APA Handbook of Psychopathology [2] 2017033373, 9781433828362, 1433828367

Table of contents :
I. Issues in Understanding Child and Adolescent Psychopathology

Introduction to Childhood and Adolescent Psychopathology
James N. Butcher and Philip C. Kendall

Developmental Perspectives on Psychopathology in Children and Adolescents
Ann S. Masten and Amanda W. Kalstabakken

A Multilevel Developmental Approach to the Prevention of Psychopathology in Children and Adolescents
Dante Cicchetti

Primary and Secondary Prevention of Child Mistreatment
Amy Damashek, Emily C. Morgan, McKenna Corlis, and Hilary Richardson

Prevention of Aggression and Bullying in Children and Adolescents
Jeffrey M. Jenson and Anne Williford

II. The Role of Assessment in Child and Adolescent Psychopathology

Recognizing Frontal-Subcortical Circuit Dimensions in Child and Adolescent Neuropsychopathology
James B. Hale, Linda A. Reddy, and Adam S. Weissman

Personality Assessment in Children With Mental Health Problems
Thomas M. Olino and Elizabeth P. Hayden

Personality Assessment of Adolescents With Psychological Problems
James N. Butcher

Assessment of Abused Youth
Jeffrey N. Wherry

III. Clinical Manifestations of Child and Adolescent Psychopathology

Trauma and Childhood Psychopathology: From Risk and Resilience to Evidence-Based Intervention
Jami M. Furr, Jonathan S. Comer, Miguel T. Villodas, Bridget Poznanski and Robin Gurwitch

Anxiety Disorders Among Children and Adolescents
Philip C. Kendall, Anna J. Swan, Matthew M. Carper, and Alexandra L. Hoff

Understanding and Managing Obsessive-Compulsive Disorder in Children and Adolescents
Carly Johnco and Eric A. Storch

Mood Disorders in Childhood and Adolescence
Mary A. Fristad and Sarah R. Black

Understanding and Treating Children and Adolescents With Neurodevelopmental Disorders
Andrew S. Davis, Kelly L. Hoover, and Angela M. Mion

Substance Use Disorders in Adolescents
Sara J. Becker and Jacqueline Horan Fisher

Eating Disorders in Children and Adolescents
Ellen E. Fitzsimmons-Craft, Anna M. Karam, and Denise E. Wilfley

Sleep Disorders in Children and Adolescents
Candice A. Alfano, Cara A. Palmer, and Joanne Louise Bower

Understanding the Development and Management of Antisocial Disorders in Adolescents
Michael S. McCloskey and Deborah A. G. Drabick

Attention-Deficit/Hyperactivity Disorder
Mary Rooney and Linda J. Pfiffner

Autism Spectrum Disorder
Matthew D. Lerner, Carla A. Mazefsky, Susan W. White, and James C. McPartland

IV. Treatment Considerations in Child and Adolescent Psychopathology

Psychological Treatment of Adolescents
Stephen R. Shirk, Allison A. Stiles, and Skyler Leonard

Treatment of Trauma in Children and Adolescents
Rochelle F. Hanson, Angela D. Moreland, and Rosaura E. Orengo-Aguayo

V. Ethical and Legal Issues in Child and Adolescent Psychopathology

Adolescent Offenders With Mental Disorders
Jeremy Colley, Bipin Subedi, and Richard Rosner

Child Custody Evaluations
H. Elizabeth King

Ethics in Assessing and Treating Children and Adolescents
Jerald Belitz

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APA Handbook of

Psychopathology

APA Handbook of Psychopathology: Child and Adolescent Psychopathology, edited by J. N. Butcher and P. C. Kendall Copyright © 2018 American Psychological Association. All rights reserved.

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APA Handbook of

Psychopathology volume 2 Child and Adolescent Psychopathology

James N. Butcher, Editor-in-Chief Philip C. Kendall, Associate Editor

Copyright © 2018 by the American Psychological Association. All rights reserved. Except as permitted under the United States Copyright Act of 1976, no part of this publication may be reproduced or distributed in any form or by any means, including, but not limited to, the process of scanning and digitization, or stored in a database or retrieval system, without the prior written permission of the publisher. The opinions and statements published are the responsibility of the authors, and such opinions and statements do not necessarily represent the policies of the American Psychological Association.

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Published by American Psychological Association 750 First Street, NE Washington, DC 20002 www.apa.org APA Order Department P.O. Box 92984 Washington, DC 20090-2984 Phone: (800) 374-2721; Direct: (202) 336-5510 Fax: (202) 336-5502; TDD/TTY: (202) 336-6123 Online: http://www.apa.org/pubs/books E-mail: [email protected] In the U.K., Europe, Africa, and the Middle East, copies may be ordered from Eurospan Group c/o Turpin Distribution Pegasus Drive Stratton Business Park Biggleswade Bedfordshire SG18 8TQ United Kingdom Phone: +44 (0) 1767 604972 Fax: +44 (0) 1767 601640 Online: https://www.eurospanbookstore.com/apa E-mail: [email protected] American Psychological Association Staff Jasper Simons, Executive Publisher Brenda Carter, Publisher, APA Books Katherine Lenz, Reference Project Editor, APA Books Typeset in Berkeley by Cenveo Publisher Services, Columbia, MD Printer: Sheridan Books, Chelsea, MI Cover Designer: Naylor Design, Washington, DC Library of Congress Cataloging-in-Publication Data Names: Butcher, James Neal, 1933– editor. | American Psychological Association. Title: APA handbook of psychopathology / James N. Butcher, editor-in-chief. Other titles: Handbook of psychopathology | APA handbooks in psychology. Description: Washington, DC : American Psychological Association, [2018] |    Series: APA handbooks in psychology series | Includes bibliographical references and indexes. Identifiers: LCCN 2017033373| ISBN 9781433828362 | ISBN 1433828367 Subjects: | MESH: Mental Disorders—pathology | Psychopathology Classification: LCC RC467 | NLM WM 140 | DDC 616.89—dc23 LC record available at https://lccn.loc.gov/2017033373 British Library Cataloguing-in-Publication Data A CIP record is available from the British Library. Printed in the United States of America First Edition http://dx.doi.org/10.1037/0000065-000 10 9 8 7 6 5 4 3 2 1

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Contents

Volume 2: Child and Adolescent Psychopathology Editorial Board ����������������������������������������������������������������������������������������������������������������������� ix Contributors��������������������������������������������������������������������������������������������������������������������������� xi Part I. Issues in Understanding Child and Adolescent Psychopathology��������������������������� 1 Chapter 1. Introduction to Childhood and Adolescent Psychopathology ������������������������������ 3 James N. Butcher and Philip C. Kendall Chapter 2. Developmental Perspectives on Psychopathology in Children and Adolescents���������������������������������������������������������������������������������������������������� 15 Ann S. Masten and Amanda W. Kalstabakken Chapter 3. A Multilevel Developmental Approach to the Prevention of Psychopathology in Children and Adolescents���������������������������������������������������� 37 Dante Cicchetti Chapter 4. Primary and Secondary Prevention of Child Maltreatment���������������������������������� 55 Amy Damashek, Emily C. Morgan, McKenna Corlis, and Hilary Richardson Chapter 5. Prevention of Aggression and Bullying in Children and Adolescents������������������ 79 Jeffrey M. Jenson and Anne Williford Part II. The Role of Assessment in Child and Adolescent Psychopathology��������������������� 95 Chapter 6. Recognizing Frontal-Subcortical Circuit Dimensions in Child and Adolescent Neuropsychopathology�������������������������������������������������������������� 97 James B. Hale, Linda A. Reddy, and Adam S. Weissman Chapter 7. Personality Assessment in Children With Mental Health Problems������������������ 123 Thomas M. Olino and Elizabeth P. Hayden Chapter 8. Personality Assessment of Adolescents With Psychological Problems�������������� 141 James N. Butcher Chapter 9. Assessment of Abused Youth������������������������������������������������������������������������������ 163 Jeffrey N. Wherry

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Contents

Part III. Clinical Manifestations of Child and Adolescent Psychopathology������������������� 185

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Chapter 10. Trauma and Child Psychopathology: From Risk and Resilience to Evidence-Based Intervention������������������������������������������������������������������������ 187 Jami M. Furr, Jonathan S. Comer, Miguel T. Villodas, Bridget Poznanski, and Robin Gurwitch Chapter 11. Anxiety Disorders Among Children and Adolescents�������������������������������������� 213 Philip C. Kendall, Anna J. Swan, Matthew M. Carper, and Alexandra L. Hoff Chapter 12. Understanding and Managing Obsessive-Compulsive Disorder in Children and Adolescents�������������������������������������������������������������������������������� 231 Carly Johnco and Eric A. Storch Chapter 13. Mood Disorders in Childhood and Adolescence���������������������������������������������� 253 Mary A. Fristad and Sarah R. Black Chapter 14. Understanding and Treating Children and Adolescents With Neurodevelopmental Disorders������������������������������������������������������������������������ 279 Andrew S. Davis, Kelly L. Hoover, and Angela M. Mion Chapter 15. Substance Use Disorders in Adolescents���������������������������������������������������������� 317 Sara J. Becker and Jacqueline Horan Fisher Chapter 16. Eating Disorders in Children and Adolescents������������������������������������������������ 343 Ellen E. Fitzsimmons-Craft, Anna M. Karam, and Denise E. Wilfley Chapter 17. Sleep Disorders in Children and Adolescents�������������������������������������������������� 369 Candice A. Alfano, Cara A. Palmer, and Joanne Louise Bower Chapter 18. Understanding the Development and Management of Antisocial Disorders in Adolescents���������������������������������������������������������������������������������� 391 Michael S. McCloskey and Deborah A. G. Drabick Chapter 19. Attention-Deficit/Hyperactivity Disorder �������������������������������������������������������� 417 Mary Rooney and Linda J. Pfiffner Chapter 20. Autism Spectrum Disorder ������������������������������������������������������������������������������ 447 Matthew D. Lerner, Carla A. Mazefsky, Susan W. White, and James C. McPartland Part IV. Treatment Considerations in Child and Adolescent Psychopathology ������������� 473 Chapter 21. Psychological Treatment of Adolescents���������������������������������������������������������� 475 Stephen R. Shirk, Allison A. Stiles, and Skyler Leonard Chapter 22. Treatment of Trauma in Children and Adolescents ���������������������������������������� 511 Rochelle F. Hanson, Angela D. Moreland, and Rosaura E. Orengo-Aguayo Part V. Ethical and Legal Issues in Child and Adolescent Psychopathology������������������� 535 Chapter 23. Adolescent Offenders With Mental Disorders�������������������������������������������������� 537 Jeremy Colley, Bipin Subedi, and Richard Rosner Chapter 24. Child Custody Evaluations������������������������������������������������������������������������������ 559 H. Elizabeth King Chapter 25. Ethics in Assessing and Treating Children and Adolescents���������������������������� 589 Jerald Belitz Index������������������������������������������������������������������������������������������������������������������������������������ 607

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Editorial Board

EDITOR-IN-CHIEF James N. Butcher, PhD, Professor Emeritus, Department of Psychology, University of Minnesota, Minneapolis ASSOCIATE EDITORS Volume 1 Jill M. Hooley, DPhil, Professor of Psychology, Department of Psychology, Harvard University, Cambridge, MA Volume 2 Philip C. Kendall, PhD, Distinguished University Professor and Laura H. Carnell Professor of Psychology, Department of Psychology, Temple University, Philadelphia, PA

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Contributors

Candice A. Alfano, PhD, Department of Psychology, University of Houston, Houston, TX Sara J. Becker, PhD, Center for Alcohol and Addictions Studies, Brown University School of Public Health, Providence, RI Jerald Belitz, PhD, Department of Psychiatry and Behavioral Sciences, University of New Mexico, Albuquerque Sarah R. Black, PhD, Department of Psychiatry and Behavioral Health, Ohio State University, Columbus Joanne Louise Bower, PhD, Department of Psychology, University of Houston, Houston, TX James N. Butcher, PhD, Department of Psychology, University of Minnesota, Minneapolis Matthew M. Carper, Doctoral Candidate, Department of Psychology, Temple University, Philadelphia, PA Dante Cicchetti, PhD, Institute of Child Development, University of Minnesota, Minneapolis Jeremy Colley, MD, Department of Psychiatry, New York University School of Medicine, and Division of Forensic Psychiatry, Bellevue Hospital, New York, NY Jonathan S. Comer, PhD, Department of Psychology, Florida International University, Miami McKenna Corlis, MA, Department of Psychology, Western Michigan University, Kalamazoo Amy Damashek, PhD, Department of Psychology, Western Michigan University, Kalamazoo Andrew S. Davis, PhD, Department of Educational Psychology, Ball State University, Muncie, IN Deborah A. G. Drabick, PhD, Department of Psychology, Temple University, Philadelphia, PA Jacqueline Horan Fisher, PhD, National Center on Addiction and Substance Abuse, New York, NY Ellen E. Fitzsimmons-Craft, PhD, Department of Psychiatry, Washington University, St. Louis, MO Mary A. Fristad, PhD, ABPP, Department of Psychiatry and Behavioral Health, Ohio State University, Columbus Jami M. Furr, PhD, Department of Psychology, Florida International University, Miami Robin Gurwitch, PhD, Department of Psychiatry and Behavioral Sciences, Duke University Medical Center, Durham, NC James B. Hale, PhD, MEd, Department of Psychology, Nanyang Technological University, Singapore; Center for Teaching Brain Literacy, Olympia, WA Rochelle F. Hanson, PhD, Department of Psychiatry and Behavioral Sciences, Medical University of South Carolina, Charleston

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Contributors

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Elizabeth P. Hayden, PhD, Department of Psychology, Western University, London, Ontario, Canada Alexandra L. Hoff, PhD, Division of Behavioral Health, Nemours Alfred I. duPont Hospital for Children, Wilmington, DE Kelly L. Hoover, Doctoral Candidate, Department of Educational Psychology, Ball State University, Muncie, IN Jeffrey M. Jenson, PhD, Graduate School of Social Work, University of Denver, Denver, CO Carly Johnco, PhD, Department of Psychology, Macquarie University, Sydney, Australia Amanda W. Kalstabakken, PhD, Institute of Child Development, University of Minnesota, Minneapolis Anna M. Karam, Doctoral Candidate, Department of Psychological and Brain Sciences, Washington University, St. Louis, MO Philip C. Kendall, PhD, ABPP, Department of Psychology, Temple University, Philadelphia, PA H. Elizabeth King, PhD, Peachtree Psychological Associates, Atlanta, GA Skyler Leonard, Doctoral Candidate, Department of Psychology, University of Denver, Denver, CO Matthew D. Lerner, PhD, Department of Psychology, Stony Brook University, Stony Brook, NY Ann S. Masten, PhD, Institute of Child Development, University of Minnesota, Minneapolis Carla A. Mazefsky, PhD, Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, PA Michael S. McCloskey, PhD, Department of Psychology, Temple University, Philadelphia, PA James C. McPartland, PhD, Department of Psychology and Yale Child Study Center, Yale University, New Haven, CT Angela M. Mion, Doctoral Candidate, Department of Educational Psychology, Ball State University, Muncie, IN Angela D. Moreland, PhD, Department of Psychiatry and Behavioral Sciences, Medical University of South Carolina, Charleston Emily C. Morgan, MA, Department of Psychology, Western Michigan University, Kalamazoo Thomas M. Olino, PhD, Department of Psychology, Temple University, Philadelphia, PA Rosaura E. Orengo-Aguayo, PhD, Department of Psychiatry and Behavioral Sciences, Medical University of South Carolina, Charleston Cara A. Palmer, PhD, Department of Psychology, University of Houston, Houston, TX Linda J. Pfiffner, PhD, Department of Psychiatry, University of California, San Francisco Bridget Poznanski, Doctoral Candidate, Department of Psychology, Florida International University, Miami Linda A. Reddy, PhD, Graduate School of Applied and Professional Psychology, Rutgers University, Piscataway, NJ Hilary Richardson, MA, Department of Psychology, Western Michigan University, Kalamazoo Mary Rooney, PhD, ABPP, Division of Services and Intervention Research, National Institute of Mental Health, Rockville, MD Richard Rosner, MD, Department of Psychiatry, New York University School of Medicine, New York Stephen R. Shirk, PhD, Department of Psychology, University of Denver, Denver, CO Allison A. Stiles, Doctoral Candidate, Department of Psychology, University of Denver, Denver, CO

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Contributors

Eric A. Storch, PhD, Menninger Department of Psychiatry and Behavioral Sciences, Baylor College of Medicine, Houston, TX Bipin Subedi, MD, Department of Psychiatry, New York University School of Medicine, and Forensic Psychology Service, Bellevue Hospital, New York, NY Anna J. Swan, PhD, Child Study Center, New York University Langone Medical Center, New York Miguel T. Villodas, PhD, Department of Psychology, Florida International University, Miami Adam S. Weissman, PhD, Child and Family Institute, New York, NY Jeffrey N. Wherry, PhD, ABPP, Department of Psychiatry and Behavioral Medicine, University of Texas Health Science Center at Tyler Susan W. White, PhD, Department of Psychology, Virginia Polytechnic Institute and State University, Blacksburg Denise E. Wilfley, PhD, Department of Psychiatry, Washington University, St. Louis, MO Anne Williford, PhD, School of Social Work, Colorado State University, Fort Collins

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Part I

Issues in Understanding Child and Adolescent PSYCHOPATHOLOGY

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Chapter 1

Introduction to Childhood and Adolescent Psychopathology

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James N. Butcher and Philip C. Kendall

We intentionally divided the APA Handbook of Psychopathology into adult and child/adolescent problems to highlight the important role age and development play in the manifestation of psychological problems. However, several chapters in Volume 1 (e.g., Chapter 5 on genetics) apply to child and adolescent adjustment as well. The psychopathology in children and adolescents can be said to be more complicated, and potentially more changeable, than adult problems. For instance, even minor changes in life circumstances can have a strong impact on a child’s daily behavior. Children, given their limited power, are in less of a position than adults to deal with some life events. Children and some adolescents have less ability to communicate their concerns and gain reassurance about changes than older people. Over the course of life, many changes in behavior, thinking, and emotions result from normal growth processes, but developmental changes are indeed more rapid and plentiful during the younger years. Some symptoms or problems may go unrecognized in children or adolescents because of their subtlety, and still others may go ignored by adults who hold the belief that young people will “grow out of it.” Simply stated, childhood and adolescence are important stages of life for the understanding, identification, and treatment of psychopathology. A primary goal of this volume is to provide a comprehensive picture of the psychological problems and related issues occurring in children and adolescents. The organization of this volume is divided into five sections to provide an overview of

the several related fields that focus somewhat differently on the issues in abnormal psychology of children and adolescents. The first section describes and details the issues and principles of child development and their importance in understanding psychopathology and its prevention. The second section focuses on the procedures and strategies for assessing psychopathology in children and adolescents. The third section highlights the contemporary research evidence defining child and adolescent disorders and the fourth section is devoted to the treatment of these disorders. The final section addresses several issues in understanding the management of child and adolescent psychopathology. Historical Perspective History offers an important perspective on the relatively belated focus on children and adolescents. The study and management of child and adolescent mental health problems is relatively recent—a little over 100 years—whereas the emphasis on adult psychopathology goes back many centuries (see ­Volume 1, Chapter 2, this handbook). Although a genuine focus on child and adolescent mental health problems did not emerge until the 20th century, interest in the behavior of young people can be found in some ancient sources. For example, Aristotle identified the period from puberty to age 21 as the last of three preadult periods he described (Berzonsky, 2000). The other childhood periods he identified were infancy (birth to 7 years) and young childhood (from age 7 to puberty).

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Santrock (2007) pointed out that Aristotle’s emphasis on the development of self-­determination during adolescence is consistent with current views of some of the contemporary themes of adolescence: autonomy, identity, and career choice. Aristotle described other characteristics of adolescents, such as moodiness, egocentrism, and impulsivity, which are consistent with modern thinking. Plato recognized the importance of cognitive development during the adolescent years, indicating adolescence as the period when reasoning first appears. Accordingly, it has been said that children should spend time in sports and music, whereas adolescents can study science and mathematics (Berzonsky, 2000; Santrock, 2007). Plato described children as intellectually and physically soft and malleable, like a “wax tablet,” and emphasized that age-appropriate books should be used to avoid a negative influence on their moral development (Bakke, 2005). In 1896 an American psychologist, Lightner Witmer, combined his interest in adolescent behavior with the application of psychology and established the first American children’s psychological clinic in Philadelphia (Witmer, 1907). Witmer’s clinic addressed the problems of mentally challenged children in terms of psychological research and therapy. To some, Witmer was the founder of clinical psychology (McReynolds, 1996), and a main impetus in encouraging others to psychologically aid children and adolescents. For example, William Healy started The Chicago Juvenile Psychopathic Institute in 1909. Healy was seminal in viewing juvenile delinquency as a symptom of urban problems, rather than inner psychological problems, and was one of the first to recognize a new area of causation—environmental, or sociocultural, factors. Interest in the study of adolescence was expanded by G. Stanley Hall (1904) during the early 20th century when he recognized that the adolescent period was not just biologically determined by puberty, but socially influenced as well (Savage, 2008). Hall recognized that the adolescent period extended beyond the youthful years, until age 21 in girls and age 25 in boys. Arnett (2006) concluded that Hall’s contributions to adolescent psychology are still relevant today. 4

The study and treatment of behavioral problems in children was strongly influenced by John B. Watson, a founder of behaviorism. His views on conditioning were extremely influential in understanding the development of children and adolescents (Watson & Raynor, 1920). Although the influence of behaviorism has fluctuated over the decades, the concepts and principles are highly relevant and remain widely used today. B. F. Skinner (1951), another behaviorist, had a profound influence on learning processes by emphasizing the roles of rewards and contingencies in the modification of behavior. Others, more recently, have also impacted the focus on childhood and adolescence as key periods in life for understanding of psychopathology. Similarly, other contemporaries have impacted the focus on the treatment of psychological problems in childhood and adolescence to reduce psychopathology in the present and in the later years. The contributing authors of this handbook bring us entirely up to date with regard to mental health problems of youth, but it will be for future authors to identify those most historically influential in these undertakings. Issues to Consider in Child and Adolescent Psychopathology As reflected in the organization of this volume, psychopathology in children and adolescents will be addressed within five sections. Before you enter these domains, however, we first would like to highlight several topics to give perspective to some of the important cross-domain issues facing psychopathology in children and adolescents.

Evolving Nature of Diagnoses in Children and Adolescents Compared with adults, the formal diagnoses of mental health problems in children and adolescents is a relatively recent venture. Over time, researchers and theoreticians developed diagnostic constructs to describe mental health problems in young people; there has been a substantial evolution of the number of potential disorders and their descriptive characteristics. Symptoms presented in youth may be different from symptoms presented by adults, despite

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Introduction to Childhood and Adolescent Psychopathology

a similar underlying issue or psychological problem (see Dulcan, 2016; Galanter & Jensen, 2016). For example, a lack of self-control in a child may be seen in taking toys from another child or interrupting adult conversation, whereas an adult may engage in substance abuse as a reflection of a lack of selfcontrol. Issues associated with measurement equivalence (e.g., the same construct/disorder may need to be measured differently—and there may be different symptoms—at different point in the lifespan) are pertinent for child and adolescent psychopathology. In recent times, new diagnoses and new diagnostic criteria have been incorporated into the accepted evaluation systems for children and adolescents. For example, in the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM–5; American Psychiatric Association, 2013), there is now a category called disruptive mood dysregulation disorder, which was introduced in 2013 partly in response to the large increase in youth being labeled with bipolar disorder. Was it that this disorder didn’t exist years ago? Is the label an effort to best describe the symptoms being presented by today’s youth? Rutter (2011) argued that there are too many separate diagnoses, and Mohr and Schneider (2013) highlighted troublesome differences between the two dominant diagnostic systems (DSM–5 and International Classification of Diseases, 10th Revision [World Health Organization, 1992]). Perhaps even more challenging and potentially beneficial is the question of whether diagnostic “categories” or “dimensions” are used to describe and catalog mental disorders (Coghill & Sonuga-Barke, 2012).

Prevalence and Incidence The occurrence of various types of mental disorders among children and adolescents is alarmingly high (Kendall & Comer, 2010), and is documented to have increased in recent years (Collishaw, 2015). A recent survey of the 12-month prevalence rate for mental disorders among 8- to 15-year-old children by the National Institute of Mental Health (2014) reported that the overall occurrence was 13.1% of the population. A report of the prevalence of youth (Kelleher et al., 2012) cited studies indicating the median prevalence of psychotic symptoms was 17% among children ages 9 to 12 years and 7.5%

among adolescents ages 13 to 18 years. Consider one condition—autism spectrum disorder—where the prevalence rate has increased dramatically (Maughan, Iervolino, & Collishaw, 2005). As Kessler, Petukhova, Sampson, Zaslavsky, and Wittchen (2012) reported, data from epidemiological studies indicate that 12-month prevalence estimates were 8.6% for major depressive episode, 12.1% for specific phobia, 7.4% for social phobia, 3.7% for posttraumatic stress disorder (PTSD), 2.0% for generalized anxiety disorder, and 1.2% for separation, among others. Comorbidity is high among these disorders (Costello, Egger, & Angold, 2005; Kendall et al., 2010). When totaled, lifetime mood and anxiety disorders are most prevalent and have been described as “gateways” to persistent mental health problems and other related adult difficulties. Indeed, data on prevalence and incidence of psychopathology in children and adolescence indicate that these disorders are an important public health issue (Perou et al., 2013).

Genetic and Physiological Factors There are some child and/or adolescent disorders that have been associated with genetics and youth in some families are susceptible to mental illnesses through gene transmissions. For example, polygenes (gene combinations) have been found to be associated with externalizing problems in youth (Dadds et al., 2014; Salvatore et al., 2015). But do you directly inherit a disorder? As it turns out, it probably is not that simple: The environment influences what genes are expressed. Research on the interaction of the environment and genes provides evidence. In Science magazine, Govind and Pearce (1986) reported on a study of lobster claws. How is this relevant? Lobsters have two claws that look alike and have similar musculature, but as they develop one becomes a crusher and one becomes a cutter. Will the right hand be the crusher and the left hand the cutter? As it turns out, in environments where the lobsters could not exercise, the claws did not differentiate. In environments where the claws could differentially exercise, the claws developed into the two different functions. The claw that was given the opportunity for exercise became the crusher; the less exercised claw became 5

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the cutter. Exercise had an impact on the genetic expression of the claw functions. Genetics provide predispositions and the environments shape what dispositions are expressed. Several chapters describe brain function and provide information about brain development and about the development of pathophysiology. Another important element to take into consideration in the development of some disorders in children and adolescents are neuropsychological factors that can occur through accidental injury or medical illnesses.

Socioeconomic Considerations Growing up in a complex society lacking or with limited family support and economic resources can seriously and negatively impact the way in which a young person develops. Not surprisingly, there is evidence to indicate that psychological adjustment is negatively influenced by low socioeconomic status (Reiss, 2013). Indeed, poverty-related stress is associated with a variety of psychological problems in youth (Santiago, Wadsworth, & Stump, 2011). Financial hardship in childhood was predictive of the onset of all classes of disorder across development (McLaughlin et al., 2011). Disadvantages and the impact of these circumstances will be noted in several chapters in this handbook.

Developmental Factors From birth to adulthood (and beyond), humans go through important developmental processes (Lerner, 2006). Attention needs to be paid to the factors in normal child development to fully understand mental health problems in children and adolescents. The timing of pubertal developmental, for example, has a relationship with adolescent depression (Benoit, Lacourse, & Claes, 2013), and age can be a factor in adjustment to trauma (Furr, Comer, Edmunds, & Kendall, 2010; Parker et al., 2016). To obtain and hold a proper perspective on problem behavior manifested by children and adolescents, it is important to have a clear understanding of the normal developmental changes and issues.

adolescence more girls than boys meet criteria for a diagnosis of depression (Hankin et al., 1998, 2015; Hyde, Mezulis, & Abramson, 2008; St. Clair et al., 2015; Zahn-Waxler, Shirtcliff, & Marceau, 2008). Though one report suggested a similar gender difference in anxiety disorders (Lewinsohn, Gotlib, Lewinsohn, Seeley, & Allen, 1998), most other reports indicate comparable incidence of anxiety disorders in boys and girls (Kendall et al., 2010). Also of interest, there are gender differences in the access to mental health services (Merikangas et al., 2010; Zimmerman, 2005). The life circumstances faced by young people differ substantially for boys versus girls, and the everyday demands can be quite different.

Perceived Threats Children and adolescents often perceive threats more extremely than they would as adults. Youth perceptions of threat are less tempered by considerations of the past or future, and tend to be seen as disproportionately important (Mash & Barkley, 2006). Trauma experiences (e.g., terrorism, disaster) influence psychological adjustment (Furr et al., 2010; Masten & Narayan, 2012). Indeed, reviews of the role of disasters in general (Goldmann & Galea, 2014) and specifically (e.g., Boston Marathon bombing; Comer et al., 2014) indicate that youth with proximity and exposure are more likely to have a higher incidence of PTSD than other youth.

Stress One general agreement in the field is that stress, in terms of biological and psychological processes, impacts mental health functioning in youth (Doom & Gunnar, 2013). The school environment, academic and peer/social, can impose stressful challenges for children and adolescence. Behavioral problems can result from several sources, such as difficulty adapting to academic demands, negative relationships with peers and teachers, or being isolated from positive relationships with classmates.

Refugee or Immigration Status Gender Differences The rates of some diagnoses differ substantially for each gender. For example, from childhood to 6

In recent times, many young people have been uprooted from their native countries around the world, and live periods of great uncertainty.

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They undergo stress in the immigration process and in adapting to a different culture (see Volume 1, C ­ hapter 7, this handbook). Children and adolescents experiencing this process have their lives changed remarkably and have considerable difficulty adapting (Blázquez et al., 2015; Butcher et al., 2015; Howard & Hodes, 2000; Savin, Sack, Clarke, Meas, & Richart, 1996). War-affected refugee children have complex trauma profiles and difficulty accessing treatments (Betancourt et al., 2012), and some data suggest that unaccompanied refugee minors have a particularly difficult time (higher levels of PTSD symptoms) in comparison with normal samples and even samples of accompanied refugee minors (Huemer et al., 2009). Among adolescents, differences between immigrant and native first-time admissions to a psychiatric facility were associated with parental separation and family breakdown.

Limited Access to Treatment Not all children and adolescents who develop mental health problems receive appropriate care. For example, fewer than half of the youth with current mental health disorders receive mental health specialty treatment (Costello, He, Sampson, Kessler, & Merikangas, 2014; Merikangas, Nakamura, & Kessler, 2009). There are related concerns expressed about the increased use of polypharmacy with youth, even when the medications have been evaluated with adults (Olfson, Druss, & Marcus, 2015). Also unfortunate, the poor (Zimmerman, 2005), members of ethnic minority groups (Alegria, Vallas, & Pumariega, 2010; Howell & McFeeters, 2008), and those whose parents lack education (Mendenhall, 2012) are all less likely to access services.

Overmedication Comer, Mojtabai, and Olfson (2011) pointed out that there is a rising and disturbing rate of psychiatric polypharmacy in young people. They conducted a study that looked across the lifespan—not just at children or adolescents—but because the results did not indicate any age effects, the findings can be said to be just as true for youth as they are for adults. Between 1996 and 2007, antipsychotic prescribing by psychiatrists for anxiety disorders increased from 10% to 21% (i.e. 1 in every 5 people

with an anxiety disorder who sees a psychiatrist is getting an antipsychotic drug). Antipsychotics are not FDA approved for treating anxiety disorders. Little is known about their efficacy for the management of anxiety and they are associated with very serious metabolic and cerebrovascular side effects. The largest increases in antipsychotic prescribing to treat anxiety disorders have been for new patients and for patients with panic disorder. Prescribing for new patients is concerning, because it means that psychiatrists are increasingly using antipsychotics as part of first-line care. Additionally, there have been no controlled trials evaluating the safety and efficacy of antipsychotics for treating panic disorder. Comer, Olfson, and Mojtabai (2010) found that from 1996 to 2007 there was a roughly 50% increase in the use of psychiatric polypharmacy by physicians to treatment of outpatients (up to 20% of all psychiatry outpatient visits in 2007 and up to 32% of all psychiatry outpatient visits with a mental disorder diagnosis in 2007). The fastest rising polypharmacy trends are for combination treatments involving antipsychotics (which have not shown safety or efficacy in treating most child problems, and in fact are associated with very serious metabolic and cerebrovascular side effect profiles). Children and adolescents are our future. There are many forces at play, causing and influencing abnormal behavior. Relatedly, there are needs in children and adolescents that if left untreated contribute to psychopathology in adults. The content of this volume will advance the identification of needs and the provision of services by mental health professionals to address these needs. Overview and Organization This volume of the APA Handbook of Psychopathology was developed to provide readers with a current and comprehensive picture of psychopathology in children and adolescents. The mental health professionals invited to contribute chapters were selected because of their current and distinguished contribution to understanding psychopathology in children and adolescents. This volume contains five sections in which chapters are grouped according to similar approaches to dealing with psychopathology. 7

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Issues in Understanding Child and Adolescent Psychopathology The chapters in this section describe and detail several key issues and principles of abnormal behavior in children and adolescents that are important to understanding the processes underlying psychopathology in this population. Many pertinent causal factors, including genetic causation, neurological causes, and sociocultural factors, are included. Chapter 2, by Ann S. Masten and Amanda W. Kalstabakken, provides a contemporary perspective on the developmental factors in psychopathology in children and adolescents that are important to understanding developmental processes, and to accurately explore deviations from the natural developmental process and the emergence of mental health problems. In Chapter 3, Dante Cicchetti addresses developmental factors in children and adolescents and how prevention of psychopathology can be approached. In Chapter 4, Amy Damashek, Emily C. Morgan, McKenna Corlis, and Hilary Richardson discuss the important issues in understanding the consequences of maltreatment of children, and how these events can result in severe psychological adjustment problems in young people. In the final chapter in this section, Jeffrey M. Jenson and Anne Williford examine the highly prominent problem of aggressive behavior among children and adolescents and provide an overview of substantial efforts being made to prevent bullying.

The Role of Assessment in Child and Adolescent Psychopathology This next section contains articles that address the psychological assessment of symptoms and behavior in children and adolescents with mental disorders. Authors focus on (a) important factors to include in assessment approaches for children and adolescents, (b) core principles and applications of the assessment strategy, (c) discussions of cultural and ethnic factors that can influence behavior, (d) limiting conditions in the assessment of children and adolescents, (e) important studies that provide valuable background for assessing children and adolescents, and (f) discussion of potentially valuable directions for the field of assessment of psychopathology for children and adolescents. 8

James B. Hale, Linda A. Reddy, and Adam S. Weissman, in Chapter 6, provide a comprehensive overview of neuropsychological assessment in children and adolescents. The authors provide a description of the core principles and applications for understanding psychopathological problems in young people who have experienced brain damage or illness. In Chapter 7, Thomas M. Olino and Elizabeth P. Hayden address the important factors to include in assessment approaches for children and describe the core principles and applications of various assessment approaches with children. In Chapter 8, James N. Butcher provides an overview of the use of objective psychological assessment procedures to assess adolescents with psychological problems. The chapter focuses on describing the major and most widely used techniques and settings for which they are used. In the final chapter of this section, Jeffrey N. Wherry provides a discussion of child maltreatment—an important consideration in child psychology today. This chapter provides a description of the extent and nature of maltreatment of children and discusses the impact such behavior can have on psychopathology of the children that are involved. Assessment strategies and instruments to understand maltreatment are described.

Clinical Manifestations of Child and Adolescent Psychopathology The chapters included in this section comprehensively address (a) description and definition of a broad range of clinical diagnoses, (b) core principles and variables, (c) information about the diagnostic areas and incidence or prevalence of specific diagnoses described, (d) current research in defining mental health problems, (e) important current research ­evidence detailing the symptom disorders, and (f) future directions for study. In Chapter 10, Jami M. Furr, Jonathan S. Comer, Miguel T. Villodas, Bridget Poznanski, and Robin Gurwitch address trauma and development of childhood psychopathology. This chapter provides an overview and discussion of how environmental risk factors can convert into illness. The incidence and prevalence of stress-related trauma and comorbidity with other conditions are included. In Chapter 11, Philip C. Kendall, Anna J. Swan, Matthew M. Carper, and Alexandra L. Hoff provide

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a comprehensive discussion of anxiety disorders among children and adolescents, including the classification of disorders in which excessive fear and anxiety in children and adolescents are manifested. They provide information on the incidence and prevalence of anxiety related disorders along with comorbidity with other diagnoses. The authors discuss diagnosis, treatment strategies, and included evaluation of current research on the anxiety disorders. Carly Johnco and Eric A. Storch explore and describe features of obsessive-compulsive disorder (OCD) in children and adolescents in Chapter 12. The current diagnostic information for OCD is identified and defined, and the incidence and prevalence of OCD and comorbidity with other diagnoses described. Relevant, specific factors pertinent to the treatment or management of OCD that is found to be effective are included. In Chapter 13, Mary A. Fristad and Sarah R. Black provide a thorough discussion of mood disorders in childhood and adolescence, including current information about their incidence and prevalence along with comorbidity of other conditions. They include a discussion of information about the characteristic symptoms of mood disorder diagnoses and potentially effective mood disorder treatments. Andrew S. Davis, Kelly L. Hoover, and Angela M. Mion provide a comprehensive discussion of the understanding and treatment of children and adolescents with neurodevelopmental disorders in Chapter 14. The important considerations in appraising young people with neurological disabilities are described, and effective assessment instruments highlighted; current research supporting prominent assessment and management approaches are included. Equally important in understanding psychological disorders, particularly among adolescents, are the etiology, manifestation, and treatment of adolescent substance abuse disorders—an increasingly common psychological problem in youth, which is addressed in Chapter 15 by Sara J. Becker and Jacqueline Horan Fisher. This chapter is devoted to providing a contemporary perspective of the causes underlying adolescent substance abuse. They also provide current information on pertinent cultural and ethnic factors that can influence substance use.

Two classes of disorders that are germane to understanding psychopathology among young people are eating disorders and sleep disorders. In Chapter 16, Ellen E. Fitzsimmons-Craft, Anna M. Karam, and Denise E. Wilfley provide an overview and description of eating disorders in children and adolescents. These disorders’ incidence and prevalence is described as well as comorbidity with any other known conditions. Information describing how eating disorder diagnoses are identified and specified and potential social influencing factors (e.g., age, ethnicity, gender) are included. Potentially effective treatments for eating disorders are also discussed. In Chapter 17, Candice A. Alfano, Cara A. Palmer, and Joanne Louise Bower address sleep disorders in children and adolescents. They discuss current diagnostic criteria and their incidence and prevalence. They also describe potential comorbidity with other known conditions. Information describing how sleep disorders are influenced by factors like age, ethnicity, and gender are included. Moreover, evidence of potentially effective treatments for sleep disorders is also provided. Several diagnostic disorders that are especially prominent in adolescents are also included in Part III: antisocial disorders, attention-deficit/ hyperactivity disorder (ADHD), and autism spectrum disorders. In Chapter 18, Michael S. McCloskey and Deborah A. G. Drabick provide understanding on the development of and management of conduct disorders and aggressive disorders in adolescents. They discuss contemporary classification of disorders and the various specific disorders related to adolescents are highlighted. They provide current information on the incidence and prevalence of personality and related conduct disorders, and their comorbidity with other diagnoses is described. In Chapter 19, Mary Rooney and Linda J. Pfiffner provide an overview and contemporary information on the development of ADHD in children and adolescents. Current diagnostic criteria are described and their incidence and prevalence clarified. Information on potentially effective treatments of ADHD is also provided. The final chapter in Part III, on autism spectrum disorders, was developed by Matthew D. Lerner, 9

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Carla A. Mazefsky, Susan W. White, and James C. McPartland. This chapter addresses the exploration and delineation of autism spectrum disorders in children and adolescents. The disorders are defined and their manifestation illustrated. Current information on the incidence and prevalence of autism spectrum disorders are explored and comorbidity with other diagnoses described. Information describing how the diagnoses are identified and specified is noted and pertinent associated factors (e.g., age, cultural factors, gender) are described as well. Current research findings regarding the various autistic spectrum disorders are summarized. Particularly effective treatments or management of schizophrenia spectrum disorders are described.

Treatment Considerations in Child and Adolescent Psychopathology Although some of the disorder-specific chapters mention treatments, the chapters included in Part IV provide information about the broad topic of treatment of psychological disorders in young people. The chapters address treatment of children and adolescents and provide information on a number of topics, including (a) description and definition of the treatment approach, (b) the core principles and applications for children and adolescents, (c) any limitations and any weaknesses of the treatment strategy for young people, (d) significant research evidence and major contributions to understanding treatment of children and adolescents, and (e) future directions for research that might be aimed at improving treatment. Stephen R. Shirk, Allison A. Stiles, and Skyler Leonard explore and provide an overview of psychological treatment methods in adolescents undergoing mental health problems in Chapter 21. Major treatment approaches are described and defined and the core principles explained. Limitations to the treatment strategy are noted. Current research evidence in support of the therapeutic approach with adolescents is described and possible future research directions are projected. In Chapter 22, Rochelle F. Hanson, Angela D. Moreland, and Rosaura E. Orengo-Aguayo discuss treatment of trauma in children and adolescents. Specific therapeutic strategies pertinent to the PTSD treatments that are effective are addressed and 10

evidence-based research supporting these treatment strategies noted.

Ethical and Legal Issues in Child and Adolescent Psychopathology The chapters in Part V include material related to ethical and legal issues in the management of psychopathology in children and adolescents. Many young people with mental health problems become involved in crimes that are referred to the criminal justice system. Chapter 23 is devoted to the topic of adolescent offenders with mental disorders. This chapter, by Jeremy Colley, Bipin Subedi, and Richard Rosner, describes the process and procedures for addressing adolescent offenders with mental disorders in court. The types of mental health problems and potential factors underlying the disorders (e.g., sociocultural and parental influences) are noted. Current research on understanding these problems is included and effective ways of dealing with them are discussed. Chapter 24, by H. Elizabeth King, is devoted to the important topic of child custody evaluations, a highly complex and often complicated professional activity for psychologists. This chapter provides an overview of information that is important for professionals involved in conducting psychological examinations in forensic custody cases. It includes a description of characteristic psychopathological problems encountered in family custody cases, discusses the contemporary standards for conducting family custody evaluations detailed by professional organizations, describes and illustrates how psychological evaluations in family custody cases are conducted, considers issues and problems psychologists might encounter in custody evaluations, and includes several case illustrations as examples. The final chapter of the handbook provides an important perspective on ethics in assessing and treating children and adolescents. In Chapter 25, Jerald Belitz addresses the principles and issues pertinent to ethical considerations for mental health professionals dealing with children and adolescents in psychopathological practice. The chapter highlights major organization guidelines for clarifying and practical applications pertinent to children and adolescents. Illustrations and case examples are discussed to illustrate major issues.

Introduction to Childhood and Adolescent Psychopathology

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Summary This introduction to Volume 2 of the APA Handbook of Psychopathology focuses its spotlight on child and adolescent disorders specifically. The understanding of psychopathology in children and adolescents is often more complicated and changeable than analysis and theoretical viewpoints for adult problems. Even minor changes in life circumstances can have a strong impact on a child’s daily behavior. Children are often less able to adapt or deal with some events than adults. A brief discussion of some of the historical contributions to the study of abnormal behavior in children and adolescents was included to provide a perspective on this field. As noted, most of the earlier historical descriptions of abnormal behavior focused on adults. It was not until the late 19th and early 20th centuries that children and adolescents became the focus of evaluation and treatment of mental disorders. This chapter provides an overview of many of the issues that can be prominent and influential in causing abnormal behavior in children and adolescents: ■■

■■ ■■ ■■ ■■ ■■ ■■

■■ ■■ ■■ ■■

Evolving nature of diagnoses in children and adolescents Prevalence and incidence Genetic and physiological factors Socioeconomic considerations Developmental factors Gender differences Perceived threats are difficult for children and adolescents Stress Refugee or immigration status Limited access to treatment Overmedication of children and adolescents with mental health problems

References Alegria, M., Vallas, M., & Pumariega, A. J. (2010). Racial and ethnic disparities in pediatric mental health. Child and Adolescent Psychiatric Clinics of North America, 19, 759–774. http://dx.doi.org/10.1016/ j.chc.2010.07.001 American Psychiatric Association. (2013). Diagnostic and statistical manual of mental disorders (5th ed.). Washington, DC: Author.

Arnett, J. J. (2006). G. Stanley Hall’s Adolescence: Brilliance and nonsense. History of Psychology, 9, 186–197. http://dx.doi.org/10.1037/ 1093-4510.9.3.186 Bakke, O. M. (2005). When children became people: The birth of childhood in early Christianity (B. McNeil, Trans.). Minneapolis, MN: Augsburg Fortress Press. Benoit, A., Lacourse, E., & Claes, M. (2013). Pubertal timing and depressive symptoms in late adolescence: The moderating role of individual, peer, and parental factors. Development and Psychopathology, 25, 455–471. http://dx.doi.org/10.1017/ S0954579412001174 Berzonsky, M. D. (2000). Theories of adolescence. In G. Adams (Ed.), Adolescent development: The essential readings (pp. 11–28). Malden, MA: Blackwell. Betancourt, T. S., Newnham, E. A., Layne, C. M., Kim, S., Steinberg, A. M., Ellis, H., & Birman, D. (2012). Trauma history and psychopathology in war-affected refugee children referred for trauma-related mental health services in the United States. Journal of Traumatic Stress, 25, 682–690. http://dx.doi.org/ 10.1002/jts.21749 Blázquez, A., Castro-Fornieles, J., Baeza, I., Morer, A., Martínez, E., & Lázaro, L. (2015). Differences in psychopathology between immigrant and native adolescents admitted to a psychiatric inpatient unit. Journal of Immigrant and Minority Health, 17, 1715–1722. http://dx.doi.org/10.1007/s10903-0140143-3 Butcher, J. N., Hass, G. A., Greene, R. L., & Nelson, L. D. (2015). Using the MMPI–2 in forensic assessment. http://dx.doi.org/10.1037/14571-000 Coghill, D., & Sonuga-Barke, E. J. (2012). Annual research review: Categories versus dimensions in the classification and conceptualisation of child and adolescent mental disorders—Implications of recent empirical study. Journal of Child Psychology and Psychiatry, 53, 469–489. http://dx.doi.org/ 10.1111/j.1469-7610.2011.02511.x Collishaw, S. (2015). Annual research review: Secular trends in child and adolescent mental health. Journal of Child Psychology and Psychiatry, 56, 370–393. http://dx.doi.org/10.1111/jcpp.12372 Comer, J. S., Dantowitz, A., Chou, T., Edson, A. L., Elkins, R. M., Kerns, C., . . . Green, J. G. (2014). Adjustment among area youth after the Boston Marathon bombing and subsequent manhunt. Pediatrics, 134, 7–14. http://dx.doi.org/10.1542/ peds.2013-4115 Comer, J. S., Mojtabai, R., & Olfson, M. (2011). National trends in the antipsychotic treatment of psychiatric outpatients with anxiety disorders. American Journal of Psychiatry, 168, 1057–1065. http://dx.doi.org/10.1176/ appi.ajp.2011.11010087 11

Butcher and Kendall

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Comer, J. S., Olfson, M., & Mojtabai, R. (2010). National trends in child and adolescent psychotropic polypharmacy in office-based practice, 1996–2007. Journal of the American Academy of Child and Adolescent Psychiatry, 49, 1001–1010. http://dx.doi.org/ 10.1016/j.jaac.2010.07.007

of depression from preadolescence to young adulthood: Emerging gender differences in a 10-year longitudinal study. Journal of Abnormal Psychology, 107, 128–140. http://dx.doi.org/10.1037/0021843X.107.1.128

Costello, E. J., Egger, H. L., & Angold, A. (2005). The developmental epidemiology of anxiety disorders: Phenomenology, prevalence, and comorbidity. Child and Adolescent Psychiatric Clinics of North America, 14, 631–648. http://dx.doi.org/10.1016/ j.chc.2005.06.003

Hankin, B. L., Young, J. F., Abela, J. R. Z., Smolen, A., Jenness, J. L., Gulley, L. D., . . . Oppenheimer, C. W. (2015). Depression from childhood into late adolescence: Influence of gender, development, genetic susceptibility, and peer stress. Journal of Abnormal Psychology, 124, 803–816. http://dx.doi.org/ 10.1037/abn0000089

Costello, E. J., He, J. P., Sampson, N. A., Kessler, R. C., & Merikangas, K. R. (2014). Services for adolescents with psychiatric disorders: 12-month data from the National Comorbidity Survey—Adolescent. Psychiatric Services, 65, 359–366. http://dx.doi.org/ 10.1176/appi.ps.201100518

Howard, M., & Hodes, M. (2000). Psychopathology, adversity, and service utilization of young refugees. Journal of the American Academy of Child and Adolescent Psychiatry, 39, 368–377. http://dx.doi.org/ 10.1097/00004583-200003000-00020

Dadds, M. R., Moul, C., Cauchi, A., Dobson-Stone, C., Hawes, D. J., Brennan, J., . . . Ebstein, R. E. (2014). Polymorphisms in the oxytocin receptor gene are associated with the development of psychopathy. Development and Psychopathology, 26, 21–31. http://dx.doi.org/10.1017/S0954579413000485 Doom, J. R., & Gunnar, M. R. (2013). Stress physiology and developmental psychopathology: Past, present, and future. Development and Psychopathology, 25, 1359–1373. http://dx.doi.org/10.1017/ S0954579413000667 Dulcan, M. (2016). Dulcan’s textbook of child and adult psychiatry (2nd ed.). http://dx.doi.org/10.1176/appi. books.9781615370306 Furr, J. M., Comer, J. S., Edmunds, J. M., & Kendall, P. C. (2010). Disasters and youth: A meta-analytic examination of posttraumatic stress. Journal of Consulting and Clinical Psychology, 78, 765–780. http://dx.doi.org/10.1037/a0021482 Galanter, C. A., & Jensen, P. S. (2016). DSM–5 casebook and treatment guide for child mental health. Washington, DC: American Psychiatric Association. Goldmann, E., & Galea, S. (2014). Mental health consequences of disasters. Annual Review of Public Health, 35, 169–183. http://dx.doi.org/10.1146/ annurev-publhealth-032013-182435 Govind, C., & Pearce, J. (1986). Differential reflex activity determines claw and closer muscle asymmetry in developing lobsters. Science, 233, 354–356. http://dx.doi.org/10.1126/science.233.4761.354 Hall, G. S. (1904). Adolescence: Its psychology and its relations to physiology, anthropology, sociology, sex, crime, religion, and education (2 vols.). Englewood Cliffs, NJ: Prentice Hall. Hankin, B. L., Abramson, L. Y., Moffitt, T. E., Silva, P. A., McGee, R., & Angell, K. E. (1998). Development

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Howell, E., & McFeeters, J. (2008). Children’s mental health care: Differences by race/ethnicity in urban/ rural areas. Journal of Health Care for the Poor and Underserved, 19, 237–247. http://dx.doi.org/10.1353/ hpu.2008.0008 Huemer, J., Karnik, N. S., Voelkl-Kernstock, S., Granditsch, E., Dervic, K., Friedrich, M. H., & Steiner, H. (2009). Mental health issues in unaccompanied refugee minors. Child and Adolescent Psychiatry and Mental Health, 3, 13–22. http://dx.doi.org/10.1186/ 1753-2000-3-13 Hyde, J. S., Mezulis, A. H., & Abramson, L. Y. (2008). The ABCs of depression: Integrating affective, biological, and cognitive models to explain the emergence of the gender difference in depression. Psychological Review, 115, 291–313. http://dx.doi.org/10.1037/ 0033-295X.115.2.291 Kelleher, I., Connor, D., Clarke, M. C., Devlin, N., Harley, M., & Cannon, M. (2012). Prevalence of psychotic symptoms in childhood and adolescence: A systematic review and meta-analysis of populationbased studies. Psychological Medicine, 42, 1857–1863. http://dx.doi.org/10.1017/S0033291711002960 Kendall, P. C., & Comer, J. S. (2010). Childhood disorders (2nd ed.). London, England: Psychology Press. Kendall, P. C., Compton, S. N., Walkup, J. T., Birmaher, B., Albano, A. M., Sherrill, J., . . . Piacentini, J. (2010). Clinical characteristics of anxiety disordered youth. Journal of Anxiety Disorders, 24, 360–365. http://dx.doi.org/10.1016/j.janxdis.2010.01.009 Kessler, R. C., Petukhova, M., Sampson, N. A., Zaslavsky, A. M., & Wittchen, H.-U. (2012). Twelve-month and lifetime prevalence and lifetime morbid risk of anxiety and mood disorders in the United States. International Journal of Methods in Psychiatric Research, 21, 169–184. http://dx.doi.org/10.1002/ mpr.1359

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Lerner, R. (Ed.). (2006). Handbook of child psychology. New York, NY, Wiley. Lewinsohn, P. M., Gotlib, I. H., Lewinsohn, M., Seeley, J. R., & Allen, N. B. (1998). Gender differences in anxiety disorders and anxiety symptoms in adolescents. Journal of Abnormal Psychology, 107, 109–117. http://dx.doi.org/10.1037/0021-843X. 107.1.109

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Mash, E. J., & Barkley, R. A. (2006). Treatment of childhood disorders (3rd ed.). New York, NY: Guilford Press. Masten, A. S., & Narayan, A. J. (2012). Child development in the context of disaster, war, and terrorism: Pathways of risk and resilience. Annual Review of Psychology, 63, 227–257. http://dx.doi.org/ 10.1146/annurev-psych-120710-100356 Maughan, B., Iervolino, A. C., & Collishaw, S. (2005). Time trends in child and adolescent mental disorders. Current Opinion in Psychiatry, 18, 381–385. http://dx.doi.org/10.1097/01.yco. 0000172055.25284.f2 McLaughlin, K. A., Breslau, J., Green, J. G., Lakoma, M. D., Sampson, N. A., Zaslavsky, A. M., & Kessler, R. C. (2011). Childhood socio-economic status and the onset, persistence, and severity of DSM–IV mental disorders in a U.S. national sample. Social Science and Medicine, 73, 1088–1096. http://dx.doi.org/10.1016/ j.socscimed.2011.06.011 McReynolds, P. (1996). Lightner Witmer: Father of clinical psychology. In G. A. Kimble, C. A. Boneau, & M. Wertheimer (Eds.), Portraits of pioneers in psychology (Vol. 2, pp. 63–71). Washington, DC: American Psychological Association. Mendenhall, A. N. (2012). Predictors of service utilization among youth diagnosed with mood disorders. Journal of Child and Family Studies, 21, 603–611. http://dx.doi.org/10.1007/s10826-011-9512-x Merikangas, K. R., He, J. P., Brody, D., Fisher, P. W., Bourdon, K., & Koretz, D. S. (2010). Prevalence and treatment of mental disorders among U.S. children in the 2001–2004 NHANES. Pediatrics, 125, 75–81. http://dx.doi.org/10.1542/peds.2008-2598 Merikangas, K. R., Nakamura, E. F., & Kessler, R. C. (2009). Epidemiology of mental disorders in children and adolescents. Dialogues in Clinical Neuroscience, 11, 7–20. Mohr, C., & Schneider, S. (2013). Anxiety disorders. European Child and Adolescent Psychiatry, 22(Suppl. 1), 17–22. http://dx.doi.org/10.1007/s00787-0120356-8 National Institute of Mental Health. (2014). Any disorder among children. Retrieved from https://www.nimh. nih.gov/health/statistics/prevalence/any-disorderamong-children.shtml

Olfson, M., Druss, B. G., & Marcus, S. C. (2015). Trends in mental health care among children and adolescents. New England Journal of Medicine, 372, 2029–2038. http://dx.doi.org/10.1056/ NEJMsa1413512 Parker, G., Lie, D., Siskind, D. J., Martin-Khan, M., Raphael, B., Crompton, D., & Kisely, S. (2016). Mental health implications for older adults after natural disasters—A systematic review and metaanalysis. International Psychogeriatrics, 28, 11–20. http://dx.doi.org/10.1017/S1041610215001210 Perou, R., Bitsko, R. H., Blumberg, S. J., Pastor, P., Ghandour, R. M., Gfroerer, J. C., . . . Huang, L. N., & the Centers for Disease Control and Prevention. (2013). Mental health surveillance among children—United States, 2005–2011. Morbidity and Mortality Weekly Report, 62, 1–35. Reiss, F. (2013). Socioeconomic inequalities and mental health problems in children and adolescents: A systematic review. Social Science and Medicine, 90, 24–31. http://dx.doi.org/10.1016/ j.socscimed.2013.04.026 Rutter, M. (2011). Research review: Child psychiatric diagnosis and classification: Concepts, findings, challenges, and potential. Journal of Child Psychology and Psychiatry, 52, 647–660. http://dx.doi.org/ 10.1111/j.1469-7610.2011.02367.x Salvatore, J. E., Aliev, F., Bucholz, K., Agrawal, A., Hesselbrock, V., Hesselbrock, M., . . . Dick, D. M. (2015). Polygenic risk for externalizing disorders: Gene-by-development and gene-by-environment effects in adolescents and young adults. Clinical Psychological Science, 3, 189–201. http://dx.doi.org/ 10.1177/2167702614534211 Santiago, C. D., Wadsworth, M. E., & Stump, J. (2011). Socioeconomic status, neighborhood disadvantage, and poverty-related stress: Prospective effects on psychological syndromes among diverse lowincome families. Journal of Economic Psychology, 32, 218–230. http://dx.doi.org/10.1016/j. joep.2009.10.008 Santrock, J. W. (2007). Adolescence (12th ed.). Boston, MA: McGraw-Hill Higher Education. Savage, J. (2008). Teenage: The prehistory of youth culture, 1875–1945. New York, NY: Penguin. Savin, D., Sack, W. H., Clarke, G. N., Meas, N., & Richart, I. (1996). The Khmer Adolescent Project: III. A study of trauma from Thailand’s Site II refugee camp. Journal of the American Academy of Child and Adolescent Psychiatry, 35, 384–391. http://dx.doi.org/ 10.1097/00004583-199603000-00021 Skinner, B. F. (1951). How to teach animals. Scientific American, 185, 26–29. http://dx.doi.org/10.1038/ scientificamerican1251-26

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St. Clair, M. C., Croudace, T., Dunn, V. J., Jones, P. B., Herbert, J., & Goodyer, I. M. (2015). Childhood adversity subtypes and depressive symptoms in early and late adolescence. Development and Psychopathology, 27, 885–899. http://dx.doi.org/ 10.1017/S0954579414000625 Watson, J., & Raynor, R. (1920). Conditioned emotional reactions. Journal of Genetic Psychology, 37, 394–419.

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Witmer, L. (1907). A case of chronic bad spelling— Amnesia visualis verbalis—due to arrest of post-natal development. Psychological Clinic, 1, 53–64.

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World Health Organization. (1992). International classification of diseases, 10th revision. Geneva, Switzerland: Author. Zahn-Waxler, C., Shirtcliff, E. A., & Marceau, K. (2008). Disorders of childhood and adolescence: Gender and psychopathology. Annual Review of Clinical Psychology, 4, 275–303. http://dx.doi.org/10.1146/ annurev.clinpsy.3.022806.091358 Zimmerman, F. J. (2005). Social and economic determinants of disparities in professional help-seeking for child mental health problems: Evidence from a national sample. Health Services Research, 40, 1514–1533. http:// dx.doi.org/10.1111/j.1475-6773.2005.00411.x

Chapter 2

Developmental Perspectives on Psychopathology in Children and Adolescents

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Ann S. Masten and Amanda W. Kalstabakken

Over the past five decades, theory and practice concerned with psychopathology in children and youth has shifted profoundly to integrate develop­ mental theory and knowledge. This transformation was heralded by the emergence of developmental psychopathology (DP) as an integrative and multi­ disciplinary perspective for research and inter­ vention on adaptive and maladaptive behavior in human development (Achenbach, 1974; Cicchetti, 1984, 1990, 2006; Cummings, Davies, & Campbell, 2000; Cummings & Valentino, 2015; Masten, 1989, 2006; Sameroff, 2000; Sroufe & Rutter, 1984). Central to this integrative movement was the simple but important idea that understanding psychopa­ thology in a developing organism would neces­ sitate a developmental perspective. This idea had profound implications for theory, diagnosis, assess­ ment, research, and practice which gradually perme­ ated multiple disciplines concerned with the mental health and well-being of young people and, subse­ quently, individuals of all ages across the develop­ mental lifespan. In this chapter, we describe the emergence of DP and the implications of this perspective for theory, research, and practice. Core principles of a develop­ mental approach to psychopathology are delineated, with an emphasis on developmental systems theory. Key models and concepts stemming from a devel­ opmental approach are described, including models of multilevel dynamics, behavioral pathways, risk, resilience, developmental cascades, and related implications for prevention and intervention.

Past contributions and future directions of a devel­ opmental perspective on psychopathology are high­ lighted in the conclusion. Emergence of Developmental Psychopathology Although the roots of DP have been traced back to early Western philosophy (Cicchetti, 1990), modern DP emerged in the 1970s as pioneering scientists began a concerted effort to study the origins of mental health problems among children believed to be at risk for psychopathology (E. J. Anthony & Koupernik, 1974; Cicchetti, 2013b; Masten, 2006). An international cadre of risk researchers undertook multiple projects aimed at delineating the etiology of psychological problems and mental illnesses, including specialists in child psychiatry, epide­ miology, clinical psychology, behavior genetics, education, and child development (Garmezy, 1974; Garmezy & Rutter, 1983; Gottesman, 1974; Gottes­ man & Shields, 1972, 1982; Sameroff & Chandler, 1975; Sroufe, 1979; Watt, Anthony, Wynne, & Rolf, 1984; Werner & Smith, 1982). By some criteria, the early studies on risk and psychopathology were disappointing, because they did not prove to be as informative as investigators anticipated. High hopes floundered as investigators began to struggle with variability in development among children in high-risk categories and more generally with the complexities of understanding human development in relation to psychopathology

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15

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Masten and Kalstabakken

or mental health (Sameroff, 2000). However, from the perspective of contemporary science on DP and its cousin, resilience science, the early risk studies were a spectacular success. Early research on risk for psychopathology initiated a crucial process of communication and integration across fields and scientists, leading to a merger of developmental and clinical sciences that opened new horizons for research and spurred a developmental revolution in mental health research and practice that is still unfolding today ­(Cicchetti, 2006, 2013b; Cummings & Valentino, 2015; Masten, 2006). Variability in the development of children classified as “at risk” because of family background, biological risks, adversity exposure, or early behavior problems pointed to the complexity of developmental processes and highlighted the fact that many children designated to be at risk grew up to be healthy. These observations gave impe­ tus to the integrated and interrelated sciences now described as DP and resilience (Cicchetti, 2016a; Masten & Cicchetti, 2016). Personal relationships played a key role in the development of DP (Masten, 2006). Influential early proponents of DP, including Norman Garmezy, Irving Gottesman, Michael Rutter, Arnold Samer­ off, Alan Sroufe, and Edward Zigler not only knew each other, but often collaborated together and with graduate students who spread their ideas. Their stu­ dents included Thomas Achenbach and Dante Cic­ chetti, among many others. Achenbach published the first book entitled Developmental Psychopathology in 1974. Cicchetti published the first special journal issue on DP in 1984 in the flagship journal of the Society for Research in Child Development, Child Development, and later founded the journal, Development and Psychopathology, among his many other contributions.

Defining Developmental Psychopathology DP has been variously defined by leading propo­ nents as an interdisciplinary science or integrative framework for understanding psychopathology in the full context of development over the lifespan (Cicchetti, 1984, 1989, 1990, 2006; Cummings & Valentino, 2015; Masten, 2006; Sroufe, 1990; Sroufe & Rutter, 1984; Zigler, 1989). Cicchetti 16

(2006), for example, described DP as “an evolving scientific discipline whose predominant focus is elu­ cidating the interplay among the biological, psycho­ logical, and social-contextual aspects of normal and abnormal development across the lifespan” (p. 1). Although definitions of DP continue to vary, there are striking consistencies in the fundamental prin­ ciples described by key proponents of this approach to psychopathology. Core Principles of Developmental Psychopathology As DP evolved, central tenets emerged and were gradually reshaped by advances in the sciences concerned with developmental change and mental health. Fundamentally, however, the core principles described by multiple proponents share the imprint of developmental systems theory, as this synthesis of developmental ideas became the prevailing concep­ tual framework for research in human development (Cummings & Valentino, 2015; Masten & Cicchetti, 2016; Zelazo, 2013).

Key Concepts From Developmental Systems Theory Developmental systems theory brings together eco­ logical models of individual development with gen­ eral systems theory, emphasizing that the course of individual development emerges from many interac­ tions among changing systems within and outside the organism, coacting to shape the structure and function of the developing individual (Bronfen­ brenner & Morris, 2006; Gottlieb, 2007; Lerner, 2006; Lickliter, 2013; Overton, 2013). Dynamic and ever-changing, individuals grow and change because of many interactions across levels of func­ tioning, from the epigenetic and neurobiological levels to cultural and societal levels. Natural eco­ system factors (e.g., air and water quality, exposure to microbiotic organisms) also play a critical role in development. The individual is embedded in socioecologi­ cal systems, proximal and distal, that directly or indirectly influence the life course. Bronfen­ brenner described the proximal ecological sys­ tems that the individual directly interacts with

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Developmental Perspectives on Psychopathology in Children and Adolescents

(e.g., family, school, peer group) as microsystems (Bronfenbrenner, 1979; Bronfenbrenner & Mor­ ris, 2006). More distal, indirect, but nevertheless influential systems include exosystems (e.g., a parent’s workplace) and macrosystems (e.g., mass media, culture, governmental policies, health care systems). Bronfenbrenner also noted the influence on development of interactions among microsystems (mesosystem) and historical time (chronosystem). Over the course of development, the relevant importance and exposure to these influential sys­ tems change. For example, in the developing fetus, all the external influences are mediated by processes within the biological mother. For example, diet, trauma, or illness experienced by a pregnant mother can alter the biology of the developing child prior to birth (Boyce & Kobor, 2015; Meaney, 2010; Monk et al., 2016). In early childhood, experiences with most systems are also regulated or mediated by caregiving adults. As children grow up, they interact with more systems directly through play, school, work, and social relationships, and they also take on greater agency in choosing interactions with specific individuals or systems. In a developmental systems framework, indi­ vidual development is shaped by many interac­ tions and the directions of influence are reciprocal and probabilistic, rather than unidirectional and deterministic (Gottlieb, 2007; Sameroff, 2000). Experiences can influence the development of psychopathology but the behavior of the indi­ vidual also influences the nature of experiences and the quality of the ecological system. Young people influence their own experiences with parents, teachers, classmates, friends, romantic partners, and other social actors who in turn play major roles in their lives. This interplay over time between children and other people, reciprocally influencing the life course of the dyad or members of larger groups, was described in developmental theory as a transactional model (Sameroff, 1975). More broadly, transactional models refer to the reciprocal influences of individuals and their con­ texts on each other (Sameroff, 2009). The idea that children influence the interactions that subsequently shape their own development

encompasses the role of human agency in develop­ ment (Bandura, 1997; Sroufe, 1979). As children grow older, they typically exert more choice about their interactions with the environment, playing an increasingly active role in their own development. At the same time, however, transactional models recognize the profound influences of external socio­ cultural conditions in constraining or altering devel­ opment (Sameroff, 2009). These influences include poverty, discrimination, war, education, and posi­ tive opportunities in many forms. The developmental systems perspective empha­ sizes that human individuals show enormous plas­ ticity, adapting to their experiences in multiple ways, ranging from brain development to social behavior and cultural beliefs (Boyce & Kobor, 2015; Del Giudice, Ellis, & Shirtcliff, 2011; Hochberg et al., 2011; Overton, 2013). Many human adaptive systems that evolved biologically and socioculturally over time show this kind of plasticity or openness, requiring calibration through experience to become fully effective. Examples range from language acqui­ sition to immune function. The human immune system, for example, requires exposure to challenges to become effec­ tive at fighting off disease or infections. Children raised on farms with extensive exposure to diverse microorganisms have a lower risk for asthma than children raised with little exposure to these microorganisms (Figueiredo et al., 2013; Guerra & Martinez, 2008; von Mutius & Radon, 2008). Find­ ings about the protective effect of early exposure to microorganisms generated the hygiene hypothesis. In terms of calibrating the human immune system, modern lives can be too clean, increasing the risk for various allergies or a less optimal immune response. Vaccination is another protective strategy for boost­ ing the effectiveness of human immune systems through deliberate exposure to manageable forms of infectious agents to build up antibodies that fight off infectious diseases. Developmental systems theory gradually became the dominant theoretical model in developmental science (Lerner, 2016). Core principles comprising a developmental approach to psychopathology, delin­ eated next, reflect central ideas from developmental systems theory. 17

Masten and Kalstabakken

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Developmental Principle As noted previously, the defining tenet of DP is that understanding psychopathology in a develop­ ing organism requires a developmental perspective. DP can be viewed as a developmental science that focuses on understanding variations and change in behavioral adaptation over the life course. Develop­ mental science is the study of such changes in living organisms as they form, mature, and decline. Multiple influences on the course of develop­ ment result in a multiplicity of possible pathways for human development, whereas individual devel­ opment also exhibits continuity and orderly pro­ gression (Cicchetti, 2006; Cummings & Valentino, 2015; Sroufe, 1979, 1997, 2007).

Systems Principle A second core principle common to developmental frameworks for psychopathology is the recognition that normative as well as pathological development emerge from interactions of many systems at mul­ tiple levels. Human individuals are living systems that encompass many neurobiological subsystems, and they are also embedded in many other sys­ tems, living and nonliving. A systems perspective underscores the idea that all human development is influenced by these interactions, leading to devel­ opmental change emerging over time. Genes, as well as environments, shape development through interactions, from the epigenetic to the social level. Consequently, development is dynamic, and the nature-versus-nurture debate is moot. Living systems are assumed to have self-­ organizing, self-regulatory, and self-righting prop­ erties that maintain vital functions while adapting to the environment (Masten, 2006). As a social species, human individuals also are regulated by relationships and cultural beliefs, influences that change over the course of development. Caregivers play crucial regulatory roles early in development and, later in life, others will gain influence through friendships, mentorships, and romantic relation­ ships. Cultural norms, passed on through socializa­ tion, influence these social relationships and the goals of self-regulated behavior. This principle has numerous implications for conceptualizing and treating psychopathology, at 18

the individual, dyadic, or system levels, and in the context of other groups engaged for treatment. For individuals, problems—as well as healthy development—arise from complex interactions among systems within an individual and also between the individual and all the multiple systems in which a human life is embedded over time. Prob­ lems can arise from deficits in adaptive skills (e.g., poor self-regulation, poor problem-solving skills), application of normative skills to antisocial or other goals disapproved by society, deviant environments or discrimination, a poor fit between the person and the context, and other kinds of adaptive challenges.

Multiple-Levels Principle Developmental systems theory presupposes that processes occurring at multiple systems levels influ­ ence the development, course, and treatment of psy­ chopathology. Multiple levels of influence can occur simultaneously or sequentially in a cascading man­ ner. Such cascades, discussed following, can result in the biological embedding of experience or genetic influences on neural development, and many other forms of influence across levels and time. Commu­ nity- or family-level violence can influence stress regulation systems within individuals, good parent­ ing can alter gene expression in children, medica­ tion can alter neurotransmitters that in turn can alter mood or behavior; there are many different examples of multilevel effects that result in cascad­ ing changes (see Masten & Cicchetti, 2010). Such multilevel dynamics and cascades have many impli­ cations for intervention and the timing of interven­ tion strategies.

Normative Principle In DP frameworks, psychopathology is defined or delineated in relation to what is generally expected for individuals of a given age, situation, socioecolog­ ical context, period in history, and culture. Norma­ tive expectations for human behavior are sometimes described as developmental tasks or milestones, or simply as “normal behavior.” This principle explicitly acknowledges that judgments about the quality of adaptation, whether they are concerned with good or poor functioning, always are based on expectations about what is typical or expected

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Developmental Perspectives on Psychopathology in Children and Adolescents

for people of a particular age, gender, situation, and culture (Masten, Burt, & Coatsworth, 2006; ­McCormick, Kuo, & Masten, 2011). Some expectations for human development are universal, reflecting the nature of our species and its typical development across most environments, whereas others are distinctive to a specific era, place, or culture. Developmental milestones, such as walk­ ing and talking, are universal developmental tasks. When these do not occur, adults (especially parents) grow concerned that something is radically amiss. Additional tasks are shared by many cultures at a given period in history, reflecting widely-shared similarities in human capabilities, cultures, and opportunities. These include the developmental task expectations of learning to obey rules at home or in public, going to school, learning to read, and getting along with other people. The exact forms of these expected achievements vary across cultures, but the fundamental expectations are similar (Masten & Coatsworth, 1998; McCormick et al., 2011). Other expectations about what is important for individu­ als to learn or do over the course of development vary dramatically across cultures and historical time. These include rites of passage and other cultural practices and the skills needed for adult life in a specific community. Expectations for behavior change as children grow up. The norms for behavior are highly tuned to age-normative development. The same behaviors that are viewed as normative in a 2- or 3-year-old (e.g., tantrums, distractibility, imaginary friends) are viewed as problematic in older children and adults because behavior is judged against developmental norms. These norms may be explicit, as in normreferenced scoring on intellectual or achievement tests, or implicit, as in community disapproval of nonnormative behavior. Salient developmental tasks wax and wane in importance over the life course. School success, for example, is important for school-age children and then wanes in importance in adolescence or adulthood as new developmental tasks begin to rise in salience (e.g., work or family formation). It is important to keep in mind that not all devia­ tions from the norm are pathological. Societies usu­ ally value exceptionally good intellectual, language,

artistic, or other desirable skills. International com­ petitions like the Olympics are held to honor “devi­ ance” in the form of exceptional athletic prowess. Nonetheless, developmental task expectations influence definitions of adaptive and maladap­ tive behavior. Classification systems for mental disorders implicitly or explicitly acknowledge these expectations in multiple ways. One key way is through age-related criteria for disorders or by designating whether behavior problems are ageinappropriate or interfere with adaptive function in these kinds of developmental task domains (e.g., interfere with school competence or peer rela­ tions). Diagnosing psychopathology requires judg­ ments about normality and deviance that are highly related to expectations about normative behavior in developmental perspective (Drabick & Kendall, 2010; Masten, 2006). There are allowances for exceptional circum­ stances. In traumatic situations, for example, behav­ ior that would ordinarily be viewed as abnormal may be widely recognized as typical and acceptable. The diagnosis of posttraumatic stress disorder in most contemporary classification systems recognizes this allowance by specifying that the symptoms are lingering past an expected window of normative recovery time (e.g., one month). This principle implies that evaluations of psy­ chopathology must account for the developmental and contextual expectations for behavior. It also raises the interesting issue of who decides what is “normal” or not. Societies, professionals, cultural leaders, families, and individuals themselves all have a stake in defining psychopathology in contrast to normal development. Cultural subgroups within a community or society may also differ in their views about what is “good” or normal behavior.

Mutually Informative Principle Another widely held idea in DP is that the study of normative development and psychopathology are mutually informative. This principle recognizes that understanding variations in adaptation from a devel­ opmental perspective is important for understand­ ing undesirable deviant behavior as well as desirable typical behavior. From this perspective, research on positive development in the context of risk for 19

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Masten and Kalstabakken

psychopathology adds important information to the knowledge base on the etiology of mental illness. Moreover, it is important to understand promo­ tive and protective influences on pathways toward or away from problems as well as the influences of risks, vulnerabilities, and adversities. Promotive factors are positively associated with good adapta­ tion regardless of risk, whereas protective factors are more important or only important when risk or adversity is high (Masten & Cicchetti, 2016). Research on competence and resilience in devel­ opment contributes important knowledge to the etiology, prevention, and treatment of psychopa­ thology (Cicchetti, 2006; Masten, 2006). Similarly, knowledge on the full range of human development, including pathological patterns, informs develop­ mental science and theories of how development proceeds.

Developmentally Sensitive Assessment and Intervention Although the final principle noted here is not always explicitly articulated, developmental approaches to psychopathology assume that assess­ ment, diagnosis, prevention, and intervention must be grounded in developmental context. There are many reasons for this view. First, the meaning of the same behavior (e.g., tantrums, distractibility, talking to an imaginary friend) changes over the course of development, as noted previously, and different behaviors (e.g., crying, texting to some­ one) may have the same meaning (e.g., seeking contact with an attachment figure) at different periods of development. Second, judgments about the course of individual development often depend on comparisons with normative-expected patterns of behavior or progress that change with develop­ ment. Third, tailoring interventions to optimize developmental timing is likely to be more effective (Toth & Cicchetti, 1999). In prevention, knowing what to do and when to do it in order to have the greatest impact (on future competence or symptoms) depends on theory and knowledge about the course of development lead­ ing up to competence or problems, the best devel­ opmental timing of a particular intervention, and the best type of intervention for different periods 20

of development. Similarly, in intervention planning and implementation more generally defined, strate­ gies always need to be informed by developmental knowledge about what has been shown or is most likely to work for whom and when. These points are often discussed in terms of developmental tim­ ing and targeting. All these principles continue to evolve and change as knowledge increases. Meanwhile, the core ideas have numerous implications for sciences and practices concerned with psychopathology. Developmental Patterns and Pathways One of the important implications of a developmen­ tal systems perspective on psychopathology as well as normative development is the focus on pathways (Cicchetti, 2006; Cummings & Valentino, 2015; Masten & Cicchetti, 2016; Sroufe, 1990). Individual development arises from complex interactions across many systems and levels, creating potentiali­ ties for many possible pathways that development may take, although some are much more likely than others. This idea was championed decades ago by Waddington (1957; see also Mitchell, 2007) in the concept of an epigenetic landscape and beautifully articulated in modern form as probabilistic epigen­ esis by Gottlieb (2007). In behavior genetics, Gottesman (1974; Gottes­ man & Shields, 1972, 1982) depicted contrasting pathways toward and away from mental illness among individuals with varying levels of initial genetic risk in his diathesis-stressor models of schizophrenia. Net liability for mental disorder (e.g., schizophrenia) fluctuated as a function of experiences over time. These compelling models illustrated the possibility of diverging pathways of mental health functioning, even for identical twins (discordant for mental illness), as well as converging pathways to disorder from different initial starting points of liability. In child psychopathology, Bowlby (1988) described multiple pathways of development, not­ ing how experiences can lead a child’s development in deviant directions. Sroufe (1997) elaborated on the idea of multiple pathways using an organic tree

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Developmental Perspectives on Psychopathology in Children and Adolescents

metaphor adapted from Waddington’s (1957) earlier branching pathway models. The course of individual development can change directions many times as a function of expe­ rience or epigenetic processes. There can be “turn­ ing points” or “branching” in new directions. In resilience science, investigators were particularly interested in positive turnarounds in the lives of young people (Masten, 2012, 2014c; Rutter, 1987; Werner & Smith, 1992, 2001). The concept of pathways also underscored two key concepts originating from systems theory but also widely observed in individual cases: equifinal­ ity and multifinality (Cicchetti, 2006; Cicchetti & Rogosch, 1996). There were different pathways to the same or similar outcomes (i.e., equifinality), as well as different outcomes among individuals with the same or similar starting points (i.e., multifinal­ ity). Just as identical twins could become discordant for a mental disorder, individuals from very differ­ ent genetic and experiential backgrounds could end up in the same kind of trouble or identified with the same diagnostic category through the influ­ ence of many interactions over time. The focus on epigenetic pathways and branching patterns in DP highlighted the complexity of human behavior and development and counteracted deterministic views of development. Multiple pathway perspectives have been validated repeatedly in research on twins ­(Grigorenko & Cicchetti, 2012; Jaffee, Price, & Reyes, 2013; van Dongen, Slagboom, Draisma, Martin, & Boomsma, 2012), by epidemiological data documenting changes in prevalence of specific disorders over the life course (Costello & Angold, 2016), in longitudinal studies of individual adjust­ ment (e.g., Hauser, Allen, & Golden, 2006; Masten & Tellegen, 2012; Werner & Smith, 1982, 1992, 2001), and in research on patterns of adjustment following mass-trauma experiences (see M ­ asten, Narayan, Silverman, & Osofsky, 2015). With advances in statistical methodology, ­investigators have begun to identify or corroborate trajectories of behavior (positive and negative) through techniques such as latent growth modeling, to capture empirically the diversity and regularity of pathways hypothesized in the literature or reported

in case studies (see Kim-Spoon & Grimm, 2016). Trajectory analyses have delineated several distinctly different patterns of response to trauma and differ­ ent patterns of development among young people with significant problems or disorders. For example, Betancourt, McBain, Newnham, and Brennan (2013) documented different recovery patterns among for­ mer child soldiers. Osofsky, Osofsky, Weems, King, and Hansel (2015) studied patterns of adaptive function among double victims of Katrina and the Deepwater Horizon oil spill. Numerous investigators have attempted to portray different pathways of sub­ stance abuse problems and desistance (see Chassin, Colder, Hussong, & Sher, 2016; Zucker, Hicks, & Heitzeg, 2016). Often, investigators seek to identify patterns within patterns, in the sense of finding distinct sub­ groups that account for general symptom or preva­ lence patterns observed in epidemiological data. Perhaps the most famous example of this effort to find subgroups with a larger pattern is provided by research on serious offending or antisocial behavior. Crime data dating back to the 1800s long have doc­ umented an age-crime curve phenomenon, where criminal behavior rises in prevalence sharply during adolescence and then falls again in early adulthood (Loeber & Farrington, 2014). Moffitt (1993) pro­ posed a developmental explanation of this crescendo pattern of misbehavior in a highly cited theoretical article, suggesting two key subgroups that generated this apparent pattern: an adolescence-limited form of antisocial behavior and a life-course-persistent pattern. She and her colleagues subsequently tested this theory through empirical analysis of data from the Dunedin cohort (e.g., Moffitt, Caspi, Har­ rington, & Milne, 2002). Similar efforts have been made to disaggregate data on substance abuse (see Masten, Faden, Zucker, & Spear, 2008). Again, distinct subgroups of individuals who share similar patterns of onset and/or offset of substance abuse problems have been identified that together com­ prise a general developmental crescendo pattern of prevalence. Some of the subgroups observed in these studies are characterized by high, chronic substance abuse; low use over time; and onset in adolescence followed by desistance. Such patterns are impor­ tant because they have very different implications 21

Masten and Kalstabakken

for prognosis, prevention, and treatment, as well as theories of etiology.

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Developmental Resilience Science and Psychopathology The history of resilience science and DP are closely intertwined (Masten & Cicchetti, 2016). Research on resilience often is viewed as a component of DP and certainly it is not a coincidence that resilience science and DP blossomed at the same time (Mas­ ten, 2014c). The search for understanding and pre­ venting the development of mental health problems, and particularly the study of young people at risk for psychopathology, played a central role in the emer­ gence of both lines of work. Pioneers in resilience science, including Norman Garmezy and Michael Rutter, were psychopathologists by training, in clini­ cal psychology and child psychiatry, respectively. They interacted and collaborated not only with each other (Garmezy & Rutter, 1983; see Masten, 2012), but also with leading developmental scientists, such as Alan Sroufe (Sroufe & Rutter, 1984). One of the crucial insights of the investigators who led the first wave of research on resilience was recognizing the importance of variation they observed in their research on children at risk for psychopathology (Masten, 2014c). Many of these children manifested normal development despite their risk status and often in the context of highly adverse conditions. Others recovered to good func­ tion when supportive caregiving or rearing environ­ ments were established or restored. The pioneers recognized that knowledge about pathways away from disorder were just as illuminating as pathways to problems, providing important clues about etiol­ ogy, particularly with respect to the practical goal of preventing psychopathology. Resilience in contemporary developmental sci­ ence refers to the capacity of a system to adapt suc­ cessfully to challenges that threaten the function, survival, or positive development of that system (Masten, 2011, 2014c). This capacity can be inferred from manifest evidence of coping effectively with significant challenges or recovering from traumatic experiences. The potential for resilience may also be assumed from the presence of well-established 22

promotive or protective factors associated with good adaptation. This definition of resilience is grounded in developmental systems theory, and has the benefit of scalability across levels of analysis, from subsys­ tems within the individual to large-scale social and ecological systems (Masten, 2011, 2014a; Masten & Obradovi´c, 2008). The concept of resilience can be applied to any dynamic adaptive system, such as a person’s immune system, a family system, or a large ecosystem. There are growing efforts to integrate resilience theory and research across multiple sys­ tem levels, perhaps because of the surge of concerns about global challenges that scale multiple levels, including climate change, natural disasters, war, and pandemics (Masten, 2014a, 2014c, 2015; Masten & Monn, 2015). Waves of resilience science have made numer­ ous contributions to theory, methods, knowledge, and interventions in DP documented over the years (Luthar, 2006; Masten, 1989; Masten & Cicchetti, 2016; Wright, Masten, & Narayan, 2013). Most fun­ damentally, the focus on positive adaptation in the context of risk or adversity shifted models of mental health and practice from a narrow and inherently limited deficit-centered perspective to a broader and more hopeful emphasis on strengths and promotive or protective influences, dynamic adaptive systems, and the potential for positive change. Influences on human development and function long overlooked and neglected in clinical sciences and practice became the focus of greater attention, complement­ ing the longstanding emphasis on risk, vulnerability, and problems in development. The overall effects of this shift transformed the frameworks for practice in multiple fields, includ­ ing family therapy (Walsh, 2016), school counsel­ ing (e.g., Akos & Galassi, 2008; Masten, Herbers, Cutuli, & Lafavor, 2008), social work (e.g., E. K. Anthony, Alter, & Jenson, 2009), military training and family services (Cozza & Lerner, 2013; Masten, 2013), positive youth development (Lerner et al., 2013; Masten, 2014b), disaster response (Masten et al., 2015), and humanitarian outreach (Ager, 2013; Lundberg & Wuermli, 2012). Models and practices of prevention and intervention for psy­ chopathology in children and youth also reflect this

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Developmental Perspectives on Psychopathology in Children and Adolescents

shift (Cicchetti, 2013a; Gest & Davidson, 2011; Luthar, Cicchetti, & Becker, 2000; Masten, 2014c; Masten et al., 2006; Winslow, Sandler, Wolchik, & Carr, 2013). Resilience science focused on variations and change in the quality of adaptive function over the life course in relation to changing experiences and development, particularly when individuals were faced with very challenging situations. Two key judgments (and corresponding measures) were critical to research on resilience, concerning the nature of challenges faced and the quality of adap­ tive functioning (Masten, 2014c). Resilience often was inferred from evidence of good adaptation in the context of significance challenge. Accounting for good outcomes when one might expect otherwise because of high risk or adversity exposure led these investigators in search of factors that functioned to prevent, counteract, or mitigate risk or spur positive function even in the presence of adversity. Investiga­ tors expected that better knowledge of preventive, promotive, and protective processes would yield better policy and practices to promote mental health and prevent problems in development. Applied models of risk and resilience encouraged better assessment of positive outcomes as well as symptoms and disorders; assets and resources that could function as potential promotive factors; and potential moderators of risk that might be malleable, and therefore a target for intervention, to protect the health or well-being of a person encountering challenges. As a result, models and measures of competence, assets, and protective factors expanded (Masten, 2014c; Masten & Tellegen, 2012). Tracking the quality of adaptation over time also required a developmental knowledge base to inform assessments as children grew up. Collaborations of clinical and developmental scientists ensued as clini­ cal scientists began to follow the development of high-risk children in longitudinal studies. From the very beginnings of resilience research, scientists were interested in pathway models because compelling data in individual or aggregated case studies of resilience indicated that some indi­ viduals showed compelling turnaround patterns in their adaptive success (judged by developmental tasks or other criteria). These turns sometimes

occurred naturally (Masten, 2014c; Rutter, 1987; Werner & Smith, 1992), but sometimes turn­ arounds were deliberately induced by intervention (e.g., Masten & O’Connor, 1989; Nelson, Fox, & Zeanah, 2014; Rutter, Sonuga-Barke, & Castle, 2010). Research on children in war, terror, and disas­ ter has been influential in resilience science (Gar­ mezy & Rutter, 1983; Masten, 2014a, 2014c; Masten et al., 2015). Differential pathways of response and recovery to mass-trauma experiences have been observed and studied since World War II. This large and expanding body of case accounts and research highlights the key roles of developmental timing, nature of exposure, previous trauma exposure, vul­ nerabilities and protective factors, and the nature of the recovery environment in the patterns of response to these extremely threatening situations. Resilience science motivated an expansion of research on competence and positive development and how individual difference in normal develop­ mental domains of adaptive function were related to psychopathology. This work would lead to new measures, research on developmental cascades, and a growing body of research linking child develop­ ment research on normative psychosocial develop­ ment to psychopathology (see Burt, Coatsworth, & Masten, 2016; Masten et al., 2006; Masten & Cic­ chetti, 2016). Initial waves of resilience studies were focused on psychosocial adaptation in children and youth, often in developed countries. Processes at other levels of analysis were relatively neglected, includ­ ing neurobiological processes and sociocultural processes that might differ in diverse cultural settings (Luthar, 2006; Masten, 2014a, 2014b; Panter-Brick & Leckman, 2013). Over the past two decades, as research on neurobiological develop­ ment and genetic processes expanded, there has been a surge of research on resilience at the biologi­ cal level and in relation to processes of gene by envi­ ronment interaction (G×E) processes (Cicchetti, 2013a; Masten & Cicchetti, 2016). Concomitantly, research on cultural influences in resilience has burgeoned (Lundberg & Wuermli, 2012; MottiStefanidi, 2015; Ungar, 2012; Ungar, Ghazinour, & Richter, 2013). 23

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Increasingly, there are calls for greater integra­ tion across levels and disciplines focused on resil­ ience (Cicchetti, 2013a; Masten, 2007, 2015, 2016; Masten & Monn, 2015; Southwick, Bonanno, Mas­ ten, Panter-Brick, & Yehuda, 2014). As noted previ­ ously, global challenges may account for some of the impetus behind these calls for more integrated the­ ory, knowledge, and action. In addition, however, a key motivation may stem from growing acceptance of a developmental systems perspective on adapta­ tion, for investigators focused on competence and resilience and those focused on preventing or ame­ liorating psychopathology in development. Adaptive and Maladaptive Perspectives: Competence and Psychopathology Developmental approaches to psychopathology highlight the importance of considering how nor­ mal and abnormal behavior are related in develop­ ment (Burt et al., 2016; Masten et al., 2006). Earlier theory and research in child psychopathology was in some respects “truncated” to focus on under­ standing patterns linking risk, vulnerabilities, and maladaptive behavior while neglecting contributors to positive adjustment and development. Earlier models also often ignored intriguing combinations of adaptive behavior, when individuals with simi­ lar symptom profiles showed markedly different success in their environments or individuals who appeared to function well in one domain struggled in another domain. It has been clear for many decades that patterns of competence in developmental tasks (e.g., school or work success) are often linked to psychopathol­ ogy, concurrently and sequentially. Numerous explanations are possible, ranging from common causal antecedents, like shared risk factors (e.g., poverty, poor parenting) to confounded measure­ ment (e.g., similar items on measures purported to assess competence and symptoms or biased reporting); symptoms may also directly affect suc­ cess in life or vice versa (Masten et al., 2006). The dual-failure model posited by Patterson, Capaldi, and colleagues suggested that antisocial behavior arising initially in the family led to failures in the 24

developmental tasks of academic achievement and getting along with peers, which in turn led to inter­ nalizing symptoms (Capaldi, 1992; Patterson & Stoolmiller, 1991). In classification, assessment, and diagnosis of mental disorders, considerations of competence and adaptive behavior often have entered research and clinical practice through the concept of impairment or in scales of adaptive function (Burt et al., 2016). For individuals with developmental disabilities, there are well-developed assessment tools designed to assess adaptive skills in specific domains of adap­ tive function (Ditterline & Oakland, 2009). The Vineland Adaptive Behavior Scale is widely used for assessing functional impairment in age-normative developmental tasks and expected skills in daily tasks of living (Sparrow, Cicchetti, & Balla, 2005). For adults with wide-ranging mental disorders, one of the most important broad-spectrum efforts to improve the assessment of adaptive “disability” level is the World Health Organization’s Disability Assess­ ment Schedule (Üstün et al., 2010), which has been translated into numerous languages. This tool, how­ ever, is not optimal for a developmentally sensitive assessment (Burt et al., 2016). Efforts are currently underway to create a comparable tool suited to ear­ lier periods of life, when developmental consider­ ations are paramount. Struggles to sort out the nature of linkages between indices of adaptive success (including impairment measures) and indices of psychopathol­ ogy (at the level of symptom and disorder) raised a host of issues, and sparked reconsiderations about how to organize research on psychopathology (Burt et al., 2016). One promising product of this struggle is the shift by the National Institute of Mental Health to research domain criteria, which focuses on constructs better suited to a dynamic systems model of human function and psychopathology. However, this approach has not been well grounded in devel­ opmental theory or evidence (Burt et al., 2016). Another promising area of advancement that arose from struggles to understand linkages among levels and domains of adaptive behavior is work on developmental cascades. Cascade models attempt to test hypotheses about progressive changes across levels, domains, and even generations that may

Developmental Perspectives on Psychopathology in Children and Adolescents

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result from the dynamic interactions of systems (Masten & Cicchetti, 2010). Some cascade analyses focus exclusively on domains within positive or negative adjustment frameworks and pathways (e.g., “competence begets competence,” studying how abuse of one substance precipitates abuse of another substance). Many cascade studies, however, attempt to encompass multiple domains of adaptive behav­ ior (positive and negative) and interactions that span levels of analysis, systems, or developmental transitions. Developmental Cascades Developmental cascades refer to the cumulative con­ sequences on development of the many interactions among systems at different levels over time whereby effects of changes in one or more levels of function, domains of adaptive function, or generation spread to change other levels, domains, and generations (Masten & Cicchetti, 2010). These types of effects were observed decades ago and sometimes described by researchers and clinicians as snowballing or pro­ gressive effects (Masten et al., 2006). It has been noted, for example, that antisocial behaviors in childhood forecast a greater risk for a multiplicity of later problems in development, including school problems, peer rejection, substance abuse, work problems, relationships problems, and criminal offending (Dodge et al., 2009; Dodge & Pettit, 2003; Hinshaw & Anderson, 1996; Kohlberg, LaCrosse, & Ricks, 1972; Maguin & Loeber, 1996; Patterson, Reid, & Dishion, 1992). Patterson’s dual-failure model described previously represents a develop­ mental cascade model (Patterson & Stoolmiller, 1991). On the favorable side of the story, childhood intellectual skills forecast later successes in life (Kohlberg et al., 1972; Masten et al., 2006). More­ over, the evidence on the predictive significance of self-control, although often studied from a negative perspective (low self-control predicting an array of poor outcomes; e.g., Moffitt et al., 2011), could also be interpreted to implicate good self-control as a key advantage for future development. Findings on the predictive significance of executive function skills and other indicators of inhibitory self-control or the

capacity to delay gratification, align well with these observations (Zelazo & Carlson, 2012). The changes studied in research on developmen­ tal cascades alter the course of development; they are not fleeting in nature. Cascades have implica­ tions for understanding etiology and co-occurrence of disorders and problems and for intervention, par­ ticularly with respect to targeting and timing. Two special issues of the journal Development and Psychopathology, edited by Masten & Cicchetti (2010), provided wide-ranging examples of research on developmental cascades. Numerous other empirical examples have been published on this theme in this and other journals since those special issues. Considerable diversity of focus is evident in this emerging body of literature. Some studies attempt to demonstrate how community violence cascades into family and peer environments, resulting in increased aggression of individual youth (e.g., Boxer et al., 2013). Others focus on pathways by which family stress can alter allostatic load in children (Repetti, Robles, & Reynolds, 2011) or family interventions that may alter biological function (e.g., inflamma­ tion; Miller, Brody, Yu, & Chen, 2014). Interven­ tions have targeted improved parenting or foster care as a strategy for lowering risks related to child maltreatment or stress (e.g., Dozier, Peloso, Lewis, Laurenceau, & Levine, 2008; Fisher, Stoolmiller, Gunnar, & Burraston, 2007). To date, the evidence base on developmental cascades remains limited (Burt et al., 2016; Cum­ mings & Valentino, 2015). Moreover, the pro­ cesses by which cascade effects occur are rarely well-articulated. Nonetheless, a new era of research has arrived, focused on multisystem processes and their consequences for development (positive or negative). The most persuasive evidence on cascade effects is provided by intervention experiments with randomized assignment to treatment or control/ comparison groups. Prevention science provides strong examples of efforts to alter development by changing a process hypothesized to play a role in the adaptation of individual children or youth. In this case, the anticipated cascade sequence is that the intervention alters the targeted moderator/ mediator of change (e.g., parenting or school 25

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Masten and Kalstabakken

engagement) which in turn precipitates changes in individual children (e.g., reduced symptoms, improved achievement). Patterson and colleagues at the Oregon Social Learning Center devoted decades to basic research and intervention development aimed at reducing the risk for antisocial behav­ ior and related problems (Patterson, Forgatch, & Degarmo, 2010; Patterson et al., 1992). Patterson et al. (2010) described some of their important pre­ ventive intervention efforts designed to interrupt cascades they had observed in basic studies, using the Parent Management Training—Oregon Model. Mothers in the experimental group were trained with the goal of altering their parenting interactions with their children, for example to become more consistent in reinforcing positive child behaviors. Results indicated that the intervention had altered their parenting and that child behavior improved. Moreover, these effects expanded over time after the intervention stopped. Cascades theoretically also would be expected to produce unexpected changes among intercon­ nected systems. Results from the Patterson et al. (2010) studies illustrate unanticipated cascades as well. Longitudinal follow-ups revealed that mothers became less depressed and more economically suc­ cessful over the long term. Implications of a Developmental Perspective for Intervention: Timing and Targeting Developmental approaches to psychopathology have yielded important models and evidence for practice while underscoring the challenges of promoting change in complex adaptive systems. Data from DP and resilience science alike point to the crucial importance of timing and targeting for intervention and the potential benefits of multiple level, multi­ disciplinary integration. Theory and basic research on development and psychopathology suggests that timing matters for intervention. There appear to be windows of vul­ nerability and opportunity in development when plasticity or malleability from experiences, including interventions, are greater (Dahl, 2004; Karatoreos & McEwen, 2013; Lupien, McEwen, Gunnar, & Heim, 26

2009; Masten, 2014c; Masten, Faden, et al., 2008; Nelson, 1999; Steinberg et al., 2006). These win­ dows are associated with developmental transitions, like the complex processes attending puberty and periods of rapid brain development, prenatal and postnatal. They also are associated with contextual changes, like the transition into formal schooling, going to college or otherwise leaving home, and ill­ ness, trauma, or other challenging experiences that disrupt function in multiple systems. These periods may reflect the dynamic nature of complex adap­ tive systems which are more open to change during times of disruption or transition. Developmental models, including cascade mod­ els, suggest that early timing could be important for initializing positive cascades or interrupting nega­ tive cascades (Masten et al., 2006; Masten & Cic­ chetti, 2010). Economist Heckman (2006), among others (see Masten, 2014c), has argued that there is a higher return on early investment in children because competence builds on competence. Analy­ ses of benefits to costs of early intervention by Heck­ man and others support this perspective (Temple & Reynolds, 2007). This high return on early invest­ ments (e.g., high quality early childcare programs) could be the result of positive cascade processes. Numerous investigators have argued that early intervention is important to interrupt pathways to antisocial behavior and related problems in chil­ dren, because of the risks to development posed by persisting aggressive and dysregulated behavior (Dishion & Patterson, 2016; Dodge et al. 2009; Shaw, Hyde, & Brennan, 2012). Very early starters may require early childhood intervention to prevent progressions along these pathways. Early interven­ tions for conduct problems often focus on parenting skills. However, as Dishion and Patterson (2016) observed, there is good evidence of successful inter­ ventions later in childhood and adolescence. Evidence on the rapid development of neural systems associated with self-regulation during the preschool years and associated control of attention and action provides another example of a widely acknowledged window of plasticity for interven­ tions to support or promote the development of skills considered essential for successful transi­ tions to school (Blair & Raver, 2015; Blair & Razza,

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Developmental Perspectives on Psychopathology in Children and Adolescents

2007; Zelazo, Blair, & Willoughby, 2016; Zelazo & Carlson, 2012). Evidence indicates that the skills involved in goal-directed problem-solving and delib­ erate self-regulation show malleability as well as rapid development in the preschool years, consistent with a sensitive period or window of opportunity (Blair & Raver, 2015; Diamond & Ling, 2016). Over the decades, prevention models have expanded to include competence promotion as well as psychopathology prevention (Burt et al., 2016). For example, in the consensus study by the Insti­ tute of Medicine on Preventing Mental, Emotional, and Behavioral Disorders Among Young People, the first recommendation of the report underscores the importance of promoting healthy development as well as preventing specific disorders: The federal government should make the healthy mental, emotional, and behav­ ioral development of young people a national priority, establish public goals for the prevention of specific [mental, emotional, and behavioral] disorders and for the promotion of healthy development among young people, and provide needed research and service resources to achieve these aims. (National Research Council & Institute of Medicine, 2009, p. 378) The committee that conducted this consensus study adopted a developmental framework for their review and they included many examples of programs that promote positive behaviors as a prevention strategy for lowering the risk of psy­ chopathology later in development. These include well-validated programs to promote parenting skills, school engagement, or prosocial behavior. Effective programs often combine strategies to reduce prob­ lems and to boost competence. Efforts to promote better self-regulation skills to boost learning and socioemotional skills for school success represent a more focused strategy of preven­ tion directed at competence promotion, targeting a set of skills considered foundational for learning and social relationships. However, there are several mul­ tifaceted efforts to improve school success through high quality early childcare and education. These include the Head Start program (Zigler & Styfco,

2010) and the Chicago Parent–Child Center (Reyn­ olds & Ou, 2011; Reynolds, Temple, White, Ou, & Robertson, 2011). Head Start and the Chicago Parent–Child Cen­ ters are examples of multicomponent programs that take a broad-based approach to intervention in early childhood, with the expectation that more compre­ hensive programs are needed for the multiplicity of risks experienced by children and their families growing up in poverty. These programs often com­ bine quality childcare and early education with parent education and outreach to support positive child development. Some programs include exten­ sive resources, all aiming to bolster the chances for successful development in children. The Harlem Achievement Zone, created by Geoffrey Canada, and related programs funded by Promise Neighborhood grants take a very comprehensive approach. Writing about the Harlem Achievement Zone, Tough (2009) described Canada’s comprehensive approach to aca­ demic success for the high-risk children of Harlem in the title of his book Whatever It Takes. Comprehensive interventions reflect an implicit, and sometimes explicit, recognition that the com­ plicated pathways of risk and adaptation in human development require a multifaceted effort to redirect the course of development. The aim of such pro­ grams is synergy, to generate a positive cascade of interactions across domains and systems of a child’s life (Masten, 2011). A recent issue of the Future of Children edited by Haskins, Garfinkel, & McLana­ han (2014) focused on two-generation mechanisms of intervention and policy that target parents and children. International efforts to promote healthy devel­ opment through humanitarian aid and economic investment also increasingly focus on multiple sys­ tems, attempting to align vertical or horizontal sys­ tems for greater impact (Leckman, Panter-Brick, & Salah, 2014; Lundberg & Wuermli, 2012; Masten, 2014a). Vertical alignment strategies can span many levels of embedded systems, from a child to public policies. At a more basic level, interventions can focus on two-generation approaches that encom­ pass parent and child in an effort to generate more synergy. Horizontal alignment includes attempts to encompass multiple systems of child or family 27

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Masten and Kalstabakken

life, such as housing, schooling, and health care. Humanitarian interventions are increasingly aligned to provide a combination of education, housing, health and nutrition, and other essentials to parents and children in the same families. Human individuals are complex adaptive sys­ tems connected to many other complex adaptive systems. The goals of facilitating specific changes in complex adaptive systems is remarkably chal­ lenging, although this complexity offers many avenues for intervention. Complex adaptive sys­ tems have many interacting components, includ­ ing many independent individuals who may act independently in ways that collectively impact systems at higher levels of organization. Efforts to change systems in a specific direction, whether one is aiming to promote mental health in chil­ dren, alter a nation’s health care system, promote economic growth, or reform education, face great opportunities and challenges related to the nature of multilevel dynamics across levels in complex adaptive systems. Well-intended programs can have unanticipated consequences because of resistance, cascades, or counter-regulatory processes, among many other influences (National Research Coun­ cil & Institute of Medicine, 2015; Reiman, Rollen­ hagen, Pietikainen, & Heikkila, 2015). Prevention and intervention sciences show progress in imple­ menting a developmental perspective, but much work remains to be done. Conclusions and Future Directions Over the past five decades, theory, research, and practice in mental health have been transformed by the infusion of a developmental systems perspective. Developmental systems theory has permeated devel­ opmental science and its applications in multiple fields. This framework has many implications for conceptualizing and addressing psychopathology, with respect to its origins, assessment, classification, prevention, treatment, and policy. Results of this transformation, often described as the emergence of DP, were evident first in work focused on children and youth. This early focus on young people probably occurred because so many early proponents of this perspective were 28

investigating adaptation and the etiology of disor­ ders in childhood and adolescence, although there were certainly champions of a lifespan perspective early on (Sroufe & Rutter, 1984; Zigler & Glick, 1986). The core principles of DP apply as well to later life as they do to early development. DP is an integrative developmental framework for the entire lifespan (Cicchetti, 2016b; Masten, 2006; Rutter, 2013; Rutter, Kim-Cohen, & Maughan, 2006). A rich developmental perspective on psychopathology during the adult years, particularly in middle and later life, remains a work in progress. Nonetheless, lifespan applications of develop­ mental perspective on psychopathology and men­ tal health are growing (Achenbach & Rescorla, 2016; Cicchetti, 2016b; Cummings & Valentino, 2015). One of the most important areas of expand­ ing research in DP is motivated by identifying the linkages between early experience and later health, including mental health. There is growing attention to childhood roots of health disparities in adulthood and the lifelong consequences of early toxic stress (Hochberg et al., 2011; Shonkoff, Boyce, & McEwen, 2009; Shonkoff & Garner, 2012). Concomitantly, a compelling case is emerg­ ing for investments in childhood with the explicit goal of promoting health and preventing health disparities in adulthood (e.g., Campbell et al., 2014; Shonkoff, 2011). Research also is rapidly expanding to encom­ pass multiple-level processes that shape pathways toward and away from mental health (Cicchetti, 2013b, 2016b; Cicchetti & Toth, 2009; Masten & Cicchetti, 2016). This expansion includes research on epigenetic processes, spurred by technological advances in assessing the human genome and gene expression (Addington & Rapoport, 2012; Boyce & Kobor, 2015; Brookes & Shi, 2014; Jaffee et al., 2013; Meaney, 2010; Roth, 2013). Further, there is growing attention to interactions of individuals with families, schools, and cultural systems as they shape development and prevent or foster competence, resilience, and psychopathology (Chen & Liu, 2016; Deater-Deckard, 2013; Durlak, Domitrovich, Weiss­ berg, & Gullotta, 2015; Kerig, 2016; Masten, 2014c; Masten & Monn, 2015; Mayes & Lewis, 2012; Motti-Stefanidi, 2015).

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Developmental Perspectives on Psychopathology in Children and Adolescents

In other words, ideas about the importance of multilevel interactions for development, norma­ tive and deviant, are plentiful. Less common are detailed delineation of processes that account for developmental cascades or diverse pathways. Similarly, although it is commonly recognized that experiments to prevent or mitigate psychopa­ thology offer powerful strategies for testing causal hypotheses about processes in the development of competence, resilience, and psychopathology, many processes remain untested to date. There­ fore, there is ample uncharted territory for devel­ opmental investigators to study. It is a time of challenges and opportunities in the development of DP. Future advances in developmental theory and evidence pertinent to psychopathology will depend on a new generation of scholars and practitioners who do not shy away from the com­ plexity of human development and its variations, who are comfortable with collaboration across disciplines, and who move well across the bidirec­ tional bridges spanning real world contexts and university laboratories. It is impossible for a single individual to master all the skills needed to gain traction along the pathways of DP. Therefore, it is crucial that training for research and practice provide experience with multidisciplinary col­ laboration as well as multiple perspectives on development. There is a parallel need for developmental and interdisciplinary training of clinicians on the front lines of evaluation and intervention. These clini­ cians are expected to address the needs of individu­ als with issues related to psychopathology at many points along the pathways of adaptive or maladap­ tive behavior. Developmental knowledge and per­ spectives could enhance the work of professionals across the lifespan: before birth, early in develop­ ment, in adulthood, and in later periods of life. Cur­ rently, the knowledge base on DP during the early decades of life is more advanced, but that is likely to change rapidly as a lifespan developmental perspec­ tive gains traction. The future looks bright for devel­ opmentally informed theory, research, and practice that aims to understand, identify, prevent, and treat psychopathology.

References Achenbach, T. M. (1974). Developmental psychopathology. Oxford, England: Ronald Press. Achenbach, T. M., & Rescorla, L. A. (2016). Developmental issues in assessment, taxonomy, and diagnosis of psychopathology: Lifespan and multicultural perspectives. In D. Cicchetti (Ed.), Developmental psychopathology: Vol. 1. Theory and method (3rd ed., pp. 46–93). http://dx.doi.org/ 10.1002/9781119125556.devpsy102 Addington, A. M., & Rapoport, J. L. (2012). Annual research review: Impact of advances in genetics in understanding developmental psychopathology. Journal of Child Psychology and Psychiatry, and Allied Disciplines, 53, 510–518. Ager, A. (2013). Annual research review: Resilience and child well-being—public policy implications. Journal of Child Psychology and Psychiatry, and Allied Disciplines, 54, 488–500. http://dx.doi.org/10.1111/ jcpp.12030 Akos, P., & Galassi, J. P. (2008). Strengths-based school counseling: Introduction to the special issue. Professional School Counseling, 12(2), 66–67. http://dx.doi.org/10.5330/PSC.n.2010-12.66 Anthony, E. J., & Koupernik, C. (Eds.). (1974). The child in his family: Children at psychiatric risk (vol. 3). New York, NY: Wiley. Anthony, E. K., Alter, C. F., & Jenson, J. M. (2009). Development of a risk and resilience-based outof-school time program for children and youths. Social Work, 54, 45–55. http://dx.doi.org/10.1093/ sw/54.1.45 Bandura, A. (1997). Self-efficacy: The exercise of control. New York, NY: Holt. Betancourt, T. S., McBain, R., Newnham, E. A., & Brennan, R. T. (2013). Trajectories of internalizing problems in war-affected Sierra Leonean youth: Examining conflict and postconflict factors. Child Development, 84, 455–470. http://dx.doi.org/10.1111/ j.1467-8624.2012.01861.x Blair, C., & Raver, C. C. (2015). School readiness and self-regulation: A developmental psychobiological approach. Annual Review of Psychology, 66, 711–731. http://dx.doi.org/10.1146/annurevpsych-010814-015221 Blair, C., & Razza, R. P. (2007). Relating effortful control, executive function, and false belief understanding to emerging math and literacy ability in kindergarten. Child Development, 78, 647–663. http://dx.doi.org/ 10.1111/j.1467-8624.2007.01019.x Bowlby, J. (1988). Developmental psychiatry comes of age. American Journal of Psychiatry, 145, 1–10. http://dx.doi.org/10.1176/ajp.145.1.1 29

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Masten and Kalstabakken

Boxer, P., Rowell Huesmann, L., Dubow, E. F., Landau, S. F., Gvirsman, S. D., Shikaki, K., & Ginges, J. (2013). Exposure to violence across the social ecosystem and the development of aggression: A test of ecological theory in the Israeli–Palestinian conflict. Child Development, 84, 163–177. http://dx.doi.org/ 10.1111/j.1467-8624.2012.01848.x Boyce, W. T., & Kobor, M. S. (2015). Development and the epigenome: The “synapse” of gene–environment interplay. Developmental Science, 18, 1–23. http://dx.doi.org/10.1111/desc.12282 Bronfenbrenner, U. (1979). Contexts of child rearing: Problems and prospects. American Psychologist, 34, 844–850. http://dx.doi.org/10.1037/0003066X.34.10.844 Bronfenbrenner, U., & Morris, P. A. (2006). The bioecological model of human development. In W. Damon & R. M. Lerner (Eds.), Handbook of child psychology: Vol. 1. Theoretical models of human development (6th ed., pp. 793–828). New York, NY: Wiley. Brookes, E., & Shi, Y. (2014). Diverse epigenetic mechanisms of human disease. Annual Review of Genetics, 48, 237–268. http://dx.doi.org/10.1146/ annurev-genet-120213-092518 Burt, K. B., Coatsworth, J. D., & Masten, A. S. (2016). Competence and psychopathology in development. In D. Cicchetti (Ed.), Developmental psychopathology: Vol. 4. Risk, resilience, and intervention (3rd ed., pp. 435–484). New York, NY: Wiley. Campbell, F., Conti, G., Heckman, J. J., Moon, S. H., Pinto, R., Pungello, E., & Pan, Y. (2014). Early childhood investments substantially boost adult health. Science, 343, 1478–1485. http://dx.doi.org/ 10.1126/science.1248429 Capaldi, D. M. (1992). Co-occurrence of conduct problems and depressive symptoms in early adolescent boys: II. A 2-year follow-up at Grade 8. Development and Psychopathology, 4, 125–144. http://dx.doi.org/10.1017/S0954579400005605 Chassin, L., Colder, C. R., Hussong, A., & Sher, K. J. (2016). Substance use and substance use disorders. In D. Cicchetti (Ed.), Developmental psychopathology: Vol. 2. Maladaptation and psychopathology (3rd ed., pp. 833–897). New York, NY: Wiley. Chen, X., & Liu, C. H. (2016). Culture, peer relationships, and developmental psychopathology. In D. Cicchetti (Ed.), Developmental psychopathology: Vol. 4. Risk, resilience, and intervention (3rd ed., pp. 723–769). New York, NY: Wiley. Cicchetti, D. (1984). The emergence of developmental psychopathology. Child Development, 55, 1–7. http://dx.doi.org/10.2307/1129830 Cicchetti, D. (1989). Past, present, and future. In D. Cicchetti (Ed.), Developmental psychopathology: 30

Vol. 1. Rochester symposium on developmental psychopathology (pp. 1–12). Hillsdale, NJ: Erlbaum. Cicchetti, D. (1990). A historical perspective on the discipline of developmental psychopathology. In J. Rolf, A. Masten, D. Cicchetti, K. Nuechterlein, & S. Weintraub (Eds.), Risk and protective factors in the development of psychopathology (pp. 2–28). http://dx.doi.org/10.1017/CBO9780511752872.003 Cicchetti, D. (2006). Development and psychopathology. In D. Cicchetti & D. J. Cohen (Eds.), Developmental psychopathology: Vol. 1. Theory and method (2nd ed., pp. 1–23). New York, NY: Wiley. Cicchetti, D. (2013a). Annual research review: Resilient functioning in maltreated children—past, present, and future perspectives. Journal of Child Psychology and Psychiatry, and Allied Disciplines, 54, 402–422. http://dx.doi.org/10.1111/j.1469-7610.2012.02608.x Cicchetti, D. (2013b). An overview of developmental psychopathology. In P. D. Zelazo (Ed.), The Oxford handbook of developmental psychology: Vol. 2. Self and other (pp. 455–480). http://doi.org/10.1093/oxfordhb/ 9780199958474.013.0018 Cicchetti, D. (Ed.). (2016a). Developmental psychopathology (3rd ed., 4 vols.). New York, NY: Wiley. Cicchetti, D. (2016b). Preface. In D. Cicchetti (Ed.), Developmental psychopathology: Vol. 1. Theory and method (3rd ed., pp. xi–xii). New York, NY: Wiley. Cicchetti, D., & Rogosch, F. (1996). Equifinality and multifinality in developmental psychopathology. Development and Psychopathology, 8, 597–600. http://dx.doi.org/10.1017/S0954579400007318 Cicchetti, D., & Toth, S. L. (2009). The past achievements and future promises of developmental psychopathology: The coming of age of a discipline. Journal of Child Psychology and Psychiatry, 50, 16–25. http://dx.doi.org/10.1111/j.1469-7610.2008.01979.x Costello, E. J., & Angold, A. (2016). Developmental epidemiology. In D. Cicchetti (Ed.) Developmental psychopathology: Vol. 1. Theory and method. (3rd ed., pp. 986–1041). New York, NY: Wiley. Cozza, S. J., & Lerner, R. M. (2013). Military children and families: Introducing the issue. Future of Children, 23(2), 3–11. http://dx.doi.org/10.1353/ foc.2013.0016 Cummings, E. M., Davies, P. T., & Campbell, S. B. (2000). Developmental psychopathology and family process. New York, NY: Guilford Press. Cummings, E. M., & Valentino, K. (2015). Developmental psychopathology. In W. F. Overton, P. M. Molenaar, & R. M. Lerner (Eds.), Handbook of child psychology and developmental science: Vol. 1. Theory and method (7th ed. pp. 566–606). http://dx.doi.org/ 10.1002/9781118963418.childpsy115

Developmental Perspectives on Psychopathology in Children and Adolescents

Dahl, R. E. (2004). Adolescent brain development: A period of vulnerabilities and opportunities. Annals of the New York Academy of Sciences, 1021, 1–22. Deater-Deckard, K. (2013). The social environment and the development of psychopathology. In P. D. Zelazo (Ed.), The Oxford handbook of developmental psychology: Vol. 2. Self and other (pp. 527–548). http://dx.doi.org/ 10.1093/oxfordhb/9780199958474.013.0021

Copyright American Psychological Association. Not for further distribution.

Del Giudice, M., Ellis, B. J., & Shirtcliff, E. A. (2011). The adaptive calibration model of stress responsivity. Neuroscience and Biobehavioral Reviews, 35, 1562–1592. http://dx.doi.org/10.1016/ j.neubiorev.2010.11.007 Diamond, A., & Ling, D. S. (2016). Conclusions about interventions, programs, and approaches for improving executive functions that appear justified and those that, despite much hype, do not. Developmental Cognitive Neuroscience, 18, 34–48. http://dx.doi.org/10.1016/j.dcn.2015.11.005 Dishion, T. J., & Patterson, G. R. (2016). The development and ecology of antisocial behavior: Linking etiology, prevention, and treatment. In D. Cicchetti (Ed.), Developmental psychopathology: Vol. 3. Maladaptation and psychopathology (3rd ed., pp. 647–678). New York, NY: Wiley. Ditterline, J., & Oakland, T. (2009). Relationships between adaptive behavior and impairment. In S. Goldstein & J. A. Naglieri (Eds.), Assessing impairment: From theory to practice (pp. 31–48). http://dx.doi.org/10.1007/978-1-387-87542-2_4 Dodge, K. A., Malone, P. S., Lansford, J. E., Miller, S., Pettit, G. S., & Bates, J. E. (2009). A dynamic cascade model of the development of substance-use onset. Monographs of the Society for Research in Child Development, 74(3), vii–119.

Figueiredo, C. A., Amorim, L. D., Alcantara-Neves, N. M., Matos, S. M., Cooper, P. J., Rodrigues, L. C., & Barreto, M. L. (2013). Environmental conditions, immunologic phenotypes, atopy, and asthma: New evidence of how the hygiene hypothesis operates in Latin America. Journal of Allergy and Clinical Immunology, 131, 1064–1068. http://dx.doi.org/ 10.1016/j.jaci.2013.01.016 Fisher, P. A., Stoolmiller, M., Gunnar, M. R., & Burraston, B. O. (2007). Effects of a therapeutic intervention for foster preschoolers on diurnal cortisol activity. Psychoneuroendocrinology, 32, 892–905. http://dx.doi.org/10.1016/j. psyneuen.2007.06.008 Garmezy, N. (1974). The study of competence in children at risk for severe psychopathology. In E. J. Anthony & C. Koupernik (Eds.), The child in his family: Children at psychiatric risk (Vol. 3, pp. 77–97). New York, NY: Wiley. Garmezy, N., & Rutter, M. (Eds.). (1983). Stress, coping, and development. New York, NY: McGraw Hill. Gest, S. D., & Davidson, A. J. (2011). A developmental perspective on risk, resilience and prevention. In M. K. Underwood (Ed.), Social development: Relationships in infancy, childhood, and adolescence (pp. 427–454). New York, NY: Guilford Press. Gottesman, I. I. (1974). Developmental genetics and ontogenetic psychology: Overdue détente and propositions from a matchmaker. In A. Pick (Ed.), The Minnesota symposia on child psychology (Vol. 8, pp. 55–80). Minneapolis: University of Minnesota Press. Gottesman, I. I., & Shields, J. (1972). Schizophrenia and genetics: A twin study vantage point. Oxford, England: Academic Press.

Dodge, K. A., & Pettit, G. S. (2003). A biopsychosocial model of the development of chronic conduct problems in adolescence. Developmental Psychology, 39, 349–371. http://dx.doi.org/10.1037/00121649.39.2.349

Gottesman, I. I., & Shields, J. (1982). Schizophrenia. Cambridge, England: Cambridge University Press.

Dozier, M., Peloso, E., Lewis, E., Laurenceau, J. P., & Levine, S. (2008). Effects of an attachment-based intervention on the cortisol production of infants and toddlers in foster care. Development and Psychopathology, 20, 845–859. http://dx.doi.org/ 10.1017/S0954579408000400

Grigorenko, E. L., & Cicchetti, D. (2012). Genomic sciences for developmentalists: The current state of affairs. Development and Psychopathology, 24, 1157–1164. http://dx.doi.org/10.1017/ S0954579412000612

Drabick, D. A., & Kendall, P. C. (2010). Developmental psychopathology and the diagnosis of mental health problems among youth. Clinical Psychology: Science and Practice, 17, 272–280. http://dx.doi.org/10.1111/ j.1468-2850.2010.01219.x Durlak, J. A., Domitrovich, C. E., Weissberg, R. P., & Gullotta, T. P. (Eds.). (2015). Handbook of social and emotional learning: Research and practice. New York, NY: Guilford Press.

Gottlieb, G. (2007). Probabilistic epigenesis. Developmental Science, 10, 1–11. http://dx.doi.org/ 10.1111/j.1467-7687.2007.00556.x

Guerra, S., & Martinez, F. D. (2008). Asthma genetics: From linear to multifactorial approaches. Annual Review of Medicine, 59, 327–341. http://dx.doi.org/ 10.1146/annurev.med.59.060406.213232 Haskins, R., Garfinkel, I., & McLanahan, S. (Eds.). (2014). Helping parents, helping children: Twogeneration mechanisms. Retrieved from http://www. futureofchildren.org/sites/futureofchildren/files/ media/helping_parents_helping_children_24_01_ full_journal.pdf 31

Masten and Kalstabakken

Hauser, S. T., Allen, J. P., & Golden, E. (2006). Out of the woods: Tales of teen resilience. Cambridge, MA: Harvard University Press. Heckman, J. J. (2006). Skill formation and the economics of investing in disadvantaged children. Science, 312, 1900–1902. http://dx.doi.org/10.1126/science. 1128898

Copyright American Psychological Association. Not for further distribution.

Hinshaw, S. P., & Anderson, C. A. (1996). Conduct and oppositional defiant disorders. In E. J. Mash & R. A. Barkley (Eds.), Child psychopathology (pp. 113–149). New York, NY: Guilford Press. Hochberg, Z., Feil, R., Constancia, M., Fraga, M., Junien, C., Carel, J. C., . . . Albertsson-Wikland, K. (2011). Child health, developmental plasticity, and epigenetic programming. Endocrine Reviews, 32, 159–224. http://dx.doi.org/10.1210/er.2009-0039 Jaffee, S. R., Price, T. S., & Reyes, T. M. (2013). Behavior genetics: Past, present, future. Development and Psychopathology, 25, 1225–1242. http://dx.doi.org/ 10.1017/S0954579413000588 Karatoreos, I. N., & McEwen, B. S. (2013). Annual research review: The neurobiology and physiology of resilience and adaptation across the life course. Journal of Child Psychology and Psychiatry, and Allied Disciplines, 54, 337–347. http://dx.doi.org/10.1111/jcpp.12054 Kerig, P. K. (2016). Family systems from a developmental psychopathology perspective. Development and Psychopathology, 1(13), 1–51. Kim-Spoon, J., & Grimm, K. J. (2016). Latent growth modeling and developmental psychopathology. In D. Cicchetti (Ed.), Developmental psychopathology: Vol. 1. Theory and method (3rd ed., pp. 986–1041). New York, NY: Wiley. Kohlberg, L., LaCrosse, J., & Ricks, D. (1972). The predictability of adult mental health from childhood behavior. In B. B. Wolman (Ed.), Manual of child psychopathology (pp. 1217–1284). New York, NY: McGraw-Hill. Leckman, J. F., Panter-Brick, C., & Salah, R. (Eds.). (2014). Pathways to peace: The transformative power of families and child development (pp. 251–271). http://dx.doi.org/10.7551/ mitpress/9780262027984.001.0001 Lerner, R. M. (2006). Developmental science, developmental systems, and contemporary theories of human development. In W. Damon & R. M. Lerner (Ed.), The handbook of child psychology. Vol. 1: Theoretical models of human development (6th ed., pp. 1–17). http://dx.doi.org/10.1002/9780470147658. chpsy0101 Lerner, R. M. (2016). Preface. In R. M. Lerner (Ed.), Handbook of child psychology and developmental science: Vol. 1. Theory and method (pp. xv–xxi). New York, NY: Wiley.

32

Lerner, R. M., Agans, J. P., Arbeit, M. R., Chase, P. A., Weiner, M. B., Schmid, K. L., & Warren, A. A. (2013). Resilience and positive youth development: A relational developmental systems model. In S. Goldstein & R. B. Brooks (Eds.), Handbook of resilience in children (2nd ed., pp. 293–308). http://dx.doi.org/10.1007/978-1-4614-3661-4_17 Lickliter, R. (2013). Biological development: Theoretical approaches, techniques, and key findings. In P. D. Zelazo (Ed.), The Oxford handbook of developmental psychology: Vol. 1. Body and mind (pp. 65–90). http://dx.doi.org/10.1093/oxfordhb/ 9780199958450.013.0004 Loeber, R., & Farrington, D. P. (2014). Age-crime curve. In G. Bruinsma & D. Weisburd (Eds.), Encyclopedia of criminology and criminal justice (pp. 12–17). http://dx.doi.org/10.1007/978-1-4614-5690-2_474 Lundberg, M., & Wuermli, A. (Eds.). (2012). Children and youth in crisis: Protecting and promoting human development in times of economic shocks. Washington, DC: World Bank. Lupien, S. J., McEwen, B. S., Gunnar, M. R., & Heim, C. (2009). Effects of stress throughout the lifespan on the brain, behavior, and cognition. Nature Reviews Neuroscience, 10, 434–445. http://dx.doi.org/10.1038/ nrn2639 Luthar, S. S. (2006). Resilience in development: A synthesis of research across five decades. In D. Cicchetti & D. J. Cohen (Eds.), Developmental psychopathology: Vol. 3. Risk, disorder, and adaptation (2nd ed., pp. 739–795). Hoboken, NJ: Wiley. Luthar, S. S., Cicchetti, D., & Becker, B. (2000). The construct of resilience: A critical evaluation and guidelines for future work. Child Development, 71, 543–562. http://dx.doi.org/10.1111/14678624.00164 Maguin, E., & Loeber, R. (1996). Academic performance and delinquency. Crime and Justice, 20, 145–264. http://dx.doi.org/10.1086/449243 Masten, A. S. (1989). Resilience in development: Implications of the study of successful adaptation for developmental psychopathology. In D. Cicchetti (Ed). Rochester symposium on developmental psychopathology. Vol. 1. The emergence of a discipline (pp. 261–294). Hillsdale. NJ: Erlbaum. Masten, A. S. (2006). Developmental psychopathology: Pathways to the future. International Journal of Behavioral Development, 30, 47–54. http://dx.doi.org/ 10.1177/0165025406059974 Masten, A. S. (2007). Resilience in developing systems: Progress and promise as the fourth wave rises. Development and Psychopathology, 19, 921–930. http://dx.doi.org/10.1017/S0954579407000442 Masten, A. S. (2011). Resilience in children threatened by extreme adversity: Frameworks for research,

Copyright American Psychological Association. Not for further distribution.

Developmental Perspectives on Psychopathology in Children and Adolescents

practice, and translational synergy. Development and Psychopathology, 23, 493–506. http://dx.doi.org/ 10.1017/S0954579411000198 Masten, A. S. (2012). Resilience in children: Vintage Rutter and beyond. In A. Slater & P. Quinn (Eds.), Developmental psychology: Revisiting the classic studies (pp. 204–221). London, England: Sage. Masten, A. S. (2013). Risk and resilience in development. In P. D. Zelazo (Ed.), Oxford handbook of developmental psychology (pp. 579–607). New York, NY: Oxford University Press. Masten, A. S. (2014a). Global perspectives on resilience in children and youth. Child Development, 85, 6–20. http://dx.doi.org/10.1111/cdev.12205 Masten, A. S. (2014b). Invited commentary: Resilience and positive youth development frameworks in developmental science. Journal of Youth and Adolescence, 43, 1018–1024. http://dx.doi.org/ 10.1007/s10964-014-0118-7 Masten, A. S. (2014c). Ordinary magic: Resilience in development. New York, NY: Guilford Press. Masten, A. S. (2015). Pathways to integrated resilience science. Psychological Inquiry, 26, 187–196. http://dx.doi.org/10.1080/1047840X.2015.1012041 Masten, A. S. (2016). Resilience in developing systems: The promise of integrated approaches. European Journal of Developmental Psychology, 13, 297–312. Masten, A. S., Burt, K. B., & Coatsworth, J. D. (2006). Competence and psychopathology in development. In D. Ciccheti & D. Cohen (Eds.), Developmental psychopathology: Vol. 3. Risk, disorder, and psychopathology (2nd ed., pp. 696–738). New York, NY: Wiley. Masten, A. S., & Cicchetti, D. (2010). Developmental cascades. Development and Psychopathology, 22, 491–495. http://dx.doi.org/10.1017/ S0954579410000222 Masten, A. S., & Cicchetti, D. (2016). Resilience in development: Progress and transformation. In D. Cicchetti (Ed.) Developmental psychopathology: Vol. 4. Risk, resilience, and intervention. (3rd ed., pp. 271–333). New York, NY: Wiley. Masten, A. S., & Coatsworth, J. D. (1998). The development of competence in favorable and unfavorable environments. Lessons from research on successful children. American Psychologist, 53, 205–220. http://dx.doi.org/10.1037/0003066X.53.2.205 Masten, A. S., Faden, V. B., Zucker, R. A., & Spear, L. P. (2008). Underage drinking: A developmental framework. Pediatrics, 121(Suppl. 4), S235–S251. http://dx.doi.org/10.1542/peds.2007-2243A Masten, A. S., Herbers, J. E., Cutuli, J. J., & Lafavor, T. L. (2008). Promoting competence and resilience

in the school context. Professional School Counseling, 12(2), 76–84. http://dx.doi.org/10.5330/PSC.n. 2010-12.76 Masten, A. S., & Monn, A. R. (2015). Child and family resilience: A call for integrated science, practice, and professional training. Family Relations: An Interdisciplinary Journal of Applied Family Studies, 64, 5–21. http://dx.doi.org/10.1111/fare.12103 Masten, A. S., Narayan, A. J., Silverman, W. K., & Osofsky, J. D. (2015). Children in war and disaster. In R. M. Lerner (Ed.), Handbook of child psychology and developmental science: Vol. 4. Ecological settings and processes in developmental systems (7th ed. pp. 704–745). http://dx.doi.org/10.1002/ 9781118963418.childpsy418 Masten, A. S., & Obradovi ´c, J. (2008). Disaster preparation and recovery: Lessons from research on resilience in human development. Ecology and Society, 13(1), 9. Retrieved from http://www.ecologyandsociety.org/ vol13/iss1/art9 Masten, A. S., & O’Connor, M. J. (1989). Vulnerability, stress, and resilience in the early development of a highrisk child. Journal of the American Academy of Child and Adolescent Psychiatry, 28, 274–278. http://dx.doi.org/ 10.1097/00004583-198903000-00021 Masten, A. S., & Tellegen, A. (2012). Resilience in developmental psychopathology: Contributions of the Project Competence Longitudinal Study. Development and Psychopathology, 24, 345–361. http://dx.doi.org/10.1017/S095457941200003X Mayes, L. C., & Lewis, M. (Eds.). (2012). The Cambridge handbook of environment in human development. http://dx.doi.org/10.1017/CBO9781139016827 McCormick, C. M., Kuo, S. I.-C., & Masten, A. S. (2011). Developmental tasks across the lifespan. In K. L. Fingerman, C. A. Berg, J. Smith, & T. C. Antonucci (Eds.), Handbook of lifespan development (pp. 117–140). New York, NY: Springer. Meaney, M. J. (2010). Epigenetics and the biological definition of gene x environment interactions. Child Development, 81, 41–79. http://dx.doi.org/10.1111/ j.1467-8624.2009.01381.x Miller, G. E., Brody, G. H., Yu, T., & Chen, E. (2014). A family-oriented psychosocial intervention reduces inflammation in low-SES African American youth. Proceedings of the National Academy of Sciences, USA, 111, 11287–11292. http://dx.doi.org/10.1073/ pnas.1406578111 Mitchell, K. J. (2007). The genetics of brain wiring: From molecule to mind. PLOS Biology, 5, e113. http://dx.doi.org/10.1371/journal.pbio.0050113 Moffitt, T. E. (1993). Adolescence-limited and life-course-persistent antisocial behavior: A developmental taxonomy. Psychological Review,

33

Copyright American Psychological Association. Not for further distribution.

Masten and Kalstabakken

100, 674–701. http://dx.doi.org/10.1037/0033295X.100.4.674 Moffitt, T. E., Arseneault, L., Belsky, D., Dickson, N., Hancox, R. J., Harrington, H., . . . Caspi, A. (2011). A gradient of childhood self-control predicts health, wealth, and public safety. Proceedings of the National Academy of Sciences, USA, 108, 2693–2698. http://dx.doi.org/10.1073/pnas.1010076108 Moffitt, T. E., Caspi, A., Harrington, H., & Milne, B. J. (2002). Males on the life-course-persistent and adolescence-limited antisocial pathways: Follow-up at age 26 years. Development and Psychopathology, 14, 179–207. http://dx.doi.org/10.1017/ S0954579402001104 Monk, C., Feng, T., Lee, S., Krupska, I., Champagne, F. A., & Tycko, B. (2016). Distress during pregnancy: Epigenetic regulation of placenta glucocorticoidrelated genes and fetal neurobehavior. American Journal of Psychiatry, 173, 705–713. http://dx.doi.org/ 10.1176/appi.ajp.2015.15091171 Motti-Stefanidi, F. (2015). Risks and resilience in immigrant youth adaptation: Who succeeds in the Greek school context and why? European Journal of Developmental Psychology, 12, 261–274. http://dx.doi.org/10.1080/ 17405629.2015.1020787 National Research Council & Institute of Medicine. (2009). Preventing mental, emotional, and behavioral disorders among young people: Progress and possibilities. Washington, DC: National Academies Press. National Research Council & Institute of Medicine. (2015). A framework for assessing effects of the food system. Washington, DC: National Academies Press. Nelson, C. A. (1999). Neural plasticity and human development. Current Directions in Psychological Science, 8, 42–45. http://dx.doi.org/10.1111/14678721.00010 Nelson, C. A., Fox, N. A., & Zeanah, C. H. (2014). Romania’s abandoned children: Deprivation, brain development, and the struggle for recovery. http://dx.doi.org/10.4159/harvard.9780674726079 Osofsky, J. D., Osofsky, H. J., Weems, C. F., King, L. S., & Hansel, T. C. (2015). Trajectories of posttraumatic stress disorder symptoms among youth exposed to both natural and technological disasters. Journal of Child Psychology and Psychiatry, and Allied Disciplines, 56, 1347–1355. http://dx.doi.org/10.1111/ jcpp.12420 Overton, W. F. (2013). A new paradigm for developmental science: Relationism and relational- developmental systems. Applied Developmental Science, 17, 94–107. http://dx.doi.org/10.1080/10888691.2013.778717 Panter-Brick, C., & Leckman, J. F. (2013). Editorial Commentary: Resilience in child development—interconnected pathways to wellbeing. Journal of Child Psychology and Psychiatry,

34

and Allied Disciplines, 54, 333–336. http://dx.doi.org/ 10.1111/jcpp.12057 Patterson, G. R., Forgatch, M. S., & Degarmo, D. S. (2010). Cascading effects following intervention. Development and Psychopathology, 22, 949–970. http://dx.doi.org/10.1017/S0954579410000568 Patterson, G. R., Reid, J. B., & Dishion, T. J. (1992). Antisocial boys (vol. 4). Eugene, OR: Castalia Pub. Patterson, G. R., & Stoolmiller, M. (1991). Replications of a dual failure model for boys’ depressed mood. Journal of Consulting and Clinical Psychology, 59, 491–498. http://dx.doi.org/10.1037/0022006X.59.4.491 Reiman, T., Rollenhagen, C., Pietikainen, E., & Heikkila, J. (2015). Principles of adaptive management in complex safety-critical organizations. Safety Science, 71, 80–92. http://dx.doi.org/10.1016/ j.ssci.2014.07.021 Repetti, R. L., Robles, T. F., & Reynolds, B. (2011). Allostatic processes in the family. Development and Psychopathology, 23, 921–938. http://dx.doi.org/ 10.1017/S095457941100040X Reynolds, A. J., & Ou, S. R. (2011). Paths of effects from preschool to adult well-being: A confirmatory analysis of the child–parent center program. Child Development, 82, 555–582. http://dx.doi.org/10.1111/ j.1467-8624.2010.01562.x Reynolds, A. J., Temple, J. A., White, B. A., Ou, S. R., & Robertson, D. L. (2011). Age 26 cost-benefit analysis of the child–parent center early education program. Child Development, 82, 379–404. http://dx.doi.org/ 10.1111/j.1467-8624.2010.01563.x Roth, T. L. (2013). Epigenetic mechanisms in the development of behavior: Advances, challenges, and future promises of a new field. Development and Psychopathology, 25, 1279–1291. http://dx.doi.org/ 10.1017/S0954579413000618 Rutter, M. (1987). Psychosocial resilience and protective mechanisms. American Journal of Orthopsychiatry, 57, 316–331. http://dx.doi.org/10.1111/j.1939-0025.1987. tb03541.x Rutter, M. (2013). Annual research review: Resilience—clinical implications. Journal of Child Psychology and Psychiatry, and Allied Disciplines, 54, 474–487. http://dx.doi.org/10.1111/j.14697610.2012.02615.x Rutter, M., Kim-Cohen, J., & Maughan, B. (2006). Continuities and discontinuities in psychopathology between childhood and adult life. Journal of Child Psychology and Psychiatry, and Allied Disciplines, 47, 276–295. http://dx.doi.org/10.1111/j.14697610.2006.01614.x Rutter, M., Sonuga-Barke, E. J., & Castle, J. (2010). Investigating the impact of early institutional

Developmental Perspectives on Psychopathology in Children and Adolescents

deprivation on development: Background and research strategy of the English and Romanian Adoptees (ERA) study. Monographs of the Society for Research in Child Development, 75, 1–20. http://dx.doi.org/10.1111/ j.1540-5834.2010.00548.x Sameroff, A. (1975). Transactional models in early social relations. Human Development, 18, 65–79. http://dx.doi.org/10.1159/000271476

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Sameroff, A. (Ed.). (2009). The transactional model of development: How children and contexts shape each other. http://dx.doi.org/10.1037/11877-000 Sameroff, A. J. (2000). Developmental systems and psychopathology. Development and Psychopathology, 12, 297–312. http://dx.doi.org/10.1017/ S0954579400003035 Sameroff, A. J., & Chandler, M. J. (1975). Reproductive risk and the continuum of caretaking casualty. Review of Child Development Research, 4, 187–244. Shaw, D. S., Hyde, L. W., & Brennan, L. M. (2012). Early predictors of boys’ antisocial trajectories. Development and Psychopathology, 24, 871–888. http://dx.doi.org/10.1017/S0954579412000429 Shonkoff, J. P. (2011). Protecting brains, not simply stimulating minds. Science, 333, 982–983. http://dx.doi.org/10.1126/science.1206014 Shonkoff, J. P., Boyce, W. T., & McEwen, B. S. (2009). Neuroscience, molecular biology, and the childhood roots of health disparities: Building a new framework for health promotion and disease prevention. JAMA: Journal of the American Medical Association, 301, 2252–2259. http://dx.doi.org/10.1001/jama. 2009.754 Shonkoff, J. P., & Garner, A. S. (2012). The lifelong effects of early childhood adversity and toxic stress. Pediatrics, 129, e232–e246. http://dx.doi.org/10.1542/ peds.2011-2663 Southwick, S. M., Bonanno, G. A., Masten, A. S., Panter-Brick, C., & Yehuda, R. (2014). Resilience definitions, theory, and challenges: Interdisciplinary perspectives. European Journal of Psychotraumatology, 5, 1–14. http://dx.doi.org/10.3402/ejpt.v5.25338 Sparrow, S. S., Cicchetti, D. V., & Balla, D. (2005). Vineland adaptive behavior scales (2nd ed.). Circle Pines, MN: AGS. Sroufe, L. A. (1979). The coherence of individual development: Early care, attachment, and subsequent developmental issues. American Psychologist, 34, 834–841. http://dx.doi.org/10.1037/0003066X.34.10.834 Sroufe, L. A. (1990). An organizational perspective on the self. In D. Cicchetti & M. Beeghly (Eds.), Transitions from infancy to childhood: The self (pp. 281–307). Chicago, IL: University of Chicago Press.

Sroufe, L. A. (1997). Psychopathology as an outcome of development. Development and Psychopathology, 9, 251–268. http://dx.doi.org/10.1017/ S0954579497002046 Sroufe, L. A. (2007). The place of development in developmental psychopathology. In A. S. Masten (Ed.), Multilevel dynamics in developmental psychopathology: Pathways to the future (pp. 285–298). Mahwah, NJ: Erlbaum. Sroufe, L. A., & Rutter, M. (1984). The domain of developmental psychopathology. Child Development, 55, 17–29. http://dx.doi.org/10.2307/1129832 Steinberg, L., Dahl, R., Keating, D., Kupfer, D. J., Masten, A. S., & Pine, D. S. (2006). Psychopathology in adolescence: Integrating affective neuroscience with the study of context. In D. Cicchetti & D. Cohen (Eds.). Developmental psychopathology: Vol. 2. Developmental neuroscience (2nd ed., pp. 710–741). Hoboken, NJ: Wiley. Temple, J. A., & Reynolds, A. J. (2007). Benefits and costs of investments in preschool education: Evidence from the Child–Parent Centers and related programs. Economics of Education Review, 26, 126–144. http://dx.doi.org/10.1016/j.econedurev.2005.11.004 Toth, S. L., & Cicchetti, D. (1999). Developmental psychopathology and child psychotherapy. In S. W. Russ, T. H. Ollendick, S. W. Russ, & T. H. Ollendick (Eds.), Handbook of psychotherapies with children and families (pp. 15–44). http://dx.doi.org/10.1007/9781-4615-4755-6_2 Tough, P. (2009). Whatever it takes: Geoffrey Canada’s quest to change Harlem and America. Boston, MA: Houghton Mifflin Harcourt. Ungar, M. (Ed.). (2012). The social ecology of resilience: A handbook of theory and practice. http://dx.doi.org/ 10.1007/978-1-4614-0586-3 Ungar, M., Ghazinour, M., & Richter, J. (2013). Annual research review: What is resilience within the social ecology of human development? Journal of Child Psychology and Psychiatry, and Allied Disciplines, 54, 348–366. http://dx.doi.org/10.1111/jcpp.12025 Üstün, T. B., Chatterji, S., Kostanjsek, N., Rehm, J., Kennedy, C., Epping-Jordan, J., . . . Pull, C., & the WHO/NIH Joint Project. (2010). Developing the World Health Organization Disability Assessment Schedule 2.0. Bulletin of the World Health Organization, 88, 815–823. http://dx.doi.org/10.2471/BLT.09.067231 van Dongen, J., Slagboom, P. E., Draisma, H. H., Martin, N. G., & Boomsma, D. I. (2012). The continuing value of twin studies in the omics era. Nature Reviews. Genetics, 13, 640–653. http://dx.doi.org/ 10.1038/nrg3243 von Mutius, E., & Radon, K. (2008). Living on a farm: Impact on asthma induction and clinical course.

35

Masten and Kalstabakken

Immunology and Allergy Clinics of North America, 28, 631–647. http://dx.doi.org/10.1016/j.iac.2008.03.010 Waddington, C. H. (1957). The strategy of the genes: A discussion of some aspects of theoretical biology. London, England: Routledge. Walsh, F. (2016). Strengthening family resilience (3rd ed.). New York, NY: Guilford Press.

Copyright American Psychological Association. Not for further distribution.

Watt, N., Anthony, E. J., Wynne, L. C., & Rolf, J. (1984). Children at risk for schizophrenia: A longitudinal perspective. Cambridge, England: Cambridge University Press. Werner, E. E., & Smith, R. S. (1982). Vulnerable but invincible: A longitudinal study of resilient children and youth. New York, NY: McGraw-Hill. Werner, E. E., & Smith, R. S. (1992). Overcoming the odds: High risk children from birth to adulthood. Ithaca, NY: Cornell University Press. Werner, E. E., & Smith, R. S. (2001). Journeys from childhood to midlife: Risk, resilience, and recovery. Ithaca, NY: Cornell University Press. Winslow, E. B., Sandler, I. N., Wolchik, S. A., & Carr, C. (2013). Building resilience in all children: A public health approach. In S. Goldstein, R. B. Brooks, S. Goldstein, R. B. Brooks (Eds.), Handbook of resilience in children (2nd ed., pp. 459–480). http://dx.doi.org/ 10.1007/978-1-4614-3661-4_27 Wright, M. O., Masten, A. S., & Narayan, A. J. (2013). Resilience processes in development: Four waves of research on positive adaptation in the context of adversity. In S. Goldstein & R. B. Brooks (Eds.), Handbook of resilience in children (2nd ed., pp. 15–37). http://dx.doi.org/10.1007/978-1-4614-3661-4_2

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Zelazo, P. D. (2013). Developmental psychology: A new synthesis. In P. D. Zelazo (Ed.), The Oxford handbook of developmental psychology: Vol. 1. Body and mind (pp. 3–12). http://dx.doi.org/10.1093/ oxfordhb/9780199958474.013.0001 Zelazo, P. D., Blair, C. B., & Willoughby, M. T. (2016). Executive function: Implications for education. Washington, DC: National Center for Education Research, Institute of Education Sciences, U.S. Department of Education. Retrieved from https:// ies.ed.gov/ncer/pubs/20172000 Zelazo, P. D., & Carlson, S. M. (2012). Hot and cool executive function in childhood and adolescence: Development and plasticity. Child Development Perspectives, 6, 354–360. http://dx.doi.org/10.1111/ j.1750-8606.2012.00246.x Zigler, E. (1989). Foreword. In D. Ciccheti (Ed.), The emergence of a discipline: Rochester symposium on developmental psychopathology (Vol. 1, pp. ix–xi). Hillsdale, NJ: Erlbaum. Zigler, E., & Glick, M. (1986). A developmental approach to adult psychopathology. New York, NY: Wiley. Zigler, E., & Styfco, S. J. (2010). The hidden history of Head Start. http://dx.doi.org/10.1093/acprof: oso/9780195393767.001.0001 Zucker, R. A., Hicks, B. M., & Heitzeg, M. M. (2016). Alcohol use and the alcohol use disorders over the life course: A cross-level developmental review. In D. Cicchetti (Ed.), Developmental psychopathology: Vol. 2. Maladaptation and psychopathology (3rd ed., pp. 793–832). New York, NY: Wiley.

Chapter 3

A Multilevel Developmental Approach to the Prevention of Psychopathology in Children and Adolescents Copyright American Psychological Association. Not for further distribution.

Dante Cicchetti

Considerable progress has occurred in our understanding of the origins, pathways, sequelae, treatment, and prevention of mental disorders (Cicchetti, 2016; Hinshaw, 2002). Despite these gains, mental disorders continue to challenge millions of individuals, as well as to place major stress on the service delivery system and the research community that strives to better understand psychopathology, thereby contributing to improved treatment and prevention efforts (Cicchetti & Toth, 2006; Ialongo et al., 2006). The numbers of people experiencing mental disorder are staggering (Costello & Angold, 2016; Drabick & Kendall, 2010). Most individuals would agree that it is preferable to prevent the emergence of mental disorders and concomitant suffering, rather than waiting for a disorder to develop and then offer treatment (Cicchetti & Hinshaw, 2002; Ialongo et al., 2006). The overarching and pragmatic goal of prevention science is to intervene during development to reduce or eliminate the emergence of maladaptation and psychopathology. Consequently, a complex understanding of the course of normal development is essential to conceptualize how deviations in normal ontogenesis give rise to psychopathology (Cicchetti & Toth, 1992; Hinshaw, 2002). A Developmental Psychopathology Perspective on Prevention The discipline of developmental psychopathology, with its major focus on the dialectic between normal

and abnormal development, is uniquely poised to provide the theoretical foundation for prevention science (Cicchetti & Toth, 1992, 2009; Ialongo et al., 2006; Institute of Medicine, 1994). From a developmental psychopathology perspective, maladaptation and mental disorder are viewed as evolving from progressive liabilities in the organization of biological and psychological systems, resulting in the undermining of the individual’s efforts to adapt effectively to stressful and adverse experiences. Prevention scientists are cognizant that there are multiple pathways to mental disorder and dysfunction (known as equifinality), and that diverse causal processes likely operate for different individuals. Moreover, prevention researchers understand that a variety of maladaptive and adaptive outcomes will occur despite a common early liability or risk condition (known as multifinality; Cicchetti & Rogosch, 1996). Theory and research conducted within the discipline of developmental psychopathology seek to unify, within a lifespan framework, the many contributions to the study of high-risk and disordered individuals emanating from multiple fields of inquiry (Cicchetti, 1990). The principles of developmental psychopathology have provided a much-needed conceptual scaffolding for the facilitation of this multidisciplinary integration, as well as for fostering an increased synergy between research and practice. The central focus of developmental psychopathology involves the elucidation of developmental processes and how they function as indicated and

The preparation of this chapter was supported by grants from the Jacobs Foundation, the National Institutes of Health (MH091070), and the Spunk Fund, Inc. http://dx.doi.org/10.1037/0000065-003 APA Handbook of Psychopathology: Vol. 2. Child and Adolescent Psychopathology, J. N. Butcher (Editor-in-Chief) Copyright © 2018 by the American Psychological Association. All rights reserved.

APA Handbook of Psychopathology: Child and Adolescent Psychopathology, edited by J. N. Butcher and P. C. Kendall Copyright © 2018 American Psychological Association. All rights reserved.

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elaborated by the examination of extremes in developmental outcome. In addition to studying extremes in the distribution (i.e., individuals with disorders), developmental psychopathologists also direct attention toward variations in the continuum between the mean and the extremes. These variations may represent individuals who are currently not divergent enough to be considered disordered, but who may progress to further extremes as development continues. Such individuals may be vulnerable to developing future disordered outcomes, or, viewed within Wakefield’s (1992) concept of harmful dysfunction, developmental deviations may, for some individuals, reflect either the earliest signs of an emerging dysfunction or an already existing dysfunction that is partially compensated for by other processes within or outside the individual. Therefore, tracking the developmental course of these individuals is likely to broaden the complexity of understanding ontogenetic processes (Richters & Cicchetti, 1993; Wakefield, 1997). The developmental conceptualization of psychopathology acknowledges human development and functioning in its full complexity and subtlety. In contrast to the often dichotomous world of mental disorder/nondisorder in psychiatry, a developmental perspective recognizes that normality often fades into abnormality, adaptive and maladaptive may take on differing definitions depending on whether one’s time referent is immediate circumstance or long-term development, and processes within the individual can be characterized as having shades, or degrees, of psychopathology. This theoretical perspective directs prevention science to focus on the progressive organization of developmental competencies and incompetencies to structure preventive efforts. To effect change during development and avert psychopathological outcomes, preventive interventions should be guided by an emphasis on promoting competence and reducing ineffective resolution of the stagesalient developmental tasks at different periods of development. In so doing, deflection of adaptation on more adaptive developmental pathways may be achieved, thereby enhancing the individual’s capacity for a greater likelihood of subsequent successful adaptation. Therefore, attending to developmental 38

competencies and liabilities, rather than a sole focus of symptom reduction, is crucial. Inherent in the developmental perspective is the value of early intervention, before developmental liabilities become more consolidated. For example, to prevent attachment insecurity, it is preferable to focus on attachment organization instead of symptoms. The focus on attachment organization will yield a deeper understanding of representation of self and other than will symptom reduction alone. For individuals who are at a specific developmental period and are more vulnerable because of a compromised developmental organization, more intensive preventive efforts may be needed to promote accessing more competent development pathways. Understanding pathways of resilient adaptation among individuals exposed to extreme risks and early adversity presents an important opportunity for prevention researchers (Luthar & Cicchetti, 2000). Identification of processes contributing to self-righting during development for these individuals may be particularly valuable to incorporate into the design of preventive interventions (Cicchetti & Rogosch, 1997; Luthar & Cicchetti, 2000). Longitudinal research in developmental psychopathology is vital for delineating the varied developmental pathways for individuals experiencing high-risk conditions and for tracking the emergence of psychopathology. Such research is invaluable for defining the mechanisms that translate risk and vulnerability into dysfunction (Rutter & Sroufe, 2000), forming a foundation on which to base preventive efforts. An ongoing goal of developmental psychopathology is to bridge fields of study and aid in the discovery of important new truths about the processes underlying adaptation across the life course. Moreover, developmental psychopathologists strive to provide the best means of preventing and ameliorating maladaptive and pathological outcomes (Cicchetti, 1990; Sroufe & Rutter, 1984). The major premise of the developmental psychopathology perspective is that psychopathology develops. Moreover, it develops according to the same principles that govern all aspects of human development, whether it is the human embryo, the brain, normal capacities (e.g., regulate emotions, engage in competent social relations), or personality

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A Multilevel Developmental Approach to the Prevention of Psychopathology in Children and Adolescents

(Cicchetti & Sroufe, 2000). Therefore, this perspective has offered enormous promise from the outset. If pathology develops in an expected manner, then it is possible to identify precursors and pathways leading to disorder, as well as factors that lead individuals along such pathways or redirect them back toward more functional adaptation. Prevention and intervention is informed. At the same time, conjoint study of normal and abnormal development further illuminates developmental processes. For example, what determines continuity or discontinuity in development? How does prior experience exercise its impact in changed environmental circumstances? Following developmental change, does prior adaptation remain latent or is it erased? A myriad of more subtle, nuanced questions also are presented. In contrast with the viewpoint that mental illnesses should be conceived as “brain disorders,” a multiple-analysis approach suggests that mental disorders can better be conceptualized in a way that reflects the probabilistic, bidirectional, and transactional nature of genetic, neurobiological, social, psychological, and pre- and postnatal environmental influences over the life course. Although the brain is clearly involved in mental disorder, many other levels contribute and transact with the brain in dynamic fashion to bring about experiencedependent brain development. Although many types of mental disorder may be characterized by strong psychobiological predispositions, the brain disorder concept may connote primacy for biology and fail to capture the transactional processes that occur psychological and social environments. An alternative to the brain disorder viewpoint would be to view mental illnesses as an evolving dysfunction among multiple and transacting developmental processes. Emerging research into the biological and genetic arenas has raised more issues than those noted by Ialongo et al. (2006), and it has underscored the complexity in understanding mechanisms that contribute to the efficacy of prevention strategies (Cicchetti & Blender, 2006). The identification of mechanisms, therefore, must be accelerated. Scientists in the field of prevention science must move beyond establishing efficacy and to focus on aligning preventive interventions with theory to identify

the mechanisms by which interventions work and to develop subgroups for whom interventions are effective (Toth et al., 2016). Preventive interventions, by design, offer an opportunity to comprehend the basic processes of development (Cicchetti & Toth, 2009; Hinshaw, 2002; Howe, Reiss, & Yuh, 2002; Ialongo et al., 2006 Szyf & Bick, 2013; Toth, Petrenko, GravenerDavis, & Handley, 2016); mediators of treatment efficacy identify developmental pathways, which can inform basic research in developmental science. These advances in basic science, promoted by an understanding of intervention mediators, can then be used to progress the field of prevention science through translational research, completing the prevention research cycle (Cicchetti & Gunnar, 2008; Cicchetti & Toth, 2006; Ialongo et al., 2006). At present, the theories, experimental designs, and measurement batteries that undergird most randomized prevention trial interventions, especially those conducted with children and adolescents, have been dominated by the assessment of processes at the psychosocial and behavioral levels of analysis (Cicchetti & Gunnar, 2008; Curtis & Cicchetti, 2003). Preventive interventions conducted with children and adolescents have paid minimal attention to neurobiological and physiological systems in their evaluations of intervention efficacy. The neglect of biological levels of analysis may be due to beliefs that biological systems are more static and less malleable to intervention (Cicchetti & Gunnar, 2008; Ialongo et al., 2006). However, research has increasingly demonstrated that psychosocial interventions can alter physiological structures and functions relating to the behavior of interest (e.g., Cicchetti, Rogosch, Toth, & Sturge-Apple, 2011; Dozier, Peloso, Lewis, Laurenceau, & Levine, 2008; Fisher, Stoolmiller, Gunnar, & Burraston, 2007; Toth, Sturge-Apple, Rogosch, & Cicchetti, 2015). Moreover, genetic moderation is being highlighted (Beach, Brody, Todorov, Gunter, & Philibert, 2010; Cicchetti, Toth, & Handley, 2015). Over the last decade, several important articles and special issues in prominent journals (e.g., Beauchaine, Neuhaus, Brenner, & Gatzke-Kopp, 2008; Belsky & van IJzendoorn, 2015) have called for increased emphasis on biological levels of analysis, especially in 39

Dante Cicchetti

genetics and gene by environment interactions (Grigorenko & Cicchetti, 2012).

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The Importance of Theory-Driven Prevention Strategies The design and evaluation of preventive interventions must be theory driven (Cicchetti & Toth, 2009; Ialongo et al., 2006). The general theoretical rationale for selecting a given approach for an intervention target is usually clearly specified. However, the hypothesized mediating mechanisms of the intervention program must also be specified and grounded in theory to adequately evaluate the process by which the preventive interventions are expected to be effective. Mediating mechanisms also must be specified a priori consideration of theorized mediating mechanisms. Moreover, subgroup analyses must be couched within a broader developmental systems perspective which emphasizes the importance of moving beyond additive or interaction models to conceptualizing dynamic reciprocal, bidirectional, and coacting processes in models (Toth et al., 2016). Ideally, detailed conceptual models reflecting these dynamic processes across levels of analysis (e.g., biological, socioemotional, cognitive, behavioral) and ecological contexts are constructed prior to designing or selecting intervention components. The model then guides the development of the preventive intervention components to accomplish the hypothesized underlying mechanisms and determine which measures are most appropriate to evaluate the efficacy of the intervention. Finally, researchers determine the appropriate trial design (e.g., comparison group, level of randomization) and statistical methodologies to answer target research questions. Furthermore, if the developmental course is altered because of the implementation of preventive interventions and the risk for negative outcomes is reduced, then prevention research has contributed to specifying the processes that are involved in the emergence of maladaptive developmental outcomes and psychopathology (Cicchetti & Rogosch, 1999; Howe et al., 2002; Kellam & Rebok, 1992). Accordingly, preventive intervention research can be conceptualized as true experiments 40

in modifying the course of development, thereby providing insight into the etiology and pathogenesis of disordered outcomes (Kellam & Rebok, 1992). Prevention research is based on theoretical models of how risk conditions are related to adverse outcomes, providing processes that link the risk condition to the negative outcome (Howe et al., 2002; Institute of Medicine, 1994). For example, the malleability of insecure and disorganized attachment among maltreated infants was investigated through a randomized preventive intervention trial (Cicchetti, Rogosch, & Toth, 2006). Findings from research of the effects of maltreatment on infant attachment were incorporated into the design and evaluation of the intervention. Thirteen-month-old infants (N = 137) and their mothers were randomly assigned to one of three intervention conditions: (a) infant–parent psychotherapy (IPP), (b) psychoeducational parenting intervention (PPI), and (c) community standard (CS) controls. A fourth group of infants who were not maltreated (N = 52) and their mothers served as an additional low-income normative comparison (NC) group. At baseline, mothers in the maltreatment groups reported greater abuse and neglect in their own childhoods, more insecure relationships with their own mothers, more maladaptive parenting attitudes, more parenting stress, and lower family support, and they were observed to evince lower maternal sensitivity. Infants in the maltreatment groups had significantly higher rates of disorganized attachment. At postintervention follow-up at age 26 months, children in the IPP and PPI groups demonstrated substantial increases in secure attachment, whereas increases were not found in the CS or NC groups. Moreover, disorganized attachment continued to predominate in the CS group. These results were maintained when intentto-treat analyses were conducted. The findings highlight the utility of translating basic research into the design and evaluation of clinical trials, as well as the importance of preventive interventions for altering attachment organization and promoting an adaptive developmental course for maltreated infants. Knowledge of developmental norms, appreciation of how developmental level may vary within

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A Multilevel Developmental Approach to the Prevention of Psychopathology in Children and Adolescents

the same age group, sensitivity to the changing meaning that problems and disorders have at different developmental levels, attention to the effects of developmental transitions and reorganizations, and understanding of the factors that are essential features to incorporate into the design and implementation of preventive interventions may serve to enhance the potential for intervention efficacy (Cicchetti & Rogosch, 1999; Cicchetti & Toth, 2009; Coie et al., 1993; Institute of Medicine, 1994; Toth et al., 2016). Developmental theory forms the foundation for the practice of prevention, and the practice of prevention needs to form a circular link back to theory to advance both. Because prevention scientists are often, and understandably, interested in the practical outcomes of a prevention program, the research design and implementation of the program are often not optimal for informing developmental theory. Randomized Prevention Trials Randomized control trials (RCTs) are optimal for determining malleability and causality and whether a prevention program works. The purpose of this section is to discuss why RCTs are considered the essential elements of prevention and treatment trials (Kendall, Comer, & Chow, 2013). Howe et al. (2002) proposed a hybrid design, called randomized prevention trials (RPTs), which combines elements of experimental clinical designs with longitudinal designs (Toth et al., 2016). RPTs can test whether the course of psychopathology can be altered through experimental manipulation. The challenge for prevention trials is to provide evidence that the resulting change in the later course of psychopathology is due to the prevention program itself and not confounding factors (Toth et al., 2016). RPTs are better able to do this by using a control or comparison condition, with random assignment of participants to intervention or control conditions. Successful randomization greatly reduces the likelihood that condition assignment will be correlated with any other third variable, as well as being better able to control for other possible confounding variables. Results from methodologically sound RPTs

can increase confidence in the conclusion that the prevention program led to the changes in outcomes, as opposed to other variables associated with the change. Even in the context of an RPT, prevention programs will have an effect only if the targeted risk factors associated with the outcome are malleable. The ability to change a risk factor is an essential precondition for using prevention trials to test theory (Howe et al., 2002; Toth et al., 2016). Some factors may change naturally, but it is through a prevention trial that malleability (i.e., the ability to change risk or protective factors) can be determined. The intervention components must also be linked conceptually to risk or maintaining factors for certain forms of psychopathology (Hinshaw, 2002). The focus of the prevention trial is to alter risk factors or mediating processes in such a way as to reduce emergence of a disorder; therefore, components that will address those factors or processes need to be included in the intervention design. An RPT design also allows researchers to test whether a change in risk and protective factors is related to a change in the likelihood of developing psychopathology. Because of the inclusion of a control condition and because the prevention program is implemented before the emergence of a disorder, it can be determined whether the change in risk and protective factors preceded the onset of psychopathology or whether the emergence of psychopathology shaped the risk and protective factors (Howe et al., 2002). Furthermore, it can lend support for the conclusion that the targeted risk and protective factors were true causal agents, and that the change was not due to any other variables (Howe et al., 2002; Kraemer et al. 1997). Developmental psychopathology theory is supported if the specific risk factors targeted by the intervention have been reduced, and it was the changes in the risk factors that accounted for the improvements in the maladaptive behavior (Baron & Kenny, 1986; Coie et al., 1993; Kendall, Carper, Olino, & Makover, 2017; MacKinnon, Fairchild, & Fritz, 2007). Modern mediational approaches require temporal precedence (Kendall et al., 2017). 41

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In a longitudinal design, Howe et al. (2002) argued RPTs can serve as tests of developmental phase and progression. Expression of psychopathology can show developmental progression over years or decades; expression of psychopathology during different periods in this progression may reflect the effects of different risk and protective mechanisms. RPTs can test this, as well as whether the preventive intervention is able to stop progression to later phases by altering risk and protective mechanisms in the current phase (Toth et al., 2016). The longitudinal aspects of the design also allow for the detection of possible negative chains of events that may intensify the course of disorder over time. From the integrative developmental psychopathology framework, RCTs are conceptualized as veridical experiments in modifying the course of development. Therefore, RPTs are viewed as tests of theory and causal mechanisms, proffering insights into the causes of maladaptive and disordered outcomes (Cicchetti & Hinshaw, 2002; Howe et al., 2002; Ialongo et al., 2006; Kellam & Rebok, 1992; Koretz, 1991). The incorporation of biological measures into the design and evaluation of RCT preventive interventions will enable prevention scientists to grasp the development of maladaptation, psychopathology, and resilience in their full complexity. Methodologically sound prevention science that incorporates a theoretically informed and guided multiple-analysis perspective will provide a unique lens through which the processes responsible for the development, maintenance, and modification of functional outcomes can be discerned (Cicchetti & Blender, 2006; Cicchetti & Dawson, 2002; Cicchetti & Hinshaw, 2002). Transactional research and multiple-analysis approaches have been increasingly implemented in the field of developmental psychopathology (Cicchetti & Toth, 2006; Gunnar & Cicchetti, 2009; Masten, 2007). Collaborative interdisciplinary preventive interventions between researchers and clinicians that consider multiple levels of influence also will help to reduce the schisms that have long existed between science and practice. The incorporation of an interdisciplinary, multiple-level perspective will enable prevention scientists to derive a more precise and comprehensive understanding of 42

the mediators and moderators underlying intervention outcomes. Differential Susceptibility to the Environment: Implications for Preventive Interventions Individual differences in biological reactivity to stress are thought to interact with the early environment to predict psychological well-being. Historically, the prevailing view of human development in the context of stressors has been explained by the diathesisstress model (Falconer, 1965; Gottesman & Shields, 1967). In this dual-risk perspective, some individuals are more vulnerable (diathesis) to developing psychological disorders when placed in the context of environmental triggers (stress). In efforts to move beyond the diathesis-stress model to understand the role of organismic characteristics in contributing to individuals’ response to positive and stressful contexts, two evolutionary accounts emerged (Ellis, Boyce, Belsky, BakermansKranenburg, & van IJzendoorn, 2011). On the basis of early studies of naturally occurring environmental adversities and biological reactivity as predictors of illness in young children, Boyce and Ellis (2005) advanced the concept of biological sensitivity to context (BSCT). The differential susceptibility hypothesis that emanated from this research posited that children differ in their susceptibility to environmental influences with respect to the possibility of either positive or negative outcomes. Differential susceptibility theory (DST) emerged in response to the question “Why are some children affected differently by exposure to the same rearing experience?” (Belsky, 1997, 2000). Although BSCT and DST were developed independently and differ in some important respects, there are many similarities relevant to the design, provision, and evaluation of interventions for maltreated individuals. Both models posit that individuals who are the most adversely affected by stressors may also be the same ones who derive the greatest benefit from supportive and enriching environments (Belsky, 1997; Belsky & Pluess, 2009; Ellis et al., 2011). The similarities between BSCT and DST were articulated in a special section of Development and

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A Multilevel Developmental Approach to the Prevention of Psychopathology in Children and Adolescents

Psychopathology devoted to differential susceptibility to the environment, a term that captures the shared concepts of these models (Ellis et al., 2011). Both models (a) are based on an evolutionary analysis of development and maintain that humans differ in their susceptibility to environmental influences, (b) believe that individuals should differ in their genetic and neurobiological susceptibility to context, (c) acknowledge that differential susceptibility can vary across development, (d) define susceptible individuals as experiencing enduring developmental change in response to exposure to positive or negative contexts, and (e) assert that neurobiological sensitivity to the environment can be positive and negative. From an evolutionary-developmental standpoint, both models suggest that differential susceptibility to heterogeneous environments is maintained through the production of diverse offspring (Ellis et al., 2011). Because a single phenotype cannot be adapted to all potential conditions, the creation of variability within families increases the probability that at least some offspring will be successful in every generation (Belsky & Pluess, 2009). Therefore, differential susceptibility to the environment presumes that individuals vary in their susceptibility to context because of genetic differences. Differential susceptibility to the environment provides a possible explanation for individual variability in responsiveness to treatment. This situation is particularly important as most treatment outcome studies, even when yielding statistically significant effects and having medium to large effect sizes, include many individuals who do not benefit from the intervention. Therefore, identifying differential susceptibility markers is key to tailoring treatments to fit individual differences. As such markers are identified, they are examined as potential moderators of relational intervention outcomes. On the basis of this understanding, it is plausible that those susceptible individuals in greatest need of intervention on the basis of prior exposure to negative environmental contexts may also be the most responsive to interventions providing more positive contexts. Cicchetti, Toth, and Handley’s (2015) RCT on the genetic moderation of depression in mothers with major depressive disorder proffers some

fascinating implications with respect to their relevance for addressing questions related to differential susceptibility theory and the provision of intervention for racially and ethnically diverse groups. Because they did not compare interpersonal psychotherapy (IPT) with another evidence-based intervention, but rather with a nonevidence-based model comprising services generally available in the community, it cannot be determined whether individuals with particular genotypes who benefited from IPT would not benefit equally from another evidence-based intervention (e.g., cognitive–behavioral therapy [CBT]). Answers to this question are particularly important with respect to determining whether individuals with certain genotypes are more likely to derive benefit from one model of treatment or another. If so, then important strides can be made with respect to ascertaining a seminal question in the intervention literature—what works for whom (Kiesler, 1966; Paul, 1967)? A central goal of the evaluation of psychological treatments has been to answer the question posed 50 years ago by Kiesler (1966) and reasked by Paul (1967)—what treatment, by whom, is most effective for this individual with that specific problem, under which set of circumstances? The provision of RCTs that include competing models of evidence-based interventions (e.g., IPT vs. CBT) would hold great promise for addressing this issue. In a related sense, individuals with the same diagnosis often vary with respect to their responsivity to the same therapeutic intervention, which further highlights the roles that genetic variation and environmental stressors play in contributing to intervention efficacy. In accord with a developmental psychopathology perspective, consideration of developmental factors will enhance the discovery of interventions that are differentially effective for individuals with differing genotypes and experiences of adversity (Gene × Environment × Development). Although the burgeoning research on the genetic moderation of intervention outcome might lead the overly zealous to conclude that we are poised to begin to provide interventions on the basis of different genetic profiles, I caution against this premature conclusion. Given the complexity of mental illness and the methodological challenges that accompany 43

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Gene × Environment investigations of intervention efficacy, extensive replications and carefully designed studies that clearly define the characteristics and risk environments of participants are needed. Even in the absence of genetic moderation, far too little is known about mediators of intervention outcome. I share the belief that the conduct of high-quality research that incorporates progress in genetic and epigenetic technology has the potential to inform a more person-specific approach to the provision of intervention, but that it is unlikely, or even advisable, that this goal will be achieved in the short term (Uher, 2011). It is important that the research suggesting that individuals with a particular genotype are less likely to respond positively to certain interventions should impel us to continue to develop and evaluate interventions that are more likely to help those who have not yet benefitted, ultimately contributing to reductions in the overall burden of mental illness for individuals, families, and society (Cicchetti et al., 2015; Toth et al., 2013). Quantitative and Molecular Genetics, Development and Preventive Interventions Molecular geneticists have discovered that gene action, like psychological growth, persists throughout the lifespan and is not only a phenomenon of the early period of existence (Goldsmith, Gottesman, & Lemery, 1997; Krebs, Goldstein, & Kilpatrick, 2011; Watson, Hopkins, Roberts, Steitz, & Weiner, 1987). Consequently, genetic factors and psychological experiences can bring about developmental change across the life course. Furthermore, just as is true for psychologically mediated effects, consequences that are genetically mediated may be modified, by subsequent experience and through later mechanisms of gene action (Goldsmith & Gottesman, 1994). Although the effects of some genes may be enduring, others may be transient. During different developmental periods, genes may be activated or deactivated, and diverse factors that regulate gene activity are likely to vary developmentally (Grigorenko & Cicchetti, 2012). Whereas genes may create particular physical structures early in ontogenesis (e.g., receptors for particular neurotransmitters 44

in a specific tissue-type), and the functioning of these structures subsequently may play a role in the unfolding of a particular behavioral disposition (e.g., withdrawal, negative affectivity, passivity), it is possible for gene action to occur at any point in the life course, which could modify these structures or cause a physiological process to unfold that affects particular individual behavioral dispositions. On the basis of the current state of the fields of quantitative and molecular genetics, there are three primary messages for prevention: ■■

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Individual characteristics can have an important and substantial impact on environment, often for genetic reasons. Because most genetic designs are populationbased, changing the environment will result in a change in outcome. Heritability does not imply immutability. Genetic factors may be protective. Although it is common to emphasize interactions in terms of a specific risk gene or genotype interacting with a specific high-risk environment, the flip side is that individuals with a different genotype or gene are protected even in high-risk environments. This notion is consistent with much of the literature on resilience. A low-risk environment is associated with fewer problems, but understanding that genes and genetic factors can operate in a similar way is also important and underscores the role of a wide range of resilience factors that may not be considered.

It also is important to consider the extent to which preventive interventions may alter gene expressions. Among genetically vulnerable individuals, offspring of parents with major mental disorders, environmental stress, and adversity are likely involved in activating genes that are influential in the manifestation of different forms of psychopathology. Through preventive interventions, the individual may be instilled with increased capacities for coping with stress and improved physiological and neurobiological self-regulation. As a result, the expression of genes associated with psychopathology may be reduced. As sophistication in the understanding of genetic processes in vulnerability to psychopathology increases, it will be important to

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A Multilevel Developmental Approach to the Prevention of Psychopathology in Children and Adolescents

examine how preventive interventions may operate in the moderation of the expression of genes contributing to mental disorders. To summarize, advances in behavior and molecular genetics may ultimately allow for the tailoring of selective and indicated preventive interventions to reflect individuals’ genetic makeup, which should facilitate the appropriate matching of individuals to efficacious interventions. Besides leading to improved intervention outcomes, such matching should ensure that the finite mental health resources available from the standpoints of cost and the number of well-trained and qualified providers are more efficiently allocated. Neuroimaging, Plasticity, and Preventive Interventions Interest in the effects of preventive interventions on brain structure and function is growing. The interest is fueled by the recognition that the brain is plastic and can change in response to internal and external experiences. Brain plasticity is complex and can occur on multiple levels, including molecular changes, cellular alterations, shaping of neuronal structures and interconnections, and reorganization of functions (Whitten, 2013). Researchers now understand that not only does behavior change when the brain changes, but that behavior can induce further brain plasticity through feedback mechanisms (Whitten, 2013). Efficacious preventive interventions may be conceptualized as a method of inducing experience-dependent plasticity to divert maladaptation and promote resilient functioning (Cicchetti & Toth, 2009). Incorporating neuroimaging methodologies into trials of preventive interventions provides various avenues with which to visualize brain structure and function and further understand the complex interplay among brain, genes, environment, and behavior. Implementing preventive interventions during the developmental stages when the neural circuits are most plastic and amenable to change is likely to have the biggest impact. Neuroimaging techniques can be used in several ways in preventive intervention research. For example, neuroimaging endophenotypes may

help to identify individuals at risk for a disorder and an optimal population to target interventions. Such endophenotypes might yield causal links in the mechanisms underlying psychopathology and resilience and refine existing theories. Neuroimaging indicators also may demonstrate effects of preventive interventions (Clark et al., 2013). Several treatment studies have demonstrated significant intervention effects on neuroimaging indicators, especially using functional magnetic resonance imaging (fMRI; Cicchetti & Gunnar, 2008; Whitten, 2013). Neuroimaging indicators could also predict which individuals are most likely to respond favorably to interventions and serve as a marker of differential susceptibility. For example, alterations or delays in cerebral maturation or functioning might predict poorer response to intervention, and could potentially lead to the use of neuroimaging to identify individuals who require more intensive or alternate prevention approaches. There are several commonly used neuroimaging methods to assess structural and functional aspects of the brain. Structural MRI can be used to examine cerebral macrostructure, like volumes (e.g., whole cerebrum, lobes, subcortical structures, delineated regions of interest), white/gray matter distributions, and cortical thickness among other novel applications. Previously, researchers manually traced structures or regions of interest for quantification; however, automatic cortical parcellation methods are commonly used now. Automated methods have many advantages including being more efficient, being less labor intensive, being less costly, having reduced rater bias and error, and requiring less training. Diffusion Tensor Imaging is another structural imaging technique that provides data on white matter microstructure and integrity by quantifying water diffusion. White matter integrity can be examined through several approaches including specified regions of interest (e.g., corpus callosum), whole brain voxel-based morphometry (VBM), and tractography. VBM statistically compares integrity values for each voxel (smallest unit of imaging visualization—comparable to pixel in photography) to identify local patterns across individuals. Tractography uses three-dimensional modeling to represent the integrity of white matter tracts in the 45

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brain. The values per tract can be compared across groups to investigate structural connectivity. fMRI techniques examine brain activity typically during tasks designed to elicit specific mental functions. fMRI indicators are likely to be more sensitive to intervention effects relative to structural analyses. Structural and functional approaches can also be combined and can be collected sequentially in the same scanner. For example, several questions about resilient adaptation could potentially be addressed using neuroimaging methodologies, including the following: (a) Do brain structure and function differ between resilient and nonresilient children matched on comparable experiences of adversity? (b) Are particular areas of the brain more or less likely to be activated in resilient functioning during challenging or stressful tasks? (c) Are there changes over time in brain structure and/or functioning in individuals classified as resilient that may reflect processes of neural plasticity? (d) Are there differences in connectivity, assessed through diffusion tensor imaging, between regions of the cerebral cortex, possibly providing evidence of neural plasticity as one of the underlying mechanisms of resilient outcomes? It has been demonstrated in animal studies that experience can exert impacts on the microstructure and biochemistry of the brain (e.g., Meaney & Szyf, 2005), therefore a vital role for continuing neural plasticity throughout epigenesis in contributing to the recovery from various forms of maladaptation and mental disorder may be suggested (Cicchetti & Curtis, 2006). The time has come to conduct interventions that not only assess behavioral changes, but also investigate whether abnormal neurobiological structures, functions, and organizations are modifiable or are refractory to therapeutic alteration (Cicchetti & Gunnar, 2008). There is growing evidence in the animal literature that efficacious interventions modify not only maladaptive behavior but also the cellular and physiological correlates of behavior (Kandel, 1979a, 1979b, 1999; Nelson, 2000; Nowakowski & Hayes, 1999). Successful preventive interventions may alter behavior and physiology through producing alterations in gene expression that create a new structural reorganization in the brain (Kandel, 1999). These data provide biologically plausible hypotheses about how 46

effective interventions in children and adolescents function to impact development. Indeed, it seems highly likely that the efficacy of any preventive intervention ultimately depends on the ability of the nervous system, either at the cellular, metabolic, or anatomical level, to be modified by experience (Nelson, 2000). Determining the multiple levels at which change is engendered through RPTs will provide more insights into the mechanisms of change, the extent to which neural plasticity may be promoted, and the interrelations between biological and psychological processes in maladaptation, psychopathology, and resilience (Cicchetti & Curtis, 2006; Nelson, 2000). Furthermore, preventive interventions with the most in-depth empirical support, on the basis of integrative multilevel theories of normality, psychopathology, and resilience, can be implemented in effectiveness trials in community or real-world settings to reach the broadest number of people and prevent, or alleviate, mental disorders (Toth, Manly, & Nilsen, 2008). Plasticity is also the hallmark of the developing nervous system. Sensitive periods exist during which plasticity is heightened in particular neural systems, and following which these systems become less open to change. One important goal of preventive intervention is to identify periods of development when a specific intervention may be more efficacious so the intervention can be targeted to that period. One hope of preventive intervention that includes measures of neural activity is to better identify sensitive periods for interventions (Zeanah et al., 2003). Greenough, Black, and Wallace (1987) identified two forms of neural plasticity in mammalian brains: experience-expectant and experience-dependent plasticity. Most notably in early development, the brain “expects” to receive information from the environment, and an early overabundance of neurons is pruned and new neuronal connections are formed as development proceeds. Appropriate timing and quality are important for optimal brain development, whereas deprivation and atypical experience may lead to detrimental consequences for neurobiological development (Cicchetti, 2002; Cicchetti & Cannon, 1999). Given the rapid rate

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A Multilevel Developmental Approach to the Prevention of Psychopathology in Children and Adolescents

of growth and organization that occurs during the early years of life, interventions that alter adverse and stressful environments may influence the type of brain that emerges during this important period. In contrast to experience-expectant plasticity, experience-dependent plasticity occurs in later periods of development as the established, yet evolving, brain responds to new experiences through the formation of new neural connections. For high-risk individuals who are confronted with multiple environmental stresses, the positive effects of preventive interventions may occur in part through alterations that are set in motion in the structure and functioning of neurobiological systems. The incorporation of a neurobiological framework into the conceptualization of preventive interventions holds considerable promise for expansion of knowledge regarding the complexity of the developmental process. By basing preventive trials on more comprehensive, integrative developmental theories of psychopathology, prevention research offers the opportunity to conduct developmental experiments that alter environment and experience in efforts to promote resilience among individuals faced with adversity. Determining the multiple levels at which change is engendered through preventive trials will provide more insights into the mechanisms of change, the extent to which neural plasticity may be promoted, and the interrelations between biological and psychological processes in risk, resilience, and psychopathology (Curtis & Cicchetti, 2003). If biological systems recover in response to the intervention, this supports arguments that the systems under study are sensitive to environmental input during development (Cicchetti & Curtis, 2006). Furthermore, if randomized interventions alter neurobiological systems associated with disorders and it can be shown that they mediate changes in psychosocial and behavioral functioning, then this fosters a better understanding of the neurobiological bases of the disorder (Cicchetti, 2002). Moreover, preventive interventions may contribute to recovery or repair of biological sequelae in ways that have only begun to be understood. Improved comprehension of the neurobiological processes that increase risk of maladaptive development may also

suggest novel targets for preventive intervention. Therefore, it is important for prevention scientists to investigate how changes in experience and psychological functioning resulting from preventive interventions may modify biological processes (Cicchetti & Gunnar, 2008). Epigenetics and Prevention Interventions The rapidly advancing field of epigenetics is making it clear that DNA is not the static entity that it was once thought to be (Mill, 2011; National Research Council & Institute of Medicine, 2009). Epigenetic processes play a dynamic role in regulating gene expression and are responsive to the environment (Bick et al., 2012; Mill, 2011; Roth, 2013). Epigenetic regulation of gene expression occurs independently of DNA sequencing and operates primarily through changes in DNA methylation and chromatin structure (Mill, 2011; Roth, 2013). Chromatin is the DNA-protein complex that forms to package DNA into a smaller volume, prevent damage, and regulate expression and replication functions. For example, chromatin packaging can be altered by a change in methylation to allow regulatory proteins to more easily access promoter regions in DNA to facilitate gene expression. These processes can be reversible, such that methylation and chromatin folding can be reversed and promoter regions may become less accessible again. Although epigenetic changes can be reversed, they are often long lasting across the course of development, and some can even be transmitted across generations (Roth, 2013). Epigenetic mechanisms may also shed light on the limited associations between gene mutations or predisposing polymorphisms and disorders. Even genes that do not carry mutations or increase risk of disease can be rendered harmful if they are not expressed in the appropriate amount in the correct type of cell at the correct time in development (Mill, 2011). Research is accumulating in human and animal models on a broad range of environmental exposures across the lifespan that are associated with epigenetic variation (Bick et al., 2012; Champagne, 2009; Monk, Spicer, & Champagne, 2012; Roth, 47

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2013). For example, several epigenetic mechanisms have been proposed linking prenatal exposure to maternal stress in early infant outcomes (Monk et al., 2012; Roth, 2013). Changes in DNA methylation have also been documented in response to prenatal exposure to cigarettes or alcohol (Haycock, 2009; Knopik, Maccani, Francazio, & McGeary, 2012). Postnatal environmental exposures, such as the quality of mother and child interactions, may also lead to shifts in developmental trajectories (for better or worse) via epigenetic pathways. Research on early adversity (e.g., child maltreatment, stressful separations from caregivers, living in impoverished environments) has found differential methylation patterns in human and animal models (Bick et al., 2012; Cicchetti, Hetzel, Rogosch, Handley, & Toth, in press; Meaney & Szyf, 2005; Monk et al., 2012; Roth, 2013). Methylation patterns have also been linked to specific patterns of HPA axis activity, immune response, and physical and mental health outcomes (Bick et al., 2012; Roth, 2013; Yang et al., 2013). Aberrant patterns of methylation have been noted in several psychiatric disorders including schizophrenia, posttraumatic stress disorder, and mood disorders (Roth, 2013). These findings highlight ripe areas for future investigation that will advance our understanding of how biological processes respond to the environment and influence developmental trajectories. Epigenetic mechanisms may be a realistic target for intervention because of their reversibility. Epigenetically informed preventive interventions can include methylation assays in their measurement batteries to evaluate the effects of the interventions on these mechanisms and refine theory. Methylation assays can be conducted genome-wide or at the level of specific regions of candidate genes with known functional properties (e.g., the glucocorticoid receptor gene; Bick et al., 2012). Tremblay (2008) provides several illustrative examples of study designs that incorporate the epigenetic level of measurement. For example, pregnant women who smoke could be randomly assigned to a preventive intervention aimed at smoking cessation and improving child outcomes in development and behavior. A reasonable hypothesis on the basis of animal and human research demonstrating altered methylation 48

could postulate that smoking during pregnancy has negative epigenetic effects on brain development that could lead to problems with self-regulation and aggression in early childhood of the offspring. An RCT on the program could test whether the program leads to changes in methylation at specific candidate genes, birth outcomes, and cognitive and behavioral development in the child across experimental and control groups. Epigenetics is a relatively new area of investigation and prevention scientists are just beginning to consider epigenetic mechanisms within the context of understanding the efficacy of interventions on complex outcomes. We anticipate the next decade will bring forth growth in this area. Conclusion and Future Perspectives It is proposed that the developmental psychopathology framework can be an integrative, multilevel approach that can be directed toward developing and evaluating preventive interventions for children, adolescents, and adults. To maintain the progress that has been made to date, increase the momentum of advances in understanding the mechanisms through which preventive interventions are efficacious, and identify the characteristics of individuals that may make them likely to benefit from a given intervention, we proffer the following recommendations. Tests of mediation of prevention or treatment effects in mental health and substance abuse research are severely lacking (Hinshaw, 2002) but are necessary for understanding mechanisms of change in prevention trials and the underlying theory guiding the prevention trial. If a change in a hypothesized mediator is not associated with a change in outcome, or if a change in outcome is observed in the absence of a change in the mediator variable, then here may be a problem with the prevention theory (Coie et al., 1993; Kendall et al., 2017). Furthermore, tests of mediation can allow researchers to elucidate intervention components that are most important to elicit change in the mediating variable, and therefore most important for achieving the desired outcomes (Kraemer, Wilson, Fairburn, & Agras, 2002).

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Prevention scientists need to be cognizant of developmental variables when examining mediators and moderators of prevention program outcomes. A program to prevent depression, for example, may or may not achieve the same outcomes for middle school students as high school students. If the program achieves similar positive outcomes across the age groups, then developmental variations among participants may not be important to achieving program outcomes. If it is found that age is a moderator, then critical periods in the developmental process of depression can be revealed, including the most effective time to implement a program to prevent depression to achieve the best outcomes. Identifying mediators and moderators is essential for prevention researchers who want to help the largest number of people with a judicious use of resources. Knowing for whom the treatment works and understanding mechanisms of change in prevention programs will allow prevention scientists to develop the most effective programs for the largest number of people across all levels of risk. Identification of the key ingredients of programs is also possible; programs can then be streamlined so the ineffective program components are modified or eliminated. Research on Gene × Environment and on epigenetics needs to incorporate, as well as to emphasize, a developmental perspective. Genes may affect how environmental experience affects the developmental process and this may operate differently at various developmental periods. Moreover, the effects of genes and experience at a specific period may be influenced by the effects of prior development. Environments may affect the timing of genetic effects and gene expression. In addition, there are experience effects on the epigenome and these also would operate differently across the course of development. There are several ways that there can be genetic effects on intervention efficacy. Are some individuals, on the basis of genetic variation, more susceptible to the positive effects of intervention? Are different interventions more effective with different individuals on the basis of genetic differences (i.e. matching intervention to genotype group)?

Does intervention affect DNA methylation resulting in changes in gene expression? DNA methylation changes in response to experience could lead to the design of prevention and intervention strategies that change the expression of genes to promote healthy physical and mental health outcomes. Given that the demethylated epigenome would be transmitted to the next generation, it will be important to determine if decreased mental illness risk through efficacious intervention alters the genome, which in turn results in a “less risky” epigenome being transmitted to the next generation. With advances in neuroimaging and molecular biology, we are increasingly able to ascertain how anomalies in brain structure and function may contribute to, or be a consequence of, the emergence of psychopathology (Cicchetti & Toth, 2009). It is important that, with increasing use of a multiplelevels-of-analysis approach to evaluating preventive interventions advocated by developmental psychopathologists (Cicchetti & Dawson, 2002), we also will be able to determine if psychosocial interventions can positively affect the brain, perhaps thereby increasing the likelihood of promoting sustained adaptive functioning.

References Baron, R. M., & Kenny, D. A. (1986). The moderatormediator variable distinction in social psychological research: Conceptual, strategic, and statistical considerations. Journal of Personality and Social Psychology, 51, 1173–1182. http://dx.doi.org/10.1037/ 0022-3514.51.6.1173 Beach, S. R. H., Brody, G. H., Todorov, A. A., Gunter, T. D., & Philibert, R. A. (2010). Methylation at SLC6A4 is linked to family history of child abuse: An examination of the Iowa Adoptee sample. American Journal of Medical Genetics Part B: Neuropsychiatric Genetics, 153, 710–713. http://dx.doi.org/10.1002/ ajmg.b.31028 Beauchaine, T. P., Neuhaus, E., Brenner, S. L., & Gatzke-Kopp, L. (2008). Ten good reasons to consider biological processes in prevention and intervention research. Development and Psychopathology, 20, 745–774. http://dx.doi.org/ 10.1017/S0954579408000369 Belsky, J. (1997). Variation in susceptibility to environmental influence: An evolutionary argument. Psychological Inquiry, 8, 182–186. http://dx.doi.org/ 10.1207/s15327965pli0803_3 49

Dante Cicchetti

Belsky, J. (2000). Fixed versus flexible strategists: Individual differences in facultative responsiveness? Behavioral and Brain Sciences, 23, 591–592. http:// dx.doi.org/10.1017/S0140525X00273378 Belsky, J., & Pluess, M. (2009). Beyond diathesis stress: Differential susceptibility to environmental influences. Psychological Bulletin, 135, 885–908. http://dx.doi.org/10.1037/a0017376

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Belsky, J., & van IJzendoorn, M. H. (2015). What works for whom? Genetic moderation of intervention efficacy. Development and Psychopathology, 27(Special Issue No. 1), 1–6. Bick, J., Naumova, O., Hunter, S., Barbot, B., Lee, M., Luthar, S. S., . . . Grigorenko, E. L. (2012). Childhood adversity and DNA methylation of genes involved in the hypothalamus-pituitary-adrenal axis and immune system: Whole-genome and candidategene associations. Development and Psychopathology, 24, 1417–1425. http://dx.doi.org/10.1017/ S0954579412000806 Boyce, W. T., & Ellis, B. J. (2005). Biological sensitivity to context: I. An evolutionary-developmental theory of the origins and functions of stress reactivity. Development and Psychopathology, 17, 271–301. http://dx.doi.org/10.1017/ S0954579405050145 Champagne, F. A. (2009). Epigenetic influence of social experiences across the lifespan. Developmental Psychobiology, 52, 299–311. http://dx.doi.org/ 10.1002/dev.20436 Cicchetti, D. (1990). A historical perspective on the discipline of developmental psychopathology. In J. Rolf, A. Masten, D. Cicchetti, K. Nuechterlein, & S. Weintraub (Eds.), Risk and protective factors in the development of psychopathology (pp. 2–28). http://dx.doi.org/10.1017/CBO9780511752872.003 Cicchetti, D. (2002). How a child builds a brain: Insights from normality and psychopathology. In W. W. Hartup & R. A. Weinberg (Eds.), The Minnesota symposia on child psychology: Vol. 32. Child psychology in retrospect and prospect (pp. 23–71). Mahwah, NJ: Erlbaum. Cicchetti, D. (Ed.). (2016). Developmental psychopathology (3rd ed., Vols. 1–4). New York, NY: Wiley. Cicchetti, D., & Blender, J. A. (2006). A multiple-levelsof-analysis perspective on resilience: Implications for the developing brain, neural plasticity, and preventive interventions. Annals of the New York Academy of Sciences, 1094, 248–258. http://dx.doi.org/ 10.1196/annals.1376.029 Cicchetti, D., & Cannon, T. D. (1999). Neurodevelopmental processes in the ontogenesis and epigenesis of psychopathology. Development and Psychopathology, 11, 375–393. http://dx.doi.org/10.1017/ S0954579499002114 50

Cicchetti, D., & Curtis, W. J. (2006). The developing brain and neural plasticity: Implications for normality, psychopathology, and resilience. In D. Cicchetti & D. Cohen (Eds.), Developmental psychopathology: Vol. 2. Developmental neuroscience (2nd ed., pp. 1–64). New York, NY: Wiley. Cicchetti, D., & Dawson, G. (2002). Multiple levels of analysis. Development and Psychopathology, 14, 417–420. http://dx.doi.org/10.1017/ S0954579402003012 Cicchetti, D., & Gunnar, M. R. (2008). Integrating biological measures into the design and evaluation of preventive interventions. Development and Psychopathology, 20, 737–743. http://dx.doi.org/ 10.1017/S0954579408000357 Cicchetti, D., Hetzel, S., Rogosch, F. A., Handley, E. D., & Toth, S. L. (in press). Epigenetic mechanisms of physical disease and mental illness risk: Illustration from the study of maltreatment. Development and Psychopathology. Cicchetti, D., & Hinshaw, S. P. (2002). Editorial: prevention and intervention science: contributions to developmental theory. Development and Psychopathology, 14, 667–671. http://dx.doi.org/ 10.1017/S0954579402004017 Cicchetti, D., & Rogosch, F. A. (1996). Equifinality and multifinality in developmental psychopathology. Development and Psychopathology, 8, 597–600. http:// dx.doi.org/10.1017/S0954579400007318 Cicchetti, D., & Rogosch, F. A. (1997). The role of self-organization in the promotion of resilience in maltreated children. Development and Psychopathology, 9, 797–815. http://dx.doi.org/10.1017/ S0954579497001442 Cicchetti, D., & Rogosch, F. A. (1999). Conceptual and methodological issues in developmental psychopathology research. In P. C. Kendall, J. N. Butcher, & G. N. Holmbeck (Eds.), Handbook of research methods in clinical psychology (pp. 433–465). New York, NY: Wiley. Cicchetti, D., Rogosch, F. A., & Toth, S. L. (2006). Fostering secure attachment in infants in maltreating families through preventive interventions. Development and Psychopathology, 18, 623–649. http://dx.doi.org/10.1017/S0954579406060329 Cicchetti, D., Rogosch, F. A., Toth, S. L., & Sturge-Apple, M. L. (2011). Normalizing the development of cortisol regulation in maltreated infants through preventive interventions. Development and Psychopathology, 23, 789–800. http://dx.doi.org/ 10.1017/S0954579411000307 Cicchetti, D., & Sroufe, L. A. (2000). The past as prologue to the future: The times, they’ve been a-changin’. Development and Psychopathology, 12, 255–264. http:// dx.doi.org/10.1017/S0954579400003011

A Multilevel Developmental Approach to the Prevention of Psychopathology in Children and Adolescents

Cicchetti, D., & Toth, S. L. (1992). The role of developmental theory in prevention and intervention. Development and Psychopathology, 4, 489–493. http:// dx.doi.org/10.1017/S0954579400004831

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Cicchetti, D., & Toth, S. L. (2006). Developmental psychopathology and preventive intervention. In A. Renninger & I. Sigel (Eds.), Handbook of child psychology (6th ed., pp. 497–547). New York, NY: Wiley. Cicchetti, D., & Toth, S. L. (2009). The past achievements and future promises of developmental psychopathology: The coming of age of a discipline. Journal of Child Psychology and Psychiatry, 50, 16–25. http://dx.doi.org/10.1111/ j.1469-7610.2008.01979.x Cicchetti, D., Toth, S. L., & Handley, E. D. (2015). Genetic moderation of interpersonal psychotherapy efficacy for low-income mothers with major depressive disorder: Implications for differential susceptibility. Development and Psychopathology, 27, 19–35. http://dx.doi.org/10.1017/ S0954579414001278 Clark, C. A. C., Sheffield, T. D., Chevalier, N., Nelson, J. M., Wiebe, S. A., & Espy, K. A. (2013). Charting early trajectories of executive control with the shape school. Developmental Psychology, 49, 1481–1493. http://dx.doi.org/10.1037/a0030578 Coie, J. D., Watt, N. F., West, S. G., Hawkins, J. D., Asarnow, J. R., Markman, H. J., . . . Long, B. (1993). The science of prevention. A conceptual framework and some directions for a national research program. American Psychologist, 48, 1013–1022. http:// dx.doi.org/10.1037/0003-066X.48.10.1013 Costello, E. J., & Angold, A. (2016). Developmental epidemiology. In D. Cicchetti. (Ed.), Developmental psychopathology: Vol. 1. Theory and method (3rd ed., pp. 94–128.). http://dx.doi.org/ 10.1002/9781119125556.devpsy103 Curtis, W. J., & Cicchetti, D. (2003). Moving research on resilience into the 21st century: Theoretical and methodological considerations in examining the biological contributors to resilience. Development and Psychopathology, 15, 773–810. http://dx.doi.org/ 10.1017/S0954579403000373 Dozier, M., Peloso, E., Lewis, E., Laurenceau, J.-P., & Levine, S. (2008). Effects of an attachment-based intervention on the cortisol production of infants and toddlers in foster care. Development and Psychopathology, 20, 845–859. http://dx.doi.org/ 10.1017/S0954579408000400 Drabick, D. A., & Kendall, P. C. (2010). Developmental psychopathology and the diagnosis of mental health problems among youth. Clinical Psychology: Science and Practice, 17, 272–280. http://dx.doi.org/10.1111/ j.1468-2850.2010.01219.x

Ellis, B. J., Boyce, W. T., Belsky, J., Bakermans-Kranenburg, M. J., & van IJzendoorn, M. H. (2011). Differential susceptibility to the environment: An evolutionary— neurodevelopmental theory. Development and Psychopathology, 23, 7–28. http://dx.doi.org/10.1017/ S0954579410000611 Falconer, D. S. (1965). The inheritance of liability to certain diseases, estimated from the incidence among relatives. Annals of Human Genetics, 29, 51–76. http:// dx.doi.org/10.1111/j.1469-1809.1965.tb00500.x Fisher, P. A., Stoolmiller, M., Gunnar, M. R., & Burraston, B. O. (2007). Effects of a therapeutic intervention for foster preschoolers on diurnal cortisol activity. Psychoneuroendocrinology, 32, 892–905. http://dx.doi.org/10.1016/ j.psyneuen.2007.06.008 Goldsmith, H. H., & Gottesman, I. (1994). Developmental psychopathology of antisocial behavior: Inserting genes into its ontogenesis and epigenesis. In C. Nelson (Ed.), The Minnesota symposia on child psychology: Vol. 27. Threats to optimal development (pp. 69–104). Hillsdale, NJ: Erlbaum. Goldsmith, H. H., Gottesman, I. I., & Lemery, K. S. (1997). Epigenetic approaches to developmental psychopathology. Development and Psychopathology, 9, 365–387. http://dx.doi.org/10.1017/ S0954579497002095 Gottesman, I. I., & Shields, J. (1967). A polygenic theory of schizophrenia. Proceedings of the National Academy of Sciences, USA, 58, 199–205. http:// dx.doi.org/10.1073/pnas.58.1.199 Greenough, W. T., Black, J. E., & Wallace, C. S. (1987). Experience and brain development. Child Development, 58, 539–559. http://dx.doi.org/ 10.2307/1130197 Grigorenko, E. L., & Cicchetti, D. (2012). Genomic sciences for developmentalists: The current state of affairs. Development and Psychopathology, 24, 1157–1164. http://dx.doi.org/10.1017/ S0954579412000612 Gunnar, M. R., & Cicchetti, D. (2009). Meeting the challenge of translational research in child psychology. In D. Cicchetti & M. R. Gunnar (Eds.), The Minnesota symposia on child psychology: Vol. 35. Meeting the challenge of translational research in child psychology (pp. 1–27). New York, NY: Wiley. Haycock, P. C. (2009). Fetal alcohol spectrum disorders: The epigenetic perspective. Biology of Reproduction, 81, 607–617. http://dx.doi.org/10.1095/ biolreprod.108.074690 Hinshaw, S. P. (2002). Intervention research, theoretical mechanisms, and causal processes related to externalizing behavior patterns. Development and 51

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Psychopathology, 14, 789–818. http://dx.doi.org/ 10.1017/S0954579402004078 Howe, G. W., Reiss, D., & Yuh, J. (2002). Can prevention trials test theories of etiology? Development and Psychopathology, 14, 673–694. http://dx.doi.org/ 10.1017/S0954579402004029

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Ialongo, N., Rogosch, F. A., Cicchetti, D., Toth, S. L., Buckley, J., Petras, H., & Neiderhiser, J. (2006). A developmental psychopathology approach to the prevention of mental health disorders. In D. Cicchetti (Ed.), Developmental psychopathology: Vol. 1. Theory and method (2nd ed., pp. 968–1018). New York, NY: Wiley. Institute of Medicine. (1994). Research on children and adolescents with mental, behavioral, and developmental disorders. Washington, DC: National Academy Press. Kandel, E. R. (1979a). Psychotherapy and the single synapse. The impact of psychiatric thought on neurobiologic research. New England Journal of Medicine, 301, 1028–1037. http://dx.doi.org/10.1056/ NEJM197911083011904 Kandel, E. R. (1979b). Small systems of neurons. Scientific American, 241, 66–76. http://dx.doi.org/ 10.1038/scientificamerican0979-66 Kandel, E. R. (1999). Biology and the future of psychoanalysis: A new intellectual framework for psychiatry revisited. American Journal of Psychiatry, 156, 505–524. Kellam, S. G., & Rebok, G. W. (1992). Building developmental and etiological theory through epidemiologically based preventive intervention trials. In J. McCord & R. E. Tremblay (Eds.), Preventing antisocial behavior: Interventions from birth through adolescence (pp. 162–195). New York, NY: Guilford Press. Kendall, P. C., Carper, M., Olino, T., & Makover, H. (2017). On the importance of temporal precedence in mediational analyses. Journal of Consulting and Clinical Psychology, 85, 80–82. Kendall, P. C., Comer, J., & Chow, C. (2013). The randomized controlled trial: Basics and beyond. In J. Comer & P. C. Kendall (Eds.), Oxford handbook of research strategies for clinical psychology. http:// dx.doi.org/10.1093/oxfordhb/9780199793549.013.0004 Kiesler, D. (1966). Some myths of psychotherapy research and search for a paradigm. Psychological Bulletin, 65, 110–136. http://dx.doi.org/10.1037/ h0022911 Knopik, V. S., Maccani, M. A., Francazio, S., & McGeary, J. E. (2012). The epigenetics of maternal cigarette smoking during pregnancy and effects on child development. Development and Psychopathology, 24, 1377–1390. http://dx.doi.org/10.1017/ S0954579412000776

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Koretz, D. S. (1991). Prevention-centered science in mental health. American Journal of Community Psychology, 19, 453–458. http://dx.doi.org/10.1007/ BF00937986 Kraemer, H. C., Kazdin, A. E., Offord, D. R., Kessler, R. C., Jensen, P. S., & Kupfer, D. J. (1997). Coming to terms with the terms of risk. Archives of General Psychiatry, 54, 337–343. http://dx.doi.org/10.1001/ archpsyc.1997.01830160065009 Kraemer, H. C., Wilson, G. T., Fairburn, C. G., & Agras, W. S. (2002). Mediators and moderators of treatment effects in randomized clinical trials. Archives of General Psychiatry, 59, 877–883. http://dx.doi.org/ 10.1001/archpsyc.59.10.877 Krebs, J. E., Goldstein, E. S., & Kilpatrick, S. T. (2011). Lewin’s Genes X. Sudbury, MA: Jones and Bartlett Publishers. Luthar, S. S., & Cicchetti, D. (2000). The construct of resilience: Implications for interventions and social policies. Development and Psychopathology, 12, 857–885. http://dx.doi.org/10.1017/ S0954579400004156 MacKinnon, D. P., Fairchild, A. J., & Fritz, M. S. (2007). Mediation analysis. Annual Review of Psychology, 58, 593–614. http://dx.doi.org/10.1146/ annurev.psych.58.110405.085542 Masten, A. S. (2007). Resilience in developing systems: Progress and promise as the fourth wave rises. Development and Psychopathology, 19, 921–930. http://dx.doi.org/10.1017/S0954579407000442 Meaney, M. J., & Szyf, M. (2005). Environmental programming of stress responses through DNA methylation: Life at the interface between a dynamic environment and a fixed genome. Dialogues in Clinical Neuroscience, 7, 103–123. Mill, J. (2011). Epigenetic effects of gene function and their role in mediating gene–environment interactions. In K. S. Kendler, S. R. Jaffee, & D. Romer (Eds.), The dynamic genome and mental health: The role of genes and environments in youth development (pp. 145–171). New York, NY: Oxford University Press. Monk, C., Spicer, J., & Champagne, F. A. (2012). Linking prenatal maternal adversity to developmental outcomes in infants: The role of epigenetic pathways. Development and Psychopathology, 24, 1361–1376. http://dx.doi.org/10.1017/S0954579412000764 National Research Council & Institute of Medicine. (2009). Preventing mental, emotional, and behavioral disorders among young people: Progress and possibilities. Washington, DC: National Academies Press. Nelson, C. A. (2000). The neurobiological basis of early intervention. In J. P. Shonkoff & S. J. Meisels (Eds.), Handbook of early childhood intervention

A Multilevel Developmental Approach to the Prevention of Psychopathology in Children and Adolescents

(2nd ed., pp. 204–228). http://dx.doi.org/10.1017/ CBO9780511529320.012 Nowakowski, R. S., & Hayes, N. L. (1999). CNS development: An overview. Development and Psychopathology, 11, 395–417. http://dx.doi.org/ 10.1017/S0954579499002126 Paul, G. L. (1967). Strategy of outcome research in psychotherapy. Journal of Consulting Psychology, 31, 109–118. http://dx.doi.org/10.1037/h0024436

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Richters, J. E., & Cicchetti, D. (1993). Toward a developmental perspective on conduct disorder. Development and Psychopathology, 5, 1–4. http:// dx.doi.org/10.1017/S0954579400004223 Roth, T. L. (2013). Epigenetic mechanisms in the development of behavior: Advances, challenges, and future promises of a new field. Development and Psychopathology, 25, 1279–1291. http://dx.doi.org/ 10.1017/S0954579413000618 Rutter, M., & Sroufe, L. A. (2000). Developmental psychopathology: Concepts and challenges. Development and Psychopathology, 12, 265–296. http://dx.doi.org/10.1017/S0954579400003023 Sroufe, L. A., & Rutter, M. (1984). The domain of developmental psychopathology. Child Development, 55, 17–29. http://dx.doi.org/10.2307/1129832 Szyf, M., & Bick, J. (2013). DNA methylation: A mechanism for embedding early life experiences in the genome. Child Development, 84, 49–57. http:// dx.doi.org/10.1111/j.1467-8624.2012.01793.x Toth, S. L., Gravener-Davis, J. A., Guild, D. J., & Cicchetti, D. (2013). Relational interventions for child maltreatment: Past, present, & future perspectives. Development and Psychopathology, 25, 1601–1617. http://dx.doi.org/10.1017/S0954579413000795 Toth, S. L., Manly, J. T., & Nilsen, W. (2008). From research to practice: Lessons learned. Journal of Applied Developmental Psychology, 29, 317–325. Toth, S. L., Petrenko, C. L. M., Gravener-Davis, J. A., & Handley, E. D. (2016). Advances in prevention science: A developmental psychopathology perspective. In D. Cicchetti (Ed.), Developmental psychopathology: Vol. 4. Risk, resilience, and intervention (3rd ed., pp. 815–873). http://dx.doi.org/ 10.1002/9781119125556.devpsy416 Toth, S. L., Sturge-Apple, M. L., Rogosch, F. A., & Cicchetti, D. (2015). Mechanisms of change: Testing

how preventative interventions impact psychological and physiological stress functioning in mothers in neglectful families. Development and Psychopathology, 27, 1661–1674. http://dx.doi.org/10.1017/ S0954579415001017 Tremblay, R. E. (2008). Understanding development and prevention of chronic physical aggression: Towards experimental epigenetic studies. Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences, 363, 2613–2622. http:// dx.doi.org/10.1098/rstb.2008.0030 Uher, R. (2011). Genes, environment, and individual differences in responding to treatment for depression. Harvard Review of Psychiatry, 19, 109–124. http:// dx.doi.org/10.3109/10673229.2011.586551 Wakefield, J. C. (1992). Disorder as harmful dysfunction: A conceptual critique of DSM–III–R’s definition of mental disorder. Psychological Review, 99, 232–247. http://dx.doi.org/10.1037/ 0033-295X.99.2.232 Wakefield, J. C. (1997). When is development disordered? Developmental psychopathology and the harmful dysfunction analysis of mental disorder. Development and Psychopathology, 9, 269–290. http:// dx.doi.org/10.1017/S0954579497002058 Watson, J. D., Hopkins, N. H., Roberts, J. W., Steitz, J. A., & Weiner, A. M. (1987). Molecular biology of the gene (4th ed.). Menlo Park, CA: Cummings. Whitten, L. A. (2013). Translational neuroscience and potential contributions of functional magnetic resonance imaging (fMRI) to the prevention of substance misuse and antisocial behavior. Prevention Science, 14, 238–246. http://dx.doi.org/10.1007/ s11121-012-0341-y Yang, B. Z. Y., Zhang, H., Ge, W., Weder, N., Douglas-Palumberi, H., Perepletchikova, F., . . . Kaufman, J. (2013). Child abuse and epigenetic mechanisms of disease risk. American Journal of Preventive Medicine, 44, 101–107. http://dx.doi.org/ 10.1016/j.amepre.2012.10.012 Zeanah, C. H., Nelson, C. A., Fox, N. A., Smyke, A. T., Marshall, P., Parker, S. W., & Koga, S. (2003). Designing research to study the effects of institutionalization on brain and behavioral development: The Bucharest Early Intervention Project. Development and Psychopathology, 15, 885–907. http://dx.doi.org/10.1017/S0954579403000452

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Chapter 4

Primary and Secondary Prevention of Child Maltreatment

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Amy Damashek, Emily C. Morgan, McKenna Corlis, and Hilary Richardson

DEFINITION AND PREVALENCE Child maltreatment is a serious public health problem that affects children’s short and long-term physical, emotional, cognitive, and social functioning. Child maltreatment has been defined by the Child Abuse Prevention and Treatment Act as “any recent act or failure to act on the part of a parent or caretaker which results in death, serious physical or emotional harm, sexual abuse or exploitation; or an act or failure to act, which presents an imminent risk of serious harm” (U.S. Department of Health and Human Services, 2016, p. viii). Approximately 702,000 children were victims of maltreatment in the United States in 2014. The most common form of maltreatment was neglect (75% of cases), followed by physical abuse (17% of cases), sexual abuse (7% of cases), and other types of maltreatment (7% of cases; U.S. Department of Health and Human Services, 2016). Consequences of Child Maltreatment Victims of child maltreatment are at risk of experiencing a variety of negative short- and long-term consequences. Short-term physical effects include minor injuries like bruises and cuts and severe injuries like burns, broken bones, hemorrhaging, and other internal injuries. Maltreatment can also cause brain regions to grow improperly which can affect cognitive, language, and academic abilities (Shonkoff & Phillips, 2000). Child maltreatment is

also associated with long-term physical (e.g., cardiovascular disease, obesity, asthma, hypertension, diabetes; Felitti & Anda, 2009) and mental health problems (e.g., depression, anxiety, posttraumatic stress disorder [PTSD], poor emotional regulation; Barrett, Katsiyannis, Zhang, & Zhang, 2014; Collishaw et al., 2007; Messman-Moore, Walsh, & DiLillo, 2010), as well as other negative outcomes including grade repetition, criminal behavior, risky sexual behaviors, pregnancy, and revictimization (Gold, Sullivan, & Lewis, 2011; Wilson, Dolan, Smith, Casanueva, & Ringeisen, 2012). These shortand long-term consequences lead to an estimated lifetime cost of $210,012 per victim, in the form of medical, child welfare, criminal justice, and special education costs, as well as productivity losses (Fang, Brown, Florence, & Mercy, 2012). The negative outcomes of maltreatment and the associated costs dictate that prevention of child maltreatment should be a public health priority. The World Health Organization recommends a prevention agenda in which programs attempt to weaken risk factors for maltreatment and strengthen protective factors in families and communities (Whitaker, Lutzker, & Shelley, 2005). Prevention programs can be categorized by level—primary/universal, secondary/selected, and tertiary/indicated. Primary prevention programs are implemented before maltreatment occurs and are typically delivered universally, at a population level that includes all caregivers, regardless of risk level. Secondary programs are delivered to families

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whose children are at high risk of being maltreated, thereby reducing the size of the focus population. Tertiary programs are similar to treatment interventions and address maltreatment once it has occurred (McMurtry, 1985; Whitaker et al., 2005).

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Evidence-Based Interventions for Child Maltreatment The field of child maltreatment has made substantial progress in identifying effective programs for preventing child maltreatment. It is crucial in the field of maltreatment to use interventions that are evidence-based (i.e., supported by research evidence). The danger of not using evidence-based interventions lies in the possibility of wasting valuable resources that could be directed to effective interventions as well as potentially harming children and families. The California Evidence-Based Clearinghouse (CEBC; http://www.cebc4cw.org) is a web-based resource developed to disseminate information about evidence-based interventions that are relevant to the child welfare system. The CEBC was created from a partnership of the California Department of Social Services, the Chadwick Center for Children and Families, and the Child and Adolescent Services Research Center. The CEBC uses experts to review practices relevant to child welfare. On the basis of the state of the science supporting each intervention, reviewers assign a scientific rating to the program from 1 to 5. There are several criteria for the ratings, but one of the primary considerations is the number of randomized controlled trials (RCTs) that have been conducted to evaluate an intervention. Interventions with a rating of “1–well supported by research evidence” must have at least 2 published RCTs showing that the intervention is more effective than a comparison practice. To receive a rating of “2–supported by research evidence,” the intervention must have been shown to be superior to a comparison practice in at least one RCT. To receive a rating of “3–promising research evidence,” the intervention must have been shown to be superior to a control condition. Interventions receiving a rating of “4–evidence fails to demonstrate effect” are those that have been tested and found to not be effective. Those rated as 56

“5–concerning practice” have been shown to have a negative impact on those who were served. Finally, a rating of “NR” indicates that there is not enough published literature to rate a program. The CEBC includes seven primary topic areas with several subtopic areas. In this chapter, we review programs that aim to prevent child maltreatment, including primary and secondary prevention programs that have earned a scientific rating of 1, 2, or 3.

Primary Prevention Programs The following section includes a review of primary prevention programs for (a) physical abuse and neglect, (b) shaken baby syndrome, and (c) child sexual abuse.

Programs for Physical Abuse and Neglect Nurse–Family Partnership.  The Nurse–Family Partnership (NFP) is a home visiting program in which registered nurses provide education related to child health and safety for low-income firsttime mothers. The goals of NFP are to improve child development by promoting healthy choices to improve pregnancy outcomes, pregnancy planning, educational achievement, and employment. These goals are addressed through education and connection to resources (Olds, Hill, O’Brien, Racine, & Moritz, 2003). Women are enrolled during early pregnancy, and can continue until their child reaches age 2. Home visits are tailored to each client’s needs. Specific domains that are addressed include personal health, environmental health, maternal role, life course development, health and human services, and family and friends (Olds et al., 2003). NFP was rated as a 1–well supported by research evidence in 2015. An early study found that primarily White at-risk mothers who participated in NFP had fewer reports of child maltreatment than did at-risk mothers who only attended regular prenatal appointments (Olds, Henderson, Chamberlin, & Tatelbaum, 1986). This effect maintained at a 15-year follow-up (Olds et al., 1997). Other changes found at a 2- to 4-year follow-up included fewer hazards in the home, less parental coping problems,

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Primary and Secondary Prevention of Child Maltreatment

and fewer child injuries compared with the control group (Olds, Henderson, & Kitzman, 1994). At the 15-year follow-up mothers also had fewer arrests, had fewer convictions, spent fewer days in jail, and had fewer child maltreatment reports than did those in the control group (Olds et al., 1997). A later RCT examined a more ethnically diverse sample of at-risk mothers who either received regular developmental screenings only, regular screenings and home visitation from a paraprofessional, or regular screenings and home visitation from a nurse (i.e., NFP). The NFP group outperformed the others on several outcomes: mothers worked more, engaged in more parent–child interactions, and had fewer, more spaced apart pregnancies. Children had better cognitive development and fewer language delays than did the other two groups (Olds et al., 2002). Many of these effects maintained at a 4-year follow-up (Olds et al., 2004). At a 9-year follow-up mothers in the NFP group used less welfare and food stamps, had longer relationships, and their children were far less likely to die of preventable causes than were those who did not receive NFP (Olds et al., 2007). In summary, NFP is a model that has been used for several decades, and research has shown strong support for its efficacy in affecting long-term outcomes that are closely related to common risk factors for child maltreatment. One study found that NFP reduced maltreatment rates, but the sample was comprised primarily of White participants. Safe Environment for Every Kid.  Safe Environment for Every Kid (SEEK) uses the pediatric primary care setting to prevent child maltreatment in at-risk families of children ages 0 to 5. The goal of SEEK is to prepare pediatric primary care professionals for addressing common psychosocial problems. Providers are trained to identify and help families with major risk factors for child maltreatment as well as to strengthen families by supporting parents and promoting children’s safety, health, and development. Health care professionals are trained via videos and informational materials on the SEEK website, which takes 2 to 3 hrs to complete. At clinics using SEEK, caregivers complete a brief screening tool called the SEEK Parent Questionnaire (PQ) before attending a regular checkup. Providers review

the questionnaire and make referrals to community resources if the PQ indicates the presence of any of the targeted problems, including: intimate partner violence; harsh punishment; food insecurity; major stress; parental depression; or substance use. Ideally, a mental health professional (e.g., social worker) is involved or consulted in this process. Providers may give parent handouts regarding the targeted problems, and SEEK incorporates motivational interviewing principles to engage parents in services (Dubowitz, 2014). SEEK was rated a 1–well supported by research evidence by the CEBC in 2014. Several RCTs conducted by the developers of SEEK have examined the program’s ability to prepare medical professionals for identifying and addressing risk factors for maltreatment; others have tested whether or not the program leads to a decrease in occurrences of child maltreatment. Two RCTs assessed how the SEEK program impacted health care professionals’ attitudes, knowledge, comfort, and improvement in identifying and addressing major risk factors for child maltreatment. Both studies found that, compared with controls, health care professionals trained in the SEEK model screened for targeted problems more often, were better able to address risk factors, and had higher levels of comfort and perceived competence in addressing psychosocial problems (Dubowitz et al., 2011; Feigelman, Dubowitz, Lane, Grube, & Kim, 2011). An RCT examining family outcomes in a diverse, low-income urban community found that families who received the SEEK model had lower rates than did the control group on every outcome measure, including reports to child protective services, instances of documented “treatment nonadherence” (i.e., possible perpetration of medical neglect), children with delayed immunizations, and parental report of harsh punishment (Dubowitz, Feigelman, Lane, & Kim, 2009). An RCT with a lower-risk sample did not find effects on reports of child maltreatment; however, the authors did find lower levels of psychological aggression and minor physical assaults for mothers in the SEEK group (Dubowitz, Lane, Semiatin, & Magder, 2012). In summary, the pediatric primary care setting is an advantageous venue for implementing child maltreatment interventions, as it provides access to 57

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hard-to-reach populations affected by a variety of risk factors. The SEEK model has strong support for decreasing reports of child maltreatment, maltreatment-related behaviors, and improving health care professionals’ ability and confidence in addressing major risk factors. Triple P—Positive Parenting Program.  Triple P is a multilevel intervention system developed for caregivers of children ages 0 to 16. The intervention was initially designed to reduce behavior problems among children and adolescents, and was later tested as a maltreatment intervention (Prinz, Sanders, Shapiro, Whitaker, & Lutzker, 2009). The system is comprised of five levels; the strategies at each level correspond with the intensity of need. Level 1 Triple P involves a community-wide distribution of parenting information via mass media (e.g., radio, newspapers, mailings) to make effective parenting strategies readily accessible for many families. The Level 2 intervention, which may be offered in a variety of settings where families have regular contact with service providers (e.g., childcare, preschool, community resource centers), involves three 90-minute seminars about common parenting challenges. In Level 3, parents receive four 20-minute consultations that incorporate behavior management advice and active skills training in individual or group settings. Level 4 of Triple P lasts eight to 10 sessions and is intended for parents with higher levels of need. This level of intervention can be delivered on an individual, group, or self-help basis, and teaches parents to generalize skills to a range of target problems in multiple settings. Level 5 Triple P provides additional support for high-risk caregivers (e.g., substance abuse, depression) for up to 11 in-home sessions. Strategies include coping skills, parental mood management, and partner communication skills (Sanders, 2012). Triple P program was rated as a 2–supported by research evidence by the CEBC in 2015. Many studies on the outcomes of Triple P have established evidence for the program’s efficacy in reducing child behavior problems (Nowak & Heinrichs, 2008) and dysfunctional parenting strategies (de Graaf, Speetjens, Smit, de Wolff, & Tavecchio, 2008; Sanders, Kirby, Tellegen, & Day, 2014). 58

A recent meta-analysis found that Triple P is associated with short-term improvements in children’s social, behavioral, and emotional functioning as well as parents’ adjustment, satisfaction, efficacy, and use of positive parenting practices. Additionally, Triple P was found to increase parenting confidence and improve parental relationships (Sanders et al., 2014). Many studies have focused specifically on the effectiveness of Triple P Level 4 interventions. A meta-analysis of research on the effects of Level 4 strategies indicated a significant decrease in dysfunctional parenting practices (e.g., overreactivity) and an increase in parental satisfaction and perceived efficacy in their parenting role, with results maintained for 3 to 12 months. Analyses also found that effects were not dependent on the treatment delivery method (i.e., individual, group, self-help format) or the severity of child disruptive behavior (de Graaf, Speetjens, Smit, de Wolff, & Tavecchio, 2008). Additional research has focused more specifically on reducing risk for child maltreatment. In one RCT for parents with anger management problems, researchers compared group Triple P enhanced with attributional retraining and anger management to a standard behavioral family intervention program. Parents in both groups demonstrated significant improvements in many aspects of family functioning (e.g., anger experience or expression), but parents in the enhanced treatment condition showed greater improvements on measures such as potential for child abuse, negative attributions, and unrealistic expectations (Sanders et al., 2004). Finally, one large RCT examined the effects of the entire Triple P system on child maltreatment outcomes at a population level. Prinz et al. (2009) randomized 18 counties in one state to either Triple P or services as usual. In counties where Triple P was implemented, the rates of substantiated cases of child maltreatment, abuserelated injuries reported in emergency settings, and out-of-home placements were significantly lower 12 months postintervention in comparison with counties that only delivered services as usual. Effect sizes were large for all three indicators (Prinz et al., 2009). A threshold analysis was conducted in Australia to evaluate the costs associated with the implementation of Triple P compared with the benefits of the program’s ability to reduce conduct disorder in

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children. On the basis of the prevalence of conduct disorder and the costs associated with it, analyses indicated that Triple P would only need to reduce the prevalence of conduct disorder by 1.5% to pay for itself. The study concluded that, with proper implementation, the Triple P program costs far less than the amount of money it saves (Mihalopoulos, Sanders, Turner, Murphy-Brennan, & Carter, 2007). Although this study focuses on the costs of conduct disorder rather than child maltreatment, the results are promising for Triple P’s ability to offset the costs of other child and family problems. In summary, Triple P is unique in its population approach to improving parenting. Although most research has supported Triple P’s effectiveness in reducing child conduct problems, one very methodologically rigorous study found that a populationbased implementation reduced rates of child maltreatment with strong effect sizes. Triple P has been disseminated in communities throughout the United States and in 25 other countries. Adults and Children Together Raising Safe Kids.  Adults and Children Together (ACT) Raising Safe Kids is a universal prevention program developed by the American Psychological Association’s Violence Prevention Office and National Association for Young Children. The program targets expectant parents and parents of children ages 0 to 10 and is designed to prevent violence in children’s lives through a variety of strategies (Portwood, Lambert, Abrams, & Nelson, 2011). The program provides caregivers with education about child development and effective parenting strategies during nine weekly 2-hr group sessions. The core components of the program include nonviolent discipline, enhancing child development, improving anger management and problem-solving skills, understanding and supervising child media exposure, and methods to reduce child exposure to violence (Knox, Burkhart, & Hunter, 2011). Parents participate through lecture, activities, observing and practicing specific skills, and completing weekly homework. The program is available in many languages, and program facilitators are trained to be culturally competent. The intervention can be delivered in any community agency, outpatient clinic, hospital, or

school. A manual and facilitator training are available from the American Psychological Association. ACT Raising Safe Kids was rated 3–promising research evidence by the CEBC in 2014. There are many studies supporting the use of ACT Raising Safe Kids. Two RCTs assessed the effectiveness of the program by randomly assigning parents to ACT Raising Safe Kids or services as usual (Knox, Burkhart, & Cromly, 2013; Portwood et al., 2011). Knox et al. (2013) used a sample from community health centers, and Portwood et al. (2011) recruited participants from a social service agency. Participants in the intervention group in both studies demonstrated increases in self-reported positive parenting behaviors (i.e., nurturing) that were higher than the control group. Those in Portwood et al.’s (2011) study also showed a decrease in use of harsh discipline, but these findings were not replicated in the Knox et al. (2013) study. However, participants in Knox et al.’s (2013) intervention group scored lower on measures of physical aggression and physical assault at posttest than did the control group. In summary, ACT Raising Safe Kids is a very cost effective program—the American Psychological Association sells the material for $50. Furthermore, complimentary trainings are offered and can be conveniently integrated into communities and/or other parenting interventions (Portwood et al., 2011). In Portwood et al.’s (2011) study, each ACT participant cost roughly $266, whereas the cost for those who continued care as usual ranged from $177 to $552. ACT Raising Safe Kids is a cost-effective, easily implemented program that demonstrates promise in reducing problematic parenting behaviors while increasing positive parenting behaviors. Additionally, it is designed to be a culturally competent program, making it available to a wide demographic. Parents as Teachers.  Parents as Teachers (PAT) is a home visiting program designed for caregivers of children from birth to kindergarten. The program is based on the belief that expert training can help all parents improve their family functioning. The goals of the program are to strengthen protective factors, increase children’s school readiness, and ensure children’s health and safety. Parents are taught about appropriate parenting practices for their children’s 59

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developmental level and how to prevent abuse and neglect. Regular developmental and health screenings are also offered, and families are connected with other needed services. Home visits may occur weekly, biweekly, or monthly, for at least 2 years, depending on the family’s needs. Families can also participate in monthly groups to obtain more information and connect with peers. The program may be delivered in adoptive or foster homes, child care centers, community agencies, outpatient clinics, and/or schools. A manual and training are available, and materials are available in several languages (Wagner, Spiker, & Linn, 2002). PAT was rated 3–promising research evidence in 2015. There is one multisite RCT on PAT in which low-income families from diverse urban areas were randomly assigned to either the PAT group or services as usual (Wagner et al., 2002). The authors examined parent (e.g., parenting knowledge, behaviors promoting child literacy) and child (i.e., social and academic development, prosocial behaviors) outcomes between groups and by income level. Groups did not differ on overall parenting or child outcomes. Trends were noted such that very low-income families in the intervention group demonstrated stronger effects on some outcomes than those in the control group (e.g., knowledge of child language and emotional development). Moderate income parents in the intervention group did report higher levels of happiness in caring for their children and acceptance of the child’s behavior compared with moderate income families in the control group. Several nonrandomized comparison trials have evaluated the effectiveness of PAT. Early studies comparing child outcomes suggested that youth whose parents participated in the program scored higher on measures of development, whereas parents also demonstrated greater parenting knowledge (Pfannenstiel & Seltzer, 1989). Additionally, as part of a state-wide assessment project, researchers found an increase in positive parenting skills related to child school readiness following participation in PAT (e.g., enrolling children in preschool, reading to children; Zigler, Pfannenstiel, & Seitz, 2008). Several adaptations of the program have been developed, including Born to Learn, which targets parents 60

of infants (Drotar, Robinson, Jeavons, & Kirchner, 2009). In a review of the cost-effectiveness of evidencebased home-visiting programs, PAT was found to be one of the most affordable programs, costing roughly $2,370 per family, whereas other programs were said to cost between $5,300 to $8,000 (Burwick et al., 2014). In summary, there is some limited evidence suggesting that PAT helps improve parenting skills and knowledge. No studies have examined the effect of the intervention on child maltreatment reports. Strong Communities for Children.  Strong Communities for Children is a comprehensive community-wide public health approach to prevent child maltreatment that emphasizes the importance of community development and support of families to reduce instances of child maltreatment (Kimbrough-Melton & Campbell, 2008). The program strives to change community norms to value supporting families. The program also aims to change the operations of community institutions (e.g., health care systems) to better support families with young children. The program relies on outreach workers who are tasked with increasing the community’s commitment to protecting children and building community capacity to work together. Encouragement of community volunteers (e.g., fire fighters gathering resources for needy families) to work toward the goals of Strong Communities for Children is also an important part of the initiative. There are eight principles that direct community outreach workers in their effort to mobilize the community: (a) activities of outreach workers should be logically related to reducing child abuse and neglect; (b) strategies should be aimed at transforming community standards, norms, and structures so that needs of families are noticed and responded to “naturally”; (c) outreach efforts should continually “push the envelope” to stimulate continuing efforts on a community level; (d) outreach is directed to recruiting volunteers, mobilizing the community, and retaining participants; (e) activities should be aimed at establishing and strengthening relationships among families and between families and institutions in the community; (f) outreach activities

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should be developed to be easily accessible, nonstigmatizing, and widely available to families and children in the community; (g) the ultimate goal is to protect children through outreach programs directed at parents that are provided and facilitated by the larger community in which they live to enhance parent leadership and community engagement; and (h) outreach activities should build on, or rely on, assets in and among the primary institutions in that community. Primary components of the program include a mailing list to parents to remind them to schedule doctor appointments or to notify them about family activities; family activity centers to create social support among families and to provide education; counseling and mentoring for families; and supportive home-based services for families of young children or those with mental health concerns (Kimbrough-Melton & Campbell, 2008). Strong Communities for Children was rated a 3–promising research evidence by the CEBC in 2015. McDonell, Ben-Arieh, and Melton (2015) conducted a two-group pretest–posttest design to examine selfreported differences in neighborhood perceptions, parent attitudes and beliefs, and parenting practices between families with children under the age of 10 living in Strong Communities for Children neighborhoods and families in comparison neighborhoods. Information was obtained on child maltreatment and injuries using social services and injury data from the International Classification of Diseases, 9th Revision (World Health Organization, 1978) for census block groups. Data were obtained for a total of 470 participants in Wave 1, and 619 in Wave 2. In comparison with the nonintervention communities, Strong Communities for Children communities showed significant changes in the expected direction for measures on social support and reciprocal helping, parental and collective efficacy, self-reported and observed parenting practices, parental stress, and child safety. Strong Communities for Children samples also showed lower rates of substantiated cases of child maltreatment and lower instances of injuries that indicate maltreatment. McLeigh et al. (2015) examined how the Strong Communities for Children program impacted low- and high-resource communities. Low-resource communities experienced the greatest number of positive impacts from

the program including greater levels of community mobilization, increases in neighboring and receiving help from neighbors, observed positive parenting in the community, and perceived household safety for children in the neighborhood. High-resource communities also experienced positive impacts, but to a lesser extent. Finally, a pre–post study examining the impact of a 5-year implementation of the initiative in South Carolina (Haski-Leventhal, Ben-Arieh, & Melton, 2008) found that Strong Communities for Children volunteers reported a high sense of neighborliness and commitment to the community, and reported high levels of integrating the program’s information into their lives. In summary, Strong Communities for Children is associated with lower rates of substantiated cases of child maltreatment and injuries that indicate maltreatment. The program has been successful at improving self-report measures of child safety, parental stress, and indicators of social support among low- and high-resource communities. A strength of the program is its ecological focus and efforts to transform communities (rather than just individuals) by enhancing natural supports. Moreover, it’s reliance on community volunteers, rather than paid professionals, may make the program more sustainable and may also lead to greater perceived cultural competence by program participants. The Safe Child Program.  The Safe Child Program is designed to prevent child maltreatment and abduction by educating children, parents, teachers, and administrators (Kraizer, Witte, & Fryer, 1989). Children receive developmentally appropriate information in group lessons to reduce their vulnerability to maltreatment and to improve their self-efficacy in promoting their own safety. Parents, teachers, and administrators can also participate in an educational component (i.e., seminars, printed information) to learn ways to prevent child abuse and to help children practice skills they are learning. The program is typically implemented in schools in five to 10 sessions for children ages 3 to 9. The content changes to be developmentally appropriate at different age levels. Typically, a school psychologist leads the teacher-training and parent-training sessions, and teachers lead the child-training session under the 61

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supervision of a school psychologist. Training DVDs are used to ensure accuracy in delivery of material and to provide models of the skills. The program also emphasizes the use of role-plays and feedback in the classroom to ensure skill mastery. A program manual is available in multiple languages. The Safe Child Program was rated as 3–promising research evidence by the CEBC in 2014. Four RCTs have been conducted to examine the effectiveness of the program. The primary outcome of all RCTs was whether children would go with a stranger in a simulated situation. In all the studies, children who participated in the Safe Child Program were less likely to go with strangers than those in the control group following training, and effects maintained at 6-month follow-up (Fryer, Kraizer, & Mlyoshi, 1987a, 1987b). More recently, Kraizer et al. (1989) examined the effectiveness of the program in reducing sexual advances specifically and found that children who participated in the program used more skills thought to reduce the chance of victimization (e.g., stopping unwanted touch; Kraizer et al., 1989). In summary, there is strong support for the Safe Child program. Future research could test parental outcomes, including whether parents reported helping their children use self-protective skills more frequently following participation.

Programs for Shaken Baby Syndrome Period of PURPLE Crying.  PURPLE is a prevention program designed to reduce the risk for Shaken Baby Syndrome (SBS) via educational materials distributed to new parents in maternity wards, pediatric practices, and prenatal classes. Parents are provided with an 11-page booklet and a 12-minute DVD produced by the National Center on Shaken Infant Syndrome (Barr et al., 2009a). The materials outline typical crying patterns of infants using the acronym PURPLE, which stands for (P) peak patterns of crying that occur in the second month of life, (U) unexpected crying bouts, (R) resistance to soothing, (P) pain-like crying facial expressions, (L) long bouts of crying, and (E) evening clustering of crying. The materials discuss that these crying patterns can often cause caregivers frustration or 62

distress, and provides three action steps that caregivers should use when soothing a crying infant (i.e., increase comforting responses, put the infant down gently and walk away to calm yourself, never hurt or shake an infant). Caregivers are also encouraged to share this information with other caregivers (Barr et al., 2009a). Period of PURPLE Crying was rated as a 3–promising research evidence by the CEBC in 2015. Three RCTs have been conducted to assess the efficacy of the program. Barr et al. (2009a) recruited new mothers from prenatal classes, maternity wards, and pediatric practices. The control condition mothers received injury-prevention materials (i.e., a DVD about infant safety and two brochures) either in person or through the mail. At a 2-month follow-up, mothers in the PURPLE group showed greater knowledge about infant crying and also reported sharing this information with other caregivers more frequently when compared with the control group (Barr et al. 2009a). Intervention and control mothers did not differ regarding their reported behaviors when dealing with a crying or inconsolable child (Barr et al., 2009a). A second RCT also found that mothers in the PURPLE intervention reported higher rates of walking away during inconsolable infant crying than mothers in the control group (Barr et al., 2009b). A third RCT conducted with a sample of Japanese mothers also found similar results (Fujiwara et al., 2012). In summary, the Period of PURPLE Crying program can be delivered quickly and in several group or individual settings, allowing for wide dissemination of SBS prevention knowledge. Mothers who participated in the prevention program consistently reported increased knowledge about shaken baby syndrome, as well as increased walking away during inconsolable infant crying. A limitation of the research (Barr et al., 2009a) is that the intervention has only been tested with mothers, even though men are more frequently the perpetrators of abuse. Upstate New York Shaken Baby Syndrome Program.  This program is designed to reduce instances of SBS by increasing parent knowledge

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through a brief (15 minute) hospital-based intervention prior to infants’ hospital discharge after their birth. Parents watch an 11-minute video on SBS and receive a one-page leaflet about the prevention and dangers of SBS. Then, trained nurses talk with the parents and review the materials with parents, and the parents’ questions are answered. Finally, parents sign a commitment statement to confirm that they received and understood the program and the materials. Prevention posters for SBS are also posted at the hospital (Dias et al., 2005). Upstate New York Shaken Baby Syndrome Program was rated as a 3–promising research evidence by the CEBC in 2015. Dias et al. (2005) assessed the impact of the program in eight counties in western New York. Data were gathered over a period of 5.5 years during which the program was delivered to 65,205 parents of newborns. Of program completers, 10% were chosen at random to engage in a telephone follow-up interview 7 months after their child’s birth to gather information about the parents’ memory of the program and instances of infant shaking and abusive head injuries. Results were compared with a historical control group (i.e., incidence rates from the previous 6 years), and with state-wide incidence rates for Pennsylvania that occurred during the study period. Over 95% of the parents who were contacted recalled receiving the program. Incidence rates of abusive head injuries decreased by 47% during the study period when compared with the historical control group. These reductions were not seen in Pennsylvania incidences rates (Dias et al., 2005). In summary, the Upstate New York Shaken Baby Syndrome Program is a brief program that can be delivered to new parents in a hospital setting. One limitation to the research is that parents who were randomly selected to participate in the follow-up only provided self-report information on SBS knowledge and instances of abusive head trauma. At the time of this review, there are no RCTs assessing change in risk reduction and knowledge gains.

Programs for Child Sexual Abuse Body Safety Training Program.  Body Safety Training (BST) program is a behaviorally based

childhood sexual abuse (CSA) prevention program for children ages 3 to 8. The program teaches children to recognize and distinguish inappropriate from appropriate touches. Children are also educated about what private parts are and who perpetrators might be (e.g., a stranger, friend, family members). Children are trained to respond effectively (e.g., saying “no,” running away and telling someone) to inappropriate requests using psychoeducation, modeling, rehearsal, and feedback. The program is implemented over a 10-day period (Wurtele, Kast, Miller-Perrin, & Kondrick, 1989). BST sessions are often school-based, but research suggests that parents are effective at teaching their children these skills at home (Wurtele, Currier, Gillispie, & Franklin, 1991). The Body Safety Training Workbook is available for parents and teachers who wish to teach children how to avoid sexual abuse. BST was rated as a 3–promising research evidence by the CEBC in 2014. Several RCTs evaluating the effectiveness of the program have found that children who participate consistently show improvements in CSA knowledge and safety behaviors compared with children in alternative programs (Lee & Tang, 1998; Sarno & Wurtele, 1997; Wurtele, 1990; Wurtele et al. 1991; Wurtele, Kast, Miller-Perrin, & Kondrick, 1989; Zhang et al., 2014) and that these skills maintain up to 5 months (Wurtele et al., 1989). The BST program has been shown to be effective when taught by teachers, parents, or a combination of the two (Wurtele, Gillispie, Currier, & Franklin, 1992; Wurtele, Kast, & Melzer, 1992). A small pilot study was conducted to examine the impact of the BST program on children with a history of CSA compared with those without a history of CSA (Currier & Wurtele, 1996). Both groups increased their knowledge of CSA and safety skills. Further, on the basis of child and parent feedback, there were not any adverse effects of program participation, and some parents reported a decrease in problematic (i.e., sexually explicit) behaviors among children who were sexually abused. Cross-cultural RCTs have also found that the BST program is effective for Chinese preschoolers (Zhang et al., 2014), Chinese adolescent girls with mild intellectual disability (Lee & Tang, 1998), and a Latino child 63

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diagnosed with autism spectrum disorder (Kenny, Bennett, Dougery, & Steele, 2013). In summary, the BST program has been found to increase children’s knowledge about sexual abuse, safety skills, and recognizing inappropriate requests for touching body parts. Although results from the BST program show that children with a history of CSA obtain knowledge on CSA and CSA prevention, there are no known studies assessing the protective values of these skills among children who used them against an offender or if the skills lead to a reduced risk for being sexually abused. Who Do You Tell?  Who Do You Tell? is a schoolbased sexual abuse prevention training for children from kindergarten to sixth grade. Small groups (15–20 children) meet for two sessions that are held on consecutive days and last between 45–60 mins. There are three variations of the program to cater to the developmental differences of younger (kindergarten–Grade 2), middle (Grades 3–4), and older (Grades 5–6) children. The program teaches sexual abuse prevention strategies and concepts through discussion and the use of pictures, videos, and role-plays. Concepts that are taught include appropriate and inappropriate touching, the different types of people who may perpetrate inappropriate touching, and giving children permission to say “no” to unwanted touches (Tutty, 1997, 2000, 2014). The Who Do You Tell? sexual abuse prevention program was rated as a 3–promising research evidence by the CEBC in 2015. An RCT was conducted to evaluate the effectiveness of the program in teaching sexual abuse prevention concepts to 231 school-age children. Children who attended the program gained more knowledge about appropriate and inappropriate touches than children in the control group. Older children gained more knowledge than younger children (Tutty, 1997). Tutty (2014) conducted a qualitative study to evaluate children’s impressions of the Who Do You Tell? program. Semi-structured group interviews were conducted with students (ages 6–12) 2 to 3 months after participating in the program to assess for the maintenance of knowledge on key concepts, and to 64

assess participants’ experiences with the program. Many of the students could recall core concepts of the program. In summary, research suggests that children who participate in the Who Do You Tell? program show increases on measurements of CSA knowledge and differentiating between appropriate versus inappropriate touching. Moreover, children are generally receptive to the program and report enjoying it. Currently, there is no research that has examined whether children who participated in the program use the skills to reduce their risk of CSA. Stewards of Children.  This program is a webbased training program about CSA prevention targeting individuals who work or volunteer in child-serving organizations (e.g., schools, daycare centers, churches). The program takes 2.5 to 3 hrs to complete at an individual’s pace throughout a 2-week access period. The program provides information about the prevalence and consequences of CSA, the circumstances in which CSA occurs, how to recognize CSA, strategies to prevent CSA, and how to respond if CSA is suspected or reported. The program uses video and audio clips, training segments with guiding text, videos of adult survivors’ descriptions of their experiences, and recommendations for best practices provided by experts (Paranal, Washington Thomas, & Derrick, 2012). Stewards of Children was rated as a 3–promising research evidence by the CEBC in October, 2014. Rheingold et al. (2015) conducted an RCT to evaluate the effects of the web-based program in comparison with an in-person program and waitlist control group with childcare professionals from three sites across the United States. Those participating in the web and in-person trainings increased their knowledge of CSA, reported more preventative behaviors, and endorsed fewer CSA myths than did those in the waitlist control. There were no significant differences between the two program modalities at a 3-month follow-up, suggesting that the web-based and in-person programs are equally effective. A nonequivalent groups design with a waitlist control examined reactions to the program among professionals from a variety of child-serving backgrounds

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(e.g., mentors, educators, foster parent advocates, local government workers; Paranal et al., 2012). Participants reported that the online format was effective, appropriate, interesting, and useful. The two most consistent barriers reported were the time it took and the technical problems faced when completing the program. Other limitations to the webbased program included an inability to discuss the content with others and ease of access. In summary, Stewards of Children allows child serving professionals to learn information about CSA prevention using an online training program format that can be completed at their convenience. Evidence suggests that the program increases knowledge about CSA prevention. Research has yet to examine the degree to which professionals implement the information that they learn in the training.

Secondary Prevention Programs The next section includes a review of secondary prevention programs for (a) physical abuse and neglect, (b) primarily physical abuse, and (c) primarily neglect.

Programs for Physical Abuse and Neglect Incredible Years.  Incredible Years (IY) is a behavior management intervention originally designed to reduce behavior problems in young children (ages 3–8). The program is delivered via group sessions and includes a curriculum for parents, teachers, and children. Video vignettes are used to help group facilitators teach basic behavior management skills, including labeled praise, ignoring, and timeout during 2-hr sessions that last for 12 weeks. In parent groups, facilitators lead discussions about parenting topics, and parents learn and practice new skills during sessions. Parents are also given homework assignments, which include contacting each other in between group meetings, to foster social support. Session topics include improving child–parent relationships through child directed play, helping children with emotion regulation, use of praise and incentives, limit setting and household rules, and effective discipline strategies (Gross et al., 2003; Reid, Webster-Stratton, & Beauchaine, 2001).

Recently, treatment developers have expanded the curriculum for caregivers of infants and toddlers. These curricula focus on promoting a positive relationship between the parent and child and helping the parent to promote the child’s development. IY was rated as a 1–well supported by research evidence by the CEBC in 2015. Several RCTs and a recent meta-analysis (Menting, Orobio de Castro, & Matthys, 2013) have found the parent training portion of IY to be effective in reducing child behavior problems and improving parenting outcomes (i.e., increases in positive parenting, appropriate discipline, consistency, and competency; decreases in critical and harsh or coercive parenting) with effects remaining up to 2 years (Posthumus, Raaijmakers, Maassen, van Engeland, & Matthys, 2012). Such effects have been found in low income communities in the United States (Baydar, Reid, & WebsterStratton, 2003; Reid et al., 2001) as well as in families from other cultures and in other countries (Axberg & Broberg, 2012; Homem, Gaspar, Santos, Azevedo, & Canavarro, 2015; Kim, Cain, & Webster-Stratton, 2008). Few studies have examined the efficacy of the intervention in a child welfare context. One RCT with foster parents found that those in IY reported greater changes in positive parenting practices, clear expectations, and collaborative coparenting versus those in the usual care condition; effects maintained up to 3 months (Linares, Montalto, Li, & Oza, 2006). Two pre–post design studies found that parents involved in child welfare agencies who participated in IY had lower parenting stress and greater empathy for their children (Marcynyszyn et al., 2011) and that foster parents who participated in the program reported lower child behavior problems at posttest (McDaniel, Braiden, Onyekwelu, Murphy, & Regan, 2011). One pre–post study testing the new toddler and infant curricula with maltreating families found that parents reported higher levels of appropriate parenting knowledge and attitudes and lower levels of parenting stress at posttest (Burger & Damashek, 2016). A study conducted in Wales found an incremental cost effectiveness ratio (the difference in cost to run the two groups vs. difference in clinical effect) of $142 for each one point decrease on a measure of 65

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child behavior problems for children in IY compared with children on the waitlist. The intervention was most cost effective for children with the highest behavioral intensity scores (Edwards, Ceilleachair, Bywater, Hughes, & Hutchings, 2007). In summary, there is a very strong evidence base for the effectiveness of IY in reducing and preventing child behavior problems. Moreover, the intervention has been found to improve parenting practices and reduce behaviors associated with maltreatment. There is limited evidence of the effectiveness of the intervention in child welfare settings. One RCT and a handful of pre–post studies have found positive changes in parenting; however, no studies to date have examined the effectiveness of the intervention in reducing or preventing child maltreatment reports. Advantages of the program include a well-developed infrastructure to provide training and support to communities adopting the program as well as materials in multiple languages. Moreover, the group delivery of the intervention promotes development of parent social support. SafeCare.  SafeCare is a home-based intervention that trains caregivers of children ages 0 to 5 to develop parenting skills in three core areas: home safety, child health, and parent–child interaction. SafeCare is based on an earlier model called Project 12 Ways or Ecobehavioral Model, which was one of the earliest interventions evaluated for child neglect (Lutzker, Frame, & Rice, 1982; Lutzker & Rice, 1987). SafeCare can be delivered by paraprofessional home visitors who use behavioral techniques, including modeling, coaching, and feedback to teach basic parenting skills. In the home safety module, home visitors help caregivers identify and remove hazards in their home. The child health module trains parents how to respond appropriately to different child health problems (i.e., illnesses and injuries) that range in severity (e.g., colds vs. high fever). In the parent–child interaction module, parents are taught how to engage in positive activities with their child on a daily basis (Lutzker & Bigelow, 2002). SafeCare can be delivered as a stand-alone intervention or as part of a more comprehensive home-based intervention. It has been implemented with families involved in child welfare, as well as 66

families who are considered at risk for maltreatment (Chaffin, Hecht, Bard, Silovsky, & Beasley, 2012; Silovsky et al., 2011). SafeCare was rated as 2–supported by research evidence by the CEBC in 2015. SafeCare has been studied extensively using single-subject or quasiexperimental comparison group designs; these studies have found improvements in parent skills for the child health and parent–child interaction modules as well as reductions in home hazards (Gershater-Molko, Lutzker, & Wesch, 2003; Lutzker, Bigelow, Doctor, & Kessler, 1998; Tertinger, Greene, & Lutzker, 1984). One quasiexperimental study found that maltreating families in SafeCare versus services as usual were less likely to recidivate at a 3-year follow-up (54% versus 85%; Gershater-Molko, Lutzker, & Wesch, 2002). Two recent RCTs have been conducted on SafeCare. Chaffin, Hecht, et al. (2012) conducted a statewide implementation study in the child welfare system in Oklahoma using a randomized cluster design in which service areas were randomized to SafeCare or services as usual. Home visiting teams were also randomized to coaching condition, so that home visitors either received coaching from a supervisor or did not receive coaching. Families who received Safe­ Care were significantly less likely to recidivate than those in services as usual. The addition of coaching was most helpful for those families who fell outside of the traditional SafeCare inclusion criteria. On the basis of results from the study, the authors estimated that treating 1,000 families with SafeCare rather than services as usual would prevent 64 to 104 first year recurrences of maltreatment among maltreating families (Chaffin, Hecht, et al., 2012). A subgroup analysis from this study found that the benefits of SafeCare versus services as usual was equivalent for American Indians versus other families (Chaffin, Bard, Bigfoot, & Maher, 2012). An additional RCT examined the effects of SafeCare+ in preventing child maltreatment among nonmaltreating, high-risk rural families (Silovsky et al., 2011). The investigators augmented SafeCare to train home visitors to use motivational interviewing and to respond to maternal depression, substance use, and interpersonal violence and compared it with standard home-based mental health

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services. Overall, there was not a significant difference in recidivism between the two groups; however, those in SafeCare+ were significantly less likely to have a report related to domestic violence than were those in services as usual. Research has also found that compared with families who receive services as usual, families who receive SafeCare are more likely to complete services (Damashek, Doughty, Ware, & Silovsky, 2011), are more satisfied with services, and perceive their providers to be more culturally competent (Damashek, Bard, & Hecht, 2012). Some adaptations to SafeCare include a culturally adapted version that was found to be acceptable to Latino families (Finno-Velasquez, Fettes, Aarons, & Hurlburt, 2014) and a computer-assisted version for at-risk fathers (Self-Brown et al., 2015). In summary, SafeCare has strong support as an intervention for child neglect. It has a firm theoretical underpinning and uses proven techniques to train parents in basic parenting skills that can protect children from physical harm and promote their emotional well-being. Besides being shown to reduce risk of recidivism among maltreating families using rigorous methodology, the program has also been found to be effective in engaging families in treatment and has been rated by diverse families as culturally sensitive. SafeCare is being disseminated in several communities in the United States, as well as six other countries, with support from the National SafeCare Training and Research Center. Effective Black Parenting Program.  The Effective Black Parenting Program is a small-group, 15-session intervention designed to provide parent training to Black parents in a manner that is culturally competent and designed to maximize engagement of Black parents. The program is facilitated by Black professionals. The training approach teaches parents behavioral parent management strategies, including labeled praise, ignoring, timeout, “mild social disapproval,” and consequences for respectful or disrespectful behavior (Myers et al., 1992, p. 133). The curriculum also uses a Family Rule Guideline Strategy to teach parents to use verbal explanations for rules, as well as A Thinking Parents Approach to teach parents to consider reasons for their child’s misbehavior and to think

before they act. In addition, the program places traditional Black discipline (with a focus on punishment and spanking) within a historical context of racism and contrasts it to modern Black discipline (with a focus on what is most effective). The program content also encourages pride in Blackness, emphasizes positive communication about ethnicity, and helps children cope with racism. Additional components of the program include content about single parenting, drugs, and effective communication with children. Effective Black Parenting Program was rated as 3–promising research evidence by the CEBC in 2015. One study using a quasi-experimental design examined the efficacy of the program in reducing child behavior problems and improving parenting skills as well as parent-child relationship quality among Black families in South Central Los Angeles. Two cohorts of families with children in first and second grade were recruited from local schools. Parents of both cohorts in the treatment group showed decreases in parental rejection, and families in the Cohort 1 treatment group showed significantly improved relationships with their child. Only parents in the treatment group in Cohort 2 showed significant improvements in parenting practices, including increases in praise and decreases in spanking behavior. Reductions in parental rejection maintained at follow-up; however, there was an increase in parents’ report of hostile/aggressive parenting (Myers et al., 1992). Regarding child gains, there was a reduction in sexual behavior problems in girls in the treatment group in Cohort 1; boys in the treatment group had lower levels of withdrawn and hyperactive behavior. In Cohort 2, children in the treatment group showed reductions in delinquency. In summary, the Effective Black Parenting Program is unique in its tailored approach to improving parenting among Black families. In the initial efficacy study, there were no changes in Cohort 1 in parenting behaviors; however, those in Cohort 2 showed reductions in spanking, which is an important finding since spanking often precedes physical abuse (Gershoff, 2002). Moreover, spanking in Black families is more common than it is in other ethnic and racial groups (MacKenzie, Nicklas, Brooks-Gunn, & Waldfogel, 2011). 67

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Exchange Parent Aide.  Exchange Parent Aide (PA) is a home-based program intended to reduce the risk for childhood physical abuse and neglect among at-risk families with children ages 0 to 12. The program is delivered by volunteers or paid parent aides (e.g., case managers) to provide families with services to improve parenting and problem-solving skills, ensure child safety, and improve social and community support. The treatment is a strengthbased program and provides 24/7 support and referrals. The weekly in-home treatment modality allows for parent aides to model parenting and problemsolving skills and provide immediate feedback in the natural environment (Harder, 2005). PA was rated as a 3–promising research evidence by the CEBC in 2015. One RCT compared PA with case management (CM) to CM only in their impact on reducing risk for maltreatment among at-risk mothers. Mothers in the CM control condition received limited services that included up to two phone contacts per month, whereas mothers in PA + CM received these contacts as well as the PA component (Guterman et al., 2013). Mothers in the PA + CM group had significant declines in self-reported physical assault toward the child as well as improvements in depression, anxiety, sense of mastery, and parenting stress. Such effects were not found for the control group; however, posttest comparisons between groups were not significant. A quasi-experimental retrospective cohort study of maltreating families examined abuse recidivism rates between families who completed the program; those who dropped out of the program; and those who refused entry into the program. Completers had fewer subsequent substantiated child protective services referrals compared with the other two groups (Harder, 2005). In summary, preliminary evidence suggests the effectiveness of this program in reducing risk for child maltreatment. Additional RCTs with larger sample sizes may provide stronger evidence for the effectiveness of this program.

Programs for Primarily Physical Abuse Combined Parent–Child Cognitive–Behavioral Therapy (CPC–CBT) is a 16-session group intervention for parents who have perpetrated childhood 68

physical abuse. The treatment aims to improve parenting skills (e.g., praise and positive reinforcement) as well as parent stress management and wellbeing, and to reduce PTSD symptoms in abused children. Therapists work with parents by increasing motivation for change using motivational interviewing, providing psychoeducation about physical abuse, teaching positive parenting (e.g., praise and positive reinforcement) and coping skills, and helping the family develop a safety plan. To address PTSD symptoms, therapists assist children in creating a trauma narrative during the final group sessions. Parents and children meet in their respective parent or child group to learn skills, and joint parent–child interactions conclude each session, during which group therapists coach parents on their interactions with their children. Over the course of treatment the time spent in joint parent–child sessions increases to allow for children to share their trauma narrative, give parents the opportunity to clarify the abuse incident (through the use of letters emphasizing that abuse is not the child’s fault), and for group therapists to provide feedback on interaction styles (Runyon, Deblinger, & Schroeder, 2009). CPC–CBT was rated as a 3–promising research evidence by the CEBC in 2015. One RCT has been conducted to test the effects of the CPC–CBT program in comparison with a parent-only CBT treatment among parents with a substantiated allegation of child physical abuse or who endorsed engaging in physical punishment of a child within the past 4 months (Runyon, Deblinger, & Steer, 2010). Children ranged in age from 7 to 13 and presented with symptoms of PTSD or had elevated scores on externalizing behaviors. Parents in the parent-only group were exposed to a more comprehensive review of parent-skills training and were not exposed to the clarification process or joint parent–child sessions. Both groups showed improvements on most parent and child measures (e.g., parent- and childreported corporal punishment, PTSD symptoms, internalizing behaviors). Between-group posttest means indicated a significant difference in total PTSD symptoms among children in the CPC–CBT group compared with those in the parent-only CBT treatment as well as a significant difference in parent reports of positive parenting. However, parents

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in the parent-only group had significantly greater decreases in the use of corporal punishment than parents in the CPC-CBT group. Other studies using pretest–posttest designs indicate that CPC–CBT is helpful in reducing child PTSD symptoms and problems behaviors, as well as reducing parental corporal punishment and increasing positive parenting (Kjellgren, Svedin, & Nilsson, 2013; Runyon et al., 2009). In summary, CPC–CBT has been found to increase positive parenting practices and decrease the use of corporal punishment. The addition of the child trauma narrative reduces children’s PTSD symptoms, more so than interventions focusing only on parenting skills.

Programs for Primarily Neglect Family Connections.  Family Connections was designed to reduce the risk of child neglect by teaching parents essential skills to meet their children’s needs. The program uses principles derived from Bronfenbrenner’s (1979) social ecological theory to guide the delivery of a culturally competent, individualized, outcome-driven treatment to increase families’ protective factors and reduce risk factors (DePanfilis & Dubowitz, 2005). Children and parents participate in 1-hr weekly meetings for 3 to 4 months in their homes. Children receive services addressing behavioral and/or emotional concerns and school attendance. Parental services involve addressing financial stress, parent–child interactions, and parental psychopathology. Families can also participate in multifamily recreational activities (e.g., holiday and cultural celebrations) that are aimed at creating networks of social support. The program is available in multiple languages (e.g., Cambodian, Korean, Spanish). Family Connections was rated a 3–promising research evidence by the CEBC in 2015. There is one RCT comparing different forms of the program. Families in a low-income urban community were randomized to receive either 3 or 9 months of the program. All families showed improvements in risk (e.g., caregiver depressive symptoms, parenting stress, and everyday stress) and protective factors (e.g., parenting attitudes, parenting sense of

competence, family functioning, and social support) for child maltreatment and neglect, with few differences between those in the 3- versus 9-month groups. Reports of maltreatment and neglect decreased for both groups, with no statistically significant differences between groups. Additionally, children’s internalizing and externalizing symptoms decreased; internalizing symptoms for those in the 9-month group showed greater declines (DePanfilis & Dubowitz, 2005). A second study using the same data found that those assigned to the 3-month group had higher completion rates than those in the 9-month group (Girvin, DePanfilis, & Daining, 2007). Researchers found that a 3-month version of Family Connections was more cost effective than a 9-month version in terms of positive changes in risk and protective factors and child safety. However, the 9-month version was more cost effective when examining child behavior change (DePanfilis, Dubowitz, & Kunz, 2008). In summary, Family Connections is an efficient, effective program that demonstrates promise in reducing risk factors for neglect while increasing protective factors. Research has yet to demonstrate effectiveness above and beyond other treatments or a no treatment control group. Nurturing Parenting Program for Parents and Their School-Age Children.  Nurturing Parenting Program for Parents and Their School-Age Children Ages 5-12 (Bavolek, Kline, McLaughlin, & Publicover, 1979) is for families who have been reported for child neglect. During 15 group sessions, parents learn appropriate parental expectations and parenting beliefs and practices. The program meets weekly; parents and their children participate in separate groups for 2.5 hrs. The program is based on the belief that changing parent–child relationships is integral to reducing risk of maltreatment and targets five parenting constructs: inappropriate developmental expectations, lack of empathy, belief in the use of physical punishment, parent–child rolereversal, and oppressing children’s independence (Cowen, 2001; Maher, Marcynyszyn, Corwin, & Hodnett, 2011). The program begins with an evaluation of parents’ life histories and parenting practices thought to contribute to child maltreatment. 69

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Subsequently, parents and facilitators meet to review parents’ strengths and weaknesses with continued evaluation throughout the program. Then, parents participate in a variety of lessons each session, including topics about child development, family values and rules, and effective discipline strategies, among others. Children engage in lessons about topics such as identifying and communicating about emotions, learning social skills, and many others. The program is available in seven languages and can also be delivered as a school-based intervention. A manual and training are available. The Nurturing Parenting Program for Parents and Their School-Age Children was rated a 3–promising research evidence by the CEBC in 2015. There are two known studies examining the effectiveness of the Nurturing Parenting Program in nonequivalent-control designs. Vespo, Capece, and Behforooz (2006) trained kindergarten teachers at two inner-city schools in Nurturing Parenting; children from the same school in the year prior to the study were used as controls. Children of teachers trained in the Nurturing Parenting curriculum showed increases in prosocial behaviors, and decreases in disruptive behaviors, aggression, dominance, social insecurity, and academic immaturity at posttest; the comparison group did not demonstrate these changes. Another study randomized participants (recruited via a variety of community agencies) to either a closed-group or open-group delivery of the program. In the closed-group, the same families participated for the duration of the program; the open-group format was a “rolling” group that allowed families to enter the program at any point. The open-group had a much higher attrition rate than the closed-group format; however, both groups demonstrated improvements in parents’ levels of appropriate beliefs and attitudes about parenting from pretest to posttest (Brock, Marek, MatteoKerney, & Bagby, 2013). Finally, a noncontrolled study with Louisiana families involved in child welfare found that parental participation in more sessions was related to a decreased likelihood of future maltreatment reports (Maher, Corwin, Hodnett, & Faulk, 2012). The study using families in Louisiana implemented the program state-wide and found that the 70

cost of implementing the program, balanced by the benefits of reducing child maltreatment, approached cost neutrality, without accounting for long-term benefits (Maher et al., 2012). In summary, although there are no RCTs comparing this intervention to an alternative treatment, research suggests that the program may be effective in increasing appropriate parenting beliefs and attitudes and may decrease the likelihood of future maltreatment reports. Additional RCTs comparing the intervention to a control group would be beneficial to further demonstrate effectiveness of the program. Step-by-Step Parenting Program.  The Step-byStep Parenting Program was designed to teach caregivers with impaired cognitive functioning (e.g., learning disabilities, traumatic brain injuries) essential skills for parenting children ages 0–3. The program aims to reduce the likelihood of children being neglected, while simultaneously improving children’s health, development, and behavior. Parents are taught appropriate developmental expectations, nutritional and health needs of young children, basic first-aid, toilet training, and healthy parent–child interactions. The program uses behavioral principles to reinforce the skills parents already have, and then builds on these skills with constructive feedback. Parents receive simple instructions and prompts, observe the skills being modeled, and engage in role-plays with feedback (Feldman, Case, & Sparks, 1992). The program monitors skill acquisition and generalization before gradual removal of in-home services. Families typically receive one weekly 1.5- to 2-hr home visit, but visits can be increased to two to three times per week. Family goals are generally achieved within 2 years. Manuals and training are available. Step-by-Step Parenting Program was rated a 3–promising research evidence by the CEBC in 2014. In one RCT, researchers randomly assigned mothers who were welfare recipients with low IQs to the parent-training group or a waitlist control. Mothers in the training program reached higher mastery of parenting skills than mothers in the control group, and the health of the children also improved following parents’ acquisition of the skills (Feldman et al., 1992). Another RCT evaluated the

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effectiveness of the program for mothers with intellectual disabilities versus mothers with no intellectual disabilities. In the treatment group, mothers with disabilities scored as high as the mothers with no disabilities, who both performed significantly better than those in the comparison group on positive parent–child interactions as well as child language abilities and social development (Feldman, Sparks, & Case, 1993). In summary, the Step-by-Step Parenting Program is effective in teaching important parenting skills to mothers with intellectual disabilities. Such programs warrant research because a recent meta-analysis concluded that mothers with intellectual disabilities may benefit from interventions designed to address their vulnerabilities (Feldman, McConnell, & Aunos, 2012). Conclusion Twenty programs have been rated as 3–promising research evidence to 1–well supported by research evidence in terms of their effectiveness in preventing child maltreatment. The programs range in terms of their delivery method and location. A few programs, including Triple P and Strong Communities are comprehensive community-wide initiatives that address risk factors for maltreatment at multiple levels. Some programs are delivered either to individual families at their homes or in medical settings, whereas others are group interventions that can be delivered in community agencies. Programs to prevent sexual abuse are primarily delivered in school settings and seek to train children as well as childserving professionals. SEEK is somewhat unique in that it trains medical professionals to detect risk factors for maltreatment and provide referrals for at-risk families. It is encouraging that there is a relatively long list of empirically supported programs to prevent maltreatment. However, 15 (75%) of these programs were rated as 3–promising research evidence, whereas only three were rated as 2–supported by research evidence and only 2 were rated as 1–well established by research evidence. This suggests that, although much progress has been made, there is still work to be done. The programs with the most evidence are designed to prevent physical abuse and

neglect (i.e., NFP, SEEK, Triple P, IY, SafeCare). Perhaps this is appropriate because most instances of maltreatment involve neglect and secondarily physical abuse. Notably, interventions to prevent sexual abuse primarily focus on training children to resist abuse (except Stewards of Children), whereas interventions for physical abuse and neglect focus on training caregivers of children to use appropriate parenting techniques. It is unclear whether programs to prevent sexual abuse aimed at potential perpetrators would be feasible or effective; however, given that only a few programs are rated as higher than 3–promising research evidence indicates that more research is needed. Although effective programs are available, their reach is still limited, and far too many families in need still do not have access to such programs. Research on ways to effectively disseminate prevention approaches within existing systems (e.g., primary care, education, public health, child welfare) is needed. A new report from the National Academies of Sciences, Engineering, and Medicine (2016) recommends that evidence-based interventions should be scaled up to serve more families and that institutions governing large service sectors (e.g., health care, education, public health) should collaborate to identify points at which interventions could be delivered (e.g., physician’s offices, schools). Another important issue that arises regarding dissemination and implementation is caregiver engagement in services. Many programs requiring a high level of caregiver involvement suffer from low rates of initial engagement and high levels of attrition. Therefore, an area for further investigation is to examine ways to effectively engage families in preventative services (National Academy of Sciences, Engineering, and Medicine, 2016). Examination of clients’ perceptions of programs, especially programs’ cultural appropriateness, may be key in determining how to more effectively engage families (Damashek et al., 2011). It’s unclear whether interventions need to be tailored specifically to specific cultural or ethnic groups (e.g., Effective Black Parenting Program) or whether interventions can be created so that they can be flexibility adapted to meet the needs of individual clients. As indicated previously, SafeCare is one intervention that has been found to be perceived 71

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as culturally sensitive by a diverse sample of parents. Research examining whether evidence-based interventions are experienced by clients as culturally sensitive and collaborative is needed. Finally, dissemination of evidence-based interventions requires funding. Some progress was made in this area with the passage of the Affordable Care Act. The Early Childhood Home Visiting Program provided $1.5 billion to states over 5 years to implement evidence-based home visiting programs to promote child well-being. As noted at the beginning of this chapter, the tangible and intangible costs of child maltreatment are tremendous, making it a worthwhile area of investment. Moreover, as noted throughout this chapter, several interventions have been found to be cost effective, meaning that upfront expenditures pay off in terms of later costs associated with maltreatment.

References Axberg, U., & Broberg, A. G. (2012). Evaluation of “the incredible years” in Sweden: The transferability of an American parent-training program to Sweden. Scandinavian Journal of Psychology, 53, 224–232. http://dx.doi.org/10.1111/j.1467-9450.2012.00955.x Barr, R. G., Barr, M., Fujiwara, T., Conway, J., Catherine, N., & Brant, R. (2009b). Do educational materials change knowledge and behaviour about crying and shaken baby syndrome? A randomized controlled trial. Canadian Medical Association Journal, 180, 727–733. http://dx.doi.org/10.1503/cmaj.081419 Barr, R. G., Rivara, F. P., Barr, M., Cummings, P., Taylor, J., Lengua, L. J., & Meredith-Benitz, E. (2009a). Effectiveness of educational materials designed to change knowledge and behaviors regarding crying and shaken-baby syndrome in mothers of newborns: A randomized, controlled trial. Pediatrics, 123, 972–980. http://dx.doi.org/10.1542/peds.2008-0908 Barrett, D. E., Katsiyannis, A., Zhang, D., & Zhang, D. (2014). Delinquency and recidivism: A multicohort, matched-control study of the role of early adverse experiences, mental health problems, and disabilities. Journal of Emotional and Behavioral Disorders, 22, 3–15. http://dx.doi.org/10.1177/1063426612470514

parenting program for Head Start mothers. Child Development, 74, 1433–1453. http://dx.doi.org/ 10.1111/1467-8624.00616 Brock, D. J. P., Marek, L. I., Matteo-Kerney, C., & Bagby, T. (2013). Open groups: Adaptations in implementing a parent training program. Health Promotion Perspectives, 3, 230–241. Bronfenbrenner, U. (1979). The ecology of human development: Experiments by design and nature. Cambridge, MA: Harvard University Press. Burger, K., & Damashek, A. (2016, June). The effects of a parenting intervention for caregivers in the foster care system. Poster presented at the 24th Annual Colloquium of the American Professional Society on the Abuse of Children, New Orleans, LA. Burwick, A., Zaveri, H., Shang, L., Boller, K., Daro, D., & Strong, D. (2014). Costs of early childhood home visiting: An analysis of programs implemented in the supporting evidence-based home visiting to prevent child maltreatment initiative: Final report. New York, NY: Mathematica Policy Research. Chaffin, M., Bard, D., Bigfoot, D. S., & Maher, E. J. (2012). Is a structured, manualized, evidencebased treatment protocol culturally competent and equivalently effective among American Indian parents in child welfare? Child Maltreatment, 17, 242–252. http://dx.doi.org/10.1177/1077559512457239 Chaffin, M., Hecht, D., Bard, D., Silovsky, J. F., & Beasley, W. H. (2012). A statewide trial of the SafeCare home-based services model with parents in Child Protective Services. Pediatrics, 129, 509–515. http://dx.doi.org/10.1542/peds.2011-1840 Collishaw, S., Pickles, A., Messer, J., Rutter, M., Shearer, C., & Maughan, B. (2007). Resilience to adult psychopathology following childhood maltreatment: Evidence from a community sample. Child Abuse and Neglect, 31, 211–229. http://dx.doi.org/10.1016/ j.chiabu.2007.02.004 Cowen, P. S. (2001). Effectiveness of a parent education intervention for at-risk families. Journal of the Society of Pediatric Nurses, 6, 73–82. http://dx.doi.org/ 10.1111/j.1744-6155.2001.tb00124.x Currier, L., & Wurtele, S. (1996). A pilot study of previously abused and non-sexually abused children’s responses to a personal safety program. Journal of Child Sexual Abuse, 5, 71–87. http:// dx.doi.org/10.1300/J070v05n01_04

Bavolek, S. J., Kline, D. F., McLaughlin, J. A., & Publicover, P. R. (1979). Primary prevention of child abuse and neglect: Identification of high-risk adolescents. Child Abuse and Neglect, 3, 1071–1080. http://dx.doi.org/10.1016/0145-2134(79)90152-2

Damashek, A., Bard, D., & Hecht, D. (2012). Provider cultural competency, client satisfaction, and engagement in home-based programs to treat child abuse and neglect. Child Maltreatment, 17, 56–66. http://dx.doi.org/10.1177/1077559511423570

Baydar, N., Reid, M. J., & Webster-Stratton, C. (2003). The role of mental health factors and program engagement in the effectiveness of a preventive

Damashek, A., Doughty, D., Ware, L., & Silovsky, J. (2011). Predictors of client engagement and attrition in home-based child maltreatment prevention

72

Primary and Secondary Prevention of Child Maltreatment

services. Child Maltreatment, 16, 9–20. http:// dx.doi.org/10.1177/1077559510388507 de Graaf, I., Speetjens, P., Smit, F., de Wolff, M., & Tavecchio, L. (2008). Effectiveness of the Triple P Positive Parenting Program on behavioral problems in children: A meta-analysis. Family Relations: An Interdisciplinary Journal of Applied Family Studies, 57, 553–566. http://dx.doi.org/10.1111/ j.1741-3729.2008.00522.x

Copyright American Psychological Association. Not for further distribution.

DePanfilis, D., & Dubowitz, H. (2005). Family connections: A program for preventing child neglect. Child Maltreatment, 10, 108–123. http://dx.doi.org/ 10.1177/1077559505275252

Fang, X., Brown, D. S., Florence, C. S., & Mercy, J. A. (2012). The economic burden of child maltreatment in the United States and implications for prevention. Child Abuse and Neglect, 36, 156–165. http:// dx.doi.org/10.1016/j.chiabu.2011.10.006 Feigelman, S., Dubowitz, H., Lane, W., Grube, L., & Kim, J. (2011). Training pediatric residents in a primary care clinic to help address psychosocial problems and prevent child maltreatment. Academic Pediatrics, 11, 474–480. http://dx.doi.org/10.1016/ j.acap.2011.07.005

DePanfilis, D., Dubowitz, H., & Kunz, J. (2008). Assessing the cost-effectiveness of Family Connections. Child Abuse and Neglect, 32, 335–351. http://dx.doi.org/10.1016/j.chiabu.2007.06.005

Feldman, M., McConnell, D., & Aunos, M. (2012). Parental cognitive impairment, mental health, and child outcomes in a child protection population. Journal of Mental Health Research in Intellectual Disabilities, 5, 66–90. http://dx.doi.org/10.1080/ 19315864.2011.587632

Dias, M. S., Smith, K., DeGuehery, K., Mazur, P., Li, V., & Shaffer, M. L. (2005). Preventing abusive head trauma among infants and young children: A hospital-based, parent education program. Pediatrics, 115, e470–e477. http://dx.doi.org/10.1542/ peds.2004-1896

Feldman, M. A., Case, L., & Sparks, B. (1992). Effectiveness of a child-care training program for parents at-risk for child neglect. Canadian Journal of Behavioural Science/Revue Canadienne des Sciences du Comportement, 24, 14–28. http://dx.doi.org/10.1037/ h0078698

Drotar, D., Robinson, J., Jeavons, L., & Kirchner, H. L. (2009). A randomized, controlled evaluation of early intervention: The Born to Learn curriculum. Child: Care, Health, and Development, 35, 643–649. http://dx.doi.org/10.1111/ j.1365-2214.2008.00915.x

Feldman, M. A., Sparks, B., & Case, L. (1993). Effectiveness of home-based early intervention on the language development of children of mothers with mental retardation. Research in Developmental Disabilities, 14, 387–408. http://dx.doi.org/ 10.1016/0891-4222(93)90010-H

Dubowitz, H. (2014). The Safe Environment for Every Kid (SEEK) model: Helping promote children’s health, development, and safety. Child Abuse and Neglect, 38, 1725–1733. http://dx.doi.org/10.1016/ j.chiabu.2014.07.011

Felitti, V. J., & Anda, R. (2009). The relationship of adverse childhood experiences to adult medical disease, psychiatric disorders, and sexual behavior: Implications for healthcare. In R. Lanius, E. Vermetten, & C. Pain (Eds.), The hidden epidemic: The impact of early life trauma on health and disease (pp. 77–88). Cambridge, England: Cambridge University Press.

Dubowitz, H., Feigelman, S., Lane, W., & Kim, J. (2009). Pediatric primary care to help prevent child maltreatment: The Safe Environment for Every Kid (SEEK) model. Pediatrics, 123, 858–864. http:// dx.doi.org/10.1542/peds.2008-1376 Dubowitz, H., Lane, W. G., Semiatin, J. N., & Magder, L. S. (2012). The SEEK model of pediatric primary care: Can child maltreatment be prevented in a low-risk population? Academic Pediatrics, 12, 259–268. http:// dx.doi.org/10.1016/j.acap.2012.03.005 Dubowitz, H., Lane, W. G., Semiatin, J. N., Magder, L. S., Venepally, M., & Jans, M. (2011). The safe environment for every kid model: Impact on pediatric primary care professionals. Pediatrics, 127, e962–e970. http://dx.doi.org/10.1542/peds.2010-1845 Edwards, R. T., Céilleachair, A., Bywater, T., Hughes, D. A., & Hutchings, J. (2007). Parenting programme for parents of children at risk of developing conduct disorder: Cost effectiveness analysis. British Medical Journal, 334, 682. http://dx.doi.org/10.1136/ bmj.39126.699421.55

Finno-Velasquez, M., Fettes, D. L., Aarons, G. A., & Hurlburt, M. S. (2014). Cultural adaptation of an evidence-based home visitation programme: Latino clients’ experiences of service delivery during implementation. Journal of Children’s Services, 9, 280–294. http://dx.doi.org/10.1108/JCS-06-2014-0030 Fryer, G. E., Jr., Kraizer, S. K., & Mlyoshi, T. (1987a). Measuring actual reduction of risk to child abuse: A new approach. Child Abuse and Neglect, 11, 173–179. http://dx.doi.org/10.1016/0145-2134(87)90055-X Fryer, G. E., Jr., Kraizer, S. K., & Mlyoshi, T. (1987b). Measuring children’s retention of skills to resist stranger abduction: Use of the simulation technique. Child Abuse and Neglect, 11, 181–185. http:// dx.doi.org/10.1016/0145-2134(87)90056-1 Fujiwara, T., Yamada, F., Okuyama, M., Kamimaki, I., Shikoro, N., & Barr, R. G. (2012). Effectiveness of educational materials designed to change knowledge 73

Damashek et al.

and behavior about crying and shaken baby syndrome: A replication of a randomized controlled trial in Japan. Child Abuse and Neglect, 36, 613–620. http://dx.doi.org/10.1016/j.chiabu.2012.07.003

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Gershater-Molko, R. M., Lutzker, J. R., & Wesch, D. (2002). Using recidivism data to evaluate project SafeCare: Teaching bonding, safety, and health care skills to parents. Child Maltreatment, 7, 277–285. http://dx.doi.org/10.1177/1077559502007003009 Gershater-Molko, R. M., Lutzker, J. R., & Wesch, D. (2003). Project SafeCare: Improving health, safety, and parenting skills in families reported for, and at-risk for child maltreatment. Journal of Family Violence, 18, 377–386. http://dx.doi.org/10.1023/ A:1026219920902 Gershoff, E. T. (2002). Corporal punishment by parents and associated child behaviors and experiences: A meta-analytic and theoretical review. Psychological Bulletin, 128, 539–579. http://dx.doi.org/ 10.1037/0033-2909.128.4.539 Girvin, H., DePanfilis, D., & Daining, C. (2007). Predicting program completion among families enrolled in a child neglect preventive intervention. Research on Social Work Practice, 17, 674–685. http:// dx.doi.org/10.1177/1049731507300285 Gold, J., Sullivan, M. W., & Lewis, M. (2011). The relation between abuse and violent delinquency: The conversion of shame to blame in juvenile offenders. Child Abuse and Neglect, 35, 459–467. http://dx.doi.org/10.1016/j.chiabu.2011.02.007 Gross, D., Fogg, L., Webster-Stratton, C., Garvey, C., Julion, W., & Grady, J. (2003). Parent training of toddlers in day care in low-income urban communities. Journal of Consulting and Clinical Psychology, 71, 261–278. http://dx.doi.org/ 10.1037/0022-006X.71.2.261 Guterman, N. B., Tabone, J. K., Bryan, G. M., Taylor, C. A., Napoleon-Hanger, C., & Banman, A. (2013). Examining the effectiveness of home-based parent aide services to reduce risk for physical child abuse and neglect: Six-month findings from a randomized clinical trial. Child Abuse and Neglect, 37, 566–577. http://dx.doi.org/10.1016/j.chiabu.2013.03.006 Harder, J. (2005). Prevention of child abuse and neglect: An evaluation of a home visitation parent aide program using recidivism data. Research on Social Work Practice, 15, 246–256. http://dx.doi.org/ 10.1177/1049731505275062 Haski-Leventhal, D., Ben-Arieh, A., & Melton, G. B. (2008). Between neighborliness and volunteerism: Participants in the strong communities initiative. Family and Community Health, 31, 150–161. http:// dx.doi.org/10.1097/01.FCH.0000314575.58905.a1 Homem, T. C., Gaspar, M. F., Santos, M. J. S., Azevedo, A. F., & Canavarro, M. C. (2015). Incredible 74

years parent training: Does it improve positive relationships in Portuguese families of preschoolers with oppositional/defiant symptoms? Journal of Child and Family Studies, 24, 1861–1875. http://dx.doi.org/ 10.1007/s10826-014-9988-2 Kenny, M., Bennett, K., Dougery, J., & Steele, F. (2013). Teaching general safety and Body Safe Training skills to a Latino preschool male with autism. Journal of Child and Family Studies, 22, 1092–1102. http:// dx.doi.org/10.1007/s10826-012-9671-4 Kim, E., Cain, K. C., & Webster-Stratton, C. (2008). The preliminary effect of a parenting program for Korean American mothers: A randomized controlled experimental study. International Journal of Nursing Studies, 45, 1261–1273. http://dx.doi.org/10.1016/ j.ijnurstu.2007.10.002 Kimbrough-Melton, R. J., & Campbell, D. (2008). Strong communities for children: A community-wide approach to prevention of child abuse and neglect. Family and Community Health, 31, 100–112. http:// dx.doi.org/10.1097/01.FCH.0000314571.28410.98 Kjellgren, C., Svedin, C., & Nilsson, D. (2013). Child physical abuse—Experienced of combined treatment for children and their parents: A pilot study. Child Care in Practice, 19, 275–290. http://dx.doi.org/ 10.1080/13575279.2013.785934 Knox, M., Burkhart, K., & Cromly, A. (2013). Supporting positive parenting in community health centers: The ACT raising safe kids program. Journal of Community Psychology, 41, 395–407. http://dx.doi.org/10.1002/ jcop.21543 Knox, M., Burkhart, K., & Hunter, K. E. (2011). ACT Against Violence Parents Raising Safe Kids Program: Effects on maltreatment-related parenting behaviors and beliefs. Journal of Family Issues, 32, 55–74. http:// dx.doi.org/10.1177/0192513X10370112 Kraizer, S., Witte, S. S., & Fryer, G. E., Jr. (1989). Child sexual abuse prevention programs: What makes them effective in protecting children? Children Today, 18, 23–27. Lee, Y. K., & Tang, C. S. (1998). Evaluation of a sexual abuse prevention program for female Chinese adolescents with mild mental retardation. American Journal on Mental Retardation, 103, 105–116. http:// dx.doi.org/10.1352/0895-8017(1998)1032.0.CO;2 Linares, L. O., Montalto, D., Li, M., & Oza, V. S. (2006). A promising parenting intervention in foster care. Journal of Consulting and Clinical Psychology, 74, 32–41. http://dx.doi.org/10.1037/0022-006X.74.1.32 Lutzker, J., & Bigelow, K. (2002). Reducing child maltreatment: A guidebook for parent services. New York, NY: Guilford Press. Lutzker, J. R., Bigelow, K. M., Doctor, R. M., & Kessler, M. L. (1998). Safety, health care, and bonding within

Primary and Secondary Prevention of Child Maltreatment

an ecobehavioral approach to treating and preventing child abuse and neglect. Journal of Family Violence, 13, 163–185. http://dx.doi.org/10.1023/ A:1022893607387 Lutzker, J. R., Frame, R. E., & Rice, J. M. (1982). Project 12 Ways: An ecobehavioral approach to the treatment and prevention of child abuse and neglect. Education and Treatment of Children, 5, 141–155.

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Lutzker, J. R., & Rice, J. M. (1987). Using recidivism data to evaluate Project 12 Ways: An ecobehavioral approach to the treatment and prevention of child abuse and neglect. Journal of Family Violence, 2, 283–290. http://dx.doi.org/10.1007/BF00993295 MacKenzie, M. J., Nicklas, E., Brooks-Gunn, J., & Waldfogel, J. (2011). Who spanks infants and toddlers? Evidence from the Fragile Families and Child Well-Being study. Children and Youth Services Review, 33, 1364–1373. http://dx.doi.org/10.1016/ j.childyouth.2011.04.007 Maher, E. J., Corwin, T. W., Hodnett, R., & Faulk, K. (2012). A cost-savings analysis of a statewide parenting education program in child welfare. Research on Social Work Practice, 22, 615–625. http:// dx.doi.org/10.1177/1049731512449873 Maher, E. J., Marcynyszyn, L. A., Corwin, T. W., & Hodnett, R. (2011). Dosage matters: The relationship between participation in the Nurturing Parenting Program for infants, toddlers, and preschoolers and subsequent child maltreatment. Children and Youth Services Review, 33, 1426–1434. http://dx.doi.org/ 10.1016/j.childyouth.2011.04.014 Marcynyszyn, L. A., Maher, E. J., & Corwin, T. W. (2011). Getting with the (evidence-based) program: An evaluation of the Incredible Years Parenting Training Program in child welfare. Children and Youth Services Review, 33, 747–757. http://dx.doi.org/ 10.1016/j.childyouth.2010.11.021 McDaniel, B., Braiden, H. J., Onyekwelu, J., Murphy, M., & Regan, H. (2011). Investigating the effectiveness of the incredible years basic parenting programme for foster carers in Northern Ireland. Child Care in Practice, 17, 55–67. http://dx.doi.org/ 10.1080/13575279.2010.522979 McDonell, J. R., Ben-Arieh, A., & Melton, G. B. (2015). Strong Communities for Children: Results of a multiyear community-based initiative to protect children from harm. Child Abuse and Neglect, 41, 79–96. http://dx.doi.org/10.1016/j.chiabu.2014.11.016 McLeigh, J. D., McDonell, J. R., & Melton, G. B. (2015). Community differences in the implementation of Strong Communities for Children. Child Abuse and Neglect, 41, 97–112. http://dx.doi.org/10.1016/ j.chiabu.2014.07.010 McMurtry, S. L. (1985). Secondary prevention of child maltreatment: A review. Social Work, 30, 42–48.

Menting, A. T., Orobio de Castro, B., & Matthys, W. (2013). Effectiveness of the Incredible Years parent training to modify disruptive and prosocial child behavior: A meta-analytic review. Clinical Psychology Review, 33, 901–913. http://dx.doi.org/10.1016/ j.cpr.2013.07.006 Messman-Moore, T. L., Walsh, K. L., & DiLillo, D. (2010). Emotion dysregulation and risky sexual behavior in revictimization. Child Abuse and Neglect, 34, 967–976. http://dx.doi.org/10.1016/ j.chiabu.2010.06.004 Mihalopoulos, C., Sanders, M. R., Turner, K. M., Murphy-Brennan, M., & Carter, R. (2007). Does the triple P-Positive Parenting Program provide value for money? Australian and New Zealand Journal of Psychiatry, 41, 239–246. http://dx.doi.org/ 10.1080/00048670601172723 Myers, H., Alvy, K. T., Arlington, A., Richardson, M. A., Marigna, M., Huff, R., . . . Newcomb, M. D. (1992). The impact of a parent training program on innercity African American families. Journal of Community Psychology, 20, 132–147. http://dx.doi.org/ 10.1002/1520-6629(199204)20:23.0.CO;2-Z National Academy of Sciences, Engineering, and Medicine. (2016). Parenting matters: Supporting parents of children ages 0–8. http://dx.doi.org/ 10.17226/21868 Nowak, C., & Heinrichs, N. (2008). A comprehensive meta-analysis of Triple P-Positive Parenting Program using hierarchical linear modeling: Effectiveness and moderating variables. Clinical Child and Family Psychology Review, 11, 114–144. http://dx.doi.org/ 10.1007/s10567-008-0033-0 Olds, D. L., Eckenrode, J., Henderson, C. R., Jr., Kitzman, H., Powers, J., Cole, R., . . . Luckey, D. (1997). Long-term effects of home visitation on maternal life course and child abuse and neglect. Fifteenyear follow-up of a randomized trial. Journal of the American Medical Association, 278, 637–643. http:// dx.doi.org/10.1001/jama.1997.03550080047038 Olds, D. L., Henderson, C. R., Jr., Chamberlin, R., & Tatelbaum, R. (1986). Preventing child abuse and neglect: A randomized trial of nurse home visitation. Pediatrics, 78, 65–78. Olds, D. L., Henderson, C. R., Jr., & Kitzman, H. (1994). Does prenatal and infancy nurse home visitation have enduring effects on qualities of parental caregiving and child health at 25 to 50 months of life? Pediatrics, 93, 89–98. Olds, D. L., Hill, P. L., O’Brien, R., Racine, D., & Moritz, P. (2003). Taking preventive intervention to scale: The nurse-family partnership. Cognitive and Behavioral Practice, 10, 278–290. http://dx.doi.org/ 10.1016/S1077-7229(03)80046-9 75

Damashek et al.

Olds, D. L., Kitzman, H., Hanks, C., Cole, R., Anson, E., Sidora-Arcoleo, K., . . . Bondy, J. (2007). Effects of nurse home visiting on maternal and child functioning: Age 9 follow-up of a randomized trial. Pediatrics, 120, e832–e845. http://dx.doi.org/ 10.1542/peds.2006-2111

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Olds, D. L., Robinson, J., O’Brien, R., Luckey, D. W., Pettitt, L. M., Henderson, C. R., Jr., . . . Talmi, A. (2002). Home visiting by paraprofessionals and by nurses: A randomized, controlled trial. Pediatrics, 110, 486–496. http://dx.doi.org/10.1542/ peds.110.3.486 Olds, D. L., Robinson, J., Pettitt, L., Luckey, D. W., Holmberg, J., Ng, R. K., . . . Henderson, C. R., Jr. (2004). Effects of home visits by paraprofessionals and by nurses: Age 4 follow-up results of a randomized trial. Pediatrics, 114, 1560–1568. http:// dx.doi.org/10.1542/peds.2004-0961 Paranal, R., Washington Thomas, K., & Derrick, C. (2012). Utilizing online training for child sexual abuse prevention: Benefits and limitations. Journal of Child Sexual Abuse, 21, 507–520. http://dx.doi.org/ 10.1080/10538712.2012.697106 Pfannenstiel, J. C., & Seltzer, D. A. (1989). New parents as teachers: Evaluation of an early parent education program. Early Childhood Research Quarterly, 4, 1–18. http://dx.doi.org/10.1016/ S0885-2006(89)90025-2 Portwood, S. G., Lambert, R. G., Abrams, L. P., & Nelson, E. B. (2011). An evaluation of the adults and children together (ACT) against violence parents raising safe kids program. Journal of Primary Prevention, 32, 147–160. http://dx.doi.org/10.1007/ s10935-011-0249-5 Posthumus, J. A., Raaijmakers, M. A. J., Maassen, G. H., van Engeland, H., & Matthys, W. (2012). Sustained effects of incredible years as a preventive intervention in preschool children with conduct problems. Journal of Abnormal Child Psychology, 40, 487–500. http://dx.doi.org/10.1007/ s10802-011-9580-9 Prinz, R. J., Sanders, M. R., Shapiro, C. J., Whitaker, D. J., & Lutzker, J. R. (2009). Population-based prevention of child maltreatment: The U.S. Triple P System population trial. Prevention Science, 10, 1–12. http:// dx.doi.org/10.1007/s11121-009-0123-3 Reid, M. J., Webster-Stratton, C., & Beauchaine, T. P. (2001). Parent training in head start: A comparison of program response among African American, Asian American, Caucasian, and Hispanic mothers. Prevention Science, 2, 209–227. http://dx.doi.org/ 10.1023/A:1013618309070 Rheingold, A. A., Zajac, K., Chapman, J. E., Patton, M., de Arellano, M., Saunders, B., & Kilpatrick, D. (2015). Child sexual abuse prevention training for childcare 76

professionals: An independent multi-site randomized controlled trial of Stewards of Children. Prevention Science, 16, 374–385. http://dx.doi.org/10.1007/ s11121-014-0499-6 Runyon, M., Deblinger, E., & Schroeder, C. (2009). Pilot evaluation of outcomes of Combined Parent–Child Cognitive–Behavioral Group therapy for families at risk for child physical abuse. Cognitive and Behavioral Practice, 16, 101–118. http://dx.doi.org/10.1016/ j.cbpra.2008.09.006 Runyon, M., Deblinger, E., & Steer, R. (2010). Group cognitive behavioral treatment for parents and children at-risk for physical abuse. An initial study. Child and Family Behavior Therapy, 32, 196–218. http://dx.doi.org/10.1080/07317107.2010.500515 Sanders, M. R. (2012). Development, evaluation, and multinational dissemination of the Triple P–Positive Parenting Program. Annual Review of Clinical Psychology, 8, 345–379. http://dx.doi.org/10.1146/ annurev-clinpsy-032511-143104 Sanders, M. R., Kirby, J. N., Tellegen, C. L., & Day, J. J. (2014). The Triple P–Positive Parenting Program: A systematic review and meta-analysis of a multilevel system of parenting support. Clinical Psychology Review, 34, 337–357. http://dx.doi.org/10.1016/ j.cpr.2014.04.003 Sanders, M. R., Pidgeon, A., Gravestock, F., Connors, M. D., Brown, S., & Young, R. M. (2004). Does parental attributional retraining and anger management enhance the effects of the Triple P–Positive Parenting Program with parents at-risk of child maltreatment? Behavior Therapy, 35, 513–535. http://dx.doi.org/10.1016/S0005-7894(04)80030-3 Sarno, J., & Wurtele, S. (1997). Effects of a personal safety program on preschoolers’ knowledge, skills, and perceptions of child sexual abuse. Child Maltreatment, 2, 35–45. http://dx.doi.org/10.1177/1077559597002001004 Self-Brown, S., Cowart-Osborne, M., Baker, E., Thomas, A., Boyd, C., Jr., Chege, E., . . . Lutzker, J. (2015). Dad2K: An adaptation of SafeCare to enhance positive parenting skills with at-risk fathers. Child and Family Behavior Therapy, 37, 138–155. Shonkoff, J. P., & Phillips, D. A. (2000). From neurons to neighborhoods: The science of early childhood development. Washington, DC: National Academy Press. Silovsky, J. F., Bard, D., Chaffin, M., Hecht, D., Burris, L., Owora, A., . . . Lutzker, J. (2011). Prevention of child maltreatment in high-risk rural families: A randomized clinical trial with child welfare outcomes. Children and Youth Services Review, 33, 1435–1444. http://dx.doi.org/10.1016/j.childyouth.2011.04.023 Tertinger, D. A., Greene, B. F., & Lutzker, J. R. (1984). Home safety: Development and validation of one component of an ecobehavioral treatment program for abused and neglected children. Journal of Applied

Primary and Secondary Prevention of Child Maltreatment

Behavior Analysis, 17, 159–174. http://dx.doi.org/ 10.1901/jaba.1984.17-159 Tutty, L. (2000). What children learn from sexual abuse prevention programs: Difficult concepts and developmental issues. Research on Social Work Practice, 10, 275–300. Tutty, L. M. (1997). Child sexual abuse prevention programs: Evaluating Who Do You Tell. Child Abuse and Neglect, 21, 869–881. http://dx.doi.org/10.1016/ S0145-2134(97)00048-3

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Tutty, L. M. (2014). Listen to the children: Kids’ impressions of Who Do You Tell. Journal of Child Sexual Abuse, 23, 17–37. http://dx.doi.org/10.1080/ 10538712.2013.841790 U.S. Department of Health and Human Services. (2016). Child maltreatment 2014. Retrieved from https:// www.acf.hhs.gov/cb/resource/child-maltreatment-2014 Vespo, J. E., Capece, D., & Behforooz, B. (2006). Effects of the nurturing curriculum on social, emotional, and academic behaviors in kindergarten classrooms. Journal of Research in Childhood Education, 20, 275–285. http:// dx.doi.org/10.1080/02568540609594567 Wagner, M., Spiker, D., & Linn, M. I. (2002). The effectiveness of the Parents as Teachers program with low-income parents and children. Topics in Early Childhood Special Education, 22, 67–81. http:// dx.doi.org/10.1177/02711214020220020101 Whitaker, D. J., Lutzker, J. R., & Shelley, G. A. (2005). Child maltreatment prevention priorities at the Centers for Disease Control and Prevention. Child Maltreatment, 10, 245–259. http://dx.doi.org/ 10.1177/1077559505274674 Wilson, E., Dolan, M., Smith, K., Casanueva, C., & Ringeisen, H. (2012). NSCAW child well-being spotlight: Adolescents with a history of maltreatment have unique service needs that may affect their transition to adulthood (OPRE Report #2012-49). Washington, DC: U.S. Department of Health and Human Services.

World Health Organization. (1978). International classification of diseases, ninth revision. Geneva, Switzerland: Author. Wurtele, S. (1990). Teaching personal safety skills to 4-year-old children: A behavioral approach. Behavior Therapy, 21, 25–32. http://dx.doi.org/10.1016/ S0005-7894(05)80186-8 Wurtele, S., Currier, L., Gillispie, E., & Franklin, C. (1991). The efficacy of a parent-implemented program for teaching preschoolers personal safety skills. Behavior Therapy, 22, 69–83. http://dx.doi.org/ 10.1016/S0005-7894(05)80245-X Wurtele, S., Kast, L., Miller-Perrin, C., & Kondrick, P. (1989). Comparison of programs for teaching personal safety skills to preschoolers. Journal of Consulting and Clinical Psychology, 57¸505-511. Wurtele, S. K., Gillispie, E. I., Currier, L. L., & Franklin, C. F. (1992). A comparison of teachers vs. parents as instructors of a personal safety program for preschoolers. Child Abuse and Neglect, 16, 127–137. http://dx.doi.org/10.1016/ 0145-2134(92)90013-H Wurtele, S. K., Kast, L. C., & Melzer, A. M. (1992). Sexual abuse prevention education for young children: A comparison of teachers and parents as instructors. Child Abuse and Neglect, 16, 865–876. http://dx.doi.org/10.1016/ 0145-2134(92)90088-9 Zhang, W., Chen, J., Feng, Y., Li, J., Liu, C., & Zhao, X. (2014). Evaluation of a sexual abuse prevention education for Chinese preschoolers. Research on Social Work Practice, 24, 428–436. http://dx.doi.org/ 10.1177/1049731513510409 Zigler, E., Pfannenstiel, J. C., & Seitz, V. (2008). The Parents as Teachers program and school success: A replication and extension. Journal of Primary Prevention, 29, 103–120. http://dx.doi.org/10.1007/ s10935-008-0132-1

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Chapter 5

Prevention of Aggression and Bullying in Children and Adolescents

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Jeffrey M. Jenson and Anne Williford

Aggression, bullying, and peer victimization affect a meaningful number of children and adolescents in the United States. Young people who are directly involved in aggressive or bullying behavior are at elevated risk for adverse developmental outcomes during childhood and adolescence. Victims of aggression and bullying often experience negative social and emotional consequences in peer relationships and at school. This chapter describes the problem of aggression and bullying in children and adolescents. Definitions, prevalence, correlates, and individual and ecological consequences of aggression and bullying are noted. Effective prevention approaches are described and ongoing challenges associated with reducing aggression, bullying, and peer victimization are outlined.

Epidemiology of Aggression, Bullying, and Victimization Aggression, bullying, and peer victimization take on many forms and have significant effects on the lives of young people. In the sections that follow, we review the definitions, prevalence, correlates, and consequences associated with these behaviors.

Definitions A common challenge in understanding the onset, persistence, and prevention of aggression and bullying is that researchers often conflate these two related, yet distinct, phenomena. Confusion

between the terms aggression and bullying makes it difficult to compare findings across studies and poses limitations to the design of preventive interventions. Peer victimization is also defined in diverse ways in practice, and in investigations addressing the epidemiology of aggression and bullying. Unique definitions for aggression, bullying, and peer victimization do exist. Aggression.  Harm, and the intent to cause harm, are critical features in definitions of aggression (Gendreau & Archer, 2005). Purposeful injury or harm to another person is generally viewed as emotionally driven and an impulsive behavior that occurs in response to a real or perceived threat; such behavior frequently is labeled as hostile (Gendreau & Archer, 2005) or reactive forms of aggression (Little, Brauner, Jones, Nock, & Hawley, 2003). Conversely, aggressive acts that are committed to achieve self-serving outcomes (e.g., gaining power or status) are often labeled as proactive (Vitaro & Brendgen, 2012) or instrumental forms of aggression (Gendreau & Archer, 2005). Further distinctions have been made to clarify and understand different forms of aggressive behavior. For example, overt aggression is defined as verbal and physical behaviors directed at individuals with the intention to physically harm or threaten them (Olweus, 1993). Overt behaviors include physical acts like hitting, kicking, and pushing as well as using intimidation, threats, and insults with the intention of causing harm. Another common

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APA Handbook of Psychopathology: Child and Adolescent Psychopathology, edited by J. N. Butcher and P. C. Kendall Copyright © 2018 American Psychological Association. All rights reserved.

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term, relational aggression, is characterized by manipulating relationships with others as a primary means to inflict harm (Putallaz, Kupersmidt, Coie, McKnight, & Grimes, 2004). Relational aggression includes talking or gossiping about others, breaking confidence and trust with peers, and ignoring or ostracizing others. Interactive communication technologies (e.g., text messaging, social media sites, online games) are important forms of communication among young people. Not surprisingly, young people have used interactive communication tools in prosocial and antisocial ways. Cyber aggression, an expression of antisocial conduct, refers to intentional activities or behaviors perpetrated by individuals or groups through electronic media that cause harm or discomfort to one or more peers (Hinduja & Patchin, 2008; Smith, 2012). There is some disagreement in the field about whether cyber aggression is a unique form of aggression, or whether it simply represents a new mechanism or context in which aggressive acts occur. Some investigators separate cyber bullying from other forms of aggression because of its unique reliance on technology. Others note the commonalities between cyber and relational aggression because exclusion, gossip, and disparaging remarks meant to damage reputations and relationships are tactics frequently used in both forms of aggression (Cassidy, Faucher, & Jackson, 2013). Bullying.  The term bullying has been used in case studies and popular literature for centuries (National Academies of Sciences, Engineering, & Medicine, 2016). In the early 1990s, Olweus (1993) set the definitional standard for bullying by characterizing it as a form of behavior in which a young person is targeted repeatedly over time by the intentional negative actions of one or more peers. Olweus further described bullying as behavior that is predicated on a power imbalance between the bully and the victim. Therefore, the key elements in the definition of bullying set forward by Olweus (1993) include intentionality, repetition, and the presence of a power imbalance. Despite general acceptance of this definition, there is considerable variation in how bullying is conceptualized and measured 80

in practice and research (Hymel & Swearer, 2015; Volk, Dane, & Marini, 2014). Peer victimization.  Peer victimization refers commonly to a relationship-based pattern of behavior that uses aggression to oppress, humiliate, or dominate others (Vernberg & Biggs, 2010). Peer victimization is a direct result of aggression and bullying, and as such, has been found to occur more frequently than any other form of aggression. One important aspect in defining victimization is related to a potential victim’s reaction to and perception of events. Some investigators (Brock, Nickerson, O’Malley, & Chang, 2006) suggest that the way in which individuals perceive a victimization experience influences their response and reaction to it. Therefore, if a child perceives that he or she has been victimized, they will interpret the aggressive act negatively. Conversely, if he or she does not perceive the event as a severe incident, they may not experience the same level of distress or characterize the event as victimization. A victim’s perception of harm is a critical key in determining whether an incident is characterized as victimization, or as a more routine peer-to-peer interaction.

Prevalence of Aggression, Bullying, and Peer Victimization Findings from nationally representative samples of youth indicate that between 10% and 33% of public school students report being victims of bullying (Eaton et al., 2012; Finkelhor, Turner, Shattuck, & Hamby, 2015; Iannotti, 2013; Robers, Kemp, & Truman, 2013; U.S. Department of Education, 2015). At the same time, evidence suggests that between 5% and 15% of students admit to bullying others (Benedict, Vivier, & Gjelsvik, 2015; Nansel et al., 2001). It is generally accepted that bullying behaviors peak during the middle school years (Currie et al., 2012). Boys typically report more bullying than girls, but girls are more likely to be victims than boys (Cook, Williams, Guerra, Kim, & Sadek, 2010). Additional evidence of the prevalence of aggression, bullying, and peer victimization comes from general surveys of risky behavior in young people. Recent findings from the Youth Risk Behavior

Prevention of Aggression and Bullying in Children and Adolescents

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Surveillance Survey found that 33% of students in Grades 9 through 12 were involved in a physical fight in the previous 12 months; 41% of boys, compared with 24% of girls, reported being in a fight in the past year (Eaton et al., 2012). Slightly more than 20% of students in Grades 9 through 12 indicated they had been bullied on school grounds with higher prevalence rates among girls (22%) than boys (18%). Sixteen percent of all students in Grades 9 through 12 reported being the victims of cyber bullying; 22% of girls and 11% of boys were victims of cyber bullying.

Risk and Protective Factors for Aggression, Bullying, and Peer Victimization A common approach to understanding child and adolescent behavior problems is based on identifying risk factors that elevate the likelihood of problems and protective factors that serve to offset or buffer levels of risk (Jenson & Bender, 2014; O’Connell, Boat, & Warner, 2009). Risk and protective factors for problems (e.g., substance use, delinquency, school dropout) have been welldocumented (Hawkins, Catalano, & Miller, 1992; Herrenkohl, Aisenberg, Williams, & Jenson, 2011; Jenson & Fraser, 2016). In recent years, risk and protective factors that increase or mitigate involvement in aggression, bullying, and peer victimization have also been identified. Individual characteristics like temperament and cognitive abilities, as well as interpersonal and environmental factors like parenting skills, peer influences, and neighborhood characteristics contribute to the onset, maintenance, and desistance of aggression, bullying, and peer victimization (Rodkin, Espelage, & Hanish, 2015). Selected risk and protective factors for aggression, bullying, and victimization are reviewed next. Aggression and bullying.  A number of individual characteristics increase risk and offer protection against aggression and bullying. Temperament from an early age is an important individual risk factor for aggression and bullying; children with difficult or uneven temperaments, often characterized by high emotionality and low personal control, are more likely than other children to engage in

aggressive behavior (Frick & Morris, 2004; Muris & Ollendick, 2005). On a related note, emotional reactivity has been identified as a key characteristic in differentiating reactively from proactively aggressive children (Vitaro & Brendgen, 2012). Children with easy temperaments who demonstrate high levels of emotional regulation tend to control their behaviors and experience less impulsivity and reactivity; these factors protect against involvement in aggression and bullying (Barton, 2016). It follows that children who display easy temperaments elicit more positive responses from parents, peers, and others in their environments. Consequently, interactions with others either protect against or exacerbate risk for involvement in aggression. For example, ineffective parenting skills and inconsistent discipline practices are related to physical and relational aggression (Ehrenreich, Beron, Brinkley, & Underwood, 2014). Finally, child maltreatment is associated with bullying and aggression (Bowes et al., 2009); evidence suggests that the relationship between maltreatment and aggression may be mediated by levels of emotional dysregulation in young people (Shields & Cicchetti, 2001). Influence from peers is an important risk factor for the onset, persistence, and desistance of aggression and bullying. All forms of aggression and bullying are more likely to occur in peer groups that have tolerant or supportive attitudes toward aggressive behavior. It follows that children who attend classrooms or schools that have permissive attitudes toward aggression have elevated levels of bullying and aggressive behavior (Mercer, McMillen, & DeRosier, 2009). Conversely, positive peers and friendship quality are related to lower rates of aggressive and bullying behavior (Bollmer, Milich, Harris, & Maras, 2005). Finally, certain environmental conditions, especially those associated with low-income neighborhoods (e.g., exposure to crime, violent victimization), increase risk for participation in aggression and bullying (Espelage, Bosworth, & Simon, 2000). Peer victimization.  Common risk factors for victimization often stem from the degree to which students belong to a minority (outgroup) versus a majority (ingroup) culture (Cartland, Ruch-Ross, & 81

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Henry, 2003). Youth who are at odds with the prevailing norms of a recognized peer group, school, or community ecology are at higher risk for victimization by peers. These characteristics include a variety of individual factors. For example, youth who identify as lesbian, gay, bisexual, transgender, or queer are at elevated risk for victimization (Collier, van Beusekom, Bos, & Sandfort, 2013; Kosciw, Diaz, & Bartkiewicz, 2010). Furthermore, belonging to a minority racial or ethnic group in a school may place students at greater risk for peer victimization (Espelage & Swearer, 2003; Juvonen, Nishina, & Graham, 2006). Youth who are socially withdrawn or who have high levels of anxiety (Cohen & Kendall, 2015) or depressive symptoms (Reijntjes, Kamphuis, Prinzie, & Telch, 2010) and children who display hyperactive, impulsive, angry, or reactive behaviors (Card & Hodges, 2008) are also at elevated risk for victimization from peers. Conversely, children who evidence more prosocial behaviors and social skills (e.g., assertiveness, effective conflict resolution) are less likely to be victimized (Card & Hodges, 2008). Moreover, higher friendship quality, peer sociability, and being well-liked by peers protect against peer victimization during childhood and adolescence (Fox & Boulton, 2006). Finally, ineffective parenting skills and inconsistent parent discipline practices are associated with increased risk for peer victimization (Perry, Hodges, & Egan, 2001). Conversely, parent support and encouragement increase protection against maladjustment problems in adolescents who experience peer victimization as young children (Stadler, Feifel, Rohrmann, Vermeiren, & Poustka, 2010).

Consequences of Aggression, Bullying, and Peer Victimization Childhood participation in physical forms of aggression and bullying frequently leads to involvement in violent behavior, delinquent conduct, and criminality during adolescence and adulthood (McDougall & Vaillancourt, 2015). Considerable evidence suggests that the association between early physical aggression and later antisocial conduct is mediated by peer rejection and by affiliation with antisocial peers (Vitaro & Brendgen, 2012). 82

The consequences and attributes associated with perpetration of relational aggression and bullying appear to follow a different pattern from that of physical forms of aggression and bullying. Although studies have identified a number of adverse consequences of participation in relational forms of aggression and bullying (Herrenkohl, Catalano, Hemphill, & Toumbourou, 2009), some investigators have reported that involvement in these behaviors is also associated with attributes like social competence, perceived popularity, and social power (Cillessen & Mayeux, 2004; Rose, Swenson, & Waller, 2004). Successful acts of relational aggression may require a certain level of social competence to effectively manipulate relationships in a manner that leads to perceived and real social gains. To illustrate, social exclusion may promote cohesion within a peer group, and as a result, the perpetrator gains power and popularity as he or she becomes more central in the peer hierarchy. On a related note, some evidence suggests that relationally aggressive youth are socially involved and central to their peer groups and are motivated to maintain their social status by engaging in behaviors like substance use to elevate their status within a group (Vitaro & Brendgen, 2012). Regardless, it is important to note that committing acts of relational aggression does not necessarily imply that perpetrators are wellliked by peers. In fact, evidence suggests that young children who are relationally aggressive experience significant peer rejection over time and that such rejection increases the likelihood of anxiety and depression, particularly for boys (Vitaro & Brendgen, 2012). Proactively aggressive children experience less peer rejection and have more friends when compared with reactively aggressive children (Vitaro & Brendgen, 2012). Reactive aggression is consistently associated with peer rejection and victimization (Salmivalli & Helteenvuori, 2007). Investigators have also found that involvement in reactive forms of aggression is associated with poor academic achievement and internalizing problem symptoms (Fite et al., 2013). It is interesting to note that some youth who participate in bullying behavior, particularly relational bullying, have relatively high levels of social

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competence (Sutton, Smith, & Swettenham, 1999) and perform well in school (Woods & Wolke, 2004). These findings call into question the common perception that all children who engage in bullying behavior are somehow “pathological.” At the same time, strong evidence does suggest that chronic involvement in physical bullying is related to more serious forms of aggression, violence, and delinquency and to poorer peer relations and school performance (Vernberg & Biggs, 2010). Children who bully others also report lower empathy when compared with victims and uninvolved students in several investigations (Jolliffe & Farrington, 2006; Williford et al., 2016). Peer victimization is consistently linked to a range of adverse outcomes including negative emotions, internalizing problems, suicidal ideation, academic difficulties, and substance use (Geoffroy et al., 2016; Kretschmer et al., 2016; Schwartz, Lansford, Dodge, Pettit, & Bates, 2015). The negative effects of peer victimization during childhood and adolescence can persist well into adulthood (McDougall & Vaillancourt, 2015). Unraveling the consequences of aggression, bullying, and peer victimization is complicated. Some investigators have even asserted that aggression, although clearly antisocial in nature, is an adaptive behavior in certain circumstances and settings (Hawley & Vaughn, 2003). From such a viewpoint, aggressive behavior may be seen by perpetrators as a necessary means of survival (Aceves, Hinshaw, Mendoza-Denton, & Page-Gould, 2010). However, even when adaptive in nature, the consequences of any kind of aggression and bullying can affect the well-being of others. Preventing Aggression, Bullying, and Peer Victimization A variety of approaches have been developed to prevent aggression, bullying, and peer victimization. These approaches have evolved considerably in theoretical sophistication in the past several decades. A brief review follows of the evolution of prevention, and evidence pertaining to the effectiveness of preventive interventions that seek to reduce aggression, bullying, and victimization.

The Evolution of Prevention In the late 1980s, experts interested in advancing preventive interventions for a host of child and adolescent problems turned to the public health field for a solution to creating a more comprehensive prevention framework. Public health experts had longrecognized the value of defining the characteristics, conditions, and behaviors that elevated or decreased risk for illness and disease. After all, national campaigns to inform the public of risk and protective factors for various types of cancer had led to considerable advances in early detection and treatment. And identifying risks—and protective factors that reduced risk—for problems like diabetes had helped countless individuals prevent, detect, or manage their diseases. A risk and protection model worked to prevent illness and disease in the public health field. Therefore, the central question for prevention advocates became “Could a public health approach be effective in preventing common child and adolescent problems like substance abuse, delinquency, and school dropout?” By the early to mid-1990s this question was expanded to include a focus on aggression, bullying, and peer victimization (Jenson & Bender, 2014). This question was met with a resounding yes by many prevention educators, practitioners, and researchers. In fact, a framework that accounted for the presence or absence of risk and protective factors for child and adolescent problem behavior seemed the ideal theoretical and implementation fit for many experts in the field (Jenson & Bender, 2014). Following 1990, the public health model of prevention received significant attention by investigators in disciplines including education, psychology, sociology, and social work. Practitioners and policymakers were also quick to identify and apply the model to practice and policy settings (Jenson & Fraser, 2016). By the end of the decade, the public health framework had become the dominant prevention approach in the country (Catalano et al., 2012). As shown in Figure 5.1, a public health model of prevention is a four-step framework used to (a) define targeted behaviors like aggression and bullying as individual and social problems, (b) identify risk and protective factors associated with the selected behaviors, (c) develop and test preventive 83

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Define the Problem

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Assure Widesp re Adoptio ad n

Identify Risk and Protecti ve Factors

Develo p and Te s Preven t tion Strateg ies

Figure 5.1.  A public health approach to prevention. Adapted from The Public Health Approach to Violence Prevention (p. 1), by the Centers for Disease Control and Prevention, 2011, Washington, DC: Author. In the public domain.

interventions that target these behaviors, and (d) assure widespread adoption of effective programs (Centers for Disease Control and Prevention, 2011). Interventions on the basis of a public health approach are generally implemented at three levels (O’Connell et al., 2009). Universal prevention programs are aimed at general child and youth populations without regard to the level of risk shown in such groups. A bullying prevention program delivered to all elementary students in a local school district is an example of a universal program. A second level of intervention is commonly known as selective or tertiary prevention. Selective programs target youth who evidence elevated levels of risk, but no specific problem symptoms, for one or more antisocial behaviors. Selective program strategies aimed at children and youth with elevated risk levels include social and cognitive skills training, academic tutoring, and mentoring. Selective prevention programs may also be implemented at group and community levels, illustrated by approaches such as behavioral parent training and neighborhood watch groups (O’Connell et al., 2009). Finally, indicated intervention strategies target children and adolescents who 84

show evidence of problem symptoms and/or young people who have become involved in antisocial conduct.

Preventing Aggression and Bully Victimization in Schools Schools and educational systems are the center of bullying prevention activities. Schools offer classroom and building-wide universal programs for all students as well as selective and indicated interventions for children and youth with early signs of learning and behavior problems. There are many advantages to concentrating bullying prevention programs in schools. Young people of all ethnic, racial, and income groups spend a majority of their childhood and adolescence attending school and participating in educational programs and activities. Schools are an obvious place to reach a wide cross-section of the nation’s children and youth. School-based programs can be implemented on an ongoing basis during the academic year, and can even be offered over the course of multiple years. In addition, prevention programs in school settings reduce typical program participation obstacles

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like recruitment, coordination, and transportation. Most important, schools provide the foundation and primary social context for children and youth to acquire and practice positive behavior. Evidence linking low rates of aggression, bullying, and other forms of problem behavior in school to academic success is an important motivation for schools to offer bullying prevention programs to all students (Zins, Bloodworth, Weissberg, & Walberg, 2007). Students who learn to stay away from involvement in aggression and bullying and to manage stress, solve problems, and improve selfefficacy are more likely to hold positive attitudes toward school and to perform well academically than students who lack these skills (Duckworth & Seligman, 2005). Conversely, young people who struggle socially or emotionally often have difficulty engaging in learning and staying focused on tasks that are necessary to achieve academic success (Caprara, Barbaranelli, Pastorelli, Bandura, & Zimbardo, 2000). As evidence presented in this chapter demonstrates, effective school-based bullying prevention programs are critical because healthy, well-functioning students exhibit fewer behavioral problems and perform at higher academic levels than other students. Intervention approaches.  The most common type of school-based bullying prevention program is a strategy known as social and emotional learning (SEL). Most SEL programs are implemented in classrooms by teachers, school social workers, or other trained staff using preestablished or manualized curricula. SEL strategies aim to increase social and emotional competence, defined as a student’s ability to manage emotions, set goals, empathize with others, maintain positive relationships, cope effectively with interpersonal conflicts, and make positive decisions (Elias et al., 1997). SEL programs focus on preventing behaviors like substance use, delinquency, violence, or school dropout. In some cases, programs target more than one of these behaviors. Regardless of which behavior problem a program targets, SEL prevention programs share a common goal of promoting social, emotional, or cognitive competencies so youth can be more successful in school and life. Youth Matters is a typical

SEL curriculum that includes structured program modules that aim to increase student awareness of the risks and consequences of becoming involved in problem behaviors like aggression and bullying (Jenson & Dieterich, 2007; Jenson, Dieterich, Brisson, Bender, & Powell, 2010). The program uses instructional strategies to teach students how to respond when confronted with high-risk situations involving aggression, bullying, and victimization. A second common prevention approach uses school-wide strategies aimed at improving school climate or culture (Sugai & Horner, 2006). Schoolwide programs seek to establish positive individual, classroom, and school norms that promote positive behavior and prevent aggression and bullying. Common elements of school-wide strategies include developing messages about the importance of positive behavior, creating school policies that shape decision-making processes to handle antisocial conduct, training staff to use effective classroom management techniques, and monitoring unstructured areas in the school (Ttofi & Farrington, 2011). For example, in some schools, students, teachers, and administrators have created antibullying policies that provide sanctions aimed at reducing bullying and victimization; other schools have increased the presence of school staff on the playground at recess to watch for bullying or other forms of negative behavior directed at peers. The single most distinguishing feature of school-wide programs is characterized by the emphasis placed on promoting a positive classroom and school environment. Unlike SEL programs that focus primarily on equipping individual students with positive skills and values, school-wide approaches seek to create the culture or conditions that are conducive to reductions in aggression and bullying. The Olweus Bullying Prevention Program is an example of an effective school-wide bullying prevention program (Olweus & Limber, 2010). The program uses classroom management techniques and school-wide components to create positive norms aimed at preventing bullying and peer victimization in the school environment. School personnel are also trained to identify and intervene in bullying situations and to help establish positive norms and 85

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rules. At the school level, surveys are first administered to assess the nature and severity of bullying in the school. Findings from surveys are used to inform the work of a coordinating committee that is designed to plan and implement bullying prevention strategies. School personnel are also trained to identify and intervene in bullying situations and to help establish positive norms and rules. Typically, the coordinating committee and school personnel discuss ways to reduce bullying and victimization in “hot spots” where students spend unstructured time in activities like recess and lunch. Comprehensive or integrated programs represent a third school-based prevention approach (Domitrovich et al., 2010). Integrated bullying prevention programs extend elements of effective school-based programs by reaching beyond school boundaries to include parents, siblings, or community members in intervention activities. One approach used in integrated programs is to involve parents in simultaneous training efforts. In such programs, parents learn new skills aimed at improving their ability to supervise their children, set limits about behavior, and support their children’s education (Hawkins, Kosterman, Catalano, Hill, & Abbott, 2008). Some integrated prevention programs combine classroom instruction with strategies aimed at educating school personnel about aggression, bullying, and victimization. An example of such an integrated universal bullying prevention strategy is the Steps to Respect program (Brown, Low, Smith, & Haggerty, 2011). In this program, classroom lessons about aggression and bullying are combined with intensive staff training and school-wide strategies to change individual behavior and norms about bullying and victimization. Effectiveness of preventive interventions.  There has been a steady increase in the number of studies examining the effects of aggression and bullying prevention programs. The school-based prevention literature includes a range of studies that vary by methodological rigor, findings, and conclusions. The diversity of studies creates challenges when summarizing current levels of evidence pertaining to bullying prevention programs. In the past decade, several investigators have conducted meta-analytic and other reviews to search 86

the aggression and bullying prevention literature for evidence of effectiveness (Evans, Fraser, & Cotter, 2014; Farrington & Ttofi, 2009; Ferguson, Miguel, Kilburn, & Sanchez, 2007; Polanin, Espelage, & Pigott, 2012; Ttofi & Farrington, 2011; Vreeman & Carroll, 2007). These reviews have been helpful in identifying both the types and the characteristics of effective programs. The reviews have produced a range of findings, with most reporting modest positive effects with regard to preventing bullying and victimization. Systematic reviews have generally found more positive effects for preventive interventions and bullying prevention studies conducted outside the U.S. (Evans et al., 2014; Farrington & Ttofi, 2009). Ttofi and Farrington (2011) and Farrington and Ttofi (2009) conducted the most comprehensive reviews of bullying prevention programs to date. Their reviews included 44 evaluations of bullying prevention interventions that used randomized or rigorous quasi-experimental designs. Studies included in the review were published between 1983 and 2009. They found, on average, a 20% to 23% decrease in perpetration of bullying and a 17% to 20% decrease in victimization across the 44 studies in their sample. The program strategies of parent training, consistent playground supervision, school conferences, videos, information for parents, and firm classroom disciplinary practices were related to a decline in bullying (Ttofi & Farrington, 2011). Structured and more intensive programs were more effective than programs of shorter duration. One program element, peer-oriented strategies, was significantly related to an increase in victimization. Whole-school strategies on the basis of the Olweus Bullying Prevention Program demonstrated generally positive effects, though results for the Olweus program were most favorable for programs implemented in Scandinavia and Europe. Evans and colleagues (2014) updated Farrington and Ttofi’s (2009) original review by examining 32 bullying prevention studies published between 2009 and 2013. The authors used criteria developed by Farrington and Ttofi (2009) and Ttofi and Farrington (2011) to select studies to be included in their review. Twenty-two studies in the review examined bully perpetration; 50% (n = 11) of these

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studies reported significant reductions in bullying. Twenty-seven studies examined peer victimization; 67% (n = 18) of these investigations reported significant positive effects (Evans et al., 2014). These findings, somewhat less positive than those reported by Ttofi and Farrington (2011), reflect a rather mixed picture. On the one hand, the review supports earlier findings that bullying prevention programs can be effective in preventing bullying and peer victimization. However, many programs included in the Evans et al. (2014) review showed no positive effects on bullying perpetration or victimization. Evans and colleagues (2014) suggested that deficiencies and inconsistencies in measuring bullying and poor specificity between bullying and general aggression make it difficult to interpret findings from evaluative studies of bullying prevention programs. In another review, Polanin and colleagues (2012) examined the effect of 12 school-based programs on increasing bystander involvement in bullying situations. They found that, overall, programs were successful in increasing bystander involvement, particularly for high school students. These findings point to the promise of including bystander behavior in bullying prevention strategies. Summary.  Evidence pertaining to the question of whether preventive interventions are effective in preventing bullying and victimization indicates that a number of programs have produced positive and significant impacts on preventing, delaying, and reducing bullying behavior and victimization. These positive effects have generally been small to medium in statistical terms. There are several possible explanations for the relatively modest effects of bullying prevention programs. First, prevention programs tend to yield greater impacts when provided and tested in small studies with tightly controlled implementation processes. Many prevention programs, however, are implemented across a number of diverse schools and neighborhoods. The large scale of school-based prevention may in turn lead to compromised implementation characterized by fewer and inconsistent leaders, less intensive programming, inadequate time to rehearse skills, and limited investment from teachers. Second, studies

vary greatly with regard to how bullying and victimization are operationalized and measured (Evans et al., 2014). The aggregation of effects across a number of studies may actually minimize or mask evidence of effectiveness for some bullying prevention programs. The question of the magnitude of an effect may be mitigated by the fact that bullying prevention programs can be implemented at a relatively low cost in many school districts. Even small to modest effects are likely to be very important in the broader context of preventing the onset of problems that may lead to costly treatment alternatives at a later age. In addition, large-scale bullying prevention programs have the capacity to affect other student outcomes. Therefore, programs targeting bullying and victimization may also have positive and longlasting effects on academic performance, delinquency, substance use, and other problem behaviors. In short, even bullying prevention programs that produce relatively modest effects may make important contributions to preventing or reducing overall problem behaviors in young people.

Program and Research Challenges Progress made in preventing aggression, bullying, and victimization has led to new program and research challenges. Selected intervention and research challenges confronting the field are reviewed next.

Creating a Positive School Climate Teaching principals, teachers, and other school personnel to recognize and appropriately handle acts of aggression, bullying, and peer victimization is critical to developing a positive school climate. A well-structured training protocol for school staff is consistent with theoretical and empirical evidence from the field of organizational science, which suggests that a highly trained workforce is essential to successful intervention efforts in school settings (Williford, 2015). In addition, evidence indicates that noncertified school staff such as paraprofessionals, aides, custodial, and lunchroom staff are seldom included in bullying prevention training 87

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efforts (Bradshaw, Sawyer, & O’Brennan, 2007). This oversight is serious because school support personnel are frequently present in locations (e.g., lunchroom, playground) where acts of bullying and peer victimization are most common. Including all school support staff is important to fully implementing and sustaining bullying prevention programs.

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Fidelity and Adaptation High levels of implementation fidelity result in programs that mirror the conditions under which an original intervention was implemented and evaluated. Unfortunately, preventive interventions often lose elements of an original intervention when they are implemented (Fixsen, Naoom, Blase, Friedman, & Wallace, 2005). For example, a local school or agency may adapt or tailor a bully prevention program to better meet the needs of students in their programs. In other cases, an organization may modify an intervention to satisfy logistical constraints including lack of time or poor financial resources (Ringwalt et al., 2003). Unfortunately, interventions are often less effective when they are altered meaningfully from their original form; results from meta-analyses of school-based aggression prevention programs reinforce the importance of implementation fidelity in reducing problem behavior among children and youth (Wilson, Lipsey, & Soydan, 2003). Prevention experts suggest that obstacles to implementation are often attributed to a school or agency’s capacity to fully implement an evidencebased program (Wandersman et al., 2008). Limited capacity may be due to a lack of training or to insufficient understanding of a new curriculum or program. In other cases, teachers, administrators, or community members may simply disagree with the need for a new intervention or program. Investigators have also noted that the implementation of bullying prevention curricula may be affected by community-level factors (e.g., awareness of bullying), school-level factors (e.g., classroom climate), administrative variables (e.g., leadership), and by teachers’ levels of confidence in assisting students (Kallestad & Olweus, 2003). To ensure implementation fidelity, program planners and 88

investigators must consider factors at all levels of influence. Adaptation in the context of preventive interventions refers to a process of changing program content to better serve one or more sub-groups of children and youth (Farrell, Henry, & Bettencourt, 2013). For example, an effective bullying prevention program that was developed primarily for White students may be modified to include cultural content that is directly relevant to Latino, Asian, or Black students. The adaptation of prevention content most often targets racial, ethnic, and cultural factors. However, in some cases, program adaptation may be based on characteristics like gender, sexual orientation, or socioeconomic status. The implementation of bullying prevention programs also involves adapting programs found to be effective in Europe and other parts of the world. Several effective programs, including the Olweus Bullying Prevention Program and a Finnish program called KiVa, have produced less successful results when tested in the United States (Bradshaw, 2015; Evans et al., 2014). Hawley and Williford (2015) suggest that an important step in strengthening the translation of bullying prevention programs tested in other countries requires establishing greater theoretical clarity of specific intervention elements. This and other strategies should be developed and tested to systematically improve the adaptation and precision of bullying prevention programs created in other countries.

Measurement and Design Inconsistent definitions of aggression and bullying in studies assessing the effects of prevention programs have led to uncertainty in the interpretation of outcomes (Bradshaw, 2015; Evans et al., 2014). In recent years, definitions of bullying have been refined to include the presence of intentional aggressive behavior, repetition of the behavior, and a power imbalance between a perpetrator and a victim (Gladden, Vivolo-Kantor, Hamburger, & Lumpkin, 2014; Volk et al., 2014). These three elements should be uniformly used by investigators to define and measure bullying and victimization. Finally, prevention scientists have noted that self-report measures of bullying and victimization may be more

Prevention of Aggression and Bullying in Children and Adolescents

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accurate than peer nomination, teacher, or parent reports (Bradshaw, 2015). There are relatively few randomized controlled trials (RCTs) of bullying prevention programs. Furthermore, many existing RCTs fail to meet standards of evidence established by groups such as the Society for Prevention Research (Flay et al., 2005; Ryan & Smith, 2009). RCTs of preventive interventions are needed to create a stronger evidence base for bulling prevention programs. Effectiveness trials to test the widespread implementation of effective programs are also lacking. Conclusion Incidents of aggression and bullying occur far too frequently among young people in the United States. In many cases, the social and emotional consequences for victims of aggression and bullying are serious and long-term in nature. Findings from a limited number of rigorous studies indicate that aggression and bullying can be prevented and reduced. Sustained efforts are needed to resolve program implementation issues in schools and other settings and to refine methodological elements in studies assessing the effects of bullying prevention programs.

References Aceves, M. J., Hinshaw, S. P., Mendoza-Denton, R., & Page-Gould, E. (2010). Seek help from teachers or fight back? Student perceptions of teachers’ actions during conflicts and responses to peer victimization. Journal of Youth and Adolescence, 39, 658–669. http:// dx.doi.org/10.1007/s10964-009-9441-9 Barton, W. H. (2016). Juvenile justice policies and programs. In J. M. Jenson & M. W. Fraser (Eds.), Social policy for children and families: A risk and resilience perspective (3rd ed., pp. 231–264). Thousand Oaks, CA: Sage. Benedict, F. T., Vivier, P. M., & Gjelsvik, A. (2015). Mental health and bullying in the United States among children aged 6 to 17 years. Journal of Interpersonal Violence, 30, 782–795. http://dx.doi.org/ 10.1177/0886260514536279 Bollmer, J. M., Milich, R., Harris, M. J., & Maras, M. A. (2005). A friend in need: The role of friendship quality as a protective factor in peer victimization and bullying. Journal of Interpersonal Violence, 20, 701–712. http:// dx.doi.org/10.1177/0886260504272897

Bowes, L., Arseneault, L., Maughan, B., Taylor, A., Caspi, A., & Moffitt, T. E. (2009). School, neighborhood, and family factors are associated with children’s bullying involvement: A nationally representative longitudinal study. Journal of the American Academy of Child and Adolescent Psychiatry, 48, 545–553. http://dx.doi.org/10.1097/CHI.0b013e31819cb017 Bradshaw, C. P. (2015). Translating research to practice in bullying prevention. American Psychologist, 70, 322–332. http://dx.doi.org/10.1037/a0039114 Bradshaw, C. P., Sawyer, A. L., & O’Brennan, L. M. (2007). Bullying and peer victimization at school: Perceptual differences between students and school staff. School Psychology Review, 36, 361–382. Brock, S. E., Nickerson, A. B., O’Malley, M. D., & Chang, Y. (2006). Understanding children victimized by their peers. Journal of School Violence, 5, 3–18. http:// dx.doi.org/10.1300/J202v05n03_02 Brown, E. C., Low, S., Smith, B. H., & Haggerty, K. P. (2011). Outcomes from a school-randomized controlled trial of Steps to Respect: A bullying prevention program. School Psychology Review, 40, 423–443. Caprara, G. V., Barbaranelli, C., Pastorelli, C., Bandura, A., & Zimbardo, P. G. (2000). Prosocial foundations of children’s academic achievement. Psychological Science, 11, 302–306. http://dx.doi.org/10.1111/1467-9280.00260 Card, N. A., & Hodges, E. V. E. (2008). Peer victimization among schoolchildren: Correlations, causes, consequences, and considerations in assessment and intervention. School Psychology Quarterly, 23, 451–461. http://dx.doi.org/10.1037/a0012769 Cartland, J., Ruch-Ross, H. S., & Henry, D. B. (2003). Feeling at home in one’s school: A first look at a new measure. Adolescence, 38, 305–319. Cassidy, W., Faucher, C., & Jackson, M. (2013). Cyberbullying among youth: A comprehensive review of current international research and its implications and application to policy and practice. School Psychology International, 34, 575–612. http:// dx.doi.org/10.1177/0143034313479697 Catalano, R. F., Fagan, A. A., Gavin, L. E., Greenberg, M. T., Irwin, C. E., Jr., Ross, D. A., & Shek, D. T. (2012). Worldwide application of prevention science in adolescent health. Lancet, 379, 1653–1664. http:// dx.doi.org/10.1016/S0140-6736(12)60238-4 Centers for Disease Control and Prevention. (2011). The public health approach to violence prevention. Retrieved from https://www.cdc.gov/violenceprevention/pdf/ PH_App_Violence-a.pdf Cillessen, A. H. N., & Mayeux, L. (2004). From censure to reinforcement: Developmental changes in the association between aggression and social status. Child Development, 75, 147–163. http://dx.doi.org/ 10.1111/j.1467-8624.2004.00660.x 89

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Cohen, J. S., & Kendall, P. C. (2015). Peer victimization among children and adolescents with anxiety disorders. Child Psychiatry and Human Development, 46, 393–405. http://dx.doi.org/10.1007/ s10578-014-0479-x

Evans, C. B. R., Fraser, M. W., & Cotter, K. L. (2014). The effectiveness of school-based bullying prevention programs: A systematic review. Aggression and Violent Behavior, 19, 532–544. http:// dx.doi.org/10.1016/j.avb.2014.07.004

Collier, K. L., van Beusekom, G., Bos, H. M. W., & Sandfort, T. G. M. (2013). Sexual orientation and gender identity/expression related peer victimization in adolescence: A systematic review of associated psychosocial and health outcomes. Journal of Sex Research, 50, 299–317. http://dx.doi.org/10.1080/ 00224499.2012.750639

Farrell, A. D., Henry, D. B., & Bettencourt, A. (2013). Methodological challenges examining subgroup differences: Examples from universal school-based youth violence prevention trials. Prevention Science, 14, 121–133. http://dx.doi.org/10.1007/ s11121-011-0200-2

Cook, C. R., Williams, K. R., Guerra, N. G., Kim, T. E., & Sadek, S. (2010). Predictors of bullying and victimization in childhood and adolescence: A metaanalytic investigation. School Psychology Quarterly, 25, 65–83. http://dx.doi.org/10.1037/a0020149 Currie, C., Zanotti, C., Morgan, A., Currie, D., de Looze, M., Roberts, C., . . . Barnekow, V. (2012). Social determinants of health and well-being among young people. Copenhagen, Denmark: World Health Organization Regional Office for Europe. Domitrovich, C. E., Bradshaw, C. P., Greenberg, M. T., Embry, D., Poduska, J. M., & Ialongo, N. S. (2010). Integrated models of school-based prevention: Logic and theory. Psychology in the Schools, 47, 71–88. Duckworth, A. L., & Seligman, M. E. P. (2005). Selfdiscipline outdoes IQ in predicting academic performance of adolescents. Psychological Science, 16, 939–944. http://dx.doi.org/10.1111/ j.1467-9280.2005.01641.x Eaton, D. K., Kann, L., Kinchen, S., Shanklin, S., Flint, K. H., Hawkins, J., . . . Wechsler, H. (2012). Youth risk behavior surveillance—United States, 2011. Morbidity and Mortality Weekly Report, 61(SS-4), 1–162.

Farrington, D. P., & Ttofi, M. M. (2009). How to reduce school bullying. Victims and Offenders, 4, 321–326. http://dx.doi.org/10.1080/15564880903227255 Ferguson, C. J., Miguel, C., Kilburn, J. C., & Sanchez, P. (2007). The effectiveness of school-based antibullying programs: A meta-analytic review. Criminal Justice Review, 32, 401–414. http:// dx.doi.org/10.1177/0734016807311712 Finkelhor, D., Turner, H. A., Shattuck, A., & Hamby, S. L. (2015). Violence, crime, and abuse exposure in a national sample of children and youth: An update. U.S. Department of Justice, Office of Justice Programs, Office of Juvenile Justice and Delinquency Prevention. Retrieved from http://www.ojjdp.gov/ pubs/248547.pdf Fite, P. J., Williford, A., Cooley, J. L., DePaolis, K., Rubens, S. L., & Vernberg, E. M. (2013). Patterns of victimization locations in elementary school children: Effects of grade level and gender. Child and Youth Care Forum, 42, 585–597. http://dx.doi.org/ 10.1007/s10566-013-9219-9 Fixsen, D., Naoom, S. F., Blase, D. A., Friedman, R. M., & Wallace, F. (2005). Implementation research: A synthesis of the literature (FMHI Publication No. 231). Tampa: University of South Florida.

Ehrenreich, S. E., Beron, K. J., Brinkley, D. Y., & Underwood, M. K. (2014). Family predictors of continuity and change in social and physical aggression from ages 9 to 18. Aggressive Behavior, 40, 421–439. http://dx.doi.org/10.1002/ab.21535

Flay, B. R., Biglan, A., Boruch, R. F., Castro, F. G., Gottfredson, D., Kellam, S., . . . Ji, P. (2005). Standards of evidence: Criteria for efficacy, effectiveness and dissemination. Prevention Science, 6, 151–175. http:// dx.doi.org/10.1007/s11121-005-5553-y

Elias, M. J., Zins, J. E., Weissberg, R. P., Frey, K. S., Greenberg, M. T., & Haynes, N. M. (1997). Promoting social and emotional learning: Guidelines for educators. Alexandria, VA: Association for Supervision and Curriculum Development.

Fox, C. L., & Boulton, M. J. (2006). Friendship as a moderator of the relationship between social skills problems and peer victimization. Aggressive Behavior, 32, 110–121. http://dx.doi.org/10.1002/ ab.20114

Espelage, D. L., Bosworth, K., & Simon, T. R. (2000). Examining the social context of bullying behaviors in early adolescence. Journal of Counseling and Development, 78, 326–333. http://dx.doi.org/10.1002/ j.1556-6676.2000.tb01914.x

Frick, P. J., & Morris, A. S. (2004). Temperament and developmental pathways to conduct problems. Journal of Clinical Child and Adolescent Psychology, 33, 54–68. http://dx.doi.org/10.1207/S15374424JCCP3301_6

Espelage, D. L., & Swearer, S. M. (2003). Research on school bullying and victimization: What have we learned and where do we go from here? School Psychology Review, 32, 365–383. 90

Gendreau, P. L., & Archer, J. (2005). Subtypes of aggression in humans and animals. In R. E. Tremblay, W. W. Hartup, & J. Archer (Eds.), Developmental origins of aggression (pp. 25–46). New York, NY: Guilford Press.

Prevention of Aggression and Bullying in Children and Adolescents

Geoffroy, M. C., Boivin, M., Arseneault, L., Turecki, G., Vitaro, F., Brendgen, M., . . . Côté, S. M. (2016). Associations between peer victimization and suicidal ideation and suicide attempt during adolescence: Results from a prospective population-based birth cohort. Journal of the American Academy of Child and Adolescent Psychiatry, 55, 99–105. http://dx.doi.org/ 10.1016/j.jaac.2015.11.010

Copyright American Psychological Association. Not for further distribution.

Gladden, R. M., Vivolo-Kantor, A. M., Hamburger, M. E., & Lumpkin, C. D. (2014). Bullying surveillance among youths: Uniform definitions for public health and recommended data elements, version 1.0. Atlanta, GA: National Center for Injury Prevention and Control, Centers for Disease Control and Prevention & U.S. Department of Education. Hawkins, J. D., Catalano, R. F., & Miller, J. Y. (1992). Risk and protective factors for alcohol and other drug problems in adolescence and early adulthood: Implications for substance abuse prevention. Psychological Bulletin, 112, 64–105. http://dx.doi.org/ 10.1037/0033-2909.112.1.64 Hawkins, J. D., Kosterman, R., Catalano, R. F., Hill, K. G., & Abbott, R. D. (2008). Effects of social development intervention in childhood 15 years later. Archives of Pediatrics and Adolescent Medicine, 162, 1133–1141. http://dx.doi.org/10.1001/archpedi.162.12.1133 Hawley, P. H., & Vaughn, B. E. (2003). Aggression and adaptative functioning: The bright side to bad behavior. Introduction to special volume. MerrillPalmer Quarterly, 49, 239–244. http://dx.doi.org/ 10.1353/mpq.2003.0012 Hawley, P. H., & Williford, A. (2015). Articulating the theory of bullying intervention programs: Views from social psychology, social work, and organizational science. Journal of Applied Developmental Psychology, 37, 3–15. http://dx.doi.org/ 10.1016/j.appdev.2014.11.006 Herrenkohl, T. I., Aisenberg, E., Williams, J. H., & Jenson, J. M. (2011). Violence in context: Current evidence on risk, protection, and prevention. New York, NY: Oxford University Press. Herrenkohl, T. I., Catalano, R. F., Hemphill, S. A., & Toumbourou, J. W. (2009). Longitudinal examination of physical and relational aggression as precursors to later problem behaviors in adolescents. Violence and Victims, 24, 3–19. http://dx.doi.org/10.1891/ 0886-6708.24.1.3 Hinduja, S., & Patchin, J. W. (2008). Cyberbullying: An exploratory analysis of factors related to offending and victimization. Deviant Behavior, 29, 129–156. http://dx.doi.org/10.1080/01639620701457816 Hymel, S., & Swearer, S. M. (2015). Four decades of research on school bullying: An introduction. American Psychologist, 70, 293–299. http:// dx.doi.org/10.1037/a0038928

Iannotti, R. J. (2013). Health behavior in school-aged children (HBSC), 2009–2010. Ann Arbor, MI: Interuniversity Consortium for Political and Social Research. Jenson, J. M., & Bender, K. A. (2014). Preventing child and adolescent problem behavior: Evidence-based strategies in schools, families, and communities. http://dx.doi.org/ 10.1093/acprof:oso/9780199766598.001.0001 Jenson, J. M., & Dieterich, W. A. (2007). Effects of a skills-based prevention program on bullying and bully victimization among elementary school children. Prevention Science, 8, 285–296. http:// dx.doi.org/10.1007/s11121-007-0076-3 Jenson, J. M., Dieterich, W. A., Brisson, D., Bender, K. A., & Powell, A. (2010). Preventing childhood bullying: Findings and lessons from the Denver public schools trial. Research on Social Work Practice, 20, 509–517. http://dx.doi.org/10.1177/1049731509359186 Jenson, J. M., & Fraser, M. W. (2016). Social policy for children and families: A risk and resilience perspective (3rd ed.). Thousand Oaks, CA: Sage. Jolliffe, D., & Farrington, D. P. (2006). Examining the relationship between low empathy and bullying. Aggressive Behavior, 32, 540–550. http://dx.doi.org/ 10.1002/ab.20154 Juvonen, J., Nishina, A., & Graham, S. (2006). Ethnic diversity and perceptions of safety in urban middle schools. Psychological Science, 17, 393–400. http:// dx.doi.org/10.1111/j.1467-9280.2006.01718.x Kallestad, J. H., & Olweus, D. (2003). Predicting teachers’ and schools’ implementation of the Olweus Bullying Prevention Program: A multilevel study. Prevention and Treatment, 6. Kosciw, G. E. A. Diaz E. M., & Bartkiewicz, M. J. (2010). The 2009 National School Climate Survey: The experiences of lesbian, gay, bisexual and transgender youth in our nation’s schools. Retrieved from http:// www.glsen.org Kretschmer, T., Sentse, M., Meeus, W., Verhulst, F. C., Veenstra, R., & Oldehinkel, A. J. (2016). Configurations of adolescents’ peer experiences: Associations with parent–child relationship quality and parental problem behavior. Journal of Research on Adolescence. Advance online publication. Little, T. D., Brauner, J., Jones, S. M., Nock, M. K., & Hawley, P. H. (2003). Rethinking aggression: A typological examination of the functions of aggression. Merrill-Palmer Quarterly, 49, 343–369. http://dx.doi.org/10.1353/mpq.2003.0014 McDougall, P., & Vaillancourt, T. (2015). Long-term adult outcomes of peer victimization in childhood and adolescence: Pathways to adjustment and maladjustment. American Psychologist, 70, 300–310. http://dx.doi.org/10.1037/a0039174

91

Jenson and Williford

Mercer, S. H., McMillen, J. S., & DeRosier, M. E. (2009). Predicting change in children’s aggression and victimization using classroom-level descriptive norms of aggression and pro-social behavior. Journal of School Psychology, 47, 267–289. http://dx.doi.org/ 10.1016/j.jsp.2009.04.001

Copyright American Psychological Association. Not for further distribution.

Muris, P., & Ollendick, T. H. (2005). The role of temperament in the etiology of child psychopathology. Clinical Child and Family Psychology Review, 8, 271–289. http://dx.doi.org/10.1007/s10567-005-8809-y Nansel, T. R., Overpeck, M., Pilla, R. S., Ruan, W. J., Simons-Morton, B., & Scheidt, P. (2001). Bullying behaviors among U.S. youth: Prevalence and association with psychosocial adjustment. JAMA, 285, 2094–2100. http://dx.doi.org/10.1001/ jama.285.16.2094 National Academies of Sciences, Engineering, and Medicine. (2016). Preventing bullying through science, policy and practice. Washington, DC: National Academies Press. O’Connell, M. E., Boat, T., & Warner, K. E. (2009). Preventing mental, emotional, and behavioral disorders among young people: Progress and possibilities. Washington, DC: National Academies Press. Olweus, D. (1993). Bullying at school. Oxford, England: Blackwell. Olweus, D., & Limber, S. P. (2010). Bullying in school: Evaluation and dissemination of the Olweus Bullying Prevention Program. American Journal of Orthopsychiatry, 80, 124–134. http://dx.doi.org/ 10.1111/j.1939-0025.2010.01015.x Perry, D. G., Hodges, E. V. E., & Egan, S. K. (2001). Determinants of chronic victimization by peers: A review and new model of family influence. In J. Juvonen & S. Graham (Eds.), Peer harassment in school: The plight of the vulnerable and victimized (pp. 73–104). New York, NY: Guilford Press. Polanin, J. R., Espelage, D. L., & Pigott, T. D. (2012). A meta-analysis of school-based bullying prevention programs’ effects on bystander intervention behavior. School Psychology Review, 41, 47–65. Putallaz, M., Kupersmidt, J. B., Coie, J. D., McKnight, K., & Grimes, C. L. (2004). A behavioral analysis of girls’ aggression and victimization. In M. Putallaz & K. L. Bierman (Eds.), Aggression, antisocial behavior, and violence among girls: A developmental perspective (pp. 110–134). New York, NY: Guilford Press.

(2003). Factors associated with fidelity to substance use prevention curriculum guides in the nation’s middle schools. Health Education and Behavior, 30, 375–391. http://dx.doi.org/10.1177/ 1090198103030003010 Robers, S., Kemp, J., & Truman, J. (2013). Indicators of school crime and safety: 2012. Washington, DC: National Center for Education Statistics. Rodkin, P. C., Espelage, D. L., & Hanish, L. D. (2015). A relational framework for understanding bullying: Developmental antecedents and outcomes. American Psychologist, 70, 311–321. http://dx.doi.org/10.1037/ a0038658 Rose, A. J., Swenson, L. P., & Waller, E. M. (2004). Overt and relational aggression and perceived popularity: Developmental differences in concurrent and prospective relations. Developmental Psychology, 40, 378–387. http://dx.doi.org/10.1037/ 0012-1649.40.3.378 Ryan, W., & Smith, J. D. (2009). Antibullying programs in schools: How effective are evaluation practices? Prevention Science, 10, 248–259. http://dx.doi.org/ 10.1007/s11121-009-0128-y Salmivalli, C., & Helteenvuori, T. (2007). Reactive, but not proactive aggression predicts victimization among boys. Aggressive Behavior, 33, 198–206. http:// dx.doi.org/10.1002/ab.20210 Schwartz, D., Lansford, J. E., Dodge, K. A., Pettit, G. S., & Bates, J. E. (2015). Peer victimization during middle childhood as a lead indicator of internalizing problems and diagnostic outcomes in late adolescence. Journal of Clinical Child and Adolescent Psychology, 44, 393–404. http://dx.doi.org/10.1080/15374416.2014.881293 Shields, A., & Cicchetti, D. (2001). Parental maltreatment and emotion dysregulation as risk factors for bullying and victimization in middle childhood. Journal of Clinical Child Psychology, 30, 349–363. http:// dx.doi.org/10.1207/S15374424JCCP3003_7 Smith, P. K. (2012). Cyberbullying and cyber aggression. In S. R. Jimerson, A. B. Nickerson, M. J. Mayer, & M. J. Furlong (Eds.), Handbook of school violence and school safety (pp. 93–103). New York, NY: Routledge. Stadler, C., Feifel, J., Rohrmann, S., Vermeiren, R., & Poustka, F. (2010). Peer-victimization and mental health problems in adolescents: Are parental and school support protective? Child Psychiatry and Human Development, 41, 371–386. http://dx.doi.org/ 10.1007/s10578-010-0174-5

Reijntjes, A., Kamphuis, J. H., Prinzie, P., & Telch, M. J. (2010). Peer victimization and internalizing problems in children: A meta-analysis of longitudinal studies. Child Abuse and Neglect, 34, 244–252. http:// dx.doi.org/10.1016/j.chiabu.2009.07.009

Sugai, G., & Horner, R. R. (2006). A promising approach for expanding and sustaining school-wide positive behavior support. School Psychology Review, 35, 245–259.

Ringwalt, C. L., Ennett, S., Johnson, R., Rohrbach, L. A., Simons-Rudolph, A., Vincus, A., & Thorne, J.

Sutton, J., Smith, P. K., & Swettenham, J. (1999). Bullying and “theory of mind”: A critique of the

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“social skills deficit” view of anti-social behaviour. Social Development, 8, 117–127. http://dx.doi.org/ 10.1111/1467-9507.00083 Ttofi, M. M., & Farrington, D. P. (2011). Effectiveness of school-based programs to reduce bullying: A systematic and meta-analytic review. Journal of Experimental Criminology, 7, 27–56. http://dx.doi.org/ 10.1007/s11292-010-9109-1

Copyright American Psychological Association. Not for further distribution.

U.S. Department of Education. (2015). Student reports of bullying and cyber-bullying: Results from the 2013 School Crime Supplement to the National Crime Victimization Survey. Washington, DC: Author. Vernberg, E. M., & Biggs, B. K. (2010). Preventing and treating bullying and victimization. New York, NY: Oxford University Press.

the gap between prevention research and practice: The interactive systems framework for dissemination and implementation. American Journal of Community Psychology, 41, 171–181. http://dx.doi.org/10.1007/ s10464-008-9174-z Williford, A. (2015). Intervening in bullying: Differences across elementary school staff members in attitudes, perceptions, and self-efficacy beliefs. Children and Schools, 37, 175–184. http://dx.doi.org/10.1093/cs/ cdv017 Williford, A., Boulton, A. J., Forrest-Bank, S. S., Bender, K. A., Dieterich, W. A., & Jenson, J. M. (2016). The effect of bullying and victimization on cognitive empathy development during the transition to middle school. Child and Youth Care Forum, 45, 525–541. http:// dx.doi.org/10.1007/s10566-015-9343-9

Vitaro, F., & Brendgen, M. (2012). Subtypes of aggressive behaviors: Etiologies, development, and consequences. In T. L. Bliesener, A. Beelman, & M. Stemmler (Eds.), Antisocial behavior and crime: Contributions of developmental and evaluation research to prevention and intervention (pp. 17–38). Cambridge, MA: Hogrefe.

Wilson, S. J., Lipsey, M. W., & Soydan, H. (2003). Are mainstream programs for juvenile delinquency less effective with minority youth than majority youth? A meta-analysis of outcome research. Research on Social Work Practice, 13, 3–26. http://dx.doi.org/ 10.1177/1049731502238754

Volk, A. A., Dane, A. V., & Marini, Z. A. (2014). What is bullying? A theoretical redefinition. Developmental Review, 34, 327–343. http://dx.doi.org/10.1016/ j.dr.2014.09.001

Woods, S., & Wolke, D. (2004). Direct and relational bullying among primary school children and academic achievement. Journal of School Psychology, 42, 135–155. http://dx.doi.org/10.1016/ j.jsp.2003.12.002

Vreeman, R. C., & Carroll, A. E. (2007). A systematic review of school-based interventions to prevent bullying. Archives of Pediatrics and Adolescent Medicine, 161, 78–88. http://dx.doi.org/10.1001/ archpedi.161.1.78 Wandersman, A., Duffy, J., Flaspohler, P., Noonan, R., Lubell, K., Stillman, L., . . . Saul, J. (2008). Bridging

Zins, J. E., Bloodworth, M. R., Weissberg, R. P., & Walberg, H. J. (2007). The scientific base linking social and emotional learning to school success. Journal of Educational and Psychological Consultation, 17, 191–210. http://dx.doi.org/10.1080/ 10474410701413145

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Chapter 6

Recognizing FrontalSubcortical Circuit Dimensions in Child and Adolescent Neuropsychopathology Copyright American Psychological Association. Not for further distribution.

James B. Hale, Linda A. Reddy, and Adam S. Weissman

One of the first things taught to a pediatric or child neuropsychologist in training is that children are not miniature adults—they have neurodevelopmental differences in brain structure and function that influence their thoughts and behavior in different ways during childhood and adolescence (SemrudClikeman & Ellison, 2009). Clinicians working with children and adolescents must recognize that the brain is much more malleable or “plastic” then we ever anticipated, especially during this developmental period. The brain shapes the world by governing an individual’s actions, which in turn affects neuronal activity and connections, modifying brain structure and function in the individual. The more the neuronal activity, the greater the connections, as suggested by Hebb’s (1949) conclusion “neurons that fire together wire together.” Although the study of neurodevelopment, plasticity, and psychopathology is just beginning (Ozonoff, 2015), understanding Hebb’s insight into neurodevelopment is important for considering how psychopathology occurs in children and adolescents. The most dramatic neurodevelopment occurs during critical periods, with the most important changes occurring early in life, yet brain plasticity allows the cortex to continue to change throughout the lifespan (Sweatt, 2016), especially in response to injury or disease. The neurodevelopmental process begins with the brain having too much gray matter that must be chiseled away (called pruning),

and too little white matter, which must be built over time (Giedd et al., 2009), especially in the brain areas that do the most complex forms of sensory integration (comprehension) or motor action (expression; Kolb, Whishaw, & Teskey, 2016). Not only is the balance between gray matter pruning and white matter growth important for healthy development, but an imbalance can lead to disability (e.g., autism; Redcay & Courchesne, 2005). Autism is of particular concern to neuropsychologists given the conflicting evidence and limited professional agreement as to whether it represents a spectrum or different disorders with similar behavioral outcomes but with different etiologies (Hain & Hale, 2010). When it comes to brain structure and function, more cortex is not necessarily better, and cortical structure and volume varies as a function of neurodevelopment. Over time, as gray matter is pruned away, white matter increasingly forms the connections (tracts) among clusters of neurons (nuclei). This allows for quick, efficient responding with little effort. In fact, an efficient, well-developed brain uses less cortex to process information, relegating much of its effort to retrieving automatized scripts or programs to respond (Koziol, Budding, & Hale, 2013). Individuals with disabilities typically show deficits in some brain/structures or function, but they also show excessive functioning in others. It is not uncommon for children with disabilities to use more cortex in solving complex problems than their

The authors thank Qiaoling Marilyn Ho and Heather Ziemba for their research efforts in the development of this chapter. http://dx.doi.org/10.1037/0000065-006 APA Handbook of Psychopathology: Vol. 2. Child and Adolescent Psychopathology, J. N. Butcher (Editor-in-Chief) APA Handbook Psychopathology: Child andAssociation. Adolescent Psychopathology, edited by Copyright ©of2018 by the American Psychological All rights reserved. J. N. Butcher and P. C. Kendall Copyright © 2018 American Psychological Association. All rights reserved.

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peers (Hale, Wilcox, & Reddy, 2016), which often leads to poor processing speed and mental fatigue, as is the case with depression (Calhoun & Mayes, 2005) and traumatic brain injury (Mathias & Wheaton, 2007). Individuals with processing speed problems not only act slower, they think slower, which would be particularly relevant for understanding and engaging in social discourse with others. Neurodevelopment is a process that allows the brain to change and adapt to environmental circumstances for efficient responding for the individual. An individual’s genetic makeup or genotype is not the sole determinant of his or her various personality characteristics or pathologies, but it is the result of that genetic code interacting with internal and environmental influences that trigger changes over time (Dudley et al., 2011). This is the focus of the epigenesis, with scientists exploring how variations in an individual’s phenotypic presentation are explained by genetic precursors and environmental circumstance (e.g., Casey, Oliveri, & Insel, 2014; Russo, Martienssen, & Riggs, 1996). During neurodevelopment, the brain adjusts to these epigenetic phenomena accordingly, in what is essentially a Darwinian natural selection process that optimizes brain functioning for each individual within the environmental context unique to that individual (Hale, Chen, et al., 2016). Brain plasticity is a double-edged sword. Although it gives hope to clinicians and affected individuals that plasticity can lead to better outcomes (e.g., Iuculano et al., 2015), it is not necessarily a “good” thing. Brain plasticity was once used to describe how the brain adjusted to injury and improved an individual’s quality of life by repairing itself and/or rerouting around a lesioned or dysfunctional area (Kolb & Gibb, 2014). However, it is important to note that changes over time are neither adaptive nor maladaptive outside of the context of an individual’s natural environment (Hale, Chen, et al., 2016). In other words, an individual’s brain will adjust to the real world, whether it is a “good” or “bad” world relative to societal norms. What may be considered adaptive brain functioning in the context of the environment that shaped it, may be considered atypical in other 98

environments. That atypicality, if meaningfully different than normative behavior, would constitute what has been referred to as psychopathology, or more appropriately, neuropsychopathology (Koziol et al., 2013). The notion of adaptive and maladaptive behavior being specific to an individual and a situation poses a considerable challenge for practitioners who must evaluate individual differences in emotional and/or behavioral functioning relative to normative data (Hale & Fitzer, 2015), and judge whether an individual’s difficulties are severe enough to warrant a clinical diagnosis that requires intervention. With neurodevelopmental expectations and norms guiding assessment and treatment practices, and recognition that the brain can change following intervention, clinicians can begin to unravel the complex interplay of genetic predisposition and environmental influence, which is a key to understanding and serving children with psychopathology. Moving Beyond Global Executive Dysfunction in Psychopathology When considering whether a child has psychopathology it is critical to evaluate executive functions. Executive functions are “brain boss” skills that allow for volition and self-control in cognitive or social problem-solving situations. Most child psychopathologies will have some form of executive dysfunction (Pennington & Ozonoff, 1996; Sergeant, Geurts, & Oosterlaan, 2002), suggesting a global approach to understanding executive deficits is relatively useless in clinical practice. Executive deficits or dysfunction on standardized measures may signal the presence of psychopathology (i.e., adequate sensitivity), but will not be as useful in determining the type of psychopathology displayed (i.e., inadequate specificity). For clinicians, the question is not whether executive dysfunction is present in child psychopathology, rather it is the type of executive dysfunction causing a child’s aberrant social, emotional, and/or behavioral functioning that is most relevant for differential diagnosis and determining treatment course (Hale, Reddy, Wilcox, et al., 2009).

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Recognizing Frontal-Subcortical Circuit Dimensions in Child and Adolescent Neuropsychopathology

Many psychopathology textbooks and instructors focus their attention on overt behavior for describing different disorders. These behavioral definitions have been used to define psychopathology, evaluate the validity of clinical assessment tools, and determine treatment course and outcomes. The notion that maladaptive symptoms reflected the “abnormal” minority, who are “different” than the personality characteristics displayed in the “normal” majority, formed the basis of categorical diagnoses and tertiary care models of psychological service delivery (Mash & Barkley, 2002). However, the categorical discrimination of abnormal and normal personality characteristics has not withheld the test of time or empirical test (Stahl, 2013). Evidence suggests that personality characteristics should be considered within the context of multiple interdependent dimensions of psychosocial functioning (Cuthbert, 2014), making the have/have not categorical psychopathology distinction problematic (Hale, Wilcox, & Reddy, 2016). Therefore, it becomes critical to understand how normal variations in personality and brain plasticity can lead to neuropsychopathology, not only to more effectively treat those affected (Hale, Semrud-Clikeman, & Kubas, 2014), but also to potentially prevent the disorders from ever occurring in the first place (Hale, Chen, et al., 2016). The impetus for this dimensional position in part came from the American Academy of Pediatric Neuropsychology (AAPN) Empirically Derived Disorders of Attention (EDDA) Work Group. EDDA was an initial attempt to move the field away from behaviorally determined diagnostic categories to one in which a neuropsychological framework could guide empirical investigation and clinical service delivery. Concluding with a capstone EDDA symposium at the 2012 AAPN conference, the participants revealed a consensus that current practices in serving individuals with attention problems (e.g., attention-deficit/hyperactivity disorder [ADHD]) must evolve beyond behavioral criteria and treatment. EDDA put forth an investigative vision that could guide researchers and practitioners alike in differentiating psychopathologies on

the basis of direct measures of neuropsychological performance rather than on behavior ratings alone (Hale et al., 2012; Koziol & Budding, 2012). The EDDA position argued that understanding the type of attention problem individuals display not only could aid in differential diagnosis of psychopathology, but also help validate neuropsychological measures and targeted intervention strategies not realized through a behavioral Diagnostic and Statistical Manual of Mental Disorders (DSM) framework (Wasserman & Wasserman, 2012). The assumption that neuropsychological tests had limited sensitivity and specificity in understanding psychopathology was based in part on circular research designs, where behavioral diagnostic criteria were used to support the “validity” of behavior ratings over neuropsychological tests in differential diagnosis (Hale, Reddy, Decker, et al., 2009). It has been argued that neuropsychological test data correlates minimally with DSM behavioral criteria, which could suggest these data sources measure different aspects of executive function (see Toplak, West, & Stanovich, 2013). In addition, a recent study showed that neuropsychological data predicted ADHD treatment response better than DSM criteria (Carmichael et al., 2015). Interestingly, DSM-determined attention problems were minimally correlated with treatment response (r range .03–.09) in this ADHD study. Clearly, informant-reported attention problems were not predictive of ADHD medication response status, a position advocated by other prominent neuroscientists who question the validity of the inattentive type of ADHD (e.g., Diamond, 2005). Although there was wide scale interest in such an empirically derived neuropsychological approach according to a large majority (85%) of EDDA’s professional member constituency, it was not until the National Institutes of Mental Health put forth its research domain criteria (RDoC; Cuthbert & Insel, 2013) approach to understanding psychopathology that the promise of EDDA was fully realized. Considering different neurodevelopmental dimensions, from normal to pathological, became the focus of researchers and clinicians alike. From a neuropsychopathology perspective, a dimensional approach

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could explain how extremes in frontal-subcortical circuit (FSC) function could explain the various psychopathologies—and their normal personality variations—seen in clinical practice and community alike. A dimensional approach to understanding normal and aberrant thoughts, emotions, and behavior has the potential to rewrite many of our beliefs about psychopathology, and resolve many of the contradictory findings that have plagued the field. The Extant Diagnostic Problem: Heterogeneity of Empirical Findings for Select Disorders One problem with the behavioral-categorical model is it is largely atheoretical and based on overt symptoms individuals display rather than underlying etiologies (Kendler, Zachar, & Craver, 2011). This problem leads to several inevitable outcomes. First, comorbid diagnoses are used to explain symptoms displayed that do not fit with the prototypical diagnosis (Pincus, Tew, & First, 2004). Comorbidity makes more sense in a traditional medical disease model than it does in understanding psychopathology. For instance, someone could have fractured leg and comorbid diabetes, where recovery may be hampered by poor circulation secondary to the diabetes. However, in psychopathology, the likelihood of a “pure” disorder in children is rare, especially as severity increases, where comorbidity is often used to explain the various additional symptoms displayed rather than considering a shared etiology (e.g., Kaplan et al., 2001). For example, if a child has ADHD, he or she may also be diagnosed with a comorbid oppositional defiant or conduct disorder (ODCD), depression (DEPD), or anxiety disorder (ANXD). However, a quick examination of all three of these “comorbid” disorders suggests all have attention problems (Hale, Reddy, Wilcox, et al., 2009). As a result, it is unclear in many cases whether the child has primary ADHD, causing both sets of problems, or whether he or she has attention problems secondary to the “real” condition. Therefore, an astute clinician must consider the type of attention problem displayed to determine the correct diagnosis. A child could have attention

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problems because of depression, but this occurs so frequently with anxiety disorder (Kendall et al., 2010) it may be difficult to ascertain whether attention problems are due to a primary depression, a primary anxiety, or the combination of both (Cummings, Caporino, & Kendall, 2014). Identifying the primary disorder and the secondary comorbidity may be important; it appears that different developmental pathways can lead in variations of a comorbidity (Cummings et al., 2014). Alternatively, comorbidity may also reflect severity of a single condition or co-occurring symptoms that vary in individuals with a disorder rather than several distinct disorders (e.g., Angst, Sellaro, & Merikangas, 2002). Therefore, comorbidity could be an artifact of the categorical diagnostic approach to understanding psychopathology (Maj, 2005). For instance, a child with ADHD and dyslexia may have a true comorbidity—a familial history reveals a genetic loading for ADHD, but dyslexia occurred because of early ear infections. However, for another child with ADHD, the underlying etiology for ADHD and reading disability may be the same—impaired executive circuits lead to both disorders. This suggests a comorbidity exists in the former situation (different etiology), but does not exist in the latter situation (shared etiology), even though both individuals would meet the same behavioral-categorical diagnoses. The other common finding in behavioralcategorical approaches is that the biological basis of these disorders remains unclear, with more symptom overlap between conditions, and disorder heterogeneity within conditions (Kaplan et al., 2001; Lilienfeld, 2003), especially when it comes to neurobiological or genetic findings. For instance, many children with ADHD experience executive function (EF) impairment on neuropsychological tests, but others do not. Although differences are being empirically considered (e.g., Lambek et al., 2010), results are often inconclusive. Not surprisingly, behavioral, categorical models lead to heterogeneity of neuroimaging findings, limited specificity of neuropsychological tests, and attenuated treatment results (Hale & Fitzer, 2015).

Recognizing Frontal-Subcortical Circuit Dimensions in Child and Adolescent Neuropsychopathology

To illustrate the inconsistency of empirical findings, a sampling of the structural and functional neuroimaging studies after 2010 for DEPD, ANXD, and obsessive-compulsive disorder (OCD;

see Table 6.1) and ADHD and ODCD externalizing disorders (combined, given diagnostic similarities; see Table 6.2) are presented in in the following tables.

Table 6.1 Neuroimaging of Internalizing Disorders Source

Sample

Main findings

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DEPD imaging studies Abe et al., 2010

Cullen et al., 2014 Fallucca et al., 2011 Hung et al., 2017 Takahashi et al., 2010 Walther et al., 2012 Yang et al., 2010 Zhong et al., 2011

21 subjects, 42 controls

DEPD gray matter reduction in right hippocampus, bilateral middle frontal and anterior cingulate, left parietal and occipital, and right superior temporal; increased mean diffusivity in bilateral hippocampus, pons, cerebellum, bilateral frontal, and left temporal 41 subjects, 29 controls DEPD lower functional connectivity between amygdala and hippocampus, parahippocampus, and brainstem, but higher amygdala and precuneus connectivity 24 subjects with DEPD, DEPD reduced cortical thickness in pericalcarine/cuneus, postcentral 24 subjects with OCD, gyrus, superior parietal gyrus, supramarginal gyrus; increased 30 controls temporal pole thickness 20 subjects, 20 controls DEPD connectivity in prefrontal and cingulate lower 29 subjects currently with DEPD, Anterior left insula volume reductions in both DEPD groups, 27 subjects remitted with DEPD, associated with introspection and emotional control 33 controls 21 subjects, 21 controls DEPD negative associations of diffusivity and activity level under left primary motor and within left parahippocampal white matter 12 subjects, 12 controls DEPD greater left amygdala, left parahippocampal gyrus, left and right anterior cingulate; less activation in cuneus 29 subjects, 31 controls DEPD elevated left amygdala and reduced left dorsolateral activation ANXD imaging studies

Brühl et al., 2014

46 subjects, 46 controls

ANXD cortical thickness increased in left insula, right anterior cingulate, right temporal pole; increased thickness in right dorsolateral and parietal ANXD greater activation in the prefrontal cortex, anterior cingulate, hippocampus, and insula

Christensen, Van 20 subjects at risk for ANXD, Ameringen, and Hall, 19 controls 2015 Etkin and Schatzberg, 2011 18 subjects with ANXD, 14 All groups deficits in activation and connectivity in ventral anterior subjects with DEPD, cingulate and amygdala, with major depressive disorder 25 subjects with ANXD and DEPD compensating using left and right lateral prefrontal Hahn et al., 2011 10 subjects, 27 controls ANXD amygdala hyperactivation and poor connectivity between left amygdala and medial prefrontal and posterior cingulate Liao et al., 2010 20 subjects, 20 controls ANXD functional connectivity decreased in motor and visual networks, but increased in medial prefrontal cortex and the dorsal attention network (middle and superior occipital gyrus, inferior and superior parietal gyrus, and middle and superior frontal gyrus) Moon, Kim, and Jeong, 22 subjects, 22 controls ANXD reduced hippocampus, midbrain, thalamus, insula, and 2014 superior temporal gyrus volumes Paulesu et al., 2010 8 subjects, 12 controls ANXD associated worry shows resting state elevated medial prefrontal and anterior cingulate activity, associated with worry Schienle, Ebner, and 16 subjects, 15 controls ANXD increased dorsal-medial prefrontal and amygdala volumes, with Schäfer, 2011 symptoms associated with dorsal-medial and anterior cingulate volumes (continues)

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Table 6.1 (Continued) Neuroimaging of Internalizing Disorders Source

Sample

Main findings

Strawn et al., 2013

15 subjects, 28 controls

Hardee et al., 2013

21 subjects behaviorally inhibited (BI), and 23 subjects not BI

ANXD gray matter greater in right posterior regions, lower in the left orbital and left posterior cingulate; white matter decreases in the left superior and medial frontal gyrus BI in youth predicted internalizing symptoms, and threat and attention-related frontal-amygdala connectivity

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OCD imaging studies Anticevic et al., 2014

27 subjects, 66 controls

Armstrong et al., 2016

21 subjects, 20 controls

Chen, Silk, Seal, Dally, and 8 subjects, 12 controls Vance, 2013 Figee et al., 2011 18 subjects, 19 controls Okada, Ota, Iida, Kishimoto, 12 subjects, 12 controls and Kishimoto, 2013 Via et al., 2014 67 subjects, 67 controls Zarei et al., 2011 26 subjects, 26 controls

OCD decreased connectivity left lateral prefrontal, increased connectivity in dorsal striatum, anterior thalamus, right putamen, ventral anterior cingulate, and left cerebellum; decreased ventral striatum and nucleus accumbens connectivity OCD increased local and decreased cross-region connectivity, suggesting difficulty with cross-network communication OCD reduced intracranial, gray, and white matter volume in bilateral cortical regions, and left cingulate and right limbic Reduced nucleus accumbens for anticipation but not receipt of reward OCD less change in oxyhemoglobin during interference task increased amygdala activation to fearful faces in OCD OCD increased gray matter volume in several regions of the basal ganglia (caudate nucleus, putamen); severity associated with higher insula and putamen gray matter volume

Note. DEPD = depression; OCD = obsessive-compulsive disorder; ANXD = anxiety disorder.

Table 6.2 Neuroimaging of Externalizing Disorders Source

Sample

Main findings ADHD imaging studies

Almeida et al., 2010

61 subjects, 61 controls

Bledsoe, SemrudClikeman, and Pliszka, 2011 Cao et al., 2010

32 subjects, 15 controls 28 subjects, 27 controls

Cha et al., 2015

30 subjects, 31 controls

Chabernaud et al., 37 subjects, 37 controls 2012 Cheng, Ji, Zhang, and 98 subjects, 141 controls Feng, 2012 Frodl et al., 2010 20 subjects with ADHD, 20 subjects with DEPD, 20 controls Helpern et al., 2011

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13 subjects, 12 controls

ADHD lower cortical thickness in right superior frontal gyrus, correlated with illness severity ADHD–combined type reduced volume in the posterior inferior cerebellar vermis ADHD decreased corpus callosum, especially in anterior middle-body and isthmus, with reduced connectivity in isthmus ADHD reduced right nucleus accumbens volume and frontal-accumbal connectivity related to increased aggression Parent rated child-behavior checklist scores related to default network resting state functional connectivity ADHD 48% cerebral volume reduction, mostly attributed to frontal and cerebellar volumes ADHD lower amygdala volumes bilaterally than DEPD or controls; In ADHD, more hyperactivity and less inattention associated with smaller right amygdala volume, and depression symptoms with larger amygdala volumes ADHD show less complex gray matter structure and stagnant white matter microstructure development from 12–18 years (controls show significant increase)

Recognizing Frontal-Subcortical Circuit Dimensions in Child and Adolescent Neuropsychopathology

Table 6.2 (Continued) Neuroimaging of Externalizing Disorders

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Source

Sample

Mahone et al., 2011 Montes et al., 2013

13 subjects, 13 controls 59 subjects, 62 controls

Posner et al., 2011

15 subjects, 15 controls

Qiu et al., 2011 Sauder, Beauchaine, Gatzke-Kopp, Shannon, and Aylward, 2012 Semrud-Clikeman, Fine, Bledsoe, and Zhu, 2017 Shaw et al., 2011

15 subjects, 15 controls 11 subjects with ADHD, 13 subjects with ADHD and ODCD, 11 controls

Tomasi and Volkow, 2012

47 subjects (ADHD–combined type and ADHD–inattentive type), 27 controls 197 subjects, 193 controls 247 subjects, 304 controls

Main findings ADHD reductions in subcortical volumes, with the greatest deficit in caudate ADHD reduced cortical thickness observed predominantly in the frontoparietal region, only in right hemishphere, but increased in occipital lobe, correlated with severity ADHD atypical activity in the medial prefrontal cortex in emotion task, attenuated by stimulants ADHD decreased white matter volume, especially in caudate Externalizing youth with comorbid internalizing symptoms smaller reductions in gray matter than individuals with externalizing psychopathology alone ADHD–combined type smaller caudate and anterior cingulate volumes than other groups; parent hyperactivity ratings predicted right anterior cingulate cortex and caudate volume High hyperactivity/impulsivity slower rate of normal cortical thinning, with ADHD children slowest ADHD increased ventral-medial frontal connectivity, and decreased default-mode and dorsal attention network connectivity; control-motivation imbalance ODCD imaging studies

Fahim et al., 2011 Fairchild et al., 2011

22 subjects, 25 controls 63 subjects, 27 controls

ODCD cortical thinning and decreased gray matter density ODCD reduced gray matter volume ventral-medial regions regardless of age of onset Fairchild et al., 2013 22 female subjects, 20 controls ODCD reduced bilateral anterior insula and right striatal gray matter volumes; aggressive CD symptoms negatively correlated with right dorsolateral volume, whereas callous-unemotional traits positively correlated with bilateral orbitofrontal volume Haney-Caron, Caprihan,17 subjects, 24 controls ODCD abnormal white matter connections in addition to the and Stevens, 2014 frontotemporal brain structures and uncinate fasciculus Hyatt, Haney-Caron, 19 subjects, 24 controls ODCD reduced cortical thickness (more posterior) and folding (more anterior, and Stevens, 2012 including left insula, ventral and dorsal-medial prefrontal, anterior cingulate, orbital frontal, right superior frontal-parietal Marsh et al., 2011 14 subjects with psychopathic Psychopathic traits related to reduced amygdala activity and functional trait, 14 controls connectivity between the amygdala and orbitofrontal cortex Marsh et al., 2013 14 subjects with psychopathic Psychopathic traits related to reduced rostral anterior cingulate, ventral trait, 21 controls striatum, amygdala activity Passamonti et al., 2012 13 subjects, 13 controls ODCD white matter microstructural abnormalities in uncinate fasciculus (affecting orbital and amygdala crosstalk) Sarkar, Craig, Catani, 27 subjects, 16 controls ODCD lower connectivity and poor maturation of the uncinate and Dell’Acqua, 2013 fasciculus Stevens and Haney24 subjects with ODCD, 24 subjects ODCD reduction in gray matter volume, associated with numerous frontal, Caron, 2012 with ADHD, 24 controls temporal, parietal and subcortical deficits Viding, Fontaine, and 30 subjects, 16 controls ODCD with high callous-unemotional traits lower amygdala activity when McCrory, 2012 presented with fear Wallace et al., 2014 22 subjects, 27 controls ODCD reduced gyrification within ventral-medial frontal and reduced amygdala and striatum volumes Wang et al., 2012 33 subjects (19 with ADHD), ODCD poor white matter integrity, but primarily associated with ADHD 46 controls symptoms

Note. ADHD = attention-deficit/hyperactivity disorder; DEPD = depression; ODCD = oppositional defiant or conduct disorder; CD = conduct disorder.

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Some interesting patterns emerge in Tables 6.1 and 6.2, including differences in amygdala activity (increased in ANXD, decreased in ODCD), but the inconsistency in findings is perhaps most remarkable. The variability in findings not only has to do with sample inclusion/exclusion criteria (often samples were behaviorally determined), but for imaging, the methodology used (e.g., stimuli type, control tasks, correction methods, significance threshold, whole brain versus region of interest analyses) could account for differences observed. In addition, left–right differences seem to be inconsistent, but the data suggest perhaps a left-hemisphere dysfunction for internalizing disorders (DEPD, ANXD, OCD), and a right-hemisphere dysfunction for externalizing ones (ADHD, ODCD). In contrast, excessive right-hemisphere functioning can lead to negative affect-avoidance symptoms (the opposite of typical ADHD and ODCD characteristics), whereas excessive left-hemisphere functioning can lead to positive affect-approach symptoms (the opposite of DEPD and ANXD), suggesting the imbalance between hemispheres becomes important in understanding the biological basis of psychopathology (Hale, Reddy, Wilcox, et al., 2009). However, it is important to note that approach and avoidance might reflect a motivation influence, whereas affect would be separate. For instance, a positive affect left-hemisphere approach behavior could be considered prosocial, but a negative affect left-hemisphere approach behavior could be aggressive (HarmonJones, Gable, & Peterson, 2010). To reconcile these inconsistencies, it is important to first turn our attention to a summary of brain structures and functions related to psychosocial functioning. Regulating Thoughts, Emotions, and Behavior: A Brain Map To understand the dimensional perspective of personality and neuropsychopathology in psychological practice, it is important to recognize the cortical and subcortical determinants of psychosocial functioning in typical development. To do this, we begin with the cortex and move to subcortical structures. Essentially, the cortex can be divided along two interpretive axes, an anterior-action 104

(frontal)–posterior-perception (occipital, temporal, parietal) axis, and left–right hemisphere axis (Hale & Fiorello, 2004). Consistent with Luria’s (1973) functional model, the sensory receptors, particularly in the right hemisphere, register social information (e.g., Adolphs, 2001), but it is the posterior occipital-parietal-temporal crossroads known as Luria’s zones of overlapping that integrate and interpret this social information (Schneider et al., 2013). Both social perception and interpersonal action are governed by Luria’s superstructure—the anterior prefrontal cortex (and associated subcortical structures). Although not localized to the frontal circuitry, this area is most often associated with the executive functions found to be deficient in most psychopathologies (Bonelli & Cummings, 2007), a point we return to later in this section. Much of social information is novel, creative, and dynamic depending on the interpersonal exchange, suggesting a right hemisphere dominance for social perception and action (Uddin, Kaplan, Molnar-Szakacs, Zaidel, & Iacoboni, 2005). However, visual representations, implicit language use, and even idiosyncrasies become familiar as a social relationship grows between individuals. This suggests social interpretation and expression shifts from right hemisphere to left hemisphere dominance in close relationships (Gorno-Tempini & Price, 2001), much like a righthemisphere novice becomes a left-hemisphere expert with experience and practice (Goldberg, 2001). Therefore, interacting with a stranger or acquaintance is much more novel and requires right-hemisphere governance, whereas developing social bonds requires a shift from novel circumstance to one of routinized and predictable understanding in the left hemisphere. Through this right to left shift in social dynamics, a cortical and personal relationship is born. In contrast to posterior–anterior and left–right horizontal axes, two important systems are top-down or vertically organized. These FSCs and corticalcerebellar circuits (CCC) constitute a third axis of interpretation, forming feedback loops that modulate thought and behavior. The cortical prefrontal regions govern or guide volitional behavior, with subcortical structures providing necessary filtering

Recognizing Frontal-Subcortical Circuit Dimensions in Child and Adolescent Neuropsychopathology

Running, Drawing

Decisions, Keeping Track, Quick, Efficient Responding

Cingulate Basal Ganglia Thalamus

Motor

Or

Watching Things, Tracking

Oculomotor

Dorso

bit

al

latera

l

Managing Life, Completing Tasks, Problem Solving

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Cerebellum Controlling Emotions and Behavior

Figure 6.1.  Basic functions of the five frontal-subcortical circuits.

or gating purposes, with information then returned to the cerebral cortex for subsequent modification, to produce adaptive (or maladaptive in the case of psychopathology) behavior (Andreasen & Pierson, 2008; Bonelli & Cummings, 2007). This circuitry prepares for social action or inaction, depending on external social or environmental cues, and internal emotional states. That action however, could be a prosocial exchange, an angry outburst, or timid withdrawal, depending on the individual and environmental circumstance. The FSCs first project to the excitatory striatum (caudate nucleus/putamen), and then to the inhibitory globus pallidus for modulation, which in turn projects to the thalamus for excitatory or inhibitory influence, and then returns to the frontal lobes for further direction. Two pathways (indirect and direct) work in opposition to create nuanced modulation of excitation and inhibition through the globus pallidus, which in turn influences thalamic excitation/inhibition of the cortex (Wall, De La Parra, Callaway, & Kreitzer, 2013). When the direct pathway is activated, behavior is released by supressing the inhibitory globus pallidus. When the indirect pathway is excited, behavior becomes less likely by activating the inhibitory globus pallidus. Therefore, the FSC fine tunes an individual’s thoughts, emotions, and behaviors to allow for adaptive behavior. FSCs also influence other

structures (e.g., hippocampus) where working memory is needed for encoding of new content during learning, or retrieving content form long-term memory. As depicted in Figure 6.1, there are at least five FSCs, including the motor, occulomotor, dorsolateral, orbital, and cingulate circuits, which interact with each other to motivate, sustain, modify, and/or suppress goal-directed behavior (Hale & Fitzer, 2015; Stuss, 2011). From these circuits, two functional systems emerge, the dorsal (dorsolateraldorsal cingulate) and ventral-medial (orbital-ventral cingulate) systems, which serve very specific functions related to executive control and psychopathology. The dorsal circuit is involved in external or environmental executive functions, including planning, organization, strategizing, monitoring, evaluating and changing behaviors (Bonelli & Cummings, 2007; Hale & Fitzer, 2015; Stuss & Alexander, 2000). The ventral-medial circuitry (orbital-ventral cingulate, in conjunction with the amygdala, nucleus accumbens, and insula) is involved in internal emotional and behavioral regulation, including emotional control, social concern, and appetitive (e.g., seek reward/avoid punishment) behaviors (Hale, Reddy, Wilcox, et al., 2009; Koziol & Budding, 2009). This is often why the dorsal system is called the “cool” cognitive EF 105

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circuitry (for cognitive control of external tasks) and the ventral system is the “hot” emotional/ behavioral EF circuitry (for internal self-control). Thus far, our review suggests a dysfunctional right hemisphere and/or FSCs are the most likely candidates for understanding child and adolescent psychopathology, with much of the evidence pointing to hot ventral-medial circuitry being most involved (Hale & Fitzer, 2015). Although the posterior brain regions are for understanding (sensory input), the frontal system is responsible for doing (motor output). Therefore, the FSC EF are not for processing thoughts, emotions, or behavior, they are for acting to produce—or inhibit—an external (dorsal) or internal (ventral-medial) motor response. In this context, it is easy to see how too little (hypoactive circuitry) or too much (hyperactive circuitry) FSC function could lead to very different disorders. For instance, too little circuit activity may lead to an exaggerated (stimulation seeking) response, whereas too much circuit activity may lead to a blunted response (stimulation avoidance). With ADHD, hypoactive circuitry leads the individual to seek external stimulation by engaging others or participating in high risk activities (to increase cortical function and control), whereas hyperactive circuitry can lead to individuals avoiding social contact and further stimulation. Essentially, the brain adapts to these internal activity levels by trying to increase (e.g., ADHD), or decrease (e.g., OCD) environmental stimulation. As suggested earlier, the goal of psychological assessment of child and adolescent psychopathology is not so much about saying whether there is an EF problem, but rather it is the type of EF problem that matters most for differential diagnosis. This imbalanced pattern appears to apply to many EFs. Too much attention (e.g., hyperfocus) is just as problematic as too little attention (e.g., distractibility) during social exchange. Too much mood (euphoria) and too little mood (anhedonia) can lead to inconsistent motivation for engaging others. Too much concern for others (hypersensitivity) or too little concern (indifference) can interfere with empathy and attachment. We begin

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to understand that optimal EF does not follow the typical psychometric notion that “more is better.” Instead, average EF is optimal, and an imbalance in FSC functions can lead to psychopathology (Hale & Fitzer, 2015). Average EF is what allows an individual to act in a flexible and adaptable way during social exchange, so it is easy to see why an imbalance leads to poor interpersonal dynamics and limited social attachment. In Hale, Reddy, Wilcox, et al.’s (2009) circuit balance theory, balance within and between these FSCs and associated structures allows a person to provide flexible, adept, prosocial behavior in real time during social exchange. Like posterior mirror neurons help individuals understand social behavior (Chong, Cunnington, Williams, Kanwisher, & Mattingley, 2008), it is these FSC action neurons that allow adaptive response to the ever changing social and emotional landscape during interpersonal exchange. Therefore, psychopathology could be related to an imbalance among the circuits (Hale, Reddy, Wilcox, et al., 2009), with circuit overactivity and underactivity leading to compensatory actions that help the individual overcome psychological or physical discomfort (Hale & Fitzer, 2015). Unfortunately, understanding neuropsychopathology is not as simple as too much or too little brain functioning, but it at least gives a framework for beginning to differentiate between psychopathologies, all of which have attention and executive problems. Given Tables 6.1 and 6.2, it is clear is that the disorders discussed are not just deficit based. In fact, there are hypo- and hyperfunctioning structures/circuits in the psychopathologies reviewed above, further adding evidence that circuit imbalance must be considered for differential diagnosis and treatment of neuropsychopathology. For instance, Davidson’s (2001) seminal work on hemispheric response to social stimuli suggests the left hemisphere is the “approach” hemisphere, whereas the right hemisphere is the “avoidant” one (Fetterman, Ode, & Robinson, 2013; Harmon-Jones et al., 2010). Additionally, lesion and other imaging data suggest that left hemisphere deficits lead to negative affect (e.g., apprehension, sadness, seriousness), whereas

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Recognizing Frontal-Subcortical Circuit Dimensions in Child and Adolescent Neuropsychopathology

right hemisphere deficits lead to positive affect (e.g., carefree, indifferent, whimsical; Fetterman et al., 2013; Hecht, 2010), perhaps because the imbalance created allows for the other hemisphere to dominate during social exchange. For instance, depression can be caused by either left prefrontal hypoactivity (e.g., deficit depression) or right prefrontal hyperactivity (e.g., compensatory depression). In circuit balance theory, the greater the imbalance, the greater the dysfunction. Therefore, what is critical to consider (and treat) is the imbalance within and between circuits that govern psychosocial functioning. Turning our attention to the CCC, it is not surprising that if EF is a motor action, then the cerebellum would also play a critical role in cognition, motivation, affect, and psychosocial functioning (Ackermann, Mathiak, & Riecker, 2007; Ito, 2008; Koziol & Budding, 2009; Schmahmann, 2004). Like the basal ganglia, the cortical areas send input to the pons, and then to the cerebellum, which then affects the quality and intensity of input, sending it back to the thalamus and then the cortex. Through CCC actions, the rate, rhythm, and force of behavior is determined. Therefore, the FSC determines the type of executive control and whether it is appropriate for a given social situation, but it is the CCC that determines how these actions will be carried out (Koziol et al., 2013). But the CCC is not just passively responsive to cortical demands. Instead, it plays an important role in creating procedural memories (Molinari, Restuccia, & Leggio, 2009) and identifying when they would be most useful to display, given the cortical input it receives, to provide anticipatory guidance in predictable situations (Grafton, Schmitt, Van Horn, & Diedrichsen, 2008; Imamizu & Kawato, 2009). In this way, the cerebellum provides the cortex with a means of achieving automaticity of function, freeing the cortex from spending time and resources when routinized actions are required (e.g., Bruya, 2010). This not only leads to several behaviors becoming routinized, but also for these behaviors to be performed in rapid succession through behavioral repertoires that are mostly outside of conscious control (Houk et al., 2007).

Therefore, it is important to recognize that effective brain functioning requires automatization of routine behaviors, but this too is a double-edged sword. According to circuit balance theory (Hale, Reddy, Wilcox, et al., 2009), balanced circuitry is more likely when the CCC can automatize effective, adaptive, prosocial behavior in real time during successful interpersonal exchange. However, if FSC imbalance exists, this deviation is likely to be accentuated by the CCC, leading to maladaptive patterns that are amplified or exaggerated, and unfortunately outside the realm of conscious control. Because these deviations are unconsciously amplified, an individual may not even recognize his or her aberrant response or adapt to social cues provided by a friend or partner, furthering the socioemotional disconnect between the two individuals during interpersonal exchange. Such is the case with cerebellar cognitive–affective syndrome, a cerebellar vermis disorder that causes attention, memory, and EF dysfunction (Schmahmann et al., 2007). Dysfunction from the top (FSC) or bottom (CCC) can lead to inconsistent, unpredictable behavior that is incongruent and incompatible with social connection and stable relationships. In summary, emotional or behavioral dysregulation in the FSC can lead to further maladaptive CCC accentuation and/or recruitment of maladaptive, routinized, behavior patterns, further hampering self-awareness, interpersonal exchange, and executive control. That is why the more one practices a problematic behavior, the more likely it is to become routinized in the CCC, making it exceedingly difficult to overcome with conscious control, hampering traditional psychotherapy (e.g., personality disorders; Hale & Fitzer, 2015). This would be a case where neural plasticity works against the individual’s adaptive, prosocial functioning. Circuit balance theory would also predict that FSC–CCC imbalance leads to an inability to adjust behavior to novel circumstances in psychosocial exchange (Hale & Fitzer, 2015), which is critical for modifying thoughts and behaviors in an adaptive way (Badre, 2008; Somerville & Casey, 2010).

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Implications for Neuropsycho­ logical Assessment, Case Conceptualization, and Differential Diagnosis To further diagnostic and treatment practices in child and adolescent psychopathology, the first step is to realize there are multiple interdependent typical–atypical dimensional continuums among the FSC and CCC circuits, which are also influenced by hemispheric preferences for positiveapproach (left hemisphere) and negative-avoid (right hemisphere) functioning. The amygdala obviously plays a role as well, with increased activity more common in ANXD (leading to emotional inhibition) and decreased activity in ODCD (leading to emotional disinhibition). On the basis of these foundations, a theoretically driven, functionally coherent model of cortical, limbic, basal ganglia, and cerebellum influences on psychosocial functioning must be developed to further clinical practice and instrument development. Understanding neuropsychopathology requires a comprehensive, multimethod approach that considers the brain, behavior, and environment simultaneously in evaluating a child’s developmental and adaptive status and need for intervention. Unfortunately, most available neuropsychological assessment tools are largely designed to examine higher-order cognitive functions (Lezak, Howieson, Bigler, & Tranel, 2012), with most instruments designed to tap conscious cognitive control thought to be a function of the cool dorsolateral-dorsal cingulate circuitry (Ardila, 2008). In contrast, the hot orbital-ventral medial structures (including amygdala, insula, and accumbens), involved in motivation, affect regulation, social judgment, and empathy, are more difficult to directly evaluate using available neuropsychological tests (Zald & Andreotti, 2010). In fact, one typical finding in children with psychopathology may be inconsistent performance on neuropsychological measures, which in turn could render nomothetic interpretation of administered measures meaningless. Therefore, informant and self-report ratings (e.g., Behavior Rating Inventory of Executive Function—Second Edition; Behavioral Assessment System for 108

Children—Third Edition; Minnesota Multiphasic Personality Inventory—Adolescent—Restructured Form), as well as careful observation and thorough clinical interviews, are all critical in any neuropsychological evaluation where child or adolescent psychopathology may be of concern. Details regarding these informant and self-report measures are discussed elsewhere in this volume. Consistent with Fox and Raichle’s (2007) notions of anticorrelated task positive and task negative brain networks, the interplay of cognitive control (external EF) and internal self-regulation (internal EF) in the manifestation of neuropsychopathology must be considered, even if there are no objective measures that successfully differentiate the two circuitries. In fact, the cool and hot circuitry dissociation is highly unlikely given that circuit balance theory posits interactions among circuits and hemispheres are the cause of adaptive behavior or psychopathology. However, these anticorrelated systems fit well with our understanding of circuit imbalance—an anxiousdepressed individual may spend too much mental energy avoiding interpersonal contact and too much time in his or her internal (task negative) world; whereas an individual with ADHD and ODCD may be too engaged in the external (task positive) world, constantly seeking stimulation instead of filtering the environment for more sustained and volitional behavior. From a dimensional perspective, typical and atypical behaviors are dependent in part on whether a child can exert cognitive control (dorsal circuitry) over them. In ANXD and ODCD, which appear to be more a manifestation of internal EF (e.g., orbitalventral medial dysfunction), atypical behaviors cannot be turned “on” (e.g., engaged) or “off” (e.g., inhibited) easily. And even more important, a child with anxiety may fear social contact, whereas a child with OCD may become obsessed with it, so there are no clear dichotomies presented here. It is also important to note that as behaviors become more automatized, they become fundamental to an individual’s adaptive or maladaptive pattern of behavior. The more automatized the behavior, the less likely it is to be responsive to traditional treatments such as cognitive–behavioral therapy (CBT). Instead, a

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Recognizing Frontal-Subcortical Circuit Dimensions in Child and Adolescent Neuropsychopathology

behavioral approach may be more useful until the routinized problematic behavior can be reversed, and the person can start to “think” about the consequences of his or her actions before acting on them. In real life, people alternate between thinking about what they do, and doing other things automatically, whether it is a social, cognitive, or academic task. Most situations require alternating episodes of automatic behaviors and higher-order cognitive control. This should represent a fundamental underlying principle for neuropsychological test interpretation as well as treatment choice. For some tasks, a child or adolescent will have considerable prior exposure to the content or task demands, so left posterior and subcortical functions are primarily responsible for performance. Other tasks that are unknown, or represent a weakness for a child, or invoke a subcortical reaction, may require FSC and right hemisphere activation as he or she attempts to use novel problem solving skills (e.g., fluid reasoning) to respond. To add interpretive complexity, task demands can also change during neurodevelopment, or even within a particular subtest. For instance, academic tests usually begin with familiarity and easy automatic responding for most children, but as item difficulty increases, so do frontal and right hemisphere functions, as children use novel problem solving strategies to respond to more difficult questions (Hale & Fiorello, 2004). Recent advances in our understanding of the biological bases of psychopathology have led to development of neuropsychological measures that assess socioemotional perception or regulation (NEPSY–II Affect Recognition, Theory of Mind), and motivational influences on behavior (e.g., Balloon Analogue Risk Task; Iowa Gambling Task), so recognition of cortical influences on behavior have extended beyond the dorsolateral-dorsal cingulate areas to include the orbital and associated ventral-medial structures (see Hale & Fitzer, 2015). However, these tests are not as sensitive or specific as we would like, nor do they have adequate standardization data to ensure technical quality, so nomothetic and idiographic interpretation is necessary for accurate case conceptualization in child and adolescent neuropsychopathology. Finally, in recognizing that inconsistent or variable performance is the norm for individuals

with psychopathology, it is important that we move beyond interpreting measures based on the assumption they measure stable psychological traits, and instead recognize that our evaluations in part assess transient psychological states (Hale, Reddy, Wilcox, et al., 2009) that should change with environmental feedback, within a session, or even possibly during a specific test. By repeating procedures during an evaluation or over successive sessions, and validating or refuting hypotheses derived from other sources (Hale & Fiorello, 2004; see Figure 6.2), we can obtain an index of and individual’s ability to learn, adapt, and even automatize his or her performance, which has dramatic implications for differential diagnosis and treatment of child neuropsychopathology. Given these caveats, it is clear effective neuropsychological evaluation of psychopathology is not necessarily about acquiring new tests that tap different functions (although that is nice too). Instead, it is using our powers of clinical observation combined with thorough history taking that leads to better case conceptualization and accurate differential diagnosis of dimensions. For instance, if a child struggles with novel task demands, this could suggest greater reliance on left hemisphere (e.g., talking oneself through the task) or subcortical responding. Perseveration could be seen in repeated statements during verbal responding, or in the inability to stop a motor response when the timed item is finished. On difficult items that lead to failure responding, a certain amount of frustration will be experienced, but if exaggerated, this could reflect cerebellar involvement. A child could be inattentive by being distracted by outside noise, something the clinician does, or irrelevant test materials (e.g., manual). Another child might get “lost” in his or her thoughts, seem preoccupied, or daydream, suggesting internal distractibility. Both lead to attention problems on standardized measures, but for very different causes. Therefore, we cannot take the clinician out of neuropsychological interpretation of data, for it is the clinician’s keen observation and notetaking skills that may help with the necessary case conceptualization skills for accurate differential diagnoses, and determining which disorder is primary and the remainder comorbidities. 109

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The Cognitive Hypothesis Testing Model Theory 1. Presenting Problem 5. Cognitive Strengths/Weaknesses 9. Intervention Consultation

Interpretation

13. Continue/Terminate/Modify

2. Intellectual/Cognitive Problem

4. Interpret IQ or Demands Analysis

6. Choose Related Construct Test

8. Interpret Constructs/Compare 12. Determine Intervention Efficacy

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Hypothesis

Data Collection

10. Choose Plausible Intervention

3. Administer/Score Intelligence Test 7. Administer/Score Related Construct Test 11. Collect Objective Intervention Data

Figure 6.2.  The cognitive hypothesis testing approach. From School Neuropsychology: A Practitioner’s Handbook (p. 129), by J. B. Hale and C. A. Fiorello, 2004, New York, NY: Guilford Press. Copyright 2004 by Guilford Press. Reprinted with permission.

Different neuropsychopathologies lead to different response patterns as described above. A variety of neuropsychological tools, offered in Table 6.3, can be used to assess these patterns. These measures are just a sampling, and there are other fine measures available that are not in this list. However, it is important to note that most EF measures presented may be sensitive to frontal lobe function, but may not be specific for any specific psychopathology, especially because most tap cool dorsolateral-dorsal cingulate functions. Therefore, there may be no “smoking gun” aberrant test score that defines which type of psychopathology is evident. Instead, it is up to the astute clinician to use all sources of data to arrive at a diagnostic impression and develop and test hypotheses about cognitive strengths and weaknesses, which in turn can lead to more targeted interventions that are sensitive and specific to a child’s needs. Linking Neuropsychological Assessment to Intervention: The Cognitive Hypothesis Testing Approach The model of brain function described in this chapter is offered to explain general adaptation, psychopathologies, and variations among clinical 110

presentation. To better understand these complex interrelationships, the cognitive hypothesis testing (CHT) model was developed (see Figure 6.2; Hale & Fiorello, 2004) to link neuropsychological assessment to intervention. In CHT, neuropsychological assessment can be used to guide intervention decisions by identifying interrelationships among individual strengths and weaknesses from a Lurian perspective, and can also be used to develop, monitor, and change interventions until treatment efficacy is achieved (Fiorello, Hale, & Wycoff, 2012). Governed by the scientific method, CHT has been used to iteratively link neuropsychological assessment results to targeted interventions, ensuring ecological and treatment validity in several studies (for a review, see Hale, Wilcox, & Reddy, 2016). Not only does it lead to effective, targeted interventions that improve a child’s functioning, CHT has also been used to develop differentiated interventions that lead to better outcomes and different changes in white matter connectivity (FitzerAttas et al., 2014). The impetus for recognizing the value of a Lurian CHT process-oriented clinical approach comes from two sources of evidence. First, over the past 20 years, neuroimaging findings have shown that not every brain processes information the same

Recognizing Frontal-Subcortical Circuit Dimensions in Child and Adolescent Neuropsychopathology

Table 6.3 Executive, Motor, and Memory Measures for Differential Diagnosis of Child Psychopathologies Test battery/instrument

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Sorting Test Trail Making Test Verbal Fluency Test Design Fluency Test Color–Word Interference Test Tower Test 20 Questions Test Word Context Test Auditory Attention and Response Set Design Fluency Animal Sorting Clocks Inhibition Speeded Naming Word Generation List Memory Memory for Designs Memory for Faces Word List Interference Affect Recognition Theory of Mind Visuomotor Precision Manual Motor Sequences Design Copying Memory for Stories Word Selective Reminding Digits Forward Digits Backward Visual Selective Reminding Manual Imitation Children’s Category Test Wisconsin Card Sorting Test Tower of London Stroop Color–Word Test Rey–Osterrieth Complex Figure Conners Continuous Performance Test II Hale–Denckla Cancellation Task

Neuropsychological constructs measured Problem solving, verbal and spatial concept formation, categorical thinking, flexibility of thinking on a conceptual task Mental flexibility, sequential processing on a visual–motor task, set shifting Verbal fluency Visual fluency Attention and response inhibition Planning, flexibility, organization, spatial reasoning, inhibition Hypothesis testing, verbal and spatial abstract thinking, inhibition Deductive reasoning, verbal abstract thinking Sustained auditory attention, vigilance, inhibition, set maintenance, mental flexibility Visual–motor fluency, mental flexibility, graphomotor responding in structured and unstructured situations Ability to formulate basic concepts and to transfer those concepts into action Planning and organization and visuoperceptual and visuospatial skils Ability to inhibit automatic responses Rapid semantic access Verbal productivity Remember list of unrelated words over multiple learning trials; one delayed trial after interference list Visual-spatial memory; also requires maintenance of rules Select previously viewed photo from an array Rote repetition of unrelated words, with each set of two followed by recall of both sets; working memory Matching photos expressing the same feeling: happy, sad, fear, anger, disgust, neutral Understand how others are feeling, understand false beliefs; also requires verbal comprehension and memory Visual–motor integration, graphomotor coordination without constructional requirements Motor imitation Visual perception of abstract stimuli, visual–motor integration, graphomotor skills Examines encoding, storage, and retrieval of lexical-semantic knowledge Learning a list of unrelated words over several trials; examines encoding, storage, and retrieval of unrelated words Auditory rote memory, sequential recall, attention; not related to EF, but useful in comparison with Digits Backward Because manipulation of digits is required to produce the correct reverse sequence, more demands on attention, working memory, executive functions Visual analogue to word selective reminding, with dots; dorsal stream, visual–motor coordination, praxis without visual discrimination Short-term visual–sequential memory, praxis Hypothesis generation and testing, pattern recognition, ability to respond to feedback Executive functions, problem solving, set maintenance, goal-oriented behavior, inhibition, ability to benefit from feedback, mental flexibility, perseveration Planning, inhibition, problem solving, monitoring, and self-regulation Better norms on the NEPSY–II and D-KEFS versions, but still popular Visual–motor integration, constructional skills, graphomotor skills, visual memory, planning, organization, problem solving Computerized measure of sustained attention, impulse control, reaction time, persistence, response variability, perseveration, visual discrimination Attention, concentration, visual scanning (continues)

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Table 6.3 (Continued) Executive, Motor, and Memory Measures for Differential Diagnosis of Child Psychopathologies Test battery/instrument

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California Verbal Learning Test—Children’s Version Comprehensive Trail-Making Test Tests of Variable Attention Purdue Pegboard Grooved Pegboard Comprehensive Assessment of Spoken Language Controlled Oral Word Association Test Boston Naming Test Green’s Word Memory Test Test of Memory Malingering

Neuropsychological constructs measured Verbal learning, long-term memory encoding and retrieval, susceptibility to interference Attention, concentration, resistance to distraction, cognitive flexibility/set shifting Computerized measure of sustained and selective attention Fine motor skills, bimanual integration, psychomotor speed Complex visual–motor–tactile integration, psychomotor speed (compare with simple sensory–motor integration) Language processing in comprehension, expression, and retrieval in these categories: lexical/semantic, syntactic, supralinguistic, pragmatic; the supralinguistic and pragmatic categories show promise in the assessment of right-hemisphere language skills (also look for prosody function during dialog) See NEPSY–II Verbal Fluency, letter fluency more related to EF (higher retrieval demands) than category fluency, which is more related to temporal lobe functioning Expressive vocabulary, free-recall retrieval from long-term memory versus cued-recall retrieval (semantic/phonemic) Distinguish between low performance because of poor effort and actual low performance Distinguish between low performance because of poor effort and actual low performance

Note. EF = executive function; D-KEFS = Delis–Kaplan Executive Function System.

way—children and adults with brain dysfunction/ damage use different brain areas/systems than typically functioning individuals when responding to test stimuli and the environment (Schneider, Parker, Crevier-Quintin, Kubas, & Hale, 2013). In many cases, a low or atypical brain function is offset by a higher functioning one that serves a compensatory function (e.g., Broca’s area activation in children with word reading disability; Shaywitz et al., 2004; Simos et al., 2007), so interpretation must examine the interrelationships of brain structures and functions. Important in this approach is an understanding that individuals with disabilities are not just functioning at the lower end of the normal distribution in a processing area, rather they attempt to use other processing strengths to compensate for their weaknesses. Therefore, unlike traditional psychological assessment where revealing deficits was the focus of evaluation, modern neuropsychological approaches to understanding psychopathology require determination of strengths and weaknesses, or in balance theory, evaluating to determine excessive functioning in one or more areas at the cost of diminished functioning in others. To make things even more 112

complex, the same score may mean different things for two different children. For instance, a child may perform poorly on the Wisconsin Card Sort Test because he or she cannot change what he or she is doing when the examiner changes the task requirements (e.g., difficulty shifting cognitive set, limited mental flexibility), whereas another may get distracted in the middle of the set (i.e., failure to maintain set). They may get the same score, but the child who fails to maintain set may say “oops” and try to change the card so it is in the correct pile. CHT clinicians also recognize that any assessment score is made of state and trait variance (Hale et al., 2013; Hale, Wilcox, & Reddy, 2016). We know that psychological states vary, but traits should remain stable. From a traditional psychometric approach, the client’s psychological state during testing becomes error variance, but from a CHT approach the client’s state becomes essential in the interpretative process. This is especially true for children with psychopathology, where variable performance is the norm, not the exception. The client’s performance on a standardized test is highly reliable during one assessment, and is also highly reliable in the next one (hence low standard errors

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Recognizing Frontal-Subcortical Circuit Dimensions in Child and Adolescent Neuropsychopathology

of measurement and high reliability), but test–retest reliability is typically lower. This is not psychometric noise or error. The changes in performance occur because the client’s state has changed (common in psychopathology) and/or learning has taken place. In fact, Luria would argue if the test performance did not change from one administration to the next it would suggest a significant problem, because the individual did not learn or modify performance on the basis of prior experience. To overcome the limitations of a fixed or flexible battery approach (often completed in a single session) and to examine, understand, and differentiate client states from client traits, CHT requires at least two test sessions, with the initial assessment followed by a hypothesis testing phase that is used to verify or refute initial findings (Hale & Fiorello, 2004). Once professional schedules are rearranged for multiple visits during a single evaluation, and clinicians begin testing children over multiple sessions, they realize how important this trait–state separation is in case formulation, especially in cases of child and adolescent neuropsychopathology. Developing clinical interventions and treatment strategies based on an understanding of the threeaxis approach to test interpretation described earlier can be useful, but what may work for one individual with a disorder may not work for another with the same disorder. Given this clinical reality, it is still essential practice to monitor interventions, and recycle (modify) them when necessary to achieve treatment efficacy. According to circuit balance theory, intervention would appear to be based on maximizing frontal-subcortical and left–right hemisphere influences (i.e., balance) on behavior so a child or adolescent responds in an adaptive, flexible way depending on each set of environmental circumstances. Findings suggest that too much higher order control, leading to inhibition, or too little leading to impulsivity, can lead to psychopathology (Hale, Reddy, Wilcox, et al., 2009). Similarly, it is well known that too little emotion or too much emotion hinders learning and memory through the interrelationship of amygdala and hippocampal functioning (Richter-Levin & Akirav, 2000). What becomes clear here is that it is critically important to achieve circuit balance to achieve optimal

psychosocial functioning (Hale, Reddy, Wilcox, et al., 2009). These assumptions are not trivial and have considerable meaning for psychological intervention. For instance, a clinician treating someone with anxiety and depression would not only want to decrease hyperactive anxiety circuitry through exposure therapy, but would also want to increase hypoactive depression circuitry through task engagement in the real world. Similarly, an imbalance between cortical and subcortical influences on behavior can lead to maladaptive behavior patterns, which are under conscious cognitive control (and exhausting) or automatic, routinized behaviors scripted by the cerebellum (less tiring, but almost never flexible or adaptive). For instance, if novel problem solving predominates and does not allow for routinized behavior to occur (e.g., too much cortical influence), EF will be taxed and processing will be slow and disconnected from the environment that demands a response. Although automatic behavior frees the cortex for higher level functioning, too much behavioral routinization or automatic responding does not allow for flexible, adaptive responding when new or incongruent information presented (too much subcortical influence; Koziol & Budding, 2009). An appropriate neuropsychological approach to intervention would be designed to maximize the balance between hemispheres and FSC and CCC to allow for automatic behavior when it is called for, with cortical adjustments as necessary to adapt to novel circumstances, changes in situation, conflicting social information, or differing relationships. In addition, what might be adaptive for one person or situation may be maladaptive for others. Overreliance on one particular interaction style or communication approach would be especially problematic, with the person’s response only adaptive in a few situations. Establishing intention scripts and overlearning to develop implicit learning strategies, on the basis of our understanding of the basal ganglia and cerebellar functions, might facilitate executive control of behavior when the problem is cortical. CBT techniques that examine faulty executive control of behavior may be very useful when the problem is cortical, and can help clients understand propensities to engage in disinhibited or 113

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restricted maladaptive behavioral repertoires before they become automatic in the CCC. Although readers may be convinced that intervention for subcortical dysfunction may be limited to medication treatment, this is hardly the case given the reciprocity of the circuits. In fact, if executive (e.g., metacognition, working memory, learning strategies instruction) and behavioral management (e.g., reinforcement, extinction, behavioral contracting) techniques are adopted and monitored, treatment efficacy will be more likely. Even techniques like exposure tasks and behavioral experiments could help individuals regulate the display or strength of subcortically mediated behaviors, and contingency management is likely to facilitate subcortical conditioned responding. What is critical, according to our integrated model, is that psychosocial well-being requires learning of adaptive patterns while reducing or extinguishing maladaptive ones, as this will foster interactions within the natural environment. Therefore, it remains critical to not only evaluate the individual but also the environment in the assessment of psychopathology. It also speaks to the importance of early intervention of psychosocial problems to prevent them from becoming routinized in the cerebellum. Summary and Future Directions Neuropsychological and neuroimaging research has evolved from viewing individual differences in thoughts, emotions, and behavior from a categorical perspective (normal versus abnormal) to a dimensional one grounded in brain–behavior relationships of individual differences (Schneider et al., 2013). A dimensional perspective recognizes that phenotypic presentation for any individual is highly variable and dependent on the neurobiology of individual differences and environmental determinants that shape them (e.g., Casey, Oliveri, & Insel, 2014), suggesting clinical presentations of individuals may vary meaningfully, even for the same diagnosis. It also recognizes that although deficits may be present during an evaluation, they may be manifest in many ways, and may be accompanied by abnormal strengths, even in the same individual. 114

Considering the complexity of understanding and diagnosing child and adolescent neuropsychopathology, inferences developed during psychological assessment must be supported with additional evidence (e.g., CHT approach) to corroborate findings. These findings that must be integrated across historical, cognitive, neuropsychological, academic, and behavioral data to ensure they have concurrent and ecological validity (Fiorello, Hale, Decker, & Coleman, 2009), and they must be used to guide interventions. The brain is neither static nor unresponsive, but surprisingly malleable, and targeted interventions can lead to brain changes that ameliorate disability (Hale, Chen, et al., 2016; Koziol et al., 2013). To link our assessment results to intervention, CHT consultation methods after comprehensive evaluation have been used in group and single subject research to establish treatment efficacy (for a review, see Hale, Wilcox, & Reddy, 2016). Relatedly, neuroimaging has moved the field away from the notion of “static” lesions by showing the brain is much more malleable and interconnected than previously thought. However, it is important to recall that brain plasticity is not always positive or adaptive, as is often suggested in the literature. Brain functioning can improve with targeted interventions, but inaccurate or delayed evaluations may lead to a more routinized, automatic problems in psychopathology which will be more difficult to overcome, even with intensive intervention (Koziol et al., 2013). The development of advanced neuroimaging technologies has provided clinicians and researchers alike with unprecedented windows into the neurobiology of many child and adolescent psychopathologies. In the future, information from neuroimaging and neuropsychological assessment will be combined to offer a more in depth understanding of brain–behavior relationships for different populations (e.g., RDoC). Given these potential benefits, future development and validation of neuropsychological assessment tools would profit from the inclusion of neuroimaging studies to show the relationship between performance differences and brain changes, and discriminant validity studies to reveal their sensitivity and

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specificity. This is especially true of measures that can assess “hot” orbital-ventral medial circuitry, as there are few available standardized measures for this important area associated with psychopathology. Neuropsychological theory and neuroscientific evidence continue to help re-write long-held notions about brain functions and psychopathology, but longitudinal research on the neurodevelopment of functional brain systems is needed (Riccio & Reynolds, 2013). Future studies are needed that examine the ecological and intervention sensitivity of assessment tools, including whether tests are measuring functional skills and predicting learning and behavior in natural environments (Reddy, Weissman, & Hale, 2013). Future research should also examine how neuropsychological assessment relates to FSC functioning, and how this influences learning, selfregulation, and environmental adaptation (Koziol et al., 2013; Riccio & Reynolds, 2013). Clearly research that identifies data-based assessment and intervention practices that are reliable and valid for known psychopathologies, and can be used to guide effective interventions, is of critical need (Hale et al., 2012; Witsken et al., 2008). Perhaps the emerging data will confirm that the purpose for conducting neuropsychological assessment is for intervention purposes, not just diagnosis. Neuropsychological assessment for intervention should be every clinician’s goal—a clinical endeavor designed to not only improve client outcomes, but also to capitalize on neural plasticity to change brain functioning for more adaptive, prosocial outcomes.

References Abe, O., Yamasue, H., Kasai, K., Yamada, H., Aoki, S., Inoue, H., & Ohtomo, K. (2010). Voxel-based analyses of gray/white matter volume and diffusion tensor data in major depression. Psychiatry Research: Neuroimaging, 181, 64–70. https://doi.org/10.1016/ j.pscychresns.2009.07.007 Ackermann, H., Mathiak, K., & Riecker, A. (2007). The contribution of the cerebellum to speech production and speech perception: Clinical and functional imaging data. Cerebellum, 6, 202–213. http://dx.doi.org/ 10.1080/14734220701266742

Adolphs, R. (2001). The neurobiology of social cognition. Current Opinion in Neurobiology, 11, 231–239. http://dx.doi.org/10.1016/S0959-4388(00)00202-6 Almeida, L. G., Ricardo-Garcell, J., Prado, H., Barajas, L., Fernández-Bouzas, A., Ávila, D., & Martínez, R. B. (2010). Reduced right frontal cortical thickness in children, adolescents and adults with ADHD and its correlation to clinical variables: A cross-sectional study. Journal of Psychiatric Research, 44, 1214–1223. https://doi.org/10.1016/j.jpsychires.2010.04.026 Andreasen, N. C., & Pierson, R. (2008). The role of the cerebellum in schizophrenia. Biological Psychiatry, 64, 81–88. http://dx.doi.org/10.1016/ j.biopsych.2008.01.003 Angst, J., Sellaro, R., & Merikangas, K. R. (2002). Multimorbidity of psychiatric disorders as an indicator of clinical severity. European Archives of Psychiatry and Clinical Neuroscience, 252, 147–154. http://dx.doi.org/10.1007/s00406-002-0357-6 Anticevic, A., Hu, S., Zhang, S., Savic, A., Billingslea, E., Wasylink, S., & Pittenger, C. (2014). Global restingstate functional magnetic resonance imaging analysis identifies frontal cortex, striatal, and cerebellar dysconnectivity in obsessive-compulsive disorder. Biological Psychiatry, 75, 595–605. https://doi.org/ 10.1016/j.biopsych.2013.10.021 Ardila, A. (2008). On the evolutionary origins of executive functions. Brain and Cognition, 68, 92–99. http:// dx.doi.org/10.1016/j.bandc.2008.03.003 Armstrong, C. C., Moody, T. D., Feusner, J. D., McCracken, J. T., Chang, S., Levitt, J. G., & O’Neill, J. (2016). Graph-theoretical analysis of resting-state fMRI in pediatric obsessive–compulsive disorder. Journal of Affective Disorders, 193(Supplement C), 175–184. https://doi.org/10.1016/j.jad.2015.12.071 Badre, D. (2008). Cognitive control, hierarchy, and the rostro-caudal organization of the frontal lobes. Trends in Cognitive Sciences, 12, 193–200. http:// dx.doi.org/10.1016/j.tics.2008.02.004 Bledsoe, J. C., Semrud-Clikeman, M., & Pliszka, S. R. (2011). Neuroanatomical and neuropsychological correlates of the cerebellum in children with attention-deficit/hyperactivity disorder–combined type. Journal of the American Academy of Child & Adolescent Psychiatry, 50, 593–601. https://doi.org/ 10.1016/j.jaac.2011.02.014 Bonelli, R. M., & Cummings, J. L. (2007). Frontalsubcortical circuitry and behavior. Dialogues in Clinical Neuroscience, 9, 141–151. Brühl, A. B., Hänggi, J., Baur, V., Rufer, M., Delsignore, A., Weidt, S., & Herwig, U. (2014). Increased cortical thickness in a frontoparietal network in social anxiety disorder. Human Brain Mapping, 35, 2966–2977. https://doi.org/10.1002/hbm.22378

115

Hale, Reddy, and Weissman

Bruya, B. (Ed.). (2010). Effortless attention: A new perspective in the cognitive science of attention and action. http://dx.doi.org/10.7551/ mitpress/9780262013840.001.0001

Copyright American Psychological Association. Not for further distribution.

Calhoun, S. L., & Mayes, S. D. (2005). Processing speed in children with clinical disorders. Psychology in the Schools, 42, 333–343. http://dx.doi.org/10.1002/ pits.20067 Cao, Q., Sun, L., Gong, G., Lv, Y., Cao, X., Shuai, L., & Wang, Y. (2010). The macrostructural and microstructural abnormalities of corpus callosum in children with attention deficit/hyperactivity disorder: A combined morphometric and diffusion tensor MRI study. Brain Research, 1310(Supplement C), 172– 180. https://doi.org/10.1016/j.brainres.2009.10.031 Carmichael, J. A., Kubas, H. A., Carlson, H. L., Fitzer, K. R., Wilcox, G., Lemay, J. F., . . . Hale, J. B. (2015). Reconsidering “inattention” in attentiondeficit hyperactivity disorder: Implications for neuropsychological assessment and intervention. Applied Neuropsychology: Child, 4, 97–105. http:// dx.doi.org/10.1080/21622965.2015.1005481 Casey, B. J., Oliveri, M. E., & Insel, T. (2014). A neurodevelopmental perspective on the research domain criteria (RDoC) framework. Biological Psychiatry, 76, 350–353. http://dx.doi.org/10.1016/ j.biopsych.2014.01.006 Cha, J., Fekete, T., Siciliano, F., Biezonski, D., Greenhill, L., Pliszka, S. R., & Posner, J. (2015). Neural correlates of aggression in medication-naive children with ADHD: Multivariate analysis of morphometry and tractography. Neuropsychopharmacology, 40, 1717–1725. https://doi.org/10.1038/npp.2015.18 Chabernaud, C., Mennes, M., Kelly, C., Nooner, K., Di Martino, A., Castellanos, F. X., & Milham, M. P. (2012). Dimensional brain-behavior relationships in children with attention-deficit/hyperactivity disorder. Biological Psychiatry, 71, 434–442. https:// doi.org/10.1016/j.biopsych.2011.08.013 Chen, J., Silk, T., Seal, M., Dally, K., & Vance, A. (2013). Widespread decreased grey and white matter in pediatric obsessive-compulsive disorder (OCD): A voxel-based morphometric MRI study. Psychiatry Research: Neuroimaging, 213, 11–17. https://doi. org/10.1016/j.pscychresns.2013.02.003 Cheng, W., Ji, X., Zhang, J., & Feng, J. (2012). Individual classification of ADHD patients by integrating multiscale neuroimaging markers and advanced pattern recognition techniques. Frontiers in Systems Neuroscience, 6 August 2012. https://doi.org/10.3389/fnsys.2012.00058 Chong, T. T. J., Cunnington, R., Williams, M. A., Kanwisher, N., & Mattingley, J. B. (2008). fMRI adaptation reveals mirror neurons in human inferior parietal cortex. Current Biology, 18, 1576–1580. http://dx.doi.org/10.1016/j.cub.2008.08.068 116

Christensen, R., Van Ameringen, M., & Hall, G. (2015). Increased activity of frontal and limbic regions to emotional stimuli in children at-risk for anxiety disorders. Psychiatry Research: Neuroimaging, 233, 9–17. https://doi.org/10.1016/j.pscychresns.2015.04.004 Cullen, K. R., Westlund, M. K., Klimes-Dougan, B., Mueller, B. A., Houri, A., Eberly, L. E., & Lim, K. O. (2014). Abnormal amygdala resting-state functional connectivity in adolescent depression. JAMA Psychiatry, 71, 1138–1147. https://doi.org/10.1001/ jamapsychiatry.2014.1087 Cummings, C. M., Caporino, N. E., & Kendall, P. C. (2014). Comorbidity of anxiety and depression in children and adolescents: 20 years after. Psychological Bulletin, 140, 816–845. http://dx.doi.org/ 10.1037/a0034733 Cuthbert, B. N. (2014). The RDoC framework: Facilitating transition from ICD/DSM to dimensional approaches that integrate neuroscience and psychopathology. World Psychiatry, 13, 28–35. http://dx.doi.org/10.1002/wps.20087 Cuthbert, B. N., & Insel, T. R. (2013). Toward the future of psychiatric diagnosis: The seven pillars of RDoC. BMC Medicine, 11, 126. http://dx.doi.org/ 10.1186/1741-7015-11-126 Davidson, R. J. (2001). Toward a biology of personality and emotion. Annals of the New York Academy of Sciences, 935, 191–207. http://dx.doi.org/ 10.1111/j.1749-6632.2001.tb03481.x Diamond, A. (2005). Attention-deficit disorder (attention-deficit/hyperactivity disorder without hyperactivity): A neurobiologically and behaviorally distinct disorder from attention-deficit/hyperactivity disorder (with hyperactivity). Development and Psychopathology, 17, 807–825. http://dx.doi.org/ 10.1017/S0954579405050388 Dudley, K. J., Li, X., Kobor, M. S., Kippin, T. E., & Bredy, T. W. (2011). Epigenetic mechanisms mediating vulnerability and resilience to psychiatric disorders. Neuroscience and Biobehavioral Reviews, 35, 1544–1551. http://dx.doi.org/10.1016/ j.neubiorev.2010.12.016 Etkin, A., & Schatzberg, A. F. (2011). Common abnormalities and disorder-specific compensation during implicit regulation of emotional processing in generalized anxiety and major depressive disorders. American Journal of Psychiatry, 168, 968–978. https:// doi.org/10.1176/appi.ajp.2011.10091290 Fahim, C., He, Y., Yoon, U., Chen, J., Evans, A., & Pérusse, D. (2011). Neuroanatomy of childhood disruptive behavior disorders. Aggressive Behavior, 37, 326–337. https://doi.org/10.1002/ab.20396 Fairchild, G., Hagan, C. C., Walsh, N. D., Passamonti, L., Calder, A. J., & Goodyer, I. M. (2013). Brain structure abnormalities in adolescent girls with

Recognizing Frontal-Subcortical Circuit Dimensions in Child and Adolescent Neuropsychopathology

conduct disorder. Journal of Child Psychology and Psychiatry, 54, 86–95. https://doi.org/10.1111/ j.1469-7610.2012.02617.x

Copyright American Psychological Association. Not for further distribution.

Fairchild, G., Passamonti, L., Hurford, G., Hagan, C. C., von dem Hagen, E. A. H., van Goozen, S. H. M., Calder, A. J. (2011). Brain structure abnormalities in early-onset and adolescent-onset conduct disorder. American Journal of Psychiatry, 168, 624–633. https://doi.org/10.1176/appi Fallucca, E., MacMaster, F. P., Haddad, J., Easter, P., Dick, R., May, G., & Rosenberg, D. R. (2011). Distinguishing between major depressive disorder and obsessive-compulsive disorder in children by measuring regional cortical thickness. Archives of General Psychiatry, 68, 527–533. https://doi. org/10.1001/archgenpsychiatry.2011.36

Giedd, J. N., Lalonde, F. M., Celano, M. J., White, S. L., Wallace, G. L., Lee, N. R., & Lenroot, R. K. (2009). Anatomical brain magnetic resonance imaging of typically developing children and adolescents. Journal of the American Academy of Child and Adolescent Psychiatry, 48, 465–470. http://dx.doi.org/ 10.1097/CHI.0b013e31819f2715 Goldberg, E. (2001). The executive brain: Frontal lobes and the civilized mind. New York, NY: Oxford University Press. Gorno-Tempini, M. L., & Price, C. J. (2001). Identification of famous faces and buildings: A functional neuroimaging study of semantically unique items. Brain: A Journal of Neurology, 124, 2087–2097. http://dx.doi.org/10.1093/ brain/124.10.2087

Fetterman, A. K., Ode, S., & Robinson, M. D. (2013). For which side the bell tolls: The laterality of approach-avoidance associative networks. Motivation and Emotion, 37, 33–38. http://dx.doi.org/10.1007/ s11031-012-9306-5

Grafton, S. T., Schmitt, P., Van Horn, J., & Diedrichsen, J. (2008). Neural substrates of visuomotor learning based on improved feedback control and prediction. NeuroImage, 39, 1383–1395. http://dx.doi.org/ 10.1016/j.neuroimage.2007.09.062

Figee, M., Vink, M., de Geus, F., Vulink, N., Veltman, D. J., Westenberg, H., & Denys, D. (2011). Dysfunctional reward circuitry in obsessivecompulsive disorder. Biological Psychiatry, 69, 867– 874. https://doi.org/10.1016/j.biopsych.2010.12.003

Hahn, A., Stein, P., Windischberger, C., Weissenbacher, A., Spindelegger, C., Moser, E., & Lanzenberger, R. (2011). Reduced resting-state functional connectivity between amygdala and orbitofrontal cortex in social anxiety disorder. NeuroImage, 56, 881–889. https:// doi.org/10.1016/j.neuroimage.2011.02.064

Fiorello, C. A., Hale, J. B., Decker, S. L., & Coleman, S. (2009). Neuropsychology in school psychology. In E. García-Vázquez, T. D. Crespi, & C. Riccio (Eds.), Handbook of education, training, and supervision of school psychologists in school and community: Vol. 1. Foundations of professional practice (pp. 213–232). New York, NY: Routledge. Fiorello, C. A., Hale, J. B., & Wycoff, K. L. (2012). Cognitive hypothesis testing: Linking test results to the real world. In D. P. Flanagan & P. L. Harrison (Eds.), Contemporary intellectual assessment: Theories, tests, and issues (3rd ed., pp. 484–496). New York, NY: Guilford Press. Fitzer-Attas, C., Fazio, P., Mrzljak, L., Martinsson, S., Landwehrmeyer, B., Bronzova, J., & Varrone, A. (2014). Imaging of phosphodiesterase 10A (PDE10A) enzyme and D2 receptor levels in the living brain of Huntington’s disease gene expansion carriers and healthy controls. Movement Disorders, 29, 1843. Fox, M. D., & Raichle, M. E. (2007). Spontaneous fluctuations in brain activity observed with functional magnetic resonance imaging. Nature Reviews Neuroscience, 8, 700–711. http://dx.doi.org/ 10.1038/nrn2201 Frodl, T., Stauber, J., Schaaff, N., Koutsouleris, N., Scheuerecker, J., Ewers, M., & Meisenzahl, E. (2010). Amygdala reduction in patients with ADHD compared with major depression and healthy volunteers. Acta Psychiatrica Scandinavica, 121, 111–118. https:// doi.org/10.1111/j.1600-0447.2009.01489.x

Hain, L. A., & Hale, J. B. (2010). “Nonverbal” learning disabilities or Asperger syndrome? Clarification through cognitive hypothesis testing. Comprehensive Evaluations: Case Reports for Psychologists, Diagnosticians, and Special Educators, 372–387. Hale, J. B., Chen, S. A., Tan, S. C., Poon, K., Fitzer, K. R., & Boyd, L. A. (2016). Reconciling individual differences with collective needs: The juxtaposition of sociopolitical and neuroscience perspectives on remediation and compensation of student skill deficits. Trends in Neuroscience and Education, 5, 41–51. http://dx.doi.org/10.1016/j.tine.2016.04.001 Hale, J. B., & Fiorello, C. A. (2004). School neuropsychology: A practitioner’s handbook. New York, NY: Guilford Press. Hale, J. B., & Fitzer, K. R. (2015). Evaluating orbitalventral medial system regulation of personal attention: A critical need for neuropsychological assessment and intervention. Applied Neuropsychology: Child, 4, 106–115. http://dx.doi.org/ 10.1080/21622965.2015.1005486 Hale, J. B., Reddy, L. A., Decker, S. L., Thompson, R., Henzel, J., Teodori, A., . . . Denckla, M. B. (2009). Development and validation of an attention-deficit/ hyperactivity disorder (ADHD) executive function and behavior rating screening battery. Journal of Clinical and Experimental Neuropsychology, 31, 897–912. http://dx.doi.org/10.1080/ 13803390802687423

117

Hale, Reddy, and Weissman

Copyright American Psychological Association. Not for further distribution.

Hale, J. B., Reddy, L. A., Weissman, A. S., Lukie, C., & Schneider, A. N. (2013). Integration of neuropsychological assessment and intervention for children with ADHD. In L. A. Reddy, A. S. Weissman, & J. B. Hale (Eds.), Neuropsychological assessment and intervention for youth: An evidencebased approach to emotional and behavioral disorders (pp. 127–154). Washington, DC: American Psychological Association. Hale, J. B., Reddy, L. A., Wilcox, G., McLaughlin, A., Hain, L., Stern, A., & Eusebio, E. (2009). Assessment and intervention for children with ADHD and other frontal-striatal circuit disorders. Best practices in School Neuropsychology: Guidelines for Effective Practice, Assessment and Evidence-Based Interventions, 225–280. Hale, J. B., Semrud-Clikeman, M., & Kubas, H. A. (2014). Best practices in medication treatment for children with emotional and behavior disorders: A primer for school psychologists. In A. Thomas & J. Grimes (Eds.), Best practices in school psychology—VI (pp. 347–360). Bethesda, MD: National Association of School Psychologists. Hale, J. B., Wilcox, G., & Reddy, L. A. (2016). Neuropsychological assessment. In J. C. Norcross, G. R.VandenBos, D. K. Freedheim, & R. Krishnamurthy (Eds.), APA handbook of clinical psychology: Applications and methods (pp. 139–165). http:// dx.doi.org/10.1037/14861-007 Hale, J. B., Yim, M., Schneider, A. N., Wilcox, G., Henzel, J. N., & Dixon, S. G. (2012). Cognitive and neuropsychological assessment of ADHD: Redefining a disruptive behavior disorder. In D. P. Flanagan & P. Harrison (Eds.), Contemporary intellectual assessment: Theories, tests, and issues (3rd ed., pp. 687–707). New York, NY: Guilford Press. Haney-Caron, E., Caprihan, A., & Stevens, M. C. (2014). DTI-measured white matter abnormalities in adolescents with conduct disorder. Journal of Psychiatric Research, 48, 111–120. https://doi.org/ 10.1016/j.jpsychires.2013.09.015 Hardee, J. E., Benson, B. E., Bar-Haim, Y., Mogg, K., Bradley, B. P., Chen, G., & Pérez-Edgar, K. (2013). Patterns of neural connectivity during an attention bias task moderate associations between early childhood temperament and internalizing symptoms in young adulthood. Biological Psychiatry, 74, 273–279. https://doi.org/10.1016/ j.biopsych.2013.01.036 Harmon-Jones, E., Gable, P. A., & Peterson, C. K. (2010). The role of asymmetric frontal cortical activity in emotion-related phenomena: A review and update. Biological Psychology, 84, 451–462. http://dx.doi.org/ 10.1016/j.biopsycho.2009.08.010 Hebb, D. O. (1949). Organization of behavior: A neuropsychological theory. New York, NY: Wiley. 118

Hecht, D. (2010). Depression and the hyperactive righthemisphere. Neuroscience Research, 68, 77–87. http:// dx.doi.org/10.1016/j.neures.2010.06.013 Helpern, J. A., Adisetiyo, V., Falangola, M. F., Caixia, H., Di Martino, A., Williams, K., … Jensen, J. H. (2011). Preliminary evidence of altered gray and white matter microstructural development in the frontal lobe of adolescents with attention-deficit hyperactivity disorder: A diffusional kurtosis imaging study. Journal of Magnetic Resonance Imaging, 33, 17–23. https://doi.org/10.1002/ jmri.22397 Houk, J. C., Bastianen, C., Fansler, D., Fishbach, A., Fraser, D., Reber, P. J., . . . Simo, L. S. (2007). Action selection and refinement in subcortical loops through basal ganglia and cerebellum. Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences, 362, 1573–1583. http://dx.doi.org/ 10.1098/rstb.2007.2063 Hung, Y., Saygin, Z. M., Biederman, J., Hirshfeld-Becker, D., Uchida, M., Doehrmann, O., & Gabrieli, J. D. E. (2017). Impaired frontal-limbic white matter maturation in children at risk for major depression. Cerebral Cortex, 27, 4478–4491. https://doi.org/ 10.1093/cercor/bhw250 Hyatt, C. J., Haney-Caron, E., & Stevens, M. C. (2012). Cortical thickness and folding deficits in conductdisordered adolescents. Biological Psychiatry, 72, 207– 214. https://doi.org/10.1016/j.biopsych.2011.11.017 Imamizu, H., & Kawato, M. (2009). Brain mechanisms for predictive control by switching internal models: Implications for higher-order cognitive functions. Psychological Research, 73, 527–544. http://dx.doi.org/ 10.1007/s00426-009-0235-1 Ito, M. (2008). Control of mental activities by internal models in the cerebellum. Nature Reviews Neuroscience, 9, 304–313. http://dx.doi.org/10.1038/nrn2332 Iuculano, T., Rosenberg-Lee, M., Richardson, J., Tenison, C., Fuchs, L., Supekar, K., & Menon, V. (2015). Cognitive tutoring induces widespread neuroplasticity and remediates brain function in children with mathematical learning disabilities. Nature Communications, 6, 8453. http://dx.doi.org/ 10.1038/ncomms9453 Kaplan, B. J., Dewey, D. M., Crawford, S. G., & Wilson, B. N. (2001). The term comorbidity is of questionable value in reference to developmental disorders: Data and theory. Journal of Learning Disabilities, 34, 555–565. http://dx.doi. org/10.1177/002221940103400608 Kendall, P. C., Compton, S. N., Walkup, J. T., Birmaher, B., Albano, A. M., Sherrill, J., . . . Piacentini, J. (2010). Clinical characteristics of anxiety disordered youth. Journal of Anxiety Disorders, 24, 360–365. http:// dx.doi.org/10.1016/j.janxdis.2010.01.009

Recognizing Frontal-Subcortical Circuit Dimensions in Child and Adolescent Neuropsychopathology

Kendler, K. S., Zachar, P., & Craver, C. (2011). What kinds of things are psychiatric disorders? Psychological Medicine, 41, 1143–1150. http://dx.doi.org/ 10.1017/S0033291710001844 Kolb, B., & Gibb, R. (2014). Searching for the principles of brain plasticity and behavior. Cortex: A Journal Devoted to the Study of the Nervous System and Behavior, 58, 251–260. http://dx.doi.org/10.1016/ j.cortex.2013.11.012

Copyright American Psychological Association. Not for further distribution.

Kolb, B., Whishaw, I. Q., & Teskey, G. C. (2016). An introduction to brain and behavior (5th ed.). London, England: Worth. Koziol, L. F., & Budding, D. (2012). Requiem for a diagnosis: Attention-deficit hyperactivity disorder. Applied Neuropsychology: Child, 1, 2–5. http://dx.doi.org/ 10.1080/21622965.2012.665774 Koziol, L. F., & Budding, D. E. (2009). Subcortical structures and cognition: Implications for neuropsychological assessment. http://dx.doi.org/ 10.1007/978-0-387-84868-6 Koziol, L. F., Budding, D. E., & Hale, J. B. (2013). Understanding neuropsychopathology in the 21st century: Current status, clinical application, and future directions. In L. A. Reddy, A. S. Weissman, & J. B. Hale (Eds.), Neuropsychological assessment and intervention for youth: An evidence-based approach to emotional and behavioral disorders (pp. 327–346). Washington, DC: American Psychological Association. Lambek, R., Tannock, R., Dalsgaard, S., Trillingsgaard, A., Damm, D., & Thomsen, P. H. (2010). Validating neuropsychological subtypes of ADHD: How do children with and without an executive function deficit differ? Journal of Child Psychology and Psychiatry, 51, 895–904. http://dx.doi.org/10.1111/ j.1469-7610.2010.02248.x Lezak, M. D., Howieson, D. B., Bigler, E. D., & Tranel, D. (2012). Neuropsychological assessment (5th ed.). New York, NY: Oxford University Press. Liao, W., Chen, H., Feng, Y., Mantini, D., Gentili, C., Pan, Z., & Zhang, W. (2010). Selective aberrant functional connectivity of resting state networks in social anxiety disorder. NeuroImage, 52, 1549–1558. https://doi.org/10.1016/j.neuroimage.2010.05.010 Lilienfeld, S. O. (2003). Comorbidity between and within childhood externalizing and internalizing disorders: Reflections and directions. Journal of Abnormal Child Psychology, 31, 285–291. http://dx.doi.org/ 10.1023/A:1023229529866 Luria, A. R. (1973). The working brain. An introduction to neuropsychology. New York, NY: Basic Books. Mahone, E. M., Crocetti, D., Ranta, M. E., Gaddis, A., Cataldo, M., Slifer, K. J., & Mostofsky, S. H. (2011). A preliminary neuroimaging study of preschool children

with ADHD. The Clinical Neuropsychologist, 25, 1009– 1028. https://doi.org/10.1080/13854046.2011.580784 Maj, M. (2005). “Psychiatric comorbidity”: An artefact of current diagnostic systems? British Journal of Psychiatry, 186, 182–184. http://dx.doi.org/10.1192/ bjp.186.3.182 Marsh, A. A., Finger, E. C., Fowler, K. A., Adalio, C. J., Jurkowitz, I. T. N., Schechter, J. C., & Blair, R. J. R. (2013). Empathic responsiveness in amygdala and anterior cingulate cortex in youths with psychopathic traits. Journal of Child Psychology and Psychiatry, 54, 900–910. https://doi.org/10.1111/jcpp.12063 Marsh, A. A., Finger, E. C., Fowler, K. A., Jurkowitz, I. T. N., Schechter, J. C., Yu, H. H., & Blair, R. J. R. (2011). Reduced amygdala–orbitofrontal connectivity during moral judgments in youths with disruptive behavior disorders and psychopathic traits. Psychiatry Research: Neuroimaging, 194, 279–286. https://doi.org/10.1016/ j.pscychresns.2011.07.008 Mash, E. J., & Barkley, R. A. (2002). Child psychopathology (2nd ed.). New York, NY: Guilford Press. Mathias, J. L., & Wheaton, P. (2007). Changes in attention and information-processing speed following severe traumatic brain injury: A metaanalytic review. Neuropsychology, 21, 212–223. http://dx.doi.org/10.1037/0894-4105.21.2.212 Molinari, M., Restuccia, D., & Leggio, M. G. (2009). State estimation, response prediction, and cerebellar sensory processing for behavioral control. Cerebellum, 8, 399–402. http://dx.doi.org/10.1007/s12311-009-0112-x Montes, L. G. A., Alcantara, H. P., Garcia, R. B. M., De La Torre, L. B., Acosta, D. A., & Duarte, M. G. (2013). Brain cortical thickness in ADHD: Age, sex, and clinical correlations. Journal of Attention Disorders, 17, 641–654. https://doi.org/10.1177/1087054711434351 Moon, C.-M., Kim, G.-W., & Jeong, G.-W. (2014). Whole-brain gray matter volume abnormalities in patients with generalized anxiety disorder: Voxelbased morphometry. NeuroReport, 25, 184. https:// doi.org/10.1097/WNR.0000000000000100 Okada, K., Ota, T., Iida, J., Kishimoto, N., & Kishimoto, T. (2013). Lower prefrontal activity in adults with obsessive–compulsive disorder as measured by near-infrared spectroscopy. Progress in NeuroPsychopharmacology and Biological Psychiatry, 43(Supplement C), 7–13. https://doi.org/10.1016/ j.pnpbp.2012.11.013 Ozonoff, S. (2015). Editorial: Early detection of mental health and neurodevelopmental disorders: The ethical challenges of a field in its infancy. Journal of Child Psychology and Psychiatry, 56, 933–935. http:// dx.doi.org/10.1111/jcpp.12452 Passamonti, L., Fairchild, G., Fornito, A., Goodyer, I. M., Nimmo-Smith, I., Hagan, C. C., & Calder, A. J. (2012). Abnormal anatomical connectivity between 119

Hale, Reddy, and Weissman

the amygdala and orbitofrontal cortex in conduct disorder. PloS One, 7, e48789. http://dx.doi.org/ 10.1371/journal.pone.0048789 Paulesu, E., Sambugaro, E., Torti, T., Danelli, L., Ferri, F., Scialfa, G., & Sassaroli, S. (2010). Neural correlates of worry in generalized anxiety disorder and in normal controls: A functional MRI study. Psychological Medicine, 40, 117–124. https://doi.org/ 10.1017/S0033291709005649

Copyright American Psychological Association. Not for further distribution.

Pennington, B. F., & Ozonoff, S. (1996). Executive functions and developmental psychopathology. Journal of Child Psychology and Psychiatry, 37, 51–87. http:// dx.doi.org/10.1111/j.1469-7610.1996.tb01380.x Pincus, H. A., Tew, J. D., & First, M. B. (2004). Psychiatric comorbidity: Is more less? World Psychiatry, 3, 18–23. Posner, J., Maia, T. V., Fair, D., Peterson, B. S., SonugaBarke, E. J., & Nagel, B. J. (2011). The attenuation of dysfunctional emotional processing with stimulant medication: An fMRI study of adolescents with ADHD. Psychiatry Research: Neuroimaging, 193, 151–160. https://doi.org/10.1016/j.pscychresns.2011.02.005 Qiu, M., Ye, Z., Li, Q., Liu, G., Xie, B., & Wang, J. (2011). Changes of brain structure and function in ADHD children. Brain Topography, 24, 243–252. https:// doi.org/10.1007/s10548-010-0168-4 Redcay, E., & Courchesne, E. (2005). When is the brain enlarged in autism? A meta-analysis of all brain size reports. Biological Pschiatry, 58, 1–9. http://dx.doi.org/ 10.1016/j.biopsych.2005.03.026 Reddy, L. A., Weissman, A., & Hale, J. B. (2013). Neuropsychological assessment and intervention for emotional and behavior disordered youth: An integrated step-by-step evidence-based approach. Washington, DC: American Psychological Association. Riccio, C. A., & Reynolds, C. R. (2013). Principles of neuropsychological assessment in children and adolescents. In C. R. Reynolds & V. L. Schwean (Eds.), The Oxford handbook of child psychological assessment (pp. 286–330). New York, NY: Oxford University Press. Richter-Levin, G., & Akirav, I. (2000). Amygdalahippocampus dynamic interaction in relation to memory. Molecular Neurobiology, 22, 11–20. http:// dx.doi.org/10.1385/MN:22:1-3:011 Russo, V. E., Martienssen, R. A., & Riggs, A. D. (1996). Epigenetic mechanisms of gene regulation. Cold Spring Harbor, NY: Cold Spring Harbor Laboratory Press. Sarkar, S., Craig, M. C., Catani, M., Dell’Acqua, F., Fahy, T., Deeley, Q., & Murphy D. G. M. (2013). Frontotemporal white-matter microstructural abnormalities in adolescents with conduct disorder: A diffusion tensor imaging study. Psychological Medicine, 43, 401–411. https://doi.org/10.1017/S003329171200116X 120

Sauder, C. L., Beauchaine, T. P., Gatzke-Kopp, L. M., Shannon, K. E., & Aylward, E. (2012). Neuroanatomical correlates of heterotypic comorbidity in externalizing male adolescents. Journal of Clinical Child & Adolescent Psychology, 41, 346–352. https:// doi.org/10.1080/15374416.2012.658612 Schienle, A., Ebner, F., & Schäfer, A. (2011). Localized gray matter volume abnormalities in generalized anxiety disorder. European Archives of Psychiatry and Clinical Neuroscience, 261, 303–307. https:// doi.org/10.1007/s00406-010-0147-5 Schmahmann, J. D. (2004). Disorders of the cerebellum: Ataxia, dysmetria of thought, and the cerebellar cognitive affective syndrome. Journal of Neuropsychiatry and Clinical Neurosciences, 16, 367–378. http://dx.doi.org/10.1176/jnp.16.3.367 Schmahmann, J. D., Pandya, D. N., Wang, R., Dai, G., D’Arceuil, H. E., de Crespigny, A. J., & Wedeen, V. J. (2007). Association fibre pathways of the brain: Parallel observations from diffusion spectrum imaging and autoradiography. Brain: A Journal of Neurology, 130, 630–653. http://dx.doi.org/10.1093/ brain/awl359 Schneider, A. N., Parker, D. J., Crevier-Quintin, E., Kubas, H. A., & Hale, J. B. (2013). Luria and learning: How neuropsychological theory translates into educational practice. In B. J. Irby, G. Brown, & R. Lara-Alecio (Eds.), Handbook of educational theories (pp. 751–760). Charlotte, NC: Information Age. Semrud-Clikeman, M., & Ellison, P. A. T. (2009). Child neuropsychology: Assessment and interventions for neurodevelopmental disorders (2nd ed.). http://dx.doi.org/ 10.1007/978-0-387-88963-4 Semrud-Clikeman, M., Fine, J. G., Bledsoe, J., & Zhu, D. C. (2017). Regional volumetric differences based on structural MRI in children with two subtypes of ADHD and controls. Journal of Attention Disorders, 21, 1040– 1049. https://doi.org/10.1177/1087054714559642 Sergeant, J. A., Geurts, H., & Oosterlaan, J. (2002). How specific is a deficit of executive functioning for attention-deficit/hyperactivity disorder? Behavioural Brain Research, 130, 3–28. http://dx.doi.org/10.1016/ S0166-4328(01)00430-2 Shaw, P., Gilliam, M., Liverpool, M., Weddle, C., Malek, M., Sharp, W., & Giedd, J. (2011). Cortical development in typically developing children with symptoms of hyperactivity and impulsivity: Support for a dimensional view of attention deficit hyperactivity disorder. American Journal of Psychiatry, 168, 143–151. https://doi.org/10.1176/ appi.ajp.2010.10030385 Shaywitz, B. A., Shaywitz, S. E., Blachman, B. A., Pugh, K. R., Fulbright, R. K., Skudlarski, P., . . . Gore, J. C. (2004). Development of left occipitotemporal

Recognizing Frontal-Subcortical Circuit Dimensions in Child and Adolescent Neuropsychopathology

Copyright American Psychological Association. Not for further distribution.

systems for skilled reading in children after a phonologically- based intervention. Biological Psychiatry, 55, 926–933. http://dx.doi.org/10.1016/ j.biopsych.2003.12.019

measures and ratings of executive function assess the same construct? Journal of Child Psychology and Psychiatry, 54, 131–143. http://dx.doi.org/10.1111/ jcpp.12001

Simos, P. G., Fletcher, J. M., Sarkari, S., Billingsley, R. L., Denton, C., & Papanicolaou, A. C. (2007). Altering the brain circuits for reading through intervention: A magnetic source imaging study. Neuropsychology, 21, 485–496. http://dx.doi.org/10.1037/ 0894-4105.21.4.485

Uddin, L. Q., Kaplan, J. T., Molnar-Szakacs, I., Zaidel, E., & Iacoboni, M. (2005). Self-face recognition activates a frontoparietal “mirror” network in the right hemisphere: An event-related fMRI study. NeuroImage, 25, 926–935. http:// dx.doi.org/10.1016/j.neuroimage.2004.12.018

Somerville, L. H., & Casey, B. J. (2010). Developmental neurobiology of cognitive control and motivational systems. Current Opinion in Neurobiology, 20, 236–241. http://dx.doi.org/10.1016/j.conb.2010.01.006

Via, E., Cardoner, N., Pujol, J., Alonso, P., López-Solà, M., Real, E., & Harrison, B. J. (2014). Amygdala activation and symptom dimensions in obsessive-compulsive disorder. The British Journal of Psychiatry, 204, 61–68. https://doi.org/10.1192/bjp.bp.112.123364

Stahl, S. M. (2013). Stahl’s essential psychopharmacology: Neuroscientific basis and practical applications. New York, NY: Cambridge University Press. Stevens, M. C., & Haney-Caron, E. (2012). Comparison of brain volume abnormalities between ADHD and conduct disorder in adolescence. Journal of Psychiatry & Neuroscience, 37, 389–398. https://doi. org/10.1503/jpn.110148 Strawn, J. R., Wehry, A. M., Chu, W.-J., Adler, C. M., Eliassen, J. C., Cerullo, M. A., & DelBello, M. P. (2013). Neuroanatomic abnormalities in adolescents with generalized anxiety disorder: A voxel-based morphometry study. Depression and Anxiety, 30, 842–848. https://doi.org/10.1002/da.22089 Stuss, D. T. (2011). Functions of the frontal lobes: Relation to executive functions. Journal of the International Neuropsychological Society, 17, 759–765. http://dx.doi.org/10.1017/ S1355617711000695 Stuss, D. T., & Alexander, M. P. (2000). Executive functions and the frontal lobes: A conceptual view. Psychological Research, 63, 289–298. http://dx.doi.org/ 10.1007/s004269900007 Sweatt, J. D. (2016). Neural plasticity and behavior—Sixty years of conceptual advances. Journal of Neurochemistry, 139, 179–199. http:// dx.doi.org/10.1111/jnc.13580 Takahashi, T., Yücel, M., Lorenzetti, V., Tanino, R., Whittle, S., Suzuki, M., & Allen, N. B. (2010). Volumetric MRI study of the insular cortex in individuals with current and past major depression. Journal of Affective Disorders, 121, 231–238. https:// doi.org/10.1016/j.jad.2009.06.003 Tomasi, D., & Volkow, N. D. (2012). Abnormal functional connectivity in children with attention-deficit/hyperactivity disorder. Biological Psychiatry, 71, 443–450. https://doi.org/10.1016/j. biopsych.2011.11.003 Toplak, M. E., West, R. F., & Stanovich, K. E. (2013). Practitioner review: Do performance-based

Viding, E., Fontaine, N. M., & McCrory, E. J. (2012). Antisocial behaviour in children with and without callous-unemotional traits. Journal of the Royal Society of Medicine, 105, 195–200. https:// doi.org/10.1258/jrsm.2011.110223 Wall, N. R., De La Parra, M., Callaway, E. M., & Kreitzer, A. C. (2013). Differential innervation of direct- and indirect-pathway striatal projection neurons. Neuron, 79, 347–360. http://dx.doi.org/10.1016/ j.neuron.2013.05.014 Wallace, G. L., White, S. F., Robustelli, B., Sinclair, S., Hwang, S., Martin, A., & Blair, R. J. R. (2014). Cortical and subcortical abnormalities in youths with conduct disorder and elevated callous-unemotional traits. Journal of the American Academy of Child & Adolescent Psychiatry, 53, 456–465.e1. https:// doi.org/10.1016/j.jaac.2013.12.008 Walther, S., Hügli, S., Höfle, O., Federspiel, A., Horn, H., Bracht, T., & Müller, T. J. (2012). Frontal white matter integrity is related to psychomotor retardation in major depression. Neurobiology of Disease, 47, 13–19. https://doi.org/10.1016/j.nbd.2012.03.019 Wang, Y., Horst, K. K., Kronenberger, W. G., Hummer, T. A., Mosier, K. M., Kalnin, A. J., … Mathews, V. P. (2012). White matter abnormalities associated with disruptive behavior disorder in adolescents with and without attention-deficit/hyperactivity disorder. Psychiatry Research: Neuroimaging, 202, 245–251. https://doi.org/10.1016/j.pscychresns.2012.01.005 Wasserman, T., & Wasserman, L. D. (2012). The sensitivity and specificity of neuropsychological tests in the diagnosis of attention deficit hyperactivity disorder. Applied Neuropsychology: Child, 1, 90–99. http://dx.doi.org/10.1080/21622965.2012.702025 Witsken, D. E., D’Amato, R. C., & Hartlage, L. C. (2008). Understanding the past, present, and future of clinical neuropsychology. Essentials of neuropsychological assessment: Treatment planning for rehabilitation (2nd ed., pp. 3–29). New York, NY: Springer. 121

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Yang, T. T., Simmons, A. N., Matthews, S. C., Tapert, S. F., Frank, G. K., Max, J. E., & Paulus, M. P. (2010). Adolescents with major depression demonstrate increased amygdala activation. Journal of the American Academy of Child & Adolescent Psychiatry, 49, 42–51. https://doi.org/10.1016/j.jaac.2009.09.004 Zald, D. H., & Andreotti, C. (2010). Neuropsychological assessment of the orbital and ventromedial prefrontal cortex. Neuropsychologia, 48, 3377–3391. http:// dx.doi.org/10.1016/j.neuropsychologia.2010.08.012

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Zarei, M., Mataix-Cols, D., Heyman, I., Hough, M., Doherty, J., Burge, L., & James, A. (2011).

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Changes in gray matter volume and white matter microstructure in adolescents with obsessivecompulsive disorder. Biological Psychiatry, 70, 1083– 1090. https://doi.org/10.1016/j.biopsych.2011.06.032 Zhong, M., Wang, X., Xiao, J., Yi, J., Zhu, X., Liao, J., & Yao, S. (2011). Amygdala hyperactivation and prefrontal hypoactivation in subjects with cognitive vulnerability to depression. Biological Psychology, 88, 233–242. https://doi.org/10.1016/ j.biopsycho.2011.08.007

Chapter 7

Personality Assessment in Children With Mental Health Problems

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Thomas M. Olino and Elizabeth P. Hayden

Even in infancy, humans show tremendous individual variation in activity level, positive emotions, irritability and fearfulness, and other early emerging behaviors. Later in early childhood, rapidly maturing neural systems contribute to the emergence of individual differences in self-regulation and executive control, alongside other increasingly nuanced and differentiated traits that unfold during development. Personality refers to these characteristic individual differences in behavior, emotional tendencies, and thinking that are relatively stable across time, and can be used to explain and predict human behavior, including mental health problems. Although interest in the concept of personality is literally ancient, until recently, modern personality science has focused largely on adults to the neglect of earlier developmental stages. Having said that, a largely independent literature examines individual difference factors or temperament in children. Historically, most experts held the notion that early emerging, biologically based temperament traits provided the foundation for the subsequent development of personality traits, which were shaped more powerfully by the environment than heritable influences. However, in recent years, there is consensus that personality and temperament are comparable in terms of heritability and stability, and are capturing the same phenomena (Caspi & Shiner, 2006), leading to efforts to integrate these two historically distinct lines of research (Shiner & Caspi, 2003) toward a lifespan perspective on individual differences. Such an approach can generate

important new knowledge concerning when and how specific traits emerge, the stability of traits across the lifespan, and associations between early individual differences and important developmental outcomes, such as mental health. Models of Personality/ Temperament Children’s personality is a “moving target” because of the increased differentiation and expansion of individual difference factors that occurs early in development. These rapid changes pose a challenge to researchers and clinicians, particularly in terms of assessment but also regarding taxonomy development, with no firm consensus in the field regarding which higher-order structure best captures the fundamental traits of childhood. In sharp contrast, an extensive literature rooted in factor-analytic methods supports the notion that adult personality can be captured by a hierarchical Big Five model comprised of five broad traits (neuroticism, extraversion, conscientiousness, agreeableness, and openness to experience), each of which can be further decomposed into more narrow-band traits (e.g., Goldberg, 1993). Of importance to developmental researchers, these five traits can be further reduced to a Big Three that includes positive emotionality (PE), negative emotionality (NE), and disinhibition, trait concepts that show marked conceptual overlap with preeminent models of individual differences in childhood. Specifically, Rothbart and Bates (2006)

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developed a highly influential model of child temperament comprised of an array of lower-order traits subsumed within three higher-order factors: extraversion/surgency, negative affectivity, and effortful control (EC), the last of which bears reasonable conceptual overlap with (low) disinhibition from adult Big Three models, as well as low conscientiousness and low agreeableness from the Big Five model. Openness to experience is not represented in the Big Three nor in child temperament models, of note in the present context given its potential role as a vulnerability for psychotic disorders (Chmielewski, Bagby, Markon, Ring, & Ryder, 2014). Despite emerging from distinct research perspectives in many cases, most child temperament/ personality questionnaires capture content related to the Big Three traits, which could facilitate examining individual difference factors across the lifetime. However, the methodological limitations of the approaches applied to the development of most models of child temperament/personality limits the ability to draw firm conclusions about the nature of child temperament structure. How Are Child Personality/ Temperament and Mental Health Outcomes Related? Despite unresolved issues concerning the nature of child personality, research supports the notion that personality traits play an important role in children’s mental health outcomes (e.g., Kagan et al., 2007). The more extensive work on adult personality and psychopathology indicates that personality serves as an intermediate marker of vulnerability (e.g., Canli, Congdon, Constable, & Lesch, 2008), thereby potentially aiding in the identification of relatively homogeneous groups of individuals within diagnostic categories that differ in trajectories and underlying etiology. Other findings with adults show that personality can successfully inform the development of tailored interventions (Quilty et al., 2008; Zinbarg, Uliaszek, & Adler, 2008). Although analogous work with children is scarce, available evidence indicates that personality may provide a means of identifying at-risk youth most likely to need early intervention (Kovacs & Lopez-Duran, 2010). Understanding 124

child personality within the mental health context may provide important benefits with respect to intervention and treatment/therapy. The relationship between children’s personalities and psychopathology is, however, complex, with behavioral and conceptual overlap characterizing many traits and disorders. For example, virtually all childhood mental health problems are characterized by high NE. Symptoms that can also be construed as aspects of low EC are also common in externalizing syndromes, whereas the fearful/wary behavior in the context of novelty that characterizes behavioral inhibition (BI; Kagan, 1997) is also found in many anxiety disorders. Distinguishing between personality and disorder at the level of measurement can be readily accomplished, for example, by removing test items that tap individual differences and mental health symptoms (e.g., Waldman, Singh, & Lahey, 2006); however, it is much more challenging to distinguish these constructs conceptually. There are various models that characterize the ways in which personality and disorder may be related, particularly in the context of depression (e.g., Klein, Kotov, & Bufferd, 2011), although associations between personality and other disorders can readily be understood within these frameworks. These include (a) the common cause model, in which personality and disorder have shared etiological influences; (b) the spectrum model, in which personality and disorder differ quantitatively rather than qualitatively; (c) personality as a precursor of disorder; (d) the vulnerability model, in which personality predisposes to disorder; (e) pathoplasticity, in which personality has influences the presentation of disorder; (f) personality features are statedependent concomitants of depressive episodes; and (g) scar model, in which personality features are consequences episodes of disorder. These models are important not only for theory, but in terms of their clinical implications as the direction of causality differs depending on the model; for example, the vulnerability model is more compatible with notions of prevention. Little work with children speaks clearly to which model or combination of models is supported in relating personality to disorder, an important direction for future work. A further complication noted by Klein et al. (2011) is that these

Personality Assessment in Children With Mental Health Problems

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models do not acknowledge the role of the wellestablished change in personality that occurs across the lifespan (e.g., Roberts & DelVecchio, 2000; Roberts & Mroczek, 2008). Although some degree of stability is a defining aspect of what we consider traits, child temperament and adult personality researchers acknowledge the malleability of traits over time, which has implications for models of how traits and disorder are related. We discuss this issue further later in this chapter. Children’s Personality and Mental Health: Which Traits Are Important? Extensive work shows cross-sectional associations between children’s individual difference factors and mental health, but longitudinal methods are a stronger methodology for establishing the relevance of child personality for adjustment. Child temperament has been tested as a predictor of later symptoms of psychopathology, changes in symptoms over time, and the onset of disorder in externalizing and internalizing symptom domains. Much of this work has focused on disinhibition and negative affectivity as predictors of children’s risk for externalizing disorders. For example, Caspi et al. (1996) found that multiple dimensions of child behavior at age 3 predicted psychopathology at age 21, with children rated as undercontrolled (i.e., impulsive and distractible) being nearly three times more likely to develop antisocial personality disorder and criminal involvement. In contrast, youth identified as inhibited (i.e., shy and low threshold to negative mood) were two times more likely to develop depressive disorders. Both dimensions were associated with suicide attempts and, in boys, with alcohol problems. In other seminal work from this group (Moffitt & Caspi, 2001), children’s early disinhibition and negativity were differentially related to the course of antisocial behavior in adolescence and adulthood, such that disinhibited and difficult children were more likely to exhibit a persistent pattern of antisocial behavior. Although compelling given the longterm scope of the study, these findings are limited by the use of broad temperament dimensions that include content from multiple narrow dimensions.

Recent studies have examined more clearly defined child temperament constructs as predictors of disorder onset. More specifically, work implicates low EC, high NE (particularly anger, a facet of the broader NE construct), and impulsivity with children’s externalizing problems (e.g., Eisenberg, Chang, Ma, & Huang, 2009; Eisenberg et al., 2005; Kotelnikova, Mackrell, Jordan, & Hayden, 2015). These same traits are also associated with internalizing problems in some studies, potentially driving the well-established comorbidity of children’s internalizing and externalizing mental health problems. For example, Bufferd et al. (2014) found that laboratory-observed low inhibitory control, a facet of EC, at age 3 was associated with onset of depressive disorders at age 6. Other traits have been implicated in predicting internalizing symptoms, including high BI and low PE (e.g., Durbin, Klein, Hayden, Buckley, & Moerk, 2005; Essex, Klein, Slattery, Goldsmith, & Kalin, 2010). A large literature indicates that early BI is associated with anxiety disorder in childhood (e.g., Hirshfeld-Becker, Biederman, Henin, Faraone, Davis, et al., 2007). Conversely, youth with greater disinhibition may be at risk for disruptive behavior disorders (e.g., Hirshfeld-Becker, Biederman, Henin, Faraone, Micco, et al., 2007), which could suggest that higher BI serves a protective function with respect to externalizing symptoms. In a multimethod longitudinal study, Dougherty et al. (2010) found that observed PE, but not NE, at age 3 predicted self-reported depressive symptoms at age 10 even after controlling for age 3 internalizing symptoms. They also found that maternal-reported child PE and NE interacted such that NE was associated with child depressive symptoms when youth had low PE, but not when youth had high PE. Therefore, there is important evidence for the role of combinations of temperament dimensions for the development of internalizing symptoms. Interest in associations between children’s traits and symptoms is widespread, but we are aware of only one published study on temperament predicting the course of disorder among children with specific diagnoses, which would be particularly useful information for those interested applying information on child personality to mental health prognosis. Bufferd et al. (2016) examined the influence of 125

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temperament on stability of anxiety disorders from age 3 to age 6. The authors found that higher levels of BI and lower levels of PE in laboratory observations and higher parental ratings of child BI predicted greater stability of anxiety disorders. The results of studies provide clues regarding which personality traits are relevant to children’s mental health problems, but much of this literature has relied on a single observer, usually a parent, for information on child personality and symptoms, likely inflating trait–symptom associations. Unsurprisingly, studies using independent assessment methods tend to yield smaller albeit meaningful associations between children’s personality and symptoms. Further, the content of questionnaires tapping child personality and symptoms often overlap, potentially inflating associations between the two. Additionally, more work is needed to establish whether children’s personality causally influences mental health. Within the context of children’s mental health, this issue could be explored through wellcontrolled experimental manipulations focused on altering children’s maladaptive traits or strengthening adaptive traits and subsequently testing whether such interventions increase children’s well-being or decrease psychopathological symptoms. Relevant work with children has largely focused on whether exercises designed to improve children’s EC and related constructs lead to adaptive outcomes (e.g., improved attention) primarily in academic domains. Such work supports the notion that interventions that focus on enhancing temperament-relevant constructs are of benefit to children (Diamond & Lee, 2011), although little is known about traits other than EC, nor whether trait-based interventions are effective in reducing mental health problems across a broader range of symptoms. Certainly, the literature focusing on the role of personality in the treatment of adult psychopathology (e.g., Quilty et al., 2008) indicates that such possibilities are worth exploring with children. Assessment Few measures have been developed with the specific goal of assessing child personality in the context of mental health problems. The methods and 126

instruments used to assess personality in children with mental health problems are generally the same as those used in the field to assess child personality for developmental or other nonclinical research. Whether this is problematic will be considered in the Future Directions section of this chapter. Additionally, although projective measures (e.g., Figure Drawing Test; Rorschach inkblot test) are used to assess personality in children, these methods are controversial and their reliability and validity are questionable (Lilienfeld, Wood, & Garb, 2000), and we do not review these measures in this chapter.

Assessment Methods: Questionnaires In the context of personality assessment in childhood, measures come from lines of research on temperament and personality. One of the first measures developed to characterize youth temperament was designed to assess a four-dimension model of temperament, the emotionality–activity– sociability–impulsivity model (Buss & Plomin, 1975, 1984). However, early in their work, Buss and Plomin (1984) found that impulsivity was not psychometrically sound, and it was therefore trimmed from subsequent iterations of the model. The three remaining dimensions are conceptually related to the Big Three dimensions of personality that characterize individual differences in adulthood (Shiner, 1998), with emotionality related to neuroticism, activity showing similarities to EC, and sociability related to social facets of the broader trait of extraversion. This measure was constructed exclusively for use as a parent report for children from preschool through the teen years. Other measures rooted in early models of temperament, the Dimensions of Temperament Survey (DOTS) and DOTS–Revised (Windle & Lerner, 1986), were developed to assess traits identified in Thomas, Chess, and Birch’s (1968) temperament model. On the basis of item-level factor analyses, the authors found support for eight dimensions: activity level, approach vs. withdrawal, flexibility, (negative) mood, rhythmicity, task orientation, distractibility, and persistence. There is good coverage of content related to neuroticism (mood), extraversion (approach vs. withdrawal, activity level), and EC (task orientation, distractibility, and persistence).

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Personality Assessment in Children With Mental Health Problems

Although rhythmicity also emerged as an important dimension, it is unclear how this dimension is related to Big Three or Big Five dimensions of personality. The DOTS–Revised can be administered to parents and youth, with early development work suggesting similar, acceptable psychometric functioning in toddlers, children, adolescents, and adults. One of the most commonly used set of parent reports in contemporary research on temperament comes from Rothbart and colleagues, who developed measures designed to assess temperament across the lifespan, including infancy (Infant Behavior Questionnaire; Gartstein & Rothbart, 2003), toddlerhood (Early Childhood Behavior Questionnaire; Putnam, Gartstein, & Rothbart, 2006), childhood (Child Behavior Questionnaire; Rothbart, Ahadi, Hershey, & Fisher, 2001), middle childhood (Temperament in Middle Childhood Questionnaire; Simonds, 2006), early adolescence (Early Adolescent Temperament Questionnaire; Capaldi & Rothbart, 1992), and adulthood (Adult Temperament Questionnaire; Evans & Rothbart, 2007). Although the number and nature of the a priori factors identified in each of the instruments differs, there is general similarity of the higher-order structures. Specifically, in the measures for infancy, toddlerhood, and childhood, three similar, broad-band dimensions of temperament are identified. In infancy, these are surgency/extraversion, negative affectivity, and orienting/regulation and, throughout the latter developmental periods, these are surgency/extraversion, negative affectivity, and EC. For the middle childhood measure, four factors were identified on the basis of scale-level factor analyses, with sociability/affiliation emerging as a separate factor. In the early adolescence, three factors were found—surgency/extraversion, negative affectivity, and high intensity pleasure, which may be similar to earlier manifestations of low EC. Finally, the adult measure identified five broad factors, extraversion, negative affectivity, EC, orienting sensitivity, and affiliativeness. In initial work on each of these instruments, general support for a three-factor structure similar to the Big Three identified in adults was found, on the basis of factor analyses of lower-order scales. However, the development of these instruments came

from a priori scales, meaning that lower-order scales were developed on the basis of theory, rather than item-level analyses. More recent investigations of the structure of the childhood and middle childhood measures have yielded significant departures from the originally proposed measures (Kotelnikova, Olino, Klein, Kryski, & Hayden, 2016; Kotelnikova, Olino, Klein, Mackrell, & Hayden, 2016). Having noted these concerns, we also acknowledge that rigorous factor-analytic methods using large samples have not been applied to most parent reports of child temperament; hence, it is certainly possible that modeling other questionnaires would also yield significant discrepancies between theory-bound models and models resulting from quantitative analyses conducted with no underlying assumptions regarding child temperament structure. Researchers have also developed downward adaptations of measures originally developed to assess adult personality. The Junior Eysenck Personality Questionnaire (H. J. Eysenck & Eysenck, 1975) was developed and assesses a Big Three measure of personality, comprised of the broad personality dimensions of extraversion, neuroticism, and psychoticism, the latter of which capturing disinhibition and nonconformity rather than symptoms of psychosis, as the name might suggest. This questionnaire was developed to conceptually map on to these same traits assessed by the Eysenck Personality Questionnaire (S. B. Eysenck & Eysenck, 1964) and is intended for use with children as young as age 7. This measure continues to be one of the more commonly used child personality self-report instruments because of consistent psychometric functioning and translations into multiple languages. Researchers have developed Big Five measures used with adults for use with children. Modeled after the Big Five Questionnaire typically used with adolescents and adults (John, Caspi, Robins, Moffitt, & Stouthamer-Loeber, 1994), the Big Five Questionnaire for Children (Barbaranelli, Caprara, Rabasca, & Pastorelli, 2003), was developed to assess extraversion, neuroticism, openness to experience, conscientiousness, and agreeableness in youth beginning in middle childhood. Initial work examined the measure as a self-report, parentreport, and teacher-report for children in Grades 4 127

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and 5 (ages 9–11), although only modest convergence was found between raters. Longitudinal measurement invariance has been reported, indicating that this measure may be sensitive to developmental changes in personality (del Barrio, Carrasco, & Holgado, 2006) and that that age-related comparisons are tenable. As developed, this instrument was designed to assess only the broad personality domains, and not more narrow dimensions that may be relevant to personality. However, more recently, the Hierarchical Personality Inventory for Children (Mervielde & De Fruyt, 1999) was developed to assess broad and narrow child personality dimensions within a Big Five organizational framework. On the basis of their work on measurement development, the authors labeled factor dimensions as conscientiousness, benevolence (conceptually related to agreeableness), extraversion, emotional stability (analogous to neuroticism), and imagination (or openness to experience). The measure has self- and parent-report formats, with the self-report version used in children as young as 7 years old. In younger children, the Infant–Toddler Social and Emotional Assessment (Carter, Briggs-Gowan, Jones, & Little, 2003) assesses content similar to the Personality Assessment Inventory (Morey, 1991) and Minnesota Multiphasic Personality Inventory (Butcher et al., 2001) by covering psychopathology symptoms and temperament. The broad externalizing dimension includes impulsivity, defiance, and aggression, and the internalizing dimension includes depression, general anxiety, separation distress, and inhibition to novelty. Dysregulation and competence are also assessed. Although NE is part of the dysregulation domain, the sleep, eating, and sensory sensitivity regulation domains covered by this instrument are only inconsistently included in models of temperament. Finally, the competence domain is comprised of items assessing compliance, attention, mastery motivation, imitation/play, empathy, and prosocial relations, with conceptual overlap found between mastery motivation and prosocial relations with extraversion and compliance, and attention with disinhibition (EC). Therefore, the ITSEA can be used as a partial marker of child traits with relevance to psychopathology. 128

Parent reports such as these are by far the most widely used means of collecting temperament data in research and clinical contexts (Else-Quest, Hyde, Goldsmith, & Van Hulle, 2006). These are generally an efficient means of collecting a vast amount of information on children with little clinician/ researcher burden. Further, it is also quite feasible to collect this information on large samples efficiently. Yet, parent reports also come with limitations. For example, parents may differ in their understanding of the items or item anchors (e.g., parents may differ on their definition of “sometimes”), and may have limited knowledge of developmental norms to inform their reports. Importantly, parent reports of youth personality may be influenced by parents’ current mood state and history of psychopathology. Given that many disorders are familial (Goodman et al., 2011), this is a salient concern particularly in clinical child settings, as parents themselves may be experiencing mental health problems. The methods used to develop models and measures of child temperament are also a limitation of this literature. Earlier, we noted that the methodologies used to develop structural models of questionnaire measures of individual differences in adults are relatively sophisticated compared with the extant structural work on parent- and self-reported temperament (Kotelnikova et al., 2015). Specifically, research on adult personality has benefitted from the availability of large samples, which has facilitated bottom-up, factor-analytic approaches that are less reliant on underlying assumptions regarding the nature of personality structure. Further, as a testimony to their validity, Big Five and Big Three models have been extensively validated across multiple stages of adult development as well as across cultures. In contrast, much of the work on the nature of children’s traits has been constrained by top-down theories regarding the structure of childhood individual differences, often tested in relatively small samples, making it difficult to draw firm conclusions about the structural nature of children’s personality. Indeed, extant work aiming to validate these models using approaches that are relatively agnostic regarding the structure of child temperament have yielded modest support for Rothbart and Bates’s (2006) model (Kotelnikova et al., 2015). The field needs

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additional bottom-up empirical work that tests competing models of children’s individual differences, with the goal of arriving at a consensus regarding the nature and structure of children’s personality. Such work will play an important role in advancing research on the biological and environmental bases of child personality, its associations with children’s mental health, gender and cross-cultural differences in traits and trait manifestations, among other important issues. Such work would also inform our understanding of which level of trait differentiation is most useful in clinical child contexts. More specifically, traits can be examined at the broad- and narrowband levels. Broad-band traits maximize coverage of a wider array of individual differences, yet do not provide precise information on more specific traitrelevant behavior. In contrast, narrow-band traits capture individual differences within a narrowly defined domain of behavior. Relevant work with adults suggests that narrow-band traits may often show stronger links to disorder than higher-order traits (e.g., Watson, Kotov, & Gamez, 2006; see also Durbin et al., 2005 for relevant work with children), although additional tests of this question are needed to inform which level of assessment should be of concern in mental health settings.

Assessment Methods: Q-Sorts Q-sorts, an alternative, less commonly used assessment strategy, call for respondents to rank specific behaviors in terms of their salience to a child’s temperament, yielding person-oriented or ipsative information on child traits. Q-sort methods provide a valuable tool to researchers interested in individualized descriptions of children’s personalities, providing unique information on the relative importance of child characteristics within, rather than between, children that parent- or self-report measures cannot provide. They can be used to characterize child behavior in almost any context, including home and other naturalistic settings (Buckley, Klein, Durbin, Hayden, & Moerk, 2002), but could also be applied to laboratory assessments. Additionally, Q-sort methods may be more amenable to post hoc analyses that allow investigators to develop measures of constructs many years after initial data collection

(Waters & Deane, 1985), and are particularly flexible with regard to scale construction and analytic methods. For example, in addition to a priori scale construction, Q-sorts can also be used to compare individual children to a criterion sort (e.g., the Q-sort of a prototypic child high in BI as the point of reference). Nevertheless, there are some important disadvantages of this approach. This method requires the same extensive knowledge of the child’s behavior across diverse contexts as parent questionnaires, and tends to be more laborious for respondents than questionnaire measures. The California Child Q-set (Block & Block, 1980) is an extensively used q-set measure in research on child personality. This is a 100-item set that calls for respondents to sort items into traits characteristic and uncharacteristic of a target child. Although this Q-sort was designed specifically to assess an a priori personality structure, including constructs of ego resiliency and ego control (Block, 1971), more recent analyses of the factor structure reveal that it can reliably assess Big Five traits (Lanning, 1994). More recently, Wilson et al. (2013) found two distinct clusters of items: the first, adaptive socialization, included items reflecting emotional stability, compliance, and intelligence, and is similar to the combination of low neuroticism and high agreeableness. The second cluster, anxious inhibition, included emotional and behavioral introversion, which are similar to low extraversion.

Assessment Methods: Laboratory Measures Questionnaires and Q-sorts rely on informants providing responses based on their knowledge of their child’s behavior in naturalistic settings. Though important, such methods may be limited in terms of the information they provide regarding how different children respond to the same stimuli or experiences; further, research (e.g., Durbin & Wilson, 2012) has identified several extraneous influences on the information informants provide on child personality. Given these limitations, extensive observational methods have been developed for use with children to permit standardized, objective evaluations of child personality/temperament (Durbin, 2010). Some of these have focused on the 129

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assessment of individual traits, such as BI and EC, and other batteries are designed to assess a broad array of traits (e.g., the Laboratory Temperament Assessment Battery [Lab-TAB]; Goldsmith, Reilly, Lemery, Longley, & Prescott, 1995). There is an array of laboratory assessments of narrow-band dimensions of temperament available, especially BI. Given that this trait is marked by fearful reactions to novelty, including social and nonsocial stimuli (Kagan, 1997), tasks used to assess BI call for children to interact with unfamiliar adults and ambiguous novel objects, and to engage in that may be perceived to be challenging (e.g., jumping off of a short height to a soft landing spot). There are also laboratory tasks designed specifically to elicit children’s EC, particularly inhibitory control (e.g., Kim, Nordling, Yoon, Boldt, & Kochanska, 2013); such tasks call for children to inhibit a dominant response, like waiting to eat a piece of candy or waiting one’s turn during a slow game. Overall, behavior elicited by such tasks has been linked to children’s mental health outcomes (e.g., Kim et al., 2013), providing evidence for their predictive validity. In contrast, the Lab-TAB (Goldsmith et al., 1995) is a flexible, comprehensive trait measure of temperament. Tasks are designed to specifically assess positive emotions and distinct negative emotions (i.e., sadness, anger, and fear), as well as multiple behavioral dimensions relevant to child temperament, including sociability, compliance, activity level, and impulsivity. Specific episodes are used to elicit behaviors indicative of traits. As such, individual differences in reactivity to these standardized situations can be observed and quantified. Further, as the complete battery is quite lengthy, investigators can sample across episodes to elicit specific behaviors of interest across the traits of interest, with support for the notion that such assessments are associated with markers of risk (Durbin et al., 2005; Kotelnikova, Olino, Mackrell, Jordan, & Hayden, 2013; Olino, Klein, Dyson, Rose, & Durbin, 2010). However, there have been very few investigations of the structure of temperament in childhood using observational methods. In preschoolers, Dyson, Olino, Durbin, Goldsmith, and Klein (2012) found that a five-factor model satisfactorily 130

described collected temperament data in 3-year-old children. The five identified dimensions included sociability, positive affect/interest, dysphoria, fear/ inhibition, and constraint. There was representative content across most of the five factors of personality, with the exception of openness to experience. The absence of an openness factor could reflect the limitations of tasks and/or coding of the Lab-TAB, which may not elicit openness well. Dyson et al. (2015) found that there was longitudinal similarity in the factor structure from age 3 to age 6. Further, the authors found homotypic continuity for each domain. Even less work exists regarding the structure of observed temperament in middle childhood. In one of the only published studies of observed middle childhood temperament, Kotelnikova et al. (2013) found that a four-factor model comprised of extraversion, fearful inhibition, disinhibition/anger, and sadness factors was a good fit to the data. These dimensions, like the observational structures found in early childhood, provide further support for factors differentiated largely on the basis of affect. Taken together, the structure of observed temperament throughout childhood is generally consistent. These dimensions coalesce around factors that are central to Big Five and Big Three conceptualizations of personality in adults. Laboratory observations of children’s temperament/personality have several significant advantages, including providing an opportunity for direct comparisons of children’s expressions of traitrelevant behavior in an identical context. Additionally, these observations are typically recorded and observed by trained raters who provide structured ratings to the children’s behavior, and are therefore free of systematic biases and informed by knowledge of developmental norms and expectations. However, these methods typically yield information on the basis of a small sample of child behavior, as most laboratory batteries are limited to a single assessment occasion; furthermore, they are laborintensive and expensive to use. The target child needs to complete the battery and the assessors need to be trained on the rating system and observe and rate the behaviors. Furthermore, laboratory measures are often poorly understood in terms of psychometric properties, or, in some cases, associated

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with important psychometric weaknesses (Lilienfeld & Treadway, 2016), a concern we will discuss in greater depth in the Future Directions section of this chapter.

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Intermethod Agreement Correspondence among different raters and different methods tapping the same trait is a key means of examining construct validity, and can also be used as a marker by which to gauge the contribution of specific methods to the prediction of outcomes of interest. There is a reasonably large literature on interparental agreement on parent-reported child temperament; for example, in the development of the Child Behavior Questionnaire, Rothbart et al. (2001) reported moderate interparental agreement (average r = .43) on child temperament traits, with relatively comparable agreement across different dimensions. In contrast, relatively scant data are available on associations between parent- and teacher-rated youth temperament, and assessment of child traits via self-reports is virtually never done with children younger than 10 years of age. There is a reasonably large literature on associations between parent-reported temperament and observational methods, with substantial variability on trait agreement evident depending on the specific dimension of temperament being assessed. Observer-completed Q-sort ratings and parent reports of child symptoms show modest yet largely meaningful associations (Buckley et al., 2002). More observable dimensions (e.g., shyness, fearfulness) tend to demonstrate modest correspondence (e.g., Dyson, Klein, Olino, Dougherty, & Durbin, 2011). In contrast, other dimensions (e.g., positive emotional expression) tend to demonstrate weaker associations. Investigations of whether these associations are moderated by youth diagnostic status have not been conducted in any existing studies. It can be considered clear that different methods of assessment often provide unique information on children’s temperament. Given that each method shows predictive validity for child mental health outcomes despite low convergence, it is challenging to recommend any single approach as the “gold standard.” Divergent associations between parent-reported traits and other methods are likely

influenced by several factors. In the context of associations between parent reports and observational methods, most parents may not have a welldeveloped understanding of typical child behavior, especially for younger children, and when they are reporting on first-born children. Therefore, lack of understanding or awareness of individual differences in child behavior may limit the validity of parent reports. Further, parents may also not have the opportunity to observe their children in the full range of environments that their children are exposed to in laboratory observations; for example, some parents may avoid introducing their children to situations likely to elicit fear, and would therefore lack information on which to base their ratings. This may be particularly true in the context of parenting an anxious child, as such children appear to elicit more controlling caregiving styles (e.g., Moore, Whaley, & Sigman, 2004). However, given the limited sample of behavior obtained by laboratory assessments, it may be that parents are better informants on traits that are expressed regularly by children. Interestingly, parents appear to rate their children’s laboratory behavior on the basis of their predictions of how their child will respond to stimuli (Durbin & Wilson, 2012), rather than rating children’s actual observed behavior. Such findings suggest that parents draw on their beliefs regarding their child’s personality as opposed to accurately perceiving, recalling, and interpreting child behavior. Perhaps the most challenging and vexing concern regarding the use of parent reports of child personality is the influence of parental individual difference characteristics on their ratings. Individual differences in parental depressive symptoms (Durbin & Wilson, 2012) and personality (Hayden, Durbin, Klein, & Olino, 2010) have both been found to influence convergence between parent ratings of child behavior and observed behavior. Across these studies, there is compelling evidence that individuals’ mood states are a significant factor in their ratings of child behavior. Whether such influences on parent-reported child personality are even more pervasive in clinical settings is unknown, although literature indicates that parent psychopathology, which may be more common in child clinical 131

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contexts, is an important concern in gathering parent reports of children’s personality.

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Child Personality in the Context of Psychopathology Numerous studies have examined associations between temperament and psychopathology in adults and in youth; however, most of these examine associations between dimensional measures of symptoms and traits. In adults, a recent meta-analysis (Kotov, Gamez, Schmidt, & Watson, 2010) of case-control studies of individuals with and without common forms of psychopathology found that all disorders were characterized by high levels of neuroticism and low levels of conscientiousness; low levels of extraversion were associated with chronic depressive disorders and social phobia; and agreeableness and openness to experience were largely unassociated with any forms of psychopathology examined. This study provides comprehensive, empirical evidence concerning how broadband personality traits are implicated in psychopathology. A comparable quantitative synthesis of the literature in childhood is not available, likely because case-control designs are rare in the literature on temperament and psychopathology in childhood. In early childhood, Dougherty et al. (2011) examined observed temperament differences between youth with and without depression, anxiety, oppositional defiant disorder (ODD), and attention-deficit/ hyperactivity disorder (ADHD). They found that depression was associated with heightened dysphoria (sadness and anger) and lower exuberance; anxiety was associated with lower sociability, heightened fear, and lower exuberance; ODD was associated with heightened dysphoria and greater disinhibition; and ADHD was unassociated with any temperament dimension examined. In later childhood, clinically anxious youth were rated as more inhibited and neurotic by their parents and less extraverted than those without anxiety disorders (Sallinen, 2006; Vreeke & Muris, 2012). The few available studies have predominantly focused on anxiety disorders. Additional attention to mood and externalizing behavior disorders is needed. 132

Future Directions Children’s traits clearly show associations with key aspects of mental health in childhood and later in life, with several factors influencing the strength of these predictions. Most longitudinal work focusing on childhood traits is relatively brief in scope; however, recent findings show that even brief assessments of personality in young adults show impressive predictive power for a broad host of physical and mental health outcomes across extensive follow-up periods (Israel et al., 2014). Such work not only begs the question of how early in life traits with long-term clinical utility can be detected, and how extensive child personality assessments must be to predict outcomes, it also highlights the promise of further refining our understanding of the role of child personality in the context of mental health. We now turn our attention to key outstanding issues in this field that warrant further research. Most of the work on children’s personality and symptoms of mental health problems has used community-based samples. Indeed, it is unclear whether there are unique considerations regarding temperament assessment in the context of children with mental health problems, calling into question the need for a chapter dedicated to the topic. The question is whether researchers and clinicians can validly apply the same assessment methods and tools applied to the study of children showing a normative pattern of development to understand personality in children with mental health problems. Although it seems likely that categorical distinctions between “clinical” (meeting diagnostic criteria for a disorder) and community-dwelling children, which reflect diagnostic cut-points rather than qualitative differences, are largely arbitrary, more work is needed that examines temperament in children with psychopathological disorders to establish that findings concerning temperament assessment are generalizable across populations. Furthermore, such studies are needed to better understand the role of temperament in course, outcome, and treatment responsiveness for disorders of childhood. Perhaps the most fundamental question is whether there is a consistent organization of personality among youth with and without

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psychopathology. Although some similarities are found in the structure of temperament yielded by observational and parent-report measures in community-based samples, structural comparisons have not been made between community-dwelling children and those with a mental health problem, an issue also unexplored in the larger literature on adults. These are critical research areas as differences in psychometric functioning between clinical and nonclinical samples render trait comparisons between those with and without a mental health problem uninterpretable. Mean-level differences in traits in healthy children compared with those with a mental health problem are difficult to interpret unless measurement invariance exists for those traits across populations. The same issue is raised if parental psychopathology is associated with structural differences in parent-reported child temperament when compared with parents with a disorder. Given that parent psychopathology influences parent-report and may be present at higher rates in children with mental health problems, this concern is worthy of further exploration. Across the studies reviewed, there is compelling evidence for the role of temperament in the emergence of psychopathology. However, the issue of specificity with respect to the role of temperament in predicting broad dimensions of externalizing and internalizing symptoms, as well as more specific symptom patterns within these broad clusters (e.g., depressive versus anxious symptoms), remains unclear. More specifically, the established associations between certain temperament traits and internalizing and externalizing problems suggest that a general psychopathology factor may exist that predisposes children to a broad array of symptoms (Caspi et al., 2014; Olino, Dougherty, Bufferd, Carlson, & Klein, 2014). In the literature on child temperament, there has been an emphasis on broader temperament dimensions compared with narrow-band traits, even though narrow-band facets provide relatively fine-grain information about behavior. Given work with adults in which narrow-band traits showed stronger links to disorder than higher-order traits (e.g., Watson et al., 2006), additional tests of this question are needed to inform which level of temperament assessment

will be most informative in mental health settings for predicting child outcomes, and could be used to streamline child temperament assessment batteries. Relatedly, most of the literature on temperament and psychopathology has focused solely on main-effect associations between traits and symptoms, although emergent evidence supports the role of interactions between dimensions of temperament in predicting child psychopathology (e.g., Dougherty et al., 2010; Kotelnikova et al., 2015). Specific temperament profiles may reveal patterns of specificity in predicting symptoms that are currently obfuscated. Whether specific traits predict children’s response to treatment, or whether adaptive traits can be strengthened as a component of effective treatment, is poorly understood. Although there is little work on this in children, dimensions relevant to extraversion predicted treatment outcome for depression in adolescents (McMakin et al., 2012) and adults (Uher et al., 2012). Similar examinations are warranted for studies of other clinical phenomena and other temperament dimensions (Shiner & Masten, 2012), especially in children. For example, there are clear links between BI and the course of child anxiety disorder (Bufferd et al., 2016), for which additional research can be informative. Likewise, additional research on the associations between EC and the course of ADHD, anger and the course of ODD, and PE and/or NE and the course of depressive disorders are all important directions for future research. Relatedly, in a systematic metaanalysis of the adult intervention literature, Roberts et al. (2017) recently showed that interventions focused on reducing symptoms of psychopathology also showed evidence of changing personality traits like neuroticism and extraversion, even though only a small proportion of the studies included were focused on personality disorder per se. Although this review covered studies of adults only, it raises the possibility that interventions with children may partially exert their effects by altering maladaptive aspects of traits. Such a notion bodes well for preventative efforts focused on child traits as well as the development of new aspects of treatment that target traits specifically. The role of positive or “resiliency” traits in predicting outcomes of children either 133

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naturalistically or in those receiving treatment may also prove useful (Shiner & Masten, 2012). Finally, certain domains of child mental health are woefully understudied with respect to the role of personality traits. For example, there is minimal attention to temperament and personality in work on developmental disabilities. Previous studies with youth with autistic spectrum disorders (Adamek et al., 2011; Barger, Campbell, & Simmons, in press) have relied on temperament measures developed and validated in typically developing samples, and they may not be sensitive to issues central to this population. There are concerns that the behaviors assessed may not be inclusive of temperament dimensions that are pertinent to youth on the autistic spectrum. Therefore, there may be additional measures that are needed that assess these dimensions. Similarly, the nature of any associations between early childhood traits and personality disorder later in life is not well understood. Longitudinal designs that comprehensively map a broad array of narrow- and broad-band childhood traits are critical toward advancing this line of inquiry. Several issues centering specifically on assessment issues in child temperament and personality require further attention. First, the field has been limited by the lack of independence of measures of children’s symptoms and children’s temperament, given that parents often provide information on both. Stricter tests of the role of personality in children’s mental health require independent measures of constructs. Another key issue in this field centers on the dynamic nature of children’s individual differences over time. Although personality change occurs across the lifespan (Roberts & DelVecchio, 2000; Roberts & Mroczek, 2008), at no developmental stage is this truer than in childhood. Substantially more is known about the rank-order stability of adult personality than children’s (cf. Durbin et al., 2007), and there is also a large literature on mean-level change in adult personality. This work has significant implications for mental health, given the mean-level changes in personality that correspond to changes in disorder rates (e.g., Littlefield, Sher, & Wood, 2009), which could be used to identify critical risk periods for prevention. Developing a parallel line of work in early childhood poses unique 134

challenges, given the need to change measurement strategies to maintain the developmental sensitivity of measures as children age. Put differently, it can be challenging to distinguish true change in child personality from the changes in measurement approach required to account for developmental change. This important issue requires further study via longitudinal, multimethod studies. For such work to occur, however, the obstacles posed by the confound between assessment strategy and developmental period require attention. Specifically, in early childhood, studies frequently rely on behavioral observations and parental reports of temperament. In middle childhood, most studies rely on parent reports of temperament and do not assess self-reported child temperament. However, it is not clear when youth are able to validly report on their temperament. Narrow-band affective dimensions are assessed via self-reports at age 10, and some studies assess personality near age 12. It is important to identify when in development self-reports are valid and useful. Alternatively, there may be a need to develop observational methods appropriate for use with older children. Observational methods are often used with preschool-age children, but rarely with children beyond middle childhood. Developmentally appropriate observational laboratory methods may yield crucial information about child temperament that neither parent nor youth selfreport yield. However, laboratory methods bring their own set of methodological concerns (Lilienfeld & Treadway, 2016). Little is known about the temporal stability of such measures, likely given the time and expense associated with their collection. The extent to which such measures are influenced by irrelevant contextual factors (e.g., experimenter characteristics) is similarly poorly understood, although a recent study by Kidd et al. (2013) provides support that such factors may have a greater impact on child behavior than is desirable. Certainly, very few investigators are motivated to exert the effort required to collect extensive laboratory assessments only to potentially establish that such measures show low stability over time, or are strongly influenced by extraneous factors rather than substantive aspects of child temperament. Fortunately, there is promising, albeit

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limited, evidence supporting the stability of laboratory indices of child temperament (Durbin, Hayden, Klein, & Olino, 2007), although further methodological study of such tasks is required. Relatedly, few studies have examined how parental individual differences influence their reports of youth temperament. The small literature indicates that parents’ own traits and psychopathology symptoms play an important role in driving the information they provide on their children (e.g., Durbin & Wilson, 2012; Hayden et al., 2010), but this literature would benefit from attention to a broader set of child temperamental and parental mental health and personality characteristics. Although there is an older literature on rating bias in maternal depression (Youngstrom, Izard, & Ackerman, 1999), more attention is warranted for traits relevant to externalizing disorders; for example, studies examining the influence of substance use disorders (as a common form of parental externalizing disorder) on parental bias in ratings of child behavior are needed. An alternative, complementary approach is to clarify whether some of these measures are less susceptible to potential biases related to parental psychopathology, personality, or current mood state. Children’s individual differences hold great promise for understanding child mental health, including predicting the onset, course, and outcomes of child psychopathology. However, there are unanswered questions surrounding the nature of child temperament itself, how child temperament influences mental health, and how to most reliably and validly gather measures of children’s individual differences.

References Adamek, L., Nichols, S., Tetenbaum, S. P., Bregman, J., Ponzio, C. A., & Carr, E. G. (2011). Individual temperament and problem behavior in children with autism spectrum disorders. Focus on Autism and Other Developmental Disabilities, 26, 173–183. http:// dx.doi.org/10.1177/1088357611405041 Barbaranelli, C., Caprara, G. V., Rabasca, A., & Pastorelli, C. (2003). A questionnaire for measuring the Big Five in late childhood. Personality and Individual Differences, 34, 645–664. http://dx.doi.org/10.1016/ S0191-8869(02)00051-X Barger, B., Campbell, J., & Simmons, C. (in press). The five factor personality model in children with ASD

during middle childhood. Focus on Autism and Other Developmental Disabilities. Block, J. (1971). Lives through time. Berkeley, CA: Bancroft Books. Block, J., & Block, J. (1980). California Child Q-set. Sunnyvale, CA: Consulting Psychologists Press. Buckley, M. E., Klein, D. N., Durbin, C. E., Hayden, E. P., & Moerk, K. C. (2002). Development and validation of a Q-sort procedure to assess temperament and behavior in preschool-age children. Journal of Clinical Child and Adolescent Psychology, 31, 525–539. http://dx.doi.org/10.1207/ S15374424JCCP3104_11 Bufferd, S. J., Dougherty, L. R., Olino, T. M., Dyson, M. W., Carlson, G. A., & Klein, D. N. (2016). Temperament distinguishes persistent/recurrent from remitting anxiety disorders across early childhood. Journal of Clinical Child and Adolescent Psychology, 1–10. http:// dx.doi.org/10.1080/15374416.2016.1212362 Bufferd, S. J., Dougherty, L. R., Olino, T. M., Dyson, M. W., Laptook, R. S., Carlson, G. A., & Klein, D. N. (2014). Predictors of the onset of depression in young children: A multimethod, multi-informant longitudinal study from ages 3 to 6. Journal of Child Psychology and Psychiatry, 55, 1279–1287. http://dx.doi.org/10.1111/ jcpp.12252 Buss, A. H., & Plomin, R. (1975). A temperament theory of personality development. New York, NY: Wiley-Interscience. Buss, A. H., & Plomin, R. (1984). Temperament: Early developing personality traits. Hillsdale, NJ: Erlbaum. Butcher, J. N., Graham, J. R., Ben-Porath, Y. S., Tellegen, A. S., Dahlstrom, W. G., & Kaemmer, B. (2001). Minnesota Multiphasic Personality Inventory-2: Manual for administration, scoring, and interpretation. Minneapolis: University of Minnesota Press. Canli, T., Congdon, E., Constable, T. R., & Lesch, K. P. (2008). Additive effects of serotonin transporter and tryptophan hydroxylase-2 gene variation on neural correlates of affective processing. Biological Psychology, 79, 118–125. http://dx.doi.org/10.1016/ j.biopsycho.2008.01.004 Capaldi, D. M., & Rothbart, M. K. (1992). Development and validation of an early adolescent temperament measure. Journal of Early Adolescence, 12, 153–173. http://dx.doi.org/10.1177/0272431692012002002 Carter, A. S., Briggs-Gowan, M. J., Jones, S. M., & Little, T. D. (2003). The infant–toddler social and emotional assessment: Factor structure, reliability, and validity. Journal of Abnormal Child Psychology, 31, 495–514. http://dx.doi.org/10.1023/A:1025449031360 Caspi, A., Houts, R. M., Belsky, D. W., Goldman-Mellor, S. J., Harrington, H., Israel, S., . . . Moffitt, T. E. (2014). The p factor: One general psychopathology 135

Olino and Hayden

factor in the structure of psychiatric disorders? Clinical Psychological Science, 2, 119–137. http:// dx.doi.org/10.1177/2167702613497473 Caspi, A., Moffitt, T. E., Newman, D. L., & Silva, P. A. (1996). Behavioral observations at age 3 years predict adult psychiatric disorders. Longitudinal evidence from a birth cohort. Archives of General Psychiatry, 53, 1033–1039. http://dx.doi.org/10.1001/ archpsyc.1996.01830110071009

Copyright American Psychological Association. Not for further distribution.

Caspi, A., & Shiner, R. L. (2006). Personality development. In W. Damon, & R. M. Lerner (Eds.), Handbook of child psychology: Vol. 3. Social, emotional, and personality development (6th ed., pp. 300–365). Hoboken, NJ: Wiley. Chmielewski, M., Bagby, R. M., Markon, K., Ring, A. J., & Ryder, A. G. (2014). Openness to experience, intellect, schizotypal personality disorder, and psychoticism: Resolving the controversy. Journal of Personality Disorders, 28, 483–499. http://dx.doi.org/ 10.1521/pedi_2014_28_128 del Barrio, V., Carrasco, M. Á., & Holgado, F. P. (2006). Factor structure invariance in the children’s big five questionnaire. European Journal of Psychological Assessment, 22, 158–167. http://dx.doi.org/ 10.1027/1015-5759.22.3.158 Diamond, A., & Lee, K. (2011). Interventions shown to aid executive function development in children 4 to 12 years old. Science, 333, 959–964. http://dx.doi.org/ 10.1126/science.1204529 Dougherty, L. R., Bufferd, S. J., Carlson, G. A., Dyson, M., Olino, T. M., Durbin, C. E., & Klein, D. N. (2011). Preschoolers’ observed temperament and psychiatric disorders assessed with a parent diagnostic interview. Journal of Clinical Child and Adolescent Psychology, 40, 295–306. http://dx.doi.org/10.1080/ 15374416.2011.546046 Dougherty, L. R., Klein, D. N., Durbin, C. E., Hayden, E. P., & Olino, T. M. (2010). Temperamental positive and negative emotionality and children’s depressive symptoms: A longitudinal prospective study from age three to age ten. Journal of Social and Clinical Psychology, 29, 462–488. http://dx.doi.org/ 10.1521/jscp.2010.29.4.462 Durbin, C. E. (2010). Modeling temperamental risk for depression using developmentally sensitive laboratory paradigms. Child Development Perspectives, 4, 168–173. http://dx.doi.org/10.1111/ j.1750-8606.2010.00137.x Durbin, C. E., Hayden, E. P., Klein, D. N., & Olino, T. M. (2007). Stability of laboratory-assessed temperamental emotionality traits from ages 3 to 7. Emotion, 7, 388–399. http://dx.doi.org/10.1037/1528-3542.7.2.388 Durbin, C. E., Klein, D. N., Hayden, E. P., Buckley, M. E., & Moerk, K. C. (2005). Temperamental emotionality in preschoolers and parental mood disorders. Journal of 136

Abnormal Psychology, 114, 28–37. http://dx.doi.org/ 10.1037/0021-843X.114.1.28 Durbin, C. E., & Wilson, S. (2012). Convergent validity of and bias in maternal reports of child emotion. Psychological Assessment, 24, 647–660. http:// dx.doi.org/10.1037/a0026607 Dyson, M. W., Klein, D. N., Olino, T. M., Dougherty, L. R., & Durbin, C. E. (2011). Social and nonsocial behavioral inhibition in preschool-age children: Differential associations with parent-reports of temperament and anxiety. Child Psychiatry and Human Development, 42, 390–405. http://dx.doi.org/10.1007/s10578-011-0225-6 Dyson, M. W., Olino, T. M., Durbin, C. E., Goldsmith, H. H., Bufferd, S. J., Miller, A. R., & Klein, D. N. (2015). The structural and rank-order stability of temperament in young children based on a laboratory-observational measure. Psychological Assessment, 27, 1388–1401. http://dx.doi.org/10.1037/pas0000104 Dyson, M. W., Olino, T. M., Durbin, C. E., Goldsmith, H. H., & Klein, D. N. (2012). The structure of temperament in preschoolers: A two-stage factor analytic approach. Emotion, 12, 44–57. http://dx.doi.org/10.1037/a0025023 Eisenberg, N., Chang, L., Ma, Y., & Huang, X. (2009). Relations of parenting style to Chinese children’s effortful control, ego resilience, and maladjustment. Development and Psychopathology, 21, 455–477. http://dx.doi.org/10.1017/S095457940900025X Eisenberg, N., Sadovsky, A., Spinrad, T. L., Fabes, R. A., Losoya, S. H., Valiente, C., . . . Shepard, S. A. (2005). The relations of problem behavior status to children’s negative emotionality, effortful control, and impulsivity: Concurrent relations and prediction of change. Developmental Psychology, 41, 193–211. http://dx.doi.org/10.1037/0012-1649.41.1.193 Else-Quest, N. M., Hyde, J. S., Goldsmith, H. H., & Van Hulle, C. A. (2006). Gender differences in temperament: A meta-analysis. Psychological Bulletin, 132, 33–72. http://dx.doi.org/10.1037/ 0033-2909.132.1.33 Essex, M. J., Klein, M. H., Slattery, M. J., Goldsmith, H. H., & Kalin, N. H. (2010). Early risk factors and developmental pathways to chronic high inhibition and social anxiety disorder in adolescence. American Journal of Psychiatry, 167, 40–46. http://dx.doi.org/ 10.1176/appi.ajp.2009.07010051 Evans, D. E., & Rothbart, M. K. (2007). Developing a model for adult temperament. Journal of Research in Personality, 41, 868–888. http://dx.doi.org/10.1016/ j.jrp.2006.11.002 Eysenck, H. J., & Eysenck, S. B. G. (1975). Manual of the Eysenck personality questionnaire (junior and adult). London, England: Hodder & Stoughton. Eysenck, S. B., & Eysenck, H. J. (1964). An improved short questionnaire for the measurement of

Personality Assessment in Children With Mental Health Problems

Copyright American Psychological Association. Not for further distribution.

extraversion and neuroticism. Life Sciences, 3, 1103–1109. http://dx.doi.org/10.1016/ 0024-3205(64)90125-0

Kagan, J. (1997). Temperament and the reactions to unfamiliarity. Child Development, 68, 139–143. http:// dx.doi.org/10.2307/1131931

Gartstein, M. A., & Rothbart, M. K. (2003). Studying infant temperament via the revised infant behavior questionnaire. Infant Behavior and Development, 26, 64–86. http://dx.doi.org/10.1016/ S0163-6383(02)00169-8

Kagan, J., Snidman, N., Kahn, V., Towsley, S., Steinberg, L., & Fox, N. A. (2007). The preservation of two infant temperaments into adolescence. Monographs of the Society for Research in Child Development, 72, 1–95.

Goldberg, L. R. (1993). The structure of phenotypic personality traits. American Psychologist, 48, 26–34. http://dx.doi.org/10.1037/0003-066X.48.1.26

Kidd, C., Palmeri, H., & Aslin, R. N. (2013). Rational snacking: Young children’s decision-making on the marshmallow task is moderated by beliefs about environmental reliability. Cognition, 126, 109–114. http://dx.doi.org/10.1016/j.cognition.2012.08.004

Goldsmith, H. H., Reilly, J., Lemery, K. S., Longley, S., & Prescott, A. (1995). Laboratory temperament assessment battery: Preschool version. Unpublished manuscript, University of Wisconsin, Madison. Goodman, S. H., Rouse, M. H., Connell, A. M., Broth, M. R., Hall, C. M., & Heyward, D. (2011). Maternal depression and child psychopathology: A metaanalytic review. Clinical Child and Family Psychology Review, 14, 1–27. http://dx.doi.org/10.1007/ s10567-010-0080-1 Hayden, E. P., Durbin, C. E., Klein, D. N., & Olino, T. M. (2010). Maternal personality influences the relationship between maternal reports and laboratory measures of child temperament. Journal of Personality Assessment, 92, 586–593. http://dx.doi.org/10.1080/ 00223891.2010.513308 Hirshfeld-Becker, D. R., Biederman, J., Henin, A., Faraone, S. V., Davis, S., Harrington, K., & Rosenbaum, J. F. (2007). Behavioral inhibition in preschool children at risk is a specific predictor of middle childhood social anxiety: A five-year follow-up. Journal of Developmental and Behavioral Pediatrics, 28, 225–233. http://dx.doi.org/ 10.1097/01.DBP.0000268559.34463.d0 Hirshfeld-Becker, D. R., Biederman, J., Henin, A., Faraone, S. V., Micco, J. A., van Grondelle, A., . . . Rosenbaum, J. F. (2007). Clinical outcomes of laboratory-observed preschool behavioral disinhibition at five-year follow-up. Biological Psychiatry, 62, 565–572. http://dx.doi.org/10.1016/ j.biopsych.2006.10.021 Israel, S., Moffitt, T. E., Belsky, D. W., Hancox, R. J., Poulton, R., Roberts, B., . . . Caspi, A. (2014). Translating personality psychology to help personalize preventive medicine for young adult patients. Journal of Personality and Social Psychology, 106, 484–498. http://dx.doi.org/10.1037/ a0035687 John, O. P., Caspi, A., Robins, R. W., Moffitt, T. E., & Stouthamer-Loeber, M. (1994). The “little five”: Exploring the nomological network of the fivefactor model of personality in adolescent boys. Child Development, 65, 160–178. http://dx.doi.org/ 10.2307/1131373

Kim, S., Nordling, J. K., Yoon, J. E., Boldt, L. J., & Kochanska, G. (2013). Effortful control in “hot” and “cool” tasks differentially predicts children’s behavior problems and academic performance. Journal of Abnormal Child Psychology, 41, 43–56. http://dx.doi.org/10.1007/s10802-012-9661-4 Klein, D. N., Kotov, R., & Bufferd, S. J. (2011). Personality and depression: Explanatory models and review of the evidence. Annual Review of Clinical Psychology, 7, 269–295. http://dx.doi.org/10.1146/ annurev-clinpsy-032210-104540 Kotelnikova, Y., Mackrell, S. V. M., Jordan, P. L., & Hayden, E. P. (2015). Longitudinal associations between reactive and regulatory temperament traits and depressive symptoms in middle childhood. Journal of Clinical Child and Adolescent Psychology, 44, 775–786. http://dx.doi.org/10.1080/ 15374416.2014.893517 Kotelnikova, Y., Olino, T. M., Klein, D. N., Kryski, K. R., & Hayden, E. P. (2016). Higher- and lower-order factor analyses of the Children’s Behavior Questionnaire in early and middle childhood. Psychological Assessment, 28, 92–108. http://dx.doi.org/10.1037/ pas0000153 Kotelnikova, Y., Olino, T. M., Klein, D. N., Mackrell, S. V., & Hayden, E. P. (2016). Higherand lower-order factor analyses of the temperament in Middle Childhood Questionnaire. Assessment. Advance online publication. http://dx.doi.org/ 10.1177/1073191116639376 Kotelnikova, Y., Olino, T. M., Mackrell, S. V., Jordan, P. L., & Hayden, E. P. (2013). Structure of observed temperament in middle childhood. Journal of Research in Personality, 47, 524–532. http:// dx.doi.org/10.1016/j.jrp.2013.04.013 Kotov, R., Gamez, W., Schmidt, F., & Watson, D. (2010). Linking “big” personality traits to anxiety, depressive, and substance use disorders: A metaanalysis. Psychological Bulletin, 136, 768–821. http://dx.doi.org/10.1037/a0020327 Kovacs, M., & Lopez-Duran, N. (2010). Prodromal symptoms and atypical affectivity as predictors 137

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of major depression in juveniles: Implications for prevention. Journal of Child Psychology and Psychiatry, 51, 472–496. http://dx.doi.org/10.1111/ j.1469-7610.2010.02230.x Lanning, K. (1994). Dimensionality of observer ratings on the California Adult Q-set. Journal of Personality and Social Psychology, 67, 151–160. http://dx.doi.org/ 10.1037/0022-3514.67.1.151

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Lilienfeld, S. O., & Treadway, M. T. (2016). Clashing diagnostic approaches: DSM–ICD versus RDoC. Annual Review of Clinical Psychology, 12, 435–463. http:// dx.doi.org/10.1146/annurev-clinpsy-021815-093122 Lilienfeld, S. O., Wood, J. M., & Garb, H. N. (2000). The scientific status of projective techniques. Psychological Science in the Public Interest, 1, 27–66. http://dx.doi.org/10.1111/1529-1006.002 Littlefield, A. K., Sher, K. J., & Wood, P. K. (2009). Is “maturing out” of problematic alcohol involvement related to personality change? Journal of Abnormal Psychology, 118, 360–374. http://dx.doi.org/10.1037/ a0015125 McMakin, D. L., Olino, T. M., Porta, G., Dietz, L. J., Emslie, G., Clarke, G., . . . Brent, D. A. (2012). Anhedonia predicts poorer recovery among youth with selective serotonin reuptake inhibitor treatmentresistant depression. Journal of the American Academy of Child and Adolescent Psychiatry, 51, 404–411. http://dx.doi.org/10.1016/j.jaac.2012.01.011 Mervielde, I., & De Fruyt, F. (1999, July). Construction of the hierarchical personality inventory for children. Paper presented at the Eighth European Conference on Personality Psychology, Tilburg, the Netherlands. Moffitt, T. E., & Caspi, A. (2001). Childhood predictors differentiate life-course persistent and adolescencelimited antisocial pathways among males and females. Development and Psychopathology, 13, 355–375. http://dx.doi.org/10.1017/S0954579401002097 Moore, P. S., Whaley, S. E., & Sigman, M. (2004). Interactions between mothers and children: Impacts of maternal and child anxiety. Journal of Abnormal Psychology, 113, 471–476. http://dx.doi.org/10.1037/ 0021-843X.113.3.471 Morey, L. C. (1991). Personality assessment inventory professional manual. Odessa, FL: Psychological Assessment Resources. Olino, T. M., Dougherty, L. R., Bufferd, S. J., Carlson, G. A., & Klein, D. N. (2014). Testing models of psychopathology in preschool-aged children using a structured interview-based assessment. Journal of Abnormal Child Psychology, 42, 1201–1211. http:// dx.doi.org/10.1007/s10802-014-9865-x Olino, T. M., Klein, D. N., Dyson, M. W., Rose, S. A., & Durbin, C. E. (2010). Temperamental emotionality in preschool-aged children and depressive disorders in parents: Associations in a large community 138

sample. Journal of Abnormal Psychology, 119, 468–478. http://dx.doi.org/10.1037/a0020112 Putnam, S. P., Gartstein, M. A., & Rothbart, M. K. (2006). Measurement of fine-grained aspects of toddler temperament: The early childhood behavior questionnaire. Infant Behavior and Development, 29, 386–401. http://dx.doi.org/10.1016/ j.infbeh.2006.01.004 Quilty, L. C., De Fruyt, F., Rolland, J.-P., Kennedy, S. H., Rouillon, P. F., & Bagby, R. M. (2008). Dimensional personality traits and treatment outcome in patients with major depressive disorder. Journal of Affective Disorders, 108, 241–250. http://dx.doi.org/10.1016/ j.jad.2007.10.022 Roberts, B. W., & DelVecchio, W. F. (2000). The rank-order consistency of personality traits from childhood to old age: A quantitative review of longitudinal studies. Psychological Bulletin, 126, 3–25. http://dx.doi.org/10.1037/0033-2909.126.1.3 Roberts, B. W., Luo, J., Briley, D. A., Chow, P., Su, R., & Hill, P. L. (2017). A systematic review of personality traits change through intervention. Psychological Bulletin, 143, 117–141. http://dx.doi.org/10.1037/ bul0000088 Roberts, B. W., & Mroczek, D. (2008). Personality trait change in adulthood. Current Directions in Psychological Science, 17, 31–35. http://dx.doi.org/ 10.1111/j.1467-8721.2008.00543.x Rothbart, M. K., Ahadi, S. A., Hershey, K. L., & Fisher, P. (2001). Investigations of temperament at 3 to 7 years: The Children’s Behavior Questionnaire. Child Development, 72, 1394–1408. http://dx.doi.org/ 10.1111/1467-8624.00355 Rothbart, M. K., & Bates, J. E. (2006). Temperament. In W. Damon, & R. M. Lerner (Eds.), Handbook of child psychology: Vol. 3. Social, emotional, and personality development (6th ed., pp. 99–166). Hoboken, NJ: Wiley. Sallinen, B. J. (2006). Parent–child interactions in the maintenance of childhood anxiety disorders (unpublished doctoral dissertation). University of Maine, Orono. Shiner, R. L. (1998). How shall we speak of children’s personalities in middle childhood? A preliminary taxonomy. Psychological Bulletin, 124, 308–332. http://dx.doi.org/10.1037/0033-2909.124.3.308 Shiner, R. L., & Caspi, A. (2003). Personality differences in childhood and adolescence: Measurement, development, and consequences. Journal of Child Psychology and Psychiatry, 44, 2–32. http://dx.doi.org/ 10.1111/1469-7610.00101 Shiner, R. L., & Masten, A. S. (2012). Childhood personality as a harbinger of competence and resilience in adulthood. Development and Psychopathology, 24, 507–528. http:// dx.doi.org/10.1017/S0954579412000120

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Simonds, J. (2006). The role of reward sensitivity and response: Execution in childhood extraversion (unpublished doctoral dissertation). University of Oregon, Eugene. Thomas, A., Chess, S., & Birch, H. G. (1968). Temperament and behavior disorders in children. New York: New York University Press.

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Uher, R., Perlis, R. H., Henigsberg, N., Zobel, A., Rietschel, M., Mors, O., . . . McGuffin, P. (2012). Depression symptom dimensions as predictors of antidepressant treatment outcome: Replicable evidence for interest-activity symptoms. Psychological Medicine, 42, 967–980. http://dx.doi.org/10.1017/ S0033291711001905 Vreeke, L. J., & Muris, P. (2012). Relations between behavioral inhibition, big five personality factors, and anxiety disorder symptoms in non-clinical and clinically anxious children. Child Psychiatry and Human Development, 43, 884–894. http://dx.doi.org/ 10.1007/s10578-012-0302-5 Waldman, I. D., Singh, A. L., & Lahey, B. B. (2006). Dispositional dimensions and the causal structure of child and adolescent conduct problems. In R. F. Krueger & J. L. Tackett (Eds.), Personality and psychopathology (pp. 112–152). New York, NY: Guilford Press. Waters, E., & Deane, K. E. (1985). Defining and assessing individual differences in attachment relationships: Q-methodology and the organization of behavior

in infancy and early childhood. Monographs of the Society for Research in Child Development, 50, 41–65. http://dx.doi.org/10.2307/3333826 Watson, D., Kotov, R., & Gamez, W. (2006). Basic dimensions of temperament in relation to personality and psychopathology. In R. F. Krueger & J. L. Tackett (Eds.), Personality and psychopathology (pp. 7–38). New York, NY: Guilford Press. Wilson, S., Schalet, B. D., Hicks, B. M., & Zucker, R. A. (2013). Identifying early childhood personality dimensions using the California Child Q-Set and prospective associations with behavioral and psychosocial development. Journal of Research in Personality, 47, 339–350. http://dx.doi.org/10.1016/ j.jrp.2013.02.010 Windle, M., & Lerner, R. M. (1986). Reassessing the dimensions of temperamental individuality across the lifespan: The Revised Dimensions of Temperament Survey (DOTS–R). Journal of Adolescent Research, 1, 213–229. http://dx.doi.org/10.1177/074355488612007 Youngstrom, E., Izard, C., & Ackerman, B. (1999). Dysphoria-related bias in maternal ratings of children. Journal of Consulting and Clinical Psychology, 67, 905–916. http://dx.doi.org/10.1037/ 0022-006X.67.6.905 Zinbarg, R. E., Uliaszek, A. A., & Adler, J. M. (2008). The role of personality in psychotherapy for anxiety and depression. Journal of Personality, 76, 1649–1688. http://dx.doi.org/10.1111/j.1467-6494.2008.00534.x

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Chapter 8

Personality Assessment of Adolescents With Psychological Problems

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James N. Butcher

Evaluating and understanding the mental health problems of adolescents can be one of the more challenging aspects of mental health services today. Problems that adolescents often encounter in their years of development can have a profound and lasting influence on them as they enter adulthood. Even with the importance of prevention or treatment of the mental health problems that young people experience, their problems may not be recognized and sufficiently treated to reduce the negative impact they can have on them. Adolescents are underserved: Many youth who need mental health services do not receive help for their problems. Crespi and Politikos (2012) pointed out that although children account for 25% of the population of the United States, less than 12% of health care funding is targeted to children and less than 20% to 30% of children who need services receive them. Psychological evaluations of adolescents become even more important in today’s practice to assure that those in need are provided the appropriate attention. The amount of stress and trauma in young people can be problematic, and the effects often go unrecognized or ignored. Stress can come from numerous sources. For example, almost half of all marriages in the United States end in divorce and over a million children each year become children of divorced parents (Behrman & Quinn, 1994). Several families experiencing marriage-related stress have children that experience periods of psychological maladjustment as a result of marital

conflict. Baker and Brassard (2013) examined mental health symptoms and parental loyalty conflicts and found that high rates of exposure to parental loyalty conflict, especially in divorced families, often resulted in significant associations with psychological maltreatment and depression. A broad range of mental health problems can be found among adolescents—some derived from neuropsychological or genetic factors, others from severe stress, and still others from learning processes in development over time. Exposure to violence (e.g., victims of assault) is associated with psychological symptoms among school-age children (Fowler, Tompsett, Braciszewski, Jacques-Tiura, & Baltes, 2009; Wilmshurst, 2015). And, personality factors can markedly influence adolescents’ emotional behavior by acting aggressive toward others. Grisso (2013) described the personality factors involved in some adolescents’ risk to harming others. The incidence and prevalence of psychopathology among adolescents is high. The 12-month prevalence rate for mental disorders among 8- to 15-year-olds was highlighted by the National Institute of Mental Health: any disorder (13.1%), attention-deficit/hyperactivity disorder (8.6%), mood disorders (3.7%), conduct disorders (2.7%), dysthymia (1.0%), anxiety disorders (.07%), generalized anxiety disorder (.03%), and eating disorders (.01%). Only about half of adolescents with mental health problems receive treatment (Merikangas et al., 2010). Merikangas and He (2014) found that most mental health problems of children and

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adolescents involve a complex mixture of multiple genetic and environmental influences.

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Reasons Why Adolescents Are Referred for Assessment of Psychopathology There are numerous reasons for conducting psychological evaluations with adolescents. The type of evaluation designed to answer the needed questions is often dependent on the assessment context. And, the assessment techniques used or tests administered will be dependent on questions to be answered. Adolescents can be referred to psychological evaluation because of following issues: ■■

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Parental concerns over the adolescent’s behavior (e.g., social isolation, avoiding school, rule breaking). Family issues that may cause problems in psychological adjustment. Behavioral problems (e.g., participating in a gang). School problems (e.g., absenteeism, improper dressing, fighting with others, inappropriate behavior toward a teacher). Legal problems (e.g., delinquent behavior, possession of drugs or alcohol, theft, violent acts against others) may require a psychological evaluation to determine the adolescent’s mental health status in subsequent court evaluations. Custody evaluations that determine the adolescent’s level of psychological adjustment from a child custody dispute (see Chapter 24, this volume). Acculturation problems resulting from deportation cases involving parents (see Volume 1, Chapter 7, this handbook).

Relationship Between the Assessor and the Examinee Establishing a positive relationship between the assessor and the examinee is a particularly important factor in conducting a psychological assessment with an adolescent. The adolescent needs to trust 142

the assessor to be cooperative in the evaluation. In a positive working relationship, friendly support of adolescents going through problems allows them to share information about their experiences and problems in a way that can be different than sharing with their family or friends. Psychological assessment, in a friendly positive relationship, avails the assessor with key information about adolescents’ capabilities and challenges. Key Factors to Consider in Personality Assessment of Adolescents Psychological assessment of adolescents describes personality functioning and provides base line information for treatment and management of mental health problems in school, home, social, and mental health settings (Crespi & Politikos, 2012).

Cooperation With the Assessment To understand the level of cooperation of the adolescent in the assessment process it is important for the practitioner to evaluate their response attitudes. Knowing whether the adolescent is cooperating fully with the evaluation is key to having credible information on which to base conclusions and recommendations. Some instruments, like the Minnesota Multiphasic Personality Inventory—Adolescent (MMPI–A), contain validity scale measures that provide information regarding adolescents’ defensiveness, over responding in an exaggerated manner, or cooperation with the evaluation. The MMPI–A validity scales (L, F, K, VRIN, and TRIN) have an extensive research base and practical use for understanding response attitudes (R. P. Archer, 2005; Williams & Butcher, 2011).

Intellectual Ability and Problem Solving Skills Knowing adolescents’ intellectual ability, their problem solving skills, or their past achievements at dealing with problems is important in determining if the adolescent can deal effectively with the problems being faced and whether they could benefit from a particular treatment approach. Past behavior predicts future behavior.

Personality Assessment of Adolescents With Psychological Problems

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Family Relationships An understanding of adolescents’ family relationships and support network can be better understood through a psychological assessment. In many adolescent assessment situations, the involvement of parents in the evaluation can bring parents and adolescents closer together.

set for ongoing, periodic monitoring. Parents can be encouraged to be actively involved in setting goals for adolescents and in assessing their progress toward those goals. These data can also be used to provide supportive evidence to be used in treatment or behavioral management programs to which they are referred.

Environmental Context

Effect of Constant Change

An understanding of adolescents’ neighborhood or environmental stress situations give the practitioner a better understanding of their behavior in an objective psychological evaluation. Adolescents may gain perspective on their environment and activities through discussing their stressful relationships with an outsider that they can trust, and thereby adolescents can openly describe their situation without feeling they will be impacted by the disclosure.

Understanding the symptomatic behavior of adolescents can be a challenge given the changing life circumstances that can occur during this time of development. There is often difficulty gaining a perspective on true pathology from temporary symptoms that result from situational factors. Assessment of psychopathology of adolescents can be made difficult because of common, and somewhat frequent, life events that occur because of environmental influences, which can dissipate as social relationships improve. Shiner and Allen (2013) concluded that the acute behaviors in adolescents may be the most attention grabbing during an initial assessment, but that it is essential to evaluate the long-term personality patterns that require attention. Some adolescent behavior problems (e.g., psychopathy) are often viewed as long-term and untreatable. However, researchers (da Silva, Rijo, & Salekin, 2013) nevertheless consider adolescents with severe personality disorder in need of assessment, because “from our point of view, there is no point in identifying psychopathic traits in children and adolescents, if the aim is not to prevent and/or treat the disorder” (p. 76).

Social Relationships Understanding adolescents’ social relationships and how they are intermixing with peers (or not) are important factors in gaining a perspective on their adjustment. Discussing their peer relationships in an assessment context can be of value in understanding adolescents’ behavior. It is important in appraising their social relationships to include in the assessment an evaluation of their social skills (e.g., whether they are introverted or extraverted) using measures such as the Social Introversion scale of the MMPI–A (see Williams & Butcher, 2011).

Long-Term Versus Temporary Maladjustment Adolescence is a period of change and challenge, and a psychological assessment can separate the transient from the troublesome and unveil potential long-term chronic personality problems that need to be addressed. Hodges (2004) pointed out that assessments can have substantial impact on adolescents’ long-term outcomes. Assessments can outline a treatment approach in which target problems are linked to goals, client and family strengths, and specific treatment steps. This can be done such that adolescents’ real-life functioning can be assessed across various domains. The initial assessment can serve as the baseline evaluation, with an expectation

Gender Differences The assessment psychologist benefits from an awareness that the problems and life circumstances adolescents face may differ substantially for boys versus girls. Gender also plays a role in diagnostic classification on the basis of systems like the Diagnostic and Statistical Manual of Mental Disorders, 5th Edition (DSM–5; American Psychiatric Association, 2013). Some categories are predominantly male, whereas others are predominantly female. The outcome of a psychological assessment might differ. On the basis of a recent study, girls are 50% less 143

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likely to use mental health services than boys (Merikangas et al., 2010).

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Cultural and Ethnic Factors The assessor must consider culture, ethnic background, and level of acculturation into adolescents’ current environment. Many adolescents undergoing mental health assessments are not fully integrated into the culture of the United States. Assessments of immigrants are increasingly being conducted (Butcher et al., 2015; Cervantes & Shelby, 2013), and understanding adolescents’ level of acculturation to the culture and environmental demands is very informative to the psychological assessment.

Presence of Comorbid Disorders in Adolescent Psychopathology Several children and adolescents cope with multiple disorders (Crespi & Politikos, 2012), particularly substance abuse conditions (Substance Abuse and Mental Health Services Administration, 2015). Psychological assessment can aid the practitioner in evaluating the possibility of comorbid conditions. Personality Assessment Methods for Evaluating Psychopathology in Adolescents Psychological evaluations of adolescents characteristically involve the use of a variety of strategies and assessment instruments to cover a broad range of pertinent content and information sources.

The Clinical Interview An effective interview can make a substantial difference in the quality of personality information obtained for an evaluation (e.g., Craig, 2009; Marin, Rey, & Silverman, 2013; Orvaschel, 2006; Sonne, 2012). The new DSM–5 Structured Clinical Interview can be found in First, Williams, Karg, and Spitzer (2016) and First, Williams, Benjamin, and Spitzer (2016). The assessment interview involves taking a history of the presenting problem, developing a clear description of the problem and the impairments that adolescents are experiencing, and 144

gaining information across various aspects of adolescents’ functioning of factors that might influence the problems experienced. Two approaches to interviewing are used in adolescent assessment. The general unstructured clinical interview can provide valuable information about adolescents’ past history and behavior, single out particular problems they are experiencing, and address their strengths. However, this approach may not allow for firm conclusions to be drawn about the specifics of their behavior. In contrast, the structured diagnostic interview provides a specific format or script for the interviewer to follow while still covering relevant symptomatology, onset, and impairment related to the symptoms. Structured interviews provide an opportunity for the assessment psychologist to evaluate many important specific elements of adolescents’ symptoms. Early in the clinical interview adolescents’ level of cooperation with the assessment, their understanding of its purpose, and their willingness to discuss the situation that led to the referral may become apparent. In addition to covering the presenting complaint and adolescents’ perspective on any problems, the psychologist also needs to determine what assets they might possess and any personal resources they might have for dealing with their problems.

Behavioral Assessment An important component to a psychological assessment of adolescents is an objective evaluation of their behavior in different circumstances. Behavioral assessment is an empirical approach to understanding performance that provides an accurate description of adolescents’ behavior and involves minimal subjective inferences. Accurate assessment of behavior requires measurement taken across multiple situations, using several methods or strategies and involving varied circumstances, and across multiple times (for discussion, see Eckert & Lovett, 2013; Heiby & Haynes, 2003). Behavioral assessment characteristically uses a variety of evaluation strategies, including behavioral rating scales, psychophysiological evaluations and observations, and assessing adolescents’ behavior on performance tasks (e.g., standardized tests).

Personality Assessment of Adolescents With Psychological Problems

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Behavior Rating Scales A valuable approach to obtaining information from parents or other adults (e.g., teachers) who have experience with the adolescents being evaluated, are the use of behavior rating scales. Parent- and therapist-rating scales are used to obtain criterion measures for new scales. For example, in the development of the MMPI–A content scales, rating scales were used for assessing the validity of the original MMPI clinical scales and code type correlates (Butcher et al., 1992; Williams & Butcher, 1989a, 1989b; Williams et al., 1992). Behavior rating scales can be informative about adolescents’ characteristic approach to problems (see behavioral rating measures in Mash & Barkley, 2007). The inclusion of information from parent-rating scales serves as a means of enlisting parents into the evaluation process and provides assurance to them that their concerns are being addressed and their role in the adolescents’ life is acknowledged. In many evaluations, the practitioner will find value in assessing parenting skills. There is a wide range of parenting assessment instruments available. (For a broad description of parent assessment instruments, see McKee, Jones, Forehand, & Cuellar, 2013; Touliatos, Perlmutter, & Straus, 2001). Behavior rating scales can provide the assessment psychologist with a unique perspective on the personality and behavior of adolescents. The information provided through behavior rating scales, when considered as part of a comprehensive and multimodal assessment approach, can add to the validity and clinical utility of the overall assessment. Behavior rating scales can provide the practitioner with data that are more reliable than those provided by procedures like unstructured interviews or performance-based techniques that require substantive conclusions. There have been advances in the development of behavior rating scales over the past 20 years, which have greatly enhanced their value among clinicians and researchers (Whitcomb, Harlacher, & Merrell, 2014). Eckert and Lovett (2013) reviewed the theoretical foundations of behavioral assessment and discussed general behavioral assessment methods, behavioral observation, behavioral interviewing and behavior rating scales, psychophysiological

measurements, performance tasks, and their utility in gaining useful information for adolescent evaluations. Useful rating scales available for assessing adolescent behavior include the Devereux Rating Scale (Reddy, Pfeiffer, & Files-Hall, 2007) and the Conners Behavioral Rating Scales (CBRS; Conners, 2009). Several versions of the CBRS are available for use in obtaining rating data on adolescents. The CBRS–Parent provides an assessment of behaviors, emotions, academic progress, and social adjustment problems in youth ages 6 to 18. The measure is available in one comprehensive length and is recommended for initial evaluations and complete reevaluations. The CBRS–Teacher provides an evaluation of behaviors, emotions, academic progress, and social or environmental problems in adolescents. This measure provides information for initial evaluations and reevaluations. The CBRS–Teacher form is often used along with the CBRS–Parent form and differences between adolescents’ behavior at home and at school are provided. The CBRS can be administered in booklet form as well as online rating (see Gianarris, Golden, & Greene, 2001). Behavior rating scales can provide summary viewpoints regarding adolescents’ behavioral characteristics. The structure of the behavioral rating scales adds objectivity to the observation of client behaviors. For example, behavior ratings such as the Child Behavior Checklist (Achenbach, 2001), which place adolescents’ behavior against a backdrop of normative levels of that behavior, can provide a perspective on low-frequency behavior or behavior that the adolescent or parent cannot readily give. Several effective procedures can be incorporated into a diagnostic evaluation: The Achenbach System of Empirically Based Assessment (Achenbach & Rescorla, 2004) provides information on adolescents’ social, emotional, and behavioral characteristics, and is in many ways considered an effective standard for child-behavior rating scales. However, it is perhaps best used for assessing child psychopathology. The Behavior Assessment System for Children—Second Edition is an expanded measure of an earlier version of the rating scale that was designed to include a 145

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screening tool (Behavioral and Emotional Screening System; Kamphaus & Reynolds, 2007). This rating scale includes a teacher form (preschool and child/ adolescent protocols), a parent form (preschool and child/adolescent protocols), and a student form (child/adolescent protocols).

categories defined in the DSM–IV (American Psychiatric Association, 1994): ■■

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Specifically Focused Assessment Instruments Some psychological assessment approaches have been developed solely for evaluating personality characteristics and mental health symptoms in adolescents. Although these instruments focus on narrowly defined characteristics or behavior patterns, the utility of such single-domain, or narrow-band, rating scales is that they allow for greater depth of information

Hare–Youth Version of the Psychopathy Checklist One of the most frequently used rating scales for adolescents, particularly for assessing personality disorder, is the Psychopathy Checklist: Youth Version (PCL:YV; Forth, Kosson, & Hare, 2003). The PCL:YV is an adaptation of the PCL–Revised for adolescents that require trained raters who examine adolescents’ behavior and background. The use of the PCL:YV requires the examination of multidomain and multisource information. This instrument includes a thorough record review and a structured interview.

Beck Youth Inventories The Beck Youth Inventories allows for the assessment of children age 7 to 18 (Beck, Beck, & Jolly, 2005). Five separate self-report inventories are available to address different aspects of children’s adjustment. The measures can be used separately or in combination to assess symptoms of depression, anxiety, anger, disruptive behavior, and self-concept. The five separate inventories contain 20 questions about thoughts, feelings, and behaviors associated with emotional and experienced social impairment relevant for children. Children respond to the question as to how often the statement has been true for them over the past 2 weeks. The questionnaires address the following behaviors, which are based on 146

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Depression: addresses symptoms of depression, including items related to negative thoughts about self, life, and the future; feelings of sadness and guilt; and sleep disturbance. Anxiety: addresses worries about school performance, the future, negative reactions of others, fears (including loss of control), and physiological symptoms associated with anxiety. Anger: addresses thoughts of being treated unfairly by others, feelings of anger, and hatred of others. Disruptive behavior: addresses thoughts and behaviors that can be associated with conduct disorder and oppositional-defiant behavior. Self-concept: addresses information about cognitions of competence, potency, and positive self-worth.

Projective Assessment Techniques Two personality assessment instruments (the Rorschach and the Thematic Apperception Test [TAT]), though in existence for many decades, are still used in some adolescent psychological evaluations. Their continued use in some assessments, in spite of many past criticisms, result from a combination of forces, including the assessor’s belief that the measures are helpful, work-environment requirements, or the fact that the assessors believe that the measures provide a unique picture of an adolescent’s personality characteristics that are not available through other means.

The Rorschach The ways in which the adolescent responds to inkblots is said to provide samples of how they problem solve and make decisions, or provide clues to underlying motivations, needs, attitudes, and worries (R. P. Archer & Newsom, 2000; Ganellen, 2001; Kelly, 2014; Teglasi, 2013; Weiner, 2003; Weiner & Meyer, 2009), although critics have questioned its value and validity (Wood et al., 2001). Though not a modern approach, there are recent studies using the Rorschach to evaluate adolescent psychopathology

Personality Assessment of Adolescents With Psychological Problems

(e.g., Reese, Viglione, & Giromini, 2014; Stokes, Pogge, & Zaccario, 2013; see also Volume 1, Chapter 8, this handbook).

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Thematic Apperception Test The TAT is considered a valuable method for engaging the adolescent in the assessment (Aronow, Altman Weiss, & Reznikoff, 2001; Murray, 1943). The TAT is comprised of a series of pictures in which the client is asked to develop a story about each of a series of pictures as they see it. The story should be imaginative with a beginning, middle, and an end. The client is asked to try to portray who the people in the picture might be and what they are feeling, thinking, and wishing. They are asked to try to tell what led to the situation shown in the picture and how everything will turn out in the end. The interpreting psychologist infers personality characteristics, motives, and problems on the basis of the story content. Some adolescents find the task of developing stories to the pictures on the test to be quite interesting (Kelly, 2014). Although assessment psychologists have questioned the utility of the TAT in personality evaluations (see Lilienfeld et al., 2001) the TAT remains widely used in evaluations of adolescents (Mittino & Maggiolini, 2013; Suzuki, Onoue, Fukui, & Ezrapour, 2012). Multiscale Personality Questionnaires Addressing Adolescent Psychopathology Some personality questionnaires have been developed for assessing adolescents by adapting the items from personality instruments that were developed for adults (e.g., Adolescent Personality Questionnaire, the Personality Assessment Inventory—Adolescents, and the MMPI–A). In some cases, specific scales were also developed to address specific adolescent problems behavior. We will consider three of these measures next.

Adolescent Personality Questionnaire The Adolescent Personality Questionnaire (APQ) was developed by Cattell and colleagues by adapting items from the adult personality instrument, the

16PF (Porter & Cattell, 1985; Schuerger, 2001). The test assesses adolescents’ personality style, problemsolving abilities, preferred work activities, and areas where adolescents might be having current problems. The test includes four sections: youth personal style (normal personality items), problem solving (a short measure of general reasoning ability), work activity preferences (measures six career-interest variables), and life’s difficulties. Derived from the basic 16PF model, these items were designed for younger age groups and have a smaller number of factors as a function of the developmental differentiation in personality that occurs in adults. The APQ addresses more “normal” range personality characteristics and behavior than it does psychopathology.

The Personality Assessment Inventory—Adolescents The Personality Assessment Inventory—Adolescents (PAI–A; Morey, 2007) is a 264-item self-report questionnaire that provides comprehensive assessment information in a variety of settings in which personality problems and mental health symptoms are being evaluated. The PAI–A represents a version of the adult PAI and uses the same scales with some modifications to address adolescent problems (developed for use with adolescents age 12–18). The author used a standardization sample of 707 adolescents who were enrolled in junior high school, high school, or college. The clinical sample used in the standardization was comprised of 1,160 adolescents in clinical or correctional settings. Items on the PAI–A were selected to be comparable with the adult version of the PAI. Empirical support for using the PAI–A in clinical evaluations has been provided (Meyer, Hong, & Morey, 2015). The Minnesota Multiphasic Personality Inventory—Adolescent The use of the MMPI with adolescents has a long and extensive empirical research history going back to the substantial work by Starke Hathaway in the 1960s. One of the most extensive longitudinal personality studies of adolescents ever conducted involved use of the original MMPI. Hathaway and his colleague, Elio Monachesi, (Hathaway & 147

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James N. Butcher

Monachesi, 1963) initially administered the MMPI to 15,300 adolescents in the ninth grade throughout the state of Minnesota (i.e., about 89% of the ninth grade students registered in public schools). They followed up this sample several years later examining behaviors like school performance, school dropout rates, and delinquent acts. Their extensive longitudinal study provided strong support for the value of the MMPI scales to measure adolescent problems in health, mental health, and correctional or juvenile delinquency applications. Hathaway and Monachesi demonstrated that some personality factors on MMPI scales were associated with high rates of juvenile delinquency and high school dropout rate. They reported, for example that three MMPI scales, Pd, Sc, and Ma, were “excitatory” scales that were associated with high rates of delinquency but two scales, D and Si, were associated with low rates of delinquency and they considered these measures to be delinquency “inhibitory” scales. After its development in the 1940s the MMPI was widely used with adolescents even though it was originally developed for adults. During the 1980s, the original MMPI was revised (Butcher, Dahlstrom, Graham, Tellegen, & Kaemmer, 1989). Some of the items were rewritten and modernized, some new items added, and new norms were collected to develop the MMPI–2. This measure was designed to focus on people who were age 18 or older. A separate form of the test was developed for adolescents from age 14 to 18 and has become one of the most widely used personality questionnaires with adolescents (Mihura, Roy, & Graceffo, 2016). Several original MMPI items were dropped from the test because they were inappropriate for assessing adolescent problems and many new items were added to develop the MMPI–A to address personality characteristics and symptom behavior more characteristic of adolescents. A new normative sample was recruited for the MMPI–A and included 805 boys and 815 girls between the ages of 14 and 18 (Butcher et al., 1992). In addition to administering the MMPI–A in the standardization study, developers also included a Biographical Information Form and a Life Events Form, developed in collaboration with Williams, as a means of studying demographic 148

and life stressors of the adolescents included in the norms (Williams & Uchiyama, 1989).

Clinical Validity Study In addition to the extensive normative study with MMPI–A, Williams and Butcher (1989a, 1989b; Williams et al., 1992) conducted a large clinical validity study to verify the utility and validity of the MMPI–A scales for describing adolescent personality and behavior. This clinical sample was also used to develop several new adolescent specific scales to more clearly focus on adolescent relevant behavior and problems (McNulty, Harkness, Ben-Porath, & Williams, 1997; Sherwood, Ben-Porath, & Williams, 1997; Weed, Butcher, & Williams, 1994). Williams and Butcher (1989a, 1989b) tested a total of 420 boys and 293 girls from inpatient alcohol/drug treatment units, psychiatric units, day treatment centers, and special school programs. Adolescents completed an experimental form of the test (Form TX) of the MMPI and the Restandardization Project’s life events and biographical information forms. Additional information about the adolescents was obtained from the Devereux Adolescent Behavior Rating Scale (a therapist rating scale; Spivack, Haimes, & Spotts, 1967), Child Behavior Checklist (a parent rating scale; Achenbach & Edelbrock, 1981), and a record review form developed by the investigators for this study (Achenbach, McConaughy, Ivanova, & Rescorla, 2011). These behavioral data sources provided objectively obtained external personality descriptors for the MMPI–A clinical and content scales (Butcher et al., 1992; Williams & Butcher, 1989a; Williams et al., 1992). Based on the results of their clinical evaluation research, the MMPI–A was published in 1992. Several MMPI–2 scales are briefly summarized next to provide a summary view of the constructs addressed. (For a more detailed examination of the information provided by the MMPI–A scales, see R. P. Archer, 2005; Williams & Butcher, 2011.)

Validity Scales The MMPI–A validity scales were developed to determine if the individual taking the test has responded to the items in a credible manner in disclosing personal information.

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Cannot say.  This measure is not really a scale but is simply a count of the total number of items that the person either failed to answer or has endorsed as true and false. Adolescents may omit items for many reasons, including carelessness, oppositional tendencies, intellectual or reading limitations, confusion, indecisiveness, depression, or defensiveness. Lie scale.  The lie (L) scale was designed to detect naive attempts by people to place themselves in a favorable light, particularly regarding personal ethics or social behavior. Adolescents with high L scores were thought to be answering the items in ways that denied relatively minor flaws or weaknesses. They were considered fairly naive and claiming excessive virtue. Infrequency scales.  The infrequency scales (F, F1, and F2) are somewhat the opposite of the L scale. These are items that have very low frequency of response in the normal population. Persons with high F scores are considered to present themselves in a negative light or responding in an exaggerated manner. The pattern can also be associated with people who randomly responded to the items. Defensiveness scale.  The items on the defensiveness (K) scale were originally selected to identify adults in psychiatric settings who displayed significant degrees of psychopathology but produced profiles that were within normal limits. People who attain high scores on the K scale are found to be evasive, denying even minor symptoms in the responding. Inconsistency scales.  The inconsistency (VRIN and TRIN) scales were developed to determine if the client is responding to similar items in an inconsistent manner. The items of each VRIN item pair have either similar or opposite content; each pair is scored for the occurrence of an inconsistency in the responses to the two items. VRIN and TRIN scores indicate the tendency of a subject to respond to items in ways that are inconsistent or contradictory. The VRIN scale is a measure of random responding; the TRIN scale is a measure of all or mostly true or false responding.

Clinical Scales The MMPI–A clinical scales are virtually identical in content as the original MMPI scales in which

Hathaway and Monachesi (1963) conducted their extensive study on adolescents. Hypochondriasis (Hs).  The Hs scale elevations reflect preoccupation with health and illness. Items present a variety of physical complaints, ranging from specific to general or vague high scores. Scores on this scale are associated with many different physical complaints. Depression (D).  This scale is an index of general dissatisfaction with one’s life, including feelings of discouragement, hopelessness, and low morale. The D scale is made up of items that address subjective depression, psychomotor retardation, physical malfunctioning, mental dullness, and brooding. Hysteria (Hy).  The items on the Hy scale were originally selected to identify individuals who respond to stress with hysterical reactions, which include sensory or motor disorders without an organic basis. High scores on the Hy scale suggest dependent, nonassertive people who tend to be capable of rapidly modifying their behavior to meet social expectations and demands. Psychopathic deviate (Pd).  This scale was developed to assess people with patterns of antisocial problems including lying, stealing, sexual promiscuity, alcohol abuse, impulsivity, and anger control problems. Masculinity/femininity (Mf).  This scale is a measure of areas of interest and not a psychopathology measure. Paranoia (Pa).  This scale consists of items that were initially selected to identify patients manifesting paranoid symptomatology. The Pa scale includes content related to having ideas of reference, suspiciousness, mistrust of others, feelings of persecution, rigidity, and moral self-righteousness. Psychasthenia (Pt).  The Pt scale was originally designed to measure a neurotic syndrome most closely related to the currently used category of anxiety or obsessive-compulsive disorder. The content of this scale covers a wide variety of symptoms including physical complaints, unhappiness, problems in concentration, obsessive thoughts, anxiety, 149

James N. Butcher

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and feelings of inferiority. High scorers tend to be described as overly self-critical, anxious, tense, nervous, and restless. Schizophrenia (Sc).  This scale contains content areas such as bizarre thought processes, peculiar perceptions, social isolation, disturbances in mood and behavior, and difficulties in concentration and impulse control. Sc scale elevations have been found to be associated with behaviors like school problems, disagreements with parents, and lack of achievement. Individuals who score high on the Sc scale tend to be viewed as having schizoid lifestyle problems or severe cognitive problems. Mania (Ma).  This scale was developed to identify patients who are manifesting symptoms of hypomania. The correlates for the Ma scale address such problems as grandiosity, irritability, flight of ideas, egocentricity, elevated mood, and cognitive and behavioral overactivity. Social introversion/extraversion (Si).  The Si scale is comprised of a shortened version of the original MMPI Si scale. Eight items were deleted from the original MMPI Si scale to reduce the overall length of the measure and one item was dropped because it was considered objectionable for adolescents. Elevated Si scores are associated with social withdrawal and low self-esteem. Low scores on Si are associated with extroverted behavior.

Content Scales A set of content measures was developed for the MMPI–2 (Butcher, Graham, Williams, & BenPorath, 1990). Based on their utility and wide acceptance of the MMPI–2 content scales, Williams et al. (1992) used them as the basis for developing similar measures for the MMPI–A. A series of rational and statistical stages of scale development, similar to the procedures used with the MMPI–2 content scales, was used in this process (for an elaboration of the multiple stages and steps involved in the development of these scales, see Williams et al., 1992). The MMPI–A content scales include the following: ■■ ■■

A-anx (adolescent anxiety) A-obs (adolescent-obsessiveness)

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A-dep (adolescent-depression) A-hea (adolescent-health concerns) A-aln (adolescent-alienation) A-biz (adolescent-bizarre mentation) A-ang (adolescent-anger) A-cyn (adolescent-cynicism) A-con (adolescent-conduct problems) A-lse (adolescent-low self-esteem) A-las (adolescent-low aspirations) A-sod (adolescent-social discomfort) A fam (adolescent-family problems) A-sch (adolescent-school problems) A-trt (adolescent-negative treatment indicators)

Substance Abuse Scales There are three widely used substance abuse scales for adolescents on the MMPI–A. The MacAndrew Alcoholism Scale–Revised (MAC-R; Andrucci, Archer, Pancoast, & Gordon, 1989; MacAndrew, 1965); the Alcohol Drug Problem Acknowledgment (ACK), developed to assess the willingness of a young person to acknowledge the problematic use of alcohol and other drugs; and the Alcohol Drug Problem Proneness, similar to the MAC-R, is an empirically derived measure developed to assess the likelihood of alcohol or drug problems in adolescents (see Weed et al., 1994). Case example.  Robert D., a 16-year-old boy from a large Midwestern city, was referred for a psychological evaluation following an incident in which he was arrested for driving under the influence of alcohol after he and two friends were involved in a minor automobile accident. His parents, Elliott and Jane have 3 children—Robert is the youngest, and his older brother and sister are in college. Elliot is a successful small business owner and Jane was a manager of a retail company before retirement. In the past 5 years, Jane has travelled extensively as a representative for a nonprofit organization. Robert’s parents provide little supervision because they believe that Robert is “mature” and able to manage himself well. Robert has a history of problematic behavior, most of which occurred when his parents were out of town and he was left under the supervision of an aunt who lived in his neighborhood. He typically

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Personality Assessment of Adolescents With Psychological Problems

stayed at home alone under computer-interactive “distant” supervision when his parents were both out of town. When he was 13 years old, he was arrested for breaking into a department store with another teenager. His parents were out of town at the time on a business trip. He received a school suspension when he was 13 years old for bullying people in his class. Although he had been evaluated earlier in the fourth grade and described as being above average in intelligence (IQ of 118), his academic performance has been marginal at best. He does not like to read books or do homework assignments and spends most of his free time playing video games. Interview.  Following his DUI arrest, Robert’s driver’s license was suspended, and he was recommended for mental health treatment. His participation in the assessment process was marginal. His aunt arranged his assessment interview and asked her son to transport Robert to the psychologist. Robert was late coming to the first interview because he asked to stop at Arby’s for a snack. Initially, he was somewhat reserved in the interview and seemed nervous about participating. As the interview progressed, he became more relaxed and friendly. However, he did not respond in a personal manner when asked specific questions. He was evasive when asked about his past drinking behavior and the problems he had encountered in school. He was reluctant to assume much responsibility for the problem and at one point blamed one of his friends for starting the drinking events. MMPI–A interpretation.  Just as Hathaway and Monachesi (1963) found with the large sample of delinquent adolescents in the 1960s, Robert obtained a clinical profile that matched this population—highly elevated Pd and Ma clinical scales indicated a likely development of personality disorder. In addition to his extreme elevations on the Pd and Ma scales, Robert also attained a significant elevation on the school problems content scale and high elevations on two substance abuse scales (the MAC-R and the ACK). Robert’s extreme elevations on the MMPI–A scales suggest that he is demonstrating behavior problems that are commonly

found among adolescents who develop personality disorders (see Figures 8.1, 8.2, and 8.3). Exhibit 8.1 shows a computer-generated report that describes Robert’s MMPI–A responses in detail and provides a summary of his personality problems that are well-established in the empirical literature. Case evaluation conclusions.  The psychological evaluation based on an integration of the MMPI–A results and the findings from the various family history and parental interviews that were conducted indicated that Robert was demonstrating several attitudes and behavioral problems that are associated with conduct disorder. The MMPI–A scale patterns, his past behavioral and legal problems, and the distant parental relationship with little guidance indicates the likely presence of a conduct disorder on the basis of criteria from the DSM–5. Conduct disorder has a median age of onset of 12 years (meaning half of those who develop this disorder have it by age 12) and a lifetime prevalence of 10% (see Nock et al., 2006). Children who develop conduct disorder over a long period of time are much more likely to develop psychopathy or antisocial personality disorder as adults than are adolescents who develop conduct disorder suddenly in adolescence (Copeland, MillerJohnson, Keeler, Angold, & Costello, 2007). About 25% to 40% of cases of early-onset conduct disorder go on to develop adult antisocial personality disorder, over 80% of boys with early-onset conduct disorder continue to have multiple problems of social dysfunction (in friendships, intimate relationships, and vocational activities) even if they do not meet all the criteria for antisocial personality disorder. Research has shown that children and adolescents with conduct disorder diagnoses are also frequently comorbid for other disorders (e.g., substance abuse disorder; Goldstein, Grant, Ruan, Smith, & Saha, 2006). Robert’s parents were interviewed on two occasions during the assessment process. The psychologist concluded that over the past few years, the parents had limited involvement in supervision of Robert and they were unaware of his negative friendship involvements, his drinking behavior, and the problematic activities outside of the home. They felt remorseful over their neglect and their “naive acceptance” that he was mature and able to take care 151

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Figure 8.1.  MMPI–A validity scale profile for Robbie. MMPI–A = Minnesota Multiphasic Personality Inventory—Adolescent; VRIN = Variable Response Inconsistency; TRIN = True Response Inconsistency; F1, F2, F = infrequency scales; L = lie scale; K = defensiveness scale. Excerpted from The Minnesota Report™: Adolescent Interpretive System (Rev. ed., p. 2), by J. N. Butcher and C. N. Williams. Copyright © 2007 by the Regents of the University of Minnesota. Portions excerpted from MMPI®—A Manual for Administration, Scoring, and Interpretation. Copyright © 1992 by the Regents of the University of Minnesota. Portions excerpted from the Supplement to the MMPI®—A Manual for Administration, Scoring, and Interpretation: The Content Component Scales, The Personality Psychopathology Five (PSY–5) Scales, The Critical Items. Copyright © 2006 by the Regents of the University of Minnesota. Reproduced by permission of the University of Minnesota Press. All rights reserved. “MMPI®” and “Minnesota Multiphasic Personality Inventory®” are registered trademarks, and “MMPI–A,” “Minnesota Multiphasic Personality Inventory—Adolescent,” and “The Minnesota Report” are trademarks of the University of Minnesota.

of himself. They concluded that they needed to be more involved with the “management” of Robert’s activities and indicated their strong interest in helping turn him around. They were open to scheduling immediately family therapy and individual treatment for Robert. They were also willing to reschedule their work and travel arrangements to help him recognize the troubled path he has taken over the past several months. They discussed transferring him to a private school to acquire more individual attention. Interestingly, one often effective treatment strategy with conduct disorder is the cohesive family model (Granic & 152

Patterson, 2006; Patterson et al., 1998). In this familyoriented approach, disorders like conduct disorder are considered to be reinforced and maintained by ineffective parenting practices. If this problem of ineffective parenting can be altered then there is some possibility of altering the course of conduct disorder. Modified Version of the MMPI–A Item Pool The MMPI publisher recently released a new test called the MMPI–A–RF (R. P. Archer, Handel,

Personality Assessment of Adolescents With Psychological Problems

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Figure 8.2.  MMPI–A clinical and supplementary scale profile for Robbie. MMPI–A = Minnesota Multiphasic Personality Inventory—Adolescent; Hs = hypochondriasis; D = depression; Hy = hysteria; Pd = psychopathic deviate; Mf = masculinity/femininity; Pa = paranoia; Pt = psychasthenia; Sc = schizophrenia; Ma = mania; Si = social introversion/extraversion; MAC-R = MacAndrew Alcoholism Scale–Revised; ACK = Alcohol Drug Problem Acknowledgment; PRO = alcohol/drug proneness; IMM = immaturity; A = anxiety; R = repression. Excerpted from The Minnesota Report™: Adolescent Interpretive System (Rev. ed., p. 3), by J. N. Butcher and C. N. Williams. Copyright © 2007 by the Regents of the University of Minnesota. Portions excerpted from MMPI®—A Manual for Administration, Scoring, and Interpretation. Copyright © 1992 by the Regents of the University of Minnesota. Portions excerpted from the Supplement to the MMPI®—A Manual for Administration, Scoring, and Interpretation: The Content Component Scales, The Personality Psychopathology Five (PSY–5) Scales, The Critical Items. Copyright © 2006 by the Regents of the University of Minnesota. Reproduced by permission of the University of Minnesota Press. All rights reserved. “MMPI®” and “Minnesota Multiphasic Personality Inventory®” are registered trademarks, and “MMPI–A,” “Minnesota Multiphasic Personality Inventory—Adolescent,” and “The Minnesota Report” are trademarks of the University of Minnesota.

Ben-Porath, & Tellegen, 2016). The methodology used by Hathaway and McKinley (1942) to develop the original MMPI scales was not followed because of what R. P. Archer and colleagues (2016) thought to be the “limitations of the criterionkeying method” (p. 3). As a result, the MMPI–A–RF has little relationship to the original MMPI or the MMPI–A other than using a portion of the MMPI–A items and being based on the norms collected in

the 1980s (Butcher et al., 1992). Test length of the MMPI–A was considered by R. P. Archer and colleagues to be a significant disadvantage, so they eliminated roughly half of the items from the inventory to produce an instrument that they thought was more appropriate for adolescents because it would take less time to complete than the hour or so it takes to complete the MMPI–A and would eliminate what they considered to be item overlap between the 153

James N. Butcher

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Figure 8.3.  MMPI–A content scale profile for Robbie. MMPI–A = Minnesota Multiphasic Personality Inventory— Adolescent; A-anx = adolescent anxiety; A-obs = adolescent obsessiveness; A-dep = adolescent depression; A-hea = adolescent health concerns; A-aln = adolescent alienation; A-biz = adolescent bizarre mentation; A-ang = adolescent anger; A-cyn = adolescent cynicism; A-con = adolescent conduct problems; A-lse = adolescent low self-esteem; A-las = adolescent low aspirations; A-sod = adolescent social discomfort; A fam = adolescent family problems; A-sch = adolescent school problems; A-trt = adolescent negative treatment indicators. Excerpted from The Minnesota Report™: Adolescent Interpretive System (Rev. ed., p. 4), by J. N. Butcher and C. N. Williams. Copyright © 2007 by the Regents of the University of Minnesota. Portions excerpted from MMPI®—A Manual for Administration, Scoring, and Interpretation. Copyright © 1992 by the Regents of the University of Minnesota. Portions excerpted from the Supplement to the MMPI®—A Manual for Administration, Scoring, and Interpretation: The Content Component Scales, The Personality Psychopathology Five (PSY–5) Scales, The Critical Items. Copyright © 2006 by the Regents of the University of Minnesota. Reproduced by permission of the University of Minnesota Press. All rights reserved. “MMPI®” and “Minnesota Multiphasic Personality Inventory®” are registered trademarks, and “MMPI–A,” “Minnesota Multiphasic Personality Inventory—Adolescent,” and “The Minnesota Report” are trademarks of the University of Minnesota.

traditional scales. The MMPI–A–RF authors reduced the length because they believed that the 478 items on the MMPI–A was flawed and “[posed] a major challenge for the attention span and concentration of some adolescents” (R. P. Archer et al., 2016, p. 3). The MMPI–A–RF consists of only 241 items. The MMPI–A–RF developers (R. P. Archer et al., 2016) provided no empirical external validation data but used protocols scored on the MMPI–A from 154

various settings provided by Pearson Assessments to serve as correlation data for the new scales. The MMPI–A–RF contains a very large number of scales for a shortened inventory, 48 separate measures (with considerable overlap between scales). Some of the scales are extremely brief (only 4 items), and therefore likely result in lower validity and reliability. No peer reviewed external validity research was available to psychologists prior to

Personality Assessment of Adolescents With Psychological Problems

Exhibit 8.1 MMPI–A Minnesota Report Narrative for Robbie

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Validity Considerations This is a valid MMPI–A. His responses to the MMPI–A validity items suggest that he cooperated with the evaluation enough to provide useful interpretive information. The resulting profiles are an adequate indication of his present personality functioning. Symptomatic Behavior This adolescent’s MMPI–A clinical profile indicates multiple serious behavior problems including school maladjustment, family discord, and authority conflicts. He can be moody, resentful, and attention-seeking. At times he may appear rebellious, impulsive, and argumentative. His poor judgment may get him into trouble. He can be self-centered and may show little remorse for his bad behavior. He may run away or lie to avoid punishment. Difficulties with the law or juvenile authorities could occur. His two highest MMPI–A clinical scales, Pd and Ma, which are clearly elevated above other scales in the clinical profile, are the most frequent two-point scale elevations among adolescents in psychiatric or alcohol/drug treatment settings. Over 10% of boys in treatment programs have this clearly defined profile pattern. It should be noted that this well-defined high-point pair is the second most frequent scale pair in the normative boys’ sample as well (representing over 2% of the sample). However, Pd and Ma scale elevations are usually lower in the normative sample than in adolescent treatment samples. In a large archival sample of MMPI–A profiles scored by Pearson Assessments (n = 19,048), this high-point pair of scale elevations (Pd and Ma) was obtained by 8.8% of the adolescent boys in the sample using well-defined criteria (i.e., each scale score being 65 or above, and more than 5 points higher than the third highest scale). His MMPI–A content scales profile reveals important areas to consider in his evaluation. This young person reports numerous difficulties in school. He probably has poor academic performance and does not participate in school activities. He may have a history of truancy or suspensions from school. He probably has very negative attitudes about school, possibly reporting that the only positive aspect of school is being with his friends. An examination of the adolescent’s underlying personality factors with the PSY–5 scales might help explain any behavioral problems he might be presently experiencing. He shows a pattern of disinhibition given his elevation on the disconstraint scale that can be manifest through high risk-taking, impulsivity, and irresponsibility. He appears to be less bound by moral restraints than other people and shows callous disregard for others. Interpersonal Relations Initially, he may seem likable and may make a good impression on others; however, his relationships tend to be very troubled. His behavior is primarily hedonistic and self-centered, and he is quite insensitive to the needs of other people, exploiting them and feeling no guilt about it. He has an average interest in being with others and is not socially isolated or withdrawn. He appears able to meet and talk with other people and does not seem overly anxious in social gatherings. However, his personal relationships may be somewhat superficial. He may be manipulative at times. Behavioral Stability The relative elevation of the highest scale (Pd) in his clinical profile shows very high profile definition. His peak score is likely to remain very prominent in his profile pattern if he is retested at a later date. Adolescents with this clinical profile may have a history of acting-out behaviors and relationship problems. Diagnostic Considerations More information is needed about his behavior problems before a definitive diagnosis can be made. His Pd elevation suggests that behavior problems should be considered. Given his elevation on the school problems scale, his diagnostic evaluation could include assessment of possible academic skills deficits and behavior problems. His extremely high score on the MAC-R scale suggests substantial problems with alcohol or other drugs. He probably engages in risktaking behaviors and tends toward exhibitionism. Further evaluation of his alcohol or other drug use is strongly recommended. He has endorsed items that confirm his increasing involvement with alcohol or other drugs. He acknowledges that his use is problematic and reports being criticized for it. He may feel that alcohol or other drugs facilitate social interactions, serving as a coping strategy. Treatment Considerations His conduct disturbance should figure prominently in any treatment planning. His clinical scales profile suggests that he is a poor candidate for traditional, insight-oriented psychotherapy. A behavioral strategy is suggested. Clearly stated contingencies that are consistently followed are important for shaping more appropriate behaviors. (continues)

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James N. Butcher

Exhibit 8.1 (Continued) MMPI–A Minnesota Report Narrative for Robbie

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His very high potential for developing alcohol or drug problems requires attention in therapy if important life changes are to be made. He has acknowledged some problems in this area, which is a valuable first step for intervention. He did endorse content suggesting a desire to succeed in life. There may be some positive aspects about school that could be reinforced. This could be an asset to build on during treatment.

Note. MMPI–A = Minnesota Multiphasic Personality Inventory—Adolescent; Pd = psychopathic deviate; Ma = mania; MAC-R = MacAndrew Alcoholism Scale–Revised. Excerpted from The Minnesota Report™: Adolescent Interpretive System (Rev. ed., pp. 5–6), by J. N. Butcher and C. L. Williams. Copyright © 2007 by the Regents of the University of Minnesota. Portions excerpted from MMPI®—A Manual for Administration, Scoring, and Interpretation. Copyright © 1992 by the Regents of the University of Minnesota. Portions excerpted from the Supplement to the MMPI®—A Manual for Administration, Scoring, and Interpretation: The Content Component Scales, The Personality Psychopathology Five (PSY–5) Scales, The Critical Items. Copyright © 2006 by the Regents of the University of Minnesota. Reproduced by permission of the University of Minnesota Press. All rights reserved. “MMPI®” and “Minnesota Multiphasic Personality Inventory®” are registered trademarks, and “MMPI–A,” “Minnesota Multiphasic Personality Inventory—Adolescent,” and “The Minnesota Report” are trademarks of the University of Minnesota.

release of the MMPI–A–RF and the computer interpretation system for the test. Shortened versions of psychological tests have been shown to be problematic measures in several studies. (For a discussion of the problems with abbreviated versions of the MMPI instruments, see Butcher & Hostetler, 1990; Butcher, Kendall, & Hoffman, 1980; Hoffmann & Butcher, 1975.) Kruyen, Emons, and Sijtsma (2013) reviewed the psychological literature for recent trends in the use of short tests and examined in depth how and to what extent test constructors and test users addressed the impact on reliability and validity and other potential consequences of using short tests. They warned that shortened test length may be more efficient from an administrative viewpoint, but their use goes against the old psychometric wisdom that many items are needed for reliable and valid measurement. Many practitioners prefer using full measures even though they may take somewhat more time to complete because the full instrument provides more extensive and reliable indicators as Bow et al. (2010) found in a survey conducted on test usage—practitioners almost always (95%) used all 567 MMPI–2 items rather than an abbreviated version of the test. The MMPI–2 restructured scales were incorporated in the MMPI–2–RF even though they have been heavily criticized in the assessment literature for their inability to detect psychopathology in various settings (e.g., research on eating disorders, Erreca, 2010; Latinos with depression, Khouri, 156

2010; substance abuse treatment, VanPortfliet, 2012; persons with psychogenic nonepileptic seizures, Locke & Thomas, 2011; internet sex offenders, Lustig, 2011). Wallace and Liljequist (2005) reported that the average T scores of clinical persons on the restructured scales were significantly lower than the scores on their original MMPI–2. They found that the majority of the test profiles among their clients (56%) had fewer scale elevations when plotted using the restructured scales compared with the original clinical scales. Moreover, in a recent family custody study using the MMPI–2–RF, E. M. Archer, Hagan, Mason, Handel, and Archer (2012) reported that most of the average Restructured Clinical (RC) scales were below a T score elevation of 50. The only RC scale that was elevated above a T score of 50 was RC6, which attained a mean score of 53.39. The RC scales that were included on the MMPI–2–A–RF were further reduced in length by a substantial number of items and differ greatly from the original restructured scales developed by Tellegen and Ben-Porath (2008). The restructured scales developed for the original MMPI–2 item pool were significantly shortened for the RC scales included in the MMPI–A–RF. The item differences between the two instruments are noted as follows: ■■

RCd (Demoralization): General unhappiness and dissatisfaction (18 items vs. 24 items on the original MMPI–2).

Personality Assessment of Adolescents With Psychological Problems

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RC1 (Somatic Complaints): Diffuse physical health complaints (23 items vs. 27 items on MMPI–2). RC2 (Low Positive Emotions): A distinctive core vulnerability factor in depression (10 items vs. 17 items on the original MMPI–2). RC3 (Cynicism): Non–self-referential beliefs that others are bad and not to be trusted (9 items vs. 15 items on the original MMPI–2). RC4 (Antisocial Behavior): Rule breaking and irresponsible behavior (20 items vs. 22 items on the original MMPI–2). RC6 (Ideas of Persecution): Self-referential beliefs that others pose a threat (9 items vs. 17 items on the original MMPI–2). RC7 (Dysfunctional Negative Emotions): Maladaptive anxiety, anger, and irritability (11 items vs. 24 items on the original MMPI–2). RC8 (Aberrant Experiences): Unusual perceptions or thoughts associated with psychosis (8 items vs. 18 items on the original MMPI–2). RC9 (Hypomanic Activation): Over-activation, aggression, impulsivity, and grandiosity, uncontrolled behavior (8 items vs. 28 items on MMPI–2).

Given the low sensitivity of the original RC scales for assessing psychopathology and their significant reduction in items in the MMPI–A–RF, the RC scales are more likely to have even greater difficulty in detecting psychopathology. Because of the generally low ranging scores on the MMPI–A–RF scales, the test authors lowered the interpretive T score level for the scales on the MMPI–A–RF to 60 (referred to as moderate elevation on MMPI–A) rather than the T of 65 indicating the clinical range on MMPI–A. This means that psychologists must consider more “average” scores to be elevated into the clinically interpretable range. The authors of the MMPI–A–RF made the decision to use nongendered T scores, using the same normative group to compare boys and girls despite the considerable differences in psychological manifestation among the two (R. P. Archer et al., 2016). For example, some diagnostic disorders have significantly different rates for men versus women (American Psychiatric Association, 2013). The authors dropped 10 female subjects from the MMPI–A normative scores to reduce the female cases to be

equivalent with the male cases (N = 805; R. P. Archer et al., 2016). The two samples were combined into one to create nongendered T scores. The use of the same nongendered T scores to assess adolescents is problematic because there are well-established gender differences between boys and girls (see American Psychiatric Association, 2013; Kenyon & Eaton, 2015; Zahn-Waxler, Shirtcliff, & Marceau, 2008; Zakriski, Wright, & Underwood, 2005). The authors of the MMPI–A–RF do not provide sufficient details about the normative sample they incorporated from the MMPI–A normative data to assist the test use in the comparison of test scores (R. P. Archer et al., 2016). Insufficient information is provided in the manual for the assessment psychologist to gain an understanding of the MMPI–A–RF. The scales differ substantially from the MMPI–A; past research on MMPI and MMPI–A with adolescents cannot be used to support decisions made on these measures. Conclusion Psychological assessment of adolescents can describe personality functioning and provide a base line for treatment and management of mental health problems. Adolescents can be referred for psychological evaluation due to parental concerns, inability to function in family relationships, school problems, legal difficulties, custody evaluations, or acculturation problems. There are several factors that need to be taken into consideration to assure that the psychological evaluation meets the intended goals. It is important to evaluate the extent of cooperation adolescents maintained in the evaluation to assure the credibility of the results. Several other factors were addressed that could influence the outcome of the evaluation. For example, the assessment should provide an appropriate perspective on adolescents’ environmental context (e.g., what their family relationships and circle of friendships are like). An evaluation needs to address adolescents’ intellectual capability and problem solving skills to assure that treatment or management recommendations could be successful. It is important to examine the extent and potential long-term nature of adolescents’ problems or whether the symptom pattern is potentially a temporary maladjustment. 157

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James N. Butcher

Psychological evaluations of adolescents are best when they use a variety of strategies and assessment instruments to cover a broad range of pertinent content and information sources. This chapter summarized several frequently used personality assessment techniques for assessing adolescents in clinical evaluations, including interview, behavioral assessment, and behavior rating scales. Several unidimensional and multidimensional personality measures were described. Finally, the MMPI–A, one of the most frequently used personality questionnaires with adolescents was described and a case illustration was provided to give the reader a perspective on the extent of information available through this approach. The new test, the MMPI–A–RF, is an entirely different measure than the MMPI–A, and does not assess the traditional constructs. Insufficient research is available to support its use in assessing adolescent psychopathology.

References Achenbach, T. M. (2001). Child Behavior Checklist for ages 6–18. Burlington, VT: Research Center for Children, Youth, and Families. Achenbach, T. M., & Edelbrock, C. S. (1981). Behavioral problems and competencies reported by parents of normal and disturbed children aged 4 through 16. Monographs for the Society for Research in Child Development, 46, 1–82. Achenbach, T. M., McConaughy, S. H., Ivanova, M. Y., & Rescorla, L. A. (2011). Manual for the ASEBA brief problem monitor. Burlington, VT: Research Center for Children, Youth, and Families. Achenbach, T. M., & Rescorla, L. A. (2004). The Achenbach System of Empirically Based Assessment (ASEBA) for ages 1.5 to 18 years. In M. E. Maruish (Ed.), The use of psychological testing for treatment planning and outcomes assessment: Instruments for children and adolescents (3rd ed., Vol. 2, pp. 179–213). Mahwah, NJ: Erlbaum. American Psychiatric Association. (1994). Diagnostic and statistical manual of mental disorders (4th ed.). Washington, DC: Author. American Psychiatric Association. (2013). Diagnostic and statistical manual of mental disorders (5th ed.). Washington, DC: Author. Andrucci, G. L., Archer, R. P., Pancoast, D. L., & Gordon, R. A. (1989). The relationship of MMPI and sensation seeking scales to adolescent drug use. Journal of Personality Assessment, 53, 253–266. http:// dx.doi.org/10.1207/s15327752jpa5302_4

158

Archer, E. M., Hagan, L. D., Mason, J., Handel, R., & Archer, R. P. (2012). MMPI–2–RF characteristics of custody evaluation litigants. Assessment, 19, 14–20. http://dx.doi.org/10.1177/1073191110397469 Archer, R. P. (2005). MMPI–A: Assessing adolescent psychopathology. New York, NY: Routledge. Archer, R. P., Handel, R. W., Ben-Porath, Y. S., & Tellegen, A. M. (2016). MMPI–A–RF: Manual for administration, scoring and interpretation manual. Bloomington, MN: Pearson Assessments. Archer, R. P., & Newsom, C. R. (2000). Psychological test usage with adolescent clients: Survey update. Assessment, 7, 227–235. http://dx.doi.org/ 10.1177/107319110000700303 Aronow, E., Altman Weiss, K., & Reznikoff, M. (2001). A practical guide to the Thematic Apperception Test: The TAT in clinical practice. New York, NY: Routledge. Baker, A. J. L., & Brassard, M. R. (2013). Adolescents caught in parental loyalty conflicts. Journal of Divorce and Remarriage, 54, 393–413. http://dx.doi.org/ 10.1080/10502556.2013.800398 Beck, J. S., Beck, A. T., & Jolly, J. B. (2005). Beck youth inventories (2nd ed.). Bloomington, MN: Pearson Assessments. Behrman, R. E., & Quinn, L. S. (1994). Children and divorce: Overview and analysis. Future of Children, 4, 4–14. http://dx.doi.org/10.2307/1602474 Bow, J. N., Flens, J. R., & Gould, J. W. (2010). MMPI–2 and MCMI–III in forensic evaluations: A survey of psychologists. Journal of Forensic Psychology Practice, 10, 37–52. http://dx.doi.org/10.1080/ 15228930903173021 Butcher, J. N., Dahlstrom, W. G., Graham, J. R., Tellegen, A., & Kaemmer, B. (1989). Manual for the restandardized Minnesota Multiphasic Personality Inventory (MMPI–2): An administrative and interpretive guide. Minneapolis: University of Minnesota Press. Butcher, J. N., Graham, J. R., Williams, C. L., & Ben-Porath, Y. S. (1990). Development and use of the MMPI–2 content scales. Minneapolis: University of Minnesota Press. Butcher, J. N., Hass, G. A., Greene, R. L., & Nelson, L. D. (2015). Using the MMPI–2 in forensic assessment. http://dx.doi.org/10.1037/14571-000 Butcher, J. N., & Hostetler, K. (1990). Abbreviating MMPI item administration: What can be learned from the MMPI for the MMPI–2. Psychological Assessment: Journal of Consulting and Clinical Psychology, 2, 12–22. http://dx.doi.org/10.1037/1040-3590.2.1.12 Butcher, J. N., Kendall, P., & Hoffman, N. G. (1980). MMPI short forms: CAUTION. Journal of Consulting and Clinical Psychology, 48, 275–278. http://dx.doi.org/ 10.1037/0022-006X.48.2.275

Personality Assessment of Adolescents With Psychological Problems

Butcher, J. N., Williams, C. L., Graham, J. R., Tellegen, A., Ben-Porath, Y. S., Archer, R. P., & Kaemmer, B. (1992). Manual for administration, scoring, and interpretation of the Minnesota Multiphasic Personality Inventory for Adolescents: MMPI–A. Minneapolis: University of Minnesota Press. Cervantes, R. C., & Shelby, T. M. (2013). Mental health issues for immigrant adolescents: Research and practice. In E. L. Grigorenko (Ed.), U.S. Immigration and Education: Cultural and policy issues across the lifespan (pp. 163–178). New York, NY: Springer.

Copyright American Psychological Association. Not for further distribution.

Conners, C. K. (2009). The Conners rating scales (3rd ed.). North Tonawanda, NY: Multihealth Systems. Copeland, W. E., Miller-Johnson, S., Keeler, G., Angold, A., & Costello, E. J. (2007). Childhood psychiatric disorders and young adult crime: A prospective, population-based study. American Journal of Psychiatry, 164, 1668–1675. http://dx.doi.org/ 10.1176/appi.ajp.2007.06122026 Craig, R. J. (2009). The clinical interview. In J. N. Butcher (Ed.), The Oxford handbook of personality assessment (pp. 201–227). http://dx.doi.org/10.1093/oxfordhb/ 9780195366877.013.0012 Crespi, T. D., & Politikos, N. N. (2012). Personality assessment for school psychologists: Considerations and issues for contemporary practice. Journal of Infant, Child and Adolescent Psychology, 11, 229–237. da Silva, D. R., Rijo, D., & Salekin, R. T. (2013). Child and adolescent psychopathy: Assessment issues and treatment needs. Aggression and Violent Behavior, 18, 71–78. http://dx.doi.org/10.1016/j.avb.2012.10.003 Eckert, T. L., & Lovett, B. J. (2013). Principles of behavioral assessment. In D. H. Saklofske, C. R. Reynolds, & V. Schwean (Eds.), The Oxford handbook of child psychological assessment (pp. 366–384). New York, NY: Oxford University Press. Erreca, K. (2010). MMPI–2–RF profiles of women with eating disorders in an inpatient setting (Doctoral dissertation). Retrieved from Dissertations and Theses database. (UMI No. 3437609) First, M. B., Williams, J. B. W., Benjamin, L. S., & Spitzer, R. L. (2016). Structured clinical interview for DSM–5 personality disorders (SCID–5–PD). Washington, DC: American Psychiatric Association. First, M. B., Williams, J. B. W., Karg, R. S., & Spitzer, R. L. (2016). Structured clinical interview for DSM–5 clinical version (SCID–5–PD). Washington, DC: American Psychiatric Association.

and mental health outcomes of children and adolescents. Development and Psychopathology, 21, 227–259. http://dx.doi.org/10.1017/ S0954579409000145 Ganellen, R. J. (2001). Weighing evidence for the Rorschach’s validity: A response to Wood et al. (1999). Journal of Personality Assessment, 77, 1–15. http://dx.doi.org/10.1207/S15327752JPA7701_01 Gianarris, W. J., Golden, C. J., & Greene, L. (2001). The Conners’ Parent Rating Scales: A critical review of the literature. Clinical Psychology Review, 21, 1061–1093. http://dx.doi.org/10.1016/ S0272-7358(00)00085-4 Goldstein, R. B., Grant, B. F., Ruan, W. J., Smith, S. M., & Saha, T. D. (2006). Antisocial personality disorder with childhood- vs. adolescence-onset conduct disorder: Results from the National Epidemiologic Survey on Alcohol and Related Conditions. Journal of Nervous and Mental Disease, 194, 667–675. http:// dx.doi.org/10.1097/01.nmd.0000235762.82264.a1 Granic, I., & Patterson, G. R. (2006). Toward a comprehensive model of antisocial development: A dynamic systems approach. Psychological Review, 113, 101–131. http://dx.doi.org/10.1037/ 0033-295X.113.1.101 Grisso, T. (2013). Forensic evaluation of juveniles (2nd ed.). Sarasota, FL: Professional Resource Press. Hathaway, S. R., & McKinley, J. C. (1942). The Minnesota Multiphasic Personality Schedule. Minneapolis: University of Minnesota Press. Hathaway, S. R., & Monachesi, E. (1963). Adolescent personality and behavior: MMPI patterns of normal, delinquent, drop-out and other outcomes. Minneapolis: University of Minnesota Press. Heiby, E. M., & Haynes, S. N. (2003). Introduction to behavioral assessment. In S. P. Haynes & E. M. Heiby (Eds.), Behavioral assessment (pp. 3–18). Hoboken, NJ: Wiley. Hodges, K. (2004). Using assessment in very day practice for the benefit of families and practitioners. Professional Psychology: Research and Practice, 35, 449–456. http:// dx.doi.org/10.1037/0735-7028.35.5.449 Hoffmann, N., & Butcher, J. N. (1975). Clinical limitations of three Minnesota Multiphasic Personality Inventory short forms. Journal of Consulting and Clinical Psychology, 43, 32–39. http://dx.doi.org/10.1037/h0076315

Forth, A. E., Kosson, D. S., & Hare, R. D. (2003). The psychopathy checklist: Youth version. Toronto, Ontario, Canada: Multi-Health Systems.

Kamphaus, R. W., & Reynolds, C. R. (2007). Behavior Assessment System for Children—Second Edition (BASC–2): Behavioral and Emotional Screening System (BESS). Bloomington, MN: Pearson Assessments.

Fowler, P. J., Tompsett, C. J., Braciszewski, J. M., JacquesTiura, A. J., & Baltes, B. B. (2009). Community violence: A meta-analysis on the effect of exposure

Kelly, F. D. (2014). The assessment of object relations in adolescents: TAT and Rorschach. New York, NY: Routledge. 159

James N. Butcher

Kenyon, K. M., & Eaton, W. O. (2015). Age at child obsessive-compulsive disorder onset and its relation to gender, symptom severity, and family functioning. Archives of Scientific Psychology, 3, 150–158. http:// dx.doi.org/10.1037/arc0000022 Khouri, R. (2010). MMPI–2 RF vs. MMPI–2: Latinos with depression (Doctoral dissertation). Retrieved from Dissertations and Theses database. (UMI No. 3416813)

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Kruyen, P. M., Emons, W. H. M., & Sijtsma, K. (2013). On the shortcomings of shortened tests: A literature review. International Journal of Testing, 13, 223–248. http://dx.doi.org/10.1080/15305058.2012.703734 Lilienfeld, S. O., Wood, J. M., & Garb, H. N. (2001, May). What’s wrong with this picture? Scientific American, 284, 80–87. http://dx.doi.org/10.1038/ scientificamerican0501-80 Locke, D. E. C., & Thomas, M. L. (2011). Initial development of Minnesota Multiphasic Personality Inventory—2—Restructured Form (MMPI–2–RF) scales to identify patients with psychogenic nonepileptic seizures. Journal of Clinical and Experimental Neuropsychology, 33, 335–343. http:// dx.doi.org/10.1080/13803395.2010.518141 Lustig, L. (2011). The MMPI–2–RF and the MCMI–III on internet sex offenders (Doctoral dissertation). Retrieved from Dissertations and Theses database. (UMI No. 3459245) MacAndrew, C. (1965). The differentiation of male alcoholic outpatients from nonalcoholic psychiatric outpatients by means of the MMPI. Quarterly Journal of Studies on Alcohol, 26, 238–246. Marin, C. E., Rey, Y., & Silverman, W. K. (2013). Interviews. In B. D. McLeod, A. Jensen-Doss, & T. H. Ollendick (Eds) Diagnostic and behavioral assessment in children and adolescents: A clinical guide (pp. 103–132). New York, NY: Guilford Press. Mash, S. J., & Barkley, R. A. (2007). Assessment of childhood disorders (4th ed.). New York, NY: Guilford Press. McKee, L. G., Jones, D. J., Forehand, R., & Cuellar, J. (2013). Assessment of parenting behaviors and style, parenting relationships, and other parent variables in child assessment. In D. H. Saklofske, C. R. Reynolds, & V. Schwean (Eds.), The Oxford handbook of child psychological assessment (pp. 788–821). http://dx.doi.org/10.1093/oxford/ 9780199796304.013.0035 McNulty, J. L., Harkness, A. R., Ben-Porath, Y. S., & Williams, C. L. (1997). Assessing the Personality Psychopathology Five (PSY–5) in adolescents: New MMPI–A scales. Psychological Assessment, 9, 250–259. http://dx.doi.org/10.1037/1040-3590.9.3.250 Merikangas, K. R., & He, J. (2014). Epidemiology of mental disorders in children and adolescents: Background and U.S. studies. In J. P. Ranaud, S. S. 160

Gau, & M. Hodes, (Eds.), From research to practice in child and adolescent mental health (pp. 19–45). Lanham, MD: Rowan & Littlefield. Merikangas, K. R., He, J. P., Brody, D., Fisher, P. W., Bourdon, K., & Koretz, D. S. (2010). Prevalence and treatment of mental disorders among U.S. children in the 2001–2004 NHANES. Pediatrics, 125, 75–81. http://dx.doi.org/10.1542/peds.2008-2598 Meyer, J. K., Hong, S. H., & Morey, L. C. (2015). Evaluating the validity indices of the Personality Assessment Inventory—Adolescent Version. Assessment, 22, 490–496. http://dx.doi.org/ 10.1177/1073191114550478 Mihura, J. L., Roy, M., & Graceffo, R. A. (2016, March). Psychological assessment in doctoral training programs. Paper presented at the annual convention of the Society for Personality Assessment, Chicago, IL. Mittino, F., & Maggiolini, A. (2013). Analisi narrativa di TAT di un campione clinico di adolescenti [TAT narrative analysis in adolescents’ clinical sample]. Psichiatria e Psicoterapia, 32, 45–58. Morey, L. C. (2007). Personality assessment inventory—Adolescent professional manual. Odessa, FL: Psychological Assessment Resources. Murray, H. A. (1943). Thematic apperception test manual. Cambridge, MA: Harvard University Press. Nock, M. K., Kazdin, A. E., Hiripi, E., & Kessler, R. C. (2006). Prevalence, subtypes, and correlates of DSM–IV conduct disorder in the National Comorbidity Survey Replication. Psychological Medicine, 36, 699–710. http://dx.doi.org/10.1017/ S0033291706007082 Orvaschel, H. (2006). Structured and semi structured interviews. In M. Hersen (Ed.), Clinician’s handbook of child behavioral assessment (pp. 159–179). http:// dx.doi.org/10.1016/B978-012343014-4/50008-3 Patterson, G. R., Reid, J. B., & Dishion, T. J. (1998). Antisocial boys. In J. M. Jenkins & K. Oatley (Eds.), Human emotions: A reader (pp. 330–336). Malden, MA: Blackwell. Porter, R. B., & Cattell, R. B. (1985). Handbook for the children’s personality questionnaire. Champaign, IL: Institute for Personality and Ability Testing. Reddy, L. A., Pfeiffer, S. I., & Files-Hall, T. M. (2007). Use of the Devereux scales of mental disorders for children and adolescents with emotional disturbance. Journal of Psychoeducational Assessment, 25, 356–372. Reese, J. B., Viglione, D. J., & Giromini, L. (2014). A comparison between comprehensive system and an early version of the Rorschach performance assessment system administration with outpatient children and adolescents. Journal of Personality Assessment, 96, 515–522. http://dx.doi.org/10.1080/ 00223891.2014.889700

Personality Assessment of Adolescents With Psychological Problems

Schuerger, J. M. (2001). 16PF Adolescent Personality Questionnaire (APQ). Champaign, IL: Institute for Personality and Ability Testing. Sherwood, N. E., Ben-Porath, Y. S., & Williams, C. L. (1997). Further refinements in content assessment with the MMPI–A: Development and application of the MMPI–A content component scales. Minneapolis: University of Minnesota Press.

Copyright American Psychological Association. Not for further distribution.

Shiner, R. L., & Allen, T. A. (2013). Assessing personality disorders in adolescents: Seven guiding principles. Clinical Psychology: Science and Practice, 20, 361–377. http://dx.doi.org/10.1111/cpsp.12047 Sonne, J. L. (2012). PsycEssentials: A pocket resource for mental health practitioners. Washington, DC: American Psychological Association.

scales. Journal of Studies on Alcohol, 55, 296–302. http://dx.doi.org/10.15288/jsa.1994.55.296 Weiner, I. B. (2003). Principles of Rorschach interpretation. New York, NY: Taylor & Francis. Weiner, I. B., & Meyer, G. J. (2009). Personality assessment with the Rorschach Inkblot Method. In J. N. Butcher (Ed.), The Oxford handbook of personality assessment (pp. 277–298). http://dx.doi.org/ 10.1093/oxfordhb/9780195366877.013.0015 Whitcomb, S. A., Harlacher, J. E., & Merrell, K. W. (2014). Behavior rating scales. In R. P. Archer & S. R. Smith (Eds.), Personality assessment (2nd ed., pp. 261–299). New York, NY: Routledge.

Spivack, G., Haimes, P. E., & Spotts, J. (1967). Deveraux adolescent behavior (DAB) rating scale manual. Devon, PA: The Deveraux Foundation.

Williams, C. L., & Butcher, J. N. (1989a). An MMPI study of adolescents: I. Empirical validity of the standard scales. Psychological Assessment: Journal of Consulting and Clinical Psychology, 1, 251–259. http://dx.doi.org/10.1037/1040-3590.1.4.251

Stokes, J. M., Pogge, D. L., & Zaccario, M. (2013). Response character styles in adolescents: A replication of convergent validity between the MMPI–A and the Rorschach. Journal of Personality Assessment, 95, 159–173. http://dx.doi.org/10.1080/ 00223891.2012.730084

Williams, C. L., & Butcher, J. N. (1989b). An MMPI study of adolescents: II. Verification and limitations of code type classifications. Psychological Assessment: Journal of Consulting and Clinical Psychology, 1, 260–265. http://dx.doi.org/10.1037/ 1040-3590.1.4.260

Substance Abuse and Mental Health Services Administration. (2015). Substance abuse treatment with persons with co-occurring disorders. Washington, DC: Author.

Williams, C. L., & Butcher, J. N. (2011). A beginner’s guide to the MMPI–A. Washington, DC: American Psychological Association.

Suzuki, L. A., Onoue, M. A., Fukui, H., & Ezrapour, S. (2012). Foundations of counseling psychology: Assessment. In N. A. Fouad, J. A. Carter, & L. M. Subich (Eds.), The APA handbook of counseling psychology: Vol. 1. Theories, research, and methods (pp. 167–199). http://dx.doi.org/10.1037/13754-007

Williams, C. L., Butcher, J. N., Ben-Porath, Y. S., & Graham, J. R. (1992). MMPI–A content scales: Assessing psychopathology in adolescents. Minneapolis: University of Minnesota Press.

Teglasi, H. (2013). The scientific status of projective techniques as performance measures of personality. In D. H. Saklofske, C. R. Reynolds, & V. Schwean (Eds.), The Oxford handbook of child psychological assessment (pp. 113–128). New York, NY: Oxford University Press. Touliatos, J., Perlmutter, B. F., & Straus, M. A. (2001). Handbook of family measurement techniques. Thousand Oaks, CA: Sage. VanPortfliet, P. (2012). The MMPI–2–RF and the prediction of completion of a substance abuse rehabilitation program (Doctoral dissertation). Retrieved from ProQuest Dissertations and Theses database. (UMI No. 3517595) Wallace, A., & Liljequist, L. (2005). A comparison of the correlational structures and elevation patterns of the MMPI–2 restructured clinical (RC) and clinical scales. Assessment, 12, 290–294. http://dx.doi.org/ 10.1177/1073191105276250 Weed, N. C., Butcher, J. N., & Williams, C. L. (1994). Development of MMPI–A alcohol/drug problem

Williams, C. L., & Uchiyama, C. (1989). Assessment of life events during adolescence: The use of self-report inventories. Adolescence, 24, 95–118. Wilmshurst, L. (2015). Essentials of child and adolescent psychopathology (2nd ed.). Hoboken, NJ: Wiley. Wood, J. M., Nezworski, M. T., Garb, H. N., & Lilienfeld, S. O. (2001). The misperception of psychopathology: Problems with the norms of the comprehensive system. Clinical Psychology: Science and Practice, 8, 360–373. Zahn-Waxler, C., Shirtcliff, E. A., & Marceau, K. (2008). Disorders of childhood and adolescence: Gender and psychopathology. Annual Review of Clinical Psychology, 4, 275–303. http://dx.doi.org/10.1146/ annurev.clinpsy.3.022806.091358 Zakriski, A. L., Wright, J. C., & Underwood, M. K. (2005). Gender similarities and differences in children’s social behavior: Finding personality in contextualized patterns of adaptation. Journal of Personality and Social Psychology, 88, 844–855. http://dx.doi.org/10.1037/ 0022-3514.88.5.844 161

Chapter 9

Assessment of Abused Youth

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Jeffrey N. Wherry

The term youth captures children and adolescents, and the term abused can encompass many different forms of child maltreatment, although the literature is scant for all but sexual abuse and physical abuse. In addition, we use the term assessment because other terms may have specific application and associated limitations. For example, psychological testing limits the focus to services delivered by psychologists. Because other professionals receive some training in assessment and deliver the services based on their state’s license, the term psychological testing would be restrictive to a broader audience of readers. Assessment reflects a variety of methods that can and should be used in the evaluation of any child—especially abused youth. These methods include a review of records; interviews of caregivers; self-reports completed by youth; caregiver ratings completed by parents, foster parents, and/or residential treatment workers; and interviews of the youth themselves. These interviews can be clinical interviews, semistructured or structured interviews, or testing of limits following the administration of a self-report measure. Of course, behavioral observations are also an important part of assessment. Controversies, Bad Practice, and Questionable Practices Any assessment tool associated with any trade or profession can be misused. This reality applies for certain practices related to the assessment of abused youth.

Did Abuse Occur? No evaluation or assessment can establish absolutely if a child was abused. Child abuse is not a diagnosis, but rather an event. In most circumstances, there are only two people who know if abuse occurred—the victim and the perpetrator. Assessment findings may be consistent with a history of abuse, but by no means can results definitively establish that abuse has occurred. Although there can be many explanations and etiologies for symptom clusters, an assessment merely describes the symptoms and does not infer that events have occurred. A dissenting assertion might be, “What about the use of the diagnosis of posttraumatic stress disorder?” Certainly, inferences are drawn about associations between symptoms and events. The distinction is in clinician’s acceptance of the child’s report of abuse. This acceptance is merely a clinical practice no different than accepting an adult client’s report of the divorce of parents when young. The veracity of the claim is not automatically suspect. Determining the veracity of such a claim is the work of a judge and jury.

Prediction Most assessments do a less-than-perfect job of predicting the future. The best predictor of future behavior is always past behavior. This is a constant regarding hurting others, hurting oneself, or the likelihood of reoffending (e.g., in the case of abuse perpetration).

http://dx.doi.org/10.1037/0000065-009 APA Handbook of Psychopathology: Vol. 2. Child and Adolescent Psychopathology, J. N. Butcher (Editor-in-Chief) Copyright © 2018 by the American Psychological Association. All rights reserved.

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Test Batteries It is relevant that many psychological tests are taught, and then implemented as a set or battery of tests. In some settings, these combinations seem to be more a function of tradition than by intentional design with the purpose of targeting known symptoms, issues, or characteristics. However, seldom are the combinations of measures designed to assess abuse-related symptoms or to answer relevant referral questions associated with child abuse. Moreover, though the number of standardized measures targeting abuse-related symptoms is sparse, it nonetheless is unfortunate that many clinicians, including psychologists, fail to use these available measures—often because graduate training programs have not taught these tests and their use with abused children.

Using Only Instruments Available in the Public Domain

related to confidentiality applies to civil and family court hearings only.

Inviting Nonoffending Fathers to Participate It is not uncommon for mothers or female caregivers to be the parental figures involved in the assessment and ongoing treatment of abused children. However, when there is a paternal figure actively involved in the life of the child, the clinician should invite fathers and paternal figures. They can make a difference. If a clinician’s experience or tendency is to avoid involvement of fathers/paternal figures, he or she might (a) ask why he or she is so inclined, and (b) at the very least, consider that all caregivers have the potential to undermine treatment or, more positively, to support it.

Provide Assessment Feedback Immediately to the Child and Caregivers

Recently, as assessment has been recognized as an important step in the treatment of abused children, clinicians of every professional discipline seem to actively seek out those measures which are available for free in the public domain. Although there are some exceptions, most of these public domain measures (a) have not been normed, (b) are not multidimensional, (c) do not include parent and child versions, and (d) have questionable reliability and validity. The enduring adage “you get what you pay for” often continues to be true.

Assessment is not therapy, but an assessment can be therapeutic—but only when feedback is provided to the child and family. Like any other skill, this requires coaching, supervision, and practice for the novice, but is an essential practice. Using the words and descriptions provided by caregivers may be essential for engagement in treatment. Also, for children and adolescents, feedback which frames some symptoms as functional responses to trauma (e.g., posttraumatic stress) can be reassuring and help that individual to make sense of their symptomatology.

Brady v. Maryland

Shape Efficient Writing Skills in Trainees

When a clinician assesses a child who allegedly has been abused, he or she must be aware that the Supreme Court has held that the prosecutor must disclose evidence or information that would prove the innocence of the defendant or would enable the defense to more effectively impeach the credibility of government witnesses (Brady v. Maryland, 1963). This applies in criminal court, but not in civil or family court. The intent of the finding is for potentially exculpatory evidence to be released to the attorney representing the defendant, to protect rights afforded by the Fourteenth Amendment of the U.S. Constitution. Invoking the client’s privilege as 164

Professors, listen up! Graduate students should be moved from taking 10–13 hours to complete a report for a grade of “A” to a more reasonable and real-world time frame (e.g., 1 hour). Inefficient patterns of behavior in graduate school are hard to unlearn in the workplace, so professors must shape efficient report-writing skills in trainees.

Not All Abuse Is Traumatic After years of ignoring trauma, the term and its symptoms are in vogue. This development is good, though in some settings the proverbial “pendulum

Assessment of Abused Youth

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has swung too far.” In short, not all abuse is traumatic. Consider a 4-year-old victim whose abuser convinces the child that the sexual abuse is a game played between children and adults. The abuser has his sexual need gratified in ways which do not physically hurt the child (e.g., oral sex), and the child is not threatened or otherwise hurt physically. This child may become sexualized, and psychologically marred, though not physically traumatized. What a Good Assessment of Child Abuse Is Not There seems to be a continuing rich tradition of using predetermined test batteries with children regardless of the presenting problem or referral question. These batteries vary on the basis of setting and the background and influence of senior psychology staff, but often included a cognitive or intellectual assessment, an assessment of academic achievement, a projective measure or set of measures (e.g., drawings, Thematic Apperception Test, Rorschach), and some form of self-report (e.g., depression, anxiety). Cashel (2002) identified the 30 most commonly administered assessments, and none of the measures recommended in this chapter appeared on the list because Cashel’s batteries of tests do not assess relevant symptoms associated with abuse and trauma. Many experienced psychologists undoubtedly have perpetuated those practices based on their own training and without questioning the rationale. A medical analogy is in order here. In what possible medical setting is it now acceptable to administer a MRI, a blood panel, an electroencephalogram, and an ultrasound of all major body systems? To the contrary, whether it is medicine, psychology in general, or assessment of abused children, assessments should be tailored to answer specific referral questions articulated by the referring source or designed around work with the population in question and assess common symptoms associated with the condition (e.g., depression) or circumstances leading to assessment (e.g., child abuse, death of a relative, a chronic health condition). In fact, identifying relevant referral questions often is no easy task and requires building

relationships among the referring agents and stakeholders involved in serving a population of children, and abused children specifically and educating these “gatekeepers” in how to articulate questions which can be addressed in an assessment. Good referral questions or issues for abused children might include the following: (a) Are the low grades in fourth and fifth grade the result of a learning disability or abuse which began about that time? (b) What recommendations should be offered for the 12-yearold who recently has disclosed sexual abuse, is cutting her ankles superficially with razors, and saying, “sometimes I wish I was never born.” (c) Identify the nature of the apparent emotional dysregulation in an 8-year-old and differentiate between developmentally appropriate behavior, bipolar disorder, and trauma-related symptoms. Although not an exhaustive list, these three examples illustrate the thought process. Occasionally, the question of a specific child’s need for therapy arises. However, need versus readiness and the ability to benefit from therapy at a specific moment often are mutually exclusive. For example, among some children’s advocacy centers (CACs), all children are referred for therapy. Similarly, in child welfare cases where custody is temporarily or permanently taken from abusive or neglectful parents and granted to the state, a child may be sentenced to a lifetime (or until they age out of the system) of therapy because the system deems it necessary for a child. The problem in both instances is that no one is asking the child if he or she wants to participate in therapy and what would make therapy helpful to them (i.e., goals of therapy). To answer a specific question about a child’s potential benefit from therapy the assessment might determine the level of distress, identify mutual goals with the child and family, and assess motivation for therapy. So, the child or adolescent should be asked, “Would you like to work with a therapist?” And because parents are the primary therapeutic agents in the life of their child, it often is advisable to ask parents about motivation and to address ambivalence and barriers to treatment. This type of interaction during assessments is advisable if families are to be meaningfully engaged in a process that is helpful. 165

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The Process of Assessment

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Conceptualizing the Assessment It is essential to conceptualize the assessment for what it is. It is not a mystical, infallible process, but rather a snapshot in time. In photography, some images are improperly captured by the photographer, and at other times, the subject of the snapshot is captured in the wrong light or in an unflattering moment. Multiple snapshots might provide a more complete portrayal of the subject as might multiple assessment sessions over time. However, the reality is that in most situations, an extended process over days is not practically feasible and/or affordable. A dose of humility is required for all professionals who conduct assessments. Specifically, a clinician might not describe the child with 100% accuracy because he or she is relying on a snapshot. Furthermore, other professionals who have been previously involved with the child may have rendered only a partially accurate snapshot. Where a more complete/accurate understanding of the child is available, the clinician should set the record straight as he or she moves forward. For example, if a child is emotionally dysregulated due to complex and chronic trauma and not bipolar disorder, the clinician must correct the diagnosis and explain that possible misdiagnosis moving forward.

Variations by Setting Children and parents are unable to be optimally helpful in an assessment unless they are safe. If there is an allegation of sexual abuse, for example, children need to be kept safe from the alleged abuser until such time that a case is substantiated or unsubstantiated. Sometimes, for children the perception of safety is quite subjective and may influence assessment findings. If a 5-year-old child has been secretly threatened by an alleged sexual abuser, that child may be too fearful to talk freely or to answer questions honestly. Similarly, parents must feel safe—safe from a spouse or partner who might make physical or financial threats (e.g., “I will not support you or our child if you move forward with these allegations”). Safety of informants in an assessment is a prerequisite for an accurate assessment. 166

Some settings, by their nature, are sites where abuse is disclosed initially. One such setting is a CAC where children may undergo a forensic interview to determine if abuse has occurred. In these settings, whether formal CACs or medical settings where exams and interviews occur, children should be screened for symptoms and other forms of adverse, stressful, or abusive events. These screenings can assist local professionals in triaging either to therapy or for further assessment. Often, children make disclosures of alleged abuse, and it is unclear if there is an official report to Child Protective Services (CPS). It is best practice to verify a report with the local or state CPS office. If there is reasonable suspicion of abuse, the assessment professional, as a mandated reporter, is required by law to make a report to CPS and/or law enforcement. Mandated reporting laws have increased reporting in large U.S. counties (Palusci, Vandervort, & Lewis, 2016), but there are studies suggesting that child abuse is underrecognized and underreported (Borres & Hägg, 2007; Raman, Holdgate, & Torrens, 2012). Professionals must trust in that process, trust in that team, and not rely merely on their own limited information and wisdom. Although the system is not perfect, it is the system, and it is the law to report suspected abuse. In addition to safety of children and parents, there is variation in the practice of screening for a variety of abusive or traumatic events based on the setting—outpatient, child placing agencies (e.g., foster homes, residential treatment, children’s homes), CPS, and other settings. Outpatient settings.  Outpatient settings can include CACs, agencies with expertise in working with abused children, or private practice practitioners. In all settings the cost of assessments, the measures and the time required to administer, may influence practices. Although costs are a relevant and practical consideration, the argument of costs associated with assessments often are invoked as though assessment before treatment is a luxury rather than a prerequisite. This tendency is perpetuated when third-party payers do not reimburse specifically for the activity of assessment, or when

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Assessment of Abused Youth

clinicians delude themselves into thinking that their “clinical acumen” is sufficient. CACs have been operational since the late 1980s. They bring together a collection of multidisciplinary team members who are tasked with investigation and prosecution of abuse, as well as treatment (and assessment) of abused children. Though mental health services always have been a necessary component of services available through CACs, only recently has the identification and treatment of abuse-related symptoms received adequate focus. This important development runs in parallel with advancements in the science of assessment and the availability of evidence-based treatments for abused children and their nonoffending parents. In fact, beginning in 2017, new standards of accreditation adopted by the National Children’s Alliance (2015) require standardized assessment of trauma-specific symptoms and events used initially to inform treatment and subsequently to assess progress. Challenges continue to impact the delivery of mental health services, generally, and assessments, specifically, in CACs. Only about 30% of CACs provide assessment and treatment services within the agency (Wherry, Huey, & Medford, 2015). Most centers rely on mental health professionals in the community. However, only about 35% of CAC executive directors agreed or strongly agreed that there were adequate numbers of providers for treatment and assessment within their communities (Wherry et al., 2015). This is particularly concerning because Wherry, Baldwin, Junco, and Floyd (2013) found that 34% of a sample of outpatient seeking treatment from a CAC experienced suicidal thinking or thoughts of self-harm. Child placing agencies.  Conradi, Wherry, and Kisiel (2011) suggested that it is critical that the child welfare system work with the mental health care system to ensure that abused children receive trauma-focused screening and assessment, and referral to the appropriate trauma-focused mental health services. It is important to note that assessment also helps to identify children who may not be candidates for trauma-focused treatment (i.e., those who are not displaying trauma-related symptoms).

In many states, children in the child welfare system have been psychologically evaluated at the time of placement outside the home. Too often those evaluations have been comprehensive in scope, but general in their focus. A broad range of domains (e.g., cognitive, academic achievement, projective, emotional, behavioral) are assessed, but clinicians exclude or neglect some abuse-specific symptoms (e.g., posttraumatic stress, dissociative symptoms, sexual concerns). In more recent years, there has been an increase in the use of unidimensional measures and broad-band rating scales (e.g., Child Behavior Checklist [CBCL]; Achenbach & Rescorla, 2001), completed by youth, caregivers, teachers, or other relevant adults. However, rarely are abusefocused measures used in these child welfare assessments. For example, in a review of current clinical practices, Cashel (2002) found that 71% of psychologists used clinical interviews, whereas 69.8% of psychologists also used IQ or cognitive assessments; no tests of trauma symptoms were identified for use with adults, let alone children. Despite increased awareness of the importance of targeted assessments in child welfare settings, clinical or diagnostic interviews continue to be ranked as the most common forms of assessment across disciplines (Summerfeldt & Antony, 2002). Residential treatment settings in the broad sense, refer to agencies where children go to live either for the short-term or the long-term. Some centers provide intensive care while children are in residence. Other centers provide basic care as professional services may not be available on-site; however, the children are supervised by home parents or shift workers, and they attend public schools. These residential or child placing agencies admit a wide variety of children and adolescents with problematic behaviors along a continuum. Nonetheless, a common denominator often includes family problems of some sort. Screening for abuse or trauma events is important in working with these children. Often, it is assumed by residential staff that children have been properly screened by CPS or other professionals as related to traumatic or abusive events. However, systematic screening is uneven at best, and proper inquiries elucidate a long history of behavioral problems while at home. When new allegations 167

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are revealed, mandated reporters must file reports as appropriate within their state. Many residential settings employ staff who are qualified to address the treatment needs of these children; however, a targeted assessment of trauma- and abuse-related symptoms still is warranted. Child Protective Services.  CPS includes first responders who are called to a variety of scenes to investigate allegations of neglect, physical abuse, or sexual abuse. Often, a specific allegation of child maltreatment (e.g., neglect) is relatively obvious based on the home situation from which the child was removed. These children may enter temporary placements, emergency shelters, foster care, or some other setting, and an assessment is requested by a CPS worker. Although the policy language is permissive and allows for the use of measures as deemed appropriate by the licensed professional, there is some increased tendency for states, working with contracted insurance companies to mandate specific procedures which may or may not include assessments that are reliable, valid, and normed. In decades past, psychologists often had the freedom to decide measures which were appropriate to the child. Due in part to inappropriate testing and reports which provided little relevant information for treatment, psychological evaluations have become increasingly rare, irrelevant, or prescribed by state agencies. Other settings.  Abused children are identified in a variety of other settings including pediatric settings, community mental health centers, schools, and inpatient psychiatric hospitals. Perhaps abused children would be properly identified more frequently and earlier if screening for abuse was routine. Use of a brief instrument to screen for exposure and abuse-related symptoms potentially would result in the identification of children in need of treatment and, where indicated, those requiring more thorough assessments (Wherry, Corson, & Hunsaker, 2013). Most inpatient hospitalizations are brief and focus on stabilization. Although thorough assessments once were a product of inpatient hospitalization, shorter length of stays allow for less assessment and less individualized treatment. Nonetheless, a 168

significant contribution by inpatient psychiatric hospitals would be the systematic screening of all children for all forms of child abuse and other potential exposure to traumatic events (e.g., natural disasters, sudden or traumatic deaths of family members, domestic discord and violence). When child abuse is disclosed, mandated reporters must report those events to CPS or law enforcement. Moreover, it is advisable to make that referral as soon as practically possible and to suspend the assessment until a forensic interview at a CAC can occur. It is important to note that these forensic interviews are quite different than the forensic tasks for which most forensic psychologists are trained (e.g., competency to stand trial). The assessment can continue after the forensic interview.

Variations Based on the Nature of the Abuse In cases of child sexual abuse, it is not advised to include the alleged perpetrator (e.g., father, uncle, aunt, stepmother) in the evaluation. Often the sexual abuse is perpetrated by someone known to the family, and it is not the father or stepfather with whom the child lives. If there is a nonabusive “paternal figure,” that individual should, at the very least, be invited to participate in the assessment process. Although nonoffending fathers may elect to not participate in an assessment, if he is not invited, he will not come. Consider offering an invitation to father figures to participate in the assessment. In cases of child physical abuse, the abuse and the setting may interact to create special circumstances. For example, if CPS orders services for the parent and child, and the child is to remain in the custody of the parent who perpetrated the physical abuse, then that parent should absolutely be involved in the assessment of the child. Their understanding, expectations, and beliefs about their child must be considered because the goal in the treatment of a physically abusive family is to change the nature of the interactions between the parent and the child. However, if the child has been removed from the custody of the parent, the assessment becomes potentially more complicated based on logistics like permissions to participate, the ability

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to participate (on the basis of geographical distance between the parent and the child placing agency), the willingness of the parent to participate, and/or the advice of legal counsel to participate. In cases of domestic violence, the interaction or complexity of abuse type and setting may come into play along with severity. First and foremost, a child should be safe from an individual who has hit the child and/or a parent. Also, there must be some recognition that some domestic violence involves both parents who are physically abusive with a child. It is imperative to screen for safety and other forms of child maltreatment and stress in these situations.

Variations Based on Timing Setting and type of abuse primarily are associated with who is involved in the assessment. Variations based on timing of the abuse may affect findings. For example, one family might seek services on the basis of a substantiated report of sexual abuse by someone outside of immediate family that occurred 12 months ago. The allegation may have been substantiated after a confession by the alleged perpetrator, but the child reported few symptoms and expressed no desire to participate in therapy at the time of disclosure. However, on returning to the home, extended family members call on each member to take a position on the abuse—often increasing the tension in a family. Over time, the child’s symptom presentation may be very different and more severe. For a child that makes a recent disclosure and then is assessed, the timing of that initial relief may influence the self-report of symptoms as there is underreporting of symptoms by the child, but not by the parent/caregiver.

Engagement Engagement of the youth and family can be a challenge especially if the family members find themselves in an assessment against their will. It is essential for professionals to establish a collaborative working relationship and communicating clearly, genuinely, and empathically their desire to help, a sense of hope, and sense of direction for the process of the assessment. Families and youth should be included, to the extent possible, in every aspect of

the decision-making process. It is imperative that the limits of confidentiality should be explained. Information about additional abuse cannot be confidential, and in some settings (e.g., CACs), clients are informed that findings will be shared with members of the team. By informing the youth and parent about the limits of confidentiality, they are then free to provide or withhold information. In some situations, teens are less than enthusiastic about the prospect of doing an assessment and following that with therapy. Often, this sort of overt resistance is apparent early in the assessment (e.g., a scowl, crossed arms, rolling eyes, verbal statements of “whatever”). In that moment, clinical judgment must inform the practitioner about next steps. Sometimes with abused children, seeing a mental health professional is confirmation of what the alleged perpetrator threatened (e.g., “They will just think you are crazy”). Explaining some possible outcomes of abuse (e.g., posttraumatic stress) as a series of responses which initially are adaptive and promote survival can be comforting and help a resistant teen to engage. Using the findings to provide feedback to parent and child is respectful, helpful, and essential. It is respectful because it honors the child’s and parent’s right and need to know what is happening. This can be a time where families and children feel like their world is out of control. Summarizing the information that they have provided is the least practitioners can do. It also is helpful because these insights, explanations, and labeling start to provide meaning to the child’s experience of symptoms. Feedback also is essential because the information belongs to them (the child and parent) even if it is shared with CPS or other members of the investigatory team. A clinician should acknowledge when he or she is uncertain about symptoms or the conceptualization of the events, symptoms, and impact. Saying, “I don’t know for certain,” is not the worst thing that can happen with the family.

Beginning the Process of Assessment After securing consent to treat/assess, explain the process to the child and family in language that they understand. With a resistant teen, it then may prove 169

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beneficial to talk first with him or her alone. Because trauma is a common outcome in some cases of physical abuse and most cases of sexual abuse, gradual exposure is a common component of most evidencebased treatments. In the service of gradual exposure and engagement, it is important to say something like, “I know you spoke with Ms. Greene last week about the abuse. I have read what you told her. I will not ask you to tell me again what happened, but I am interested in learning more about your thoughts and feelings—before, during, and after the abuse.” Thus, “abuse” has been introduced gradually as the reason for the assessment, but the child is not required to retell the story at this juncture. In this way, assessment is truly the start of therapy, though it will inform the treatment as well. With each measure, form, or self-report, it is imperative to ask follow-up questions (a) to show that the child’s time spent completing the measures/ procedures was important, (b) to gather examples of specific events and symptoms, and (c) to immediately assess risk if critical items are endorsed (e.g., thoughts of self-harm). While this follow-up or “testing of limits” can provide details and examples helpful in treatment, it should occur only after the measure has been completed. Also, if the youth experiences the follow-up as badgering by the examiner rather than interest, follow-up then should cease in the service of engagement. In addition to following up on critical items, measures should be scored immediately if possible and integrated into the feedback provided to the child and caregivers. When concerns about selfharm or suicidality are identified, it is imperative clinically and from a risk management perspective to inform the parent, implement a safety plan, and provide resources and contacts in the event of afterhour emergencies. In a CAC setting, after-hour emergencies may be precipitated by changes in the status of the investigation or related activities (e.g., the release of an incarcerated alleged perpetrator, dismissal of a case or opting not to pursue prosecution, accusations by extended family members directed at the alleged victim, receiving news from a medical lab confirming the presence of a sexually transmitted disease, or a change in the status of a pregnancy for the victim). These potential situations 170

may trigger responses in the child or changes in the status of family support, and alter concerns about suicidality. Each case is unique, so referrals to inpatient psychiatric hospitals may require thoughtful consideration although deliberate and well-planned outpatient alternatives (e.g., intensive and frequent outpatient care) also may be a consideration.

Differential Diagnosis, Feedback, and Treatment The process of differential diagnosis is less about assigning a diagnostic label and more about formulating and testing hypotheses about symptoms, triggering events, and conceptualizing the case. This process begins before the evaluation as professionals read and consider the material available about the child and the case. For example, either in the existing records or as the assessment unfolds, professionals may hear about concentration problems for the child. This symptom alone might be explained as a posttraumatic stress disorder (PTSD) symptom of arousal, as a symptom of depression, or as a symptom of attention-deficit/hyperactivity disorder (ADHD). Pursuing each line of questioning will be important in your assessment and for the recommendations for treatment which will follow. For some beginners who work with CACs, there can be a healthy reluctance about assigning a diagnostic label to a child. Remember, the diagnosis is fluid and not meant to be permanent. Others are not compelled to agree with a diagnosis because it too is a snapshot in time. However, a clinician is not required to see the child in the same way that another clinician perceived the child 12 months ago. Professionals must get over their discomfort with diagnosis and do it. Remember, we are not treating sexual abuse, physical abuse, or domestic violence. We are treating the symptoms that result from these events, and the assessment informs that process. Moreover, good treatment, even evidence-based treatment, is predicated on, and informed by a good and accurate assessment. When a clinician has conceptualized the case, he or she must provide feedback to the family. This feedback can and should be done on the day of the assessment. The feedback should be provided in language that the child and parents can understand.

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Clinicians should be truthful with children and with parents and make clear the associations between the symptoms and the treatments which can be provided (preferably those which are evidence-based). When communicating with parents and children, clinicians should consider the following: (a) explain what they are not saying (e.g., “I am not saying your behavior is crazy”); (b) if the trauma symptoms are evident, they must explain how these symptoms initially may be a functional response promoting survival, but once safe, the overgeneralized learning becomes dysfunctional; (c) they should initiate the process of psychoeducation about the normality of symptom clusters like trauma responses (e.g., posttraumatic stress, dissociation), sexual concerns, worries, and regressive behaviors; (d) they should explain how avoidance can be a relief in the shortterm, but it can make matters worse in the longterm; (e) they should describe to parents and the child how certain exposure therapies like the trauma narrative of trauma-focused cognitive–behavioral therapy (Deblinger, Behl, & Glickman, 2012) are predicated on gradual exposure and meaningful doses of stress management prior to explicit discussion and exposure to detailed accounts of the abuse; (f) they should explain the primacy of parent support while predicting how common “wellmeaning” intuitive advice from friends and family is not always the best advice (e.g., “Move on,” “Don’t dwell on the negative,” “Forgive your family”); (g) they should reduce the mystery associated with therapy and predict course and length of treatment; and (h) they should encourage parents and the child to be active “consumers” in the process of treatment and invite their “course corrections” in the event that the treatment is not meeting their needs.

Written Reports of Findings Few, if any, mental health clinicians decide to do this work (i.e., psychology, assessment, therapy) for the joy of paperwork. In the world of clinicians, it is an insufferable annoyance which leads to avoidance. Part of this dilemma is compounded by graduate programs which initially reward students for spending 10 or more hours scoring, interpreting, and writing up assessment results. The problem is that such a reinforcement pattern does not square with the

real world. Although 10 hours of effort often will result in a grade of “A” in graduate school, similar documentation will not result in bonuses if one is employed by an agency or reimbursement if one is in private practice. We must encourage students and ourselves to spend less time in obsessing over writing reports and establish a goal early in training to spend 1 to 2 hours on a report. This is achievable. And for some, software programs which support dictation to the typed word is part of the solution. For those who teach assessment, consider classes designed where students must complete a report in person and in the presence of a professor, and doing so in decreasing increments of time (e.g., starting with 3 hours, then 2 hours, and finally 1 hour. Methods of Assessment There are two potential approaches or stages to assessment of child abuse: screening and full assessment. Determining if and when screening occurs will depend on resources which are available in the community or in agencies serving the child and family. For example, few communities have enough trained and experienced professionals to provide services to all children and families in a timely manner. Even if an agency or community has adequate resources, there are variations in the flow of referrals that are seasonal. For example, during holiday periods, children may be mandated by visitation orders to visit with family members who ultimately perpetrate abuse. During the summer, similar mandated visits occur while children are simultaneously not attending school and do not have access to mandated reporters like teachers. During the fall (following summer visits), there may be a spike in referrals. Regardless of the timing, this ebb and flow of referrals may require some triage on the basis of the needs of children in relation to limited resources. If routine screening occurs, this can assist in determining and prioritizing needs. Full assessments might occur routinely in some communities or be determined by the screening. Theoretically, in a world free of practical restraints like budgets and staffing, screenings would occur with all children, followed by full assessments, and subsequent therapy where indicated. 171

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Regarding child abuse, there is growing recognition that screening and assessment should target polyvictimization (the experience of multiple abuse events and/or stressful and traumatic events) and symptoms which are evident. Note that some symptoms and problems may predate abuse, but warrant treatment. When children present with multiple traumatic events and symptoms with varied potential origins, the work of assessment is much like good detective work as all the clues are combined to provide a clearer understanding of what influences are affecting the child and the family.

Screening Annually, neglect is the primary form of child maltreatment reported. Year after year, and decade after decade, child neglect hovers near 70% of all reported forms of child maltreatment. And although the effects of neglect can be negative in the form of hostility/aggression, negative self-esteem, emotional instability, negative worldview, negative selfadequacy, and dependence (Khaleque, 2015), we are increasingly learning about the issue of polyvictimization (i.e., experiencing multiple forms of child abuse). For example, exposure to multiple forms of abuse or trauma is common with polyvictimization occurring in 20% to 48% of youth who report (Finkelhor, Ormond, & Turner, 2007). However, routine screening for traumatic events in CPS or in child psychology is not commonplace. The systematic screening by CPS remains an understudied practice despite policies which vary from state to state. Moreover, in many studies of children in residential care, agreement between the child and a CPS worker is low for a history of child sexual abuse (Baker, Curtis, & Papa-Lentini, 2006; Collin-Vézina, Coleman, Milne, Sell, & Daigneault, 2011; Dale, Baker, Anastasio, & Purcell, 2007; Milne & Collin-Vézina, 2014). Amaya-Jackson, Socolar, Hunter, Runyan, and Colindres (2000) noted that using methods other than direct inquiry of the child often yield imprecise estimates of abuse prevalence. Children should be asked directly about their abuse experience in clinical settings since, otherwise a significant number of abuse victims may go unrecognized (Ungar, Barter, McConnell, Tutty, & Fairholm, 2009). 172

Traditionally, caregiver reports have been the primary source of information for a child’s exposure to other abuse and trauma-related events. Including reports from children, rather than relying on reports from caregivers alone, yields a more comprehensive assessment of a child’s exposure to violence (Ceballo, Dahl, Aretakis, & Ramirez, 2001). For some cases under investigation, a child may reveal sexual abuse by a neighbor. While the responsibility of that abuse is strictly attributed to the abuser, a parent may be reluctant to disclose lapses in supervision or mood swings leading to physical abuse. Depending on the timing of the screening relative to the abuse and its circumstances, some parents, preoccupied by their child’s recent disclosure, may also fail to endorse other items which remain salient to the child (e.g., the death of a grandparent). A variety of checklists and semistructured interviews exist (with varying psychometric properties) which serve to screen or assess for a history of stressful events. In working with children, it is imperative to separately screen the child and have the parent complete a screening tool for their child. Sometimes children and parents are in full agreement about the events which have occurred. On other occasions, children and parents may merely not think to record or “check off” an event. Still in other situations, children and/or parents may deliberately fail to mention events. It is in the best interest of the child that potential disparities in reporting are addressed to better understand the situation, the motivations of reporters (clients), and the accuracy of the information. Younger children may be less reliable regarding the timing (e.g., onset and duration) of events, the frequency of occurrence, and the number of referent events. Also, event items, like symptom items, require follow-up questioning on the part of the clinician. As an example, a child may endorse an item indicating “an attack” by a dog. On inquiry, the child may describe an innocuous, playful interaction with a puppy that led to scratches or inconsequential bite wounds from sharp puppy teeth. Conversely, another child may endorse an attack by a dog and reveal wounds and describe events that resulted in hundreds of stitches to repair the injury. As expected, the potential trauma from these two dissimilar events is dramatically different. Moreover, for some children,

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as heinous as certain abuse events might be in the perception of the clinician (e.g., vaginal or rectal penetration of the child), other identified events may be more traumatic to the child and have a variety of implications for treatment planning. Next are several instruments (i.e., checklists, procedures, and interviews) that can be used to screen for history of traumatic events. For the trauma screening measures, it is important to acknowledge that reliability and validity studies can be difficult to conduct with these instruments, and endorsement of items by children or parents cannot be used as forensic evidence to establish that these events have happened. Rather, the value of these instruments is for the clinician who is tasked with treating these children and families. Each of the following instruments has it strengths and weaknesses. Clinicians should review several and pick one that can be used comfortably with the parent and one that can be used with the child. ■■

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Traumatic Events Screening Inventory—Parent Report Revised (Ford, 2002a); Self Report Revised (Ford, 2002b) UCLA PTSD Reaction Index for the DSM–IV: Child and Adolescent Version (Pynoos, Rodriquez, Steinberg, Stuber, & Frederick, 1998) UCLA PTSD Reaction Index for the DSM–IV: Parent Version (Pynoos et al., 1998) The Adolescent Trauma History Checklist and Interview (Habib & Labruna, 2006) Childhood Trauma Questionnaire (Bernstein et al., 1994) Juvenile Victim Questionnaire (Finkelhor, Hamby, Ormrod, & Turner, 2005) Child and Adolescent Trauma Screen (Foa, Johnson, Feeny, & Treadwell, 2001)

If no other trauma has been identified, it is important to screen for forms of child abuse other than the referent event. We know from research that the various forms of child maltreatment often cooccur (Saunders, 2003), and that family/domestic violence is frequently present where there is child maltreatment. Because effective treatment is more likely when children first can be protected from the many forms of abuse they experience, it is important to conduct a thorough assessment of potentially

traumatic events aside from the initial referent event. In the final analysis, personal preferences may prevail in the selection of screening tools that screen for stress, abuse, and trauma. However, it is important to recognize that not all abuse is traumatic, and not all stressful events are traumatic. For example, a 4-year-old child is abused by an extended relative. The abuse may follow weeks of grooming or gradual preparation of the victim. The actual abuse may involve touching or oral contact, which when introduced gradually, is neither frightening nor painful at the moment. The child may be sexualized without being traumatized.

Full Assessment Clinicians often do not assess a broad range of diagnoses (Angold, 2002), seek out information to confirm a diagnosis (Angold, 2002), assign diagnoses for which criteria have not been met (Garb, 1998), and stop collecting assessment data prematurely once criteria for a diagnosis is made (Garb, 2005). A good assessment of a youth should include (a) multiple informants (e.g., child, parent, teacher), (b) multiple methods (e.g., review of records, interviews, self-report, adult ratings), and (c) assessment of multiple domains of abuserelated symptoms (e.g., posttraumatic stress, acute stress, dissociative symptomatology, depression, self-harm and suicidality, anxiety, anger and aggression, sexual concerns. If only one or two dimensions (e.g., trauma and depression) are assessed, important symptoms including possible self-harm and sexualized behavior may be missed. For example, Meyer et al. (2001) found that there were several studies that reported a high number of diagnostic errors when only one assessment measure or method was used. Unidimensional vs. broad-band rating scales.  Best practice in assessing children dictates obtaining information from multiple sources; including caregivers, teachers, and child self-report. Regarding specific areas to assess, it is recommended that caregivers, teachers, and/or other relevant adults complete broad-band rating scales which assess a wide range of behaviors and symptoms (e.g., the CBCL). 173

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Although there are psychometrically sound unidimensional measures (e.g., the Children’s Depression Inventory—2, Kovacs, 2010; UCLA PTSD Reaction Index, Pynoos et al., 1998), a focus on a single construct limits the breadth of information that is needed to understand a child’s premorbid and current functioning, as well abuse-related symptoms. An additional concern with unidimensional measures is that they can force the rater to attempt to fit multiple problems into the single measure being assessed. Not surprisingly, this then can result in a false positive for the specific symptom being measured or the absence of endorsement (because the measure does not fit with the child’s problems), leading the evaluator to conclude mistakenly that the child is exhibiting no symptoms. Projectives.  Projective tests are those where children theoretically project their own psychology or personality into the drawing, story, or description of a stimulus. Although there is a tradition of using these approaches with children, their administration, scoring, and interpretation remain more art than science. The chief complaint related to these approaches is one of poor reliability in scoring as well as a lack of evidence for validity in interpretations. Seldom do these measures have norms on the basis of age, sex, or clinical versus nonclinical status. Nonetheless, a child may tell his or her story, draw his or her family, or make reference to his or her experience, and this can be informative. Occasionally rare responses occur with the use of projectives (e.g., genitalia on a drawing) which may be revealing; however, there are no studies where specificity and sensitivity have been calculated to allow such occurrences to serve as a pathognomonic indicator for abusive events in the life of a child. Furthermore, as with drawings, anatomically detailed dolls, and even parent ratings (e.g., Child Sexual Behavior Inventory, Friedrich, 1998), the inclusion of sexual content or aggressive responses is not diagnostic for sexual abuse or physical abuse. In the end, some of these projective drawing procedures, such as the Draw-A-Person, Kinetic Family Drawing, or House-Tree-Person test (Greene & Weiner, 2008; Murstein, 1965), may serve as a useful warm-up activity in the initial 174

work with a child, if clinicians do not over-interpret the findings. Clinical interview.  The clinical interview (e.g., clinical intake, psychosocial interview, biopsychosocial interview, mental status exam) is the most frequently used evaluation procedure by clinicians across all disciplines, but it too is not without its problems. Structured clinical interviews, which often are used in psychiatric studies, are time consuming and may have components or modules with limited reliability and validity. Additionally, the use of these procedures often results in high rates of comorbidity (i.e., multiple diagnoses made) with no process of differential diagnosis used. This may result in overdiagnosis or misdiagnosis and as a result, ill-informed treatment. Semistructured interviews allow more flexibility by the clinician, but they are fraught with the same shortcomings as structured clinical interviews. A key limitation of semistructured clinical interviews is that, even for the skilled clinician, it is possible that preconceived hypotheses are the only ones explored in this process. As a result, “pet” diagnoses may be overidentified, and children and adolescents may receive inappropriate treatment. In a review of psychological testing and assessment, Meyer et al. (2001) concluded that clinicians who relied solely on clinical interviews were prone to incomplete understanding of their clients. Every test or procedure has its potential flaws or misuses, so it is recommended that more than the clinical interview be used. Despite these shortcomings, some form of clinical interviewing, when used in conjunction with reliable, valid, and normed measures can elucidate descriptive findings. A variation of semistructured interviewing is the testing of limits when using formal assessment procedures. For example, when using a self-report measure, the clinician might administer the entire measure and then score and interpret it as per the stated instructions. Then, the evaluator might use the child’s answers as a launching point for interviewing and inquiry about examples of situations associated with the endorsed thoughts, feelings or behaviors as described by the instruments. A similar approach can occur with

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caregiver ratings. This “testing of limits,” when done without changing formal scores, can put “clinical flesh on the assessment skeleton.” Behavioral observations.  Behavioral observations can be informative. However, like all other forms of assessment, the observations are only a snapshot in time and/or place, and may not be representative of the good and bad behavior of a child. When overt, externalizing behaviors are identified in a home, classroom, or office, one may be able to hypothesize and test the role of various stimuli or events in the possible establishment or maintenance of behaviors. For some abused children, these same observations may provide clues to stimuli or triggers which elicit otherwise inexplicable behavior by the child. For example, a certain tone of voice, a look from an adult, or another sensory experience (e.g., smell or taste) may serve as a reminder of past abuse and result in a response which otherwise cannot be explained by the seemingly innocuous situation. In the final analysis, there is no single approach or instrument that always will be superior (Verhulst, 1995) to others. Even instruments that are reliable, valid, and normed (though these features are preferred) may be rejected by a child and/or caregivers or fail to delineate a clear picture of symptoms and treatment needs. Moreover, lack of agreement among raters (parents, teachers, and youth; Achenbach, 1993) may inadvertently affect case conceptualization and treatment. We recommend an approach that is ongoing and includes multiple methods and informants. Differential Diagnosis There are several obstacles to the assessment of the sequelae of child abuse. Child victims may deny that the abuse event occurred (Shapiro & Dominiak, 1990), may recant sexual abuse (22% of children do so even when the evidence for abuse is compelling; Sorensen & Snow, 1991), may be too ashamed about the event to report its impact (Wyatt, Loeb, Solis, & Carmona, 1999), and may have difficulty with direct questioning (Perrin, Smith, & Yule, 2000). Further, young children

may lack the metacognitive skills needed to report symptoms accurately. Therefore, the parents’ reports of symptoms are critical in the assessment of PTSD. Alternative PTSD diagnostic criteria for young children have been proposed for the fifth version of the Diagnostic and Statistical Manual of Mental Disorders because of perceived shortcomings of the fourth edition (American Psychiatric Association, 1994; Iselin, LeBrocque, Kenardy, Anderson, & McKinlay, 2010). However, the problems related to children and their parents not endorsing avoidant cluster symptoms relative to reexperiencing and arousal symptoms continues. This has resulted in various suggested alternatives in the number of required avoidant symptoms from one of five to one of seven (Scheeringa, Myers, Putnam, & Zeanah, 2012). One of the great challenges in assessing abused children involves an accurate gathering of symptoms present and the concise conceptualization of symptomatology, so that a coherent treatment plan might follow. Again, some children may not be traumatized and may exhibit other symptoms (e.g., sexualized behavior), whereas some children may have symptoms below the threshold for a diagnosis of PTSD, or may be misdiagnosed with another disorder mimicking the symptoms of PTSD. An important step in the assessment process prior to the delivery of treatment is the differential diagnosis of various symptoms. Although it is true that certain behaviors, symptoms, or disorders may predate child abuse or cooccur by chance (e.g., psychotic symptoms, bipolar disorder, ADHD), there is no empirical basis to conclude that child abuse precipitates or causes psychosis, bipolar disorder, or ADHD. Rather, some symptoms may be unrecognized developmental equivalents of PTSD symptoms in children. The American Academy of Child and Adolescent Psychiatry’s (AACAP, 2010) Practice Parameter for the Assessment and Treatment of Children and Adolescents With Posttraumatic Stress Disorder recommends that “the psychiatric assessment should consider differential diagnoses of other psychiatric disorders and physical conditions that may mimic PTSD” (p. 420). A good history, including the onset and duration of symptoms, is necessary to make the distinctions 175

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required for a good differential diagnosis. Specifically, details about abuse often are discovered years later, but coincide with the onset of other symptoms misdiagnosed during an earlier time in the child’s life. The following are presentations of symptoms, which may serve the same developmentally sensitive and functional purpose (i.e., alarming, promoting safety) of PTSD symptoms (reexperiencing, arousal, and avoidance) and, as a result, can lead to misdiagnosis.

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Reexperiencing Symptoms Psychosis.  Rather than psychosis, it is far more likely that a maltreated child may demonstrate behavior inconsistent with the demands of a given situation because unidentified stimuli are triggering a conditioned, anxious response. Visual hallucinations.  Rather than visual hallucinations, a child may have reexperiencing symptoms of trauma, which when described, mimic visual hallucinations (Carter & Wherry, 2007). Hypnopompic and hypnogogic hallucinations (dream states) may be confused, especially among inexperienced clinicians, as visual hallucinations.

Arousal Symptoms Bipolar disorder.  Rather than bipolar disorder, a child may be emotionally dysregulated because of chronic activation of the fight or flight response; this child should not be considered for a diagnosis of bipolar disorder (Wherry, Davis-Deniz, et al., 2008). Similar children may exhibit sexualized behaviors, which certainly should not be misconstrued as manic symptoms of sexual indiscretions like those evidenced among adults. Panic disorder.  Rather than panic disorder, a child may present with anxiety, which rises to the level of panic; however, in some cases the anxiety may (unlike panic) be “triggered” by a stimulus unidentified, or symbolic in its nature. For example, a relationship may have characteristics similar to a formerly abusive relationship and those characteristics (e.g., controlling, demanding) may result in panic even though there is no physical or sexual abuse in that relationship. 176

Attention-deficit/hyperactivity disorder.  Concentration problems alone do not warrant the diagnosis of ADHD. Moreover, concentration problems can be symptomatic of PTSD hyperarousal symptoms or symptoms of depression secondary to abuse.

Avoidance Symptoms Separation anxiety/school phobia.  Rather than separation anxiety or school phobia, a young child, who is reluctant to separate from a safe parent, may be exhibiting adaptive avoidant behavior designed to protect the child from an undetected abuser (Berres, Smith, Junko, & Wherry, 2007; Wherry & Marrs, 2008). Somatization.  In some families, somatic symptoms serve a purpose similar to those found among children with separation anxiety features. That is, somatic symptoms (e.g., as mild as headaches or stomachaches and as extreme as conversion disorders of paralysis) may bring a safe parent to the child’s side or in the extreme, remove the child from an unsafe home and result in admission to a hospital (Wherry, McMillan, & Hutchison, 1991). Substance abuse.  Substance abuse has been documented as a coping strategy for adolescents with PTSD who have been sexually abused (Hawke, Jainchill, & Leon, 2000; Kilpatrick et al., 2000; Raghavan & Kingston, 2006). Although effective in nullifying or avoiding the PTSD symptoms of arousal and reexperiencing, the additional problems in functioning can be debilitating. If the substance use continues to the point of addictive abuse, then a separate diagnosis related to substance abuse may require specialized treatment prior to beginning work on the sexual or physical abuse.

Recommended Measures for Evaluation of Multiple Symptoms Associated With Abuse A good starting point for evaluation is to use measures designed to assess symptoms, which are most common among abused children, including anxiety, depression, anger, trauma symptoms (including PTSD and dissociation), and sexualized behaviors

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(Kendall-Tackett, Williams, & Finkelhor, 1993). However, to assess only one of these dimensions is inadequate. It is true that virtually any symptom can result from child abuse, it is also true that no one single symptom is pathognomonic or diagnostic for abuse. Even sexualized behaviors are not a definitive sign of sexual abuse as there may be alternative explanations (e.g., exposure to sexually explicit behavior via the internet). Because of the diverse and often complex presentation among many abused/traumatized children, there is a need to use trauma-focused measures, which can capture this complexity. Examples include the Trauma Symptom Checklist for Children (TSCC; Briere, 1996) and the Trauma Symptom Checklist for Young Children (TSCYC; Briere, 2005). Additionally, two new, 20-item screening measures the TSCC–Short Form (TSCC-SF) and the TSCYC–Short Form (TSCYC-SF) have been normed and published.

instrument is targeted for CACs and can be used to identify clients who need additional assessment or for triage to therapy.

TSCC–Short Form and TSCYC–Short Form.  The TSCC-SF and the TSCYC-SF were developed to address the need for screening and triage of abused children (Briere & Wherry, 2016). Both measures are based on items from their respective longer forms, the TSCC (for use with children ages 8–16) and the TSCYC (for use with caregivers of children ages 3–12). Twelve items of general trauma and eight items related to sexual concerns were selected on the basis of their ability to predict overall trauma- and sexual-related symptomatology within in their respective normative samples. Unlike the full versions, the TSCC-SF and TSCYC-SF items are not clustered into a variety of separate subscales; rather, they are clustered into general trauma and sexual concerns. The basis for item selection was determined by (a) multivariate prediction of the respective measure’s posttraumatic stress scale score and sexual concerns scale score, using step-wise multiple regression analyses; (b) maximized alpha reliability (α ≥ .80); (c) representation of the scale content domain; (d) inclusion of at least one suicide risk item; and (e) correlations (i.e., r ≥ .90) with the full TSCC or TSCYC scale. Recommended cutoffs are provided on the basis of percentile scores from the normative sample. The distribution of the

TSCYC.  The TSCYC is a 90-item, caregiver rating scale completed for children ages 3 to 12. It also uses two validity scales for assessment of underreporting (response level) and atypical responses (atypical response). The raw scores, t-scores, and percentile scores are reported for each of the following scales: anxiety, depression, anger/aggression, posttraumatic stress—intrusion, posttraumatic stress—avoidance, posttraumatic stress—arousal, posttraumatic stress—total, dissociation, and sexual concerns. In Briere’s (1999) initial study, the clinical scales had good reliability with alphas ranging from .81 to .93. Additionally, TSCYC scales were predictive of exposure to sexual abuse, physical abuse, and witnessing domestic violence. Subsequent studies have demonstrated the convergent validity of the TSCYC with other parent ratings (e.g., the CBCL, Child Sexual Behavior Inventory, and the UCLA PTSD Reaction Index). The convergent validity of the TSCYC and the TSCC has been found to be moderate (Lanktree et al., 2008) to weak (Wherry, Graves, & Rhodes King, 2008), perhaps illustrating the lack of agreement often found between children and caregivers and further highlighting the need for a multi-informant approach to screening and assessment of abused children. The TSCYC also has been

TSCC.  The TSCC is designed for use with children and adolescents ages 8 to 16. It is a 54-item, self-report measure with a validity scale that examines underreporting (underresponse) and overreporting of symptoms (hyperresponse). Raw scores and t-scores are reported for each of the following scales: anxiety, depression, anger/aggression, posttraumatic stress, dissociation (overt and fantasy), and sexual concerns (preoccupation and distress). The alpha coefficients for clinical scales range from .77 to .89 in the standardization sample. Adequate convergent, discriminant, and predictive validity have been demonstrated in normative and clinical samples. Normative data were derived from 3,008 nonclinical children; 53% were girls, and the distribution of the sample was 44% White, 27% Black, and 22% Hispanic.

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used in screening children for PTSD with a model correctly classifying 100% of the PTSD-negative and 72.7% of the PTSD-positive participants. These findings suggest that the TSCYC may be used as an economical and time-efficient screening device for PTSD (Pollio, Glover-Orr, & Wherry, 2008). The TSCYC also has been normed with a sample of 750 parents stratified to match U.S. census data by region, parent educational level, child’s age, race, ethnicity, and gender. Evaluation of Single Constructs Using Unidimensional Measures When a measure like the TSCC or TSCYC indicates the presence of significantly elevated frequencies of symptoms for depression, PTSD, sexual concerns, anger, anxiety, or dissociation, there may be value in using a unidimensional measure to understand further the nature of that elevated symptom. For maltreated children, there are several unidimensional measures which can be useful including, the Child Sexual Behavior Inventory (sexual concerns; CSBI; Friedrich, 1998), the UCLA PTSD Reaction Index (Steinberg, Brymer, Decker, & Pynoos, 2004), and the Child Dissociative Checklist (dissociation; Putnam, Helmers, & Trickett, 1993).

Sexualized Behavior Child Sexual Behavior Inventory.  The CSBI, a 38-item instrument completed by caregivers for children ages 2 to 12, assesses sexualized behavior in children. Studies indicate that it is reliable and valid, and it has been normed by age and sex. The measure is comprised of two subscales: developmentally related sexual behavior (DRSB) and sexual abuse specific items (SASI). There also is a CSBI total score. The DRSB includes behaviors which, at age 3 might be normative, but at age 12 would be highly unlikely, unusual, or atypical. The SASI subscale includes items consistent with behaviors that may be exhibited by sexually abused children. However, an elevation of the SASI scale does not indicate or prove that a child has been sexually abused. According to the developer, William Friedrich, these sexualized behaviors might be present because of other family 178

behaviors such as watching movies with explicit sexual content, family nudity, or observing parents having sex (Friedrich, 1998). An alpha coefficient of .72 was obtained for the CSBI total score indicating good internal consistency. Test–retest reliability was .91 when administered an average of two weeks later. Interrater reliability for mother–father pairs was .83. The manual cites data supporting the convergent, discriminant, and construct validity of the CSBI. Additionally, the measure was standardized on a nonclinical sample of 1,114 children and on a primarily White (76%) clinical sample of 512 children. Adolescent Clinical Sexual Behavior Inventory—Self-Report.  The Adolescent Clinical Sexual Behavior Inventory—Self-Report (ACSBI-S; Friedrich, Lysne, Sim, & Shamos, 2004) is a 45-item self-report scale for assessing a broad range of sexual behaviors and attitudes among adolescents. Two research studies have produced (a) a five-factor solution (sexual knowledge/interest, sexual risk/misuse, divergent sexual interest, concerns about appearance, and fear/discomfort) accounting for 37.6% of the total variance (Friedrich et al., 2004), and (b) a three-factor solution (sexual knowledge/interest, sexual risk/misuse, and concerns about appearance) accounting for 41.58% of the variance (Wherry, Berres, Sim, & Friedrich, 2009). Research suggested each scale has adequate internal consistency with alpha coefficients that ranged from .65 to .84, except for the fear/discomfort scale, which had an alpha coefficient of .45. Test–retest reliability at 1 week was .74. The ACSBI-S total score was correlated with the CBCL’s delinquency subscale (r = .25); Adolescent Sexual Concern Questionnaire sexual concerns items (r = .72); and the TSCC subscales of sexual concerns (r = .73), sexual concerns—distress (r = .54), and sexual concerns—preoccupation (r = .68). However, the ACSBI-S has no norms, and the samples used to date have been very homogenous regarding race and ethnicity.

Posttraumatic Stress Disorder There are numerous unidimensional measures for assessing PTSD in children and adolescents. Most of these measures list the PTSD symptoms from the

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corresponding Diagnostic and Statistical Manual and provide for a rating of frequency and/or severity. Many of these checklists or rating scales provide different forms for youth and caregivers, and most have determined cutoff scores which correspond to the possible diagnosis of PTSD. However, these scales have not been normed by age or sex. So, if the symptom of concentration problems (a PTSD arousal symptom) is endorsed for a 4-year-old as occurring very often, the conundrum is does this same endorsement have the same meaning for a 14-year-old? UCLA PTSD Reaction Index.  The UCLA PTSD Reaction Index for DSM–IV is available for completion by children, adolescents, and caregivers. Internal consistency for the full scales is excellent (α = .90). Test–retest is reported at .84 after a median number of seven days. Convergent validity is good, though to date, there are no published norms for the instrument. Children’s PTSD Inventory.  The Children’s PTSD Inventory (Saigh, 2003) is a semistructured interview that consists of five subscales. Moderate (.58) to high (.89) alphas are reported, and the internal consistency for the overall diagnosis is a Cronbach’s alpha of .95. Interrater reliability and test–retest reliability are good. The author reports moderate to high sensitivity and specificity, good convergent validity, and discriminant validity. Two small clinical samples (n = 150; n = 42) were used to establish the psychometric properties.

Dissociation Child Dissociative Checklist.  The Child Dissociative Checklist (Putnam et al., 1993) is a 20-item parent rating scale. Items are rated on a scale ranging from 0 (not true) to 2 (very true). These ratings are summed, and a cutoff score equal to or greater than 12 is considered clinically meaningful, particularly in older children. The checklist has a 1-year test–retest reliability coefficient of rho = .69 (N = 73, p = .0001) in a sample that included sexually abused girls and a nonabused comparison group. Putnam et al. (1993) report good discriminant validity. Concurrent validity with

other externalizing behavior measures has been demonstrated (Wherry, Jolly, Feldman, Adam, & Manjanatha, 1994). However, items representative of pathological dissociation may deserve special attention (Wherry, Neil, & Taylor, 2009). Adolescent Dissociative Experiences Scale.  The Adolescent Dissociative Experiences Scale (Armstrong, Putnam, Carlson, Libero, & Smith, 1997) is a screening instrument developed to detect dissociative behavior in adolescents between 11 and 17 years of age. Reliability and validity of the scale have been demonstrated in at least two different studies (Armstrong et al., 1997; Smith & Carlson, 1996) though there are no normative data available.

Broad-Band Rating Scales Broad-band rating scales are often lengthy measures completed by caregivers, teachers, or adolescents. They have value in screening maltreated children because of the breadth of the domains assessed. However, these measures do not assess specifically for trauma-related symptoms, like PTSD, and they may have very few items devoted to the assessment of sexualized behaviors or concerns. One of the primary benefits is identifying symptoms which otherwise might not have been reported or detected in screening for abuse-related symptoms. These newly identified symptoms may not be a focus of the treatment for abuse, but may require treatment from another provider. There are two broad-band rating scales used primarily in clinical and school settings. They have been studied extensively and possess outstanding psychometric qualities (reliability, validity, excellent normative samples). The CBCL (Achenbach & Rescorla, 2001) is often used in clinical settings and the Behavioral Assessment Scale for Children, Second Edition (Reynolds & Kamphaus, 2006) is often used in school settings, though they work equally well in either setting. The reliability and validity of both scales are well established. A third easily accessible, free, and brief measure is the Strengths and Difficulty Questionnaire (Goodman & Scott, 1999), which is highly correlated with the CBCL. It has been translated into many 179

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languages, but like many translated instruments, the normative data are sparse for specific cultural groups speaking these languages. Also, the domains are more general and nonclinical (i.e., emotional symptoms, conduct problems, hyperactivity/inattention, peer relationship problems, and prosocial behavior). One drawback in the use of the questionnaire is the absence of any item assessing for suicidal thoughts/ behavior or thoughts/behavior indicative of self-harm.

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Other Procedures Two additional procedures deserve mention. These procedures are not clinical procedures in the traditional sense. The Extended Forensic Evaluation (Carnes, NelsonGardell, Wilson, & Orgassa, 2001) is a procedure with a defined protocol that requires specific, intensive training. It can be considered an extension of the forensic interview and should be done by individuals with training in forensic interviewing and with a mental health background. It is used in some jurisdictions as a follow-up to a forensic interview, which might have resulted in a partial or vague disclosure of abuse. A second procedure is the Child and Adolescent Needs and Strengths–Trauma Version (Kisiel, Blaustein, Fogler, Ellis, & Saxe, 2009). This is a method for summarizing existing assessment results from reports and case file data. The procedure is an information integration tool and not an assessment measure. Specifically, it systematically documents trauma experiences, symptoms, functional difficulties and strengths, caregiver needs and strengths, and management and planning needs. To illustrate the potential errors associated with such data, Hambrick, Tunno, Gabrielli, Jackson, and Belz (2014) found that youth reported more psychological abuse and physical abuse, and younger children reported more sexual abuse than documented in their file. However, as an information integration tool, the results are only as helpful and accurate as the original information gathered during an assessment. Special Considerations

Cultural Diversity One of the basic requirements in the field of assessment is standardization of norms using a 180

representative sample for sex, age, and race/ ethnicity. Some of the instruments have not been standardized, whereas others (e.g., the CSBI) were standardized on a primarily White sample. Standard interpretations of t-scores are appropriate when the child is among the racial or ethnic groups included in the sample. Unfortunately, for less-represented cultures (i.e., non-White, non-Black, and nonHispanic), the availability of appropriate, normed measures is scant. Several principles of cultural competence deserve mention. First, the interpretation of any behavior or event should be neither ethnocentric (i.e., on the basis of the majority cultural norm) nor completely relative to the culture (i.e., cultural relativism). However, there often is as much variability within a racial group as between racial groups. It is important to learn about global and idiosyncratic cultural beliefs and practices related to child-rearing, family structure, sex roles, and religious beliefs, as well as levels of acculturation. Secondly, although much has been written about the potential advantages of matching clinicians with children by race, there is little research that has specifically focused on children who have been abused. One exception is the study by Springman, Wherry, and Notaro (2006) which found that race match predicted disclosure rates in forensic interviews, but in the opposite direction which was hypothesized. That is, White children were more likely to disclose to a Black interviewer, and Black children were more likely to disclose to a White interviewer. Finally, there are other cultural considerations that may be particularly salient in the case of child maltreatment, including concerns regarding disproportionate representation of some racial/ethnic groups in the child welfare system, differences in rates of substantiation and out of home placement and greater likelihood of other system involvement for many children of color, that may impact their level of trust and engagement in the assessment and treatment process. However, as noted by some researchers and public policymakers (Alliance for Racial Equality in Child Welfare, 2011; Bartholet, 2009; Drake et al., 2011), these are very complex issues, which are confounded by factors (e.g., socioeconomic status, higher rates of child maltreatment among

Assessment of Abused Youth

some racial/ethnic groups) that may account for this apparent disproportionality. Nonetheless, this speaks to the importance of cultural sensitivity and awareness in the assessment process.

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Prosecution Needs Versus Mental Health Needs In some instances, there can be resistance among prosecutors regarding assessments conducted in CACs. As stated previously, one objection hinges on items included in some of the assessment measures which can adversely impact the legal proceedings. However, this can be addressed by including expert testimony as part of the prosecution. Most important, a comprehensive assessment and treatment should never be withheld because of concerns that this will negatively influence the prosecution.

Conclusion Assessment of children under any circumstance, presents its challenges. When assessing children who have or may have been abused, the task becomes exponentially complicated. The approaches and tools outlined in this chapter can be helpful, but it is the clinical judgment of the practitioner, in the context of training and experience with child abuse, which influences the ultimate conclusions, diagnoses, and specific treatment recommendations.

References Achenbach, T. M. (1993). Empirically based taxonomy: How to use syndromes and profile types derived from the CBCL/4–18, TRF, and YSR. Burlington: University of Vermont. Achenbach, T. M., & Rescorla, L. A. (2001). Manual for the ASEBA school-age forms and profiles. Burlington: University of Vermont. Alliance for Racial Equality in Child Welfare. (2011). Disparities and disproportionality in child welfare: Analysis of the research. Washington, DC: Center for the Study of Social Policy & Annie E. Casey Foundation. Amaya-Jackson, L., Socolar, R. R. S., Hunter, W., Runyan, D. K., & Colindres, R. (2000). Directly questioning children and adolescents about maltreatment a review of survey measures used. Journal of Interpersonal Violence, 15, 725–759. http://dx.doi.org/ 10.1177/088626000015007005

American Psychiatric Association. (1994). Diagnostic and statistical manual of mental disorders (4th ed.). Washington, DC: Author. Angold, A. (2002). Diagnostic interviews with parents and children. In M. Rutter & E. Taylor (Eds.), Child and adolescent psychiatry: Modern approaches (4th ed., pp. 32–51). Oxford, England: Blackwell Scientific. Armstrong, J. G., Putnam, F. W., Carlson, E. B., Libero, D. Z., & Smith, S. R. (1997). Development and validation of a measure of adolescent dissociation: The Adolescent Dissociative Experiences Scale. Journal of Nervous and Mental Disease, 185, 491–497. http:// dx.doi.org/10.1097/00005053-199708000-00003 Baker, A. J. L., Curtis, P. A., & Papa-Lentini, C. (2006). Sexual abuse histories of youth in child welfare residential treatment centers: Analysis of the Odyssey Project population. Journal of Child Sexual Abuse, 15, 29–49. http://dx.doi.org/10.1300/J070v15n01_02 Bartholet, E. (2009). Racial disproportionality. Arizona Law Review, 51, 871–932. Bernstein, D. P., Fink, L., Handelsman, L., Foote, J., Lovejoy, M., Wenzel, K., . . . Ruggiero, J. (1994). Initial reliability and validity of a new retrospective measure of child abuse and neglect. American Journal of Psychiatry, 151, 1132–1136. http://dx.doi.org/ 10.1176/ajp.151.8.1132 Berres, A. Smith, M., Junko, K., & Wherry, J. N. (2007, October). Empirical support for alternative avoidant criteria for the diagnosis of PTSD in abused children. Paper presented at the meeting of the American Academy of Child and Adolescent Psychiatry, Boston, MA. Borres, M. P., & Hägg, A. (2007). Child abuse study among Swedish physicians and medical students. Pediatrics International, 49, 177–182. http:// dx.doi.org/10.1111/j.1442-200X.2007.02331.x Brady v. Maryland, 373 U.S. 83 (1963). Briere, J. (1996). The Trauma Symptom Checklist for Children. Odessa, FL: Psychological Assessment Resources. Briere, J. (1999). The Trauma Symptom Checklist for Young Children. Odessa, FL: Psychological Assessment Resources. Briere, J. (2005). The Trauma Symptom Checklist for Young Children. Odessa, FL: Psychological Assessment Resources. Briere, J., & Wherry, J. N. (2016). Development and validation of the TSCC and TSCYC screening forms (TSCC-SF and TSCYC-SF). Odessa, FL: Psychological Assessment Resources. Carnes, C. N., Nelson-Gardell, D., Wilson, C., & Orgassa, U. C. (2001). Extended forensic evaluation when 181

Jeffrey N. Wherry

sexual abuse is suspected: A multisite field study. Child Maltreatment, 6, 230–242. http://dx.doi.org/ 10.1177/1077559501006003004 Carter, C., & Wherry, J. N. (2007, October). Co-morbidity/misdiagnosis of psychotic symptoms in abused children with PTSD. Paper presented at the meeting of the American Academy of Child and Adolescent Psychiatry, Boston, MA.

Copyright American Psychological Association. Not for further distribution.

Cashel, M. L. (2002). Child and adolescent psychological assessment: Current clinical practices and the impact of managed care. Professional Psychology: Research and Practice, 33, 446–453. http://dx.doi.org/ 10.1037/0735-7028.33.5.446 Ceballo, R., Dahl, T. A., Aretakis, M. T., & Ramirez, C. (2001). Inner-city children’s exposure to community violence: How much do parents know? Journal of Marriage and Family, 63, 927–940. http://dx.doi.org/ 10.1111/j.1741-3737.2001.00927.x Collin-Vézina, D., Coleman, K., Milne, L., Sell, J., & Daigneault, I. (2011). Trauma experiences, maltreatment-related impairments, and resilience among child welfare youth in residential care. International Journal of Mental Health and Addiction, 9, 577–589. http://dx.doi.org/10.1007/s11469-011-9323-8 Conradi, L., Wherry, J., & Kisiel, C. (2011). Linking child welfare and mental health using traumainformed screening and assessment practices. Child Welfare, 90(6), 129–147. Dale, N., Baker, A. J., Anastasio, E., & Purcell, J. (2007). Characteristics of children in residential treatment in New York State. Child Welfare: Journal of Policy Practice, and Program, 86, 5–27. Deblinger, E., Behl, L., & Glickman, A. (2012). Trauma-focused cognitive–behavior therapy. In P. C. Kendall (Ed.), Child and adolescent therapy: Cognitive–behavioral procedures (4th ed., pp. 345–376). New York, NY: Guilford Press. Drake, B., Jolley, J. M., Lanier, P., Fluke, J., Barth, R. P., & Jonson-Reid, M. (2011). Racial bias in child protection? A comparison of competing explanations using national data. Pediatrics, 127, 471–478. http:// dx.doi.org/10.1542/peds.2010-1710 Finkelhor, D., Hamby, S. L., Ormrod, R., & Turner, H. (2005). The juvenile victimization questionnaire: Reliability, validity, and national norms. Child Abuse and Neglect, 29, 383–412. http://dx.doi.org/10.1016/ j.chiabu.2004.11.001 Finkelhor, D., Ormrod, R. K., & Turner, H. A. (2007). Polyvictimization and trauma in a national longitudinal cohort. Development and Psychopathology, 19, 149–166. http://dx.doi.org/10.1017/S0954579407070083 Foa, E. B., Johnson, K. M., Feeny, N. C., & Treadwell, K. R. (2001). The child PTSD Symptom Scale: A preliminary examination of its psychometric

182

properties. Journal of Clinical Child Psychology, 30, 376–384. http://dx.doi.org/10.1207/ S15374424JCCP3003_9 Ford, J. (2002a). Traumatic Events Screening Inventory—Parent Report Revised (TESI). Unpublished manuscript, University of Connecticut, Mansfield. Ford, J. (2002b). Traumatic Events Screening Inventory—Self Report Revised (TESI). Unpublished manuscript, University of Connecticut, Mansfield. Friedrich, W. N. (1998). The child sexual behavior inventory professional manual. Odessa, FL: Psychological Assessment Resources. Friedrich, W. N., Lysne, M., Sim, L., & Shamos, S. (2004). Assessing sexual behavior in high-risk adolescents with the adolescent clinical sexual behavior inventory (ACSBI). Child Maltreatment, 9, 239–250. http://dx.doi.org/10.1177/ 1077559504266907 Garb, H. N. (1998). Studying the clinician: Judgment research and psychological assessment. Washington, DC: American Psychological Association. Garb, H. N. (2005). Clinical judgment and decision making. Annual Review of Clinical Psychology, 1, 67–89. http://dx.doi.org/10.1146/ annurev.clinpsy.1.102803.143810 Goodman, R., & Scott, S. (1999). Comparing the strengths and difficulties questionnaire and the child behavior checklist: Is small beautiful? Journal of Abnormal Child Psychology, 27, 17–24. http://dx.doi.org/ 10.1023/A:1022658222914 Greene, R. L., & Weiner, I. B. (2008). Handbook of personality assessment (2nd ed.). Hoboken, NJ: Wiley. Habib, M., & Labruna, V. (2006). The adolescent trauma history checklist and interview. Unpublished measure. Hambrick, E. P., Tunno, A. M., Gabrielli, J., Jackson, Y., & Belz, C. (2014). Using multiple informants to assess child maltreatment: Concordance between case file and youth self-report. Journal of Aggression, Maltreatment, and Trauma, 23, 751–771. http:// dx.doi.org/10.1080/10926771.2014.933463 Hawke, J. M., Jainchill, N., & Leon, G. D. (2000). The prevalence of sexual abuse and its impact on the onset of drug use among adolescents in therapeutic community drug treatment. Journal of Child and Adolescent Substance Abuse, 9, 35–49. http://dx.doi.org/ 10.1300/J029v09n03_03 Iselin, G., Le Brocque, R., Kenardy, J., Anderson, V., & McKinlay, L. (2010). Which method of posttraumatic stress disorder classification best predicts psychosocial function in children with traumatic brain injury? Journal of Anxiety Disorders, 24, 774–779. http://dx.doi.org/10.1016/ j.janxdis.2010.05.011

Assessment of Abused Youth

Kendall-Tackett, K. A., Williams, L. M., & Finkelhor, D. (1993). Impact of sexual abuse on children: A review and synthesis of recent empirical studies. Psychological Bulletin, 113, 164–180. http://dx.doi.org/ 10.1037/0033-2909.113.1.164

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Khaleque, A. (2015). Perceived parental neglect, and children’s psychological maladjustment, and negative personality dispositions: A meta-analysis of multicultural studies. Journal of Child and Family Studies, 24, 1419–1428. http://dx.doi.org/10.1007/ s10826-014-9948-x Kilpatrick, D. G., Acierno, R., Saunders, B., Resnick, H. S., Best, C. L., & Schnurr, P. P. (2000). Risk factors for adolescent substance abuse and dependence: Data from a national sample. Journal of Consulting and Clinical Psychology, 68, 19–30. http://dx.doi.org/ 10.1037/0022-006X.68.1.19 Kisiel, C. L., Blaustein, M., Fogler, J., Ellis, H., & Saxe, G. (2009). Treating children with traumatic experiences: Understanding and assessing needs and strengths. Emotional and Behavioral Disorders in Youth, 9, 13–19. Kovacs, M. (2010). The Child Depression Inventory 2—Professional manual. San Antonio, TX: Psychological Corporation. Lanktree, C. B., Gilbert, A. M., Briere, J., Taylor, N., Chen, K., Maida, C. A., & Saltzman, W. R. (2008). Multi-informant assessment of maltreated children: Convergent and discriminant validity of the TSCC and TSCYC. Child Abuse and Neglect, 32, 621–625. http://dx.doi.org/10.1016/j.chiabu.2007.10.003 Meyer, G. J., Finn, S. E., Eyde, L. D., Kay, G. G., Moreland, K. L., Dies, R. R., . . . Reed, G. M. (2001). Psychological testing and psychological assessment. A review of evidence and issues. American Psychologist, 56, 128–165. http://dx.doi.org/ 10.1037/0003-066X.56.2.128

and adolescents. Journal of Child Psychology and Psychiatry, 41, 277–289. http://dx.doi.org/ 10.1111/1469-7610.00612 Pollio, E. S., Glover-Orr, L. E., & Wherry, J. N. (2008). Assessing posttraumatic stress disorder using the Trauma Symptom Checklist for Young Children. Journal of Child Sexual Abuse, 17, 89–100. http:// dx.doi.org/10.1080/10538710701884557 Putnam, F. W., Helmers, K., & Trickett, P. K. (1993). Development, reliability, and validity of a child dissociation scale. Child Abuse and Neglect, 17, 731–741. http://dx.doi.org/10.1016/ S0145-2134(08)80004-X Pynoos, R., Rodriguez, N., Steinberg, A., Stuber, M., & Frederick, C. (1998). UCLA PTSD Index for DSM–IV. Los Angeles: University of California. Raghavan, C., & Kingston, S. (2006). Child sexual abuse and posttraumatic stress disorder: The role of age at first use of substances and lifetime traumatic events. Journal of Traumatic Stress, 19, 269–278. http:// dx.doi.org/10.1002/jts.20117 Raman, S., Holdgate, A., & Torrens, R. (2012). Are our frontline clinicians equipped with the ability and confidence to address child abuse and neglect? Child Abuse Review, 21, 114–130. http://dx.doi.org/ 10.1002/car.1180 Reynolds, C. R., & Kamphaus, R. W. (2006). BASC-2: Behavior Assessment System for Children (2nd ed.). Upper Saddle River, NJ: Pearson Education. Saigh, P. A. (2003). The Children’s Posttraumatic Stress Disorder Inventory test manual. San Antonio, TX: Psychological Corporation. Saunders, B. E. (2003). Understanding children exposed to violence: Toward an integration of overlapping fields. Journal of Interpersonal Violence, 18, 356–376. http://dx.doi.org/10.1177/0886260502250840

Milne, L., & Collin-Vézina, D. (2014). Disclosure of sexual abuse among youth in residential treatment care: A multiple informant comparison. Journal of Child Sexual Abuse, 23, 398–417. http://dx.doi.org/ 10.1080/10538712.2014.896841

Scheeringa, M. S., Myers, L., Putnam, F. W., & Zeanah, C. H. (2012). Diagnosing PTSD in early childhood: An empirical assessment of four approaches. Journal of Traumatic Stress, 25, 359–367. http://dx.doi.org/ 10.1002/jts.21723

Murstein, B. (1965). Handbook of projective techniques. New York, NY: Basic Books.

Shapiro, S., & Dominiak, G. (1990). Common psychological defenses seen in the treatment of sexually abused adolescents. American Journal of Psychotherapy, 44, 68–74.

National Children’s Alliance. (2015). Standards for accredited members revised effective–2017. Washington, DC: Author. Palusci, V. J., Vandervort, F. E., & Lewis, J. M. (2016). Does changing mandated reporting laws improve child maltreatment reporting in large U.S. counties? Children and Youth Services Review, 66, 170–179. http://dx.doi.org/10.1016/j.childyouth.2016.05.002 Perrin, S., Smith, P., & Yule, W. (2000). The assessment of Posttraumatic Stress Disorder in children

Smith, R. S., & Carlson, E. B. (1996). Reliability and validity of the Adolescent Dissociative Experiences Scale. Dissociation: Progress in the Dissociative Disorders, 9, 125–129. Sorensen, T., & Snow, B. (1991). How children tell: The process of disclosure in child sexual abuse. Child Welfare: Journal of Policy, Practice, and Program, 70, 3–15.

183

Jeffrey N. Wherry

Springman, R. E., Wherry, J. N., & Notaro, P. C. (2006). The effects of interviewer race and child race on sexual abuse disclosures in forensic interviews. Journal of Child Sexual Abuse, 15, 99–116. http:// dx.doi.org/10.1300/J070v15n03_06

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Steinberg, A. M., Brymer, M. J., Decker, K. B., & Pynoos, R. S. (2004). The University of California at Los Angeles Posttraumatic Stress Disorder Reaction Index. Current Psychiatry Reports, 6, 96–100. http:// dx.doi.org/10.1007/s11920-004-0048-2 Summerfeldt, L. J., & Antony, M. M. (2002). Structured and semi-structured diagnostic interviews. In M. M. Antony & D. H. Barlow (Eds.), Handbook of assessment and treatment planning for psychological disorders (pp. 3–37). New York, NY: Guilford Press. Ungar, M., Barter, K., McConnell, S. M., Tutty, L. M., & Fairholm, J. (2009). Patterns of abuse disclosure among youth. Qualitative Social Work: Research and Practice, 8, 341–356. http://dx.doi.org/ 10.1177/1473325009337842 Verhulst, F. C. (1995). Recent developments in assessment and diagnosis of child psychopathology. European Child and Adolescent Psychiatry, 11, 203–212. Wherry, J. N., Baldwin, S., Junco, K., & Floyd, B. (2013). Suicidal thoughts/behaviors in sexually abused children. Journal of Child Sexual Abuse, 22, 534–551. http://dx.doi.org/10.1080/10538712.2013.800938 Wherry, J. N., Berres, A. K., Sim, L., & Friedrich, W. N. (2009). Factor structure of the Adolescent Clinical Sexual Behavior Inventory. Journal of Child Sexual Abuse, 18, 233–246. http://dx.doi.org/ 10.1080/10538710902881428 Wherry, J. N., Corson, K., & Hunsaker, S. (2013). A short form of the Trauma Symptom Checklist for Young Children using a clinical sample of sexually abused outpatients. Journal of Child Sexual Abuse, 22, 796–821. http://dx.doi.org/10.1080/ 10538712.2013.830667 Wherry, J. N., Davis-Deniz, A., Carter, C., Bartholomew, M., VanCleave, A. B., & Marrs, A. (2008, April).

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Comorbidity or misdiagnosis in abused children with PTSD. Paper presented at the meeting of the Southwestern Psychological Association, Kansas City, KS. Wherry, J. N., Graves, L. E., & Rhodes King, H. M. (2008). The convergent validity of the Trauma Symptom Checklist for Young Children for a sample of sexually abused outpatients. Journal of Child Sexual Abuse, 17, 38–50. http://dx.doi.org/ 10.1080/10538710701884441 Wherry, J. N., Huey, C., & Medford, E. (2015). A national survey of child advocacy center directors regarding knowledge of assessment, treatment referral, and training needs in physical and sexual abuse. Journal of Child Sexual Abuse, 24, 280–299. http://dx.doi.org/10.1080/10538712.2015.1009606 Wherry, J. N., Jolly, J. B., Feldman, J., Adam, B., & Manjanatha, S. (1994). The Child Dissociative Checklist: Preliminary findings of a screening measure. Journal of Child Sexual Abuse, 3(3), 51–66. http://dx.doi.org/10.1300/J070v03n03_04 Wherry, J. N., & Marrs, A. (2008). Anxious school refusers and symptoms of PTSD in abused children. Journal of Child and Adolescent Trauma, 1, 109–117. http://dx.doi.org/10.1080/19361520802084061 Wherry, J. N., McMillan, S. L., & Hutchison, H. T. (1991). Differential diagnosis and treatment of conversion disorder and Guillain-Barre syndrome. Clinical Pediatrics, 30, 578–585, 585. http://dx.doi.org/ 10.1177/000992289103001002 Wherry, J. N., Neil, D. A., & Taylor, T. N. (2009). Pathological dissociation as measured by the child dissociative checklist. Journal of Child Sexual Abuse, 18, 93–102. http://dx.doi.org/ 10.1080/10538710802584643 Wyatt, G. E., Loeb, T. B., Solis, B., & Carmona, J. V. (1999). The prevalence and circumstances of child sexual abuse: Changes across a decade. Child Abuse and Neglect, 23, 45–60. http://dx.doi.org/10.1016/ S0145-2134(98)00110-0

Chapter 10

Trauma and Child Psychopathology: From Risk and Resilience to EvidenceBased Intervention Copyright American Psychological Association. Not for further distribution.

Jami M. Furr, Jonathan S. Comer, Miguel T. Villodas, Bridget Poznanski, and Robin Gurwitch

Most children regrettably experience a traumatic event by the time they reach adulthood. Epidemiologic studies find that almost 66% of children in the general population experience or witness actual or threatened death or serious injury by adolescence (e.g., Copeland et al., 2007; McLaughlin et al., 2013). Despite these daunting statistics, most exposed children and adolescents show great resilience and endure remarkably well (Comer & Olfson, 2010; Copeland et al., 2007; McLaughlin et al., 2013). Nonetheless, research also finds that, for a sizable minority of exposed children, trauma exposure confers a very heavy mental health toll. For example, childhood exposure to trauma and related adversities has been linked with a wide range of mental disorders (Green et al., 2010; McLaughlin et al., 2013; Molnar, Buka, & Kessler, 2001), family and social impairments, physical comorbidities (Jackson et al., 2016; La Greca, Comer, & Lai, 2016; Villodas, Litrownik, Newton, & Davis, 2016), reduced quality of life (Corso et al., 2008), substance misuse (Green et al., 2010; Kilpatrick et al., 2003; Molnar et al., 2001), and suicidal behaviors (Bruffaerts et al., 2010). Identifying patterns and predictors of risk and resilience in children and adolescents exposed to trauma is critical to inform the optimal treatment of trauma-related symptoms. The developmental psychopathology notion of multifinality asserts that similar environmental stressors can eventuate in a diverse range of outcomes (Cicchetti & Rogosch, 1996). Children exposed to similar traumas can develop widely different clinical manifestations. Accordingly,

researchers are increasingly moving away from simplistic main effects models of the impact of child exposure to traumatic stress (McLaughlin, 2016), and are conducting more longitudinal research using data analytic strategies that address transactional relationships among potentially traumatic experiences, contextual influences, and child developmental capacities. Such studies are being increasingly leveraged to reveal more sophisticated and clinically informative accounts of child trauma trajectories. The progress is to be applauded. This chapter reviews the range of potentially traumatic exposures experienced by children and adolescents, the various forms of child psychopathology outcomes that have been associated with exposure to trauma, and risk factors for the development of psychopathology following exposure to trauma. We then turn our attention to key emerging research areas in the study of childhood traumatic exposure—complex trauma, disasters and children, and trauma exposure in developing regions of the world. The chapter closes with a review of the evidence-based treatment practices for children exposed to trauma and a discussion of future directions in the study of child trauma exposure and psychopathology. Types of Childhood Trauma Potentially traumatic events include those in which children experience or witness actual or threatened death, serious injury, or sexual violence. Further, the Diagnostic and Statistical Manual of Mental

http://dx.doi.org/10.1037/0000065-010 APA Handbook of Psychopathology: Vol. 2. Child and Adolescent Psychopathology, J. N. Butcher (Editor-in-Chief) Copyright © 2018 by the American Psychological Association. All rights reserved.

APA Handbook of Psychopathology: Child and Adolescent Psychopathology, edited by J. N. Butcher and P. C. Kendall Copyright © 2018 American Psychological Association. All rights reserved.

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Furr et al.

Disorders, Fifth Edition (DSM–5; American Psychiatric Association, 2013) has newly specified that indirect exposure to a traumatic event, such as via media-based contact, does not qualify as a potentially traumatic event, although learning that serious threat, injury, or unexpected death has happened to a loved one can qualify as a traumatic event. Roughly 60% to 65% of children and adolescents experience at least one such event prior to adulthood, and 37% experience two or more (Copeland et al., 2007; McLaughlin et al., 2013). Moreover, a range of accumulated childhood adversities that may not individually constitute any direct and immediate physical threat (e.g., economic disadvantage, parental mental illness, parental incarceration) can also be enormously stressful for developing children, particularly when occurring in combination with one another (Centers for Disease Control and Prevention & Kaiser Permanente, 2016; Finkelhor, Shattuck, Turner, & Hamby, 2013), and can have particularly difficult impacts on trauma-exposed children. Exposure to violence makes up the most prevalent category of potentially traumatic events that children experience, with epidemiologic studies in the United States finding that between 25% to 33% of American children are either the direct victim of serious violence or witness a violent event at some point in their childhood (Copeland et al., 2007; Finkelhor et al., 2015). Across forms of violence exposure, the most common events experienced by children are physical assault with a weapon and/ or physical assault resulting in injury (9%–15%), being mugged or threatened with a weapon (8%), witnessing domestic violence (8%–20%), and suffering physical abuse by a relative (7%–10%; Copeland et al., 2007; Finkelhor et al., 2015; McLaughlin et al., 2013). Sexual traumas, including sexual abuse, rape, and other sexual assault experiences, are experienced by an additional 11% of children (Copeland et al., 2007; Finkelhor et al., 2014). Other forms of injury and trauma that are regrettably common in childhood and adolescence include involvement in a serious accident (12%), exposure to a natural or manmade disaster (11%–15%), and diagnosis of a serious physical illness (11%; Copeland et al., 2007; McLaughlin et al., 2013). 188

The nature of potentially traumatic events experienced varies meaningfully across several sociodemographic factors. Younger children are more likely than older children and adolescents to experience physical abuse by a caregiver, witness domestic violence, or receive diagnosis of a serious physical illness (McLaughlin et al., 2013). In contrast, older children and adolescents are more likely than younger children to experience sexual victimization, serious accidents, or exposure to interpersonal violence outside of the home (McLaughlin et al., 2013). There are significant gender differences as well. Girls are at greater risk than boys for experiencing a sexual trauma (McLaughlin et al., 2013), which is reported by approximately 25% of girls, relative to 5% of boys (Finkelhor et al., 2014). In contrast, boys are at greater risk than girls for experiencing a serious accident or physical assault (McLaughlin et al., 2013). Moreover, children dwelling in urban regions are at greater risk for experiencing physical abuse by a family member than children in rural areas. Psychopathology Related to Trauma The data are consistent—children exposed to traumatic stress show elevated rates of psychopathology across a range of domains, including posttraumatic stress disorder (PTSD), depression, anxiety problems, disruptive behavior problems, and substance abuse (see Chapters 13, 15, 18, and 22, this volume). These disorders commonly co-occur in complex comorbid patterns (Au et al., 2013; De Young, Kenardy, Cobham, & Kimble, 2012; Dixon et al., 2005). We consider each class of disorder next.

Posttraumatic Stress Disorder The most frequent psychopathology finding related to children’s traumatic stress is that exposed children show elevated rates of PTSD symptoms and diagnosis (Copeland et al., 2007; Furr et al., 2010; McLaughlin et al., 2013). According to the DSM–5 (American Psychiatric Association, 2013), PTSD refers to a specific set of disturbances in cognition, mood, behavior, and arousal that can emerge after an individual experiences or witnesses actual or threatened death or serious injury, or learns

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that a close family member or friend experienced actual or threatened death. Disturbances in cognition can include memory impairments (e.g., difficulty remembering key aspects of the event), repeated thoughts about the event and a sense that the trauma is being relived, avoidance of thoughts related to the event, lack of interest in previously enjoyed activities, nightmares about the event, and/ or negative self-cognitions or negative thoughts about the world. Disturbances in mood can include maladaptive feelings of self-blame and guilt and/or avoidance of feelings related to the traumatic event. Disturbances in behavior can include avoiding places, events, and objects that can serve as reminders of the traumatic event. Disturbances in arousal can include experiencing an exaggerated startle response, feeling tense or “on edge,” showing anger outbursts, and/or having difficulty sleeping. To qualify for a DSM–5 diagnosis of PTSD, these disturbances must last for at least 1 month, and must be severe enough to result in significant functional impairment. PTSD symptoms can manifest differently in children relative to adults (De Young, Kenardy, & Cobham, 2011; Salmon & Bryant, 2002; Scheeringa et al., 2003). For example, younger children tend to report fewer cognitive symptoms than adolescents and adults (Salmon & Bryant, 2002). Developmental factors may prevent younger children from feeling as though they are reliving a past event. Instead, the intrusiveness of traumatic reexperiencing can manifest in the repetitive posttraumatic play (e.g., a child who has been in an automobile accident may repeatedly crash toy cars into one another; Gaensbauer, 1995; Lieberman & Knorr, 2007). Children may also show generalized nightmares with unrecognizable content or about vague threats or monsters (Scheeringa et al., 2003), rather than nightmares specifically about the traumatic event that are seen in adults and adolescents. Moreover, the anger outbursts seen in adults with PTSD can look more like low-level irritability or fussiness in younger children. In addition, the diminished interest in usual activities seen in adult PTSD can manifest in younger children as constricted play or restricted exploratory behavior (De Young et al., 2011; Scheeringa et al., 2003).

Epidemiologic research drawing on populationbased sampling estimates that roughly 5% of adolescents have met diagnostic criteria for PTSD at some point in their lives (McLaughlin et al., 2013). Overall, roughly 8% of children who experience a traumatic event go on to develop PTSD by the time they reach adulthood (McLaughlin et al., 2013). Among the various types of potentially traumatic experiences, exposure to interpersonal violence and/or sexual trauma in children is associated with the greatest risk of developing PTSD. Experiencing rape, sexual assault, or physical abuse by a caregiver show the strongest links with the development of PTSD in children (McLaughlin et al., 2013). In the United States, the rate of PTSD is significantly higher in girls (7%) than in boys (2%), and girls show almost 4 times the odds of developing PTSD than boys after traumatic exposure (McLaughlin et al., 2013; see also Kilpatrick et al., 2003). What experiences, when accompanied by PTSD symptoms, qualify for diagnosis of PTSD? Although the general concept of a PTSD-like syndrome has been described for centuries under various names (e.g., railway spine, soldier’s heart, shellshock, battered woman syndrome; Chou et al., 2016; Friedman et al., 2011; Luz et al., 2011), the precise definitional boundaries of the disorder have shifted across taxonomies and DSM iterations. Though various DSM iterations agree that diagnostic criteria for PTSD include exposure to a traumatic event and associated cognitive, behavioral, and arousal symptoms, the optimal definition of a qualifying “traumatic exposure” has been a source of spirited debate (Weathers & Keane, 2007). Across DSM iterations, evolving components of what qualifies as a traumatic event for a PTSD diagnosis have relied on shifting factors. In the Diagnostic and Statistical Manual of Mental Disorders, Third Edition (DSM–III; American Psychiatric Association, 1980), a qualifying traumatic event was described as a distressing situation falling outside the range of usual human experience. This wording of the DSM–III definition characterized traumatic events as rare (i.e., outside usual human experience), however research indicates a relatively high incidence of extreme events and stressors in the general population (Copeland et al., 2007; McLaughlin et al., 2013). 189

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The Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition (DSM–IV; American Psychiatric Association, 1994) included an expanded definition of qualifying traumatic events and changed the focus from the commonality of the experience to the subjective responses they elicit. For example, the DSM–IV required that for an event to qualify for PTSD diagnosis, the individual needed to experience fear, helplessness, or horror at the time of the event. Regrettably, clinical applications of this DSM–IV definition revealed that such subjective criteria were considerably broader and more inclusive than previous definitions of qualifying traumatic experiences, leading to concerns about “bracket creep” in which less objectively threatening events could qualify for eligibility as a traumatic event in the definition of PTSD (e.g., seeing a car accident on television; McNally, 2009). The most recent diagnostic definition of PTSD in the DSM–5 (American Psychiatric Association, 2013) now reorganizes and delineates eligible traumatic experiences qualifying for diagnosis of PTSD as “exposure to actual or threatened death, serious injury, or sexual violence” (p. 280) via (a) direct experience, (b) witnessing the event in person, (c) learning that a family member/close friend experienced the event, or (d) repeated, extreme exposure to related details of a qualifying event (although not through media-based encounters). This current definition now offers a clearer and more exclusive definition of qualifying traumatic events for a diagnosis of PTSD that no longer relies on subjective distress and makes explicit that indirect exposure (e.g., television-based contact with an event) cannot qualify. There is now considerable evidence from large child samples supporting this current definition of a qualifying traumatic event for diagnosis of PTSD (e.g., Chou et al., 2016). There is also evidence that the current definition of a qualifying traumatic experience for PTSD requires further modification for children under the age of 6 (Chou et al., 2016). For example, media-based contact with traumatic events have been associated with PTSD symptoms in many children (Busso et al., 2014; Comer, Dantowitz, et al., 2014; Comer, DeSerisy, & Green, 2016; Comer & Kendall, 2007; Duarte et al., 2011), and it has been suggested that children at early stages 190

of cognitive development may not readily distinguish between televised and real in-person events. As such, the explicit exclusion of media-based encounters for diagnosis in the current PTSD definition may be misguided regarding younger children. Interestingly, there is strong epidemiologic evidence that children can show significant and interfering PTSD symptoms following exposure to low magnitude life stressors in the absence of qualifying traumatic events (Copeland et al., 2010). Importantly, elevated rates of PTSD and PTSD symptomatology are the most frequently identified form of psychopathology among children exposed to traumatic events, but this may be due, in part, to the simple fact that it is PTSD symptoms that are most often assessed in trauma-exposed children. More comprehensive evaluations that have examined domains of psychopathology reflective of the full range of problems experienced by children and adolescents who experience potentially traumatic events find that exposed children are also vulnerable to elevated rates of depression, anxiety, disruptive behavior problems, and substance use.

Depression In addition to the disturbances in cognition, behavior, mood, and arousal associated with PTSD resulting from children’s exposure to trauma, a considerable proportion of children exposed to trauma experience episodes of profound sadness, hopelessness, despondency, and/or fatigue. Major depressive disorder is characterized by multiple episodes (each lasting at least 2 weeks) of interfering sadness, emptiness, and/or irritability, accompanied by decreased energy, difficulty concentrating, low self-esteem, restlessness, changes in sleep, changes in appetite, and/or suicidal thoughts or behaviors (American Psychiatric Association, 2013). Roughly 10% of children who experience a traumatic event meet full diagnostic criteria for a depressive disorder at some point before they reach adulthood (Copeland et al., 2007). In fact, experiencing a traumatic event is associated with three times the odds of developing a depressive disorder (Copeland et al., 2007). Experiencing multiple childhood traumas is associated with a further linear increase in risk for depression (Suliman et al., 2009).

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Up to 25% of individuals who experience child maltreatment develop a major depressive disorder at some point in their lifetime (Scott, McLaughlin, Smith, & Ellis, 2012). Among depressed individuals, those who have experienced child maltreatment have earlier onset of depression and more depressive episodes across time, relative to depressed individuals who have not experienced child maltreatment (Scott et al., 2012). Additionally, earlier onset of child maltreatment is associated with more severe and intractable depression across development, as well as increased suicidal ideation (Dunn, McLaughlin, Slopen, Rosand, & Smoller, 2013; Nanni, Uher, & Danese, 2012; see also Chapter 9, this volume). Moreover, depression has been found to mediate the association between victimization and suicidal ideation among children who have been exposed to violence (M. D. Bennett & Joe, 2015; Miller et al., 2014). Across major child adversities, sexual abuse is associated with the greatest subsequent risk for development of a mood disorder (e.g., Green et al., 2010).

Anxiety Problems Anxiety problems refer to patterns of fear, anxious apprehension, and behavioral avoidance that are disproportionate to actual threats, difficult to control, and considerably interfere with life functioning. Anxiety disorders are the most prevalent class of mental disorders affecting children (Costello et al., 2003), and are associated with considerable comorbidity (Cummings, Caporino, & Kendall, 2014), peer dysfunction (Ginsburg et al., 1998; Verduin & Kendall, 2008), sleep problems (Caporino et al., 2015; Weiner et al., 2015), substance use (Wu et al., 2010), and overall reduced quality of life (Comer et al., 2011; Swan & Kendall, 2017). Research has increasingly documented a significant link between traumatic exposure in childhood and development of anxiety disorders (Hoven et al., 2004; Pynoos, Steinberg, & Piacentini, 1999). Roughly 12% of children experiencing a traumatic event meet diagnostic criteria for an anxiety disorder (Copeland et al., 2007), with children exposed to family violence particularly vulnerable to the development of anxiety problems (Briggs-Gowan et al., 2015). The most common anxiety disorders among

trauma-exposed children are generalized anxiety disorder (Copeland et al., 2007) and separation anxiety disorder, although timing of trauma exposure appears to play an important role in determining which anxiety disorder an exposed child is at greatest risk to develop. For example, trauma exposure during puberty confers greatest risk for onset of social anxiety disorder, relative to other disorders (Marshall, 2016).

Disruptive Behavior Problems Although most research examining associations between childhood trauma and psychopathology have examined internalizing problems, a growing body of literature finds that traumatic exposure in childhood can place children at elevated risk for the development of disruptive behavior problems, including oppositional defiant disorder and conduct disorder (Crum et al., 2017; Hoven et al., 2005; Villodas et al., 2015). Indeed, researchers have identified enormously high rates of trauma exposure (as high as 92%) among detained juvenile delinquents (Abram et al., 2004; Ford, Hartman, Hawke, & Chapman, 2008). Moreover, children exposed to trauma have roughly twice the odds of meeting diagnostic criteria for a disruptive behavior disorder by the time they reach adolescence (Copeland et al., 2007). Exposure to community violence and child maltreatment independently and prospectively predict the onset of significant disruptive behavior problems (Litrownik et al., 2003; McCabe et al., 2005). Across major child adversities, physical abuse and exposure to family violence are associated with the greatest subsequent risk for development of a disruptive behavior disorder, and have immediate and prolonged impacts on disruptive behavior problems (Green et al., 2010; Keiley et al., 2001; Lansford et al., 2007; Thornberry et al., 2001; Yates et al., 2003). The specific mechanisms that explain the association between family violence and disruptive behavior problems remain relatively unclear. Although social learning and social information processing models attempt to explain the development of aggressive behavior among children who have been exposed to violence (Bandura, 1977; Crick & Dodge, 1996), these theories do not account for 191

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elevated levels of other disruptive behaviors (e.g., delinquency). Child maltreatment has been found to disrupt multiple physiological, cognitive, emotional, and social developmental processes (Appleyard, Yang, & Runyan, 2010; Kerig, Bennett, Thompson, & Becker, 2012; Kim & Cicchetti, 2010; Shonk & Cicchetti, 2001), that contribute to the development, maintenance, and exacerbation of disruptive behavior problems (Kaplow & Widom, 2007; Keiley et al., 2001; Kotch et al., 2008; Lansford et al., 2007; Manly, Kim, Rogosch, & Cicchetti, 2001; Thornberry, Henry, Ireland, & Smith, 2010). Researchers have found that exposure to violence is associated with a secondary variant of the juvenile psychopathy profile, which is characterized by elevated levels of callous and unemotional traits (Howard, Kimonis, Muñoz, & Frick, 2012; Kimonis, Fanti, Isoma, & Donoghue, 2013). This secondary psychopathy, sometimes referred to as acquired callousness, is associated with differences in emotional processing, including increased attention to emotionally distressing stimuli, and is theorized to develop because of emotional numbing following exposure to trauma (D. C. Bennett & Kerig, 2014; Kerig, Bennett, et al., 2012; Kimonis, Frick, Cauffman, Goldweber, & Skeem, 2012). Moreover, children with these presentations often have more severe antisocial behavior problems that are more likely to persist across the life course (Kimonis, Skeem, Cauffman, & Dmitrieva, 2011).

Substance Use Rates of childhood trauma are considerably elevated among adolescents with substance use disorders (Dube et al., 2003; Khoury et al., 2010; Lipschitz et al., 2003; Simpson & Miller, 2002). Across child traumas, sexual and physical abuse seem to show the strongest links with the development of substance use disorders (Simpson & Miller, 2002), and multiple traumatic exposures in childhood have been associated with more serious substance use problems later in life (Khoury et al., 2010). Roughly 10% of children exposed to childhood trauma meet criteria for a formal substance use disorder by adolescence (Copeland et al., 2007). The rate of substance use disorders is particularly elevated (18%) among trauma-exposed children who report 192

unwanted, painful, and distressing recollections, memories, thoughts, or images of the traumatic event (Copeland et al., 2007). One theoretical explanation of the elevated rates of substance use disorders among trauma-exposed children posits that using substances facilitates the avoidance of trauma-related stimuli (e.g., memories, thoughts, images) and provides temporary relief from the distressing cognitive and emotional arousal that results from these stimuli (De Bellis, 2002; Jacobsen, Southwick, & Kosten, 2001). Researchers have found support for this theory, as children who have been physically or sexually abused and/ or exposed to family violence report they were more likely to use drugs and alcohol to cope with psychological distress (Flannery & Singer, 1998; Harrison, Fulkerson, & Beebe, 1997). Therefore, substance use may serve as a method of self-medication that could develop immediately following trauma exposure or over a prolonged time.

Attention Problems Distractibility and difficulty sustaining attention are among the cognitive consequences associated with trauma in children. Indeed, researchers have found high rates of abuse among children who have been diagnosed with attention-deficit/hyperactivity disorder (ADHD; Briscoe-Smith & Hinshaw, 2006). In addition, longitudinal research has shown that early childhood physical and sexual abuse are associated with subsequent behavioral profiles characterized by attention problems and high rates of ADHD diagnoses (Villodas et al., 2015). Although ADHD is heritable and rooted in deficits in neuropsychological functioning, it is possible that such vulnerabilities and deficits are exacerbated by experiences of trauma. For example, deficits in executive functioning, higher order cognitive functions, and social information processing among children who have been maltreated could present as inattention and impulsivity, but might be manifestations of dissociation (Becker-Blease & Freyd, 2008; Endo, Sugiyama, & Someya, 2006; Lee & Hoaken, 2007). These dissociative presentations could be misinterpreted as ADHD or could worsen existing symptoms of ADHD if the child’s trauma history is not fully considered.

Trauma and Child Psychopathology

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Risk and Resilience in Trauma-Exposed Children Given the extraordinary diversity in clinical outcomes among children exposed to trauma, research has increasingly examined mediators and moderators of associations between trauma exposure and psychopathology. Biopsychosocial models (e.g., McLaughlin & Lambert, 2017) consider the complex interplay among risk and resilience factors that powerfully influence individual trajectories among trauma-exposed children. Biological factors may explain some of the variability in vulnerability to the development of posttrauma psychopathology. Twin studies suggest almost 33% of the risk for developing PTSD following trauma exposure is accounted for by genetic factors (e.g., Bomyea, Risbrough, & Lang, 2012), with remaining variance due to other causal factors. Molecular genetics studies suggest the 5-HT transporter, catechol-O-methyltransferase, and FKBP5 gene variants may play important roles in individual vulnerability (Bomyea et al., 2012). Moreover, hypersensitivity or overreactivity in the hypothalamic–pituitary–adrenal systems that regulate physiological responses to stress may function as further biological vulnerability to the development of psychopathology following exposure to traumatic stress (Bomyea et al., 2012). Finally, interactions between a functional polymorphism involving the promoter region of the monoamine oxidase A (MAOA) gene, which results in low levels of MAOA, and physical abuse during childhood have been linked to an increased risk for the development of various forms of psychopathology (Kim-Cohen et al., 2006). McLaughlin and Lambert (2017) outlined four central mechanisms in threat processing that are being suggested to link trauma exposure to subsequent psychopathology. They argue that exposure to traumatic events in childhood alters affective and neurobiological processes to optimize identification of potential future threats in the environment and to amplify emotional responses to such threats. Such processes may be adaptive in promoting safety in extreme environments, but this heightened reactivity may be maladaptive in other contexts, and

promote patterns of internalizing and externalizing psychopathology across development. The four specific threat processing alterations proposed by McLaughlin and Lambert (2017) in the aftermath of childhood trauma exposure facilitate the subsequent development of psychopathology. First, trauma-exposed children show social information processing biases. For example, they are more sensitive to anger in others (McLaughlin & Lambert, 2017; Pollak et al., 2000; Pollak & Sinha, 2002). Trauma-exposed children are also more likely to attend to threatening stimuli and to make hostile attributions when interpreting the intentions of other children (Dodge et al., 1995). Disproportionate focus on threat information, in turn, interferes with encoding of other important contextual information that helps children distinguish harmless from threatening situations. Second, traumaexposed children show altered emotional learning and disruptions in learning mechanisms that underlie the acquisition of anxiety and fear. During laboratory-based fear conditioning paradigms, trauma-exposed children show similar skin conductance responses during conditioned cues that predict safety and conditioned cues that predict threat. In contrast, children without trauma exposure exhibit discriminating skin conductance responses across these two stimuli (McLaughlin et al., 2016). This failure to differentiate across stimuli in fear conditioning paradigms, in turn, mediates associations between trauma exposure and externalizing psychopathology (McLaughlin et al., 2016). The third specific threat processing alteration outlined by McLaughlin and Lambert (2017) is heightened emotional response to potential threats. Trauma-exposed children show elevated amygdala response to negative stimuli, as well as increased sympathetic nervous system activation (Heleniak et al., 2016). These alterations in turn have been found to mediate links between trauma exposure and psychopathology (e.g., Kim-Spoon, Cicchetti, & Rogosch, 2013). Finally, trauma-exposed children show difficulty disengaging from negative emotional content. They are more likely to engage in ruminative emotion regulation strategies and to have difficulty engaging in more adaptive strategies like cognitive reappraisal (McLaughlin et al., 2015). 193

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Not all trauma-exposed children show these four threat processing alterations, and not all children who show these alterations go on to develop psychopathology. Key moderators serve important protective functions and buffer against the negative effects of trauma exposure. These protective factors include supportive family and social relationships, heightened sensitivity to rewarding stimuli, and mature amygdala–prefrontal circuitry (McLaughlin & Lambert, 2017). Indeed, after trauma exposure, (a) children with supportive caregivers show lower rates of psychopathology (Trickey et al., 2012) and amygdala reactivity to threat is lower in children with supportive mothers, (b) increased sensitivity to positive stimuli at neural and behavioral levels reduces risk for psychopathology in traumaexposed children, and (c) greater medial prefrontal cortex (mPFC)-amygdala functional connectivity among trauma-exposed children during threat tasks protects against the onset of internalizing psychopathology (Herringa et al., 2016; McLaughlin & Lambert, 2017). Emerging Areas in the Study of Trauma Exposure and Child Psychopathology Despite progress and increased awareness, there remain several areas in need of study regarding childhood traumatic exposure. Specifically, we need to know more about complex trauma, disasters and children, and trauma exposure in developing regions of the world.

Complex Trauma Children who have been exposed to stressful and traumatic experiences are often exposed to multiple and/or recurring traumatic events (Copeland et al., 2007; McLaughlin et al., 2013). For example, a recent epidemiologic study found nearly 41% of children reported being victims of more than one incident of violence within the past year and 10% reported being victims of six or more incidents (Finkelhor et al., 2015). Researchers have found that children’s traumatic experiences are often interrelated, such that exposure to a single traumatic event could increase the risk for experiencing other 194

traumatic events (Dong et al., 2004; Herrenkohl & Herrenkohl, 2007). This is particularly true in the cases of child maltreatment and family violence, in which an ongoing threat from a perpetrator exists and may be associated with a variety of other stressors within the home (e.g., substance use, poverty, mental illness). Based on reports to child protective services agencies, which only include maltreatment experiences that were detected by these agencies, researchers have found that children who have been maltreated are rarely reported for a single type of maltreatment (Villodas et al., 2012), and many are reported repeatedly for chronic maltreatment across development (Proctor et al., 2012). Risk for exposure to additional traumatic events could also be exacerbated by ecological risk factors, including living in communities and attending schools with high rates of violence and crime (Zielinski & Bradshaw, 2006). Repeated exposures to traumatic events across development complicate the identification of reactions to specific traumatic experiences. For example, researchers have found that the accumulation of traumatic exposures during childhood is associated with increasing symptom presentation complexity (Cloitre et al., 2009; Ford, Elhai, Connor, & Frueh, 2010). Moreover, exposure to more trauma, regarding variety and chronicity of traumatic experiences, is associated with more severe psychopathology (English, Graham, Litrownik, Everson, & Bangdiwala, 2005; Finkelhor, Ormrod, & Turner, 2007a, 2007b; Villodas et al., 2012). These findings support the conceptualization of complex trauma, a term often used to refer to the accumulation of traumatic experiences and the multifaceted traumatic reactions that are often associated with these experiences. Complex trauma reactions do not necessarily include symptoms that meet full criteria for a diagnosis of PTSD and can often include additional symptoms and functional impairments that may not fit within existing psychiatric diagnostic categories (D’Andrea et al., 2012). Researchers have conceptualized and proposed a developmental trauma disorder, which involves the development of diagnostically complex, multidomain impairments including attachment problems, impulsivity, emotional and behavioral dysregulation, maladaptive

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cognitive processes, hypervigilance to threat, low self-concept, and hypothalamus–pituitary–adrenal axis dysregulation in reaction to multiple and often recurring traumatic exposures (Kearney et al., 2010; Teague, 2013). Based on a developmental psychopathology framework, it is theorized that these impairments arise becuase of disruptions in developmental processes that initiate a cascading sequence of changes in emotional, cognitive, and physiological regulatory processes during development (Teague, 2013; van der Kolk et al., 2005, 2009). The impairments caused by these disrupted developmental processes preclude the successful mastery of subsequent milestones, which can result in further functional deficits. For example, negative alterations in cognitions and mood that result from repeated exposure to trauma (e.g., depressive or aggressive problems) could result in withdrawal and/or rejection from peer groups. This social isolation can, in turn, reduce opportunities to learn and practice adaptive social skills. Alternatively, avoidance of trauma-related thoughts or feelings and emotional numbing can limit a child’s development of empathy for others and result in a callous-unemotional presentation similar to that of children with secondary psychopathy. Empirical support for the role of trauma in the complex reactions of trauma-exposed children and adolescents is accumulating. For example, research has found that PTSD symptoms partially mediate associations between trauma exposure and externalizing psychopathology (e.g., aggression, rulebreaking), internalizing psychopathology, suicidal ideation, and physical health concerns (Cromer & Villodas, 2017; Kerig, Vanderzee, Becker, & Ward, 2012; Kerig, Ward, Vanderzee, & Arnzen Moeddel, 2009; Mazza & Reynolds, 1999; Ruchkin, Henrich, Jones, Vermeiren, & Schwab-Stone, 2007; Villodas, Cromer, et al., 2016). These findings indicate that associations between trauma and many of these outcomes can be at least partially explained by symptoms of posttraumatic stress reactions. However, PTSD symptoms cannot fully account for these associations, which indicates that additional mechanisms must be identified to explain the complex heterogeneity of reactions to trauma exposure.

Disasters and Children Disasters are destructive events that disrupt and overwhelm entire communities, confront every society, and globally affect millions each year. The number of disasters appears to be on the rise. The Intergovernmental Panel on Climate Change (2014) forecasts an increase in temperatures and subsequent frequency in disasters around the world. When disasters strike, a great many children and adolescents are nearby and are vulnerable to directly witnessing massive destruction, seeing dead or injured people, losing a family member or friend, being involved in a school evacuation, viewing physical damage or ruins, and/or being forced to relocate housing. Such diverse phenomena as earthquakes, floods, hurricanes, tsunamis, terrorist attacks, nuclear waste accidents, and mass transportation disasters are linked with increased psychopathology in children (e.g., Comer, Bry, Poznanski, & Golik, 2016; Furr et al., 2010; Hoven et al., 2005; La Greca et al., 2013; La Greca, Silverman, Vernberg, & Prinstein, 1996; Lochman et al., 2017). Children and adolescents living in regions that have experienced a disaster commonly show subsequent increases in PTSD symptoms, depression, anxiety, conduct problems, substance use, and overall reduced quality of life (e.g., Comer, Bry, et al., 2016; Comer, Dantowitz, et al., 2014; Furr et al., 2010; Lochman et al., 2017; Rowe et al., 2010; Tang et al., 2017). As with other forms of child trauma, disaster exposure is associated with great heterogeneity in child outcomes (Furr et al., 2010). For example, whereas Swenson et al. (1996) did not find a significant disaster exposure effect on children’s PTSD symptoms 14 months after Hurricane Hugo, Mullett-Hume, Anshel, Guevara, and Cloitre (2008) found a large effect of the 9/11 World Trade Center attack on children’s PTSD symptoms 2 years after the attack. Focused research on disasters and children has revealed that specific aspects of the disaster and its consequences can greatly influence the effect it has on child and adolescent psychopathology. Disasters with larger death tolls, relative to disasters with smaller death tolls, generally have greater impacts on child psychopathology (Furr et al., 2010). Notably, disasters in which more than 1,000 people 195

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perished have been associated with the highest rates of postdisaster psychopathology in children (Furr et al., 2010). Importantly, the cause of the disaster (i.e., whether it was natural or man-made) does not differentially influence associations between exposure and child psychopathology. The specific source of the disaster seems to matter far less in determining child outcomes than the extent of destruction, children’s subjective distress at the time of the disaster, and whether children lost a loved one in the event (Furr et al., 2010). Unlike many other forms of child trauma, disasters are unique in that they can profoundly impact the full array of contexts in which children develop (e.g., family, peer groups, school systems, national economic climate, international policies and relations; Comer & Kendall, 2007). Child adaptation in the aftermath of disaster unfolds within a set of embedded contexts that have also been altered by the traumatic event. A child’s long-term ability to function and cope with disaster exposure is intricately linked to the ways in which that child’s developmental contexts function and cope with the disaster. Indeed, research finds that in the aftermath of disaster, child psychopathology is more severe when caregivers experience elevated disaster-related distress themselves and avoid household discussions of the event (Carpenter et al., 2017; Kerns et al., 2014), and when schools are disorganized and less equipped to help children process the event (Green et al., 2015, in press). Some disasters reveal devastating patterns of prejudice that can, in turn, further promote maladjustment. Following Hurricane Katrina, researchers found many in the country were less sympathetic to economically disadvantaged individuals of color who were stranded in New Orleans and even placed more blame on them for their situation (see Weems & Overstreet, 2008). Discrimination and other forms of social injustices that emerge in postdisaster recovery contexts, in turn, can promote psychopathology in trauma-exposed children and adolescents (Weems et al., 2007). Postdisaster breakdowns in social cohesion and community structure can also further promote psychopathology in trauma-exposed children (Weems & Overstreet, 2008). 196

Disaster-related life disruptions can predict which exposed children and adolescents subsequently show the greatest psychopathology. In the aftermath of disaster, schools can close for prolonged periods of time, families can be forced to relocate, travel can be disrupted, and attack-related economic ripples can affect parental employment and family stress (Comer, Bry, et al., 2016). Importantly, each of these disaster-related life disruptions can, in turn, affect postdisaster functioning and adjustment in exposed children and adolescents. For example, following the 9/11 World Trade Center attacks, Comer and colleagues (2010) found that New York City children with a family member who lost their job due to the attacks were roughly twice as likely to suffer from PTSD and anxiety disorders as their peers whose family members’ jobs were not affected by the attacks. In addition, New York City children and adolescents whose postattack travel throughout the city was restricted were nearly three times as likely to exhibit PTSD and two times as likely to exhibit anxiety or depression than their peers whose typical travel was not restricted (Comer et al., 2010; Comer, Bry, et al., 2016). Children of first responders (e.g., firefighters, police officers, EMTs) are a particularly vulnerable group (Comer, Bry, et al., 2016; Comer, Kerns, et al., 2014; Duarte et al., 2006; Hoven et al., 2009). For example, following the Boston Marathon bombing, the proportion of Boston-area children and adolescents with likely PTSD was about 6 times higher among those who had a relative in law enforcement who participated in the manhunt for the bombing suspects (Comer, Kerns, et al., 2014). Even after controlling for children’s own direct exposure to the bombing and manhunt, having a relative participate in the manhunt was significantly associated with elevated child PTSD symptoms, emotional symptoms, hyperactivity, and inattention (Comer, Bry, et al., 2016; Comer, Kerns, et al., 2014).

Child Trauma in Developing Regions of the World Until recently most of the quality research on trauma exposure in children had been conducted in developed (and largely Western) regions of the world. Within the past decade, however, studies on

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child exposure to trauma have examined a much broader range of regions of the world (e.g., Comer, Bry, et al., 2016; Neuner et al., 2006; Tol et al., 2013; Walker et al., 2007) affording opportunity to empirically consider the generalizability of prior findings to children and adolescents living in less developed regions of the world. Children and adolescents account for approximately 33% of the world’s population, with 90% living in developing or low- and middle-income countries (LMICs; Kieling et al., 2011). There are over 200 million children under the age of 5 years living in developing countries (Grantham-McGregor et al., 2007) and more than 80% of children with disabilities live in LMICs (Peek & Stough, 2010). LMIC children are generally living in communities dependent on the environment for their livelihood, increasing risk for mental health problems following climate/weather-related disasters (Dodgen et al., 2016). In addition to greater exposure to climate and weather-related disasters, children in developing countries are also more likely to be exposed to war, armed conflict, and community and political violence (Walker et al., 2007), and most disaster victims who are adversely affected dwell in LMICs (Comer, Bry, et al., 2016; Neuner et al., 2006). Given these statistics, it is important to understand the mental health care risks and needs of these children and adolescents. Mental health concerns following traumatic events in LMICs, like in high-income and developed countries, include PTSD, depression and suicidal ideation, anxiety, and disruptive behavior problems. These concerns are greater in children than adults (Bonanno et al., 2010), and problems further increase in children with prior disabilities (Weissbecker et al., 2008). Studies have identified that greater trauma exposure is predictive of increased mental health risk, and this effect may be even more pronounced in LMICs. Whereas postdisaster PTSD rates in developed regions of the world tend to hover between 10% and 30% (Furr et al., 2010), following the 1988 Armenian earthquake, 70% of children living in the most affected communities met diagnosis for PTSD (Pynoos et al., 1993). PTSD rates for children and adolescents in the aftermath of the 2004 Sri Lankan tsunami approached 40%

with the best predictors again being severity of trauma exposure and loss; predictors also included prior trauma. For these children, almost 8% were additionally diagnosed with PTSD unrelated to the tsunami (Neuner, Schauer, Catani, Ruf, & Elbert, 2006). Prior to the tsunami, over 85% of children had already been exposed to a traumatic event. Exposure to disaster-related trauma may serve as a trigger for other prior traumatic experiences, heightening risk for mental health concerns. Traumatic loss is another risk factor, particularly when death and destruction overwhelm a community and may lead to epidemic rates of complicated grief. After the Sri Lankan tsunami, in families with a greater number of losses, mothers reported higher rates of grief reactions, PTSD, and depression in their children. Adolescents with greater loss and displacement reported greater levels of PTSD and depression even after controlling for direct traumatic exposure (Wickrama & Kaspar, 2007). Exposure to violence is associated with meaningful mental health concerns for children in LMICs. For example, South African children living in communities with significant violence had higher rates of PTSD, depression, aggression and behavior problems than those children with minimal or no exposure (Magwaza, Killian, Petersen, & Pillay, 1993). Children and adolescents living in the Middle East have been exposed to chronic intra- and intergroup violence. Estimated prevalence rates of PTSD among Palestinian adolescents approaches 35%, with children living in areas of greatest conflict reporting the highest rates (Neria et al., 2010). War and other armed conflicts produce large numbers of refugees in LMICs. Affected children and adolescents are often exposed to multiple losses, direct exposure to violence (e.g., seeing killings, injuries, torture) and perceived and actual threat to their lives. There is a dose-effect relationship between these cumulative traumas and mental health problems, which may be further impacted by unaccompanied minor status, illegal entry status, and the attitudes of countries receiving refugees (Bui et al., 2015). As in developed and high-income countries, family variables impact mental health in LMIC children and adolescents in the aftermath of traumatic events (Dodgen et al., 2016). Children and adolescents 197

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are largely dependent on their caregivers; therefore, their well-being can be adversely affected if their caregivers are not coping well or have their own mental health problems (Grantham-McGregor et al., 2007). Tol and colleagues (2013) noted that although PTSD in LMICs is best predicted by trauma exposure, family level variables are better predictors of other subsequent mental health care problems. Greater maternal depression and lower sensitivity to child concerns, for example, have been associated with greater disruptive behavior problems in trauma-exposed preschool children in LMICs (Walker et al., 2007). Treatment of Children Exposed to Trauma The past several decades have witnessed tremendous advances in evidence-based treatment practices for trauma-exposed children (Dorsey et al., 2017; Silverman et al., 2008). There have now been dozens of controlled evaluations drawing on rigorous clinical trial methodologies that have compared alternative treatment approaches. One of the most consistent findings has been that, regardless of theoretical orientation and child age, trauma-focused child treatments (i.e., those that specifically address the traumatic event) are more effective in treating children’s PTSD symptoms than nondirective or nonspecific programs (Cohen et al., 2010). Indeed, trauma-exposed children in child-centered treatments rarely bring up their traumatic experiences spontaneously (Cohen et al., 2004). Consensus practice guidelines suggest trauma-focused psychological treatment should be first line care for trauma-exposed children showing signs of distress (Cohen et al., 2004). The most studied and well-supported family of treatments for children and adolescents exposed to trauma has been trauma-informed cognitive–behavioral therapy (CBT; see Kendall, 2012), which can be delivered in a variety of formats and is now recognized as well-established for the treatment of trauma-exposed children (Dorsey et al., 2017). These CBT programs can be delivered as individual child treatments, individual child treatments with parental involvement, group 198

child treatments, and group child treatments with parental involvement. On average, the inclusion of parents and other key caregivers in the treatment of trauma-exposed children is associated with superior outcomes relative to treatments that only work with children and adolescents (Cohen et al., 2010). Among CBT interventions for children exposed to trauma, trauma-focused CBT (TF-CBT; Cohen, Mannarino, & Deblinger, 2006) has shown the most favorable outcomes. TF-CBT is a relatively short-term treatment (typically lasting 12–16 sessions) that was originally developed for child victims of sexual abuse. Core TF-CBT treatment components can be summarized and organized using the PRACTICE acronym: ■■

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Psychoeducation and Parenting Skills: Educate child and caregivers about common trauma symptoms and about the nature of the specific traumatic event the child experienced; normalize trauma reactions; teach caregivers positive attending skills, selective attention, and related contingency management skills. Relaxation Skills: Teach progressive muscle relaxation and diaphragmatic breathing exercises. Affective Modulation Skills: Help children identify and appropriately label emotional experiences; help children learn positive emotion regulation strategies to productively manage emotional experiences. Cognitive Coping and Processing: Help children recognize relationships between thoughts, feelings, and behaviors; challenge dysfunctional attitudes and interpretations. Trauma Narrative: Collaboratively construct narrative of the traumatic experiences, adjusting maladaptive cognitive distortions and interpretations about the experiences; place the event in the broader context of the child’s life. In-Vivo Mastery of Trauma Reminders: Engage children in graduated exposure to places, events, and objects that serve as reminders of the traumatic event. Conjoint Child–Parent Sessions: Address family issues; child shares trauma narrative with parent. Enhancing Safety and Development: Address prevention of future trauma.

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Over a dozen randomized trials have now demonstrated the significant superiority of TF-CBT over a number of rigorous control interventions, including usual community treatment, nondirective supportive therapy, child-centered psychotherapy, and waitlist controls (Cohen et al., 2010). Treatment outcomes have been somewhat more positive regarding reducing children’s PTSD symptoms relative to reducing symptoms of depression and anxiety that also commonly accompany trauma exposure. Long-term follow-up evaluations have also been positive, demonstrating that many of the gains associated with TF-CBT are maintained across time. For these reasons, the Substance Abuse and Mental Health Services Administration recognizes TF-CBT as a model program. A similar program to TF-CBT that is designed for group administration in the school setting is Cognitive–Behavioral Intervention for Trauma in Schools (CBITS; Stein et al., 2003). CBITS covers most of the PRACTICE components, although parental involvement is optional. CBITS also includes a teacher component that addresses the impact of trauma exposure on student learning and classroom behavior. Trauma narrative sessions are typically held as individual sessions, rather than as group exercises. There is some evidence that adolescents benefit specifically from prolonged exposure therapy, a form of behavioral therapy initially developed for adult populations that focuses predominantly on having the patient repeatedly recount traumatic memories and systematically confront places, events, and objects that serve as reminders of the traumatic event. It is believed that such repeated exposure reduces distress evoked by traumatic memories in the long-term. Downward extensions of prolonged exposure therapy geared toward trauma-exposed adolescent populations have demonstrated very favorable outcomes, relative to supportive counseling, in reducing PTSD symptoms, depression, and functional impairments (Foa et al., 2013). Given the elevated rates of trauma exposure among adolescents with substance use disorders (Dube et al., 2003; Khoury et al., 2010; Lipschitz et al., 2003; Simpson & Miller, 2002), there is evidence that trauma-exposed adolescents at risk

for substance abuse can particularly benefit from integrated treatment programs that merge traumafocused intervention with substance use treatment. Seeking Safety (Najavits, 2002) is a group intervention initially designed for adults that incorporates substance abuse risk reduction into treatment focused on cognitive processing of the traumatic event. Research has begun to support the downward extension of the Seeking Safety program for adolescent populations (Najavits et al., 2006). One challenge in the implementation of traumafocused interventions is the treatment of complex trauma. Given the emphasis on specific traumatic events in trauma-focused interventions, implementation may be more difficult with children and adolescents with multiple and recurrent traumatic experiences (Ford & Cloitre, 2009). Cohen and colleagues (2012) have recommended adaptations of TF-CBT for complex trauma. These adaptations include enhancing the emphasis on coping skills and processing, addressing safety and development early in treatment and repeating it as often as needed, implementing exposures more gradually in response to children’s distress levels, identifying unifying trauma themes and integrating them throughout treatment, and including traumatic grief and safety and trust generalization components, as needed. Although they also recommend identifying and engaging any supportive adult with whom the child has substantial contact in the absence of a primary caregiver, it is important to recognize that many trauma-exposed children may not have an identifiable supportive adult available to participate in treatment. For example, children in foster care, group homes, juvenile detention, or other institutional settings may not have supportive adults in their lives. Trauma Affect Regulation: Guide for Education and Therapy for Adolescents (TARGET-A) was developed to address complex trauma reactions and focuses on developing a sequential set of cognitive and behaviorally based emotion regulation skills among trauma-exposed children (Ford & Hawke, 2012; Ford et al., 2012; Frisman et al., 2008). Based on CBT and dialectical behavioral therapy models, these skills include value identification, goal setting, paying attention to physical sensations, cognitive 199

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restructuring, identifying and recognizing distress triggers, emotion identification and regulation strategies, and actively contributing to the treatment process. TARGET-A has demonstrated preliminary evidence of its efficacy among adolescents with complex trauma in juvenile detention centers, and can be delivered in individual or group formats (Ford & Hawke, 2012; Ford et al., 2012; Frisman et al., 2008). Although caregiver and family involvement is not essential to the model, a family-based component can be implemented as well. Importantly, given the focus on higher-order skill development, this model may be less applicable for younger children.

Trauma-Informed Care Although trauma-focused interventions are critical for many children in acute posttraumatic distress, more general trauma-informed care is necessary for a broader population of children. Trauma-informed care is an ideological approach to providing services to individuals that recognizes the potential impact of trauma in the life of the client and adapts all aspects of service provision accordingly (Elliott, Bjelajac, Fallot, Markoff, & Reed, 2005). It requires an organizational paradigm shift among service providers and all their employees. Given the high rates of trauma exposure in children, trauma-informed care models suggest providers treat all clients as potential trauma survivors, using a trauma-informed approach, particularly for those with known risk factors for trauma exposure. It should also be noted that a trauma-informed approach to care has implications for all service provider employees regarding training, supervision, and self-care, given the potential for vicarious traumatization. Ten principles of trauma-informed care have been identified (Elliott et al., 2005): ■■

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Recognize the potential impact of trauma on development. Target trauma as a primary treatment goal. Focus on empowering the client. Provide opportunities for choice and control throughout treatment. Work collaboratively with the client. Promote client safety and respect during treatment.

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Use a strengths-based approach, recognizing resilience. Reduce the risk for retraumatization. Strive for cultural competence. Solicit consumer feedback for service planning and improvement.

Not all service providers will provide traumafocused services. However, trauma-informed service providers will be able to recognize and respond to potential trauma so that individuals receive appropriate services to address their trauma reactions. This can be achieved through trauma screening and assessment in organizations that serve individuals who may have been exposed to trauma (Milne & Collin-Vézina, 2015). Identifying histories of trauma exposure will facilitate appropriate case conceptualization and service referrals. As emphasized throughout this chapter, reactions to traumatic experiences are heterogeneous; therefore, case conceptualizations based exclusively on the presenting symptoms are often misguided.

Trauma Services for Children in Developing Regions of the World Mental health problems are disproportionately high in children and adolescents in LMICs, and evidence-based services in LMICs are lacking and are not viewed as a priority (Kieling et al., 2011). Rates of unmet mental health care needs for children in developing countries has been estimated to approach 100% (Patel et al., 2007). Mental health care, when provided, is often provided by nonmental health professionals, including primary health care providers and lay health workers. These providers rarely have mental health care training, access to appropriate psychiatric medications, and/or knowledge of mental health care systems, if these even exist (Eisenman et al., 2006). To widen the reach of helpful mental health resources for trauma-exposed children in LMICs, potential treatment providers need to be expanded by training of relief volunteers, educators, traditional healers, social workers, those working in suicide and substance abuse prevention/intervention programs, religious leaders, and law enforcement in evidence-based treatment techniques (Davidson & McFarlane, 2006; Eisenman et al., 2006).

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Shelter workers may not know how to manage children—particularly those with special health care needs—and should also be included in training efforts (Institute of Medicine, 2001). To best improve mental health services to LMIC children and adolescents, integration with existing services, such as health care, is paramount. In addition to long-term service planning, longterm monitoring and consultation with individuals and programs providing mental health care is strongly recommended, especially as these services are outside of their traditional roles (Davidson & McFarlane, 2006). Families also play a crucial role in supporting children after disaster and other traumatic events, as better parent–child relationships are associated with better mental health outcomes (Wickrama & Kaspar, 2007). Use of families, particularly in the delivery of psychological first aid, may help improve mental health outcomes after disasters in LMICs (Eisenman et al., 2006). Parent training programs in LMICs for very young children have been associated with improved cognitive development, reduced disruptive behavior problems, and improved physical outcomes (Kieling et al., 2011). Disaster mental health programs must also plan for long-term services, as trauma-exposed families in LMICs may not immediately present for aid, but mental health needs may emerge across time (Eisenman et al., 2006). Finally, working with disaster survivors or survivors of extreme violence takes a toll on providers, particularly in LMICs; therefore, mental health training must also include training to address compassion fatigue, burnout, and self-care. Children mental health services are a very low priority in most LMICs and international organizations (Kieling et al., 2011). Worldwide, less than 10% of countries have a clearly defined mental health policy for children and adolescents, and no low-income country has a policy (Patel et al., 2007). Mental health research and programs in LMICs versus developed countries has a 10% to 90% gap, with LMICs accounting for only 10% of all randomized controlled trials. The World Health Organization has developed guidelines for families and educators to help with developmental and behavioral problems in children in LMICs. Research in LMICs for children exposed to disasters and trauma is challenging

because of on-going presence of other risk factors in their lives: preconceptual risk factors (poor pregnancy health care and substance exposure), family health and mental health problems, dietary and nutritional deficiencies, HIV/AIDS and other disease exposure, lack of appropriate child care and stimulation, lack of education, child labor, sexual abuse and extreme use of corporal discipline, child soldiers, and exposure to violence (Kieling et al., 2011). Cultural factors merit consideration in research, program development, or training activities in LMIC. For example, when reviewing outcomes of Ugandan children exposed to armed conflict in Uganda, researchers noted that children initially did not present with psychological distress. Culturally, children were to remain silent and were not to discuss distress as it could upset others who may be suffering; somatic complaints were high in these children. In some countries, gender separation is culturally important. Therefore, services and training in these countries must incorporate this cultural dictate. Additionally, if traditional healers play a crucial role in a culture, gaining their support for any program will aid in its success (Davidson & McFarlane, 2006). Conclusion Almost 66% of the general population of children and adolescents experience or witness actual or threatened death or serious injury at least once by the time they reach adulthood. Although most trauma-exposed children can adaptively process and adjust to such experiences with time, for a sizable minority, trauma exposure confers increased risk for a range of internalizing and externalizing psychopathologies. Well-studied outcomes of trauma exposure in childhood and adolescence include PTSD, depression, anxiety, disruptive behavior problems, and substance use. Further research is needed to delineate the impact of experiencing multiple and/or repeated traumatic events on complex and heterogeneous trauma reactions among children and adolescents. Understanding patterns of risk and resilience among trauma-exposed children is critical for informing intervention efforts. Biopsychosocial 201

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models examine the complex interplay among vulnerability and protective factors that determine individual trajectories among trauma-exposed children. Altered threat processing mechanisms that emerge in trauma-exposed children may mediate associations between trauma exposure and child psychopathology, particularly in children with limited caregiver support, reduced neural and behavioral sensitivity to positive stimuli, and poorer mPFCamygdala functional connectivity. Fortunately, recent decades have witnessed tremendous advances in trauma-informed care and evidence-based interventions for children exposed to traumatic events. Trauma-focused CBT and related interventions focusing on repeated exposure to traumatic experiences and reminders of the event have shown tremendous support across a range of rigorous clinical trials, and positive outcomes are maintained across time. Effective dissemination and implementation efforts are now needed to broaden the reach of supported treatment models and connect the best evidence-based practices with traumaexposed children most in need.

References Abram, K. M., Teplin, L. A., Charles, D. R., Longworth, S. L., McClelland, G. M., & Dulcan, M. K. (2004). Posttraumatic stress disorder and trauma in youth in juvenile detention. Archives of General Psychiatry, 61, 403–410. http://dx.doi.org/10.1001/ archpsyc.61.4.403 American Psychiatric Association. (1980). Diagnostic and statistical manual of mental disorders (3rd ed.). Washington, DC: Author. American Psychiatric Association. (1994). Diagnostic and statistical manual of mental disorders (4th ed.). Washington, DC: Author. American Psychiatric Association. (2013). Diagnostic and statistical manual of mental disorders (5th ed.). Washington, DC: Author. Appleyard, K., Yang, C., & Runyan, D. K. (2010). Delineating the maladaptive pathways of child maltreatment: A mediated moderation analysis of the roles of self-perception and social support. Development and Psychopathology, 22, 337–352. http://dx.doi.org/10.1017/S095457941000009X Au, T. M., Dickstein, B. D., Comer, J. S., SaltersPedneault, K., & Litz, B. T. (2013). Co-occurring posttraumatic stress and depression symptoms after sexual assault: A latent profile analysis. Journal of 202

Affective Disorders, 149, 209–216. http://dx.doi.org/ 10.1016/j.jad.2013.01.026 Bandura, A. (1977). Social learning theory. Oxford, England: Prentice Hall. Becker-Blease, K. A., & Freyd, J. J. (2008). A preliminary study of ADHD symptoms and correlates: Do abused children differ from nonabused children? Journal of Aggression, Maltreatment, and Trauma, 17, 133–140. http:// dx.doi.org/10.1080/10926770802250736 Bennett, D. C., & Kerig, P. K. (2014). Investigating the construct of trauma-related acquired callousness among delinquent youth: Differences in emotion processing. Journal of Traumatic Stress, 27, 415–422. http://dx.doi.org/10.1002/jts.21931 Bennett, M. D., Jr., & Joe, S. (2015). Exposure to community violence, suicidality, and psychological distress among African American and Latino youths: Findings from the CDC Youth Violence Survey. Journal of Human Behavior in the Social Environment, 25, 775–789. http://dx.doi.org/ 10.1080/10911359.2014.922795 Bomyea, J., Risbrough, V., & Lang, A. J. (2012). A consideration of select pre-trauma factors as key vulnerabilities in PTSD. Clinical Psychology Review, 32, 630–641. http://dx.doi.org/10.1016/ j.cpr.2012.06.008 Bonanno, G. A., Brewin, C. R., Kaniasty, K., & Greca, A. M. (2010). Weighing the costs of disaster: Consequences, risks, and resilience in individuals, families, and communities. Psychological Science in the Public Interest, 11, 1–49. http://dx.doi.org/ 10.1177/1529100610387086 Briggs-Gowan, M. J., Pollak, S. D., Grasso, D., Voss, J., Mian, N. D., Zobel, E., . . . Pine, D. S. (2015). Attention bias and anxiety in young children exposed to family violence. Journal of Child Psychology and Psychiatry, 56, 1194–1201. http://dx.doi.org/ 10.1111/jcpp.12397 Briscoe-Smith, A. M., & Hinshaw, S. P. (2006). Linkages between child abuse and attention-deficit/ hyperactivity disorder in girls: Behavioral and social correlates. Child Abuse and Neglect, 30, 1239–1255. http://dx.doi.org/10.1016/j.chiabu.2006.04.008 Bruffaerts, R., Demyttenaere, K., Borges, G., Haro, J. M., Chiu, W. T., Hwang, I., . . . Nock, M. K. (2010). Childhood adversities as risk factors for onset and persistence of suicidal behaviour. British Journal of Psychiatry, 197, 20–27. http://dx.doi.org/ 10.1192/bjp.bp.109.074716 Bui, E., Ohye, B., Palitz, S., Olliac, B., Goutaudier, N., Raynard, J. P., . . . Stoddard, F. J. (2015). Acute and chronic reactions to trauma in children and adolescents. In J. M. Rey (Ed.), IACAPAP e-Textbook of child and adolescent mental health. Geneva,

Trauma and Child Psychopathology

Switzerland: International Association for Child and Adolescent Psychiatry and Allied Professions. Busso, D. S., McLaughlin, K. A., & Sheridan, M. A. (2014). Media exposure and sympathetic nervous system reactivity predict PTSD symptoms after the Boston marathon bombings. Depression and Anxiety, 31, 551–558. http://dx.doi.org/10.1002/da.22282

Copyright American Psychological Association. Not for further distribution.

Caporino, N. E., Read, K. L., Shiffrin, N., Settipani, C., Kendall, P. C., Compton, S. N., . . . Albano, A. M. (2015). Sleep-related problems and the effects of anxiety treatment in children and adolescents. Journal of Clinical Child and Adolescent Psychology, 1–11. http://dx.doi.org/10.1080/15374416.2015.1063429 Carpenter, A. L., Elkins, R. M., Kerns, C., Chou, T., Greif Green, J., & Comer, J. S. (2017). Eventrelated household discussions following the Boston Marathon bombing and associated posttraumatic stress among area youth. Journal of Clinical Child and Adolescent Psychology, 46, 331–342. http://dx.doi.org/ 10.1080/15374416.2015.1063432 Centers for Disease Control and Prevention & Kaiser Permanente. (2016). The ACE Study survey data. Retrieved from https://www.cdc.gov/ violenceprevention/acestudy/about.html Chou, T., Carpenter, A. L., Kerns, C. E., Elkins, R. M., Green, J. G., & Comer, J. S. (2016). Disqualified qualifiers: Evaluating the utility of the revised DSM–5 definition of potentially traumatic events among area youth following the Boston marathon bombing. Depression and Anxiety, 34, 367–373. http:// dx.doi.org/10.1002/da.22543 Cicchetti, D., & Rogosch, F. A. (1996). Equifinality and multifinality in developmental psychopathology. Development and Psychopathology, 8, 597–600. http:// dx.doi.org/10.1017/S0954579400007318 Cloitre, M., Stolbach, B. C., Herman, J. L., van der Kolk, B., Pynoos, R., Wang, J., & Petkova, E. (2009). A developmental approach to complex PTSD: Childhood and adult cumulative trauma as predictors of symptom complexity. Journal of Traumatic Stress, 22, 399–408. http://dx.doi.org/ 10.1002/jts.20444 Cohen, J. A., Bukstein, O., Walter, H., Benson, S. R., Chrisman, A., Farchione, T. R., . . . Medicus, J. (2010). Practice parameter for the assessment and treatment of children and adolescents with posttraumatic stress disorder. Journal of the American Academy of Child and Adolescent Psychiatry, 49, 414–430. Cohen, J. A., Deblinger, E., Mannarino, A. P., & Steer, R. A. (2004). A multisite, randomized controlled trial for children with sexual abuse-related PTSD symptoms. Journal of the American Academy of Child and Adolescent Psychiatry, 43, 393–402. http:// dx.doi.org/10.1097/00004583-200404000-00005

Cohen, J. A., Mannarino, A. P., & Deblinger, E. (2006). Treating trauma and traumatic grief in children and adolescents. New York, NY: Guilford Press. Cohen, J. A., Mannarino, A. P., Kliethermes, M., & Murray, L. A. (2012). Trauma-focused CBT for youth with complex trauma. Child Abuse and Neglect, 36, 528–541. http://dx.doi.org/10.1016/ j.chiabu.2012.03.007 Comer, J. S., Blanco, C., Hasin, D. S., Liu, S. M., Grant, B. F., Turner, J. B., & Olfson, M. (2011). Health-related quality of life across the anxiety disorders: Results from the national epidemiologic survey on alcohol and related conditions (NESARC). Journal of Clinical Psychiatry, 72, 43–50. http://dx.doi.org/10.4088/ JCP.09m05094blu Comer, J. S., Bry, L. J., Poznanski, B., & Golik, A. M. (2016). Children’s mental health in the context of terrorist attacks, ongoing threats, and possibilities of future terrorism. Current Psychiatry Reports, 18, 79. http://dx.doi.org/10.1007/s11920-016-0722-1 Comer, J. S., Dantowitz, A., Chou, T., Edson, A. L., Elkins, R. M., Kerns, C., . . . Green, J. G. (2014). Adjustment among area youth after the Boston Marathon bombing and subsequent manhunt. Pediatrics, 134, 7–14. http:// dx.doi.org/10.1542/peds.2013-4115 Comer, J. S., DeSerisy, M., & Green, J. G. (2016). Caregiver-reports of Internet exposure and posttraumatic stress symptoms among Bostonarea youth following the 2013 Marathon bombing. Evidence-Based Practice in Child and Adolescent Mental Health, 1, 86–102. http://dx.doi.org/ 10.1080/23794925.2016.1203737 Comer, J. S., Fan, B., Duarte, C. S., Wu, P., Musa, G. J., Mandell, D. J., . . . Hoven, C. W. (2010). Attackrelated life disruption and child psychopathology in New York City public schoolchildren 6-months post-9/11. Journal of Clinical Child and Adolescent Psychology, 39, 460–469. http://dx.doi.org/ 10.1080/15374416.2010.486314 Comer, J. S., & Kendall, P. C. (2007). Terrorism: The psychological impact on youth. Clinical Psychology: Science and Practice, 14, 179–212. http://dx.doi.org/ 10.1111/j.1468-2850.2007.00078.x Comer, J. S., Kerns, C., Elkins, R. M., Edson, A. L., Chou, T., Dantowitz, A., ... Green, J. G. (2014). Adjustment among children with relatives who participated in the manhunt following the Boston Marathon attack. Depression and Anxiety, 31, 542–550. http://dx.doi.org/ 10.1002/da.22281 Comer, J. S., & Olfson, M. (2010). The epidemiology of anxiety disorders. In H. B. Simpson, F. Schneier, Y. Neria, & R. Lewis-Fernandez (Eds.), Anxiety disorders: Theory, research, and clinical perspectives (pp. 6–19). http://dx.doi.org/10.1017/ CBO9780511777578.004 203

Furr et al.

Copeland, W. E., Keeler, G., Angold, A., & Costello, E. J. (2007). Traumatic events and posttraumatic stress in childhood. Archives of General Psychiatry, 64, 577–584. http://dx.doi.org/10.1001/archpsyc.64.5.577 Copeland, W. E., Keeler, G., Angold, A., & Costello, E. J. (2010). Posttraumatic stress without trauma in children. American Journal of Psychiatry, 167, 1059–1065. http:// dx.doi.org/10.1176/appi.ajp.2010.09020178

Copyright American Psychological Association. Not for further distribution.

Corso, P. S., Edwards, V. J., Fang, X., & Mercy, J. A. (2008). Health-related quality of life among adults who experienced maltreatment during childhood. American Journal of Public Health, 98, 1094–1100. http://dx.doi.org/10.2105/AJPH.2007.119826 Costello, E. J., Mustillo, S., Erkanli, A., Keeler, G., & Angold, A. (2003). Prevalence and development of psychiatric disorders in childhood and adolescence. Archives of General Psychiatry, 60, 837–844. http:// dx.doi.org/10.1001/archpsyc.60.8.837 Crick, N. R., & Dodge, K. A. (1996). Social informationprocessing mechanisms in reactive and proactive aggression. Child Development, 67, 993–1002. http:// dx.doi.org/10.2307/1131875 Cromer, K. D., & Villodas, M. T. (2017). The role of posttraumatic stress as a pathway to psychopathology among youth at high-risk for victimization. Psychology of Violence, 67, 182–192. Crum, K. I., Cornacchio, D., Coxe, S., Greif Green, J., & Comer, J. S. (2017). Conduct problems among Boston-area youth following the 2013 marathon bombing: The moderating role of prior violent crime exposure. Journal of Clinical Child and Adolescent Psychology, 46, 343–352. http://dx.doi.org/10.1080/ 15374416.2015.1077450 Cummings, C. M., Caporino, N. E., & Kendall, P. C. (2014). Comorbidity of anxiety and depression in children and adolescents: 20 years after. Psychological Bulletin, 140, 816–845. http://dx.doi.org/10.1037/a0034733 D’Andrea, W., Ford, J., Stolbach, B., Spinazzola, J., & van der Kolk, B. A. (2012). Understanding interpersonal trauma in children: Why we need a developmentally appropriate trauma diagnosis. American Journal of Orthopsychiatry, 82, 187–200. http:// dx.doi.org/10.1111/j.1939-0025.2012.01154.x Davidson, J. R. T., & McFarlane, A. C. (2006). The extent and impact of mental health problems after disaster. Journal of Clinical Psychiatry, 67(Suppl. 2), 9–14. De Bellis, M. D. (2002). Developmental traumatology: A contributory mechanism for alcohol and substance use disorders. Psychoneuroendocrinology, 27, 155–170. http://dx.doi.org/10.1016/ S0306-4530(01)00042-7 De Young, A. C., Kenardy, J. A., & Cobham, V. E. (2011). Trauma in early childhood: A neglected population. Clinical Child and Family Psychology Review, 14, 231–250. http://dx.doi.org/10.1007/s10567-011-0094-3 204

De Young, A. C., Kenardy, J. A., Cobham, V. E., & Kimble, R. (2012). Prevalence, comorbidity and course of trauma reactions in young burn-injured children. Journal of Child Psychology and Psychiatry, 53, 56–63. http://dx.doi.org/10.1111/j.1469-7610.2011.02431.x Dixon, A., Howie, P., & Starling, J. (2005). Trauma exposure, posttraumatic stress, and psychiatric comorbidity in female juvenile offenders. Journal of the American Academy of Child and Adolescent Psychiatry, 44, 798–806. http://dx.doi.org/ 10.1097/01.chi.0000164590.48318.9c Dodge, K. A., Pettit, G. S., Bates, J. E., & Valente, E. (1995). Social information-processing patterns partially mediate the effect of early physical abuse on later conduct problems. Journal of Abnormal Psychology, 104, 632–643. http://dx.doi.org/ 10.1037/0021-843X.104.4.632 Dodgen, D., Donato, D., Kelly, N., & La Greca, A., . . . Ursano, R. (2016) Mental health and well-being. In A. J. Crimmins, J. Balbus, J. L. Gamble, C. B. Bard, J. E. Bell, D. Dodgen, . . . L. Ziska (Eds.), The impacts of climate change on human health in the United States: A scientific assessment (pp. 217–246). Washington, DC: U.S. Global Change Research Program. Dong, M., Anda, R. F., Felitti, V. J., Dube, S. R., Williamson, D. F., Thompson, T. J., . . . Giles, W. H. (2004). The interrelatedness of multiple forms of childhood abuse, neglect, and household dysfunction. Child Abuse and Neglect, 28, 771–784. http://dx.doi.org/10.1016/j.chiabu.2004.01.008 Dorsey, S., McLaughlin, K. A., Kerns, S. E. U., Harrison, J. P., Lambert, H. K., Briggs, E. C., . . . AmayaJackson, L. (2017). Evidence base update for psychosocial treatments for children and adolescents exposed to traumatic events. Journal of Clinical Child and Adolescent Psychology, 46, 303–330. http:// dx.doi.org/10.1080/15374416.2016.1220309 Duarte, C. S., Hoven, C. W., Wu, P., Bin, F., Cotel, S., Mandell, D. J., . . . Markenson, D. (2006). Posttraumatic stress in children with first responders in their families. Journal of Traumatic Stress, 19, 301–306. http://dx.doi.org/10.1002/jts.20120 Duarte, C. S., Wu, P., Cheung, A., Mandell, D. J., Fan, B., Wicks, J., . . . Hoven, C. W. (2011). Media use by children and adolescents from New York City 6 months after the WTC attack. Journal of Traumatic Stress, 24, 553–556. http://dx.doi.org/10.1002/jts.20687 Dube, S. R., Felitti, V. J., Dong, M., Chapman, D. P., Giles, W. H., & Anda, R. F. (2003). Childhood abuse, neglect, and household dysfunction and the risk of illicit drug use: The adverse childhood experiences study. Pediatrics, 111, 564–572. http://dx.doi.org/10.1542/peds.111.3.564 Dunn, E. C., McLaughlin, K. A., Slopen, N., Rosand, J., & Smoller, J. W. (2013). Developmental timing of child maltreatment and symptoms of depression

Trauma and Child Psychopathology

and suicidal ideation in young adulthood: Results from the National Longitudinal Study of Adolescent Health. Depression and Anxiety, 30, 955–964. Eisenman, D., Weine, S., Green, B., Jong, J., Rayburn, N., Ventevogel, P., . . . Agani, F. (2006). The ISTSS/ Rand guidelines on mental health training of primary healthcare providers for trauma-exposed populations in conflict-affected countries. Journal of Traumatic Stress, 19, 5–17. http://dx.doi.org/10.1002/jts.20094

Copyright American Psychological Association. Not for further distribution.

Elliott, D. E., Bjelajac, P., Fallot, R. D., Markoff, L. S., & Reed, B. G. (2005). Trauma-informed or trauma-denied: Principles and implementation of trauma-informed services for women. Journal of Community Psychology, 33, 461–477. http://dx.doi.org/10.1002/jcop.20063 Endo, T., Sugiyama, T., & Someya, T. (2006). Attentiondeficit/hyperactivity disorder and dissociative disorder among abused children. Psychiatry and Clinical Neurosciences, 60, 434–438. http:// dx.doi.org/10.1111/j.1440-1819.2006.01528.x English, D. J., Graham, J. C., Litrownik, A. J., Everson, M., & Bangdiwala, S. I. (2005). Defining maltreatment chronicity: Are there differences in child outcomes? Child Abuse and Neglect, 29, 575–595. http://dx.doi.org/10.1016/ j.chiabu.2004.08.009 Finkelhor, D., Ormrod, R. K., & Turner, H. A. (2007a). Polyvictimization: A neglected component in child victimization. Child Abuse and Neglect, 31, 7–26. http://dx.doi.org/10.1016/j.chiabu.2006.06.008

counseling for sexual abuse-related PTSD in adolescent girls: A randomized clinical trial. JAMA: Journal of the American Medical Association, 310, 2650–2657. http://dx.doi.org/10.1001/ jama.2013.282829 Ford, J. D., & Cloitre, M. (2009). Best practices in psychotherapy for children and adolescents. In C. A. Courtois & J. D. Ford (Eds.), Treating complex traumatic stress disorders: An evidence-based guide (pp. 59–81). New York, NY: Guilford Press. Ford, J. D., Elhai, J. D., Connor, D. F., & Frueh, B. C. (2010). Poly-victimization and risk of posttraumatic, depressive, and substance use disorders and involvement in delinquency in a national sample of adolescents. Journal of Adolescent Health, 46, 545–552. http://dx.doi.org/10.1016/ j.jadohealth.2009.11.212 Ford, J. D., Hartman, J. K., Hawke, J., & Chapman, J. F. (2008). Traumatic victimization, posttraumatic stress disorder, suicidal ideation, and substance abuse risk among juvenile justice-involved youth. Journal of Child and Adolescent Trauma, 1, 75–92. http:// dx.doi.org/10.1080/19361520801934456 Ford, J. D., & Hawke, J. (2012). Trauma affect regulation psychoeducation group and milieu intervention outcomes in juvenile detention facilities. Journal of Aggression, Maltreatment, and Trauma, 21, 365–384. http://dx.doi.org/10.1080/10926771.2012.673538

Finkelhor, D., Ormrod, R. K., & Turner, H. A. (2007b). Polyvictimization and trauma in a national longitudinal cohort. Development and Psychopathology, 19, 149–166. http://dx.doi.org/ 10.1017/S0954579407070083

Ford, J. D., Steinberg, K. L., Hawke, J., Levine, J., & Zhang, W. (2012). Randomized trial comparison of emotion regulation and relational psychotherapies for PTSD with girls involved in delinquency. Journal of Clinical Child and Adolescent Psychology, 41, 27–37. http://dx.doi.org/10.1080/15374416.2012.632343

Finkelhor, D., Shattuck, A., Turner, H., & Hamby, S. (2013). Improving the adverse childhood experiences study scale. JAMA Pediatrics, 167, 70–75. http://dx.doi.org/10.1001/jamapediatrics.2013.420

Friedman, M. J., Resick, P. A., Bryant, R. A., & Brewin, C. R. (2011). Considering PTSD for DSM–5. Depression and Anxiety, 28, 750–769. http:// dx.doi.org/10.1002/da.20767

Finkelhor, D., Shattuck, A., Turner, H. A., & Hamby, S. L. (2014). The lifetime prevalence of child sexual abuse and sexual assault assessed in late adolescence. Journal of Adolescent Health, 55, 329–333. http:// dx.doi.org/10.1016/j.jadohealth.2013.12.026

Frisman, L., Ford, J., Lin, H.-J., Mallon, S., & Chang, R. (2008). Outcomes of trauma treatment using the TARGET model. Journal of Groups in Addiction and Recovery, 3, 285–303. http:// dx.doi.org/10.1080/15560350802424910

Finkelhor, D., Turner, H. A., Shattuck, A., & Hamby, S. L. (2015). Prevalence of childhood exposure to violence, crime, and abuse. JAMA Pediatrics, 169, 746–754. http://dx.doi.org/10.1001/ jamapediatrics.2015.0676

Furr, J. M., Comer, J. S., Edmunds, J. M., & Kendall, P. C. (2010). Disasters and youth: A meta-analytic examination of posttraumatic stress. Journal of Consulting and Clinical Psychology, 78, 765–780. http://dx.doi.org/10.1037/a0021482

Flannery, D. J., & Singer, M. (1998). Adolescent violence exposure and victimization at home: Coping and psychological trauma symptoms. International Review of Victimology, 6, 29–48. http://dx.doi.org/ 10.1177/026975809800600103

Gaensbauer, T. J. (1995). Trauma in the preverbal period. Symptoms, memories, and developmental impact. Psychoanalytic Study of the Child, 50, 122–149.

Foa, E. B., McLean, C. P., Capaldi, S., & Rosenfield, D. (2013). Prolonged exposure vs. supportive

Ginsburg, G. S., La Greca, A. M., & Silverman, W. K. (1998). Social anxiety in children with anxiety disorders: Relation with social and emotional functioning. Journal of Abnormal Child Psychology, 205

Furr et al.

26, 175–185. http://dx.doi.org/10.1023/ A:1022668101048 Grantham-McGregor, S., Cheung, Y. B., Cueto, S., Glewwe, P., Richter, L., & Strupp, B. (2007). Developmental potential in the first 5 years for children in developing countries. Lancet, 369, 60–70. http://dx.doi.org/10.1016/S0140-6736(07)60032-4

Copyright American Psychological Association. Not for further distribution.

Green, J. G., Holt, M. K., Kwong, L., Reid, G., Xuan, Z., & Comer, J. S. (2015). School- and classroombased support for children following the 2013 Boston Marathon attack and manhunt. School Mental Health, 7, 81–91. http://dx.doi.org/10.1007/ s12310-014-9140-x Green, J. G., McLaughlin, K. A., Berglund, P. A., Gruber, M. J., Sampson, N. A., Zaslavsky, A. M., & Kessler, R. C. (2010). Childhood adversities and adult psychiatric disorders in the national comorbidity survey replication I: Associations with first onset of DSM–IV disorders. Archives of General Psychiatry, 67, 113–123. http://dx.doi.org/10.1001/ archgenpsychiatry.2009.186 Green, J. G., Xuan, Z., Kwong, L., Holt, M. K., & Comer, J. S. (in press). Teachers’ reports of outreach to school-based mental health service providers following the 2013 Boston Marathon attack. Children and Schools. Harrison, P. A., Fulkerson, J. A., & Beebe, T. J. (1997). Multiple substance use among adolescent physical and sexual abuse victims. Child Abuse and Neglect, 21, 529–539. http://dx.doi.org/10.1016/ S0145-2134(97)00013-6 Heleniak, C., McLaughlin, K. A., Ormel, J., & Riese, H. (2016). Cardiovascular reactivity as a mechanism linking child trauma to adolescent psychopathology. Biological Psychology, 120, 108–119. http:// dx.doi.org/10.1016/j.biopsycho.2016.08.007 Herrenkohl, T. I., & Herrenkohl, R. C. (2007). Examining the overlap and prediction of multiple forms of child maltreatment, stressors, and socioeconomic status: A longitudinal analysis of youth outcomes. Journal of Family Violence, 22, 553–562. http:// dx.doi.org/10.1007/s10896-007-9107-x Herringa, R. J., Burghy, C. A., Stodola, D. E., Fox, M. E., Davidson, R. J., & Essex, M. J. (2016). Enhanced prefrontal-amygdala connectivity following childhood adversity as a protective mechanism against internalizing in adolescence. Biological Psychiatry: Cognitive Neuroscience and Neuroimaging, 1, 326–334. Hoven, C. W., Duarte, C. S., Lucas, C. P., Wu, P., Mandell, D. J., Goodwin, R. D., . . . Susser, E. (2005). Psychopathology among New York city public school children 6 months after September 11. Archives of General Psychiatry, 62, 545–552. http:// dx.doi.org/10.1001/archpsyc.62.5.545

206

Hoven, C. W., Duarte, C. S., Wu, P., Doan, T., Singh, N., Mandell, D. J., . . . Cohen, P. (2009). Parental exposure to mass violence and child mental health: The First Responder and WTC Evacuee Study. Clinical Child and Family Psychology Review, 12, 95–112. http://dx.doi.org/10.1007/s10567-009-0047-2 Hoven, C. W., Duarte, C. S., Wu, P., Erickson, E. A., Musa, G. J., & Mandell, D. J. (2004). Exposure to trauma and separation anxiety in children after the WTC attack. Applied Developmental Science, 8, 172–183. http://dx.doi.org/10.1207/ s1532480xads0804_1 Howard, A. L., Kimonis, E. R., Muñoz, L. C., & Frick, P. J. (2012). Violence exposure mediates the relation between callous-unemotional traits and offending patterns in adolescents. Journal of Abnormal Child Psychology, 40, 1237–1247. http://dx.doi.org/ 10.1007/s10802-012-9647-2 Jackson, Y., Cushing, C. C., Gabrielli, J., Fleming, K., O’Connor, B. M., & Huffhines, L. (2016). Child maltreatment, trauma, and physical health outcomes: The role of abuse type and placement moves on health conditions and service use for youth in foster care. Journal of Pediatric Psychology, 41, 28–36. http://dx.doi.org/10.1093/jpepsy/jsv066 Jacobsen, L. K., Southwick, S. M., & Kosten, T. R. (2001). Substance use disorders in patients with posttraumatic stress disorder: A review of the literature. American Journal of Psychiatry, 158, 1184–1190. http://dx.doi.org/10.1176/ appi.ajp.158.8.1184 Institute of Medicine. (2001). Neurological, psychiatric, and developmental disorders: Meeting the challenge in the developing world. Washington, DC: National Academies Press. Intergovernmental Panel on Climate Change. (2014). Impacts, adaptations and vulnerability. New York, NY: Cambridge University Press. Kaplow, J. B., & Widom, C. S. (2007). Age of onset of child maltreatment predicts long-term mental health outcomes. Journal of Abnormal Psychology, 116, 176–187. http://dx.doi.org/10.1037/ 0021-843X.116.1.176 Kearney, C. A., Wechsler, A., Kaur, H., & LemosMiller, A. (2010). Posttraumatic stress disorder in maltreated youth: A review of contemporary research and thought. Clinical Child and Family Psychology Review, 13, 46–76. http://dx.doi.org/10.1007/ s10567-009-0061-4 Keiley, M. K., Howe, T. R., Dodge, K. A., Bates, J. E., & Petti, G. S. (2001). The timing of child physical maltreatment: A cross-domain growth analysis of impact on adolescent externalizing and internalizing problems. Development and Psychopathology, 13, 891–912.

Copyright American Psychological Association. Not for further distribution.

Trauma and Child Psychopathology

Kendall, P. C. (Ed.). (2012). Child and adolescent therapy: Cognitive–behavioral procedures (4th ed.). New York, NY: Guilford Press.

New evidence and a meta-analysis. Molecular Psychiatry, 11, 903–913. http://dx.doi.org/10.1038/ sj.mp.4001851

Kerig, P. K., Bennett, D. C., Thompson, M., & Becker, S. P. (2012). “Nothing really matters”: Emotional numbing as a link between trauma exposure and callousness in delinquent youth. Journal of Traumatic Stress, 25, 272–279. http://dx.doi.org/10.1002/ jts.21700

Kimonis, E. R., Fanti, K. A., Isoma, Z., & Donoghue, K. (2013). Maltreatment profiles among incarcerated boys with callous-unemotional traits. Child Maltreatment, 18, 108–121. http://dx.doi.org/ 10.1177/1077559513483002

Kerig, P. K., Vanderzee, K. L., Becker, S. P., & Ward, R. M. (2012). Deconstructing PTSD: Traumatic experiences, posttraumatic symptom clusters, and mental health problems among delinquent youth. Journal of Child and Adolescent Trauma, 5, 129–144. http://dx.doi.org/10.1080/19361521.2012.671796 Kerig, P. K., Ward, R. M., Vanderzee, K. L., & Arnzen Moeddel, M. (2009). Posttraumatic stress as a mediator of the relationship between trauma and mental health problems among juvenile delinquents. Journal of Youth and Adolescence, 38, 1214–1225. http://dx.doi.org/10.1007/s10964-008-9332-5 Kerns, C. E., Elkins, R. M., Carpenter, A. L., Chou, T., Green, J. G., & Comer, J. S. (2014). Caregiver distress, shared traumatic exposure, and child adjustment among area youth following the 2013 Boston Marathon bombing. Journal of Affective Disorders, 167, 50–55. http://dx.doi.org/10.1016/ j.jad.2014.05.040 Khoury, L., Tang, Y. L., Bradley, B., Cubells, J. F., & Ressler, K. J. (2010). Substance use, childhood traumatic experience, and posttraumatic stress disorder in an urban civilian population. Depression and Anxiety, 27, 1077–1086. http://dx.doi.org/ 10.1002/da.20751 Kieling, C., Baker-Henningham, H., Belfer, M., Conti, G., Ertem, I., Omigbodun, O., . . . Rahman, A. (2011). Child and adolescent mental health worldwide: Evidence for action. Lancet, 378, 1515–1525. http:// dx.doi.org/10.1016/S0140-6736(11)60827-1 Kilpatrick, D. G., Ruggiero, K. J., Acierno, R., Saunders, B. E., Resnick, H. S., & Best, C. L. (2003). Violence and risk of PTSD, major depression, substance abuse/ dependence, and comorbidity: Results from the National Survey of Adolescents. Journal of Consulting and Clinical Psychology, 71, 692–700. http://dx.doi.org/ 10.1037/0022-006X.71.4.692 Kim, J., & Cicchetti, D. (2010). Longitudinal pathways linking child maltreatment, emotion regulation, peer relations, and psychopathology. Journal of Child Psychology and Psychiatry, 51, 706–716. http:// dx.doi.org/10.1111/j.1469-7610.2009.02202.x Kim-Cohen, J., Caspi, A., Taylor, A., Williams, B., Newcombe, R., Craig, I. W., & Moffitt, T. E. (2006). MAOA, maltreatment, and gene-environment interaction predicting children’s mental health:

Kimonis, E. R., Frick, P. J., Cauffman, E., Goldweber, A., & Skeem, J. (2012). Primary and secondary variants of juvenile psychopathy differ in emotional processing. Development and Psychopathology, 24, 1091–1103. http://dx.doi.org/10.1017/ S0954579412000557 Kimonis, E. R., Skeem, J. L., Cauffman, E., & Dmitrieva, J. (2011). Are secondary variants of juvenile psychopathy more reactively violent and less psychosocially mature than primary variants? Law and Human Behavior, 35, 381–391. http:// dx.doi.org/10.1007/s10979-010-9243-3 Kim-Spoon, J., Cicchetti, D., & Rogosch, F. A. (2013). A longitudinal study of emotion regulation, emotion lability-negativity, and internalizing symptomatology in maltreated and nonmaltreated children. Child Development, 84, 512–527. http://dx.doi.org/10.1111/ j.1467-8624.2012.01857.x Kotch, J. B., Lewis, T., Hussey, J. M., English, D., Thompson, R., Litrownik, A. J., . . . Dubowitz, H. (2008). Importance of early neglect for childhood aggression. Pediatrics, 121, 725–731. http:// dx.doi.org/10.1542/peds.2006-3622 La Greca, A., Silverman, W. K., Vernberg, E. M., & Prinstein, M. J. (1996). Symptoms of posttraumatic stress in children after Hurricane Andrew: A prospective study. Journal of Consulting and Clinical Psychology, 64, 712–723. http://dx.doi.org/ 10.1037/0022-006X.64.4.712 La Greca, A. M., Comer, J. S., & Lai, B. S. (2016). Trauma and child health: An introduction to the Special Issue. Journal of Pediatric Psychology, 41, 1–4. http:// dx.doi.org/10.1093/jpepsy/jsv087 La Greca, A. M., Lai, B. S., Joormann, J., Auslander, B. B., & Short, M. A. (2013). Children’s risk and resilience following a natural disaster: Genetic vulnerability, posttraumatic stress, and depression. Journal of Affective Disorders, 151, 860–867. http:// dx.doi.org/10.1016/j.jad.2013.07.024 Lansford, J. E., Miller-Johnson, S., Berlin, L. J., Dodge, K. A., Bates, J. E., & Pettit, G. S. (2007). Early physical abuse and later violent delinquency: A prospective longitudinal study. Child Maltreatment, 12, 233–245. http://dx.doi.org/10.1177/1077559507301841 Lee, V., & Hoaken, P. N. S. (2007). Cognition, emotion, and neurobiological development: Mediating the

207

Furr et al.

relation between maltreatment and aggression. Child Maltreatment, 12, 281–298. http://dx.doi.org/ 10.1177/1077559507303778 Lieberman, A. F., & Knorr, K. (2007). The impact of trauma: A developmental framework for infancy and early childhood. Pediatric Annals, 36, 209–215. http://dx.doi.org/10.3928/0090-4481-20070401-10

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Lipschitz, D. S., Rasmusson, A. M., Anyan, W., Gueorguieva, R., Billingslea, E. M., Cromwell, P. F., & Southwick, S. M. (2003). Posttraumatic stress disorder and substance use in inner-city adolescent girls. Journal of Nervous and Mental Disease, 191, 714–721. http://dx.doi.org/10.1097/ 01.nmd.0000095123.68088.da Litrownik, A. J., Newton, R., Hunter, W. M., English, D., & Everson, M. D. (2003). Exposure to family violence in young at-risk children: A longitudinal look at the effects of victimization and witnessed physical and psychological aggression. Journal of Family Violence, 18, 59–73. http://dx.doi.org/ 10.1023/A:1021405515323 Lochman, J. E., Vernberg, E., Powell, N. P., Boxmeyer, C. L., Jarrett, M., McDonald, K., . . . Kassing, F. (2017). Pre–post tornado effects on aggressive children’s psychological and behavioral adjustment through one-year postdisaster. Journal of Clinical Child and Adolescent Psychology, 46, 136–149. http:// dx.doi.org/10.1080/15374416.2016.1228460 Luz, M. P., Mendlowicz, M., Marques-Portella, C., Gleiser, S., Berger, W., Neylan, T. C., . . . Figueira, I. (2011). PTSD criterion A1 events: A literaturebased categorization. Journal of Traumatic Stress, 24, 243–251. http://dx.doi.org/10.1002/jts.20633 Magwaza, A. S., Killian, B. J., Petersen, I., & Pillay, Y. (1993). The effects of chronic violence on preschool children living in South African townships. Child Abuse and Neglect, 17, 795–803. http:// dx.doi.org/10.1016/S0145-2134(08)80010-5 Manly, J. T., Kim, J. E., Rogosch, F. A., & Cicchetti, D. (2001). Dimensions of child maltreatment and children’s adjustment: Contributions of developmental timing and subtype. Development and Psychopathology, 13, 759–782. Marshall, A. D. (2016). Developmental timing of trauma exposure relative to puberty and the nature of psychopathology among adolescent girls. Journal of the American Academy of Child and Adolescent Psychiatry, 55, 25–32.e1. http://dx.doi.org/10.1016/ j.jaac.2015.10.004 Mazza, J. J., & Reynolds, W. M. (1999). Exposure to violence in young inner-city adolescents: Relationships with suicidal ideation, depression, and PTSD symptomatology. Journal of Abnormal Child Psychology, 27, 203–213. http://dx.doi.org/ 10.1023/A:1021900423004 208

McCabe, K. M., Lucchini, S. E., Hough, R. L., Yeh, M., & Hazen, A. (2005). The relation between violence exposure and conduct problems among adolescents: A prospective study. American Journal of Orthopsychiatry, 75, 575–584. http://dx.doi.org/ 10.1037/0002-9432.75.4.575 McLaughlin, K. A. (2016). Future directions in childhood adversity and youth psychopathology. Journal of Clinical Child and Adolescent Psychology, 45, 361–382. http://dx.doi.org/10.1080/15374416.2015. 1110823 McLaughlin, K. A., Koenen, K. C., Hill, E. D., Petukhova, M., Sampson, N. A., Zaslavsky, A. M., & Kessler, R. C. (2013). Trauma exposure and posttraumatic stress disorder in a national sample of adolescents. Journal of the American Academy of Child and Adolescent Psychiatry, 52, 815–830.e14. http:// dx.doi.org/10.1016/j.jaac.2013.05.011 McLaughlin, K. A., & Lambert, H. K. (2017). Child trauma exposure and psychopathology: Mechanisms of risk and resilience. Current Opinion in Psychology, 14, 29–34. http://dx.doi.org/10.1016/ j.copsyc.2016.10.004 McLaughlin, K. A., Peverill, M., Gold, A. L., Alves, S., & Sheridan, M. A. (2015). Child maltreatment and neural systems underlying emotion regulation. Journal of the American Academy of Child and Adolescent Psychiatry, 54, 753–762. http:// dx.doi.org/10.1016/j.jaac.2015.06.010 McLaughlin, K. A., Sheridan, M. A., Gold, A. L., Duys, A., Lambert, H. K., Peverill, M., . . . Pine, D. S. (2016). Maltreatment exposure, brain structure, and fear conditioning in children and adolescents. Neuropsychopharmacology, 41, 1956–1964. http:// dx.doi.org/10.1038/npp.2015.365 McNally, R. J. (2009). Can we fix PTSD in DSM–V? Depression and Anxiety, 26, 597–600. http:// dx.doi.org/10.1002/da.20586 Miller, A. B., Adams, L. M., Esposito-Smythers, C., Thompson, R., & Proctor, L. J. (2014). Parents and friendships: A longitudinal examination of interpersonal mediators of the relationship between child maltreatment and suicidal ideation. Psychiatry Research, 220, 998–1006. http://dx.doi.org/10.1016/ j.psychres.2014.10.009 Milne, L., & Collin-Vézina, D. (2015). Assessment of children and youth in child protective services outof-home care: An overview of trauma measures. Psychology of Violence, 5, 122–132. http:// dx.doi.org/10.1037/a0037865 Molnar, B. E., Buka, S. L., & Kessler, R. C. (2001). Child sexual abuse and subsequent psychopathology: Results from the National Comorbidity Survey. American Journal of Public Health, 91, 753–760. http://dx.doi.org/10.2105/AJPH.91.5.753

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Mullett-Hume, E., Anshel, D., Guevara, V., & Cloitre, M. (2008). Cumulative trauma and posttraumatic stress disorder among children exposed to the 9/11 World Trade Center attack. American Journal of Orthopsychiatry, 78, 103–108. http://dx.doi.org/ 10.1037/0002-9432.78.1.103

Pynoos, R. S., Goenjian, A., Tashjian, M., Karakashian, M., Manjikian, R., Manoukian, G., . . . Fairbanks, L. A. (1993). Posttraumatic stress reactions in children after the 1988 Armenian earthquake. British Journal of Psychiatry, 163, 239–247. http:// dx.doi.org/10.1192/bjp.163.2.239

Najavits, L. M. (2002). Seeking safety: A treatment manual for PTSD and substance abuse. New York, NY: Guilford Press.

Pynoos, R. S., Steinberg, A. M., & Piacentini, J. C. (1999). A developmental psychopathology model of childhood traumatic stress and intersection with anxiety disorders. Biological Psychiatry, 46, 1542–1554. http:// dx.doi.org/10.1016/S0006-3223(99)00262-0

Najavits, L. M., Gallop, R. J., & Weiss, R. D. (2006). Seeking safety therapy for adolescent girls with PTSD and substance use disorder: A randomized controlled trial. Journal of Behavioral Health Services and Research, 33, 453–463. http://dx.doi.org/10.1007/ s11414-006-9034-2 Nanni, V., Uher, R., & Danese, A. (2012). Childhood maltreatment predicts unfavorable course of illness and treatment outcome in depression: A meta-analysis. American Journal of Psychiatry, 169, 141–151. http://dx.doi.org/10.1176/appi. ajp.2011.11020335 Neria, Y., Bravova, M., & Halper, J. (2010). Trauma and PTSD among civilians in the middle east. PTSD Research Quarterly, 21, 1–8. Neuner, F., Schauer, E., Catani, C., Ruf, M., & Elbert, T. (2006). Post-tsunami stress: A study of posttraumatic stress disorder in children living in three severely affected regions in Sri Lanka. Journal of Traumatic Stress, 19, 339–347. http://dx.doi.org/10.1002/ jts.20121 Patel, V., Flisher, A. J., Hetrick, S., & McGorry, P. (2007). Mental health of young people: A global publichealth challenge. Lancet, 369, 1302–1313. http:// dx.doi.org/10.1016/S0140-6736(07)60368-7 Peek, L., & Stough, L. M. (2010). Children with disabilities in the context of disaster: A social vulnerability perspective. Child Development, 81, 1260–1270. http://dx.doi.org/10.1111/ j.1467-8624.2010.01466.x Pollak, S. D., Cicchetti, D., Hornung, K., & Reed, A. (2000). Recognizing emotion in faces: Developmental effects of child abuse and neglect. Developmental Psychology, 36, 679–688. http:// dx.doi.org/10.1037/0012-1649.36.5.679 Pollak, S. D., & Sinha, P. (2002). Effects of early experience on children’s recognition of facial displays of emotion. Developmental Psychopathology, 38, 784–791. http://dx.doi.org/10.1037/ 0012-1649.38.5.784 Proctor, L. J., Aarons, G. A., Dubowitz, H., English, D. J., Lewis, T., Thompson, R., . . . Roesch, S. C. (2012). Trajectories of maltreatment re-reports from ages 4 to 12: Evidence for persistent risk after early exposure. Child Maltreatment, 17, 207–217. http:// dx.doi.org/10.1177/1077559512448472

Rowe, C. L., La Greca, A. M., & Alexandersson, A. (2010). Family and individual factors associated with substance involvement and PTS symptoms among adolescents in greater New Orleans after Hurricane Katrina. Journal of Consulting and Clinical Psychology, 78, 806–817. http://dx.doi.org/10.1037/a0020808 Ruchkin, V., Henrich, C. C., Jones, S. M., Vermeiren, R., & Schwab-Stone, M. (2007). Violence exposure and psychopathology in urban youth: The mediating role of posttraumatic stress. Journal of Abnormal Child Psychology, 35, 578–593. http://dx.doi.org/ 10.1007/s10802-007-9114-7 Salmon, K., & Bryant, R. A. (2002). Posttraumatic stress disorder in children. The influence of developmental factors. Clinical Psychology Review, 22, 163–188. http://dx.doi.org/10.1016/S0272-7358(01)00086-1 Scheeringa, M. S., Zeanah, C. H., Myers, L., & Putnam, F. W. (2003). New findings on alternative criteria for PTSD in preschool children. Journal of the American Academy of Child and Adolescent Psychiatry, 42, 561–570. http://dx.doi.org/10.1097/01. CHI.0000046822.95464.14 Scott, K. M., McLaughlin, K. A., Smith, D. A. R., & Ellis, P. M. (2012). Childhood maltreatment and DSM–IV adult mental disorders: Comparison of prospective and retrospective findings. British Journal of Psychiatry, 200, 469–475. http://dx.doi.org/10.1192/ bjp.bp.111.103267 Shonk, S. M., & Cicchetti, D. (2001). Maltreatment, competency deficits, and risk for academic and behavioral maladjustment. Developmental Psychology, 37, 3–17. http://dx.doi.org/10.1037/0012-1649.37.1.3 Silverman, W. K., Ortiz, C. D., Viswesvaran, C., Burns, B. J., Kolko, D. J., Putnam, F. W., & AmayaJackson, L. (2008). Evidence-based psychosocial treatments for children and adolescents exposed to traumatic events. Journal of Clinical Child and Adolescent Psychology, 37, 156–183. http://dx.doi. org/10.1080/15374410701818293 Simpson, T. L., & Miller, W. R. (2002). Concomitance between childhood sexual and physical abuse and substance use problems. A review. Clinical Psychology Review, 22, 27–77. http://dx.doi.org/ 10.1016/S0272-7358(00)00088-X 209

Furr et al.

Stein, B. D., Jaycox, L. H., Kataoka, S. H., Wong, M., Tu, W., Elliott, M. N., & Fink, A. (2003). A mental health intervention for schoolchildren exposed to violence: A randomized controlled trial. JAMA: Journal of the American Medical Association, 290, 603–611. http://dx.doi.org/10.1001/jama.290.5.603

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Suliman, S., Mkabile, S. G., Fincham, D. S., Ahmed, R., Stein, D. J., & Seedat, S. (2009). Cumulative effect of multiple trauma on symptoms of posttraumatic stress disorder, anxiety, and depression in adolescents. Comprehensive Psychiatry, 50, 121–127. http:// dx.doi.org/10.1016/j.comppsych.2008.06.006 Swan, A., & Kendall, P. C. (2016). Fear and missing out: Youth anxiety and functional outcomes. Clinical Psychology: Science and Practice, 23, 417–435.

van der Kolk, B. A., Pynoos, R. S., Cicchetti, D., Cloitre, M., D’Andrea, W., Ford, J. D., . . . Spinazzola, J. (2009). Proposal to include a developmental trauma disorder diagnosis for children and adolescents in DSM–V. Unpublished manuscript. van der Kolk, B. A., Roth, S., Pelcovitz, D., Sunday, S., & Spinazzola, J. (2005). Disorders of extreme stress: The empirical foundation of a complex adaptation to trauma. Journal of Traumatic Stress, 18, 389–399. http://dx.doi.org/10.1002/jts.20047 Verduin, T. L., & Kendall, P. C. (2008). Peer perceptions and liking of children with anxiety disorders. Journal of Abnormal Child Psychology, 36, 459–469. http:// dx.doi.org/10.1007/s10802-007-9192-6

Swenson, C. C., Saylor, C. F., Powell, M. P., Stokes, S. J., Foster, K. Y., & Belter, R. W. (1996). Impact of a natural disaster on preschool children: Adjustment 14 months after a hurricane. American Journal of Orthopsychiatry, 66, 122–130. http://dx.doi.org/ 10.1037/h0080162

Villodas, M. T., Cromer, K. D., Moses, J. O., Litrownik, A. J., Newton, R. R., & Davis, I. P. (2016). Unstable child welfare permanent placements and early adolescent physical and mental health: The roles of adverse childhood experiences and posttraumatic stress. Child Abuse and Neglect, 62, 76–88. http:// dx.doi.org/10.1016/j.chiabu.2016.10.014

Tang, W., Zhao, J., Lu, Y., Yan, T., Wang, L., Zhang, J., & Xu, J. (2017). Mental health problems among children and adolescents experiencing two major earthquakes in remote mountainous regions: A longitudinal study. Comprehensive Psychiatry, 72, 66–73. http://dx.doi.org/10.1016/ j.comppsych.2016.09.004

Villodas, M. T., Litrownik, A. J., Newton, R. R., & Davis, I. P. (2016). Long-term placement trajectories of children who were maltreated and entered the child welfare system at an early age: Consequences for physical and behavioral well-being. Journal of Pediatric Psychology, 41, 46–54. http://dx.doi.org/ 10.1093/jpepsy/jsv031

Teague, C. M. (2013). Developmental Trauma Disorder: A Provisional Diagnosis. Journal of Aggression, Maltreatment, and Trauma, 22, 611–625. http:// dx.doi.org/10.1080/10926771.2013.804470

Villodas, M. T., Litrownik, A. J., Thompson, R., Jones, D., Roesch, S. C., Hussey, J. M., . . . Dubowitz, H. (2015). Developmental transitions in presentations of externalizing problems among boys and girls at risk for child maltreatment. Development and Psychopathology, 27, 205–219. http:// dx.doi.org/10.1017/S0954579414000728

Thornberry, T. P., Henry, K. L., Ireland, T. O., & Smith, C. A. (2010). The causal impact of childhood-limited maltreatment and adolescent maltreatment on early adult adjustment. Journal of Adolescent Health, 46, 359–365. http://dx.doi.org/10.1016/ j.jadohealth.2009.09.011 Thornberry, T. P., Ireland, T. O., & Smith, C. A. (2001). The importance of timing: The varying impact of childhood and adolescent maltreatment on multiple problem outcomes. Development and Psychopathology, 13, 957–979. Tol, W. A., Song, S., & Jordans, M. J. D. (2013). Annual research review: Resilience and mental health in children and adolescents living in areas of armed conflict—a systematic review of findings in low- and middle-income countries. Journal of Child Psychology and Psychiatry, 54, 445–460. http://dx.doi.org/ 10.1111/jcpp.12053 Trickey, D., Siddaway, A. P., Meiser-Stedman, R., Serpell, L., & Field, A. P. (2012). A meta-analysis of risk factors for posttraumatic stress disorder in children and adolescents. Clinical Psychology Review, 32, 122–138. http://dx.doi.org/10.1016/ j.cpr.2011.12.001 210

Villodas, M. T., Litrownik, A. J., Thompson, R., Roesch, S. C., English, D. J., Dubowitz, H., . . . Runyan, D. K. (2012). Changes in youth’s experiences of child maltreatment across developmental periods in the LONGSCAN consortium. Psychology of Violence, 2, 325–338. http://dx.doi.org/10.1037/a0029829 Walker, S. P., Wachs, T. D., Gardner, J. M., Lozoff, B., Wasserman, G. A., Pollitt, E., & Carter, J. A. (2007). Child development: Risk factors for adverse outcomes in developing countries. Lancet, 369, 145–157. http://dx.doi.org/10.1016/ S0140-6736(07)60076-2 Weathers, F. W., & Keane, T. M. (2007). The Criterion A problem revisited: Controversies and challenges in defining and measuring psychological trauma. Journal of Traumatic Stress, 20, 107–121. http:// dx.doi.org/10.1002/jts.20210 Weems, C. F., & Overstreet, S. (2008). Child and adolescent mental health research in the context of Hurricane Katrina: An ecological needs-based perspective and introduction to the special section.

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Copyright American Psychological Association. Not for further distribution.

Journal of Clinical Child and Adolescent Psychology, 37, 487–494. http://dx.doi.org/ 10.1080/15374410802148251

adolescents: Findings from a pilot study in Sri Lanka. Social Science and Medicine, 64, 713–723. http:// dx.doi.org/10.1016/j.socscimed.2006.09.031

Weems, C. F., Watts, S. E., Marsee, M. A., Taylor, L. K., Costa, N. M., Cannon, M. F., . . . Pina, A. A. (2007). The psychosocial impact of Hurricane Katrina: Contextual differences in psychological symptoms, social support, and discrimination. Behaviour Research and Therapy, 45, 2295–2306. http:// dx.doi.org/10.1016/j.brat.2007.04.013

Wu, P., Goodwin, R. D., Fuller, C., Liu, X., Comer, J. S., Cohen, P., & Hoven, C. W. (2010). The relationship between anxiety disorders and substance use among adolescents in the community: Specificity and gender differences. Journal of Youth and Adolescence, 39, 177–188. http://dx.doi.org/ 10.1007/s10964-008-9385-5

Weiner, C. L., Meredith Elkins, R., Pincus, D., & Comer, J. (2015). Anxiety sensitivity and sleep-related problems in anxious youth. Journal of Anxiety Disorders, 32, 66–72. http://dx.doi.org/10.1016/ j.janxdis.2015.03.009

Yates, T. M., Dodds, M. F., Sroufe, L. A., & Egeland, B. (2003). Exposure to partner violence and child behavior problems: A prospective study controlling for child physical abuse and neglect, child cognitive ability, socioeconomic status, and life stress. Development and Psychopathology, 15, 199–218. http://dx.doi.org/10.1017/ S0954579403000117

Weissbecker, I., Sephton, S., Martin, M., & Simpson, D. (2008). Psychological and physiological correlates of stress in children exposed to disaster: Review of current research and recommendations for intervention. Children, Youth and Environments, 18, 30–70. Wickrama, K. A. S., & Kaspar, V. (2007). Family context of mental health risk in Tsunami-exposed

Zielinski, D. S., & Bradshaw, C. P. (2006). Ecological influences on the sequelae of child maltreatment: A review of the literature. Child Maltreatment, 11, 49–62. http://dx.doi.org/10.1177/1077559505283591

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Chapter 11

Anxiety Disorders Among Children and Adolescents

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Philip C. Kendall, Anna J. Swan, Matthew M. Carper, and Alexandra L. Hoff

Anxiety disorders are prevalent in children and adolescents (Costello, Egger, & Angold, 2005), and they are associated with concurrent and future functional impairment across domains (for a review, see Swan & Kendall, 2016). Studies support that children and adolescents with anxiety experience increased interpersonal and peer difficulties compared with their nonanxious peers (e.g., Settipani & Kendall, 2013; Verduin & Kendall, 2008). Children and adolescents with anxiety often experience heightened anxiety at school, resulting in difficulty concentrating and decreased academic achievement (e.g., Mychailyszyn, Mendez, & Kendall, 2010; Van Ameringen, Mancini, & Farvolden, 2003). Additionally, anxiety in children and adolescents is associated with increased family dysfunction and caregiver strain (e.g., Essau, Lewinsohn, Olaya, & Seeley, 2014; Wood, McLeod, Sigman, Hwang, & Chu, 2003). Beyond causing interference in daily functioning, anxiety disorders are linked to long-term negative outcomes. Anxiety disorders are chronic and unlikely to remit without treatment (Pine, Cohen, Gurley, Brook, & Ma, 1998): children and adolescents with anxiety evolve into adults with anxiety. When left untreated, anxiety confers risk for the later development of commonly comorbid concerns like depression (Angold, Costello, & Erkanli, 1999), suicidal ideation, and substance-use problems (C. L. Benjamin, Harrison, Settipani, Brodman, & Kendall, 2013; Kendall, Safford, FlannerySchroeder, & Webb, 2004). Similarly, impairment associated with child and adolescent anxiety endures as youth enter adulthood (see Volume 1,

Chapter 18, this handbook). The presence of an anxiety disorder in children and adolescents has been linked to lower educational attainment (Wittchen, Nelson, & Lachner, 1998; Woodward & Fergusson, 2001), decreased life satisfaction (Essau et al., 2014), and delayed achievement of developmental milestones in adulthood (e.g., independent living; Last, Hansen, & Franco, 1997). Fortunately, psychological therapies have been developed to treat anxiety in children and adolescents: Cognitive–behavioral therapy (CBT) is considered a well-established treatment for anxiety disorders in children and adolescents (Hollon & Beck, 2013) with approximately 60% of CBT-treated children responding positively to treatment (Kendall et al., 2008; Walkup et al., 2008). Despite favorable results, approximately 30% to 40% of children continue to experience interfering anxiety symptoms following a full course of CBT (e.g., Ginsburg et al., 2014). Understanding factors that contribute to the onset, maintenance, and exacerbation of anxiety symptoms throughout development is essential to identify targets for intervention and to better tailor existing treatments. This chapter focuses on three of the most common anxiety disorders in children and adolescents: generalized anxiety disorder (GAD), social anxiety disorder (SAD, formerly social phobia), and separation anxiety disorder (SepAD). These disorders have high overlap in symptomatology (Kendall et al., 2010), and are often comorbid with each other and with depression (Cummings, Caporino, & Kendall, 2014). We use a

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213

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developmental psychopathology framework to provide (a) an introduction to how anxiety expresses in children and adolescents; (b) a review of familial, behavioral, cognitive, and social factors that contribute to the development and maintenance of anxiety disorders in children and adolescents; (c) a description of psychometrically sound assessment methods and tools; and (d) an overview of empirically supported treatments for anxiety disorders in children and adolescents. Common Anxiety Disorders and Developmental Considerations in Children and Adolescents For most children and adolescents, fear and anxiety are normally occurring emotional reactions that coordinate a set of adaptive physiological, behavioral, and cognitive responses to address a threat or perceived threat. A child may experience mild anticipatory anxiety before a test that is associated with task-orienting thoughts (e.g., “I need to study now so that I can get a good night’s sleep”) and helpful behaviors (reviewing test-related materials). However, children and adolescents with anxiety disorders experience excessive anxiety that interferes with adaptive functioning and causes distress. For example, an adolescent with severe test-related worries may experience task-interfering thoughts (e.g., “I am going to fail. I can’t do this”) that inspire maladaptive behaviors (e.g., procrastination to avoid negative emotions triggered by studying; even overpreparation at the expense of other responsibilities or activities). Clinically elevated anxiety indicative of a disorder can be differentiated from everyday anxiety by (a) the intensity of the anxiety (elevated compared with peers), (b) the endurance of the anxiety (typically more than 6 months), and (c) anxiety-related interference in individual and/ or family functioning (Kendall, Crawford, Kagan, Furr, & Podell, in press). Symptoms of SepAD, SAD, and GAD as delineated by the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (American Psychiatric Association, 2013)—the classification system for mental disorders used by researchers and practitioners in the United States—are reviewed next. 214

Separation Anxiety Disorder SepAD is characterized by an age-inappropriate fear of being separated from a caregiver or group of caregivers. In anticipation of separating from caregivers or at the moment of separation, children and adolescents with SepAD experience extreme distress. They may cling to their parents, cry, tantrum, and/or experience physical symptoms (e.g., headaches/nausea). These children and adolescents often endorse worry about harm befalling themselves or their caregivers when separated. As a result, pathological behaviors reflect a desire to minimize time spent away from caregivers. Children and adolescents may avoid going over to friends’ houses, or sleeping independently at night. Parents often describe these children as “clingy,” following them from room to room in the home. Some children and adolescents report dreams/nightmares with a separation anxiety theme. Additionally, they may experience extreme distress separating from parents in the morning and throughout the school-day, as evidenced by repeated attempts to call home, to leave school early, or outright refusal to attend school. To qualify for a diagnosis, symptoms must be present for at least 4 weeks and cause meaningful impairment in individual and/ or family functioning (APA, 2013).

Developmental Considerations Some heightened separation anxiety is considered normative during early development (e.g., fear of strangers around 1 year old; Rheingold & Eckerman, 1974). Children and adolescents with SepAD experience anxiety that is elevated compared with their peers, and onset can occur as early as preschool age. Separation anxiety is more common in children than adolescents with 12-month prevalence rates estimated to be 4% for children and 1.6% for adolescents (American Psychiatric Association, 2013). Additionally, symptoms may manifest differently across development. Younger children may have difficulty verbalizing the content of their fears; behavioral and physiological symptoms are the primary indicators of separation anxiety. Adolescents are often better able to identify their anxious cognition. SepAD is the most common anxiety disorder in children younger than 12 years old (American Psychiatric Association, 2013).

Anxiety Disorders Among Children and Adolescents

If unsuccessfully treated, SepAD is associated with anxiety and related disorders later in life (Aschenbrand et al., 2003).

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Social Anxiety Disorder Children and adolescents with SAD have an excessive fear of negative social evaluation. As a result, feared situations are almost always experienced as anxiety-provoking, resulting in avoidance of these situations or endurance with distress. Children with SAD commonly fear performance situations (e.g., giving a class presentation; answering questions in class), or other social situations (e.g., joining a conversation; meeting new people; attending social events). Avoidance can be overt (e.g., refusal to participate) or subtle (e.g., minimal eye contact and speech in social interactions). Experiencing anxiety in some social situations is developmentally normative; however, for children and adolescents with SAD, heightened anxiety does not abate over time. To meet diagnostic criteria for SAD, symptoms must be present for at least 6 months and cause meaningful interference in academic, family, or social functioning. Additionally, social fears must not be circumscribed to interactions with adults; rather, social anxiety must also occur in peer settings to qualify for a diagnosis in childhood (American Psychiatric Association, 2013).

Developmental Considerations During adolescence increased emphasis on social evaluation is normative. As children orient away from family toward peers, peer social interactions become increasingly important, and normally developing children report an increased sensitivity to negative peer evaluation (Miers, Blöte, de Rooij, Bokhorst, & Westenberg, 2013). For some children, however, the increased importance of social interactions with peers is experienced as highly anxiety-provoking, resulting in the maintenance and intensification of social fears and avoidance in adolescence. Prevalence rates suggest that SAD is common, affecting 5% to 15% of children and adolescents (Kashdan & Herbert, 2001) with an average age of onset between 10 and 14 years old (Kovacs & Devlin, 1998). Given that children place increased importance on peer relationships during

adolescence, and SAD is characterized by excessive fear of embarrassment or humiliation in social situations, it is unsurprising that adolescence represents a key period for the onset and intensification of social anxiety symptoms (Miers et al., 2013).

Generalized Anxiety Disorder The cardinal feature of GAD is excessive, uncontrollable worry about a variety of events and activities. Many children and adolescents with GAD experience “flavor of the day” worries that change as new stressors arise. Common GAD concerns include worry about school (e.g., grades, tests, homework), world affairs (e.g., terrorism, war, natural disasters), harm befalling themselves or loved ones, family affairs (e.g., finances), and the future (e.g., getting into college, death or dying). Children and adolescents with GAD may also be highly perfectionistic, worrying excessively about making a mistake, or failing to meet high expectations. To mitigate distress associated with their worries, children and adolescents with GAD often seek reassurance from parents or other authority figures. They may also avoid situations that trigger their anxious feelings. To meet diagnostic criteria, children must experience at least one physical symptom of anxiety (e.g., muscle tension, difficulty concentrating or sleeping, restlessness). These worries and associated physical symptoms must persist for at least 6 months and cause meaningful interference in academic, family, and/or social functioning (American Psychiatric Association, 2013).

Developmental Considerations The average age of onset for GAD is later than other anxiety disorders (American Psychiatric Association, 2013), and prevalence rates in children and adolescents are estimated to range from 3% to 10% (C. L. Benjamin, Beidas, Comer, Puliafico, & Kendall, 2011). When race is considered, a higher percentage of White versus non-White children and adolescents received a principal diagnosis of GAD only (Kendall et al., 2010). As children develop, the content of their worries may change as well. Younger children are more likely to worry about school and performances, whereas older adults are more likely to report health-related worries (Lindesay et al., 2006). 215

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Nature of Anxiety Disorders in Children and Adolescents

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Anxiety disorders are highly common among children and adolescents, with 12-month prevalence estimates of meeting diagnostic criteria for any anxiety disorder ranging from 8% to 21% of children and adolescents (Costello et al., 2005). Anxiety disorders are also highly comorbid, with other anxiety disorders and with nonanxiety disorders (Kendall et al., 2010), particularly depression (Axelson & Birmaher, 2001; Essau, 2008).

Review of Epidemiological Data Comorbidity with other anxiety disorders may be explained by overlap in diagnostic criteria or by developmental changes that occur throughout childhood and adolescence: A child’s anxious temperament may manifest itself as different disorders depending on developmental stage (i.e., SepAD as a younger child and GAD or SAD as an adolescent). Multiple pathways have been suggested to explain the high degree of comorbidity between anxiety and depressive disorders (for review, see Cummings et al., 2014), but current work in this area is theoretical and additional work is needed to explicitly test the proposed pathways.

Etiology of Anxiety Disorders in Children and Adolescents There is no single cause for anxiety disorders among children and adolescents. Rather, multiple biological and environmental factors interact with each other and lead to the development of an anxiety disorder. Biological and temperamental factors.  The findings from twin and adoption studies suggest that anxiety disorders often run in families, indicating that part of the development of anxiety may be familial (with possible genetic factors; for review, see Gregory & Eley, 2007). However, research seeking to identify a specific “anxiety gene” has not yet been successful. Perhaps multiple genes play a small role in the etiology of child anxiety rather than a single gene acting as the primary causal factor. Of the specific genes that have been examined, the serotonin system has been studied most frequently, particularly the serotonin transporter gene 5-HTT, which 216

has also been implicated in other internalizing disorders (e.g., Caspi et al., 2003). Given the efficacy of selective-serotonin reuptake inhibitors in treating children and adolescents with anxiety (Walkup et al., 2008), it is likely that the serotonin system contributes to anxiety among this population. However, there has also been some work examining dopamine genes, catechol-O-methyltransferase, and corticotrophin-releasing hormone/corticotrophinreleasing factor genes (see Gregory & Eley, 2007). One factor commonly implicated in anxiety disorders in children and adolescents is reward system activity. Although rewards are behavioral factors, the biological reward system has also been implicated. Specifically, children and adolescents with anxiety are less motivated by reward and more risk averse than their nonanxious peers (e.g., Lorian & Grisham, 2010; Richards et al., 2015). More recent work has demonstrated that children and adolescents with no anxiety demonstrate greater activation in the striatum (an important part of the reward system) compared with their more anxious peers (Guyer et al., 2006). The lower striatal activity seen in children and adolescents with anxiety has been found to be a result of lower intrinsic functional connectivity within the striatum, which the authors suggested “may contribute to the perturbations in the processing of motivational, emotional, interoceptive, and motor information seen in pediatric anxiety” (Dorfman, Benson, Farber, Pine, & Ernst, 2016, p. 159). Temperament has been found to play a role in the etiology of anxiety disorders in youth. One finding regarding temperament is that children whose temperament is characterized by high levels of emotionality and neuroticism, and low levels of effortful control are at increased risk for the development of psychopathology in childhood and adolescence (Calkins & Fox, 2002; Lonigan & Phillips, 2001). These temperamental factors likely interact with each other and with environmental variables (e.g., parenting behavior) to lead to the development of psychopathological symptoms in many children (for review, see Muris & Ollendick, 2005). Other temperamental factors have been found to be linked to anxiety disorders specifically. Behavioral inhibition has been examined as a prospective

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Anxiety Disorders Among Children and Adolescents

risk factor for the development of anxiety in childhood and adolescence, particularly in the development of social anxiety. Behavioral inhibition refers to a persistent pattern of reticence, fearfulness, and avoidance in novel situations (J. Kagan, Reznick, & Snidman, 1988). Approximately 10% to 15% of children are behaviorally inhibited, and this trait has been shown to be stable from the preschool years to middle childhood (Hirshfeld-Becker et al., 2008; J. Kagan et al., 1988). Prospective longitudinal studies of behavioral inhibition report behavioral inhibition to be associated with increased risk for social anxiety in childhood and early adolescence (for review, see Hirshfeld-Becker et al., 2008). However, its contribution to the development of other anxiety disorders is less well understood. Parenting factors related to child anxiety.  Although the potential hereditary factors involved in the etiology of anxiety in youth have been acknowledged, parents also exhibit certain behaviors and characteristics that contribute to the development and maintenance of anxiety in their children (see Drake & Ginsburg, 2012; Wei & Kendall, 2014). Perhaps the most direct way parents may contribute to anxiety in children and adolescents is by modeling anxious behavior and thought (Fisak & Grills-Taquechel, 2007). When children observe their parents displaying outward signs of anxiety (e.g, hyperventilating), verbally expressing their anxiety, or avoiding anxiety-provoking situations, they learn to respond to similar situations with fear and avoidance. This process is consistent with social learning theory (Bandura, 1986), and research has supported the notion that children learn anxious behavior by observing their parents. Higher parent-reported anxious modeling has been correlated with higher child-reported fear levels (Muris, Steerneman, Merckelbach, & Meesters, 1996), and parental social withdrawal has been associated with increased social anxiety in children (Drake & Ginsburg, 2012; Fisak & Grills-Taquechel, 2007). Parents may also model anxiety via communication with their children about harm and safety in a way that promotes an exaggerated sense of danger. For example, the frequency of parents’ messages

about harmful situations may instill a view of the world as an overall dangerous place (Fisak & Grills-Taquechel, 2007). Additionally, the degree of parents’ tendency to interpret ambiguous situations as threatening in their communication with their children has been shown to increase children’s likelihood to avoid these situations (Barrett, Rapee, Dadds, & Ryan, 1996). Finally, parents may reinforce their children’s avoidance of anxiety-provoking situations in a variety of ways, including avoiding encountering anxious stimuli (e.g., staying at a playdate so a child does not experience separation anxiety), accommodating children’s desire to avoid anxiety-provoking situations (e.g., allowing a child to stay home from school on the day of a test), removing children from a situation when they becomes anxious (e.g., leaving the park because a child became upset about a bee flying nearby), or by providing excessive reassurance. Although parents may allow or encourage avoidance of anxiety-provoking stimuli with the intention of reducing children’s distress, such reinforcing of avoidance stops children from mastering appropriate coping skills and maintains anxiety in the long term. Indeed, recent research indicates that parental accommodation of children’s anxiety is associated with increased anxiety severity (e.g., Lebowitz, Scharfstein, & Jones, 2014). Parenting behaviors are also associated with anxiety in children and adolescents. Various forms of parental overcontrol have shown quite a strong association with anxiety (Drake & Ginsburg, 2012; McLeod, Wood, & Weisz, 2007; Wei & Kendall, 2014). Parental overcontrol includes behaviors like granting less autonomy to youth, being overinvolved in their lives and actions, and acting overprotective. Parents who engage in higher levels of control with their children may be more restrictive regarding what youth can or cannot do, may ask them a lot of questions or seek out knowledge of every detail of their children’s lives, and may frequently make choices or decisions for them, even dictating how their children should feel. It is theorized that parental overcontrol prevents children from developing a sense of independence and autonomy, creating a lack of control that leads to anxiety (Chorpita & Barlow, 1998; Wei & Kendall, 2014). Meta-analyses 217

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investigating the parent factors associated with child anxiety have suggested that parental overcontrol, particularly low autonomy-granting and overinvolvement, has the most robust association with anxiety in children and adolescents (McLeod et al., 2007; van der Bruggen, Stams, & Bögels, 2008). High levels of criticism and rejection from parents have also been associated with higher levels of anxiety in children and adolescents (Hudson, Dodd, & Bovopoulos, 2011; Lieb et al., 2000), presumably because of a lower sense of self-worth and selfefficacy stemming from frequent disapproval, hostility, and conflict (Drake & Ginsburg, 2012; Ginsburg & Schlossberg, 2002). Although the correlation between anxiety in parents and anxiety in children and adolescents has been noted, other parental psychopathology has also been associated with increased risk for anxiety in this population. For example, depression in parents has been associated with an increased likelihood of various anxiety disorders in children (Biederman et al., 2001; Lieb, Isensee, Höfler, Pfister, & Wittchen, 2002; Weissman et al., 2006), as well as maternal bipolar disorder (Hammen, Burge, Burney, & Adrian, 1990). It should be noted that the complex nature of the relationship between parental factors and anxiety in children is not currently known. Although there is some evidence that parents who are themselves anxious are more likely to exhibit behaviors such as anxious modeling, overprotectiveness, and overcontrol, which in turn may lead to anxiety in children, there is also evidence that anxiety in children elicits certain parental behaviors that may contribute to the maintenance of anxiety (Drake & Ginsburg, 2012; Hudson, Doyle, & Gar, 2009).

Behavioral Signs of Anxiety Perhaps the hallmark behavioral sign of anxiety across the lifespan is avoidance of anxiety-provoking stimuli (Albano, Chorpita, & Barlow, 2003; Barlow, 1988; Beesdo, Knappe, & Pine, 2009; Chorpita & Barlow, 1998). Avoidance can manifest as either experiential avoidance (i.e., avoidance of specific activities or situations) or emotional avoidance (i.e., avoidance of negative emotions). That said, experiential avoidance has been most frequently linked 218

with the anxiety disorders (e.g., Kashdan et al., 2013). Children and adolescents with SepAD may avoid being alone or in other situations without their parents. Children and adolescents with SAD may avoid a variety of social situations, including school, friends’ birthday parties, extra-curricular activities, among others. Children and adolescents with GAD may avoid any situation that could be an object of their worry, including taking tests, doing homework, and watching the news. Parents of children and adolescents with anxiety may model and/ or accommodate avoidance behaviors, which can be a factor that serves to maintain unwanted levels of anxiety (Ginsburg, Siqueland, Masia-Warner, & Hedtke, 2004; E. R. Kagan, Peterman, Carper, & Kendall, 2016). CBT treatments of anxiety specifically target avoidance behaviors through the use of exposure tasks, which provide children and adolescents with an opportunity to confront feared stimuli rather than avoiding them. It is frequently the case that avoidance of social situations is simply viewed as shyness by parents, teachers, or peers. As with any behavior, there is a continuum of normalcy, and pathology only arises toward the extreme end of the spectrum. However, even shyness has been associated with a variety of negative sequelae. For example, longitudinal research has found that shy children at age 5 years are more likely to be shy in later childhood and adolescence, and to be less likely to marry, have children, and establish a stable career (Caspi, Elder, & Bem, 1988). Even subclinical anxiety symptoms can lead to negative functional outcomes when avoidance is involved.

Cognitive Symptoms of Anxiety Children and adolescents with anxiety present with a variety of cognitive symptoms. For example, the internal dialogue of children and adolescents with anxiety tends to revolve around a variety of potential catastrophes. Indeed, past research has found that the content of self-talk of a child with anxiety is more negative than that of nonanxious child (Sood & Kendall, 2007), and that children and adolescents with anxiety exhibit a higher ratio of negative to positive self-statements compared with their nonanxious peers (Treadwell & Kendall, 1996).

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Anxiety Disorders Among Children and Adolescents

This negative self-talk typically involves threatrelated future thinking and a lack of perceived control (Barrett et al., 1996). For example, children and adolescents with SepAD may think that their parents will be harmed when they are not together. Children and adolescents with SAD may think that others will judge them for saying something stupid, and that participation in any social interaction will result in embarrassment. Children and adolescents with GAD may worry about failing a test even though they typically earn A’s. It is easy for one to see how these negative cognitions can lead to avoidant behavior. CBT treatment aims to change maladaptive cognitions and replace them with more adaptive ways of thinking. However, negative self-talk is not the only cognitive manifestation of anxiety in children and adolescents. In addition to overestimating the likelihood of catastrophes, children and adolescents with anxiety exhibit low levels of perceived control over threatrelated stimuli (Barlow, 1988, 2002). Research has found that perceived control over anxiety-related events is negatively correlated with self-reported anxiety: Higher perceived control is associated with lower levels of self-reported anxiety (Weems, Silverman, Rapee, & Pina, 2003). Exposure tasks aim to increase children’s perceived control over anxietyrelated events by encouraging approach rather than avoidance behaviors and allowing youth to make new cognitive appraisals of anxiety-provoking events (Craske et al., 2008; Foa & Kozak, 1986). Exposure tasks are theorized to increase coping skills among children and adolescents with anxiety (Kendall et al., 2005), and children’s belief in their ability to manage anxiety-provoking situations is termed coping efficacy (Kendall et al., 2016). As would be expected, decreased coping efficacy has been associated with higher levels of anxiety symptoms (Kendall et al., 2016).

their nonanxious peers. Anxiety in children and adolescents has also been associated with decreased social competency, as rated by teachers, parents, and children themselves (Chansky & Kendall, 1997; Strauss, Lease, Kazdin, Dulcan, & Last, 1989). Studies support a reciprocal relationship between interpersonal difficulties and the development of youth anxiety disorders: Elevated anxiety symptoms predicted decreased peer acceptance in a longitudinal study of youth followed from elementary to middle school (Grover, Ginsburg, & Ialongo, 2007); conversely, frequent peer victimization was found to predict increased internalizing symptoms 1 year later in a community sample of adolescents (Stapinski, Araya, Heron, Montgomery, & Stallard, 2015). Peer problems may contribute to the onset and exacerbation of anxiety symptoms, whereas existing anxiety symptoms simultaneously increase social difficulties. SAD is most robustly associated with elevated interpersonal difficulties. Studies suggest that children and adolescents with SAD, but not other anxiety disorders, are rated as less likeable by peers (Verduin & Kendall, 2008), and have more difficulty making friends (Scharfstein, Alfano, Beidel, & Wong, 2011). Given that social anxiety is characterized by fear and avoidance of social situations, increased social impairment is coherent: Children and adolescents with social anxiety have fewer opportunities to practice social skills, and, when around others, may appear withdrawn or visibly anxious. As a result, these children experience more interpersonal difficulties (e.g., bullying) than children with other anxiety disorders, and peer victimization has been shown to exacerbate social anxiety symptoms over time (Storch, Masia-Warner, Crisp, & Klein, 2005). Fortunately, treatment studies suggest that as anxiety decreases, social competency and skill increases (Settipani & Kendall, 2013; Suveg et al., 2009; Wood, 2006).

Social Correlates of Anxiety Many children and adolescents with anxiety experience social difficulties. Indeed, teachers have reported that children with elevated anxiety symptoms (Muris & Meesters, 2002) and anxiety disorders (R. S. Benjamin, Costello, & Warren, 1990) have more problematic peer relationships than

Assessment of Anxiety Disorders in Children and Adolescents Accurate assessment and diagnosis is a necessary precursor to effective intervention for anxiety disorders in children and adolescents. Mental 219

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health professionals use a variety of methods and tools, including clinician-administered interviews, behavioral observations, self-report measures, and multiple-informant reports to guide case conceptualization. Semistructured clinical interviews like the Anxiety Disorders Interview Schedule for Children (ADIS C/P; Albano & Silverman, 2016) are considered the gold standard; however, self-, parent-, and teacher-report forms require less time and personnel resources to administer. Many mental health professionals use a combination of assessment tools to render diagnoses and monitor treatment progress. Commonly used and psychometrically sound assessment tools are reviewed next.

Clinician-Administered Measures Anxiety Disorders Interview Schedule for Children—Child and Parent Versions.  The ADISC/P is a semi-structured interview that assesses the major anxiety, mood, and externalizing disorders experienced by children and adolescents (Albano & Silverman, 2016). Composite diagnoses are rendered on the basis of information gathered from the children and parents. To meet diagnostic criteria, children and parents must endorse sufficient symptoms, and the interviewer must indicate a clinical severity rating of 4 or greater on a scale from 0 to 8. Interrater reliability for the ADIS-C/P is excellent (kappa = .98 for the parent interview and kappa = .93 for the child; Silverman & Nelles, 1988). In addition, its reliability (Lyneham, Abbott, & Rapee, 2007), and sensitivity to treatment-related changes (Hudson, Rapee, et al., 2009; Walkup et al., 2008) are documented. Pediatric Anxiety Rating Scale.  The Pediatric Anxiety Rating Scale is a clinician-rated measure of youth anxiety symptom severity (Research Units on Pediatric Psychopharmacology Anxiety Study Group, 2002). It consists of a 50-item anxiety symptom checklist targeting GAD, SAD, and SepAD symptoms, as well as 7 global items. Clinicians rate symptoms as having occurred over the past week, on the basis of parent and child report. The clinician then rates seven dimensions of overall severity, including overall symptom frequency, distress, and associated impairment on a scale from 0 to 5. The 220

scale has demonstrated adequate interrater reliability, retest reliability, internal consistency, and convergent and divergent validity (Ginsburg, Keeton, Drazdowski, & Riddle, 2011; Research Units on Pediatric Psychopharmacology Anxiety Study Group, 2002). It has also demonstrated sensitivity to treatment effects (e.g., Walkup et al., 2008).

Questionnaires Multidimensional Anxiety Scale for Children.  The Multidimensional Anxiety Scale for Children (MASC; J. S. March, 1997) is a 39-item measure that assesses the presence of general, social, and separation anxiety symptoms over the past week. There are parallel parent- and youth-report forms, and respondents rate items on a 0 to 4 point Likert scale. The MASC yields a total score, as well as four subscale scores assessing: physical symptoms, social anxiety, separation anxiety, and harm avoidance. The MASC has demonstrated reliability (internal consistency, retest reliability; J. S. March, Sullivan, & Parker, 1999), and convergent validity with other measures (Baldwin & Dadds, 2007) and interviews (Villabø et al., 2012). It has also demonstrated sensitivity to treatment changes (Manassis et al., 2002). Screen for Child Anxiety and Related Emotional Disorders.  The Screen for Child Anxiety and Related Emotional Disorders is a 41-item screening questionnaire that assesses parent- and youth-reported symptoms of SepAD, GAD, SAD, school phobia, and somatic anxiety symptoms over the past three months on a 0- to 2-point rating scale (Birmaher, 1999). It has demonstrated internal consistency and retest reliability (Birmaher et al., 1997, 1999). Spence Children’s Anxiety Scale.  The Spence Children’s Anxiety Scale is a 44-item questionnaire that assesses generalized anxiety, panic/agoraphobia, social phobia, separation anxiety, obsessive-­ compulsive disorder, and physical injury fears (Spence, 1998). There are parallel parent- and youthreport questionnaires, and respondents provide ratings on a 0- to 3-point scale. It has demonstrated internal consistency, retest reliability, and convergent validity with other measures of anxiety symptoms (Spence, Barrett, & Turner, 2003). Spence and Rapee (1999)

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also developed a parent-report preschool anxiety scale to assess anxiety symptoms in young children ages 3 to 6. Other questionnaires.  Other questionnaires used to assess youth anxiety symptoms and related concerns include the Revised Children’s Manifest Anxiety Scale (C. R. Reynolds & Richmond, 1985), the State Trait Anxiety Inventory for Children (Spielberger, 1973), the Child Behavior Checklist–Anxiety and Teacher-Report Form–Anxiety (Kendall et al., 2007), and the Child Behavior Checklist Anxiety Subscale (Achenbach, Dumenci, & Rescorla, 2003). The Child Anxiety Impact Scale (Langley, Bergman, McCracken, & Piacentini, 2004) assesses anxiety-related interference in home/family, social, and school functioning as reported by parents and children.

Informant Discrepancies Gathering information from multiple sources (youth, parents, teachers) is recommended when working with youth. Multiple informant reports aid case conceptualization and accurate diagnoses. However, informant reports are often discrepant (Choudhury, Pimentel, & Kendall, 2003; Comer & Kendall, 2004), and there is no one single method to use when interpreting reports that disagree (De Los Reyes & Kazdin, 2005). Potential explanations for informant discrepancies include individual biases, poor reporting, and differences in available information (De Los Reyes & Kazdin, 2005). Children with a social desirability bias may underreport their anxiety symptoms (Rapee, Barrett, Dadds, & Evans, 1994). In contrast, anxious parents may overreport their child’s anxiety symptoms (Frick, Silverthorn, & Evans, 1994). Accurate reporting is further complicated given that many anxiety symptoms are internal (e.g., worries) and may not be outwardly expressed. Indeed, parent–child agreement is greater for observable symptoms of anxiety (Comer & Kendall, 2004). When interpreting discrepant reports, clinicians are encouraged to use the “or” rule to maximize the identification of clinically elevated anxiety in children and adolescents: If either the parent or the child endorse sufficient symptoms and related impairment to meet diagnostic criteria, the

child receives the diagnosis. Additional research examining how to integrate differing informant reports is warranted. Psychological Treatments for Anxiety Disorders in Children and Adolescents CBT has received the strongest empirical support for the psychological treatment of child anxiety (Hollon & Beck, 2013; Kendall, 2012; Ollendick & King, 2012). CBT for children and adolescents typically consists of developing awareness of anxious feelings, thoughts (i.e., self-talk), and behavior (e.g., avoidance), followed by learning skills to cope with anxiety and practicing these skills in real-life behavioral exposure tasks. Children and adolescents learn to use relaxation and to address their anxious self-talk so that they feel more prepared when facing feared situations, in therapy sessions and at home or in the community. CBT may be conducted in an individual or group format. Parents are often involved in treatment to varying degrees, whether participating fully in a family-based treatment or consulting with a child’s therapist to help support the child in conducting between-session exposure tasks. Although treatment for anxiety disorders typically occurs in weekly sessions for several weeks, intensive single-session CBT formats have been developed for treating specific phobias (Ollendick & Davis, 2013). The details regarding both the procedures for treating children and adolescents with anxiety (Kendall et al., in press) and the research evaluations of the outcome (Hollon & Beck, 2013; Ollendick & King, 2012) are available. Given the efficacy of face-to-face CBT in treating youth anxiety disorders, there has been a call in recent years to develop alternative strategies for the delivery of CBT that could help increase the accessibility of CBT treatments in community settings (Kazdin & Blase, 2011). To this end, technology has been applied (Carper, McHugh, & Barlow, 2013), and computer-based (standalone) and computer-assisted (in combination with faceto-face therapy) CBT programs have proliferated. Research examining the efficacy of these modalities suggests that the outcomes are comparably favorable 221

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for computer-assisted CBT compared with face-toface CBT (Andrews, Cuijpers, Craske, McEvoy, & Titov, 2010; Khanna & Kendall, 2010; S. March, Spence, & Donovan, 2009; Reger & Gahm, 2009). Accordingly, computer-based and computer-assisted programs represent a promising method for the delivery of mental health care to children and adolescents with anxiety (Kendall, Carper, Khanna, & Harris, 2015; Khanna, Kerns, & Carper, 2014). Recent developments in mindfulness-based CBT suggest that stress reduction techniques using mindfulness may be an effective component of anxiety treatment for adults (Arch et al., 2013), and some research has begun evaluating these techniques for children and adolescents. Although only preliminary evidence exists, studies suggest that mindfulnessbased CBT can be efficacious for anxiety in children and adolescents (Burke, 2010; Semple, Lee, Rosa, & Miller, 2010). Additional developments in anxiety treatment include a parent-training program for pre-school age children with anxiety, on the basis of parent–child interaction therapy techniques (Comer et al., 2012; Zisser & Eyberg, 2010), and an online parent training program entitled Child Anxiety Tales (see http://www.copingcatparents.com). CBT has been deemed to meet the criteria for a “well-established” treatment for anxiety in children and adolescents (see Chambless & Hollon, 1998; Hollon & Beck, 2013). In large randomized controlled trials, CBT for child anxiety typically achieves response rates of roughly 60% (S. Reynolds, Wilson, Austin, & Hooper, 2012; Walkup et al., 2008). CBT for child and adolescent anxiety has been found to be comparably efficacious to acute pharmacological treatment (i.e., sertraline; Walkup et al., 2008) and to have long-term benefits in terms of reduced substance use problems (Puleo et al., 2011) and suicidality (Wolk et al., 2015). Predictor variables identify factors that are associated with treatment outcome and have no interactive effect with treatment modality (Kraemer, Wilson, Fairburn, & Agras, 2002). Research examining predictors of CBT treatment response has generally found that CBT is comparably effective for children and adolescents of both genders, across ages (Kendall & Peterman, 2015), and across parental involvement (Bennett et al., 2013). 222

One study found that the presence of depressive symptoms was associated with poorer treatment response (Berman, Weems, Silverman, & Kurtines, 2000), and some data suggest that adolescents with anxiety who show depressive symptoms and are socially anxious have follow-up outcomes that are good but not as good as adolescents without this pattern (Kerns et al., 2013). Other research suggests that higher levels of maternal- and teacher-reported internalizing symptoms, higher levels of maternal self-reported depressive symptoms, and older age may be associated with poorer treatment response (Southam-Gerow, Kendall, & Weersing, 2001). Lower levels of caregiver strain have been associated with better treatment outcomes (Compton et al., 2014). Finally, there is mixed evidence regarding the contribution of anxiety severity to treatment response, with some studies finding lower levels of anxiety severity at pretreatment to predict better outcomes (e.g., Compton et al., 2014) and others finding higher anxiety severity at pretreatment to be associated with better treatment outcome (e.g., Kley, Heinrichs, Bender, & Tuschen-Caffier, 2012). Moderating variables describe for whom and/ or under what circumstances a treatment works (Baron & Kenny, 1986; Kraemer et al., 2002). Recent meta-analytic reviews have concluded that the majority of findings suggest that there are no demographic or clinical (i.e., duration, diagnosis, pretreatment severity, comorbidity) characteristics that consistently moderate treatment outcome (Nilsen, Eisemann, & Kvernmo, 2013). Nilsen et al. (2013) note that the lack of statistical power, poor study design for testing moderating variables, and lack of variability in the moderator of interest may be partially to blame for the lack of significant moderation effects. That said, the absence of significant moderators could be evidence that treatments are comparably effective for children and adolescents across the variables studied. To date, there is not enough evidence regarding treatment moderators of favorable outcomes for children and adolescents with anxiety. Mediating variables are variables that describe possible mechanisms through which a treatment achieves its beneficial effects (Kraemer et al., 2002). Unfortunately, studies examining mediation in CBT

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for anxiety have suffered from a variety of methodological shortcomings, including lack of demonstrating temporal precedence of the mediator (i.e., that change in the mediating variable occurs prior to change in the outcome variable) and lack of a treatment comparison condition. Despite the methodological issues, research suggests that changes in negative self-talk may be a mediator of change in CBT for child anxiety (Kendall & Treadwell, 2007; Treadwell & Kendall, 1996). Changing in coping efficacy has also been found to mediate 3-month follow-up gains in CBT for child anxiety (Kendall et al., 2016). The therapeutic alliance has also been found to be a potential mediator of CBT for child anxiety (Marker, Comer, Abramova, & Kendall, 2013). Overall, CBT produces large effects on behavioral variables and moderate effects of physiological, cognitive, and coping variables (Chu & Harrison, 2007). Future Directions Much has been learned about the nature, development, maintenance, and treatment for anxiety and its disorders in children and adolescents. Indeed, the number and quality of research studies on anxiety in youth has provided a valuable resource for our understanding and for effective intervention. However, there are lingering matters that will require increased attention. Anxiety in children and adolescents appears to be on the rise and, as we know, anxiety involves social, cognitive, behavioral, biological, and emotional factors. It can be a debilitating condition. When left untreated, these forces constitute a gateway disorder (Kessler, 2010), with unwanted sequelae later in development (e.g., adult anxiety, depression, substance use problems, suicidality). With international terrorist events, financial concerns increasingly expressed by parents, and social technological changes that pose potential risk, childhood and adolescence can no longer be described as a time of worry-free development. Children, at earlier ages, seem to carry worries and concerns that, in the past, were reserved for more mature ages. Knowing these needs, we are poised to offer adjustments to reduce the interfering distress. A goal for the near future is to reduce the unwanted rise of anxious distress.

Are the anxiety disorders in youth sufficiently distinct to justify various labels or will the field be better served by a more unified construct (anxiety) that represents a maladaptive experience that displays itself in different ways and differentially over the ages? Perhaps a single continuum of anxious distress will serve researchers, and eventually service providers, more effectively than the categorical approach that has been in place for decades. There is some initial good news. Anxiety in youth is treatable, though not all types of treatment are effective. Parents can help reduce anxious distress, but we need to learn more about how parents can reduce their contribution to the development and maintenance of anxiety in youth. CBT has received widespread endorsement, but the number of mental health service providers who are trained in these procedures is less than the number of youth with identified needs. The future merits a sustained effort for the dissemination and implementation of empirically supported treatments for children and adolescents with anxiety.

References Achenbach, T. M., Dumenci, L., & Rescorla, L. A. (2003). DSM-oriented and empirically based approaches to constructing scales from the same item pools. Journal of Clinical Child and Adolescent Psychology, 32, 328–340. http://dx.doi.org/10.1207/ S15374424JCCP3203_02 Albano, A. M., Chorpita, B. F., & Barlow, D. H. (2003). Childhood anxiety disorders. In E. J. Mash & R. A. Barkley (Eds.), Child psychopathology (2nd ed., pp. 279–329). New York, NY: Guilford Press. Albano, A. M., & Silverman, W. (2016). The anxiety disorders interview schedule for children for DSM–V (child and parent versions). San Antonio, TX: Psychological Corporation. American Psychiatric Association. (2013). Diagnostic and statistical manual of mental disorders (5th ed.). Washington, DC: Author. Andrews, G., Cuijpers, P., Craske, M. G., McEvoy, P., & Titov, N. (2010). Computer therapy for the anxiety and depressive disorders is effective, acceptable and practical health care: A meta-analysis. PLOS ONE, 5, e13196. http://dx.doi.org/10.1371/journal. pone.0013196 Angold, A., Costello, E. J., & Erkanli, A. (1999). Comorbidity. Journal of Child Psychology and Psychiatry, 40, 57–87. http://dx.doi.org/ 10.1111/1469-7610.00424 223

Kendall et al.

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Arch, J. J., Ayers, C. R., Baker, A., Almklov, E., Dean, D. J., & Craske, M. G. (2013). Randomized clinical trial of adapted mindfulness-based stress reduction versus group cognitive–behavioral therapy for heterogeneous anxiety disorders. Behaviour Research and Therapy, 51, 185–196. http://dx.doi.org/ 10.1016/j.brat.2013.01.003 Aschenbrand, S. G., Kendall, P. C., Webb, A., Safford, S. M., & Flannery-Schroeder, E. (2003). Is childhood separation anxiety disorder a predictor of adult panic disorder and agoraphobia? A seven-year longitudinal study. Journal of the American Academy of Child and Adolescent Psychiatry, 42, 1478–1485. http:// dx.doi.org/10.1097/00004583-200312000-00015 Axelson, D. A., & Birmaher, B. (2001). Relation between anxiety and depressive disorders in childhood and adolescence. Depression and Anxiety, 14, 67–78. http://dx.doi.org/10.1002/da.1048 Baldwin, J. S., & Dadds, M. R. (2007). Reliability and validity of parent and child versions of the multidimensional anxiety scale for children in community samples. Journal of the American Academy of Child and Adolescent Psychiatry, 46, 252–260. http:// dx.doi.org/10.1097/01.chi.0000246065.93200.a1 Bandura, A. (1986). Social foundations of thought and action: A social cognitive theory. Englewood Cliffs, NJ: Prentice Hall. Barlow, D. H. (1988). Anxiety and its disorders: The nature and treatment of anxiety and panic. New York, NY: Guilford Press. Barlow, D. H. (2002). Anxiety and its disorders: The nature and treatment of anxiety and panic (2nd ed.). New York, NY: Guilford Press. Baron, R. M., & Kenny, D. A. (1986). The moderator–mediator variable distinction in social psychological research: Conceptual, strategic, and statistical considerations. Journal of Personality and Social Psychology, 51, 1173–1182. http://dx.doi.org/ 10.1037/0022-3514.51.6.1173 Barrett, P. M., Rapee, R. M., Dadds, M. M., & Ryan, S. M. (1996). Family enhancement of cognitive style in anxious and aggressive children. Journal of Abnormal Child Psychology, 24, 187–203. http://dx.doi.org/ 10.1007/BF01441484 Beesdo, K., Knappe, S., & Pine, D. S. (2009). Anxiety and anxiety disorders in children and adolescents: Developmental issues and implications for DSM–V. Psychiatric Clinics of North America, 32, 483–524. http://dx.doi.org/10.1016/j.psc.2009.06.002

and related outcomes in young adults 7 to 19 years after receiving treatment for child anxiety. Journal of Consulting and Clinical Psychology, 81, 865–876. http://dx.doi.org/10.1037/a0033048 Benjamin, R. S., Costello, E. K., & Warren, M. (1990). Anxiety disorders in a pediatric sample. Journal of Anxiety Disorders, 4, 293–316. Bennett, K., Manassis, K., Walter, S. D., Cheung, A., Wilansky-Traynor, P., Diaz-Granados, N., . . . Wood, J. J. (2013). Cognitive–behavioral therapy age effects in child and adolescent anxiety: An individual patient data meta-analysis. Depression and Anxiety, 30, 829–841. http://dx.doi.org/10.1002/da.22099 Berman, S. L., Weems, C. F., Silverman, W. K., & Kurtines, W. M. (2000). Predictors of outcome in exposure-based cognitive and behavioral treatments for phobic and anxiety disorders in children. Behavior Therapy, 31, 713–731. http://dx.doi.org/ 10.1016/S0005-7894(00)80040-4 Biederman, J., Faraone, S. V., Hirshfeld-Becker, D. R., Friedman, D., Robin, J. A., & Rosenbaum, J. F. (2001). Patterns of psychopathology and dysfunction in high-risk children of parents with panic disorder and major depression. American Journal of Psychiatry, 158, 49–57. http://dx.doi.org/ 10.1176/appi.ajp.158.1.49 Birmaher, B. (1999). Screen for child anxiety related emotional disorders (SCARED). Pittsburgh, PA: Western Psychiatric Institute and Clinic. Birmaher, B., Brent, D. A., Chiappetta, L., Bridge, J., Monga, S., & Baugher, M. (1999). Psychometric properties of the screen for child anxiety related emotional disorders (SCARED): A replication study. Journal of the American Academy of Child and Adolescent Psychiatry, 38, 1230–1236. http:// dx.doi.org/10.1097/00004583-199910000-00011 Birmaher, B., Khetarpal, S., Brent, D., Cully, M., Balach, L., Kaufman, J., & Neer, S. M. (1997). The screen for child anxiety related emotional disorders (SCARED): Scale construction and psychometric characteristics. Journal of the American Academy of Child and Adolescent Psychiatry, 36, 545–553. http:// dx.doi.org/10.1097/00004583-199704000-00018 Burke, C. A. (2010). Mindfulness-based approaches with children and adolescents: A preliminary review of current research in an emergent field. Journal of Child and Family Studies, 19, 133–144. http:// dx.doi.org/10.1007/s10826-009-9282-x

Benjamin, C. L., Beidas, R. S., Comer, J. S., Puliafico, A. C., & Kendall, P. C. (2011). Generalized anxiety disorder in youth: Diagnostic considerations. Depression and Anxiety, 28, 173–182. http:// dx.doi.org/10.1002/da.20747

Calkins, S. D., & Fox, N. A. (2002). Self-regulatory processes in early personality development: A multilevel approach to the study of childhood social withdrawal and aggression. Development and Psychopathology, 14, 477–498. http:// dx.doi.org/10.1017/S095457940200305X

Benjamin, C. L., Harrison, J. P., Settipani, C. A., Brodman, D. M., & Kendall, P. C. (2013). Anxiety

Carper, M. M., McHugh, R. K., & Barlow, D. H. (2013). The dissemination of computer-based

224

Anxiety Disorders Among Children and Adolescents

psychological treatment: A preliminary analysis of patient and clinician perceptions. Administration and Policy in Mental Health and Mental Health Services Research, 40, 87–95. http://dx.doi.org/ 10.1007/s10488-011-0377-5 Caspi, A., Elder, G. H., & Bem, D. J. (1988). Moving away from the world: Life-course patterns of shy children. Developmental Psychology, 24, 824–831. http:// dx.doi.org/10.1037/0012-1649.24.6.824

Copyright American Psychological Association. Not for further distribution.

Caspi, A., Sugden, K., Moffitt, T. E., Taylor, A., Craig, I. W., Harrington, H., . . . Poulton, R. (2003). Influence of life stress on depression: Moderation by a polymorphism in the 5-HTT gene. Science, 301, 386–389. http://dx.doi.org/10.1126/science.1083968 Chambless, D. L., & Hollon, S. D. (1998). Defining empirically supported therapies. Journal of Consulting and Clinical Psychology, 66, 7–18. http:// dx.doi.org/10.1037/0022-006X.66.1.7 Chansky, T. E., & Kendall, P. C. (1997). Social expectancies and self-perceptions in anxietydisordered children. Journal of Anxiety Disorders, 11, 347–363. http://dx.doi.org/10.1016/ S0887-6185(97)00015-7 Chorpita, B. F., & Barlow, D. H. (1998). The development of anxiety: The role of control in the early environment. Psychological Bulletin, 124, 3–21. http://dx.doi.org/10.1037/0033-2909.124.1.3 Choudhury, M. S., Pimentel, S. S., & Kendall, P. C. (2003). Childhood anxiety disorders: Parent-child (dis)agreement using a structured interview for the DSM–IV. Journal of the American Academy of Child and Adolescent Psychiatry, 42, 957–964. http:// dx.doi.org/10.1097/01.CHI.0000046898.27264.A2 Chu, B. C., & Harrison, T. L. (2007). Disorder-specific effects of CBT for anxious and depressed youth: A meta-analysis of candidate mediators of change. Clinical Child and Family Psychology Review, 10, 352–372. http://dx.doi.org/10.1007/s10567-007-0028-2 Comer, J. S., & Kendall, P. C. (2004). A symptom-level examination of parent-child agreement in the diagnosis of anxious youths. Journal of the American Academy of Child and Adolescent Psychiatry, 43, 878–886. http://dx.doi.org/ 10.1097/01.chi.0000125092.35109.c5 Comer, J. S., Puliafico, A. C., Aschenbrand, S. G., McKnight, K., Robin, J. A., Goldfine, M. E., & Albano, A. M. (2012). A pilot feasibility evaluation of the CALM Program for anxiety disorders in early childhood. Journal of Anxiety Disorders, 26, 40–49. http://dx.doi.org/10.1016/j.janxdis.2011.08.011 Compton, S. N., Peris, T. S., Almirall, D., Birmaher, B., Sherrill, J., Kendall, P. C., . . . Albano, A. M. (2014). Predictors and moderators of treatment response in childhood anxiety disorders: Results from the CAMS trial. Journal of Consulting and Clinical Psychology, 82, 212–224. http://dx.doi.org/10.1037/a0035458

Costello, E. J., Egger, H. L., & Angold, A. (2005). The developmental epidemiology of anxiety disorders: Phenomenology, prevalence, and comorbidity. Child and Adolescent Psychiatric Clinics of North America, 14, 631–648. http://dx.doi.org/10.1016/ j.chc.2005.06.003 Craske, M. G., Kircanski, K., Zelikowsky, M., Mystkowski, J., Chowdhury, N., & Baker, A. (2008). Optimizing inhibitory learning during exposure therapy. Behaviour Research and Therapy, 46, 5–27. http://dx.doi.org/10.1016/j.brat.2007.10.003 Cummings, C. M., Caporino, N. E., & Kendall, P. C. (2014). Comorbidity of anxiety and depression in children and adolescents: 20 years after. Psychological Bulletin, 140, 816–845. http:// dx.doi.org/10.1037/a0034733 De Los Reyes, A., & Kazdin, A. E. (2005). Informant discrepancies in the assessment of childhood psychopathology: A critical review, theoretical framework, and recommendations for further study. Psychological Bulletin, 131, 483–509. http:// dx.doi.org/10.1037/0033-2909.131.4.483 Dorfman, J., Benson, B., Farber, M., Pine, D., & Ernst, M. (2016). Altered striatal intrinsic functional connectivity in pediatric anxiety. Neuropsychologia, 85, 159–168. http://dx.doi.org/10.1016/ j.neuropsychologia.2016.03.019 Drake, K. L., & Ginsburg, G. S. (2012). Family factors in the development, treatment, and prevention of childhood anxiety disorders. Clinical Child and Family Psychology Review, 15, 144–162. http:// dx.doi.org/10.1007/s10567-011-0109-0 Essau, C. A. (2008). Comorbidity of depressive disorders among adolescents in community and clinical settings. Psychiatry Research, 158, 35–42. http:// dx.doi.org/10.1016/j.psychres.2007.09.007 Essau, C. A., Lewinsohn, P. M., Olaya, B., & Seeley, J. R. (2014). Anxiety disorders in adolescents and psychosocial outcomes at age 30. Journal of Affective Disorders, 163, 125–132. http://dx.doi.org/10.1016/ j.jad.2013.12.033 Fisak, B., Jr., & Grills-Taquechel, A. E. (2007). Parental modeling, reinforcement, and information transfer: Risk factors in the development of child anxiety? Clinical Child and Family Psychology Review, 10, 213–231. http://dx.doi.org/10.1007/s10567-007-0020-x Foa, E. B., & Kozak, M. J. (1986). Emotional processing of fear: Exposure to corrective information. Psychological Bulletin, 99, 20–35. http://dx.doi.org/ 10.1037/0033-2909.99.1.20 Frick, P. J., Silverthorn, P., & Evans, C. (1994). Assessment of childhood anxiety using structured interviews: Patterns of agreement among informants and association with maternal anxiety. Psychological Assessment, 6, 372–379. http:// dx.doi.org/10.1037/1040-3590.6.4.372 225

Kendall et al.

Copyright American Psychological Association. Not for further distribution.

Ginsburg, G. S., Becker, E. M., Keeton, C. P., Sakolsky, D., Piacentini, J., Albano, A. M., . . . Kendall, P. C. (2014). Naturalistic follow-up of youths treated for pediatric anxiety disorders. JAMA Psychiatry, 71, 310–318. http://dx.doi.org/10.1001/ jamapsychiatry.2013.4186

clinically anxious children. Journal of Clinical Child and Adolescent Psychology, 38, 256–262. http:// dx.doi.org/10.1080/15374410802698438

Ginsburg, G. S., Keeton, C. P., Drazdowski, T. K., & Riddle, M. A. (2011). The utility of clinicians’ ratings of anxiety using the pediatric anxiety rating scale (PARS). Child and Youth Care Forum, 40, 93–105. http://dx.doi.org/10.1007/s10566-010-9125-3

Hudson, J. L., Rapee, R. M., Deveney, C., Schniering, C. A., Lyneham, H. J., & Bovopoulos, N. (2009). Cognitive–behavioral treatment versus an active control for children and adolescents with anxiety disorders: A randomized trial. Journal of the American Academy of Child and Adolescent Psychiatry, 48, 533–544. http://dx.doi.org/10.1097/ CHI.0b013e31819c2401

Ginsburg, G. S., & Schlossberg, M. C. (2002). Familybased treatment of childhood anxiety disorders. International Review of Psychiatry, 14, 143–154. http://dx.doi.org/10.1080/09540260220132662

Kagan, E. R., Peterman, J. S., Carper, M. M., & Kendall, P. C. (2016). Accommodation and treatment of anxious youth. Depression and Anxiety, 33, 840–847. http://dx.doi.org/10.1002/da.22520

Ginsburg, G. S., Siqueland, L., Masia-Warner, C., & Hedtke, K. A. (2004). Anxiety disorders in children: Family matters. Cognitive and Behavioral Practice, 11, 28–43. http://dx.doi.org/10.1016/S1077-7229(04)80005-1

Kagan, J., Reznick, J. S., & Snidman, N. (1988). Biological bases of childhood shyness. Science, 240, 167–171. http://dx.doi.org/10.1126/science.3353713

Gregory, A. M., & Eley, T. C. (2007). Genetic influences on anxiety in children: What we’ve learned and where we’re heading. Clinical Child and Family Psychology Review, 10, 199–212. http://dx.doi.org/ 10.1007/s10567-007-0022-8 Grover, R. L., Ginsburg, G. S., & Ialongo, N. (2007). Psychosocial outcomes of anxious first graders: A seven-year follow-up. Depression and Anxiety, 24, 410–420. http://dx.doi.org/10.1002/da.20241 Guyer, A. E., Nelson, E. E., Perez-Edgar, K., Hardin, M. G., Roberson-Nay, R., Monk, C. S., . . . Ernst, M. (2006). Striatal functional alteration in adolescents characterized by early childhood behavioral inhibition. Journal of Neuroscience, 26, 6399–6405. http://dx.doi.org/10.1523/JNEUROSCI.0666-06.2006 Hammen, C., Burge, D., Burney, E., & Adrian, C. (1990). Longitudinal study of diagnoses in children of women with unipolar and bipolar affective disorder. Archives of General Psychiatry, 47, 1112–1117. http:// dx.doi.org/10.1001/archpsyc.1990.01810240032006 Hirshfeld-Becker, D. R., Micco, J., Henin, A., Bloomfield, A., Biederman, J., & Rosenbaum, J. (2008). Behavioral inhibition. Depression and Anxiety, 25, 357–367. http://dx.doi.org/10.1002/da.20490 Hollon, S. D., & Beck, A. T. (2013). Cognitive and cognitive–behavioral therapies. In M. J. Lambert (Ed.), Handbook of psychotherapy and behavior change (6th ed., pp. 393–442). Hoboken, NJ: Wiley. Hudson, J. L., Dodd, H. F., & Bovopoulos, N. (2011). Temperament, family environment and anxiety in preschool children. Journal of Abnormal Child Psychology, 39, 939–951. http://dx.doi.org/10.1007/ s10802-011-9502-x Hudson, J. L., Doyle, A. M., & Gar, N. (2009). Child and maternal influence on parenting behavior in 226

Kashdan, T. B., Farmer, A. S., Adams, L. M., Ferssizidis, P., McKnight, P. E., & Nezlek, J. B. (2013). Distinguishing healthy adults from people with social anxiety disorder: Evidence for the value of experiential avoidance and positive emotions in everyday social interactions. Journal of Abnormal Psychology, 122, 645–655. http://dx.doi.org/10.1037/a0032733 Kashdan, T. B., & Herbert, J. D. (2001). Social anxiety disorder in childhood and adolescence: Current status and future directions. Clinical Child and Family Psychology Review, 4, 37–61. http:// dx.doi.org/10.1023/A:1009576610507 Kazdin, A. E., & Blase, S. L. (2011). Rebooting psychotherapy research and practice to reduce the burden of mental illness. Perspectives on Psychological Science, 6, 21–37. http:// dx.doi.org/10.1177/1745691610393527 Kendall, P. C. (2012). Anxiety disorders in youth. In P. C. Kendall (Ed.), Child and adolescent therapy: Cognitive–behavioral procedures (4th ed., pp. 143–189). New York, NY: Guilford Press. Kendall, P. C., Carper, M. M., Khanna, M. S., & Harris, M. S. (2015). Computer technology and children’s mental health. In R. Scott & S. Kosslyn (Eds.), Emerging trends in the social and behavioral sciences (pp. 1–16). http:// dx.doi.org/10.1002/9781118900772.etrds0049 Kendall, P. C., Compton, S. N., Walkup, J. T., Birmaher, B., Albano, A. M., Sherrill, J., . . . Piacentini, J. (2010). Clinical characteristics of anxiety disordered youth. Journal of Anxiety Disorders, 24, 360–365. http://dx.doi.org/10.1016/j.janxdis.2010.01.009 Kendall, P. C., Crawford, E., Kagan, E., Furr, J., & Podell, J. (in press). Child-focused treatment of anxiety. In J. R. Weisz and A. E. Kazdin (Eds.) Evidence-based psychotherapies for children and adolescents (3rd ed.). New York, NY: Guilford Press.

Anxiety Disorders Among Children and Adolescents

Kendall, P. C., Cummings, C. M., Villabø, M. A., Narayanan, M. K., Treadwell, K., Birmaher, B., . . . Albano, A. M. (2016). Mediators of change in the Child/Adolescent Anxiety Multimodal Treatment Study. Journal of Consulting and Clinical Psychology, 84, 1–14. http://dx.doi.org/10.1037/a0039773

Copyright American Psychological Association. Not for further distribution.

Kendall, P. C., Hudson, J. L., Gosch, E., Flannery-Schroeder, E., & Suveg, C. (2008). Cognitive–behavioral therapy for anxiety disordered youth: A randomized clinical trial evaluating child and family modalities. Journal of Consulting and Clinical Psychology, 76, 282–297. http://dx.doi.org/ 10.1037/0022-006X.76.2.282 Kendall, P. C., & Peterman, J. S. (2015). CBT for adolescents with anxiety: Mature yet still developing. American Journal of Psychiatry, 172, 519–530. http:// dx.doi.org/10.1176/appi.ajp.2015.14081061 Kendall, P. C., Puliafico, A. C., Barmish, A. J., Choudhury, M. S., Henin, A., & Treadwell, K. S. (2007). Assessing anxiety with the Child Behavior Checklist and the Teacher Report Form. Journal of Anxiety Disorders, 21, 1004–1015. http:// dx.doi.org/10.1016/j.janxdis.2006.10.012 Kendall, P. C., Robin, J. A., Hedtke, K., Suveg, C. M., Flannery-Schroeder, E., & Gosch, E. (2005). Considering CBT with anxious youth? Think exposures. Cognitive and Behavioral Practice, 12, 136–148. http:// dx.doi.org/10.1016/S1077-7229(05)80048-3 Kendall, P. C., Safford, S., Flannery-Schroeder, E., & Webb, A. (2004). Child anxiety treatment: Outcomes in adolescence and impact on substance use and depression at 7.4-year follow-up. Journal of Consulting and Clinical Psychology, 72, 276–287. http://dx.doi.org/10.1037/0022-006X.72.2.276 Kendall, P. C., & Treadwell, K. R. H. (2007). The role of self-statements as a mediator in treatment for youth with anxiety disorders. Journal of Consulting and Clinical Psychology, 75, 380–389. http:// dx.doi.org/10.1037/0022-006X.75.3.380 Kerns, C. M., Read, K. L., Klugman, J., & Kendall, P. C. (2013). Cognitive–behavioral therapy for youth with social anxiety: Differential short and long-term treatment outcomes. Journal of Anxiety Disorders, 27, 210–215. http://dx.doi.org/10.1016/ j.janxdis.2013.01.009 Kessler, R. (2010). Anxiety as a gateway disorder. Cambridge, MA: Harvard University Press. Khanna, M. S., & Kendall, P. C. (2010). Computerassisted cognitive–behavioral therapy for child anxiety: Results of a randomized clinical trial. Journal of Consulting and Clinical Psychology, 78, 737–745. http://dx.doi.org/10.1037/a0019739 Khanna, M. S., Kerns, C. M., & Carper, M. M. (2014). Internet-based dissemination and implementation of cognitive–behavioral therapy for child anxiety.

In R. S. Beidas & P. C. Kendall (Eds.), Dissemination and implementation of evidence-based practices in child and adolescent mental health (pp. 313–335). New York, NY: Oxford University Press. Kley, H., Heinrichs, N., Bender, C., & TuschenCaffier, B. (2012). Predictors of outcome in a cognitive–behavioral group program for children and adolescents with social anxiety disorder. Journal of Anxiety Disorders, 26, 79–87. http://dx.doi.org/ 10.1016/j.janxdis.2011.09.002 Kovacs, M., & Devlin, B. (1998). Internalizing disorders in childhood. Journal of Child Psychology and Psychiatry, 39, 47–63. http://dx.doi.org/10.1017/ S0021963097001765 Kraemer, H. C., Wilson, G. T., Fairburn, C. G., & Agras, W. S. (2002). Mediators and moderators of treatment effects in randomized clinical trials. Archives of General Psychiatry, 59, 877–883. http://dx.doi.org/ 10.1001/archpsyc.59.10.877 Langley, A. K., Bergman, R. L., McCracken, J., & Piacentini, J. C. (2004). Impairment in childhood anxiety disorders: Preliminary examination of the child anxiety impact scale-parent version. Journal of Child and Adolescent Psychopharmacology, 14, 105–114. http://dx.doi.org/10.1089/ 104454604773840544 Last, C. G., Hansen, C., & Franco, N. (1997). Anxious children in adulthood: A prospective study of adjustment. Journal of the American Academy of Child and Adolescent Psychiatry, 36, 645–652. http:// dx.doi.org/10.1097/00004583-199705000-00015 Lebowitz, E. R., Scharfstein, L. A., & Jones, J. (2014). Comparing family accommodation in pediatric obsessive-compulsive disorder, anxiety disorders, and nonanxious children. Depression and Anxiety, 31, 1018–1025. http://dx.doi.org/10.1002/da.22251 Lieb, R., Isensee, B., Höfler, M., Pfister, H., & Wittchen, H. U. (2002). Parental major depression and the risk of depression and other mental disorders in offspring: A prospective-longitudinal community study. Archives of General Psychiatry, 59, 365–374. http://dx.doi.org/10.1001/archpsyc.59.4.365 Lieb, R., Wittchen, H. U., Höfler, M., Fuetsch, M., Stein, M. B., & Merikangas, K. R. (2000). Parental psychopathology, parenting styles, and the risk of social phobia in offspring: A prospective-longitudinal community study. Archives of General Psychiatry, 57, 859–866. http://dx.doi.org/10.1001/archpsyc.57.9.859 Lindesay, J., Baillon, S., Brugha, T., Dennis, M., Stewart, R., Araya, R., & Meltzer, H. (2006). Worry content across the lifespan: An analysis of 16- to 74-yearold participants in the British National Survey of Psychiatric Morbidity 2000. Psychological Medicine, 36, 1625–1633. http://dx.doi.org/10.1017/ S0033291706008439 227

Kendall et al.

Lonigan, C. J., & Phillips, B. M. (2001). Temperamental influences on the development of anxiety disorders. In M. W. Vasey & M. R. Dadds (Eds.), The developmental psychopathology of anxiety (pp. 60–91). http://dx.doi.org/10.1093/ med:psych/9780195123630.003.0004

Copyright American Psychological Association. Not for further distribution.

Lorian, C. N., & Grisham, J. R. (2010). The safety bias: Risk-avoidance and social anxiety pathology. Behaviour Change, 27, 29–41. http://dx.doi.org/ 10.1375/bech.27.1.29 Lyneham, H. J., Abbott, M. J., & Rapee, R. M. (2007). Interrater reliability of the Anxiety Disorders Interview Schedule for DSM–IV: Child and parent version. Journal of the American Academy of Child and Adolescent Psychiatry, 46, 731–736. http://dx.doi.org/ 10.1097/chi.0b013e3180465a09 Manassis, K., Mendlowitz, S. L., Scapillato, D., Avery, D., Fiksenbaum, L., Freire, M., . . . Owens, M. (2002). Group and individual cognitive–behavioral therapy for childhood anxiety disorders: A randomized trial. Journal of the American Academy of Child and Adolescent Psychiatry, 41, 1423–1430. http://dx.doi.org/ 10.1097/00004583-200212000-00013 March, J. S. (1997). Manual for the multidimensional anxiety scale for children (MASC). Toronto, Ontario, Canada: Multi-Health Systems. March, J. S., Sullivan, K., & Parker, J. (1999). Test-retest reliability of the multidimensional anxiety scale for children. Journal of Anxiety Disorders, 13, 349–358. http://dx.doi.org/10.1016/S0887-6185(99)00009-2 March, S., Spence, S. H., & Donovan, C. L. (2009). The efficacy of an internet-based cognitive–behavioral therapy intervention for child anxiety disorders. Journal of Pediatric Psychology, 34, 474–487. http:// dx.doi.org/10.1093/jpepsy/jsn099 Marker, C. D., Comer, J. S., Abramova, V., & Kendall, P. C. (2013). The reciprocal relationship between alliance and symptom improvement across the treatment of childhood anxiety. Journal of Clinical Child and Adolescent Psychology, 42, 22–33. http:// dx.doi.org/10.1080/15374416.2012.723261 McLeod, B. D., Wood, J. J., & Weisz, J. R. (2007). Examining the association between parenting and childhood anxiety: A meta-analysis. Clinical Psychology Review, 27, 155–172. http://dx.doi.org/ 10.1016/j.cpr.2006.09.002 Miers, A. C., Blöte, A. W., de Rooij, M., Bokhorst, C. L., & Westenberg, P. M. (2013). Trajectories of social anxiety during adolescence and relations with cognition, social competence, and temperament. Journal of Abnormal Child Psychology, 41, 97–110. http://dx.doi.org/10.1007/s10802-012-9651-6 Muris, P., & Meesters, C. (2002). Symptoms of anxiety disorders and teacher-reported school functioning of 228

normal children. Psychological Reports, 91, 588–590. http://dx.doi.org/10.2466/pr0.2002.91.2.588 Muris, P., & Ollendick, T. H. (2005). The role of temperament in the etiology of child psychopathology. Clinical Child and Family Psychology Review, 8, 271–289. http://dx.doi.org/ 10.1007/s10567-005-8809-y Muris, P., Steerneman, P., Merckelbach, H., & Meesters, C. (1996). The role of parental fearfulness and modeling in children’s fear. Behaviour Research and Therapy, 34, 265–268. http://dx.doi.org/ 10.1016/0005-7967(95)00067-4 Mychailyszyn, M., Mendez, J., & Kendall, P. (2010). School functioning in youth with and without anxiety disorders: Comparisons by diagnosis and comorbidity. School Psychology Review, 39, 106–121. Nilsen, T. S., Eisemann, M., & Kvernmo, S. (2013). Predictors and moderators of outcome in child and adolescent anxiety and depression: A systematic review of psychological treatment studies. European Child and Adolescent Psychiatry, 22, 69–87. http:// dx.doi.org/10.1007/s00787-012-0316-3 Ollendick, T. H., & Davis, T. E. I., III. (2013). One-session treatment for specific phobias: A review of Öst’s single-session exposure with children and adolescents. Cognitive Behaviour Therapy, 42, 275–283. http:// dx.doi.org/10.1080/16506073.2013.773062 Ollendick, T. H., & King, N. J. (2012). Evidence-based treatments for children and adolescents: Issues and commentary. In P. C. Kendall (Ed.), Child and adolescent therapy: Cognitive–behavioral procedures (4th ed., pp. 499–519). New York, NY: Guilford Press. Pine, D. S., Cohen, P., Gurley, D., Brook, J., & Ma, Y. (1998). The risk for early-adulthood anxiety and depressive disorders in adolescents with anxiety and depressive disorders. Archives of General Psychiatry, 55, 56–64. http://dx.doi.org/10.1001/ archpsyc.55.1.56 Puleo, C. M., Conner, B. T., Benjamin, C. L., & Kendall, P. C. (2011). CBT for childhood anxiety and substance use at 7.4-year follow-up: A reassessment controlling for known predictors. Journal of Anxiety Disorders, 25, 690–696. http://dx.doi.org/10.1016/ j.janxdis.2011.03.005 Rapee, R. M., Barrett, P. M., Dadds, M. R., & Evans, L. (1994). Reliability of the DSM–III–R childhood anxiety disorders using structured interview: Interrater and parent-child agreement. Journal of the American Academy of Child and Adolescent Psychiatry, 33, 984–992. http://dx.doi.org/10.1097/00004583199409000-00008 Reger, M. A., & Gahm, G. A. (2009). A meta-analysis of the effects of internet- and computer-based cognitive–behavioral treatments for anxiety. Journal

Anxiety Disorders Among Children and Adolescents

of Clinical Psychology, 65, 53–75. http://dx.doi.org/ 10.1002/jclp.20536 Research Units on Pediatric Psychopharmacology Anxiety Study Group. (2002). The Pediatric Anxiety Rating Scale (PARS): Development and psychometric properties. Journal of the American Academy of Child and Adolescent Psychiatry, 41, 1061–1069. http://dx.doi.org/10.1097/00004583200209000-00006

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Reynolds, C. R., & Richmond, B. O. (1985). Revised children’s manifest anxiety scale. Los Angeles, CA: Western Psychological Services. Reynolds, S., Wilson, C., Austin, J., & Hooper, L. (2012). Effects of psychotherapy for anxiety in children and adolescents: A meta-analytic review. Clinical Psychology Review, 32, 251–262. http://dx.doi.org/ 10.1016/j.cpr.2012.01.005 Rheingold, H. L., & Eckerman, C. O. (1974). Fear of the stranger: A critical examination. Advances in Child Development and Behavior, 8, 185–222. http:// dx.doi.org/10.1016/S0065-2407(08)60496-6

Southam-Gerow, M. A., Kendall, P. C., & Weersing, V. R. (2001). Examining outcome variability: Correlates of treatment response in a child and adolescent anxiety clinic. Journal of Clinical Child Psychology, 30, 422–436. http://dx.doi.org/10.1207/ S15374424JCCP3003_13 Spence, S. H. (1998). A measure of anxiety symptoms among children. Behaviour Research and Therapy, 36, 545–566. http://dx.doi.org/10.1016/ S0005-7967(98)00034-5 Spence, S. H., Barrett, P. M., & Turner, C. M. (2003). Psychometric properties of the Spence Children’s Anxiety Scale with young adolescents. Journal of Anxiety Disorders, 17, 605–625. http:// dx.doi.org/10.1016/S0887-6185(02)00236-0 Spence, S. H., & Rapee, R. (1999). Preschool anxiety scale (parent report). Brisbane, Australia: University of Queensland. Spielberger, C. D. (1973). State-trait anxiety inventory for children (STAIC): Preliminary manual. Sunnyvale, CA: Consulting Psychologists Press.

Richards, J. M., Patel, N., Daniele-Zegarelli, T., MacPherson, L., Lejuez, C. W., & Ernst, M. (2015). Social anxiety, acute social stress, and reward parameters interact to predict risky decisionmaking among adolescents. Journal of Anxiety Disorders, 29, 25–34. http://dx.doi.org/10.1016/ j.janxdis.2014.10.001

Stapinski, L. A., Araya, R., Heron, J., Montgomery, A. A., & Stallard, P. (2015). Peer victimization during adolescence: Concurrent and prospective impact on symptoms of depression and anxiety. Anxiety, Stress, and Coping: An International Journal, 28, 105–120. http://dx.doi.org/10.1080/10615806.201 4.962023

Scharfstein, L., Alfano, C., Beidel, D., & Wong, N. (2011). Children with generalized anxiety disorder do not have peer problems, just fewer friends. Child Psychiatry and Human Development, 42, 712–723. http://dx.doi.org/10.1007/s10578-011-0245-2

Storch, E. A., Masia-Warner, C., Crisp, H., & Klein, R. G. (2005). Peer victimization and social anxiety in adolescence: A prospective study. Aggressive Behavior, 31, 437–452. http://dx.doi.org/10.1002/ ab.20093

Semple, R. J., Lee, J., Rosa, D., & Miller, L. F. (2010). A randomized trial of mindfulness-based cognitive therapy for children: Promoting mindful attention to enhance social-emotional resiliency in children. Journal of Child and Family Studies, 19, 218–229. http://dx.doi.org/10.1007/s10826-009-9301-y

Strauss, C. C., Lease, C. A., Kazdin, A. E., Dulcan, M. K., & Last, C. G. (1989). Multimethod assessment of the social competence of children with anxiety disorders. Journal of Clinical Child Psychology, 18, 184–189. http://dx.doi.org/10.1207/ s15374424jccp1802_10

Settipani, C. A., & Kendall, P. C. (2013). Social functioning in youth with anxiety disorders: Association with anxiety severity and outcomes from cognitive–behavioral therapy. Child Psychiatry and Human Development, 44, 1–18. http://dx.doi.org/ 10.1007/s10578-012-0307-0

Suveg, C., Hudson, J. L., Brewer, G., FlannerySchroeder, E., Gosch, E., & Kendall, P. C. (2009). Cognitive–behavioral therapy for anxiety-disordered youth: Secondary outcomes from a randomized clinical trial evaluating child and family modalities. Journal of Anxiety Disorders, 23, 341–349. http:// dx.doi.org/10.1016/j.janxdis.2009.01.003

Silverman, W. K., & Nelles, W. B. (1988). The anxiety disorders interview schedule for children. Journal of the American Academy of Child and Adolescent Psychiatry, 27, 772–778. http://dx.doi.org/ 10.1097/00004583-198811000-00019 Sood, E., & Kendall, P. C. (2007). Assessing anxious selftalk in youth: The Negative Affectivity Self-Statement Questionnaire—Anxiety Scale. Cognitive Therapy and Research, 31, 603–618. http://dx.doi.org/10.1007/ s10608-006-9043-8

Swan, A. J., & Kendall, P. C. (2016). Fear and missing out: Youth anxiety and functional outcomes. Clinical Psychology: Science and Practice, 23, 417–435. http:// dx.doi.org/10.1111/cpsp.12169 Treadwell, K. R. H., & Kendall, P. C. (1996). Self-talk in youth with anxiety disorders: States of mind, content specificity, and treatment outcome. Journal of Consulting and Clinical Psychology, 64, 941–950. http://dx.doi.org/10.1037/0022-006X.64.5.941 229

Copyright American Psychological Association. Not for further distribution.

Kendall et al.

Van Ameringen, M., Mancini, C., & Farvolden, P. (2003). The impact of anxiety disorders on educational achievement. Journal of Anxiety Disorders, 17, 561–571. http://dx.doi.org/10.1016/ S0887-6185(02)00228-1

Weissman, M. M., Wickramaratne, P., Nomura, Y., Warner, V., Pilowsky, D., & Verdeli, H. (2006). Offspring of depressed parents: 20 years later. American Journal of Psychiatry, 163, 1001–1008. http://dx.doi.org/10.1176/ajp.2006.163.6.1001

van der Bruggen, C. O., Stams, G. J. J. M., & Bögels, S. M. (2008). Research review: The relation between child and parent anxiety and parental control: A meta-analytic review. Journal of Child Psychology and Psychiatry, 49, 1257–1269. http://dx.doi.org/10.1111/ j.1469-7610.2008.01898.x

Wittchen, H.-U., Nelson, C. B., & Lachner, G. (1998). Prevalence of mental disorders and psychosocial impairments in adolescents and young adults. Psychological Medicine, 28, 109–126. http:// dx.doi.org/10.1017/S0033291797005928

Verduin, T. L., & Kendall, P. C. (2008). Peer perceptions and liking of children with anxiety disorders. Journal of Abnormal Child Psychology, 36, 459–469. http:// dx.doi.org/10.1007/s10802-007-9192-6 Villabø, M., Gere, M., Torgersen, S., March, J. S., & Kendall, P. C. (2012). Diagnostic efficiency of the child and parent versions of the Multidimensional Anxiety Scale for Children. Journal of Clinical Child and Adolescent Psychology, 41, 75–85. http:// dx.doi.org/10.1080/15374416.2012.632350 Walkup, J. T., Albano, A. M., Piacentini, J., Birmaher, B., Compton, S. N., Sherrill, J. T., . . . Kendall, P. C. (2008). Cognitive–behavioral therapy, sertraline, or a combination in childhood anxiety. New England Journal of Medicine, 359, 2753–2766. http:// dx.doi.org/10.1056/NEJMoa0804633 Weems, C. F., Silverman, W. K., Rapee, R. M., & Pina, A. A. (2003). The role of control in childhood anxiety disorders. Cognitive Therapy and Research, 27, 557–568. http://dx.doi.org/10.1023/ A:1026307121386 Wei, C., & Kendall, P. C. (2014). Parental involvement: Contribution to childhood anxiety and its treatment. Clinical Child and Family Psychology Review, 17, 319–339. http://dx.doi.org/10.1007/ s10567-014-0170-6

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Wolk, C. B., Kendall, P. C., & Beidas, R. S. (2015). Cognitive–behavioral therapy for child anxiety confers long-term protection from suicidality. Journal of the American Academy of Child and Adolescent Psychiatry, 54, 175–179. http://dx.doi.org/10.1016/ j.jaac.2014.12.004 Wood, J. J. (2006). Effect of anxiety reduction on children’s school performance and social adjustment. Developmental Psychology, 42, 345–349. http:// dx.doi.org/10.1037/0012-1649.42.2.345 Wood, J. J., McLeod, B. D., Sigman, M., Hwang, W.-C., & Chu, B. C. (2003). Parenting and childhood anxiety: Theory, empirical findings, and future directions. Journal of Child Psychology and Psychiatry, 44, 134–151. http://dx.doi.org/10.1111/ 1469-7610.00106 Woodward, L. J., & Fergusson, D. M. (2001). Life course outcomes of young people with anxiety disorders in adolescence. Journal of the American Academy of Child and Adolescent Psychiatry, 40, 1086–1093. http:// dx.doi.org/10.1097/00004583-200109000-00018 Zisser, A., & Eyberg, S. M. (2010). Parent–child interaction therapy and the treatment of disruptive behavior disorders. In J. R. Weisz & A. E. Kazdin (Eds.), Evidence-based psychotherapies for children and adolescents (2nd ed., pp. 179–193). New York, NY: Guilford Press.

Chapter 12

Understanding and Managing Obsessive-Compulsive Disorder in Children and Adolescents

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Carly Johnco and Eric A. Storch

Obsessive-compulsive disorder (OCD) is characterized by recurrent and intrusive thoughts, images and impulses (obsessions), and repetitive patterns of overt or covert behavior (compulsions) that serve to neutralize feelings of anxiety and discomfort. Despite being conceptualized under one diagnostic label, symptom presentation of OCD is heterogeneous. There have been considerable advances in the understanding, assessment, and treatment of OCD in children and adolescents, and this chapter explores the current diagnostic definition and conceptualization of OCD, including common phenotypic symptom presentations and nosology. Epidemiological literature is reviewed to summarize incidence and prevalence rates, as well as gender distribution, age/developmental differences, and etiological factors. Diagnostic comorbidity and the impact of OCD on social, family, and functional outcomes during this critical developmental phase is also discussed. Clinical and selfreport assessment measures are reviewed, and a critical review of the literature assessing psychological and pharmacological treatment options for pediatric OCD is provided. We focus on an overview of specific factors relevant to the successful amelioration of symptoms, including core treatment components for OCD (e.g., exposure and response prevention), and treatment targets (e.g., family accommodation).

Diagnosis and Clinical Presentation of ObsessiveCompulsive Disorder in Children and Adolescents Until the fifth version of the Diagnostic and Statistical Manual of Mental Disorders (DSM–5; American Psychiatric Association, 2013), OCD was classified as an anxiety disorder. The reclassification has OCD as an independent condition under the obsessive-compulsive related disorders, alongside conditions like body dysmorphic disorder, trichotillomania (hair pulling), hoarding disorder, excoriation disorder (skin picking), and tic disorders. Although not without debate (Storch, Abramowitz, & Goodman, 2008), this shift in categorization was in response to an increasing body of research that suggests considerable diagnostic and symptom overlap between these conditions. However, comorbidity between OCD and anxiety disorders remains commonplace. The DSM–5 diagnostic criteria for OCD (American Psychiatric Association, 2013) requires the presence of obsessions, compulsions, or both. Obsessions are defined as recurrent and persistent thoughts, urges, or impulses that are experienced as intrusive and unwanted, and cause marked anxiety or distress. An individual attempts to ignore or suppress these thoughts, urges, or images, or to neutralize them with some other thought or action

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(i.e., performing a compulsion). Compulsions are repetitive behaviors (e.g., hand washing, ordering, checking) or mental acts (e.g., praying, counting, repeating words silently) that an individual feels driven to perform in response to an obsession or according to rules that are rigidly applied. These behaviors or mental acts are aimed at preventing or reducing anxiety or distress, or to prevent some dreaded event or situation. Notably, these behaviors or mental acts are not connected in a realistic way with what they are designed to neutralize or prevent, or are clearly excessive (although young children are often unable to articulate the aims of these behaviors and mental acts). To meet diagnostic criteria, obsessions and/or compulsions must be timeconsuming or cause clinically meaningful distress or impairment in social, occupational (e.g., school), or other important areas of functioning. Diagnostic criteria specify that symptoms cannot be attributable to the effects of a substance or medical condition, or to another mental disorder. There are also a number of symptoms that can potentially overlap with other mental health conditions, and it is important to differentiate the diagnostic specificity of these symptoms (e.g., obsessions vs. excessive worry in generalized anxiety disorder; obsessions vs. preoccupation with appearance in body dysmorphic disorder or eating disorders; obsessions vs. sexual urges/fantasies as in paraphilic disorders; compulsions vs. difficulty discarding items in hoarding disorder; compulsions vs. ritualized eating behavior in eating disorders; compulsions vs. repetitive patterns of behavior in autism spectrum disorder or complex tic symptoms). Engaging in compulsive behaviors (including behavioral or mental rituals, suppression, and avoidance) is one of the key factors that maintains OCD symptoms. Individuals with OCD believe that their compulsions serve the purpose of preventing danger, neutralizing obsessional thoughts, and/ or reducing urges/obsessional distress. Engaging in these behaviors is reinforced when the feared outcome does not come to pass and when the discomfort/urge reduces, increasing the likelihood of engagement in compulsive behavior in the future. For example, a child who has an intrusive obsessional thought about a parent being in a car accident 232

is likely to feel an inflated sense of responsibility to prevent this outcome (see the Cognitive Factors section) and believe that thinking about their parent being in an accident will influence the realistic probability of the accident occurring. In response, this child may engage in some sort of compulsive behavior (e.g., ritualized pattern of touching objects) with the belief that this will neutralize the thought and prevent the accident. When the parent returns safely, the child’s use of the compulsion is reinforced as protective, increasing the use of this strategy in response to subsequent intrusive thoughts. By engaging in compulsions, children do not learn that their internal thought process is unrelated to real-world risk, and that their parent would (in all likelihood) have returned safely regardless of their actions or thoughts. Despite the single diagnosis of OCD, symptom presentations vary widely. Within the OCD diagnosis, there are 13 major symptom categories: aggressive obsessions, contamination obsessions, sexual obsessions, hoarding obsessions, religious obsessions, symmetry obsessions, somatic obsessions, cleaning compulsions, checking compulsions, repeating compulsions, counting compulsions, ordering compulsions, and hoarding compulsions (Mataix-Cols, Nakatani, Micali, & Heyman, 2008). This list underscores the considerable variability in OCD presentations of which practitioners and researchers need to be aware. Although core treatment components are usually quite similar (e.g., exposure to the stimulus while preventing compulsive responses), the treatment will look considerably different on the basis of the particular symptom nosology. Although there are areas of considerable overlap between child, adolescent, and adult OCD symptomatology (Selles, Storch, & Lewin, 2014), there are also several developmental factors (e.g., limited insight, verbal ability, familial responses to symptoms) that make child and adolescent OCD differ somewhat to adult presentations. Insight can vary considerably in children and adolescents. In comparison with parent reports, children often minimize their symptoms (Canavera, Wilkins, Pincus, & Ehrenreich-May, 2009). This minimization could be the result of several factors, including social desirability, embarrassment, and/or poor insight.

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Understanding and Managing Obsessive-Compulsive Disorder in Children and Adolescents

Children often do not recognize their behaviors as excessive (Lewin et al., 2010; Selles et al., 2014), and as a result, will vary in their intrinsic motivation to change. The relationship between insight and verbal ability can further complicate matters in children with OCD. Especially in younger children, they may have difficulty articulating their symptoms (especially obsessive beliefs). Rather, children commonly report feeling an “urge” or “need” to complete a specific ritual. Children and adolescents also tend to present with a range of comorbidities that can make differential diagnosis complex. For example, tics are commonly comorbid in children with OCD, making it important to assess whether repetitive patterns of movements are a ritualized compulsive behavior, or more likely to be a complex motor tic. Children often find it difficult to articulate their experience of this movement, making it important to assess whether children experience a premonitory urge to tic, or whether the movement is a voluntary behavior in response to OCD-related discomfort (Lewin & Piacentini, 2010; Palumbo & Kurlan, 2007). Similarly, young children with OCD often present with behavioral and cognitive rigidity, avoidance, extreme routine adherence, drive for sameness, excessive sensory sensitivity, and reassurance seeking (Dar, Kahn, & Carmeli, 2012). These symptoms can be challenging to differentiate from autism spectrum disorders and several anxiety disorders (Kerns & Kendall, 2012; Kerns et al., 2014; Zandt, Prior, & Kyrios, 2007). One of the most common challenges for differential diagnosis in children and adolescents can be differentiating symptoms of OCD from generalized anxiety disorder (GAD), given there is considerable overlap between symptoms of worry and obsessions. OCD and GAD are underpinned by an intolerance of uncertainty, and involve uncontrollable repetitive cognitive activity that is often experienced as intrusive (Comer, Kendall, Franklin, Hudson, & Pimentel, 2004). The adult literature suggests differentiation may be possible by assessing whether the thought process has an identifiable trigger (worry is commonly associated with identifiable triggers, whereas obsessions are not), however it may be more difficult in children who often find it difficult

to identify and/or articulate triggers and cognitions (Comer et al., 2004). It may be easier to attribute symptoms to OCD in cases where the child experiences vivid imagery related to the thought (more common in OCD than worry), stereotypical OCD content (e.g., contamination), and metacognitive beliefs (see the Cognitive Factors section), as these features can be more suggestive of obsessions than worry. Further, behaviors resulting from worry tend to be more logical in nature (e.g., calling a parent following a worry about them being injured), but are often starkly illogical in nature in OCD (e.g., ritualized tapping to prevent harm; Comer et al., 2004). Age-appropriate development and stages are also important diagnostic considerations in children and adolescents with OCD. Certain rituals or behaviors can be typical behaviors at different stages of childhood. For example, bedtime rituals are a normal (and often important) part of childhood. Similarly, it is common for younger children to line up their toys, insist on having a specific toy to sleep with, and to jump or step in ritualized ways as part of a game (Leonard, Goldberger, Rapoport, Cheslow, & Swedo, 1990). Some level of magical thinking and rigidity is normal in children (Evans, Milanak, Medeiros, & Ross, 2002). When diagnosing and treating OCD, it is important for clinicians to consider normative and nonpathological behaviors from those that are more maladaptive, interfering, and impairing in nature. Epidemiology It is estimated that somewhere between 1% and 4% of children are affected by OCD (American Psychiatric Association, 2013; Douglass, Moffitt, Dar, McGee, & Silva, 1995; Geller, 2006; Zohar, 1999). Symptoms of OCD may begin at any age, however there tends to be a bimodal pattern to the age of symptom onset, with symptoms onset common during either childhood (between ages 8–12) or in early adulthood (around age 20; Anholt et al., 2014; Geller, 2006). Particularly during the prepubertal period, there tends to be an increased prevalence of OCD in boys (estimated ratio 3:2), with girls being more likely to experience onset during puberty or adolescence, and a more even gender split (or slight 233

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increased prevalence in women) during adulthood (Geller, 2006; Hanna, 1995; Kaufman et al., 1997; Zohar, 1999). Early onset of OCD symptoms (i.e., before age 10) has been associated with a somewhat different pattern from those who experience onset of symptoms at older ages. Specifically, early onset OCD has been associated with higher heritability, longer illness duration, and greater tic comorbidity (Garcia et al., 2009; Mancebo et al., 2008; Nakatani et al., 2011). In comparison to adults with OCD, studies have found that children and adolescents are less likely to experience sexual preoccupations, but are more likely to have harm and hoarding symptoms (Geller, 2006; Geller et al., 2001; Mancebo et al., 2008). Studies that have examined differences between children and adolescents with OCD have been less consistent. Overall, children tend to report slightly lower levels of obsessions, with an increase in obsessive and ruminative thinking during adolescence. Compulsions tend to be similar in children and adolescents, although hoarding tends to be more common in childhood. OCD is a chronic condition, and left untreated is often unremitting into adulthood. Approximately 41% of children and adolescents with OCD continue to experience OCD into their adult life, with this rate increasing to 61% when subthreshold levels of symptoms were included (Micali et al., 2010; Stewart et al., 2004). Age of onset, longer duration, and greater mental health comorbidity are all associated with poorer prognosis (Bloch et al., 2009; Garcia et al., 2009; Mancebo et al., 2014, 2008; Nakatani et al., 2011; Stewart et al., 2004). Risks and Maintaining Factors Despite decades of research, there is no clearly defined answer to this question of what causes OCD. The current evidence suggests that OCD is most likely the result of an interaction between a variety of genetic, neurological, cognitive, behavioral, and environmental factors. There are several factors that appear to place children at an increased risk for developing OCD. Some of these risk factors are open to being modified during early intervention or treatment programs for OCD. 234

Family Transmission It is common for OCD to run in families. Family studies vary slightly in their findings, with most indicating that between 7% and 30% of children with OCD have at least one first degree relative with OCD (do Rosario-Campos et al., 2005; Lenane et al., 1990; Nestadt, Grados, & Samuels, 2010; Pauls, Alsobrook, Goodman, Rasmussen, & Leckman, 1995). Risk estimates suggest that OCD is 32.5 times more common among the first-degree relatives of children with OCD than among children without OCD (do RosarioCampos et al., 2005). This family link is strongest in children who experience OCD onset prior to age 7 (do Rosario-Campos et al., 2005; Nestadt et al., 2000; Pauls et al., 1995). The transmission of OCD within families can be the result of genetic pathways, as well as the result of shared environmental factors.

Genetic Factors In children, it is estimated that genetic influences account for 45% to 65% of the variance in OCD symptoms (compared with 27%–47% in adults; van Grootheest, Cath, Beekman, & Boomsma, 2005). Twin studies have consistently found that OCD concordance is more common in monozygotic twins compared with dizygotic twins (Pauls, 2010; van Grootheest et al., 2005). However, despite investigation into several candidate genes, no “OCD gene” has been identified. Rather than a specific genetic predisposition to developing OCD, the genetic transmission of OCD is likely to be far more complex. Although a thorough review of candidate genes is beyond the scope of this chapter, there is increasing evidence to suggest that there are certain genetic mutations in individuals with OCD that affect how the serotonergic and dopaminergic neurotransmitter systems operate (e.g., mutations in the serotonin transporter gene hSERT and 5-HTTLPR polymorphism; see Nestadt et al., 2010; Pauls, 2010; Pauls, Abramovitch, Rauch, & Geller, 2014; Taylor, 2013 for a review).

Neurobiological Factors Most information about neurobiological functioning in OCD comes from adult samples. With few studies using child and adolescent samples, it is unclear how relevant these findings are to children with OCD.

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Understanding and Managing Obsessive-Compulsive Disorder in Children and Adolescents

Neurotransmitters.  Most research on neurotransmitters and neurobiological functioning has been conducted with adults with OCD, although there are a small number of trials using pediatric samples. Overall, there is some evidence that individuals with OCD show some abnormalities in neurotransmitter functioning, with the primary neurotransmitters implicated being serotonin, dopamine, and glutamine (Goodman et al., 1990; Pauls, 2010; Pauls et al., 2014). Rather than there being too much or too little of these neurotransmitters, it is the way in which they are processed that appears to be affected in individuals with OCD. Serotonin is the primary neurotransmitter associated with OCD. The serotonin transporter genes are responsible for terminating the action of serotonin in the synaptic gap and recycling it. Mutations in the transporter (e.g., 5-HTTLPR polymorphism and hSERT) result in the transporter being overactive, removing and metabolizing serotonin before it has had a chance to adequately activate the receiver neuron. Support for the serotonin hypothesis of OCD has primarily been gained from the efficacy of selective serotonin reuptake inhibitor (SSRI) medications and clomipramine (a tricyclic antidepressant) for treating OCD, both of which limit the reabsorption of serotonin, allowing more time for it to bind to the postsynaptic neuron (Geller et al., 2003). Animal studies have shown an increase in compulsive behaviors in response to suppression of serotonin (Chou-Green, Holscher, Dallman, & Akana, 2003; Tsaltas et al., 2005), further supporting the role of serotonin in OCD. Although serotonin is the primary neurotransmitter implicated in OCD, there is some evidence suggesting a role of dopamine and glutamate. Serotonin is an important modulator of dopaminergic activity, and most likely implicated in the dysregulation of dopamine. Dopamine is a neurotransmitter that is highly implicated in reward and rewardmotivated behavior. Compulsive behaviors are often associated with a (short-term) level of pleasure or relief in individuals with OCD. Research findings in relation to dopamine and OCD are mixed, but overall evidence suggests some association between altered dopamine transmission and OCD symptoms. Support for dopamine has primarily come from rodent studies that have shown an increase in

compulsive behaviors when dopamine pathways are activated, and from the efficacy of pharmacological treatments using dopaminergic antagonists (Campbell et al., 1999; Denys, Zohar, & Westenberg, 2004; Feinberg, 1991; Leonard et al., 1991; Szechtman, Sulis, & Eilam, 1998). The role of glutamate is not well understood. However, some studies have shown increased levels of glutamate in the cerebrospinal fluid of adults with OCD (Chakrabarty, Bhattacharyya, Christopher, & Khanna, 2005), and others have found decreased levels of glutamate in certain brain regions (Rosenberg et al., 2000). Similar to hSERT, mutations in the glutamate transporter gene SLC1A1 has also been found in individuals with OCD, metabolizing glutamate too rapidly (Porton et al., 2013). Overall, it is unclear precisely how glutamate is implicated in OCD and more research is needed. Given that most of the research has been with adults, there is reason for readers to interpret these findings with caution. Neurobiological functioning.  Advancements in neuroimaging technology has facilitated research into functioning within the brains of individuals with OCD. Although it is unclear whether differences in neurological functioning are the cause of OCD or the result of experiencing OCD, there does appear to be some differences in neurological functioning between individuals who experience OCD and those who do not. There is little evidence that actual brain structures are impaired in individuals with OCD. Rather, it appears to be the communication between certain regions that is affected. There are three main brain regions affected in OCD: the cortex, striatum, and thalamus (Harrison et al., 2009; Nakao, Okada, & Kanba, 2014; Saxena & Rauch, 2000). These areas communicate with each other via neurological circuits called corticostriato-thalamic pathways. These pathways communicate messages from the basal ganglia (responsible for the coordination of movement), and the motor/premotor cortex (the area that initiates nerve impulses for voluntary muscle movement), and the orbitofrontal cortex (involved in decision making). Overall, these brain regions are involved in initiating behavior and terminating behavior. Dysfunctional 235

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communication between these structures results in children with OCD getting “stuck” in repetitive loops of behaviors and thoughts (Nakao et al., 2014). For example, most people lock the door once and leave. Dysfunction in these brain circuits mean that individuals with OCD can get stuck repeating this processing, checking the locks repeatedly, even when they do not want to perform the action. Neuropsychological functioning.  Neurological dysfunction would usually indicate impairments in neuropsychological functioning. Although this tends to be the case in adult OCD (Abramovitch, Abramowitz, & Mittelman, 2013), results from studies of children and adolescents with OCD have found inconsistent findings in relation to the impact of OCD on planning, response inhibition, set shifting and cognitive flexibility, verbal memory, nonverbal memory, processing speed, working memory, visuospatial functions, and attention. Some studies have found children who experience OCD perform similarly to controls (e.g., Beers et al., 1999), whereas others find comparatively poorer functioning (e.g., Andrés et al., 2007). However, a recent meta-analysis of 11 studies concluded that there is no evidence of a significant difference in neuropsychological functioning in children and adolescents with OCD (Abramovitch et al., 2015). Some have suggested that children who display deficits in these neuropsychological functions may be at an increased risk of developing OCD, there is no conclusive evidence to support this. Autoimmunity.  There has been debate about and interest in the phenomenology of children and adolescents who develop rapid-onset OCD symptoms following infection. Most notably, it has been suggested that OCD may develop as an autoimmune response to streptococcal infections, a condition called pediatric autoimmune neuropsychiatric disorders associated with streptococcal infections (PANDAS). This condition is characterized by an abrupt (often overnight) onset of OCD and/or tic symptoms following recent streptococcal infection, accompanied by neuropsychiatric symptoms, cognitive decline, and behavioral regression, including restricted eating, anxiety, significant declines in handwriting and math abilities, increased 236

urinary frequency and sensory hypersensitivity (Swedo et al., 1998). The most notable differences to typical OCD, is the presence of neurological abnormalities, particularly choreiform movements (a repetitive, jerky, and involuntary movement), and a significant regression in handwriting skills and math abilities (Bernstein, Victor, Pipal, & Williams, 2010). The proposed mechanism underlying PANDAS is an autoimmune inflammation of the basal ganglia, a brain region heavily implicated in OCD symptomatology (Snider & Swedo, 2004). In more recent years this condition has been renamed pediatric acute-onset neuropsychiatric syndrome (PANS) to encompasses cases who meet the symptom phenotype but are negative for Group A Streptococcal infection (Swedo, Leckman, & Rose, 2012). The onset of PANS has been noted in response to other triggers, including Lyme’s disease, H1N1 virus, and following prophylactic antibiotic treatment (Murphy, Gerardi, & ParkerAthill, 2014).

Cognitive Factors There are several characteristic errors in the way children with OCD think about and interpret information (Clark & Purdon, 1993; Libby, Reynolds, Derisley, & Clark, 2004; Obsessive Compulsive Cognitions Working Group, 1997). These patterns of cognitive processing are proposed to increase risk for—and maintain—OCD. The experience of intrusive thoughts is not unique to OCD. In fact, intrusions are a common occurrence, and can be bizarre and/or unwanted in nature (Purdon & Clark, 1994; Rachman & de Silva, 1978; Salkovskis & Harrison, 1984). Although most individuals quickly disregard intrusions, individuals with OCD typically inflate the importance of these thoughts and/or images. The difference between a normal intrusive thought and an obsession is not the content, but the individual’s reaction to it. Table 12.1 describes several common patterns of cognitive processing in individuals with OCD. Depending on the content, children with OCD will often experience intense feelings of anxiety, guilt, shame, and/or embarrassment in response to these intrusions, and use compulsive behaviors in an attempt to “un-do” or neutralize the intrusions.

Understanding and Managing Obsessive-Compulsive Disorder in Children and Adolescents

Table 12.1 Common Patterns of Cognitive Processing in Individuals With Obsessive-Compulsive Disorder (OCD) Description

Cognitive error

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Overimportance of thoughts and thought–action fusion

Inflated responsibility and magical thinking

Excessive concern with controlling thoughts

Overestimation of danger Intolerance of uncertainty

Perfectionism

This pattern of thinking refers to the degree of importance that is attached to thoughts or images. It is common for individuals with OCD to believe that simply thinking about a behavior/action is equivalent to carrying it out. This pattern can lead to increased guilt, shame and embarrassment about behaviors that the person has never actually engaged in. Thought–action fusion can also commonly lead people to believe that their experience of having the thought or image will make the event more likely to happen, causing increased risk to oneself of others. Individuals with OCD often feel that they are critically and personally responsible for preventing harm and critical negative outcomes. Magical thinking (or superstitious thinking) is closely related to inflated responsibility. This cognitive error refers to the belief that the individual can perform some special action that will (magically) prevent a negative outcome from happening. In this circumstance, there is no objective link between the action and the prevention of harm (e.g., engaging in ritualized touching to prevent a parent having a heart attack). Having an overvalued belief in the importance of thoughts and an inflated sense of personal responsibility, individuals with OCD often believe that it is important (and possible) to control their thoughts (including images). They often believe that controlling their thoughts will prevent the obsessional content or action from happening (thought–action fusion). However, thought suppression research has shown that attempting to avoid or prevent a specific thought usually has a rebound effect, with the particular thought or image increasing in frequency. Similar to individuals with anxiety disorders, individuals with OCD typically overestimate the level of danger or threat in a given situation, and underestimate their ability to cope. Because we cannot see into the future, uncertainty is unavoidable. Many people with OCD believe that they need to know (and that it is possible to know) for certain that nothing bad will happen. To plan and prepare for every possible negative outcome, individuals with this belief commonly exhibit doubting, excessive reassurance seeking, and compulsive rituals. Paradoxically, the more that the individual attempts to plan and prepare to avoid danger, the more extreme their reaction is likely to be when an unforeseen situation arises—maintaining their anxiety about uncertainty. Perfectionism involves putting relentless pressure on oneself to meet high standards, and tends to include a judgement of self-worth on the basis of achievement of these (usually unreasonable) high standards. Many individuals with OCD will engage in repetitive compulsions until they are “just right” or done perfectly. This is often coupled with the belief that obsessions can be neutralized (and risk prevented) if the compulsion is done perfectly or just right (overimportance of thoughts/inflated responsibility).

Behavioral Responses The behavioral model of OCD suggests that individuals learn to associate certain situations or object with danger, and as a result, begin to avoid them or perform rituals to reduce their anxiety. Avoidance is a normal reaction to fear, and is a reaction that is

intended to protect us and keep us safe from danger. In the case of OCD, the level of anxiety is excessive given the objective (if any) level of danger. Rituals often function as a form of subtle avoidance, with compulsions designed to induce an artificial sense of having eliminated or reduced the danger (Salkovskis, 237

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1985, 1999). Unfortunately, although avoidance and compulsive behaviors reduce anxiety and fear in the short-term, they exacerbate these very same symptoms in the long term. Avoidance behaviors are negatively reinforced over time. In the context of perceived danger, the individual uses avoidance or compulsions, and nothing bad happens. The individual mistakenly believes that the lack of danger is due to their behavior, increasing the likelihood that these behaviors will be used in subsequent situations or encounters (Clark & Purdon, 1993; Salkovskis, 1989, 1999). This vicious cycle ultimately maintains and exacerbates OCD over time, and usually leads to an overgeneralization of fear to other situations and objects. Treatment of OCD usually involves reversing this cycle, encouraging approach and engagement with the stimuli while refraining from compulsions. This leads to the individual learning that negative outcomes are less likely than they anticipate and that they can handle the outcomes.

Parent and Family Factors The role of genetics and heritability have been discussed but, there are other ways in which families impact children’s OCD. Symptoms of OCD are known to have an unwanted impact on family members and to cause disruption to family functioning. However, the way in which family members react to children’s OCD can also play a critical role in maintaining symptoms. The impact of OCD symptoms and family responses are most likely to be reciprocal and/or cyclical, with each influencing the other (Waters & Barrett, 2000). Family accommodation is a widely studied phenomenon in pediatric OCD. Family accommodation encompasses the direct and indirect participation of family members (e.g., parents, siblings) in children’s OCD rituals, and the modification of family functioning to facilitate compulsions (Calvocoressi et al., 1995, 1999; Lebowitz, Panza, Su, & Bloch, 2012). Family members usually engage in these behaviors to reduce children’s distress or to avoid triggering distress in children. However, like the behavioral models of OCD, allowing avoidance and facilitating compulsions serve to reinforce OCD symptomatology. Although children experience short-term relief, they ultimately experience greater long-term distress, impairment, and 238

illness severity. Around half of parents reporting daily participation in their children’s compulsive behaviors, and just over half proving daily reassurance (Albert et al., 2010; Peris et al., 2008); virtually all families accommodate on a weekly basis (Caporino et al., 2012; Storch, Geffken, Merlo, Jacob, et al., 2007). Family accommodation is an important clinical and prognostic factor to assess in children with OCD, and has been associated with increased symptom severity (Wu et al., 2016) and poorer treatment outcome (Garcia et al., 2010; Merlo, Lehmkuhl, Geffken, & Storch, 2009; Storch, Merlo, Larson, Marien, et al., 2008). Models of family accommodation often indicate that children’s use of coercive behaviors and/ or externalizing behaviors increase the likelihood of family accommodation (Caporino et al., 2012; Lebowitz, Omer, & Leckman, 2011; McGuire et al., 2015; Storch et al., 2012; Storch, Lewin, Geffken, Morgan, & Murphy, 2010). Children and adolescents with OCD commonly demand that parents and siblings engage in (or refrain from) certain behaviors to appease their OCD. When family members refuse, it is common for children to display a variety of behaviors designed to elicit accommodation, with behaviors ranging from coercive (e.g., demands, pleading, guilt, tantrums) to outright rage (e.g., verbal and physical aggression).

Life Events Twin studies have found that around 20% to 41% of the variability in OCD is attributable to environmental factors (van Grootheest et al., 2005), although others suggest that this rate is as high as 80% (Krebs, Waszczuk, Zavos, Bolton, & Eley, 2015). Although not all studies have replicated this finding, there are some studies that have found higher rates of stressful life events and exposure to traumatic events among children with OCD, including higher rates of abuse, victimization, bereavement, parental divorce, and illness (Fontenelle, Cocchi, Harrison, Miguel, & Torres, 2011; Rosso, Albert, Asinari, Bogetto, & Maina, 2012). Several studies have found these rates to be particularly elevated in the few years preceding OCD symptom onset. This elevated rate of exposure to traumatic and stressful events have also been found in a number of other

Understanding and Managing Obsessive-Compulsive Disorder in Children and Adolescents

pediatric mental health conditions (Kilpatrick et al., 2003; Manly, Kim, Rogosch, & Cicchetti, 2001); however, these are unlikely to cause OCD in and of themselves. Rather, these factors are likely to interact with several environmental, biological and psychological factors to increase children’s overall level of risk for OCD. The notable exception to this is traumatic brain injury, where compulsions can be a result of structural brain damage.

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Assessment There are several worthy methods used to diagnoses, assess, and track symptoms of OCD in children and adolescents. There are important benefits to incorporating evidence-based assessment into assessment during routine clinical practice. Assessment of OCD has several aims: screening for symptoms, establishment of a diagnosis and symptom presentation, identifying comorbidity, identifying the impact on psychosocial functioning, and tracking symptom change during treatment or over time. The primary methodologies used to assess symptoms are clinical interview measures and parent- or self-report measures.

Diagnostic Assessment of ObsessiveCompulsive Disorder Clinician-administered structured diagnostic interviews based on diagnostic criteria are the most common (and deemed the gold-standard) in assessment of OCD. These diagnostic interviews are most commonly used in research settings because of their length, although integrating their use into clinical settings is ideal. Most diagnostic measures are currently based on criteria specified in the DSM–IV–TR (American Psychiatric Association, 2000); however, these will be updated to reflect diagnostic criteria in the DSM–5 (American Psychiatric Association, 2013) over the coming years. The typical structure of structured interviews involves a separate assessment with parents and children, and final diagnoses being made on the basis of clinician judgement using information from both informants. There are several clinical interviews available to diagnose OCD in children and adolescents, although they vary in the extent to which they exclusively evaluate OCD or serve as a general screener for mental

health disorders, including OCD as one module. For example, the ADIS–IV (Silverman & Albano, 1996), Schedule for Affective Disorders and Schizophrenia for School-Age Children—Present and Lifetime version (K-SADS-PL; Kaufman et al., 1997), Diagnostic Interview Schedule for Children Version—IV (DISC–IV; Shaffer, Fisher, Lucas, Dulcan, & SchwabStone, 2000), and The Mini-International Neuropsychiatric Interview (MINI-KID; Sheehan et al., 2010) are four commonly used structured diagnostic clinical interviews for mental health conditions in children and adolescents. These measures assess a range of mental health concerns, including anxiety, depression, and externalizing psychopathology in addition to OCD. The K-SADS-PL, DISC–IV, and MINI-KID assess a range of disorders. The ADIS–IV, in contrast, has a stronger and more comprehensive focus on diagnosing anxiety, although also includes modules to assess other nonanxiety psychopathology in children. Except for tic disorders, these measures are ideal for broad-based diagnostic assessment of the diagnostic presence of OCD and comorbid psychopathology (tic disorders are poorly identified using the clinician-interviews and practice parameters suggest that tic disorders are best diagnosed using expert clinical assessment; Murphy, Lewin, Storch, & Stock, 2013). Once OCD diagnosis is established, it is commonplace to assess the severity of OCD symptomatology. Clinician-rated measures of severity are usually preferred over self- or parent-reported ratings of severity. Clinician-rated severity can be benchmarked against more generalized standard of OCD-related impairment and severity to facilitate comparison across children and adolescents, as well as to more objectively measure symptom change for an individual. Of the clinician interviews already mentioned, the ADIS–IV is the only one to provide a clinician-rated measure of OCD severity. In addition to providing information on diagnosis presence, the ADIS–IV also includes a clinical severity rating for each diagnosis on a 0–8 scale, with severity ratings ≥4 being indicative of diagnostic caseness (scores below 4 indicate subthreshold symptom presence). For a more detailed and thorough assessment specifically of OCD, the Children’s Yale-Brown Obsessive Compulsive Scale (CY-BOCS; 239

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Scahill et al., 1997) is a semistructured clinicianadministered measure of OCD symptom presence and severity over the previous week. Although clinician-rated diagnostic interviews are less commonly used in clinical practice, the CY-BOCS is more readily integrated into these settings and can be administered within a standard clinic session. The CY-BOCS is considered the gold-standard measure of OCD symptom nosology and severity for clinical and research purposes in children and adolescents. The CY-BOCS is often used in conjunction with one of the diagnostic interviews mentioned previously to facilitate assessment of comorbidity and severity (Scahill et al., 1997). The first section of the CY-BOCS involves a symptoms checklist to assess the presence of contamination, aggressive, sexual, hoarding, superstitious, somatic, religious, and miscellaneous obsessions, as well as washing/cleaning, checking, repeating, counting, ordering/arranging, hoarding/saving, superstitious, and miscellaneous compulsions. After identifying individual symptom presentation 10 severity items are administered to assess the frequency, distress, interference, resistance and controllability of obsessional and compulsive symptoms respectively. A separate obsession severity, compulsion severity, and total score can be computed from this measure. The CY-BOCS demonstrates excellent psychometric properties, including good internal consistency, convergent validity with measures other OCD measures, divergent validity from measures of general anxiety, depressive symptoms and tics, and treatment sensitivity (Flessner et al., 2011; Scahill et al., 1997; Storch et al., 2004). Factor analysis results vary in findings, with some studies supporting the obsession and compulsion two factor structure (McKay et al., 2003; Scahill et al., 1997) with an alternate factor structure representing a disturbance and severity factor also supported (McKay et al., 2003; Storch et al., 2006).

Parent- and Self-Report Measures of Obsessive-Compulsive Disorder in Children and Adolescents In addition to clinician-rated measures, it is helpful to collect child self-report and parent-report measures about symptom severity, along with other related constructs. Three self-report measures have 240

acceptable psychometric properties for assessing OCD symptoms in children and adolescents. Children’s Obsessional Compulsive Inventory–Revised (ChOCI-R; Uher, Heyman, Turner, & Shafran, 2008) is a 32-item parent- and self-report measure of OCD symptoms content and severity. Ten items assess the presence of obsession symptoms and 10 items assess compulsive symptoms on a 3-point scale. Following the items assessing symptom presence, six items assess severity and impairment associated with obsessional and compulsive symptoms, and include assessment of the duration, interference, distress, resistance, control, and avoidance related to obsession and compulsions respectively. These items parallel the format and content of the CY-BOCS. The ChOCI-R has shown good internal consistency, convergent validity with the CY-BOCS, strong parent–child agreement, and good divergent validity from measures of hyperactivity and conduct and peer problems (Uher et al., 2008). Children’s Florida Obsessive Compulsive Inventory (C-FOCI; Storch, Khanna, et al., 2009) is a 17-item self-report screening measure that assesses the presence (or absence) of obsessions and compulsions. Part B includes 5 questions that assess duration, distress, avoidance, controllability, and interference of symptoms. The C-FOCI has shown good internal consistency, treatment sensitivity, convergent validity with the CY-BOCS and with parentand child-ratings of OCD-related impairment, as well as good discriminant validity from measures of externalizing pathology (Storch, Khanna, et al., 2009). The Obsessive Compulsive Inventory–Child Version (Foa et al., 2010) is a 21-item self-report measure of OCD symptom severity in six domains: doubting/checking, obsessing, hoarding, washing, ordering, and neutralizing. This measure shows good test–retest reliability, convergent validity, divergent validity, and treatment sensitivity (Foa et al., 2010; Jones et al., 2013; Rodríguez-Jiménez et al., 2017). Two versions of the Leyton Obsessional Inventory–Child Version (LOI-CV) have been validated for youth, however psychometric studies have suggested that neither are ideal for screening purposes, or for measuring symptom severity. The original version consists of 20-items assessing the

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Understanding and Managing Obsessive-Compulsive Disorder in Children and Adolescents

presence of symptoms, and the degree of interference that each symptom caused (Berg, Rapoport, & Flament, 1986); a subsequent short-form iteration of this measure (Bamber, Tamplin, Park, Kyte, & Goodyer, 2002) consists of 11-items assessing frequency of symptoms in three domains: obsessions/ incompleteness, compulsions, and concern with cleanliness. Although initial psychometric assessment was promising in nonclinical samples and small clinical samples, further studies have shown problematic psychometric properties, including poor treatment sensitivity (Geller et al., 2003), poor internal consistency, poor convergent validity with the CY-BOCS and with parent-rated measures of OCD impairment (Storch et al., 2011). Overall, the LOI-CV is not an ideal measure of OCD severity.

Assessment of Related Psychosocial, Adaptive, and Family Functioning In addition to measures of symptom presence and severity, it is common to assess for the level of functional impairment related to children’s OCD symptoms. The most widely used measure of this is the parent- and child-report version of the Child Obsessive Compulsive Impact Scale–Revised (COIS; Piacentini, Peris, Bergman, Chang, & Jaffer, 2007). This measure assesses the level of impairment over the past month in various domains, including daily living, school, social, and family/activities. As noted, family accommodation has received considerable attention in the pediatric OCD literature. There are clinician-administered and parent-report measures of family accommodation that can be used to assess this construct. The interviewer-rated Family Accommodation Scale (FAS-IR; Calvocoressi et al., 1999) is a 13-item clinician-administered measure of family accommodation, initially developed for use with adults with OCD. This measure assesses family members’ active participation in OCD rituals (e.g., providing reassurance, performing rituals, providing items needed for rituals), and modification of family functioning because of children’s OCD symptoms (e.g., facilitating avoidance of certain places/items, changing family routines). The FAS-IR begins with an OCD symptom checklist, followed by 12 items probing about accommodation behaviors. The FAS-IR takes

around 30 mins to 45 mins to administer, and shows good interrater reliability, internal consistency, and convergent validity with measures of OCD symptom severity in adults (Calvocoressi et al., 1999). Although possible, the FAS-IR is rarely used in child and adolescent populations. Assessment of family accommodation in childhood has primarily used a 13-item parent-report measure that was derived from the initial clinician-administered FAS (Flessner et al., 2011; Peris et al., 2008; Stewart et al., 2008; Storch, Geffken, Merlo, Jacob, et al., 2007). A subsequent factor analysis suggested dropping one of these items, to form a 12-item measure that assesses two subscales: avoidance of OCD triggers and involvement in compulsions (Flessner et al., 2009). This 12-item measure demonstrates good internal consistency, convergent validity with measures of OCD, including the CY-BOCS and COIS, and discriminant validity from measures of trauma and self-concept (Flessner et al., 2009). Rage, externalizing symptoms, and coercive behaviors are all common in children and adolescents with OCD, and can be important constructs to assess given their relationship to symptoms severity, adaptive functioning, treatment outcome, and family accommodation. These constructs differ somewhat in their definition, but including at least one measure of these types of behaviors in children with OCD is important. The most commonly used measure of externalizing behaviors is the externalizing subscale of the Child Behavior Checklist (CBCL; Achenbach & Rescorla, 2001). Parallel forms exist for children ages 1 to 5.5 years and adolescents age 6 to 18 years. The CBCL is one of the most widely used parent-report measures of emotional functioning in children, and includes assessment of internalizing and externalizing symptoms. Age-normed t-scores are available, with the externalizing problems subscale including attention deficit/hyperactivity problems, oppositional defiant problems, and conduct problems. The Rage Outbursts and Anger Rating Scale (Budman et al., 2008) is a 3-item clinician rated measure of the intensity, duration, and frequency of rage outbursts over the past week. Rage encompasses a brief explosive episode of destructive and/ 241

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or aggressive behaviors directed toward others and/ or property. Rage episodes are common in children with OCD, and are one of the driving forces that increase family accommodation (Storch et al., 2012). This measure has shown good internal consistency, convergent validity, and divergent validity from symptom measures in samples of children with OCD (Storch et al., 2012) and other psychiatric conditions (Johnco et al., 2015; Nadeau et al., 2016). The Coercive–Disruptive Behavior Scale for Pediatric OCD (Lebowitz et al., 2011) is an 18-item parent-rated checklist of coercive behaviors common in children with OCD. These behaviors impose rules and restrictions on family members, and are usually used to elicit accommodation of OCD symptoms. Items are rated on a 5-point scale about the frequency with which children engage in them. This measure has shown good internal consistency, convergent validity, and divergent validity (Lebowitz et al., 2011; Lebowitz, Storch, MacLeod, & Leckman, 2015). Importantly, this scale captures a range of coercive and disruptive behaviors common to children with OCD that are not identified using other broad-based measures of externalizing pathology (Lebowitz et al., 2011). Treatment Psychological interventions and pharmacological interventions have shown efficacy for the treatment of OCD in children and adolescents. The current evidence suggests that psychological treatment, specifically cognitive–behavioral therapy (CBT) using exposure and response prevention, either alone or in combination with medication, should be offered as the first-line treatment for pediatric OCD (Geller & March, 2012; National Institute of Clinical Excellence, 2005). These treatments are detailed next.

Psychological Interventions CBT refers to a group of skills-based treatments that teach individuals how to change the way they think and the way they behave, and have been applied to a range of child and adolescent mental health problems (Kendall, 2012). The most effective treatment components include psychoeducation about OCD symptoms, course and maintaining 242

factors, cognitive therapy skills aimed at challenging inaccurate beliefs, and gradual exposure to feared situations with response prevention and relapse prevention skills (Rosa-Alcázar et al., 2015). The most effective type of CBT for OCD is exposure and response prevention (ERP), which involves exposing the individual to feared thoughts, images, objects, or situations and having them refrain from engaging in associated compulsive behaviors and/ or avoidance. Over time, the individual experiences reduction (or remission) of their OCD by challenging the validity of their OCD-related beliefs, and stopping their compulsions. Preventing ritual usage allows the young person to learn that their feared outcomes do not happen (or that they can cope with the outcomes), and breaks the cycle of triggerritual-anxiety reduction. Tasks involved in ERP are tailored to the symptom presentation of the child. For example, children with contamination-based OCD will increasingly engage with contaminated or unclean object while refraining from washing. Children with symmetry compulsions may deliberately make things uneven and leave them that way, or touch things in an uneven manner. There is consistent and robust evidence that CBT including ERP either with or without medication is the most effective treatment for OCD. Meta-analyses show greater effect sizes for CBT relative to pharmacotherapy (McGuire et al., 2015; Watson & Rees, 2008). As such, current practice parameters and clinical guidelines for pediatric OCD recommend using CBT alone in mild to moderate cases, and combined treatment in severe cases and those who do not respond to CBT in the first instance (Geller & March, 2012; National Institute of Clinical Excellence, 2005). The Pediatric OCD Treatment Study (2004), a landmark study of treatment for children with OCD, compared with CBT monotherapy, sertraline monotherapy (an SSRI), combined CBT and sertraline treatment, and placebo. This study found that combination treatment was superior to placebo, or CBT and sertraline monotherapy, however the efficacy for CBT and sertraline monotherapy differed between sites. Combined treatment did not differ from CBT in terms of remission of OCD, with remission rates of 53.6% in the combined condition, 39.3% for the CBT alone,

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Understanding and Managing Obsessive-Compulsive Disorder in Children and Adolescents

21.4% for sertraline alone, and 3.6% for placebo. This is consistent with other studies that have found no difference between combined treatment and CBT monotherapy in children (Storch et al., 2013) and adults with OCD (Foa et al., 2005). Predictor studies have found that greater psychiatric comorbidity, specifically comorbid disruptive behavior, ADHD, and depression, has been associated with poorer treatment outcome for CBT (Storch, Merlo, Larson, Geffken, et al., 2008). In the Pediatric OCD Treatment Study, less severe OCD symptoms, lower levels of functional impairment, fewer externalizing symptoms, lower levels of family accommodation, and better insight have been associated with better treatment response to CBT with ERP and SSRI treatment for OCD (Garcia et al., 2010). The same study found that children with a family history of OCD were six times less likely to benefit from CBT alone, but did respond to combination CBT and sertraline (Garcia et al., 2010). Although the core skills included in CBT with ERP do not vary, various formats have been trialed with children who have OCD. Pediatric OCD has a significant impact on families (Cooper, 1996; Storch, Lehmkuhl, et al., 2009; Waters & Barrett, 2000), and family accommodation behaviors have been implicated as an important maintaining factor. Although CBT with ERP for OCD with children has usually involved some level of involvement from parents to facilitate completion of ERP tasks between sessions, more recent trials for pediatric OCD have heavily emphasized involvement of parents in the treatment (Barrett, Healy-Farrell, & March, 2004; Freeman et al., 2003, 2008; Marien, Storch, Geffken, & Murphy, 2009; Storch, ­Geffken, Merlo, Mann, et al., 2007; Storch et al., 2016). These family-based treatments differ somewhat from earlier treatments (e.g., Pediatric OCD Treatment Study, 2004) where parents participated in a few specific sessions, or were involved briefly at the end of sessions to review homework assignment. In contrast, during family-based treatments parents receive psychoeducation about OCD, instruction on how to reduce family accommodation, and support around how to facilitate and reinforce ERP compliance in children. Family-based treatment has shown efficacy using individual and group formats, with OCD

remission in 88% and 76% of cases, respectively (Barrett et al., 2004). It has also been effectively implemented using weekly and intensive session frequency with 50% and 75% no longer meeting diagnostic criteria for OCD at the end of treatment (Storch, Geffken, Merlo, Mann, et al., 2007). In addition to varying the child- vs. family-focus, CBT with ERP has also been delivered using intensive and weekly session frequencies. The ability to access appropriately trained clinicians is commonly cited as a barrier to accessing effective treatment for children with OCD. As such, intensive formats of treatment have been proposed as one way to rapidly alleviate symptoms. Intensive treatment can allow families to travel to suitable practitioners for a brief period to receive effective treatment. The cost of accommodation and travel is usually lower than inpatient treatment, and often parallels the costs of sustained weekly treatment (usually 16 sessions) and/or the costs of impairment from unremitting OCD. Families commonly schedule this type of treatment during school breaks. Intensive treatment of OCD usually involves daily sessions for brief intervals of time. For example, children in some studies received 90 min sessions 5 days/week for 3 weeks (Marien et al., 2009), whereas children in other studies received twice daily sessions (50 mins–75 mins) for 5 days (Whiteside, Brown, & Abramowitz, 2008; Whiteside & Jacobsen, 2010; Whiteside et al., 2014) or a single 3-hour session followed by webcam follow-up (Farrell & Oar, 2014). In the only trial comparing the two, intensive CBT showed similar effectiveness to weekly CBT (Storch, Geffken, Merlo, Mann, et al., 2007). A more recent development in the pediatric OCD treatment literature has been the assessment of whether CBT efficacy can be enhanced using a medication originally used to treat tuberculosis, the partial N-methyl-D-aspartate (NMDA) agonist, d-cycloserine (DCS). Administration of DCS prior to or immediately after ERP is proposed to enhance fear extinction during exposure therapy (Hofmann et al., 2006; Norberg, Krystal, & Tolin, 2008). Although several studies have suggested efficacy for DCS in adult populations, there is little evidence of this in child and adolescent samples. Two studies have found no difference between DCS and placebo conditions (Storch, 243

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Murphy, et al., 2010; Storch et al., 2016). Another study found no difference between DCS and placebo at posttreatment, but found that children in the DCS group showed greater improvement at a 1-month follow-up (Farrell et al., 2013).

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Pharmacological Interventions SSRIs (e.g., fluvoxamine, fluoxetine, sertraline) and clomipramine (a tricyclic antidepressant), have shown efficacy for the treatment of OCD in children, particularly when these are used in conjunction with CBT with ERP (Pediatric OCD Treatment Study, 2004). Although clomipramine may be more effective than SSRIs for pediatric OCD (Geller et al., 2003), it is also associated with higher adverse effects (Feinberg, 1991), so SSRIs are generally the preferred pharmacological agent for children (Bandelow et al., 2012). Although SSRIs are more effective than placebo conditions, they are not more effective than CBT with ERP when used alone (Abramowitz, Whiteside, & Deacon, 2005; Pediatric OCD Treatment Study, 2004; Storch et al., 2013; Watson & Rees, 2008), and there is question if the combination is superior to CBT alone for children with OCD of “average” severity (Pediatric OCD Treatment Study, 2004; Storch et al., 2013). Although there is potential for symptom improvement, pharmacotherapies carry a risk of adverse side effects (e.g., behavioral activation in children taking SSRIs; Goodman, Murphy, & Storch, 2007; Murphy, Segarra, Storch, & Goodman, 2008), and symptoms often relapse after discontinuation of mediation (Asbahr et al., 2005; Leonard et al., 1991). As such, current professional guidelines for pediatric OCD recommend using CBT alone as a first-line treatment, with SSRI augmentation or monotherapy recommended in cases of extreme symptom severity or inability to access appropriate CBT treatment (Geller & March, 2012; National Institute of Clinical Excellence, 2005). Conclusion Pediatric OCD is a heterogeneous mental health condition that affects many children and adolescents and confers meaningful disability and reduced quality of life when left untreated. Research can build on the description of the nature of the condition, 244

prevailing theories of etiology and maintenance, and information about assessment and treatment. It is our hope that such research leads to greater understanding about this complex condition. In addition, it is our hope that there will be dissemination and implementation of the extremely effective interventions, especially psychological ones, to ameliorate obsessive-compulsive symptoms among children and adolescents.

References Abramovitch, A., Abramowitz, J. S., & Mittelman, A. (2013). The neuropsychology of adult obsessivecompulsive disorder: A meta-analysis. Clinical Psychology Review, 33, 1163–1171. http://dx.doi.org/ 10.1016/j.cpr.2013.09.004 Abramovitch, A., Abramowitz, J. S., Mittelman, A., Stark, A., Ramsey, K., & Geller, D. A. (2015). Research Review: Neuropsychological test performance in pediatric obsessive-compulsive disorder—A metaanalysis. Journal of Child Psychology and Psychiatry, 56, 837–847. http://dx.doi.org/10.1111/jcpp.12414 Abramowitz, J. S., Whiteside, S. P., & Deacon, B. J. (2005). The effectiveness of treatment for pediatric obsessive-compulsive disorder: A meta-analysis. Behavior Therapy, 36, 55–63. http://dx.doi.org/ 10.1016/S0005-7894(05)80054-1 Achenbach, T. M., & Rescorla, L. A. (2001). Manual for the ASEBA school-age forms and profiles: Child Behavior Checklist for Ages 6–18, Teacher’s Report Form, and Youth Self-Report. Burlington: University of Vermont. Albert, U., Bogetto, F., Maina, G., Saracco, P., Brunatto, C., & Mataix-Cols, D. (2010). Family accommodation in obsessive-compulsive disorder: Relation to symptom dimensions, clinical and family characteristics. Psychiatry Research, 179, 204–211. http://dx.doi.org/10.1016/j.psychres.2009.06.008 American Psychiatric Association. (2000). Diagnostic and statistical manual of mental disorders (4th ed., text revision). Washington, DC: Author. American Psychiatric Association. (2013). Diagnostic and statistical manual of mental disorders (5th ed.). Washington, DC: Author. Andrés, S., Boget, T., Lázaro, L., Penadés, R., Morer, A., Salamero, M., & Castro-Fornieles, J. (2007). Neuropsychological performance in children and adolescents with obsessive-compulsive disorder and influence of clinical variables. Biological Psychiatry, 61, 946–951. http://dx.doi.org/10.1016/ j.biopsych.2006.07.027 Anholt, G. E., Aderka, I. M., van Balkom, A. J., Smit, J. H., Schruers, K., van der Wee, N. J., . . . van Oppen, P.

Understanding and Managing Obsessive-Compulsive Disorder in Children and Adolescents

(2014). Age of onset in obsessive-compulsive disorder: Admixture analysis with a large sample. Psychological Medicine, 44, 185–194. http:// dx.doi.org/10.1017/S0033291713000470

Copyright American Psychological Association. Not for further distribution.

Asbahr, F. R., Castillo, A. R., Ito, L. M., Latorre, M. R., Moreira, M. N., & Lotufo-Neto, F. (2005). Group cognitive–behavioral therapy versus sertraline for the treatment of children and adolescents with obsessive-compulsive disorder. Journal of the American Academy of Child and Adolescent Psychiatry, 44, 1128–1136. http://dx.doi.org/10.1097/ 01.chi.0000177324.40005.6f Bamber, D., Tamplin, A., Park, R. J., Kyte, Z. A., & Goodyer, I. M. (2002). Development of a short leyton obsessional inventory for children and adolescents. Journal of the American Academy of Child and Adolescent Psychiatry, 41, 1246–1252. http:// dx.doi.org/10.1097/00004583-200210000-00015 Bandelow, B., Sher, L., Bunevicius, R., Hollander, E., Kasper, S., Zohar, J., & Möller, H. J. (2012). Guidelines for the pharmacological treatment of anxiety disorders, obsessive-compulsive disorder and posttraumatic stress disorder in primary care. International Journal of Psychiatry in Clinical Practice, 16, 77–84. http://dx.doi.org/10.3109/13651501. 2012.667114

Budman, C., Coffey, B. J., Shechter, R., Schrock, M., Wieland, N., Spirgel, A., & Simon, E. (2008). Aripiprazole in children and adolescents with Tourette disorder with and without explosive outbursts. Journal of Child and Adolescent Psychopharmacology, 18, 509–515. http://dx.doi.org/ 10.1089/cap.2007.061 Calvocoressi, L., Lewis, B., Harris, M., Trufan, S. J., Goodman, W. K., McDougle, C. J., & Price, L. H. (1995). Family accommodation in obsessivecompulsive disorder. American Journal of Psychiatry, 152, 441–443. http://dx.doi.org/ 10.1176/ajp.152.3.441 Calvocoressi, L., Mazure, C. M., Kasl, S. V., Skolnick, J., Fisk, D., Vegso, S. J., . . . Price, L. H. (1999). Family accommodation of obsessive-compulsive symptoms: Instrument development and assessment of family behavior. Journal of Nervous and Mental Disease, 187, 636–642. http://dx.doi.org/10.1097/ 00005053-199910000-00008 Campbell, K. M., de Lecea, L., Severynse, D. M., Caron, M. G., McGrath, M. J., Sparber, S. B., . . . Burton, F. H. (1999). OCD-like behaviors caused by a neuropotentiating transgene targeted to cortical and limbic D1+ neurons. Journal of Neuroscience, 19, 5044–5053.

Barrett, P., Healy-Farrell, L., & March, J. S. (2004). Cognitive–behavioral family treatment of childhood obsessive-compulsive disorder: A controlled trial. Journal of the American Academy of Child and Adolescent Psychiatry, 43, 46–62. http:// dx.doi.org/10.1097/00004583-200401000-00014

Canavera, K. E., Wilkins, K. C., Pincus, D. B., & Ehrenreich-May, J. T. (2009). Parent-child agreement in the assessment of obsessivecompulsive disorder. Journal of Clinical Child and Adolescent Psychology, 38, 909–915. http:// dx.doi.org/10.1080/15374410903258975

Beers, S. R., Rosenberg, D. R., Dick, E. L., Williams, T., O’Hearn, K. M., Birmaher, B., & Ryan, C. M. (1999). Neuropsychological study of frontal lobe function in psychotropic-naive children with obsessivecompulsive disorder. American Journal of Psychiatry, 156, 777–779.

Caporino, N. E., Morgan, J., Beckstead, J., Phares, V., Murphy, T. K., & Storch, E. A. (2012). A structural equation analysis of family accommodation in pediatric obsessive-compulsive disorder. Journal of Abnormal Child Psychology, 40, 133–143. http:// dx.doi.org/10.1007/s10802-011-9549-8

Berg, C. J., Rapoport, J. L., & Flament, M. (1986). The Leyton Obsessional Inventory-Child Version. Journal of the American Academy of Child Psychiatry, 25, 84–91. http://dx.doi.org/10.1016/S00027138(09)60602-6

Chakrabarty, K., Bhattacharyya, S., Christopher, R., & Khanna, S. (2005). Glutamatergic dysfunction in OCD. Neuropsychopharmacology, 30, 1735–1740. http://dx.doi.org/10.1038/sj.npp.1300733

Bernstein, G. A., Victor, A. M., Pipal, A. J., & Williams, K. A. (2010). Comparison of clinical characteristics of pediatric autoimmune neuropsychiatric disorders associated with streptococcal infections and childhood obsessive-compulsive disorder. Journal of Child and Adolescent Psychopharmacology, 20, 333–340. http://dx.doi.org/10.1089/cap.2010.0034 Bloch, M. H., Craiglow, B. G., Landeros-Weisenberger, A., Dombrowski, P. A., Panza, K. E., Peterson, B. S., & Leckman, J. F. (2009). Predictors of early adult outcomes in pediatric-onset obsessive-compulsive disorder. Pediatrics, 124, 1085–1093. http:// dx.doi.org/10.1542/peds.2009-0015

Chou-Green, J. M., Holscher, T. D., Dallman, M. F., & Akana, S. F. (2003). Compulsive behavior in the 5-HT2C receptor knockout mouse. Physiology and Behavior, 78, 641–649. http://dx.doi.org/10.1016/ S0031-9384(03)00047-7 Clark, D. A., & Purdon, C. (1993). New perspectives for a cognitive theory of obsessions. Australian Psychologist, 28, 161–167. http://dx.doi.org/ 10.1080/00050069308258896 Comer, J. S., Kendall, P. C., Franklin, M. E., Hudson, J. L., & Pimentel, S. S. (2004). Obsessing/worrying about the overlap between obsessive-compulsive disorder and generalized anxiety disorder in youth. Clinical 245

Johnco and Storch

Psychology Review, 24, 663–683. http://dx.doi.org/ 10.1016/j.cpr.2004.04.004 Cooper, M. (1996). Obsessive-compulsive disorder: Effects on family members. American Journal of Orthopsychiatry, 66, 296–304. http://dx.doi.org/ 10.1037/h0080180

Copyright American Psychological Association. Not for further distribution.

Dar, R., Kahn, D. T., & Carmeli, R. (2012). The relationship between sensory processing, childhood rituals and obsessive-compulsive symptoms. Journal of Behavior Therapy and Experimental Psychiatry, 43, 679–684. http://dx.doi.org/10.1016/j.jbtep.2011. 09.008 Denys, D., Zohar, J., & Westenberg, H. G. (2004). The role of dopamine in obsessive-compulsive disorder: Preclinical and clinical evidence. Journal of Clinical Psychiatry, 65(Suppl. 14), 11–17. do Rosario-Campos, M. C., Leckman, J. F., Curi, M., Quatrano, S., Katsovitch, L., Miguel, E. C., & Pauls, D. L. (2005). A family study of early-onset obsessivecompulsive disorder. American Journal of Medical Genetics. Part B, Neuropsychiatric Genetics, 136, 92–97. http://dx.doi.org/10.1002/ajmg.b.30149 Douglass, H. M., Moffitt, T. E., Dar, R., McGee, R., & Silva, P. (1995). Obsessive-compulsive disorder in a birth cohort of 18-year-olds: Prevalence and predictors. Journal of the American Academy of Child and Adolescent Psychiatry, 34, 1424–1431. http:// dx.doi.org/10.1097/00004583-199511000-00008 Evans, D. W., Milanak, M. E., Medeiros, B., & Ross, J. L. (2002). Magical beliefs and rituals in young children. Child Psychiatry and Human Development, 33, 43–58. http://dx.doi.org/10.1023/A:1016516205827 Farrell, L. J., & Oar, E. L. (2014). Intensive cognitive–behavioural treatment for obsessive compulsive disorder in children and adolescents. Psychopatholgy Review, 1, 182–188. Farrell, L. J., Waters, A. M., Boschen, M. J., Hattingh, L., McConnell, H., Milliner, E. L., . . . Storch, E. A. (2013). Difficult-to-treat pediatric obsessivecompulsive disorder: Feasibility and preliminary results of a randomized pilot trial of D-cycloserineaugmented behavior therapy. Depression and Anxiety, 30, 723–731. http://dx.doi.org/10.1002/da.22132 Feinberg, M. (1991). Clomipramine for obsessivecompulsive disorder. American Family Physician, 43, 1735–1738. Flessner, C. A., Freeman, J. B., Sapyta, J., Garcia, A., Franklin, M. E., March, J. S., & Foa, E. (2011). Predictors of parental accommodation in pediatric obsessive-compulsive disorder: Findings from the Pediatric Obsessive-Compulsive Disorder Treatment Study (POTS) trial. Journal of the American Academy of Child and Adolescent Psychiatry, 50, 716–725. http://dx.doi.org/10.1016/j.jaac.2011.03.019 246

Flessner, C. A., Sapyta, J., Garcia, A., Freeman, J. B., Franklin, M. E., Foa, E., & March, J. (2009). Examining the psychometric properties of the Family Accommodation Scale-Parent-Report (FAS-PR). Journal of Psychopathology and Behavioral Assessment, 31, 38–46. Foa, E. B., Coles, M., Huppert, J. D., Pasupuleti, R. V., Franklin, M. E., & March, J. (2010). Development and validation of a child version of the obsessive compulsive inventory. Behavior Therapy, 41, 121–132. http://dx.doi.org/10.1016/j.beth. 2009.02.001 Foa, E. B., Liebowitz, M. R., Kozak, M. J., Davies, S., Campeas, R., Franklin, M. E., . . . Tu, X. (2005). Randomized, placebo-controlled trial of exposure and ritual prevention, clomipramine, and their combination in the treatment of obsessivecompulsive disorder. American Journal of Psychiatry, 162, 151–161. http://dx.doi.org/10.1176/ appi.ajp.162.1.151 Fontenelle, L. F., Cocchi, L., Harrison, B. J., Miguel, E. C., & Torres, A. R. (2011). Role of stressful and traumatic life events in obsessive–compulsive disorder. Neuropsychiatry, 1, 61–69. http:// dx.doi.org/10.2217/npy.10.1 Freeman, J. B., Garcia, A. M., Coyne, L., Ale, C., Przeworski, A., Himle, M., . . . Leonard, H. L. (2008). Early childhood OCD: Preliminary findings from a family-based cognitive–behavioral approach. Journal of the American Academy of Child and Adolescent Psychiatry, 47, 593–602. http://dx.doi.org/10.1097/ CHI.0b013e31816765f9 Freeman, J. B., Garcia, A. M., Fucci, C., Karitani, M., Miller, L., & Leonard, H. L. (2003). Family-based treatment of early-onset obsessive-compulsive disorder. Journal of Child and Adolescent Psychopharmacology, 13(Suppl. 1), 71–80. http:// dx.doi.org/10.1089/104454603322126368 Garcia, A. M., Freeman, J. B., Himle, M. B., Berman, N. C., Ogata, A. K., Ng, J., . . . Leonard, H. (2009). Phenomenology of early childhood onset obsessive compulsive disorder. Journal of Psychopathology and Behavioral Assessment, 31, 104–111. http:// dx.doi.org/10.1007/s10862-008-9094-0 Garcia, A. M., Sapyta, J. J., Moore, P. S., Freeman, J. B., Franklin, M. E., March, J. S., & Foa, E. B. (2010). Predictors and moderators of treatment outcome in the Pediatric Obsessive Compulsive Treatment Study (POTS I). Journal of the American Academy of Child and Adolescent Psychiatry, 49, 1024–1033. http:// dx.doi.org/10.1016/j.jaac.2010.06.013 Geller, D. A. (2006). Obsessive-compulsive and spectrum disorders in children and adolescents. Psychiatric Clinics of North America, 29, 353–370. http:// dx.doi.org/10.1016/j.psc.2006.02.012

Understanding and Managing Obsessive-Compulsive Disorder in Children and Adolescents

Geller, D. A., Biederman, J., Faraone, S., Agranat, A., Cradock, K., Hagermoser, L., . . . Coffey, B. J. (2001). Developmental aspects of obsessive compulsive disorder: Findings in children, adolescents, and adults. Journal of Nervous and Mental Disease, 189, 471–477. http://dx.doi.org/10.1097/00005053200107000-00009

Copyright American Psychological Association. Not for further distribution.

Geller, D. A., Biederman, J., Stewart, S. E., Mullin, B., Martin, A., Spencer, T., & Faraone, S. V. (2003). Which SSRI? A meta-analysis of pharmacotherapy trials in pediatric obsessive-compulsive disorder. American Journal of Psychiatry, 160, 1919–1928. http://dx.doi.org/10.1176/appi.ajp.160.11.1919 Geller, D. A., & March, J. (2012). Practice parameter for the assessment and treatment of children and adolescents with obsessive-compulsive disorder. Journal of the American Academy of Child and Adolescent Psychiatry, 51, 98–113. http:// dx.doi.org/10.1016/j.jaac.2011.09.019 Goodman, W. K., McDougle, C. J., Price, L. H., Riddle, M. A., Pauls, D. L., & Leckman, J. F. (1990). Beyond the serotonin hypothesis: a role for dopamine in some forms of obsessive compulsive disorder? Journal of Clinical Psychiatry, 51 Suppl, 36–43. Goodman, W. K., Murphy, T. K., & Storch, E. A. (2007). Risk of adverse behavioral effects with pediatric use of antidepressants. Psychopharmacology, 191, 87–96. http://dx.doi.org/10.1007/s00213-006-0642-6 Hanna, G. L. (1995). Demographic and clinical features of obsessive-compulsive disorder in children and adolescents. Journal of the American Academy of Child and Adolescent Psychiatry, 34, 19–27. http:// dx.doi.org/10.1097/00004583-199501000-00009 Harrison, B. J., Soriano-Mas, C., Pujol, J., Ortiz, H., López-Solà, M., Hernández-Ribas, R., . . . Cardoner, N. (2009). Altered corticostriatal functional connectivity in obsessive-compulsive disorder. Archives of General Psychiatry, 66, 1189–1200. http:// dx.doi.org/10.1001/archgenpsychiatry.2009.152 Hofmann, S. G., Meuret, A. E., Smits, J. A., Simon, N. M., Pollack, M. H., Eisenmenger, K., . . . Otto, M. W. (2006). Augmentation of exposure therapy with D-cycloserine for social anxiety disorder. Archives of General Psychiatry, 63, 298–304. http://dx.doi.org/ 10.1001/archpsyc.63.3.298 Johnco, C., Salloum, A., De Nadai, A. S., McBride, N., Crawford, E. A., Lewin, A. B., & Storch, E. A. (2015). Incidence, clinical correlates and treatment effect of rage in anxious children. Psychiatry Research, 229, 63–69. http://dx.doi.org/10.1016/j.psychres. 2015.07.071 Jones, A. M., De Nadai, A. S., Arnold, E. B., McGuire, J. F., Lewin, A. B., Murphy, T. K., & Storch, E. A. (2013). Psychometric properties of the obsessive compulsive inventory: Child version in children and

adolescents with obsessive-compulsive disorder. Child Psychiatry and Human Development, 44, 137–151. http://dx.doi.org/10.1007/s10578-012-0315-0 Kaufman, J., Birmaher, B., Brent, D., Rao, U., Flynn, C., Moreci, P., . . . Ryan, N. (1997). Schedule for affective disorders and schizophrenia for school-age children-present and lifetime version (K-SADS-PL): Initial reliability and validity data. Journal of the American Academy of Child and Adolescent Psychiatry, 36, 980–988. http://dx.doi.org/10.1097/00004583199707000-00021 Kendall, P. C. (2012). Child and adolescent therapy: Cognitive–behavioral procedures (4th ed.). New York, NY: Guilford Press. Kerns, C. M., & Kendall, P. C. (2012). The presentation and classification of anxiety in autism spectrum disorder. Clinical Psychology: Science and Practice, 19, 323–347. http://dx.doi.org/10.1111/cpsp.12009 Kerns, C. M., Kendall, P. C., Berry, L., Souders, M. C., Franklin, M. E., Schultz, R. T., . . . Herrington, J. (2014). Traditional and atypical presentations of anxiety in youth with autism spectrum disorder. Journal of Autism and Developmental Disorders, 44, 2851–2861. http://dx.doi.org/10.1007/s10803-014-2141-7 Kilpatrick, D. G., Ruggiero, K. J., Acierno, R., Saunders, B. E., Resnick, H. S., & Best, C. L. (2003). Violence and risk of PTSD, major depression, substance abuse/ dependence, and comorbidity: Results from the National Survey of Adolescents. Journal of Consulting and Clinical Psychology, 71, 692–700. http:// dx.doi.org/10.1037/0022-006X.71.4.692 Krebs, G., Waszczuk, M. A., Zavos, H. M., Bolton, D., & Eley, T. C. (2015). Genetic and environmental influences on obsessive-compulsive behaviour across development: A longitudinal twin study. Psychological Medicine, 45, 1539–1549. http:// dx.doi.org/10.1017/S0033291714002761 Lebowitz, E. R., Omer, H., & Leckman, J. F. (2011). Coercive and disruptive behaviors in pediatric obsessive-compulsive disorder. Depression and Anxiety, 28, 899–905. http://dx.doi.org/10.1002/ da.20858 Lebowitz, E. R., Panza, K. E., Su, J., & Bloch, M. H. (2012). Family accommodation in obsessive-compulsive disorder. Expert Review of Neurotherapeutics, 12, 229–238. http://dx.doi.org/ 10.1586/ern.11.200 Lebowitz, E. R., Storch, E. A., MacLeod, J., & Leckman, J. F. (2015). Clinical and family correlates of coercive–disruptive behavior in children and adolescents with obsessive–compulsive disorder. Journal of Child and Family Studies, 24, 2589–2597. http://dx.doi.org/10.1007/s10826-014-0061-y Lenane, M. C., Swedo, S. E., Leonard, H., Pauls, D. L., Sceery, W., & Rapoport, J. L. (1990). Psychiatric 247

Johnco and Storch

disorders in first degree relatives of children and adolescents with obsessive compulsive disorder. Journal of the American Academy of Child and Adolescent Psychiatry, 29, 407–412. http://dx.doi.org/ 10.1097/00004583-199005000-00012

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Leonard, H. L., Goldberger, E. L., Rapoport, J. L., Cheslow, D. L., & Swedo, S. E. (1990). Childhood rituals: Normal development or obsessivecompulsive symptoms? Journal of the American Academy of Child and Adolescent Psychiatry, 29, 17–23. http://dx.doi.org/10.1097/00004583199001000-00004 Leonard, H. L., Swedo, S. E., Lenane, M. C., Rettew, D. C., Cheslow, D. L., Hamburger, S. D., & Rapoport, J. L. (1991). A double-blind desipramine substitution during long-term clomipramine treatment in children and adolescents with obsessive-compulsive disorder. Archives of General Psychiatry, 48, 922–927. http://dx.doi.org/10.1001/ archpsyc.1991.01810340054007 Lewin, A. B., Bergman, R. L., Peris, T. S., Chang, S., McCracken, J. T., & Piacentini, J. (2010). Correlates of insight among youth with obsessive-compulsive disorder. Journal of Child Psychology and Psychiatry, 51, 603–611. http://dx.doi.org/10.1111/ j.1469-7610.2009.02181.x Lewin, A. B., & Piacentini, J. (2010). Evidence-based assessment of child obsessive compulsive disorder: Recommendations for clinical practice and treatment research. Child and Youth Care Forum, 39, 73–89. http://dx.doi.org/10.1007/s10566-009-9092-8 Libby, S., Reynolds, S., Derisley, J., & Clark, S. (2004). Cognitive appraisals in young people with obsessivecompulsive disorder. Journal of Child Psychology and Psychiatry, 45, 1076–1084. http://dx.doi.org/ 10.1111/j.1469-7610.2004.t01-1-00300.x Mancebo, M. C., Boisseau, C. L., Garnaat, S. L., Eisen, J. L., Greenberg, B. D., Sibrava, N. J., . . . Rasmussen, S. A. (2014). Long-term course of pediatric obsessive-compulsive disorder: 3 years of prospective follow-up. Comprehensive Psychiatry, 55, 1498–1504. http://dx.doi.org/10.1016/j.comppsych.2014.04.010 Mancebo, M. C., Garcia, A. M., Pinto, A., Freeman, J. B., Przeworski, A., Stout, R., . . . Rasmussen, S. A. (2008). Juvenile-onset OCD: Clinical features in children, adolescents and adults. Acta Psychiatrica Scandinavica, 118, 149–159. http://dx.doi.org/ 10.1111/j.1600-0447.2008.01224.x Manly, J. T., Kim, J. E., Rogosch, F. A., & Cicchetti, D. (2001). Dimensions of child maltreatment and children’s adjustment: Contributions of developmental timing and subtype. Development and Psychopathology, 13, 759–782. Marien, W. E., Storch, E. A., Geffken, G. R., & Murphy, T. K. (2009). Intensive family-based 248

cognitive–behavioral therapy for pediatric obsessivecompulsive disorder: Applications for treatment of medication partial- or nonresponders. Cognitive and Behavioral Practice, 16. http://dx.doi.org/10.1016/ j.cbpra.2008.1012.1006. Mataix-Cols, D., Nakatani, E., Micali, N., & Heyman, I. (2008). Structure of obsessive-compulsive symptoms in pediatric OCD. Journal of the American Academy of Child and Adolescent Psychiatry, 47, 773–778. http:// dx.doi.org/10.1097/CHI.0b013e31816b73c0 McGuire, J. F., Piacentini, J., Lewin, A. B., Brennan, E. A., Murphy, T. K., & Storch, E. A. (2015). A meta-analysis of cognitive–behavior therapy and medication for child obsessive–compulsive disorder: Moderators of treatment efficacy, response, and remission. Depression and Anxiety, 32, 580–593. http://dx.doi.org/10.1002/da.22389 McKay, D., Piacentini, J., Greisberg, S., Graae, F., Jaffer, M., Miller, J., . . . Yaryura-Tobias, J. A. (2003). The Children’s Yale-Brown Obsessive-Compulsive Scale: Item structure in an outpatient setting. Psychological Assessment, 15, 578–581. http:// dx.doi.org/10.1037/1040-3590.15.4.578 Merlo, L. J., Lehmkuhl, H. D., Geffken, G. R., & Storch, E. A. (2009). Decreased family accommodation associated with improved therapy outcome in pediatric obsessive-compulsive disorder. Journal of Consulting and Clinical Psychology, 77, 355–360. http://dx.doi.org/10.1037/a0012652 Micali, N., Heyman, I., Perez, M., Hilton, K., Nakatani, E., Turner, C., & Mataix-Cols, D. (2010). Longterm outcomes of obsessive-compulsive disorder: Follow-up of 142 children and adolescents. British Journal of Psychiatry, 197, 128–134. http:// dx.doi.org/10.1192/bjp.bp.109.075317 Murphy, T. K., Gerardi, D. M., & Parker-Athill, E. C. (2014). The PANDAS controversy: Why (and how) is it still unsettled? Current Developmental Disorders Reports, 1, 236–244. http://dx.doi.org/10.1007/ s40474-014-0025-3 Murphy, T. K., Lewin, A. B., Storch, E. A., & Stock, S. (2013). Practice parameter for the assessment and treatment of children and adolescents with tic disorders. Journal of the American Academy of Child and Adolescent Psychiatry, 52, 1341–1359. http:// dx.doi.org/10.1016/j.jaac.2013.09.015 Murphy, T. K., Segarra, A., Storch, E. A., & Goodman, W. K. (2008). SSRI adverse events: How to monitor and manage. International Review of Psychiatry, 20, 203–208. http://dx.doi.org/ 10.1080/09540260801889211 Nadeau, J. M., McBride, N. M., Dane, B. F., Collier, A. B., Keene, A. C., Hacker, L. E., . . . Storch, E. A. (2016). Psychometric evaluation of the rage outbursts and anger rating scale in an outpatient psychiatric sample.

Understanding and Managing Obsessive-Compulsive Disorder in Children and Adolescents

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Journal of Child and Family Studies, 25, 1229–1234. http://dx.doi.org/10.1007/s10826-015-0303-7 Nakao, T., Okada, K., & Kanba, S. (2014). Neurobiological model of obsessive-compulsive disorder: Evidence from recent neuropsychological and neuroimaging findings. Psychiatry and Clinical Neurosciences, 68, 587–605. http:// dx.doi.org/10.1111/pcn.12195

Pediatric OCD Treatment Study. (2004). Cognitive–behavior therapy, sertraline, and their combination for children and adolescents with obsessive-compulsive disorder: The Pediatric OCD Treatment Study (POTS) randomized controlled trial. JAMA: Journal of the American Medical Association, 292, 1969–1976. http:// dx.doi.org/10.1001/jama.292.16.1969

Nakatani, E., Krebs, G., Micali, N., Turner, C., Heyman, I., & Mataix-Cols, D. (2011). Children with very early onset obsessive-compulsive disorder: Clinical features and treatment outcome. Journal of Child Psychology and Psychiatry, 52, 1261–1268. http:// dx.doi.org/10.1111/j.1469-7610.2011.02434.x

Peris, T. S., Bergman, R. L., Langley, A., Chang, S., McCracken, J. T., & Piacentini, J. (2008). Correlates of accommodation of pediatric obsessive-compulsive disorder: Parent, child, and family characteristics. Journal of the American Academy of Child and Adolescent Psychiatry, 47, 1173–1181. http:// dx.doi.org/10.1097/CHI.0b013e3181825a91

National Institute of Clinical Excellence. (2005). NICE guidelines: Obsessive compulsive disorder (OCD) and body dysmorphic disorder (BDD). http://guidance. nice.org.uk/CG31 Nestadt, G., Grados, M., & Samuels, J. F. (2010). Genetics of obsessive-compulsive disorder. Psychiatric Clinics of North America, 33, 141–158. http://dx.doi.org/10.1016/j.psc.2009.11.001 Nestadt, G., Samuels, J., Riddle, M., Bienvenu, O. J., III, Liang, K. Y., LaBuda, M., . . . Hoehn-Saric, R. (2000). A family study of obsessive-compulsive disorder. Archives of General Psychiatry, 57, 358–363. http:// dx.doi.org/10.1001/archpsyc.57.4.358 Norberg, M. M., Krystal, J. H., & Tolin, D. F. (2008). A meta-analysis of D-cycloserine and the facilitation of fear extinction and exposure therapy. Biological Psychiatry, 63, 1118–1126. http://dx.doi.org/ 10.1016/j.biopsych.2008.01.012 Obsessive Compulsive Cognitions Working Group. (1997). Cognitive assessment of obsessivecompulsive disorder. Behaviour Research and Therapy, 35, 667–681. http://dx.doi.org/10.1016/ S0005-7967(97)00017-X Palumbo, D., & Kurlan, R. (2007). Complex obsessive compulsive and impulsive symptoms in Tourette’s syndrome. Neuropsychiatric Disease and Treatment, 3, 687–693. Pauls, D. L. (2010). The genetics of obsessive-compulsive disorder: A review. Dialogues in Clinical Neuroscience, 12, 149–163. Pauls, D. L., Abramovitch, A., Rauch, S. L., & Geller, D. A. (2014). Obsessive-compulsive disorder: An integrative genetic and neurobiological perspective. Nature Reviews Neuroscience, 15, 410–424. http:// dx.doi.org/10.1038/nrn3746 Pauls, D. L., Alsobrook, J. P., II, Goodman, W., Rasmussen, S., & Leckman, J. F. (1995). A family study of obsessive-compulsive disorder. American Journal of Psychiatry, 152, 76–84. http://dx.doi.org/ 10.1176/ajp.152.1.76

Piacentini, J., Peris, T. S., Bergman, R. L., Chang, S., & Jaffer, M. (2007). Functional impairment in childhood OCD: Development and psychometrics properties of the Child Obsessive-Compulsive Impact Scale-Revised (COIS-R). Journal of Clinical Child and Adolescent Psychology, 36, 645–653. http:// dx.doi.org/10.1080/15374410701662790 Porton, B., Greenberg, B. D., Askland, K., Serra, L. M., Gesmonde, J., Rudnick, G., . . . Kao, H. T. (2013). Isoforms of the neuronal glutamate transporter gene, SLC1A1/EAAC1, negatively modulate glutamate uptake: Relevance to obsessive-compulsive disorder. Translational Psychiatry, 3, e259. http:// dx.doi.org/10.1038/tp.2013.35 Purdon, C., & Clark, D. A. (1994). Obsessive intrusive thoughts in nonclinical subjects. Part II. Cognitive appraisal, emotional response and thought control strategies. Behaviour Research and Therapy, 32, 403–410. http://dx.doi.org/10.1016/ 0005-7967(94)90003-5 Rachman, S., & de Silva, P. (1978). Abnormal and normal obsessions. Behaviour Research and Therapy, 16, 233–248. http://dx.doi.org/10.1016/ 0005-7967(78)90022-0 Rodríguez-Jiménez, T., Godoy, A., Piqueras, J. A., Gavino, A., Martínez-González, A. E., & Foa, E. B. (2017). Factor structure and measurement invariance of the obsessive-compulsive inventory—Child version (OCI-CV) in general population. European Journal of Psychological Assessment, 33, 97–103. Rosa-Alcázar, A. I., Sánchez-Meca, J., Rosa-Alcázar, Á., Iniesta-Sepúlveda, M., Olivares-Rodríguez, J., & Parada-Navas, J. L. (2015). Psychological treatment of obsessive-compulsive disorder in children and adolescents: A meta-analysis. Spanish Journal of Psychology, 18, E20. http://dx.doi.org/10.1017/ sjp.2015.22 Rosenberg, D. R., MacMaster, F. P., Keshavan, M. S., Fitzgerald, K. D., Stewart, C. M., & Moore, G. J. (2000). Decrease in caudate glutamatergic concentrations in pediatric obsessive-compulsive 249

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disorder patients taking paroxetine. Journal of the American Academy of Child and Adolescent Psychiatry, 39, 1096–1103. http://dx.doi.org/10.1097/ 00004583-200009000-00008 Rosso, G., Albert, U., Asinari, G. F., Bogetto, F., & Maina, G. (2012). Stressful life events and obsessivecompulsive disorder: Clinical features and symptom dimensions. Psychiatry Research, 197, 259–264. http://dx.doi.org/10.1016/j.psychres.2011.10.005

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Salkovskis, P. M. (1985). Obsessional-compulsive problems: A cognitive–behavioural analysis. Behaviour Research and Therapy, 23, 571–583. http:// dx.doi.org/10.1016/0005-7967(85)90105-6 Salkovskis, P. M. (1989). Cognitive–behavioural factors and the persistence of intrusive thoughts in obsessional problems. Behaviour Research and Therapy, 27, 677–682. http://dx.doi.org/ 10.1016/0005-7967(89)90152-6 Salkovskis, P. M. (1999). Understanding and treating obsessive-compulsive disorder. Behaviour Research and Therapy, 37(Suppl. 1), S29–S52. http://dx.doi.org/ 10.1016/S0005-7967(99)00049-2 Salkovskis, P. M., & Harrison, J. (1984). Abnormal and normal obsessions—A replication. Behaviour Research and Therapy, 22, 549–552. http://dx.doi.org/ 10.1016/0005-7967(84)90057-3 Saxena, S., & Rauch, S. L. (2000). Functional neuroimaging and the neuroanatomy of obsessivecompulsive disorder. Psychiatric Clinics of North America, 23, 563–586. http://dx.doi.org/10.1016/ S0193-953X(05)70181-7 Scahill, L., Riddle, M. A., McSwiggin-Hardin, M., Ort, S. I., King, R. A., Goodman, W. K., . . . Leckman, J. F. (1997). Children’s Yale-Brown Obsessive Compulsive Scale: Reliability and validity. Journal of the American Academy of Child and Adolescent Psychiatry, 36, 844–852. http://dx.doi.org/ 10.1097/00004583-199706000-00023 Selles, R. R., Storch, E. A., & Lewin, A. B. (2014). Variations in symptom prevalence and clinical correlates in younger versus older youth with obsessive-compulsive disorder. Child Psychiatry and Human Development, 45, 666–674. http://dx.doi.org/ 10.1007/s10578-014-0435-9 Shaffer, D., Fisher, P., Lucas, C. P., Dulcan, M. K., & Schwab-Stone, M. E. (2000). NIMH Diagnostic Interview Schedule for Children Version IV (NIMH DISC–IV): Description, differences from previous versions, and reliability of some common diagnoses. Journal of the American Academy of Child and Adolescent Psychiatry, 39, 28–38. http://dx.doi.org/ 10.1097/00004583-200001000-00014 Sheehan, D. V., Sheehan, K. H., Shytle, R. D., Janavs, J., Bannon, Y., Rogers, J. E., . . . Wilkinson, B. (2010). Reliability and validity of the Mini International 250

Neuropsychiatric Interview for Children and Adolescents (MINI-KID). Journal of Clinical Psychiatry, 71, 313–326. http://dx.doi.org/10.4088/ JCP.09m05305whi Silverman, W. K., & Albano, A. M. (1996). The anxiety disorders interview schedule for DSM–IV—Child and parent versions. London, England: Oxford University Press. Snider, L. A., & Swedo, S. E. (2004). PANDAS: Current status and directions for research. Molecular Psychiatry, 9, 900–907. http://dx.doi.org/10.1038/ sj.mp.4001542 Stewart, S. E., Beresin, C., Haddad, S., Egan Stack, D., Fama, J., & Jenike, M. (2008). Predictors of family accommodation in obsessive-compulsive disorder. Annals of Clinical Psychiatry, 20(2), 65–70. http:// dx.doi.org/10.1080/10401230802017043 Stewart, S. E., Geller, D. A., Jenike, M., Pauls, D., Shaw, D., Mullin, B., & Faraone, S. V. (2004). Long-term outcome of pediatric obsessive-compulsive disorder: A meta-analysis and qualitative review of the literature. Acta Psychiatrica Scandinavica, 110, 4–13. http://dx.doi.org/10.1111/j.1600-0447.2004.00302.x Storch, E. A., Abramowitz, J., & Goodman, W. K. (2008). Where does obsessive-compulsive disorder belong in DSM–V? Depression and Anxiety, 25, 336–347. http:// dx.doi.org/10.1002/da.20488 Storch, E. A., Bussing, R., Small, B. J., Geffken, G. R., McNamara, J. P., Rahman, O., . . . Murphy, T. K. (2013). Randomized, placebo-controlled trial of cognitive–behavioral therapy alone or combined with sertraline in the treatment of pediatric obsessivecompulsive disorder. Behaviour Research and Therapy, 51, 823–829. http://dx.doi.org/10.1016/j.brat.2013.09.007 Storch, E. A., Geffken, G. R., Merlo, L. J., Jacob, M. L., Murphy, T. K., Goodman, W. K., . . . Grabill, K. (2007). Family accommodation in pediatric obsessive-compulsive disorder. Journal of Clinical Child and Adolescent Psychology, 36, 207–216. http:// dx.doi.org/10.1080/15374410701277929 Storch, E. A., Geffken, G. R., Merlo, L. J., Mann, G., Duke, D., Munson, M., . . . Goodman, W. K. (2007). Family-based cognitive–behavioral therapy for pediatric obsessive-compulsive disorder: Comparison of intensive and weekly approaches. Journal of the American Academy of Child and Adolescent Psychiatry, 46, 469–478. http://dx.doi.org/10.1097/ chi.0b013e31803062e7 Storch, E. A., Jones, A. M., Lack, C. W., Ale, C. M., Sulkowski, M. L., Lewin, A. B., . . . Murphy, T. K. (2012). Rage attacks in pediatric obsessivecompulsive disorder: Phenomenology and clinical correlates. Journal of the American Academy of Child and Adolescent Psychiatry, 51, 582–592. http:// dx.doi.org/10.1016/j.jaac.2012.02.016

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Storch, E. A., Khanna, M., Merlo, L. J., Loew, B. A., Franklin, M., Reid, J. M., . . . Murphy, T. K. (2009). Children’s Florida Obsessive Compulsive Inventory: Psychometric properties and feasibility of a self-report measure of obsessive-compulsive symptoms in youth. Child Psychiatry and Human Development, 40, 467–483. http://dx.doi.org/10.1007/s10578-009-0138-9

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Storch, E. A., Lehmkuhl, H. D., Pence, S. L., Geffken, G. R., Ricketts, E., Storch, J. F., & Murphy, T. K. (2009). Parental experiences of having a child with obsessive-compulsive disorder: Associations with clinical characteristics and caregiver adjustment. Journal of Child and Family Studies, 18, 249–258. http://dx.doi.org/10.1007/s10826-008-9225-y Storch, E. A., Lewin, A. B., Geffken, G. R., Morgan, J. R., & Murphy, T. K. (2010). The role of comorbid disruptive behavior in the clinical expression of pediatric obsessive-compulsive disorder. Behaviour Research and Therapy, 48, 1204–1210. http:// dx.doi.org/10.1016/j.brat.2010.09.004 Storch, E. A., Merlo, L. J., Larson, M. J., Geffken, G. R., Lehmkuhl, H. D., Jacob, M. L., . . . Goodman, W. K. (2008). Impact of comorbidity on cognitive–behavioral therapy response in pediatric obsessive-compulsive disorder. Journal of the American Academy of Child and Adolescent Psychiatry, 47, 583–592. http://dx.doi.org/10.1097/ CHI.0b013e31816774b1 Storch, E. A., Merlo, L. J., Larson, M. J., Marien, W. E., Geffken, G. R., Jacob, M. L., . . . Murphy, T. K. (2008). Clinical features associated with treatmentresistant pediatric obsessive-compulsive disorder. Comprehensive Psychiatry, 49, 35–42. http:// dx.doi.org/10.1016/j.comppsych.2007.06.009 Storch, E. A., Murphy, T. K., Adkins, J. W., Lewin, A. B., Geffken, G. R., Johns, N. B., . . . Goodman, W. K. (2006). The children’s Yale-Brown obsessivecompulsive scale: Psychometric properties of child- and parent-report formats. Journal of Anxiety Disorders, 20, 1055–1070. http:// dx.doi.org/10.1016/j.janxdis.2006.01.006 Storch, E. A., Murphy, T. K., Geffken, G. R., Soto, O., Sajid, M., Allen, P., . . . Goodman, W. K. (2004). Psychometric evaluation of the Children’s YaleBrown Obsessive-Compulsive Scale. Psychiatry Research, 129, 91–98. http://dx.doi.org/10.1016/ j.psychres.2004.06.009 Storch, E. A., Murphy, T. K., Goodman, W. K., Geffken, G. R., Lewin, A. B., Henin, A., . . . Geller, D. A. (2010). A preliminary study of D-cycloserine augmentation of cognitive–behavioral therapy in pediatric obsessive-compulsive disorder. Biological Psychiatry, 68, 1073–1076. http://dx.doi.org/ 10.1016/j.biopsych.2010.07.015 Storch, E. A., Park, J. M., Lewin, A. B., Morgan, J. R., Jones, A. M., & Murphy, T. K. (2011). The Leyton

Obsessional Inventory—Child Version Survey Form does not demonstrate adequate psychometric properties in American youth with pediatric obsessive-compulsive disorder. Journal of Anxiety Disorders, 25, 574–578. http://dx.doi.org/10.1016/ j.janxdis.2011.01.005. Storch, E. A., Wilhelm, S., Sprich, S., Henin, A., Micco, J., Small, B. J., . . . Geller, D. A. (2016). Efficacy of augmentation of cognitive behavior therapy with weight-adjusted d-cycloserine vs placebo in pediatric obsessive-compulsive disorder: A randomized clinical trial. JAMA Psychiatry, 73, 779–788. http://dx.doi.org/ 10.1001/jamapsychiatry.2016.1128 Swedo, S. E., Leckman, J. F., & Rose, N. R. (2012). From research subgroup to clinical syndrome: Modifying the PANDAS criteria to describe PANS (pediatric acute-onset neuropsychiatric syndrome). Pediatrics and Therapeutics, 2, 113. http:// dx.doi.org/10.4172/2161-0665.1000113 Swedo, S. E., Leonard, H. L., Garvey, M., Mittleman, B., Allen, A. J., Perlmutter, S., . . . Dubbert, B. K. (1998). Pediatric autoimmune neuropsychiatric disorders associated with streptococcal infections: Clinical description of the first 50 cases. American Journal of Psychiatry, 155, 264–271. Szechtman, H., Sulis, W., & Eilam, D. (1998). Quinpirole induces compulsive checking behavior in rats: A potential animal model of obsessive-compulsive disorder (OCD). Behavioral Neuroscience, 112, 1475–1485. http:// dx.doi.org/10.1037/0735-7044.112.6.1475 Taylor, S. (2013). Molecular genetics of obsessivecompulsive disorder: A comprehensive meta-analysis of genetic association studies. Molecular Psychiatry, 18, 799–805. http://dx.doi.org/10.1038/mp.2012.76 Tsaltas, E., Kontis, D., Chrysikakou, S., Giannou, H., Biba, A., Pallidi, S., . . . Rabavilas, A. (2005). Reinforced spatial alternation as an animal model of obsessivecompulsive disorder (OCD): Investigation of 5-HT2C and 5-HT1D receptor involvement in OCD pathophysiology. Biological Psychiatry, 57, 1176–1185. http://dx.doi.org/10.1016/j.biopsych.2005.02.020 Uher, R., Heyman, I., Turner, C. M., & Shafran, R. (2008). Self-, parent-report and interview measures of obsessive-compulsive disorder in children and adolescents. Journal of Anxiety Disorders, 22, 979–990. http://dx.doi.org/10.1016/j.janxdis. 2007.10.001 van Grootheest, D. S., Cath, D. C., Beekman, A. T., & Boomsma, D. I. (2005). Twin studies on obsessivecompulsive disorder: A review. Twin Research and Human Genetics, 8, 450–458. http:// dx.doi.org/10.1375/twin.8.5.450 Waters, T. L., & Barrett, P. M. (2000). The role of the family in childhood obsessivecompulsive disorder. Clinical Child and 251

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Family Psychology Review, 3, 173–184. http:// dx.doi.org/10.1023/A:1009551325629 Watson, H. J., & Rees, C. S. (2008). Meta-analysis of randomized, controlled treatment trials for pediatric obsessive-compulsive disorder. Journal of Child Psychology and Psychiatry, 49, 489–498. http:// dx.doi.org/10.1111/j.1469-7610.2007.01875.x Whiteside, S. P., Brown, A. M., & Abramowitz, J. S. (2008). Five-day intensive treatment for adolescent OCD: A case series. Journal of Anxiety Disorders, 22, 495–504. http://dx.doi.org/10.1016/j.janxdis. 2007.05.001 Whiteside, S. P., & Jacobsen, A. B. (2010). An uncontrolled examination of a 5-day intensive treatment for pediatric OCD. Behavior Therapy, 41, 414–422. http://dx.doi.org/10.1016/ j.beth.2009.11.003 Whiteside, S. P., McKay, D., De Nadai, A. S., Tiede, M. S., Ale, C. M., & Storch, E. A. (2014). A baseline

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controlled examination of a 5-day intensive treatment for pediatric obsessive-compulsive disorder. Psychiatry Research, 220, 441–446. http:// dx.doi.org/10.1016/j.psychres.2014.07.006 Wu, M. S., McGuire, J. F., Martino, C., Phares, V., Selles, R. R., & Storch, E. A. (2016). A meta-analysis of family accommodation and OCD symptom severity. Clinical Psychology Review, 45, 34–44. http:// dx.doi.org/10.1016/j.cpr.2016.03.003 Zandt, F., Prior, M., & Kyrios, M. (2007). Repetitive behaviour in children with high functioning autism and obsessive compulsive disorder. Journal of Autism and Developmental Disorders, 37, 251–259. http://dx.doi.org/10.1007/ s10803-006-0158-2 Zohar, A. H. (1999). The epidemiology of obsessivecompulsive disorder in children and adolescents. Child and Adolescent Psychiatric Clinics of North America, 8, 445–460.

Chapter 13

Mood Disorders in Childhood and Adolescence

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Mary A. Fristad and Sarah R. Black

Childhood depression was a controversial diagnosis in the 1980s (Fristad & Algorta, 2013) but is now considered a highly prevalent disorder. Suicide, a not infrequent but most unfortunate consequence of depression, is the second leading cause of death in 10- to 14-year-olds and 15- to 24-year-olds (Kann et al., 2014). Related to depression, bipolar disorder in childhood received very little empirical attention until this century; many practicing clinicians have received little to no training in its diagnosis and treatment (Fristad & Algorta, 2013). We describe these diagnoses; outline the current diagnostic criteria; summarize evidence about incidence, prevalence, and comorbidity; and review clinical profiles, treatments, and etiologic theories. Depressive Disorders Although depressive and bipolar disorders were grouped together under the mood disorders section of fourth edition of the Diagnostic and Statistical Manual of Mental Diseases (DSM–IV; American Psychiatric Association, 2000), the current fifth edition (DSM–5; American Psychiatric Association, 2013) separates the disorders into two sections: depressive disorders and bipolar and related disorders.

Diagnostic Criteria The DSM–5 identifies six separate depressive disorders, all of which can affect children and adolescents as well as adults. Chief among these is the diagnosis of major depressive disorder (MDD), with core symptoms of dysphoric mood and/or anhedonia;

in children, however, irritable mood may stand in for depressed mood. A major challenge in diagnosing MDD comes when defining the core symptoms; how much depression warrants a diagnosis? The DSM–5 provides some guidance in requiring that a depressed or irritable mood, or anhedonia, last “most of the day, nearly every day” (p. 160). Therefore, practitioners must ascertain how much of a typical day an individual feels depressed/irritable/ anhedonic, and for how long that level of daily depression/irritability/anhedonia has lasted. Diagnostic criteria for MDD require that children experience at least one major depressive episode (MDE). The core requirement for an MDE is a period of depressed mood, irritable mood, or anhedonia that lasts for at least 2 weeks; once this criterion is met, an individual must also experience at least four of the following associated symptoms: appetite disturbance (increase or decrease) and/or substantial weight loss or gain; sleep disturbance (insomnia or hypersomnia); psychomotor agitation or retardation that is observable by others; fatigue or loss of energy; feelings of worthlessness or excessive, inappropriate guilt; diminished ability to concentrate or make decisions; or suicidal ideation (ranging from a wish to die to a suicide attempt). As with the core symptoms, these associated symptoms should be present most of the day, nearly every day for at least a 2-week period. It is important to note that not only should these symptoms represent a change from an individual’s typical functioning, but should also only occur or occur more intensely during the period of depressed

http://dx.doi.org/10.1037/0000065-013 APA Handbook of Psychopathology: Vol. 2. Child and Adolescent Psychopathology, J. N. Butcher (Editor-in-Chief) Copyright © 2018 by the American Psychological Association. All rights reserved.

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mood. For example, an individual who suffers from chronic insomnia should not be considered to be experiencing depression-related insomnia unless it is meaningfully worse than usual (e.g., initial insomnia increasing from 1 to 3 hours), or if the insomnia is experienced in a different way (e.g., initial insomnia vs. early morning waking) when he or she is feeling depressed. Similarly, a change in sleeping patterns would not be considered a symptom of depression if the change occurs outside of the depressed mood. Notably, unlike in previous versions, the DSM–5 does not exclude an individual from receiving an MDD diagnosis if they have recently experienced the loss of a loved one. This change is in response to research suggesting that depressive episodes following a death may present challenges to the bereaved, and in some cases, may warrant therapeutic intervention. The DSM–5 encourages clinicians to consider typical responses to loss when assessing for depressive disorders, however, and provides guidance on how to judge symptoms as typical or atypical. For example, although individuals with MDD often focus on pessimistic or self-critical ruminations, bereaved individuals typically focus on thoughts and memories of the deceased. Regardless of content, however, clinicians should consider duration when distinguishing between typical grief and MDD, as long periods of grief may result in social, physical, or occupational impairment (Melhem, Moritz, Walker, Shear, & Brent, 2007). Persistent depressive disorder (PDD) represents a new category, encompassing dysthymic disorder and chronic MDD. As its name suggests, PDD requires a more prolonged course of depressed mood, but does not require the same level of intensity. Specifically, individuals may be diagnosed with PDD if they experience depressed or irritable mood most of the day, more days than not; this is typically conceptualized as greater than 50% of an individual’s waking hours. Although adults are required to experience a persistent depressed mood for at least 2 years to warrant a diagnosis of PDD, children and adolescents need only experience it for 1 year; additionally, as in MDD, irritable mood may be substituted for depressed mood in childhood. Associated symptoms of PDD include appetite disturbance 254

(increase or decrease), sleep disturbance (insomnia or hypersomnia), low energy or fatigue, low selfesteem, poor concentration or indecision, and feelings of hopelessness. To receive a diagnosis of PDD, children must exhibit at least two of these associated symptoms during the period of depressed mood. Additionally, they must not be without the symptoms for more than 2 months during this period (i.e., have no more than 2 well months). Premenstrual dysphoric disorder (PMDD) is a controversial diagnosis. Opponents cite concerns about pathologizing typical premenstrual distress, as well as broader consequences of having an official diagnosis that could be used to label women as emotionally unstable because of their menstrual cycle (Zachar & Kendler, 2014). Proponents, however, cite research suggesting that PMDD can be successfully distinguished from MDD, significantly impacts women’s functioning, and can be effectively treated with psychotherapeutic and pharmacological interventions (Epperson et al., 2012). Central to the diagnosis of PMDD is the observation and careful monitoring of symptoms across the menstrual cycle. Specifically, PMDD symptoms must be present in the week before menstruation, start improving within a few days after the onset of menses, and remit completely in the week following menstruation. Core symptoms of PMDD include at least one of the following, occurring only during the time specified: affective instability, irritable mood, depressed mood, or marked anxiety. Additionally, women must also experience at least one of the following associated symptoms: anhedonia, difficulty concentrating, lethargy, appetite disturbance, sleep disturbance, feeling overwhelmed, or physical symptoms (e.g., joint or muscle pain, bloating, breast tenderness or swelling). Individuals must report a total of at least five symptoms from either category during the period of disturbance. Necessarily, only women who have experienced menarche (first menstrual period) can be diagnosed with PMDD. The diagnoses described thus far pertain to children and adults, but the disruptive mood dysregulation disorder (DMDD) is a diagnosis added to the DSM–5 to address concerns about of overdiagnosis of bipolar disorders in children and adolescents (Carlson, 2016); DMDD is only applied to children

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between the ages of 6 and 18. Diagnosing DMDD is challenging because its core symptom—marked irritability—is present in many other disorders (e.g., oppositional defiant disorder [ODD], bipolar disorder) as well as in nondisordered children. The DSM–5 defines DMDD-related irritability as being “chronic, severe, and persistent,” and specifies that children must manifest this irritability in two forms. First, children with DMDD frequently (i.e., three or more times per week) have developmentally inappropriate temper outbursts, during which they may become verbally or physically aggressive in response to minor stressors or setbacks. It is especially important for clinicians diagnosing this disorder to consider what is developmentally appropriate for children of different ages. For example, episodic irritability (as demonstrated by easy annoyance and temper outbursts) is typical in healthy toddlers and preschoolers, and is often responsive to parental behavior management strategies. Such outbursts would not be considered developmentally typical, however, when seen in a school-age child or adolescent. Persons of all ages can become irritable when hot, hungry, tired or stressed; however, this should result in transitory rather than chronic irritability. Culturally, it is also important to understand what expressions of anger and irritability are considered acceptable by parents and extended family versus the community in which the family lives. Second, in the times between outbursts, children with DMDD exhibit irritable and angry moods most of the day, nearly every day, for a year or longer. Such irritability must be present in more than one setting, must be considered “severe” in at least one setting, and must be observable by others; furthermore, symptoms must occur prior to the age of 10, and the child cannot have exhibited full symptom criteria for hypomania or mania for more than one day. These criteria diverge from that of bipolar disorders in that irritability in DMDD is chronic rather than episodic. Importantly, a diagnosis of DMDD should not be applied if a child meets criteria for intermittent explosive disorder or bipolar disorder, and the symptoms may not exclusively occur in the context of another mental disorder (e.g., autism spectrum disorder, separation anxiety disorder, MDD, PDD), drug use, or a medical condition.

Several specific antecedents may trigger depressive episodes, including medical conditions and certain substances; two diagnoses, therefore, exist to classify these depressive conditions properly. Substance/medication-induced depressive disorder is diagnosed when a markedly depressed or anhedonic mood episode is clearly preceded by either the introduction or withdrawal of a substance or medication (e.g., alcohol, hallucinogenic drugs, opioids, sedatives, cocaine, stimulants). Because depressive disorders are highly comorbid with substance use disorders (Brière, Rohde, Seeley, Klein, & Lewinsohn, 2013; Pettinati, O’Brien, & Dundon, 2013), it is crucial for clinicians to determine that the depressed mood followed the use or withdrawal of substances, rather than preceded it. A similar rule can be applied to diagnoses of depressive disorder because of another medical condition, wherein a medical condition rather than a substance is deemed to have caused a depressive episode. As with substance/medication-induced depressive disorder, the medical condition causing depressed mood must precede the mood. Additionally, this diagnosis should be most seriously considered when the medical condition is one of several known triggers of depressed mood (e.g., stroke, Huntington’s Disease, Parkinson’s Disease, traumatic brain injury, thyroid dysfunction). Although other medical disorders may be related to depressed mood, a diagnosis of adjustment disorder with depressed mood may be more appropriate if a causal relationship has not yet been established between the two. Finally, there often exist situations wherein an individual is experiencing clinically significant symptoms of depression that do not meet criteria for one of the disorders discussed previously. Other specified depressive disorder is a moniker assigned to previously defined depressive presentations, including recurrent brief depression (depressive episodes between 2–14 days long, occurring at least once a month), short-duration depressive episode (depressive episodes lasting between 4–14 days), or depressive episode with insufficient symptoms (depressive episodes meeting duration but not full symptom count). Unspecified depressive disorder, in contrast, is used to describe a symptom presentation that is similar to a depressive disorder, but 255

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does not meet criteria for any of the other disorders detailed in this section. The DSM–5 notes that unspecified depressive disorder may be a useful diagnosis when a clinician chooses not to specify the exact nature of an individual’s symptom presentation, or when time or setting limitations prevent a thorough assessment of all associated symptoms. The DSM–5 also includes several specifiers that may be added to a diagnosis to more fully represent an individual’s symptom profile. An anxious distress specifier indicates that an individual is experiencing marked anxious symptoms accompanying their depressive disorder, whereas a mixed features specifier suggests that some manic or hypomanic symptoms are present during the depressive illness. A with melancholic features specifier is added if the depressed individual exhibits pronounced anhedonia (beyond that required to make the depressive diagnosis) or lack of reactivity to pleasurable stimuli, whereas a with atypical features specifier includes symptoms such as mood reactivity, interpersonal rejection sensitivity, and hypersomnia, among other symptoms. Catatonia refers to a pattern of negative symptoms that may accompany depression, including stupor, mutism, and stereotypy, among others. A depressive disorder with psychotic features is present if the individual experiences either mood-congruent or mood-incongruent psychosis exclusively during their depressive illness, and a seasonal pattern is indicated if there has been a regular, temporal relationship between the onset of depressive symptoms and the time of year (typically fall or winter). Finally, depressive disorders with peripartum onset are those which onset during or within four weeks following pregnancy.

Developmental Differences in Symptom Presentation In addition to heterogeneity in symptom expression, as evidenced by the multiple depressive diagnoses reviewed previously, heterogeneity in symptom expression also occurs across persons of different ages and sex. Next, we discuss how age and developmental level may influence the expression of depression in children and adolescents. Infants and preschool-age children.  Although depression in very young children is a rare phenomenon, infants have been shown to demonstrate 256

depression-like symptoms, including fussiness, feeding and sleep problems, withdrawal, and apathy (Guedeney, 2007). These symptoms are also consistent with diagnoses of failure to thrive, which has been proposed as a possible early life manifestation of mood disturbance (Powell & Bettes, 1992). Depression in preschool-age children has been demonstrated in several investigations (Bufferd, Dougherty, Carlson, Rose, & Klein, 2012; Egger & Angold, 2006; Luby, Belden, Pautsch, Si, & Spitznagel, 2009; Luby et al., 2003). Depressed preschoolers often present with sad affect and somberness, as well as feeding and sleep disturbances and lethargy that is seen across settings (Carlson & Kashani, 1988). Research also suggests that although the most depressed preschoolers do meet strict diagnostic criteria, modified criteria may identify preschoolers for whom subthreshold depression is still a problem. Modified criteria (as proposed by Luby et al., 2002) include relaxing the 2-week MDD duration criteria, defining anhedonia as also relating to play activities, and allowing for endorsement of inappropriate guilt and suicidal ideation when they emerged as play themes. Luby et al. (2002) reported that the most significant modification was the relaxation of duration criteria, as 76% of the children who were identified as having MDD using modified criteria would not have met traditional DSM–IV duration criteria for depression. School-age children and adolescents.  Rates of depression steadily increase with age (Angold, Costello, & Worthman, 1998). As children enter formal schooling, depression also becomes more visible as children begin to navigate new academic and social challenges. Although dysphoric mood may still manifest as sadness, clinginess to adults, or tearfulness, it may also begin to present as increased irritability (Krieger, Leibenluft, Stringaris, & Polanczyk, 2013). Additionally, as children’s capacity for reflective thought increases and they become more aware of social norms, inappropriate guilt, low self-esteem, and hopelessness may begin to emerge as part of the clinical picture. In a meta-analysis of the literature on phenomenology of depression across childhood and adolescence, Weiss and Garber (2003) reported that symptoms of anhedonia, hopelessness, hypersomnia,

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weight gain, and social withdrawal were reported more often in older children and adolescents than in younger children. Yorbik et al.’s (2004) investigation of mood symptoms in over 2,000 outpatient children and adolescents with depression also found evidence for increased hopelessness, fatigue, and hypersomnia, as well as heightened severity and medical lethality of suicidal acts in adolescents. Notably, their adolescent participants reported more weight loss than children, in contrast to Weiss and Garber’s finding of increased weight gain in adolescence; it could be that these findings together suggest more weight disturbance generally in adolescent depression. Finally, Yorbik et al. reported that in contrast to adolescent depressive episodes, children’s depressive or irritable moods tended to be associated with specific events or thoughts rather than general distress. Notably, prevalence rates, course of disorder, and comorbidities vary by developmental level, and may predict the quality and degree of psychosocial functioning deficits.

Prevalence Rates of depression tend to increase with age, and are especially prevalent following the pubertal transition (Angold, Costello, & Worthman, 1998). Previous research has shown that rates of preschool depression are between 0.3% and 2.1% (Bufferd, Dougherty, Carlson, & Klein, 2011; Egger & Angold, 2006; Ezpeleta, de la Osa, & Doménech, 2014), whereas prevalence of depression in schoolage children is typically around 2.8% (Merikangas, Nakamura, & Kessler, 2009). In adolescence, rates of depression increase and are approximately at the level one would see in adult samples, around 11% lifetime prevalence for MDD and 1.8% lifetime prevalence for dysthymic disorder (Avenevoli et al., 2015). Notably, prior to puberty, boys and girls have roughly equal rates of depressive diagnoses, but following puberty, girls have twice as much depression (Nolen-Hoeksema, 2001). Several theories have been posited to explain this discrepancy, including gender differences in stress experience and responding, social and biological changes surrounding puberty, and coping styles (Hamilton, Stange, Abramson, & Alloy, 2015; Kendler & Gardner, 2014; Nolen-Hoeksema, 2001).

Course of the Disorder Duration of depressive episodes in children and adolescents range from an average of 3 to 13 months, although some research has reported slightly longer episodes in children (Birmaher, Arbelaez, & Brent, 2002; Birmaher et al., 2004). Additionally, younger age of onset is significantly related to risk of relapse or recurrence (Birmaher et al., 1996; Emslie et al., 1997; Kovacs, 1996). Luby et al. (2014) found that preschoolers with depression were three times more likely to develop depression during middle childhood and early adolescence, whereas others have demonstrated homotypic continuity between middle childhood and adolescent depression, as well as heterotypic continuity between earlier depression and later anxiety disorders (Costello, Mustillo, Erkanli, Keeler, & Angold, 2003).

Comorbidities Children and adolescents diagnosed with a depressive disorder are quite likely to be diagnosed with a comorbid disorder; indeed, rates of comorbidity range between 42% and 75% (Angold, Costello, & Erkanli, 1999; Essau, 2008; Kovacs, 1996; Rohde, Lewinsohn, & Seeley, 1991; Sørensen, Nissen, Mors, & Thomsen, 2005). Notably, anxiety frequently co-occurs with childhood depression, with rates of comorbidity as high as 75% (Angold, Costello, & Erkanli, 1999; Avenevoli, Stolar, Dierker, & Ries Merikangas, 2001; Yorbik, Birmaher, Axelson, Williamson, & Ryan, 2004). Cummings, Caporino, and Kendall (2014) proposed that this high degree of correlation is likely because of shared etiological processes and symptom overlap between disorders, and suggest several avenues of future research that may further illuminate these relationships. Regarding distinctions between childhood and adolescent depression, Yorbik et al. (2004) reported that depressed children were more likely to experience comorbid attention-deficit/hyperactivity disorder (ADHD), ODD, or separation anxiety than depressed adolescents, whereas adolescents experienced more substance use disorders. Rohde et al. (2013) reported that children with MDD were five times more likely to experience an anxiety disorder 257

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and eight times as likely to experience a substance use disorder, whereas adolescents were four times more likely to experience anxiety but only two times more likely to develop a substance use disorder. Taken together, these findings suggest that childhood depression may be a broad predictor of risk for psychopathology, whereas adolescent depression may impose a specific risk for later depression or anxiety.

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Bipolar Disorders Broadly speaking, bipolar disorders are defined by the presence of manic, hypomanic, and/or depressive episodes. Detailed criteria for each DSM–5 diagnosis are discussed in the sections that follow.

Diagnostic Criteria “Classic” mania is the core symptom of bipolar I disorder, featuring at least 7 days of expansive and elated mood (or any length of time if hospitalization is required). However, controversies exist about the extent to which children and adolescents experience “classic” mania (Leibenluft, Charney, Towbin, Bhangoo, & Pine, 2003). Importantly, euphoric mood should only be endorsed if the mood represents a change from an individual’s typical functioning. A helpful tool in assessing for elated mood is the notion of “too much”; for example, loved ones may note that the individual appeared “too happy,” so much so that they may have been worried. Individuals experiencing euphoric mood may be able to identify a feeling of “jumping out of my skin” or feeling “on top of the world.” As in the depressive disorders, irritability plays an important role in the diagnosis of bipolar disorders, as irritable mood may be substituted for euphoric mood. Manic irritability, however, can be distinguished from depressive irritability by its severity and accompanying symptoms. Irritability in bipolar disorder often has an “explosive” quality, with individuals often starting fights, lashing out at others verbally or physically, or destroying property (Hunt et al., 2009). In contrast, irritability as seen in the depressive disorders often has a cranky, overly sensitive presentation. In both cases, friends and family interacting with someone who displays 258

irritability may report the feeling of “walking on eggshells,” but although an irritably depressed individual may avoid or withdraw, an irritably manic individual may approach in an extreme or even violent manner. Manic episodes, marked by distinct periods of euphoric or irritable mood, also feature several associated symptoms. These include grandiosity or inflated self-esteem, decreased need for sleep (which should be distinguished from insomnia by assessing level of fatigue when waking from a limited amount of sleep), increased talkativeness or pressured speech (talking more quickly or loudly than usual), flight of ideas (bouncing from topic to topic) or racing thoughts, distractibility, increase in goal-directed activity (e.g., starting many new projects, feeling extremely ambitious) or psychomotor agitation, or excessive involvement in risky or dangerous activities. To be diagnosed with bipolar I disorder, an individual must have experienced at least 7 days of euphoric or irritable mood, accompanied by at least three associated symptoms (or four if irritable mood is being used to make a diagnosis); as noted previously, however, any duration of mania meets criteria for bipolar I disorder if hospitalization is required. Like depressive disorders, associated symptoms of bipolar I disorder must either be present exclusively during mania, or be much worse during manic periods. For example, a child with ADHD is typically distractible, displays excessive energy and often talks rapidly and exhibits poor judgement. Although these symptoms of ADHD can differ in their expression on the basis of environmental management, they would only “count” as symptoms of mania if they were significantly more pronounced in the context of a manic mood state and represented a significant change from the child’s typical functioning. Unlike the other bipolar and related disorders, bipolar I disorder does not require the presence of an MDE to make the diagnosis; this presentation, however, is less common than bipolar I disorder featuring manic and depressive episodes (Hunt et al., 2009). Bipolar II disorder, in contrast, does require the individual to have experienced at least one MDE as well as hypomanic episodes. Hypomanic episodes are characterized by euphoric or irritable mood

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that lasts for a shorter time (at least 4 days) and is typically less psychosocially impairing than in mania. Duration of euphoric/irritable mood and the requirement for an MDE are the primary differences between the two disorders, as bipolar II disorder also requires a minimum of three of the same associated symptoms (four if mood is only irritable). As with the depressive disorders, several specifiers are available to further characterize bipolar disorders. Anxious distress, mixed, melancholic, atypical, or psychotic features, catatonia, peripartum onset, and seasonal pattern may all be applied to bipolar as well as depressive diagnoses. Additionally, a specifier of with rapid cycling may be applied if a child experiences at least four separate mood episodes within the past year that all meet full criteria for manic, hypomanic, or depressive episodes. Notably, although the DSM–IV (American Psychiatric Association, 2000) defined mixed episodes as occurring when an individual simultaneously met criteria for a manic episode and an MDE, the DSM–5 only allows for an episode specifier of with mixed features (American Psychiatric Association, 2013). This specifier applies when an individual meets criteria for a manic or hypomanic episode while simultaneously experiencing at least three nonoverlapping symptoms of depression. Also, although a mixed episode in DSM–IV was associated only with bipolar I disorder, a mixed features specifier may be attached to either bipolar I or bipolar II disorder in the DSM–5. Cyclothymic disorder is diagnosed when an individual vacillates between experiencing depressive symptoms that do not meet criteria for an MDE and hypomanic symptoms that do not meet criteria for a manic episode. These alternating periods must last for at least 2 years in adults, whereas 1 year of disturbance is sufficient to diagnose the disorder in children and adolescents. Additionally, the individual must not be symptom-free for more than 2 months during the 2-year period. Unlike bipolar I and II disorders, no specific number of depressive or hypomanic symptoms is required for this diagnosis. Notably, if at any point the individual experiences symptoms sufficient to warrant a diagnosis of MDD or bipolar disorder, they no longer meet criteria for cyclothymic disorder.

Like depressive disorders, substance/medicationinduced bipolar and related disorder is diagnosed when marked euphoric or irritable mood is preceded by the use of or withdrawal from a substance or medication. Substances that may trigger a bipolar episode include alcohol, hallucinogenic drugs, sedatives, stimulants, or cocaine. Steroid medications may also be related to emergence of bipolar episodes. Notably, use of antidepressant medications to treat depressive disorders may result in manic or hypomanic symptoms in undiagnosed cases of bipolar disorder; when manic or hypomanic symptoms persist after removal of the medication; however, a diagnosis of substance/medication-induced bipolar and related disorder would not be appropriate, as the use of antidepressants merely revealed an underlying bipolar diagnosis, rather than triggered it. A diagnosis of bipolar and related disorder because of another medical condition may be considered when an individual develops marked euphoric or irritable mood following a medical diagnosis (e.g., Cushing’s disease, multiple sclerosis, stroke, traumatic brain injury). Finally, the DSM–5 presents two categories to be used if an individual fails to meet minimum symptom criteria, but nevertheless experiences impairing mood symptoms. Other specified bipolar and related disorders are diagnosed when an individual fails to meet criteria for one of the previously described bipolar disorders, but demonstrates symptoms consistent with another specific condition. Shortduration cyclothymia (less than 2 years), MDEs paired with hypomanic episodes of insufficient duration or symptom count to lead to a diagnosis of bipolar II disorder, and hypomanic episodes without previous depressive episodes would qualify for a diagnosis of other specified bipolar and related disorder. When a clinician notes significant manic and depressive symptoms but is unable to determine if full criteria are met for one of the primary bipolar and related disorders, a diagnosis of unspecified bipolar and related disorder is appropriate. Many investigators have begun highlighting the value of discussing bipolar disorders on a continuum, referring to the collective group as bipolar spectrum disorders. Youngstrom (2009) discusses narrow, intermediate, and broad phenotypes of 259

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bipolar disorder, as originally posited by Leibenluft Charney, Towbin, Bhangoo, and Pine (2003). In this model, narrow criteria require children to exhibit elated mood or grandiosity to qualify for bipolar disorder, whereas the intermediate classification most closely mirrors current DSM–5 criteria for a bipolar disorder, and the broad criteria include individuals who do not meet full criteria for bipolar disorders because of reasons like chronic irritability, recurrent hypomanic episodes without full mania, or insufficient length of episodes. Although not meeting for full diagnostic criteria, individuals with the broad phenotype of bipolar disorder typically experience marked impairment that may be amenable to psychosocial or pharmacological intervention.

Developmental Differences in Symptom Presentation In the past three decades there has been a surge of attention and research on pediatric bipolar disorder; with this surge in research, however, has also come much controversy about the diagnostic boundaries of bipolar disorder, and how much allowance should be made in diagnosing a traditionally “adult” condition in children. One controversy around pediatric bipolar disorder comes in the definition of mania. As noted, the core symptom of bipolar disorder may manifest as either euphoria/elated mood or severe irritability. The DSM–5 specifies that mania in either form must be episodic to meet criteria for bipolar disorder. Although elated mood almost always presents episodically, irritability can often have a chronic presentation, especially in children (Biederman, 1998; R. G. Klein, Pine, & Klein, 1998). Some researchers have claimed that extreme chronic irritability with mood lability may represent a childhood presentation of bipolar disorder, in contrast to the nature of mania in adults. Others, however, have noted that extreme irritability that does not present episodically may be better classified in other diagnostic categories (Leibenluft, 2011). Controversies persist regarding the presentation of pediatric bipolar disorder, but its existence is now considered accepted. Demeter et al. (2013) investigated the extent to which bipolar disorder presentation differs across childhood and adolescence. In 260

their sample of over 500 children (ages 4–17) with diagnosed bipolar spectrum disorders, the authors found that irritability, motor activity, and aggression, as rated on the Young Mania Rating Scale (YMRS; Young et al. 1978) decreased linearly with age; odd thought content, in contrast, increased as children age. Importantly, the authors noted that the small difference in scores, while statistically significant when considered on a symptom level, did not contribute to significantly different total YMRS scores. This observation led authors to suggest that there are not prominent developmental differences in symptom presentation of mania. In contrast, and in line with previous investigations of depressive disorders, depression symptoms (as measured by the Children’s Depression Rating Scale; Poznanski et al., 1984) in children diagnosed with bipolar spectrum disorders steadily increased with age (peaking in the 14–17-year-old group), with the exception of guilt and physical complaints, which did not differ significantly across six age groups (ages 4–6, 7–8, 9–10, 11–13, and 14–17).

Prevalence Although the rate of bipolar diagnoses has increased over the past 20 years (Blader & Carlson, 2007; Moreno et al., 2007), prevalence of bipolar disorder in children has remained steady (Van Meter, Moreira, & Youngstrom, 2011). This suggests that increased rates of bipolar disorder are the result of heightened awareness and detection of bipolar disorders in children, rather than increased occurrence. Merikangas, Nakamura, and Kessler (2009) reported that the lifetime prevalence rate of bipolar disorders in children was between 0% and 2.1%, whereas prevalence of hypomania was between 0% and 0.4%. Among 13- to 18-year-old adolescents, however, the lifetime prevalence of either bipolar I or bipolar II disorder has been reported as 2.9%, with higher rates for girls and older adolescents (Merikangas et al., 2010). In a comprehensive meta-analysis of 12 studies with over 16,000 participants, Van Meter et al. (2011) reported a lifetime prevalence rate of 1.8% for bipolar spectrum disorders, with lower rates for bipolar I disorder prevalence (1.2%) and higher rates of bipolar spectrum disorders among older adolescents (ages 12 and over; 2.7%).

Mood Disorders in Childhood and Adolescence

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Course of the Disorder First episodes of bipolar disorders tend to occur between 15 and 19 years old (Nusslock & Frank, 2011), and the illness typically has a chronic, recurring course (Birmaher, 2013). Specifically, although most individuals will recover from their index episode, as many as 80% will experience a recurrence of either a full manic or depressive episode, as well as frequent interepisode impairment because of subthreshold symptomatology (Birmaher, 2007; Birmaher et al., 2009; Carlson et al., 2012; DelBello et al., 2007; Diler, 2007; Geller, Tillman, Bolhofner, & Zimerman, 2008). Birmaher et al. (2009) found that children with bipolar II disorder had the highest rate of recurrence over a 40year follow-up period (81%), followed by those with bipolar I disorder (65.2%) and bipolar disorder not otherwise specified (53.7%). Other investigations have shown that bipolar diagnoses in childhood and adolescence tend to remain stable into adulthood (Geller, Tillman, Bolhofner, & Zimerman, 2008; Lewinsohn, Klein, & Seeley, 2000).

Comorbidities Substantial disagreements exist between investigators regarding the diagnosis of pediatric bipolar disorder, with one source disagreement being the frequent occurrence of accompanying comorbid diagnoses, which interacts with heterogeneous symptom presentation to complicate diagnostic decision-making (Van Meter, Burke, Kowatch, Findling, & Youngstrom, 2016). Van Meter et al. (2016) determined that the most common symptoms of bipolar disorder, measured in over 2,000 children, were increased energy, irritability, mood lability, distractibility, and goal-directed activity; these symptoms were all present in over 70% of children with bipolar disorder. Notably, the five most common symptoms of pediatric bipolar disorder are also commonly present in disruptive behavior or depressive disorders. Furthermore, although symptoms overlap between disorders, some children and adolescents may truly have more than one psychiatric disorder, which further complicates diagnosis. Data taken from community and clinical samples suggests that children with bipolar disorder often also meet criteria for ADHD (48%–62%), ODD (31%–53%), or

anxiety disorders (27%–54%; Frías, Palma, & Farriols, 2015; Kowatch, Youngstrom, Danielyan, & Findling, 2005). Considering symptom overlap between bipolar disorder and other conditions, careful differential diagnosis is crucial in pediatric populations. As noted, ADHD has a very high rate of comorbidity with bipolar disorder, and some investigators suggest that situations arise wherein bipolar disorder is diagnosed when ADHD would be more appropriate. For example, a child may present as irritable and emotionally labile in structured situations that require sustained attention (e.g., school), but demonstrate euthymic, stable mood when in play or home settings that require less structure; the inconsistency in mood across settings is not consistent with the diagnosis of bipolar disorder. Additionally, children with ADHD are often hyperactive, impulsive, and excessively talkative, symptoms which may also be present during manic episodes; in ADHD, however, children are likely to present as chronically hyperactive, impulsive, and talkative, whereas bipolar disorder would expect a more episodic presentation. Finally, it is essential that the symptoms of mania represent a marked change from a child’s typical presentation and impair functioning. When distinguishing bipolar disorder from other disruptive behavior disorders, therefore, it is essential for clinicians to assess severity, periodicity, and typical functioning. Irritability also spans across multiple pediatric diagnoses. As noted, depressive irritability may be distinguished from bipolar irritability by considering its severity and whether is it more approach vs. avoidance motivated (i.e., either lashing out or withdrawing). Anxious irritability, in contrast, may only emerge when a child is in an anxiety-provoking situation, whereas irritability of ODD should be present chronically and across multiple settings (American Psychiatric Association, 2013). Relatedly, irritability in the face of a change in routine may be representative of an autism spectrum disorder diagnosis (Simonoff et al., 2012), whereas irritable mood when being prevented from engaging in compulsive behaviors could be related to obsessive-compulsive disorder. It is also crucial to assess associated symptoms when categorizing irritability. For example, 261

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irritability accompanied by rule-breaking behavior may be indicative of conduct disorder, whereas increased energy, decreased need for sleep, or grandiosity may accompany irritable mood in bipolar disorders (Krieger, Leibenluft, Stringaris, & Polanczyk, 2013).

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Risk and Etiological Factors for Mood Disorders As our knowledge of the phenomenology and course of mood disorders grows, so does our interest in understanding the factors that may confer risk for mood disorders in certain individuals or groups. Next, we discuss these factors and the research supporting their relationship to mood disorders.

Biological Factors Genetics.  To date, twin studies have provided some of the most robust evidence of genetic contributions to the transmission of mood disorders; based on such studies, the heritability estimate for MDD is estimated to be between 30% and 50%, with earlier age of onset associated with higher heritability (Bierut et al., 1999; Fernandez-Pujals et al., 2015; Kendler, Thornton, & Gardner, 2001; Levinson, 2006; McGuffin et al., 1996, Sullivan, Neale, & Kendler, 2000). Children of depressed parents, especially mothers, have also been extensively studied. A 20-year follow-up of children of depressed parents demonstrated that the risk for anxiety, mood, and substance use disorders is three times higher in children of depressed parents than in children raised by nondepressed parents (Weissman et al., 2006). Although confounded with environmental influences, Weissman et al. (2006) suggested that children of depressed parents constitute a group at heightened risk for depression and other psychopathology, and may warrant early detection and intervention. The heritability of bipolar disorders is considered more robust than that of depressive disorders (Bertelsen, Harvald, & Hauge, 1977; Jones, Kent, & Craddock, 2002). Specifically, twin studies suggest that the heritability of bipolar disorders is around 85%, with monozygotic twins over eight times more 262

likely to develop bipolar disorder than their dizygotic twin counterparts (McGuffin et al., 2003). The same investigation showed that bipolar disorder was also associated with risk for depressive disorders, as 26.7% of monozygotic twins of those with bipolar disorder were diagnosed with unipolar depression. Neuroendocrinology.  Disruptions of the hypothalamic-pituitary-adrenal (HPA) axis have been implicated in depressive and bipolar disorders. The HPA axis is responsible for the production and regulation of the stress hormone cortisol, and in depressed children, the HPA axis may be overactive, resulting in higher basal cortisol levels than their nondepressed counterparts (LopezDuran, Kovacs, & George, 2009; Luby et al., 2003). Additionally, altered cortisol levels may be a biological indicator of risk for depression, as children of depressed mothers have been shown to exhibit elevated morning cortisol and cortisol reactivity to a laboratory stressor (Dougherty et al., 2011; Dougherty, Klein, Olino, Dyson, & Rose, 2009). A recent meta-analysis also implicates the HPA axis in bipolar disorder (Murri et al., 2016). Using data gathered from 41 empirical studies, the authors found that bipolar disorder in adults was associated with higher basal cortisol levels, which were significantly positively associated with being in a manic phase of the disorder and age. Although fewer investigations have been completed about children with bipolar disorder, at-risk children and adolescents (offspring of bipolar parents) have been shown to have higher levels of basal cortisol, but no differences in cortisol reactivity to laboratory stressors (Ellenbogen, Hodgins, & Walker, 2004; Ellenbogen, Hodgins, Walker, Couture, & Adam, 2006; Ellenbogen, Santo, Linnen, Walker, & Hodgins, 2010). Structural and functional brain abnormalities.  Depression in childhood has been associated with several structural brain abnormalities, including reduced hippocampal volume (MacQueen & Frodl, 2011) and reduced left frontal volume (Nolan et al., 2002); such findings have even been demonstrated in children with subthreshold depression (Vulser et al., 2015), and are often present in children at risk for depression (Rao, Hammen, & Poland, 2010).

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Children with depression also have reduced amygdala volume compared with healthy controls (Rosso et al., 2005), as well as white matter abnormalities that may impede communication between neurons (Cullen et al., 2010; Rajkowska & MiguelHidalgo, 2007). Functional neuroimaging studies indicate that depressed children and adolescents have increased levels of activity in their amygdala and ventral striatum in response to negative emotional stimuli and decreased levels in response to positive emotional stimuli (Hasler & Northoff, 2011; Leppänen, 2006). In a recent meta-analysis, Miller, Hamilton, Sacchet, and Gotlib (2015) reported that children with MDD demonstrated hyperactivation in the thalamus and parahippocampal gyrus during affective processing tasks, as well as hypoactivation in the dorsal cingulate cortex and dorsal anterior insula during executive functioning tasks, and differential patterns of activity during positive and negative emotional tasks. Neuroimaging studies of children with bipolar disorder have found evidence of decreased hippocampal volume compared with control participants (Otten & Meeter, 2015), amygdala hyperactivation during emotional face recognition tasks, and hypoactivation in the anterior cingulate cortex in response to emotional and nonemotional images and tasks (Wegbreit et al., 2014). Smaller amygdala volume has also been reported in children, but not adults, with bipolar disorder (Pfeifer, Welge, Strakowski, Adler, & DelBello, 2008), whereas unmedicated pediatric bipolar disorder has been associated with abnormally decreased right inferior frontal gyrus activity (Hafeman et al., 2014). Furthermore, children at risk for bipolar disorder have demonstrated amygdala hyperactivity in response to fearful faces (Olsavsky et al., 2012). In a longitudinal investigation of children at risk for bipolar disorder, Bertocci et al. (2014) found that compared with children with low and decreasing levels of emotional and behavioral dysregulation, children with high and decreasing levels of dysregulation demonstrated less activity in the dorsolateral prefrontal cortex (dlPFC) and less functional connectivity between brain regions related to emotion generation and regulation. In a similar sample, Bebko et al. (2014) determined that emotionally dysregulated children,

regardless of diagnosis, demonstrated greater left middle prefrontal cortical activity. These results are significant considering research implicating the prefrontal cortex, and especially the dlPFC, in the process of emotion regulation.

Psychological and Social Factors Temperament.  Temperament is generally defined as early-emerging, relatively stable individual differences in how individuals regulate emotions and react to unfamiliar environmental stimuli (Rothbart & Bates, 2006). Reviews of previous literature indicate that temperament is heritable and influenced by the early family environment (Saudino, 2005). The tripartite model posits that depressive disorders are characterized by high levels of negative emotionality (NE) and low levels of positive emotionality (PE), whereas bipolar disorder is characterized by high levels of PE (Clark & Watson, 1991; Gruber, Oveis, Keltner, & Johnson, 2008; Stanton & Watson, 2014). In research assessing temperament styles in children of depressed parents, Durbin et al. (2005) demonstrated that preschoolers with low levels of PE were more likely to have a depressed mother than preschoolers who had high PE levels. Relatedly, Dougherty et al. (2011) found that temperamental dysphoria and low exuberance were associated with depression in preschoolers; similar findings supporting the tripartite model have been found in childhood and adolescent samples (Anderson & Hope, 2008). Previous research has also demonstrated that low PE best discriminates depression from anxiety in children (Lonigan, Carey, & Finch, 1994). In contrast, although Olino et al. (2010) also reported that preschool children with depressed mothers had higher levels of NE and behavioral inhibition (BI) than children whose mothers were not depressed, this effect was qualified by an interaction with PE. Specifically, higher NE and BI predicted higher likelihood of having a depressed mother in children with high and moderate levels of PE, but not low levels. The authors suggested that higher NE and lower PE may be sufficient but not necessary risk factors for the development of depression, and noted that exposure to stress (i.e., the experience of being parented by a 263

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depressed mother) could influence temperament across development. In adults, positive emotionality has been implicated as a risk factor for bipolar disorder in some investigations (Gruber, Johnson, Oveis, & Keltner, 2008), but not others (Bagby et al., 1996, 1997; Gruber & Johnson, 2009). Another construct, the behavioral activation system (BAS) has received more attention and empirical support as a temperamental risk factor for bipolar disorder (Alloy & Abramson, 2010). Individuals with bipolar disorder are theorized to have high BAS “sensitivity,” meaning that they are hyperresponsive to rewards, which in turn makes rewards more salient and desirable and may lead to increases in risk-taking behavior. Research largely supports this theory, with many investigations finding an association between selfreport and observational measures of BAS sensitivity and bipolar disorder, within and outside of manic illness (Alloy, Abramson, Uroševic´, Bender, & ­Wagner, 2009; Alloy, Abramson, Walshaw, et al., 2009; Johnson, Fulford, & Carver, 2012; Meyer & Hofmann, 2005; Uroševi´c et al., 2008). In a nonclinical sample of 14- to 19-year-olds, Alloy et al. (2012) found that high BAS sensitivity predicted higher likelihood and earlier onset of bipolar spectrum disorder than moderate or low BAS sensitivity. Relatedly, Tillman et al. (2003) found that children and early adolescents with bipolar disorder exhibited greater novelty seeking than children in a healthy control group. Taken together, these investigations suggest that temperament and mood may be related even in the earliest phases of childhood. Cognitive risk.  There has been strong support for the notion that certain cognitive styles are related to increased risk of depression in children (Hankin, 2012). Negative cognitive style, which includes a tendency to attribute negative life events to personal, stable, and global causes, as well perfectionistic values and external validation of self-worth, has been associated with risk for depression as well as depressive illness (Abela et al., 2011; Carter & Garber, 2011; Kendall, Stark, & Adam, 1990). Rumination is especially likely to lead to depression when it interacts with stressful events, as children may compulsively focus on the events rather than 264

engaging with problem-solving strategies (Abela & Hankin, 2011); adolescent girls are especially at risk for engaging in corumination, or the act of discussing problems and emotions to the exclusion of other activities (Rose, 2002; Starr & Davila, 2009; Stone, Hankin, Gibb, & Abela, 2011). Children and adolescents with bipolar disorder demonstrate similar negative cognitive styles to depressed children when they are in depressive phases of their illness; during manic or hypomanic phases, however, they also may exhibit a positive attributional bias (Alloy, Abramson, Walshaw, & Neeren, 2006). A study of affected versus nonaffected offspring of bipolar parents found that affected children demonstrated significantly lower self-esteem, increased sensitivity to punishment, and increased ruminations compared with nonaffected children (Pavlickova, Turnbull, & Bentall, 2014). Experiencing hypomania, however, may influence these negative styles (Alloy, Reilly-Harrington, Fresco, Whitehouse, & Zechmeister, 1999). Furthermore, individuals who experience mania or hypomania without depression may exhibit exclusively positive attributional styles, whereas individuals who experience both phases of bipolar illness may vacillate between the styles (Alloy et al., 2006).

Environmental Factors Family factors.  Parental socialization of emotion may be an important factor in the development and maintenance of mood disorders. Schwartz, Sheeber, Dudgeon, and Allen (2012) reviewed observational studies, and found that depressed children and adolescents who exhibit dysphoria are often met by increased positivity and decreased anger from their parents; this reaction has a reinforcing effect, as positive reactions to dysphoria help to maintain the behavior (Sheeber et al., 1998, 2000). Moreover, mothers of irritable adolescents often tend to mirror aggressive and conflictual behavior from their children, which then prolongs conflict between them (Sheeber et al., 2000). Furthermore, aggressive responses to adolescent aggression may predict the later onset of MDD (Schwartz et al., 2011). Expressed emotion (EE), or criticism, hostility, or emotional overinvolvement with a psychiatrically

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ill family member, has also been identified as a family factor that may influence psychosocial dysfunction associated with mood disorders (Belardinelli et al., 2008; Fristad, 2006; Miklowitz et al., 2009). Nader et al. (2013) found that families of children and adolescents with bipolar disorder demonstrated significantly less positive EE, more negative EE, less cohesion, and more conflict than families of children without bipolar disorder. Furthermore, adolescents with bipolar disorder who experience higher levels of negative EE are at heightened risk for suicidal ideation (Ellis et al., 2014). These findings highlight the importance of family involvement in the diagnosis and treatment of mood disorders in children, as heightened parental understanding of psychiatric illness has been shown to translate to better outcomes for their children (Fristad, Verducci, Walters, & Young, 2009). Stressful life events and early adversity.  Crosssectional and prospective studies have demonstrated that stressful life events are associated with the onset and maintenance of mood disorders in children and adolescents (Cole, Nolen-Hoeksema, Girgus, & Paul, 2006; Grant et al., 2006; Monroe, Rohde, Seeley, & Lewinsohn, 1999; Patton, Coffey, Posterino, Carlin, & Bowes, 2003). Consistent with a diathesis-stress model, children who are at biological risk for a mood disorder (because of genetics or temperament) are most vulnerable to the effects of life stress, although significant stressors may also trigger mood episodes in less vulnerable children (Hankin & Abramson, 2001). Early life adversity, including chronic stressors (e.g., low socioeconomic status) and parental maltreatment may trigger biological changes that increase children’s vulnerability to psychopathology (Nusslock & Miller, 2016), including brain alterations that impact the affective dysregulation seen in major depression and bipolar disorder (Dvir, Ford, Hill, & Frazier, 2014; Whittle et al., 2013). In their review of the literature on child maltreatment and psychopathology, Teicher and Samson (2013) found that depressed individuals who had a history of maltreatment demonstrated earlier age of onset, greater symptom severity, and poorer treatment response than nonmaltreated individuals with depression. Exposure to stressful life

events and early adversity, therefore, has a demonstrable effect on the occurrence, recurrence, and treatment of mood disorders in children. Treatment of Mood Disorders in Children Understanding the phenomenology and etiology of mood disorders in children is important for the development of effective psychopharmacological, somatic, and psychosocial treatments to assist children and families living with mood disorders.

Biological Interventions Pharmacological treatment for children with depressive disorders are typically indicated in the event of moderate to severe depressive symptomatology, or in cases where suicidal ideation is present (Sakolsky & Birmaher, 2012). Selective serotonin reuptake inhibitors (SSRIs) have been the most investigated and empirically supported treatments for children depression, although they are not without their difficulties. Randomized controlled trials of SSRIs have found strong evidence for the use of fluoxetine (Emslie et al., 1997, 2002; March et al., 2004) and escitalopram (Emslie et al., 2009; Wagner et al., 2006), limited evidence for the use of citalopram (von Knorring et al., 2006; Wagner et al., 2004) and sertraline (Wagner, 2003), and mixed evidence for the use of paroxetine (Berard et al., 2006; Emslie et al., 2006; Keller et al., 2001) in child populations. SSRIs have been found to be superior to tricyclic antidepressants in children and adolescents (Qin et al., 2014). Pharmacological treatment of pediatric bipolar disorder is most often in the form of atypical/ second-generation antipsychotics (SGAs; e.g., risperidone, aripiprazole, olanzapine) or mood stabilizers (e.g., lithium, divalproex sodium, carbamazepine). Monotherapy is typically the firstline approach, and adjunctive medications may be added in the event of incomplete or nonresponse to treatment (Kowatch, Fristad, et al., 2005). Liu et al. (2011) reported on 17 randomized controlled trials and 29 open-label trials of medication for children with bipolar disorder, and found that divalproex sodium monotherapy had demonstrated 265

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efficacy in double-blinded trials, whereas lithium and carbamazepine monotherapies had only demonstrated efficacy in open-label trials. Additionally, SGA monotherapy demonstrated efficacy in openlabel and double-blind studies. Notably, when the results of the available studies were collapsed in a meta-analysis, SGA monotherapy was determined to be significantly more efficacious than the other monotherapy treatments. The authors also highlighted the higher tolerability of this class of medications as a potential benefit of SGAs. Relatedly, Kowatch, Fristad, et al. (2005) outlined a standardized algorithm that can be used to make pharmacological treatment decisions for children with bipolar disorder, indicating that clinicians should first use mood stabilizer or SGA monotherapy, and only move to adjunctive augmentation in the event of a partial response to the initial treatment. In the event of nonresponse, Kowatch, Fristad, et al. suggest that clinicians move to different monotherapies twice more before using a combination treatment. Importantly, some research has highlighted problematic side effects that have been associated with psychopharmacological treatment of bipolar disorder in children, including weight gain, metabolic illness, hormonal effects, and neurological effects (see Cohen, Bonnot, Bodeau, Consoli, & Laurent, 2012 for a review). Previous work has also suggested that antipsychotic medication effects in children with mood disorders only persist if the drug is being used, necessitating continued use of the medications indefinitely. These factors, therefore, should be thoughtfully considered before electing to use pharmacological agents in the treatment of bipolar disorder in children and highlight the importance of adjunctive treatments, including psychotherapy and nutritional interventions, to decrease psychotropic burden.

Psychological and Behavioral Interventions Like the adult literature, the most robustly supported treatment for child and adolescent depression is cognitive–behavioral therapy (CBT; J. B. Klein, Jacobs, & Reinecke, 2007); additionally, interpersonal therapy for adolescents (IPT-A) has 266

also been evaluated favorably (Mufson et al., 1999). CBT treatments for children and adolescents typically focus on cognitive restructuring, challenging of negative and irrational thoughts, development of problem-solving skills, and increased involvement in pleasurable activities (Harrington, Whittaker, Shoebridge, & Campbell, 1998; Kendall, 2011; Lewinsohn, Clarke, Hops, & Andrews, 1990; Stark et al., 2006). IPT-A consists of addressing common adolescent developmental issues like separation from parents, increasing importance of friendships and romantic relationships, and peer pressure (Mufson et al., 1999). Several meta-analyses have pointed to the efficacy of psychosocial treatments for childhood depression, especially in light of the minimal to absent side effects of psychological treatment (David-Ferdon & Kaslow, 2008; SommersFlanagan & Campbell, 2009; Watanabe, Hunot, Omori, Churchill, & Furukawa, 2007); little evidence has differentiated between these treatments in terms of efficacy, however. Fristad and MacPherson (2014) reviewed the accumulated research on psychosocial treatments for pediatric bipolar disorder, specifically focusing on empirically supported treatments. Two interventions were deemed to be of the highest quality, as indicated by their investigation in double-blind, randomized-controlled trials (RCTs) with adequate sample sizes and appropriate data analytic techniques, psychoeducational psychotherapy (PEP; Fristad, Goldberg-Arnold, & Leffler, 2011) and family-focused treatment for adolescents with bipolar spectrum disorders (FFT-A; Miklowitz et al., 2004). Since that review, a third intervention has met these criteria—child and family focused cognitive–behavioral therapy (CFF:CBT; West et al., 2014). PEP is offered in multifamily and individualfamily formats, as is CFF-CBT, whereas FFT-A is offered in only an individual-family format. All three interventions use psychoeducation about bipolar disorder and its treatments, are familybased, and emphasize family skill building with a focus on communication and problem solving and emotion-regulation skills for children. Other treatment approaches for pediatric bipolar disorder that have received some empirical support include CBT (Feeny et al., 2006), dialectic behavior therapy

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(Goldstein, Axelson, Birmaher, & Brent, 2007), and interpersonal and social rhythm therapy (Hlastala, Kotler, McClellan, & McCauley, 2010).

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Nutritional Interventions Nutritional supplements have also been investigated as treatment for mood disorders in children, especially considering the myriad side effects associated with pharmacological treatment. The use of omega-3 fatty acids has been most thoroughly investigated. In adults, a meta-analysis of 10 studies including over 300 adults with mood disorders found that use of omega-3 results in a significant antidepressant effect, with greater effects at higher doses (Lin & Su, 2007). In children, Nemets, Nemets, Apter, Bracha, and Belmaker (2006) completed a pilot RCT in 28 children ages 6 to 12 with MDD that demonstrated omega-3 was more likely than placebo to result in a ≥ 50% reduction in depressive symptoms as well as remission from depression. In a pilot RCT of 72 children with depression, Fristad et al. (2016) found moderating effects of maternal depression and psychosocial stress on outcomes, such that children whose mothers had current or past depression and/or less psychosocial stress (i.e., suggesting a more endogenous depression) had a significantly better response to combined PEP plus omega-3 fatty acids, as well as each treatment individually, compared with placebo. Wozniak et al. (2007) conducted an open-label trial of omega-3 for bipolar spectrum disorder in 20 children ages 6 to 17, and reported that participants’ YMRS scores decreased by 30% in 50% of the sample and by 50% in 35% of the sample; additionally, depressive symptoms were rated as much or very much improved by 40% of the sample. Relatedly, in a 6-week open label adjunctive trial of 360mg EPA and 1560 mg DHA per day in 18 children with bipolar disorder, Clayton et al. (2009) reported decreased levels of clinician-rated depression and mania, and greater psychosocial functioning, as well as decreased parent ratings of internalizing or externalizing psychopathology. In a pilot RCT focused on 23 children ages 7 to 14 with bipolar disorder not otherwise specified or cyclothymic disorder, Fristad et al. (2015) demonstrated that 2g/day of omega-3 fatty acids in combination with PEP led to a large reduction in depressive symptoms

compared with placebo and active monitoring (d = 1.70, p = .018). Small trials examining the use of multivitamin/ minerals and other multinutrient supplements as treatment for mood disturbance have demonstrated promising results in children and adults (e.g., Frazier, Fristad, & Arnold, 2012; Kaplan, Fisher, Crawford, Field, & Kolb, 2004; Rucklidge, Gately, & Kaplan, 2010; Sylvia, Peters, Deckersbach, & Nierenberg, 2012); authors recommend that large, randomized trials be conducted to further validate the use of such supplements in the treatment of mood disorders. Conclusion and Future Directions Diagnosis of mood disorders in childhood and adolescence requires thorough understanding of the typical and atypical developmental changes of youth. With appropriate diagnosis and thoughtful treatment approaches, children with mood disorders have an enhanced trajectory. Lack of attention to the unique needs of these children and families, however, may lead to increased psychosocial impairment and dysfunction, the effects of which can be farreaching and long-lasting. Further research on etiology, treatment, and prevention of mood disorders in children, as well as dissemination of this research to community providers, will reduce the public health costs of disability associated with mood disorders in childhood and adolescence.

References Abela, J. R., & Hankin, B. L. (2011). Rumination as a vulnerability factor to depression during the transition from early to middle adolescence: A multiwave longitudinal study. Journal of Abnormal Psychology, 120, 259–271. http://dx.doi.org/10.1037/ a0022796 Abela, J. R., Stolow, D., Mineka, S., Yao, S., Zhu, X. Z., & Hankin, B. L. (2011). Cognitive vulnerability to depressive symptoms in adolescents in urban and rural Hunan, China: A multiwave longitudinal study. Journal of Abnormal Psychology, 120, 765–778. http:// dx.doi.org/10.1037/a0025295 Alloy, L. B., & Abramson, L. Y. (2010). The role of the behavioral approach system (BAS) in bipolar spectrum disorders. Current Directions in Psychological Science, 19, 189–194. http://dx.doi.org/ 10.1177/0963721410370292 267

Fristad and Black

Alloy, L. B., Abramson, L. Y., Uroševi´c, S., Bender, R. E., & Wagner, C. A. (2009). Longitudinal predictors of bipolar spectrum disorders: A behavioral approach system (BAS) perspective. Clinical Psychology: Science and Practice, 16, 206–226. http://dx.doi.org/ 10.1111/j.1468-2850.2009.01160.x

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Alloy, L. B., Abramson, L. Y., Walshaw, P. D., Gerstein, R. K., Keyser, J. D., Whitehouse, W. G., . . . HarmonJones, E. (2009). Behavioral approach system (BAS)-relevant cognitive styles and bipolar spectrum disorders: Concurrent and prospective associations. Journal of Abnormal Psychology, 118, 459–471. http://dx.doi.org/10.1037/a0016604 Alloy, L. B., Abramson, L. Y., Walshaw, P. D., & Neeren, A. M. (2006). Cognitive vulnerability to unipolar and bipolar mood disorders. Journal of Social and Clinical Psychology, 25, 726–754. http://dx.doi.org/10.1521/ jscp.2006.25.7.726 Alloy, L. B., Bender, R. E., Whitehouse, W. G., Wagner, C. A., Liu, R. T., Grant, D. A., . . . Abramson, L. Y. (2012). High behavioral approach system (BAS) sensitivity, reward responsiveness, and goal-striving predict first onset of bipolar spectrum disorders: A prospective behavioral high-risk design. Journal of Abnormal Psychology, 121, 339–351. http:// dx.doi.org/10.1037/a0025877 Alloy, L. B., Reilly-Harrington, N. A., Fresco, D. M., Whitehouse, W. G., & Zechmeister, J. S. (1999). Cognitive styles and life events in subsyndromal unipolar and bipolar mood disorders: Stability and prospective prediction of depressive and hypomanic mood swings. Journal of Cognitive Psychotherapy, 13, 21–40. American Psychiatric Association. (2000). Diagnostic and statistical manual of mental disorders (4th ed., text revision). Washington, DC: Author. American Psychiatric Association. (2013). Diagnostic and statistical manual of mental disorders (5th ed.). Washington, DC: Author. Anderson, E. R., & Hope, D. A. (2008). A review of the tripartite model for understanding the link between anxiety and depression in youth. Clinical Psychology Review, 28, 275–287. http://dx.doi.org/10.1016/ j.cpr.2007.05.004 Angold, A., Costello, E. J., & Erkanli, A. (1999). Comorbidity. Journal of Child Psychology and Psychiatry, 40, 57–87. http://dx.doi.org/10.1111/ 1469-7610.00424 Angold, A., Costello, E. J., & Worthman, C. M. (1998). Puberty and depression: The roles of age, pubertal status and pubertal timing. Psychological Medicine, 28, 51–61. http://dx.doi.org/10.1017/ S003329179700593X Avenevoli, S., Stolar, M., Li, J., Dierker, L., & Ries Merikangas, K. (2001). Comorbidity of depression 268

in children and adolescents: Models and evidence from a prospective high-risk family study. Biological Psychiatry, 49, 1071–1081. http://dx.doi.org/ 10.1016/S0006-3223(01)01142-8 Avenevoli, S., Swendsen, J., He, J. P., Burstein, M., & Merikangas, K. R. (2015). Major depression in the national comorbidity survey-adolescent supplement: Prevalence, correlates, and treatment. Journal of the American Academy of Child and Adolescent Psychiatry, 54, 37–44.e2. http://dx.doi.org/10.1016/ j.jaac.2014.10.010 Bagby, R. M., Bindseil, K. D., Schuller, D. R., Rector, N. A., Young, L. T., Cooke, R. G., . . . Joffe, R. T. (1997). Relationship between the five-factor model of personality and unipolar, bipolar and schizophrenic patients. Psychiatry Research, 70, 83–94. http:// dx.doi.org/10.1016/S0165-1781(97)03096-5 Bagby, R. M., Young, L. T., Schuller, D. R., Bindseil, K. D., Cooke, R. G., Dickens, S. E., . . . Joffe, R. T. (1996). Bipolar disorder, unipolar depression and the five-factor model of personality. Journal of Affective Disorders, 41, 25–32. http://dx.doi.org/10.1016/ 0165-0327(96)00060-2 Bebko, G., Bertocci, M. A., Fournier, J. C., Hinze, A. K., Bonar, L., Almeida, J. R., . . . Phillips, M. L. (2014). Parsing dimensional vs diagnostic category-related patterns of reward circuitry function in behaviorally and emotionally dysregulated youth in the longitudinal assessment of manic symptoms study. JAMA Psychiatry, 71, 71–80. http://dx.doi.org/ 10.1001/jamapsychiatry.2013.2870 Belardinelli, C., Hatch, J. P., Olvera, R. L., Fonseca, M., Caetano, S. C., Nicoletti, M., . . . Soares, J. C. (2008). Family environment patterns in families with bipolar children. Journal of Affective Disorders, 107, 299–305. http://dx.doi.org/10.1016/j.jad.2007.08.011 Berard, R., Fong, R., Carpenter, D. J., Thomason, C., & Wilkinson, C. (2006). An international, multicenter, placebo-controlled trial of paroxetine in adolescents with major depressive disorder. Journal of Child and Adolescent Psychopharmacology, 16, 59–75. http:// dx.doi.org/10.1089/cap.2006.16.59 Bertelsen, A., Harvald, B., & Hauge, M. (1977). A Danish twin study of manic-depressive disorders. British Journal of Psychiatry, 130, 330–351. http:// dx.doi.org/10.1192/bjp.130.4.330 Bertocci, M. A., Bebko, G., Olino, T., Fournier, J., Hinze, A. K., Bonar, L., . . . Phillips, M. L. (2014). Behavioral and emotional dysregulation trajectories marked by prefrontal-amygdala function in symptomatic youth. Psychological Medicine, 44, 2603–2615. http:// dx.doi.org/10.1017/S0033291714000087 Biederman, J. (1998). Attention-deficit/hyperactivity disorder: A lifespan perspective. Journal of Clinical Psychiatry, 59(Suppl. 7), 4–16.

Mood Disorders in Childhood and Adolescence

Copyright American Psychological Association. Not for further distribution.

Bierut, L. J., Heath, A. C., Bucholz, K. K., Dinwiddie, S. H., Madden, P. A., Statham, D. J., . . . Martin, N. G. (1999). Major depressive disorder in a communitybased twin sample: Are there different genetic and environmental contributions for men and women? Archives of General Psychiatry, 56, 557–563. http:// dx.doi.org/10.1001/archpsyc.56.6.557

Comprehensive Psychiatry, 52, 359–369. http:// dx.doi.org/10.1016/j.comppsych.2010.08.006 Bufferd, S. J., Dougherty, L. R., Carlson, G. A., Rose, S., & Klein, D. N. (2012). Psychiatric disorders in preschoolers: Continuity from ages 3 to 6. American Journal of Psychiatry, 169, 1157–1164. http:// dx.doi.org/10.1176/appi.ajp.2012.12020268

Birmaher, B. (2007). Longitudinal course of pediatric bipolar disorder. American Journal of Psychiatry, 164, 537–539. http://dx.doi.org/10.1176/ ajp.2007.164.4.537

Carlson, G. A. (2016). Disruptive mood dysregulation disorder: Where did it come from and where is it going. Journal of Child and Adolescent Psychopharmacology, 26, 90–93. http://dx.doi.org/10.1089/cap.2016.29102.gca

Birmaher, B. (2013). Bipolar disorder in children and adolescents. Child and Adolescent Mental Health, 18, 140–148. http://dx.doi.org/10.1111/camh.12021

Carlson, G. A., & Kashani, J. H. (1988). Phenomenology of major depression from childhood through adulthood: Analysis of three studies. American Journal of Psychiatry, 145, 1222–1225. http:// dx.doi.org/10.1176/ajp.145.10.1222

Birmaher, B., Arbelaez, C., & Brent, D. (2002). Course and outcome of child and adolescent major depressive disorder. Child and Adolescent Psychiatric Clinics of North America, 11, 619–637. http:// dx.doi.org/10.1016/S1056-4993(02)00011-1 Birmaher, B., Axelson, D., Goldstein, B., Strober, M., Gill, M. K., Hunt, J., . . . Keller, M. (2009). Fouryear longitudinal course of children and adolescents with bipolar spectrum disorders: The Course and Outcome of Bipolar Youth (COBY) study. American Journal of Psychiatry, 166, 795–804. http:// dx.doi.org/10.1176/appi.ajp.2009.08101569 Birmaher, B., Ryan, N. D., Williamson, D. E., Brent, D. A., Kaufman, J., Dahl, R. E., . . . Nelson, B. (1996). Childhood and adolescent depression: A review of the past 10 years. Part I. Journal of the American Academy of Child and Adolescent Psychiatry, 35, 1427–1439. http://dx.doi.org/10.1097/00004583199611000-00011 Birmaher, B., Williamson, D. E., Dahl, R. E., Axelson, D. A., Kaufman, J., Dorn, L. D., & Ryan, N. D. (2004). Clinical presentation and course of depression in youth: Does onset in childhood differ from onset in adolescence? Journal of the American Academy of Child and Adolescent Psychiatry, 43, 63–70. http://dx.doi.org/10.1097/00004583200401000-00015 Blader, J. C., & Carlson, G. A. (2007). Increased rates of bipolar disorder diagnoses among U.S. child, adolescent, and adult inpatients, 1996-2004. Biological Psychiatry, 62, 107–114. http://dx.doi.org/ 10.1016/j.biopsych.2006.11.006 Brière, F. N., Rohde, P., Seeley, J. R., Klein, D., & Lewinsohn, P. M. (2014). Comorbidity between major depression and alcohol use disorder from adolescence to adulthood. Comprehensive Psychiatry, 55, 526–533. http://dx.doi.org/10.1016/ j.comppsych.2013.10.007 Bufferd, S. J., Dougherty, L. R., Carlson, G. A., & Klein, D. N. (2011). Parent-reported mental health in preschoolers: Findings using a diagnostic interview.

Carlson, G. A., Kotov, R., Chang, S. W., Ruggero, C., & Bromet, E. J. (2012). Early determinants of four-year clinical outcomes in bipolar disorder with psychosis. Bipolar Disorders, 14, 19–30. http:// dx.doi.org/10.1111/j.1399-5618.2012.00982.x Carter, J. S., & Garber, J. (2011). Predictors of the first onset of a major depressive episode and changes in depressive symptoms across adolescence: Stress and negative cognitions. Journal of Abnormal Psychology, 120, 779–796. http://dx.doi.org/10.1037/a0025441 Clark, L. A., & Watson, D. (1991). Tripartite model of anxiety and depression: Psychometric evidence and taxonomic implications. Journal of Abnormal Psychology, 100, 316–336. http://dx.doi.org/ 10.1037/0021-843X.100.3.316 Clayton, E. H., Hanstock, T. L., Hirneth, S. J., Kable, C. J., Garg, M. L., & Hazell, P. L. (2009). Reduced mania and depression in juvenile bipolar disorder associated with long-chain omega-3 polyunsaturated fatty acid supplementation. European Journal of Clinical Nutrition, 63, 1037–1040. http://dx.doi.org/ 10.1038/ejcn.2008.81 Cohen, D., Bonnot, O., Bodeau, N., Consoli, A., & Laurent, C. (2012). Adverse effects of secondgeneration antipsychotics in children and adolescents: A Bayesian meta-analysis. Journal of Clinical Psychopharmacology, 32, 309–316. http:// dx.doi.org/10.1097/JCP.0b013e3182549259 Cole, D. A., Nolen-Hoeksema, S., Girgus, J., & Paul, G. (2006). Stress exposure and stress generation in child and adolescent depression: A latent trait-stateerror approach to longitudinal analyses. Journal of Abnormal Psychology, 115, 40–51. http://dx.doi.org/ 10.1037/0021-843X.115.1.40 Costello, E. J., Mustillo, S., Erkanli, A., Keeler, G., & Angold, A. (2003). Prevalence and development of psychiatric disorders in childhood and adolescence. Archives of General Psychiatry, 60, 837–844. http:// dx.doi.org/10.1001/archpsyc.60.8.837 269

Fristad and Black

Cullen, K. R., Klimes-Dougan, B., Muetzel, R., Mueller, B. A., Camchong, J., Houri, A., . . . Lim, K. O. (2010). Altered white matter microstructure in adolescents with major depression: A preliminary study. Journal of the American Academy of Child and Adolescent Psychiatry, 49, 173–83.e1.

Copyright American Psychological Association. Not for further distribution.

Cummings, C. M., Caporino, N. E., & Kendall, P. C. (2014). Comorbidity of anxiety and depression in children and adolescents: 20 years after. Psychological Bulletin, 140, 816–845. http:// dx.doi.org/10.1037/a0034733 David-Ferdon, C., & Kaslow, N. J. (2008). Evidencebased psychosocial treatments for child and adolescent depression. Journal of Clinical Child and Adolescent Psychology, 37, 62–104. http:// dx.doi.org/10.1080/15374410701817865 Delbello, M. P., Hanseman, D., Adler, C. M., Fleck, D. E., & Strakowski, S. M. (2007). Twelve-month outcome of adolescents with bipolar disorder following first hospitalization for a manic or mixed episode. American Journal of Psychiatry, 164, 582–590. http://dx.doi.org/10.1176/ ajp.2007.164.4.582 Demeter, C. A., Youngstrom, E. A., Carlson, G. A., Frazier, T. W., Rowles, B. M., Lingler, J., . . . Findling, R. L. (2013). Age differences in the phenomenology of pediatric bipolar disorder. Journal of Affective Disorders, 147, 295–303. http:// dx.doi.org/10.1016/j.jad.2012.11.021 Diler, R. S. (2007). Pediatric bipolar disorder: A global perspective. New York, NY: Nova. Dougherty, L. R., Bufferd, S. J., Carlson, G. A., Dyson, M., Olino, T. M., Durbin, C. E., & Klein, D. N. (2011). Preschoolers’ observed temperament and psychiatric disorders assessed with a parent diagnostic interview. Journal of Clinical Child and Adolescent Psychology, 40, 295–306. http://dx.doi.org/10.1080/15374416. 2011.546046 Dougherty, L. R., Klein, D. N., Olino, T. M., Dyson, M., & Rose, S. (2009). Increased waking salivary cortisol levels and depression risk in preschoolers: The role of maternal history of melancholic depression and early child temperament. Journal of Child Psychology and Psychiatry, 50, 1495–1503. http://dx.doi.org/ 10.1111/j.1469-7610.2009.02116.x Durbin, C. E., Klein, D. N., Hayden, E. P., Buckley, M. E., & Moerk, K. C. (2005). Temperamental emotionality in preschoolers and parental mood disorders. Journal of Abnormal Psychology, 114, 28–37. http://dx.doi.org/ 10.1037/0021-843X.114.1.28 Dvir, Y., Ford, J. D., Hill, M., & Frazier, J. A. (2014). Childhood maltreatment, emotional dysregulation, and psychiatric comorbidities. Harvard Review of Psychiatry, 22, 149–161. http://dx.doi.org/10.1097/ HRP.0000000000000014 270

Egger, H. L., & Angold, A. (2006). Common emotional and behavioral disorders in preschool children: Presentation, nosology, and epidemiology. Journal of Child Psychology and Psychiatry, 47, 313–337. http:// dx.doi.org/10.1111/j.1469-7610.2006.01618.x Ellenbogen, M. A., Hodgins, S., & Walker, C. D. (2004). High levels of cortisol among adolescent offspring of parents with bipolar disorder: A pilot study. Psychoneuroendocrinology, 29, 99–106. http:// dx.doi.org/10.1016/S0306-4530(02)00135-X Ellenbogen, M. A., Hodgins, S., Walker, C. D., Couture, S., & Adam, S. (2006). Daytime cortisol and stress reactivity in the offspring of parents with bipolar disorder. Psychoneuroendocrinology, 31, 1164–1180. http://dx.doi.org/10.1016/j.psyneuen.2006.08.004 Ellenbogen, M. A., Santo, J. B., Linnen, A. M., Walker, C. D., & Hodgins, S. (2010). High cortisol levels in the offspring of parents with bipolar disorder during two weeks of daily sampling. Bipolar Disorders, 12, 77–86. http://dx.doi.org/10.1111/j.1399-5618.2009.00770.x Ellis, A. J., Portnoff, L. C., Axelson, D. A., Kowatch, R. A., Walshaw, P., & Miklowitz, D. J. (2014). Parental expressed emotion and suicidal ideation in adolescents with bipolar disorder. Psychiatry Research, 216, 213–216. http://dx.doi.org/10.1016/ j.psychres.2014.02.013 Emslie, G. J., Heiligenstein, J. H., Wagner, K. D., Hoog, S. L., Ernest, D. E., Brown, E., . . . Jacobson, J. G. (2002). Fluoxetine for acute treatment of depression in children and adolescents: A placebo-controlled, randomized clinical trial. Journal of the American Academy of Child and Adolescent Psychiatry, 41, 1205–1215. http://dx.doi.org/10.1097/00004583200210000-00010 Emslie, G. J., Rush, A. J., Weinberg, W. A., Kowatch, R. A., Hughes, C. W., Carmody, T., & Rintelmann, J. (1997). A double-blind, randomized, placebocontrolled trial of fluoxetine in children and adolescents with depression. Archives of General Psychiatry, 54, 1031–1037. http://dx.doi.org/10.1001/ archpsyc.1997.01830230069010 Emslie, G. J., Ventura, D., Korotzer, A., & Tourkodimitris, S. (2009). Escitalopram in the treatment of adolescent depression: A randomized placebo-controlled multisite trial. Journal of the American Academy of Child and Adolescent Psychiatry, 48, 721–729. http://dx.doi.org/10.1097/ CHI.0b013e3181a2b304 Emslie, G. J., Wagner, K. D., Kutcher, S., Krulewicz, S., Fong, R., Carpenter, D. J., . . . Wilkinson, C. (2006). Paroxetine treatment in children and adolescents with major depressive disorder: A randomized, multicenter, double-blind, placebo-controlled trial. Journal of the American Academy of Child and Adolescent Psychiatry, 45, 709–719. http://dx.doi.org/ 10.1097/01.chi.0000214189.73240.63

Copyright American Psychological Association. Not for further distribution.

Mood Disorders in Childhood and Adolescence

Epperson, C. N., Steiner, M., Hartlage, S. A., Eriksson, E., Schmidt, P. J., Jones, I., & Yonkers, K. A. (2012). Premenstrual dysphoric disorder: Evidence for a new category for DSM–5. American Journal of Psychiatry, 169, 465–475. http://dx.doi.org/10.1176/ appi.ajp.2012.11081302

Fristad, M. A., Verducci, J. S., Walters, K., & Young, M. A. (2009). Impact of multifamily psychoeducational psychotherapy in treating children aged 8 to 12 years with mood disorders. Archives of General Psychiatry, 66, 1013–1021. http://dx.doi.org/10.1001/ archgenpsychiatry.2009.112

Essau, C. A. (2008). Comorbidity of depressive disorders among adolescents in community and clinical settings. Psychiatry Research, 158, 35–42. http:// dx.doi.org/10.1016/j.psychres.2007.09.007

Fristad, M. A., Vesco, A. T., Young, A. S., Nader, E. S., Healy, K. Z., Seidenfeld, A., . . . Arnold, L. E. (2016). Pilot randomized control trial of omega-3 and individual-family psychoeducational psychotherapy for children and adolescents with depression. Journal of Clinical Child and Adolescent Psychology, 7, 1–14. Advance online publication. http://dx.doi.org/ 10.1080/15374416.2016.1233500

Ezpeleta, L., de la Osa, N., & Doménech, J. M. (2014). Prevalence of DSM–IV disorders, comorbidity and impairment in 3-year-old Spanish preschoolers. Social Psychiatry and Psychiatric Epidemiology, 49, 145–155. http://dx.doi.org/10.1007/s00127-013-0683-1 Feeny, N. C., Danielson, C. K., Schwartz, L., Youngstrom, E. A., & Findling, R. L. (2006). Cognitive–behavioral therapy for bipolar disorders in adolescents: A pilot study. Bipolar Disorders, 8, 508–515. http://dx.doi.org/ 10.1111/j.1399-5618.2006.00358.x Fernandez-Pujals, A. M., Adams, M. J., Thomson, P., McKechanie, A. G., Blackwood, D. H. R., Smith, B. H., . . . McIntosh, A. M. (2015). Epidemiology and heritability of major depressive disorder, stratified by age of onset, sex, and illness course in Generation Scotland: Scottish Family Health Study (GS:SFHS). PLOS ONE, 10, e0142197. http://dx.doi.org/10.1371/ journal.pone.0142197 Frazier, E. A., Fristad, M. A., & Arnold, L. E. (2012). Feasibility of a nutritional supplement as treatment for pediatric bipolar spectrum disorders. Journal of Alternative and Complementary Medicine, 18, 678–685. http://dx.doi.org/10.1089/acm.2011.0270 Frías, Á., Palma, C., & Farriols, N. (2015). Comorbidity in pediatric bipolar disorder: Prevalence, clinical impact, etiology and treatment. Journal of Affective Disorders, 174, 378–389. http://dx.doi.org/10.1016/ j.jad.2014.12.008 Fristad, M. A. (2006). Psychoeducational treatment for school-aged children with bipolar disorder. Development and Psychopathology, 18, 1289–1306. http://dx.doi.org/10.1017/S0954579406060627

Fristad, M. A., Young, A. S., Vesco, A. T., Nader, E. S., Healy, K. Z., Gardner, W., . . . Arnold, L. E. (2015). A randomized controlled trial of individual family psychoeducational psychotherapy and omega-3 fatty acids in youth with subsyndromal bipolar disorder. Journal of Child and Adolescent Psychopharmacology, 25, 764–774. http://dx.doi.org/10.1089/ cap.2015.0132 Geller, B., Tillman, R., Bolhofner, K., & Zimerman, B. (2008). Child bipolar I disorder: Prospective continuity with adult bipolar I disorder; characteristics of second and third episodes; predictors of 8-year outcome. Archives of General Psychiatry, 65, 1125–1133. http://dx.doi.org/10.1001/ archpsyc.65.10.1125 Goldstein, T. R., Axelson, D. A., Birmaher, B., & Brent, D. A. (2007). Dialectical behavior therapy for adolescents with bipolar disorder: A 1-year open trial. Journal of the American Academy of Child and Adolescent Psychiatry, 46, 820–830. http://dx.doi.org/ 10.1097/chi.0b013e31805c1613 Grant, K. E., Compas, B. E., Thurm, A. E., McMahon, S. D., Gipson, P. Y., Campbell, A. J., . . . Westerholm, R. I. (2006). Stressors and child and adolescent psychopathology: Evidence of moderating and mediating effects. Clinical Psychology Review, 26, 257–283. http://dx.doi.org/10.1016/ j.cpr.2005.06.011

Fristad, M. A., & Algorta, G. P. (2013). Future directions for research on youth with bipolar spectrum disorders. Journal of Clinical Child and Adolescent Psychology, 42, 734–747. http://dx.doi.org/10.1080/ 15374416.2013.817312

Gruber, J., & Johnson, S. L. (2009). Positive emotional traits and ambitious goals among people at risk for mania: The need for specificity. International Journal of Cognitive Therapy, 2, 176–187. http://dx.doi.org/ 10.1521/ijct.2009.2.2.176

Fristad, M. A., Goldberg-Arnold, J. S., & Leffler, J. M. (2011). Psychotherapy for children with bipolar and depressive disorders. New York, NY: Guilford Press.

Gruber, J., Johnson, S. L., Oveis, C., & Keltner, D. (2008). Risk for mania and positive emotional responding: Too much of a good thing? Emotion, 8, 23–33. http://dx.doi.org/10.1037/1528-3542.8.1.23

Fristad, M. A., & MacPherson, H. A. (2014). Evidencebased psychosocial treatments for child and adolescent bipolar spectrum disorders. Journal of Clinical Child and Adolescent Psychology, 43, 339–355. http://dx.doi.org/10.1080/15374416.2013.822309

Gruber, J., Oveis, C., Keltner, D., & Johnson, S. L. (2008). Risk for mania and positive emotional responding: Too much of a good thing? Emotion, 8, 23–33. http://dx.doi.org/10.1037/1528-3542.8.1.23 271

Fristad and Black

Guedeney, A. (2007). Withdrawal behavior and depression in infancy. Infant Mental Health Journal, 28, 393–408. http://dx.doi.org/10.1002/imhj.20143

Copyright American Psychological Association. Not for further distribution.

Hafeman, D. M., Bebko, G., Bertocci, M. A., Fournier, J. C., Bonar, L., Perlman, S. B., . . . Phillips, M. L. (2014). Abnormal deactivation of the inferior frontal gyrus during implicit emotion processing in youth with bipolar disorder: Attenuated by medication. Journal of Psychiatric Research, 58, 129–136. http:// dx.doi.org/10.1016/j.jpsychires.2014.07.023 Hamilton, J. L., Stange, J. P., Abramson, L. Y., & Alloy, L. B. (2015). Stress and the development of cognitive vulnerabilities to depression explain sex differences in depressive symptoms during adolescence. Clinical Psychological Science, 3, 702–714. http://dx.doi.org/ 10.1177/2167702614545479 Hankin, B. L. (2012). Future directions in vulnerability to depression among youth: Integrating risk factors and processes across multiple levels of analysis. Journal of Clinical Child and Adolescent Psychology, 41, 695–718. http://dx.doi.org/10.1080/15374416. 2012.711708 Hankin, B. L., & Abramson, L. Y. (2001). Development of gender differences in depression: An elaborated cognitive vulnerability-transactional stress theory. Psychological Bulletin, 127, 773–796. http:// dx.doi.org/10.1037/0033-2909.127.6.773 Harrington, R., Whittaker, J., Shoebridge, P., & Campbell, F. (1998). Systematic review of efficacy of cognitive behaviour therapies in childhood and adolescent depressive disorder. BMJ, 316, 1559–1563. http://dx.doi.org/10.1136/ bmj.316.7144.1559 Hasler, G., & Northoff, G. (2011). Discovering imaging endophenotypes for major depression. Molecular Psychiatry, 16, 604–619. http://dx.doi.org/10.1038/ mp.2011.23 Hlastala, S. A., Kotler, J. S., McClellan, J. M., & McCauley, E. A. (2010). Interpersonal and social rhythm therapy for adolescents with bipolar disorder: Treatment development and results from an open trial. Depression and Anxiety, 27, 457–464. http://dx.doi.org/10.1002/da.20668 Hunt, J., Birmaher, B., Leonard, H., Strober, M., Axelson, D., Ryan, N., . . . Keller, M. (2009). Irritability without elation in a large bipolar youth sample: Frequency and clinical description. Journal of the American Academy of Child and Adolescent Psychiatry, 48, 730–739. http://dx.doi.org/10.1097/ CHI.0b013e3181a565db Johnson, S. L., Fulford, D., & Carver, C. S. (2012). The double-edged sword of goal engagement: Consequences of goal pursuit in bipolar disorder. Clinical Psychology and Psychotherapy, 19, 352–362. http://dx.doi.org/10.1002/cpp.1801 272

Jones, I., Kent, L., & Craddock, N. (2002). Genetics of affective disorders. In P. McGuffin, M. J. Owen, & I. I. Gottesman (Eds.), Psychiatric genetics and genomics (pp. 211–245). Oxford, England: Oxford University Press. Kann, L., Kinchen, S., Shanklin, S. L., Flint, K. H., Kawkins, J., Harris, W. A., . . . Zaza, S. (2014). Youth risk behavior surveillance—United States, 2013. MMWR Supplements, 63, 1–168. Kaplan, B. J., Fisher, J. E., Crawford, S. G., Field, C. J., & Kolb, B. (2004). Improved mood and behavior during treatment with a mineral-vitamin supplement: An open-label case series of children. Journal of Child and Adolescent Psychopharmacology, 14, 115–122. http://dx.doi.org/10.1089/104454604773840553 Keller, M. B., Ryan, N. D., Strober, M., Klein, R. G., Kutcher, S. P., Birmaher, B., . . . McCafferty, J. P. (2001). Efficacy of paroxetine in the treatment of adolescent major depression: A randomized, controlled trial. Journal of the American Academy of Child and Adolescent Psychiatry, 40, 762–772. http:// dx.doi.org/10.1097/00004583-200107000-00010 Kendall, P. C. (Ed.). (2011). Child and adolescent therapy: Cognitive–behavioral procedures. New York, NY: Guilford Press. Kendall, P. C., Stark, K. D., & Adam, T. (1990). Cognitive deficit or cognitive distortion in childhood depression. Journal of Abnormal Child Psychology, 18, 255–270. http://dx.doi.org/10.1007/BF00916564 Kendler, K. S., & Gardner, C. O. (2014). Sex differences in the pathways to major depression: A study of opposite-sex twin pairs. American Journal of Psychiatry, 171, 426–435. http://dx.doi.org/10.1176/ appi.ajp.2013.13101375 Kendler, K. S., Thornton, L. M., & Gardner, C. O. (2001). Genetic risk, number of previous depressive episodes, and stressful life events in predicting onset of major depression. American Journal of Psychiatry, 158, 582–586. http://dx.doi.org/10.1176/ appi.ajp.158.4.582 Klein, J. B., Jacobs, R. H., & Reinecke, M. A. (2007). Cognitive–behavioral therapy for adolescent depression: A meta-analytic investigation of changes in effect-size estimates. Journal of the American Academy of Child and Adolescent Psychiatry, 46, 1403–1413. http:// dx.doi.org/10.1097/chi.0b013e3180592aaa Klein, R. G., Pine, D. S., & Klein, D. F. (1998). Resolved: Mania is mistaken for ADHD in prepubertal children. Journal of the American Academy of Child and Adolescent Psychiatry, 37, 1093–1096. http:// dx.doi.org/10.1097/00004583-199810000-00020 Kovacs, M. (1996). Presentation and course of major depressive disorder during childhood and later years of the lifespan. Journal of the American Academy of

Mood Disorders in Childhood and Adolescence

Child and Adolescent Psychiatry, 35, 705–715. http:// dx.doi.org/10.1097/00004583-199606000-00010 Kowatch, R. A., Fristad, M., Birmaher, B., Wagner, K. D., Findling, R. L., & Hellander, M. (2005). Treatment guidelines for children and adolescents with bipolar disorder. Journal of the American Academy of Child and Adolescent Psychiatry, 44, 213–235. http:// dx.doi.org/10.1097/00004583-200503000-00006

Copyright American Psychological Association. Not for further distribution.

Kowatch, R. A., Youngstrom, E. A., Danielyan, A., & Findling, R. L. (2005). Review and meta-analysis of the phenomenology and clinical characteristics of mania in children and adolescents. Bipolar Disorders, 7, 483–496. http://dx.doi.org/10.1111/ j.1399-5618.2005.00261.x Krieger, F. V., Leibenluft, E., Stringaris, A., & Polanczyk, G. V. (2013). Irritability in children and adolescents: Past concepts, current debates, and future opportunities. Revista Brasileira de Psiquiatria, 35(Suppl. 1), S32–S39. http://dx.doi.org/10.1590/ 1516-4446-2013-S107 Leibenluft, E. (2011). Severe mood dysregulation, irritability, and the diagnostic boundaries of bipolar disorder in youths. American Journal of Psychiatry, 168, 129–142. http://dx.doi.org/10.1176/appi. ajp.2010.10050766 Leibenluft, E., Charney, D. S., Towbin, K. E., Bhangoo, R. K., & Pine, D. S. (2003). Defining clinical phenotypes of juvenile mania. American Journal of Psychiatry, 160, 430–437. http://dx.doi.org/10.1176/ appi.ajp.160.3.430 Leppänen, J. M. (2006). Emotional information processing in mood disorders: A review of behavioral and neuroimaging findings. Current Opinion in Psychiatry, 19, 34–39. http://dx.doi.org/10.1097/ 01.yco.0000191500.46411.00 Levinson, D. F. (2006). The genetics of depression: A review. Biological Psychiatry, 60, 84–92. http:// dx.doi.org/10.1016/j.biopsych.2005.08.024 Lewinsohn, P. M., Clarke, G. N., Hops, H., & Andrews, J. (1990). Cognitive–behavioral treatment for depressed adolescents. Behavior Therapy, 21, 385–401. http:// dx.doi.org/10.1016/S0005-7894(05)80353-3 Lewinsohn, P. M., Klein, D. N., & Seeley, J. R. (2000). Bipolar disorder during adolescence and young adulthood in a community sample. Bipolar Disorders, 2, 281–293. http://dx.doi.org/0.1034/ j.1399-5618.2000.20309.x

Pharmacologic treatments for pediatric bipolar disorder: A review and meta-analysis. Journal of the American Academy of Child and Adolescent Psychiatry, 50, 749–62.e39. http://dx.doi.org/10.1016/ j.jaac.2011.05.011 Lonigan, C. J., Carey, M. P., & Finch, A. J., Jr. (1994). Anxiety and depression in children and adolescents: Negative affectivity and the utility of self-reports. Journal of Consulting and Clinical Psychology, 62, 1000–1008. http://dx.doi.org/10.1037/ 0022-006X.62.5.1000 Lopez-Duran, N. L., Kovacs, M., & George, C. J. (2009). Hypothalamic-pituitary-adrenal axis dysregulation in depressed children and adolescents: A meta-analysis. Psychoneuroendocrinology, 34, 1272–1283. http:// dx.doi.org/10.1016/j.psyneuen.2009.03.016 Luby, J. L., Belden, A. C., Pautsch, J., Si, X., & Spitznagel, E. (2009). The clinical significance of preschool depression: Impairment in functioning and clinical markers of the disorder. Journal of Affective Disorders, 112, 111–119. http://dx.doi.org/10.1016/ j.jad.2008.03.026 Luby, J. L., Gaffrey, M. S., Tillman, R., April, L. M., & Belden, A. C. (2014). Trajectories of preschool disorders to full DSM depression at school age and early adolescence: Continuity of preschool depression. American Journal of Psychiatry, 171, 768–776. http:// dx.doi.org/10.1176/appi.ajp.2014.13091198 Luby, J. L., Heffelfinger, A. K., Mrakotsky, C., Brown, K. M., Hessler, M. J., Wallis, J. M., & Spitznagel, E. L. (2003). The clinical picture of depression in preschool children. Journal of the American Academy of Child and Adolescent Psychiatry, 42, 340–348. http:// dx.doi.org/10.1097/00004583-200303000-00015 Luby, J. L., Heffelfinger, A. K., Mrakotsky, C., Hessler, M. J., Brown, K. M., & Hildebrand, T. (2002). Preschool major depressive disorder: Preliminary validation for developmentally modified DSM–IV criteria. Journal of the American Academy of Child and Adolescent Psychiatry, 41, 928–937. http://dx.doi.org/ 10.1097/00004583-200208000-00011 MacQueen, G., & Frodl, T. (2011). The hippocampus in major depression: Evidence for the convergence of the bench and bedside in psychiatric research? Molecular Psychiatry, 16, 252–264. http://dx.doi.org/ 10.1038/mp.2010.80

Lin, P. Y., & Su, K. P. (2007). A meta-analytic review of double-blind, placebo-controlled trials of antidepressant efficacy of omega-3 fatty acids. Journal of Clinical Psychiatry, 68, 1056–1061. http:// dx.doi.org/10.4088/JCP.v68n0712

March, J., Silva, S., Petrycki, S., Curry, J., Wells, K., Fairbank, J., . . . Severe, J. (2004). Fluoxetine, cognitive–behavioral therapy, and their combination for adolescents with depression: Treatment for Adolescents With Depression Study (TADS) randomized controlled trial. JAMA, 292, 807–820. http://dx.doi.org/10.1001/jama.292.7.807

Liu, H. Y., Potter, M. P., Woodworth, K. Y., Yorks, D. M., Petty, C. R., Wozniak, J. R., . . . Biederman, J. (2011).

McGuffin, P., Katz, R., Watkins, S., & Rutherford, J. (1996). A hospital-based twin register of the 273

Fristad and Black

heritability of DSM–IV unipolar depression. Archives of General Psychiatry, 53, 129–136. http://dx.doi.org/ 10.1001/archpsyc.1996.01830020047006

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McGuffin, P., Rijsdijk, F., Andrew, M., Sham, P., Katz, R., & Cardno, A. (2003). The heritability of bipolar affective disorder and the genetic relationship to unipolar depression. Archives of General Psychiatry, 60, 497–502. http://dx.doi.org/10.1001/ archpsyc.60.5.497 Melhem, N. M., Moritz, G., Walker, M., Shear, M. K., & Brent, D. (2007). Phenomenology and correlates of complicated grief in children and adolescents. Journal of the American Academy of Child and Adolescent Psychiatry, 46, 493–499. http://dx.doi.org/ 10.1097/chi.0b013e31803062a9 Merikangas, K. R., He, J. P., Burstein, M., Swanson, S. A., Avenevoli, S., Cui, L., . . . Swendsen, J. (2010). Lifetime prevalence of mental disorders in U.S. adolescents: Results from the National Comorbidity Survey Replication—Adolescent Supplement (NCS–A). Journal of the American Academy of Child and Adolescent Psychiatry, 49, 980–989. http:// dx.doi.org/10.1016/j.jaac.2010.05.017 Merikangas, K. R., Nakamura, E. F., & Kessler, R. C. (2009). Epidemiology of mental disorders in children and adolescents. Dialogues in Clinical Neuroscience, 11, 7–20. Meyer, T. D., & Hofmann, B. U. (2005). Assessing the dysregulation of the behavioral activation system: The hypomanic personality scale and the BIS–BAS scales. Journal of Personality Assessment, 85, 318–324. http://dx.doi.org/10.1207/s15327752jpa8503_08 Miklowitz, D. J., Axelson, D. A., George, E. L., Taylor, D. O., Schneck, C. D., Sullivan, A. E., . . . Birmaher, B. (2009). Expressed emotion moderates the effects of family-focused treatment for bipolar adolescents. Journal of the American Academy of Child and Adolescent Psychiatry, 48, 643–651. http://dx.doi.org/ 10.1097/CHI.0b013e3181a0ab9d Miklowitz, D. J., George, E. L., Axelson, D. A., Kim, E. Y., Birmaher, B., Schneck, C., . . . Brent, D. A. (2004). Family-focused treatment for adolescents with bipolar disorder. Journal of Affective Disorders, 82, S113–S128. http://dx.doi.org/10.1016/j.jad.2004.05.020 Miller, C. H., Hamilton, J. P., Sacchet, M. D., & Gotlib, I. H. (2015). Meta-analysis of functional neuroimaging of major depressive disorder in youth. JAMA Psychiatry, 72, 1045–1053. http://dx.doi.org/ 10.1001/jamapsychiatry.2015.1376 Monroe, S. M., Rohde, P., Seeley, J. R., & Lewinsohn, P. M. (1999). Life events and depression in adolescence: Relationship loss as a prospective risk factor for first onset of major depressive disorder. Journal of Abnormal Psychology, 108, 606–614. http:// dx.doi.org/10.1037/0021-843X.108.4.606 274

Moreno, C., Laje, G., Blanco, C., Jiang, H., Schmidt, A. B., & Olfson, M. (2007). National trends in the outpatient diagnosis and treatment of bipolar disorder in youth. Archives of General Psychiatry, 64, 1032–1039. http://dx.doi.org/10.1001/ archpsyc.64.9.1032 Mufson, L., Weissman, M. M., Moreau, D., & Garfinkel, R. (1999). Efficacy of interpersonal psychotherapy for depressed adolescents. Archives of General Psychiatry, 56, 573–579. http://dx.doi.org/10.1001/ archpsyc.56.6.573 Murri, M. B., Prestia, D., Mondelli, V., Pariante, C., Patti, S., Olivieri, B., . . . Amore, M. (2016). The HPA axis in bipolar disorder: Systematic review and metaanalysis. Psychoneuroendocrinology, 63, 327–342. http://dx.doi.org/10.1016/j.psyneuen.2015.10.014 Nader, E. G., Kleinman, A., Gomes, B. C., Bruscagin, C., Santos, B., Nicoletti, M., . . . Caetano, S. C. (2013). Negative expressed emotion best discriminates families with bipolar disorder children. Journal of Affective Disorders, 148, 418–423. http://dx.doi.org/ 10.1016/j.jad.2012.11.017 Nemets, H., Nemets, B., Apter, A., Bracha, Z., & Belmaker, R. H. (2006). Omega-3 treatment of childhood depression: A controlled, double-blind pilot study. American Journal of Psychiatry, 163, 1098–1100. http:// dx.doi.org/10.1176/ajp.2006.163.6.1098 Nolan, C. L., Moore, G. J., Madden, R., Farchione, T., Bartoi, M., Lorch, E., . . . Rosenberg, D. R. (2002). Prefrontal cortical volume in childhood-onset major depression: Preliminary findings. Archives of General Psychiatry, 59, 173–179. http://dx.doi.org/10.1001/ archpsyc.59.2.173 Nolen-Hoeksema, S. (2001). Gender differences in depression. Current Directions in Psychological Science, 10, 173–176. http://dx.doi.org/10.1111/ 1467-8721.00142 Nusslock, R., & Frank, E. (2011). Subthreshold bipolarity: Diagnostic issues and challenges. Bipolar Disorders, 13, 587–603. http://dx.doi.org/10.1111/ j.1399-5618.2011.00957.x Nusslock, R., & Miller, G. E. (2016). Early-life adversity and physical and emotional health across the lifespan: A neuroimmune network hypothesis. Biological Psychiatry, 80, 23–32. http://dx.doi.org/ 10.1016/j.biopsych.2015.05.017 Olino, T. M., Klein, D. N., Dyson, M. W., Rose, S. A., & Durbin, C. E. (2010). Temperamental emotionality in preschool-aged children and depressive disorders in parents: Associations in a large community sample. Journal of Abnormal Psychology, 119, 468–478. http://dx.doi.org/10.1037/a0020112 Olsavsky, A. K., Brotman, M. A., Rutenberg, J. G., Muhrer, E. J., Deveney, C. M., Fromm, S. J., . . . Leibenluft, E. (2012). Amygdala hyperactivation during face

Mood Disorders in Childhood and Adolescence

emotion processing in unaffected youth at risk for bipolar disorder. Journal of the American Academy of Child and Adolescent Psychiatry, 51, 294–303. http:// dx.doi.org/10.1016/j.jaac.2011.12.008 Otten, M., & Meeter, M. (2015). Hippocampal structure and function in individuals with bipolar disorder: A systematic review. Journal of Affective Disorders, 174, 113–125. http://dx.doi.org/10.1016/j.jad.2014.11.001

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Patton, G. C., Coffey, C., Posterino, M., Carlin, J. B., & Bowes, G. (2003). Life events and early onset depression: Cause or consequence? Psychological Medicine, 33, 1203–1210. http://dx.doi.org/10.1017/ S0033291703008626 Pavlickova, H., Turnbull, O., & Bentall, R. P. (2014). Cognitive vulnerability to bipolar disorder in offspring of parents with bipolar disorder. British Journal of Clinical Psychology, 53, 386–401. http:// dx.doi.org/10.1111/bjc.12051 Pettinati, H. M., O’Brien, C. P., & Dundon, W. D. (2013). Current status of co-occurring mood and substance use disorders: A new therapeutic target. American Journal of Psychiatry, 170, 23–30. http://dx.doi.org/ 10.1176/appi.ajp.2012.12010112 Pfeifer, J. C., Welge, J., Strakowski, S. M., Adler, C. M., & DelBello, M. P. (2008). Meta-analysis of amygdala volumes in children and adolescents with bipolar disorder. Journal of the American Academy of Child and Adolescent Psychiatry, 47, 1289–1298. http:// dx.doi.org/10.1097/CHI.0b013e318185d299 Powell, G. F., & Bettes, B. A. (1992). Infantile depression, nonorganic failure to thrive, and DSM–III–R: A different perspective. Child Psychiatry and Human Development, 22, 185–198. http://dx.doi.org/10.1007/BF00705891 Poznanski, E. O., Grossman, J. A., Buchsbaum, Y., Banegas, M., Freeman, L., & Gibbons, R. (1984). Preliminary studies of the reliability and validity of the children’s depression rating scale. Journal of the American Academy of Child Psychiatry, 23, 191–197. http:// dx.doi.org/10.1097/00004583-198403000-00011 Qin, B., Zhang, Y., Zhou, X., Cheng, P., Liu, Y., Chen, J. . . . Xie, P. (2014). Selective serotonin reuptake inhibitors versus tricyclic antidepressants in young patients: A meta-analysis of efficacy and acceptability. Clinical Therapeutics, 36, 1087–1095. http://dx.doi.org/10.1016/j.clinthera.2014.06. 001 0149-2918/$. Rajkowska, G., & Miguel-Hidalgo, J. J. (2007). Gliogenesis and glial pathology in depression. CNS and Neurological Disorders—Drug Targets, 6, 219–233. http://dx.doi.org/0.2174/187152707780619326 Rao, U., Hammen, C. L., & Poland, R. E. (2010). Longitudinal course of adolescent depression: Neuroendocrine and psychosocial predictors. Journal of the American Academy of Child and Adolescent Psychiatry, 49, 141–151.

Rohde, P., Lewinsohn, P. M., Klein, D. N., Seeley, J. R., & Gau, J. M. (2013). Key characteristics of major depressive disorder occurring in childhood, adolescence, emerging adulthood, and adulthood. Clinical Psychological Science, 1, 41–53. http:// dx.doi.org/10.1177/2167702612457599 Rohde, P., Lewinsohn, P. M., & Seeley, J. R. (1991). Comorbidity of unipolar depression: II. Comorbidity with other mental disorders in adolescents and adults. Journal of Abnormal Psychology, 100, 214–222. http://dx.doi.org/10.1037/0021-843X.100.2.214 Rose, A. J. (2002). Co-rumination in the friendships of girls and boys. Child Development, 73, 1830–1843. http://dx.doi.org/10.1111/1467-8624.00509 Rosso, I. M., Cintron, C. M., Steingard, R. J., Renshaw, P. F., Young, A. D., & Yurgelun-Todd, D. A. (2005). Amygdala and hippocampus volumes in pediatric major depression. Biological Psychiatry, 57, 21–26. http://dx.doi.org/10.1016/j.biopsych.2004.10.027 Rothbart, M. K., & Bates, J. E. (2006). Temperament in children’s development. In W. Damon, R. Lerner, & N. Eisenberg (Eds.), Handbook of child psychology, Vol. 3: Social, emotional, and personality development (6th ed., pp. 99–166). New York, NY: Wiley. Rucklidge, J. J., Gately, D., & Kaplan, B. J. (2010). Database analysis of children and adolescents with bipolar disorder consuming a micronutrient formula. BMC Psychiatry, 10, 74. http://dx.doi.org/ 10.1186/1471-244X-10-74 Sakolsky, D., & Birmaher, B. (2012). Developmentally informed pharmacotherapy for child and adolescent depressive disorders. Child and Adolescent Psychiatric Clinics of North America, 21, 313–325. http:// dx.doi.org/10.1016/j.chc.2012.01.005 Saudino, K. J. (2005). Behavioral genetics and child temperament. Journal of Developmental and Behavioral Pediatrics, 26, 214–223. http://dx.doi.org/ 10.1097/00004703-200506000-00010 Schwartz, O. S., Dudgeon, P., Sheeber, L. B., Yap, M. B. H., Simmons, J. G., & Allen, N. B. (2011). Observed maternal responses to adolescent behaviour predict the onset of major depression. Behaviour Research and Therapy, 49, 331–338. http://dx.doi.org/ 10.1016/j.brat.2011.02.008 Schwartz, O. S., Sheeber, L. B., Dudgeon, P., & Allen, N. B. (2012). Emotion socialization within the family environment and adolescent depression. Clinical Psychology Review, 32, 447–453. http://dx.doi.org/ 10.1016/j.cpr.2012.05.002 Sheeber, L., Allen, N., Davis, B., & Sorensen, E. (2000). Regulation of negative affect during mother–child problem-solving interactions: Adolescent depressive status and family processes. Journal of Abnormal Child Psychology, 28, 467–479. http://dx.doi.org/ 10.1023/A:1005135706799 275

Copyright American Psychological Association. Not for further distribution.

Fristad and Black

Sheeber, L., Hops, H., Andrews, J., Alpert, T., & Davis, B. (1998). Interactional processes in families with depressed and nondepressed adolescents: Reinforcement of depressive behavior. Behaviour Research and Therapy, 36, 417–427. http://dx.doi.org/ 10.1016/S0005-7967(97)10030-4

Teicher, M. H., & Samson, J. A. (2013). Childhood maltreatment and psychopathology: A case for ecophenotypic variants as clinically and neurobiologically distinct subtypes. American Journal of Psychiatry, 170, 1114–1133. http://dx.doi.org/ 10.1176/appi.ajp.2013.12070957

Simonoff, E., Jones, C. R. G., Pickles, A., Happé, F., Baird, G., & Charman, T. (2012). Severe mood problems in adolescents with autism spectrum disorder. Journal of Child Psychology and Psychiatry, 53, 1157–1166. http://dx.doi.org/10.1111/j.1469-7610.2012.02600.x

Tillman, R., Geller, B., Craney, J. L., Bolhofner, K., Williams, M., Zimerman, B., . . . Beringer, L. (2003). Temperament and character factors in a prepubertal and early adolescent bipolar disorder phenotype compared to attention deficit hyperactive and normal controls. Journal of Child and Adolescent Psychopharmacology, 13, 531–543. http://dx.doi.org/ 10.1089/104454603322724922

Sommers-Flanagan, J., & Campbell, D. G. (2009). Psychotherapy and (or) medications for depression in youth: An evidence-based review with recommendations for treatment. Journal of Contemporary Psychotherapy, 39, 111–120. http:// dx.doi.org/10.1007/s10879-008-9106-0 Sørensen, M. J., Nissen, J. B., Mors, O., & Thomsen, P. H. (2005). Age and gender differences in depressive symptomatology and comorbidity: An incident sample of psychiatrically admitted children. Journal of Affective Disorders, 84, 85–91. http://dx.doi.org/ 10.1016/j.jad.2004.09.003 Stanton, K., & Watson, D. (2014). Positive and negative affective dysfunction in psychopathology. Social and Personality Psychology Compass, 8, 555–567. http:// dx.doi.org/10.1111/spc3.12132 Stark, K. D. J., Hargrave, J., Sander, B., Custer, G., Schnoebelen, S., Simpson, J., & Molnar, J. (2006). Treatment of depression in childhood and adolescence: Cognitive–behavioral procedures for the individual and family. In P. C. Kendall (Ed.), Cognitive–behavioral therapy with children and adolescents (pp. 169–216). New York, NY: Guilford Press. Starr, L. R., & Davila, J. (2009). Clarifying co-rumination: Associations with internalizing symptoms and romantic involvement among adolescent girls. Journal of Adolescence, 32, 19–37. http://dx.doi.org/ 10.1016/j.adolescence.2007.12.005 Stone, L. B., Hankin, B. L., Gibb, B. E., & Abela, J. R. Z. (2011). Co-rumination predicts the onset of depressive disorders during adolescence. Journal of Abnormal Psychology, 120, 752–757. http://dx.doi.org/ 10.1037/a0023384 Sullivan, P. F., Neale, M. C., & Kendler, K. S. (2000). Genetic epidemiology of major depression: Review and meta-analysis. American Journal of Psychiatry, 157, 1552–1562. http://dx.doi.org/10.1176/ appi.ajp.157.10.1552 Sylvia, L. G., Peters, A. T., Deckersbach, T., & Nierenberg, A. A. (2012). Nutrient-based therapies for bipolar disorder: A systematic review. Psychotherapy and Psychosomatics, 82, 10–19. http:// dx.doi.org/10.1159/000341309 276

Uroševi´c, S., Abramson, L. Y., Harmon-Jones, E., & Alloy, L. B. (2008). Dysregulation of the behavioral approach system (BAS) in bipolar spectrum disorders: Review of theory and evidence. Clinical Psychology Review, 28, 1188–1205. http://dx.doi.org/ 10.1016/j.cpr.2008.04.004 Van Meter, A. R., Burke, C., Kowatch, R. A., Findling, R. L., & Youngstrom, E. A. (2016). Ten-year updated meta-analysis of the clinical characteristics of pediatric mania and hypomania. Bipolar Disorders, 18, 19–32. http://dx.doi.org/10.1111/bdi.12358 Van Meter, A. R., Moreira, A. L. R., & Youngstrom, E. A. (2011). Meta-analysis of epidemiologic studies of pediatric bipolar disorder. Journal of Clinical Psychiatry, 72, 1250–1256. http://dx.doi.org/ 10.4088/JCP.10m06290 von Knorring, A. L., Olsson, G. I., Thomsen, P. H., Lemming, O. M., & Hultén, A. (2006). A randomized, double-blind, placebo-controlled study of citalopram in adolescents with major depressive disorder. Journal of Clinical Psychopharmacology, 26, 311–315. http://dx.doi.org/10.1097/01. jcp.0000219051.40632.d5 Vulser, H., Lemaitre, H., Artiges, E., Miranda, R., Penttilä, J., Struve, M., . . . Stephens, D. (2015). Subthreshold depression and regional brain volumes in young community adolescents. Journal of the American Academy of Child and Adolescent Psychiatry, 54, 832–840. http://dx.doi.org/10.1016/j.jaac.2015.07.006 Wagner, K. D. (2003). Major depression in children and adolescents. Psychiatric Annals, 33, 266–270. http:// dx.doi.org/10.3928/0048-5713-20030401-07 Wagner, K. D., Jonas, J., Findling, R. L., Ventura, D., & Saikali, K. (2006). A double-blind, randomized, placebo-controlled trial of escitalopram in the treatment of pediatric depression. Journal of the American Academy of Child and Adolescent Psychiatry, 45, 280–288. http://dx.doi.org/10.1097/01. chi.0000192250.38400.9e Wagner, K. D., Robb, A. S., Findling, R. L., Jin, J., Gutierrez, M. M., & Heydorn, W. E. (2004). A

Mood Disorders in Childhood and Adolescence

randomized, placebo-controlled trial of citalopram for the treatment of major depression in children and adolescents. American Journal of Psychiatry, 161, 1079–1083. http://dx.doi.org/10.1176/appi. ajp.161.6.1079

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Watanabe, N., Hunot, V., Omori, I. M., Churchill, R., & Furukawa, T. A. (2007). Psychotherapy for depression among children and adolescents: A systematic review. Acta Psychiatrica Scandinavica, 116, 84–95. http:// dx.doi.org/10.1111/j.1600-0447.2007.01018.x Wegbreit, E., Cushman, G. K., Puzia, M. E., Weissman, A. B., Kim, K. L., Laird, A. R., & Dickstein, D. P. (2014). Developmental meta-analyses of the functional neural correlates of bipolar disorder. JAMA Psychiatry, 71, 926–935. http://dx.doi.org/ 10.1001/jamapsychiatry.2014.660 Weiss, B., & Garber, J. (2003). Developmental differences in the phenomenology of depression. Development and Psychopathology, 15, 403–430. http://dx.doi.org/ 10.1017/S0954579403000221 Weissman, M. M., Wickramaratne, P., Nomura, Y., Warner, V., Pilowsky, D., & Verdeli, H. (2006). Offspring of depressed parents: 20 years later. American Journal of Psychiatry, 163, 1001–1008. http://dx.doi.org/10.1176/ajp.2006.163.6.1001 West, A. E., Weinstein, S. M., Peters, A. T., Katz, A. C., Henry, D. B., Cruz, R. A., & Pavuluri, M. N. (2014). Child- and family-focused cognitive–behavioral therapy for pediatric bipolar disorder: A randomized clinical trial. Journal of the American Academy of Child and Adolescent Psychiatry, 53, 1168–1178. http:// dx.doi.org/10.1016/j.jaac.2014.08.013

Whittle, S., Dennison, M., Vijayakumar, N., Simmons, J. G., Yücel, M., Lubman, D. I., . . . Allen, N. B. (2013). Childhood maltreatment and psychopathology affect brain development during adolescence. Journal of the American Academy of Child and Adolescent Psychiatry, 52, 940–952.e1. http://dx.doi.org/10.1016/ j.jaac.2013.06.007 Wozniak, J., Biederman, J., Mick, E., Waxmonsky, J., Hantsoo, L., Best, C., . . . Laposata, M. (2007). Omega-3 fatty acid monotherapy for pediatric bipolar disorder: A prospective open-label trial. European Neuropsychopharmacology, 17, 440–447. http://dx.doi.org/10.1016/j.euroneuro.2006.11.006 Yorbik, O., Birmaher, B., Axelson, D., Williamson, D. E., & Ryan, N. D. (2004). Clinical characteristics of depressive symptoms in children and adolescents with major depressive disorder. Journal of Clinical Psychiatry, 65, 1654–1659. http://dx.doi.org/10.4088/ JCP.v65n1210 Young, R. C., Biggs, J. T., Ziegler, V. E., & Meyer, D. A. (1978). A rating scale for mania: Reliability, validity and sensitivity. British Journal of Psychiatry, 133, 429–435. http://dx.doi.org/10.1192/bjp.133.5.429 Youngstrom, E. A. (2009). Definitional issues in bipolar disorder across the life cycle. Clinical Psychology: Science and Practice, 16, 140–160. http://dx.doi.org/ 10.1111/j.1468-2850.2009.01154.x Zachar, P., & Kendler, K. S. (2014). A diagnostic and statistical manual of mental disorders history of premenstrual dysphoric disorder. Journal of Nervous and Mental Disease, 202, 346–352. http://dx.doi.org/ 10.1097/NMD.0000000000000128

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Chapter 14

Understanding and Treating Children and Adolescents With Neurodevelopmental Disorders Copyright American Psychological Association. Not for further distribution.

Andrew S. Davis, Kelly L. Hoover, and Angela M. Mion

Healthy neurodevelopment relies on the integration of multiple physiological, genetic, epigenetic, and ecological factors. Perturbation or disruption in any of these variables can result in long-lasting, synergistic, and pervasive effects on the development and expression of age-appropriate academic, social, behavioral, vocational, and emotional functioning (Davis & Phelps, 2008). These issues often require children with neurodevelopmental disorders to interact with several systems that, at times, may feel redundant, incompatible, or confusing for their caregivers. For example, children with neurodevelopmental disorders frequently become involved in the special education, juvenile justice, developmental disabilities, vocational rehabilitation, social security, and health insurance systems. Psychologists who work with children and adolescents with neurodevelopmental problems are encouraged to consider the approach typically used by pediatric neuropsychologists. Pediatric neuropsychology can be viewed as the practical application of the understanding of the relationship between children’s central nervous system, genetics, and their environment. Encapsulated within the concept of children’s central nervous system functioning is the development and execution of neurological, neuroendocrine, and other organ systems that effect children’s cognition, which takes place within the context of their environment. The study of neurodevelopmental disorders relies on the understanding that neurocognitive development is influenced by a combination of biological and psychosocial factors. As such, the etiology of a neurodevelopmental

disorder may be multidimensional, and it is important to consider the contribution of each of the systems and factors to determine the focus of an intervention. Additionally, psychologists who work with children with neurodevelopmental disorders need to adopt a multidisciplinary approach to assessment and intervention. Children with neurodevelopmental disorders are at a high risk for other health problems that can exacerbate neurocognitive delays, as well as impede the execution of otherwise intact abilities. Any health concern which reduces children’s ability to interact with the environment at an age-appropriate level can increase the risk of neurodevelopmental delay. Limited exposure to learning opportunities can be reflected on normreferenced and standardized tests. Neurodevelopment was defined by Kindsvatter and Geroski (2014) as a paradigm that incorporates human growth and development along with neurobiological processes. Indeed, key to working with children with known or suspected neurodevelopmental disorders is knowledge of typical or healthy development. Psychologists who are unsure of what level of cognitive, language, physical, motor, sensory, social, and moral development is age-appropriate will be unable to ascertain what constitutes a delay in these areas. Being able to readily access a lexicon of expected abilities and milestones at each age when interacting with children is an essential skill. The understanding of neurodevelopmental disorders in childhood may be used as a preventative method for more serious and debilitating dysfunction in adults (Pitman, 2014). Indeed, early intervention for a

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Davis, Hoover, and Mion

congenital or acquired encephalopathy is more effective than late intervention, and primary and secondary intervention have more potential to disrupt synergistic complications than do tertiary interventions. The neurodevelopmental disorders “are a group of heterogeneous conditions characterized by a delay or disturbance in the acquisition of skills in a variety of developmental domains, including motor, social, language, and cognition” (Jeste, 2015, p. 690). In the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition, (DSM–5; American Psychiatric Association; 2013), several disorders were reclassified into a new section called Neurodevelopmental Disorders. This structural change was partly designed to enhance clinical utility and align with the International Classification of Diseases, Eleventh Edition (World Health Organization, in press), but also to reflect a lifespan perspective and incorporate research from genetics and neuroimaging (American Psychiatric Association, 2013). Indeed, structural and functional neuroimaging research has increasingly identified morphological aberrations in children with neurodevelopmental disorders which has shifted the etiological emphasis for many of these conditions toward organic contributors. This paradigm shift has accelerated the mandate that child psychologists expand their understanding of central nervous system contributors to these conditions. The neurodevelopmental disorders are comorbid with one another (American Psychiatric Association, 2013), which can be a complicating factor in differential diagnosis. Reflective of the developmental perspective and lifespan approach, the disorders in the Neurodevelopmental Disorders section of the DSM–5 typically have their onset during childhood, although they may not be recognized or diagnosed until later in adolescence or adulthood (American Psychiatric Association, 2013). As such, this collection of disorders which includes intellectual developmental disorder (IDD), communication disorders, autism spectrum disorder (ASD), attention-deficit/­hyperactivity disorder (ADHD), specific learning disorder (SLD), and motor disorders are likely to have a meaningful impact typically seen during childhood (American Psychiatric Association, 2013). 280

This chapter discusses some of these conditions but also considers other health concerns which have a direct effect on neurodevelopment and with which psychologists who work with children should be familiar. This includes ASD and ADHD. ASD is estimated to be the fastest growing neurodevelopmental disorder (Frasier-Robinson, 2015). The prevalence in 2012 was reported as 1 in 88, and the prevalence in 2014 was reported as 1 in 68—a 30% increase (Diament, 2014). The most common neurodevelopmental disorder in children is ADHD (Jeste, 2015). However, ASD and ADHD will not be covered in this chapter as they are covered elsewhere in this volume. The principles of neuropsychological assessment are also covered in this volume, although we will discuss some concepts of pediatric neuropsychological assessment germane to some of the conditions described in this chapter. Neurodevelopmental Disorders This section discusses psychiatric disorders, sensory– motor concerns, and genetic disorders that interfere with the child’s neurodevelopmental trajectory.

Psychiatric Neurodevelopmental Disorders These conditions appear in the Neurodevelopmental Disorders section of the DSM–5. Intellectual developmental disorder.  A plethora of neurodevelopmental disorders associated with IDD (e.g., genetic conditions and perinatal complications) do not appear in the DSM–5. This diversity of conditions argues that the diagnosis of IDD, previously referred to as mental retardation, does not infer that there is a specific etiology, prognosis, or salient pattern of neurodevelopmental delay or neurocognitive dysfunction. Rather, it simply means there is impairment in intellectual functioning and adaptive functioning; both deficits must occur during the developmental period (American Psychiatric Association, 2013). As such, although there can be somewhat of a common approach to the assessment of the neurofunctional deficits seen in these individuals, children with this disorder do not constitute a homogeneous group. Although we argue for a comprehensive neuropsychological evaluation for virtually all conditions,

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Understanding and Treating Children and Adolescents With Neurodevelopmental Disorders

some key areas will be emphasized. For children with IDD this includes measures of adaptive and intellectual functioning. Regarding the former, there are several approaches that can be taken. Parental report during a diagnostic interview is of high utility if the interviewer is intimately familiar with what age-appropriate adaptive functions are expected of the patient. Another approach to the assessment of adaptive functioning is through observation of children during a diagnostic interview, mental status examination, and performance on assessment tasks. The inclusion of standardized and norm-referenced rating measures of adaptive functioning is recommended when working with children suspected of neurodevelopmental delay. This would include objective rating measures such as the Adaptive Behavior Assessment System (Harrison & Oakland, 2015) and the Vineland Adaptive Behavior Scales (Sparrow, Cicchetti, & Saulnier, 2016). This approach allows parents to rate their child’s adaptive functioning in several areas that correspond to the type of impairment that would be seen in children with IDD. A potential concern with this approach is that there are not validity scales in some of these types of instruments; therefore, clinicians need to be vigilant of parents rating their child as more impaired than they are, regardless of the rationale, and ratings should be considered in the context of other tests, embedded and stand-alone measures of suboptimal effort, and the psychologist’s observation and clinical judgment. Although it is beyond the scope of this chapter, the inclusion of symptom validity tests when conducting pediatric neuropsychological assessments is of paramount importance in interpreting results (see Bush et al., 2005; DeRight & Carone, 2015; Kirkwood, 2015; Slick, Tan, Sherman, & Strauss, 2011). A second essential area that must be assessed in patients with known or suspected IDD is intellectual functioning. Although clinicians may make a qualitative judgement about a patient’s intellectual abilities, a far more effective approach lies within the use of well-validated norm-referenced and standardized measures. The previous iteration of the DSM–5, the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition, Text Revision (DSM–IV–TR; American Psychiatric Association, 2000), required

a diagnosis of mental retardation (the predecessor to IDD) to include intellectual functioning at least two standard deviations below the mean; this equated to a standard score of 70 on many intelligence tests. Some of the tests psychologists considered include the Wechsler Intelligence Scale for Children (Wechsler, 2014), the Wechsler Preschool and Primary Scale of Intelligence (Wechsler, 2012), the Woodcock-Johnson Tests of Cognitive Abilities (Schrank, McGrew, & Mather, 2014), the StanfordBinet Intelligence Scales (Roid, 2003), the Reynolds Intelligence Assessment Scales (Reynolds & Kamphaus, 2016), the Differential Ability Scales (Elliott, 2007), the Leiter International Performance Scale (Roid, Miller, Pomplun, & Koch, 2013), the Cognitive Assessment System (Naglieri, Das, & Goldstein, 2014), and the Kaufman Assessment Battery for Children (Kaufman & Kaufman, 2004). Using the DSM–5 approach for IDD will result in diagnostic classifications of mild, moderate, severe, or profound severity levels which are assigned on the basis of dysfunction in conceptual, social, and practical domains of functioning (American Psychiatric Association, 2013). Intervention for individuals with IDD are guided by the symptomology with which children present given the large number of etiologies that can result in this condition. Therefore, extrapolation of research on evidence-based interventions is done with caution when conducted on a group of children whose only commonality is this psychiatric diagnosis. Rather, psychologists consider whether the intervention is generalizable to their patient if it addresses a target construct or maladaptive behavior that the patient has in common with the intervention group as opposed to the two simply sharing a diagnosis of intellectual disability. Inherent in this process is the consideration of individual factors that would serve to increase or decrease the effectiveness of the adapted evidence-based intervention. A successful component of virtually all interventions for children with IDD is the involvement of caregivers, educators, and the community at large. Children with IDD are likely to need increased support, guidance, and structure from these groups. Andrews, Falkmer, and Girdler (2015) reviewed thirteen studies that evaluated types of community 281

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involvement for children with intellectual disability. Community programs were found to be able to increase friendships, recreation, quality of life, and self-esteem in intellectually disabled children (Andrews, et al., 2015). Community programs achieved this when recreation and friendship facilitation are paired, when normally developing peers were included, when children could provide input into programming, and when impairments of individuals were accommodated (Andrews, et al., 2015). Other types of interventions target the other health conditions to which this group is vulnerable. Indeed, comorbid health and mental health conditions are common in this population (Armstrong et al., 2011; Munir, 2016; Zeilinger, Nader, BrehmerRinderer, Koller, & Weber, 2013). Rimmer, Yamaki, Lowry, Wang, and Vogel (2010) found that in adolescents, rates of obesity and secondary conditions (e.g., asthma, diabetes, hypertension, high cholesterol) were two to four times higher in IDD groups (e.g., ASD, Down syndrome, spina bifida) when compared with healthy controls. Psychologists can help physicians and caregivers via behavioral intervention plans to increase treatment adherence, decrease unhealthy behaviors, and encourage healthy behaviors. For example, a recent study found that the use of behavioral intervention in addition to laxative therapy used at home and school was effective in helping two adolescents with ASD and IDD with a history of constipation and encopresis (Axelrod, Tornehl, & Fontanini-Axelrod, 2016). Children with IDD typically display learning problems, and academic interventions are generally warranted throughout schooling. It is important to reiterate that the type of intervention should be, in part, guided by the underlying etiology of the IDD, as that etiology may offer the clinician prognostic knowledge that could influence the selection of an intervention. For example, a child who is likely to show regression in academic skills versus a child who is likely to demonstrate delayed academic growth are likely to benefit from different approaches to intervention in the long-term. In general, however, when psychologists design interventions in the school, it is essential that they coordinate with children’s caregivers to ensure consistency and allow children to generalize the skills they learn in school 282

to real-world settings, particularly when adaptive functioning is the target of the intervention. Another guiding principle should be the level of severity of intellectual disability. For example, a child with mild intellectual disability will likely, in the absence of regression or decline, have more independence than a child with severe intellectual disability; academic and learning interventions should have different goals. There is a wealth of evidence-based adaptive, academic, social–emotional, and behavioral interventions for these children, and interested readers are directed to the special education and school psychology literature (e.g., Rathvon, 2008; Shapiro, 2011). Communication disorders.  Language is an extremely complicated concept and is influenced by genetic, biological, and environmental factors. From a neurofunctional standpoint, language is one of the most widely studied neurocognitive domains, and is highly susceptible to neurological dysfunction as well as cultural variation. Although language is typically associated with the perisylvian area of the dominant hemisphere, intact expressive and receptive language are dependent on the integration of multiple healthy neurological areas. As such, it is relatively easy to disrupt language development via numerous avenues. Language development starts extremely early in life, and there is evidence that infants can differentiate between the sound of their mother’s voice and a stranger’s voice (e.g., Yovel & Belin, 2013). Expressive language development typically proceeds in a very strict order (e.g., phonemes, morphemes, words, sentences), and development and use of expressive and receptive language are influenced by the social or pragmatic components of language. Disruption at one stage in language development can have long-lasting ripple effects on future language development, which is a concern given the concept of critical and sensitive periods influenced by neural plasticity. One of the most important concepts for psychologists to consider when working with children with language delay is the potentially highly variable nature of language neurodevelopment (e.g., Bergen & Woodin, 2011). This situation engenders a cautious approach to making prognostications about language development when a delay is present. Certitude can be increased when there is

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a known neurogenic condition or etiology underlying the delay, although serial testing is suggested. Although not a panacea, the use of norm-referenced standardized tests can assist in determining the severity of a language delay with more severe impairment suggesting a finding outside of typical developmental variation. Regardless of whether the assessed deficit is transient or persistent, it is important to consider how the future acquisition of age-appropriate skills, which are dependent on the delayed language function, could be affected. Cultural considerations are paramount when evaluating children for a language disorder. It is important to determine if cultural factors have resulted in less exposure to the dominant culture’s language and social communication prior to concluding there is an organic language disorder. The communication disorders in DSM–5 include language disorder, speech sound disorder, childhood-onset fluency disorder (stuttering), and social (pragmatic) communication disorder (American Psychiatric Association, 2013). Speech sound disorder is characteristic of difficulty with producing speech, including articulation or trouble with producing phonemes, which inhibit intelligibility in communication (M. M. Allen, 2013). Childhoodonset fluency disorder (stuttering) is diagnosed when there is a difficulty with fluency of speech and includes occurrences of repetitions, prolongations, broken words, blocking (pauses), circumlocutions (substitutions), or physically tense words (American Psychiatric Association, 2013). Psychologists should exercise great caution when diagnosing these two conditions as speech-language pathologists are likely better suited to determine the degree of impairment and intervene when the problem is with the musculature of speech. In general, caution is suggested in assessing communication disorders without the input of speech-language pathologists. Additionally, intervention is also more likely to fall within the realm of speech-language therapists regarding actually improving the patient’s speech and language abilities; however, psychologists should still be heavily involved in designing interventions focused on the behavioral, social–emotional, and academic difficulties that frequently accompany these conditions.

Social (pragmatic) communication disorder (SCD) is identified by difficulties in communication involving social purposes, social context, rules of conversation, and nonliteral language (American Psychiatric Association, 2013). This condition can present with some of the same features as ASD, although the other characteristics seen with ASD (e.g., adherence to nonfunctional routines) are typically absent. Assessment of this condition may be hampered by the lack of culturally valid tests to facilitate differential diagnosis, and few evidencebased interventions are present (Norbury, 2014). Adams and colleagues (2012) conducted a randomized controlled trial to test the efficacy of a social communication intervention for children with SCD and children with ASD with pragmatic language impairments. Participants were randomly assigned to one of two conditions: an intensive social communication intervention or typical speech-language services. Results indicated positive findings in the treatment group for competency in conversations, parent-reported pragmatic language use and social communication skills, and teacher-rated classroom learning skills (Adams et al., 2012). These results suggest that children with SCD and children with ASD with pragmatic language difficulties benefit from targeted social language interventions. Specific learning disorder.  DSM–5 defines SLD as difficulty acquiring and using one or more academic skills beyond what would be expected given a child’s age, and which causes impairment in academic, occupational, or adaptive domains of functioning (American Psychiatric Association, 2013). Indeed, as with all neurodevelopmental disorders, it is important to consider what would be expected given a child’s age as well as whether children have had the opportunity to acquire the skills. A key consideration for psychologists regarding this disorder is that the criteria specified in the DSM–5 may not be consistent with the criteria required for special education eligibility in a child’s school. This can result in a situation in which children have been diagnosed with a SLD by a psychologist or other health care professional but do not qualify for special education services as students with a specific learning disability, or vice versa. This can be a source of frustration 283

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for children and their parents; children may have been in special education for the entirety of their schooling but do not meet criteria for the psychiatric disorder of SLD and may not receive the accommodations to which they have become accustomed on standardized testing for college entry or in college. Discussing the neurological basis and intervention of reading, mathematics, and written language in a comprehensive fashion far exceeds the present mission. Suffice it to say, these neurodevelopmental disorders can meaningfully disrupt children’s functioning not only academically, but regarding social, emotional, behavioral, and vocational functioning (American Psychiatric Association, 2013). For example, children with SLD may present with poor social skills, including difficulty processing social information (Brooks, Floyd, Robins, & Chan, 2015). Dyslexia, dyscalculia, and dysgraphia are terms used to describe difficulties with reading, math calculation, and written language that assume a neurological underpinning. In addition to quantifiably measuring academic skills, assessment of SLD should attempt to look for an underlying pattern of strengths and weaknesses which can be used to ascertain the cause and then address that cause in the most effective way possible. Understanding the timeline of neural plasticity is important in designing interventions in these conditions given critical and sensitive periods associated with academic skills (see also Davis, 2010; Feifer & Della Toffalo, 2007; Flanagan &Alfonso, 2011; Lee, Rojewski, Gregg, & Jeong, 2015; Lerner & Johns, 2014; Maehler & Schuchardt, 2016; Shaywitz, 2003).

Sensory–Motor Disorders The sensory and motor systems are one of the first neurological systems to develop, and are among the first to be myelinated (Bergen & Woodin, 2011). Early damage or dysfunction to the sensory–motor systems can have a pervasive impact on children’s ability to acquire neurocognitive milestones, and can reduce their ability to explore and interact with their environment. Although early to develop, the corticospinal, extrapyramidal, and cerebellar motor tracts rely on multiple intact cortical and subcortical structures and serve as a sensitive measure of 284

the integrity of the central nervous system. Sensory dysfunction may also be reflective of not only damage to peripheral organ systems but also to efferent sensory tracts, cranial nerves, and other neurological structures. Assessment of sensory functioning could be performed via observation of species-wide expectations, using screening measures, or via standardized norm-referenced testing. A typical neurological examination includes assessment of a patient’s upper and lower extremity functioning; several neuropsychological tests do the same albeit with a norm-referenced approach. Useful tests include the Halstead–Reitan Neuropsychological Battery (Reitan & Wolfson, 1993), the Luria–Nebraska Neuropsychological Battery (Golden, Hammeke, & Purisch, 1980), the NEPSY-II (Korkman, Kirk, & Kemp, 2007), the Dean-Woodcock Sensory Motor Battery (R. S. Dean & Woodcock, 1994), and the Grooved Pegboard Test (Trites, 1989). These measures include versions of classic neuropsychological and neurological measures that, in part, assess for hemiparesis, gait disturbance, balance difficulties, fine motor speed and coordination, visual–motor integration, vision, hearing, graphesthesia, stereognosis, finger gnosis, and other tactile awareness/ discrimination functions. A critical component of any neuropsychological assessment of children must entail comprehensive assessment of the sensory–motor systems as they may not only reflect subtle to severe neurological impairment, but could interfere with children’s ability to perform on other neuropsychological tests, which could lead to inaccurate interpretation. DSM–5 does not include diagnostic classifications for sensory disorders like blindness or deafness; however, psychologists do need to consider if their findings are related to these conditions. It is important to consider that in the absence of these conditions, sensory functions exist on a continuum, and they should be tested prior to assessment. The motor disorders in DSM–5 include developmental coordination disorder (DCD), stereotypic movement disorder, and tic disorders. DCD is characterized by a discrepancy between children’s acquisition and their execution of coordinating motor skills given their age (American Psychiatric Association, 2013). DCD may be defined as significant difficulty

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or impairment in development of motor coordination skills (Hertza & Estes, 2011). Children with DCD are often clumsy, and slow and inaccurate in their movements; these difficulties interfere with adaptive, academic, vocational, and leisure activities (American Psychiatric Association, 2013). Stereotypic movement disorder involves repetitive, driven, and purposeless motor behavior that interferes with social, academic, or other domains and may be self-injurious (Stein & Woods, 2014). Examples of the movements in this disorder are hand shaking, rocking, or head banging (American Psychiatric Association, 2013); it is important to distinguish this condition from the same features seen in ASD. Helping children, families, and educators understand that motor disturbance can affect other areas of children’s functioning is a key role for psychologists and is critical in the design of interventions to address these concerns. Tic disorders in DSM–5 include three types of disorders: Tourette’s syndrome, persistent (chronic) motor or vocal tic disorder, and provisional tic disorder (American Psychiatric Association, 2013; Woods & Thomsen, 2014). In Tourette’s syndrome, multiple motor and one or multiple vocal tics are present; in persistent (chronic) motor or vocal tic disorder, one or more motor or vocal tics are present (American Psychiatric Association, 2013). Provisional tic disorder is used when one or more motor or vocal tics have been present for less than a year (American Psychiatric Association, 2013). Research on the neurological correlates of tic disorders has mainly focused on the basal ganglia and associated circuitry, which includes the caudate and other nuclei (Phelps & Smerbeck, 2011). Associated neurocognitive deficits have been found to include verbal and visual memory, visual–motor integration, and executive functioning and are useful as targeted areas of assessment and intervention (Phelps & Smerbeck, 2011). Assessment of tic disorders depends on informant and self-report, as well as psychologists observations; some tic disorder rating scales have been developed. Interventions for tic disorders may be multidimensional and include behavioral and psychopharmacological approaches. A literature review investigating behavioral and psychosocial

interventions for individuals with tics is suggestive of evidence for the use of habit reversal training and exposure, with response prevention as first-line treatments with adolescents and adults and with second line treatments including contingency management, function based intervention, and relaxation training (Verdellen, van de Griendt, Hartmann, & Murphy, 2011). The authors also concluded that massed negative practice lacks evidence, despite it being the oldest reported intervention. Recent research indicates that individual types of tics may respond differently to interventions (McGuire et al., 2015). In essence, behavioral interventions seem to be effective regarding addressing the difficulties seen in tic disorders although individual factors merit consideration.

Neurodevelopmental Genetic Disorders Healthy human cells have 23 pairs of chromosomes (other than the sperm and egg cells which just have 23 chromosomes) on which are embedded the genetic material that helps determine children’s physiological and mental health makeup. Numerical and structural problems with the chromosomes and mutations to the genetic material can result in mortality and morbidity in children. The medical field has begun to document some of these conditions, and the next several decades may see an increase in our ability to diagnose these conditions pre- and postnatally. Over 6,000 genetic disorders have been identified (http://www.geneticdiseasefoundation. org) and although it is beyond our ability to cover them all, the more common genetic disorders that impact neurodevelopment are considered next. Down syndrome.  Down syndrome is a chromosomal condition that has three different etiologies. The most common etiology is nondisjunction, which is an error in cell division that occurs in about 92% to 95% of cases of Down syndrome; nondisjunction results in three copies of chromosome 21 (Trisomy 21; Davis & Escobar, 2010). Another etiology is translocation, in which part of chromosome 21 attaches to another chromosome; translocation accounts for about 3% to 4% of Down syndrome cases. The third etiology is mosaicism, which accounts for the remaining 2% to 4% of cases; mosaicism occurs when some 285

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but not all cells have three copies of chromosome 21 (Davis & Escobar, 2010). Children with Down syndrome have a distinctive physical phenotype which includes short stature, facial dysmorphology, hypotonia, epicanthal folds, and hand and feet dysmorphology (Davis & Escobar, 2010). This condition is also associated with other psychiatric and physiological problems, including dysarthria, hearing and vision impairments, congenital heart problems, sleep apnea, certain forms of leukemia, hypothyroidism, and an increased chance of developing Alzheimer’s disease later in life (Davis & Escobar, 2010; Jensen & Davis, 2013; Nordstrøm, Paus, Andersen, & Kolset, 2015). Like many of the neurodevelopmental genetic disorders, psychologists working with children with Down syndrome do not provide the diagnosis but determine the type and extent of the neurofunctional impairment that is associated with the condition, and design and/or implement interventions. This process includes a comprehensive neuropsychological assessment including measures of intelligence, adaptive functioning, memory, language, visual–spatial processing, attention, executive functioning, and sensory–motor functioning. Although Down syndrome is associated with IDD, neurocognitively, children with Down syndrome tend to have strengths in visual–spatial processing and weaknesses in language and verbal memory, as opposed to an overall flat profile (Davis & Escobar, 2010). This pattern suggests that these children are likely to benefit from academic, social, and behavioral interventions and reminders that rely more on visual cues, graphs, pictures, charts, and figures than verbal instruction; similarly, verbal instruction should be supplemented with these visual stimuli. Fragile X syndrome.  Fragile X syndrome is a genetic condition caused by a mutation on the FMR-1 gene on the X chromosome (Wheeler et al., 2015). Men are affected about twice as often as women (McDuffie, Thurman, Hagerman, & Abbeduto, 2015), and age of diagnosis is typically at about 35 to 37 months in boys and 42 months in girls (Centers for Disease Control and Prevention [CDC], 2015a). Diagnosis of Fragile X is confirmed with genetic testing (Schwarte, 2008), although the 286

physical dysmorphology associated with the condition may become somewhat obvious, particularly as children age. Fragile X is characterized by a specific phenotypic presentation that typically is developed in adolescence: “elongated face, large ears, prominent jaw, macrocephaly, macroorchidism, flat feet, a narrow, high-arched palate, and hyperextensible joints” (Schwarte, 2008, p. 290). Fragile X is the most common inherited cause of IDD (Schwarte, 2008). Symptoms of Fragile X include concerns related to intelligence, anxiety, hyperactivity and impulsivity, and symptoms commonly associated with ASD (Wheeler et al., 2015). Indeed, the high rates of ASD-like symptomology seen in these children suggests that psychologists who are evaluating children for ASD familiarize themselves with this condition. Indeed, in general, it is sound practice when determining that children have IDD to consider whether a referral for genetic testing is warranted, particularly in the absence of a known explanation for the etiology of the IDD. Medical management is recommended for Fragile X syndrome, specifically for ear, speech and language, aggression and behavior problems, seizures, mood problems, and obsessive-compulsive symptoms (Schwarte, 2008). If emotional, academic, cognitive, and behavioral problems are present in school, children with Fragile X should receive special education that accommodates their unique needs (Schwarte, 2008). Pharmacotherapy can be used to treat behavior disruption in Fragile X syndrome (Davenport, Schaefer, Friedmann, Fitzpatrick, & Erickson, 2016). (For a comprehensive review of pharmacological treatments for Fragile X syndrome, see Davenport et al., 2016.) Rett syndrome.  This genetic condition was listed in DSM–IV–TR (American Psychiatric Association, 2000) although it was removed in the DSM–5. A mutation on the MECP2 gene causes Rett syndrome, which occurs predominantly in women, and is characterized by typical development until 6 to 18 months of age, followed by a loss of motor and language skills and the development of stereotypical hand movements (Andrews et al., 2014; Downs et al., 2016). Physical impairment is common and may include problems with feeding, gastrointestinal

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functioning, and breathing, as well as comorbid conditions including epilepsy and scoliosis (Andrews et al., 2014). Additionally, severe intellectual disability disorder is common in patients with Rett syndrome (Andrews et al., 2014; Volker, Thomeer, & Lopata, 2011). Prader–Willi syndrome.  Prader–Willi syndrome is a genetic condition characterized by intellectual disability (typically mild to moderate in severity), a physical phenotype, and a behavioral phenotype (Tunnicliffe, Woodcock, Bull, Oliver, & Penhallow, 2014). Individuals with Prader–Willi syndrome are at an increased risk of health problems; data from a recent study in France suggested a high prevalence of a number of conditions. Seventy-five percent of their sample (ages 16–54) had respiratory problems, 26% had hypothyroidism, and 25% had Type 2 diabetes (Laurier et al., 2015). Each of these conditions carries their own independent risk factors for poor outcomes. Chromosome 15 is implicated by several possible abnormalities in Prader–Willi syndrome, thought to be through random error, and involves a lack of expression of paternally inherited genes (Kundert, 2008). The physical dysmorphology of Prader–Willi syndrome includes short stature, small hands and feet, underdeveloped genitals, and facial dysmorphology (i.e., almond-shaped eyes, a narrow face and forehead, small triangular-shaped mouth; Cassidy, Schwartz, Miller, & Driscoll, 2012; Kundert, 2008; Tunnicliffe et al., 2014). Hypotonia is also a major feature of this condition (Kundert, 2008). Behavior problems in these patients include insatiable appetite and overeating, skin picking, impulsivity, emotional lability, verbal perseveration, lethargy, and obsessive-compulsive behaviors (Cassidy et al., 2012; Kundert, 2008; Tunnicliffe et al., 2014). Hyperphagia (excessively consuming food) is caused by dysfunction of the hypothalamus and leads to overeating and obesity (Kundert, 2008). Adolescents with Prader–Willi often develop symptoms of depression and anxiety including withdrawal, low energy levels, lethargy, and confusion; a recent systematic review also suggests depression with psychotic symptoms may be more likely in individuals with Prader–Willi syndrome (Kundert, 2008; Walton & Kerr, 2016).

Males and females are about equally affected. Although Prader–Willi syndrome is more common in European Americans, it may be underdiagnosed in African Americans (Kundert, 2008). Based on results of a meta-analysis of research on psychiatric problems and intellectual abilities in patients with Prader–Willi syndrome, Yang and colleagues (2013) suggested persons who present with the paternal deletion type of Prader–Willi syndrome tend to have lower levels of intellectual functioning, particularly verbal reasoning abilities, than patients with the maternal uniparental disomy (mUPD) type; however, persons with mUPD are much more likely to experience psychiatric problems. According to Kundert (2008), few interventions have been evaluated in patients with Prader–Willi syndrome. Growth hormones are used with about 71% of children and work to increase rate of growth, and ultimately, height (Kundert, 2008). One recent review examined the use of psychotropic intervention for patients with Prader–Willi syndrome (Bonnot et al., 2016). Although the authors commented that there is a great need for additional research in this area, results suggested positive effects of some psychotropic medications (e.g., ­topiramate, N-acetyl cysteine, antidepressants, ­Risperdone) for the treatment of aggressive and impulsive behavior, self-injury, obsessive-­compulsive symptoms, and psychotic symptoms. To prevent obesity in these patients, balanced diets ranging from 1000 to 1200 calories are recommended (Kundert, 2008). Research also suggests diet modifications can result in lower body fat and body mass index (Miller, Lynn, Shuster, & Driscoll, 2013). When behavioral issues occur, behavioral interventions and strategies may be effective for tantrums, aggression, and transitioning between activities (Kundert, 2008). Occupational and physical therapy, as well as services in school, may help to remediate developmental issues in children with Prader–Willi syndrome (Kundert, 2008). Sotos syndrome.  Sotos syndrome is a genetic condition associated with dysmorphic facial features, overgrowth in childhood, and learning disability (Tatton-Brown & Rahman, 2007). Common health concerns associated with this syndrome are cardiac problems, kidney problems, seizures, and scoliosis 287

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(Tatton-Brown & Rahman, 2007). The distinctive craniofacial features in Sotos syndrome consist of macrocephaly, a high broad forehead and pointed chin, sparseness of hair in the frontal and temporal areas, discoloration of the cheeks (i.e., malar flushing), and slanted palpebral fissures (Sotos, 2014; Tatton-Brown & Rahman, 2007). Height and head circumference are often greater than two standard deviations above the mean in children (TattonBrown & Rahman, 2007). A mutation on the NSD1 gene is the most common cause of Sotos syndrome, accounting for about 90% of cases (Lane, Milne, & Freeth, 2016; Tatton-Brown & Rahman, 2007). Lane and colleagues (2016) conducted a metareview to synthesize research on cognitive skills and behavior in patients with Sotos syndrome. Aggregated data suggested that most patients with Sotos syndrome had mild intellectual disability (with IQ scores ranging from 50 to 69) or were in the borderline range of cognitive skills (with IQ scores ranging from 70 to 84; Lane et al., 2016). Additionally, Sotos syndrome patients showed a profile with higher language skills than performance (nonverbal) skills (Lane et al., 2016). Behavior problems may be more common in patients with Sotos syndrome compared with sameage peers; this may be partly attributable to their larger size making them appear older than they are (Lane et al., 2016). In these situations, psychologists should consult with parents and school psychologists to help them provide education to friends, teachers, and classmates.

the same muscle weakness as Duchenne’s, but it is also characterized by muscle atrophy and may include cramping because of activity and flexion contractures in the elbows (Darras et al., 2000). Additionally, Becker muscular dystrophy has a later onset and progresses more slowly than does Duchenne muscular dystrophy (Zebracki & Drotar, 2008). Duchenne and Becker muscular dystrophy are characterized by impairment in mobility; however, wheelchair dependency typically occurs before age 13 in Duchenne muscular dystrophy and after age 16 in Becker muscular dystrophy; impairments in motor development, cardiomyopathy, and cognitive impairment are also common, to varying degrees (Darras et al., 2000). Snow and colleagues (2013) reviewed recent research in intellectual functioning, psychosocial functioning, and neurobehavioral functioning in patients with Duchenne muscular dystrophy. Results indicated overall lower intellectual functioning, particularly verbal intelligence, and indicated that memory and executive functioning are more likely to be impaired in this population, whereas expressive and receptive language and visuospatial skills are largely intact (Snow et al., 2013). In a recent study by Conway et al. (2015), over half of males with dystrophinopathies in the sample had behavioral problems, ADHD, depression, or some combination of these. Rates of ADHD and depressed mood in this sample were described as almost three times higher than estimates of the general population.

Dystrophinopathies.  Darras, Miller, and Urion (2000) described dystrophinopathies as a spectrum of muscular diseases, with the severe end including progressive muscular degeneration diseases like Duchenne muscular dystrophy and Becker muscular dystrophy. The dystrophinopathies are caused by variants in the DMD gene; genetic testing and muscle biopsy are used in diagnosis (Darras, et al., 2000). Duchenne muscular dystrophy predominantly affects males (Snow, Anderson, & Jakobson, 2013). Children typically develop symptoms during early childhood and present before age 5 with bilateral progressive muscle weakness; weakness tends to be more proximal than distal (Darras et al., 2000). Becker muscular dystrophy shares

Mitochondrial disorders.  According to Marin and Saneto (2016), mitochondrial disorders are a relatively rare (1 in 2,000) and eclectic group of diseases that are caused by dysfunction of the cells’ mitochondrial respiratory chain; confirmation of a diagnosis requires clinical and laboratory testing, which can be extensive. The most commonly presented mitochondrial disease in children is Leigh syndrome, which is a condition characterized by a period of typical neurodevelopment, disease onset with progressive encephalopathy at age 2, and death by age 3 (Lake, Bird, Isohanni, & Paetau, 2015). Although persons may exhibit different clinical presentations, bilateral symmetrical lesions and brainstem and basal ganglia damage is common to all

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patients (Lake et al., 2015). For additional information, see Antshel (2010).

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Health Conditions and Neurodevelopment The conditions discussed in this section include conditions which are acquired, for which there may be a genetic predisposition, or conditions that are likely genetic but have not been identified as such. The neurodevelopmental impact of all the conditions discussed are influenced by risk and resiliency factors. Risk factors represent increased probability of a problem; when risk factors are present, development of a disorder may be more likely, and associated outcomes are more likely to be poor. In addition, the probability a risk factor will manifest as a neurodevelopmental problem and/or exacerbate neurodevelopmental delay depends on the degree of exposure to the risk factor as well as the additive impact of other multiple risk factors on children. In contrast, when resiliency factors are present, a disorder may be less likely to develop or less likely to have negative neurodevelopmental outcomes because resiliency factors ameliorate the impact of risk factors and promote good health outcomes. Many risk and resiliency outcomes fall on a continuum; whereas low levels of education and low socioeconomic status are risk factors for poor outcomes of neurodevelopmental conditions, high levels of education and high socioeconomic status may serve as protective or resiliency factors. For example, Noble and colleagues (2015) found that family income and parental education explained a significant proportion of the variance in brain structure, particularly in areas associated with language development, memory, and executive functioning. There is also a wealth of research linking demographic variables, including socioeconomic status and parental level of education, to intellectual functioning. The synergistic impact of multiple risk and resiliency factors help to determine the overall neurodevelopmental impact on children.

HIV In addition to the transmission methods seen in adults, children can acquire HIV via vertical

transmission when an HIV-/AIDS-positive mother exposes her child during the perinatal period (i.e., in utero, during birth, or through breastfeeding; A. B. Allen, Jesse, & Forsyth, 2011; CDC, 2015c). Unfortunately, because children’s nervous systems are developing, pediatric HIV has especially adverse outcomes on neurodevelopment, particularly when it is untreated or barriers to treatment are present. In a review of outcomes for children with pediatric HIV, Willen (2006) described pediatric HIV-related developmental disruptions as nonlinear and resulting in “disparate patterns of behavior affecting cognitive, psychosocial as well as emotional functions” (p. 223). Neurologically, the brains of children with HIV/ AIDS may be characterized by reduction in white matter, demyelination, and atrophy or calcification, particularly in subcortical areas (Cohen et al., 2015; Uban et al., 2015; Willen, 2006). Children with HIV tend to demonstrate deficits in general cognitive functioning and in psychomotor functioning, as well as adaptive functioning, language, social–emotional functioning, and academic achievement (Lazarus, Rutstein, & Lowenthal, 2015; Willen, 2006). Specific cognitive deficits include deficits in sequential processing, immediate memory, visuomotor integration, and attention (Willen, 2006). More recently, Cohen and colleagues (2015) found that when compared with healthy control children matched by age, sex, socioeconomic status, and ethnicity, children with HIV performed significantly more poorly on measures of intelligence, processing speed, attention, and working memory. Uban and colleagues (2015) proposed that white matter changes in children with HIV mediate the relationship between HIV and working memory. Psychologists can work to not only help design academic interventions and accommodations for cognitive deficits for children with HIV/AIDS, but also for social functioning and to help children and families cope with the stigma of the disease. Indeed, the stigma associated with HIV/AIDS can effect an individual’s health outcomes and treatment adherence, particularly when considered in conjunction with racial/ethnic status, socioeconomic status, social support, and coping (Earnshaw, Bogart, Dovidio, & Williams, 2015; Katz et al., 2013). Stigma associated with having a parent with 289

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HIV/AIDS can also have an impact on children. A review of the global literature on the impact of a parent having HIV/AIDS on children was conducted by Chi and Li (2013). They found that children with parents who had HIV/AIDS had more psychological concerns than did children without parents who had HIV/AIDS. Their review indicated that several studies showed children with parents who had HIV/AIDS had higher levels of internalizing problems, externalizing problems, and more difficulty with social adjustment than did their peers who were not affected by HIV/AIDS. It would seem this is a group who would benefit from psychological intervention even though the children themselves may not have HIV/AIDS. Bennett, Hersh, Herres, and Foster (2016) looked at internalizing behaviors in adolescents (ages 12–24) with HIV and found stigma related to HIV, being prone to shame, and avoidant coping were related to increased depressive, anxious, and posttraumatic stress disorder symptomatology, which suggests interventions are needed in these areas. Regarding medicinal intervention for pediatric HIV, the Panel on Antiretroviral Therapy and Medical Management of HIV-Infected Children (2016) recommends all children with HIV be treated with antiretroviral treatment. This recommendation is particularly important considering recent research on highly active antiretroviral therapy (HAART) in children infected with HIV in the perinatal period. In a study of archival data from children and adolescents perinatally infected with HIV, Lazarus and colleagues (2015) found when HAART was initiated prior to onset of AIDS, this treatment was associated with higher scores on measures of intelligence as well as prevention/delay of AIDS and associated progressive or static encephalopathy. Preventative measures related to vertical transmission of HIV are also critical. Boivin, Kakooza, Warf, Davidson, and Grigorenko (2015) stated that, “one of the greatest public health initiatives developed in the modern era of infectious disease is the prevention of mother-to-child transmission of HIV” (p. 156). This involves a medication regimen for mothers infected with HIV that is administered during pregnancy; preventative interventions like this have been shown to drastically reduce the rate of 290

mother-to-child transmission of HIV (Boivin et al., 2015; National Institutes of Health, 2014).

Diabetes Mellitus Diabetes mellitus is another health condition that has the potential to impact affected children’s neurodevelopment. All three types of diabetes (i.e., Type 1 diabetes, Type 2 diabetes, and maternal gestational diabetes) can have adverse impacts on development. In Type 1 diabetes, formerly known as juvenile diabetes, the pancreas does not produce insulin, which is needed to convert glucose into energy and regulate blood glucose levels. Prevalence for Type 1 diabetes in 2009 was estimated to be 1.93 per 1000 (Dabelea et al., 2014). The impact of Type 1 diabetes on intellectual functioning and central nervous system integrity has been studied by multiple researchers. Northam and colleagues (2009) compared intellectual functioning (as measured by the Wechsler Abbreviated Scale of Intelligence; Wechsler, 1999) and magnetic resonance spectroscopy and magnetic resonance imaging (MRI) results of children with Type 1 diabetes and healthy children. Results suggested children with Type 1 diabetes demonstrated significantly lower general intellectual functioning (p = .03) and verbal reasoning ability (p = .05) at 12 years postdiagnosis when compared with healthy children. In addition, MRI results suggested children with Type 1 diabetes showed “decreased gray matter volume in bilateral thalami, right parahippocampal gyrus, and right insular cortex” (p. 447) and decreased white matter volume in the left temporal lobe as well as bilateral mesial temporal lobes. Age of onset, rather than duration of the condition, is thought to be a key factor to predicting the severity of adverse cognitive outcomes. Children who develop Type 1 diabetes early (i.e., before age 7) are thought to have poorer outcomes than children who develop the condition later in life (Ack, Miller, & Weil, 1961; Ferguson et al., 2005). Ferguson and colleagues (2005) sought to determine whether adults diagnosed with Type 1 diabetes early in life continued to demonstrate cognitive deficits. In addition, they hypothesized that cognitive deficits would be accompanied by organic evidence (i.e., atrophy). Results suggested that adults with a history

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of early diagnosis of Type 1 diabetes demonstrate lower nonverbal reasoning skills and slowed processing speed on the Wechsler Adult Intelligence Scale, Revised (Wechsler, 1981) when compared with adults diagnosed with Type 1 diabetes after the age of 7 (Ferguson et al., 2005). In addition, Ferguson and colleagues (2005) stated that early-onset Type 1 diabetes had an impact on cognitive functioning “independently of retinopathy and diabetes duration” (p. 1435). Regarding organic evidence of cognitive deficits, Ferguson and colleagues (2005) noted that structural brain abnormalities (e.g., larger lateral ventricles, ventricular atrophy) were more common in participants with early-onset Type 1 diabetes than in participants diagnosed after age 7. Type 2 diabetes is characterized by the body’s resistance to or ineffective use of insulin. Although Type 2 diabetes is more prevalent than Type 1 diabetes, it is more common in adults as it develops gradually over time; however, increasing cases of Type 2 diabetes are being diagnosed in children and adolescents (H. J. Dean & Sellers, 2015). Prevalence for Type 2 diabetes in 2009 was estimated to be 0.46 per 1000 (Dabelea et al., 2014). Because Type 2 diabetes is less common in children, less research on its neurodevelopmental impact is available. However, one longitudinal study by Olsson, Hulting, and Montgomery (2008) examined whether Type 2 diabetes later in life was related to poorer cognitive functioning in childhood. Participants in the National Child Development Study in England were followed from birth through age 42. Numerous data were collected including significant diagnoses, family history of diabetes, cognitive functioning and reading comprehension at age 11, results of a medical exam at age 16, and interviews at ages 33 and 42. Results suggest participants diagnosed with Type 2 diabetes had lower scores on measures of general cognitive ability and reading comprehension at age 11. According to Olsson and colleagues (2008), this could suggest cognitive deficits may be an early indicator of subclinical Type 2 diabetes; high levels of glucose in the blood may impact myelin production which may then negatively impact cognitive functioning. Gestational diabetes, a type of diabetes a mother can develop during pregnancy, can have adverse

impacts on the mother and the developing fetus. According to the CDC (2015d), gestational diabetes is associated with increased birth weight (i.e., fetal macrosomia) and higher risk of developing obesity, breathing difficulties, or Type 2 diabetes for the child. Mann, Pan, Rao, McDermott, and Hardin (2013) hypothesized that maternal diabetes (either gestational diabetes or otherwise) is related to greater risk of intellectual deficits in children. Results of a retrospective study of children receiving Medicaid benefits suggested that children born to mothers with diabetes did in fact have increased risk of intellectual disability (Mann et al., 2013); however, results of an earlier study by Ornoy, Ratzon, Greenbaum, Wolf, and Dulitzky (2001) suggested no cognitive differences (as measured by the Wechsler Intelligence Scale for Children, Revised [Wechsler, 1974]) between children born to mothers with pregestational or gestational diabetes and control group children. Results of studies by Ornoy and colleagues (2001) and Ratzon, Greenbaum, Dulitzky, and Ornoy (2000) suggest children born to mothers with pregestational or gestational diabetes may have other developmental difficulties. For example, Ornoy and colleagues (2001) found that children whose mothers had pregestational or gestational diabetes demonstrated poorer attention and motor functioning than control children. More specifically, Ratzon and colleagues (2000) found motor functioning of children born to mothers with diabetes was negatively correlated with hyperglycemia severity. They concluded that maternal hyperglycemia may impact motor development in a mild, but negative way (Ratzon et al., 2000). Psychologists may not be accustomed to considering health conditions like diabetes when working with children as care of children with diabetes are traditionally in the domain of medical providers. However, any conditions, including diabetes, in which risk and resiliency factors are powerful are ripe for psychologist involvement. For diabetes, this can include psychologists working to increase medication and diet compliance to facilitate glycemic control, helping adolescents gain increased understanding of the potentially dire health consequences of not managing their condition, reducing fears and phobias regarding needles, and reducing fear 291

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associated with the potential short and long-term health consequences. For example, several interventions for fear of hypoglycemia in children and adolescents with Type 1 diabetes and their parents were discussed in a recent review by Driscoll, Raymond, Naranjo, and Patton (2016). Suggested interventions include cognitive–behavioral therapy (CBT), graduated exposure, and psychoeducation (e.g., Blood Glucose Awareness Training [Cox et al., 2001] and HypoCOMPaSS [Little et al., 2014]; Driscoll et al., 2016). A recent meta-analysis by Buchberger and colleagues (2016) sought to determine the prevalence of depression and anxiety in children with Type 1 diabetes as well as how symptoms of depression and anxiety relate to management of the disease. Results suggest 17.2% to 63% of children with Type 1 diabetes in the studies analyzed experienced symptoms of depression and 32% experienced symptoms of anxiety. Symptoms of anxiety and symptoms of depression were shown to be related to poorer glycemic control (i.e., higher HbA1c levels). Buchberger and colleagues (2016) concluded that these findings underscore the need for psychological screening and integrated treatment teams for children with diabetes. Diabetes can be difficult to manage in childhood and adolescence. Therefore, it is important that caregivers and other adults who interact with children with diabetes play a role in treatment management and fostering academic success and emotional well-being at home and at school. In a review of research on parenting interventions for children diagnosed with Type 1 diabetes, Lohan, Morawska, and Mitchell (2015) discussed a number of interventions designed to improve treatment adherence and health outcomes as well as target behavioral issues and parental distress. A recent intervention study conducted by Maranda, Lau, Stewart, and Gupta (2015) looked at the effectiveness of a behavioral intervention to improve glycemic control in a group of 28 children with Type 1 diabetes. They assigned half of the group to an intervention condition and the other half to a control condition. The children in the intervention were taught to associate caring for a Betta fish with self-management of their diabetes. They were instructed to feed their fish twice a day and to check their blood glucose at that 292

time and then change the water once a week and review their logs with their caregiver at that time. After three months, the children in the intervention demonstrated a significant decrease in A1C level (a marker of blood glucose) compared with the control condition. Although this study used a relatively small sample size, it shows the range of inexpensive and noncomplicated interventions that may be effective in improving self-management in these types of health conditions. In a review of research on school-based interventions for students with diabetes, Pansier and Schulz (2015) broke down school-based interventions into two main categories: diabetes education interventions for school staff (e.g., teachers, nurses) and school-based interventions for students with diabetes that target health, academics, and emotional well-being. Pansier and Schulz (2015) noted that there has been an increase in interventions related to diabetes in the past 10 years, particularly long-term, often theory-based interventions which Pansier and Schulz described as integrative (i.e., interventions which promote coordination of care). Psychologists and educators should be aware of the warning signs of diabetes complications when they are working with children with diabetes so they know when to intervene. An additional consideration is that a diabetic complication could present as or exacerbate cognitive deficits during standardized evaluation. For example, hypoglycemia can present acutely with headaches, sweating, pale skin, shaking, dizziness, feeling tired, and poor coordination, whereas hyperglycemia can present with malaise, fatigue, excessive thirst, and frequent urination (Wyckoff, Hanchon, & Gregg, 2015).

Hypertension According to George, Tong, Wigington, Gillespie, and Hong (2014), the prevalence of hypertension, or high blood pressure, in children and adolescents is on the rise. Persistent pulmonary hypertension of the newborn (PPHN) is one type of hypertension with adverse neurodevelopmental outcomes for children (Hosono et al., 2009). Although there are many possible causes of PPHN, newborns with the condition typically present with “high pulmonary vascular resistance with right-to-left shunting of venous

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blood through ductus arteriosus and/or foramen ovale, resulting in severe hypoxemia” (Hosono et al., 2009, p. 79). Mortality rates for infants with PPHN are high, and those that survive often have hearing impairments and other developmental issues. Asymptomatic idiopathic intracranial hypertension is another type of hypertension in children and adolescents (most commonly during puberty) that causes intracranial pressure to increase for no known reason (Bassan, Berkner, Stolovitch, & Kesler, 2008). Risk factors for pediatric intracranial hypertension include obesity and female gender (Bassan et al., 2008; Dessardo, Dessardo, Sasso, Saruni´c, & Dezulovi´c, 2010). Symptoms of intracranial hypertension include symptoms of intracranial pressure (e.g., headaches, nausea, visual difficulties); however, many children present without these symptoms. Some researchers examined the impact of maternal hypertension and hypertensive medications on neurodevelopment of the developing fetus. Koren (2013) reported up to 15% of women have high blood pressure during pregnancy. According to Olusanya and Solanke (2012), maternal hypertensive conditions during pregnancy include chronic hypertension, pregnancy-induced hypertension, preeclampsia, and eclampsia. On the basis of a study of mothers with these conditions in a low-income country, Olusanya and Solanke (2012) concluded that maternal hypertensive disorders were associated with a variety of adverse outcomes including low birth weight, fetal growth restriction, and preterm delivery. Preventative and educational measures targeted toward women at high risk of hypertension while pregnant may help reduce the risk of problematic outcomes in their children. Interventions for children with hypertension are also pharmacological and lifestyle-change-based. Chaturvedi, Lipszyc, Licht, Craig, and Parekh (2014) reviewed several randomized controlled trials on medications for hypertension for children. Although results suggest many antihypertensive medications studied reduce blood pressure in children with hypertension, very limited information is available about the long-term impact of these medications on children (e.g., long-term efficacy, organ damage). Hypertension is often comorbid with other

risk factors for heart disease and cerebrovascular concerns including hyperlipidemia, hyperglycemia, and excess body weight. Lifestyle interventions have also been shown to be associated with positive outcomes in children with obesity; that is, weight loss, healthier body mass index, and a reduction in symptoms associated with metabolic syndrome (e.g., reduced blood pressure, improved blood glucose levels; Reinehr, Kleber, & Toschke, 2009).

Traumatic Brain Injury Mechanical injury to the brain has the potential to result in neural death through a variety of mechanisms. Primary injuries (injuries which occur at the time of the mechanical trauma) may include direct penetration of a foreign object as in an open/ penetrating head injury, the coup–contrecoup or acceleration/deceleration injuries seen in closed head injuries, and secondary injuries which arise as a result of the mechanical trauma like hematoma. Similar to many conditions in this chapter, traumatic brain injury (TBI) cannot fully be covered in this space because of the multitude of research that has been published on pediatric TBI regarding causes, effects, and outcomes. Readers looking for more detailed information on the epidemiology of TBI in children are directed to Thurman (2016), and readers interested in more comprehensive information on assessment and outcomes associated with TBI in children and adolescents are directed to Semrud-Clikeman and Bledsoe (2011) and Lloyd, Wilson, Tenovuo, and Saarijärvi (2015). Recently, Lloyd and colleagues (2015) published a comprehensive, systematic review of TBI outcomes for children and adolescents. They analyzed TBI literature published between 2008 and 2013 and reported on neuropsychological, psychosocial, and academic outcomes of mild TBI and moderate TBI. As expected given the variability in how TBIs are sustained (e.g., motor vehicle accidents, abuse, falls, sports-related injury), which areas of the brain are most impacted, pre- and postmorbid ecological and psychiatric factors, and the severity of the injury, outcomes were quite variable; however, it was noted that 48.9% of the studies reviewed indicated poor psychosocial outcomes in children and adolescents with mild TBI (e.g., psychiatric diagnoses, poorer 293

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quality of life), and 40.9% indicated poor neuropsychological outcomes in children and adolescents with moderate TBI (e.g., difficulties with executive functioning; Lloyd et al., 2015). Overall, results suggest some children and adolescents have long-term adverse outcomes following a TBI, whereas others recover more quickly. Recently, Königs and colleagues (2015) examined the impact of pediatric TBI by comparing behavioral ratings, intellectual functioning, and attention of children with TBI to children who had other types of traumatic injury. Results suggested children with TBI were rated to have significantly more difficulty with attention and internalizing behavior (as rated by parents and teachers) as well as externalizing behavior (as rated by parents). In addition, children with TBI were found to have significantly lower full scale IQ (FSIQ) scores and significantly longer lapses of attention on an attention task than children with a history of other types of traumatic injury. As with many of the other conditions discussed, intervention for TBI in children and adolescents hinges on individual differences. In determining whether interventions are needed, what type of intervention to implement, and when to implement an intervention, it is important to consider the severity of the injury, the recovery process to date, and the individual needs of the child or adolescent. Interventions for mild TBI, for instance, may include medical treatment for symptoms (e.g., headache, nausea), psychoeducation, rest, and active rehabilitation (Winkler & Taylor, 2015). Medical intervention for more severe TBI might aim to reduce intracranial pressure of cerebral perfusion pressure thereby reducing the risk of secondary injury (Geyer, Meller, Kulpan, & Mowery, 2013). For children and adolescents who experience lasting behavioral issues and/or psychological distress following a TBI, CBT has also been shown to be an effective and important area of intervention (Pastore et al., 2011). Parenting interventions have also been shown to improve emotional and behavioral symptoms in children who have sustained a TBI; for example, Brown, Whittingham, Boyd, McKinlay, and Sofronoff (2014) found an intervention that combined a parenting group and a stress-management 294

component resulted in lasting improvements in behavior and emotional problems. Overall, Winkler and Taylor (2015) explained that it is quite challenging to evaluate the efficacy of intervention for children and adolescents with TBI because it is difficult to parse apart the influence of the intervention from the influence of recovery. Perinatal Complications and Neurodevelopment Perinatal complications (i.e., deviations from expected development which occur prenatally, during birth, and shortly after birth) also have the potential to adversely impact the course of children’s neurodevelopment. There are numerous problems that can occur from the time of conception through birth that affect neurodevelopment. The prognosis for these conditions is highly variable depending on the specific complication as well as risk and resiliency factors.

Neural Tube Defects Neural tube defects (NTDs) occur when the neural tube does not close completely at the caudal or rostral ends (Meeks Corn & Bishop, 2011). When the rostral side of the neural tube fails to close, anencephaly can result and when the caudal side fails to close, spina bifida can result. Anencephaly, a condition where the forebrain does not develop, is not compatible with life, although the pregnancy may progress to term. Other types of NTDs include hydraencephaly, exencephaly, and holoprosencephaly. In hydraencephaly, the forebrain does not develop appropriately, and there is a fluid-filled sac in its place; in exencephaly, the brain develops outside the cranial vault of the skull; and in holoprosencephaly, the forebrain does not divide into the left and right hemispheres (Meeks Corn & Bishop, 2011). There are many types of spina bifida, varying in prevalence and severity. The least severe form of spina bifida is spina bifida occulta, a condition which may not be associated with any outwardly observable differences or functional deficits (Loveday & Edginton, 2011). In one of the most common types of spina bifida (i.e., spina bifida myelomeningocele) the spinal cord is herniated and loss

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of sensation, paralysis, and loss of bladder and/or bowel control can occur (Meeks Corn & Bishop, 2011). Many children with spina bifida meningomyelocele also have hydrocephalus. Loveday and Edginton (2011) provided information on neuropsychological functioning and evidence-based interventions for children with spina bifida and hydrocephalus. They state that because of the varying severity levels of spina bifida, cognitive functioning is quite variable from case to case; nonetheless, cognitive functioning tends to follow a pattern characterized by low average overall intellectual functioning with slowed processing speed and better-developed verbal reasoning than nonverbal reasoning abilities. Other common areas of weakness include visuospatial abilities and language (Loveday & Edginton, 2011). Recommended interventions for children and adolescents with spina bifida include medical interventions (e.g., shunt placement) and neuropsychological interventions tailored to the specific, individual needs of the patient. Evidence-based interventions focused on speech and language, attention, inhibition, memory, anxiety or other emotional concerns may be relevant depending on the unique cognitive, behavioral, and emotional needs of the child or adolescent. Psychologists should also consider consulting with educators and other school personnel to provide accommodations given the longer time these children take to complete toilet activities which can negatively interfere with learning and socialization (Gribble, Parsons, Donlau, & Falkmer, 2013). Loveday and Edginton (2011) also recommended general interventions including, “extra time for study and examinations as well as breaks, sufficient rest and exercise, adequate nutrition and hydration, bowel and bladder management, and effective pain control” (p. 780). For the most severe NTDs, prevention, rather than intervention, is the primary focus as some of the NTDs are either associated with quite severe functional deficits or incompatibility with life; however, research supports the use of some specific and nonspecific interventions and accommodations for children with conditions that result from perinatal complications. Prevention methods for NTDs have centered on folic acid since the 1990s; in fact, enriched cereal grain products are mandated to be

fortified with folic acid as a public health intervention in the United States. Since this mandate, there has been a reduction of cases of NTD-affected births in the United States although folic acid is not a panacea, NTDs can still occur even when mothers are fully compliant with recommendations. There appears to be a health disparity in the United States regarding NTD rates; although rates have fallen for all ethnic groups since the mandatory fortification, there is a higher rate of NTDs for Hispanic women (Ginsburg, Mendez, & Haskard-Zolnierek, 2016). Ginsburg and colleagues (2016) raised the possibility, based on a review of the literature, that this may be in part accounted for by genetic influences, more unplanned pregnancies, and lower levels of information about folic acid. Based on the results of a study, they suggested more targeted information about folic acid is needed. The United States Preventive Services Task Force (2014) also recommended women who plan to become pregnant and women who are pregnant take a daily prenatal vitamin supplement with 0.4 to 0.8 mg of folic acid. This is particularly important in other countries who do not have mandated folic acid fortification (Bestwick, Huttly, Morris, & Wald, 2014).

Migration Deficits Neuronal migration disorders include agenesis of the corpus callosum, lissencephaly, subcortical band heterotopia, and periventricular heterotopia. In lissencephaly, a gene mutation results in the surface of the cortex being thick and smooth (Meeks Corn & Bishop, 2011). Children with this disorder tend to have profound intellectual disability; however, some research suggests a more variable phenotype for lissencephaly (Leventer, Cardoso, Ledbetter, & Dobyns, 2001; Meeks Corn & Bishop, 2011). Based on results of a study of five patients with lissencephaly, Leventer and colleagues (2001) suggested some variations of lissencephaly are milder than others in terms of functional deficits; they noted one of the patients they studied had the mildest form of lissencephaly reviewed to date with an IQ estimated to be in the average range. Periventricular heterotopia is a neuronal migration disorder where neurons fail to form the layers of the cortex; children with this disorder often have 295

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“microcephaly, developmental delay, and neonatal epilepsy” (Meeks Corn & Bishop, 2011, p. 10). Subcortical band heterotopia, also referred to as double cortex syndrome, is a migration disorder which occurs during the third to fifth month of prenatal development in which the migration problem results in an extra layer of neural tissue in the subcortex (Jacobs, Anderson, & Harvey, 2001). SpencerSmith, Leventer, Jacobs, Luca, and Anderson (2009) looked at the neuropsychological profile of seven children (ages 4–15 years) with subcortical band heterotopia and, in general, intellectual deficits were found with more variability in academic, social, and behavioral functioning. A more consistent deficit was seen in processing speed, which is commensurate with the white matter pathology found in this condition (Spencer-Smith et al., 2009). Agenesis of the corpus callosum occurs when the corpus callosum fails to form. A comprehensive review of the literature evaluating the neuropsychological profile of this condition was conducted by Siffredi, Anderson, Leventer, and Spencer-Smith (2013) in which they could extrapolate mean IQ scores for children (76.35, SD = 30.12) and adolescents (85.56, SD = 18.8). They also found strong evidence of pragmatic language and mathematics deficits and variability in other functions including that 75% of individuals experienced expressive and receptive language impairment, 43% demonstrated visual and spatial impairment, and 36% demonstrated attention impairment. The authors also concluded that there are some relatively preserved functions in individuals with this condition, including vocabulary, visual–spatial long term memory, reading, and spelling and noted that variability within this group would be expected given the high possibility of comorbid neurological conditions. In addition to the neurocognitive concerns, recent research has started to emerge which suggests that children with agenesis of the corpus callosum present with autistic features. For example, Lau and colleagues (2013) found that of a group of 106 individuals with agenesis of the corpus callosum 45% of children and 35% of adolescents demonstrated rating scale scores consistent with autism. The authors of this study recommend that agenesis of the corpus callosum be considered as a possible etiology for 296

ASD and the children with agenesis of the corpus callosum should be screened for ASD.

Intrauterine Toxicity Neurodevelopment can also be impacted by exposure to illegal and legal toxic substances (e.g., alcohol, tobacco, illicit drugs, prescription medications, heavy metals) in utero. These toxic substances are termed teratogens, many of which are described in detail in the sections that follow; however, for a more thorough review of teratogens and viruses that can adversely impact neurodevelopment, readers are encouraged to refer to Diav-Citrin (2011) and Jamkhande, Chintawar, and Chandak (2014). The conditions in this section differ from all previous conditions discussed in this chapter in that they are preventable. Although there is likely a genetic component regarding the propensity toward substance abuse as well as ecological risk factors, many toxins in this section are volitionally introduced to the fetus and psychologists, health care professionals, governmental agencies, and nongovernmental agencies should be working toward the prevention of these conditions. Shahin and Einarson (2011) noted that knowledge transfer regarding the impact of teratogens on pregnancy outcomes to women and medical personnel is important in preventing teratogen exposure; however, based on a review of the literature on knowledge transfer in this area, Shahin and Einarson concluded that there is room for improvement in how information about teratogens is communicated and disseminated. In addition to communicating the risks associated with prenatal exposure to alcohol, tobacco, illicit drugs, and heavy metals with pregnant women, it is also imperative that medical personnel consider and communicate the risks associated with teratogenic prescription medications. Given the teratogenic risk associated with commonly prescribed medications (e.g., antiepileptic drugs [AEDs], selective serotonin reuptake inhibitors [SSRIs]) and the limited public knowledge of teratogenic risks associated with such medications, Gottlieb (2013) stated, “it is the ethical and legal obligation of every health care provider to consider the side effects of any medication recommended and to educate patients about the associated risks and

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necessary precautions” (p. 795). Exposure to some teratogens, including mercury, can not only occur in utero but also via breastfeeding (Peters, Rhoades, Ezzell, Holland, & Weatherspoon, 2013). To prevent exposure to mercury, it is recommended that pregnant and lactating women be educated regarding fish consumption and mercury exposure. In general, it is recommended that pregnant and lactating women avoid eating certain predatory fish that are more likely to contain methylmercury (e.g., shark, swordfish) and limit consumption of other fish; a table which includes recommendations for several types of fish (based on recommendations from the American Pregnancy Association and the Mayo Clinic) is included in Peters and colleagues (2013). Alcohol.  Consuming alcohol during pregnancy can lead to range of neurodevelopmental issues. Fetal alcohol spectrum disorders (FASD) are the conditions which occur when children experience functional impairment as a result of prenatal alcohol exposure. FASD is an umbrella term that encompasses a variety of diagnoses including fetal alcohol syndrome, fetal alcohol effects, prenatal alcohol effects, alcohol-related birth defects, partial fetal alcohol syndrome, and alcohol-related neurodevelopmental disorder. This wide range of terms emphasizes that the effects of exposure to alcohol in utero are variable (Jones & Streissguth, 2010; Leibson, Neuman, Chudley, & Koren, 2014; Sokol, Delaney-Black, & Nordstrom, 2003). That is, although some children with FASDs have facial dysmorphology associated with fetal alcohol syndrome (i.e., widely-set eyes, smooth philtrum, thin upper lip, short palpebral fissures), others do not (Jones & Streissguth, 2010; Leibson et al., 2014; Mattson et al., 2010; Sokol et al., 2003). In addition, children with FASDs may demonstrate variable cognitive deficits although some children with intrauterine alcohol exposure demonstrate typical intelligence. Deficits that have been in this population include intellectual functioning, executive functioning, attention, language, visuospatial construction, motor functioning, and adaptive functioning (Mattson & Riley, 1998; McGee, Bjorkquist, Riley, & Mattson, 2009; Streissguth, 2007; Streissguth, Barr, & Sampson,

1990; Vaurio, Crocker, & Mattson, 2011). FASDs are highly comorbid with psychiatric disorders including ADHD, specific learning disabilities, mood disorders, oppositional defiant disorder, and self-injurious behavior (Burd, Klug, Martsolf, & Kerbeshian, 2003). For a recent thorough review of intervention and prevention efforts in the United States see Davis, Hoover, Moore, and Petrenko (2017). Tobacco.  Prevention efforts to reduce intrauterine tobacco exposure have focused on educating the public and selective targeting of high-risk groups. Although there has been a dramatic decrease in smoking among adults and high school students in recent years (i.e., smokers decreased from 22.8% to 16.8% of adults and 28.5% to 15.7% of high school students from 2001 to 2014; CDC, 2016), it is still important to consider the impact of exposure to tobacco smoke on the developing fetus. Herrmann, King, and Weitzman (2008) summarized the relationship between tobacco smoke exposure and neurodevelopmental outcomes and stated tobacco exposure has been shown to be associated with decreased birth weight and decreased brain growth which seem to stem from fetal hypoxia. Results of a study by Wiebe and colleagues (2015) indicated a relationship between tobacco exposure and behavioral problems: When children exposed to tobacco smoke prenatally were compared with healthy control children, children exposed to tobacco smoke demonstrated significantly more difficulty with self-regulation tasks (Wiebe et al., 2015). Research has also determined prenatal exposure to tobacco smoke impacts the vision of the developing fetus. Pueyo and colleagues (2011) reported that prenatal exposure to tobacco smoke specifically impacts the development of the optic nerve (Pueyo et al., 2011). They concluded that optic nerve damage (i.e., to the retinal nerve fiber layer) observed in children exposed to cigarette smoke in utero “may be a sign of the toxicity of smoking on [central nervous system] development as a whole” (p. 333). In a review of the literature on exposure to cigarette smoking during pregnancy and vision in children, Fernandes, Yang, Li, and Cheikh Ismail (2015) found that 18 of 24 studies reviewed indicated children prenatally exposed to cigarette smoke were 297

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at risk for vision problems including strabismus, refraction errors, and retinal problems. Illicit drugs.  There are a sufficient number of illicit drugs to which intrauterine exposure results in neurodevelopmental delays that they cannot all be reviewed here, so some of the more common ones will be discussed. One issue for psychologists to consider is the increasing number of synthetic drugs which have not been extensively studied and the effects of which are largely unknown. Accornero et al. (2007) suggest attention may be negatively impacted by prenatal cocaine exposure through age 7; however, deficits in response time and consistency were not present at age 5. This finding underscores the importance of serial assessment for children prenatally exposed to cocaine, as some related cognitive deficits may not present until later. Chiriboga, Kuhn, and Wasserman (2014) assessed cocaine-exposed infants and compared them with unexposed children at 6, 12, and 24 months of age in the domains of development, behavior, and neurological outcomes. Results suggest a negative developmental trajectory in cocaine-exposed children as well as hypertonia and impaired fetal growth prior to 6 months (when cocaine-exposed infants appear to “catch-up” in size). More recently, Gkioka and colleagues (2016) concluded from their review of the literature on cocaine exposure that “exposure seems to be associated with various adverse neurodevelopmental sequelae, including motor problems, impairment of memory function, social dysfunction and anxiety, attention deficits, and IQ alterations” (p. 8). Methamphetamine use has increased in recent years; however, limited information is available regarding the neurodevelopmental impact of children exposed to methamphetamine in utero. Smith and colleagues (2011) sought to study the relationship between methamphetamine exposure and neurodevelopmental outcomes (e.g., mental and motor development). Based on results from a large group of infants who were given the Bayley Scales of Infant Development (Bayley, 1993), Smith and colleagues concluded that children exposed to methamphetamine prenatally demonstrate fine motor difficulties that are “mostly resolved by 3 years” (p. 176). 298

Similarly, Kiblawi and colleagues (2014) found similar neonatal neurobehavioral scale scores in infants exposed prenatally to methamphetamines to matched controls at 1 month with improvement in stress and arousal in the exposed infants. Opioid use can take many forms, including use of illicit drugs like heroin and through the use or abuse of legally prescribed opioid pills or methadone. Beckwith and Burke (2015) examined the impact of prenatal exposure to heroin, methadone, and other opioids on neurodevelopment. Study participants included 28 infants who had received rehabilitation services (i.e., medication treatment, therapy to stimulate development in a variety of domains) for methadone or opioid withdrawal symptoms (i.e., neonatal abstinence syndrome). Participants’ performance on the Bayley Scales of Infant and Toddler Development-Third Edition (Bayley, 2005) at discharge from the rehabilitation program was compared with performance of similar-age infants and children. Study participants’ performance on the language, motor, and cognitive sections was significantly poorer than performance of infants/children in control sample; however, when results for the study sample were compared with a smaller subgroup of the control sample (i.e., 300 infants and toddlers from the first three age bands), performance of infants/children in the study sample was significantly poorer than similar-age peers for language and cognition, but not motor functioning (Beckwith & Burke, 2015). Other prescribed and over-the-counter medications.  According to Andrade and colleagues (2004), nearly 50% of pregnant women are prescribed medication during pregnancy; however, some legally prescribed medications are known to have teratogenic potential. The United States Food and Drug Administration (FDA) now requires prescription medications to be labeled with a summary of risk for pregnancy, lactation, and reproductive potential for men and women (FDA, 2014). Previously, the FDA (2006) required prescription medications to be labeled with a pregnancy category (i.e., a letter: A, B, C, D, X, or N). Category A drugs were medications for which controlled, human studies found no fetal risk, and Category B drugs were

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drugs for which no fetal risk was indicated in animal studies or fetal risk was indicated in animal but not human studies (FDA, 2006). Category C drugs were medications for which risk was not yet ruled out, Category D drugs were medications with evidence of fetal risk, and Category X drugs were medications for which human and/or animal studies suggested fetal abnormalities or risks to the fetus which outweigh the benefits of the medication (FDA, 2006). Finally, Category N drugs were medications which were not yet categorized by the FDA (FDA, 2006). Of concern is that in the over 150,000 deliveries studied by Andrade and colleagues (2004), 40% of women were prescribed category C medication and, “an estimated 120,000 pregnant women are exposed to drugs with evidence of potential fetal risk each year” (p. 403). One class of medication that can have teratogenic effects is AEDs. According to Motamedi and Meador (2006), previous research suggests adverse consequences of seizure activity to the developing fetus outweigh the risk of AEDs on neurodevelopment. Nonetheless, based on their review of the literature on AEDs and neurodevelopmental outcomes, Motamedi and Meador (2006) concluded that congenital malformations are associated with exposure to AEDs, and further research is needed to determine whether exposure to AEDs in utero has lasting cognitive and behavioral effects. Velez-Ruiz and Meador (2015) concluded exposure to AEDs in utero is associated with risk of developmental delays and intellectual difficulties; however, they stated certain AEDs may have more severe adverse outcomes than others. For example, Valproate is known to cause apoptosis in animal studies and have more severe effects on development. SSRIs are another type of prescribed medication that may adversely impact neurodevelopment if taken by pregnant women; this may include congenital malformations and neurocognitive deficits. Knickmeyer and colleagues (2014) found children whose mothers were depressed and took SSRIs during their pregnancy were more likely to meet criteria for Arnold-Chiari Type I malformation when compared with children without a history of maternal depression and SSRI exposure. They did not find differences in risk for Arnold-Chiari

Type I malformation when they compared a group of children whose mothers were depressed but did not take SSRIs during their pregnancy and a control group. Several risk factors increased the likelihood of meeting Arnold-Chiari Type I malformation criteria including being exposed to SSRIs for three trimesters, being exposed to SSRI at the time of conception, and a family history of mood disturbance. The authors cautioned that research is needed before prescribers alter their practice. Casper and colleagues (2003) compared postnatal neurodevelopmental functioning of children born to mothers with major depressive disorder who did and did not take antidepressant medications. Although children who were exposed to SSRIs in utero did not differ significantly from nonexposed children on measures of weight, height, or head circumference, significant differences were found on measures of motor development and behavior. On the Bayley Scales of Infant Development (Bayley, 1993), children exposed to SSRIs in utero scored significantly lower than unexposed children on the psychomotor development and behavioral rating indices, whereas performance on the mental development index between the two groups was not significantly different. Casper and colleagues (2003) concluded motor development and control may be an area of weakness for children exposed to SSRIs in utero, which may also be associated with lower Apgar scores at birth. A recent meta-analysis on SSRIs and pregnancy by Ross and colleagues (2013) highlights a variety of studies which suggest adverse outcomes associated with SSRI exposure (e.g., congenital malformations, neonatal hypertension, low birth weight); however, the authors comment on the importance of parsing apart the impact of SSRI medication and the impact of maternal depression itself on neurodevelopment, as some studies have found adverse outcomes in children whose mothers were depressed but not taking SSRI medication. Psychologists may need to work with prescribers to help them determine the severity of the maternal depression to help the patient and prescriber determine if the risk to the developing fetus of the untreated depression exceeds that associated with SSRI use. Some recent research has examined the impact of over-the-counter medication (e.g., acetaminophen) on 299

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neurodevelopmental outcomes. Liew, Ritz, Rebordosa, Lee, and Olsen (2014) studied the relationship between over-the-counter and prescription acetaminophen use during pregnancy with hyperkinetic disorders and ADHD-like behavior in children. Results suggest over half of the study participants reported using acetaminophen during pregnancy; based on results of sensitivity and regression analyses, Liew and colleagues (2014) concluded that acetaminophen use during pregnancy increased the risk of parent-reported ADHD-like behavior, ADHD medication prescriptions, and diagnoses of hyperkinetic disorders. Heavy metals.  Exposure to certain heavy metals has also been shown to be associated with adverse neurodevelopmental outcomes. Prenatal exposure to high levels of methylmercury, for example, has been associated with microencephaly and impaired motor, language, memory, and visual–spatial functioning (Callejo & Geer, 2012). Golding and colleagues (2016) noted that although it has long been assumed that any level of mercury could harm the developing fetus, results of research on mercury exposure and neurodevelopment have not been as clear as would be expected (i.e., teratogenic effects of methylmercury may depend on the type of seafood consumed, protective levels of blood selenium in the mother). Golding and colleagues (2016) sought to examine the neurodevelopmental impact of low levels of prenatal mercury exposure of preschool children. While pregnant, mothers gave blood samples and completed a dietary questionnaire which assessed how frequently they consumed different types of seafood during pregnancy. Children of mothers who consumed fish during pregnancy and children whose mothers did not consume fish during pregnancy were compared four times over the course of 42 months on measures of social skills, motor skills, and language skills. Regression analyses suggest associations between maternal prenatal mercury levels and overall development scores at each measurement time point were either not statistically significant or positive (i.e., prenatal mercury levels were associated with no significant difference in total development score or prenatal mercury levels were associated with higher total development scores; Golding et al., 2016). 300

Lead is another metal that can have deleterious effects on child neurodevelopment. Vigeh, Yokoyama, Matsukawa, Shinohara, and Ohtani (2014) cited past research which indicates prenatal lead exposure has the potential to impact later cognitive development. To further examine the impact of lead exposure at varying points during pregnancy, Vigeh and colleagues (2014) devised a longitudinal study in Iran whereby first, second, and third trimester blood lead levels of pregnant women and umbilical cord blood lead levels were analyzed and statistically compared with early childhood development inventory scores. Results from Vigeh and colleagues (2014) suggest higher first trimester blood lead levels are associated with poorer developmental outcomes; overall, results suggest with each measured unit increase of prenatal blood lead, there is nearly double the risk of lower early child development inventory scores (Ireton & Glascoe, 1995; Ireton & Thwing, 1976). In addition to mercury and lead, other metallic trace elements are known to have neurotoxic or teratogenic effects. Rodríguez-Barranco and colleagues (2013) conducted a meta-analysis of 41 studies related to the potential adverse neurodevelopmental impact of arsenic, cadmium, and manganese. Based on results of their meta-analyses, Rodríguez-Barranco and colleagues (2013) concluded that increased arsenic in drinking water is associated with negative neurodevelopmental outcomes, and increased arsenic levels in urine is associated with significant decreases in FSIQ and verbal IQ; however, no studies on arsenic suggested significant effects on behavioral symptomology. According to Rodríguez-Barranco et al., cadmium is ranked as the third most threatening metallic trace element to human health (lead and mercury were the only elements ranked higher) by the Agency for Toxic Substances and Disease Registry. Only one of the four studies included in the meta-analysis suggested cadmium had an adverse impact on neurodevelopment (i.e., high cord blood cadmium levels were associated with lower FSIQ and performance IQ); another study suggested higher levels of cadmium in children’s hair were associated with difficulties with attention and social functioning as well as withdrawal. Results of

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the meta-analysis suggested manganese, a metallic trace element found in drinking water, food, and cigarette smoke, is associated with significant decreases in FSIQ and verbal reasoning abilities and significant increases in behavioral disorders (Rodríguez-Barranco et al., 2013).

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Maternal Trauma Research has suggested maternal trauma is associated with a host of negative outcomes in the parent–child relationship (e.g., difficulty understanding and responding to children’s emotions, greater risk of child maltreatment). Briggs and colleagues (2014) summarized this issue by highlighting the connection between responsive parenting and child stress by stating, “appropriate and responsive caregiving is a protective factor against the potentially toxic effects of stress, but parents with their own traumatic histories may not be able to provide this type of environment” (p. 167). That is, children of parents who have experienced trauma may in turn be more vulnerable to levels of stress which can negatively impact their neurodevelopment. Results from Briggs and colleagues (2014) suggest a significant association between caregiver childhood trauma (e.g., parents used drugs, time spent in foster care, abuse) and their children being at risk for social–emotional development problems at the age of 3; specifically, children of mothers who experienced trauma when they were children were nine times more likely to have scores above the risk cutoff on Ages and Stages Questionnaires: Social Emotional (Squires, Bricker, & Twombly, 2002). Given concerns related to social–emotional functioning in children born to mothers with a history of childhood trauma, Briggs and colleagues (2014) designed a study to assess the impact of the Healthy Steps intervention on social–emotional functioning of children whose mothers experienced trauma. The Healthy Steps program involves wellchild visits, outpatient intervention for children and caregivers, and social–emotional screening of children at various time points. Results suggest the intervention likely moderates the effect of maternal trauma on child social–emotional development (Briggs et al., 2014).

Low Birth Weight Low birth weight has been extensively researched as a risk factor for poor functional outcomes in part because other perinatal complications can present with low birth weight; it can be an outcome of other perinatal complications and a predictor of future problems by itself, and it serves as an example of the additive and synergistic effects of risk factors discussed previously. Despite low birth weight being associated with adverse developmental outcomes, recent research suggests advances in medical technology and care have led to increased survival rates and better neurodevelopmental outcomes for children with low birth weight (Gardella et al., 2015). However, in a comprehensive review of outcomes associated with preterm birth and low birth weight, Westrupp, Howard, & Anderson (2011) stated that as many as 50% of children born preterm or with low birth weight have at least “subtle learning, cognitive, and behavioral problems” (p. 749). For example, Anderson and Doyle (2004) studied the potential impact of preterm birth and low birth weight on executive functioning of children. They followed a cohort of very preterm/extremely low birth weight infants born in Australia and compared their cognitive functioning to a cohort of infants of normal birth weight. Participants completed the Wechsler Intelligence Scale for Children-Third Edition (Wechsler, 1991) as well as measures of executive functioning. Results suggest children with extremely low birth weight had significantly lower overall intellectual functioning and executive functioning (i.e., verbal and visual abstract reasoning, verbal working memory, planning, and spatial conceptualization/organization) than their normal birth weight peers. Children born very premature or with very low birth weight are also at greater risk for developing DCD (Edwards et al., 2011). Early intervention for infants born very preterm or with very low birth weight is important in improving neurodevelopmental outcomes for this population. For example, Hsiao, Tsai, Chen, and Lin (2014) stated that following a nutrition plan which includes intravenous feeding with high parenteral lipids, enteral feeding/probiotics/lactoferrin, and protein-fortified milk/formula may prevent some of the neurodevelopmental impairment common in 301

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children born preterm or with low birth weight by preventing other conditions including extrauterine growth restriction and sepsis. Recently, caffeine therapy has also been shown to improve neuro­ developmental outcomes in infants born with very low birth weight (Gupte, Gupta, Ravichandran, Ma, & Chouthai, 2016). Results suggest infants who received caffeine therapy within the first 48 hours of life had better neurodevelopmental outcomes (as measured by the Bayley Scales of Infant Development) than infants who received caffeine therapy later and infants who did not receive caffeine therapy (Gupte et al., 2016). Results of another recent study support the use of a Family Nurture Intervention with preterm infants (Welch et al., 2015). Given the nature of the neonatal intensive care unit, infants are separated from their mothers; the Family Nurture Intervention was designed to foster an emotional connection between preterm infants and their mothers via calming sessions that included scent cloth exchanging, touch, eye contact, and speaking to the infant. Results suggest infants who participated in the Family Nurture Intervention performed better on several neurodevelopmental and socioemotional measures (e.g., cognitive and language sections of the Bayley Scales of Infant Development, the Modified Checklist for Autism in Toddlers, and the Child Behavior Checklist) at 18 months old (Welch et al., 2015). Postnatal Environmental Influences on Neurodevelopment The environment has a tremendous impact on the course of children’s neurodevelopment via risk and resiliency factors that interact with organic features to predict functional outcomes. One of the primary responsibilities of psychologists when conducting diagnostic interviews is to explore children’s psychosocial history to help determine the presence of these environmental factors for the purpose of facilitating differential diagnosis and designing strengthbased interventions which rely on children’s resiliency factors to compensate for weaknesses. The introduction of an environmental concern has the potential to alter children’s neurodevelopment in what may have been an otherwise healthy trajectory. 302

This section reviews some of postnatal environmental influences on development. Children are vulnerable to toxins found in and outside of the home, given their lack of knowledge about safety. An area for psychologists to consider is that many of the toxins that affect children prenatally can also affect children postnatally. This includes exposure to illicit substances, legal substances like tobacco and alcohol, as well as household and environmental toxins like air pollutants, lead, polychlorinated biphenyls (PCBs), mercury, and methylmercury. Children with neurodevelopmental disorders are likely to encounter some of these toxins more than adults in the same environment as they may put things in their mouth (e.g., household cleaners, lead paint) and may lack the reading and/or safety skills to keep themselves safe past an age when they would otherwise be able to do so. Child maltreatment (i.e., child abuse and neglect) is an environmental determinant that can have a profound and pervasive impact on neurodevelopment. This is discussed briefly here, as there is a separate chapter of this handbook dedicated to the topic. Child maltreatment is a key risk factor for neurocognitive problems, academic problems, motor deficits, and socioemotional symptomatology (Davis, Moss, Nogin, & Webb, 2015; Majer, Nater, Lin, Capuron, & Reeves, 2010). Common neurocognitive deficits in children who have experienced maltreatment include lower general intellectual functioning, and deficits in language, memory and learning, attention, and executive functioning (De Bellis, Hooper, Spratt, & Woolley, 2009). Children who are victims of child maltreatment will each have a unique pattern of neurocognitive strengths and weaknesses; therefore, interventions which capitalize on neurocognitive strengths and support weaknesses should be chosen or designed and implemented with children based on their individual needs. Environmental factors that affect neurodevelopment do not necessarily need to be actively aversive. Neurodevelopment is generally viewed as to what would be expected of a typical, or healthy, pattern of development as compared with other children at the same age. Therefore, when an environment is not as enriching as what would be found for a typical

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child, a neurodevelopmental delay may result, at least when normative comparisons are made. For example, a lack of exposure to educational materials and opportunities can have an important impact on neurodevelopment. The quality of a school accounts for between 2% and 10% of the variance in cognitive functioning in children (Sattler, 2008). This is a substantial amount, especially when considering that some school districts and psychologists use cut off scores on intelligence tests to make determinations about whether a student qualifies for a special education classification of a cognitive disability or SLD. The quality and number of years of education children have can help determine their developmental trajectory. Indeed, research has consistently shown that years of education and IQ scores have a positive relationship. Despite this well-known finding, it is important to note many IQ tests, including the widely used Wechsler Intelligence Scales, do not have education-adjusted norms. In general, each year of schooling completed adds between one and three-and-a-half points to students’ estimated IQ score as adults (Sattler, 2008). Higher IQ is associated with graduating from high school, attending college, being professionally employed, and having a higher income (Ceci & Williams, 1997; Sattler, 2008; Schneider, Niklas, & Schmiedeler, 2014). Children who miss out on educational opportunities lose approximately 1.8 IQ points from their estimated adult IQ scores (Sattler, 2008). The Influence of Culture and Acculturation on Neurodevelopment Neurodevelopment proceeds in a relatively fixed trajectory for healthy typically developing children, which is in part determined by genetics and affected by ecological components previously discussed. The DSM–5 accounts for cultural variations in behavior throughout the text, and psychologists must consider children’s cultural background when making a decision about whether a neurodevelopmental delay or disorder is present. The diagnosis of neurodevelopmental disorders and determination of the associated functional deficits is typically facilitated via normreferenced comparisons. Inherent in this approach

is the expectation that a determination can be made for each area of children’s functioning that it is average, below average, or above average, with average functioning representing typical or healthy development. The use of psychometrically sound, normreferenced instruments allows for the generation of scaled and standard scores that result in a hypothetical comparison with a sample representative of the population. As discussed previously, normal variations in development present as a complication to this process. An additional intricacy is the potentially very meaningful effect that culture can have on the measurement of neurocognitive functioning when norm-referenced instruments are used. Without considering the effect of culture, it would be very easy to mistakenly determine that children are exhibiting a deficit in some area of functioning when in fact their performance on that measurement is typical for someone given their cultural background. The field of neuropsychology must continue to develop instruments that are psychometrically sound given the rapid changing demographics in the United States. Byrd, Arentoft, Scheiner, Westerveld, and Baron (2008) conducted a review of the literature on multicultural neuropsychological assessment in children and in part argued for the necessity for the establishment of cross-cultural psychometric integrity for measures used to evaluate diverse groups of children. This critical component of determining the validity and reliability of cognitive tests in groups that differ from the normative sample is an essential first step toward a comprehensive crosscultural examination of the utility of neuropsychological measures. (p. 220) A thorough discussion of the effects of cultural and linguistic diversity on assessment and intervention is beyond the scope of this chapter and interested readers are directed to Ortiz (2008), Linan-­ Thompson and Ortiz (2009), Camacho, Wong, and Llorente (2011), and Wong, Strickland, FletcherJanzen, Ardila, and Reynolds (2000). However, the importance of acculturation is critical to understanding the assessment of neurodevelopment and is discussed briefly next. 303

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Acculturation has been widely discussed in the assessment literature given the frequent finding that acculturation accounts for a sufficient proportion of the variance in some test results and should be considered when conducting neuropsychological assessments. For example, Boone, Victor, Wren, Razani, and Pontón (2007) evaluated the relationship between a group of 161 patients’ ethnicity and cognitive test scores. They found a significant inverse relationship between several verbal tests and acculturation variables including the Boston Naming Test (Kaplan, Goodglass, & Weintraub, 2001) and age at which conversational English was learned, the number of years of education obtained in the United States and the number of years lived in the United States. Similarly, they found a significant inverse relationship between Digit Span from a version of the Wechsler Intelligence Scales, age at which English was learned, and the number of years lived in the United States as well as an inverse correlation with the verbal fluency test and number of years in the United States. There have been multiple suggestions for working with diverse groups via neuropsychological and psychological assessment and some examples are listed here that should be considered when assessing children with known or suspected neurodevelopmental disorders. In 2009, the National Academy of Neuropsychology released a position paper (Judd et al., 2009) focused on improving the neuropsychological assessment of Hispanics. According to the United States Census Bureau, “the United States is projected to become more racially and ethnically diverse in the coming years” (Colby & Ortman, 2015, p. 8); by 2060, the United States will have no true majority group as people who identify as non-Hispanic Whites will make up less than 50% of the total United States population. Neuropsychologists, who typically rely on normative instruments, must be able to use culturally competent assessment techniques when working with patients from diverse backgrounds. Included in the Judd and colleagues (2009) paper were 20 goals and objectives for improving neuropsychological evaluations with Hispanic patients. Many of these are applicable to other groups as well and they include goals to obtain training and competence 304

in cross-cultural evaluation, possess the relevant general and neuropsychological knowledge of the patient’s language and culture, to assess the client’s language history, conduct the evaluation in the client’s preferred language, and to identify the client’s cultural context and acculturation process; readers are directed to Judd and colleagues (2009) for the entire list. Hsieh and Bean (2014) pointed out that it is important to understand that different levels of acculturation may be present in the same family; for example, an acculturation gap between Chinese American parents and adolescents in the same family may result in problems regarding sexual development and dating. Different levels of acculturation could certainly interfere with the process of working with families of children with neurodevelopmental concerns regarding help designing social interventions for children that rely on family support and investment. Ortiz (2008) also provided a number of useful suggestions regarding best practices in nondiscriminatory assessment. Finally, psychologists are encouraged to consider that acculturation scales can be useful for clinical and research purposes and a relatively recent list of published measures is available from Substance Abuse and Mental Health Services Administration (2014). General Thoughts on EvidenceBased Interventions for Children With Neurodevelopmental Disorders Examples of interventions for some different concerns are included in their relevant conditions, but some general thoughts about evidence-based interventions for children with neurodevelopmental disorders are provided next. Children with these disorders tend to need interventions for a wide array of problematic behaviors and concerns including academic, social, emotional, and behavioral issues. This may include teaching the adaptive skills necessary to increase independence, reduce maladaptive behaviors, reduce self-injurious behaviors, and improve safety skills. The American Psychological Association Presidential Task Force on EvidenceBased Practice (2006) defined evidence-based practice in psychology as, “the integration of the

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Understanding and Treating Children and Adolescents With Neurodevelopmental Disorders

best available research with clinical expertise in the context of patient characteristics, culture, and preferences” (p. 273). According to the task force, intervention encompasses a variety of direct services including assessment and diagnosis, prevention programs, treatment, and consultation; however, they state that the bulk of the research on evidence-based practice in psychology focuses on treatment. In general, the goal of interventions is to either adjust a developmental trajectory or to reduce the severity of clinical symptoms (Jeste, 2015). It is our opinion that a key component of this process when working with children with neurodevelopmental disorders is the comprehensive neuropsychological assessment, given the heterogeneity of most of conditions. Indeed, it is important to remember that every child with whom a practitioner works has a unique background, psychological profile, personality, and prognosis; therefore, “like any child with special needs, interventions should be based on the unique needs of each child” (Schwarte, 2008, p. 294). An area of intervention for neurodevelopmental genetic conditions is genetic counseling. According to Bhat (2015), “communication of the nature and implications of these disorders to the ‘at risk’ family is the basis of genetic counselling” (p. 217). Genetic counselors can help families understand the etiology of genetic conditions, as well as prognosis and available interventions; they can also help couples to assess the risk of genetic complications for future children (Bhat, 2015; CDC, 2015b). For example, genetic counselors can play an important role in helping parents fully understand risks, consider implications within the context of their own cultural beliefs and values, and make decisions regarding whether to try to conceive in the future, whether to participate in prenatal tests, etc. (Bhat, 2015). Although psychologists, at least now, do not actually conduct or interpret the results of genetic testing, it is important for psychologists who work with children with neurodevelopmental disorders to be familiar with the process as they may make recommendations and referrals for such. Being conversant with the criteria for testing will help psychologists make appropriate referrals as well as provide support for children and families. For example, McConkie-Rosell and colleagues (2007) reported

the recommendations from multidisciplinary focus groups on cascade testing and genetic counseling for Fragile X-associated disorders. Part of the recommendations were for which patient groups testing should be offered, including “patients presenting with a personal or family history of mental retardation, developmental disability, or autism” (p. 597). Psychologists are on the front lines of identifying and diagnosing these conditions, and familiarity with this information will guide recommendations. Conclusion There are conditions in which the healthy development of the central nervous system and associated organ systems is delayed or disrupted, resulting in neurocognitive deficits and functional problems for children. Children progress through relatively orderly stages of neurodevelopment in which one stage is hierarchically dependent on healthy and intact functioning at the previous stage. As such, disruption in a neurocognitive or neurofunctional ability at one stage may have a longitudinal ripple effect throughout children’s neurodevelopment. Assessment of children considers whether or not neurodevelopment is progressing in each functional domain relative to their same age peers. This includes assessment of children’s current abilities in language, attention, memory, executive functioning, visual–spatial processing, sensory and motor abilities, adaptive functioning, social–emotional functioning, and academic abilities. This information needs to be integrated with children’s psychiatric and psychosocial history. The importance of considering children’s culture is an essential part of this process. A neuropsychological assessment approach for differential diagnosis, determining neurofunctional abilities, and designing and implementing strengths-based evidence-based interventions adopts this approach and is recommended when working with children with known or suspected neurodevelopmental disorders.

References Accornero, V. H., Amado, A. J., Morrow, C. E., Xue, L., Anthony, J. C., & Bandstra, E. S. (2007). Impact of prenatal cocaine exposure on attention and response 305

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inhibition as assessed by continuous performance tests. Journal of Developmental and Behavioral Pediatrics, 28, 195–205. http://dx.doi.org/10.1097/ 01.DBP.0000268560.72580.f9

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Ack, M., Miller, I., & Weil, W. B., Jr. (1961). Intelligence of children with diabetes mellitus. Pediatrics, 28, 764–770. Adams, C., Lockton, E., Freed, J., Gaile, J., Earl, G., McBean, K., . . . Law, J. (2012). The Social Communication Intervention Project: A randomized controlled trial of the effectiveness of speech and language therapy for school-age children who have pragmatic and social communication problems with or without autism spectrum disorder. International Journal of Language and Communication Disorders, 47, 233–244. http://dx.doi.org/10.1111/j.14606984.2011.00146.x Allen, A. B., Jesse, M. T., & Forsyth, B. (2011). Pediatric HIV/AIDS. In A. S. Davis (Ed.), Handbook of pediatric neuropsychology (pp. 865–876). New York, NY: Springer. Allen, M. M. (2013). Intervention efficacy and intensity for children with speech sound disorder. Journal of Speech, Language, and Hearing Research, 56, 865–877. http://dx.doi.org/10.1044/1092-4388(2012/11-0076) American Psychiatric Association. (2000). Diagnostic and statistical manual of mental disorders (4th ed., text revision). Washington, DC: Author. American Psychiatric Association. (2013). Diagnostic and statistical manual of mental disorders (5th ed.). Washington, DC: Author. American Psychological Association Presidential Task Force on Evidence-Based Practice. (2006). Evidence-based practice in psychology. American Psychologist, 61, 271–285. http://dx.doi.org/10.1037/0003-066X.61.4.271 Anderson, P. J., & Doyle, L. W. (2004). Executive functioning in school-aged children who were born very preterm or with extremely low birth weight in the 1990s. Pediatrics, 114, 50–57. http://dx.doi.org/ 10.1542/peds.114.1.50 Andrade, S. E., Gurwitz, J. H., Davis, R. L., Chan, K. A., Finkelstein, J. A., Fortman, K., . . . Platt, R. (2004). Prescription drug use in pregnancy. American Journal of Obstetrics and Gynecology, 191, 398–407. http:// dx.doi.org/10.1016/j.ajog.2004.04.025 Andrews, J., Falkmer, M., & Girdler, S. (2015). Community participation interventions for children and adolescents with a neurodevelopmental intellectual disability: A systematic review. Disability and Rehabilitation, 37, 825–833. http://dx.doi.org/ 10.3109/09638288.2014.944625 Andrews, J., Leonard, H., Hammond, G. C., Girdler, S., Rajapaksa, R., Bathgate, K., & Downs, J. (2014). Community participation for girls and women living with Rett syndrome. Disability and Rehabilitation, 36, 306

894–899. http://dx.doi.org/10.3109/09638288.2013. 813083 Antshel, K. M. (2010). Neurocognition in mitochondrial disorders. In C. L. Armstrong & L. Morrow (Eds.), Handbook of medical neuropsychology: Applications of cognitive neuroscience (pp. 491–501). http:// dx.doi.org/10.1007/978-1-4419-1364-7_27 Armstrong, K. H., Hangauer, J., Agazzi, H., Nunez, A., & Gieron-Korthals, M. (2011). Individuals with intellectual and developmental disabilities. In A. S. Davis (Ed.), Handbook of pediatric neuropsychology (pp. 537–549). New York, NY: Springer. Axelrod, M. I., Tornehl, M., & Fontanini-Axelrod, A. (2016). Co-occurring autism and intellectual disability: A treatment for encopresis using a behavioral intervention plus laxative across settings. Clinical Practice in Pediatric Psychology, 4, 1–10. http://dx.doi.org/10.1037/cpp0000131 Bassan, H., Berkner, L., Stolovitch, C., & Kesler, A. (2008). Asymptomatic idiopathic intracranial hypertension in children. Acta Neurologica Scandinavica, 118, 251–255. http://dx.doi.org/ 10.1111/j.1600-0404.2008.01007.x Bayley, N. (1993). Bayley scales of infant development (2nd ed.). San Antonio, TX: The Psychological Corporation. Bayley, N. (2005). Bayley scales of infant and toddler development (3rd ed.). San Antonio, TX: Harcourt Assessment, Inc. Beckwith, A. M., & Burke, S. A. (2015). Identification of early developmental deficits in infants with prenatal heroin, methadone, and other opioid exposure. Clinical Pediatrics, 54, 328–335. http://dx.doi.org/ 10.1177/0009922814549545 Bennett, D. S., Hersh, J., Herres, J., & Foster, J. (2016). HIV-related stigma, shame, and avoidant coping: Risk factors for internalizing symptoms among youth living with HIV? Child Psychiatry and Human Development, 47, 657–664. http://dx.doi.org/10.1007/ s10578-015-0599-y Bergen, D., & Woodin, M. (2011). Neuropsychological development of newborns, infants, and toddlers (0 to 3 years old). In A. S. Davis (Ed.), Handbook of pediatric neuropsychology (pp. 15–30). New York, NY: Springer. Bestwick, J. P., Huttly, W. J., Morris, J. K., & Wald, N. J. (2014). Prevention of neural tube defects: A cross-sectional study of the uptake of folic acid supplementation in nearly half a million women. PLOS One, 9, e89354. http://dx.doi.org/10.1371/ journal.pone.0089354 Bhat, M. (2015). Social and cultural issues in genetic counselling. Journal of Biosciences, 40, 217–220. http://dx.doi.org/10.1007/s12038-015-9528-2

Understanding and Treating Children and Adolescents With Neurodevelopmental Disorders

Boivin, M. J., Kakooza, A. M., Warf, B. C., Davidson, L. L., & Grigorenko, E. L. (2015). Reducing neurodevelopmental disorders and disability through research and interventions. Nature, 527, S155–S160. http://dx.doi.org/10.1038/nature16029

Copyright American Psychological Association. Not for further distribution.

Bonnot, O., Cohen, D., Thuilleaux, D., Consoli, A., Cabal, S., & Tauber, M. (2016). Psychotropic treatments in Prader–Willi syndrome: A critical review of published literature. European Journal of Pediatrics, 175, 9–18. http://dx.doi.org/10.1007/s00431-015-2670-x Boone, K. B., Victor, T. L., Wen, J., Razani, J., & Pontón, M. (2007). The association between neuropsychological scores and ethnicity, language, and acculturation variables in a large patient population. Archives of Clinical Neuropsychology, 22, 355–365. http://dx.doi.org/10.1016/j.acn.2007.01.010 Briggs, R. D., Silver, E. J., Krug, L. M., Mason, Z. S., Schrag, R. D. A., Chinitz, S., & Racine, A. D. (2014). Healthy Steps as a moderator: The impact of maternal trauma on child social-emotional development. Clinical Practice in Pediatric Psychology, 2, 166–175. http://dx.doi.org/10.1037/cpp0000060 Brooks, B. A., Floyd, F., Robins, D. L., & Chan, W. Y. (2015). Extracurricular activities and the development of social skills in children with intellectual and specific learning disabilities. Journal of Intellectual Disability Research, 59, 678–687. http:// dx.doi.org/10.1111/jir.12171 Brown, F. L., Whittingham, K., Boyd, R. N., McKinlay, L., & Sofronoff, K. (2014). Improving child and parenting outcomes following paediatric acquired brain injury: A randomised controlled trial of Stepping Stones Triple P plus Acceptance and Commitment Therapy. Journal of Child Psychology and Psychiatry, 55, 1172–1183. http://dx.doi.org/ 10.1111/jcpp.12227 Buchberger, B., Huppertz, H., Krabbe, L., Lux, B., Mattivi, J. T., & Siafarikas, A. (2016). Symptoms of depression and anxiety in youth with type 1 diabetes: A systematic review and meta-analysis. Psychoneuroendocrinology, 70, 70–84. http:// dx.doi.org/10.1016/j.psyneuen.2016.04.019 Burd, L., Klug, M. G., Martsolf, J. T., & Kerbeshian, J. (2003). Fetal alcohol syndrome: Neuropsychiatric phenomics. Neurotoxicology and Teratology, 25, 697–705. http://dx.doi.org/10.1016/j.ntt.2003.07.014 Bush, S. S., Ruff, R. M., Tröster, A. I., Barth, J. T., Koffler, S. P., Pliskin, N. H., . . . Silver, C. H. (2005). Symptom validity assessment: Practice issues and medical necessity NAN policy & planning committee. Archives of Clinical Neuropsychology, 20, 419–426. http:// dx.doi.org/10.1016/j.acn.2005.02.002 Byrd, D., Arentoft, A., Scheiner, D., Westerveld, M., & Baron, I. S. (2008). State of multicultural neuropsychological assessment in children: Current

research issues. Neuropsychology Review, 18, 214–222. http://dx.doi.org/10.1007/s11065-008-9065-y Callejo, F. P., & Geer, L. A. (2012). A community-based approach to disseminate health information on the hazards of prenatal mercury exposure in Brooklyn, NY. Journal of Community Health, 37, 745–753. http://dx.doi.org/10.1007/s10900-012-9575-7 Camacho, M., Wong, T. M., & Llorente, A. M. (2011). Cultural considerations in pediatric neuropsychology. In A. S. Davis (Ed.), Handbook of pediatric neuropsychology (pp. 995–1004). New York, NY: Springer. Casper, R. C., Fleisher, B. E., Lee-Ancajas, J. C., Gilles, A., Gaylor, E., DeBattista, A., & Hoyme, H. E. (2003). Follow-up of children of depressed mothers exposed or not exposed to antidepressant drugs during pregnancy. Journal of Pediatrics, 142, 402–408. http:// dx.doi.org/10.1067/mpd.2003.139 Cassidy, S. B., Schwartz, S., Miller, J. L., & Driscoll, D. J. (2012). Prader–Willi syndrome. Genetics in Medicine, 14, 10–26. http://dx.doi.org/10.1038/ gim.0b013e31822bead0 Ceci, S. J., & Williams, W. M. (1997). Schooling, intelligence, and income. American Psychologist, 52, 1051–1058. http://dx.doi.org/10.1037/ 0003-066X.52.10.1051 Centers for Disease Control and Prevention. (2015a). Data and statistics. Fragile X Syndrome (FXS). Retrieved from http://www.cdc.gov/ncbddd/fxs/data.html Centers for Disease Control and Prevention. (2015b). Genetic counseling. Public Health Genomics. Retrieved from https://www.cdc.gov/genomics/ gtesting/genetic_counseling.htm Centers for Disease Control and Prevention. (2015c). HIV among pregnant women, infants, and children in the United States. HIV/AIDS. Retrieved from https://www. cdc.gov/hiv/group/gender/pregnantwomen/index.html Centers for Disease Control and Prevention. (2015d). What is gestational diabetes? Diabetes Home. Retrieved from http://www.cdc.gov/diabetes/basics/ gestational.html Centers for Disease Control and Prevention. (2016). Trends in current cigarette smoking among high school students and adults, United States, 1965–2014. Smoking and Tobacco Use. Retrieved from http://www.cdc.gov/tobacco/data_statistics/ tables/trends/cig_smoking Chaturvedi, S., Lipszyc, D. H., Licht, C., Craig, J. C., & Parekh, R. (2014). Pharmacological interventions for hypertension in children. Evidence-Based Child Health: A Cochrane Review Journal, 9, 498–580. http://dx.doi.org/10.1002/ebch.1974 Chi, P., & Li, X. (2013). Impact of parental HIV/AIDS on children’s psychological well-being: A systematic review 307

Davis, Hoover, and Mion

of global literature. AIDS and Behavior, 17, 2554–2574. http://dx.doi.org/10.1007/s10461-012-0290-2

prevention and intervention for FASD in the United State. Journal of Pediatric Neuropsychology, 3, 7–24.

Chiriboga, C. A., Kuhn, L., & Wasserman, G. A. (2014). Neurobehavioral and developmental trajectories associated with level of prenatal cocaine exposure. Journal of Neurology and Psychology, 2, 12.

Davis, A. S., Moss, L. E., Nogin, M. M., & Webb, N. E. (2015). Neuropsychology of child maltreatment and implications for school psychologists. Psychology in the Schools, 52, 77–91. http://dx.doi.org/10.1002/ pits.21806

Copyright American Psychological Association. Not for further distribution.

Cohen, S., Ter Stege, J. A., Geurtsen, G. J., Scherpbier, H. J., Kuijpers, T. W., Reiss, P., . . . Pajkrt, D. (2015). Poorer cognitive performance in perinatally HIVinfected children versus healthy socioeconomically matched controls. Clinical Infectious Diseases, 60, 1111–1119. Colby, S. L., & Ortman, J. M. (2015). Projections of the size and composition of the U. S. population: 2014 to 2060. Population estimates and projections. Retrieved from https://www.census.gov/content/dam/ Census/library/publications/2015/demo/p25-1143.pdf Conway, K., Mathews, K. D., Paramsothy, P., Oleszek, J., Trout, C., Zhang, Y., & Romitti, P. A. (2015). Neurobehavioral concerns among males with dystrophinopathy using population-based surveillance data from the Muscular Dystroophy Surveillance, Tracking, and Research Network. Journal of Developmental and Behavioral Pediatrics, 36, 455–463. http://dx.doi.org/10.1097/ DBP.0000000000000177 Cox, D. J., Gonder-Frederick, L., Polonsky, W., Schlundt, D., Kovatchev, B., & Clarke, W. (2001). Blood glucose awareness training (BGAT-2): Long-term benefits. Diabetes Care, 24, 637–642. http:// dx.doi.org/10.2337/diacare.24.4.637 Dabelea, D., Mayer-Davis, E. J., Saydah, S., Imperatore, G., Linder, B., Divers, J., . . . Hamman, R. F., & the SEARCH for Diabetes in Youth Study. (2014). Prevalence of type 1 and type 2 diabetes among children and adolescents from 2001 to 2009. Journal of the American Medical Association, 311, 1778–1786. http://dx.doi.org/10.1001/jama.2014.3201 Darras, B. T., Miller, D. T., & Urion, D. K. (2000). Dystrophinopathies. GeneReviews. Retrieved from http://www.ncbi.nlm.nih.gov/books/NBK1119 Davenport, M. H., Schaefer, T. L., Friedmann, K. J., Fitzpatrick, S. E., & Erickson, C. A. (2016). Pharmacotherapy for Fragile X syndrome: Progress to date. Drugs, 76, 431–445. http://dx.doi.org/ 10.1007/s40265-016-0542-y Davis, A. S. (Ed.). (2010). Handbook of pediatric neuropsychology. New York, NY: Springer. Davis, A. S., & Escobar, L. F. (2010). Down syndrome. In A. S. Davis (Ed.), Handbook of pediatric neuropsychology (pp. 785–793). New York, NY: Springer. Davis, A. S., Hoover, K. L., Moore, B. M., & Petrenko, C. L. M. (2017). Neuropsychological aspects of 308

Davis, A. S., & Phelps, L. (2008). Psychoeducational implications of neurodevelopmental genetic disorders. School Psychology Quarterly, 23, 243–245. http://dx.doi.org/10.1037/1045-3830.23.2.243 Dean, H. J., & Sellers, E. A. C. (2015). Children have type 2 diabetes too: An historical perspective. Biochemistry and Cell Biology, 93, 425–429. http:// dx.doi.org/10.1139/bcb-2014-0152 Dean, R. S., & Woodcock, R. W. (1994). Dean-Woodcock neuropsychological assessment system: Sensory and motor assessment battery. Itasca, IL: Riverside. De Bellis, M. D., Hooper, S. R., Spratt, E. G., & Woolley, D. P. (2009). Neuropsychological findings in childhood neglect and their relationships to pediatric PTSD. Journal of the International Neuropsychological Society, 15, 868–878. http://dx.doi.org/10.1017/ S1355617709990464 DeRight, J., & Carone, D. A. (2015). Assessment of effort in children: A systematic review. Child Neuropsychology, 21, 1–24. http://dx.doi.org/ 10.1080/09297049.2013.864383 Dessardo, N. S., Dessardo, S., Sasso, A., Saruni´c, A. V., & Dezulovi´c, M. S. (2010). Pediatric idiopathic intracranial hypertension: Clinical and demographic features. Collegium Antropologicum, 34(Suppl. 2), 217–221. Diament, M. (2014). Bits and pieces: Autism rates spike again, CDC says. Disability Scoop. Retrieved from https://www.disabilityscoop.com/2014/03/27/autismrates-cdc-says/19237 Diav-Citrin, O. (2011). Prenatal exposures associated with neurodevelopmental delay and disabilities. Developmental Disabilities Research Reviews, 17, 71–84. http://dx.doi.org/10.1002/ddrr.1102 Downs, J., Stahlhut, M., Wong, K., Syhler, B., Bisgaard, A. M., Jacoby, P., & Leonard, H. (2016). Validating the Rett Syndrome Gross Motor Scale. PLOS One, 11, e0147555. http://dx.doi.org/10.1371/journal.pone.0147555 Driscoll, K. A., Raymond, J., Naranjo, D., & Patton, S. R. (2016). Fear of hypoglycemia in children and adolescents and their parents with type 1 diabetes. Current Diabetes Reports, 16, 77. http://dx.doi.org/ 10.1007/s11892-016-0762-2 Earnshaw, V. A., Bogart, L. M., Dovidio, J. F., & Williams, D. R. (2013). Stigma and racial/ethnic HIV disparities: Moving toward resilience. American Psychologist, 68, 225–236. http://dx.doi.org/10.1037/a0032705

Understanding and Treating Children and Adolescents With Neurodevelopmental Disorders

Edwards, J., Berube, M., Erlandson, K., Haug, S., Johnstone, H., Meagher, M., . . . Zwicker, J. G. (2011). Developmental coordination disorder in school-aged children born very preterm and/or at very low birth weight: A systematic review. Journal of Developmental and Behavioral Pediatrics, 32, 678–687. http://dx.doi.org/10.1097/ DBP.0b013e31822a396a Elliott, C. D. (2007). Differential ability scales (2nd ed.). San Antonio, TX: Pearson.

Copyright American Psychological Association. Not for further distribution.

Feifer, S. G., & Della Toffalo, D. A. (2007). Integrating RTI with cognitive neuropsychology: A scientific approach to reading. Lutz, FL: Psychological Assessment Resources. Ferguson, S. C., Blane, A., Wardlaw, J., Frier, B. M., Perros, P., McCrimmon, R. J., & Deary, I. J. (2005). Influence of an early-onset age of type 1 diabetes on cerebral structure and cognitive function. Diabetes Care, 28, 1431–1437. http://dx.doi.org/10.2337/ diacare.28.6.1431 Fernandes, M., Yang, X., Li, J. Y., & Cheikh Ismail, L. (2015). Smoking during pregnancy and vision difficulties in children: A systematic review. Acta Ophthalmologica, 93, 213–223. http://dx.doi.org/ 10.1111/aos.12627 Flanagan, D. P., & Alfonso, V. C. (2011). Essentials of specific learning disability identification. Hoboken, NJ: Wiley. Food and Drug Administration. (2006). Requirements on content and format of labeling for human prescription drug and biological products. Final rule. Federal Register, 71, 3921–3997. Food and Drug Administration. (2014). Pregnancy and lactation labeling (drugs) final rule. Retrieved from https://www.fda.gov/drugs/ developmentapprovalprocess/developmentresources/ labeling/ucm093307.htm Frasier-Robinson, M. (2015). Autism spectrum disorder: A guide to the latest resources. Reference and User Services Quarterly, 22, 113–125. Gardella, B., Iacobone, A. D., Bogliolo, S., Musacchi, V., Orcesi, S., Tzialla, C., & Spinillo, A. (2015). Obstetric risk factors and time trends of neurodevelopmental outcome at 2 years in verylow-birthweight infants: A single institution study. Developmental Medicine and Child Neurology, 57, 1035–1041. http://dx.doi.org/10.1111/dmcn.12859 George, M. G., Tong, X., Wigington, C., Gillespie, C., & Hong, Y. (2014). Hypertension screening in children and adolescents—National Ambulatory Medical Care Survey, National Hospital Ambulatory Medical Care Survey, and Medical Expenditure Panel Survey, United States, 2007–2010. Morbidity and Mortality Weekly Report, 63, 47–53.

Geyer, K., Meller, K., Kulpan, C., & Mowery, B. D. (2013). Traumatic brain injury in children: Acute care management. Pediatric Nursing, 39, 283–289. Ginsburg, H. J., Mendez, R. V., & Haskard-Zolnierek, K. B. (2016). Immediate recognition for folic acid supplementation information about preventing neural tube birth defects. North American Journal of Psychology, 18, 211–228. Gkioka, E., Korou, L. M., Daskalopoulou, A., Misitzi, A., Batsidis, E., Bakoyiannis, I., & Pergialiotis, V. (2016). Prenatal cocaine exposure and its impact on cognitive functions of offspring: A pathophysiological insight. Reviews in the Neurosciences, 27, 523–534. http://dx.doi.org/ 10.1515/revneuro-2015-0064 Golden, C. J., Hammeke, T. A., & Purisch, A. D. (1980). The Luria–Nebraska Neuropsychological Battery: Manual. Los Angeles, CA: Western Psychological Services. Golding, J., Gregory, S., Iles-Caven, Y., Hibbeln, J., Emond, A., & Taylor, C. M. (2016). Associations between prenatal mercury exposure and early child development in the ALSPAC study. Neurotoxicology, 53, 215–222. http://dx.doi.org/10.1016/j.neuro. 2016.02.006 Gottlieb, A. S. (2013). A teachable moment for providers and patients alike: Contraceptive counseling when prescribing medications with teratogenic potential. Journal of Women’s Health, 22, 795–796. http:// dx.doi.org/10.1089/jwh.2013.4557 Gribble, N., Parsons, R., Donlau, M., & Falkmer, T. (2013). Predictors of time to complete toileting for children with spina bifida. Australian Occupational Therapy Journal, 60, 343–349. Gupte, A. S., Gupta, D., Ravichandran, S., Ma, M. M., & Chouthai, N. S. (2016). Effect of early caffeine on neurodevelopmental outcome of very low-birth weight newborns. Journal of Maternal-Fetal and Neonatal Medicine, 29, 1233–1237. http://dx.doi.org/ 10.3109/14767058.2015.1041493 Harrison, P., & Oakland, T. (2015). Adaptive behavior assessment system (3rd ed.). Bloomington, MN: Pearson. Herrmann, M., King, K., & Weitzman, M. (2008). Prenatal tobacco smoke and postnatal secondhand smoke exposure and child neurodevelopment. Current Opinion in Pediatrics, 20, 184–190. http:// dx.doi.org/10.1097/MOP.0b013e3282f56165 Hertza, J., & Estes, B. (2011). Developmental dyspraxia and developmental coordination disorder. In A. S. Davis (Ed.), Handbook of pediatric neuropsychology (pp. 593–602). New York, NY: Springer. Hosono, S., Ohno, T., Kimoto, H., Shimizu, M., Takahashi, S., & Harada, K. (2009). Developmental 309

Davis, Hoover, and Mion

outcomes in persistent pulmonary hypertension treated with nitric oxide therapy. Pediatrics International, 51, 79–83. http://dx.doi.org/10.1111/ j.1442-200X.2008.02664.x

Copyright American Psychological Association. Not for further distribution.

Hsiao, C. C., Tsai, M. L., Chen, C. C., & Lin, H. C. (2014). Early optimal nutrition improves neurodevelopmental outcomes for very preterm infants. Nutrition Reviews, 72, 532–540. http:// dx.doi.org/10.1111/nure.12110 Hsieh, A. L., & Bean, R. A. (2014). Understanding familial/cultural factors in adolescent depression: A culturally competent treatment for working with Chinese American families. American Journal of Family Therapy, 42, 398–412. http://dx.doi.org/ 10.1080/01926187.2014.884414 Ireton, H., & Glascoe, F. P. (1995). Assessing children’s development using parents’ reports. Clinical Pediatrics, 34, 248–255. http://dx.doi.org/ 10.1177/000992289503400504 Ireton, H., & Thwing, E. (1976). Appraising the development of a preschool child by means of a standardized report prepared by the mother. The Minnesota Child Development Inventory. Clinical Pediatrics, 15, 875–882. http://dx.doi.org/ 10.1177/000992287601501004 Jacobs, R., Anderson, V., & Harvey, A. S. (2001). Neuropsychological profile of a 9-year-old child with subcortical band heterotopia or “double cortex”. Developmental Medicine and Child Neurology, 43, 628–633. http://dx.doi.org/10.1017/ S0012162201001141 Jamkhande, P. G., Chintawar, K. D., & Chandak, P. G. (2014). Teratogenicity: A mechanism based short review on common teratogenic agents. Asian Pacific Journal of Tropical Disease, 4, 421–432. http:// dx.doi.org/10.1016/S2222-1808(14)60600-9 Jensen, K. M., & Davis, M. M. (2013). Health care in adults with Down syndrome: A longitudinal cohort study. Journal of Intellectual Disability Research, 57, 947–958. http://dx.doi.org/10.1111/j.13652788.2012.01589.x Jeste, S. S. (2015). Neurodevelopmental behavioral and cognitive disorders. CONTINUUM: Lifelong Learning in Neurology, 21, 690–714. Jones, K., & Streissguth, A. P. (2010). Fetal alcohol syndrome and fetal alcohol spectrum disorders: A brief history. Journal of Psychiatry and Law, 38, 373–382. http://dx.doi.org/10.1177/ 009318531003800402 Judd, T., Capetillo, D., Carrión-Baralt, J., Mármol, L. M., Miguel-Montes, L. S., Navarrete, M. G., . . . Valdés, J., & the NAN Policy and Planning Committee. (2009). Professional considerations for improving the neuropsychological evaluation of Hispanics: A National Academy of Neuropsychology education 310

paper. Archives of Clinical Neuropsychology, 24, 127–135. http://dx.doi.org/10.1093/arclin/acp016 Kaplan, E., Goodglass, H., & Weintraub, S. (2001). Boston naming test (2nd ed.). Philadelphia, PA: Lippincott Williams & Wilkins. Katz, I. T., Ryu, A. E., Onuegbu, A. G., Psaros, C., Weiser, S. D., Bangsberg, D. R., & Tsai, A. C. (2013). Impact of HIV-related stigma on treatment adherence: Systematic review and meta-synthesis. Journal of the International AIDS Society, 16(Suppl. 2), 18640. http://dx.doi.org/10.7448/IAS.16.3.18640 Kaufman, A. S., & Kaufman, N. L. (2004). Kaufman assessment battery for children (2nd ed.). Circle Pines, MN: American Guidance Service. Kiblawi, Z. N., Smith, L. M., Diaz, S. D., LaGasse, L. L., Derauf, C., Newman, E., . . . Lester, B. (2014). Prenatal methamphetamine exposure and neonatal and infant neurobehavioral outcome: Results from the IDEAL study. Substance Abuse, 35, 68–73. http:// dx.doi.org/10.1080/08897077.2013.814614 Kindsvatter, A., & Geroski, A. (2014). The impact of early life stress on the neurodevelopment of the stress response system. Journal of Counseling and Development, 92, 472–480. http://dx.doi.org/10.1002/ j.1556-6676.2014.00173.x Kirkwood, M. W. (2015). Pediatric validity assessment. NeuroRehabilitation, 36, 439–450. http://dx.doi.org/ 10.3233/NRE-151232 Knickmeyer, R. C., Meltzer-Brody, S., Woolson, S., Hamer, R. M., Smith, J. K., Lury, K., & Gilmore, J. H. (2014). Rate of Chiari I malformation in children of mothers with depression with and without prenatal SSRI exposure. Neuropsychopharmacology, 39, 2611–2621. http://dx.doi.org/10.1038/npp.2014.114 Königs, M., Heij, H. A., van der Sluijs, J. A., Vermeulen, R. J., Goslings, J. C., Luitse, J. S. K., . . . Oosterlaan, J. (2015). Pediatric traumatic brain injury and attention deficit. Pediatrics, 136, 534–541. http:// dx.doi.org/10.1542/peds.2015-0437 Koren, G. (2013). Systematic review of the effects of maternal hypertension in pregnancy and antihypertensive therapies on child neurocognitive development. Reproductive Toxicology, 39, 1–5. http://dx.doi.org/10.1016/j.reprotox.2013.03.006 Korkman, M., Kirk, U., & Kemp, S. L. (2007). NEPSY (2nd ed.). San Antonio, TX: Psychological Corporation. Kundert, D. K. (2008). Prader–Willi syndrome. School Psychology Quarterly, 23, 246–257. http://dx.doi.org/ 10.1037/1045-3830.23.2.246 Lake, N. J., Bird, M. J., Isohanni, P., & Paetau, A. (2015). Leigh syndrome: Neuropathology and pathogenesis. Journal of Neuropathology and Experimental Neurology, 74, 482–492. http://dx.doi.org/10.1097/ NEN.0000000000000195

Understanding and Treating Children and Adolescents With Neurodevelopmental Disorders

Lane, C., Milne, E., & Freeth, M. (2016). Cognition and behaviour in Sotos syndrome: A systematic review. PLOS ONE, 11, e0149189. http://dx.doi.org/10.1371/ journal.pone.0149189

Copyright American Psychological Association. Not for further distribution.

Lau, Y. C., Hinkley, L. B., Bukshpun, P., Strominger, Z. A., Wakahiro, M. L., Baron-Cohen, S., . . . Marco, E. J. (2013). Autism traits in individuals with agenesis of the corpus callosum. Journal of Autism and Developmental Disorders, 43, 1106–1118. http:// dx.doi.org/10.1007/s10803-012-1653-2 Laurier, V., Lapeyrade, A., Copet, P., Demeer, G., Silvie, M., Bieth, E., . . . Molinas, C. (2015). Medical, psychological, and social features in a large cohort of adults with Prader–Willi syndrome: Experience from a dedicated centre in France. Journal of Intellectual Disability Research, 59, 411–421. http://dx.doi.org/ 10.1111/jir.12140 Lazarus, J. R., Rutstein, R. M., & Lowenthal, E. D. (2015). Treatment initiation factors and cognitive outcome in youth with perinatally acquired HIV infection. HIV Medicine, 16, 355–361. http:// dx.doi.org/10.1111/hiv.12220 Lee, I. H., Rojewski, J. W., Gregg, N., & Jeong, S. (2015). Postsecondary education persistence of adolescents with specific learning disabilities or emotional/ behavioral disorders. Journal of Special Education, 49, 77–88. http://dx.doi.org/10.1177/0022466914524826 Leibson, T., Neuman, G., Chudley, A. E., & Koren, G. (2014). The differential diagnosis of fetal alcohol spectrum disorder. Journal of Population Therapeutics and Clinical Pharmacology, 21, e1–e30. Lerner, J., & Johns, B. (2014). Learning disabilities and related disabilities: Strategies for success (13th ed.). Belmont, CA: Wadsworth Cengage Learning. Leventer, R. J., Cardoso, C., Ledbetter, D. H., & Dobyns, W. B. (2001). LIS1 missense mutations cause milder lissencephaly phenotypes including a child with normal IQ. Neurology, 57, 416–422. http:// dx.doi.org/10.1212/WNL.57.3.416 Liew, Z., Ritz, B., Rebordosa, C., Lee, P. C., & Olsen, J. (2014). Acetaminophen use during pregnancy, behavioral problems, and hyperkinetic disorders. JAMA Pediatrics, 168, 313–320. http://dx.doi.org/ 10.1001/jamapediatrics.2013.4914

and continuous with conventional glucose selfmonitoring. Diabetes Care, 37, 2114–2122. http:// dx.doi.org/10.2337/dc14-0030 Lloyd, J., Wilson, M. L., Tenovuo, O., & Saarijärvi, S. (2015). Outcomes from mild and moderate traumatic brain injuries among children and adolescents: A systematic review of studies from 2008–2013. Brain Injury, 29, 539–549. http://dx.doi.org/10.3109/ 02699052.2014.1002003 Lohan, A., Morawska, A., & Mitchell, A. (2015). A systematic review of parenting interventions for parents of children with type 1 diabetes. Child: Care, Health and Development, 41, 803–817. http:// dx.doi.org/10.1111/cch.12278 Loveday, C., & Edginton, T. (2011). Spina bifida and hydrocephalus. In A. S. Davis (Ed.), Handbook of pediatric neuropsychology (pp. 769–783). New York, NY: Springer. Maehler, C., & Schuchardt, K. (2016). Working memory in children with specific learning disorders and/or attention deficits. Learning and Individual Differences, 49, 341–347. http://dx.doi.org/10.1016/j.lindif. 2016.05.007 Majer, M., Nater, U. M., Lin, J. M., Capuron, L., & Reeves, W. C. (2010). Association of childhood trauma with cognitive function in healthy adults: A pilot study. BMC Neurology, 10, 61. http://dx.doi.org/ 10.1186/1471-2377-10-61 Mann, J. R., Pan, C., Rao, G. A., McDermott, S., & Hardin, J. W. (2013). Children born to diabetic mothers may be more likely to have intellectual disability. Maternal and Child Health Journal, 17, 928–932. http://dx.doi.org/10.1007/s10995-0121072-1 Maranda, L., Lau, M., Stewart, S. M., & Gupta, O. T. (2015). A novel behavioral intervention in adolescents with type 1 diabetes mellitus improves glycemic control: Preliminary results from a pilot randomized control trial. Diabetes Educator, 41, 224–230. http:// dx.doi.org/10.1177/0145721714567235 Marin, S. E., & Saneto, R. P. (2016). Neuropsychiatric features in primary mitochondrial disease. Neurologic Clinics, 34, 247–294. http://dx.doi.org/10.1016/ j.ncl.2015.08.011

Linan-Thompson, S., & Ortiz, A. A. (2009). Response to intervention and English-language learners: Instructional and assessment considerations. Seminars in Speech and Language, 30, 105–120. http:// dx.doi.org/10.1055/s-0029-1215718

Mattson, S. N., & Riley, E. P. (1998). A review of the neurobehavioral deficits in children with fetal alcohol syndrome or prenatal exposure to alcohol. Alcoholism: Clinical and Experimental Research, 22, 279–294. http://dx.doi.org/10.1111/j.1530-0277.1998.tb03651.x

Little, S. A., Leelarathna, L., Walkinshaw, E., Tan, H. K., Chapple, O., Lubina-Solomon, A., . . . Shaw, J. A. (2014). Recovery of hypoglycemia awareness in long-standing type 1 diabetes: A multicenter 2 × 2 factorial randomized controlled trial comparing insulin pump with multiple daily injections

Mattson, S. N., Roesch, S. C., Fagerlund, A., Autti-Rämö, I., Jones, K. L., May, P. A., . . . Riley, E. P. (2010). Toward a neurobehavioral profile of fetal alcohol spectrum disorders. Alcoholism: Clinical and Experimental Research, 34, 1640–1650. http:// dx.doi.org/10.1111/j.1530-0277.2010.01250.x 311

Davis, Hoover, and Mion

McConkie-Rosell, A., Abrams, L., Finucane, B., Cronister, A., Gane, L. W., Coffey, S. M., . . . Hagerman, R. J. (2007). Recommendations from multi-disciplinary focus groups on cascade testing and genetic counseling for fragile X-associated disorders. Journal of Genetic Counseling, 16, 593–606. http://dx.doi.org/ 10.1007/s10897-007-9099-y

Copyright American Psychological Association. Not for further distribution.

McDuffie, A., Thurman, A. J., Hagerman, R. J., & Abbeduto, L. (2015). Symptoms of autism in males with fragile X syndrome: A comparison to nonsyndromic ASD using current ADI-R scores. Journal of Autism and Developmental Disorders, 45, 1925–1937. http://dx.doi.org/10.1007/s10803013-2013-6 McGee, C. L., Bjorkquist, O. A., Riley, E. P., & Mattson, S. N. (2009). Impaired language performance in young children with heavy prenatal alcohol exposure. Neurotoxicology and Teratology, 31, 71–75. http:// dx.doi.org/10.1016/j.ntt.2008.09.004 McGuire, J. F., Piacentini, J., Scahill, L., Woods, D. W., Villarreal, R., Wilhelm, S., . . . Peterson, A. L. (2015). Bothersome tics in patients with chronic tic disorders: Characteristics and individualized treatment response to behavior therapy. Behaviour Research and Therapy, 70, 56–63. http://dx.doi.org/ 10.1016/j.brat.2015.05.006 Meeks Corn, H. J., & Bishop, D. L. (2011). Intrauterine development of the central nervous system. In A. S. Davis (Ed.), Handbook of pediatric neuropsychology (pp. 1–14). New York, NY: Springer. Miller, J. L., Lynn, C. H., Shuster, J., & Driscoll, D. J. (2013). A reduced-energy intake, well-balanced diet improves weight control in children with Prader–Willi syndrome. Journal of Human Nutrition and Dietetics, 26, 2–9. http://dx.doi.org/10.1111/ j.1365-277X.2012.01275.x Motamedi, G. K., & Meador, K. J. (2006). Antiepileptic drugs and neurodevelopment. Current Neurology and Neuroscience Reports, 6, 341–346. http:// dx.doi.org/10.1007/s11910-006-0028-5 Munir, K. M. (2016). The co-occurrence of mental disorders in children and adolescents with intellectual disability/intellectual developmental disorder. Current Opinion in Psychiatry, 29, 95–102. http://dx.doi.org/10.1097/YCO.0000000000000236 Naglieri, J. A., Das, J. P., & Goldstein, S. (2014). Cognitive assessment system (2nd ed.). Austin, TX: Pro-Ed. National Institutes of Health. (2014). NIH-sponsored study identifies superior drug regimen for preventing mother-to-child HIV transmission. Retrieved from https://www.nih.gov/news-events/news-releases/nihsponsored-study-identifies-superior-drug-regimenpreventing-mother-child-hiv-transmission Noble, K. G., Houston, S. M., Brito, N. H., Bartsch, H., Kan, E., Kuperman, J. M., . . . Sowell, E. R. (2015). 312

Family income, parental education and brain structure in children and adolescents. Nature Neuroscience, 18, 773–778. http://dx.doi.org/10.1038/nn.3983 Norbury, C. F. (2014). Practitioner review: Social (pragmatic) communication disorder conceptualization, evidence and clinical implications. Journal of Child Psychology and Psychiatry, 55, 204–216. http://dx.doi.org/10.1111/jcpp.12154 Nordstrøm, M., Paus, B., Andersen, L. F., & Kolset, S. O. (2015). Dietary aspects related to health and obesity in Williams syndrome, Down syndrome, and Prader–Willi syndrome. Food and Nutrition Research, 59, 25487. http://dx.doi.org/10.3402/fnr.v59.25487 Northam, E. A., Rankins, D., Lin, A., Wellard, R. M., Pell, G. S., Finch, S. J., . . . Cameron, F. J. (2009). Central nervous system function in youth with type 1 diabetes 12 years after disease onset. Diabetes Care, 32, 445–450. http://dx.doi.org/10.2337/dc08-1657 Olsson, G. M., Hulting, A. L., & Montgomery, S. M. (2008). Cognitive function in children and subsequent type 2 diabetes. Diabetes Care, 31, 514–516. http://dx.doi.org/10.2337/dc07-1399 Olusanya, B. O., & Solanke, O. A. (2012). Perinatal outcomes associated with maternal hypertensive disorders of pregnancy in a developing country. Hypertension in Pregnancy, 31, 120–130. http:// dx.doi.org/10.3109/10641955.2010.525280 Ornoy, A., Ratzon, N., Greenbaum, C., Wolf, A., & Dulitzky, M. (2001). School-age children born to diabetic mothers and to mothers with gestational diabetes exhibit a high rate of inattention and fine and gross motor impairment. Journal of Pediatric Endocrinology and Metabolism, 14(Suppl.), 681–689. http://dx.doi.org/10.1515/JPEM.2001.14.S1.681 Ortiz, S. O. (2008). Best practices in nondiscriminatory assessment. In A. Thomas & J. Grimes (Eds.), Best practices in school psychology V (pp. 661–679). Bethesda, MD: National Association of School Psychologists. Panel on Antiretroviral Therapy and Medical Management of HIV-Infected Children. (2016). Guidelines for the use of antiretroviral agents in pediatric HIV infection. Retrieved from https:// aidsinfo.nih.gov/contentfiles/lvguidelines/ pediatricguidelines.pdf Pansier, B., & Schulz, P. J. (2015). School-based diabetes interventions and their outcomes: A systematic literature review. Journal of Public Health Research, 4, 467–473. http://dx.doi.org/10.4081/jphr.2015.467 Pastore, V., Colombo, K., Liscio, M., Galbiati, S., Adduci, A., Villa, F., & Strazzer, S. (2011). Efficacy of cognitive–behavioural therapy for children and adolescents with traumatic brain injury. Disability and Rehabilitation, 33, 675–683. http://dx.doi.org/ 10.3109/09638288.2010.506239

Understanding and Treating Children and Adolescents With Neurodevelopmental Disorders

Peters, K., Rhoades, S., Ezzell, D. A., Holland, J., & Weatherspoon, D. (2013). Environmental mercury exposure during pregnancy and lactation: Practical advice to minimize risks. International Journal of Childbirth Education, 28, 19–26. Phelps, L., & Smerbeck, A. (2011). Pediatric tic disorders. In A. S. Davis (Ed.), Handbook of pediatric neuropsychology (pp. 603–611). New York, NY: Springer.

Copyright American Psychological Association. Not for further distribution.

Pitman, M. M. (2014). Mental disorders, brain disorders, neurodevelopmental disorders: Challenges for the philosophy of psychopathology after DSM–5. South African Journal of Philosophy, 33, 131–144. http:// dx.doi.org/10.1080/02580136.2014.912472 Pueyo, V., Güerri, N., Oros, D., Valle, S., Tuquet, H., González, I., . . . Pablo, L. E. (2011). Effects of smoking during pregnancy on the optic nerve neurodevelopment. Early Human Development, 87, 331–334. http://dx.doi.org/10.1016/ j.earlhumdev.2011.01.042 Rathvon, N. (2008). Effective school interventions: Evidence-based strategies for improving student outcomes (2nd ed.). New York, NY: Guilford Press. Ratzon, N., Greenbaum, C., Dulitzky, M., & Ornoy, A. (2000). Comparison of the motor development of school-age children born to mothers with and without diabetes mellitus. Physical and Occupational Therapy in Pediatrics, 20, 43–57. http://dx.doi.org/ 10.1080/J006v20n01_04 Reinehr, T., Kleber, M., & Toschke, A. M. (2009). Lifestyle intervention in obese children is associated with a decrease of the metabolic syndrome prevalence. Atherosclerosis, 207, 174–180. http:// dx.doi.org/10.1016/j.atherosclerosis.2009.03.041 Reitan, R. M., & Wolfson, D. (1993). The Halstead–Reitan neuropsychological test battery: Theory and clinical interpretation (2nd ed.). Tucson, AZ: Neuropsychology Press. Reynolds, C. R., & Kamphaus, R. W. (2016). Reynolds intellectual assessment scales (2nd ed.). Lutz, FL: Psychological Assessment Resources. Rimmer, J. H., Yamaki, K., Lowry, B. M., Wang, E., & Vogel, L. C. (2010). Obesity and obesityrelated secondary conditions in adolescents with intellectual/developmental disabilities. Journal of Intellectual Disability Research, 54, 787–794. http:// dx.doi.org/10.1111/j.1365-2788.2010.01305.x Rodríguez-Barranco, M., Lacasaña, M., Aguilar-Garduño, C., Alguacil, J., Gil, F., González-Alzaga, B., & RojasGarcía, A. (2013). Association of arsenic, cadmium and manganese exposure with neurodevelopment and behavioural disorders in children: A systematic review and meta-analysis. Science of the Total Environment, 454–455, 562–577. http://dx.doi.org/ 10.1016/j.scitotenv.2013.03.047

Roid, G. H. (2003). Stanford-Binet intelligence scale (5th ed.). Itasca, IL: Riverside. Roid, G. H., Miller, L. J., Pomplun, M., & Koch, C. (2013). Leiter international performance scale (3rd ed.). Torrance, CA: Western Psychological Services. Ross, L. E., Grigoriadis, S., Mamisashvili, L., Vonderporten, E. H., Roerecke, M., Rehm, J., . . . Cheung, A. (2013). Selected pregnancy and delivery outcomes after exposure to antidepressant medication: A systematic review and meta-analysis. JAMA Psychiatry, 70, 436–443. http://dx.doi.org/ 10.1001/jamapsychiatry.2013.684 Sattler, J. M. (2008). Assessment of children: Cognitive foundations. San Diego, CA: Jerome M. Sattler Publisher, Inc. Schneider, W., Niklas, F., & Schmiedeler, S. (2014). Intellectual development from early childhood to early adulthood: The impact of early IQ differences on stability and change over time. Learning and Individual Differences, 32, 156–162. http://dx.doi.org/ 10.1016/j.lindif.2014.02.001 Schrank, F. A., McGrew, K. S., & Mather, N. (2014). Woodcock-Johnson tests of cognitive abilities (4th ed.). Rolling Meadows, IL: Riverside. Schwarte, A. R. (2008). Fragile X syndrome. School Psychology Quarterly, 23, 290–300. Semrud-Clikeman, M., & Bledsoe, J. (2011). Traumatic brain injury in children and adolescents. In A. S. Davis (Ed.), Handbook of pediatric neuropsychology (pp. 963–978). New York, NY: Springer. Shahin, I., & Einarson, A. (2011). Knowledge transfer and translation: Examining how teratogen information is disseminated. Birth Defects Research, 91, 956–961. http://dx.doi.org/10.1002/bdra.22851 Shapiro, E. S. (2011). Academic skills problems: Direct assessment and intervention (4th ed.). New York, NY: Guilford Press. Shaywitz, S. (2003). Overcoming dyslexia: A new and complete science-based program for reading problems at any level. New York, NY: Knopf. Siffredi, V., Anderson, V., Leventer, R. J., & SpencerSmith, M. M. (2013). Neuropsychological profile of agenesis of the corpus callosum: A systematic review. Developmental Neuropsychology, 38, 36–57. http:// dx.doi.org/10.1080/87565641.2012.721421 Slick, D. J., Tan, J. E., Sherman, E. M. S., & Strauss, E. (2011). Malingering and relating conditions in pediatric populations. In A. S. Davis (Ed.), Handbook of pediatric neuropsychology (pp. 457–470). New York, NY: Springer. Smith, L. M., LaGasse, L. L., Derauf, C., Newman, E., Shah, R., Haning, W., . . . Della Grotta, S. (2011). Motor and cognitive outcomes through three 313

Davis, Hoover, and Mion

years of age in children exposed to prenatal methamphetamine. Neurotoxicology and Teratology, 33, 176–184. http://dx.doi.org/10.1016/ j.ntt.2010.10.004 Snow, W. M., Anderson, J. E., & Jakobson, L. S. (2013). Neuropsychological and neurobehavioral functioning in Duchenne muscular dystrophy: A review. Neuroscience and Biobehavioral Reviews, 37, 743–752. http://dx.doi.org/10.1016/j.neubiorev.2013.03.016

Copyright American Psychological Association. Not for further distribution.

Sokol, R. J., Delaney-Black, V., & Nordstrom, B. (2003). Fetal alcohol spectrum disorder. JAMA: Journal of the American Medical Association, 290, 2996–2999. http://dx.doi.org/10.1001/jama.290.22.2996 Sotos, J. F. (2014). Sotos syndrome 1 and 2. Pediatric Endocrinology Reviews, 12, 2–16. Sparrow, S. S., Cicchetti, D. V., & Saulnier, C. A. (2016). Vineland adaptive behavior scales (3rd ed.). Bloomington, MN: Pearson. Spencer-Smith, M., Leventer, R., Jacobs, R., Luca, C. D., & Anderson, V. (2009). Neuropsychological profile of children with subcortical band heterotopia. Developmental Medicine and Child Neurology, 51, 909–916. http://dx.doi.org/10.1111/j.14698749.2009.03309.x Squires, J., Bricker, D., & Twombly, E. (2002). Ages and stages questionnaire: Social-emotional. Baltimore, MD: Brookes. Stein, D. J., & Woods, D. W. (2014). Stereotyped movement disorder in ICD–1. Revista Brasileira de Psiquiatria, 36, S65–S68. http://dx.doi.org/ 10.1590/1516-4446-2014-3606 Streissguth, A. P. (2007). Offspring effects of prenatal alcohol exposure from birth to 25 years: The Seattle Prospective Longitudinal Study. Journal of Clinical Psychology in Medical Settings, 14, 81–101. http:// dx.doi.org/10.1007/s10880-007-9067-6 Streissguth, A. P., Barr, H. M., & Sampson, P. D. (1990). Moderate prenatal alcohol exposure: Effects on child IQ and learning problems at age 7 1/2 years. Alcoholism: Clinical and Experimental Research, 14, 662–669. http://dx.doi.org/ 10.1111/j.1530-0277.1990.tb01224.x Substance Abuse and Mental Health Services Administration. (2014). Treatment improvement protocol: Improving cultural competence. Retrieved from http://store.samhsa.gov/shin/content/SMA144849/SMA14-4849.pdf Tatton-Brown, K., & Rahman, N. (2007). Sotos syndrome. European Journal of Human Genetics, 15, 264–271. http://dx.doi.org/10.1038/sj.ejhg.5201686 Thurman, D. J. (2016). The epidemiology of traumatic brain injury in children and youths: A review of research since 1990. Journal of Child Neurology, 31, 20–27. http://dx.doi.org/10.1177/0883073814544363 314

Trites, R. (1989). Grooved pegboard test. Lafayette, IN: Lafayette Instruments. Tunnicliffe, P., Woodcock, K., Bull, L., Oliver, C., & Penhallow, J. (2014). Temper outbursts in Prader–Willi syndrome: Causes, behavioural and emotional sequence and responses by carers. Journal of Intellectual Disability Research, 58, 134–150. http:// dx.doi.org/10.1111/jir.12010 Uban, K. A., Herting, M. M., Williams, P. L., Ajmera, T., Gautam, P., Huo, Y., . . . Sowell, E. R. (2015). White matter microstructure among youth with perinatally acquired HIV is associated with disease severity. AIDS, 29, 1035–1044. http://dx.doi.org/10.1097/ QAD.0000000000000648 United States Preventive Services Task Force. (2014). Final recommendation statement: Folic acid to prevent neural tube defects. Preventive Medication. Retrieved from http://www.uspreventiveservicestaskforce.org/ Page/Document/RecommendationStatementFinal/ folic-acid-to-prevent-neural-tube-defects-preventivemedication#Pod2 Vaurio, L., Crocker, N., & Mattson, S. N. (2011). Fetal alcohol spectrum disorders. In A. S. Davis (Ed.), Handbook of pediatric neuropsychology (pp. 877–886). New York, NY: Springer. Velez-Ruiz, N. J., & Meador, K. J. (2015). Neurodevelopmental effects of fetal antiepileptic drug exposure. Drug Safety, 38, 271–278. http:// dx.doi.org/10.1007/s40264-015-0269-9 Verdellen, C., van de Griendt, J., Hartmann, A., & Murphy, T., & the ESSTS Guidelines Group. (2011). European clinical guidelines for Tourette syndrome and other tic disorders. Part III: Behavioural and psychosocial interventions. European Child and Adolescent Psychiatry, 20, 197–207. http://dx.doi.org/ 10.1007/s00787-011-0167-3 Vigeh, M., Yokoyama, K., Matsukawa, T., Shinohara, A., & Ohtani, K. (2014). Low level prenatal blood lead adversely affects early childhood mental development. Journal of Child Neurology, 29, 1305–1311. http://dx.doi.org/10.1177/ 0883073813516999 Volker, M. A., Thomeer, M. L., & Lopata, C. (2011). Pervasive developmental disorders. In A. S. Davis (Ed.), Handbook of pediatric neuropsychology (pp. 501–535). New York, NY: Springer. Walton, C., & Kerr, M. (2016). Prader–Willi syndrome: Systematic review of the prevalence and nature of presentation of unipolar depression. Advances in Mental Health and Intellectual Disabilities, 10, 172–184. http://dx.doi.org/10.1108/AMHID-082015-0037 Wechsler, D. (1974). Wechsler intelligence scale for children—Revised. New York, NY: The Psychological Corporation.

Understanding and Treating Children and Adolescents With Neurodevelopmental Disorders

Wechsler, D. (1981). Wechsler adult intelligence scale— Revised manual. New York, NY: The Psychological Corporation. Wechsler, D. (1991). Wechsler intelligence scale for children (3rd ed.). San Antonio, TX: The Psychological Corporation. Wechsler, D. (1999). Wechsler abbreviated scale of intelligence manual. San Antonio, TX: The Psychological Corporation. Wechsler, D. (2012). Wechsler preschool and primary scale of intelligence (4th ed.). Bloomington, MN: Pearson.

Copyright American Psychological Association. Not for further distribution.

Wechsler, D. (2014). Wechsler intelligence scale for children (5th ed.). Bloomington, MN: Pearson. Welch, M. G., Firestein, M. R., Austin, J., Hane, A. A., Stark, R. I., Hofer, M. A., . . . Myers, M. M. (2015). Family Nurture Intervention in the neonatal intensive care unit improves social-relatedness, attention, and neurodevelopment of preterm infants at 18 months in a randomized controlled trial. Journal of Child Psychology and Psychiatry, 56, 1202–1211. http://dx.doi.org/10.1111/jcpp.12405 Westrupp, E. M., Howard, K., & Anderson, P. J. (2011). Long-term outcomes following preterm birth. In A. S. Davis (Ed.), Handbook of pediatric neuropsychology (pp. 955–962). New York, NY: Springer. Wheeler, A., Mussey, J., Villagomez, A., Bishop, E., Raspa, M., Edwards, A., & . . . Bailey, D. (2015). DSM–5 changes and the prevalence of parentreported autism spectrum symptoms in fragile X syndrome. Journal of Autism and Developmental Disorders, 45, 816–829. http://dx.doi.org/10.1007/ s10803-014-2246-z Wiebe, S. A., Clark, C. A. C., De Jong, D. M., Chevalier, N., Espy, K. A., & Wakschlag, L. (2015). Prenatal tobacco exposure and self-regulation in early childhood: Implications for developmental psychopathology. Development and Psychopathology, 27, 397–409. http://dx.doi.org/10.1017/ S095457941500005X Willen, E. J. (2006). Neurocognitive outcomes in pediatric HIV. Mental Retardation and Developmental Disabilities Research Reviews, 12, 223–228. http:// dx.doi.org/10.1002/mrdd.20112 Winkler, R., & Taylor, N. F. (2015). Do children and adolescents with mild traumatic brain injury and

persistent symptoms benefit from treatment? A systematic review. Journal of Head Trauma Rehabilitation, 30, 324–333. http://dx.doi.org/ 10.1097/HTR.0000000000000114 Wong, T. M., Strickland, T. L., Fletcher-Janzen, E., Ardila, A., & Reynolds, C. R. (2000). Theoretical and practical issues in the neuropsychological assessment and treatment of culturally dissimilar patients. In E. Fletcher-Janzen, T. L., Strickland, & C. Reynolds (Eds.), Handbook of cross-cultural neuropsychology (pp. 3–18). New York, NY: Kluwer Academic. Woods, D. W., & Thomsen, P. H. (2014). Tourette and tic disorders in ICD–11: Standing at the diagnostic crossroads. Revista Brasileira de Psiquiatria, 36, S51–S58. http://dx.doi.org/10.1590/1516-44462013-1274 World Health Organization. (in press). International statistical classification of diseases and related health problems (11th ed.). Geneva, Switzerland: Author. Wyckoff, L., Hanchon, T., & Gregg, S. R. (2015). Psychological, behavioral, and educational considerations for students with diabetes, psychology in the schools. Psychology in the Schools, 52, 672–682. https://doi.org/10.1002/pits.21848 Yang, L., Zhan, G., Ding, H., Ma, D., Huang, G., & Zhou, W. (2013). Psychiatric illness and intellectual disability in the Prader–Willi syndrome with different molecular defects—A meta-analysis. PLOS ONE, 8, e72640. https://doi.org/10.1371/journal. pone.0072640 Yovel, G., & Belin, P. (2013). A unified coding strategy for processing faces and voices. Trends in Cognitive Sciences, 17, 263–271. http://dx.doi.org/10.1016/ j.tics.2013.04.004 Zebracki, K., & Drotar, D. (2008). Pain and activity limitations in children with Duchenne or Becker muscular dystrophy. Developmental Medicine and Child Neurology, 50, 546–552. http://dx.doi.org/ 10.1111/j.1469-8749.2008.03005.x Zeilinger, E. L., Nader, I. W., Brehmer-Rinderer, B., Koller, I., & Weber, G. (2013). CAPs-IDD: Characteristics of assessment instruments for psychiatric disorders in persons with intellectual developmental disorders. Journal of Intellectual Disability Research, 57, 737–746. http:// dx.doi.org/10.1111/jir.12003

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Chapter 15

Substance Use Disorders in Adolescents

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Sara J. Becker and Jacqueline Horan Fisher

Adolescence is a developmental period marked by profound biological and psychological changes. It is also a time during which experimentation with risky behaviors, including alcohol and other drug use, commonly occurs (Steinberg, 2007). As noted in a review by Hernandez and colleagues (2015), alcohol and drug use are typically initiated during adolescence, increase substantially across this development period, and decrease in early adulthood. This pattern is so common across countries and cultures that some theorists have argued that substance use disorders (SUDs) should be characterized as developmental disorders (Masten, Faden, Zucker, & Spear, 2008; Sher & Gotham, 1999). Prevalence estimates from a National Institute on Drug Abuse funded survey of eighth through 12th graders (Johnston, O’Malley, Bachman, & Schulenberg, 2016) indicate that about 26% of eighth graders in the United States have tried alcohol, 16% have tried marijuana, and 21% have tried other illicit drugs. By 12th grade, these rates more than double: 64% of have tried alcohol, 45% have tried marijuana, and 49% have tried drugs. Although experimentation with alcohol or drug use is a developmentally normative phenomenon, about 5% of adolescents will develop problems sufficient to merit a diagnosis of SUD (Center for Behavioral Health Statistics and Quality, 2015). If left untreated, these adolescents face a cascade of negative health consequences that often persist into adulthood, highlighting the importance of evidence-based assessment and intervention for this vulnerable population.

This chapter overviews the contemporary diagnostic criteria for adolescent SUDs, followed by a discussion of prevailing etiological theories and the prevalence and consequences of adolescent substance use and SUDs in this age cohort. Building on this material, we review prevailing state-of-the-art and evidence-based approaches to treating adolescents with substance-related problems. We conclude with a discussion of the next frontier for clinical research for adolescents at risk of or affected by SUDs. Contemporary Diagnostic Criteria There are two major systems for classifying mental health and substance use conditions: the Diagnostic and Statistical Manual of Mental Disorders (DSM) of the American Psychiatric Association and the International Classification of Diseases of the World Health Organization. The DSM is the system most commonly used for diagnostic classification in the United States. In the first two versions of the DSM published in 1952 and 1968, addiction was classified as a personality disorder, reflecting the belief that addiction was merely a symptom of a disordered personality and not a disorder (see White, 2007). When the third edition of DSM was published in 1980, SUDs were listed as separate disorders for the first time under the headings substance abuse and substance dependence (White, 2007). This classification system was retained across three subsequent revisions—DSM–III–R, DSM–IV, and DSM–IV–TR—although some of the specific criteria were refined in each version.

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Over the 20-year period when the DSM–IV–TR was the prevailing classification system, a number of empirical studies raised concerns about the developmental fit of the SUD diagnostic criteria for adolescents. First, examinations of the criteria for abuse and dependence confirmed that they did not differ systematically in prevalence, sensitivity, specificity, severity, or age of onset (Martin, Chung, Kirisci, & Langenbucher, 2006). Second, several factor analyses and latent class analyses failed to support the distinction between the abuse and dependence criteria, instead supporting the presence of a single dimension of substance problems (Chung & Martin, 2001; Martin et al., 2006). Third, multiple cross-sectional studies showed that substantial proportions of adolescents were “diagnostic orphans” who met 1 or 2 symptoms of dependence but did not meet criteria for substance abuse (e.g., Hasin & Paykin, 1998, 1999). Prevalence estimates of diagnostic orphans in these studies ranged from 2% and 17% among general population samples and 8% and 34% in clinical samples (see Harford, Yi, & Grant, 2010); further, longitudinal studies demonstrated that diagnostic orphans were more like those with substance abuse than those with no symptoms, suggesting that use of two distinct sets of criteria was problematic (Pollock & Martin, 1999; Schuckit et al., 2008). Fourth, empirical investigators provided evidence that the “legal problems” symptom lacked relevance for women and early adolescents, and was often reflective of comorbid conduct disorder (Martin et al., 2006). Combined, this body of research did not support the use of two discrete diagnoses or the use of a legal problems criterion in this age cohort, and suggested that a unidimensional set of criteria might be more developmentally appropriate. Several meaningful changes were made to the classification of SUDs in the fifth and most recent edition (DSM–5; American Psychiatric Association, 2013) which addressed many of the these concerns. The most important change was that the separate substance abuse and dependence diagnoses were abandoned in favor of a single, continuous diagnosis of SUD. In addition, the legal problems symptom was dropped and a new symptom indicating craving 318

for substances was added. A visual depiction of the changes from the DSM–IV to DSM–5 criteria created by the National Institute on Alcohol Abuse and Alcoholism (2015) is presented in Figure 15.1. Under the new system, an SUD diagnosis may be obtained if an individual exhibits at least two of 11 symptoms within a 12-month period. Additional specification may be provided to indicate the severity of the disorder, such as mild (e.g., 2–3 symptoms are present), moderate (e.g., 4–5 symptoms are present), or severe (e.g., 6 or more symptoms are present). There are numerous substances for which these diagnostic criteria may be applied and a diagnosis of SUD reached, including alcohol, cannabis, hallucinogens, inhalants, opioids, sedatives/ hypnotics/anxiolytics, stimulants, tobacco, and other/unknown substances. Each specific substance is addressed as a separate use disorder (e.g., alcohol use disorder, cannabis use disorder, etc.), but nearly all substances are diagnosed on the basis of the same overarching criteria. Concerns persist about the current criteria’s developmental appropriateness for adolescents, however. The SUD symptom criteria were designed to capture maladaptive behaviors and symptoms related to substance use across four domains (see Hernandez et al., 2015): (a) social difficulties (e.g., persistent interpersonal problems caused by or exacerbated by substance use), (b) loss of control (e.g., frequently using more of the substance than intended), (c) risky behaviors (e.g., continuing to use a substance despite recurrent physical or psychological problems), and (d) physiological changes (e.g., symptoms of tolerance, withdrawal, craving). A primary concern is whether the physiological change domain is relevant for adolescents. In a critique of DSM–5 criteria, Winters, Martin, and Chung (2011) asserted that symptoms such as tolerance (i.e., needing more of the substance to get the same high) may be developmentally normative for adolescents and young adults as they move from experimental use to regular use, whereas symptoms of withdrawal (i.e., physical symptoms experienced when use of the substance is stopped) may be a relatively rare phenomenon in this age group because of the time required to become physically dependent. Moreover, they note that the manifestation of the

Substance Use Disorders in Adolescents

Any 1 = ALCOHOL ABUSE

DSM–5 In the past year, have you:

Found that drinking—or being sick from drinking—often 1 interfered with taking care of your home or family? Or caused job troubles? Or school problems?

Had times when you ended up drinking more, or longer, than you intended?

More than once gotten into situations while or after drinking that increased your chances of getting hurt (such as driving, swimming, using machinery, walking in a dangerous area, or having unsafe sex)?

2

More than once wanted to cut down or stop drinking, or tried to, but couldn’t?

More than once gotten arrested, been held at a police station, or had other legal problems because of your drinking? **This is not included in DSM–5**

3

Spent a lot of time drinking? Or being sick or getting over other aftereffects?

Continued to drink even though it was causing trouble with your family or friends?

4

Wanted a drink so badly you couldn’t think of anything else? **This is new to DSM–5**

5

Found that drinking—or being sick from drinking—often interfered with taking care of your home or family? Or caused job troubles? Or school problems?

Had to drink much more than you once did to get the effect you want? Or found that your usual number of drinks had much less effect than before? Found that when the effects of alcohol were wearing off, you had withdrawal symptoms, such as trouble sleeping, shakiness, restlessness, nausea, sweating, a racing heart, or a seizure? Or sensed things that were not there?

Any 3 = ALCOHOL DEPENDENCE

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DSM–IV In the past year, have you:

6

Had times when you ended up drinking more, or longer, 7 than you intended?

More than once wanted to cut down or stop drinking, or tried to, but couldn’t?

Spent a lot of time drinking? Or being sick or getting over other aftereffects?

8

9

The presence of at least 2 of these symptoms indicates an Alcohol Use Disorder (AUD). The severity of the AUD is defined as:

Continued to drink even though it was causing trouble with your family or friends? Mild: The presence of 2 to 3 Given up or cut back on activities that were symptoms important or interesting to you, or gave you pleasure, in order to drink? Moderate: More than once gotten into situations while The presence of 4 to 5 or after drinking that increased your symptoms chances of getting hurt (such as driving, swimming, using machinery, walking in a dangerous area, or having unsafe sex)? Continued to drink even though it was making you feel depressed or anxious or adding to another health problem? Or after having had a memory blackout?

Given up or cut back on activities that were important or interesting to you, or gave you pleasure, in order to drink?

Had to drink much more than you once did to get the effect you want? Or found 10 that your usual number of drinks had much less effect than before?

Continued to drink even though it was making you feel depressed or anxious or adding to another health problem? Or after having had a memory blackout?

Found that when the effects of alcohol were wearing off, you had withdrawal symptoms, 11 such as trouble sleeping, shakiness, restlessness, nausea, sweating, a racing heart, or a seizure? Or sensed things that were not there?

Severe: The presence of 6 or more symptoms

Figure 15.1.  Overview of changes from DSM–IV to DSM–5 criteria for alcohol use disorder. From Alcohol Use Disorder: A Comparison Between DSM–IV and DSM–5, by the National Institute on Alcohol Abuse and Alcoholism, 2015, Bethesda, MD: Author. In the public domain.

craving symptom during adolescence is not well understood and requires further study. Winters and colleagues (2011) conclude by calling for greater

clarification of the operational definitions of tolerance, withdrawal, and craving to improve SUD diagnosis for adolescents. 319

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Other developmental concerns with the new DSM–5 criteria include the hazardous use criterion and the two-symptom diagnostic threshold. The hazardous use criterion is endorsed far less often among adolescents (Martin et al., 2006; Martin, Chung, & Langenbucher, 2008), in part because adolescents have less access to automobiles. As for the two-symptom threshold, it has been argued that several of the proposed DSM–5 symptoms are developmentally normative for adolescents, mild, and/or prone to being overendorsed (Chung, Martin, Winters, & Langenbucher, 2001). Requiring only two symptoms to be endorsed may result in adolescents being diagnosed whose problem severity may be mild and whose substance use pattern may be intermittent and likely to remit (Winters et al., 2011). A final and related concern is that the new criteria might increase the heterogeneity of adolescents meeting criteria for a disorder. SUDs are polythetic disorders, meaning they share common characteristics but lack a singular trait essential for a diagnosis (see McBride, Teesson, Slade, & Baillie, 2010). This contributes to exceptional variability in the configuration of symptoms experienced by adolescents who meet criteria for an SUD. In theory, there are over 55 possible permutations of symptoms needed to meet criteria for a mild SUD, over 330 to meet criteria for a moderate SUD, and over 460 to meet criteria for a severe SUD. To further complicate the issue, there are multiple substance-specific diagnoses and there is substantial comorbidity across these diagnoses. For instance, 74% of those seeking treatment for cannabis use disorder report additional problems with one or more substances, and it has been estimated that over 50% of individuals who meet criteria for a cannabis use disorder experience a lifetime alcohol use disorder (American Psychiatric Association, 2013). It is therefore not an exaggeration to assert that there are thousands of possible symptom and substance-specific permutations that could lead to a SUD disorder under contemporary diagnostic criteria. Etiological Models With such extraordinary heterogeneity of adolescent SUD symptoms and drugs of misuse, it should 320

not be surprising that there is no uniform etiological pathway to a disorder. It is widely accepted that substance misuse and SUDs are influenced by a myriad of risk, promotive, and protective factors (see Cleveland, Feinberg, Bontempo, & Greenberg, 2008). Risk factors and promotive factors are at opposite ends of the continuum: risk factors predict an increased probability of substance use, whereas promotive factors predict a decreased probability of substance use (see Fergus & Zimmerman, 2005). Protective factors are variables that buffer the effects of specific risk factors. Some factors can be protective and promotive; for instance, parental monitoring has been shown to protect against the effects of peer substance use and has also been shown to reduce the overall likelihood of adolescent substance use, independent of the presence of risk factors (Fairlie, Wood, & Laird, 2012). Although a comprehensive review is beyond the scope of this chapter (see Hawkins, Catalano, & Miller, 1992; Wiers, Fromme, Latvala, & Stewart, 2012), we briefly review the research in support of two prevailing etiological models that have been used to explain addictive behaviors: biopsychosocial and dual process.

Biopsychosocial Models Biopsychosocial models view addiction as the result of a complex interplay of risk, promotive, and protective factors across three domains: biological, psychological, and social (Griffiths, 2005; Zucker & Gomberg, 1986). Biological factors that influence adolescent SUDs encompass, but are not limited to, genetics and pubertal changes. Psychological factors include personality, motivations, and expectancies. Finally, social factors pertain to interpersonal environments (e.g., family, peers) and broader societal, cultural, and contextual factors. These three domains are not independent, but rather interact to affect one another in a myriad of ways; we review some of the most extensively studied factors in each domain next. Biological factors.  Relative to other mental health disorders in adolescents, SUDs have moderately high heritable effects. Data from over 1,400 twin pairs ages 8 to 16 in the Virginia Twin Study of

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Substance Use Disorders in Adolescents

Adolescent Behavioral Development (Maes et al., 1999) found that heritable effects were strong for lifetime tobacco (84%) and alcohol (72%) use, moderate for lifetime drug use (45%), and modest for lifetime marijuana use (22%). Another study of the heritability of seven psychiatric disorders found that alcohol and drug use disorders had large diseasespecific genetic effects (Kendler, Prescott, Myers, & Neale, 2003). Furthermore, the underlying structure of the genetic and environmental factors for SUDs was very similar in men and women. Although genes are perhaps the least malleable risk factor for adolescent substance use, it is also well established that interactions between genes and the environment are core to SUD risk formation (see Hussong, Jones, Stein, Baucom, & Boeding, 2011). Another biological risk factor, pubertal change, has also been implicated in the onset and maintenance of substance use during the adolescent years. A cross-sectional survey of 5,769 10- to 15-year old students found that pubertal stage was independently associated with higher rates of substance use and SUDs, independent of age and school grade level (Patton et al., 2004). Other studies have provided evidence that pubertal timing among girls predicts patterns of substance use, with girls who mature earlier reporting higher levels of alcohol and tobacco use during their early teenage years (Stattin & Magnusson, 2003; Wilson et al., 1994). One interpretation of these findings is that girls who mature early may be more prone to peer rejection and low self-esteem, and may be more likely to turn to substance use (Dick, Rose, Viken, & Kaprio, 2000). Other possible explanations are that puberty affects neurobiologic systems (Angold, Costello, Erkanli, & Worthman, 1999) and/or promotes changes in cognitive and emotional styles (Moore & Rosenthal, 1993). Studies of animals have provided partial support for this hypothesis; among animals, the onset of puberty has been shown to predict increased exploration and novelty seeking, changes that are linked to the limbic system so often implicated in SUDs among humans (e.g., Spear & Brake, 1983) Psychological factors.  Personality traits represent a robust psychological risk factor. Childhood

personality assessments have consistently been shown to predict substance use problems in adulthood (e.g., Cloninger, Sigvardsson, & Bohman, 1988), with the greatest risks among those adolescents with “externalizing” personality traits such as disinhibition, impulsivity, sensation seeking, novelty seeking, and psychoticism (see Wiers et al., 2012). It has been theorized that these personality traits increase the likelihood that an adolescent will engage in risk taking behaviors in pursuit of short-term gains (i.e., intoxication or a high) in part because they are less likely to consider long-term consequences (Wiers et al., 2012). Internalizing traits (e.g., neuroticism, harm avoidance, negative affectivity) have also been theorized as etiologically relevant to the development of SUDs (Hussong et al., 2011; King, Iacono, & McGue, 2004); self-medication of internalizing symptoms has been implicated as one putative mechanism explaining the relationship between internalizing traits and SUDs (see McMahon, Kouzekanani, Demarco, Kusel, & Davidson, 1992). Although less studied than externalizing traits, prospective research has shown that internalizing traits like neuroticism and anxiety sensitivity are associated with increased alcohol problems in young adulthood (Jackson & Sher, 2003). Further, successful treatment of internalizing traits (i.e., anxiety) has been found to be associated with fewer substance use problems 7.4 years after treatment even when controlling for known predictors (Puleo, Conner, Benjamin, & Kendall, 2011). Two types of substance use-related cognitions have been identified as important psychological factors related to adolescent substance use: substance-related expectancies and substance use motives. Expectancies, defined as expected effects of substance use (positive or negative) have been mostly extensively studied with alcohol (see Brown, Christiansen, & Goldman, 1987; Jones, Corbin, & Fromme, 2001), although some research has been conducted with other drugs (e.g., marijuana and stimulants; Aarons, Brown, Stice, & Coe, 2001). Early studies in adolescent populations (Brown et al., 1987; Christiansen, Goldman, & Inn, 1982) indicate that there were at least seven discrete factors influencing alcohol expectancies: (a) global 321

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positive changes, (b) changes in social behavior, (c) improved cognitive and motor abilities, (d) sexual enhancement, (e) cognitive and motor impairment, (f) increased arousal, and (g) relaxation and tension reduction. Some studies have found that expectancies explain up to 50% of the variance in drinking behavior, though longitudinal studies that examine the influence of expectancies over time typically find more modest levels of prediction (1%–5%; for a review, see Jones et al., 2001). Further research has provided evidence that well-crystallized expectancies exist prior to alcohol use, but that experiences with alcohol can shape future expectancies (Jones et al., 2001). Early research only assessed positive expectancies (Brown, Goldman, & Christiansen, 1985), but more recent research has demonstrated the importance of considering negative expectancies and using a multidimensional approach (Pabst, Baumeister, & Kraus, 2010). In addition, dosespecific expectancies have been shown to influence subsequent substance use behavior. An early study by Wiers, Hoogeveen, Sergeant, and Gunning (1997) demonstrated that adolescents were the only age group in which positive expectancies of a high dose contributed unique variance in drinking behavior. Like expectancies, substance use motives have been primarily studied for alcohol, although some studies have tested motives for other drugs (e.g., Bonn-Miller, Zvolensky, & Bernstein, 2007). Cooper (1994) developed a two by two factorial model to assess drinking motives. The model crosses the drive to drink (categorized as internal or external) with the desire for reinforcement (categorized as positive or negative), thereby yielding four motivational factors: enhancement (internal drive for positive reinforcement; i.e., drinking to increase positive affect), social (external drive for positive reinforcement; i.e., drinking to affiliate), coping (internal drive for negative reinforcement; i.e., drinking to reduce negative affect), and conformity (external drive for negative reinforcement; i.e., drinking to avoid peer pressure). These four factors have been cross-validated in adolescent samples across the United States, Canada, and Switzerland (Kuntsche, Stewart, & Cooper, 2008). In all three 322

countries, enhancement and coping motives were positive related to alcohol use and risky drinking, whereas coping motives were associated with ­alcohol-related problems (Kuntsche et al., 2008). Social influences.  Regarding social influences, there is extensive evidence that adolescents and their peers tend to have similar patterns of substance use and SUDs (see Becker & Curry, 2014). The two leading theories to account for the relationship between adolescent and peer substance use are socialization and selection. According to the socialization model, adolescents adopt the beliefs, attitudes, and behaviors of their peers because of modeling and pressure to conform (e.g., Gardner & Steinberg, 2005). Meanwhile, the selection model theorizes that adolescents select and affiliate with peers with similar beliefs, attitudes, and behaviors (e.g., Jaccard, Blanton, & Dodge, 2005). Several longitudinal studies have suggested that the relationship between adolescent and peer substance use is reciprocal, driven by selection and socialization processes (Curran, Stice, & Chassin, 1997; Read, Wood, & Capone, 2005). One study by Burk and colleagues (2012) suggested that the relative importance of these processes may vary over adolescence: specifically, peer selection was found to be more robust than socialization in early adolescence, whereas the two processes had relatively equal contributions in middle and late adolescence. Parents also play a critical role in the development and maintenance of adolescent substance use and SUDs via biological (i.e., genetic) and social (i.e., environmental) pathways (see Rose, Dick, Viken, & Kaprio, 2001). Parental substance use has shown prospective associations with the initiation and severity of adolescent substance use (Hussong, Bauer, & Chassin, 2008). Other key parenting processes of a social nature that have been associated with adolescent substance use include parental monitoring and supervision (Tobler & Komro, 2010), parent–teen communication (Ennett, Bauman, Foshee, Pemberton, & Hicks, 2001), parental involvement in the adolescents’ activities (Ryan, Jorm, & Lubman, 2010), parental disapproval of substance use (Mrug & McCay, 2013), and general family management practices (Wang, Dishion, Stormshak, & Willett, 2011).

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Substance Use Disorders in Adolescents

Finally, broader societal and cultural factors have been linked to adolescent substance use and SUDs. Cultural processes that have been associated with adolescent substance use include acculturation, cultural identity, immigration status, and perceived discrimination (see Hernandez et al., 2015). Acculturation (i.e., the process of acquiring or adapting to a new culture) has been found to predict increased adolescent substance use, with adolescents classified as “more acculturated” demonstrating higher rates of alcohol use than their “less acculturated” peers (Epstein, Botvin, & Diaz, 2001). A discrepancy between the parents’ and adolescents’ level of acculturation has also been shown to be an independent risk factor for adolescent substance use (Unger, Ritt-Olson, Soto, & Baezconde-Garbanati, 2009). It is also well-established that immigrants have lower rates of substance use and substance-related problems than adolescents born in the United States, and that immigrants experience greater risk of substance use as they acculturate to the U.S. culture (Salas-Wright, Vaughn, Clark, Terzis, & Córdova, 2014). Somewhat relatedly, minority adolescents with lower levels of ethnic, racial and cultural identity (Holley, Kulis, Marsiglia, & Keith, 2006) and lower levels of ethnic/racial pride (Castro, Stein, & Bentler, 2009) have been found to be at increased risk of substance use. Finally, adolescent reports of ethnic/racial discrimination have been associated with increased levels of smoking (Bennett, Wolin, Robinson, Fowler, & Edwards, 2005), alcohol consumption (Kwate, Valdimarsdottir, Guevarra, & Bovbjerg, 2003), and use of other drugs (Minior, Galea, Stuber, Ahern, & Ompad, 2003).

Dual Process Models Dual process models have increasingly been used to explain addictive behaviors (for a review, see Wiers et al., 2007). In contrast to biopsychosocial models that focus on the interplay of a wide range of factors, dual process models focus on cognitive factors within the individual. As noted in a review by Stacy and Wiers (2010), dual process models view addictive behaviors as the joint outcome of two distinct sets of cognitive processes: relatively automatic or impulsive processes and relatively controlled or reflective processes, consistent with more general

dual process models in the field of psychology (Evans, 2008; Strack & Deutsch, 2004). The relatively automatic processes are associated with attributes like being impatient or distractible, having difficulty in delaying gratification, and being easily frustrated. In structural models, these attributes load on a common factor associated with behavioral disinhibition, which is consistently related to higher levels of risk factors for substance use (e.g., negative life events, affiliation with deviant peers; Wills et al., 2001; Wills & Dishion, 2004). Meanwhile, the relatively controlled processes are associated with attributes (e.g., the tendency to consider alternatives before acting, plan ahead, and link behaviors with potential consequences; Carver & Scheier, 2000). These attributes also load on a common factor associated with self-control, which has been related to promotive and protective factors for substance use (e.g., academic performance; Wills et al., 2001). The theory underlying dual-process models is that addiction, once established, is perpetuated by strong automatic processes (i.e., behavioral disinhibition), which can be triggered outside of conscious awareness. Deficits in reflective processes (i.e., selfcontrol) then contribute to continued use despite the inevitable experience of negative consequences. Although some scholars have questioned the evidence in support of dual process models (Keren & Schul, 2009; Kruglanski & Gigerenzer, 2011), other scholars have asserted that the dual-process distinction is supported by a wide range of converging experimental, psychometric, and neuroscientific methods (for a review, see Evans & Stanovich, 2013). For example, experimental evidence indicates that proxies for good self-control (i.e., decision making and future time perspective), are inversely related to frequency of substance use (AudrainMcGovern, Rodriguez, Tercyak, Neuner, & Moss, 2005), whereas indicators of behavioral disinhibition (i.e., impulsiveness, affect lability) are positively related to substance use among adolescents and adults (Tarter et al., 2003). Meanwhile, some neuroscientific data supports the presence of two brain networks: a cognitive control network (which is responsible for self-control) and a socioemotional network (which involves reward-oriented functions 323

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and contributes to disinhibition) which interact to influence risk taking (see Steinberg, 2007). Regarding adolescent SUDs, a number of recent studies have confirmed a central hypothesis from dual-process models: the influence of impulsive or automatic cognitive processes (i.e., memory associations, automatic tendencies to approach the substance) is stronger among adolescents with relatively weaker executive control compared with adolescents with relatively good executive control (Grenard et al., 2008; Peeters et al., 2013; Thush et al., 2008). Intervention implications of these findings include the need to address reflective processes (i.e., ability and motivation to control impulses) and/or interfere with impulsive processes (Wiers et al., 2007). To date, several studies have evinced that implicit cognitive processes can be successfully changed, with positive effects on treatment in adults with problematic drinking (Fadardi & Cox, 2009) and alcohol dependence (Eberl et al., 2013). Of relevance is a trial by Eberl and colleagues (2013) that compared the effects of bias modification training to reduce automatic alcohol-approach tendencies to treatment as usual in a sample of adults. Results indicated bias modification training was associated with significantly lower relapse rates and that this effect was mediated by change in alcohol-approach bias. These results are promising, but further research is needed to determine if the findings hold in an adolescent population. Prevalence Estimates and Trends With variability in symptom expression and putative etiological pathways, prevalence estimates of adolescent SUDs tend to present an overly simplistic picture of the adolescent substance use phenomenon. For this reason, two national annual surveys in the United States—the National Survey on Drug Use and Health (NSDUH) and the Monitoring the Future (MTF) survey—annually collect detailed information about the prevalence, patterns, and consequences of substance use and SUDs among adolescents in the United States. The NSDUH focuses on substance use trends among adolescents ages 12 to 17, whereas the MTF takes a closer look at drug use and attitudes among eighth, 10th, and 12th graders. 324

Data from the NSDUH and MTF surveys have provided encouraging news about adolescent substance use since the 1990s. Specifically, the surveys have revealed steadily decreasing use of alcohol, cigarettes, inhalants, synthetic drugs, and prescription pain relievers, and relatively stable use of marijuana among adolescents. In the 2015 MTF survey (Johnston et al., 2016), 10.7% of students reported binge drinking (five or more drinks in a row over the past 2 weeks), relative to 21.9% of students at the peak level in 1998. Similarly, past year illicit drug use was endorsed by 26.8% of students in 2015 down from its peak of 34.1% in 1997. Cigarette smoking was at its lowest rate in the survey’s 30-year history; in 1997, 16.9% of students were daily smokers, while in 2015 only 3.2% of students reported smoking daily. According to the 2015 NSDUH, 5% of adolescents between the ages of 12 and 17 met diagnostic criteria for an SUD during the past year, down from a maximum of 8.9% in 2003. The data from the NSDUH and MTF surveys have not been all positive, however. In recent years, there has been a softening of attitudes toward marijuana use (i.e., decreases in perceived harm and disapproval of use) and rising rates of e-cigarette use among adolescents. Perhaps of greatest concern, there is a persistent gap between those adolescents who meet diagnostic criteria for an SUD over the past year and those who report receiving any treatment. Of the 1.3 million adolescents who met diagnostic criteria for a SUD in the 2015 NSDUH, only 122,000 (9.1% of those with a diagnosis) received any specialty substance use treatment, leaving 1.2 million adolescents with an unmet need for treatment. The proportion of adolescents receiving treatment—fewer than 1 out of 10—has hovered around the same level for over the past decade, highlighting a critical need for more proactive strategies to bridge the treatment gap. Substance-Related Consequences When left untreated or ineffectively treated, adolescent substance use and SUDs are often associated with far-reaching consequences and societal costs. Adverse consequences associated with adolescent substance use include sexually transmitted

Substance Use Disorders in Adolescents

infections, unintended pregnancy, criminal involvement, school truancy, psychiatric disorders, and physical health problems (Substance Abuse and Mental Health Services Administration [SAMHSA], 1999). We briefly review some of these consequences next.

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Sexually Transmitted Infections and Pregnancy Prevalence estimates suggest that adolescents ages 15 to 19 and young adults ages 20 to 24 currently account for half of all new sexually transmitted infections (STIs; Satterwhite et al., 2013) and that one in four sexually active adolescent girls has an STI (Forhan et al., 2009). Relative to adolescents who do not use substances, adolescents who use alcohol or other drugs are more likely to have sexual intercourse (Tapert, Aarons, Sedlar, & Brown, 2001), have an earlier sexual debut (Rothman, Wise, Bernstein, & Bernstein, 2009), and engage in highrisk sexual activities that increase the risk of STIs (e.g., unprotected sex, sexual relations with multiple partners; see Yan, Chiu, Stoesen, & Wang, 2007). The association between adolescent substance use and sexually risky practices is at least partly influenced by the acute effects of alcohol and other drugs on decision making. National survey data collected by the Centers for Disease Control and Prevention (Kann et al., 2016) found that over 20% of sexually active high school students drank alcohol or used drugs before their last sexual experience. Adolescents who use alcohol or drugs and engage in high-risk sexual activity also experience high rates of unplanned pregnancy (see National Center on Addiction and Substance Abuse, 2011). This association is partly explained by behavioral choices; the more addictive substances that adolescents use in their lifetime, the less likely they are to report condom use at last intercourse (National Center on Addiction and Substance Abuse, 2011; Santelli, Robin, Brener, & Lowry, 2001). For women of all ages, unintended pregnancies are associated with potential health risks to the fetus because of delayed pregnancy recognition; it is estimated that about 60% of unplanned pregnancies are not confirmed until after five weeks gestation (Naimi, Lipscomb, Brewer, & Gilbert, 2003).

For adolescents, the health risks associated with unintended pregnancies are often compounded by continued substance use. Connery and colleagues (2014) analyzed pooled national survey data from 2011 through 2012 and concluded that pregnant adolescents used substances at significantly higher rates than pregnant women in other age groups. As an example, 18.3% of pregnant adolescents reported past-month illicit drug use, a rate that was double that of pregnant young adults ages 18 to 25 (9%), and 6 times that of pregnant women ages 26 to 44 (3.4%). Sizable proportions of pregnant adolescents also reported engaging in past month drinking and cigarette use. These rates are alarming considering the teratogenic effects of prenatal exposure to addictive substances (Behnke & Smith, 2013). Hence, many researchers and clinicians have called for integrated universal substance use and sexual risk screening among adolescents (e.g., Connery et al., 2014; Levy, Sherritt, Gabrielli, Shrier, & Knight, 2009).

School-Related Problems Adolescent substance use has been linked to a range of school-related consequences including declining grades, absenteeism from school and other activities, and increased likelihood of dropping out (for a review, see National Center on Addiction and Substance Abuse, 2011). Several developmental theories have pointed to change in school engagement variables (e.g., school bonding, school performance, truancy) as important predictive factors for adolescent substance use and other related problems (see Patterson, Reid, & Shishion, 1992; Thornberry & Krohn, 2001). Conversely, several specific substances have been shown to predict poor educational outcomes and negatively affect school engagement. For instance, marijuana has demonstrated negative effects on attention, memory, and learning that can last anywhere from days to weeks after the acute effects of the drug wear off (Tapert, Schweinsburg, & Brown, 2008). Marijuana use has also been associated with reduced chances of finishing high school or obtaining a degree (Macleod et al., 2004; Silins et al., 2014). Of concern is the strong association between adolescent substance use and truancy (Chou, Ho, Chen, & Chen, 2006; Henry, Thornberry, & Huizinga, 2009), because truancy has been 325

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related to later problems in romantic relationships, in jobs, and with violence, adult criminality, and incarceration (see Development Services Group Inc., 2010).

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Juvenile Delinquency and Crime There is a well-established link between adolescent substance use and delinquency (Tripodi & Bender, 2011). Relative to nonoffending adolescents, juvenile offenders report higher rates of alcohol and drug use, with some studies suggesting that over 60% of adolescents in the juvenile justice system require treatment for SUDs (Henderson et al., 2007; Young, Dembo, & Henderson, 2007). In an analysis of national survey data, Chassin (2008) found that the rate of SUDs among adolescents ages 12 to 17 who had ever been in jail or detained (24.7%) was more than three times greater than the rate of SUDs among adolescents who had never been involved with the criminal justice system (8%). Chassin further noted that the criminal justice system accounted for most referrals to publicly funded SUD treatment programs, arguably rendering the criminal justice system the primary SUD treatment system in the United States. The association between adolescent substance use and delinquency is complex and difficult to disentangle. By definition, adolescent alcohol or drug use is illegal. For many adolescents who use substances, intervention by the juvenile justice system (e.g., detention, arrest, probation) is an eventual consequence of actions associated with alcohol or drug use. By the same token, adolescents who are attracted to alcohol or other drugs may have a predisposition to delinquent behavior (Loeber, 1988), and substance use predicts later antisocial behavior in adolescents (Popovici, French, Pacula, Maclean, & Antonaccio, 2014). The two behaviors are strongly correlated and likely bidirectional, with behaviors sharing mutual causal influences and risk factors (see Mulvey, Schubert, & Chassin, 2010). The combination of substance use and juvenile delinquency is associated with particularly negative outcomes. Drug and alcohol use is associated with increased recidivism rates and increases the likelihood that an adolescent offender will have a prolonged interaction with the juvenile justice system 326

(Young et al., 2007). Severe SUDs are also associated with increased rates of offending and more serious offending (Tripodi & Bender, 2011). For these reasons, substance use intervention has been recognized as a vital component of juvenile rehabilitative efforts and several evidence-based treatment approaches (described later in this chapter) have been designed specifically for this high-risk population.

Mental Health Problems Adolescent substance use also commonly co-occurs with, follows, and/or exacerbates existing mental health problems. Community-based studies of adolescents suggest that about half of adolescents who meet criteria for an SUD also meet criteria for a co-occurring mental health condition (Armstrong & Costello, 2002). In clinical samples, rates of comorbid substance use and mental health are even higher, with up to 75% of adolescents meeting criteria for a dual diagnosis (Grella, Hser, Joshi, & Rounds-Bryant, 2001). The mental health diagnoses that most commonly co-occur with adolescent SUDs are externalizing disorders (e.g., conduct disorder, oppositional defiant disorder, and ­attention-deficit/hyperactivity disorder; Armstrong & Costello, 2002). Internalizing disorders (e.g., anxiety disorders, depression, posttraumatic stress disorder) also commonly occur with SUDs in this age cohort (Chan, Dennis, & Funk, 2008). Dually diagnosed adolescents generally begin using substances at an earlier age, and use more frequently and for a longer duration than do those without a comorbid condition (Grella et al., 2001; Rowe, Liddle, Greenbaum, & Henderson, 2004). Relative to adolescents with only an SUD diagnosis, those with a dual diagnosis report more school and interpersonal problems, criminal involvement, history of sexual or physical abuse, suicidal behavior, and parental substance use or mental health disorders (for a review, see Hersh, Curry, & Becker, 2013).

Physical Health Problems Physical health problems associated with adolescent substance use and SUDS include injuries due to accidents (including car crashes), physical disabilities and diseases, and the effects of possible overdoses (for a review, see Crowe, 1998). National

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data on emergency room admissions from 2011 found that there were nearly 440,000 drug-related admissions to the emergency department among individuals age 20 and under; just under half of the admissions (43%) were associated with alcohol use, whereas the remainder were associated with other illicit drug use, nonmedical use of prescription drugs, need for acute detoxification, drug-related suicide attempts, and adverse reactions to pharmaceuticals. Alcohol and illicit drug use are consistently associated with leading causes of death in this age cohort: suicide, accidents, and violent crime (Dawkins, 1997; Keyes, Brady, & Li, 2015; Wong, Zhou, Goebert, & Hishinuma, 2013). Longitudinal studies have confirmed that adolescent substance use is also associated with future physical health problems. A longitudinal study of early adolescents found that substance use predicted physical health impairments in later adolescence (Hansell & White, 1991). Along the same lines, a 4-year study found that late adolescents who reported cannabis and other illicit drug use were more likely to experience increased objective and subjective trouble with their health during their early adulthood (Newcomb & Bentler, 1987).

Summary of Consequences Adolescent substance use is associated with negative consequences including sexually transmitted infections, unintended pregnancy, truancy, delinquency, mental health problems, physical health problems, and death. Longitudinal studies have indicated that the relationship between many of these consequences and adolescent substance use is bidirectional, with negative risk factors predicting increased likelihood of substance use and substance use predicting increased likelihood of negative consequences (e.g., Begle et al., 2011; Chou et al., 2006; Mulvey et al., 2010). Furthermore, data from clinical and community samples indicates that these consequences rarely occur in isolation; adolescents who use substances and who meet criteria for SUDs often have a host of co-occurring problems (SAMHSA, 1999). Collectively these consequences highlight the need for evidence-based intervention models to address and prevent the escalation of adolescent substance use.

Evidence-Based Treatment Approaches To date, research has examined the effectiveness of behavioral treatment approaches for adolescent substance use problems across the continuum of care ranging from early intervention for adolescents experimenting with alcohol or drugs to residential care for adolescents with severe SUDs. Evaluating outpatient behavioral interventions is especially important because over 80% of adolescents receive SUD treatment in outpatient settings (SAMHSA, 2014). In 2008, Becker and Curry reviewed 57 outpatient interventions for adolescent SUDs and found that cognitive–behavioral therapy (CBT), ecological family therapy, and brief motivational interviewing (MI) models had the greatest evidentiary support. Around the same time, Waldron and Turner (2008) published a meta-analytic review and concluded that three specific treatment models: functional family therapy, multidimensional family therapy, and group CBT met criteria to be deemed well established. A more recent 2014 review by Hogue and colleagues used a similar classification system as Becker and Curry (2008) by focusing on type of therapy (e.g., ecological family therapy) instead of specific “brands” of therapy (e.g., multidimensional family therapy). This recent review deemed CBT models (individual and group), ecological family therapy models, and integrated models (combining motivation enhancement therapy and CBT) as well established, and family-based behavioral treatments and motivational models as probably efficacious. We review the evidence in support of prevailing outpatient models next.

Family Therapy Models Early literature examining adolescent SUD treatment focused almost exclusively on family-based modalities (e.g., Liddle & Dakof, 1995; Stanton & Shadish, 1997; Waldron, 1997) and found support for many of these modalities across the board. However, much of this research suffered from weak methodology, including small and homogeneous sample sizes, short follow-up windows, limited outcome measures, and lack of comparison groups (see Becker & Curry, 2008). More recently, 327

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family therapies continue to be the most widely researched, and studies have been more methodologically rigorous (Hogue et al., 2014). Within this large body of literature, there have been five predominant types of family models: behavioral, systems functional, ecological, and educational (see Becker & Curry, 2008). Classifications are based on how the treatment addresses intrafamilial and extrafamilial family influences on the development and maintenance of adolescent substance use. Behavioral approaches apply principles of operant and social learning within the family context to promote social behaviors and reduce substance use, whereas systems approaches attempt to restructure problematic family interaction patterns associated with adolescents’ substance use. Functional approaches integrate principles of systems and behavioral models. Ecological models directly target intrafamilial relationships as well as multiple interacting, nested systems in which adolescents develop (e.g., school, peers, juvenile justice). Finally, educational models focus on providing psychoeducation to the families of adolescents with SUDs. Of these five types of family therapy, ecological family models have the most evidentiary support (Becker & Curry, 2008). These models typically offer intensive home-based care and include specific brands such as multidimensional family therapy (MDFT; Liddle, 1991), multisystemic therapy (Henggeler et al., 1991), and ecologically based family therapy (Slesnick & Prestopnik, 2005). Over the past decade, ecological family models have been tested in three efficacy studies and four effectiveness studies, many of which specifically focused on juvenile justice involved adolescents (see Hogue et al., 2014). Across these studies, ecological family models were consistently more effective than standard office-based care (e.g., drug court, uncontrolled therapy) in reducing adolescent substance use. Other than ecological family models, the only family therapy model with sufficient data to be deemed well established was functional family therapy (FFT; Barton & Alexander, 1981); FFT performed as well as other active conditions and outperformed standard care in two recent randomized trials (Hogue et al., 2014). 328

Cognitive–Behavioral Therapy According to CBT models, adolescent substance use is a learned behavior as a result of social interactions with parents, siblings, peers, or the media and a consequence of environmental contingencies (Akers, Krohn, Lanza-Kaduce, & Radosevich, 1979). CBT approaches combine cognitive strategies that aim to identify and alter distorted thinking, with behavioral strategies to learn coping, communication, problemsolving, and substance refusal skills (see Kendall, 2012). CBT models are often delivered as part of an integrated package consisting of at least one other behavioral treatment. For instance, over the past 10 years three efficacy studies have tested CBT models integrated with motivation enhancement therapy (MET; see Hogue et al., 2014). These studies provided evidence that MET/CBT outperforms treatment as usual and performs as well as more intensive treatment models. Waldron and Turner’s 2008 meta-analysis concluded that group based CBT was a well-established treatment, whereas individual CBT was probably efficacious and in need of further research. More recently, Hogue et al. (2014) concluded that the evidence base in support of individual CBT had improved such that both types of CBT models merited designation as well established. The systematic review by Becker and Curry (2008) rated the quality of evidence in support of different models and concluded that although CBT was not the most frequently tested model, it was the model tested in the highest proportion of methodologically strong studies.

Motivational Interviewing/Motivation Enhancement Therapy MI is a brief treatment model consisting of one or two sessions, which is designed to enhance intrinsic motivation for behavior change using a combination of client-centered and directive techniques aimed toward exploring and resolving ambivalence (Miller & Rollnick, 2002). MET models are an adaptation of MI that include one or more client feedback sessions during which the counselor presents normative feedback in a nonconfrontational manner. Hogue and colleagues (2014) identified five effectiveness studies over the past decade that

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specifically tested MI/MET models, all of which recruited outside of traditional treatment settings (e.g., schools, emergency departments, primary care). Data in support of MI/MET models across these studies were mixed, with some studies finding that MI/MET was more effective than assessment only and other studies finding that MI/MET was no more effective than a minimal contact control (Hogue et al., 2014). A potential strength of MI/MET models is their low cost—systematic reviews indicate that most interventions examining the efficacy of MI/MET have consisted of single treatment sessions and been implemented by clinical trainees (JensenDoss & Hawley, 2011). Additionally, several studies have provided data that MI/MET is associated with positive short-term effects, suggesting that it may be a promising short-term option for adolescents with less severe substance use and substance related problems (e.g., McCambridge & Strang, 2004; Peterson, Baer, Wells, Ginzler, & Garrett, 2006). Based on the body of research in support of motivational models, Hogue and colleagues (2014) concluded that MET had evidence of effectiveness when delivered as a precursor to CBT (MET+CBT and MET+CBT+FFT), but that neither MI nor MET had yet proven sufficient as a stand-alone treatment for adolescent SUD. Of note, however, a recent meta-analysis by Tanner-Smith and colleagues (2013) reported that the combination of MET and CBT did not perform consistently better than a variety of comparison conditions. Therefore, there is a need for more effectiveness research on MI and MET, particularly when integrated with other evidence-based treatment models.

Summary of Models There are multiple evidence-based treatment models for adolescents with SUDs that have been shown to be superior to standard care (see Table 15.1). Specifically, there are several well-established treatment models (i.e., ecological family therapy, CBT, and integrated MET/CBT models with or without FFT) as well as models that are probably efficacious (i.e., family behavioral therapy and MI/MET). In contrast to the adult treatment literature, in which the use of contingency management

(CM; i.e., motivational incentives) has a robust evidence base (see Dutra et al., 2008); no studies have tested CM as a stand-alone intervention for adolescents (though two studies found encouraging results when combining CM with other models; see Hogue et al., 2014) suggesting that CM may be worthy of further exploration. The effect sizes of various adolescent SUD treatment models are typically small (Waldron & Turner, 2008), suggesting that even among the strongest performing models there is room for improvement. Furthermore, relatively little research has been conducted on moderators (defined as variables that are present at baseline and interact with treatment condition to influence treatment outcome) and mediators (defined as variables that can partially explain treatment outcome) of adolescent SUD treatment. Studies of moderators are needed to answer the vital question, “which treatment works best for whom,” whereas studies of mediators address the question, “why does the treatment work” (see Kiesler, 1966; Kraemer, Wilson, Fairburn, & Agras, 2002). As synthesized by Hogue et al. (2014), the few extant studies of moderators suggest that adolescents with greater clinical complexity (e.g., more severe impairment, more co-occurring disorders) may be likely to benefit from ecological family approaches that target multiple systems. Moreover, there is evidence that age and ethnicity moderate treatment response to specific models, though there has not been a consistently interpretable pattern of moderation across studies. Studies of mediation have been rarer. To date, only one study by Henderson et al. (2009) has demonstrated mediation of adolescent SUD treatment outcomes; this study found that the effects of MDFT on adolescent SUD symptoms could be explained by improvement in parental monitoring. More studies of moderators and mediators are warranted to facilitate the matching of adolescents to specific ingredients and distill the essential treatment ingredients. New Frontiers in Adolescent Substance Use Research The description of treatment models summarizes the extensive and promising knowledge base developed 329

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Table 15.1 Prevailing Treatment Models for Adolescent Substance Use Disorders Approach Family therapy models

Model Ecological

Functional

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Systems

Behavioral

Educational Cognitive–behavioral therapy

Individual

Group Motivational techniques

Motivational interviewing

Motivation enhancement therapy Integrated models

Motivation enhancement therapy + cognitive– behavioral therapy Motivation enhancement therapy + cognitive– behavioral therapy + family behavioral

for adolescent SUD treatment, with research-tested interventions covering multiple treatment formats (e.g., individual, group, family) and orientations (e.g., behavioral, motivational, educational, systems). These treatment approaches are designed to address the key risk, protective, and promotive factors identified in prevailing etiological models of adolescent SUDs. As such, these disparate treatment 330

Description

Support

Directly targets intrafamilial relationships and interacting, nested systems in which adolescents develop. Integrates principles of operant and social learning with the process of restructuring problematic family interaction patterns. Restructures problematic family interaction patterns associated with substance use. Applies principles of operant and social learning within the family context to promote social behaviors and reduce substance use. Provides psychoeducation to adolescents and their families. Combines cognitive strategies that aim to identify and alter distorted thinking with behavioral strategies to learn coping, communication, problemsolving, and substance refusal skills. Applies strategies described above in a group format. Enhances intrinsic motivation for behavior change using client-centered and directive techniques to explore and resolve ambivalence. Presents normative feedback in a nonconfrontational manner to reduce substance use. Combines nonconfrontation, normative feedback with cognitive and behavioral strategies to reduce substance use.

Well established

Combines integrated motivation enhancement therapy + cognitive– behavioral therapy model above with family sessions focused on promoting new coping skills.

Well established

Probably efficacious

Needs more research

Probably efficacious

Needs more research Well established

Probably efficacious Probably efficacious

Well established

models share a common goal: to ameliorate some of the most serious consequences associated with adolescent substance use. Although major advances have been made in the field in terms of the ability to assess, diagnose, and treat adolescents with or at risk of SUD, progress is hindered by the persistent “treatment gap” between those adolescents who need treatment and those who

Substance Use Disorders in Adolescents

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receive an effective intervention. As noted previously, over 90% of adolescents who meet criteria for an SUD diagnosis will not receive any type of specialty SUD treatment (Center for Behavioral Health Statistics and Quality, 2015). When considering the size and scope of the treatment gap, Hogue and colleagues (2014) made the following observation: These alarming trends crystallize a harsh truth in our current behavioral healthcare system: Our very best ASU treatments are not being effectively delivered to the vast majority of the intended consumer base, thereby dramatically undercutting the potential public health benefits afforded by the advances in treatment research. (p. 713) To capitalize on the field’s prior investments in treatment research, it is imperative that we develop new, proactive strategies to increase the use of evidencebased behavioral treatment. There are three priority areas for future work: (a) expanded availability of technology-delivered assessment and intervention; (b) promotion of Screening, Brief Intervention, and Referral to Treatment for Adolescents; and (c) broader use of direct-to-consumer marketing.

Technology-Delivered Assessment and Intervention In recent years, technology-delivered assessment and intervention models have emerged as a means of improving the reach of traditional behavioral treatments. Technology-delivered assessment models have several appealing attributes for patients in general and for adolescents specifically. For treatment providers, the use of electronically administered assessments may reduce administrative burden, decrease the likelihood of missed or inaccurate data, facilitate incorporation of assessment results into existing records (including the electronic medical record if applicable), and enable instantaneous scoring to inform subsequent treatment decisions (see Buntin, Burke, Hoaglin, & Blumenthal, 2011). For adolescents, it has been shown that technologydelivered questionnaires are perceived as more confidential than paper-based measures (Pedersen, Grow, Duncan, Neighbors, & Larimer, 2012) and

that adolescents have exceptional comfort with technology (Lenhart, 2015). Several studies have shown that adolescents reporter higher (and presumably more accurate) levels of substance use on technologydelivered assessment measures than via more traditional assessment modalities; furthermore, this sensitivity to delivery channel is more pronounced among adolescents than adults (for a review, see Brener, Billy, & Grady, 2003). Similarly, interventions delivered via technology confer several advantages. A key benefit for treatment providers is the ability to balance flexibility with fidelity, by allowing content to be individually tailored to the patient’s specific needs, whereas retaining core elements that can be delivered at low-cost with low burden on provider time (Buntin et al., 2011). Thus far, several technology-delivered screening and assessment tools have been validated for the detection of high risk substance use, predominantly in adults (see Rooke, Thorsteinsson, Karpin, Copeland, & Allsop, 2010). Intervention models for adolescents have also recently begun to emerge, though these models have primarily focused on cigarette use (Pallonen et al., 1998) and on prevention rather than intervention (Marsch, Bickel, & Grabinski, 2007; Paperny, Aono, Lehman, Hammar, & Risser, 1990). As the ubiquity of technology increases in our society, technology-delivered assessment and intervention models—delivered via computer, tablet, smartphone, or social media—represent an exciting and timely avenue for future work.

Screening, Brief Intervention, and Referral to Treatment Another potential strategy to address the vast underidentification of adolescent substance use across adolescent-involved settings is more widespread use of Screening, Brief Intervention, and Referral to Treatment (SBIRT) procedures. SBIRT is an early intervention approach designed to promote universal screening of substance use in settings where adolescents spend a great deal of time (SAMHSA, 2013). Universal screening is then followed by the use of basic algorithms to match the adolescent to the appropriate level of care: (a) brief advice; (b) a stand-alone brief intervention (BI), typically rooted in an MI framework, to modify substance 331

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use behavior and enhance readiness to seek treatment; or (c) a BI paired with a referral to more intensive treatment (RT; see Mitchell, Gryczynski, O’Grady, & Schwartz, 2013). In theory, these treatment elements are ideally suited to improve the detection of adolescents at risk of SUDs, create a viable pathway for adolescents in need of services, and help to close the gap between treatment need and use (SAMHSA, 2013). It is precisely because of these theoretical benefits that virtually every leading behavioral health organization in the United States has endorsed the use of SBIRT procedures including the American Academy of Pediatrics, American Medical Association, National Institute on Alcohol Abuse and Alcoholism, National Institute on Drug Abuse, and SAMHSA. In practice, however, the data in support of SBIRT for adolescents have been limited. The United States Preventative Services Task Force reviewed the evidence base in support of SBIRT in 2014 and deemed it insufficient to recommend routine use of the model in pediatric primary care (Moyer, 2013); data on the effectiveness of screening plus BI were relatively inconsistent, whereas no prior studies evaluated the effectiveness of RT. As noted in a recent review (Ozechowski, Becker, & Hogue, 2016), a primary reason why SBIRT has failed to live up to its potential among adolescents may be a lack of consideration of the developmental appropriateness of the model. Building on the extant literature and developmental theory, Ozechowski et al. (2016) developed a set of recommended adaptations to the SBIRT model to more optimally serve adolescent populations. Collectively, the set of recommendations is referred to as the SBIRT-Adolescent framework. Overarching principles within the framework include a reliance on proactive (versus reactive) procedures to engage adolescents, involvement of the primary caregiver in all stages of the approach, and use of technology to streamline service delivery. Although the specific techniques in SBIRTAdolescent have not yet been subject to rigorous empirical testing, Ozechowski and colleagues (2016) reviewed a robust empirical and conceptual literature base supporting these broad treatment principles as a means of enhancing the detection of adolescent substance use in nonclinical settings. 332

Direct-to-Consumer Marketing A complementary strategy to address the adolescent SUD treatment gap (see Becker, 2015a; Santucci, McHugh, & Barlow, 2012) is use of direct-toconsumer (DTC) marketing to encourage adolescents and caregivers to request substance use screening and intervention. In contrast to traditional treatment dissemination and implementation approaches that attempt to bring services to the patient (or “push” the service to patients who enter the treatment system), a DTC marketing approach aims to bring patients to the service (or “pull” the service from the treatment system more actively). To be successful, DTC marketing should target not only those in need of a specific health service (i.e., adolescents in need of SUD screening) but also those responsible for making decisions about the initiation of service (i.e., primary caregivers). DTC marketing campaigns that encourage caregivers to request a comprehensive behavioral health screening for their teens in gateway service settings (e.g., primary care offices, schools, juvenile justice), could thereby offer a complementary approach to the SBIRT-Adolescent principles described previously. To date, DTC marketing has been used to promote a range of public health efforts, including smoking cessation, HIV prevention, immunization, and breastfeeding (Andreasen, 1995; Centers for Disease Control and Prevention, 2016), and data on initiatives designed to encourage patients to request medical services (e.g., genetic cancer screening; Myers et al., 2006) have been encouraging. The most heavily studied example is the marketing of pharmaceutical products (see Porter, 2011). Data from almost two decades of DTC pharmaceutical marketing reveal that it is associated with increases in both patient requests for medication and physician prescribing behaviors (see Becker & Midoun, 2016). The success of DTC marketing in getting patients to request psychiatric medication suggests that it has the potential to shape patients’ treatmentseeking behavior, and may be useful for promoting behavioral treatments. To live up to its potential, DTC efforts need to use language that is clear, easily understandable, and relevant to the target population (see Becker, 2015a). Currently, several national organizations

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that support behavioral health research have created websites to promote health education about adolescent SUD treatment for the public (see http://www. teendrugabuse.gov; http://www.effectivechildtherapy. com); unfortunately, recent qualitative research suggests that many of the terms used in existing health education materials (i.e., evidence-based practice) are misunderstood and viewed negatively by adolescents and caregivers (Becker, Spirito, & Vanmali, 2016). Researchers and clinicians interested in using DTC marketing are advised to solicit feedback from the target population about their unmet needs and communication preferences. Several well-validated frameworks from the marketing field (e.g., the Marketing Mix; see Becker, 2015b) are available to facilitate efforts in this area. Conclusion We presented contemporary diagnostic criteria for SUDs, described prevailing etiological models, discussed prevalence estimates and primary consequences of untreated substance use, and reviewed the evidence-base in support of outpatient behavioral treatments. We emphasized the need for a new frontier of adolescent substance use research focused on addressing the treatment gap between those adolescents who need treatment for an SUD and those who receive an effective intervention. We recommended greater reliance on technologydelivered assessment and intervention, widespread use of SBIRT-A, and investment in DTC marketing approaches. Effectively addressing the adolescent SUD treatment gap will likely require concerted efforts in each of these areas. Push strategies are needed to increase the supply of evidence-based treatment in the community (e.g., SBIRT), whereas pull strategies to increase adolescent and caregiver awareness of effective treatment models (e.g., DTC marketing). A comprehensive approach will therefore require coordination among multiple stakeholders including adolescents, caregivers, treatment developers, front-line providers, policymakers, research funders, and insurance payers. Adolescent SUDs rarely occur in a vacuum and are usually complicated by co-occurring physical and behavioral

health problems. Therefore, attempts to improve the quality and use of behavioral treatments for SUDs in adolescents would benefit from collaboration with medical and behavioral care providers treating the other conditions covered in this handbook. Such collaboration will be critical to enhance the assessment and treatment of adolescents with or at risk of SUDs, and ultimately to improve the outcomes and health of adolescents affected by substance use.

References Aarons, G. A., Brown, S. A., Stice, E., & Coe, M. T. (2001). Psychometric evaluation of the marijuana and stimulant effect expectancy questionnaires for adolescents. Addictive Behaviors, 26, 219–236. http:// dx.doi.org/10.1016/S0306-4603(00)00103-9 Akers, R. L., Krohn, M. D., Lanza-Kaduce, L., & Radosevich, M. (1979). Social learning and deviant behavior: A specific test of a general theory. American Sociological Review, 44, 636–655. http://dx.doi.org/ 10.2307/2094592 American Psychiatric Association. (2013). Diagnostic and statistical manual of mental disorders (5th ed.). Washington, DC: Author. Andreasen, A. (1995). Marketing social change: Changing behavior to promote health, social development and the environment. San Francisco, CA: Jossey-Bass. Angold, A., Costello, E. J., Erkanli, A., & Worthman, C. M. (1999). Pubertal changes in hormone levels and depression in girls. Psychological Medicine, 29, 1043–1053. http://dx.doi.org/10.1017/ S0033291799008946 Armstrong, T. D., & Costello, E. J. (2002). Community studies on adolescent substance use, abuse, or dependence and psychiatric comorbidity. Journal of Consulting and Clinical Psychology, 70, 1224–1239. http://dx.doi.org/10.1037/0022-006X.70.6.1224 Audrain-McGovern, J., Rodriguez, D., Tercyak, K. P., Neuner, G., & Moss, H. B. (2005). The impact of self-control indices on peer smoking and adolescent smoking progression. Journal of Pediatric Psychology, 31, 139–151. http://dx.doi.org/10.1093/jpepsy/jsi079 Barton, C., & Alexander, J. F. (1981). Functional family therapy. In A. S. Gurman & D. P. Kniskern (Eds.), Handbook of family therapy (Vol. 1, pp. 403–442). New York, NY: Brunner. Becker, S. J. (2015a). Direct-to-consumer marketing: A complementary approach to traditional dissemination and implementation efforts for mental health and substance abuse interventions. Clinical 333

Becker and Fisher

Psychology: Science and Practice, 22, 85–100. http:// dx.doi.org/10.1111/cpsp.12086 Becker, S. J. (2015b). Evaluating whether directto-consumer marketing can increase demand for evidence-based practice among parents of adolescents with substance use disorders: Rationale and protocol. Addiction Science and Clinical Practice, 10, 4. http://dx.doi.org/10.1186/s13722-015-0028-3

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Becker, S. J., & Curry, J. F. (2008). Outpatient interventions for adolescent substance abuse: A quality of evidence review. Journal of Consulting and Clinical Psychology, 76, 531–543. http://dx.doi.org/ 10.1037/0022-006X.76.4.531 Becker, S. J., & Curry, J. F. (2014). Testing the effects of peer socialization versus selection on alcohol and marijuana use among treated adolescents. Substance Use and Misuse, 49, 234–242. http://dx.doi.org/ 10.3109/10826084.2013.824479 Becker, S. J., & Midoun, M. M. (2016). Effects of directto-consumer advertising on patient prescription requests and physician prescribing: A systematic review of psychiatry-relevant studies. Journal of Clinical Psychiatry, 77, e1293–e1300. http:// dx.doi.org/10.4088/JCP.15r10325 Becker, S. J., Spirito, A., & Vanmali, R. (2016). Perceptions of “evidence-based practice” among the consumers of adolescent substance use treatment. Health Education Journal, 75, 358–369. http:// dx.doi.org/10.1177/0017896915581061 Begle, A. M., Hanson, R. F., Danielson, C. K., McCart, M. R., Ruggiero, K. J., Amstadter, A. B., . . . Kilpatrick, D. G. (2011). Longitudinal pathways of victimization, substance use, and delinquency: Findings from the National Survey of Adolescents. Addictive Behaviors, 36, 682–689. http://dx.doi.org/10.1016/ j.addbeh.2010.12.026 Behnke, M., & Smith, V. C., (2013). Prenatal substance abuse: Short- and long-term effects on the exposed fetus. Pediatrics, 131, e1009–e1024. http://dx.doi.org/ 10.1542/peds.2012-3931 Bennett, G. G., Wolin, K. Y., Robinson, E. L., Fowler, S., & Edwards, C. L. (2005). Perceived racial/ethnic harassment and tobacco use among African American young adults. American Journal of Public Health, 95, 238–240. http://dx.doi.org/10.2105/ AJPH.2004.037812 Bonn-Miller, M. O., Zvolensky, M. J., & Bernstein, A. (2007). Marijuana use motives: Concurrent relations to frequency of past 30-day use and anxiety sensitivity among young adult marijuana smokers. Addictive Behaviors, 32, 49–62. http://dx.doi.org/ 10.1016/j.addbeh.2006.03.018 Brener, N. D., Billy, J. O., & Grady, W. R. (2003). Assessment of factors affecting the validity of 334

self-reported health-risk behavior among adolescents: Evidence from the scientific literature. Journal of Adolescent Health, 33, 436–457. http:// dx.doi.org/10.1016/S1054-139X(03)00052-1 Brown, S. A., Christiansen, B. A., & Goldman, M. S. (1987). The Alcohol Expectancy Questionnaire: An instrument for the assessment of adolescent and adult alcohol expectancies. Journal of Studies on Alcohol, 48, 483–491. http://dx.doi.org/10.15288/jsa.1987.48.483 Brown, S. A., Goldman, M. S., & Christiansen, B. A. (1985). Do alcohol expectancies mediate drinking patterns of adults? Journal of Consulting and Clinical Psychology, 53, 512–519. http://dx.doi.org/ 10.1037/0022-006X.53.4.512 Buntin, M. B., Burke, M. F., Hoaglin, M. C., & Blumenthal, D. (2011). The benefits of health information technology: A review of the recent literature shows predominantly positive results. Health Affairs, 30, 464–471. http://dx.doi.org/ 10.1377/hlthaff.2011.0178 Burk, W. J., van der Vorst, H., Kerr, M., & Stattin, H. (2012). Alcohol use and friendship dynamics: Selection and socialization in early-, middle-, and late-adolescent peer networks. Journal of Studies on Alcohol and Drugs, 73, 89–98. http://dx.doi.org/ 10.15288/jsad.2012.73.89 Carver, C. S., & Scheier, M. F. (2000). On the structure of behavioral self-regulation. In M. Boekarts, P. R. Pintrich, & M. Zeidner (Eds.), Handbook of selfregulation (pp. 41–84). http://dx.doi.org/10.1016/ B978-012109890-2/50032-9 Castro, F. G., Stein, J. A., & Bentler, P. M. (2009). Ethnic pride, traditional family values, and acculturation in early cigarette and alcohol use among Latino adolescents. Journal of Primary Prevention, 30, 265–292. http://dx.doi.org/10.1007/s10935-009-0174-z Center for Behavioral Health Statistics and Quality. (2015). Behavioral health trends in the United States: Results from the 2014 National Survey on Drug Use and Health. Rockville, MD: Substance Abuse and Mental Health Services Administration. Centers for Disease Control and Prevention. (2016). Gateway to health communication and social marketing practice: Campaigns. Retrieved from http://www.cdc. gov/healthcommunication/campaigns/index.html Chan, Y. F., Dennis, M. L., & Funk, R. R. (2008). Prevalence and comorbidity of major internalizing and externalizing problems among adolescents and adults presenting to substance abuse treatment. Journal of Substance Abuse Treatment, 34, 14–24. http://dx.doi.org/10.1016/j.jsat.2006.12.031 Chassin, L. (2008). Juvenile justice and substance use. Future of Children, 18, 165–183. http://dx.doi.org/ 10.1353/foc.0.0017

Substance Use Disorders in Adolescents

Chou, L. C., Ho, C. Y., Chen, C. Y., & Chen, W. J. (2006). Truancy and illicit drug use among adolescents surveyed via street outreach. Addictive Behaviors, 31, 149–154. http://dx.doi.org/10.1016/ j.addbeh.2005.04.011

Copyright American Psychological Association. Not for further distribution.

Christiansen, B. A., Goldman, M. S., & Inn, A. (1982). Development of alcohol-related expectancies in adolescents: Separating pharmacological from sociallearning influences. Journal of Consulting and Clinical Psychology, 50, 336–344. http://dx.doi.org/10.1037/ 0022-006X.50.3.336 Chung, T., & Martin, C. S. (2001). Classification and course of alcohol problems among adolescents in addictions treatment programs. Alcoholism: Clinical and Experimental Research, 25, 1734–1742. http:// dx.doi.org/10.1111/j.1530-0277.2001.tb02184.x Chung, T., Martin, C. S., Winters, K. C., & Langenbucher, J. W. (2001). Assessment of alcohol tolerance in adolescents. Journal of Studies on Alcohol, 62, 687–695. http://dx.doi.org/10.15288/ jsa.2001.62.687 Cleveland, M. J., Feinberg, M. E., Bontempo, D. E., & Greenberg, M. T. (2008). The role of risk and protective factors in substance use across adolescence. Journal of Adolescent Health, 43, 157–164. http:// dx.doi.org/10.1016/j.jadohealth.2008.01.015 Cloninger, C. R., Sigvardsson, S., & Bohman, M. (1988). Childhood personality predicts alcohol abuse in young adults. Alcoholism: Clinical and Experimental Research, 12, 494–505. http://dx.doi.org/10.1111/ j.1530-0277.1988.tb00232.x Connery, H. S., Albright, B. B., & Rodolico, J. M. (2014). Adolescent substance use and unplanned pregnancy: Strategies for risk reduction. Obstetrics and Gynecology Clinics of North America, 41, 191–203. http://dx.doi.org/10.1016/j.ogc.2014.02.011 Cooper, M. L. (1994). Motivations for alcohol use among adolescents: Development and validation of a fourfactor model. Psychological Assessment, 6, 117–128. http://dx.doi.org/10.1037/1040-3590.6.2.117 Crowe, A. H. (1998). Drug identification and testing in the juvenile justice system: Summary. Washington, DC: U.S. Department of Justice. Retrieved from https:// www.ncjrs.gov/html/ojjdp/167889/contents.html Curran, P. J., Stice, E., & Chassin, L. (1997). The relation between adolescent alcohol use and peer alcohol use: A longitudinal random coefficients model. Journal of Consulting and Clinical Psychology, 65, 130–140. http://dx.doi.org/10.1037/0022-006X.65.1.130 Dawkins, M. P. (1997). Drug use and violent crime among adolescents. Adolescence, 32, 395–405. Development Services Group Inc. (2010). Truancy prevention: Literature review. Washington, DC: Office of Juvenile Justice and Delinquency Prevention.

Retrieved from http://www.ojjdp.gov/mpg/litreviews/ Truancy_Prevention.pdf Dick, D. M., Rose, R. J., Viken, R. J., & Kaprio, J. (2000). Pubertal timing and substance use: Associations between and within families across late adolescence. Developmental Psychology, 36, 180–189. http:// dx.doi.org/10.1037/0012-1649.36.2.180 Dutra, L., Stathopoulou, G., Basden, S. L., Leyro, T. M., Powers, M. B., & Otto, M. W. (2008). A metaanalytic review of psychosocial interventions for substance use disorders. American Journal of Psychiatry, 165, 179–187. http://dx.doi.org/10.1176/ appi.ajp.2007.06111851 Eberl, C., Wiers, R. W., Pawelczack, S., Rinck, M., Becker, E. S., & Lindenmeyer, J. (2013). Approach bias modification in alcohol dependence: Do clinical effects replicate and for whom does it work best? Developmental Cognitive Neuroscience, 4, 38–51. http://dx.doi.org/10.1016/j.dcn.2012.11.002 Ennett, S. T., Bauman, K. E., Foshee, V. A., Pemberton, M., & Hicks, K. A. (2001). Parent–child communication about adolescent tobacco and alcohol use: What do parents say and does it affect youth behavior? Journal of Marriage and Family, 63, 48–62. http://dx.doi.org/ 10.1111/j.1741-3737.2001.00048.x Epstein, J. A., Botvin, G. J., & Diaz, T. (2001). Linguistic acculturation associated with higher marijuana and polydrug use among Hispanic adolescents. Substance Use and Misuse, 36, 477–499. http:// dx.doi.org/10.1081/JA-100102638 Evans, J. S. B. (2008). Dual-processing accounts of reasoning, judgment, and social cognition. Annual Review of Psychology, 59, 255–278. http://dx.doi.org/ 10.1146/annurev.psych.59.103006.093629 Evans, J. S. B., & Stanovich, K. E. (2013). Dual-process theories of higher cognition: Advancing the debate. Perspectives on Psychological Science, 8, 223–241. http://dx.doi.org/10.1177/1745691612460685 Fadardi, J. S., & Cox, W. M. (2009). Reversing the sequence: Reducing alcohol consumption by overcoming alcohol attentional bias. Drug and Alcohol Dependence, 101, 137–145. http://dx.doi.org/ 10.1016/j.drugalcdep.2008.11.015 Fairlie, A. M., Wood, M. D., & Laird, R. D. (2012). Prospective protective effect of parents on peer influences and college alcohol involvement. Psychology of Addictive Behaviors, 26, 30–41. http:// dx.doi.org/10.1037/a0023879 Fergus, S., & Zimmerman, M. A. (2005). Adolescent resilience: A framework for understanding healthy development in the face of risk. Annual Review of Public Health, 26, 399–419. http://dx.doi.org/ 10.1146/annurev.publhealth.26.021304.144357 Forhan, S. E., Gottlieb, S. L., Sternberg, M. R., Xu, F., Datta, S. D., McQuillan, G. M., . . . Markowitz, L. E. 335

Becker and Fisher

(2009). Prevalence of sexually transmitted infections among female adolescents aged 14 to 19 in the United States. Pediatrics, 124, 1505–1512. http:// dx.doi.org/10.1542/peds.2009-0674

Copyright American Psychological Association. Not for further distribution.

Gardner, M., & Steinberg, L. (2005). Peer influence on risk taking, risk preference, and risky decision making in adolescence and adulthood: An experimental study. Developmental Psychology, 41, 625–635. http://dx.doi.org/10.1037/00121649.41.4.625 Grella, C. E., Hser, Y. I., Joshi, V., & Rounds-Bryant, J. (2001). Drug treatment outcomes for adolescents with comorbid mental and substance use disorders. Journal of Nervous and Mental Disease, 189, 384–392. http:// dx.doi.org/10.1097/00005053-200106000-00006 Grenard, J. L., Ames, S. L., Wiers, R. W., Thush, C., Sussman, S., & Stacy, A. W. (2008). Working memory capacity moderates the predictive effects of drug-related associations on substance use. Psychology of Addictive Behaviors, 22, 426–432. http://dx.doi.org/10.1037/0893-164X.22.3.426 Griffiths, M. (2005). A “components” model of addiction within a biopsychosocial framework. Journal of Substance Use, 10, 191–197. http:// dx.doi.org/10.1080/14659890500114359 Hansell, S., & White, H. R. (1991). Adolescent drug use, psychological distress, and physical symptoms. Journal of Health and Social Behavior, 32, 288–301. http://dx.doi.org/10.2307/2136809 Harford, T. C., Yi, H. Y., & Grant, B. F. (2010). The five-year diagnostic utility of “diagnostic orphans” for alcohol use disorders in a national sample of young adults. Journal of Studies on Alcohol and Drugs, 71, 410–417. http://dx.doi.org/10.15288/ jsad.2010.71.410 Hasin, D., & Paykin, A. (1998). Dependence symptoms but no diagnosis: Diagnostic “orphans” in a community sample. Drug and Alcohol Dependence, 50, 19–26. http://dx.doi.org/10.1016/S0376-8716(98)00007-6 Hasin, D., & Paykin, A. (1999). DSM–IV alcohol abuse: Investigation in a sample of at-risk drinkers in the community. Journal of Studies on Alcohol, 60, 181–187. http://dx.doi.org/10.15288/ jsa.1999.60.181 Hawkins, J. D., Catalano, R. F., & Miller, J. Y. (1992). Risk and protective factors for alcohol and other drug problems in adolescence and early adulthood: Implications for substance abuse prevention. Psychological Bulletin, 112, 64–105. http://dx.doi.org/ 10.1037/0033-2909.112.1.64 Henderson, C. E., Rowe, C. L., Dakof, G. A., Hawes, S. W., & Liddle, H. A. (2009). Parenting practices as mediators of treatment effects in an early-intervention trial of multidimensional family therapy. American 336

Journal of Drug and Alcohol Abuse, 35, 220–226. http:// dx.doi.org/10.1080/00952990903005890 Henderson, C. E., Young, D. W., Jainchill, N., Hawke, J., Farkas, S., & Davis, R. M. (2007). Program use of effective drug abuse treatment practices for juvenile offenders. Journal of Substance Abuse Treatment, 32, 279–290. http://dx.doi.org/10.1016/j.jsat.2006.12.021 Henggeler, S. W., Borduin, C. M., Melton, G. B., Mann, B. J., Smith, L., Hall, J. A., . . . Fucci, B. R. (1991). Effects of multisystemic therapy on drug use and abuse in serious juvenile offenders: A progress report from two outcome studies. Family Dynamics of Addiction Quarterly, 1(3), 40–51. Henry, K. L., Thornberry, T. P., & Huizinga, D. H. (2009). A discrete-time survival analysis of the relationship between truancy and the onset of marijuana use. Journal of Studies on Alcohol and Drugs, 70, 5–15. http://dx.doi.org/10.15288/ jsad.2009.70.5 Hernandez, L., Lavingne, A., Wood, M., & Weirs, R. W. (2015). Moderators and mediators of treatments for youth with substance use disorders. In M. Maric, P. J. M. Prins, & T. H. Ollendick (Eds.), Moderators and mediators of youth treatment outcomes (pp. 174–209). New York, NY: Oxford University Press. Hersh, J., Curry, J. F., & Becker, S. J. (2013). The influence of comorbid depression and conduct disorder on MET/CBT treatment outcome for adolescent substance use disorders. International Journal of Cognitive Therapy, 6, 325–341. http:// dx.doi.org/10.1521/ijct.2013.6.4.325 Hogue, A., Henderson, C. E., Ozechowski, T. J., & Robbins, M. S. (2014). Evidence base on outpatient behavioral treatments for adolescent substance use: Updates and recommendations, 2007–2013. Journal of Clinical Child and Adolescent Psychology, 43, 695–720. http://dx.doi.org/10.1080/15374416.2014. 915550 Holley, L. C., Kulis, S., Marsiglia, F. F., & Keith, V. M. (2006). Ethnicity versus ethnic identity: What predicts substance use norms and behaviors? Journal of Social Work Practice in the Addictions, 6(3), 53–79. http://dx.doi.org/10.1300/J160v06n03_06 Hussong, A., Bauer, D., & Chassin, L. (2008). Telescoped trajectories from alcohol initiation to disorder in children of alcoholic parents. Journal of Abnormal Psychology, 117, 63–78. http://dx.doi.org/ 10.1037/0021-843X.117.1.63 Hussong, A. M., Jones, D. J., Stein, G. L., Baucom, D. H., & Boeding, S. (2011). An internalizing pathway to alcohol use and disorder. Psychology of Addictive Behaviors, 25, 390–404. http://dx.doi.org/10.1037/ a0024519 Jaccard, J., Blanton, H., & Dodge, T. (2005). Peer influences on risk behavior: An analysis of the

Substance Use Disorders in Adolescents

effects of a close friend. Developmental Psychology, 41, 135–147. http://dx.doi.org/10.1037/00121649.41.1.135 Jackson, K. M., & Sher, K. J. (2003). Alcohol use disorders and psychological distress: A prospective state-trait analysis. Journal of Abnormal Psychology, 112, 599–613. http://dx.doi.org/10.1037/0021843X.112.4.599

Copyright American Psychological Association. Not for further distribution.

Jensen-Doss, A., & Hawley, K. M. (2011). Understanding clinicians’ diagnostic practices: Attitudes toward the utility of diagnosis and standardized diagnostic tools. Administration and Policy in Mental Health and Mental Health Services Research, 38, 476–485. http:// dx.doi.org/10.1007/s10488-011-0334-3 Johnston, L., O’Malley, P., Bachman, J., & Schulenberg, J. (2016). Monitoring the Future national survey results on drug use, 1975–2015: Overview, key findings on adolescent drug use. Retrieved from http://www. monitoringthefuture.org/pubs/monographs/ mtf-overview2015.pdf Jones, B. T., Corbin, W., & Fromme, K. (2001). A review of expectancy theory and alcohol consumption. Addiction, 96, 57–72. http://dx.doi.org/10.1046/ j.1360-0443.2001.961575.x Kann, L., McManus, T., Harris, W. A., Shanklin, S. L., Flint, K. H., Hawkins, J., . . . Zaza, S. (2016). Youth risk behavior surveillance–United States, 2015. MMWR: Surveillance Summaries, 65, 1–174. Kendall, P. C. (Ed.). (2012). Child and adolescent therapy: Cognitive–behavioral procedures (4th ed.). New York, NY: Guilford Press. Kendler, K. S., Prescott, C. A., Myers, J., & Neale, M. C. (2003). The structure of genetic and environmental risk factors for common psychiatric and substance use disorders in men and women. Archives of General Psychiatry, 60, 929–937. http://dx.doi.org/10.1001/ archpsyc.60.9.929 Keren, G., & Schul, Y. (2009). Two is not always better than one: A critical evaluation of two-system theories. Perspectives on Psychological Science, 4, 533–550. http://dx.doi.org/10.1111/j.1745-6924.2009.01164.x Keyes, K. M., Brady, J. E., & Li, G. (2015). Effects of minimum legal drinking age on alcohol and marijuana use: Evidence from toxicological testing data for fatally injured drivers aged 16 to 25 years. Injury Epidemiology, 2, 1. http://dx.doi.org/10.1186/ s40621-014-0032-1 Kiesler, D. J. (1966). Some myths of psychotherapy research and the search for a paradigm. Psychological Bulletin, 65, 110–136. http://dx.doi.org/10.1037/ h0022911 King, S. M., Iacono, W. G., & McGue, M. (2004). Childhood externalizing and internalizing psychopathology in the prediction of early substance

use. Addiction, 99, 1548–1559. http://dx.doi.org/ 10.1111/j.1360-0443.2004.00893.x Kraemer, H. C., Wilson, G. T., Fairburn, C. G., & Agras, W. S. (2002). Mediators and moderators of treatment effects in randomized clinical trials. Archives of General Psychiatry, 59, 877–883. http:// dx.doi.org/10.1001/archpsyc.59.10.877 Kruglanski, A. W., & Gigerenzer, G. (2011). Intuitive and deliberate judgments are based on common principles. Psychological Review, 118, 97–109. http:// dx.doi.org/10.1037/a0020762 Kuntsche, E., Stewart, S. H., & Cooper, M. L. (2008). How stable is the motive-alcohol use link? A cross-national validation of the Drinking Motives Questionnaire Revised among adolescents from Switzerland, Canada, and the United States. Journal of Studies on Alcohol and Drugs, 69, 388–396. http:// dx.doi.org/10.15288/jsad.2008.69.388 Kwate, N. O. A., Valdimarsdottir, H. B., Guevarra, J. S., & Bovbjerg, D. H. (2003). Experiences of racist events are associated with negative health consequences for African American women. Journal of the National Medical Association, 95, 450–460. Lenhart, A. (2015). Teens, social media, and technology overview. Retrieved from http://www.pewinternet. org/files/2015/04/PI_TeensandTech_Update2015_ 0409151.pdf Levy, S., Sherritt, L., Gabrielli, J., Shrier, L. A., & Knight, J. R. (2009). Screening adolescents for substance use-related high-risk sexual behaviors. Journal of Adolescent Health, 45, 473–477. http:// dx.doi.org/10.1016/j.jadohealth.2009.03.028 Liddle, H. (1991). A multidimensional model for treating the adolescent who is abusing alcohol or other drugs. In W. Snyder & T. Ooms (Eds.), Empowering families, helping adolescents: Family-centered treatment of adolescents with mental health and substance abuse problems (pp. 91–100). Washington, DC: United States Public Health Service. Liddle, H., & Dakof, G. A. (1995). Family-based treatments for adolescent drug abuse: State of the science. In E. Rahdert & D. Czechowicz (Eds.), Adolescent drug abuse: Clinical assessment and therapeutic interventions (pp. 218–254). Rockville, MD: National Institute on Drug Abuse. Loeber, R. (1988). Natural histories of conduct problems, delinquency, and associated substance use. In B. B. Lahey & A. E. Kazdin (Eds.), Advances in clinical child psychology (pp. 73–124). http://dx.doi.org/ 10.1007/978-1-4613-9829-5_2 Macleod, J., Oakes, R., Copello, A., Crome, I., Egger, M., Hickman, M., . . . Smith, G. (2004). Psychological and social sequelae of cannabis and other illicit drug use by young people: A systematic review of 337

Becker and Fisher

longitudinal, general population studies. Lancet, 363, 1579–1588. http://dx.doi.org/10.1016/ S0140-6736(04)16200-4

Copyright American Psychological Association. Not for further distribution.

Maes, H. H., Woodard, C. E., Murrelle, L., Meyer, J. M., Silberg, J. L., Hewitt, J. K., . . . Eaves, L. J. (1999). Tobacco, alcohol and drug use in eight- to sixteen-year-old twins: The Virginia Twin Study of Adolescent Behavioral Development. Journal of Studies on Alcohol, 60, 293–305. http://dx.doi.org/ 10.15288/jsa.1999.60.293 Marsch, L. A., Bickel, W. K., & Grabinski, M. J. (2007). Application of interactive, computer technology to adolescent substance abuse prevention and treatment. Adolescent Medicine: State of the Art Reviews, 18, 342–356. Martin, C. S., Chung, T., Kirisci, L., & Langenbucher, J. W. (2006). Item response theory analysis of diagnostic criteria for alcohol and cannabis use disorders in adolescents: Implications for DSM–5. Journal of Abnormal Psychology, 115, 807–814. http:// dx.doi.org/10.1037/0021-843X.115.4.807 Martin, C. S., Chung, T., & Langenbucher, J. W. (2008). How should we revise diagnostic criteria for substance use disorders in the DSM–5? Journal of Abnormal Psychology, 117, 561–575. http:// dx.doi.org/10.1037/0021-843X.117.3.561 Masten, A. S., Faden, V. B., Zucker, R. A., & Spear, L. P. (2008). Underage drinking: A developmental framework. Pediatrics, 121(Suppl. 4), S235–S251. http://dx.doi.org/10.1542/peds.2007-2243A McBride, O., Teesson, M., Slade, T., & Baillie, A. (2010). Theoretical and observed subtypes of DSM–IV alcohol- and cannabis-use disorders in the Australian population. Journal of Studies on Alcohol and Drugs, 71, 597–606. http://dx.doi.org/10.15288/ jsad.2010.71.597 McCambridge, J., & Strang, J. (2004). The efficacy of single-session motivational interviewing in reducing drug consumption and perceptions of drug-related risk and harm among young people: Results from a multi-site cluster randomized trial. Addiction, 99, 39–52. http://dx.doi.org/10.1111/ j.1360-0443.2004.00564.x McMahon, R. C., Kouzekanani, K., DeMarco, L. A., Kusel, S. J., & Davidson, R. S. (1992). Cognitive motivations for drinking among alcoholics: Factor structure and correlates. American Journal of Drug and Alcohol Abuse, 18, 477–487. http://dx.doi.org/ 10.3109/00952999209051043 Miller, W. R., & Rollnick, S. (2002). Motivational interviewing: Preparing people for change (2nd ed.). New York, NY: Guilford Press. Minior, T., Galea, S., Stuber, J., Ahern, J., & Ompad, D. (2003). Racial differences in discrimination 338

experiences and responses among minority substance users. Ethnicity and Disease, 13, 521–527. Mitchell, S. G., Gryczynski, J., O’Grady, K. E., & Schwartz, R. P. (2013). SBIRT for adolescent drug and alcohol use: Current status and future directions. Journal of Substance Abuse Treatment, 44, 463–472. http://dx.doi.org/10.1016/j.jsat.2012.11.005 Moore, S. M., & Rosenthal, D. A. (1993). Venturesomeness, impulsiveness, and risky behavior among older adolescents. Perceptual and Motor Skills, 76, 98–98. http://dx.doi.org/10.2466/ pms.1993.76.1.98 Moyer, V. A., & the Preventive Services Task Force. (2013). Screening and behavioral counseling interventions in primary care to reduce alcohol misuse: U.S. preventive services task force recommendation statement. Annals of Internal Medicine, 159, 210–218. Mrug, S., & McCay, R. (2013). Parental and peer disapproval of alcohol use and its relationship to adolescent drinking: Age, gender, and racial differences. Psychology of Addictive Behaviors, 27, 604–614. http://dx.doi.org/10.1037/a0031064 Mulvey, E. P., Schubert, C., & Chassin, L. (2010). Substance use and delinquent behavior among serious adolescent offenders. OJJDP Juvenile Justice Bulletin: Pathways to Desistance. Washington, DC: U.S. Department of Justice. Myers, M. F., Chang, M.-H., Jorgensen, C., Whitworth, W., Kassim, S., Litch, J. A., . . . Bradley, L. A. (2006). Genetic testing for susceptibility to breast and ovarian cancer: Evaluating the impact of a directto-consumer marketing campaign on physicians’ knowledge and practices. Genetics in Medicine, 8, 361–370. http://dx.doi.org/10.1097/ 01.gim.0000223544.68475.6c Naimi, T. S., Lipscomb, L. E., Brewer, R. D., & Gilbert, B. C. (2003). Binge drinking in the preconception period and the risk of unintended pregnancy: Implications for women and their children. Pediatrics, 111, 1136–1141. National Center on Addiction and Substance Abuse. (2011). Adolescent substance use: America’s no. 1 public health problem. Retrieved from http://www. centeronaddiction.org/addiction-research/reports/ adolescent-substance-use National Institute on Alcohol Abuse and Alcoholism. (2015). Alcohol use disorder: A comparison between DSM–IV and DSM–5 (NIH Publication No. 13-7999). Retrieved from http://pubs.niaaa.nih.gov/ publications/dsmfactsheet/DSMfact.pdf Newcomb, M. D., & Bentler, P. M. (1987). The impact of late adolescent substance use on young adult health status and utilization of health services: A structural-equation

Substance Use Disorders in Adolescents

model over four years. Social Science and Medicine, 24, 71–82. http://dx.doi.org/10.1016/ 0277-9536(87)90141-9 Ozechowski, T. J., Becker, S. J., & Hogue, A. (2016). SBIRT-A: Adapting SBIRT to maximize developmental fit for adolescents in primary care. Journal of Substance Abuse Treatment, 62, 28–37. http://dx.doi.org/10.1016/j.jsat.2015.10.006

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Pabst, A., Baumeister, S. E., & Kraus, L. (2010). Alcoholexpectancy dimensions and alcohol consumption at different ages in the general population. Journal of Studies on Alcohol and Drugs, 71, 46–53. http:// dx.doi.org/10.15288/jsad.2010.71.46 Pallonen, U. E., Velicer, W. F., Prochaska, J. O., Rossi, J. S., Bellis, J. M., Tsoh, J. Y., . . . Prokhorov, A. V. (1998). Computer-based smoking cessation interventions in adolescents: Description, feasibility, and six-month follow-up findings. Substance Use and Misuse, 33, 935–965. http://dx.doi.org/10.3109/ 10826089809056250 Paperny, D. M., Aono, J. Y., Lehman, R. M., Hammar, S. L., & Risser, J. (1990). Computer-assisted detection and intervention in adolescent high-risk health behaviors. Journal of Pediatrics, 116, 456–462. http:// dx.doi.org/10.1016/S0022-3476(05)82844-6 Patterson, G. R., Reid, J. B., & Shishion, T. J. (1992). Antisocial boys. Eugene, OR: Castilia. Patton, G. C., McMorris, B. J., Toumbourou, J. W., Hemphill, S. A., Donath, S., & Catalano, R. F. (2004). Puberty and the onset of substance use and abuse. Pediatrics, 114, e300–e306. http://dx.doi.org/ 10.1542/peds.2003-0626-F Pedersen, E. R., Grow, J., Duncan, S., Neighbors, C., & Larimer, M. E. (2012). Concurrent validity of an online version of the Timeline Followback assessment. Psychology of Addictive Behaviors, 26, 672–677. http://dx.doi.org/10.1037/a0027945 Peeters, M., Monshouwer, K., van de Schoot, R. A., Janssen, T., Vollebergh, W. A., & Wiers, R. W. (2013). Automatic processes and the drinking behavior in early adolescence: A prospective study. Alcoholism: Clinical and Experimental Research, 37, 1737–1744. Peterson, P. L., Baer, J. S., Wells, E. A., Ginzler, J. A., & Garrett, S. B. (2006). Short-term effects of a brief motivational intervention to reduce alcohol and drug risk among homeless adolescents. Psychology of Addictive Behaviors, 20, 254–264. http://dx.doi.org/ 10.1037/0893-164X.20.3.254 Pollock, N. K., & Martin, C. S. (1999). Diagnostic orphans: Adolescents with alcohol symptom who do not qualify for DSM–IV abuse or dependence diagnoses. American Journal of Psychiatry, 156, 897–901. http://dx.doi.org/10.1176/ajp.156.6.897

Popovici, I., French, M. T., Pacula, R. L., Maclean, J. C., & Antonaccio, O. (2014). Cannabis use and antisocial behavior among youth. Sociological Inquiry, 84, 131–162. http://dx.doi.org/10.1111/soin.12027 Porter, D. (2011). Direct-to-Consumer (DTC) pharmaceutical marketing: Impacts and policy implications. SPNHA Review, 7, 5. Puleo, C., Conner, B., Benjamin, C., & Kendall, P. C. (2011). CBT for childhood anxiety and substance use at 7.4-year follow-up: A reassessment controlling for known predictors. Journal of Anxiety Disorders, 25, 690–696. http://dx.doi.org/10.1016/ janxdis.2011.03.005 Read, J. P., Wood, M. D., & Capone, C. (2005). A prospective investigation of relations between social influences and alcohol involvement during the transition into college. Journal of Studies on Alcohol, 66, 23–34. http://dx.doi/org/10.15288/jsa.2005.66.23 Rooke, S., Thorsteinsson, E., Karpin, A., Copeland, J., & Allsop, D. (2010). Computer-delivered interventions for alcohol and tobacco use: A meta-analysis. Addiction, 105, 1381–1390. http://dx.doi.org/ 10.1111/j.1360-0443.2010.02975.x Rose, R. J., Dick, D. M., Viken, R. J., & Kaprio, J. (2001). Gene-environment interaction in patterns of adolescent drinking: Regional residency moderates longitudinal influences on alcohol use. Alcoholism: Clinical and Experimental Research, 25, 637–643. http://dx.doi.org/10.1111/j.1530-0277.2001. tb02261.x Rothman, E. F., Wise, L. A., Bernstein, E., & Bernstein, J. (2009). The timing of alcohol use and sexual initiation among a sample of Black, Hispanic, and White adolescents. Journal of Ethnicity in Substance Abuse, 8, 129–145. http://dx.doi.org/10.1080/ 15332640902896984 Rowe, C. L., Liddle, H. A., Greenbaum, P. E., & Henderson, C. E. (2004). Impact of psychiatric comorbidity on treatment of adolescent drug abusers. Journal of Substance Abuse Treatment, 26, 129–140. http://dx.doi.org/10.1016/S0740-5472(03)00166-1 Ryan, S. M., Jorm, A. F., & Lubman, D. I. (2010). Parenting factors associated with reduced adolescent alcohol use: A systematic review of longitudinal studies. Australian and New Zealand Journal of Psychiatry, 44, 774–783. http://dx.doi.org/10.1080/ 00048674.2010.501759 Salas-Wright, C. P., Vaughn, M. G., Clark, T. T., Terzis, L. D., & Córdova, D. (2014). Substance use disorders among first- and second- generation immigrant adults in the United States: Evidence of an immigrant paradox? Journal of Studies on Alcohol and Drugs, 75, 958–967. http://dx.doi.org/10.15288/ jsad.2014.75.958

339

Becker and Fisher

Santelli, J. S., Robin, L., Brener, N. D., & Lowry, R. (2001). Timing of alcohol and other drug use and sexual risk behaviors among unmarried adolescents and young adults. Family Planning Perspectives, 33, 200–205. http://dx.doi.org/10.2307/2673782

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Santucci, L. C., McHugh, R. K., & Barlow, D. H. (2012). Direct-to-consumer marketing of evidence-based psychological interventions: Introduction. Behavior Therapy, 43, 231–235. http://dx.doi.org/10.1016/ j.beth.2011.07.003 Satterwhite, C. L., Torrone, E., Meites, E., Dunne, E. F., Mahajan, R., Ocfemia, M. C., . . . Weinstock, H. (2013). Sexually transmitted infections among U.S. women and men: Prevalence and incidence estimates, 2008. Sexually Transmitted Diseases, 40, 187–193. http://dx.doi.org/10.1097/ OLQ.0b013e318286bb53 Schuckit, M. A., Danko, G. P., Smith, T. L., Bierut, L. J., Bucholz, K. K., Edenberg, H. J., . . . Trim, R. (2008). The prognostic implications of DSM–IV abuse criteria in drinking adolescents. Drug and Alcohol Dependence, 97, 94–104. http://dx.doi.org/10.1016/ j.drugalcdep.2008.03.020 Sher, K. J., & Gotham, H. J. (1999). Pathological alcohol involvement: A developmental disorder of young adulthood. Development and Psychopathology, 11, 933–956. http://dx.doi.org/10.1017/ S0954579499002394 Silins, E., Horwood, L. J., Patton, G. C., Fergusson, D. M., Olsson, C. A., Hutchinson, D. M., . . . Mattick, R. P. (2014). Young adult sequelae of adolescent cannabis use: An integrative analysis. Lancet Psychiatry, 1, 286–293. http://dx.doi.org/10.1016/S22150366(14)70307-4 Slesnick, N., & Prestopnik, J. L. (2005). Ecologically based family therapy outcome with substance abusing runaway adolescents. Journal of Adolescence, 28, 277–298. http://dx.doi.org/10.1016/ j.adolescence.2005.02.008 Spear, L. P., & Brake, S. C. (1983). Periadolescence: Agedependent behavior and psychopharmacological responsivity in rats. Developmental Psychobiology, 16, 83–109. http://dx.doi.org/10.1002/dev.420160203 Stacy, A. W., & Wiers, R. W. (2010). Implicit cognition and addiction: A tool for explaining paradoxical behavior. Annual Review of Clinical Psychology, 6, 551–575. http://dx.doi.org/10.1146/annurev. clinpsy.121208.131444 Stanton, M. D., & Shadish, W. R. (1997). Outcome, attrition, and family–couples treatment for drug abuse: A metaanalysis and review of the controlled, comparative studies. Psychological Bulletin, 122, 170–191. Stattin, H., & Magnusson, D. (2003). Pubertal maturation in female development. Hillsdale, NJ: Erlbaum.

340

Steinberg, L. (2007). Risk taking in adolescence: New perspectives from brain and behavioral science. Current Directions in Psychological Science, 16, 55–59. http://dx.doi.org/10.1111/j.1467-8721.2007.00475.x Strack, F., & Deutsch, R. (2004). Reflective and impulsive determinants of social behavior. Personality and Social Psychology Review, 8, 220–247. http:// dx.doi.org/10.1207/s15327957pspr0803_1 Substance Abuse and Mental Health Services Administration. (1999). Treatment of adolescents with substance use disorders. Rockville, MD: Author. Substance Abuse and Mental Health Services Administration. (2013). Systems-level implementation of screening, brief intervention, and referral to treatment. Rockville, MD: Author. Substance Abuse and Mental Health Services Administration. (2014). Discharges from substance abuse treatment services. Rockville, MD: Author. Tanner-Smith, E. E., Wilson, S. J., & Lipsey, M. W. (2013). The comparative effectiveness of outpatient treatment for adolescent substance abuse: A metaanalysis. Journal of Substance Abuse Treatment, 44, 145–158. http://dx.doi.org/10.1016/j.jsat.2012.05.006 Tapert, S. F., Aarons, G. A., Sedlar, G. R., & Brown, S. A. (2001). Adolescent substance use and sexual risk-taking behavior. Journal of Adolescent Health, 28, 181–189. http://dx.doi.org/10.1016/S1054139X(00)00169-5 Tapert, S. F., Schweinsburg, A. D., & Brown, S. A., (2008). The influence of marijuana use on neurocognitive functioning in adolescents. Current Drug Abuse Reviews, 1, 99–111. http://dx.doi.org/ 10.2174/1874473710801010099 Tarter, R. E., Kirisci, L., Mezzich, A., Cornelius, J. R., Pajer, K., Vanyukov, M., . . . Clark, D. (2003). Neurobehavioral disinhibition in childhood predicts early age at onset of substance use disorder. American Journal of Psychiatry, 160, 1078–1085. http:// dx.doi.org/10.1176/appi.ajp.160.6.1078 Thornberry, T. P., & Krohn, M. D. (2001). The development of delinquency: An interactional perspective. In S. O. White (Ed.), Handbook for youth and justice (pp. 289–305). http://dx.doi.org/ 10.1007/978-1-4615-1289-9_15 Thush, C., Wiers, R. W., Ames, S. L., Grenard, J. L., Sussman, S., & Stacy, A. W. (2008). Interactions between implicit and explicit cognition and working memory capacity in the prediction of alcohol use in at-risk adolescents. Drug and Alcohol Dependence, 94, 116–124. http://dx.doi.org/10.1016/ j.drugalcdep.2007.10.019 Tobler, A. L., & Komro, K. A. (2010). Trajectories or parental monitoring and communication and effects on drug use among urban young adolescents. Journal

Substance Use Disorders in Adolescents

of Adolescent Health, 46, 560–568. http://dx.doi.org/ 10.1016/j.jadohealth.2009.12.008 Tripodi, S. J., & Bender, K. (2011). Substance abuse treatment for juvenile offenders: A review of quasiexperimental and experimental research. Journal of Criminal Justice, 39, 246–252. http://dx.doi.org/ 10.1016/j.jcrimjus.2011.02.007

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Unger, J. B., Ritt-Olson, A., Soto, D. W., & BaezcondeGarbanati, L. (2009). Parent–child acculturation discrepancies as a risk factor for substance use among Hispanic adolescents in Southern California. Journal of Immigrant and Minority Health, 11, 149–157. http:// dx.doi.org/10.1007/s10903-007-9083-5 Waldron, H. (1997). Adolescent substance abuse and family therapy outcome: A review of randomized trials. In T. H. Ollendick & R. J. Prinz (Eds.), Advances in clinical child psychology (pp. 199–234). http://dx.doi.org/10.1007/978-1-4757-9035-1_6 Waldron, H. B., & Turner, C. W. (2008). Evidence-based psychosocial treatments for adolescent substance abuse. Journal of Clinical Child and Adolescent Psychology, 37, 238–261. http://dx.doi.org/ 10.1080/15374410701820133 Wang, M.-T., Dishion, T. J., Stormshak, E. A., & Willett, J. B. (2011). Trajectories of family management practices and early adolescent behavioral outcomes. Developmental Psychology, 47, 1324–1341. http://dx.doi.org/10.1037/a0024026 White, W. (2007). Substance abuse versus substance dependence: Implications for management of the DUI offender [Briefing paper for the Administrative Office of the Illinois Courts and the Illinois Secretary of State]. Retrieved from http://www.williamwhitepapers.com/pr/ Abuse%26dependencepaperFinal5-30-2007.pdf Wiers, R. W., Bartholow, B. D., van den Wildenberg, E., Thush, C., Engels, R. C., Sher, K. J., . . . Stacy, A. W. (2007). Automatic and controlled processes and the development of addictive behaviors in adolescents: A review and a model. Pharmacology, Biochemistry and Behavior, 86, 263–283. http://dx.doi.org/10.1016/ j.pbb.2006.09.021 Wiers, R. W., Fromme, K., Latvala, A., & Stewart, S. H. (2012). Risk and protective factors for underage drinking. In P. D. Witte & M. C. Mitchell, Jr., (Eds.), Underage drinking: A report on drinking in the second half of life in Europe and North America (pp. 79–146). Louvain-la-Neuve, Belgium: Presses Universitaires de Louvain.

Wiers, R. W., Hoogeveen, K. J., Sergeant, J. A., & Gunning, W. (1997). High- and low-dose alcoholrelated expectancies and the differential associations with drinking in male and female adolescents and young adults. Addiction, 92, 871–888. http:// dx.doi.org/10.1111/j.1360-0443.1997.tb02956.x Wills, T. A., Cleary, S., Filer, M., Shinar, O., Mariani, J., & Spera, K. (2001). Temperament related to earlyonset substance use: Test of a developmental model. Prevention Science, 2, 145–163. http:// dx.doi.org/10.1023/A:1011558807062 Wills, T. A., & Dishion, T. J. (2004). Temperament and adolescent substance use: A transactional analysis of emerging self-control. Journal of Clinical Child and Adolescent Psychology, 33, 69–81. http:// dx.doi.org/10.1207/S15374424JCCP3301_7 Wilson, D. M., Killen, J. D., Hayward, C., Robinson, T. N., Hammer, L. D., Kraemer, H. C., . . . Taylor, C. B. (1994). Timing and rate of sexual maturation and the onset of cigarette and alcohol use among teenage girls. Archives of Pediatrics and Adolescent Medicine, 148, 789–795. http://dx.doi.org/10.1001/ archpedi.1994.02170080019004 Winters, K. C., Martin, C. S., & Chung, T. (2011). Substance use disorders in DSM–5 when applied to adolescents. Addiction, 106, 882–884. http:// dx.doi.org/10.1111/j.1360-0443.2010.03334.x Wong, S. S., Zhou, B., Goebert, D., & Hishinuma, E. S. (2013). The risk of adolescent suicide across patterns of drug use: A nationally representative study of high school students in the United States from 1999 to 2009. Social Psychiatry and Psychiatric Epidemiology, 48, 1611–1620. http://dx.doi.org/10.1007/ s00127-013-0721-z Yan, A. F., Chiu, Y. W., Stoesen, C. A., & Wang, M. Q. (2007). STD-/HIV-related sexual risk behaviors and substance use among U.S. rural adolescents. Journal of the National Medical Association, 99, 1386–1394. Young, D. W., Dembo, R., & Henderson, C. E. (2007). A national survey of substance abuse treatment for juvenile offenders. Journal of Substance Abuse Treatment, 32, 255–266. http://dx.doi.org/10.1016/ j.jsat.2006.12.018 Zucker, R. A., & Gomberg, E. S. (1986). Etiology of alcoholism reconsidered. The case for a biopsychosocial process. American Psychologist, 41, 783–793. http:// dx.doi.org/10.1037/0003-066X.41.7.783

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Chapter 16

Eating Disorders in Children and Adolescents

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Ellen E. Fitzsimmons-Craft, Anna M. Karam, and Denise E. Wilfley

Eating disorders (EDs) are serious mental health problems (Klump, Bulik, Kaye, Treasure, & Tyson, 2009). Mortality from anorexia nervosa (AN) is the highest of all mental disorders, with a 50-fold increase in the relative risk of death from suicide (Keel et al., 2003). Common mental health comorbidities include depression, anxiety, and substance abuse; medical complications include cardiovascular and neurological problems, and these disorders are associated with marked impairment in functioning (Klump et al., 2009). Delayed treatment results in poorer prognosis and greater relapse rates (American Psychiatric Association, 2006), underscoring the need for prevention, early identification, and effective intervention of these serious problems in youth. Diagnosing Eating Disorders in Youth New diagnostic criteria for EDs were published in 2013 in the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM–5; American Psychiatric Association, 2013). The previous DSM–IV–TR (American Psychiatric Association, 2000) recognized only two specific EDs—AN and bulimia nervosa (BN). However, a large number of individuals with disturbing eating problems presenting for care did not meet criteria for these disorders and were assigned the residual or “leftover” diagnostic category of ED not otherwise specified (EDNOS). Indeed, EDNOS, which encompassed a wide spectrum of EDs, including subthreshold AN, subthreshold BN, and binge eating disorder

(BED), accounted for the majority of ED diagnoses among youth presenting for treatment (Thomas, Vartanian, & Brownell, 2009). This ambiguity led to questions regarding the clinical meaningfulness of EDNOS, despite research showing that EDNOS was associated with similar levels of psychological and physiological morbidity compared with recognized ED diagnoses (Thomas et al., 2009). The DSM–5 addressed these issues by broadening the diagnostic criteria for AN and BN, formally recognizing BED, and further clarifying other EDs.

Anorexia Nervosa The DSM–5 identifies three core diagnostic features of AN: significantly low body weight (Criterion A); intense fear of gaining weight or becoming fat and/ or engaging in behaviors that interfere with weight gain regardless of a low body weight (Criterion B); and disturbance in how one’s weight or shape is experienced, self-evaluation overly influenced by weight or shape, or denial of the seriousness of one’s low body weight (Criterion C). Youth with AN often present with extreme weight loss or increasing growth in height without corresponding weight gain. Calculating body mass index (BMI) percentile is useful for determining whether body weight is significantly low (Criterion A), and significantly low body weight is considered less than what would minimally be expected given a child’s age, sex, developmental trajectory, and physical health. The Centers for Disease Control and Prevention (CDC) has used BMI-for-age below the 5th percentile as suggestive of underweight;

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however, youth with a BMI above this threshold may be deemed significantly underweight if they fail to maintain their expected growth trajectory (American Psychiatric Association, 2013). Individuals with AN typically display an intense fear of weight gain or becoming fat (Criterion B), accompanied by general preoccupation with weight, shape, and food (e.g., counting calories). These feelings are usually not alleviated by weight loss and may increase as weight loss progresses. However, some individuals with AN, particularly younger individuals, deny a fear of weight gain. In these cases, persistent engagement in behaviors that prevent weight gain can be used to establish Criterion B, including strict dieting, restriction of whole food groups (e.g., new-onset vegetarianism), and/or excessive physical activity that is engaged in rigidly. Dietary restriction and exercise may be pursued to the exclusion of other activities, and individuals may refuse to eat foods that were once enjoyed or avoid meals with others. The experience and significance of weight and shape are often distorted among individuals with AN (Criterion C). Many perceive themselves as being globally overweight despite being emaciated. Others may recognize their overall thinness but perceive certain body parts (e.g., abdomen, buttocks, thighs) as “too fat.” Furthermore, individuals often engage in a variety of body checking techniques to continually evaluate their shape and weight (e.g., frequent weighing, excessive mirror use), and selfesteem is highly dependent on weight and shape. Notably, amenorrhea (the loss or lack of onset of the menstrual period) was removed as a diagnostic criteria for AN in the DSM–5 given that it excluded boys, premenarchal girls, and girls taking contraceptives; however, delayed or interrupted pubertal development is often present among youth with AN (Rosen, 2010). Most individuals with AN also experience their symptoms as ego-syntonic and have pride in their ability to engage in extreme dietary restriction and exercise. Current symptoms, over the past three months, may be described as falling into one of two subtypes: (a) restricting type, whereby the individual does not engage in recurrent episodes of binge eating or purging; or (b) binge-eating/purging type for individuals who 344

have engaged in recurrent binge eating or purging (self-induced vomiting or the misuse of laxatives, diuretics, or enemas).

Bulimia Nervosa There are three essential features of BN according to the DSM–5: recurrent episodes of binge eating (i.e., eating an objectively large amount of food while experiencing loss of control [LOC]; Criterion A), recurrent inappropriate compensatory behaviors to prevent weight gain (e.g., self-induced vomiting; misuse of laxatives, diuretics, or other medications; fasting; excessive exercise; Criterion B), and undue influence of body shape and weight on self-evaluation (Criterion D). The binge eating and compensatory behaviors must occur, on average, at least once per week for three months (Criterion C), and the diagnosis of BN excludes individuals meeting criteria for AN (Criterion E; American Psychiatric Association, 2013). To be defined as a binge eating episode, the amount of food consumed must be larger than most individuals would eat in a similar period under similar circumstances. Context needs to be considered, as an amount that would be regarded as excessive for a regular meal may not be considered excessive in certain circumstances (e.g., a holiday meal). The food must also be consumed in a discrete period, typically defined as less than 2 hours, and snacking or grazing throughout the day would not be considered a binge. This excessive food consumption must be accompanied by LOC, defined as the inability to refrain from eating or to stop eating once started. Binge eating episodes are followed by a desire to purge, or compensate for the calories consumed, with self-induced vomiting being the most common compensatory behavior among individuals with BN (American Psychiatric Association, 2013). Individuals with BN usually weigh within the normal or high normal range, with lower BMIs associated with a history of AN; however, the percentage of individuals with BN who are overweight or obese has increased in recent years (Bulik, Marcus, Zerwas, Levine, & La Via, 2012).

Binge Eating Disorder The DSM–5 is the first edition to formally recognize BED, but past research has established its clinical significance (Wilfley, Wilson, & Agras, 2003).

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Eating Disorders in Children and Adolescents

The DSM–5 defines BED as recurrent episodes of binge eating (Criterion A) that occur, on average, at least once per week for three months (Criterion D). Additionally, the binge eating must cause marked distress (Criterion C) and include three or more of the following five features: (a) eating much more rapidly than normal; (b) eating until feeling uncomfortably full; (c) eating large amounts when not feeling physically hungry; (d) eating alone because of embarrassment by how much one is eating; and (e) feeling disgusted, depressed, or guilty after binge eating (Criterion B). The distinguishing feature between BED and BN is that binge eating episodes are not accompanied by compensatory behavior in BED, and the diagnosis of BED excludes individuals meeting criteria for AN or BN (Criterion E; American Psychiatric Association, 2013). The guidelines for determining whether an eating episode qualifies as a binge are the same for BN. In terms of weight status, nearly three-quarters of those with BED are overweight or obese (Kessler et al., 2013).

Other Diagnostic Categories Additional diagnostic categories include other specified feeding or eating disorder (OSFED), avoidant/restrictive food intake disorder (ARFID), and unspecified feeding or eating disorder (UFED). OSFED refers to EDs that cause clinically significant distress or impairment but that do not meet full criteria for AN, BN, or BED. Examples that can be specified using OSFED include atypical AN (i.e., weight within normal range), subthreshold BN (i.e., low frequency and/or limited duration), subthreshold BED (i.e., low frequency and/or limited duration), purging disorder (i.e., recurrent purging in the absence of binge eating), and night eating syndrome (i.e., recurrent episodes of night eating; eating after awakening from sleep or excessive food consumption after the evening meal). ARFID involves avoidance or restriction of food intake that manifests in a failure to meet appropriate nutritional and/or energy needs. It is distinct from AN in that shape and weight concerns do not appear to be the driving force behind the food restriction or avoidance (American Psychiatric Association, 2013). Rather, those behaviors may be driven by sensory characteristics of the food (e.g., textural aversions,

extreme sensitivity to color or smell, fear of choking or vomiting). UFED comprises any other ED that causes clinically significant distress or impairment that does not fit into other categories (American Psychiatric Association, 2013). Assessment Relative to assessing adults for EDs, assessing youth is particularly challenging. Certain difficulties are present with adults as well (e.g., reliance on self-report, the ego-syntonic nature of many EDs). However, there are difficulties unique to the assessment of youth: cognitive limitations inherent to normal developmental stage, which may lead to difficulty understanding somewhat abstract concepts like LOC and overvaluation of shape and weight; changing healthy weight targets due to physical maturation; variability in the onset of pubertal indicators (e.g., menarche) that relate to ED diagnostic criteria; lack of insight into the severity of the problem; and motivation to want to actively deny or minimize symptoms to avoid intervention efforts that may be imposed by caretakers to continue ED behaviors (Couturier & Lock, 2006; Loeb, Brown, & Goldstein, 2011). Effective assessment of EDs in youth must accurately measure the ED symptoms, consider information gathered considering normal youth development, and use multiple informants (Lock & Le Grange, 2013; Loeb, Brown, & Goldstein, 2011; Micali & House, 2011). To conduct the most accurate assessment, it is best when the youth and parents are interviewed separately (Lock & Le Grange, 2013).

Interview Measures In addition to the usual clinical interview, there are several standardized, semi-structured interviews used specifically for evaluating and diagnosing EDs in youth. Semi-structured interviews are considered the gold standard for generating ED diagnoses. Interviewers play an active role in the process and can help define and clarify constructs. See Table 16.1 for an overview.

Self-Report Questionnaires Although self-report questionnaires cannot replace a clinical or semi-structured interview 345

Fitzsimmons-Craft, Karam, and Wilfley

Table 16.1 Semi-Structured Interviews for Assessing Eating Disorders and Their Psychopathology in Children and Adolescents

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Interview

Citation

Eating Disorder Examination

Fairburn, Cooper, and O’Connor, 2008

Child Version of the EDE

Bryant-Waugh, Cooper, Taylor, and Lask, 1996

Structured Interview for Anorexic and Bulimic Syndromes for Expert Rating

Fichter, Herpertz, Quadflieg, and HerpertzDahlmann, 1998

Clinical Eating Disorders Rating Instrument

Palmer, Christie, Cordle, Davies, and Kenrick, 1987

Description Assesses the frequency and severity of behavioral and attitudinal symptoms of eating disorders; considered the “gold standard” for assessing eating disorder psychopathology; first developed for use with adults but commonly used for the assessment of adolescents. Modified from the Eating Disorder Examination with the goal of making the interview more understandable and more accurate for use with youth. Used for diagnosing EDs and also for assessing a broad array of psychopathology related to EDs, including body image disturbance, substance abuse, social integration, sexuality, depression, anxiety, and compulsion. Assesses the behaviors and beliefs associated with anorexia nervosa and bulimia nervosa, as well as features of general psychopathology.

Note. EDE = Eating Disorder Examination; ED = eating disorder.

for making a diagnosis of an ED, they can serve a number of useful purposes: (a) they can serve as a screening instrument to suggest that a clinical interview is warranted; (b) they can provide a numerical indication of symptom severity; (c) given the heterogeneity in symptom presentation, they can inform treatment planning by identifying which issues may be most salient to address; and (d) they can be useful for assessing treatment progress, as they can be administered numerous times (Crowther & Sherwood, 1997). See Table 16.2 for an overview of self-report measures, including those originally designed for adults that have been used with children and adolescents and measures specifically designed for use in youth.

Parental Involvement Parents are particularly important for assessing youth for an ED. In terms of a clinical interview, the Eating

346

Disorder Examination has been modified for use with parents (Couturier, Lock, Forsberg, Vanderheyden, & Yen, 2007; Loeb, 2008). Research has demonstrated that parent information is particularly important for diagnosing youth with AN or restrictive-type EDs, given their tendency to underreport dietary restraint and weight concerns (Couturier et al., 2007). If possible, it is best to have both parents present for the evaluation, especially when both are involved in the care of the individual; this sets the tone for the involvement of both parents in the health of the individual and provides comprehensive information about the youth and family from multiple perspectives (Lock & Le Grange, 2013). In terms of a self-report measure for parents, the Questionnaire of Eating and Weight Patterns–Parent (Johnson, Grieve, Adams, & Sandy, 1999) can be used to diagnostically assess the presence of BN and BED in youth, and a parent version of the Eating Disorder Examination-Questionnaire has also been developed (Loeb, 2007).

Eating Disorders in Children and Adolescents

Table 16.2 Self-Report Questionnaires for Assessing Eating Disorders and Their Psychopathology in Children and Adolescents Questionnaire

Citation

Construct assessed

Number of items

Scale

Strengths and weaknesses

Bulimia Test—Revised

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Eating Attitudes Test—26

Eating Disorder Diagnostic Scale

Eating Disorder ExaminationQuestionnaire

Eating Disorder Inventory

Self-report measures originally developed for adults but used with youth Thelen, Farmer, Symptoms of BN 36 5-point scale, with Demonstrated Wonderlich, and differing anchors psychometric Smith, 1991 properties in youth Garner, Olmstead, Eating disorder 26 6-point scale Score of 20 indicates Bohr, and Garfinkel, attitudes and ranging from never a probable eating 1982 behaviors to always disorder; limited evidence of reliability and validity in youth 22 Uses a combination Demonstrated Stice, Telch, and Symptoms of AN, psychometric of Likert-type Rizvi, 2000 BN, and BED to properties in youth (0 to 6), yes/no, generate DSM–IV frequency, and diagnoses write-in response formats Self-report measure 36 Most items Fairburn and Beglin, Disordered eating that was adapted assessed on a 1994 thoughts and from the gold 0 to 6 scale, with behaviors over standard interview, differing anchors, the past 28 days; the EDE; support and others use a 4 subscales are for its use in youth write-in response generated (i.e., but provides format eating concern, less accurate shape concern, information than weight concern, the EDE and dietary restraint), as well as a global score and frequencies of eating disorder behaviors (e.g., binge eating and purging) 64 6-point scale Some evidence for Garner, Olmstead, Psychological ranging from its psychometric and Polivy, 1983 and behavioral never to always properties in youth traits common in AN and BN; 8 subscales (i.e., drive for thinness, bulimia, body dissatisfaction, ineffectiveness, perfectionism, interpersonal distrust, interoceptive awareness, and maturity fears) (continues)

347

Fitzsimmons-Craft, Karam, and Wilfley

Table 16.2 (Continued) Self-Report Questionnaires for Assessing Eating Disorders and Their Psychopathology in Children and Adolescents Questionnaire

Citation

Construct assessed

Number of items

Scale

Strengths and weaknesses

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Eating Pathology Symptoms Inventory

SCOFF

Forbush et al., 2013 Multidimensional measure of eating pathology; 8 subscales (i.e., body dissatisfaction, binge eating, cognitive restraint, purging, restricting, excessive exercise, negative attitudes toward obesity, and muscle building) Morgan, Reid, and Screening Lacey, 1999 questionnaire that addresses the core features of AN and BN

45

5-point scale ranging from never to very often

Psychometric properties not yet demonstrated in youth

 5

Yes/no

Can be administered orally or in written form; some evidence for its psychometric properties in youth

Self-report measures developed for youth Maloney, McGuire, Eating disorder 26 6-point scale and Daniels, 1988 attitudes and ranging from behaviors never to always 91 6-point scale Eating Disorder Garner, 1991 Multidimensional ranging from Inventory for symptoms of never to always Children eating disorders; 11 subscales (i.e., body dissatisfaction, drive for thinness, bulimia, interoceptive awareness, impulse regulation, asceticism, social insecurity, maturity fears, ineffectiveness, perfectionism, and interpersonal distrust) Eating disorder 14 Yes/no/? Kids’ Eating Childress, symptoms in Disorders Survey Brewerton, children Hodges and Jarrell, 1993

Children’s Eating Attitudes Test

348

May be reliably used for children as young as 8 years Specifically formulated for children and adolescents and demonstrated psychometric properties in youth

Developed specifically to assess eating disorder symptoms in children and demonstrated psychometric properties in youth

Eating Disorders in Children and Adolescents

Table 16.2 (Continued) Self-Report Questionnaires for Assessing Eating Disorders and Their Psychopathology in Children and Adolescents Questionnaire

Citation

Construct assessed

Number of items

Scale

Strengths and

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weaknesses Questionnaire for Eating and Weight Patterns— Adolescent Version

Johnson, Grieve, BN and BED in youth Adams, and Sandy, 1999

12

Youth Eating Disorder ExaminationQuestionnaire

Goldschmidt, Doyle, Disordered eating and Wilfley, 2007 thoughts and behaviors over the past 28 days; 4 subscales are generated (i.e., eating concern, shape concern, weight concern, dietary restraint), as well as a global score and frequencies of eating disorder behaviors (e.g., binge eating and purging)

39

Uses a combination of yes/no and 5-point scales

Generates diagnoses of BN and BED and assesses their behavioral and cognitive features; parent version available Adapted for a thirdMost items grade reading assessed on a level and examples 0 to 6 scale, with and pictures are differing anchors, provided and others use a write-in response format

Note. BN = bulimia nervosa; AN = anorexia nervosa; BED = binge eating disorder; DSM–IV = Diagnostic and Statistical Manual of Mental Disorders, 4th edition; EDE = Eating Disorder Examination.

Other Essential Components of Eating Disorders Assessment in Youth In addition to conducting a thorough assessment of the ED symptoms, it is also necessary to gain a complete mental health picture of the patient, which can be facilitated by using a semi-structured interview like the Schedule for Affective Disorders and Schizophrenia for School-Age Children–Present and Lifetime Version (Kaufman et al., 1997). This interview has demonstrated psychometric properties, and it integrates parent and child report for making diagnoses and gathering a comprehensive history. It is also recommended that assessment include a family psychiatric history to understand the history of mental health problems, including eating and other disorders. Additionally, ED assessment in youth should involve a basic medical examination, including a

complete physical as well as laboratory tests, as by the time the patient presents for treatment, there may have been a prolonged period of weight loss and/or binge eating and purging, which can have serious medical consequences. Indeed, EDs can affect every organ system, and serious medical complications can occur at any weight (Campbell & Peebles, 2014). Among low-weight patients, a standard initial assessment should include a complete blood count; an electrolyte battery; an electrocardiogram; liver, kidney, and thyroid function tests; and a dual-energy X-ray absorptiometry to measure bone mineral density. Among individuals who purge, an electrolyte battery and a dental evaluation should be included in the standard evaluation (Crow & Swigart, 2007). These examinations help to assess the degree of illness and chronicity and can also 349

Fitzsimmons-Craft, Karam, and Wilfley

help to rule out organic causes for weight loss, such as thyroid disease or diabetes. Given the severe, possibly life-threatening effects of EDs, physicians should remain involved throughout the course of treatment, and clinicians need to be aware that at any time the need for acute medical hospitalization may arise (Lock & Le Grange, 2013).

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Influencing Factors to Consider There are additional factors that may influence the diagnosis of EDs in youth. First, as noted, age is an influencing factor, as denial of symptoms is more common among younger individuals with EDs (Couturier & Lock, 2006). Second, although EDs do not discriminate and affect youth across all racial and ethnic groups, individuals from racial/ ethnic minority backgrounds are significantly less likely than their White counterparts to receive care, a referral for further evaluation, or to even be asked by a doctor about ED symptoms (Becker, Franko, Speck, & Herzog, 2003; Marques et al., 2011). Additionally, racial/ethnic differences in clinical presentation are apparent (e.g., Black and Asian individuals with AN may not present with body image distortion) and may influence the likelihood of an individual being diagnosed with an ED (Gilbert, 2003). Third, gender may influence the likelihood that a child or adolescent receives an ED diagnosis. Indeed, girls are more likely to receive an ED diagnosis, even when their symptoms are identical to those of boys (Currin, Schmidt, & Waller, 2007). Therefore, clinician bias in the form of perceptions that boys or individuals of certain racial/ethnic minority backgrounds do not have EDs, may be an important barrier to access to care.

Differential Diagnosis Before concluding that disordered eating symptoms should be attributed to an ED diagnosis, general medical conditions and other psychiatric disorders should be considered. Many serious medical illnesses are associated with substantial weight loss as may be seen in AN, but these are relatively uncommon in children and adolescents (e.g., Crohn’s disease, brain tumor, Type I diabetes; Walsh & Satir, 2005). However, the intense fear of weight gain, importance of weight and shape to 350

self-evaluation, and reward associated with weight loss are not characteristic of these conditions. Likewise, there are some medical and neurological conditions that are associated with binge eating (e.g., Kleine-Levin syndrome) but would not be accompanied by overconcern with weight and shape (Walsh & Satir, 2005). Several other mental health disorders merit consideration before making an ED diagnosis. Notably, it is important to distinguish between ARFID and other EDs. ARFID is a particularly important diagnosis for consideration among youth, with one study indicating that nearly 15% of youth presenting for an ED evaluation receive this diagnosis (Ornstein et al., 2013). Other mental disorders (e.g., major depressive disorder) may be associated with weight loss or other disturbances in eating behavior (e.g., overeating). Likewise, some of the psychological symptoms experienced by those with social phobia, obsessive-compulsive disorder (OCD), and body dysmorphic disorder may resemble features seen among those with AN. However, individuals with these other disorders would not display the same intensity of weight/shape concern as typically seen with an ED (Walsh & Satir, 2005). Prevalence, Incidence, and Course Work examining the lifetime prevalence of EDs in children and adolescents has found rates for AN ranging from 0.3% to 2.0%, 0.8% to 2.6% for BN, 1.4% to 4.1% for BED, and 3.6% to 11.5% for OSFED among girls (Allen, Byrne, Oddy, & Crosby, 2013; Fairweather-Schmidt & Wade, 2014; Smink, van Hoeken, Oldehinkel, & Hoek, 2014; Stice, Marti, & Rohde, 2013). Limited information is available regarding boys, but existing data suggest that the prevalence of any DSM–5 ED in boys, including OSFED, ranges from 1.2% to 2.9% (Allen et al., 2013; Smink et al., 2014). Boys typically account for roughly 10% of ED cases (Rosen, 2010), but studies have found that young patients with EDs are more likely to be male than older patients with EDs (Peebles, Wilson, & Lock, 2006). In longitudinal representative populationbased studies, incidence of AN for girls ages 15 to 19 years has been found to be 270 per 100,000

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(Keski-Rahkonen et al., 2007) and 300 per 100,000 for BN for girls ages 16 to 20 years (Keski-Rahkonen et al., 2009). Regarding boys, Raevuori et al. (2009) found the incidence of AN in boys ages 10 to 24 years to be 16 per 100,000 in a longitudinal representative population-based study. From clinical registry data, the incidence rate of BN in boys ages 10 to 19 years was reported to be 3 per 100,000 (Currin, Schmidt, Treasure, & Jick, 2005). There are few studies reporting on EDNOS specifically, but Lahortiga-Ramos et al. (2005) found the incidence rate of EDNOS in girls 12 to 22 years to be 2,800 per 100,000. At this juncture, information is not available on incidence of EDs using the specific diagnostic criteria in DSM–5. In terms of course, AN typically begins in adolescence or young adulthood. Early work described a bimodal distribution of age at onset for AN, with two peaks at 14 and 18 years (Halmi, Casper, Eckert, Goldberg, & Davis, 1979). More recent work has identified peak age of onset for AN to be between 15 and 19 years (Micali, Hagberg, Petersen, & Treasure, 2013) or 18 and 20 years (Stice et al., 2013; Volpe et al., 2016). Cases of early onset, prior to puberty commencing, have also been identified (Russell, 2013). The course of AN is variable, with some individuals recovering after one episode and others experiencing a chronic course or periods of recovery and relapse (American Psychiatric Association, 2013). A review indicated that less than half (46%) of individuals with AN fully recover and 20% experience a chronic course, but younger age at onset of illness is associated with better outcome (Steinhausen, 2002). AN has a mortality rate of at least 5% to 6% (Steinhausen, 2002)—the highest mortality rate of any psychiatric disorder (Sullivan, 1995). BN also typically begins in adolescence or young adulthood, with the peak age of onset between 15 and 20 years (Micali et al., 2013; Stice et al., 2013; Volpe et al., 2016). Onset before puberty is uncommon (American Psychiatric Association, 2013). Course may be chronic or intermittent (American Psychiatric Association, 2013), and a review indicated that less than half (45%) of individuals with BN exhibit full recovery (Steinhausen & Weber, 2009). Notably, crossover to other EDs in the course

of BN occurs in a minority of cases (10%–32%), with crossover to an unspecified ED most common (Steinhausen & Weber, 2009). Although the mortality rate of BN is lower than that of AN (2%; Fichter & Quadflieg, 2004), the risk of suicide and suicide attempts is higher (Herpertz-Dahlmann, 2009). BED usually begins in adolescence or young adulthood but is more likely than the other EDs to develop later in life. Notably, recovery rates in natural course and treatment outcome studies are higher for BED than for AN or BN. Fairburn, Cooper, Doll, Norman, and O’Connor (2000) found that only 18% of individuals with BED in the community retained a clinical ED diagnosis at 5-year follow-up. Crossover from BED to other EDs is relatively uncommon (American Psychiatric Association, 2013). Children who experience LOC eating, regardless of amount of food, are more likely to develop full or subthreshold BED; LOC eating is a concerning precursor for the development of this disorder (Tanofsky-Kraff et al., 2011). Common Mental Health Comorbidities Youth with EDs often present with at least one other mental disorder and even more have met criteria for a comorbid mental health disorder at some point in their lives. Indeed, one study found that 55% of adolescents with AN, 88% with BN, and 84% with BED met criteria for at least one other DSM–IV disorder (Swanson, Crow, Le Grange, Swendsen, & Merikangas, 2011). More specifically, Swanson et al. (2011) demonstrated that among adolescents with BN or BED, respectively, 50% or 45% had a lifetime mood disorder, 66% or 65% had a lifetime anxiety disorder, 20% or 27% had a lifetime substance abuse or dependence disorder, and 58% or 43% had a lifetime behavioral disorder. Bühren et al. (2014) found that nearly 50% of adolescents with AN met criteria for at least one comorbid psychiatric disorder, with mood and anxiety disorders being the most common comorbidities. The binge–purge subtype was associated with increased rates of psychiatric comorbidity relative to the restricting subtype (Bühren et al., 2014). Similarly, Root et al. (2010) found that substance use disorders were more common among those with the binge–purge vs. the restricting 351

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subtype of AN. EDs with binge and/or purge behaviors may be associated with the highest levels of psychiatric comorbidity. Retrospective studies indicate that many individuals with EDs had an anxiety disorder in childhood prior to the onset of their ED, with OCD and social phobia being most common, supporting the possibility that childhood anxiety may be one vulnerability factor for the development of an ED (Kaye et al., 2004). A growing body of literature suggests that attention-deficit/hyperactivity disorder (ADHD) is also an important ED comorbidity. Biederman et al. (2007) found that girls with ADHD were 3.6 times more likely to meet criteria for an ED compared with girls without ADHD. Results from a nationally representative sample also suggested that those with clinical ADHD were more likely to experience a clinical ED (Bleck, DeBate, & Olivardia, 2014). Etiology There is general agreement in the field that the etiology of EDs is multifactorial and is influenced by biological, psychological, and social factors. EDs are understood using a biopsychosocial framework (Polivy & Herman, 2002). In terms of biology, evidence exists that EDs are biologically-based, serious mental health problems (Klump et al., 2009). First, EDs run in families, and twin studies reveal that additive genetic factors account for 40% to 60% of liability to AN, BN, and BED (Trace, Baker, Peñas-Lledó, & Bulik, 2013). Additionally, neurobiological abnormalities are present among those with EDs (e.g., alterations in the dopaminergic and serotonergic systems, disturbances of higher-order circuits related to reward), but it is unclear whether these features contribute to the development of EDs or are their result—whether they are “traits” or “scars” (Kaye, Wierenga, Bailer, Simmons, & Bischoff-Grethe, 2013). Research is also ongoing to identify specific alterations in gene expression that may be involved in ED development (Trace et al., 2013). Temperament is considered biologically based and may contribute to ED risk as well, with harm avoidance and persistence emerging as particularly important facets (Atiye, Miettunen, & Raevuori-Helkamaa, 2015). Finally, gender should 352

be considered an important risk factor given the preponderance of EDs in girls (Jacobi, Hayward, de Zwaan, Kraemer, & Agras, 2004). Several psychological risk factors have been identified. First, body dissatisfaction or weight concerns has emerged as one of the most consistent and robust risk factor for EDs (Jacobi et al., 2004; Stice, 2002). For example, Killen et al. (1996) found that adolescent girls scoring in the highest quartile on weight concerns had the highest incidence (10%) of EDs over 4 years compared with none of the girls in the lowest quartile. Second, negative emotionality, including tendencies to experience depression or anxiety, negative self-evaluation, and low selfesteem, has also been identified as an important risk factor for EDs (Jacobi et al., 2004; Keel & Forney, 2013; Polivy & Herman, 2002; Stice, 2002). Third, perfectionism, a personality trait typified by striving for flawlessness, is a risk factor for EDs in that it may promote an individual’s relentless pursuit of the thin ideal (Stice, 2002). Fourth, dieting may contribute to the onset of an ED in that either the caloric deprivation associated with dieting may trigger binge eating as a way to counteract reduced caloric intake; violating dietary rules may result in disinhibited eating; or dieting may contribute to negative affect, which in turn may contribute to disordered eating (Polivy & Herman, 2002; Stice, 2002). Fifth, thin-ideal internalization or the extent to which an individual “buys into” societal ideals of attractiveness is an important causal risk factor for not only body dissatisfaction but also eating disturbance (Stice, 2002). Sixth, impulsivity may leave an individual vulnerable to episodes of binge eating, and relatedly, substance use has also been found to be a risk factor for development of an ED (Stice, 2002). Finally, other cognitive and personality features may contribute to ED development, including obsessive thoughts and need for control (Polivy & Herman, 2002). Social factors contribute to ED development. As suggested by data showing an increase in disordered eating after the introduction of television in Fiji, cultural context, including a high emphasis on the thin ideal, may be one contributor to ED development (Becker, Burwell, Gilman, Herzog, & Hamburg, 2002). However, cultural context is not a

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specific causal factor as most people in cultures that value thinness do not develop EDs. Family factors, including criticism, may also play a role in ED etiology, but current research refutes the idea that family is an exclusive or even primary causal risk factor (Le Grange, Lock, Loeb, & Nicholls, 2010). Peer influence is an important risk factor as well, with friends’ dieting being predictive of use of unhealthy and extreme weight control behaviors and binge eating in girls 5 years later (Eisenberg & NeumarkSztainer, 2010). Other factors that have been identified as contributing to ED development include early childhood eating and gastrointestinal problems, sexual abuse and other adverse experiences, general psychiatric morbidity, and elevated BMI (Hilbert et al., 2014; Jacobi et al., 2004; Stice, 2002). In sum, the etiology of EDs should be considered multifactorial, including a combination of genetic, biological, and temperamental vulnerabilities that may interact with psychological, social, and environmental factors to increase risk (Klump et al., 2009).

Evidence-Based Treatments Several treatment approaches for children and adolescents with EDs have been investigated, and treatments are categorized as well-established, probably efficacious, possibly efficacious, or experimental based on guidelines used in the most recent review

on evidence-based psychosocial treatments for EDs in youth (Lock, 2015). See Table 16.3 for an overview.

Anorexia Nervosa Although treatment studies of childhood and adolescent EDs remain few in number, treatment for youth with AN is the most studied, and a number of randomized controlled trials (RCTs) have been conducted (i.e., Agras et al., 2014; Eisler et al., 2000; Geist, Heinmaa, Stephens, Davis, & Katzman, 2000; Godart et al., 2012; Gowers et al., 2007; Le Grange, Eisler, Dare, & Russell, 1992; Le Grange et al., 2016; Lock, Agras, Bryson, & Kraemer, 2005; Lock et al., 2010; Madden et al., 2015; Robin et al., 1999; Russell, Szmukler, Dare, & Eisler, 1987). Collectively, these studies indicate that family-based treatment (FBT) is the most well-established treatment for youth with AN (Keel & Haedt, 2008; Lock, 2015). Family-based treatment.  FBT is a well-established treatment for youth with AN (Lock, 2015) and is often referred to as the “Maudsley method” to pay homage to the original development and testing of FBT at the Maudsley Hospital in London. Two early studies indicated its superiority to individual therapy. First, Russell et al. (1987) compared FBT with individual supportive therapy. Although both treatments were efficacious, adolescents with a short duration of AN (i.e., less than 3 years) who received FBT had a significantly higher weight at the end of treatment

Table 16.3 Evidence-Based Psychological Treatments for Eating Disorders in Youth Anorexia nervosa Well-established treatments Family-based treatment Probably efficacious treatments Systemic family therapy; adolescent-focused therapy Possibly efficacious treatments None Experimental treatments

Bulimia nervosa None None

Binge eating disorder None None

Family-based treatment; Internet-facilitated cognitive–behavioral therapy cognitive–behavioral guided self-help therapy–self-help Cognitive–behavioral therapy; Supportive psychotherapy; Interpersonal psychotherapy; cognitive remediation therapy cognitive–behavioral therapy dialectical behavior therapy

Note. From “An Update on Evidence-Based Psychosocial Treatments for Eating Disorders in Children and Adolescents,” by J. Lock, 2015, Journal of Clinical Child and Adolescent Psychology, 44, p. 714. Copyright 2015 by Taylor and Francis. Adapted with permission. 353

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than those randomized to individual supportive therapy (Russell et al., 1987). Second, Robin et al. (1999) compared FBT with individual ego-oriented individual therapy, which included a focus on adolescent self-efficacy, self-awareness, and autonomy. FBT was superior in terms of weight gain and return of menses at the end of treatment and at a 1-year followup, but both treatments produced comparably large improvements in eating-related cognitions and more general psychopathology. Initial studies examining family therapy for AN were similar in their approach, but the exact protocol used differed depending on the study (e.g., Eisler et al., 2000; Le Grange et al., 1992; Russell et al., 1987). There is now one treatment manual for FBT that is widely used by clinicians (Lock & Le Grange, 2013). The basic tenants of FBT include (a) the family is not blamed as the cause of the illness—FBT takes an agnostic stance on ED etiology and externalizes the disorder from the patient; (b) the adolescent is embedded in the family and the parents’ involvement in therapy is vitally important for the ultimate success of the treatment; therefore, parents are tasked with taking charge of and facilitating weight gain in their malnourished child; (c) the entire family is an important part of treatment success and recovery from the ED; and (d) normal adolescent development is seen as having been interrupted by AN (Lock & Le Grange, 2013). FBT is marked by three phases (Lock & Le Grange, 2013). Phase 1 is devoted to weight restoration of the youth; the therapist tasks parents with this responsibility and supports and reinforces their efforts to refeed their child. The transition to Phase 2 occurs when the youth can take back control of eating and his or her weight, with therapist and parental oversight. During this phase, the focus remains on the youth’s ED symptoms in addition to other significant family issues. Phase 3 addresses issues related to adolescence (e.g., puberty, appropriate family boundaries, the transition to being given more personal autonomy) with disordered eating symptoms no longer a central topic. In a study comparing the efficacy of FBT and adolescent-focused therapy (AFT), an individual treatment for adolescents with AN, Lock et al. (2010) found no statistically significant difference 354

in remission rates between the two treatments at the end of treatment. However, FBT was superior to AFT in terms of maintaining remission status at 6- and 12-month follow-ups. More recently, Agras et al. (2014) investigated the relative efficacy of a specific focus on ED symptoms and behaviors as targeted in FBT compared with systemic family therapy, with its more general focus on the family system. Findings supported both forms of family therapy for adolescent AN but highlighted some advantages of FBT: it was rated as more acceptable to parents; it produced more rapid patient weight restoration; and it was associated with reduced use of hospitalization, which inherently lowers the cost of treatment. Notably, three RCTs have investigated whether the family should be considered a single unit or whether parents should be seen separately from the youth. First, Le Grange et al. (1992) compared conjoint FBT to parents and the youth being seen separately during the therapy session (Le Grange et al., 1992). Both forms of treatment brought about similar benefits. Second, Eisler et al. (2000) also compared conjoint to separated family therapy and found that both worked similarly in terms of nutritional and psychological improvements and global outcome. However, a moderator effect revealed that for patients with high levels of maternal criticism, separated family therapy was more beneficial. Third, Le Grange et al. (2016) evaluated the relative efficacy of FBT and parent-focused treatment (PFT), where the therapist met with the parents only while a nurse monitored the youth. PFT was more efficacious than FBT in precipitating remission; however, differences in remission rates were not apparent at follow-up. Collectively, these studies indicate that treatment for youth with AN that separates parents from the patient may be just as, if not more, efficacious than conjoint treatment. Systemic family therapy.  Systemic family-therapy is considered probably efficacious for the treatment of youth with AN (Agras et al., 2014; Godart et al., 2012; Lock, 2015). Although similar to FBT in its philosophy to involve parents and family members in the treatment of adolescent AN, systemic family therapy differs significantly from FBT in that the

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treatment focuses specifically on the family system instead of disordered eating behavior or normalization of weight (although the therapist will help the family address these issues if they so choose). The therapist adopts a neutral stance and facilitates communication among the family. Systemic family therapy assumes difficulties are precipitated not in individuals themselves but in the relationships, interactions, and communication patterns that develop between individuals in a family system. The treatment assumes and highlights existing family strengths and builds on them to aid in recovery and empowerment, as well as to foster problem solving in the context of the family’s issues that brought them to therapy (Agras et al., 2014). Godart et al. (2012) compared posthospitalization outcome for adolescents with AN who received treatment as usual or treatment as usual plus a form of systemic family therapy. Among completers, those who received family therapy demonstrated greater rates of good and intermediate outcomes. Adolescent-focused therapy.  AFT, which was originally described by Robin et al. (1999) as ego-oriented individual therapy, is also considered a probably efficacious treatment for adolescent AN (Lock, 2015; Lock et al., 2010; Robin et al., 1999). AFT theorizes that individuals with AN have ego deficits and difficulty clarifying biological needs from self-control (Lock et al., 2010). Patients learn to identify and describe emotions and how to tolerate negative affect rather than escaping it with starvation. Phase 1 is marked by establishing rapport, assessing motivation, and developing the patient formulation. The therapist promotes normal eating by encouraging patients to discontinue dieting and promotes weight gain by setting weight goals. Until the patient is weight restored, weight gain is actively discussed and encouraged. The patient is asked to take responsibility for food-related issues instead of tasking parents with this charge. The therapist encourages patients to explore and interpret their behaviors, emotions, and motives, and helps them distinguish emotions from bodily needs. Phase 2 promotes separation and individuation and seeks to increase tolerance of negative mood. Phase 3 marks the end of treatment and focuses on termination (Lock et al., 2010).

Cognitive–behavioral therapy.  Cognitive– behavioral therapy–enhanced (CBT-E) is considered an experimental treatment for youth with AN (Dalle Grave, Calugi, Doll, & Fairburn, 2013; Fairburn, 2008; Lock, 2015). CBT-E was originally developed for adults with EDs but has since been adapted for treating adolescents with AN. CBT-E has three phases and emphasizes the core ED psychopathology of overvaluation of body weight and shape, and routinely involves the patient’s parents (Fairburn, 2008). Phase 1 of CBT-E is marked by the adolescent being prompted to explore the current state of their life and the experience of maintaining the ED, followed by the adolescent being gently challenged to consider the advantages and disadvantages of addressing the illness. If the patient is willing and motivated to proceed with treatment, the initiation of Phase 2 begins with emphasis on weight restoration through the promotion of regular eating while continuing to address other ED psychopathology (e.g., body dissatisfaction). Homework is regularly assigned throughout treatment. Phase 3 is entered into when the youth has made good progress and the emphasis can be shifted to maintenance of the changes made. A benchmarking study on CBT-E for youth with AN revealed that CBT-E produced considerable increases in weight and significant reductions in ED psychopathology (Dalle Grave et al., 2013). Further, patients could maintain their treatment progress with little change over the 60-week posttreatment follow-up period. Cognitive remediation therapy.  Cognitive remediation therapy (CRT; Dahlgren, Lask, Landrø, & Rø, 2013; Pretorius et al., 2012; Wood, Al-Khairulla, & Lask, 2011) shows promise and is considered an experimental treatment for youth with AN (Lock, 2015). CRT addresses cognitive processes that are theorized to maintain the rigid thinking (e.g., dysfunctional attention to detail, difficulties with cognitive flexibility, set shifting, central coherence, and general ability to see the “bigger picture”) shown to be present in patients with AN (McAnarney et al., 2011), instead of focusing directly on the ED symptoms or psychopathology (Pretorius et al., 2012). Therefore, CRT is an adjunctive treatment with the hope to increase motivation and cognitive 355

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abilities to maximize efficacy of other ED treatments (Baldock & Tchanturia, 2007). CRT uses cognitive exercises with the aim to alter dysfunctional cognitive patterns and strengthen thinking skills (e.g., cognitive flexibility, “bigger picture” thinking strategies). In a pilot study, Pretorius et al. (2012) found a small effect of increased cognitive flexibility at the end of a CRT group. Dahlgren et al. (2013) also examined the effects of CRT in terms of neuropsychological functioning in adolescents with AN. CRT showed significant improvements in weight, depression, visio-spatial memory, perceptual disembedding abilities, and verbal fluency (Dahlgren et al., 2013). More research is needed to better understand the benefits of CBT-E and CRT when treating adolescents with AN.

Bulimia Nervosa To date, the evidence base for treatment for youth with BN is limited compared with AN, with only three published RCTs examining psychosocial interventions for this patient population. Two possibly efficacious and two experimental treatments have been identified (Lock, 2015). Family-based treatment.  FBT has been identified as a possibly efficacious treatment for youth with BN (Le Grange, Crosby, Rathouz, & Leventhal, 2007; Le Grange, Lock, Agras, Bryson, & Jo, 2015; Lock, 2015). FBT-BN is similar to the FBT treatment for youth with AN, but with a stronger emphasis in Phase 1 on regulating eating and eliminating binge eating and purging as opposed to weight restoration. Further, the adolescent maintains some control over their eating in addition to parental involvement, instead of complete control over eating put in the parents’ hands. Compared with supportive psychotherapy for adolescents with BN, Le Grange et al. (2007) found FBT-BN to be superior on abstinence from binge eating and purging at posttreatment and a 6-month follow-up. Further, FBT-BN more rapidly reduced core bulimic symptoms compared with supportive psychotherapy. Additionally, Le Grange et al. (2015) compared FBT-BN and CBT for adolescents (CBT-A) for the treatment of adolescent BN. FBT-BN showed short-term superiority and brought about higher binge eating and purging abstinence rates at 356

the end of treatment and a 6-month follow-up; however, abstinence rates did not differ between treatment groups at a 12-month follow-up. Cognitive–behavioral therapy guided selfhelp.  CBT guided self-help (CBTgsh) has also been identified as a possibly efficacious treatment for youth with BN (Lock, 2015; Schmidt & Treasure, 1997). CBTgsh is a workbook-based psychosocial intervention that was adapted from a self-help treatment for adult patients with BN (Schmidt & Treasure, 1997). The therapist and patient meet weekly, with the therapist’s role being to motivate the patient and guide him or her through the workbook. CBTgsh establishes patient motivation to change in the beginning of treatment and focuses on the function of BN in the patient’s life. Thoughts, feelings, and behaviors are self-monitored to illuminate to the patient the patterns that emerge between these constructs and how their ED symptoms are maintained. Patients are tasked with problem solving through behavioral experiments and goal setting to help recognize and intervene in the vicious cycle of binge eating and compensatory behaviors. Homework is regularly assigned throughout treatment, and follow-up sessions occur after the core weekly sessions to give attention to relapse prevention. Schmidt et al. (2007) compared the efficacy and cost-effectiveness of FBT-BN and CBTgsh in adolescents with BN. At the end of treatment, CBTgsh produced significantly greater reductions in binge eating; however, treatment effects were comparable at a 6-month follow-up. No other treatment group differences emerged. One notable advantage of CBTgsh was the lower direct cost compared with FBT-BN. Supportive psychotherapy.  Supportive psychotherapy has been identified as an experimental treatment for youth with BN (Lock, 2015). Supportive psychotherapy was designed as a comparison condition for FBT-BN and was intentionally designed to have no overlap with cognitive–behavioral, interpersonal, or analytic therapy (Le Grange et al., 2007). Phase 1 of this treatment seeks to establish a strong therapeutic alliance, gather personal and family history, and understand the development and course of the ED. The patient is also encouraged to identify underlying issues that might have contributed to the

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start and maintenance of the illness. Phase 2 emphasizes emotional problems and encourages the patient to express his or her feelings. To foster independence and empowerment, the patient is responsible for generating topics for discussion during sessions. During Phase 3, important discoveries from the initial phases are reviewed, including underlying issues that maintain disordered eating symptoms. The patient and therapist discuss how these issues continue to impact the patient’s journey toward ED recovery and how he or she might address similar problems if they arise in the future. Although Le Grange et al. (2007) found FBT-BN to be superior to supportive psychotherapy, the treatment produced improvements in binge eating and purging episodes and ED psychopathology. Cognitive–behavioral therapy.  CBT is an experimental treatment for youth with BN, with encouraging preliminary results. As previously mentioned, CBT-A was implemented in a recent RCT (Le Grange et al., 2015). CBT-A was derived from the Fairburn and colleagues CBT for adult BN treatment manual (Fairburn, Marcus, & Wilson, 1993) and follows three treatment phases. Significant treatment modifications from CBT for adult BN include an initial focus on therapeutic alliance as demonstrated through increased contact with a therapist; conjoint sessions with parents to provide psychoeducation about BN and foster their support of the treatment; use of concrete examples; and exploration of developmental issues (e.g., autonomy) in the context of BN (Le Grange et al., 2015). Le Grange et al. (2015) found FBT-BN was superior to CBT-A in the shortterm, but treatment effects were indistinguishable at a 12-month follow-up. Further, CBT-A precipitated significant reductions in binge eating and purging rates (Le Grange et al., 2015).

Binge Eating Disorder To date, there is a notable paucity of RCTs and treatment research conducted among youth with BED (Lock, 2015). One possibly efficacious and two experimental treatments have been identified, although more research in this area is warranted. Internet-facilitated cognitive–behavioral therapy– self-help.  Internet-facilitated CBT–self-help

has been identified as a possibly efficacious treatment for youth with BED (Jones et al., 2008; Lock, 2015). Jones et al. (2008) investigated an Internetfacilitated CBT–self-help intervention for binge eating and weight maintenance in adolescents. The intervention combined psychoeducation and behavioral interventions, including self-monitoring, goal setting, stimulus control, and appetite awareness, as well as introduced emotion regulation skills. Weekly letters were sent to participants to reinforce participation, and the program included an asynchronous moderated discussion group. Those who received the Internet-facilitated intervention had significantly lower BMI z scores and BMI from baseline to follow-up, compared with the waitlist control group. Further, those randomized to the Internet-facilitated intervention had significant reductions in objective binge episodes, subjective binge episodes, and weight and shape concerns from baseline to posttreatment and from baseline to follow-up. Relatedly, DeBar et al. (2013) conducted a pilot study of an in-person CBT group in a sample of adolescents who reported current binge eating with or without compensatory behaviors. Participants randomized to the CBT group reported significantly fewer binge eating episodes at posttreatment compared with those in the treatment-as-usual/delayed treatment control group. Additional studies regarding the efficacy of in-person CBT for youth with BED are needed. Interpersonal psychotherapy.  Interpersonal psychotherapy (IPT) is an experimental treatment for youth with BED (Lock, 2015; Tanofsky-Kraff et al., 2010, 2014). IPT for the prevention of weight gain (IPT-WG; Tanofsky-Kraff et al., 2010) was adapted from the IPT-Adolescent Skills Training manual for the prevention of depression (Young & Mufson, 2003) and IPT for the treatment of adult BED (Wilfley, MacKenzie, Welch, Ayres, & Weissman, 2000). At the beginning of treatment, the youth’s symptoms are conceptualized in one of four problems areas: interpersonal deficits, interpersonal role disputes, role transitions, or grief. IPT-WG consists of three phases. Phase 1 emphasizes psychoeducation, provides the theoretical rationale for the treatment approach, and develops rapport between group 357

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members. Phase 2 is marked by fostering interpersonal skills that can be applied to different relationships within the youth’s identified interpersonal problem area, and group members are encouraged to share personal relationship experience and develop improved communication. In Phase 3, group members prepare to terminate and plan to work on future goals. A hallmark feature of the treatment is to continually link episodes of LOC eating and overeating to the maladaptive interpersonal context, which has contributed to and helps maintain symptoms (Tanofsky-Kraff et al., 2010). In a pilot study with adolescent girls at-risk for excess weight gain with and without LOC eating, Tanofsky-Kraff et al. (2010) compared IPT-WG with a standardof-care health education group. IPT-WG produced significantly greater reductions in LOC eating than the health education group (Tanofsky-Kraff et al., 2010). In a more adequately powered RCT, Tanofsky-Kraff et al. (2014) compared IPT-WG with a health education group among adolescent girls at high risk of obesity and EDs based on BMI and reported LOC eating. Both groups had significant reductions in expected BMI gain, percent of body fat, symptoms of depression and anxiety, and LOC eating frequency over a 12-month follow-up, but IPT-WG was more efficacious than health education at reducing objective binge eating at a 12-month follow-up (Tanofsky-Kraff et al., 2014). Dialectical behavioral therapy.  Safer, Couturier, and Lock (2007) developed an adolescent-specific version of dialectical behavioral therapy (DBT) for the treatment of BED. Results of the case report provide preliminary support that DBT may be a therapeutic option for adolescents with BED, making it an experimental treatment, but it must be systemically studied further. The adapted DBT for adolescent BED was modeled heavily after DBT for BED in adults, with some important modifications (e.g., meeting conjointly with patient and parents in the first part of the initial session). In this adapted treatment, the first half of every session reviews the patient’s practice of DBT skills and behavioral chain analysis from the prior week, with the remainder of the session spent focusing on the acquisition of new skills. The behavior chain analysis is a key tool used 358

throughout treatment to thoroughly analyze a problematic eating behavior, examine what triggered the episode, identify factors that made the adolescent especially vulnerable, review the behavioral “steps” (or “links” of the chain) that led to the episode, and generate behaviors the patient could do in the future to replace the problematic eating behavior. If the patient identifies parental interactions as a key link in the behavioral chain, parents are invited to join treatment for a family session. The use and consistency of family sessions depends on the individual adolescent. Skills covered in treatment include distress tolerance, mindfulness, emotion regulation, and interpersonal effectiveness.

Early Treatment for Eating Disorders Notably, long-term outcome data have consistently illuminated the benefit of early treatment for EDs. Shorter latency between onset and start of treatment is associated with better outcomes, and likewise, longer duration is associated with lower remission rates (Loeb, Craigen, Goldstein, Lock, & Le Grange, 2011). Further, untreated EDs tend to take a more chronic and debilitating course (Lewinsohn, Striegel-Moore, & Seeley, 2000). These findings highlight that seeking treatment early in ED onset is critical. As such, eating pathology in children and adolescents should not go ignored, and these problems should be treated as quickly as possible to ensure the greatest chance at lasting recovery. Pharmacological Interventions for Eating Disorders To date, there is a lack of scientific data to document an evidence-based pharmacological treatment for children and adolescents with EDs, and there are currently no FDA-approved pharmacological treatments for youth with these problems (Powers & Cloak, 2012). Treatment guidelines suggest psychosocial and medical interventions should be the first line of treatment for this patient population (van den Heuvel & Jordaan, 2014), and weight restoration should be addressed first in underweight individuals (Reinblatt, Redgrave, & Guarda, 2008). It is recommended that medication only be prescribed prudently and with thorough consideration given

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the lack of empirical support in this area (van den Heuvel & Jordaan, 2014). Research on pharmacological treatment for youth with AN has mostly investigated atypical antipsychotics (e.g., olanzapine, quetiapine) and antidepressants (e.g., fluoxetine; van den Heuvel & Jordaan, 2014). A case series review evaluated the use of atypical antipsychotics in this patient population, and concluded that they generally have beneficial effects on psychopathology (e.g., anxiety and rigidity) with limited negative side effects, but that further research is needed (Mehler-Wex, Romanos, Kirchheiner, & Schulze, 2008). Likewise, in a retrospective naturalistic study of youth with AN, the use of medication (mostly antidepressants) was associated with improvements in ED behaviors, weight, mood, and obsessive symptoms with limited adverse effects (Rossi et al., 2007). However, other studies have found no benefit of adding an antidepressant to psychological treatment for adolescents with AN (Attia, Haiman, Walsh, & Flater, 1998; Holtkamp et al., 2005; Kaye et al., 2001). Currently there is no clear evidence to recommend the addition of medication to psychological interventions in treating youth with AN, except in cases where pharmacotherapy is used to treat comorbid conditions (Aigner, Treasure, Kaye, & Kasper, 2011). Few studies have formally investigated pharmacotherapy in youth with BN, but one study found that the addition of 60 mg of fluoxetine to supportive psychotherapy in an open trial led to significantly decreased binge eating and purging episodes and was well tolerated (Kotler, Devlin, Davies, & Walsh, 2003). Although fluoxetine is not FDA-approved to treat youth with BN, it is approved for use with adults with BN and is FDA-approved to treat depression and OCD in youth. Despite the dearth of evidence, taken together, these results and the relatively robust evidence for using fluoxetine in the treatment of adult BN (Powers & Cloak, 2012), fluoxetine as part of a multimodal treatment plan in patients with significant comorbid mood symptoms or in psychotherapy nonresponders may be judiciously considered (Mitchell, Roerig, & Steffen, 2013; van den Heuvel & Jordaan, 2014). To date no studies have systematically evaluated

pharmacotherapy for BED in children and adolescents (van den Heuvel & Jordaan, 2014). Predictors and Moderators of Treatment Response A few key treatment response predictors have been identified for youth with EDs. First, parental criticism of the youth with AN can adversely affect the family’s ability to remain in treatment and overall treatment outcomes (Eisler et al., 2000; Le Grange et al., 1992). High levels of expressed emotion, a marker of parental criticism, in mothers predicted early dropout from family therapy but not from individual treatment (Szmukler, Eisler, Russell, & Dare, 1985). Conversely, parental warmth increased the likelihood of a better treatment outcome for adolescents with AN (Le Grange, Hoste, Lock, & Bryson, 2011). Given these findings, it is of the utmost important to address family criticism during therapy sessions, and therapists should attempt to facilitate family warmth by eliciting empathy from family members. Furthermore, adolescents with AN who come from highly critical families may benefit from separated family therapy (Le Grange et al., 1992). Second, early weight gain in AN treatment may be a critical indicator of short- and long-term treatment outcomes, as rapid response to treatment, indicated by a weight gain of approximately 2kg by Session 4, was a strong predictor of attaining recovery by the end of FBT (Doyle, Le Grange, Loeb, Doyle, & Crosby, 2010). Further, Lock et al. (2013) found that reaching 95% of expected BMI by the end of FBT was a strong predictor of recovery at long-term follow-up. These findings highlight the importance of fostering early change among youth with AN. Third, brief duration of illness and earlier age of onset may be important predictors of treatment response, with Agras et al. (2014) finding that such patients gained more weight in two forms of family treatment. Several treatment moderators have been identified for youth with AN. Moderators identify which psychosocial intervention works best for whom or under what circumstances (Kraemer, Wilson, Fairburn, & Agras, 2002). Le Grange et al. (2012) found that having higher eating-related 359

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obsessionality or global ED pathology predicted patients benefitting more from FBT rather than AFT, and Agras et al. (2014) found that individuals with greater obsessive-compulsive symptoms gained significantly more weight with systemic family therapy compared with FBT by the end of treatment. Finally, in their recent RCT for youth with AN comparing FBT and PFT, Le Grange et al. (2016) found no moderators for end of treatment outcome. However, dietary restraint, eating concern, shape concern, global ED psychopathology, eatingrelated obsessive-compulsive features, and duration of illness were moderators of treatment effect at 12-month follow-up. Individuals with elevated EDrelated obsessions and compulsions did better in FBT, but those with lower scores on these measures did markedly better in PFT. Le Grange, Crosby, and Lock (2008) evaluated predictors and moderators of treatment outcome for adolescents with BN who participated in FBT-BN or individual supportive psychotherapy. Participants with less severe eating concerns at baseline were more likely to have remitted after treatment and at follow-up, regardless of treatment received. Adolescents with lower depressive symptoms at baseline were more likely to be partially remitted after treatment. In terms of moderators, participants with less severe global ED psychopathology receiving FBT-BN were more likely to be partially remitted at followup, suggesting that FBT-BN may be most helpful for individuals with less severe ED pathology (Le Grange et al., 2008). Furthermore, Le Grange et al.’s (2015) RCT for adolescent BN identified family environment conflict as a treatment effect moderator, such that those with less conflict responded better to FBTBN compared with CBT-A. This study also found that boys, individuals with lower eating-related obsessionality, and individuals reporting higher family cohesion all showed better ED abstinence rates at the end of treatment (Le Grange et al., 2015). Prevention ED prevention involves the reduction or elimination of key, modifiable risk factors for EDs and/or the promotion of factors that are protective against EDs and can be done at multiple levels: individual, 360

family, group, institutional, community, or societal (Neumark-Sztainer, 2011). Further, prevention efforts can be divided into two main types: primary prevention, which targets the whole population with the aim to prevent the onset of an ED before any sign of the disorder occurs, and secondary prevention, which targets individuals at high risk of developing an ED who are already starting to show symptoms and includes early identification and intervention to prevent the occurrence of a fullblown ED. Most prevention programs for youth have been implemented at the school level, which provides easy access to children of diverse backgrounds. Several reviews have been conducted on the effectiveness of ED prevention programming, with findings suggesting that these programs have modest success in decreasing risk factors and increasing protective factors and that further work is needed to improve program effectiveness (e.g., Holt & Ricciardelli, 2008; Pratt & Woolfenden, 2002; Stice, Shaw, & Marti, 2007). Larger effects are found for programs that are secondary (vs. primary) prevention, interactive (vs. didactic), multisession (vs. single session), offered to only girls (vs. co-ed), offered to participants over age 15 (vs. younger participants), and delivered by professional interventionists (vs. endogenous providers; Stice et al., 2007). Furthermore, a recent systematic review indicated that a small number of prevention studies including parents have led to reductions in ED risk factors, suggesting that it may be of value to determine how to creatively and effectively engage parents in future prevention efforts (Hart, Cornell, Damiano, & Paxton, 2015). Importantly, the availability and use of computers, the Internet, and mobile phones has expanded tremendously in recent years. In 2015, over 90% of teens ages 13 to 17 reported going online daily, including 24% who reported being online “almost constantly” (Lenhart, 2015). There is great potential for the Internet to serve as a vehicle to provide psychological interventions (Kendall, Carper, Khanna, & Harris, 2015), including programs to prevent EDs. Using the Internet for intervention has numerous purported benefits including lack of geographic boundaries, allowing for widespread dissemination and the ability to reach individuals

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who may otherwise have limited access to prevention programming; easily accessible from anywhere at any time; cost and time efficient; anonymity; and high user acceptability (Aardoom, Dingemans, Spinhoven, & Van Furth, 2013). Research indicates that Internet-based prevention programs can effectively decrease ED risk factors in youth (e.g., My Body, My Life, [Heinicke, Paxton, McLean, & Wertheim, 2007]; Student Bodies [Abascal, Brown, Winzelberg, Dev, & Taylor, 2004]), and the Internet is increasingly being used for intervention as well. For example, Aardoom et al. (2016) found that a fully automated Internet-based self-monitoring and feedback intervention was effective in reducing ED and comorbid psychopathology among youth 16 years and older with self-reported ED symptoms. The Internet and mobile technology holds great promise for the ED intervention in youth, but a challenge is keeping up with technology and user preferences. Future Directions In recent decades, the evidence base for understanding and treating youth with EDs has increased dramatically. However, there are still too few treatment studies to provide sufficient understanding. Further, AN has received most of the empirical attention, leaving a plethora of unanswered questions particularly when it comes to treating youth with BN, BED, and other EDs. Additionally, although it is important to continue to examine and improve interventions for youth with EDs, the next imperative step is to disseminate and implement these evidence-based treatments into community and clinical settings. Indeed, a major barrier to effectively treating youth with EDs is the lack of adoption of evidence-based interventions in settings in which these patients are usually seen (Novins, Green, Legha, & Aarons, 2013). Dissemination and implementation science is a burgeoning field that seeks to distribute materials and information to a clinical practice audience with the goal of increasing the routine use of evidencebased care (Novins et al., 2013). Given the limited nature of the literature on treatments for youth EDs and the relatively new field of dissemination and implementation science, it is unsurprising that there

is a dearth of studies in this area. There are preliminary findings to support that it may be possible to disseminate FBT (Couturier, Isserlin, & Lock, 2010; Loeb et al., 2007); however, treatment fidelity has been largely unexplored. The use of online platforms that are widely accessible will be an important strategy both for innovative treatment, as well as a method of training clinicians in empirically supported approaches (Fairburn & Patel, 2014). Moving forward, it will be important to identify barriers to uptake of evidence-based practices and to evaluate effective strategies for training clinicians in these approaches (Lock, 2015). Conclusion EDs are serious mental health problems with complex etiologies and are associated with high medical and psychiatric comorbidity, poor quality of life, and high mortality. Mortality from AN is the highest of all mental disorders. Effective prevention, early recognition, and treatment is needed to prevent these devastating illnesses, complications, and a chronic course. Family involvement is typically recommended in the assessment and treatment of EDs in children and adolescents, and to date, FBT for AN is the only well-established treatment for any youth ED. Future research is needed to identify other wellestablished treatments. Further, the development and examination of dissemination and implementation strategies is a critical next step to enhance quality of care for youth with EDs by effectively training usual-care clinicians in evidence-based treatments.

References Aardoom, J. J., Dingemans, A. E., Spinhoven, P., & Van Furth, E. F. (2013). Treating eating disorders over the internet: A systematic review and future research directions. International Journal of Eating Disorders, 46, 539–552. http://dx.doi.org/10.1002/eat.22135 Aardoom, J. J., Dingemans, A. E., Spinhoven, P., van Ginkel, J. R., de Rooij, M., & van Furth, E. F. (2016). Web-based fully automated self-help with different levels of therapist support for individuals with eating disorder symptoms: A randomized controlled trial. Journal of Medical Internet Research, 18, e159. http://dx.doi.org/10.2196/jmir.5709 Abascal, L., Brown, J. B., Winzelberg, A. J., Dev, P., & Taylor, C. B. (2004). Combining universal and 361

Fitzsimmons-Craft, Karam, and Wilfley

targeted prevention for school-based eating disorder programs. International Journal of Eating Disorders, 35, 1–9. http://dx.doi.org/10.1002/eat.10234

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Agras, W. S., Lock, J., Brandt, H., Bryson, S. W., Dodge, E., Halmi, K. A., . . . Woodside, B. (2014). Comparison of two family therapies for adolescent anorexia nervosa: A randomized parallel trial. JAMA Psychiatry, 71, 1279–1286. http://dx.doi.org/10.1001/ jamapsychiatry.2014.1025 Aigner, M., Treasure, J., Kaye, W., & Kasper, S. (2011). World Federation of Societies of Biological Psychiatry (WFSBP) guidelines for the pharmacological treatment of eating disorders. World Journal of Biological Psychiatry, 12, 400–443. http://dx.doi.org/ 10.3109/15622975.2011.602720 Allen, K. L., Byrne, S. M., Oddy, W. H., & Crosby, R. D. (2013). DSM–IV–TR and DSM–5 eating disorders in adolescents: Prevalence, stability, and psychosocial correlates in a population-based sample of male and female adolescents. Journal of Abnormal Psychology, 122, 720–732. http://dx.doi.org/10.1037/a0034004 American Psychiatric Association. (2000). Diagnostic and statistical manual of mental disorders (4th ed., text revision). Washington, DC: Author. American Psychiatric Association. (2006). Treatment of patients with eating disorders, third edition. American Journal of Psychiatry, 163(Suppl.), 4–54. American Psychiatric Association. (2013). Diagnostic and statistical manual of mental disorders (5th ed.). Washington, DC: Author. Atiye, M., Miettunen, J., & Raevuori-Helkamaa, A. (2015). A meta-analysis of temperament in eating disorders. European Eating Disorders Review, 23, 89–99. http://dx.doi.org/10.1002/erv.2342 Attia, E., Haiman, C., Walsh, B. T., & Flater, S. R. (1998). Does fluoxetine augment the inpatient treatment of anorexia nervosa? American Journal of Psychiatry, 155, 548–551. http://dx.doi.org/10.1176/ajp.155.4.548 Baldock, E., & Tchanturia, K. (2007). Translating laboratory research into practice: Foundations, functions and future of cognitive remediation therapy for anorexia nervosa. Therapy, 4, 285–292. http://dx.doi.org/10.2217/14750708.4.3.285 Becker, A. E., Burwell, R. A., Gilman, S. E., Herzog, D. B., & Hamburg, P. (2002). Eating behaviours and attitudes following prolonged exposure to television among ethnic Fijian adolescent girls. British Journal of Psychiatry, 180, 509–514. http://dx.doi.org/10.1192/ bjp.180.6.509 Becker, A. E., Franko, D. L., Speck, A., & Herzog, D. B. (2003). Ethnicity and differential access to care for eating disorder symptoms. International Journal of Eating Disorders, 33, 205–212. http://dx.doi.org/ 10.1002/eat.10129 362

Biederman, J., Ball, S. W., Monuteaux, M. C., Surman, C. B., Johnson, J. L., & Zeitlin, S. (2007). Are girls with ADHD at risk for eating disorders? Results from a controlled, five-year prospective study. Journal of Developmental and Behavioral Pediatrics, 28, 302–307. http://dx.doi.org/10.1097/ DBP.0b013e3180327917 Bleck, J. R., DeBate, R. D., & Olivardia, R. (2014). The comorbidity of ADHD and eating disorders in a nationally representative sample. Journal of Behavioral Health Services and Research, 42, 437–451. http://dx.doi.org/10.1007/s11414-014-9422-y Bryant-Waugh, R. J., Cooper, P. J., Taylor, C. L., & Lask, B. D. (1996). The use of the eating disorder examination with children: A pilot study. International Journal of Eating Disorders, 19, 391–397. http://dx.doi.org/10.1002/(SICI)1098108X(199605)19:43.0.CO;2-G Bühren, K., Schwarte, R., Fluck, F., Timmesfeld, N., Krei, M., Egberts, K., . . . Herpertz-Dahlmann, B. (2014). Comorbid psychiatric disorders in female adolescents with first-onset anorexia nervosa. European Eating Disorders Review, 22, 39–44. http://dx.doi.org/10.1002/erv.2254 Bulik, C. M., Marcus, M. D., Zerwas, S., Levine, M. D., & La Via, M. (2012). The changing “weightscape” of bulimia nervosa. American Journal of Psychiatry, 169, 1031–1036. http://dx.doi.org/10.1176/appi. ajp.2012.12010147 Campbell, K., & Peebles, R. (2014). Eating disorders in children and adolescents: State of the art review. Pediatrics, 134, 582–592. http://dx.doi.org/10.1542/ peds.2014-0194 Childress, A. C., Brewerton, T. D., Hodges, E. L., & Jarrell, M. P. (1993). The Kids’ Eating Disorders Survey (KEDS): A study of middle school students. Journal of the American Academy of Child and Adolescent Psychiatry, 32, 843–850. http://dx.doi.org/ 10.1097/00004583-199307000-00021 Couturier, J., Isserlin, L., & Lock, J. (2010). Family-based treatment for adolescents with anorexia nervosa: A dissemination study. Eating Disorders, 18, 199–209. http://dx.doi.org/10.1080/10640261003719443 Couturier, J., Lock, J., Forsberg, S., Vanderheyden, D., & Yen, H. L. (2007). The addition of a parent and clinician component to the eating disorder examination for children and adolescents. International Journal of Eating Disorders, 40, 472–475. http://dx.doi.org/10.1002/eat.20379 Couturier, J. L., & Lock, J. (2006). Denial and minimization in adolescents with anorexia nervosa. International Journal of Eating Disorders, 39, 212–216. http://dx.doi.org/10.1002/eat.20241 Crow, S., & Swigart, S. (2007). Medical assessment. In J. E. Mitchell & C. B. Peterson (Eds.), Assessment

Eating Disorders in Children and Adolescents

of eating disorders (pp. 120–128). New York, NY: Guilford Press. Crowther, J. H., & Sherwood, N. E. (1997). Assessment. In D. M. Garner & P. E. Garfinkel (Eds.), Handbook of treatment for eating disorders (2nd ed., pp. 34–49). New York, NY: Guilford Press.

Copyright American Psychological Association. Not for further distribution.

Currin, L., Schmidt, U., Treasure, J., & Jick, H. (2005). Time trends in eating disorder incidence. British Journal of Psychiatry, 186, 132–135. http:// dx.doi.org/10.1192/bjp.186.2.132

Fairburn, C. G., Cooper, Z., Doll, H. A., Norman, P., & O’Connor, M. (2000). The natural course of bulimia nervosa and binge eating disorder in young women. Archives of General Psychiatry, 57, 659–665. http:// dx.doi.org/10.1001/archpsyc.57.7.659 Fairburn, C. G., Cooper, Z., & O’Connor, M. E. (2008). Eating disorder examination. In C. G. Fairburn (Ed.), Cognitive–behavior therapy and eating disorders (12th ed., pp. 265–308). New York, NY: Guilford Press.

Currin, L., Schmidt, U., & Waller, G. (2007). Variables that influence diagnosis and treatment of the eating disorders within primary care settings: A vignette study. International Journal of Eating Disorders, 40, 257–262. http://dx.doi.org/10.1002/eat.20355

Fairburn, C. G., Marcus, M. D., & Wilson, G. T. (1993). Cognitive–behaviour therapy for binge eating and bulimia nervosa: A comprehensive treatment manual. In C. G. Fairburn & G. T. Wilson (Eds.), Binge eating: Nature, assessment, and treatment (12th ed., pp. 361–404). New York, NY: Guilford Press.

Dahlgren, C. L., Lask, B., Landrø, N. I., & Rø, Ø. (2013). Neuropsychological functioning in adolescents with anorexia nervosa before and after cognitive remediation therapy: A feasibility trial. International Journal of Eating Disorders, 46, 576–581. http:// dx.doi.org/10.1002/eat.22155

Fairburn, C. G., & Patel, V. (2014). The global dissemination of psychological treatments: A road map for research and practice. American Journal of Psychiatry, 171, 495–498. http://dx.doi.org/10.1176/ appi.ajp.2013.13111546

Dalle Grave, R., Calugi, S., Doll, H. A., & Fairburn, C. G. (2013). Enhanced cognitive–behaviour therapy for adolescents with anorexia nervosa: An alternative to family therapy? Behaviour Research and Therapy, 51, R9–R12. http://dx.doi.org/10.1016/j.brat.2012.09.008 DeBar, L. L., Wilson, G. T., Yarborough, B. J., Burns, B., Oyler, B., Hildebrandt, T., . . . Striegel, R. H. (2013). Cognitive–behavioral treatment for recurrent binge eating in adolescent girls: A pilot trial. Cognitive and Behavioral Practice, 20, 147–161. http://dx.doi.org/ 10.1016/j.cbpra.2012.04.001 Doyle, P. M., Le Grange, D., Loeb, K., Doyle, A. C., & Crosby, R. D. (2010). Early response to familybased treatment for adolescent anorexia nervosa. International Journal of Eating Disorders, 43, 659–662. http://dx.doi.org/10.1002/eat.20764 Eisenberg, M. E., & Neumark-Sztainer, D. (2010). Friends’ dieting and disordered eating behaviors among adolescents five years later: Findings from Project EAT. Journal of Adolescent Health, 47, 67–73. http://dx.doi.org/10:1016/j.jadohealth.2009.12.030 Eisler, I., Dare, C., Hodes, M., Russell, G., Dodge, E., & Le Grange, D. (2000). Family therapy for adolescent anorexia nervosa: The results of a controlled comparison of two family interventions. Journal of Child Psychology and Psychiatry, 41, 727–736. http:// dx.doi.org/10.1111/1469-7610.00660

Fairweather-Schmidt, A. K., & Wade, T. D. (2014). DSM–5 eating disorders and other specified eating and feeding disorders: Is there a meaningful differentiation? International Journal of Eating Disorders, 47, 524–533. http://dx.doi.org/10.1002/ eat.22257 Fichter, M. M., Herpertz, S., Quadflieg, N., & HerpertzDahlmann, B. (1998). Structured Interview for Anorexic and Bulimic disorders for DSM–IV and ICD–10: Updated (third) revision. International Journal of Eating Disorders, 24, 227–249. http://dx.doi.org/10.1002/(SICI)1098108X(199811)24:33.0.CO;2-O Fichter, M. M., & Quadflieg, N. (2004). Twelveyear course and outcome of bulimia nervosa. Psychological Medicine, 34, 1395–1406. http:// dx.doi.org/10.1017/S0033291704002673 Forbush, K. T., Wildes, J. E., Pollack, L. O., Dunbar, D., Luo, J., Patterson, K., . . . Watson, D. (2013). Development and validation of the Eating Pathology Symptoms Inventory (EPSI). Psychological Assessment, 25, 859–878. http://dx.doi.org/10.1037/ a0032639 Garner, D. M. (1991). The eating disorder inventory—c. Lutz, FL: Psychological Assessment Resources.

Fairburn, C. G. (2008). Cognitive–behavioral therapy and eating disorders. New York, NY: Guilford Press.

Garner, D. M., Olmstead, M. P., Bohr, Y., & Garfinkel, P. E. (1982). The eating attitudes test: Psychometric features and clinical correlates. Psychological Medicine, 12, 871–878. http://dx.doi.org/10.1017/ S0033291700049163

Fairburn, C. G., & Beglin, S. J. (1994). Assessment of eating disorders: Interview or self-report questionnaire? International Journal of Eating Disorders, 16, 363–370.

Garner, D. M., Olmstead, M. P., & Polivy, J. (1983). Development and validation of a multidimensional eating disorder inventory for anorexia nervosa and bulimia. International Journal of Eating 363

Fitzsimmons-Craft, Karam, and Wilfley

Disorders, 2, 15–34. http://dx.doi.org/10.1002/ 1098-108X(198321)2:23.0.CO;2-6 Geist, R., Heinmaa, M., Stephens, D., Davis, R., & Katzman, D. K. (2000). Comparison of family therapy and family group psychoeducation in adolescents with anorexia nervosa. Canadian Journal of Psychiatry, 45, 173–178.

Copyright American Psychological Association. Not for further distribution.

Gilbert, S. C. (2003). Eating disorders in women of color. Clinical Psychology: Science and Practice, 10, 444–455. http://dx.doi.org/10.1093/clipsy.bpg045 Godart, N., Berthoz, S., Curt, F., Perdereau, F., Rein, Z., Wallier, J., . . . Jeammet, P. (2012). A randomized controlled trial of adjunctive family therapy and treatment as usual following inpatient treatment for anorexia nervosa adolescents. PLOS ONE, 7, e28249. http://dx.doi.org/10.1371/journal.pone.0028249 Goldschmidt, A. B., Doyle, A. C., & Wilfley, D. E. (2007). Assessment of binge eating in overweight youth using a questionnaire version of the Child Eating Disorder Examination with instructions. International Journal of Eating Disorders, 40, 460–467. http://dx.doi.org/10.1002/eat.20387 Gowers, S. G., Clark, A., Roberts, C., Griffiths, A., Edwards, V., Bryan, C., . . . Barrett, B. (2007). Clinical effectiveness of treatments for anorexia nervosa in adolescents: Randomised controlled trial. British Journal of Psychiatry, 191, 427–435. http:// dx.doi.org/10.1192/bjp.bp.107.036764 Halmi, K. A., Casper, R. C., Eckert, E. D., Goldberg, S. C., & Davis, J. M. (1979). Unique features associated with age of onset of anorexia nervosa. Psychiatry Research, 1, 209–215. http://dx.doi.org/ 10.1016/0165-1781(79)90063-5 Hart, L. M., Cornell, C., Damiano, S. R., & Paxton, S. J. (2015). Parents and prevention: A systematic review of interventions involving parents that aim to prevent body dissatisfaction or eating disorders. International Journal of Eating Disorders, 48, 157–169. http://dx.doi.org/10.1002/eat.22284 Heinicke, B. E., Paxton, S. J., McLean, S. A., & Wertheim, E. H. (2007). Internet-delivered targeted group intervention for body dissatisfaction and disordered eating in adolescent girls: A randomized controlled trial. Journal of Abnormal Child Psychology, 35, 379–391. http://dx.doi.org/10.1007/s10802-006-9097-9 Herpertz-Dahlmann, B. (2009). Adolescent eating disorders: Definitions, symptomatology, epidemiology and comorbidity. Child and Adolescent Psychiatric Clinics of North America, 18, 31–47. http://dx.doi.org/10.1016/j.chc.2008.07.005 Hilbert, A., Pike, K. M., Goldschmidt, A. B., Wilfley, D. E., Fairburn, C. G., Dohm, F. A., . . . Striegel Weissman, R. (2014). Risk factors across the eating disorders. 364

Psychiatry Research, 220, 500–506. http://dx.doi. org/10.1016/j.psychres.2014.05.054 Holt, K. E., & Ricciardelli, L. A. (2008). Weight concerns among elementary school children: A review of prevention programs. Body Image, 5, 233–243. http:// dx.doi.org/10.1016/j.bodyim.2008.02.002 Holtkamp, K., Konrad, K., Kaiser, N., Ploenes, Y., Heussen, N., Grzella, I., & Herpertz-Dahlmann, B. (2005). A retrospective study of SSRI treatment in adolescent anorexia nervosa: Insufficient evidence for efficacy. Journal of Psychiatric Research, 39, 303–310. http:// dx.doi.org/10.1016/j.jpsychires.2004.08.001 Jacobi, C., Hayward, C., de Zwaan, M., Kraemer, H. C., & Agras, W. S. (2004). Coming to terms with risk factors for eating disorders: Application of risk terminology and suggestions for a general taxonomy. Psychological Bulletin, 130, 19–65. http://dx.doi.org/ 10.1037/0033-2909.130.1.19 Johnson, W. G., Grieve, F. G., Adams, C. D., & Sandy, J. (1999). Measuring binge eating in adolescents: Adolescent and parent versions of the questionnaire of eating and weight patterns. International Journal of Eating Disorders, 26, 301–314. http://dx.doi.org/10.1002/(SICI)1098108X(199911)26:33.0.CO;2-M Jones, M., Luce, K. H., Osborne, M. I., Taylor, K., Cunning, D., Doyle, A. C., . . . Taylor, C. B. (2008). Randomized, controlled trial of an internetfacilitated intervention for reducing binge eating and overweight in adolescents. Pediatrics, 121, 453–462. http://dx.doi.org/10.1542=peds.2007-1173 Kaufman, J., Birmaher, B., Brent, D., Rao, U., Flynn, C., Moreci, P., . . . Ryan, N. (1997). Schedule for Affective Disorders and Schizophrenia for SchoolAge Children—Present and Lifetime Version (K-SADS-PL): Initial reliability and validity data. Journal of the American Academy of Child and Adolescent Psychiatry, 36, 980–988. http://dx.doi.org/ 10.1097/00004583-199707000-00021 Kaye, W. H., Bulik, C. M., Thornton, L., Barbarich, N., & Masters, K. (2004). Comorbidity of anxiety disorders with anorexia and bulimia nervosa. American Journal of Psychiatry, 161, 2215–2221. http:// dx.doi.org/10.1176/appi.ajp.161.12.2215 Kaye, W. H., Nagata, T., Weltzin, T. E., Hsu, L. K., Sokol, M. S., McConaha, C., . . . Deep, D. (2001). Double-blind placebo-controlled administration of fluoxetine in restricting- and restricting-purging-type anorexia nervosa. Biological Psychiatry, 49, 644–652. http://dx.doi.org/10.1016/S0006-3223(00)01013-1 Kaye, W. H., Wierenga, C. E., Bailer, U. F., Simmons, A. N., & Bischoff-Grethe, A. (2013). Nothing tastes as good as skinny feels: The neurobiology of anorexia nervosa. Trends in Neurosciences, 36, 110–120. http:// dx.doi.org/10.1016/j.tins.2013.01.003

Eating Disorders in Children and Adolescents

Keel, P. K., Dorer, D. J., Eddy, K. T., Franko, D., Charatan, D. L., & Herzog, D. B. (2003). Predictors of mortality in eating disorders. Archives of General Psychiatry, 60, 179–183. http://dx.doi.org/10.1001/ archpsyc.60.2.179 Keel, P. K., & Forney, K. J. (2013). Psychosocial risk factors for eating disorders. International Journal of Eating Disorders, 46, 433–439. http://dx.doi.org/ 10.1002/eat.22094

Copyright American Psychological Association. Not for further distribution.

Keel, P. K., & Haedt, A. (2008). Evidence-based psychosocial treatments for eating problems and eating disorders. Journal of Clinical Child and Adolescent Psychology, 37, 39–61. http://dx.doi.org/ 10.1080/15374410701817832 Kendall, P. C., Carper, M. M., Khanna, M. S., & Harris, M. S. (2015). Computer technology and children’s mental health. In R. Scott & S. Kosslyn (Eds.), Emerging trends in the social and behavioral sciences. http:// dx.doi.org/10.1002/9781118900772.etrds0049 Keski-Rahkonen, A., Hoek, H. W., Linna, M. S., Raevuori, A., Sihvola, E., Bulik, C. M., . . . Kaprio, J. (2009). Incidence and outcomes of bulimia nervosa: A nationwide population-based study. Psychological Medicine, 39, 823–831. http://dx.doi.org/10.1017/ S0033291708003942 Keski-Rahkonen, A., Hoek, H. W., Susser, E. S., Linna, M. S., Sihvola, E., Raevuori, A., . . . Rissanen, A. (2007). Epidemiology and course of anorexia nervosa in the community. American Journal of Psychiatry, 164, 1259–1265. http://dx.doi.org/10.1176/ appi.ajp.2007.06081388 Kessler, R. C., Berglund, P. A., Chiu, W. T., Deitz, A. C., Hudson, J. I., Shahly, V., . . . Xavier, M. (2013). The prevalence and correlates of binge eating disorder in the World Health Organization World Mental Health Surveys. Biological Psychiatry, 73, 904–914. http:// dx.doi.org/10.1016/j.biopsych.2012.11.020 Killen, J. D., Taylor, C. B., Hayward, C., Haydel, K. F., Wilson, D. M., Hammer, L., . . . Strachowski, D. (1996). Weight concerns influence the development of eating disorders: A 4-year prospective study. Journal of Consulting and Clinical Psychology, 64, 936–940. http://dx.doi.org/10.1037/0022-006X.64.5.936 Klump, K. L., Bulik, C. M., Kaye, W. H., Treasure, J., & Tyson, E. (2009). Academy for eating disorders position paper: Eating disorders are serious mental illnesses. International Journal of Eating Disorders, 42, 97–103. http://dx.doi.org/10.1002/eat.20589 Kotler, L. A., Devlin, M. J., Davies, M., & Walsh, B. T. (2003). An open trial of fluoxetine for adolescents with bulimia nervosa. Journal of Child and Adolescent Psychopharmacology, 13, 329–335. http://dx.doi.org/ 10.1089/104454603322572660 Kraemer, H. C., Wilson, G. T., Fairburn, C. G., & Agras, W. S. (2002). Mediators and moderators

of treatment effects in randomized clinical trials. Archives of General Psychiatry, 59, 877–883. http:// dx.doi.org/10.1001/archpsyc.59.10.877 Lahortiga-Ramos, F., De Irala-Estévez, J., Cano-Prous, A., Gual-García, P., Martínez-González, M. Á., & Cervera-Enguix, S. (2005). Incidence of eating disorders in Navarra (Spain). European Psychiatry, 20, 179–185. http://dx.doi.org/10.1016/ j.eurpsy.2004.07.008 Le Grange, D., Crosby, R. D., & Lock, J. (2008). Predictors and moderators of outcome in familybased treatment for adolescent bulimia nervosa. Journal of the American Academy of Child and Adolescent Psychiatry, 47, 464–470. http://dx.doi.org/ 10.1097/CHI.0b013e3181640816 Le Grange, D., Crosby, R. D., Rathouz, P. J., & Leventhal, B. L. (2007). A randomized controlled comparison of family-based treatment and supportive psychotherapy for adolescent bulimia nervosa. Archives of General Psychiatry, 64, 1049–1056. http:// dx.doi.org/10.1001/archpsyc.64.9.1049 Le Grange, D., Eisler, I., Dare, C., & Russell, G. F. (1992). Evaluation of family treatments in adolescent anorexia nervosa: A pilot study. International Journal of Eating Disorders, 12, 347–357. http://dx.doi.org/ 10.1002/1098-108X(199212)12:43.0.CO;2-W Le Grange, D., Hoste, R. R., Lock, J., & Bryson, S. W. (2011). Parental expressed emotion of adolescents with anorexia nervosa: Outcome in family-based treatment. International Journal of Eating Disorders, 44, 731–734. http://dx.doi.org/10.1002/eat.20877 Le Grange, D., Hughes, E. K., Court, A., Yeo, M., Crosby, R. D., & Sawyer, S. M. (2016). Randomized clinical trial of parent-focused treatment and family-based treatment for adolescent anorexia nervosa. Journal of the American Academy of Child and Adolescent Psychiatry, 55, 683–692. http://dx.doi.org/10.1016/ j.jaac.2016.05.007 Le Grange, D., Lock, J., Agras, W. S., Bryson, S. W., & Jo, B. (2015). Randomized clinical trial of familybased treatment and cognitive–behavioral therapy for adolescent bulimia nervosa. Journal of the American Academy of Child and Adolescent Psychiatry, 54, 886–942. http://dx.doi.org/10.1016/j.jaac.2015.08.008 Le Grange, D., Lock, J., Agras, W. S., Moye, A., Bryson, S. W., Jo, B., & Kraemer, H. C. (2012). Moderators and mediators of remission in family-based treatment and adolescent focused therapy for anorexia nervosa. Behaviour Research and Therapy, 50, 85–92. http:// dx.doi.org/10.1016/j.brat.2011.11.003 Le Grange, D., Lock, J., Loeb, K., & Nicholls, D. (2010). Academy for Eating Disorders position paper: The role of the family in eating disorders. International Journal of Eating Disorders, 43, 1–5. 365

Fitzsimmons-Craft, Karam, and Wilfley

Copyright American Psychological Association. Not for further distribution.

Lenhart, A. (2015). Teens, social media, and technology overview, 2015. Retrieved from http://www. pewinternet.org/2015/04/09/teens-social-mediatechnology-2015

nervosa in adolescence: Evidence of successful dissemination. Journal of the American Academy of Child and Adolescent Psychiatry, 46, 792–800. http:// dx.doi.org/10.1097/chi.0b013e318058a98e

Lewinsohn, P. M., Striegel-Moore, R. H., & Seeley, J. R. (2000). Epidemiology and natural course of eating disorders in young women from adolescence to young adulthood. Journal of the American Academy of Child and Adolescent Psychiatry, 39, 1284–1292. http:// dx.doi.org/10.1097/00004583-200010000-00016

Madden, S., Miskovic-Wheatley, J., Wallis, A., Kohn, M., Lock, J., Le Grange, D., . . . Touyz, S. (2015). A randomized controlled trial of in-patient treatment for anorexia nervosa in medically unstable adolescents. Psychological Medicine, 45, 415–427. http://dx.doi.org/10.1017/S0033291714001573

Lock, J. (2015). An update on evidence-based psychosocial treatments for eating disorders in children and adolescents. Journal of Clinical Child and Adolescent Psychology, 44, 707–721. http:// dx.doi.org/10.1080/15374416.2014.971458

Maloney, M. J., McGuire, J. B., & Daniels, S. R. (1988). Reliability testing of a children’s version of the Eating Attitude Test. Journal of the American Academy of Child and Adolescent Psychiatry, 27, 541–543. http:// dx.doi.org/10.1097/00004583-198809000-00004

Lock, J., Agras, W. S., Bryson, S., & Kraemer, H. C. (2005). A comparison of short- and long-term family therapy for adolescent anorexia nervosa. Journal of the American Academy of Child and Adolescent Psychiatry, 44, 632–639. http://dx.doi.org/ 10.1097/01.chi.0000161647.82775.0a

Marques, L., Alegria, M., Becker, A. E., Chen, C. N., Fang, A., Chosak, A., & Diniz, J. B. (2011). Comparative prevalence, correlates of impairment, and service utilization for eating disorders across U.S. ethnic groups: Implications for reducing ethnic disparities in health care access for eating disorders. International Journal of Eating Disorders, 44, 412–420. http://dx.doi.org/10.1002/eat.20787

Lock, J., Agras, W. S., Le Grange, D., Couturier, J., Safer, D., & Bryson, S. W. (2013). Do end of treatment assessments predict outcome at follow-up in eating disorders? International Journal of Eating Disorders, 46, 771–778. http://dx.doi.org/10.1002/eat.22175 Lock, J., & Le Grange, D. (2013). Treatment manual for anorexia nervosa: A family-based approach (2nd ed.). New York, NY: Guilford Press. Lock, J., Le Grange, D., Agras, W. S., Moye, A., Bryson, S. W., & Jo, B. (2010). Randomized clinical trial comparing family-based treatment with adolescentfocused individual therapy for adolescents with anorexia nervosa. Archives of General Psychiatry, 67, 1025–1032. http://dx.doi.org/10.1001/ archgenpsychiatry.2010.128 Loeb, K. L. (2007). Eating disorder examination questionnaire–parent version (P-EDEQ), version 1.4. Unpublished measure. Loeb, K. L. (2008). Eating disorder examination–parent version (P-EDE), version 1.4. Unpublished measure. Loeb, K. L., Brown, M., & Goldstein, M. M. (2011). Assessment of eating disorders in children and adolescents. In D. Le Grange & J. Lock (Eds.), Eating disorders in children and adolescents: A clinical handbook (pp. 156–198). New York, NY: Guilford Press. Loeb, K. L., Craigen, K. E., Goldstein, M. M., Lock, J., & Le Grange, D. (2011). Early treatment for eating disorders. In J. Lock & D. Le Grange (Eds.), Eating disorders in children and adolescents: A clinical handbook (pp. 337–361). New York, NY: Guilford Press. Loeb, K. L., Walsh, B. T., Lock, J., Le Grange, D., Jones, J., Marcus, S., . . . Dobrow, I. (2007). Open trial of family-based treatment for full and partial anorexia 366

McAnarney, E. R., Zarcone, J., Singh, P., Michels, J., Welsh, S., Litteer, T., . . . Klein, J. D. (2011). Restrictive anorexia nervosa and set-shifting in adolescents: A biobehavioral interface. Journal of Adolescent Health, 49, 99–101. http://dx.doi.org/ 10.1016/j.jadohealth.2010.11.259 Mehler-Wex, C., Romanos, M., Kirchheiner, J., & Schulze, U. M. (2008). Atypical antipsychotics in severe anorexia nervosa in children and adolescents—Review and case reports. European Eating Disorders Review, 16, 100–108. http:// dx.doi.org/10.1002/erv.843 Micali, N., Hagberg, K. W., Petersen, I., & Treasure, J. L. (2013). The incidence of eating disorders in the UK in 2000–2009: Findings from the General Practice Research Database. BMJ Open, 3, e002646. http:// dx.doi.org/10.1136/bmjopen-2013-002646 Micali, N., & House, J. (2011). Assessment measures for child and adolescent eating disorders: A review. Child and Adolescent Mental Health, 16, 122–127. http://dx.doi.org/10.1111/j.1475-3588.2010.00579.x Mitchell, J. E., Roerig, J., & Steffen, K. (2013). Biological therapies for eating disorders. International Journal of Eating Disorders, 46, 470–477. http://dx.doi.org/ 10.1002/eat.22104 Morgan, J. F., Reid, F., & Lacey, J. H. (1999). The SCOFF questionnaire: Assessment of a new screening tool for eating disorders. BMJ (Clinical Research Ed.), 319, 1467–1468. http://dx.doi.org/10.1136/ bmj.319.7223.1467 Neumark-Sztainer, D. (2011). Prevention of eating disorders in children and adolescents. In D. Le

Eating Disorders in Children and Adolescents

Grange & J. Lock (Eds.), Eating disorders in children and adolescents: A clinical handbook (pp. 421–439). New York, NY: Guilford Press.

Copyright American Psychological Association. Not for further distribution.

Novins, D. K., Green, A. E., Legha, R. K., & Aarons, G. A. (2013). Dissemination and implementation of evidence-based practices for child and adolescent mental health: A systematic review. Journal of the American Academy of Child and Adolescent Psychiatry, 52, 1009–1025.e18. http://dx.doi.org/10.1016/ j.jaac.2013.07.012 Ornstein, R. M., Rosen, D. S., Mammel, K. A., Callahan, S. T., Forman, S., Jay, M. S., . . . Walsh, B. T. (2013). Distribution of eating disorders in children and adolescents using the proposed DSM–5 criteria for feeding and eating disorders. Journal of Adolescent Health, 53, 303–305. http://dx.doi.org/10.1016/ j.jadohealth.2013.03.025 Palmer, R., Christie, M., Cordle, C., Davies, D., Kenrick, J. (1987). The clinical eating disorder rating instrument (CEDRI): A preliminary description. Eating Disorders, 6, 9–16. http://dx.doi.org/10.1002/1098-108X(198701) 6:13.0.CO;2-Z Peebles, R., Wilson, J. L., & Lock, J. D. (2006). How do children with eating disorders differ from adolescents with eating disorders at initial evaluation? Journal of Adolescent Health, 39, 800–805. http://dx.doi.org/ 10.1016/j.jadohealth.2006.05.013 Polivy, J., & Herman, C. P. (2002). Causes of eating disorders. Annual Review of Psychology, 53, 187–213. http://dx.doi.org/10.1146/annurev. psych.53.100901.135103 Powers, P. S., & Cloak, N. L. (2012). Psychopharmacologic treatment of obesity and eating disorders in children and adolescents. Child and Adolescent Psychiatric Clinics of North America, 21, 831–859. http:// dx.doi.org/10.1016/j.chc.2012.07.003 Pratt, B. M., & Woolfenden, S. R. (2002). Interventions for preventing eating disorders in children and adolescents. Cochrane Database of Systematic Reviews, 2, CD002891. Pretorius, N., Dimmer, M., Power, E., Eisler, I., Simic, M., & Tchanturia, K. (2012). Evaluation of a cognitive remediation therapy group for adolescents with anorexia nervosa: Pilot study. European Eating Disorders Review, 20, 321–325. http://dx.doi.org/ 10.1002/erv.2176

Robin, A. L., Siegel, P. T., Moye, A. W., Gilroy, M., Dennis, A. B., & Sikand, A. (1999). A controlled comparison of family versus individual therapy for adolescents with anorexia nervosa. Journal of the American Academy of Child and Adolescent Psychiatry, 38, 1482–1489. http://dx.doi.org/10.1097/00004583199912000-00008 Root, T. L., Pinheiro, A. P., Thornton, L., Strober, M., Fernandez-Aranda, F., Brandt, H., . . . Bulik, C. M. (2010). Substance use disorders in women with anorexia nervosa. International Journal of Eating Disorders, 43, 14–21. Rosen, D. S. (2010). Identification and management of eating disorders in children and adolescents. Pediatrics, 126, 1240–1253. http://dx.doi.org/10.1542/peds.2010-2821 Rossi, G., Balottin, U., Rossi, M., Chiappedi, M., Fazzi, E., & Lanzi, G. (2007). Pharmacological treatment of anorexia nervosa: A retrospective study in preadolescents and adolescents. Clinical Pediatrics, 46, 806–811. http://dx.doi.org/10.1177/0009922807303929 Russell, G. F., Szmukler, G. I., Dare, C., & Eisler, I. (1987). An evaluation of family therapy in anorexia nervosa and bulimia nervosa. Archives of General Psychiatry, 44, 1047–1056. http://dx.doi.org/10.1001/ archpsyc.1987.01800240021004 Russell, G. F. M. (2013). Anorexia nervosa of early onset and its impact on puberty. In P. J. Cooper & A. Stein (Eds.), Childhood feeding problems and adolescent eating disorders (pp. 160–187). New York, NY: Routledge. Safer, D. L., Couturier, J. L., & Lock, J. (2007). Dialectical behavior therapy modified for adolescent binge eating disorder: A case report. Cognitive and Behavioral Practice, 14, 157–167. http://dx.doi.org/ 10.1016/j.cbpra.2006.06.001 Schmidt, U., Lee, S., Beecham, J., Perkins, S., Treasure, J., Yi, I., . . . Eisler, I. (2007). A randomized controlled trial of family therapy and cognitive–behavior therapy guided self-care for adolescents with bulimia nervosa and related disorders. American Journal of Psychiatry, 164, 591–598. http://dx.doi.org/10.1176/ ajp.2007.164.4.591 Schmidt, U., & Treasure, J. (1997). Clinician’s guide to getting better bit(e) by bit(e): A survival kit for sufferers of bulimia nervosa and binge eating disorders. Hove, England: Psychology Press.

Raevuori, A., Hoek, H. W., Susser, E., Kaprio, J., Rissanen, A., & Keski-Rahkonen, A. (2009). Epidemiology of anorexia nervosa in men: A nationwide study of Finnish twins. PLOS ONE, 4, e4402. http://dx.doi.org/ 10.1371/journal.pone.0004402

Smink, F. R. E., van Hoeken, D., Oldehinkel, A. J., & Hoek, H. W. (2014). Prevalence and severity of DSM–5 eating disorders in a community cohort of adolescents. International Journal of Eating Disorders, 47, 610–619. http://dx.doi.org/10.1002/eat.22316

Reinblatt, S. P., Redgrave, G. W., & Guarda, A. S. (2008). Medication management of pediatric eating disorders. International Review of Psychiatry, 20, 183–188. http:// dx.doi.org/10.1080/09540260801889120

Steinhausen, H. C. (2002). The outcome of anorexia nervosa in the 20th century. American Journal of Psychiatry, 159, 1284–1293. http://dx.doi.org/ 10.1176/appi.ajp.159.8.1284 367

Fitzsimmons-Craft, Karam, and Wilfley

Steinhausen, H. C., & Weber, S. (2009). The outcome of bulimia nervosa: Findings from one-quarter century of research. American Journal of Psychiatry, 166, 1331–1341. http://dx.doi.org/10.1176/appi. ajp.2009.09040582

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Stice, E. (2002). Risk and maintenance factors for eating pathology: A meta-analytic review. Psychological Bulletin, 128, 825–848. http://dx.doi.org/ 10.1037/0033-2909.128.5.825 Stice, E., Marti, C. N., & Rohde, P. (2013). Prevalence, incidence, impairment, and course of the proposed DSM–5 eating disorder diagnoses in an 8-year prospective community study of young women. Journal of Abnormal Psychology, 122, 445–457. http:// dx.doi.org/10.1037/a0030679 Stice, E., Shaw, H., & Marti, C. N. (2007). A metaanalytic review of eating disorder prevention programs: Encouraging findings. Annual Review of Clinical Psychology, 3, 207–231. http://dx.doi.org/ 10.1146/annurev.clinpsy.3.022806.091447 Stice, E., Telch, C. F., & Rizvi, S. L. (2000). Development and validation of the Eating Disorder Diagnostic Scale: A brief self-report measure of anorexia, bulimia, and binge-eating disorder. Psychological Assessment, 12, 123–131. http://dx.doi.org/ 10.1037/1040-3590.12.2.123 Sullivan, P. F. (1995). Mortality in anorexia nervosa. American Journal of Psychiatry, 152, 1073–1074. http://dx.doi.org/10.1176/ajp.152.7.1073 Swanson, S. A., Crow, S. J., Le Grange, D., Swendsen, J., & Merikangas, K. R. (2011). Prevalence and correlates of eating disorders in adolescents. Results from the national comorbidity survey replication adolescent supplement. Archives of General Psychiatry, 68, 714–723. http://dx.doi.org/10.1001/ archgenpsychiatry.2011.22 Szmukler, G. I., Eisler, I., Russell, G. F., & Dare, C. (1985). Anorexia nervosa, parental “expressed emotion” and dropping out of treatment. British Journal of Psychiatry, 147, 265–271. http://dx.doi.org/ 10.1192/bjp.147.3.265 Tanofsky-Kraff, M., Shomaker, L. B., Olsen, C., Roza, C. A., Wolkoff, L. E., Columbo, K. M., . . . Yanovski, J. A. (2011). A prospective study of pediatric loss of control eating and psychological outcomes. Journal of Abnormal Psychology, 120, 108–118. http:// dx.doi.org/10.1037/a0021406 Tanofsky-Kraff, M., Shomaker, L. B., Wilfley, D. E., Young, J. F., Sbrocco, T., Stephens, M., . . . Yanovski, J. A. (2014). Targeted prevention of excess weight gain and eating disorders in high-risk adolescent girls: A randomized controlled trial. American Journal

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of Clinical Nutrition, 100, 1010–1018. http:// dx.doi.org/10.3945/ajcn.114.092536 Tanofsky-Kraff, M., Wilfley, D. E., Young, J. F., Mufson, L., Yanovski, S. Z., Glasofer, D. R., . . . Schvey, N. A. (2010). A pilot study of interpersonal psychotherapy for preventing excess weight gain in adolescent girls at-risk for obesity. International Journal of Eating Disorders, 43, 701–706. http://dx.doi.org/ 10.1002=eat.20773 Thelen, M. H., Farmer, J., Wonderlich, S., & Smith, M. (1991). A revision of the Bulimia Test: The BULIT-R. Psychological Assessment: A Journal of Consulting and Clinical Psychology, 3, 119–124. http://dx.doi.org/ 10.1037/1040-3590.3.1.119 Thomas, J. J., Vartanian, L. R., & Brownell, K. D. (2009). The relationship between eating disorder not otherwise specified (EDNOS) and officially recognized eating disorders: Meta-analysis and implications for DSM. Psychological Bulletin, 135, 407–433. http://dx.doi.org/10.1037/a0015326 Trace, S. E., Baker, J. H., Peñas-Lledó, E., & Bulik, C. M. (2013). The genetics of eating disorders. Annual Review of Clinical Psychology, 9, 589–620. http:// dx.doi.org/10.1146/annurev-clinpsy-050212-185546 van den Heuvel, L. L., & Jordaan, G. P. (2014). The psychopharmacological management of eating disorders in children and adolescents. Journal of Child and Adolescent Mental Health, 26, 125–137. http://dx.doi.org/10.2989/17280583.2014.909816 Volpe, U., Tortorella, A., Manchia, M., Monteleone, A. M., Albert, U., & Monteleone, P. (2016). Eating disorders: What age at onset? Psychiatry Research, 238, 225–227. http://dx.doi.org/10.1016/ j.psychres.2016.02.048 Walsh, B. T., & Satir, D. A. (2005). Diagnostic issues. In J. E. Mitchell & C. B. Peterson (Eds.), Assessment of eating disorders (pp. 1–16). New York, NY: Guilford Press. Wilfley, D. E., MacKenzie, R. K., Welch, R. R., Ayres, V. E., & Weissman, M. M. (2000). Interpersonal psychotherapy for group. New York, NY: Basic Books. Wilfley, D. E., Wilson, G. T., & Agras, W. S. (2003). The clinical significance of binge eating disorder. International Journal of Eating Disorders, 34(Suppl.), S96–S106. http://dx.doi.org/10.1002/eat.10209 Wood, L., Al-Khairulla, H., & Lask, B. (2011). Group cognitive remediation therapy for adolescents with anorexia nervosa. Clinical Child Psychology and Psychiatry, 16, 225–231. http://dx.doi.org/ 10.1177/1359104511404750 Young, J. F., & Mufson, L. (2003). Manual for interpersonal psychotherapy–adolescent skills training (IPT-AST). New York, NY: Columbia University Press.

Chapter 17

Sleep Disorders in Children and Adolescents

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Candice A. Alfano, Cara A. Palmer, and Joanne Louise Bower

Up to 30% of children and adolescents suffer from sleep problems (Mindell & Owens, 2009). For many children, these problems persist over time and result in adverse daytime consequences (Kataria, Swanson, & Trevathan, 1987; Lam, Hiscock, & Wake, 2003; Sadeh, 2007; Wake et al., 2006; Zuckerman, Stevenson, & Bailey, 1987). Impaired attention, problems with impulse control, hyperactivity, decrements in working memory, and poor academic performance have each been shown to result from insufficient sleep (see McLaughlin-Crabtree & Witcher, 2008). Potential effects on physical health are similarly deleterious and include cardiovascular risk, compromised immune function, and metabolic changes (e.g., insulin resistance; de la Eva, Baur, Donaghue, & Waters, 2002; Gozal & KheirandishGozal, 2008). Sleep disturbances also overlap considerably with child psychopathology (Alfano & Gamble, 2009), and most children presenting with sleep complaints meet criteria for a mental health problem (Ivanenko, Barnes, Crabtree, & Gozal, 2004). These collective data provide undeniable evidence of the critical role of sleep in physical, cognitive, and emotional development. This chapter overviews sleep disorders in childhood, including evidence-based assessment and treatment approaches. We begin with a brief review of the neuroscience of sleep, including a description of the two-process model of sleep regulation, basic sleep staging, and physiology. Next, we discuss key normative, developmental changes in sleep that occur during the childhood years. Although http://dx.doi.org/10.1037/0000065-017

problems sleeping can manifest at any age, we focus on school-age children age 6 and older who have achieved monophasic sleep (i.e., one nighttime sleep period during a 24-hr day). Then, diagnostic classifications of sleep disorders and validated methods for the assessment and diagnosis of these disorders are outlined. We consider several specific sleep disorders common in children and adolescents, including those most likely to be encountered by mental health professionals because of their high prevalence and frequent comorbidity with various forms of psychopathology. For each disorder, we provide information on its prevalence, comorbidities, diagnostic considerations, and evidence-based intervention approaches. Neurobiology of Sleep Sleep occurs in several distinct stages, which arise through the interplay of different biological processes. In the sections that follow, we describe these stages and their underlying neurobiology in more detail.

Sleep Architecture Sleep consists of several stages that occur cyclically throughout the night. These stages include two main subtypes: rapid eye movement sleep (REM) and non-rapid eye movement sleep (NREM). NREM sleep is further divided into three stages (N1–N3) corresponding with increasing depth of sleep. Whereas N3 sleep dominates the first half of the

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night, REM sleep is predominant during the second half, with REM periods becoming progressively longer and denser across the night. NREM sleep is generally marked by parasympathetic dominance, including decreased muscle tone, reductions in blood pressure/heart rate, and slowed, rhythmic respiration. A marker of sleep homeostasis (N3), also known as slow-wave sleep, occupies 25% to 30% of total sleep in school-age children (as compared with approximately 20% in adults), and is therefore believed to play a critical role in brain maturation, including increased synaptic density (Campbell & Feinberg, 2009; Kurth et al., 2010). By early adolescence, an approximate 40% decrease in N3 is observed (Campbell & Feinberg, 2009; Carskadon & Dement, 2011; Colrain & Baker, 2011). This decline is largely responsible for observable reductions in the total sleep time of adolescents compared with children (Karacan, Anch, Thornby, Okawa, & Williams, 1975). REM sleep is often referred to as “paradoxical sleep” because of the presence of brain wave activity that resembles wakefulness. Increased eye movements, irregular respiration, and rapid changes in heart rate and blood pressure characterize REM sleep. Most dreaming activity occurs during this stage, with muscle atonia inhibiting motor activity so dreams do not create bodily responses. REM sleep accounts for approximately 25% of nighttime sleep (Anders, Sadeh, & Appareddy, 1995) with mild decreases observed after the transition to adolescence. Unlike N3 sleep however, REM sleep percentage does not change significantly in relation to total sleep time during adolescence (Ohayon, Carskadon, Guilleminault, & Vitiello, 2004).

Two-Process Model of Sleep Regulation Two intrinsic processes determine the timing and structure of sleep: a homeostatic process (Process S) that results in increased sleep pressure as time spent awake lengthens, and a circadian process (Process C) that regulates alertness levels across a 24-hr day (Borbély, 1982). Proxies for homeostatic sleep drive accumulation include increases in electroencephalography (EEG) power density in the low frequency range (0.75 Hz–4.5 Hz; also known as slow wave activity), and decreases in latency to 370

sleep onset (Carskadon, Acebo, & Jenni, 2004). Homeostatic sleep pressure builds more rapidly in infants and young children than adults, necessitating brief daytime nap periods. Process C operates relatively independent from Process S, contributing to the timing of sleep by providing certain internal signals at specific times. An increase in melatonin secretion serves as the most reliable biological index of circadian sleep phase (Carskadon et al., 2004). Although circadian rhythms are endogenous, they can be entrained to the environment through external cues called zeitgebers. Light serves as the most potent environmental regulator of circadian sleep phase. Homeostatic and circadian processes also interact such that the scheduling of sleep can alter exposure to sleep cues (e.g., availability of natural light) and circadian phase can modulate sleep pressure. Pioneering work by Carskadon (1990) and other researchers has shown both sleep processes undergo meaningful changes during the adolescent years. Slowed accumulation of homeostatic sleep pressure together with a delay of circadian phase contribute to the shifted sleep schedules commonly observed in adolescents (Carskadon, 2002). This pattern of later bedtimes combined with difficulty waking in the morning also relates to psychosocial changes that occur during this developmental period, including greater independence in deciding sleep schedules, more academic responsibilities, a broadening social network and activities, and increased use of electronic media. Although a shift toward an eveningness chronotype (i.e., an individual’s preference for the later timing of daily activities) is closely associated with the adolescent years (Carskadon & Acebo, 2002), sleep schedule preferences can be reliably observed in children as young as 7 and 8 years old (Werner, Lebourgeois, Geiger, & Jenni, 2009). Sleep in the Context of Development Infancy and early childhood are distinguished in part by the greatest need for sleep. By the time children reach 2 years of age, one half of their life will have been spent sleeping, achieved through a polyphasic (i.e., multiple sleep periods during a

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24-hr day) sleep–wake pattern that characterizes the first months and years of life. Over the next few years sleep–wake patterns continue to shift until one primary nocturnal sleep period is achieved, around the age of 6 years. Whereas 100% of children at age 2 nap regularly, by age 6, less than 10% of children take daytime naps (Iglowstein, Jenni, Molinari, & Largo, 2003; Weissbluth, 1995). However, even when daytime naps are relinquished for one consolidated nighttime sleep period, total sleep need remains considerably greater in children than in adults (Anders, Sadeh, & Appareddy, 1995). Recommended total sleep for children ages 6 to 12 years is between 9 and 12 hours per night, and between 8 and 10 hours during adolescence (Paruthi et al., 2016). These pronounced changes in sleep duration and timing closely reflect maturation of homeostatic and chronobiological mechanisms that regulate the sleep–wake cycle (Wolfson, 1996). In addition to developmental changes in sleep duration and timing, early sleep–wake behaviors are influenced by factors that function outside of children’s control, including family sleep practices (e.g., bedtimes and nighttime interactions), cultural traditions and beliefs regarding sleep (e.g., cosleeping), sleep environment conditions (e.g., noise and light), and caregiver appreciation of the importance of healthy sleep (Lozoff, Askew, & Wolf, 1996; Mindell, Sadeh, Wiegand, How, & Goh, 2010; Willinger, Ko, Hoffman, Kessler, & Corwin, 2003). Furthermore, children who experience problems with sleep do not always complain of sleepiness or other daytime impairments, and may not view their sleep to be problematic. In many cases therefore, caregivers rather than children serve as the primary reporters of these problems. However, reliance solely on parent report can also be problematic because caregivers may not be aware of all aspects of their children’s sleep, including the amount of time required to initiate sleep after a child gets in bed (i.e., sleep onset latency; Fricke-Oerkermann et al., 2007; Owens, Spirito, McGuinn, & Nobile, 2000). Overall, the ways in which these factors interact with normative developmental changes in sleep ultimately define which sleep behaviors can be considered abnormal at any given developmental stage.

Nosology of Sleep Disorders Sleep disorders are physical or psychological conditions characterized by some form of abnormality in the sleep–wake cycle. The fifth edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM–5; American Psychiatric Association, 2013) includes 10 sleep–wake disorder groups, including insomnia disorders, hypersomnolence disorders, narcolepsy, sleep-related breathing disorders, circadian rhythm sleep–wake disorders, NREM sleep arousal disorder, nightmare disorder, REM sleep behavior disorder, restless legs syndrome, and substance-/medication-induced sleep disorder. This group of disorders is notably expanded compared with the DSM–IV–TR (American Psychiatric Association, 2000), which included only four broad sleep disorder categories. Although mental health practitioners tend to be most familiar with DSM classifications, the International Classification of Sleep Disorders, Third Edition (ICSD–3; American Academy of Sleep Medicine [AASM], 2014) is more commonly used among sleep researchers and professionals. The ICSD–3 includes 60 specific diagnoses organized into seven major categories, as well as sleep disorders associated with medical/neurologic disorders. In addition, the ICSD–3 provides information on validated assessment and treatment approaches for individual disorders. Measurement of Sleep Overall, the evaluation, diagnosis, and treatment of sleep disorders in children and adolescents are more complex than in adults. This complexity is due to constant changes in sleep duration and timing, the role of caregivers in child sleep–wake patterns, and potential discrepancies in parent and child sleep reports. Although a range of methods is available for assessing sleep, all evaluations should begin with a thorough clinical interview with children and parents.

Clinical Interviews Diagnostic evaluation for a sleep disorder should always include a structured interview with parents 371

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and children. Information regarding children’s current and historical sleep patterns/behaviors, duration and persistence of the sleep problem, and description of children’s sleep routine and environment should be collected in detail. Although not always present, attention should be paid to potential behavioral indicators of insufficient sleep, including an inability to awaken in the morning, increased sleep on weekends, and sleepiness at inappropriate times (e.g., in school). For children with behaviorally based sleep problems (e.g., refusal to sleep independently), gaining an understanding of how caregivers respond to these behaviors is often critical for effective intervention. Child behaviors that contribute to poor sleep should also be evaluated, including late day caffeine consumption, intense social/sports/activity schedules, and nighttime use of electronic media (e.g., texting, video games). Likewise, understanding whether certain physical problems and/ or medical conditions contribute to children’s sleep difficulties (e.g., eczema, gastroesophageal reflux, pain syndromes) can inform treatment approaches.

Subjective Measures A sleep log or diary is a prospective record of an individual’s sleep–wake patterns and related information, completed for at least 1 week. Despite the lack of a standardized format, the sleep diary is often regarded as the gold standard for subjective sleep assessment. Typical information assessed includes specific bed and wake times, sleep onset latency, number and duration of nighttime awakenings, and quality of sleep. Questionnaires are also commonly used to assess various types of sleep disturbances which may or may not be captured by objective sleep measures (e.g., bedtime resistance, sleepwalking). Questionnaire-based assessments can be an important tool either alone or in conjunction with objective indicators of sleep. A wide number of validated questionnaires for children are now available (see Spruyt & Gozal, 2011), though they differ in terms of informant (e.g., child self-report or caregiver-report), specific sleep patterns and behaviors assessed, and time periods during which sleep is evaluated. 372

Polysomnography Polysomnography (PSG) measures the biological and physiological changes that occur during sleep, enabling the scoring of specific sleep stages. Basic PSG assessment uses electrodes for the measurement of EEG to record brain activity, electrooculography to assess eye movements, and facial electromyography (EMG) to assess muscle tone. Additional measures provide information on respiration, cardiovascular functioning, and body movement (e.g., chest/abdominal respiration belts, pulse oximetry, electrocardiogram, leg EMG). These additional channels are necessary to diagnose certain medically based sleep disorders, including obstructive sleep apnea (OSA) which is characterized by upper airway obstruction that results in pauses in breathing during sleep. PSG is performed overnight, either in a sleep laboratory or the individual’s home using ambulatory equipment. Recordings are subsequently scored and evaluated by a certified sleep medicine professional. Consistent with the ongoing developmental changes in sleep observed in infants and children, there are considerable differences in the scoring parameters for adult versus pediatric studies (Beck & Marcus, 2009). Although PSG is considered the gold standard of sleep measurement, it is not necessary for the diagnosis of all sleep disorders (e.g., insomnia, circadian sleep disorders).

Multiple Sleep Latency Test The Multiple Sleep Latency Test (MSLT) is a laboratory-based test of daytime sleepiness and sleep propensity used after the age of 5 years (Aurora et al., 2012; Carskadon et al., 1986). The PSG-based test consists of four to five 20-min nap opportunities spaced apart in 2-hr intervals, beginning 3 hr after waking from the previous night’s sleep. The MSLT is preceded by overnight PSG to ensure the patient is not sleep deprived and to rule out the presence of certain sleep disorders. During nap periods, sleep latencies less than 10 minutes may be indicative of pathological daytime sleepiness (Arand et al., 2005). In addition, occurrence of REM sleep during two or more naps may be suggestive of narcolepsy (Arand et al., 2005; Aurora et al., 2012). However, normative data based on the MSLT in children are limited, and the 20-min nap protocol in prepubertal children

Sleep Disorders in Children and Adolescents

may underestimate sleepiness during childhood (Aurora et al., 2012).

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Actigraphy Actigraphy does not provide information about sleep stages and physiology, but does provide a more naturalistic assessment of typical sleep patterns than PSG. An actigraph is an accelerometerbased monitor that collects data continuously for up to several weeks. These monitors often resemble wrist watches and are worn for 24 hr a day. Data are downloaded and scored using reliable algorithms for distinguishing sleep from wake. Sleep logs or diaries are typically completed in conjunction with actigraphy, although some actigraphs feature an event marker button that allows for more precise assessment of total time in bed and sleep onset latency. Common variables that are derived from actigraphy-based measurement include total sleep time, sleep onset latency, sleep efficiency (e.g., total time in bed divided by time spent sleeping), and total wake minutes after sleep onset. Actigraphy is a well-validated measure of sleep in children and more cost-effective than PSG. However, seven or more nights of recording may be needed to provide valid estimates of sleep in pediatric populations (Acebo et al., 1999). Further, actigraphy is less sensitive than PSG for documenting motionless periods of wake, which may be present in patients with insomnia (Sadeh & Acebo, 2002). Common Sleep Disorders in Children and Adolescents In the sections that follow, we outline some of the most commonly experienced sleep disorders in children and adolescents.

Insomnias Insomnia-related disorders are broadly characterized by difficulty with sleep initiation, consolidation, duration, or quality despite adequate opportunity for sleep. In children, insomnia can manifest as difficulty or refusal to sleep alone, being unable to initiate sleep without a caregiver present, excessive fears/anxiety about sleep, and/or sleep avoidance/ refusal. Problems must be persistent and result in

daytime impairment (e.g., fatigue, impaired attention or concentration, poor academic performance, mood disturbance). In children, insomnia can produce increased family conflict and dysfunction as well (Kelly & El-Sheikh, 2011; Meltzer & Mindell, 2007). Diagnostic criteria.  The ICSD–3 breaks insomnia into three diagnostic categories: chronic insomnia disorder, short-term insomnia disorder, and other insomnia disorder. To meet criteria for chronic insomnia disorder, sleep difficulties (and their daytime impairments) must occur at least three times per week and be present for at least 3 months. If all conditions except symptom duration are met, a diagnosis of short-term insomnia disorder may be assigned. A diagnosis of other insomnia disorder is reserved for cases where criteria are not met for either chronic or short-term insomnia or can be given on a provisional basis when more information is needed. Notably, diagnostic criteria for insomnia in ICSD–3 represent a major change from the previous classification manual. The International Classification of Sleep Disorders, Second Edition (AASM, 2005) defined two developmental subtypes of behavioral insomnia of childhood: sleep-onset association subtype and limit-setting subtype. The former was characterized by difficulty falling asleep in the absence of certain conditions (e.g., rocking, nursing) and was primarily diagnosed in infants and young children. The latter subtype included difficulty initiating sleep and resisting/refusing bed because of inadequate structure, limit setting, and/or behavior management by a caregiver and was more common in preschool- and school-age children. Much research has been conducted on the basis of this behaviorally based conceptualization of childhood insomnia and it remains valuable from the standpoint of assessment and behavioral intervention. A DSM–5 diagnosis of insomnia is largely similar to the ICSD–3 classification, but categorizes insomnia as persistent (symptoms last 3 months or longer), episodic (symptoms lasting at least 1 month but less than 3 months), or recurrent (two or more episodes within 1 year). Prevalence and course.  Insomnia is the most commonly reported sleep problem in children 373

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(Archbold, Pituch, Panahi, & Chervin, 2002; Owens & Mindell, 2011), though it tends to be underdiagnosed in primary care settings (Meltzer, Johnson, Crosette, Ramos, & Mindell, 2010). Upward of 20% to 30% of children suffer from insomnia (Owens & Mindell, 2011) with problems reported by the child and/or the caregiver. Somewhat lower rates are found in adolescents ( Johnson, Roth, Schultz, & Breslau, 2006), although short-term or more episodic forms of insomnia may be more common. Still, the true prevalence of insomnia in children is somewhat unknown, because of a lack of agreed on definition for childhood forms of the disorder (Glaze, Rosen, & Owens, 2002). Further, little is known about the course of childhood insomnia because longitudinal data are rare. Most longitudinal research has focused on child sleep problems more broadly. Left untreated however, these problems can become chronic (Owens & Mindell, 2011) and have been routinely shown to increase risk for developing various psychiatric conditions including anxiety disorders and depression (Alvaro, Roberts, & Harris, 2013). In one recent study, bedtime problems but not night waking problems in third grade were significant predictors of internalizing problems in adolescence (Reynolds & Alfano, 2016a). Comorbidity and diagnostic considerations.  Psychological and medical comorbidities are the rule rather than the exception among all insomnia patients. In fact, the change from primary insomnia in the DSM–IV–TR (American Psychiatric Association, 2000) to insomnia disorder in DSM–5 (American Psychiatric Association, 2013) was purposely made to avoid primary versus secondary distinctions when insomnia co-occurs with other psychiatric or medical conditions. Insomnia symptoms are particularly common in children and adolescents with mood and anxiety disorders (Alfano, Pina, Zerr, & Villalta, 2010; Ivanenko, Barnes, Crabtree, & Gozal, 2004; Johnson, Roth, & Breslau, 2006; Peterman, Carper, & Kendall, 2015), with several studies showing these problems to be reciprocally related (e.g., Alvaro et al., 2013; Shanahan, Copeland, Angold, Bondy, & Costello, 2014). Insomnia is also common in children with autism-spectrum disorders (Cortesi, Giannotti, 374

Ivanenko, & Johnson, 2010) and attention-deficit/ hyperactivity disorder (ADHD; Cortese, Faraone, Konofal, & Lecendreux, 2009; Yoon, Jain, & Shapiro, 2012), although it is critical to ensure that daytime inattention and hyperactivity are not a result of inadequate sleep as opposed to a separate comorbid condition. Children with chronic illness such as rheumatological conditions, and pain-related conditions also report insomnia at higher rates than found in population based studies (Lazaratou, Soldatou, & Dikeos, 2012). In these children it can be difficult to determine whether the medical condition gives rise to sleep problems, or whether sleep problems exacerbate medical conditions and pain. Treatments.  There are no FDA-approved medications for the treatment of pediatric insomnia and few data exist to support the safety and efficacy of pharmacological treatments (Badin, Haddad, & Shatkin, 2016). Behavioral interventions by comparison are highly effective and should be considered the first line of treatment for insomnia in children (Meltzer & Mindell, 2014; Mindell, Kuhn, Lewin, Meltzer, & Sadeh, 2006). Such treatments typically include teaching good sleep hygiene practices and graduated extinction procedures (Clementi, Balderas, Cowie, & Alfano, 2014; Mindell et al., 2006). Sleep hygiene refers to a variety of behavioral and environmental practices that promote quality sleep. One of the most important sleep hygiene measures is the maintenance of a regular, appropriate sleep–wake schedule 7 days a week. Other recommendations include avoiding daytime naps, reducing caffeine intake, avoiding worry (and the experience of other negative emotions) while in bed, limiting exposure to light in the hours before sleep, getting out of bed if sleep onset is difficult, and keeping the bedroom comfortable, cool, quiet, and dark. Although sleep hygiene is a central component of behavioral interventions for insomnia, it represents a common aspect of treatment for virtually all sleep disorders. Graduated extinction aims to reduce maladaptive/inappropriate child behaviors at bedtime (e.g., refusal to sleep alone, nighttime fears) and/or during the night (e.g., getting into the parents’ bed) via a removal of reinforcing parental attention/interaction.

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Graduated extinction, defined as ignoring inappropriate behaviors and systematically increasing the amount of time before responding, can be implemented in various ways depending on the individual child’s problems and the ability of the parents to set firm limits surrounding sleep (Clementi et al., 2014). Extinction procedures can be combined with other procedures to increase bedtime compliance and facilitate sleep transitions (Kuhn & Elliott, 2003; Tikotzky & Sadeh, 2010). For example, bedtime fading refers to delaying a child’s bedtime until he or she is naturally sleepy. When sleep initiation is achieved within 15 min of getting into bed, the child’s bedtime can be moved back, incrementally, to an earlier bedtime. This procedure aims not only to reduce the amount of time spent in bed awake (thereby disassociating the bed with arousal and frustration) but to strengthen a child’s intrinsic sleep drive (see Sadeh, Gruber, & Raviv, 2003). Older children and adolescents may benefit from additional strategies that reduce bedtime arousal, including relaxation techniques, guided imagery, or cognitive restructuring of problematic sleep-related thoughts (Moturi & Avis, 2010). More evidence is needed, however, on the efficacy of behavioral treatments for older children and adolescents, children with comorbid medical or psychiatric disorders, and children with neurodevelopmental disorders (Meltzer & Mindell, 2014).

Hypersomnias Hypersomnias are a group of disorders characterized by the core feature of excessive daytime sleepiness (hypersomnolence) that is not due to another sleep, medical, or psychiatric disorder. Although less common than other sleep disorders, hypersomnia is associated with significant functional impairments and reduced quality of life. We focus here on two types of hypersomnia; narcolepsy and idiopathic hypersomnia. Narcolepsy.  Narcolepsy is a disorder involving irregular patterns in REM sleep and major disruptions of the normal sleep–wake cycle. Diagnosing narcolepsy in children can be challenging because of difficulties completing assessment procedures (e.g., MSLT), a lack of published normative sleep values

in children, and confirmation of symptoms using child reports. Daytime sleepiness in children also may not be obvious to parents and possibly masked by irritability, aggressiveness, or social withdrawal (Guilleminault & Pelayo, 1998, 2000; Nevsimalova, 2009). Other symptoms (e.g., cataplexy) may present differently in children, which can delay diagnosis. Diagnostic criteria.  To meet criteria for an ICSD–3 diagnosis of narcolepsy, patients must experience a consistent irrepressible need for sleep or daytime lapses into sleep for at least 3 months. In young children, sleepiness can present as the reappearance of daytime naps after they have been discontinued, or as excessively long sleep during the night. Decreased sleep onset latency and sleep-onset REM periods must also be present during an MSLT. Commonly, patients additionally experience sleep paralysis, hypnagogic (sleep onset) and/or hypnopompic (sleep offset) hallucinations. In the ICSD–3, narcolepsy is separated into two types, both of which are characterized by excessive daytime sleepiness and signs of REM-sleep disruption. In Type 1, cataplexy (i.e., sudden and transient episodes of muscle weakness typically precipitated by experiencing strong emotions) and low or absent hypocretin-1 levels in cerebrospinal fluid are often present. In Type 2, there are no signs of cataplexy, but this symptom can sometimes develop later, in which case a new diagnosis of Type 1 should be given. In children, cataplexy can affect gait and facial muscles. For example, involuntary grimacing, jaw-opening with tongue thrusting, or hypotonia in the absence of clear emotional triggers would be sufficient to fulfill cataplexy criteria in children. DSM–5 diagnostic criteria overlaps with ICSD–3, but with some differences. DSM–5 narcoleptic patients experience recurrent periods of an irresistible need to sleep, or will fall asleep, or nap within the same day at least 3 times per week for a minimum of 3 months. However only one of the following symptoms must be present: cataplexy, hypocretin-1 deficiency, or objective evidence of REM sleep abnormality (e.g., REM sleep latency ≤ 15 min). The DSM–5 does not separate narcolepsy into types, but includes specifiers about whether cataplexy or hypocretin-1 deficiency is present. 375

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Prevalence and course.  Understanding of the prevalence of narcolepsy in children is limited by the fact that the disorder is often underdiagnosed or initially misdiagnosed (Dahl, Holttum, & Trubnick, 1994; Nevsimalova, 2009). Available research suggests prevalence rates of around 0.05% in the United States and Western Europe, with some countries experiencing slightly higher (e.g., Japan) or lower (e.g., Israel) prevalence (Peterson & Husain, 2008). Despite relatively low rates of incidence, the magnitude of impairment caused by the disorder is often substantial. Narcolepsy is a lifelong condition. Symptoms often begin in childhood or adolescence but formal diagnosis typically occurs in adulthood (Morrish, King, Smith, & Shneerson, 2004; Ohayon, FeriniStrambi, Plazzi, Smirne, & Castronovo, 2005). Sleepiness is usually the first symptom to emerge in children (Ohayon et al., 2005). Sleep-onset/offset hallucinations, sleep paralysis, or disturbed sleep at night may also become increasingly present with age (Green & Stillman, 1998). Comorbidity and diagnostic considerations.  Narcolepsy often results in substantial behavioral changes and is commonly comorbid with a variety of mental health conditions, including ADHD behavior problems, depression, and anxiety (Lecendreux et al., 2015; Nevsimalova, 2009; Ohayon, 2013). Certain symptoms (e.g., inattentiveness, bizarre hallucinations, insomnia) can also lead to misdiagnosis of a psychiatric disorder (Dahl et al., 1994). Children may be misdiagnosed with ADHD or depression, as daytime sleepiness can present as inattentiveness and result in decreased school performance (Guilleminault & Pelayo, 1998; Pearl, Weiss, & Stein, 2001). Children with narcolepsy may also become more withdrawn, irritable, and emotional labile (Dahl et al., 1994; Nevsimalova, 2009). Precocious puberty and increased weight gain/obesity are also common in children with narcolepsy (Poli et al., 2013). Treatment.  Because of the potential for meaningful impairments in all aspects of functioning, treatment should be implemented as early as possible. For example, although mild cataplexy may not be dangerous, severe cataplexy can result in fatal injury (e.g., while driving). Generally, a regular sleep–wake routine and lifestyle changes combined 376

with pharmacological treatment show the greatest efficacy for narcolepsy. In adults, modafinil (a nonamphetamine type stimulant) is the first–line treatment for daytime sleepiness, whereas sodium oxybate is effective for symptoms of cataplexy, daytime sleepiness, and disrupted sleep (Morgenthaler et al., 2007). Neither medication is FDA-approved for use in pediatric patients, however results from a few pilot studies suggest both compounds to be effective and well tolerated by children (Ivanenko, Tauman, & Gozal, 2003; Murali & Kotagal, 2006). Behavioral interventions are also beneficial and should be included in all intervention approaches for children. These typically include keeping a regular sleep–wake schedule, participating in stimulating activities/sports, and scheduled naps (Nevsimalova, 2009). One to two daytime naps of approximately 30 minutes in duration is often recommended for discharging sleep pressure and increasing alertness. Idiopathic hypersomnia.  The core feature of idiopathic hypersomnia (IH) is excessive sleepiness. Patients with IH often complain of feeling tired regardless of how much sleep they obtain. Sleep inertia, which consists of difficulty waking up, irritability, and confusion, is also commonly present. Because these symptoms coincide with a broad range of sleep and medical conditions, IH tends to be a diagnosis of exclusion. Diagnostic assessment that includes comprehensive interviews, PSG, and MSLT procedures is therefore required. Diagnostic criteria.  On the basis of ICSD–3 criteria, daily periods of irrepressible need to sleep or daytime lapses into sleep occurring for at least 3 months must be present. IH can be distinguished from narcolepsy through the absence of cataplexy, and lack of REM-sleep abnormalities in MSLT monitoring. However, reduced sleep latency during an MSLT or extended sleep during a 24-hr period when not sleep deprived must be observed. Obviously, developmental sleep need must be taken into consideration when determining the presence of “extended sleep.” In the DSM–5, hypersomnolence disorder most closely resembles an ICSD–3 diagnosis of IH (Sowa, 2016). Diagnosis of hypersomnolence

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disorder requires recurrent periods of sleep or lapses into sleep, excessive sleepiness with a prolonged sleep period that is nonrestorative, or difficulty being fully awake after abrupt awakening. Symptoms must occur at least 3 times per week for at least 3 months and be accompanied by distress or impairment. The DSM–5 includes specifiers for acute (less than 1 month), subacute (duration of 1–3 months) or persistent (more than 3 months) hypersomnolence, and severity (number of days a week difficulty maintaining alertness is experienced). Prevalence and course.  The prevalence of IH is unknown as no epidemiological studies have been conducted and excessive daytime sleepiness and fatigue are common to a range of conditions. The estimated prevalence is low and similar to narcolepsy (Bassetti & Aldrich, 1997), though higher rates of IH have been reported in women than men (Ali, Auger, Slocumb, & Morgenthaler, 2009; Bassetti & Aldrich, 1997). Average age of onset is typically in late adolescence or young adulthood (Sowa, 2016), though diagnosis may not occur until several years after symptom onset (Ali et al., 2009; Anderson, Pilsworth, Sharples, Smith, & Shneerson, 2007). The disorder is thought to be stable and long lasting, although fluctuations in the condition may be observed and spontaneous remissions have been reported (Anderson et al., 2007; Bassetti & Aldrich, 1997). Comorbidity and diagnostic considerations.  According to DSM–5, hypersomnia can occur in a range of several mental health disorders including depressive disorders. Depression is also common in patients with primary IH (Sowa, 2016) and IH patients commonly experience social, school, and work impairments that can produce depressive symptoms. Careful assessment is necessary for determining whether separate diagnoses are appropriate (Billiard & Sonka, 2016). When both disorders are present in children, depression is more severe than when it occurs without hypersomnia (Liu et al., 2007). Research in adults shows hypersomnia to be prevalent in bipolar depression as well (Akiskal & Benazzi, 2005; Bowden, 2005), though studies in children are lacking. The differentiation of IH from other sleep conditions is rarely straightforward. Commonly, IH

is confused with narcolepsy or severe OSA. In adolescents, inadequate sleep, poor sleep hygiene, and shifted sleep schedules need to be considered. Further, a range of medical conditions can cause impairing hypersomnia (e.g., endocrine disorders, anemia), and a comprehensive medical assessment is necessary to diagnosis IH (Chokroverty, 2000). Treatment.  Given the general lack of knowledge regarding IH, management of the disorder has evolved from effective treatments for narcolepsy. The first randomized, placebo-controlled trial of modafinil was recently conducted among adults with IH (Mayer, Benes, Young, Bitterlich, & Rodenbeck, 2015), with results showing significant reductions in daytime sleepiness and napping. Studies regarding the efficacy and safety in children are lacking, however. Unfortunately, behavioral treatments tend to be ineffective (Billiard & Sonka, 2016).

Circadian Rhythm Sleep Disorders Circadian rhythm sleep disorders are characterized by a misalignment of the internal circadian clock and actual clock time that results in disrupted sleep–wake schedules. These disorders can be temporary (e.g., jet lag) or long lasting. Delayed sleep–wake phase disorder.  Delayed sleep–wake phase disorder (DSWPD) is the most common circadian rhythm sleep disorder diagnosed in children and adolescents (Moturi & Avis, 2010). The onset of puberty coincides with biological and psychosocial changes that contribute to a diagnosis of DSWPD, including slowed accumulation of homeostatic sleep pressure, a delay in circadian timing, and greater independence from parents (Carskadon, 2002; Carskadon, Vieira, & Acebo, 1993). Together these changes result in a desire to stay up later at night and subsequent difficulty waking in the morning. Compensation for inadequate sleep during the school week commonly takes place on weekends when sleep duration is extended (Giannotti, Cortesi, Sebastiani, & Ottaviano, 2002). Diagnostic criteria.  The ICSD–3 defines DSWPD as an intractable delay in the phase of the major sleep period evidenced by chronic or recurrent (i.e., at least 3 months) complaints of inability to fall asleep at a desired bedtime and inability to awaken 377

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at a desired time in the morning. The delay in sleep timing is usually more than 2 hours relative to conventional or socially acceptable timing and when allowed to select their own sleep–wake schedule, problems initiating or waking from sleep are not experienced. In DSWPD, sleep–wake schedules are typically unresponsive to conventional attempts to advance the sleep phase to earlier hours. DSM–5 criteria also include distress or daytime functional impairment as additional diagnostic criterion, which may be particularly helpful for distinguishing cases of DSWPD from age-appropriate shifts in sleep phase. Prevalence and course.  DSWPD has an overall prevalence rate of 7% to 16% in adolescents (AASM, 2014), although considerable variability in prevalence estimates can be found across communitybased studies (e.g., Hazama, Inoue, Kojima, Ueta, & Nakagome, 2008; Saxvig, Pallesen, WilhelmsenLangeland, Molde, & Bjorvatn, 2012). Gradisar, Gardner, and Dohnt (2011) noted the importance of considering the role of culture because of the variability in acceptable bedtimes. Variability in school start times also likely influences degree of impairment because of delayed sleep timing (Crowley, Acebo, & Carskadon, 2007). The typical course of DSWPD emerges during adolescence, and if untreated, is a chronic condition continuing into adulthood (AASM, 2014). Patients report that preference for delayed sleep–wake time persists for many years (Barion & Zee, 2007), however in older adulthood, circadian phase advances (Monk, 2005), reducing risk. Comorbidity and diagnostic considerations.  The complaints of DSWPD (e.g., problems falling asleep, excessive daytime sleepiness) overlap with a host of other sleep disorders which can make diagnosis challenging. Many people with DSWPD also meet criteria for insomnia. For example, more than 50% of adolescents in one large study met criteria for both disorders (Sivertsen et al., 2013). Importantly, DSWPD was associated with significantly greater daytime sleepiness, poorer school attendance, and more depressive symptoms as compared with insomnia. In some cases, however, overlap between DSWPD and insomnia may arise because of a lack of diagnostic specificity. Multimodal assessments are 378

therefore necessary for differentiating DSWPD from other disorders. The ICSD–3 recommends at least 1 week (preferably 2) of sleep logs and actigraphy monitoring showing delayed (>2 hr) sleep onset and offset. DSWPD is closely associated with mood disorders. Rates of the disorder are elevated in unipolar (18%; Glozier et al., 2014) and bipolar (62%; Robillard et al., 2013) depression. Similarly, in teens with DSWPD, 35% report symptoms of depression (Saxvig et al., 2012). Relationships are difficult to disentangle, but bidirectional pathways are likely. For example, depressed adolescents who avoid school or social activities may be disinclined to keep a regular sleep schedule, and are less compliant with treatment for comorbid DSWPD (AASM, 2014). Conversely, impairments imposed by misaligned sleep–wake schedules (e.g., school absenteeism and truancy) may be a risk factor for mood disturbances. Treatment.  Treatments for DSWPD aim to realign the circadian phase with the desired sleep–wake schedule. Practice guidelines (Auger et al., 2015) for DSWPD include carefully timed exogenous melatonin to advance circadian phase. Administration should occur approximately 4 to 6 hours before the shifted sleep onset time (Bartlett, Biggs, & Armstrong, 2013). Administration and bedtimes should shift concurrently until the desired sleep-wake schedule is obtained. Evidence for the efficacy and safety of melatonin in adolescents, however, remains limited (Auger et al., 2015). Morning light exposure has been shown to promote phase shifting in adults (Rosenthal et al., 1990). However, effectiveness, optimal timing, duration, and dosing of morning light remain to be determined in adolescents. Phase advance treatment includes incremental advances in the sleep period by 15 minutes per night until the desired schedule is achieved (Kazaglis et al., 2016). For more extreme DSWPD cases (i.e., phase shifts of more than 3 hr), phase delay is sometimes used. This involves shifting the sleep–wake cycle forward 2 to 3 hours per night over several days (e.g., Day 1 sleep time: 3am–11am, Day 2: 5am–1pm, etc.) until the desired bedtime is achieved (Moturi & Avis, 2010). However, compliance with this procedure requires considerable motivation on the part of the adolescent

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to be successful. Further, even when successfully treated, relapse is common as minor deviations from scheduled bed and wake times can produce sleep phase shifts (Millman, 2005). Practice guidelines do not recommend stand-alone light therapy or behavioral therapies, but combination treatments show promise (Gradisar, Dohnt, et al., 2011), and are recommended for adolescents with and without comorbid psychiatric diagnoses (Auger et al., 2015).

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Parasomnias Parasomnias include a range of undesirable movements or behaviors that occur during the sleep period. Like numerous waking behaviors, many of these nighttime events are considered normative during the childhood years and become pathological only when occurring regularly and causing impairment. Although this category encompasses a wide variety of behaviors/events, we focus on three common childhood parasomnias. Nightmares/nightmare disorder.  Diagnostic criteria.  ICSD–3 nightmare disorder is characterized by frequent nightmares, usually emerging from REM sleep, that portray the individual in a situation that jeopardizes their life or personal safety. On awakening, orientation and alertness occur rapidly and dream content is remembered. Although distressing nightmares commonly occur in childhood, children with nightmare disorder experience them with a greater frequency and experience substantial distress or functional impairment as a result. Impairment may occur across a range of functional domains, including mood, bedtime/sleep resistance, daytime sleepiness, or adverse effects on the family/caregivers. The DSM–5 considers nightmares mild if they occur less than once per week, moderate when occurring more than once a week, and severe when they occur nightly. The DSM–5 also highlights the fact that nightmares (and their significance) can vary by culture, and that sensitivity to these differences can facilitate nightmare disclosure. Nightmares can be idiopathic or posttraumatic. Idiopathic nightmares are almost exclusively associated with REM sleep and tend to be experienced in the second half of the sleep period when REM sleep

dominates. Their content is generally random. Posttraumatic nightmares, by comparison, can emerge from any sleep stage and may occur at any time during the night. However, such nightmares may or may not reflect aspects of the original trauma. Prevalence and course.  The prevalence of nightmare disorder in children is difficult to determine because occasional nightmares are normative and nightmare frequency is not often reported. Whereas most children report having had at least one nightmare (Mindell & Barrett, 2002), approximately 3% to 5% of children experience nightmares on a frequent basis (i.e., at least once per week; Hublin, Kaprio, Partinen, & Koskenvuo, 1999; Nevéus, Cnattingius, Olsson, & Hetta, 2001). Most children will outgrow nightmares with age, but persistent nightmares can result in other sleep problems that persist over time (e.g., delayed sleep onset, fear of sleeping alone, middle of the night wakings). Comorbidity and diagnostic considerations.  Nightmares are intimately associated with anxiety. Trait anxiety and nightmare frequency are linearly related (Mindell & Barrett, 2002) and increased levels of child anxiety at 3 years of age predicts the occurrence of “bad dreams” at age 5 (Simard et al., 2008). Children with separation and generalized anxiety disorders commonly complain of frequent nightmares (Alfano, Ginsburg, & Kingery, 2007). However, the subjective reports of anxious children may overestimate the true frequency of nightmares when compared with prospective assessment (Reynolds & Alfano, 2016b), making it unlikely that nightmare disorder criteria would be met. Frequent nightmares have also been associated with hyperactivity, behavioral problems, and mood disturbance in children (Li et al., 2011). Nightmares are also prominent reexperiencing features of posttraumatic stress disorder (PTSD), independently associated with distress and functional impairment. Although research on nightmares in trauma-exposed adults and those who develop PTSD is considerable (see Alfano & Mellman, 2009), data are comparatively limited in children and adolescents. Available research suggests that more than 50% of children exposed to a trauma subsequently experience nightmares (Dutton & Greene, 2010). Among children exposed to a natural 379

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disaster, bad dreams following the event have been found to possess strong diagnostic efficacy for a PTSD diagnosis (Lonigan, Phillips, & Richey, 2003). In young children, nightmares should be distinguished from sleep terrors (also called night terrors) which are also common. Unlike nightmares, sleep terrors predominantly occur during NREM Stage 3 sleep, earlier in the sleep period than most nightmares. During these episodes, children may partially awaken, cry, scream, and exhibit physical signs of intense fear (e.g., racing heartbeat or sweating). Sleep terrors are not caused by nightmares however and, unlike nightmares, sleep terrors are rarely recalled on awakening (Mindell & Owens, 2009). Nightmares may result from sleep deprivation which can in turn result in increased amounts of REM sleep. Treatment.  There are no practice parameters for the treatment of nightmare disorder in children and adolescents, though treatment recommendations often include ensuring adequate amounts of sleep and reducing exposure to upsetting/violent images/media, especially at night. In adults, imagery rehearsal treatment (IRT) is recommended. IRT requires individuals to recall their nightmare by writing it down and then changing the content to be more positive (Krakow et al., 2001). The new dream is then rehearsed for 10 to 20 minutes per day. A few studies have adapted IRT for use with children and adolescents with successful outcomes reported (Krakow et al., 2001; Simard & Nielsen, 2009; St.-Onge, Mercier, & De Koninck, 2009). Modification includes the use of drawings instead of written descriptions of dreams and having parents assist with practicing new content (Simard & Nielsen, 2009). IRT appears to be effective in reducing nightmare frequency and nighttime distress in children though more controlled trials are needed. Nocturnal enuresis.  Diagnostic criteria.  Nocturnal enuresis (NE) refers to the recurrent, involuntary passage of urine during sleep. Most children wet the bed occasionally or even nightly as toddlers when bladder control is developing. The diagnosis is typically not given to children under the age of 5 years. After this time, the DSM–5 and ISCD–3 NE criteria require that ­bedwetting occur at least twice a week for more than 380

3 months. Using DSM–5 criteria, a diagnosis can be given for lower frequency and/or duration of wetting if substantial distress or functional impairment is present. Enuresis can be either primary (the child has never consistently maintained dryness during sleep) or secondary (the child had previously maintained dryness for at least 6 months). It is also necessary that bedwetting is not better accounted for by the effects of medications or a medical condition (e.g., epilepsy). Prevalence and course.  At 4.5 years of age, about 30% of children still wet the bed (Butler & Heron, 2008). The estimated prevalence of NE varies considerably from 5% to 20% of children over the age of 5 (AASM, 2014; American Psychiatric Association, 2013). Prevalence declines rapidly with age (Özkan, Garipardic, Toktamis, Karabiber, & Sahinkanat, 2004), with a prevalence of 3% to 5% among 10-year-olds and 1% in adolescents age 15 and older. Primary NE is more common than secondary NE (Özkan et al., 2004) and prevalence is 2 to 3 times higher in boys than girls (Bakker, van Sprundel, van der Auwera, van Gool, & Wyndaele, 2002; Butler, 2004). Prevalence rates across cultures remain relatively stable, with differences being attributed to variations in measurement rather than cultural changes in prevalence (Fritz & Rockney, 2004). Spontaneous remission of NE is common during younger years (AASM, 2014). However, this may be largely true of milder cases only, based on a large epidemiological study showing greater persistence of symptoms for those who wet the bed more frequently (Yeung, Sreedhar, Sihoe, Sit, & Lau, 2006). Secondary NE can emerge at any age but is most common between ages 5 and 8 (American Psychiatric Association, 2013). Furthermore, elevated levels of stress and/or anxiety during critical developmental periods may delay attainment of dryness. Comorbidity and diagnostic considerations.  Children with NE tend to have more behavioral problems in childhood (Stone, Malone, Atwill, McGrigor, & Hill, 2008) and internalizing and externalizing disorders in adolescence (Feehan, McGee, Stanton, & Silva, 1990). Linkages with anxiety have been documented. There is evidence that children with separation anxiety and generalized anxiety

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­ isorders experience higher rates of NE than nonpsyd chiatric controls (Alfano, Beidel, Turner, & Lewin, 2006; Verduin & Kendall, 2003). Secondary NE is a common regressive symptom in young children exposed to stress or trauma (Osofsky, 1995; Pynoos, 1993). Involuntary waking enuresis can also occur in anxious or trauma-exposed children. However daytime, combined with nighttime, wetting may signal an underlying medical issue where an NE diagnosis may not be appropriate (e.g., undiagnosed diabetes). Certain medications (e.g., antipsychotics) and diuretics also can cause incontinence and approximately 23% of NE cases are associated with constipation/ encopresis (Sureshkumar et al., 2009). There is considerable evidence linking NE with sleep-related breathing disorders (e.g., OSA). Children with a respiratory disturbance index of greater than one (i.e., at least one or more respiratoryrelated events per hour of sleep) during PSG monitoring show NE prevalence rates of almost 50% (Brooks & Topol, 2003). Presence of NE shows high sensitivity for detecting OSA but also low specificity for ruling out the disorder (Alexopoulos et al., 2014) given the range of possible causes of NE. When NE is secondary to the upper airway obstruction that characterizes OSA, treatment with adenotonsillectomy or other interventions (e.g., nasal corticosteroids) usually reduce or eliminate bedwetting episodes (Jeyakumar et al., 2012; Kaditis, Kheirandish-Gozal, & Gozal, 2012). Treatment.  When medical causes of NE are suspected, these should be the primary target of treatment (Fritz & Rockney, 2004). Similarly, where psychosocial causes are suspected, individual and/ or family based therapies like trauma-focused cognitive–behavioral therapy are recommended (e.g., Scheeringa et al., 2011). In instances where NE occurs in the absence of other medical or psychiatric conditions, behavioral interventions are highly effective. These include lifestyle changes and an enuresis alarm. Lifestyle changes include having children void regularly throughout the day and just prior to bed, and limiting fluid intake in the hours prior to sleep. During alarm treatment (also called the bell and pad method), children attach a small alarm to their underwear that sounds when it senses the first drops of urine. The child gradually learns

to awaken earlier and recognize the sensation of a full bladder and get up to use the bathroom. Alarm therapy results in nighttime dryness in about 66% of children (Glazener, Evans, & Peto, 2006) and is particularly effective when combined with behavioral contracts, positive reinforcement, and parental assistance (Fritz & Rockney, 2004). Pharmacological intervention for NE typically includes imipramine or desmopressin. Both are associated with modest to moderate success rates (up to 65%), however relapse rates are high. Additionally, unwanted side effects include cardiac arrhythmia with imipramine and water intoxication with desmopressin, requiring careful monitoring. Behavioral intervention should therefore be the first line of approach. Sleepwalking.  Diagnostic criteria.  Sleepwalking (also known as somnambulism) is an arousal disorder resulting from incomplete awakening during NREM sleep. Episodes are characterized by inappropriate or lack of responsiveness to the environment and limited associated cognition or dream content. Sleepwalkers can perform bizarre and dangerous behaviors (e.g., urinating in a closet, leaving the home). There is partial or complete amnesia for these events on awakening from the sleep period. Sleepwalking occurs in the first third of the sleep period, typically during slow wave sleep. Criteria for a sleepwalking diagnosis using ICSD–3 and DSM–5 are largely similar, though DSM–5 requires that episodes cause clinical distress or functional impairment. Prevalence and course.  Occasional sleepwalking is common in childhood, with approximately 14.5% of children age 2.5 to 6 years having experienced at least one episode (Petit, Touchette, Tremblay, Boivin, & Montplaisir, 2007). Instances of sleepwalking decrease as childhood progresses, such that the population prevalence by age 13 reduces to 3.3% (Laberge, Tremblay, Vitaro, & Montplaisir, 2000). However, because sleepwalking in childhood is common and normally benign (Zadra, Desautels, Petit, & Montplaisir, 2013), fewer cases meet threshold for a clinical disorder. Most children outgrow sleepwalking by adolescence. Comorbidity and diagnostic considerations.  Sleep-related breathing disorders are common 381

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in sleepwalkers (Guilleminault, Palombini, Pelayo, & Chervin, 2003), possibly because breathing-related microarousals trigger sleepwalking episodes (Zadra et al., 2013). Parasomnias are also interrelated; children and adolescents who sleepwalk are more likely to talk in their sleep (somniloquy), and experience confusional arousals and night terrors (Laberge et al., 2000). In fact, confusional arousals mark the beginning of sleepwalking episodes for many children, and may be a common underlying risk factor for other arousal disorders (Laberge et al., 2000). If comorbid sleep disorders are present, treating these conditions may resolve episodes of sleepwalking altogether (Guilleminault et al., 2003). There is some evidence that links sleepwalking with elevated rates of psychopathology in children and adolescents. For example, the disorder has been associated with separation anxiety in young and school-age children (Alfano et al., 2006; Petit et al., 2007) and with anxiety disorders and suicidal thoughts in adolescents (Gau & Suen Soong, 1999). Several other disorders and conditions may have similar presentations to sleepwalking and require consideration (Aurora et al., 2012). For example, nocturnal seizures can mimic features of sleepwalking. By comparison, however, seizures can occur multiple times at any point during the sleep period, and have repetitive dystonic and dyskinetic features not seen in sleepwalking (Tinuper et al., 2007). Unlike sleepwalking, PSG with video monitoring is required for the diagnosis of nocturnal seizures. Treatment.  Because occasional sleepwalking typically reaches spontaneous resolution without intervention, there are minimal practice guidelines for the condition in childhood. However, sleep schedules should be closely reviewed as sleepwalking may result from sleep deprivation and poor sleep hygiene (Mindell & Owens, 2009). Importantly, caregivers should be given information regarding the potential risks of sleepwalking, including injury within the home or leaving the home. Recommendations for keeping children safe during episodes typically include gates or barriers (e.g., at the top of stairwells) and a means by which family 382

members are alerted when an event occurs (e.g., hanging a bell on the child’s bedroom door). Treatment using scheduled awakenings has shown some success. Typically, such treatment requires parents to wake children 15 to 20 minutes prior to the typical onset of a sleepwalking episode. Once the child has been awakened, they may return to sleep. To date, evidence for the effectiveness of this method has relied solely on case studies, but findings include immediate elimination of sleepwalking, with effects maintained at 3- and 6-month follow-up (Frank, Spirito, Stark, & Owens-Stively, 1997). In severe cases where there is a high risk of or previous occurrence of injury, pharmacological treatment may be considered (Mindell & Owens, 2009). Most commonly, low doses of short-acting benzodiazepines are used at bedtime because these medications suppress slow wave sleep. Summary Sleep is a multifaceted, complex phenomena, linked with various aspects of health and overall well-being. Sleep is also critical for healthy physical, cognitive, and emotional development during the childhood years. Unfortunately, disturbances in sleep are common in children and adolescents, with upward of 30% of children experiencing persistent problems in one or more aspects of sleep. These problems do not necessarily remit on their own and can result in daytime impairments within the domains of family relationships, physical health, and academic performance. Overlap with psychopathology is also pervasive, as most children referred for sleep disturbances exhibit emotional and behavioral problems, most commonly anxiety and depression. At the same time, a range of factors render the diagnosis of sleep disorders in children a more arduous task as compared with adults. Ongoing changes in sleep patterns that characterize the childhood years are shaped not only by biological factors but by psychological, cultural, social, and family influences. Such complexity necessitates the use of comprehensive assessment approaches for evaluating sleep in children and adolescents. The DSM–5 (American Psychiatric Association, 2013) includes 10 sleep–wake disorder groups.

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Some of the most common disorders found in children have been described in this chapter. Notably, sleep–wake disorders included in the DSM–5 represent a considerably expanded list in contrast to previous DSM editions. This expansion is a direct result of the abundance of sleep-focused research conducted during the past decade. At the same time however, sleep research in children and adolescents has accumulated at a slower rate and far less is known regarding precise prevalence rates, typical course of these disorders, and effective treatments. This is particularly true for the hypersomnia disorders, including narcolepsy and idiopathic hypersomnia, where research in children and adolescents is highly limited and misdiagnosis may be common. Specific challenges relate to high rates of comorbidity, developmental differences in presentation, need for PSG/ MSLT monitoring to rule out other disorders (e.g., OSA), and lack of normative sleep values in children. Studies investigating effective treatments for childhood sleep disorders lag adult-based studies. For all disorders reviewed in this chapter, considerable gaps in treatment-based knowledge were identified, including treatment with pharmacological and behavioral interventions. For most sleep disorders, behavioral interventions are considered the first line of treatment, including improving sleep hygiene, and ensuring appropriate, regular bedtimes that permit adequate total sleep.

References Acebo, C., Sadeh, A., Seifer, R., Tzischinsky, O., Wolfson, A. R., Hafer, A., & Carskadon, M. A. (1999). Estimating sleep patterns with activity monitoring in children and adolescents: How many nights are necessary for reliable measures? Sleep, 22, 95–103. http://dx.doi.org/10.1093/sleep/22.1.95 Akiskal, H. S., & Benazzi, F. (2005). Atypical depression: A variant of bipolar II or a bridge between unipolar and bipolar II? Journal of Affective Disorders, 84, 209–217. http://dx.doi.org/10.1016/j.jad.2004.05.004 Alexopoulos, E. I., Malakasioti, G., Varlami, V., Miligkos, M., Gourgoulianis, K., & Kaditis, A. G. (2014). Nocturnal enuresis is associated with moderate-to-severe obstructive sleep apnea in children with snoring. Pediatric Research, 76, 555–559. http://dx.doi. org/10.1038/pr.2014.137 Alfano, C. A., & Mellman, T. (2009). Sleep in anxiety disorders. In J. W. Winkelman & D. T. Plante (Eds.),

Foundations of psychiatric sleep medicine (pp. 286–297). New York, NY: Cambridge University Press. Alfano, C. A., Beidel, D. C., Turner, S. M., & Lewin, D. S. (2006). Preliminary evidence for sleep complaints among children referred for anxiety. Sleep Medicine, 7, 467–473. http://dx.doi.org/10.1016/j. sleep.2006.05.002 Alfano, C. A., & Gamble, A. L. (2009). The role of sleep in childhood psychiatric disorders. Child and Youth Care Forum, 38, 327–340. http://dx.doi.org/10.1007/ s10566-009-9081-y Alfano, C. A., Ginsburg, G. S., & Kingery, J. N. (2007). Sleep-related problems among children and adolescents with anxiety disorders. Journal of the American Academy of Child and Adolescent Psychiatry, 46, 224–232. http://dx.doi.org/10.1097/01. chi.0000242233.06011.8e Alfano, C. A., Pina, A. A., Zerr, A. A., & Villalta, I. K. (2010). Presleep arousal and sleep problems of anxiety-disordered youth. Child Psychiatry and Human Development, 41, 156–167. http://dx.doi.org/ 10.1007/s10578-009-0158-5 Ali, M., Auger, R. R., Slocumb, N. L., & Morgenthaler, T. I. (2009). Idiopathic hypersomnia: Clinical features and response to treatment. Journal of Clinical Sleep Medicine, 5, 562–568. Alvaro, P. K., Roberts, R. M., & Harris, J. K. (2013). A systematic review assessing bidirectionality between sleep disturbances, anxiety, and depression. Sleep: Journal of Sleep and Sleep Disorders Research, 36, 1059–1068. http://dx.doi.org/10.5665/sleep.2810 American Academy of Sleep Medicine. (2005). International classification of sleep disorders diagnostic and coding manual (2nd ed.). Westchester, NY: Author. American Academy of Sleep Medicine. (2014). International classification of sleep disorders (3rd ed.). Darien, IL: Author. American Psychiatric Association. (2000). Diagnostic and statistical manual of mental disorders (4th ed., text revision). Washington, DC: Author. American Psychiatric Association. (2013). Diagnostic and statistical manual of mental disorders (5th ed.). Washington, DC: Author. Anders, T. F., Sadeh, A., & Appareddy, V. (1995). Normal sleep in neonates and children. In R. Ferber & M. Kryger (Eds.), Principles and practice of sleep medicine in the child (p. 7). Philadelphia, PA: W. B. Saunders. Anderson, K. N., Pilsworth, S., Sharples, L. D., Smith, I. E., & Shneerson, J. M. (2007). Idiopathic hypersomnia: A study of 77 cases. Sleep: Journal of Sleep and Sleep Disorders Research, 30, 1274–1281. http://dx.doi.org/10.1093/sleep/30.10.1274 383

Alfano, Palmer, and Bower

Arand, D., Bonnet, M., Hurwitz, T., Mitler, M., Rosa, R., & Sangal, R. B. (2005). The clinical use of the MSLT and MWT. Sleep, 28, 123–144. http://dx.doi.org/ 10.1093/sleep/28.1.123

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Archbold, K. H., Pituch, K. J., Panahi, P., & Chervin, R. D. (2002). Symptoms of sleep disturbances among children at two general pediatric clinics. Journal of Pediatrics, 140, 97–102. http://dx.doi.org/10.1067/ mpd.2002.119990 Auger, R. R., Burgess, H. J., Emens, J. S., Deriy, L. V., Thomas, S. M., & Sharkey, K. M. (2015). Clinical practice guideline for the treatment of intrinsic circadian rhythm sleep-wake disorders. Journal of Clinical Sleep Medicine, 11, 1199–1236. http://dx.doi.org/ 10.5664/jcsm.5100 Aurora, R. N., Lamm, C. I., Zak, R. S., Kristo, D. A., Bista, S. R., Rowley, J. A., & Casey, K. R. (2012). Practice parameters for the nonrespiratory indications for polysomnography and multiple sleep latency testing for children. Sleep: Journal of Sleep and Sleep Disorders Research, 35, 1467–1473. http://dx.doi.org/ 10.5665/sleep.2190 Badin, E., Haddad, C., & Shatkin, J. P. (2016). Insomnia: The sleeping giant of pediatric public health. Current Psychiatry Reports, 18, 47–54. http://dx.doi.org/ 10.1007/s11920-016-0687-0 Bakker, E., van Sprundel, M., van der Auwera, J. C., van Gool, J. D., & Wyndaele, J. J. (2002). Voiding habits and wetting in a population of 4,332 Belgian schoolchildren aged between 10 and 14 years. Scandinavian Journal of Urology and Nephrology, 36, 354–362. http://dx.doi.org/ 10.1080/003655902320783863 Barion, A., & Zee, P. C. (2007). A clinical approach to circadian rhythm sleep disorders. Sleep Medicine, 8, 566–577. http://dx.doi.org/10.1016/j. sleep.2006.11.017 Bartlett, D. J., Biggs, S. N., & Armstrong, S. M. (2013). Circadian rhythm disorders among adolescents: Assessment and treatment options. Medical Journal of Australia, 199, 16–20. http://dx.doi.org/10.5694/ mja13.10912 Bassetti, C., & Aldrich, M. S. (1997). Idiopathic hypersomnia. A series of 42 patients. Brain: A Journal of Neurology, 120, 1423–1435. http://dx.doi.org/ 10.1093/brain/120.8.1423 Beck, S. E., & Marcus, C. L. (2009). Pediatric polysomnography. Sleep Medicine Clinics, 4, 393–406. http://dx.doi.org/10.1016/j. jsmc.2009.04.007 Billiard, M., & Sonka, K. (2016). Idiopathic hypersomnia. Sleep Medicine Reviews, 29, 23–33. http://dx.doi.org/ 10.1016/j.smrv.2015.08.007 Borbély, A. A. (1982). A two-process model of sleep regulation. Human Neurobiology, 1, 195–204. 384

Bowden, C. L. (2005). A different depression: Clinical distinctions between bipolar and unipolar depression. Journal of Affective Disorders, 84, 117–125. http://dx.doi.org/10.1016/S01650327(03)00194-0 Brooks, L. J., & Topol, H. I. (2003). Enuresis in children with sleep apnea. Journal of Pediatrics, 142, 515–518. http://dx.doi.org/10.1067/mpd.2003.158 Butler, R. J. (2004). Childhood nocturnal enuresis: Developing a conceptual framework. Clinical Psychology Review, 24, 909–931. http://dx.doi.org/ 10.1016/j.cpr.2004.07.001 Butler, R. J., & Heron, J. (2008). The prevalence of infrequent bedwetting and nocturnal enuresis in childhood. Scandinavian Journal of Urology and Nephrology, 42, 257–264. http://dx.doi.org/ 10.1080/00365590701748054 Campbell, I. G., & Feinberg, I. (2009). Longitudinal trajectories of non-rapid eye movement delta and theta EEG as indicators of adolescent brain maturation. Proceedings of the National Academy of Sciences, USA, 106, 5177–5180. http://dx.doi.org/ 10.1073/pnas.0812947106 Carskadon, M. A. (1990). Patterns of sleep and sleepiness in adolescents. Pediatrician, 17, 5–12. Carskadon, M. A. (2002). Factors influencing sleep patterns of adolescents. In M. A. Carskadon (Ed.), Adolescent sleep patterns: Biological, social, and psychological influences (pp. 4–26). http://dx.doi.org/ 10.1017/CBO9780511499999.005 Carskadon, M. A., & Acebo, C. (2002). Regulation of sleepiness in adolescents: Update, insights, and speculation. Sleep: Journal of Sleep and Sleep Disorders Research, 25, 606–614. http://dx.doi.org/10.1093/ sleep/25.6.606 Carskadon, M. A., Acebo, C., & Jenni, O. G. (2004). Regulation of adolescent sleep: Implications for behavior. Annals of the New York Academy of Sciences, 1021, 276–291. http://dx.doi.org/10.1196/ annals.1308.032 Carskadon, M. A., & Dement, W. C. (2011). Monitoring and staging human sleep. In M. H. Kryger, T. Roth, & W. C. Dement (Eds.), Principles and practice of sleep medicine (5th ed., pp. 16–26). http://dx.doi.org/ 10.1016/B978-1-4160-6645-3.00002-5 Carskadon, M. A., Dement, W. C., Mitler, M. M., Roth, T., Westbrook, P. R., & Keenan, S. (1986). Guidelines for the multiple sleep latency test (MSLT): A standard measure of sleepiness. Sleep, 9, 519–524. http://dx.doi.org/10.1093/sleep/9.4.519 Carskadon, M. A., Vieira, C., & Acebo, C. (1993). Association between puberty and delayed phase preference. Sleep: Journal of Sleep and Sleep Disorders Research, 16, 258–262. http://dx.doi.org/10.1093/ sleep/16.3.258

Sleep Disorders in Children and Adolescents

Chokroverty, S. (2000). Diagnosis and treatment of sleep disorders caused by co-morbid disease. Neurology, 54(Suppl. 1), S8–S15. Clementi, M. A., Balderas, J., Cowie, J., & Alfano, C. A. (2014). Treatment of childhood insomnia and nighttime fears. In C. A. Alfano & D. C. Beidel (Eds.), Comprehensive evidence based interventions for children and adolescents (pp. 261–274). New York, NY: Wiley.

Copyright American Psychological Association. Not for further distribution.

Colrain, I. M., & Baker, F. C. (2011). Changes in sleep as a function of adolescent development. Neuropsychology Review, 21, 5–21. http://dx.doi.org/ 10.1007/s11065-010-9155-5 Cortese, S., Faraone, S. V., Konofal, E., & Lecendreux, M. (2009). Sleep in children with attention-deficit/ hyperactivity disorder: Meta-analysis of subjective and objective studies. Journal of the American Academy of Child and Adolescent Psychiatry, 48, 894–908. Cortesi, F., Giannotti, F., Ivanenko, A., & Johnson, K. (2010). Sleep in children with autistic spectrum disorder. Sleep Medicine, 11, 659–664. http://dx.doi.org/ 10.1016/j.sleep.2010.01.010 Crowley, S. J., Acebo, C., & Carskadon, M. A. (2007). Sleep, circadian rhythms, and delayed phase in adolescence. Sleep Medicine, 8, 602–612. http:// dx.doi.org/10.1016/j.sleep.2006.12.002 Dahl, R. E., Holttum, J., & Trubnick, L. (1994). A clinical picture of child and adolescent narcolepsy. Journal of the American Academy of Child and Adolescent Psychiatry, 33, 834–841. http://dx.doi.org/ 10.1097/00004583-199407000-00009 de la Eva, R. C., Baur, L. A., Donaghue, K. C., & Waters, K. A. (2002). Metabolic correlates with obstructive sleep apnea in obese subjects. Journal of Pediatrics, 140, 654–659. http://dx.doi.org/10.1067/ mpd.2002.123765 Dutton, M. A., & Greene, R. (2010). Resilience and crime victimization. Journal of Traumatic Stress, 23, 215–222. Feehan, M., McGee, R., Stanton, W., & Silva, P. A. (1990). A 6-year follow-up of childhood enuresis: Prevalence in adolescence and consequences for mental health. Journal of Paediatrics and Child Health, 26, 75–79. http://dx.doi.org/ 10.1111/j.1440-1754.1990.tb02390.x Frank, N. C., Spirito, A., Stark, L., & Owens-Stively, J. (1997). The use of scheduled awakenings to eliminate childhood sleepwalking. Journal of Pediatric Psychology, 22, 345–353. http://dx.doi.org/ 10.1093/jpepsy/22.3.345 Fricke-Oerkermann, L., Plück, J., Schredl, M., Heinz, K., Mitschke, A., Wiater, A., & Lehmkuhl, G. (2007). Prevalence and course of sleep problems in childhood. Sleep: Journal of Sleep and Sleep Disorders

Research, 30, 1371–1377. http://dx.doi.org/10.1093/ sleep/30.10.1371 Fritz, G., & Rockney, R. (2004). Practice parameter for the assessment and treatment of children and adolescents with enuresis. Journal of the American Academy of Child and Adolescent Psychiatry, 43, 1540–1550. http://dx.doi.org/10.1097/01. chi.0000142196.41215.cc Gau, S. F., & Suen Soong, W. T. (1999). Psychiatric comorbidity of adolescents with sleep terrors or sleepwalking: A case-control study. Australian and New Zealand Journal of Psychiatry, 33, 734–739. http://dx.doi.org/10.1080/j.1440-1614.1999.00610.x Giannotti, F., Cortesi, F., Sebastiani, T., & Ottaviano, S. (2002). Circadian preference, sleep and daytime behaviour in adolescence. Journal of Sleep Research, 11, 191–199. http://dx.doi.org/10.1046/j.13652869.2002.00302.x Glaze, D. G., Rosen, C. L., & Owens, J. A. (2002). Toward a practical definition of pediatric insomnia. Current Therapeutic Research, 63, B4–B17. http:// dx.doi.org/10.1016/S0011-393X(02)80100-X Glazener, C. M. A., Evans, J. H. C., & Peto, R. E. (2006). Alarm interventions for nocturnal enuresis in children. Evidence-Based Child Health: A Cochrane Review Journal, 1, 9–97. http://dx.doi.org/10.1002/ebch.4 Glozier, N., O’Dea, B., McGorry, P. D., Pantelis, C., Amminger, G. P., Hermens, D. F., . . . Hickie, I. B. (2014). Delayed sleep onset in depressed young people. BMC Psychiatry, 14, 33. http://dx.doi.org/ 10.1186/1471-244X-14-33 Gozal, D., & Kheirandish-Gozal, L. (2008). The multiple challenges of obstructive sleep apnea in children: Morbidity and treatment. Current Opinion in Pediatrics, 20, 654–658. http://dx.doi.org/10.1097/ MOP.0b013e328316ec2d Gradisar, M., Dohnt, H., Gardner, G., Paine, S., Starkey, K., Menne, A., . . . Trenowden, S. (2011). A randomized controlled trial of cognitive–behavior therapy plus bright light therapy for adolescent delayed sleep phase disorder. Sleep: Journal of Sleep and Sleep Disorders Research, 34, 1671–1680. http://dx.doi.org/10.5665/ sleep.1432 Gradisar, M., Gardner, G., & Dohnt, H. (2011). Recent worldwide sleep patterns and problems during adolescence: A review and meta-analysis of age, region, and sleep. Sleep Medicine, 12, 110–118. http:// dx.doi.org/10.1016/j.sleep.2010.11.008 Green, P. M., & Stillman, M. J. (1998). Narcolepsy. Signs, symptoms, differential diagnosis, and management. Archives of Family Medicine, 7, 472–478. http:// dx.doi.org/10.1001/archfami.7.5.472 Guilleminault, C., Palombini, L., Pelayo, R., & Chervin, R. D. (2003). Sleepwalking and sleep terrors in prepubertal children: What triggers them? 385

Alfano, Palmer, and Bower

Pediatrics, 111, e17–e25. http://dx.doi.org/10.1542/ peds.111.1.e17 Guilleminault, C., & Pelayo, R. (1998). Narcolepsy in prepubertal children. Annals of Neurology, 43, 135–142. http://dx.doi.org/10.1002/ana.410430125

Copyright American Psychological Association. Not for further distribution.

Guilleminault, C., & Pelayo, R. (2000). Narcolepsy in children: A practical guide to its diagnosis, treatment and follow-up. Paediatric Drugs, 2, 1–9. http://dx.doi.org/ 10.2165/00148581-200002010-00001 Hazama, G. I., Inoue, Y., Kojima, K., Ueta, T., & Nakagome, K. (2008). The prevalence of probable delayed-sleep-phase syndrome in students from junior high school to university in Tottori, Japan. Tohoku Journal of Experimental Medicine, 216, 95–98. http://dx.doi.org/10.1620/tjem.216.95 Hublin, C., Kaprio, J., Partinen, M., & Koskenvuo, M. (1999). Nightmares: Familial aggregation and association with psychiatric disorders in a nationwide twin cohort. American Journal of Medical Genetics, 88, 329–336. http://dx.doi.org/10.1002/ (SICI)1096-8628(19990820)88:43.0.CO;2-E Iglowstein, I., Jenni, O. G., Molinari, L., & Largo, R. H. (2003). Sleep duration from infancy to adolescence: Reference values and generational trends. Pediatrics, 111, 302–307. http://dx.doi.org/10.1542/ peds.111.2.302 Ivanenko, A., Barnes, M. E., Crabtree, V. M., & Gozal, D. (2004). Psychiatric symptoms in children with insomnia referred to a pediatric sleep medicine center. Sleep Medicine, 5, 253–259. http://dx.doi.org/ 10.1016/j.sleep.2004.02.001 Ivanenko, A., Tauman, R., & Gozal, D. (2003). Modafinil in the treatment of excessive daytime sleepiness in children. Sleep Medicine, 4, 579–582. http://dx.doi.org/ 10.1016/S1389-9457(03)00162-X Jeyakumar, A., Rahman, S. I., Armbrecht, E. S., & Mitchell, R. (2012). The association between sleepdisordered breathing and enuresis in children. Laryngoscope, 122, 1873–1877. http://dx.doi.org/ 10.1002/lary.23323 Johnson, E. O., Roth, T., & Breslau, N. (2006). The association of insomnia with anxiety disorders and depression: Exploration of the direction of risk. Journal of Psychiatric Research, 40, 700–708. http:// dx.doi.org/10.1016/j.jpsychires.2006.07.008 Johnson, E. O., Roth, T., Schultz, L., & Breslau, N. (2006). Epidemiology of DSM–IV insomnia in adolescence: Lifetime prevalence, chronicity, and an emergent gender difference. Pediatrics, 117, e247–e256. http:// dx.doi.org/10.1542/peds.2004-2629 Kaditis, A., Kheirandish-Gozal, L., & Gozal, D. (2012). Algorithm for the diagnosis and treatment of pediatric OSA: A proposal of two pediatric sleep centers. Sleep 386

Medicine, 13, 217–227. http://dx.doi.org/ 10.1016/j.sleep.2011.09.009 Karacan, I., Anch, M., Thornby, J. I., Okawa, M., & Williams, R. L. (1975). Longitudinal sleep patterns during pubertal growth: Four-year follow up. Pediatric Research, 9, 842–846. Kataria, S., Swanson, M. S., & Trevathan, G. E. (1987). Persistence of sleep disturbances in preschool children. Journal of Pediatrics, 110, 642–646. http:// dx.doi.org/10.1016/S0022-3476(87)80571-1 Kazaglis, L., Molero, H., Azeem, M. W., & Khawaja, I. S. (2016). Overview and management of common sleep disorders in children: A review for psychiatrists. Psychiatric Annals, 46, 39–44. http://dx.doi.org/ 10.3928/00485713-20151209-01 Kelly, R. J., & El-Sheikh, M. (2011). Marital conflict and children’s sleep: Reciprocal relations and socioeconomic effects. Journal of Family Psychology, 25, 412–422. http://dx.doi.org/10.1037/a0023789 Krakow, B., Sandoval, D., Schrader, R., Keuhne, B., McBride, L., Yau, C. L., & Tandberg, D. (2001). Treatment of chronic nightmares in adjudicated adolescent girls in a residential facility. Journal of Adolescent Health, 29, 94–100. http://dx.doi.org/ 10.1016/S1054-139X(00)00195-6 Kuhn, B. R., & Elliott, A. J. (2003). Treatment efficacy in behavioral pediatric sleep medicine. Journal of Psychosomatic Research, 54, 587–597. http://dx.doi.org/ 10.1016/S0022-3999(03)00061-8 Kurth, S., Ringli, M., Geiger, A., LeBourgeois, M., Jenni, O. G., & Huber, R. (2010). Mapping of cortical activity in the first two decades of life: A highdensity sleep electroencephalogram study. Journal of Neuroscience, 30, 13211–13219. http://dx.doi.org/ 10.1523/JNEUROSCI.2532-10.2010 Laberge, L., Tremblay, R. E., Vitaro, F., & Montplaisir, J. (2000). Development of parasomnias from childhood to early adolescence. Pediatrics, 106, 67–74. http:// dx.doi.org/10.1542/peds.106.1.67 Lam, P., Hiscock, H., & Wake, M. (2003). Outcomes of infant sleep problems: A longitudinal study of sleep, behavior, and maternal well-being. Pediatrics, 111, e203–e207. http://dx.doi.org/10.1542/ peds.111.3.e203 Lazaratou, H., Soldatou, A., & Dikeos, D. (2012). Medical comorbidity of sleep disorders in children and adolescents. Current Opinion in Psychiatry, 25, 391–397. http://dx.doi.org/10.1097/ YCO.0b013e3283556c7a Lecendreux, M., Lavault, S., Lopez, R., Inocente, C. O., Konofal, E., Cortese, S., . . . Dauvilliers, Y. (2015). Attention-deficit/hyperactivity disorder (ADHD) symptoms in pediatric narcolepsy: A cross-sectional study. Sleep: Journal of Sleep and Sleep Disorders

Sleep Disorders in Children and Adolescents

Research, 38, 1285–1295. http://dx.doi.org/10.5665/ sleep.4910

Copyright American Psychological Association. Not for further distribution.

Li, S. X., Yu, M. W., Lam, S. P., Zhang, J., Li, A. M., Lai, K. Y., & Wing, Y. K. (2011). Frequent nightmares in children: Familial aggregation and associations with parent-reported behavioral and mood problems. Sleep: Journal of Sleep and Sleep Disorders Research, 34, 487–493. http://dx.doi.org/10.1093/ sleep/34.4.487 Liu, X., Buysse, D. J., Gentzler, A. L., Kiss, E., Mayer, L., Kapornai, K., . . . Kovacs, M. (2007). Insomnia and hypersomnia associated with depressive phenomenology and comorbidity in childhood depression. Sleep: Journal of Sleep and Sleep Disorders Research, 30, 83–90. http://dx.doi.org/10.1093/ sleep/30.1.83 Lonigan, C. J., Phillips, B. M., & Richey, J. A. (2003). Posttraumatic stress disorder in children: Diagnosis, assessment, and associated features. Child and Adolescent Psychiatric Clinics of North America, 12, 171–194. http://dx.doi.org/10.1016/S10564993(02)00105-0 Lozoff, B., Askew, G. L., & Wolf, A. W. (1996). Cosleeping and early childhood sleep problems: Effects of ethnicity and socioeconomic status. Journal of Developmental and Behavioral Pediatrics, 17, 9–15. http://dx.doi.org/10.1097/00004703-19960200000002 Mayer, G., Benes, H., Young, P., Bitterlich, M., & Rodenbeck, A. (2015). Modafinil in the treatment of idiopathic hypersomnia without long sleep time—A randomized, double-blind, placebo-controlled study. Journal of Sleep Research, 24, 74–81. http://dx.doi.org/ 10.1111/jsr.12201 McLaughlin-Crabtree, V., & Witcher, L. A. (2008). Impact of sleep loss on children and adolescents. In A. Ivanenko (Ed.), Sleep and psychiatric disorders in children and adolescents (pp. 139–148). New York, NY: Informa Healthcare. Meltzer, L. J., Johnson, C., Crosette, J., Ramos, M., & Mindell, J. A. (2010). Prevalence of diagnosed sleep disorders in pediatric primary care practices. Pediatrics, 125, e1410–e1418. http://dx.doi.org/ 10.1542/peds.2009-2725 Meltzer, L. J., & Mindell, J. A. (2007). Relationship between child sleep disturbances and maternal sleep, mood, and parenting stress: A pilot study. Journal of Family Psychology, 21, 67–73. http://dx.doi.org/ 10.1037/0893-3200.21.1.67 Meltzer, L. J., & Mindell, J. A. (2014). Systematic review and meta-analysis of behavioral interventions for pediatric insomnia. Journal of Pediatric Psychology, 39, 932–948. http://dx.doi.org/10.1093/jpepsy/jsu041 Millman, R. P. (2005). Excessive sleepiness in adolescents and young adults: Causes, consequences, and

treatment strategies. Pediatrics, 115, 1774–1786. http://dx.doi.org/10.1542/peds.2005-0772 Mindell, J. A., & Barrett, K. M. (2002). Nightmares and anxiety in elementary-aged children: Is there a relationship. Child: Care, Health and Development, 28, 317–322. http://dx.doi.org/10.1046/j.13652214.2002.00274.x Mindell, J. A., Kuhn, B., Lewin, D. S., Meltzer, L. J., & Sadeh, A., & the American Academy of Sleep Medicine. (2006). Behavioral treatment of bedtime problems and night wakings in infants and young children. Sleep: Journal of Sleep and Sleep Disorders Research, 29, 1263–1276. Mindell, J. A., & Owens, J. A. (2009). A clinical guide to pediatric sleep: Diagnosis and management of sleep problems in children and adolescents (2nd ed.). Philadelphia, PA: Lippincott Williams & Wilkins Publishers. Mindell, J. A., Sadeh, A., Wiegand, B., How, T. H., & Goh, D. Y. T. (2010). Cross-cultural differences in infant and toddler sleep. Sleep Medicine, 11, 274–280. http://dx.doi.org/10.1016/j.sleep.2009.04.012 Monk, T. H. (2005). Aging human circadian rhythms: Conventional wisdom may not always be right. Journal of Biological Rhythms, 20, 366–374. http:// dx.doi.org/10.1177/0748730405277378 Morgenthaler, T. I., Kapur, V. K., Brown, T., Swick, T. J., Alessi, C., Aurora, R. N., . . . Zak, R. (2007). Practice parameters for the treatment of narcolepsy and other hypersomnias of central origin. Sleep: Journal of Sleep and Sleep Disorders Research, 30, 1705–1711. http:// dx.doi.org/10.1093/sleep/30.12.1705 Morrish, E., King, M. A., Smith, I. E., & Shneerson, J. M. (2004). Factors associated with a delay in the diagnosis of narcolepsy. Sleep Medicine, 5, 37–41. http://dx.doi.org/10.1016/j.sleep.2003.06.002 Moturi, S., & Avis, K. (2010). Assessment and treatment of common pediatric sleep disorders. Innovations in Clinical Neuroscience, 7, 24–37. Murali, H., & Kotagal, S. (2006). Off-label treatment of severe childhood narcolepsy-cataplexy with sodium oxybate. Sleep: Journal of Sleep and Sleep Disorders Research, 29, 1025–1029. http://dx.doi.org/10.1093/ sleep/29.8.1025 Nevéus, T., Cnattingius, S., Olsson, U., & Hetta, J. (2001). Sleep habits and sleep problems among a community sample of schoolchildren. Acta Paediatrica, 90, 1450–1455. http://dx.doi.org/ 10.1111/j.1651-2227.2001.tb01612.x Nevsimalova, S. (2009). Narcolepsy in childhood. Sleep Medicine Reviews, 13, 169–180. http://dx.doi.org/ 10.1016/j.smrv.2008.04.007 Ohayon, M. M. (2013). Narcolepsy is complicated by high medical and psychiatric comorbidities: A 387

Alfano, Palmer, and Bower

comparison with the general population. Sleep Medicine, 14, 488–492. http://dx.doi.org/10.1016/j. sleep.2013.03.002 Ohayon, M. M., Carskadon, M. A., Guilleminault, C., & Vitiello, M. V. (2004). Meta-analysis of quantitative sleep parameters from childhood to old age in healthy individuals: Developing normative sleep values across the human lifespan. Sleep: Journal of Sleep and Sleep Disorders Research, 27, 1255–1273.

Copyright American Psychological Association. Not for further distribution.

Ohayon, M. M., Ferini-Strambi, L., Plazzi, G., Smirne, S., & Castronovo, V. (2005). How age influences the expression of narcolepsy. Journal of Psychosomatic Research, 59, 399–405. http://dx.doi.org/10.1016/j. jpsychores.2005.06.065 Osofsky, J. D. (1995). The effects of exposure to violence on young children. American Psychologist, 50, 782–788. http://dx.doi.org/10.1037/0003066X.50.9.782 Owens, J. A., & Mindell, J. A. (2011). Pediatric insomnia. Pediatric Clinics of North America, 58, 555–569. http://dx.doi.org/10.1016/j.pcl.2011.03.011 Owens, J. A., Spirito, A., McGuinn, M., & Nobile, C. (2000). Sleep habits and sleep disturbance in elementary school-aged children. Journal of Developmental and Behavioral Pediatrics, 21, 27–36. http://dx.doi.org/10.1097/00004703-200002000-00005 Özkan, K. U., Garipardic, M., Toktamis, A., Karabiber, H., & Sahinkanat, T. (2004). Enuresis prevalence and accompanying factors in schoolchildren: A questionnaire study from southeast Anatolia. Urologia Internationalis, 73, 149–155. http://dx.doi.org/ 10.1159/000079696 Paruthi, S., Brooks, L. J., D’Ambrosio, C., Hall, W. A., Kotagal, S., Lloyd, R. M., . . . Wise, M. S. (2016). Recommended amount of sleep for pediatric populations: A consensus statement of the American Academy of Sleep Medicine. Journal of Clinical Sleep Medicine, 12, 785–786. http:// dx.doi.org/10.5664/jcsm.5866 Pearl, P. L., Weiss, R. E., & Stein, M. A. (2001). Medical mimics. Medical and neurological conditions simulating ADHD. Annals of the New York Academy of Sciences, 931, 97–112. http://dx.doi.org/ 10.1111/j.1749-6632.2001.tb05775.x Peterman, J. S., Carper, M. M., & Kendall, P. C. (2015). Anxiety disorders and comorbid sleep problems in school-aged youth: Review and future research directions. Child Psychiatry and Human Development, 46, 376–392. http://dx.doi.org/10.1007/s10578-014-0478-y Peterson, P. C., & Husain, A. M. (2008). Pediatric narcolepsy. Brain and Development, 30, 609–623. http://dx.doi.org/10.1016/j.braindev.2008.02.004 Petit, D., Touchette, E., Tremblay, R. E., Boivin, M., & Montplaisir, J. (2007). Dyssomnias and parasomnias in early childhood. Pediatrics, 119, e1016–e1025. http://dx.doi.org/10.1542/peds.2006-2132 388

Poli, F., Pizza, F., Mignot, E., Ferri, R., Pagotto, U., Taheri, S., . . . Plazzi, G. (2013). High prevalence of precocious puberty and obesity in childhood narcolepsy with cataplexy. Sleep: Journal of Sleep and Sleep Disorders Research, 36, 175–181. http://dx.doi.org/ 10.5665/sleep.2366 Pynoos, R. S. (1993). Traumatic stress and developmental psychopathology in children and adolescents. In J. M. Oldham, M. B. Riba, & A. Tasman (Eds.), American Psychiatric Press Review of Psychiatry (Vol. 12, pp. 205–238). Washington, DC: American Psychiatric Press. Reynolds, K. C., & Alfano, C. A. (2016a). Childhood bedtime problems predict adolescent internalizing symptoms through emotional reactivity. Journal of Pediatric Psychology, 41, 971–982. http://dx.doi.org/ 10.1093/jpepsy/jsw014 Reynolds, K. C., & Alfano, C. A. (2016b). Things that go bump in the night: Frequency and predictors of nightmares in anxious and nonanxious children. Behavioral Sleep Medicine, 14, 442–456. http://dx.doi.org/ 10.1080/15402002.2015.1017099 Robillard, R., Naismith, S. L., Rogers, N. L., Ip, T. K. C., Hermens, D. F., Scott, E. M., & Hickie, I. B. (2013). Delayed sleep phase in young people with unipolar or bipolar affective disorders. Journal of Affective Disorders, 145, 260–263. http://dx.doi.org/10.1016/j. jad.2012.06.006 Rosenthal, N. E., Joseph-Vanderpool, J. R., Levendosky, A. A., Johnston, S. H., Allen, R., Kelly, K. A., . . . Starz, K. E. (1990). Phase-shifting effects of bright morning light as treatment for delayed sleep phase syndrome. Sleep: Journal of Sleep and Sleep Disorders Research, 13, 354–361. Sadeh, A. (2007). Consequences of sleep loss or sleep disruption in children. Sleep Medicine Clinics, 2, 513–520. http://dx.doi.org/10.1016/j.jsmc.2007.05.012 Sadeh, A., & Acebo, C. (2002). The role of actigraphy in sleep medicine. Sleep Medicine Reviews, 6, 113–124. http://dx.doi.org/10.1053/smrv.2001.0182 Sadeh, A., Gruber, R., & Raviv, A. (2003). The effects of sleep restriction and extension on school-age children: What a difference an hour makes. Child Development, 74, 444–455. http://dx.doi.org/ 10.1111/1467-8624.7402008 Saxvig, I. W., Pallesen, S., Wilhelmsen-Langeland, A., Molde, H., & Bjorvatn, B. (2012). Prevalence and correlates of delayed sleep phase in high school students. Sleep Medicine, 13, 193–199. http://dx.doi.org/ 10.1016/j.sleep.2011.10.024 Scheeringa, M. S., Weems, C. F., Cohen, J. A., AmayaJackson, L., & Guthrie, D. (2011). Trauma-focused cognitive–behavioral therapy for posttraumatic stress disorder in 3-through 6-year-old children: A randomized clinical trial. Journal of Child Psychology

Sleep Disorders in Children and Adolescents

and Psychiatry, 52, 853–860. http://dx.doi.org/ 10.1111/j.1469-7610.2010.02354.x Shanahan, L., Copeland, W. E., Angold, A., Bondy, C. L., & Costello, E. J. (2014). Sleep problems predict and are predicted by generalized anxiety/ depression and oppositional defiant disorder. Journal of the American Academy of Child and Adolescent Psychiatry, 53, 550–558. http://dx.doi.org/10.1016/j. jaac.2013.12.029

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Simard, V., & Nielsen, T. (2009). Adaptation of imagery rehearsal therapy for nightmares in children: A brief report. Psychotherapy: Theory, Research, Practice, Training, 46, 492–497. http://dx.doi.org/10.1037/a0017945 Simard, V., Nielsen, T. A., Tremblay, R. E., Boivin, M., & Montplaisir, J. Y. (2008). Longitudinal study of bad dreams in preschool-aged children: Prevalence, demographic correlates, risk and protective factors. Sleep: Journal of Sleep and Sleep Disorders Research, 31, 62–70. http://dx.doi.org/10.1093/sleep/31.1.62

Movement disorders in sleep: Guidelines for differentiating epileptic from non-epileptic motor phenomena arising from sleep. Sleep Medicine Reviews, 11, 255–267. http://dx.doi.org/10.1016/j. smrv.2007.01.001 Verduin, T. L., & Kendall, P. C. (2003). Differential occurrence of comorbidity within childhood anxiety disorders. Journal of Clinical Child and Adolescent Psychology, 32, 290–295. http://dx.doi.org/10.1207/ S15374424JCCP3202_15 Wake, M., Morton-Allen, E., Poulakis, Z., Hiscock, H., Gallagher, S., & Oberklaid, F. (2006). Prevalence, stability, and outcomes of cry-fuss and sleep problems in the first 2 years of life: Prospective community-based study. Pediatrics, 117, 836–842. http://dx.doi.org/10.1542/peds.2005-0775 Weissbluth, M. (1995). Naps in children: 6 months–7 years. Sleep, 18, 82–87. http://dx.doi.org/10.1093/ sleep/18.2.82

Sivertsen, B., Pallesen, S., Stormark, K. M., Bøe, T., Lundervold, A. J., & Hysing, M. (2013). Delayed sleep phase syndrome in adolescents: Prevalence and correlates in a large population based study. BMC Public Health, 13, 1163. http://dx.doi.org/ 10.1186/1471-2458-13-1163

Werner, H., Lebourgeois, M. K., Geiger, A., & Jenni, O. G. (2009). Assessment of chronotype in 4- to 11-year-old children: Reliability and validity of the Children’s Chronotype Questionnaire (CCTQ). Chronobiology International, 26, 992–1014. http:// dx.doi.org/10.1080/07420520903044505

Sowa, N. A. (2016). Idiopathic hypersomnia and hypersomnolence disorder: A systematic review of the literature. Psychosomatics: Journal of Consultation and Liaison Psychiatry, 57, 152–164. http://dx.doi.org/ 10.1016/j.psym.2015.12.006

Willinger, M., Ko, C. W., Hoffman, H. J., Kessler, R. C., & Corwin, M. J. (2003). Trends in infant bed sharing in the United States, 1993–2000: The National Infant Sleep Position study. Archives of Pediatrics and Adolescent Medicine, 157, 43–49. http://dx.doi. org/10.1001/archpedi.157.1.43

Spruyt, K., & Gozal, D. (2011). Pediatric sleep questionnaires as diagnostic or epidemiological tools: A review of currently available instruments. Sleep Medicine Reviews, 15, 19–32. http://dx.doi.org/ 10.1016/j.smrv.2010.07.005 Stone, J., Malone, P. S. J., Atwill, D., McGrigor, V., & Hill, C. M. (2008). Symptoms of sleep-disordered breathing in children with nocturnal enuresis. Journal of Pediatric Urology, 4, 197–202. http:// dx.doi.org/10.1016/j.jpurol.2007.11.011 St.-Onge, M., Mercier, P., & De Koninck, J. (2009). Imagery rehearsal therapy for frequent nightmares in children. Behavioral Sleep Medicine, 7, 81–98. http:// dx.doi.org/10.1080/15402000902762360 Sureshkumar, P., Jones, M., Caldwell, P. H., & Craig, J. C. (2009). Risk factors for nocturnal enuresis in schoolage children. Journal of Urology, 182, 2893–2899. http://dx.doi.org/10.1016/j.juro.2009.08.060 Tikotzky, L., & Sadeh, A. (2010). The role of cognitive–behavioral therapy in behavioral childhood insomnia. Sleep Medicine, 11, 686–691. http://dx.doi.org/ 10.1016/j.sleep.2009.11.017 Tinuper, P., Provini, F., Bisulli, F., Vignatelli, L., Plazzi, G., Vetrugno, R., . . . Lugaresi, E. (2007).

Wolfson, A. R. (1996). Sleeping patterns of children and adolescents: Developmental trends, disruptions, and adaptations. Child and Adolescent Psychiatric Clinics of North America, 5, 549–568. Yeung, C. K., Sreedhar, B., Sihoe, J. D. Y., Sit, F. K. Y., & Lau, J. (2006). Differences in characteristics of nocturnal enuresis between children and adolescents: A critical appraisal from a large epidemiological study. BJU International, 97, 1069–1073. http:// dx.doi.org/10.1111/j.1464-410X.2006.06074.x Yoon, S. Y. R., Jain, U., & Shapiro, C. (2012). Sleep in attention-deficit/hyperactivity disorder in children and adults: Past, present, and future. Sleep Medicine Reviews, 16, 371–388. http://dx.doi.org/10.1016/j. smrv.2011.07.001 Zadra, A., Desautels, A., Petit, D., & Montplaisir, J. (2013). Somnambulism: Clinical aspects and pathophysiological hypotheses. Lancet Neurology, 12, 285–294. http://dx.doi.org/10.1016/S14744422(12)70322-8 Zuckerman, B., Stevenson, J., & Bailey, V. (1987). Sleep problems in early childhood: Continuities, predictive factors, and behavioral correlates. Pediatrics, 80, 664–671. 389

Chapter 18

Understanding the Development and Management of Antisocial Disorders in Adolescents Copyright American Psychological Association. Not for further distribution.

Michael S. McCloskey and Deborah A. G. Drabick

Adolescence is characterized by numerous neurobiological, cognitive, and social changes. Peers gain in importance among adolescents, and they experience concurrent changes in their cognitive control, reward-seeking behaviors, and autonomy seeking. It is critical to consider typical development in determining whether adolescents’ behaviors are problematic. Although research indicates that children exhibit appropriate information processing and determination of consequences for risk-taking behavior (Steinberg, 2008), adolescents are more willing to engage in risk-taking behavior among peers (Weigard, Chein, Albert, Smith, & Steinberg, 2014). Oppositional behavior is expected among adolescents, and it is crucial to identify whether this behavior exceeds what is expected on the basis of normative development. Similarly, engaging in what might be labeled conduct disorder (CD) and/ or substance use behaviors may be expected given the increases in risk-taking and sensation-seeking that are normative among adolescents (Drabick & Kendall,2010; Drabick & Steinberg, 2011). This chapter considers several externalizing disorders that are associated with adolescence within the context of this developmental framework. For example, taking a dimensional (rather than categorical) approach to understanding symptoms, many externalizing behaviors are relatively common among adolescents. Given that the categorical approach is dominant within the mental health field, we also consider the categorical approach in examining oppositional defiant disorder (ODD), CD, antisocial personality disorder (ASPD),

and intermittent explosive disorder (IED). For each, we provide information regarding the diagnoses, risk factors and correlates, assessment, and intervention strategies with a focus on adolescents. Psychological Disorders In the following section, we consider four externalizing disorders that are often identified in adolescence: ODD, CD, ASPD, and IED. We provide prevalence rates, and discuss current and past diagnostic criteria and specifiers associated with each of these disorders.

Oppositional Defiant Disorder ODD is characterized by a recurrent pattern of angry and irritable mood, argumentative and defiant behavior toward authority figures, and vindictiveness that persists for at least 6 months. Prevalence rates of ODD range from 1% to 11% in the general population, with an average of 3.3%. Prior to puberty, boys are more likely to evidence ODD than girls; however, rates are similar among boys and girls after that point (American Psychiatric Association, 2013). Lifetime prevalence of ODD is 10.2%, with boys reporting 11.2% and girls reporting 9.2% (Nock, Kazdin, Hiripi, & Kessler, 2007). ODD was first included in the Diagnostic and Statistical Manual of Mental Disorders, Third Edition (DSM–III; American Psychiatric Association, 1980). To meet DSM–III criteria for ODD, two of five behaviors (i.e., violations of minor rules, temper tantrums, argumentativeness, provocative behavior,

The authors contributed equally to this chapter. http://dx.doi.org/10.1037/0000065-018 APA Handbook of Psychopathology: Vol. 2. Child and Adolescent Psychopathology, J. N. Butcher (Editor-in-Chief) Copyright © 2018 by the American Psychological Association. All rights reserved.

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and stubbornness) had to be present for at least 6 months, with onset after the age of 3. A hierarchical exclusion rule involving CD was also included that indicated ODD could not be diagnosed if diagnostic criteria for CD were met. This criterion was based on the facts that ODD is often a developmental precursor to CD, that ODD and CD share numerous risk factors, and the disorders are closely related in taxonomic and developmental terms (Burke, Waldman, & Lahey, 2010). The revised version of DSM–III (DSM–III–R; American Psychiatric Association, 1987) expanded criteria to include nine behaviors, five of which had to be present for at least 6 months. The word often was added to criteria, along with the statement that the behaviors had to occur more frequently than is typical for adolescents of comparable mental age and developmental level. Finally, the DSM–III requirement of onset after age 3 was removed. The number of symptoms required was reduced from five to four in the DSM–IV (American Psychiatric Association, 1994), and the symptom related to swearing was eliminated, leaving a list of eight symptoms with an impairment criterion added. Although these symptoms and criteria were maintained in the subsequent text revision (DSM–IV–TR; American Psychiatric Association, 2000), several substantive changes have been made in the current edition (DSM–5; American Psychiatric Association, 2013). First, the hierarchical exclusion criterion with CD was removed, given that it obfuscated the high rates of overlap between ODD and CD. Second, ODD symptoms were organized into three categories: angry or irritable mood, argumentative or defiant behavior, and vindictiveness. This decision stemmed from evidence that the “behavioral” symptoms of ODD (e.g., argues, defies, annoys) are differentially associated with attention-deficit/ hyperactivity disorder (ADHD) and CD, whereas “emotional” symptoms of ODD (i.e., loses temper, is touchy or easily annoyed, is angry or resentful) are predictive of mood and anxiety disorders. Third, the DSM–5 provides further guidance regarding how to operationalize often for each of the symptoms. Specifically, the symptom of spitefulness and vindictiveness should occur at least twice in the past 6 months. All other ODD symptoms must occur on 392

most days among children under age 5, and at least once per week among individuals age 5 and older. Although the disorder is defined in a categorical manner, the DSM–5 includes a continuous severity specifier related to the pervasiveness of ODD symptoms (mild: one setting; moderate: two settings; and severe: three or more settings).

Conduct Disorder To meet criteria for CD based on the DSM–5 (American Psychiatric Association, 2013), three of 15 symptoms have to be met in the past 12 months, with at least one in the past 6 months. Criteria include aggression toward people and animals, destruction of property, deceitfulness or theft, and serious violations of rules. The estimated lifetime prevalence of CD is 9.5%, with boys reporting 12% and girls reporting 7.1% (Nock, Kazdin, Hiripi, & Kessler, 2006). CD, like ODD, was first listed as a separate condition with operationalized criteria in the DSM–III. The CD category included subtypes that differed in terms of whether behaviors were socialized/ undersocialized and aggressive/nonaggressive. The undersocialized component had to do with failure to establish a normal degree of affection, empathy, or bond with others (e.g., experiencing guilt and concern for the welfare of friends). The nonaggressive component involved a repetitive and persistent pattern in which either basic rights of others or major developmentally appropriate societal norms or rules were violated (e.g., truancy, running away), whereas the aggressive component involved aggression toward people and/or animals. The DSM–III–R (American Psychiatric Association, 1987) omitted symptoms related to disobedience, substance abuse, and blaming others from the CD category, reducing the total number of symptoms to 13, and maintained the duration criterion of 6 months. In addition, the required number of symptoms was increased from one to three, and the subtyping approach was modified to include three subtypes: group type, solitary aggressive type, and undifferentiated type. The DSM–IV (American Psychiatric Association, 1994) added two symptoms to CD (i.e., bullying, staying out late), modified the lying criterion, and changed the duration criteria.

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Understanding the Development and Management of Antisocial Disorders in Adolescents

Specifically, the requirement of three criteria was maintained, though the duration criteria were modified to indicate that at least three criteria should be met within the past 12 months, with at least one in the past 6 months. The DSM–IV introduced an age-of-onset specifier, differentiating a childhood-onset type (at least one symptom prior to age 10), an adolescent-onset type (absence of any symptom prior to age 10), and an unspecified onset. The addition of these specifiers stemmed from multiple lines of inquiry. Specifically, research examining childhood- and adolescent-onset CD indicates that compared with individuals with adolescent-onset CD, individuals with childhoodonset CD exhibit (a) higher levels of aggressive and antisocial behavior; (b) a more persistent course of CD; (c) more cognitive, verbal, and neuropsychological deficits; (d) higher levels of familial risk factors; (e) different patterns of comorbid conditions; (f) greater impairment in occupational and interpersonal functioning across developmental periods; and (g) higher levels of antisocial and substance-abusing behaviors in adulthood (Connor, Ford, Albert, & Doerfler, 2007; Frick & Viding, 2009; Loeber, Burke, & Pardini, 2009; Moffitt, 1993; Moffitt & Caspi, 2001; Odgers et al., 2007). The DSM–5 maintained the symptoms, criteria, and the age-of-onset specifier for CD; however, the additional specifier “with limited prosocial emotions” was included. This specifier stemmed from research indicating that adolescents with CD with and without callous-unemotional (CU) traits differ in terms of CD symptom severity, course, genetic, neurobiological, cognitive, emotional, and psychosocial risk factors and correlates, and in response to contextual factors and interventions (Frick, Ray, Thornton, & Kahn, 2014a, 2014b; Frick & White, 2008). This specifier requires at least two characteristics over at least 12 months across relationships and settings, including lack of remorse or guilt, lack of empathy or callous behaviors, lack of concern about performance, and shallow or deficient affect.

Antisocial Personality Disorder Though not diagnosed until age 18, ASPD is the development of antisocial behaviors, which began

in childhood or early adolescence, that was (by definition) identified as CD (American Psychiatric Association, 2013). As such, CD and ASPD share the core feature of disregard for and/or violation of the rights of others. Symptoms of ASPD include a failure to conform to societal norms, impulsivity and poor planning, aggressiveness, a disregard for safety, lack of responsibility and a lack of remorse, though only three of the preceding symptoms (plus CD prior to age 18) are needed to meet criteria for ASPD (American Psychiatric Association, 2013). ASPD is differentiated from the older, related construct of psychopathy (which is not included in the DSM–5) in that individuals with ASPD are not required to display the interpersonal and affective symptoms of psychopathy, including shallow affect, lack of empathy, or grandiose sense of worth (Cleckley, 1976; Hare, 1991); these affective components are similar to CU traits that served as the foundation for the addition of the specifier of limited prosocial emotions to the DSM–5 CD category (Frick et al., 2014a, 2014b). Because of this, most individuals identified as psychopaths meet criteria for ASPD, but only a minority of individuals with ASPD would be considered as evidencing psychopathy. It has been argued that the almost exclusive focus on antisocial behavior, rather than potential underlying personality characteristics and interpersonal traits, may not best capture a personality disorder and may overpathologize criminal behavior and fail to distinguish more and less severe subtypes of ASPD (Ogloff, 2006). Reflecting this, proposed alternate criteria for ASPD that focuses more on the affective and interpersonal traits typically associated with psychopathy (e.g., egocentrism, lack of empathy) is included “for further review” in the DSM–5 (American Psychiatric Association, 2013). ASPD has a lifetime prevalence of 2% to 4% of the general population, occurring several times more often in men than women (American Psychiatric Association, 2013; National Institute for Health and Clinical Excellence, 2010; Robins, Tipp, & Przybeck, 1991). The prevalence of ASPD is much higher in prison samples with estimates of approximately 50% (Fazel & Danesh, 2002). Among those with ASPD, some individuals show an adolescent time-limited pattern in 393

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which antisocial symptoms remit in early adulthood; however, most individuals with ASPD show a more severe and chronic course that lasts well into adulthood (Moffitt, 1993). Not surprisingly, ASPD is associated with high levels of violent and nonviolent crime (Fazel & Danesh, 2002), and is associated with considerable costs to public service agencies (T. Kendall et al., 2009). ASPD is also associated with early death, with one study finding that individuals with ASPD under age 40 were 33 times more likely to die than similar-age men without ASPD (Black, Baumgard, Bell, & Kao, 1996).

Intermittent Explosive Disorder Of the different disorders associated with antisocial behavior in adolescence, IED is the only disorder that requires excessive levels of aggressive behavior. In fact, excessive aggression is pathognomonic of an IED diagnosis, with the excessive aggression being evidenced as frequent minor aggressive acts (i.e., verbal aggression and/ or minor physical aggression that causes no harm or damage occurring twice a week or more on average and/ for 3 or more months) and/or less frequent acts (three or more in a year) of major aggression (i.e., damaging objects and/or injuring people; American Psychiatric Association, 2013). This aggression must be disproportionate to the provocation and must be anger-based. Frequent acts of non–anger-based instrumental aggression, such as attacking an individual to rob him or her would not be considered as meeting IED criteria. Though it is grouped with other disruptive behavioral disorders within the Disruptive, Impulse-Control, and Conduct Disorders section of the DSM–5, IED can be considered a disorder of affective aggression. IED was initially thought to be rare. However, as diagnostic criteria and assessment measures have improved, estimates of IED have increased, and it is currently believed that the lifetime prevalence rate is closer to 5%, with the 1-month to 1-year prevalence rate at approximately 2% to 3% (Kessler et al., 2006). Adolescence is a critical time in the development of IED, with the typical age of onset for individuals with IED between 13 and 21 (Coccaro, 2012). This age of onset reflects the developmental tendency for aggression to increase throughout adolescence, though in 394

IED the aggression is more pronounced and longer lasting as IED tends to run a chronic, waxing and waning course lasting on average from 12 to over 20 years. Gender differences are small with some studies suggesting IED may occur more commonly in men (Coccaro, 2012). There does seem to be a gender × age of onset interaction with men tending to have an earlier age of onset than women. Most individuals with IED display minor and major aggression in sufficient frequency to meet criteria for the disorder (Kulper, Kleiman, McCloskey, Berman, & Coccaro, 2015). This aggression is often an overresponse to minor provocation and can include acts as varied as yelling, threatening, throwing or hitting objects, or physically attacking a person or animal. In general, these aggressive outbursts involve close friends and family (or their property), but it is not uncommon for those with IED to lash out at acquaintances (e.g., co-workers) or even strangers (e.g., road rage). When aggressive outbursts occur, those with IED report feeling more overwhelmed and less in control of their anger during the outburst, and more disappointed, embarrassed, and remorseful after the outbursts as compared with those without IED (Kulper et al., 2015). These aggressive outbursts cause considerable distress for the individual with IED and often lead to problems in interpersonal relationships, sleep disruption, and lowered job satisfaction. There is also a financial cost, as individuals with IED will on average cause about $1600 worth of property damage and have 2–3 aggressive outbursts that require medical attention over their lifetime (Kessler et al., 2006). Furthermore, IED is associated with several negative health outcomes including heart disease, hypertension, and stroke (McCloskey, Kleabir, Berman, Chen, & Coccaro, 2010). Therefore, IED is a prevalent, chronic, and severe disorder. Despite the severity of IED and the adolescent age of onset, there are surprisingly few studies focusing on the expression of IED in adolescence. One epidemiological study found that adolescents with IED reported an average age of onset and levels of chronicity (measured as 1-year persistence) and comorbidity that was consistent with that found for adults with

Understanding the Development and Management of Antisocial Disorders in Adolescents

IED (McLaughlin, et al., 2012). The field needs studies that directly compare the frequency of aggression and related symptoms among adolescents and adults with IED concurrently.

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Shared Risk Factors There are a variety of factors that are associated with increased risk for externalizing behaviors, as well as ODD, CD, ASPD, and IED. These risk factors involve numerous domains (e.g., neurobiological, psychological, social) and are often shared by or common to these disorders. These predictors carry valuable information for prevention and treatment.

Oppositional Defiant Disorder and Conduct Disorder A wide range of risk factors is associated with ODD and CD, which differ based on the developmental period and consequent tasks considered. Children are exposed to different factors that could confer added risk or buffer and reduce risk for ODD and CD as they enter distinct developmental periods, and there is the possibility that risk factors from earlier developmental periods can be carried forward to inform risk for continued ODD and/or CD behaviors (Drabick & Kendall, 2010; Drabick & Steinberg, 2011). Note that many of these factors are not specific to ODD or CD and may serve as shared risk factors for comorbid conditions. In terms of earlier developmental processes, prenatal exposure to substances is associated with ODD and CD. Children who exhibit temperamental difficulties (e.g., difficult temperament more broadly, as well as specific features of negative emotionality, inflexibility, and poor effortful control) are also at risk for developing ODD, particularly if parental behaviors are not a good fit for children with these temperamental features (Burke, Loeber, & Birmaher, 2002; Lavigne, Gouze, Hopkins, & Bryant, 2016; Loeber, Burke, & Pardini, 2009). Given that ODD confers risk for CD, it is not surprising that CD is associated with similar temperamental features. However, it is likely that there are different developmental trajectories associated with an individual’s temperament that might lead to different CD presentations. A common pathway for ODD

and CD can be found among children who exhibit strong reactions to anger and frustration. A pathway more specific to CD involves low levels of fear-based inhibitions that might be associated with higher levels of CU traits, and these children exhibit higher levels of approach and are less responsive to contextual demands including parenting behaviors that might shape behavior (Frick et al., 2014b; Krieger & Stringaris, 2016). Therefore, this presentation is more likely to be associated with CD than ODD. Parents of adolescents with ODD and CD report elevated levels of their own psychopathology (e.g., depression, substance use), which could interfere with provision of consistent discipline, optimal parent–child communication, and parental positive interactions with their children (Burke et al., 2002; Drabick, Gadow, & Loney, 2007; Lavigne et al., 2016; Loeber et al., 2009). Parental behaviors associated with ODD and CD in adolescents include low warmth, low support, high harsh parental behaviors, and high intrusiveness. In addition, CD is associated with inconsistent discipline, low parental monitoring, and coercive interchanges among parents and their children (Burke et al., 2002; Dishion, Bullock, & Granic, 2002; Dodge et al., 2008). Nevertheless, because parents and children engage in transactional relations, parental behaviors likely shape and exacerbate symptoms, particularly among children with difficult temperament. For example, if a child reacts to an adult command or request by becoming irritable or oppositional and the adult subsequently withdraws the request, the child may learn that these behaviors lead to desirable outcomes. These coercive interchanges among adults and children contribute to additional difficulties across domains and developmental periods (Patterson, 1982). Indeed, prospective research indicates that parenting behaviors worsen over time among adolescents with ODD (Burke, Pardini, & Loeber, 2008). Difficulties managing adolescent behavior lead to decreased monitoring and poor parent–child interactions that ultimately confer risk for associating with deviant peers, lack of investment in school, and CD (Dodge et al., 2008; Fosco, Stormshak, Dishion, & Winter, 2012). Given these challenges among parents and their children with ODD and/ or CD, it is not surprising that families of children 395

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with these behavior problems report higher levels of conflict and maltreatment, as well as lower levels of cohesion and expressiveness (Burke et al., 2002; Drabick et al., 2007; Lavigne et al., 2016). Therefore, these families may model aggressive and hostile behaviors, and consequently socialize children to engage in similar behaviors. Several neurobiological correlates of ODD and CD have been identified, though these factors are more associated with externalizing behaviors in general. These correlates include behavioral and molecular genetic factors, low salivary cortisol level, decreased autonomic nervous system activity, atypical frontal lobe activation patterns, and reduced functional connectivity between the amygdala and the medial prefrontal cortex (Beauchaine & Hinshaw, 2016; Blair, 2010; Burke et al., 2002; Loeber et al., 2009; Lorber, 2004). In terms of cognitive correlates, adolescents with ODD and CD exhibit deficits in executive functioning, processing of rewards and punishments, cognitive flexibility, and decision making (Beauchaine & Hinshaw, 2016; Blair, 2010; Crowe & Blair, 2008; Loeber et al., 2009). These correlates may underpin other adolescent-specific risk factors, such as difficult temperament and emotional lability, and provide convergent evidence that biological factors confer risk for ODD and CD. However, some adolescents with CD exhibit age-appropriate executive functioning and verbal abilities (Fontaine, Barker, Salekin, & Viding, 2008), which could allow these adolescents to recruit others in engaging in CD behaviors and potentially escape detection (Drabick, Bubier, Chen, Price, & Lanza, 2011). Adolescents with CD also exhibit deficits in social information processing, including increased likelihood of attributing hostile intent to others in ambiguous situations (i.e., hostile attribution bias), selecting aggressive responses to conflict, and expecting that aggressive responses are likely to lead to desired outcomes (Crick & Dodge, 1996). Regarding peer relationships, adolescents with ODD demonstrate hostile attribution biases and are likely to experience victimization and rejection (Burke et al., 2002; Drabick et al., 2011). Adolescents with ODD and CD are also more likely to engage in proactive and reactive aggression than adolescents without these disorders (Blair, 396

2010; Drabick et al., 2011; Loeber et al., 2009). Parent–child and family factors associated with ODD and CD may lead adolescents to develop these hostile attribution biases, as well as exhibit difficulties with emotion regulation and conflict resolution. In childhood, these individuals are potentially ill-prepared to problem solve and manage interpersonal relationships. Demonstrating oppositional behavior toward teachers, who are sanctioned authority figures, might further contribute to peer rejection among these children. As they become adolescents and the peer group increases in importance, adolescents with ODD may be more likely to select similar children as friends (e.g., adolescents who exhibit ODD symptoms or emotion regulation difficulties) and then to be socialized by these adolescents ­(Dishion & Tipsord, 2011). Earlier problematic interpersonal processes may contribute to the selection of aggressive and dysregulated peers as adolescents with ODD age, and these factors may contribute to further impairment, CD symptoms, and associated negative outcomes across domains (Dodge et al., 2008). Aggressive children are likely to experience peer rejection, which attenuates children’s ability to develop prosocial relationships and appropriate means of resolving conflict in peer relationships. Given limited experiences with typically developing peers, these children may select deviant or antisocial peers in adolescence, which is one of the most proximal predictors of CD during this developmental period (Chen, Drabick, & Burgers, 2015; Dishion, Bullock, & Granic, 2002). Challenges with parenting a child with behavior problems might lead to lower levels of monitoring, further enabling adolescents with CD to associate with and be socialized by deviant peers (Dishion & Tipsord, 2011; Fosco et al., 2012). Indeed, adolescents with CD often engage in deviancy training, characterized by peer support and encouragement of antisocial behaviors, which is related to increases in antisocial behavior (Dishion & Tipsord, 2011). Communities characterized by violence, crime, drug use, and poverty have been linked to ODD and CD symptoms (Leventhal & Brooks-Gunn, 2004). Although some studies suggest that the relation between neighborhood characteristics and ODD and CD is mediated by other factors (e.g., parental

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psychopathology, parenting practices, family conflict, heightened stress responses), such neighborhood environments may model aggressive behavior and increase the likelihood that adolescents will engage in ODD and CD behaviors (Burke et al., 2002; Crowe & Blair, 2008; Drabick et al., 2011; Loeber et al., 2009). CD behaviors may be viewed as adaptive or even protective in truly dangerous contexts (Drabick et al., 2011; Leventhal & Brooks-Gunn, 2004). More recent research has considered correlates among adolescents with CD with and without CU traits. This work indicates that adolescents with CU traits exhibit difficulty labeling sad and fearful expressions, though they can recognize other emotions (Blair, 2010; Crowe & Blair, 2008; Dolan & Fullam, 2010; Jones, Laurens, Herba, Barker, & Viding, 2009). In addition, adolescents with CU traits have difficulty with stimulus-reinforcement and reversal learning (i.e., responding to changes in contingencies), and this pattern is evidenced with positive and negative stimuli, as well as punishing and rewarding contingencies (Blair, 2010; Budhani & Blair, 2005; Finger et al., 2008; Marini & Stickle, 2010). Finally, adolescents with CU traits perform similarly to comparison children on tasks involving cognitive perspective-taking, but have difficulty with affective perspective-taking (AnastassiouHadjicharalambous & Warden, 2008) and tasks requiring recall of emotional information (Dolan & Fullam, 2010). Given these developmental pathways and potential impairment across domains in which we would like children to be successful (e.g., school, home, peers), adolescents may develop additional psychological conditions secondary to ODD or CD. Although oppositional behavior can be considered normative during some developmental periods, it has become clear that ODD confers risk for a variety of subsequent conditions, including CD, anxiety and mood disorders, and substance use disorders, even after controlling for other co-occurring conditions and initial levels of co-occurring symptoms (Drabick, Ollendick, & Bubier, 2010; Loeber et al., 2009; Nock et al., 2007). Because of the early age of onset, it is not surprising that ODD developmentally precedes most co-occurring conditions; however,

ODD is typically secondary to ADHD, phobias, and separation anxiety disorder. In general, co-occurring conditions are associated with greater persistence of ODD and increased ODD symptom severity as is typically the case with comorbidity (Nock et al., 2007). Nevertheless, anxiety may have a differential effect on ODD and CD based on the developmental period considered. Specifically, anxiety may attenuate behavior problems in childhood and adulthood; however, during adolescence, anxiety may exacerbate ODD and CD behaviors and lead to other co-occurring conditions and negative sequelae (Drabick et al., 2010). As CD is more likely to develop in later childhood or adolescence, CD typically follows the development of conditions with earlier onset (e.g., ADHD, ODD). ODD and CD confer risk for depression, which might stem from “failures” related to developmental expectations (Capaldi, 1991). Cooccurring CD and depression are associated with increased rates of substance use, delinquency, and school drop-out compared with either condition alone, though depression is more likely to follow CD than the reverse (Angold, Costello, & Erkanli, 1999; Maughan et al., 2004). The change to the DSM–5 with regard to considering emotional vs. behavioral symptoms of ODD can inform these developmental pathways, as the irritability associated with ODD might be a precursor for adolescent depression among adolescents with behavior problems, whereas the behavioral symptoms of ODD might be more likely to lead to CD characterized by CU traits (Stringaris & Goodman, 2009). Patterns of comorbid conditions differ depending on the presence of CU traits as well. Adolescents with CD without CU traits exhibit elevated levels of impulsivity (including ADHD) and anxiety, whereas CU traits are generally associated with lower levels of anxiety and internalizing problems over time (Frick et al., 2014a, 2014b; Frick & White, 2008).

Antisocial Personality Disorder The literature suggests genetic, biological, and psychosocial/environmental influences associated with the development and maintenance of ASPD. For example, twin, family, and adoption studies 397

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have suggested that antisocial behavior in general and ASPD specifically is roughly 50% heritable (Ferguson, 2010), and that the genetic contribution to antisocial behavior is stronger for offenders with a lifelong persistent pattern (similar to ASPD) as compared with adolescents with a limited pattern of offending (Barnes, Beaver, & Boutwell, 2011). Furthermore, genetic influences interact with environmental factors to facilitate ASPD. Early imaging research had suggested functional and structural brain anomalies among individuals with ASPD, including reduced prefrontal gray matter and abnormal patterns (hypo- and hyperactivation) of limbic response to emotional stimuli (McCloskey, Phan, & Coccaro, 2005). Findings from more recent studies have qualified these initial results, suggesting that the relationship between ASPD and structural prefrontal lobe deficits may be, in part, driven by psychopathy, substance use, and other comorbidities (Glenn, Johnson, & Raine, 2013; Gregory et al., 2012). Likewise, individuals high on the interpersonal–affective dimension of psychopathy show lower limbic (e.g., amygdala) activity to emotional/salient stimuli, whereas individuals higher on the antisocial-lifestyle dimension often show increased limbic activity to emotional stimuli (Seara-Cardoso & Viding, 2015). Individuals higher on affective-interpersonal psychopathic traits show reduced neural responses to others’ pain, whereas those higher on antisocial-lifestyle traits show increased neural responses to others’ pain (Seara-Cardoso, Viding, Lickley, & Sebastian, 2015). Conversely, increased striatal response to reward seems to be present among those with high antisocial traits independent of psychopathy, though the psychopathic group showed greater connectivity between the striatum and dorsomedial prefrontal cortex during reward (Geurts et al., 2016). These studies highlight the need to disambiguate ASPD and psychopathy when conducting research on antisocial behavior, consistent with calls to consider CD with and without CU traits (i.e., DSM–5 limited prosocial emotions specifier to the CD category). Individuals with ASPD (and antisocial behavior in general) also show deficits in a variety of higher order cognitive processes (e.g., planning, decision 398

making, cognitive control/impulsivity, attention) often referred to collectively as executive functioning (e.g., Morgan & Lilienfeld, 2000), consistent with adolescents with CD. Regarding decision making, individuals with ASPD are overly influenced by the prospect of large rewards (Mazas, Finn, & Steinmetz, 2000) and have difficulty altering behavior in response to punishment cues and/or changing contingencies (De Brito, Viding, Kumari, Blackwood, & Hodgins, 2013). These difficulties appear to be, in part, associated with a general tendency toward impulsive behavior (Chamberlain, Derbyshire, Leppink, & Grant, 2016; Swann, Lijffijt, Lane, Steinberg, & Moeller, 2009), but also may reflect general information processing difficulties as individuals with ASPD made poorer choices even after taking longer to deliberate on their decision than a comparison group (DeBrito et al., 2013). Preliminary research also suggests that these deficits occur (at least among young adults with ASPD) even in the absence of substance use disorder (Chamberlain et al., 2016), and are not limited to those who are elevated on psychopathy (Zeier, Baskin-Sommers, Hiatt Racer, & Newman, 2012). In psychopathy, these learning deficits have been linked to physiological and neural underarousal to aversive stimuli that impairs learning (Birbaumer et al., 2005; Veit et al., 2013). However, this physiological underarousal appears to be more linked to affective-interpersonal facets of psychopathy than to antisocial behavior (Veit et al., 2013), as the association between antisocial behavior is more complicated and includes increased reactivity to some negative stimuli (Lorber, 2004). There appear to be similarities in terms of correlates for (a) CD and ASPD and (b) CD with CU traits and psychopathy, suggesting that there also may be higher levels of developmental continuity among these conditions from adolescence to adulthood.

Intermittent Explosive Disorder Most research on correlates of IED has focused on cognitive–affective processes and their underlying neurobiological and neurochemical mechanisms. Individuals with IED consistently show heightened levels of anger and anger reactivity supporting the conceptualization of IED as a disorder of “affective”

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aggression (McCloskey, Berman, Noblett, & Coccaro, 2006; McCloskey, Lee, Berman, Noblett, & Coccaro, 2008). However, emotion regulation problems in IED are not limited to anger, with several studies showing increased global affective lability relative to individuals with other psychological disorders (Fettich, McCloskey, Look, & Coccaro, 2015; McCloskey et al., 2006; McCloskey, Lee, et al., 2008). Furthermore, those with IED also show greater intensity in their negative emotions than other psychiatric comparison groups (Fettich et al., 2015). Problems with emotion regulation in IED are exacerbated by difficulties in socioemotional information processing. Like others with aggression problems, individuals with IED tend to demonstrate a hostile attribution bias, in which benign or ambiguous acts are seen as hostile and/or intentionally malicious (Crick & Dodge, 1996). As evidence of this, individuals with IED exhibit difficulty in reading ambiguous facial expressions, emotional cues, and intention-based cues, all of which lead to the misattribution of nonhostile actions as threatening (Best et al., 2002; Coccaro et al., 2014; Coccaro, Noblett, & McCloskey, 2009). Further, those with IED are more likely to have a negative emotional response to situations in which an ambiguously intended act is directed toward them, evincing their reduced cognitive empathy, which appears to be a driving factor in aggression (Coccaro, Lee, & Kavoussi, 2009; Murray-Close et al., 2010). There is also some evidence that these socioemotional information processing deficits may go beyond hostile attributions as those with IED also show generalized deficits in correctly identifying facial expressions across emotions (Best et al., 2002). Research using self-report measures of impulsivity suggests that individuals with IED are more impulsive (McCloskey, Lee, et al., 2008), but these findings are not consistent with equivocal results from behavioral impulsivity tasks (e.g., Best et al., 2002). It has been suggested that individuals with IED may not be more impulsive across all impulsivity domains, but may show more negative urgency (Puhalla, Ammerman, Uyeji, Berman, & McCloskey, 2016). That is, individuals with IED are more impulsive specifically in the context of a significant

negative emotion. This is consistent with the findings on IED and emotion dysregulation (Fettich et al., 2015) and it would also explain the previous self-report and behavioral impulsivity findings. The combination of information processing deficits, emotional dysregulation, and emotional impulsivity is reflected in the tendency of individuals with IED to react disproportionately aggressively to provocation situations (McCloskey, Ben-Zeev, Lee, Berman, & Coccaro, 2009; McCloskey et al., 2006; McCloskey, Lee, et al., 2008). IED has been linked to several biological correlates. Regarding neurochemistry, IED is most strongly associated with serotonin, as multiple indices of impaired serotonin functioning (e.g., lower serotonin levels, lower platelet serotonin content, and decreased number of serotonin transporter binding sites) have been found among individuals with IED (Coccaro, 2012), whereas serotonin-enhancing drugs have been shown to reduce affective aggression in IED (Coccaro, Lee, & Kavoussi, 2009). Functional and structural brain abnormalities are also present among those with IED. Functional magnetic resonance imaging studies show increased amygdala activation (Coccaro, McCloskey, Fitzgerald, & Phan, 2007; McCloskey et al., 2016) and reduced prefrontal cortex (PFC) activation (Coccaro et al., 2007) in IED when looking at angry faces and negative evocative pictures, suggesting an increased limbic (and potentially decreased prefrontal) response to social threat. Dysregulation of this fronto-cortical circuit that is thought to govern emotion regulation is supported by the lack of a negative amygdala-PFC coupling in IED when responding to emotional stimuli (Coccaro et al., 2007; McCloskey et al., 2016). These functional deficits may reflect structural brain abnormalities. Magnetic resonance imaging studies have revealed that individuals with IED showed significantly lower grey matter volume in fronto-limbic brain structures as well as inward shape deformations of the amygdala and hippocampus (Coccaro, Fitzgerald, Lee, McCloskey, & Phan, 2016; Coccaro, Lee, McCloskey, Csernansky, & Wang, 2015). Few studies have examined familial and contextual factors as related to IED. Though limited in number, research findings show that adverse 399

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childhood experiences are associated with IED. Increased and earlier exposure to trauma was associated with IED (Nickerson, Aderka, Bryant, & Hofmann, 2012), with IED participants reporting more physical and emotional abuse and neglect and lower parent care (particularly maternal) relative to psychiatric controls (Fanning, Meyerhoff, Lee, & Coccaro, 2014; Lee, Meyerhoff, & Coccaro, 2014). More research on these and other contextual factors is needed. Developmental Pathways It is important to consider normative or typical development in determining whether behavior is problematic among adolescents. In addition, it is critical to consider developmental relations among these disorders and co-occurring conditions over time, as some disorders may confer risk for other conditions and/or sequelae that in turn lead to other psychological disorders. In the following section, we consider normative levels of externalizing behaviors, as well as the developmental relations among these conditions.

Oppositional Defiant Disorder and Conduct Disorder Some ODD and CD behaviors are normative during different developmental periods (e.g., oppositional behavior among toddlers and adolescents; Drabick, 2009; Steinberg, 2008). As such, criteria often include explicit statements that compare individuals with others of the same developmental level to determine whether behaviors are atypical or problematic relative to peers. The developmental pathways and interrelations among these conditions also should be considered during adolescence, though a number of different pathways may be evidenced. For example, there may be a common underlying process that underpins many externalizing conditions, but the behaviors manifested change over time in accordance with developmental changes and opportunities for exhibiting externalizing behaviors (Beauchaine & Hinshaw, 2016; Frick & Nigg, 2012). Another possibility is that correlates or sequelae of one condition may confer risk for another condition and facilitate successive or 400

concurrent comorbidity among disorders (Drabick, Beauchaine, Gadow, Carlson, & Bromet, 2006). For example, adolescents with ADHD often experience interpersonal and academic difficulties, which may confer risk for ODD. Adolescents with ADHD and ODD may be rejected by typically developing peers and be more likely to select and/or to be socialized by deviant peers, increasing risk for CD. When considering potential developmental pathways among conditions, ODD onset generally occurs prior to age 10 (retrospective self-reported onset of ODD begins at age 4 and increases steadily into adolescence; Nock et al., 2007). Childhood-onset CD requires at least one symptom prior to age 10; this CD subtype can occur with other earlier-onset conditions, now that the CD hierarchical exclusion criterion has been removed for ODD in the DSM–5. In particular, behavioral symptoms of ODD are expected to be associated with CD (Frick & Nigg, 2012; Stringaris & Goodman, 2009). To be consistent with the diagnostic nomenclature, ASPD cannot be diagnosed prior to age 18 and requires that individuals meet criteria for CD prior to age 15. Accordingly, continuity between CD and ASPD is expected among some individuals. However, it is unclear whether ASPD is more likely to be continuous with CD when the latter is exhibited with or without limited prosocial emotions. Given that CU traits and this specifier are expected to identify adolescents with a more persistent and pernicious course (Frick et al., 2014a, 2014b), it is possible that continuity will be associated with adolescents with CD with limited prosocial emotions, though research is wanting.

Antisocial Personality Disorder Independent of its relation to CD, ASPD has a very high level of comorbidity, with some community studies showing over 90% of individuals with ASPD have another DSM disorder (e.g., Swanson, Bland, & Newman, 1994). The greatest comorbidity is with psychopathy, as many studies indicate that almost all individuals with psychopathy also meet criteria for ASPD (Hare, 1996), though the reverse is not true; in fact, only a portion of those with ASPD also meet criteria for psychopathy (e.g., 32%; Coid & Ullrich, 2010). As such, psychopathy

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has been conceptualized as a severe subtype of ASPD, and this is supported by studies showing ASPD with (vs. without) comorbid psychopathy is associated with greater impairment and poorer treatment response (Glenn et al., 2013; Gregory et al., 2012; Ogloff, 2006). However, findings of different cognitive and neurobiological impairments associated with ASPD vs. psychopathy (e.g., differences in fear reactivity) suggest a more complex relationship between ASPD and psychopathy (Anton et al., 2012). Among DSM disorders, ASPD is most related to substance use disorders with estimates of 50% for alcohol use disorders and over 80% for any substance disorder (Glenn et al., 2013; Trull et al., 2010). This comorbidity is important when considering research on ASPD as many of the cognitive and neurobiological deficits associated with ASPD are also associated with substance use disorders (e.g., executive functioning), though some of these deficits are likely present prior to substance use initiation given that these correlates are associated also with CD. Like ODD and CD, ASPD co-occurs with several other disorders including mood disorders, anxiety disorders, and other personality disorders (e.g., borderline personality disorder). The specific comorbidity for ASPD seems to vary somewhat as a function of gender; men with ASPD are more likely to meet criteria for comorbid mood disorders, whereas women with ASPD are more likely to meet criteria for comorbid histrionic or borderline personality disorder (Sher et al., 2015). For most of these disorders, the presence of comorbid ASPD is associated with a more severe and treatment-refractory course (Glenn et al., 2013).

Intermittent Explosive Disorder IED typically has an onset in adolescence (ages 12–21), with a slightly earlier onset in boys (Coccaro, 2012). Similar to ODD, CD, and ASPD, most individuals (75%–80%) with IED meet criteria for another psychological disorder. These comorbidities are especially high for anxiety, depressive, disruptive (i.e., CD and ODD), and substance use disorders, all of which are 2.5 to 4 times more likely to occur among those with IED (Kessler et al., 2006). Personality disorders also have a high rate

of comorbidity with IED, particularly borderline personality disorder and ASPD, with about 25% of those with IED meeting criteria for borderline personality disorder and/or ASPD in community samples and much higher rates in research samples (Coccaro, 2012). This extensive comorbidity raises questions about IED as a distinct disorder. However, like ODD, IED tends to precede any comorbid disorders (e.g., Kessler at al., 2006) suggesting that rather than a symptom of another disorder, IED promoted the development of comorbid psychopathology. Supporting the differentiation between IED and its many comorbidities, the underlying nature of IED appears to be qualitatively different from subclinical aggression (Ahmed, Green, McCloskey, & Berman, 2010), whereas many of IED’s comorbid disorders (including ASPD) appear more dimensional in nature (Marcus, Lilienfeld, Edens, & Poythress, 2006). There also appears to be some specificity in the heritability of IED in that family members of an individual with IED were more likely to have a diagnosis of IED relative to other potentially related disorders (e.g., ASPD; Coccaro, 2012). Finally, the presence of IED seems to exacerbate the impairment associated with comorbid disorders rather than vice versa (Keyes, McLaughlin, Vo, Galbraith, & Heimberg, 2016). Assessment and Intervention Considering the prevalence, impact, correlates, courses, comorbidity, and sequelae of these disorders, the utility of valid and reliable assessment approaches, as well as evidence-based interventions, is clear. It is important to use multiple informants and assessment approaches during adolescence, given that informants often have access to different information and their responses may not be grounded in developmental processes (e.g., teachers may be more likely to determine whether a behavior is problematic than parents who have less experience with typically developing adolescents). However, at least regarding treatment, the evidence is mixed at best, with somewhat more positive results for ODD and CD relative to IED and ASPD. Next, we review current knowledge regarding best practices for these disorders. 401

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Oppositional Defiant Disorder and Conduct Disorder A multimethod, multi-informant assessment approach is typically recommended to determine whether adolescents meet criteria for ODD or CD, as well as to identify risk factors and correlates that can inform prognosis and interventions (De Los Reyes et al., 2015; McMahon & Frick, 2005). The use of behavioral checklists from multiple informants (e.g., parents, teachers, older children), diagnostic interviews, and observational methods is recommended to assess ODD behaviors. This approach is particularly important for assessing ODD given the necessity of determining whether these behaviors occur often relative to peers and the addition to DSM–5 of a severity indicator (i.e., number of settings). Given the covert nature of many CD behaviors and potential issues related to social desirability in responding, it is important to obtain information from multiple informants for CD through behavioral checklists and/or diagnostic interviews. CD is a particularly heterogeneous disorder; assessments should take this issue into account and consider aspects related to this heterogeneity (e.g., presence of CU traits, co-occurring ADHD, aggressive and nonaggressive behaviors, age of onset). Commonly used behavioral checklists that assess for ODD and CD behaviors include the Achenbach Child Behavior Checklist family of instruments, the Behavioral Assessment System for Children, Conners Rating Scales, and the Child and Adolescent Symptom Inventory. The most frequently used and empirically supported structured interviews for ODD and CD are the Diagnostic Interview Schedule for Children and the Diagnostic Interview for Children and Adolescents, although other structured interviews contain ODD and CD modules. In terms of observational coding systems for ODD, the Behavioral Coding System and Dyadic Parent–Child Interaction Coding System II are structured, microanalytic observation methods for assessing parental interactions with younger children. Another valid and reliable observational method for assessing ODD among preschool children is the Disruptive Behavior Diagnostic Observation Schedule, which is designed to assess problems in behavioral regulation and anger modulation in a laboratory setting. The 402

Revised Edition of the School Observation Coding System can be useful for evaluating ODD behaviors in the school setting. To assess for CU behaviors, reliable and valid questionnaires that have been used in a variety of settings (e.g., community, clinical, forensic) and among adolescents include the Antisocial Process Screening Device and Inventory of Callous/Unemotional Traits (for a review of assessment instruments and approaches, see ­McMahon & Frick, 2005). The most effective treatment approaches for ODD and CD involve cognitive–behavioral strategies and multiple levels of functioning, with combined parent and child involvement typically superior to either component alone and to control conditions (for a review, see Eyberg, Nelson, & Boggs, 2008; P. C. Kendall, 2012). Several parent and family treatments have been shown to be effective among children with ODD. Parent–Child Interaction Training (Brinkmeyer & Eyberg, 2003) is designed for children ages 2–7 years and uses (a) child-directed interactions to develop nondirective play skills and thereby improve the quality of parent–child interactions and (b) parent-directed interaction, which involves improving parenting skills related to giving instructions, addressing noncompliance, and rewarding compliance. Helping the Noncompliant Child (McMahon & Forehand, 2003) is a secondary prevention program designed for children ages 3–8 years with noncompliant behavior. Parents learn skills that can decrease coercive parent–child interactions, improve parents’ provision of positive feedback and clear directions, and increase use of appropriate discipline strategies. The Incredible Years intervention (Webster-Stratton & Reid, 2003) includes treatment programs designed for parents, children (ages 2–10), and teachers. The Incredible Years program is designed to reduce children’s behavior problems and increase social competence, which is addressed by improving parental monitoring and discipline strategies, as well as children’s problem-solving skills. Parent Management Training–Oregon Model (Patterson, Chamberlain, & Reid, 1982) involves teaching parents behavioral principles for modifying and monitoring child behavior and for implementing behavior modification programs for their children

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(ages 3–12 years). Sanders’s (1999) Positive Parenting Program (Triple P) is a multilevel system that includes universal prevention (Level 1); provision of parenting skills that vary on the basis of the number of sessions, format, and content (Levels 2–4); and a behavioral family intervention that targets family stressors in addition to children’s disruptive behaviors (Level 5). A variety of child-focused cognitive–behavioral interventions for adolescent behavior problems (including aggression and CD) also have received support, particularly among children in elementary school and early adolescence. These include Lochman et al.’s Anger Control Training, which is the predecessor for the Coping Power Program (Lochman, Barry, & Pardini, 2003). Both programs are based on the social information-processing model of anger control and include problem-solving approaches. In Kazdin’s (2003) Problem-Solving Skills Training, adolescents are taught problemsolving strategies and encouraged to generalize these strategies to real-life problems. As another example, Group Assertive Training (Huey & Rank, 1984) is a school-based intervention designed to address aggressive classroom behavior among adolescents. Regarding CD and delinquency, Multidimensional Treatment Foster Care (Chamberlain & Smith, 2003) is an efficacious community-based program that involves placing adolescents who have engaged in delinquent behavior in a foster home for 6 to 9 months during which time the adolescent receives treatment. The foster parents obtain preservice training that focuses on behavioral reinforcement and consistent discipline, and caregivers to whom the adolescent will return following the foster care placement receive intensive parent management training. This intervention has been shown to be superior to typical group home care for adolescents with histories of chronic delinquency. Multisystemic Therapy (Henggeler & Lee, 2003) is an individualized and flexible intervention that addresses the many domains in which adolescents with CD exhibit difficulties. This intervention involves cognitive–behavioral approaches, pragmatic family therapies, and pharmacological interventions as appropriate and is delivered within the child’s context (e.g., school, home) with

at least weekly meetings with the therapist over a period of 3 to 5 months. Little research has examined mediators and moderators of treatment outcome among adolescents with ODD or CD. However, changes in parenting behaviors (e.g., increased parental monitoring) and, among older adolescents, changes in social information processing and peer relationships likely mediate treatment effects (Lochman & Wells, 2002). Regarding treatment moderators, harsh parental behaviors, parental psychological functioning, marital adjustment, family stressors, and child comorbid internalizing symptoms moderate treatment effects (Beauchaine, Webster-Stratton, & Reid, 2005), and not only confer risk for externalizing behaviors, but also may have implications for intervention outcomes.

Antisocial Personality Disorder Diagnostic interviews are the gold standard assessment measure for ASPD. The Structured Clinical Interview for DSM Personality Disorders (SCID–II; First, Spitzer, Williams, & Gibbon, 1997) and the Structured Interview of Disorders of Personality (SID–P; Pfohl, Blum, & Zimmerman, 1995) are semi-structured interviews that are designed to diagnosis ASPD (as well as all other DSM personality disorders) by asking questions directly relevant to the seven ASPD criteria and additional questions relevant to CD. The full SCID–II and SID–P take 45 to 90 minutes to administer, but the ASPD questions themselves take only about 10 to 15 minutes. There are always risks associated with using just a portion of a measure that has some validation data on the full measure. However, if one were to do that, the SCID–II would be the easier of the two measures as the SCID–II, but not the SID–P, organizes its questions by diagnosis. Likewise, the 20-item Hare Psychopathy Checklist—Revised (Hare, 1991) and its adolescent (ages 12–18) complement (Forth, Kosson, & Hare, 2003) consist of two primary factors that assess both behaviors associated with ASPD (antisocial lifestyle) and more interpersonal–affective traits that are considered the core of psychopathy (e.g., lack of empathy). The checklist is widely used in criminal and psychiatric settings to assess risk of reoffending and/ 403

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or engaging in violent behavior, and is considered the gold standard in this regard (Salekin et al., 1996), though a more recent meta-analysis that found it had the lowest predictive validity of nine risk assessment tools (median: area under the curve = .66, positive predictive value = .52, negative predictive value = .68) has called this into question (Singh, Grann, & Fazel, 2011). Several omnibus questionnaire inventories assess ASPD including the Personality Assessment Inventory (Morey, 2007), Personality Diagnostic Questionnaire (Hyler, 1994) and the Millon Clinical Multiaxial Inventory (Millon, Grossman, & Millon, 2015). Overall, these measures correlate moderately well at the symptom level, but only modestly at the diagnostic level with interview measures of ASPD (e.g., Guy, Poythress, Douglas, Skeem, & Edens, 2008). ASPD is considered among the most treatmentresistant psychological disorders in part because of the lack of desire among individuals with ASPD to seek treatment. For this reason, treatments for ASPD often occur within the context of the justice system. This research, which suggests modest to moderate effects of cognitive–behavioral and related interventions, has limited applicability to ASPD in that ASPD is not required (or often assessed) for treatment (National Institute for Health and Clinical Excellence, 2010). In addition, these studies typically focus on reducing criminal recidivism rather than the treatment of ASPD. There are few well-controlled trials examining the efficacy of treatments for ASPD. An early exploratory randomized control trial of 59 male community participants with ASPD found that cognitive–behavioral therapy (CBT) was not better than treatment as usual in reducing ASPD symptoms (e.g., anger, aggression, substance use). Relatedly, only 11 of the 25 men in the CBT condition completed 10 or more of the scheduled 15 to 30 treatment sessions (Davidson et al., 2009). A second study of a behavioral intervention for opioid dependent outpatients with ASPD found improvement on family/social adjustment relative to standard care for the behavioral treatment group, but no difference between behavioral and treatment as usual groups on either the rate of drug negative specimens or the substance use scales of the addiction severity index (Neufeld et al., 2008). 404

A mentalization-based treatment was found to reduce mood symptoms and self-harm among individuals with ASPD and comorbid borderline personality disorder (Bateman, O’Connell, Lorenzini, Gardner, & Fonagy, 2016), but it is not clear to what extent the treatment affected ASPD pathology outside of general aggressiveness. Studies of pharmacological interventions for ASPD are similarly limited, with a few “poor quality” studies generally showing no difference between psychotropic medication (e.g., antidepressants) and placebo (Khalifa et al., 2010). Overall, the findings reflect a paucity of research on treatments specifically for ASPD independent of age, let alone in late adolescence/ early adulthood, where the behavioral symptoms of ASPD may be most severe.

Intermittent Explosive Disorder The Structured Clinical Interview for DSM–5 (First, Williams, Karg, & Spitzer, 2015) includes a module that assesses for IED. Otherwise, there are few published measures to assess IED. The Intermittent Explosive Disorder Interview (IED-I) is an unpublished instrument that has been used in several research studies of IED (McCloskey & Coccaro, 2003) and distinguishes between individuals with IED and psychiatric controls (Kulper et al., 2015). In addition, two measures of aggression frequency are often used in IED research. The Life-History of Aggression (LHA; Coccaro, Berman, & Kavoussi, 1997) is an 11-item self-report measure of aggression, self-harm, and antisocial behavior that is used to assess frequency of lifetime verbal and physical aggression. The five-item aggression scale of the LHA has been shown to be a valid and reliable measure of aggression frequency (Coccaro et al., 1997). Complementing this, the Overt Aggression Scale–Modified (OAS-M; Coccaro, Harvey, KupsawLawrence, Herbert, & Bernstein, 1991) is an interview that assesses the frequency and intensity of aggressive acts in which an individual engaged over the past week. Items on the OAS-M are weighted so that a more intense act of aggression (e.g., punching someone vs. yelling at them) contributes more to the total aggression score. The OAS-M has adequate psychometric properties with reported interrater reliabilities (ICC) > .90 for the aggression and

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irritability scales (Coccaro et al., 1997), as well as significant correlations between the OAS-M aggression scale and lifetime measures of aggression frequency (r = .45–.52; Coccaro, Berman, & Kavoussi, 1997; see McCloskey & Coccaro, 2003, for a full review of the OAS-M). The OAS-M has also been found to be sensitive to change. For this reason, the OAS-M is used in most IED treatment studies (e.g., Coccaro, Lee, & Kavoussi, 2009; McCloskey, Noblett, Deffenbacher, Gollan, & Coccaro, 2008). Despite the prevalence and impact of IED, there is a dearth of treatment research on the disorder. A double-blind placebo-controlled trial found that fluoxetine reduced aggression (Coccaro, Lee, & Kavoussi, 2009); however, a more recent (albeit smaller) study did not find an effect of fluoxetine on aggression (Coccaro, Lee, Breen, & Irwin, 2015). Likewise, there has been inconsistent support for the use of anticonvulsants in the treatment of IED, with randomized clinical trials showing that Divalproex (Hollander et al., 2003) and Oxcarbazepine (Mattes, 2005), but not Levetiracetam (Mattes, 2008), reduced aggression among patients with IED. There has been even less research on psychological interventions for IED. A study of a brief (four 90-min sessions) cognitive–behavioral program for aggressive drivers found drivers with IED tended to improve less than drivers without IED, leading the authors to suggest that IED individuals may benefit from longer, more intensive therapy (Galovski & Blanchard, 2002). A later randomized clinical trial of a 12-session cognitive–behavioral treatment for IED found that cognitive–behavioral treatment was superior to waitlist in reducing anger, aggression, and hostility in IED (McCloskey, Noblett, et al., 2008). However, this study was limited to adults ages 25–53. It is still unknown if this intervention is effective for adolescents with IED. Controversies and Future Directions For all the antisocial disorders reviewed, controversies remain. The DSM–5 includes several modifications to the diagnostic criteria that were somewhat controversial and will require additional research to examine the utility of these changes. There are

also several gaps in the literature regarding intervention and interrelations among these disorders, particularly during adolescence when the phenotypic presentations of these conditions may be difficult to disentangle. We present several controversies and gaps in current knowledge next, in addition to related directions for future research.

Oppositional Defiant Disorder and Conduct Disorder For ODD, one concern is that findings regarding the predictive utility of emotional vs. behavioral symptoms are mixed and differ depending on the informant considered and strategy for dividing emotional and behavioral symptoms (Drabick & Gadow, 2012; Stringaris & Goodman, 2009). Nevertheless, changes to the DSM–5 provide a consistent framework for division of symptoms into subgroups and a foundation for future research. In addition, DSM–5 provides the first operationalization of often for ODD symptoms; however, further evaluation is needed to determine whether these descriptions can increase reliability and whether the frequency anchors represent valid comparisons to adolescents across developmental periods (Burke et al., 2010; Frick & Nigg, 2012). The addition of a severity criterion for ODD also requires further examination. Although there is an expectation that adolescents who exhibit ODD symptoms across settings and individuals will experience greater severity, more research is needed. Last, the addition of the specifier “with limited prosocial emotions” to the DSM–5 CD category will require further research to determine its utility, particularly in differentiating adolescents with childhood-onset CD who differ in the persistence of CD symptoms (Frick & Nigg, 2012; Moffitt et al., 2008). There are several directions for future research on ODD. First, there is a need to evaluate concurrent and predictive utility of the disorder, independent of co-occurring conditions (e.g., ADHD, CD), especially now that the CD exclusion criterion has been abandoned. Second, future research should continue to examine the predictive validity of ODD symptom subgroups using the DSM–5 approach to separating symptoms. Third, greater operationalization of severity and pervasiveness criteria is included in the 405

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DSM–5, and effects on reliability and validity for ODD should be evaluated. Last, given that symptom severity and correlates of ODD differ depending on the informant used to rate symptoms, additional research regarding contextual demands, informants, and settings is needed to supplement research regarding the severity criterion added to the DSM–5 ODD category (Burke et al., 2010; De Los Reyes et al., 2015; Frick & Nigg, 2012; Moffitt et al., 2008). For CD, continued consideration of the limited prosocial emotions specifier is an important direction for future research. Given the high levels of treatment seeking among families of adolescents with ODD and/or CD, future research should evaluate mediators and moderators of treatment outcomes to inform assessment and modifications to existing interventions, as well as improve intervention outcomes. Additional issues that have been raised regarding CD involve (a) the potential utility of a childhood-limited subgroup and (b) whether changes to the conceptualization of CD should be made to better accommodate girls (Frick & Nigg, 2012; Moffitt et al., 2008). There is considerable heterogeneity among adolescents with childhood-onset CD in that some exhibit a more persistent path of antisocial behavior, whereas others are more likely to desist. Although the former group may exhibit higher levels of CU traits (and meet criteria for the limited prosocial emotions specifier), children who desist are not well-characterized, suggesting that future research is necessary to better understand them. To date, although discussion continues regarding whether different criteria, symptom thresholds, duration, or some combination would better characterize CD behaviors among girls, data do not support modifying diagnostic criteria for CD on the basis of sex. Finally, despite the diagnostic link between CD and ASPD, future research should evaluate continuity from CD to ASPD, taking into consideration age of onset for CD as well as the presence of limited prosocial emotions that might be more predictive of psychopathy than ASPD more broadly (Burke et al., 2010; Frick et al., 2014a, 2014b).

Antisocial Personality Disorder The diagnosis of ASPD has been criticized as being too behaviorally focused and overemphasizing 406

criminal behavior (in effect making criminal behavior a psychological disorder) at the cost of affective–interpersonal characteristics associated with an “antisocial personality” (Gurley, 2009). Despite minor changes in the DSM criteria, these criticisms persisted. To address these problems, the proposed DSM–5 restructuring of personality disorders included significant changes to ASPD that would focus more on traditional psychopathic traits including callousness and lack of empathy, and would have a “with psychopathic traits” specifier that would mirror the “with limited prosocial emotions” specifier for CD (American Psychiatric Association, 2013). However, in response to concerns about the new approach to personality disorders (e.g., Shedler et al., 2010), these changes to ASPD were not enacted. Rather, they remain in the DSM–5 as an alternative (more trait-based) model of personality disorders for further research. Therefore, the actual DSM–5 criteria are identical to its DSM–IV–TR predecessor, with its heavy behavioral/criminal focus. As a result, many studies of ASPD will likely continue without full attention to the heterogeneity of these potentially core interpersonal–affective deficits. ASPD is a heterogeneous disorder that overlaps, but is separate from, psychopathy and criminality. Though some studies have started to focus on subtyping ASPD (Poythress et al., 2010), more research is needed to identify key subtypes of ASPD and assess to what extent genetic, cognitive, environmental, and neurobiological deficits are specific to an ASPD subtype or consistent across all subtypes. This includes a greater understanding of how the significant comorbidity between ASPD and other psychiatric disorders affects the course and severity of ASPD (Glenn et al., 2013). It is well-established that adolescents with CD with and without CU traits differ on many of these correlates, as well as course and severity (Frick et al., 2014a, 2014b); future research could take a similar approach to identifying consistencies and useful discriminating processes among individuals with ASPD with and without various co-occurring conditions. Once key deficits associated with more homogenous subtypes of ASPD are identified, treatments can be better tailored to address those deficits. This would

Understanding the Development and Management of Antisocial Disorders in Adolescents

include treatment of adolescents and young adults with ASPD outside of the criminal system, an area of study that is clearly lacking.

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Intermittent Explosive Disorder The DSM–5 included several revisions of the criteria for IED. Chief among these is the inclusion of verbal aggression and the related multiple pathways (frequent minor aggression or less frequent major aggression) to meet the aggression severity/ frequency criterion. This change is consistent with research showing that frequent verbal aggression can be highly impairing (e.g., McCloskey, Lee, et al., 2008). Other changes introduced in the DSM–5 improved the specificity of the disorder. This included specifying that the aggressive outbursts are (a) anger-based, (b) cause distress or impairment, and (c) are developmentally inappropriate (by specifying that the diagnosis cannot be given to children under age 6). Despite recent increases in research on IED, the disorder is still understudied and the changes made to the DSM–5 should increase research on the disorder, as previously most research on IED did not use DSM criteria because of limitations with earlier DSM diagnoses (Coccaro, 2012). Research on IED, though limited, has identified several neurobiological deficits including serotonin dysregulation and structural and functional deficits of the corticolimbic circuits posited to underpin emotion regulation. Such findings are consistent with the findings that IED participants show global emotion dysregulation and impulsivity in the face of negative emotion. However, the role of related cognitive and biological processes is less clear. Other areas of IED research have been given even more limited attention and require further examination. For example, very few studies have considered environmental factors as they relate to IED; those few that have are cross-sectional and focused almost exclusively on abuse/trauma/ aggression as opposed to correlates of IED. In fact, there is a dearth of longitudinal research on IED. Individuals with IED present with varying levels of comorbidity, aggression presentation and severity, and associated deficits, but no research has examined whether there are reliable subtypes within the

IED diagnosis. Relatedly, the IED criteria is in many ways similar to the new childhood disorder introduced in the DSM–5—disruptive mood dysregulation disorder—and research is needed to determine the extent these two disorders are related. Finally, considering the severity and prevalence of IED, more treatment research is needed, especially among adolescents. Conclusion Adolescence is a critical period for the development of cognitive control and peer relationships, and as such, is key in the developmental trajectory of pathological, antisocial behavior. Categorical manifestations of such pathological antisocial behaviors in adolescence include ODD, CD, ASPD, and IED. Though there are clear differences between these disorders regarding symptom presentation (e.g., ODD and IED symptoms are more focused on angry, defiant and aggressive behavior, CD and ASPD symptoms assess more global antisocial behavior), there is also significant overlap with regard to not only presentation (e.g., most include aggressiveness as a symptom), but also comorbidity, risk factors, and sequelae. All four disorders show a great deal of comorbidity, both with each other, and more generally with other mood, anxiety, and substance use disorders, suggesting more severe, global, and overlapping deficits. There are common cognitive (e.g., poor executive functioning, limited cognitive flexibility, hostile attributional biases) and neurobiological (e.g., atypical frontal lobe activation and decreased cortico-limbic functional connectivity) impairments across the four disorders. It may be that distinctions within these disorders (e.g., the differentiation of CD with vs. without limited prosocial emotions, ASPD with vs. without psychopathy) may be as important to differentiating developmental trajectories as differences between the disorders. Regarding treatment, the findings presented here highlight the importance of early intervention for adolescents with CD/ODD, as treatments for these disorders have demonstrated efficacy in contrast to treatments for IED and ASPD, which are significantly more limited in demonstrated efficacy and effectiveness. 407

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References Ahmed, A. O., Green, B. A., McCloskey, M. S., & Berman, M. E. (2010). Latent structure of intermittent explosive disorder in an epidemiological sample. Journal of Psychiatric Research, 44, 663–672. http:// dx.doi.org/10.1016/j.jpsychires.2009.12.004 American Psychiatric Association. (1980). Diagnostic and statistical manual of mental disorders (3rd ed.). Washington, DC: Author.

Copyright American Psychological Association. Not for further distribution.

American Psychiatric Association. (1987). Diagnostic and statistical manual of mental disorders (3rd ed., rev.). Washington, DC: Author. American Psychiatric Association. (1994). Diagnostic and statistical manual of mental disorders (4th ed.). Washington, DC: Author. American Psychiatric Association. (2000). Diagnostic and statistical manual of mental disorders (4th ed., text revision). Washington, DC: Author. American Psychiatric Association. (2013). Diagnostic and statistical manual of mental disorders (5th ed.). Washington, DC: Author. Anastassiou-Hadjicharalambous, X., & Warden, D. (2008). Physiologically-indexed and self-perceived affective empathy in conduct-disordered children high and low on callous–unemotional traits. Child Psychiatry and Human Development, 39, 503–517. http://dx.doi.org/10.1007/s10578-008-0104-y Angold, A., Costello, E. J., & Erkanli, A. (1999). Comorbidity. Journal of Child Psychology and Psychiatry, 40, 57–87. http://dx.doi.org/ 10.1111/1469-7610.00424 Anton, M. E., Baskin-Sommers, A. R., Vitale, J. E., Curtin, J. J., & Newman, J. P. (2012). Differential effects of psychopathy and antisocial personality disorder symptoms on cognitive and fear processing in female offenders. Cognitive, Affective, and Behavioral Neuroscience, 12, 761–776. http://dx.doi.org/10.3758/ s13415-012-0114-x Barnes, J. C., Beaver, K. M., & Boutwell, B. B. (2011). Examining the genetic underpinnings to Moffitt’s developmental taxonomy: A behavioral genetic analysis. Criminology: An Interdisciplinary Journal, 49, 923–954. http://dx.doi.org/10.1111/ j.1745-9125.2011.00243.x

Beauchaine, T. P., Webster-Stratton, C., & Reid, M. J. (2005). Mediators, moderators, and predictors of 1-year outcomes among children treated for early-onset conduct problems: A latent growth curve analysis. Journal of Consulting and Clinical Psychology, 73, 371–388. http://dx.doi.org/ 10.1037/0022-006X.73.3.371 Best, M., Williams, J. M., & Coccaro, E. F. (2002). Evidence for a dysfunctional prefrontal circuit in patients with an impulsive aggressive disorder. Proceedings of the National Academy of Sciences, USA, 99, 8448–8453. http://dx.doi.org/10.1073/pnas.112604099 Birbaumer, N., Veit, R., Lotze, M., Erb, M., Hermann, C., Grodd, W., & Flor, H. (2005). Deficient fear conditioning in psychopathy: A functional magnetic resonance imaging study. Archives of General Psychiatry, 62, 799–805. http://dx.doi.org/10.1001/ archpsyc.62.7.799 Black, D. W., Baumgard, C. H., Bell, S. E., & Kao, C. (1996). Death rates in 71 men with antisocial personality disorder. A comparison with general population mortality. Psychosomatics: Journal of Consultation and Liaison Psychiatry, 37, 131–136. http://dx.doi.org/10.1016/S0033-3182(96)71579-7 Blair, R. J. R. (2010). Psychopathy, frustration, and reactive aggression: The role of ventromedial prefrontal cortex. British Journal of Psychology, 101, 383–399. http://dx.doi.org/ 10.1348/000712609X418480 Brinkmeyer, M. Y., & Eyberg, S. M. (2003). Parent-child interaction therapy for oppositional children. In A. E. Kazdin & J. R. Weisz (Eds.), Evidence-based psychotherapies for children and adolescents (pp. 204–223). New York, NY: Guilford Press. Budhani, S., & Blair, R. J. R. (2005). Response reversal and children with psychopathic tendencies: Success is a function of salience of contingency change. Journal of Child Psychology and Psychiatry, 46, 972–981. http://dx.doi.org/10.1111/ j.1469-7610.2004.00398.x Burke, J. D., Loeber, R., & Birmaher, B. (2002). Oppositional defiant disorder and conduct disorder: A review of the past 10 years, part II. Journal of the American Academy of Child and Adolescent Psychiatry, 41, 1275–1293. http:// dx.doi.org/10.1097/00004583-200211000-00009

Bateman, A., O’Connell, J., Lorenzini, N., Gardner, T., & Fonagy, P. (2016). A randomised controlled trial of mentalization-based treatment versus structured clinical management for patients with comorbid borderline personality disorder and antisocial personality disorder. BMC Psychiatry, 16, 304. http:// dx.doi.org/10.1186/s12888-016-1000-9

Burke, J. D., Pardini, D. A., & Loeber, R. (2008). Reciprocal relationships between parenting behavior and disruptive psychopathology from childhood through adolescence. Journal of Abnormal Child Psychology, 36, 679–692. http://dx.doi.org/10.1007/ s10802-008-9219-7

Beauchaine, T. P., & Hinshaw, S. P. (Eds.). (2016). The Oxford handbook of externalizing spectrum disorders. New York, NY: Oxford University Press.

Burke, J. D., Waldman, I., & Lahey, B. B. (2010). Predictive validity of childhood oppositional defiant disorder and conduct disorder: Implications for

408

Understanding the Development and Management of Antisocial Disorders in Adolescents

the DSM–V. Journal of Abnormal Psychology, 119, 739–751. http://dx.doi.org/10.1037/a0019708 Capaldi, D. M. (1991). Co-occurrence of conduct problems and depressive symptoms in early adolescent boys: I. Familial factors and general adjustment at Grade 6. Development and Psychopathology, 3, 277–300. http://dx.doi.org/ 10.1017/S0954579400005319

Copyright American Psychological Association. Not for further distribution.

Chamberlain, S. R., Derbyshire, K. L., Leppink, E. W., & Grant, J. E. (2016). Neurocognitive deficits associated with antisocial personality disorder in non-treatment-seeking young adults. Journal of the American Academy of Psychiatry and the Law, 44, 218–225. Chamberlain, S. R., & Smith, D. K. (2003). Antisocial behavior in children and adolescents: The Oregon Multidimensional Treatment Foster Care Model. In A. E. Kazdin & J. R. Weisz (Eds.), Evidence-based psychotherapies for children and adolescents (pp. 282–300). New York, NY: Guilford Press. Chen, D., Drabick, D. A., & Burgers, D. E. (2015). A developmental perspective on peer rejection, deviant peer affiliation, and conduct problems among youth. Child Psychiatry and Human Development, 46, 823–838. http://dx.doi.org/10.1007/s10578014-0522-y Cleckley, H. (1976). The mask of sanity (5th ed.). St. Louis, MO: Mosby. Coccaro, E. F. (2012). Intermittent explosive disorder as a disorder of impulsive aggression for DSM–5. American Journal of Psychiatry, 169, 577–588. http:// dx.doi.org/10.1176/appi.ajp.2012.11081259 Coccaro, E. F., Berman, M. E., & Kavoussi, R. J. (1997). Assessment of life history of aggression: Development and psychometric characteristics. Psychiatry Research, 73, 147–157. http://dx.doi.org/ 10.1016/S0165-1781(97)00119-4 Coccaro, E. F., Fitzgerald, D. A., Lee, R., McCloskey, M. S., & Phan, K. L. (2016). Frontolimbic morphometric abnormalities in intermittent explosive disorder and aggression. Biological Psychiatry: Cognitive Neuroscience and Neuroimaging, 1, 32–38. Coccaro, E. F., Harvey, P. D., Kupsaw-Lawrence, E., Herbert, J. L., & Bernstein, D. P. (1991). Development of neuropharmacologically based behavioral assessments of impulsive aggressive behavior. Journal of Neuropsychiatry and Clinical Neurosciences, 3, S44–S51. Coccaro, E. F., Lee, R., Breen, E. C., & Irwin, M. R. (2015). Inflammatory markers and chronic exposure to fluoxetine, divalproex, and placebo in intermittent explosive disorder. Psychiatry Research, 229, 844–849. http://dx.doi.org/10.1016/ j.psychres.2015.07.078

Coccaro, E. F., Lee, R., McCloskey, M., Csernansky, J. G.,& Wang, L. (2015). Morphometric analysis of amygdala and hippocampus shape in impulsively aggressive and healthy control subjects. Journal of Psychiatric Research, 69, 80–86. http://dx.doi.org/ 10.1016/j.jpsychires.2015.07.009 Coccaro, E. F., Lee, R., & McCloskey, M. S. (2014). Relationship between psychopathy, aggression, anger, impulsivity, and intermittent explosive disorder. Aggressive Behavior, 40, 526–536. http:// dx.doi.org/10.1002/ab.21536 Coccaro, E. F., Lee, R. J., & Kavoussi, R. J. (2009). A double-blind, randomized, placebo-controlled trial of fluoxetine in patients with intermittent explosive disorder. Journal of Clinical Psychiatry, 70, 653–662. http://dx.doi.org/10.4088/ JCP.08m04150 Coccaro, E. F., McCloskey, M. S., Fitzgerald, D. A., & Phan, K. L. (2007). Amygdala and orbitofrontal reactivity to social threat in individuals with impulsive aggression. Biological Psychiatry, 62, 168–178. http://dx.doi.org/10.1016/j. biopsych.2006.08.024 Coccaro, E. F., Noblett, K. L., & McCloskey, M. S. (2009). Attributional and emotional responses to socially ambiguous cues: Validation of a new assessment of social/emotional information processing in healthy adults and impulsive aggressive patients. Journal of Psychiatric Research, 43, 915–925. http://dx.doi.org/10.1016/j. jpsychires.2009.01.012 Coid, J., & Ullrich, S. (2010). Antisocial personality disorder is on a continuum with psychopathy. Comprehensive Psychiatry, 51, 426–433. http://dx.doi.org/ 10.1016/j.comppsych.2009.09.006 Connor, D. F., Ford, J. D., Albert, D. B., & Doerfler, L. A. (2007). Conduct disorder subtype and comorbidity. Annals of Clinical Psychiatry, 19, 161–168. http:// dx.doi.org/10.1080/10401230701465269 Crick, N. R., & Dodge, K. A. (1996). Social informationprocessing mechanisms in reactive and proactive aggression. Child Development, 67, 993–1002. http:// dx.doi.org/10.2307/1131875 Crowe, S. L., & Blair, R. J. R. (2008). The development of antisocial behavior: What can we learn from functional neuroimaging studies? Development and Psychopathology, 20, 1145–1159. http://dx.doi.org/ 10.1017/S0954579408000540 Davidson, K. M., Tyrer, P., Tata, P., Cooke, D., Gumley, A., Ford, I., . . . Crawford, M. J. (2009). Cognitive behaviour therapy for violent men with antisocial personality disorder in the community: An exploratory randomized controlled trial. Psychological Medicine, 39, 569–577. http://dx.doi.org/ 10.1017/S0033291708004066 409

Copyright American Psychological Association. Not for further distribution.

McCloskey and Drabick

De Brito, S. A., Viding, E., Kumari, V., Blackwood, N., Hodgins, S. (2013). Cool and hot executive function impairments in violent offenders with antisocial personality disorder with and without psychopathy. PLOS ONE, 8, e65566. http://dx.doi.org10.1371/ journal.pone.0065566 23840340

Drabick, D. A., Gadow, K. D., & Loney, J. (2007). Sourcespecific oppositional defiant disorder: Comorbidity and risk factors in referred elementary schoolboys. Journal of the American Academy of Child and Adolescent Psychiatry, 46, 92–101. http://dx.doi.org/ 10.1097/01.chi.0000242245.00174.90

De Los Reyes, A., Augenstein, T. M., Wang, M., Thomas, S. A., Drabick, D. A., Burgers, D. E., & Rabinowitz, J. (2015). The validity of the multi-informant approach to assessing child and adolescent mental health. Psychological Bulletin, 141, 858–900. http://dx.doi.org/ 10.1037/a0038498

Drabick, D. A., & Kendall, P. C. (2010). Developmental psychopathology and the diagnosis of mental health problems among youth. Clinical Psychology: Science and Practice, 17, 272–280. http://dx.doi.org/10.1111/ j.1468-2850.2010.01219.x

Dishion, T. J., Bullock, B. M., & Granic, I. (2002). Pragmatism in modeling peer influence: Dynamics, outcomes, and change processes. Development and Psychopathology, 14, 969–981. http://dx.doi.org/ 10.1017/S0954579402004169

Drabick, D. A., Ollendick, T. H., & Bubier, J. L. (2010). Co-occurrence of ODD and anxiety: Shared risk processes and evidence for a dual-pathway model. Clinical Psychology: Science and Practice, 17, 307–318. http://dx.doi.org/10.1111/j.14682850.2010.01222.x

Dishion, T. J., & Tipsord, J. M. (2011). Peer contagion in child and adolescent social and emotional development. Annual Review of Psychology, 62, 189–214. http://dx.doi.org/10.1146/annurev. psych.093008.100412

Drabick, D. A., & Steinberg, L. (2011). Developmental psychopathology. In B. B. Brown & M. Prinstein (Eds.), Encyclopedia of adolescence (vol. 3, pp. 136–142). http://dx.doi.org/10.1016/B978-0-12373951-3.00109-5

Dodge, K. A., Greenberg, M. T., & Malone, P. S. (2008). Testing an idealized dynamic cascade model of the development of serious violence in adolescence. Child Development, 79, 1907–1927. http://dx.doi.org/ 10.1111/j.1467-8624.2008.01233.x

Eyberg, S. M., Nelson, M. M., & Boggs, S. R. (2008). Evidence-based psychosocial treatments for children and adolescents with disruptive behavior. Journal of Clinical Child and Adolescent Psychology, 37, 215–237. http://dx.doi.org/ 10.1080/15374410701820117

Dolan, M. C., & Fullam, R. (2010). Emotional memory and psychopathic traits in conduct disordered adolescents. Personality and Individual Differences, 48, 327–331. http://dx.doi.org/10.1016/ j.paid.2009.10.029 Drabick, D. A. (2009). Can a developmental psychopathology perspective facilitate a paradigm shift toward a mixed categorical–dimensional classification system? Clinical Psychology: Science and Practice, 16, 41–49. http://dx.doi.org/10.1111/ j.1468-2850.2009.01141.x Drabick, D. A., Beauchaine, T. P., Gadow, K. D., Carlson, G. A., & Bromet, E. J. (2006). Risk factors for conduct problems and depressive symptoms in a cohort of Ukrainian children. Journal of Clinical Child and Adolescent Psychology, 35, 244–252. http:// dx.doi.org/10.1207/s15374424jccp3502_8 Drabick, D. A., Bubier, J., Chen, D., Price, J., & Lanza, H. I. (2011). Source-specific oppositional defiant disorder among inner-city children: Prospective prediction and moderation. Journal of Clinical Child and Adolescent Psychology, 40, 23–35. http://dx.doi.org/10.1080/ 15374416.2011.533401 Drabick, D. A., & Gadow, K. D. (2012). Deconstructing oppositional defiant disorder: Clinic-based evidence for an anger/irritability phenotype. Journal of the American Academy of Child and Adolescent Psychiatry, 51, 384–393. http://dx.doi.org/10.1016/j. jaac.2012.01.010 410

Fanning, J. R., Meyerhoff, J. J., Lee, R., & Coccaro, E. F. (2014). History of childhood maltreatment in intermittent explosive disorder and suicidal behavior. Journal of Psychiatric Research, 56, 10–17. http://dx.doi.org/10.1016/ j.jpsychires.2014.04.012 Fazel, S., & Danesh, J. (2002). Serious mental disorder in 23,000 prisoners: A systematic review of 62 surveys. Lancet, 359, 545–550. http://dx.doi.org/10.1016/ S0140-6736(02)07740-1 Ferguson, C. J. (2010). Genetic contributions to antisocial personality and behavior: A meta-analytic review from an evolutionary perspective. Journal of Social Psychology, 150, 160–180. http://dx.doi.org/ 10.1080/00224540903366503 Fettich, K. C., McCloskey, M. S., Look, A. E., & Coccaro, E. F. (2015). Emotion regulation deficits in intermittent explosive disorder. Aggressive Behavior, 41, 25–33. http://dx.doi.org/10.1002/ab.21566 Finger, E. C., Marsh, A. A., Mitchell, D. G., Reid, M. E., Sims, C., Budhani, S., . . . Blair, J. R. (2008). Abnormal ventromedial prefrontal cortex function in children with psychopathic traits during reversal learning. Archives of General Psychiatry, 65, 586–594. http://dx.doi.org/10.1001/archpsyc.65.5.586 First, M., Williams, J., Karg, R., & Spitzer, R. (2015). Structured Clinical Interview for DSM–5 research

Understanding the Development and Management of Antisocial Disorders in Adolescents

version. Arlington, VA: American Psychiatric Association. First, M. B., Spitzer, R. L., Williams, J. B. W., & Gibbon, M. (1997). Structured Clinical Interview for DSM–IV Axis II Personality Disorders (SCID–II) user’s guide and interview. Washington, DC: American Psychiatric Press.

Copyright American Psychological Association. Not for further distribution.

Fontaine, N., Barker, E. D., Salekin, R. T., & Viding, E. (2008). Dimensions of psychopathy and their relationships to cognitive functioning in children. Journal of Clinical Child and Adolescent Psychology, 37, 690–696. http://dx.doi.org/ 10.1080/15374410802148111 Forth, A. E., Kosson, D., & Hare, R. D. (2003). The Hare psychopathy checklist: Youth version. Toronto, Ontario, Canada: Multi-Health Systems. Fosco, G. M., Stormshak, E. A., Dishion, T. J., & Winter, C. E. (2012). Family relationships and parental monitoring during middle school as predictors of early adolescent problem behavior. Journal of Clinical Child and Adolescent Psychology, 41, 202–213. http:// dx.doi.org/10.1080/15374416.2012.651989 Frick, P. J., & Nigg, J. T. (2012). Current issues in the diagnosis of attention deficit hyperactivity disorder, oppositional defiant disorder, and conduct disorder. Annual Review of Clinical Psychology, 8, 77–107. http://dx.doi.org/10.1146/annurevclinpsy-032511-143150 Frick, P. J., Ray, J. V., Thornton, L. C., & Kahn, R. E. (2014a). Annual research review: A developmental psychopathology approach to understanding callous–unemotional traits in children and adolescents with serious conduct problems. Journal of Child Psychology and Psychiatry, 55, 532–548. http://dx.doi.org/10.1111/jcpp.12152 Frick, P. J., Ray, J. V., Thornton, L. C., & Kahn, R. E. (2014b). Can callous–unemotional traits enhance the understanding, diagnosis, and treatment of serious conduct problems in children and adolescents? A comprehensive review. Psychological Bulletin, 140, 1–57. http://dx.doi.org/10.1037/a0033076 Frick, P. J., & Viding, E. (2009). Antisocial behavior from a developmental psychopathology perspective. Development and Psychopathology, 21, 1111–1131. http://dx.doi.org/10.1017/S0954579409990071 Frick, P. J., & White, S. F. (2008). Research review: The importance of callous–unemotional traits for developmental models of aggressive and antisocial behavior. Journal of Child Psychology and Psychiatry, 49, 359–375. http://dx.doi.org/10.1111/j. 1469-7610.2007.01862.x Galovski, T. E., & Blanchard, E. B. (2002). The effectiveness of a brief psychological intervention on court-referred and self-referred aggressive drivers. Behaviour Research and Therapy, 40, 1385–1402. http://dx.doi.org/10.1016/S0005-7967(01)00100-0

Geurts, D. E., von Borries, K., Volman, I., Bulten, B. H., Cools, R., & Verkes, R. J. (2016). Neural connectivity during reward expectation dissociates psychopathic criminals from noncriminal individuals with high impulsive/antisocial psychopathic traits. Social Cognitive and Affective Neuroscience, 11, 1326–1334. http://dx.doi.org/10.1093/scan/nsw040 Glenn, A. L., Johnson, A. K., & Raine, A. (2013). Antisocial personality disorder: A current review. Current Psychiatry Reports, 15, 427. http://dx.doi.org/ 10.1007/s11920-013-0427-7 Gregory, S., ffytche, D., Simmons, A., Kumari, V., Howard, M., Hodgins, S., & Blackwood, N. (2012). The antisocial brain: Psychopathy matters. JAMA Psychiatry, 69, 962–972. http://dx.doi.org/10.1001/ archgenpsychiatry.2012.222 Gurley, J. R. (2009). A history of changes to the criminal personality in the DSM. History of Psychology, 12, 285–304. http://dx.doi.org/10.1037/a0018101 Guy, L. S., Poythress, N. G., Douglas, K. S., Skeem, J. L., & Edens, J. F. (2008). Correspondence between self-report and interview-based assessments of antisocial personality disorder. Psychological Assessment, 20, 47–54. http://dx.doi.org/ 10.1037/1040-3590.20.1.47 Hare, R. D. (1991). The Hare Psychopathy Checklist—Revised. Toronto, Ontario, Canada: Multi-Health Systems. Hare, R. D. (1996). Psychopathy: A clinical construct whose time has come. Criminal Justice and Behavior, 23, 25–54. http://dx.doi.org/ 10.1177/0093854896023001004 Henggeler, S. W., & Lee, T. (2003). Multisystemic treatment of serious clinical problems. In A. E. Kazdin & J. R. Weisz (Eds.), Evidence-based psychotherapies for children and adolescents (pp. 301–322). New York, NY: Guilford Press. Hollander, E., Tracy, K. A., Swann, A. C., Coccaro, E. F., McElroy, S. L., Wozniak, P., . . . Nemeroff, C. B. (2003). Divalproex in the treatment of impulsive aggression: Efficacy in cluster B personality disorders. Neuropsychopharmacology, 28, 1186–1197. Huey, W. C., & Rank, R. C. (1984). Effects of counselor and peer-led group assertive training on Black adolescent aggression. Journal of Counseling Psychology, 31, 95–98. http://dx.doi.org/ 10.1037/0022-0167.31.1.95 Hyler, S. E. (1994). PDQ-4 + personality diagnostic questionnaire. New York: New York State Psychiatric Institute. Jones, A. P., Laurens, K. R., Herba, C. M., Barker, G. J.,& Viding, E. (2009). Amygdala hypoactivity to fearful faces in boys with conduct problems and 411

McCloskey and Drabick

callous–unemotional traits. American Journal of Psychiatry, 166, 95–102. http://dx.doi.org/10.1176/appi. ajp.2008.07071050 Kazdin, A. E. (2003). Problem-solving skills training and parent management training for conduct disorder. In A. E. Kazdin & J. R. Weisz (Eds.), Evidence-based psychotherapies for children and adolescents (pp. 241–262). New York, NY: Guilford Press.

Copyright American Psychological Association. Not for further distribution.

Kendall, P. C. (Ed.). (2012). Child and adolescent therapy: Cognitive–behavioral procedures (4th ed.). New York, NY: Guilford Press. Kendall, T., Pilling, S., Tyrer, P., Duggan, C., Burbeck, R., Meader, N., & Taylor, C. (2009). Borderline and antisocial personality disorders: Summary of NICE guidance. BMJ (Clinical Research Ed.), 338, b93. http://dx.doi.org/10.1136/bmj.b93 Kessler, R. C., Coccaro, E. F., Fava, M., Jaeger, S., Jin, R., & Walters, E. (2006). The prevalence and correlates of DSM–IV intermittent explosive disorder in the National Comorbidity Survey Replication. Archives of General Psychiatry, 63, 669–678. http://dx.doi.org/ 10.1001/archpsyc.63.6.669 Keyes, K. M., McLaughlin, K. A., Vo, T., Galbraith, T., & Heimberg, R. G. (2016). Anxious and aggressive: The co-occurrence of IED with anxiety disorders. Depression and Anxiety, 33, 101–111. http://dx.doi.org/ 10.1002/da.22428 Khalifa, N., Duggan, C., Stoffers, J., Huband, N., Völlm, B. A., Ferriter, M., & Lieb, K. (2010). Pharmacological interventions for antisocial personality disorder. Cochrane Database of Systematic Reviews, (8): CD007667. Advance online publication. Krieger, F. V., & Stringaris, A. (2016). Temperament and vulnerability to externalizing behavior. In T. Beauchaine & S. Hinshaw (Eds.), The Oxford handbook of externalizing behaviors (pp. 170–183). New York, NY: Oxford University Press. Kulper, D. A., Kleiman, E. M., McCloskey, M. S., Berman, M. E., & Coccaro, E. F. (2015). The experience of aggressive outbursts in intermittent explosive disorder. Psychiatry Research, 225, 710–715. http:// dx.doi.org/10.1016/j.psychres.2014.11.008 Lavigne, J. V., Gouze, K. R., Hopkins, J., & Bryant, F. B. (2016). A multidomain cascade model of early childhood risk factors associated with oppositional defiant disorder symptoms in a community sample of 6-year-olds. Development and Psychopathology, 28, 1547–1562. http://dx.doi.org/10.1017/ S0954579415001194 Lee, R., Meyerhoff, J., & Coccaro, E. F. (2014). Intermittent explosive disorder and aversive parental care. Psychiatry Research, 220, 477–482. http:// dx.doi.org/10.1016/j.psychres.2014.05.059 412

Leventhal, T., & Brooks-Gunn, J. (2004). A randomized study of neighborhood effects on low-income children’s educational outcomes. Developmental Psychology, 40, 488–507. http://dx.doi.org/ 10.1037/0012-1649.40.4.488 Lochman, J. E., Barry, T. D., & Pardini, D. A. (2003). Anger control training for aggressive youth. In A. E. Kazdin & J. R. Weisz (Eds.), Evidence-based psychotherapies for children and adolescents (pp. 263–281). New York, NY: Guilford Press. Lochman, J. E., & Wells, K. C. (2002). Contextual socialcognitive mediators and child outcome: A test of the theoretical model in the Coping Power program. Development and Psychopathology, 14, 945–967. http://dx.doi.org/10.1017/S0954579402004157 Loeber, R., Burke, J. D., & Pardini, D. A. (2009). Development and etiology of disruptive and delinquent behavior. Annual Review of Clinical Psychology, 5, 291–310. http://dx.doi.org/10.1146/ annurev.clinpsy.032408.153631 Lorber, M. F. (2004). Psychophysiology of aggression, psychopathy, and conduct problems: A metaanalysis. Psychological Bulletin, 130, 531–552. http:// dx.doi.org/10.1037/0033-2909.130.4.531 Marcus, D. K., Lilienfeld, S. O., Edens, J. F., & Poythress, N. G. (2006). Is antisocial personality disorder continuous or categorical? A taxometric analysis. Psychological Medicine, 36, 1571–1581. http://dx.doi.org/ 10.1017/S0033291706008245 Marini, V. A., & Stickle, T. R. (2010). Evidence for deficits in reward responsivity in antisocial youth with callous-unemotional traits. Personality Disorders: Theory, Research, and Treatment, 1, 218–229. http://dx.doi.org/10.1037/a0017675 Mattes, J. A. (2005). Oxcarbazepine in patients with impulsive aggression: A double-blind, placebo-controlled trial. Journal of Clinical Psychopharmacology, 25, 575–579. http://dx.doi.org/ 10.1097/01.jcp.0000186739.22395.6b Mattes, J. A. (2008). Levetiracetam in patients with impulsive aggression: A double-blind, placebocontrolled trial. Journal of Clinical Psychiatry, 69, 310–315. http://dx.doi.org/10.4088/JCP.v69n0218 Maughan, B., Rowe, R., Messer, J., Goodman, R., & Meltzer, H. (2004). Conduct disorder and oppositional defiant disorder in a national sample: Developmental epidemiology. Journal of Child Psychology and Psychiatry, 45, 609–621. http:// dx.doi.org/10.1111/j.1469-7610.2004.00250.x Mazas, C. A., Finn, P. R., & Steinmetz, J. E. (2000). Decision-making biases, antisocial personality, and early-onset alcoholism. Alcoholism: Clinical and Experimental Research, 24, 1036–1040. http://dx.doi.org/ 10.1111/j.1530-0277.2000.tb04647.x

Understanding the Development and Management of Antisocial Disorders in Adolescents

McCloskey, M. S., Ben-Zeev, D., Lee, R., Berman, M. E., & Coccaro, E. F. (2009). Acute tryptophan depletion and self-injurious behavior in aggressive patients and healthy volunteers. Psychopharmacology, 203, 53–61. http://dx.doi.org/10.1007/s00213-0081374-6

Copyright American Psychological Association. Not for further distribution.

McCloskey, M. S., Berman, M. E., Noblett, K. L., & Coccaro, E. F. (2006). Intermittent explosive disorder-integrated research diagnostic criteria: Convergent and discriminant validity. Journal of Psychiatric Research, 40, 231–242. http://dx.doi.org/ 10.1016/j.jpsychires.2005.07.004 McCloskey, M. S., & Coccaro, E. F. (2003). Questionnaire and interview measures of aggression in adults. In E. F. Coccaro (Ed.), Aggression: psychiatric assessment and treatment (pp. 167–193). http://dx.doi.org/10.1201/b14206-11 McCloskey, M. S., Kleabir, K., Berman, M. E., Chen, E. Y.,& Coccaro, E. F. (2010). Unhealthy aggression: Intermittent explosive disorder and adverse physical health outcomes. Health Psychology, 29, 324–332. http://dx.doi.org/10.1037/a0019072 McCloskey, M. S., Lee, R., Berman, M. E., Noblett, K. L., & Coccaro, E. F. (2008). The relationship between impulsive verbal aggression and intermittent explosive disorder. Aggressive Behavior, 34, 51–60. http://dx.doi.org/10.1002/ab.20216 McCloskey, M. S., Noblett, K. L., Deffenbacher, J. L., Gollan, J. K., & Coccaro, E. F. (2008). Cognitive–behavioral therapy for intermittent explosive disorder: A pilot randomized clinical trial. Journal of Consulting and Clinical Psychology, 76, 876–886. http://dx.doi.org/ 10.1037/0022-006X.76.5.876 McCloskey, M. S., Phan, K. L., Angstadt, M., Fettich, K. C., Keedy, S., & Coccaro, E. F. (2016). Amygdala hyperactivation to angry faces in intermittent explosive disorder. Journal of Psychiatric Research, 79, 34–41. http://dx.doi.org/10.1016/j. jpsychires.2016.04.006 McCloskey, M. S., Phan, K. L., & Coccaro, E. F. (2005). Neuroimaging and personality disorders. Current Psychiatry Reports, 7, 65–72. http://dx.doi.org/ 10.1007/s11920-005-0027-2 McLaughlin, K. A., Green, J. G., Hwang, I., Sampson, N. A., Zaslavsky, A. M., & Kessler, R. C. (2012). Intermittent explosive disorder in the National Comorbidity Survey Replication Adolescent Supplement. JAMA Psychiatry, 69, 1131–1139. http:// dx.doi.org/10.1001/archgenpsychiatry.2012.592 McMahon, R. J., & Forehand, R. L. (2003). Helping the noncompliant child: Family-based treatment for oppositional behavior (2nd ed.). New York, NY: Guilford Press.

McMahon, R. J., & Frick, P. J. (2005). Evidence-based assessment of conduct problems in children and adolescents. Journal of Clinical Child and Adolescent Psychology, 34, 477–505. http://dx.doi.org/10.1207/ s15374424jccp3403_6 Millon, T., Grossman, S., & Millon, C. (2015). MCMI–IIV manual. Minneapolis, MN: Pearson Education. Moffitt, T. E. (1993). Adolescence-limited and lifecourse persistent and adolescence-limited antisocial behavior: A developmental taxonomy. Psychological Review, 100, 674–701. http://dx.doi.org/ 10.1037/0033-295X.100.4.674 Moffitt, T. E., Arseneault, L., Jaffee, S. R., Kim-Cohen, J., Koenen, K. C., Odgers, C. L., . . . Viding, E. (2008). Research review: DSM–V conduct disorder: research needs for an evidence base. Journal of Child Psychology and Psychiatry, 49, 3–33. http://dx.doi.org/ 10.1111/j.1469-7610.2007.01823.x Moffitt, T. E., & Caspi, A. (2001). Childhood predictors differentiate life-course persistent and adolescencelimited antisocial pathways among males and females. Development and Psychopathology, 13, 355–375. http://dx.doi.org/10.1017/ S0954579401002097 Morey, L. (2007). Personality assessment inventory professional manual (2nd ed.). Lutz, FL: Psychological Assessment Resources. Morgan, A. B., & Lilienfeld, S. O. (2000). A meta-analytic review of the relation between antisocial behavior and neuropsychological measures of executive function. Clinical Psychology Review, 20, 113–136. Murray-Close, D., Ostrov, J. M., Nelson, D. A., Crick, N. R., & Coccaro, E. F. (2010). Proactive, reactive, and romantic relational aggression in adulthood: Measurement, predictive validity, gender differences, and association with intermittent explosive disorder. Journal of Psychiatric Research, 44, 393–404. http:// dx.doi.org/10.1016/j.jpsychires.2009.09.005 National Institute for Health and Clinical Excellence. (2010). Antisocial personality disorder: The NICE guideline on treatment, management, and prevention. Leicester, England: British Psychological Society & Royal College of Psychiatrists. Neufeld, K. J., Kidorf, M. S., Kolodner, K., King, V. L., Clark, M., & Brooner, R. K. (2008). A behavioral treatment for opioid-dependent patients with antisocial personality. Journal of Substance Abuse Treatment, 34, 101–111. http://dx.doi.org/10.1016/ j.jsat.2007.02.009 Nickerson, A., Aderka, I. M., Bryant, R. A., & Hofmann, S. G. (2012). The relationship between childhood exposure to trauma and intermittent explosive disorder. Psychiatry Research, 197, 128–134. http:// dx.doi.org/10.1016/j.psychres.2012.01.012 413

McCloskey and Drabick

Nock, M. K., Kazdin, A. E., Hiripi, E., & Kessler, R. C. (2006). Prevalence, subtypes, and correlates of DSM–IV conduct disorder in the National Comorbidity Survey Replication. Psychological Medicine, 36, 699–710. http://dx.doi.org/10.1017/ S0033291706007082

Copyright American Psychological Association. Not for further distribution.

Nock, M. K., Kazdin, A. E., Hiripi, E., & Kessler, R. C. (2007). Lifetime prevalence, correlates, and persistence of oppositional defiant disorder: Results from the National Comorbidity Survey Replication. Journal of Child Psychology and Psychiatry, 48, 703–713. http://dx.doi.org/10.1111/ j.1469-7610.2007.01733.x Odgers, C. L., Milne, B. J., Caspi, A., Crump, R., Poulton, R., & Moffitt, T. E. (2007). Predicting prognosis for the conduct-problem boy: Can family history help? Journal of the American Academy of Child and Adolescent Psychiatry, 46, 1240–1249. http://dx.doi.org/ 10.1097/chi.0b013e31813c6c8d Ogloff, J. R. (2006). Psychopathy/antisocial personality disorder conundrum. Australian and New Zealand Journal of Psychiatry, 40, 519–528. http://dx.doi.org/ 10.1080/j.1440-1614.2006.01834.x Patterson, G. R. (1982). Coercive family process. Eugene, OR: Castalia. Patterson, G. R., Chamberlain, P., & Reid, J. B. (1982). A comparative evaluation of a parent-training program. Behavior Therapy, 13, 638–650. http://dx.doi.org/ 10.1016/S0005-7894(82)80021-X Pfohl, B., Blum, N., & Zimmerman, M. (1995). Structured Clinical Interview for DSM–IV Personality. Iowa City: University of Iowa College of Medicine. Poythress, N. G., Edens, J. F., Skeem, J. L., Lilienfeld, S. O., Douglas, K. S., Frick, P. J., . . . Wang, T. (2010). Identifying subtypes among offenders with antisocial personality disorder: A cluster-analytic study. Journal of Abnormal Psychology, 119, 389–400. http://dx.doi.org/ 10.1037/a0018611 Puhalla, A. A., Ammerman, B. A., Uyeji, L. L., Berman, M. E., & McCloskey, M. S. (2016). Negative urgency and reward/punishment sensitivity in intermittent explosive disorder. Journal of Affective Disorders, 201, 8–14. http://dx.doi.org/10.1016/j.jad.2016.04. 045 27155024 Robins, L. N., Tipp, J., & Przybeck, T. (1991). Antisocial personality. In L. N. Robins & D. A. Reiger (Eds.), Psychiatric disorders in America (pp. 258–290). New York, NY: Free Press. Salekin, R. T., Rogers, R., Sewell, K. W. (1996). A review and meta-analysis of the Psychopathy Checklist and Psychopathy Checklist—Revised: Predictive validity of dangerousness. Clinical Psychology, 3, 203–215. Sanders, M. R. (1999). Triple P-Positive Parenting Program: Towards an empirically validated 414

multilevel parenting and family support strategy for the prevention of behavior and emotional problems in children. Clinical Child and Family Psychology Review, 2, 71–90. http://dx.doi.org/ 10.1023/A:1021843613840 Seara-Cardoso, A., & Viding, E. (2015). Functional Neuroscience of Psychopathic Personality in Adults. Journal of Personality, 83, 723–737. http://dx.doi.org/ 10.1111/jopy.12113 Seara-Cardoso, A., Viding, E., Lickley, R. A., & Sebastian, C. L. (2015). Neural responses to others’ pain vary with psychopathic traits in healthy adult males. Cognitive, Affective, and Behavioral Neuroscience, 15, 578–588. http://dx.doi.org/10.3758/s13415015-0346-7 Shedler, J., Beck, A., Fonagy, P., Gabbard, G. O., Gunderson, J., Kernberg, O., . . . Westen, D. (2010). Personality disorders in DSM–5. American Journal of Psychiatry, 167, 1026–1028. http://dx.doi.org/ 10.1176/appi.ajp.2010.10050746 Sher, L., Siever, L. J., Goodman, M., McNamara, M., Hazlett, E. A., Koenigsberg, H. W., & New, A. S. (2015). Gender differences in the clinical characteristics and psychiatric comorbidity in patients with antisocial personality disorder. Psychiatry Research, 229, 685–689. http://dx.doi.org/ 10.1016/j.psychres.2015.08.022 Singh, J. P., Grann, M., & Fazel, S. (2011). A comparative study of violence risk assessment tools: A systematic review and metaregression analysis of 68 studies involving 25,980 participants. Clinical Psychology Review, 31, 499–513. http://dx.doi.org/10.1016/j. cpr.2010.11.009 Steinberg, L. (2008). A social neuroscience perspective on adolescent risk-taking. Developmental Review, 28, 78–106. http://dx.doi.org/10.1016/j.dr.2007.08.002 Stringaris, A., & Goodman, R. (2009). Three dimensions of oppositionality in youth. Journal of Child Psychology and Psychiatry, 50, 216–223. http:// dx.doi.org/10.1111/j.1469-7610.2008.01989.x Swann, A. C., Lijffijt, M., Lane, S. D., Steinberg, J. L., & Moeller, F. G. (2009). Trait impulsivity and response inhibition in antisocial personality disorder. Journal of Psychiatric Research, 43, 1057–1063 http://dx.doi.org/ 10.1016/j.jpsychires.2009.03.003 19345957 Swanson, M. C., Bland, R. C., & Newman, S. C. (1994). Antisocial personality disorders. Acta Psychiatrica Scandinavica, 89(Suppl. 376), 63–70. http://dx.doi.org/ 10.1111/j.1600-0447.1994.tb05792.x Trull, T. J., Jahng, S., Tomko, R. L., Wood, P. K., & Sher, K. J. (2010). Revised NESARC personality disorder diagnoses: Gender, prevalence, and comorbidity with substance dependence disorders. Journal of Personality Disorders, 24, 412–426. http://dx.doi.org/ 10.1521/pedi.2010.24.4.412

Understanding the Development and Management of Antisocial Disorders in Adolescents

Veit, R., Konicar, L., Klinzing, J. G., Barth, B., Yilmaz, O., & Birbaumer, N. (2013). Deficient fear conditioning in psychopathy as a function of interpersonal and affective disturbances. Frontiers in Human Neuroscience, 7, 706. http://dx.doi.org/ 10.3389/fnhum.2013.00706

Zeier, J. D., Baskin-Sommers, A. R., Hiatt Racer, K. D., Newman, J. P. (2012). Cognitive control deficits associated with antisocial personality disorder and psychopathy. Personality Disorders: Theory, Research, and Treatment, 3, 283–293. http://dx.doi.org/ 10.1037/a0023137 22452754

Copyright American Psychological Association. Not for further distribution.

Webster-Stratton, C., & Reid, M. (2003). The Incredible Years parents, teachers, and children training series: A multifaceted treatment approach for young children with conduct problems. In A. E. Kazdin & J. R. Weisz (Eds.), Evidence-based psychotherapies for children and adolescents (pp. 224–240). New York, NY: Guilford Press.

Weigard, A., Chein, J., Albert, D., Smith, A., & Steinberg, L. (2014). Effects of anonymous peer observation on adolescents’ preference for immediate rewards. Developmental Science, 17, 71–78. http://dx.doi.org/ 10.1111/desc.12099

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Chapter 19

Attention-Deficit/ Hyperactivity Disorder

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Mary Rooney and Linda J. Pfiffner

Attention-deficit/hyperactivity disorder (ADHD) is a neurodevelopmental disorder characterized by symptoms of inattention, hyperactivity, and impulsivity. ADHD is associated with meaningful impairment across multiple domains of functioning, including academic, social, occupational, and financial. ADHD frequently co-occurs with internalizing and externalizing disorders across the lifespan, and is a significant risk factor for the development of substance use disorders in adolescence and adulthood. As the most frequently diagnosed mental health disorder in childhood, ADHD is estimated to occur in 5% to 7% of children. It is now recognized that ADHD persists into adulthood in most cases, and occurs in approximately 4% of adults. Although the exact causes of ADHD are unknown, it is a highly heritable disorder with a developmental course that is significantly influenced by environmental factors. Historical Context Our current conceptualization of ADHD evolved gradually over time, and in recent years has been advanced through substantial neurobiological and genetic research. The first description of attention disorders in the medical literature appeared over 200 years ago (Barkley & Peters, 2012), but a formal definition of what is now known as ADHD did not appear until 1980 (for a detailed history see Taylor, 2011). It was the second version of the Diagnostic and Statistical Manual of Mental Disorders (DSM;

American Psychiatric Association, 1968) that first included childhood disorders and contained the precursor to ADHD, hyperkinetic reaction of childhood. Although this diagnostic category ultimately helped put ADHD on the map, its emphasis on hyperactivity and marginalization of inattentive features was viewed as misguided by influential cognitive researchers. In 1972, Douglas proposed a three-component model of self-regulation (attentional, inhibitory, and organizational) that provoked further research and influenced the renaming of the disorder to attention deficit disorder (ADD) in the DSM–III (American Psychiatric Association, 1980). The DSM–III included two subtypes, ADD with hyperactivity and ADD without hyperactivity, with the latter recognizing children who were inattentive but not hyperactive. This diagnostic classification of ADD subtypes brought increased awareness to the symptoms of inattention, but led to concerns that the symptoms of hyperactivity and impulsivity were now being minimized. Concurrently, technological advances enabled researchers to apply computerized factor analyses to the disorder’s symptoms for the first time. In DSM–III–R (American Psychiatric Association, 1987), the disorder was renamed attention-deficit/hyperactivity disorder, and the two symptom clusters were consolidated into a single symptom list. The subtype of ADD without hyperactivity was subsumed under a new classification named undifferentiated ADD. During the 1980s, emerging theories of cognitive control, motivation, and information processing

Work on this chapter was supported, in part, by a grant from the Institute of Education Sciences, U.S. Department of Education, to the University of California, San Francisco (Award No. R324A120358). http://dx.doi.org/10.1037/0000065-019 417 APA Handbook of Psychopathology: Vol. 2. Child and Adolescent Psychopathology, J. N. Butcher (Editor-in-Chief) APA Handbook of2018 Psychopathology: Child andAssociation. Adolescent Psychopathology, edited by Copyright © by the American Psychological All rights reserved. J. N. Butcher and P. C. Kendall Copyright © 2018 American Psychological Association. All rights reserved.

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provided the foundation for the conceptualization of ADHD as an executive functioning-based disorder (Barkley, 2001). Additional factor analytic studies led to refined item sets and symptom clusters. In preparation for the publication of the DSM–IV a large field trial was undertaken to validate the disorder’s diagnostic criteria. When the DSM–IV was published (American Psychiatric Association, 1994), specific diagnostic criteria were presented for two distinct, equally weighted subtypes: the predominantly hyperactive/impulsive type and the predominantly inattentive type. A third, combined subtype, was also introduced to capture children who met symptom thresholds in the inattentive and hyperactive/impulsive domains. An age of symptom onset criterion was added (age 7), and the persistence of ADHD into adulthood was acknowledged for the first time. Almost 20 years passed before the publication of the current DSM–5 (American Psychiatric Association, 2013). During this time, theoretical conceptualizations continued to broaden, fueled in part by advancements in our knowledge of the biological/ genetic correlates of the disorder. Recognition of the lifelong-chronic nature of the disorder also continued to expand, and research into adult ADHD has proliferated. Despite these advancements, few changes were made to the ADHD diagnostic criteria in the DSM–5. The symptom lists remained unchanged, as did the symptom cut-off score for children. The word subtype was replaced with presentation to emphasize the changing nature of symptom presentations across development. In addition, the age of onset guideline was increased to age 12 to better reflect individuals whose symptoms do not become impairing until preadolescence. Perhaps most important, the adult presentation of ADHD was more formally incorporated into the diagnostic criteria with the introduction of a symptom threshold specific to adults (five or more in one symptom domain) and adult-specific symptom qualifiers. Today ADHD is recognized as a universal disorder, having been identified in every country and culture in which it has been studied. Great strides have been made in our understanding of the disorder’s etiology and pathogeneses, but additional work is needed. The heterogeneity observed in ADHD is still poorly understood, and we have only 418

a limited understanding of the effect that various environmental influences have on the disorder’s developmental course and long term outcomes. Understanding these core features is essential to the development of advanced targeted interventions. Diagnostic Criteria and Considerations The ADHD diagnostic criteria defined in the DSM–5 (American Psychiatric Association, 2013) represent the current consensus of experts in the field. The DSM–5 criteria are based on symptom lists derived from field trials conducted with children and adolescents ages 4 to 16 (Lahey et al., 1994). The absence of adult participants in these field trials, as well as the preponderance of boys in the study samples (4 times as many boys as girls), indicate that these criteria may not adequately capture the presentation of ADHD observed in many girls, older adolescents, and adults.

Symptom Clusters ADHD symptoms are clustered into two domains originally derived through factor analysis: inattention and hyperactive/impulsive (Willcutt et al., 2012). The inattention domain is comprised of nine symptoms reflecting difficulty sustaining attention, persisting at tasks or play activities, following through on instructions, giving close attention to details, organizing tasks and activities, and keeping track of belongings. The hyperactivity and impulsivity domain is also comprised of nine symptoms reflecting excessive movement including difficulty remaining seated, fidgeting, and constantly being on-the-go as if “driven by a motor,” as well as excessive talkativeness and impulsive behavior (e.g., blurting out answers, difficulty waiting, frequently interrupting others). For children, six or more of the nine symptoms in at least one of the two domains must be present for at least 6 months, to a degree that is inconsistent with developmental level and that negatively impacts social and academic activities. For adults (17 and older), only five or more of these symptoms must be present since childhood at an impairing level. In all cases, symptoms must be present beginning in childhood (before age 12), and

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may not solely be a manifestation of oppositional behavior, failure to understand tasks or instructions, or be primarily attributed to another disorder.

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Presentations Individuals diagnosed with ADHD are assigned one of three presentations: predominantly hyperactive impulsive presentation, predominantly inattentive presentation, and combined presentation (American Psychiatric Association, 2013). Although the diagnosis of ADHD itself is quite stable over time (59% of children diagnosed with ADHD continue to meet diagnostic criteria 5 years later), the assigned presentation is relatively unstable and typically shifts over the course of development (Willcutt et al., 2012). Predominantly hyperactive–impulsive presentation.  Developmentally, problems with impulsivity and hyperactivity initially appear during the preschool years and decline with age. ADHD with hyperactive–impulsive presentation (ADHD-HI) is the least common presentation of the disorder, comprising an estimated 13% to 17% of all individuals with ADHD (Willcutt et al., 2012). Not surprisingly, the prevalence of ADHD-HI is highest in preschool children and declines through adolescence, with only 1.1% to 14% of adolescents with ADHD meeting criteria for ADHD-HI (Willcutt et al., 2012). It has been suggested that ADHD-HI is a developmental precursor to the combined presentation, which is diagnosed once academic and social demands increase and inattention symptoms become more impairing (Willcutt et al., 2012). ADHD-HI is associated with those impairments specific to the hyperactive–impulsive symptom cluster. During peer interactions impulsive behavior often results in peer rejection (Hoza et al., 2005). In classrooms, hyperactivity and impulsivity are often disruptive and result in frequent punishment and criticism (Garner et al., 2013). Externalizing disorders, such as oppositional defiant disorder (ODD) and conduct disorder (CD), are the most common comorbid conditions associated with ADHD-HI (Lahey et al., 2009). When present, these externalizing disorders predict significantly greater impairment and negative long term outcomes than is observed in ADHD-HI alone (Willcutt et al., 2012).

Predominantly inattentive presentation.  The predominantly inattentive presentation of ADHD (ADHD-I) is the most common form of ADHD in community settings (Willcutt, 2012). However, children and adolescents with ADHD-I are referred to clinics less frequently than children with the combined or HI presentations, because they often lack the overt behavior problems that typically lead to referrals. When children are referred, it is usually at an older age than for combined or HI presentations (Milich, Balentine, & Lynam, 2001). Academically, individuals with ADHD-I frequently experience underachievement and poor productivity and fluency, and they may be more likely to struggle in math and reading relative to those with the combined presentation (Massetti et al., 2008; Pennington et al., 2009). Socially, children and adolescents with ADHD-I tend to be more withdrawn, less assertive, and are more likely to be overlooked by their peers than those with combined or HI presentation (Mikami, Huang-Pollock, Pfiffner, McBurnett, & Hangai, 2007; Willcutt et al., 2012). Girls with ADHD-I may be particularly susceptible to internalizing problems associated with negative social preference (Becker, McBurnett, Hinshaw, & Pfiffner, 2013). Comorbid conditions are common in ADHD-I, with anxiety, mood, and learning disorders occurring most frequently (Willcutt et al., 2012). Externalizing disorders are less common in children and adolescents with ADHD-I than in those with combined or HI presentations. ADHD-I is associated with an increased risk for substance use problems at a rate comparable with other subtypes of ADHD (De Alwis, Lynskey, Reiersen, & Agrawal, 2014). Combined presentation.  The combined presentation of ADHD (ADHD-C) is the most common form of ADHD in clinic samples and children and adolescents with ADHD-C are typically more impaired across multiple domains than those with ADHD-I or ADHD-HI (Willcutt, 2012). This situation is likely because they struggle with the aspects of functioning associated with both symptom dimensions, rather than only one of the dimensions as in the other two presentations (American Psychiatric Association, 2013). ADHD-C is associated with academic impairment, poor social skills and a tendency to be disliked by peers, and higher rates of family 419

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conflict (Willcutt et al., 2012). In contrast to children and adolescents with ADHD-I however, those with ADHD-C are less likely to be withdrawn and passive (Willcutt et al., 2012). In terms of comorbid conditions, rates externalizing and internalizing disorders are both elevated across the lifespan. As with ADHD-I, individuals with ADHD-C are at increased risk for the development of substance use disorder in adulthood (De Alwis et al., 2014).

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Diagnostic Considerations Preschool children.  DSM–5 criteria may not adequately capture ADHD in preschool children. ADHD symptom lists are not developmentally sensitive for this age range (Merkt, Siniatchkin, & Petermann, 2016), and symptoms that are present during preschool may only be predictive of long-term impairment when they persist for at least 12 months or beyond the age of 4 (Lahey et al., 2004; Riddle et al., 2013). The challenge lies in differentiating ADHDrelated inattention, hyperactivity, and impulsivity from typical preschool behavior or symptoms related to other psychopathologies (Merkt et al., 2016). Given the importance of early intervention, identifying and treating young children with ADHD is a priority and additional research is needed to improve diagnostic accuracy in preschool children. Adolescents.  Cognitive impairments and behavioral challenges associated with ADHD (e.g., attention span, emotional control, hyperactivity) improve as children develop into adolescents (McAuley, Crosbie, Charach, & Schachar, 2014). However, for adolescents with ADHD these improvements are less likely to keep pace with increased social and academic demands and expectations for greater autonomy (Langberg et al., 2008). For children presenting predominantly with inattentive symptoms of ADHD, their symptoms may not become impairing until they reach adolescence and struggle to keep up with their peers (Willcutt et al., 2012). In the DSM–5, developmental descriptors were added to the symptom lists in an effort to better capture ADHD symptoms in adolescence and reduce the likelihood of missed diagnoses in this age group (American Psychiatric Association, 2013; Sibley et al., 2012). A recent study suggests that these added descriptors 420

may in fact help parents identify more symptoms in this age group (Sibley & Kuriyan, 2016). Adults.  As mentioned previously, DSM symptom lists and clusters were derived from field trials of children and adolescents with ADHD and do not optimally characterize the disorder in adulthood. In the DSM–5, a handful of relatively minor changes were made to address this weakness, including the addition of adult descriptors to existing symptom lists, increasing the age of symptom onset from 7 to 12 years, and lowering the symptom threshold for adults (ages 17 and over) to five symptoms in a single cluster (American Psychiatric Association, 2013). These changes however, were not empirically based (Clarke et al., 2013), and do not fully describe the presentation of ADHD symptoms and impairments in adulthood (Matte et al., 2015). Retrospective symptom reporting also presents a challenge in this age group, since the childhood onset of symptoms is required for an adult ADHD diagnosis. Therefore, it is recommended that clinicians gather clinical information from the patient directly, as well as from an adult who knew the patient as a child (American Psychiatric Association, 2013). Despite these limitations, recent studies suggest that the DSM–5 criteria do capture many adults with moderate to severe ADHD (Matte et al., 2015).

Cognitive Styles Associated With Attention-Deficit/Hyperactivity Disorder Sluggish cognitive tempo.  Sluggish cognitive tempo (SCT) is a cognitive–emotional style characterized by excessive daydreaming, mental confusion or “fogginess,” sluggishness, poor motivation, and drowsiness (Becker et al., 2016). SCT correlates highly with ADHD inattentive symptoms (Becker et al., 2016), and some studies have estimated that 30% to 63% of youth with ADHD-I have high levels of SCT (Garner et al., 2013). Clinically, relative to ADHD, SCT is more highly associated with internalizing symptoms and less correlated with ODD symptoms (Barkley, 2013; Becker et al., 2016; Lee, Burns, Snell, & McBurnett, 2014). Whether SCT represents a subtype of ADHD or a distinct disorder is currently a matter of debate (Barkley, 2013). Supporting the view that SCT constitutes a separate

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disorder, multiple factor analytic studies have found SCT symptoms to represent a separate symptom dimension from the ADHD-HI and ADHD-I symptom clusters (Barkley, 2013; Jacobson et al., 2012). In addition, SCT symptoms contribute to academic, social, and behavioral impairment above and beyond the influence of ADHD (Becker et al., 2016). Conversely, the high rate of individuals who meet criteria for SCT and ADHD-I supports the notion that SCT may be best classified as a restrictive inattentive subtype with few hyperactive–impulsive symptoms (Barkley, 2013). Positive illusory bias.  Children with ADHD experience difficulties in peer relationships and academic performance, yet many children with ADHD s­ elf-report competency in these domains ­ ( J. S. Owens, Goldfine, Evangelista, Hoza, & Kaiser, 2007). In fact, research suggests that children with ADHD overestimate their own competence relative to objective measures and parent and teacher reports (Hoza et al., 2004; Hoza, Pelham, Dobbs, Owens, & Pillow, 2002). This phenomenon is referred to as the positive illusory bias (PIB). Positive illusions are not unique to individuals with ADHD, and are found in the general population. However, as noted by J. S. Owens and colleagues (2007), the PIB found in children with ADHD is unique in three ways: (a) the discrepancy between perceived and actual competence is greater in children with ADHD than in the general population (Hoza et al., 2002; J. S. Owens & Hoza, 2003); (b) PIB does not enhance motivation, performance, or task persistence in children with ADHD, yet it does appear to have this effect in the general population; and (c) it is a counterintuitive phenomenon among children with ADHD who have histories marked with failure in multiple domains (e.g., Hoza et al., 2002). Studies suggest that PIB in children with ADHD may negatively affect response to interventions (Mikami, 2010), possibly because inflated perceived competence interferes with the ability to recognize a need for improvement or be receptive to negative feedback ( J. S. Owens et al., 2007). PIB is associated with lower levels of prosocial behavior and lower levels of effortful behavior in children with ADHD (Linnea, Hoza, Tomb, & Kaiser, 2012). Conversely,

PIB is also associated with lower levels of depressive symptoms, suggesting that there may be some protective effects (Mikami, 2010). Reward discounting.  Another meaningful cognitive feature of ADHD is the temporal discounting of rewards, or delay discounting. This phenomenon refers to the tendency to overvalue an immediate reward over a delayed reward, even in cases where the delayed reward is greater (Patros et al., 2016). All individuals engage in reward discounting to a certain extent, and studies have shown that the ability to tolerate delayed rewards increases linearly as we age (Steinberg et al., 2009). However, across development individuals with ADHD engage in reward discounting more than individuals without ADHD, and experience significant impairment as a result (Luman, van Meel, Oosterlaan, & Geurts, 2012). The dynamic developmental theory of ADHD posits that reward discounting is in fact central to ADHD symptoms and impairment (Sagvolden, Johansen, Aase, & Russell, 2005). The strong tendency to prefer smaller immediate rewards over larger delayed rewards may contribute motivational difficulties as well as impulsivity in ADHD. Individuals with elevated reward discounting may struggle to sustain the motivation to complete challenging or boring tasks in the face of more appealing alternatives, and may struggle to resist impulsive choices that come with immediate rewards while dismissing negative long term consequences (Luman, Oosterlaan, & Sergeant, 2005). Clinically, behavioral interventions for ADHD aim to accommodate these difficulties by using frequent external rewards and immediate consequences to shape behavior (Pfiffner & Haack, 2014). However, studies suggest individuals with ADHD continue to struggle in real-world situations when predictable reward and consequence schedules are not present (Alsop et al., 2016).

Executive Functioning Deficits ADHD symptoms and impairments are highly correlated with deficits in executive functioning (EF; Barkley, 2001). EFs are coordinated mental processes that direct an individual’s thoughts, actions, and emotions to accomplish a goal in a flexible manner. 421

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These mental processes can be broken down into seven skill sets: self-awareness, inhibition, nonverbal working memory, verbal working memory, emotion self-regulation, self-motivation, and planning/ problem solving (Barkley, 2012). Many clinical researchers and theorists consider deficits in EF to be central to the symptoms and impairments observed in individuals with ADHD (Sonuga-Barke, Sergeant, Nigg, & Willcutt, 2008). Debate exists however, on how to best measure EFs and understand their relationship to the real-world impairment observed in ADHD. On neuropsychological tests of EFs there is a great deal of variability in the performance across individuals with ADHD, and a unifying EF deficit has not been identified (Barkley, 2015; Nigg, Willcutt, Doyle, & Sonuga-Barke, 2005). In contrast, on rating scales measuring EF in daily life, ADHD symptoms in children and adults are highly correlated with EF. Barkley (2015) notes that these correlations are so high that they suggest ratings of ADHD and EF are essentially assessing the same construct. Therefore, despite inconclusive evidence of a direct, consistent association between ADHD and EF on laboratory measures, the strong association between ADHD and ratings of real word EF provides evidence for ADHD as a disorder of EF.

Emotion Dysregulation Once included in diagnostic criteria and now classified only as an associated feature, clinical scientists are again focusing on the role of emotion dysregulation in ADHD (Shaw, Stringaris, Nigg, & Leibenluft, 2014). Emotion regulation is generally defined as an individual’s ability to modify an emotional state to promote adaptive, goal-oriented behaviors (Shaw et al., 2014). Within the context of ADHD, the clinical expression of emotion dysregulation is typically conceptualized as excessive irritability and mood lability (Ambrosini, Bennett, & Elia, 2013). In a recent meta-analysis, Shaw and colleagues (2014) confirmed that emotion dysregulation is prevalent in ADHD throughout the lifespan and is a major contributor to ADHD-related impairment. Their metaanalysis suggests that the emotion dysregulation observed in ADHD may stem from deficits in appropriately orienting toward and allocating attention to emotional stimuli. Although the association 422

between emotion dysregulation and ADHD is not yet well understood, three separate theoretical models have been proposed (Shaw et al., 2014): (a) emotion regulation and ADHD symptoms represent correlated but distinct dimensions (Nigg & Casey, 2005), (b) emotion dysregulation is a core feature of ADHD (Barkley & Murphy, 2010a), and (c) the combination of emotion dysregulation and ADHD represents a distinct entity (Biederman et al., 2012; Surman et al., 2011).

Impairments and Adverse Outcomes Academic.  ADHD is associated with academic impairment throughout schooling (American Psychiatric Association, 2013). Many of the core symptoms of ADHD, such as difficulty completing tasks or assignments, making careless mistakes, difficulty paying attention, and difficulty organizing tasks and materials, directly interfere with learning and educational attainment (Vile Junod, DuPaul, Jitendra, Volpe, & Cleary, 2006). Research shows that elementary school children with ADHD are more likely to have a history of academic underachievement, low productivity, homework challenges, learning disorders, and grade retention relative to their peers without ADHD (DuPaul, Morgan, Farkas, Hillemeier, & Maczuga, 2016). In middle school, academic underperformance and homework struggles often become even more pronounced as academic demands increase and levels of parent and teacher support decrease (Evans et al., 2016). By high school, if ADHD is untreated and academic supports are not put into place, the disorder’s effect on academic outcomes becomes stark. Relative to high school students without the disorder, those with ADHD have significantly poorer school attendance, lower grade point averages, higher rates of course failure, and higher rates of school dropout (Kent et al., 2011). The emerging literature on college students with ADHD shows a similar pattern (Gormley, DuPaul, Weyandt, & Anastopoulos, 2016). Social.  Social impairment is common among individuals with ADHD across the lifespan. Children and adolescents with ADHD are rated lower on social preference, have fewer reciprocated

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friendships and are more often disliked or ignored by their peers (Hoza et al., 2005). Peer rejection and peer neglect limit a child’s opportunity to practice social skills, and may exacerbate social problems (Hoza, 2007). The social problems associated with ADHD have primarily been attributed to the symptoms of inattention and impulsivity, as well as comorbid conditions (Mikami, Ransone, & Calhoun, 2011; Solanto, Pope-Boyd, Tryon, & Stepak, 2009). Social information processing deficits, EF deficits, and deficits in pragmatic skills also contribute to social difficulties (Staikova, Gomes, Tartter, McCabe, & Halperin, 2013). Social impairments continue into adulthood, where they contribute to frequent job loss, lower rates of marital satisfaction, lower levels of social support, increased rates of divorce, and greater loneliness (Michielsen et al., 2015; Philipsen et al., 2009). Occupational and financial.  Problems in occupational functioning are pervasive among adults with ADHD. Clinic-referred adults with ADHD are more likely to have been unemployed, fired from a job, have impulsively quit a job, and applied for disability (Barkley & Murphy, 2010b). Adults with ADHD also report more work-related anxiety, external conflict regarding their careers, and higher rates of workplace accidents (de Graaf et al., 2008; Painter, Prevatt, & Welles, 2008). In 2008, for example, the estimated increased expenditure per ADHD employee was $4,336 annually, excluding accident-related costs (de Graaf et al., 2008). The occupational problems associated with ADHD likely contribute to the financial challenges experienced by those with the disorder, including higher rates of financial stress, lower earnings, greater dependence on state welfare systems, and greater financial dependence on family members (Altszuler et al., 2016; Biederman & Faraone, 2006; Brook, Brook, Zhang, Seltzer, & Finch, 2013). Driving.  ADHD is associated with driving-related impairment and negative outcomes, with the greatest risk found among those with ADHD and comorbid ODD or CD (Vaa, 2014). Studies suggest that individuals with ADHD have higher rates of traffic accidents, are found to be at fault for more accidents, and have more bodily injuries from accidents

( Jerome, Segal, & Habinski, 2006). They also report higher rates of traffic citations and suspended or revoked licenses (Vaa, 2014). During driving simulator tasks, individuals with ADHD demonstrate slower and more variable reaction times, more steering variability, and poorer steering control (Barkley, 2004; Jerome et al., 2006), and greater impairment when under the influence of alcohol than adults without ADHD (Weafer, Camarillo, Fillmore, Milich, & Marczinski, 2008). Results are not uniform across all retrospective and simulator studies however, and some investigators have called for more rigorous research in this area (Vaa, 2014). Health outcomes.  ADHD is associated with several health risk factors and negative health outcomes across the lifespan. Among children with ADHD, the greatest physical health risk appears to be increased susceptibility to accident and injury, which is two to four times higher for children with ADHD than for children without the disorder (Lange et al., 2016). Parent-reported injuries attributed to a child’s carelessness, impulsivity, or poor judgment may be up to seven times higher for children with ADHD than for children without ADHD (Lahey et al., 2004). In adults, ADHD is associated with a variety of health risk factors including poorer lipid profiles and vital signs, and a higher body mass index (BMI; Spencer, Faraone, Tarko, McDermott, & Biederman, 2014). In addition, adults with ADHD have higher rates of asthma and musculoskeletal complaints relative to adults without ADHD (Spencer et al., 2014). Unhealthy lifestyle behaviors are also found at higher rates, including smoking, alcohol and drug use, poor eating habits, and sleep problems (Barkley, Murphy, & Fischer, 2010; Semeijn et al., 2013). It is therefore not surprising that adults with ADHD have higher rates of health care utilization and costs than adults without ADHD (Doshi et al., 2012). Obesity is an increasingly identified problem among individuals with ADHD. A recent metaanalysis found that the pooled prevalence of obesity was increased by about 70% in adults with ADHD and 40% in children with ADHD (Cortese, MoreiraMaia, et al., 2016). The authors also found a significant association between having a BMI classified as overweight (but not obese) and ADHD. In this same 423

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study, individuals who were pharmacologically treated for ADHD had rates of obesity that were about 40% lower than those with untreated ADHD. Sleep problems and disorders are also associated with ADHD in childhood and adulthood (Cortese et al., 2013). As many as 70% of children with ADHD display mild to severe sleep problems (Sung, Hiscock, Sciberras, & Efron, 2008), including bedtime resistance, delayed sleep-onset, frequent awakenings, difficulty waking-up in the morning, daytime sleepiness, and breathing problems during sleep (Cortese, Konofal, Yateman, Mouren, & Lecendreux, 2006). Rates of diagnosed sleep disorders are also significantly higher in children and adults with ADHD. These include delayed sleepphase syndrome, partial arousal parasomnias, sleep disordered breathing, obstructive sleep apnea, restless leg syndrome, and periodic limb movement disorder (Vélez-Galarraga, Guillén-Grima, CrespoEguílaz, & Sánchez-Carpintero, 2016).

Differential Diagnosis The core symptoms of inattention, hyperactivity, and impulsivity, are not specific to ADHD and are found in a variety of mental health and physical conditions. Anxiety, stress, and mood disorders are associated with inattention across the lifespan, and with hyperactivity and impulsivity, as well as inattention, during childhood (American Psychiatric Association, 2013). Learning disorders often include difficulty concentrating at school and poor academic productivity as part of their clinical picture (American Psychiatric Association, 2013). In adolescents and adults, substance use can impair EF, resulting in symptoms and impairments like those observed in ADHD (R. Z. Goldstein & Volkow, 2011). Lastly, in older adults, mild cognitive impairment and normal aging can cause or exacerbate EF weaknesses, making differential diagnosis particularly challenging (Tucker-Drob, Johnson, & Jones, 2009). Physical health and lifestyle factors also play a significant role in the presentation of ADHD-like symptoms and impairments in children and adults. A wide range of health conditions (e.g., seizure disorders, autoimmune disorders, thyroid conditions, concussions) can result in attention problems or impaired impulse control (Taras & Potts-Datema, 2005). 424

Chronic insufficient sleep caused by lifestyle or sleep disorders can also cause difficulty with concentration and activity level throughout the day (  J. A. Owens, 2009). Given that many of these physical health and mental health conditions occur frequently in the population, and can also co-occur with ADHD, differential diagnosis can be challenging and necessitates a thorough detailed assessment of symptoms and health.

Co-Occurring Disorders ADHD is highly comorbid with other mental health conditions. In community samples, approximately 44% of children with ADHD have at least one additional disorder, and 43% have at least two additional disorders (Willcutt et al., 2012). Disruptive behavior disorders.  Disruptive behavior disorders are the most common co-occurring disorders among children and adolescents with ADHD. It is estimated that ODD co-occurs with ADHD in about 50% of school-age children with ADHD (Connor, Steeber, & McBurnett, 2010). ODD is characterized by a pattern of negativistic, hostile, defiant, and disobedient behavior toward authority figures, as well as interpersonal sensitivity and high emotional reactivity (American Psychiatric Association, 2013). About 66% of children diagnosed with ADHD and ODD go on to develop CD (Burke, Loeber, & Birmaher, 2002; Loeber, Burke, Lahey, Winters, & Zera, 2000). The prevalence of disruptive behavior disorders is higher among children and adolescents with the combined presentation of ADHD than the inattentive presentation (Connor et al., 2010). The presence of comorbid ODD or CD symptoms in children and adolescents with ADHD is associated with poorer social, academic, and psychiatric outcomes (Pliszka, 2007). Having ODD and/or CD during childhood or adolescence significantly increases the risk of future substance use problems among individuals with ADHD (Molina & Pelham, 2014). Learning disorders.  Learning disorders (LDs) co-occur with ADHD in about 20% to 40% of ADHD cases (Seidman, Biederman, Monuteaux, Doyle, & Faraone, 2001). This rate is significantly higher than the 10% of individuals with LDs in the general population (Butterworth & Kovas, 2013). Studies

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suggest a strong familial effect for the association between LDs and ADHD (Del’Homme, Kim, Loo, Yang, & Smalley, 2007; Pennington et al., 2009), particularly reading and writing disorders (Del’Homme et al., 2007). Reading disorders (RD) have been studied most frequently among children with ADHD, with relatively little attention given to math disorders or other LDs. The strong association between ADHD and RD may be due in part to shared genetic risk factors, particularly among RD and inattention (Gayán et al., 2005; Rosenberg, Pennington, Willcutt, & Olson, 2012). Environmental factors also play an important role however, with gene × environment interaction studies showing significant main effects of parent education and socioeconomic status on the development of RDs in children with ADHD (Pennington et al., 2009; Rosenberg et al., 2012). Additional studies are needed to better understand the development and maintenance of LDs among children with ADHD. Anxiety and depression.  Approximately 25% to 50% of children and adolescents with ADHD experience a co-occurring anxiety disorder, including simple phobias, obsessive-compulsive disorder, separation anxiety, social anxiety, and generalized anxiety (Jarrett & Ollendick, 2008). Anxiety disorders are also prevalent among adults with ADHD, occurring in about 38% of adult cases (Simon, Czobor, & Bitter, 2013). Anxiety appears to adversely affect functioning among those with ADHD, and is associated with greater attentional and EF difficulties (Bowen, Chavira, Bailey, Stein, & Stein, 2008; Newcorn et al., 2001). The co-occurrence of two or more anxiety disorders is associated with the greatest level of impairment (Sciberras et al., 2014). Depression also frequently occurs in children and adolescents with ADHD, with rates ranging from 12% to 50% (Angold, Costello, & Erkanli, 1999; Daviss, 2008). Among adults with ADHD, depression is observed in 16% to 31% of cases (Knouse, Zvorsky, & Safren, 2013). The comorbidity of ADHD and depression appears to be associated with greater functional impairment and illness severity than is observed in either disorder alone (Miller, Nigg, & Faraone, 2007; Rohde, Clarke, Lewinsohn, Seeley, & Kaufman, 2001). In addition,

individuals with ADHD are more likely to have an earlier age of depression onset, experience longer depressive episodes, have higher rates of reoccurrence, increased suicidality, and higher health care costs than individuals without ADHD who experience depression (Biederman et al., 2008; Fishman, Stang, & Hogue, 2007; Knouse et al., 2013). Tic disorders.  Tic disorders occur in children with ADHD with roughly the same frequency as they occur in the general population, affecting approximately 20% of children and 12% of adults (Peterson, Pine, Cohen, & Brook, 2001; Spencer et al., 2001). Tourette’s Disorder (TD), the most severe chronic tic disorder, affects less than 1% of the general population and less than 1% of those with ADHD (Kurlan et al., 2002). However, 50% or more of those diagnosed with TD also meet diagnostic criteria for ADHD (Kurlan et al., 2002). Researchers have suggested that ADHD and TD share core inhibitory deficits that manifest as impulsive behavior in ADHD and tics (sudden movements or sounds prompted by unpleasant sensations) in TD (Bloch, Panza, Landeros-Weisenberger, & Leckman, 2009). Recent studies have also identified shared genetic factors in ADHD and TD as well as shared environmental influences (Hirschtritt et al., 2015; Pinto et al., 2016). Autism spectrum disorders.  Autism spectrum disorders (ASD) are characterized by impairments in communication, social reciprocity, and stereotypic and/or repetitive behaviors (American Psychiatric Association, 2013). Most children with ASD demonstrate significant symptoms of inattention, hyperactivity, and impulsivity (Antshel, Zhang-James, & Faraone, 2013), and it is estimated that up to 59% of individuals with ASD also meet diagnostic criteria for ADHD (S. Goldstein & Schwebach, 2004). Conversely, children with ADHD display higher levels of ASD traits than typically developing children (Mayes, Calhoun, Mayes, & Molitoris, 2012). These traits include deficits in reciprocal social interactions and language pragmatics, restricted and repetitive interests, and reduced responsiveness to social reinforcers (for a review see Antshel et al., 2013). Although there is symptom overlap, the clinical presentation of social impairments differs between the two disorders. Overall, social interactions are 425

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significantly more impaired in children with ASD relative to those with ADHD (Mulligan et al., 2009), and although children with ADHD are often less responsive to social motivators and cues than their typically developing peers, they are still significantly more socially motivated than those with ASD (Geurts, Luman, & van Meel, 2008). In addition, children with ADHD do not experience pervasive impairments in nonverbal communication (e.g., eye contact, use of gestures to accompany conversation) and the unusual sensory interests or preoccupations that are central to ASD (Mulligan et al., 2009). When ADHD and ASD co-occur however, internal­ izing, externalizing, and social problems and adaptive functioning are more severe than when either disorder occurs alone (Holtmann, Bolte, & Poustka, 2007; Yerys et al., 2009). Eating disorders.  Emerging research suggests comorbidity between ADHD and eating disorders (EDs), indicating that girls with ADHD are over three times more likely to develop a clinical or subclinical ED than girls without ADHD (Biederman et al., 2007). EDs can present at a clinical level in one of four DSM–5 categories (anorexia nervosa, bulimia nervosa, binge-eating disorder, or eating disorder not otherwise specified) or at a subclinical level (American Psychiatric Association, 2013). Studies suggest that ADHD may be more strongly associated with binging and/or purging behaviors than restrictive eating behaviors. In a clinical sample, adolescent girls with ADHD had a significantly higher risk of developing bulimia nervosa than other eating disorders (Biederman et al., 2007). In a nationally representative sample of adolescents, ADHD symptoms predicted binging and/or purging behaviors but not restrictive behaviors (Bleck & DeBate, 2013). In clinical and nonclinical samples, ADHD symptoms have been found to be associated with binge eating disorder tendencies (Cortese, Bernardina, & Mouren, 2007; Steadman & Knouse, 2016). However, results from a recent epidemiological study suggest that ADHD is associated with clinical-level binge and/or purge eating behaviors and clinically significant restrictive eating behaviors (Bleck, DeBate, & Olivardia, 2015). Although additional research is needed to clarify the relationship 426

between ADHD and specific EDs, several theories have emerged regarding factors underlying the association between ADHD and binge and/or purge behaviors. Davis and colleagues (2006) posit that the planning and self-monitoring deficits associated with ADHD lead to binge eating, whereas others suggest that impulsivity is driving the association (Rosval et al., 2006; Steadman & Knouse, 2016), or that binge eating is a compensatory mechanism that helps control frustration stemming from ADHDassociated attentional and organizational difficulties (Cortese et al., 2007). Personality disorders.  Children diagnosed with ADHD are at increased risk for the development of a personality disorder beginning in late adolescence (Matthies & Philipsen, 2014). The most prominent risks are associated with the development of borderline personality disorder (BPD) in women, and antisocial personality disorder (ASPD) in men. The high rate of childhood ADHD (59.5%) among adults with BPD and the considerable overlap in clinical features of impulsivity, emotion dysregulation, and poor social relationships, have raised questions about whether ADHD symptoms may be a precursor to BPD or whether the two disorders share common pathological mechanisms (Matthies & Philipsen, 2014). Although relatively little is known about the association between ADHD and ASPD among men, studies suggest that impulsivity, callous–unemotional traits, and the presence of co-occurring CD are important factors in the development of ASPD in adulthood (LeMoine, Romirowsky, Woods, & Chronis-Tuscano, 2015). Overall, multiple biological factors, temperament characteristics, and unfavorable environmental conditions appear to interact across development to create the association between ADHD and personality disorders (Matthies & Philipsen, 2016). However, additional research is needed.

Prevalence and Epidemiology ADHD is among the most common childhood disorders, with an estimated worldwide prevalence rate of 5.3% (Polanczyk, Willcutt, Salum, Kieling, &

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Rohde, 2014). In the United States, prevalence estimates generally range from 5% to 7% of children and adolescents (Willcutt et al., 2012) and 4.4% of adults (Kessler et al., 2006), and rates of diagnosis are on the rise (Visser et al., 2014). Variability in the measurement methodology across studies make it challenging to discern whether this increase is primarily due to a greater number of individuals meeting true diagnostic criteria for ADHD, or whether the diagnosis is simply being applied more frequently to those presenting with ADHD-like symptoms (Polanczyk et al., 2014). Some researchers propose that the rise in ADHD diagnoses is driven by changes in special education policy, increased awareness about ADHD, improved access to health care, and increased rates of noncredible ADHD presentations among older adolescents and young adults seeking access to stimulant medication for academic, social, and/or financial gain. (Collins & Cleary, 2016; Hinshaw & Scheffler, 2014; Musso & Gouvier, 2014; Schneider & Eisenberg, 2006). However, there is some evidence to suggest that when ADHD is objectively measured with standardized diagnostic criteria, prevalence rates have remained constant (Polanczyk et al., 2014).

Gender Differences ADHD has traditionally been considered a disorder that disproportionally affects boys. In community samples the ratios of boys to girls meeting ADHD diagnostic criteria is 2:1, and in clinic referred samples the ratio is as high as 9:1 (American Psychiatric Association, 2013; Robison, Skaer, Sclar, & Galin, 2002). In adulthood this gap almost disappears (Barkley et al., 2010; Kessler et al., 2006). Studies examining patterns of gender differences in ADHD have often provided mixed results, often because of differences in sample composition and methods of diagnostic classification (Cortese, Faraone, Bernardi, Wang, & Blanco, 2016). Across studies however, results indicate that boys and girls with ADHD experience persistent impairment (Hinshaw et al., 2012; Uchida, Spencer, Faraone, & Biederman, 2015). A handful of consistent patterns of subtype differences and comorbidities have emerged between boys and girls with ADHD. Specifically, boys are more likely to present with ADHD-HI or ADHD-C,

which are comprised of symptoms that are more disruptive to parents and teachers (American Psychiatric Association, 2013; Kessler et al., 2006). In contrast, girls are more likely to present with the less-overtly disruptive predominantly inattentive presentation (Nussbaum, 2012). Gender differences in patterns of ADHD comorbidity and mental health risks generally mirror those in the general population. Across development boys are more likely than girls to experience co-occurring externalizing disorders (Cortese, Faraone, et al., 2016), and girls are more likely to struggle with internalizing disorders, low self-esteem, poor coping skills, and self-harm behaviors (Cortese, Faraone, et al., 2016; Rucklidge & Tannock, 2001). The higher rate of disruptive symptoms (i.e., impulsively and hyperactivity) and externalizing disorders observed in boys likely contributes to more boys receiving an ADHD diagnosis in childhood than girls with similar levels of academic and social impairment (Cortese, Faraone, et al., 2016).

Socioeconomic Factors Numerous studies have found associations between higher rates of ADHD diagnoses and socioeconomic status (SES), although across studies patterns of correlates have been mixed. A recent meta-analysis found evidence to support the assertion that socioeconomic disadvantage is indeed associated with increased prevalence of childhood ADHD (Russell, Ford, Williams, & Russell, 2016). Specifically, results indicate that a child in a low SES family is on average 2.21 times more likely to have ADHD than their high SES peers. Looking at specific factors within the SES composite, children from families whose parents have low levels of educational attainment are 1.91 times more likely to have ADHD or elevated ADHD symptoms than their peers with highly educated parents. Similarly, children in single-parent households are 1.85 times more likely to have ADHD than children in two-parent households. Several putative mechanisms underlying the association between socioeconomic disadvantage and increased rates of ADHD have been proposed. These include higher rates parental mental health problems, poor maternal health behaviors during 427

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pregnancy (Russell et al., 2016), negative parenting behaviors (Kiernan & Huerta, 2008), and parental and childhood exposure to stressful and traumatic events (Webb, 2013). A bidirectional heritability theory has also been proposed, where individuals with mental health difficulties like ADHD are at greater risk for socioeconomic disadvantage (Reiss, 2013). Therefore, children are raised in disadvantaged environments which increase their own risk for psychological difficulties, particularly when combined with preexisting genetic risk factors.

Race and Ethnicity The prevalence of ADHD within the United States has been studied in several recent population-based longitudinal studies with mixed results (­ Morgan, Staff, Hillemeier, Farkas, & Maczuga, 2013; ­Pastor & Reuben, 2005; Visser et al., 2014). These studies varied in their methodology, but all relied on parent report. A review of these studies conducted by Siegel and colleagues (2016) showed that Hispanic children were significantly less likely to have received an ADHD diagnosis than White or Black children. Black children were less likely to receive a diagnosis than White children in one study, but rates were comparable among the two racial groups in other samples. Multiple mechanisms for the disparities have been hypothesized, including (a) limited access to affordable health care (Coker et al., 2009), (b) non-English language spoken in the home (Morgan et al., 2013), (c) negative views and stigma surrounding mental health issues (Hervey-Jumper, Douyon, Falcone, & Franco, 2007), and (d) cultural differences in symptom norms and reporting (Gerdes, Lawton, Haack, & Hurtado, 2013). A recent analysis of data collected through the Early Childhood Longitudinal Study-Birth Cohort study supports the non-English language hypothesis (Morgan et al., 2013). Although Hispanic children in this sample initially appeared to be underdiagnosed with ADHD relative to White children, the disparity became statistically insignificant once the researchers controlled for a non-English language being spoken in the home. The variability and disparity in ADHD diagnosis among racial and ethnic groups in the United 428

States is likely to change over time. A recent analysis of trends in parent-reported ADHD prevalence between 2003 and 2011 found that although rates of parent-reported ADHD diagnosis rose for all racial and ethnic groups during this time (by 43% overall), the largest increases were seen among Hispanics (83.3%) and non-English speakers (107.1%; Collins & Cleary, 2016). This finding may reflect increasing access to affordable mental health care more broadly and Spanish mental health resources more specifically, as well as greater cultural acceptance of ADHD within the Hispanic community. However, additional research is needed to better understand the complex and nuanced factors underlying racial and ethnic differences in rates of ADHD diagnosis. Theories of Attention-Deficit/ Hyperactivity Disorder Core Psychopathology Theories of ADHD core psychopathology have largely focused on neuropsychological factors and EF deficits that influence the behavior observed in children and adolescents with ADHD. Although older models focused on single cognitive deficits that may be at the core of ADHD symptomatology, more recent multiple-deficit and multiple-pathway models have taken a broader approach. Research suggests that these comprehensive, multiplecomponent theories better capture the heterogeneity that is inherent in ADHD (Willcutt, Sonuga-Barke, Nigg, & Sergeant, 2008). Competing single-component theories of ADHD can be grouped into models that emphasize cognitive control (“top-down” theories) and those that emphasize motivational or energetic factors (“bottom-up” theories). The most influential contemporary top-down theories focus on response inhibition and response variability, whereas the most influential bottom-up theories focus on delay aversion and motivation (Willcutt, 2015). Barkley’s (1997, 2001) theory of response inhibition is perhaps the most studied top-down theory. Barkley theorizes that inhibition is primary to other EFs in that a response must be inhibited long enough to allow other EFs to occur. Inhibition,

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according to Barkley, encompasses the processes of response inhibition (inhibiting a prepotent response or stopping an ongoing response) and interference control. The inhibitory deficit found in individuals with ADHD results in impulsive behavior and difficulty discontinuing actions that others are easily able to stop. Tasks without preconditioned responses, as well as those requiring resistance to temptation or delayed gratification are particularly reliant on the process of response inhibition. Barkley’s most recent iteration of his theory also identifies deficits in metacognition, specifically selfawareness and working memory, and deficits in selfdirected attention as core deficits in ADHD that lead to impulsive actions and variable patterns of attention and behavior (Barkley, 2015). A comprehensive bottom-up theory, emphasizing the role of motivational control and reward sensitivity, has been presented by Sagvolden and colleagues (2005). Their dynamic developmental behavioral theory is based on the hypothesis that altered dopaminergic function plays a pivotal role in ADHD symptomatology by failing to modulate nondopaminergic signal transmission appropriately. Hypofunctioning across four dopamine branches produces altered behavioral reinforcement and behavioral inhibition and delay in aversion, EF, and motor functions. Sagvolden et al.’s theory describes how these core neurocognitive deficits interact within an individual with the external environment over time to influence the developmental course of ADHD. Recent theories constitute neurobiologically based multiple pathway models that aim to capture the heterogeneity of ADHD. Castellanos and Tannock (2002) presented an influential multiple pathway model that sparked research into the response variability observed in ADHD and the neurocircuitry underlying ADHD symptoms. Their model integrated three endophenotypes grounded in neuroscience: (a) a specific abnormality in reward-related circuitry that leads to delay aversion, (b) deficits in temporal processing that result in within-individual variability in response and attention, and (c) deficits in working memory. Nigg and Casey (2005) aimed to integrate the science of emotion regulation with the cognitive neuroscience of attention. Their theory focuses on three circuits involved in cognitive

control, learning, and emotion regulation, and the coordinated way in which they operate to produce ADHD symptoms. Sonuga-Barke (2005) sought to integrate the top-down (inhibition) and bottom-up (delay aversion and motivation) aspects into a parallel model. He suggested that inattention is a result of a breakdown of EF and response inhibition, and that impulsivity is caused by a weakness in motivational responding. The neural processes involved in EF and motivational responding occur simultaneously, resulting in the inconsistent and impulsive behavior observed in individuals with ADHD. Etiological Influences

Genetic Influences Numerous family, adoption, and twin studies indicate that ADHD is highly familial, with first degree relatives of an individual having a two-to-fourfold increased risk of having ADHD themselves (Mick & Faraone, 2008). Analyses of twin and adoption studies indicate that much of this risk is due to shared genetic influences rather than environmental factors (Mick & Faraone, 2008). In fact, the heritability coefficient of ADHD is estimated to be as high as .76, comparable with the heritability coefficient of height (Derks et al., 2008). Given the high heritability factor for ADHD, researchers have pursued molecular genetic studies to identify candidate genes, genetic markers, and genetic pathways underlying the development of the disorder. To date at least six candidate genes reliably associated with ADHD have been identified, including catecholaminergic (DRD4), dopamine transporter (DAT1), dopamine receptor (DRD5, DRD2, DRD3, DRD1), serotonin transporter (5-HTTLPR), and serotonin receptor (HTR1B) genes (Z. Li, Chang, Zhang, Gao, & Wang, 2014). Although several candidate genes have been identified, the search for specific genetic markers and pathways has yielded few results. Consistent with other disorders, the genetic underpinnings of ADHD are complex with many genes exerting small individual effects, and epigenetic factors making significant contributions to the development and presentation of the disorder (Martin, O’Donovan, Thapar, Langley, & Williams, 2015; Sánchez-Mora et al., 2015). 429

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Environmental Factors A host of environmental factors have been found to be associated with ADHD. However, causal associations have been scarce as controlled randomized trials of suspected environmental risk factors and their manipulation are not possible. In addition, most putative environmental factors (e.g., exposure to tobacco in utero) may be attributable to other factors, such as maternal ADHD, that shape an individual’s exposure to these risks. Nonetheless, a growing body of evidence suggests that certain highly correlated environmental risk factors may indeed influence the development or course of ADHD.

Exposure to Toxins Exposure to environmental toxins has been studied as a possible cause of ADHD or a contributing factor in the development of the disorder. The three toxins that have been the greatest focus of this line of research are lead, mercury, and polychlorinated biphenyls (PCBs) (Banerjee, Middleton, & Faraone, 2007). Multiple large scale studies have shown that lead contamination can result in increased distractibility, hyperactivity, and decreased intellectual functioning (Daneshparvar et al., 2016; Needleman, 1982). The symptoms of inattention and hyperactivity associated with lead contamination are like those observed in children with ADHD. However, most children with ADHD do not test positive for lead contamination, and many children with high levels of lead exposure do not meet diagnostic criteria for ADHD. Mercury is a neurodevelopmental toxicant most commonly encountered through individual or maternal consumption of contaminated fish. Studies have shown that prenatal exposure to high levels of mercury adversely affect cognitive ability, language development, visual-spatial skills, gross motor skills, memory, and attention (Sanders, Claus Henn, & Wright, 2015) and may be associated with ADHD symptoms. PCBs represent a class of compounds was once widely used in the manufacturing of flame retardants, sealants, pesticides, carbonless copy paper, among others. Although no longer produced, their environmental impact remains significant because of the longevity of the products manufactured with PCBs. Human exposure to PCBs most often occurs through food consumption. Prenatal 430

PCB exposure has been linked to symptoms that overlap with ADHD, including poorer concentration, decreased performance accuracy, and slower processing speed (Caspersen et al., 2016).

Prenatal Risk Factors Exposure to toxins in utero represents one prenatal risk for ADHD. However, there are several maternal lifestyle factors that also increase the risk of a child developing ADHD. Maternal consumption of nicotine and alcohol during pregnancy, as well as psychological stress, have all been studied as factors contributing to the development of ADHD in offspring (Markussen Linnet et al., 2006). Maternal illicit drug use during pregnancy has been explored as well. However, it so frequently occurs in conjunction with the use of alcohol and nicotine, as well as maternal stress, that it is extremely difficult for researchers to parse out the unique contribution of prenatal illicit drug exposure to the development of ADHD. Numerous studies have linked prenatal nicotine exposure to the development of ADHD (Knopik et al., 2016). Establishing a causal relationship is challenging however, given that rates of cigarette smoking are high among individuals with ADHD, and it is possible that a genetic link is a more potent factor than maternal smoking per se. In addition, studies have rarely controlled for postnatal exposure to second-hand smoke, which may also contribute to the development of ADHD in exposed infants (Knopik et al., 2016). Alcohol is widely recognized as a teratogenic agent causing impaired mental functioning, including a spectrum of fetal alcohol disorders, which incorporate core symptoms of ADHD (Khoury & Milligan, 2016). Several studies have identified an association between moderate or heavy maternal alcohol consumption and the development of ADHD symptoms (Han et al., 2015; Knopik et al., 2005). However, results are mixed, with at least one large-scale longitudinal study not finding an association (Hill, Lowers, LockeWellman, & Shen, 2000). Maternal stress has been associated with higher levels of attention and behavior problems in children (Grizenko, Shayan, Polotskaia, Ter-Stepanian, & Joober, 2008). However, maternal stress is often accompanied by a host of additional environmental

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risk factors including substance use and maternal psychopathology. In a well-controlled study, Grizenko et al. (2012) found that children with ADHD were more likely to have been exposed to maternal stress in utero than their siblings, even when the mother’s patterns of cigarette smoking and alcohol use remained constant across all pregnancies.

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Premature Birth and Low Birthweight Premature birth and low birthweight have been shown to increase the risk of ADHD (AarnoudseMoens, Weisglas-Kuperus, van Goudoever, & Oosterlaan, 2009). Most studies of premature birth have focused on very preterm births (below 32 weeks gestation), rather than moderate (32–33 weeks) and late (34–36 weeks) preterm births ­ (de Jong, Verhoeven, & van Baar, 2012). A recent population-based study examined the risk of developing ADHD by each week of gestational age (Sucksdorff et al., 2015). After controlling for maternal factors associated with preterm birth and ADHD (i.e., smoking, age, SES) findings showed a dose effect, with each declining week of gestational age (counting back from week 40) increasing the risk of ADHD. This same study examined ADHD risk associated with weight for gestational age. Infants born small for gestational age (SD  –2 to –1) as well as infants born large for gestational age (SD  2) had a significantly greater risk of developing ADHD. These findings are consistent with population-based (de Jong et al., 2012) and twin studies (Pettersson et al., 2015). Results are not consistent across all studies however, and additional research is needed to clarify the associations between preterm birth, birth weight, and ADHD.

Family and Social Influences on Developmental Trajectory Research strongly supports a genetic link for ADHD, and studies suggest that exposure to certain environmental toxins confers additional risk for the development of the disorder. In addition, family and other social influences exert a significant effect on the trajectory of ADHD symptomology and the development of comorbid conditions (Ullsperger, Nigg, & Nikolas, 2016). Parent–child interactions in families of children with ADHD are often characterized

by high levels of parenting stress along with lower levels of positive parenting, and higher levels of harsh and inconsistent discipline (Haack, Villodas, McBurnett, Hinshaw, & Pfiffner, 2016; Johnston & Mash, 2001). Research indicates that early parenting impacts the severity of ADHD symptoms (Hawes, Dadds, Frost, & Russell, 2013). However, studies also indicate a bidirectional relationship, with a child’s challenging ADHD-related behaviors eliciting inconsistent and ineffective parenting (Johnston & Mash, 2001; Kaiser, McBurnett, & Pfiffner, 2011). These negative parenting strategies and the weakened parent–child relationship interact with the child’s existing ADHD symptoms to set the stage for increases in ADHD symptom severity, the development of comorbid disorders, and negative academic and social outcomes (Humphreys et al., 2013; Musser, Karalunas, Dieckmann, Peris, & Nigg, 2016; Pfiffner & McBurnett, 2006; Pfiffner, McBurnett, Rathouz, & Judice, 2005). Parenting behavior has been the most widely studied social factor influencing the developmental trajectory of ADHD, but quality of peer relationships (Humphreys et al., 2013) as well as stressful life events and exposure to parental psychopathology (Biederman, Petty, Clarke, Lomedico, & Faraone, 2011) have all emerged as important factors in the maintenance and worsening of ADHD symptoms, impairments, and co-occurring conditions. Treatment Approaches for Attention-Deficit/Hyperactivity Disorder The chronic, impairing nature of ADHD results in the need for treatment across the lifespan. Empirically supported treatments include pharmacotherapy and psychosocial interventions, which can be provided as individual treatments or in combination. Professional practice guidelines often recommend multimodal approaches (e.g., American Academy of Pediatrics, 2011); however, specific recommendations depend on age, symptom severity, and the presence of co-occurring conditions. Additional discussion of treatments for adolescents with ADHD is provided elsewhere (see Chapter 21, this volume). 431

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Pharmacological Treatment There is a large body of literature documenting the efficacy of pharmacological treatments for improving the core symptoms of ADHD (Kaplan & Newcorn, 2011). Stimulant medications (amphetamine and methylphenidate) are considered the most effective treatment for ADHD in children (ages 6 and older), adolescents, and adults. Stimulants reduce the hyperactivity and impulsivity associated with ADHD, and improve on-task behavior, academic productivity, and performance on academic testing (Faraone & Buitelaar, 2010). ADHD symptoms diminish shortly after medication is administered, and improvement continues for as long as the medication is in effect (3–6 hours for immediate-release formulas and up to 10 hours for controlled-release formulations; Solomon, Volkow, & Swanson, 2013). Nonstimulant ADHD medications may be prescribed when an individual has an inadequate response to stimulant mediation, experiences significant side effects, or when there are concerns about simulant abuse potential. Nonstimulant medications (e.g., atomoxetine, guanfacine, clonidine) require up to 12 weeks for a full treatment effect to emerge, but show sustained effects throughout the day once a therapeutic dose has been reached. Nonstimulants have been found to be more effective than placebo at improving ADHD symptoms, but are potentially less effective than simulant medications (Kaplan & Newcorn, 2011).

Nonpharmacological Interventions Pharmacotherapy is effective at reducing the symptoms of ADHD in individuals who respond to medication and have minimal side effects. However treatment with medication is not always an option because of adverse side effects, contraindications, and patient preference. In addition, stimulant medication may not cover all times of the day when ADHD symptoms are impairing (e.g., early morning and evenings). Pharmacotherapy is also unable to directly target the specific skill deficits associated with ADHD (e.g., social skills, organizational skills) and impaired parent–child relationships (Daley et al., 2014). There is a large body of literature documenting the effectiveness of behavioral interventions for children with ADHD, and there is an 432

expanding evidence-base supporting the use of these interventions with adolescents. For adults with ADHD, there is growing support for treatment with cognitive–behavioral therapy (CBT) and emerging evidence for the effectiveness of dialectical behavior therapy (DBT). In addition to behavioral and cognitive–behavioral treatments, cognitive training and neurofeedback are currently being studied as treatments that target the neurocognitive deficits associated with ADHD. Behavioral interventions for children.  The most widely studied and implemented behavioral interventions for ADHD include behavioral parent training (BPT), behavioral classroom management (BCM), child skills training, behavioral multicomponent interventions (Pfiffner & Haack, 2015). In BPT, clinicians work with parents to provide psychoeducation, to teach behavior modification principles and strategies, and to adapt these strategies to individual families’ home environments. Most BPT curricula focus on strategies for improving parent–child relationships, increasing positive behaviors using effective instructions and positive reinforcement (e.g., praise, rewards), and reducing problem behaviors by implementing prudent negative consequences (Pfiffner & Haack, 2015). In BCM, teachers provide behavioral support for students with ADHD through the provision of clear expectations and instructions, positive reinforcement, and negative consequences. BCM often includes the use of a daily report card, which provides students with specific behavior goals and the opportunity to earn points for meeting their goals throughout the day (Pfiffner & Haack, 2015). In contrast to BPT and BCM, which train parents and teachers to administer interventions with children, child skills training programs directly train children in skills that they apply themselves (Pfiffner & Haack, 2015). Child skills training has generally focused on teaching organizational skills and/or social skills (Mikami, Jia, & Na, 2014), and includes a combination of didactic instruction and opportunities for skill practice and reinforcement. Results from randomized controlled trials of organizational skills training programs show sustained functional improvements following

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the interventions (Abikoff et al., 2013; Langberg, Becker, Epstein, Vaughn, & Girio-Herrera, 2013). Evidence for social skills training as a stand-alone intervention is mixed, with improvements in social skills observed only within the treatment group setting without generalization to real-world environments (Mikami et al., 2014). Driven by limited generalizability of treatment gains, several multicomponent behavioral interventions recently have been developed and evaluated. These interventions include a combination of BPT, BCM, and/or child skills, which target problems across home and school contexts and aim to facilitate skill generalization (Pfiffner & Haack, 2015). Results from randomized controlled trials indicate that multicomponent interventions can in fact have significant effects across multiple settings on ADHD-related symptoms as well as behavioral, organizational, and social impairment (Evans et al., 2016; Pfiffner et al., 2014, 2016; Power et al., 2012), and that these approaches can be superior to single-component interventions (Pfiffner et al., 2014). Psychotherapy for adults.  In contrast to behavioral interventions with children and adolescents which rely heavily on supervising adults to implement treatment protocols, nonpharmacological treatments for adult ADHD have largely focused on cognitive–behavioral interventions that target maladaptive thought processes as well as specific behavioral skills like time management, organization, and planning (Knouse, Cooper-Vince, Sprich, & Safren, 2008). Individual and group CBT have been shown to reduce ADHD symptoms and impairments in randomized controlled trials (Safren et al., 2010; Solanto et al., 2010). DBT is a cognitive–behavioral approach that blends traditional CBT skills with acceptance-based and mindfulness-based skills (Knouse & Safren, 2010). Results from initial trials suggest that DBT may be effective in reducing symptoms and impairments related to ADHD as well as co-occurring affective disorders (Fleming, McMahon, Moran, Peterson, & Dreessen, 2015; Philipsen et al., 2007). Cognitive training and neurofeedback.  In recent years, treatment efforts have been directed to modify neurocognitive deficits, which are thought

to underlie many of the core symptoms and impairments of children with ADHD. Cognitive training involves computer-based training exercises designed to facilitate the development of hypothesized areas of EF and attention deficiencies in children and adolescents with ADHD (Pfiffner & Haack, 2015). Evidence for treatment effects on daily functioning is limited. In a recent meta-analysis found that despite observed improvements in working memory following cognitive training, these interventions had limited effects on ADHD symptoms on the basis of blinded measures (Cortese et al., 2015). Neurofeedback has been used to alter brainwave patterns posited to underlie ADHD and has been evaluated as a potential treatment for children and adolescents with ADHD (Arns, Heinrich, & Strehl, 2014). Children are taught how to control their brain activity patterns through auditory and visual feedback on changes in the brain’s electrical activity measured by scalp electrodes. Several studies have reported evidence that this treatment can improve ADHD symptoms, but effects on measures of social, academic, and home functioning have not been found (Arns et al., 2014). Limitations in study methodology complicate the interpretations and conclusions that can be drawn from current ADHD neurofeedback research (Lofthouse, Arnold, & Hurt, 2012). Future Directions The immediate future of ADHD research is largely focused on developing a better understanding the vast heterogeneity observed in the disorder, as well as patterns and predictors of comorbidities and negative outcomes. These important areas of research will contribute to the development of personalized, targeted assessment, prevention, and intervention protocols that aim to reduce the individual and societal burden of the disorder. Researchers in the field of epigenetics are currently exploring interactions between genes associated with ADHD the influence of social and environmental factors on gene expression ( J. J. Li & Lee, 2013). Neuroimaging researchers are examining brain development in ADHD, seeking to understand how differences in brain structure and functioning map on to the EF deficits and behavioral profiles observed in 433

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individuals with the disorder (Silk et al., 2016). Clinical scientists are also seeking to identify refined diagnostic criteria that best capture ADHD symptoms and impairments in men and women across the lifespan (Faraone, 2013). The launch of the Research Domains Criteria Initiative (RDoC) research framework in 2010 by the National Institute of Mental Health has advanced investigations into specific cognitive neuroscience-based constructs associated with ADHD (Baroni & Castellanos, 2015). RDoC takes a dimensional approach to the classification of symptoms and impairments and seeks to identify associated genetic and neurological biomarkers that may contribute to the development and maintenance of psychopathology (Cuthbert & Insel, 2010). The goal of RDoC-based research is to develop targeted interventions and improved diagnostic profiles that reflect an advanced, nuanced understanding of ADHD and other mental health disorders.

References Aarnoudse-Moens, C. S. H., Weisglas-Kuperus, N., van Goudoever, J. B., & Oosterlaan, J. (2009). Metaanalysis of neurobehavioral outcomes in very preterm and/or very low birth weight children. Pediatrics, 124, 717–728. http://dx.doi.org/10.1542/ peds.2008-2816 Abikoff, H., Gallagher, R., Wells, K. C., Murray, D. W., Huang, L., Lu, F., & Petkova, E. (2013). Remediating organizational functioning in children with ADHD: Immediate and long-term effects from a randomized controlled trial. Journal of Consulting and Clinical Psychology, 81, 113–128. http://dx.doi.org/10.1037/ a0029648 Alsop, B., Furukawa, E., Sowerby, P., Jensen, S., Moffat, C., & Tripp, G. (2016). Behavioral sensitivity to changing reinforcement contingencies in attention-deficit hyperactivity disorder. Journal of Child Psychology and Psychiatry, 57, 947–956. http://dx.doi.org/10.1111/ jcpp.12561 Altszuler, A. R., Page, T. F., Gnagy, E. M., Coxe, S., Arrieta, A., Molina, B. S. G., & Pelham, W. E. (2016). Financial dependence of young adults with childhood ADHD. Journal of Abnormal Child Psychology, 44, 1217–1229. Ambrosini, P. J., Bennett, D. S., & Elia, J. (2013). Attention deficit hyperactivity disorder characteristics: II. Clinical correlates of irritable mood. Journal of Affective Disorders, 145, 70–76. http://dx.doi.org/10.1016/j.jad.2012.07.014 434

American Academy of Pediatrics. (2011). ADHD: Clinical practice guideline for the diagnosis, evaluation, and treatment of attention-deficit/hyperactivity disorder in children and adolescents. Pediatrics, 128, 1007–1022. http://doi.org/10.1542/peds.2011-2654 American Psychiatric Association. (1968). Diagnostic and statistical manual of mental disorders (2nd ed.). Washington, DC: Author. American Psychiatric Association. (1980). Diagnostic and statistical manual of mental disorders (3rd ed.). Washington, DC: Author. American Psychiatric Association. (1987). Diagnostic and statistical manual of mental disorders (3rd ed., rev.). Washington, DC: Author. American Psychiatric Association. (1994). Diagnostic and statistical manual of mental disorders (4th ed.). Washington, DC: Author. American Psychiatric Association. (2013). Diagnostic and statistical manual of mental disorders (5th ed.). Washington, DC: Author. Angold, A., Costello, E. J., & Erkanli, A. (1999). Comorbidity. Journal of Child Psychology and Psychiatry, 40, 57–87. http://dx.doi.org/ 10.1111/1469-7610.00424 Antshel, K. M., Zhang-James, Y., & Faraone, S. V. (2013). The comorbidity of ADHD and autism spectrum disorder. Expert Review of Neurotherapeutics, 13, 1117–1128. http://dx.doi.org/10.1586/14737175. 2013.840417 Arns, M., Heinrich, H., & Strehl, U. (2014). Evaluation of neurofeedback in ADHD: The long and winding road. Biological Psychology, 95, 108–115. http:// dx.doi.org/10.1016/j.biopsycho.2013.11.013 Banerjee, T. D., Middleton, F., & Faraone, S. V. (2007). Environmental risk factors for attention-deficit hyperactivity disorder. Acta Paediatrica, 96, 1269–1274. http://doi.org/10.1111/j.1651-2227.2007.00430.x Barkley, R. A. (1997). ADHD and the nature of self-control. New York, NY: Guilford Press. Barkley, R. A. (2001). The executive functions and selfregulation: An evolutionary neuropsychological perspective. Neuropsychology Review, 11, 1–29. http://dx.doi.org/10.1023/A:1009085417776 Barkley, R. A. (2004). Driving impairments in teens and adults with attention-deficit/hyperactivity disorder. Psychiatric Clinics of North America, 27, 233–260. http://dx.doi.org/10.1016/S0193-953X(03)00091-1 Barkley, R. A. (2012). Executive functions: What they are, how they work, and why they evolved. New York, NY: Guilford Press. Barkley, R. A. (2013). Distinguishing sluggish cognitive tempo from ADHD in children and adolescents: Executive functioning, impairment, and comorbidity.

Attention-Deficit/Hyperactivity Disorder

Journal of Clinical Child and Adolescent Psychology, 42, 161–173. http://dx.doi.org/10.1080/15374416. 2012.734259 Barkley, R. A. (2015). Attention-deficit hyperactivity disorder: A handbook for diagnosis and treatment (4th ed.). New York, NY: Guilford Press.

Copyright American Psychological Association. Not for further distribution.

Barkley, R. A., & Murphy, K. R. (2010a). Deficient emotional self-regulation in children and adults with attention-deficit/hyepractivity disorder. Journal of ADHD and Related Disorders, 1, 5–29. Barkley, R. A., & Murphy, K. R. (2010b). Impairment in occupational functioning and adult ADHD: The predictive utility of executive function (EF) ratings versus EF tests. Archives of Clinical Neuropsychology, 25, 157–173. http://dx.doi.org/10.1093/arclin/ acq014 Barkley, R. A., Murphy, K. R., & Fischer, M. (2010). ADHD in adults: What the science says. New York, NY: Guilford Press. Barkley, R. A., & Peters, H. (2012). The earliest reference to ADHD in the medical literature? Melchior Adam Weikard’s description in 1775 of “attention deficit.” Journal of Attention Disorders, 16, 623–630. http:// dx.doi.org/10.1177/1087054711432309 Baroni, A., & Castellanos, F. X. (2015). Neuroanatomic and cognitive abnormalities in attention-deficit/ hyperactivity disorder in the era of “high definition” neuroimaging. Current Opinion in Neurobiology, 30, 1–8. http://dx.doi.org/10.1016/j.conb.2014.08.005 Becker, S. P., Leopold, D. R., Burns, G. L., Jarrett, M. A., Langberg, J. M., Marshall, S. A., . . . Willcutt, E. G. (2016). The internal, external, and diagnostic validity of sluggish cognitive tempo: A meta-analysis and critical review. Journal of the American Academy of Child and Adolescent Psychiatry, 55, 163–178. http://dx.doi.org/10.1016/j.jaac.2015.12.006 Becker, S. P., McBurnett, K., Hinshaw, S. P., & Pfiffner, L. J. (2013). Negative social preference in relation to internalizing symptoms among children with ADHD predominantly inattentive type: Girls fare worse than boys. Journal of Clinical Child and Adolescent Psychology, 42, 784–795. http://dx.doi.org/10.1080/ 15374416.2013.828298 Biederman, J., Ball, S. W., Monuteaux, M. C., Mick, E., Spencer, T. J., McCreary, M., . . . Faraone, S. V. (2008). New insights into the comorbidity between ADHD and major depression in adolescent and young adult females. Journal of the American Academy of Child and Adolescent Psychiatry, 47, 426–434. http://dx.doi.org/10.1097/CHI.0b013e31816429d3 Biederman, J., Ball, S. W., Monuteaux, M. C., Surman, C. B., Johnson, J. L., & Zeitlin, S. (2007). Are girls with ADHD at risk for eating disorders? Results from a controlled, five-year prospective

study. Journal of Developmental and Behavioral Pediatrics, 28, 302–307. http://dx.doi.org/10.1097/ DBP.0b013e3180327917 Biederman, J., & Faraone, S. V. (2006). The effects of attention-deficit/hyperactivity disorder on employment and household income. Medscape General Medicine, 8(3), 12. Biederman, J., Petty, C. R., Clarke, A., Lomedico, A., & Faraone, S. V. (2011). Predictors of persistent ADHD: An 11-year follow-up study. Journal of Psychiatric Research, 45, 150–155. http://dx.doi.org/ 10.1016/j.jpsychires.2010.06.009 Biederman, J., Spencer, T., Lomedico, A., Day, H., Petty, C. R., & Faraone, S. V. (2012). Deficient emotional self-regulation and pediatric attention deficit hyperactivity disorder: A family risk analysis. Psychological Medicine, 42, 639–646. http://dx.doi.org/ 10.1017/S0033291711001644 Bleck, J., & DeBate, R. D. (2013). Exploring the co-morbidity of attention-deficit/hyperactivity disorder with eating disorders and disordered eating behaviors in a nationally representative communitybased sample. Eating Behaviors, 14, 390–393. http:// dx.doi.org/10.1016/j.eatbeh.2013.05.009 Bleck, J. R., DeBate, R. D., & Olivardia, R. (2015). The comorbidity of ADHD and eating disorders in a nationally representative sample. Journal of Behavioral Health Services and Research, 42, 437–451. http://dx.doi.org/10.1007/s11414-014-9422-y Bloch, M. H., Panza, K. E., Landeros-Weisenberger, A., & Leckman, J. F. (2009). Meta-analysis: Treatment of attention-deficit/hyperactivity disorder in children with comorbid tic disorders. Journal of the American Academy of Child and Adolescent Psychiatry, 48, 884–893. http://dx.doi.org/10.1097/ CHI.0b013e3181b26e9f Bowen, R., Chavira, D. A., Bailey, K., Stein, M. T., & Stein, M. B. (2008). Nature of anxiety comorbid with attention deficit hyperactivity disorder in children from a pediatric primary care setting. Psychiatry Research, 157, 201–209. http://dx.doi.org/10.1016/ j.psychres.2004.12.015 Brook, J. S., Brook, D. W., Zhang, C., Seltzer, N., & Finch, S. J. (2013). Adolescent ADHD and adult physical and mental health, work performance, and financial stress. Pediatrics, 131, 5–13. http://dx.doi.org/ 10.1542/peds.2012-1725 Burke, J. D., Loeber, R., & Birmaher, B. (2002). Oppositional defiant disorder and conduct disorder: A review of the past 10 years, part II. Journal of the American Academy of Child and Adolescent Psychiatry, 41, 1275–1293. http://dx.doi.org/ 10.1097/00004583-200211000-00009 Butterworth, B., & Kovas, Y. (2013). Understanding neurocognitive developmental disorders can improve 435

Rooney and Pfiffner

Copyright American Psychological Association. Not for further distribution.

education for all. Science, 340, 300–305. http:// dx.doi.org/10.1126/science.1231022

Psychiatry, 77, e421–e428. http://dx.doi.org/10.4088/ JCP.14m09630

Caspersen, I. H., Aase, H., Biele, G., Brantsæter, A. L., Haugen, M., Kvalem, H. E., . . . Knutsen, H. K. (2016). The influence of maternal dietary exposure to dioxins and PCBs during pregnancy on ADHD symptoms and cognitive functions in Norwegian preschool children. Environment International, 94, 649–660. http://dx.doi.org/10.1016/ j.envint.2016.06.033

Cortese, S., Ferrin, M., Brandeis, D., Buitelaar, J., Daley, D., Dittmann, R. W., . . . Sonuga-Barke, E. J. S. (2015). Cognitive training for attention-deficit/ hyperactivity disorder: Meta-analysis of clinical and neuropsychological outcomes from randomized controlled trials. Journal of the American Academy of Child and Adolescent Psychiatry, 54, 164–174. http:// dx.doi.org/10.1016/j.jaac.2014.12.010

Castellanos, F. X., & Tannock, R. (2002). Neuroscience of attention-deficit/hyperactivity disorder: The search for endophenotypes. Nature Reviews Neuroscience, 3, 617–628. http://dx.doi.org/10.1038/nrn896

Cortese, S., Konofal, E., Yateman, N., Mouren, M.C., & Lecendreux, M. (2006). Sleep and alertness in children with attention-deficit/hyperactivity disorder: A systematic review of the literature. Sleep: Journal of Sleep and Sleep Disorders Research, 29, 504–511.

Clarke, D. E., Narrow, W. E., Regier, D. A., Kuramoto, S. J., Kupfer, D. J., Kuhl, E. A., . . . Kraemer, H. C. (2013). DSM–5 field trials in the United States and Canada, Part I: Study design, sampling strategy, implementation, and analytic approaches. American Journal of Psychiatry, 170, 43–58. http://dx.doi.org/ 10.1176/appi.ajp.2012.12070998 Coker, T. R., Elliott, M. N., Kataoka, S., Schwebel, D. C., Mrug, S., Grunbaum, J. A., . . . Schuster, M. A. (2009). Racial/ethnic disparities in the mental health care utilization of fifth grade children. Academic Pediatrics, 9, 89–96. http://dx.doi.org/10.1016/j. acap.2008.11.007 Collins, K. P., & Cleary, S. D. (2016). Racial and ethnic disparities in parent-reported diagnosis of ADHD: National Survey of Children’s Health (2003, 2007, and 2011). Journal of Clinical Psychiatry, 77, 52–59. http://dx.doi.org/10.4088/JCP.14m09364 Connor, D. F., Steeber, J., & McBurnett, K. (2010). A review of attention-deficit/hyperactivity disorder complicated by symptoms of oppositional defiant disorder or conduct disorder. Journal of Developmental and Behavioral Pediatrics, 31, 427–440. http://dx.doi.org/10.1097/ DBP.0b013e3181e121bd Cortese, S., Bernardina, B. D., & Mouren, M.-C. (2007). Attention-deficit/hyperactivity disorder (ADHD) and binge eating. Nutrition Reviews, 65, 404–411. http:// dx.doi.org/10.1111/j.1753-4887.2007.tb00318.x Cortese, S., Brown, T. E., Corkum, P., Gruber, R., O’Brien, L. M., Stein, M., . . . Owens, J. (2013). Assessment and management of sleep problems in youths with attention-deficit/hyperactivity disorder. Journal of the American Academy of Child and Adolescent Psychiatry, 52, 784–796. http:// dx.doi.org/10.1016/j.jaac.2013.06.001 Cortese, S., Faraone, S. V., Bernardi, S., Wang, S., & Blanco, C. (2016). Gender differences in adult attention-deficit/hyperactivity disorder: Results from the National Epidemiologic Survey on Alcohol and Related Conditions (NESARC). Journal of Clinical

436

Cortese, S., Moreira-Maia, C. R., St. Fleur, D., MorcilloPeñalver, C., Rohde, L. A., & Faraone, S. V. (2016). Association between ADHD and obesity: A systematic review and meta-analysis. American Journal of Psychiatry, 173, 34–43. http://dx.doi.org/ 10.1176/appi.ajp.2015.15020266 Cuthbert, B. N., & Insel, T. R. (2010). Toward new approaches to psychotic disorders: The NIMH Research Domain Criteria project. Schizophrenia Bulletin, 36, 1061–1062. http://dx.doi.org/10.1093/ schbul/sbq108 Daley, D., van der Oord, S., Ferrin, M., Danckaerts, M., Doepfner, M., Cortese, S., & Sonuga-Barke, E. J. S. (2014). Behavioral interventions in attentiondeficit/hyperactivity disorder: A meta-analysis of randomized controlled trials across multiple outcome domains. Journal of the American Academy of Child and Adolescent Psychiatry, 53, 835–847. http:// dx.doi.org/10.1016/j.jaac.2014.05.013 Daneshparvar, M., Mostafavi, S.-A., Zare Jeddi, M., Yunesian, M., Mesdaghinia, A., Mahvi, A. H., & Akhondzadeh, S. (2016). The role of lead exposure on attention-deficit/ hyperactivity disorder in children: A systematic review. Iranian Journal of Psychiatry, 11, 1–14. Davis, C., Levitan, R. D., Smith, M., Tweed, S., & Curtis, C. (2006). Associations among overeating, overweight, and attention deficit/hyperactivity disorder: A structural equation modelling approach. Eating Behaviors, 7, 266–274. http://dx.doi.org/ 10.1016/j.eatbeh.2005.09.006 Daviss, W. B. (2008). A review of co-morbid depression in pediatric ADHD: Etiology, phenomenology, and treatment. Journal of Child and Adolescent Psychopharmacology, 18, 565–571. http://dx.doi.org/ 10.1089/cap.2008.032 De Alwis, D., Lynskey, M. T., Reiersen, A. M., & Agrawal, A. (2014). Attention-deficit/hyperactivity disorder subtypes and substance use and use disorders in

Attention-Deficit/Hyperactivity Disorder

NESARC. Addictive Behaviors, 39, 1278–1285. http:// dx.doi.org/10.1016/j.addbeh.2014.04.003

Copyright American Psychological Association. Not for further distribution.

de Graaf, R., Kessler, R. C., Fayyad, J., ten Have, M., Alonso, J., Angermeyer, M., . . . Posada-Villa, J. (2008). The prevalence and effects of adult attentiondeficit/hyperactivity disorder (ADHD) on the performance of workers: Results from the WHO World Mental Health Survey Initiative. Occupational and Environmental Medicine, 65, 835–842. http:// dx.doi.org/10.1136/oem.2007.038448 de Jong, M., Verhoeven, M., & van Baar, A. L. (2012). School outcome, cognitive functioning, and behaviour problems in moderate and late preterm children and adults: A review. Seminars in Fetal and Neonatal Medicine, 17, 163–169. http:// dx.doi.org/10.1016/j.siny.2012.02.003 Del’Homme, M., Kim, T. S., Loo, S. K., Yang, M. H., & Smalley, S. L. (2007). Familial association and frequency of learning disabilities in ADHD sibling pair families. Journal of Abnormal Child Psychology, 35, 55–62. http://dx.doi.org/10.1007/s10802-006-9080-5 Derks, E. M., Hudziak, J. J., Dolan, C. V., van Beijsterveldt, T. C. E. M., Verhulst, F. C., & Boomsma, D. I. (2008). Genetic and environmental influences on the relation between attention problems and attention deficit hyperactivity disorder. Behavior Genetics, 38, 11–23. http:// dx.doi.org/10.1007/s10519-007-9178-8 Doshi, J. A., Hodgkins, P., Kahle, J., Sikirica, V., Cangelosi, M. J., Setyawan, J., . . . Neumann, P. J. (2012). Economic impact of childhood and adult attention-deficit/hyperactivity disorder in the United States. Journal of the American Academy of Child and Adolescent Psychiatry, 51, 990–1002. http:// dx.doi.org/10.1016/j.jaac.2012.07.008 Douglas, V. I. (1972). Stop, look, and listen: The problem of sustained attention and impulse control in hyperactive and normal children. Canadian Journal of Behavioural Science, 4, 259–282. http://dx.doi.org/ 10.1037/h0082313 DuPaul, G. J., Morgan, P. L., Farkas, G., Hillemeier, M. M., & Maczuga, S. (2016). Academic and social functioning associated with attention-deficit/ hyperactivity disorder: Latent class analyses of trajectories from kindergarten to fifth grade. Journal of Abnormal Child Psychology, 44, 1425–1438. http:// dx.doi.org/10.1007/s10802-016-0126-z Evans, S. W., Langberg, J. M., Schultz, B. K., Vaughn, A., Altaye, M., Marshall, S. A., & Zoromski, A. K. (2016). Evaluation of a school-based treatment program for young adolescents with ADHD. Journal of Consulting and Clinical Psychology, 84, 15–30. http://dx.doi.org/10.1037/ccp0000057 Faraone, S. V. (2013). Attention-defici/hyperactivity disorder and the shifting sands of psychiatric

nosology. British Journal of Psychiatry, 203, 81–83. http://dx.doi.org/10.1192/bjp.bp.113.126524 Faraone, S. V., & Buitelaar, J. (2010). Comparing the efficacy of stimulants for ADHD in children and adolescents using meta-analysis. European Child and Adolescent Psychiatry, 19, 353–364. http://dx.doi. org/10.1007/s00787-009-0054-3 Fishman, P. A., Stang, P. E., & Hogue, S. L. (2007). Impact of comorbid attention deficit disorder on the direct medical costs of treating adults with depression in managed care. Journal of Clinical Psychiatry, 68, 248–253. http://dx.doi.org/10.4088/ JCP.v68n0210 Fleming, A. P., McMahon, R. J., Moran, L. R., Peterson, A. P., & Dreessen, A. (2015). Pilot randomized controlled trial of dialectical behavior therapy group skills training for ADHD among college students. Journal of Attention Disorders, 19, 260–271. http:// dx.doi.org/10.1177/1087054714535951 Garner, A. A., O’Connor, B. C., Narad, M. E., Tamm, L., Simon, J., & Epstein, J. N. (2013). The relationship between ADHD symptom dimensions, clinical correlates, and functional impairments. Journal of Developmental and Behavioral Pediatrics: JDBP, 34, 469–477. http://doi.org/10.1097/ DBP.0b013e3182a39890 Gayán, J., Willcutt, E. G., Fisher, S. E., Francks, C., Cardon, L. R., Olson, R. K., . . . DeFries, J. C. (2005). Bivariate linkage scan for reading disability and attention-deficit/hyperactivity disorder localizes pleiotropic loci. Journal of Child Psychology and Psychiatry, 46, 1045–1056. http://dx.doi.org/10.1111/ j.1469-7610.2005.01447.x Gerdes, A. C., Lawton, K. E., Haack, L. M., & Hurtado, G. D. (2013). Assessing ADHD in Latino families: Evidence for moving beyond symptomatology. Journal of Attention Disorders, 17, 128–140. http:// dx.doi.org/10.1177/1087054711427396 Geurts, H. M., Luman, M., & van Meel, C. S. (2008). What’s in a game: The effect of social motivation on interference control in boys with ADHD and autism spectrum disorders. Journal of Child Psychology and Psychiatry, 49, 848–857. http://dx.doi.org/10.1111/ j.1469-7610.2008.01916.x Goldstein, R. Z., & Volkow, N. D. (2011). Dysfunction of the prefrontal cortex in addiction: Neuroimaging findings and clinical implications. Nature Reviews Neuroscience, 12, 652–669. http://dx.doi.org/10.1038/ nrn3119 Goldstein, S., & Schwebach, A. J. (2004). The comorbidity of pervasive developmental disorder and attention deficit hyperactivity disorder: Results of a retrospective chart review. Journal of Autism and Developmental Disorders, 34, 329–339. http://dx.doi. org/10.1023/B:JADD.0000029554.46570.68 437

Rooney and Pfiffner

Gormley, M. J., DuPaul, G. J., Weyandt, L. L., & Anastopoulos, A. D. (2016). First-year GPA and academic service use among college students with and without ADHD. Journal of Attention Disorders. Advance online publication.

Copyright American Psychological Association. Not for further distribution.

Grizenko, N., Fortier, M.-E., Zadorozny, C., Thakur, G., Schmitz, N., Duval, R., & Joober, R. (2012). Maternal stress during pregnancy, ADHD symptomatology in children and genotype: Geneenvironment interaction. Journal of the Canadian Academy of Child and Adolescent Psychiatry, 21, 9–15. Grizenko, N., Shayan, Y. R., Polotskaia, A., Ter-Stepanian, M., & Joober, R. (2008). Relation of maternal stress during pregnancy to symptom severity and response to treatment in children with ADHD. Journal of Psychiatry and Neuroscience, 33, 10–16. Haack, L. M., Villodas, M. T., McBurnett, K., Hinshaw, S., & Pfiffner, L. J. (2016). Parenting mediates symptoms and impairment in children with ADHDinattentive type. Journal of Clinical Child and Adolescent Psychology, 45, 155–166. http://dx.doi.org/ 10.1080/15374416.2014.958840 Han, J.-Y., Kwon, H.-J., Ha, M., Paik, K.-C., Lim, M.-H., Gyu Lee, S., . . . Kim, E.-J. (2015). The effects of prenatal exposure to alcohol and environmental tobacco smoke on risk for ADHD: A large population-based study. Psychiatry Research, 225, 164–168. http://dx.doi.org/10.1016/j. psychres.2014.11.009 Hawes, D. J., Dadds, M. R., Frost, A. D. J., & Russell, A. (2013). Parenting practices and prospective levels of hyperactivity/inattention across early- and middlechildhood. Journal of Psychopathology and Behavioral Assessment, 35, 273–282. http://dx.doi.org/10.1007/ s10862-013-9341-x Hervey-Jumper, H., Douyon, K., Falcone, T., & Franco, K. N. (2007). Identifying, evaluating, diagnosing, and treating ADHD in minority youth. Journal of Attention Disorders, 11, 522–528. http:// dx.doi.org/10.1177/1087054707311054 Hill, S. Y., Lowers, L., Locke-Wellman, J., & Shen, S. (2000). Maternal smoking and drinking during pregnancy and the risk for child and adolescent psychiatric disorders. Journal of Studies on Alcohol, 61, 661–668. Hinshaw, S. P., Owens, E. B., Zalecki, C., Huggins, S. P., Montenegro-Nevado, A. J., Schrodek, E., & Swanson, E. N. (2012). Prospective follow-up of girls with attention-deficit/hyperactivity disorder into early adulthood: Continuing impairment includes elevated risk for suicide attempts and self-injury. Journal of Consulting and Clinical Psychology, 80, 1041–1051. http://dx.doi.org/10.1037/a0029451 Hinshaw, S. P., & Scheffler, R. M. (2014). The ADHD explosion: Myths, medication, money, and today’s push

438

for performance. New York, NY: Oxford University Press. Hirschtritt, M. E., Lee, P. C., Pauls, D. L., Dion, Y., Grados, M. A., Illmann, C., . . . Mathews, C. A. (2015). Lifetime prevalence, age of risk, and genetic relationships of comorbid psychiatric disorders in Tourette syndrome. JAMA Psychiatry, 72, 325–333. http://dx.doi.org/10.1001/ jamapsychiatry.2014.2650 Holtmann, M., Bolte, S., & Poustka, F. (2007). Attention deficit hyperactivity disorder symptoms in pervasive developmental disorders: Association with autistic behavior domains and coexisting psychopathology. Psychopathology, 40, 172–177. http://dx.doi.org/ 10.1159/000100007 Hoza, B. (2007). Peer functioning in children with ADHD. Ambulatory Pediatrics, 7(1, Suppl.), 101–106. http://dx.doi.org/10.1016/j.ambp.2006.04.011 Hoza, B., Gerdes, A. C., Hinshaw, S. P., Arnold, L. E., Pelham, W. E., Jr., Molina, B. S. G., . . . Wigal, T. (2004). Self-perceptions of competence in children with ADHD and comparison children. Journal of Consulting and Clinical Psychology, 72, 382–391. http://dx.doi.org/10.1037/0022-006X.72.3.382 Hoza, B., Mrug, S., Gerdes, A. C., Hinshaw, S. P., Bukowski, W. M., Gold, J. A., . . . Arnold, L. E. (2005). What aspects of peer relationships are impaired in children with attention-deficit/ hyperactivity disorder? Journal of Consulting and Clinical Psychology, 73, 411–423. http://dx.doi.org/ 10.1037/0022-006X.73.3.411 Hoza, B., Pelham, W. E., Jr., Dobbs, J., Owens, J. S., & Pillow, D. R. (2002). Do boys with attention-deficit/ hyperactivity disorder have positive illusory self-concepts? Journal of Abnormal Psychology, 111, 268–278. http://dx.doi.org/10.1037/0021843X.111.2.268 Humphreys, K. L., Katz, S. J., Lee, S. S., Hammen, C., Brennan, P. A., & Najman, J. M. (2013). The association of ADHD and depression: Mediation by peer problems and parent-child difficulties in two complementary samples. Journal of Abnormal Psychology, 122, 854–867. http://dx.doi.org/10.1037/a0033895 Jacobson, L. A., Murphy-Bowman, S. C., Pritchard, A. E., Tart-Zelvin, A., Zabel, T. A., & Mahone, E. M. (2012). Factor structure of a sluggish cognitive tempo scale in clinically referred children. Journal of Abnormal Child Psychology, 40, 1327–1337. http:// dx.doi.org/10.1007/s10802-012-9643-6 Jarrett, M. A., & Ollendick, T. H. (2008). A conceptual review of the comorbidity of attention-deficit/ hyperactivity disorder and anxiety: Implications for future research and practice. Clinical Psychology Review, 28, 1266–1280. http://dx.doi.org/10.1016/j. cpr.2008.05.004

Attention-Deficit/Hyperactivity Disorder

Jerome, L., Segal, A., & Habinski, L. (2006). What we know about ADHD and driving risk: A literature review, meta-analysis and critique. Journal of the Canadian Academy of Child and Adolescent Psychiatry, 15, 105–125.

Copyright American Psychological Association. Not for further distribution.

Johnston, C., & Mash, E. J. (2001). Families of children with attention-deficit/hyperactivity disorder: Review and recommendations for future research. Clinical Child and Family Psychology Review, 4, 183–207. http://dx.doi.org/10.1023/A:1017592030434 Kaiser, N. M., McBurnett, K., & Pfiffner, L. J. (2011). Child ADHD severity and positive and negative parenting as predictors of child social functioning: Evaluation of three theoretical models. Journal of Attention Disorders, 15, 193–203. http://dx.doi.org/ 10.1177/1087054709356171 Kaplan, G., & Newcorn, J. H. (2011). Pharmacotherapy for child and adolescent attention-deficit hyperactivity disorder. Pediatric Clinics of North America, 58, 99–120. http://dx.doi.org/10.1016/j. pcl.2010.10.009 Kent, K. M., Jr., Pelham, W. E., Jr., Molina, B. S., Sibley, M. H., Waschbusch, D. A., Yu, J., . . . Karch, K. M. (2011). The academic experience of male high school students with ADHD. Journal of Abnormal Child Psychology, 39, 451–462. http://dx.doi.org/10.1007/ s10802-010-9472-4 Kessler, R. C., Adler, L., Barkley, R., Biederman, J., Conners, C. K., Demler, O., . . . Zaslavsky, A. M. (2006). The prevalence and correlates of adult ADHD in the United States: Results from the National Comorbidity Survey Replication. American Journal of Psychiatry, 163, 716–723. http://dx.doi.org/ 10.1176/ajp.2006.163.4.716 Khoury, J. E., & Milligan, K. (2016). Comparing executive functioning in children and adolescents with fetal alcohol spectrum disorders and ADHD: A meta-analysis. Journal of Attention Disorders. Advance online publication. Kiernan, K. E., & Huerta, M. C. (2008). Economic deprivation, maternal depression, parenting and children’s cognitive and emotional development in early childhood. British Journal of Sociology, 59, 783–806. http://dx.doi.org/10.1111/j.14684446.2008.00219.x

prenatal substance exposure, and genetic transmission to child ADHD risk: A female twin study. Psychological Medicine, 35, 625–635. http:// doi.org/10.1017/S0033291704004155 Knouse, L. E., Cooper-Vince, C., Sprich, S., & Safren, S. A. (2008). Recent developments in the psychosocial treatment of adult ADHD. Expert Review of Neurotherapeutics, 8, 1537–1548. http:// dx.doi.org/10.1586/14737175.8.10.1537 Knouse, L. E., & Safren, S. A. (2010). Current status of cognitive behavioral therapy for adult attentiondeficit hyperactivity disorder. Psychiatric Clinics of North America, 33, 497–509. http://dx.doi.org/ 10.1016/j.psc.2010.04.001 Knouse, L. E., Zvorsky, I., & Safren, S. A. (2013). Depression in adults with attention-deficit/ hyperactivity disorder (ADHD): The mediating role of cognitive–behavioral factors. Cognitive Therapy and Research, 37, 1220–1232. http:// dx.doi.org/10.1007/s10608-013-9569-5 Kurlan, R., Como, P. G., Miller, B., Palumbo, D., Deeley, C., Andresen, E. M., . . . McDermott, M. P. (2002). The behavioral spectrum of tic disorders: A community-based study. Neurology, 59, 414–420. http://dx.doi.org/10.1212/WNL.59.3.414 Lahey, B. B., Applegate, B., McBurnett, K., Biederman, J., Greenhill, L., Hynd, G. W., . . . Shafer, D. (1994). DSM–IV field trials for attention deficit hyperactivity disorder in children and adolescents. American Journal of Psychiatry, 151, 1673–1685. http:// dx.doi.org/10.1176/ajp.151.11.1673 Lahey, B. B., Pelham, W. E., Loney, J., Kipp, H., Ehrhardt, A., Lee, S. S., . . . Massetti, G. (2004). Three-year predictive validity of DSM–IV attention deficit hyperactivity disorder in children diagnosed at 4–6 years of age. American Journal of Psychiatry, 161, 2014–2020. http://dx.doi.org/10.1176/appi. ajp.161.11.2014 Lahey, B. B., Van Hulle, C. A., Rathouz, P. J., Rodgers, J. L., D’Onofrio, B. M., & Waldman, I. D. (2009). Are oppositional-defiant and hyperactiveinattentive symptoms developmental precursors to conduct problems in late childhood? Genetic and environmental links. Journal of Abnormal Child Psychology, 37, 45–58. http://dx.doi.org/10.1007/ s10802-008-9257-1

Knopik, V. S., Marceau, K., Bidwell, L. C., Palmer, R. H. C., Smith, T. F., Todorov, A., . . . Heath, A. C. (2016). Smoking during pregnancy and ADHD risk: A genetically informed, multiple-rater approach. American Journal of Medical Genetics: Part B. Neuropsychiatric Genetics, 171, 971–981. http:// doi.org/10.1002/ajmg.b.32421

Langberg, J. M., Becker, S. P., Epstein, J. N., Vaughn, A. J., & Girio-Herrera, E. (2013). Predictors of response and mechanisms of change in an organizational skills intervention for students with ADHD. Journal of Child and Family Studies, 22, 1000–1012. http://dx.doi.org/ 10.1007/s10826-012-9662-5

Knopik, V. S., Sparrow, E. P., Madden, P. a. F., Bucholz, K. K., Hudziak, J. J., Reich, W., . . . Heath, A. C. (2005). Contributions of parental alcoholism,

Langberg, J. M., Epstein, J. N., Altaye, M., Molina, B. S. G., Arnold, L. E., & Vitiello, B. (2008). The transition to middle school is associated

439

Rooney and Pfiffner

with changes in the developmental trajectory of ADHD symptomatology in young adolescents with ADHD. Journal of Clinical Child and Adolescent Psychology, 37, 651–663. http://doi.org/ 10.1080/15374410802148095

Copyright American Psychological Association. Not for further distribution.

Lange, H., Buse, J., Bender, S., Siegert, J., Knopf, H., & Roessner, V. (2016). Accident proneness in children and adolescents affected by ADHD and the impact of medication. Journal of Attention Disorders, 20, 501–509. http://dx.doi.org/ 10.1177/1087054713518237 Lee, S., Burns, G. L., Snell, J., & McBurnett, K. (2014). Validity of the sluggish cognitive tempo symptom dimension in children: Sluggish cognitive tempo and ADHD-inattention as distinct symptom dimensions. Journal of Abnormal Child Psychology, 42, 7–19. http://dx.doi.org/10.1007/s10802-013-9714-3 LeMoine, K. A., Romirowsky, A. M., Woods, K. E., & Chronis-Tuscano, A. (2015). Paternal antisocial behavior (but not paternal ADHD) is associated with negative parenting and child conduct problems. Journal of Attention Disorders. Advance online publication. Li, J. J., & Lee, S. S. (2013). Interaction of dopamine transporter gene and observed parenting behaviors on attention-deficit/hyperactivity disorder: A structural equation modeling approach. Journal of Clinical Child and Adolescent Psychology, 42, 174–186. http://doi.org/10.1080/15374416.2012. 736355 Li, Z., Chang, S. H., Zhang, L. Y., Gao, L., & Wang, J. (2014). Molecular genetic studies of ADHD and its candidate genes: A review. Psychiatry Research, 219, 10–24. http://dx.doi.org/10.1016/j. psychres.2014.05.005 Linnea, K., Hoza, B., Tomb, M., & Kaiser, N. (2012). Does a positive bias relate to social behavior in children with ADHD? Behavior Therapy, 43, 862–875. http://dx.doi.org/10.1016/j. beth.2012.05.004 Loeber, R., Burke, J. D., Lahey, B. B., Winters, A., & Zera, M. (2000). Oppositional defiant and conduct disorder: A review of the past 10 years, part I. Journal of the American Academy of Child and Adolescent Psychiatry, 39, 1468–1484. http:// dx.doi.org/10.1097/00004583-200012000-00007 Lofthouse, N., Arnold, L. E., & Hurt, E. (2012). Current status of neurofeedback for attention-deficit/ hyperactivity disorder. Current Psychiatry Reports, 14, 536–542. http://dx.doi.org/10.1007/s11920-012-0301-z Luman, M., Oosterlaan, J., & Sergeant, J. A. (2005). The impact of reinforcement contingencies on ADHD: A review and theoretical appraisal. Clinical Psychology Review, 25, 183–213. http://dx.doi.org/10.1016/j. cpr.2004.11.001

440

Luman, M., van Meel, C. S., Oosterlaan, J., & Geurts, H. M. (2012). Reward and punishment sensitivity in children with ADHD: Validating the Sensitivity to Punishment and Sensitivity to Reward Questionnaire for children (SPSRQ-C). Journal of Abnormal Child Psychology, 40, 145–157. http://dx.doi.org/10.1007/ s10802-011-9547-x Markussen Linnet, K., Obel, C., Bonde, E., Hove Thomsen, P., Secher, N. J., Wisborg, K., & Brink Henriksen, T. (2006). Cigarette smoking during pregnancy and hyperactive-distractible preschooler’s: A follow-up study. Acta Paediatrica, 95, 694–700. http://doi.org/10.1080/08035250500459709 Martin, J., O’Donovan, M. C., Thapar, A., Langley, K., & Williams, N. (2015). The relative contribution of common and rare genetic variants to ADHD. Translational Psychiatry, 5, e506. http://dx.doi.org/ 10.1038/tp.2015.5 Massetti, G. M., Lahey, B. B., Pelham, W. E., Loney, J., Ehrhardt, A., Lee, S. S., & Kipp, H. (2008). Academic achievement over 8 years among children who met modified criteria for attention-deficit/hyperactivity disorder at 4–6 years of age. Journal of Abnormal Child Psychology, 36, 399–410. http://dx.doi.org/ 10.1007/s10802-007-9186-4 Matte, B., Rohde, L. A., Turner, J. B., Fisher, P. W., Shen, S., Bau, C. H. D., . . . Grevet, E. H. (2015). Reliability and validity of proposed DSM–5 ADHD symptoms in a clinical sample of adults. Journal of Neuropsychiatry and Clinical Neurosciences, 27, 228–236. http:// dx.doi.org/10.1176/appi.neuropsych.13060137 Matthies, S., & Philipsen, A. (2016). Comorbidity of personality disorders and adult attention deficit hyperactivity disorder (ADHD)—Review of recent findings. Current Psychiatry Reports, 18, 33. http:// dx.doi.org/10.1007/s11920-016-0675-4 Matthies, S. D., & Philipsen, A. (2014). Common ground in attention deficit hyperactivity disorder (ADHD) and borderline personality disorder (BPD)—Review of recent findings. Borderline Personality Disorder and Emotion Dysregulation, 1, 3. http://dx.doi.org/ 10.1186/2051-6673-1-3 Mayes, S. D., Calhoun, S. L., Mayes, R. D., & Molitoris, S. (2012). Autism and ADHD: Overlapping and discriminating symptoms. Research in Autism Spectrum Disorders, 6, 277–285. http://dx.doi.org/ 10.1016/j.rasd.2011.05.009 McAuley, T., Crosbie, J., Charach, A., & Schachar, R. (2014). The persistence of cognitive deficits in remitted and unremitted ADHD: A case for the state-independence of response inhibition. Journal of Child Psychology and Psychiatry, 55, 292–300. http:// dx.doi.org/10.1111/jcpp.12160 Merkt, J., Siniatchkin, M., & Petermann, F. (2016). Neuropsychological measures in the diagnosis of

Attention-Deficit/Hyperactivity Disorder

ADHD in preschool: Can developmental research inform diagnostic practice? Journal of Attention Disorders. Advance online publication.

Copyright American Psychological Association. Not for further distribution.

Michielsen, M., Comijs, H. C., Aartsen, M. J., Semeijn, E. J., Beekman, A. T. F., Deeg, D. J. H., & Kooij, J. J. S. (2015). The relationships between ADHD and social functioning and participation in older adults in a population-based study. Journal of Attention Disorders, 19, 368–379.

Mulligan, A., Anney, R. J. L., O’Regan, M., Chen, W., Butler, L., Fitzgerald, M., . . . Gill, M. (2009). Autism symptoms in attention-deficit/hyperactivity disorder: A familial trait which correlates with conduct, oppositional defiant, language and motor disorders. Journal of Autism and Developmental Disorders, 39, 197–209. http://dx.doi.org/10.1007/s10803-008-0621-3

Mick, E., & Faraone, S. V. (2008). Genetics of attention deficit hyperactivity disorder. Child and Adolescent Psychiatric Clinics of North America, 17, 261–284. http://doi.org/10.1016/j.chc.2007.11.011

Musser, E. D., Karalunas, S. L., Dieckmann, N., Peris, T. S., & Nigg, J. T. (2016). Attention-deficit/ hyperactivity disorder developmental trajectories related to parental expressed emotion. Journal of Abnormal Psychology, 125, 182–195. http:// dx.doi.org/10.1037/abn0000097

Mikami, A. Y. (2010). The importance of friendship for youth with attention-deficit/hyperactivity disorder. Clinical Child and Family Psychology Review, 13, 181–198. http://dx.doi.org/10.1007/s10567-0100067-y

Musso, M. W., & Gouvier, W. D. (2014). “Why is this so hard?” A review of detection of malingered ADHD in college students. Journal of Attention Disorders, 18, 186–201. http://dx.doi.org/10.1177/ 1087054712441970

Mikami, A. Y., Huang-Pollock, C. L., Pfiffner, L. J., McBurnett, K., & Hangai, D. (2007). Social skills differences among attention-deficit/hyperactivity disorder types in a chat room assessment task. Journal of Abnormal Child Psychology, 35, 509–521. http://dx.doi.org/10.1007/s10802-0079108-5

Needleman, H. L. (1982). Lead and impaired abilities. Developmental Medicine and Child Neurology, 24, 196–197. http://dx.doi.org/10.1111/ j.1469-8749.1982.tb08802.x

Mikami, A. Y., Jia, M., & Na, J. J. (2014). Social skills training. Child and Adolescent Psychiatric Clinics of North America, 23, 775–788. http://dx.doi.org/ 10.1016/j.chc.2014.05.007 Mikami, A. Y., Ransone, M. L., & Calhoun, C. D. (2011). Influence of anxiety on the social functioning of children with and without ADHD. Journal of Attention Disorders, 15, 473–484. http://dx.doi.org/ 10.1177/1087054710369066 Milich, R., Balentine, A. C., & Lynam, D. R. (2001). ADHD combined type and ADHD predominantly inattentive type are distinct and unrelated disorders. Clinical Psychology: Science and Practice, 8, 463–488. http://dx.doi.org/10.1093/clipsy.8.4.463 Miller, T. W., Nigg, J. T., & Faraone, S. V. (2007). Axis I and II comorbidity in adults with ADHD. Journal of Abnormal Psychology, 116, 519–528. http://dx.doi.org/ 10.1037/0021-843X.116.3.519 Molina, B. S. G., & Pelham, W. E., Jr. (2014). Attentiondeficit/hyperactivity disorder and risk of substance use disorder: Developmental considerations, potential pathways, and opportunities for research. Annual Review of Clinical Psychology, 10, 607–639. http://dx.doi.org/10.1146/annurevclinpsy-032813-153722 Morgan, P. L., Staff, J., Hillemeier, M. M., Farkas, G., & Maczuga, S. (2013). Racial and ethnic disparities in ADHD diagnosis from kindergarten to eighth grade. Pediatrics, 132, 85–93. http://doi.org/10.1542/ peds.2012-2390

Newcorn, J. H., Halperin, J. M., Jensen, P. S., Abikoff, H. B., Arnold, L. E., Cantwell, D. P., . . . Vitiello, B. (2001). Symptom profiles in children with ADHD: Effects of comorbidity and gender. Journal of the American Academy of Child and Adolescent Psychiatry, 40, 137–146. http://dx.doi.org/10.1097/00004583200102000-00008 Nigg, J. T., & Casey, B. J. (2005). An integrative theory of attention-deficit/ hyperactivity disorder based on the cognitive and affective neurosciences. Development and Psychopathology, 17, 785–806. http://dx.doi.org/ 10.1017/S0954579405050376 Nigg, J. T., Willcutt, E. G., Doyle, A. E., & Sonuga-Barke, E. J. S. (2005). Causal heterogeneity in attentiondeficit/hyperactivity disorder: Do we need neuropsychologically impaired subtypes? Biological Psychiatry, 57, 1224–1230. http:// dx.doi.org/10.1016/j.biopsych.2004.08.025 Nussbaum, N. L. (2012). ADHD and female specific concerns: A review of the literature and clinical implications. Journal of Attention Disorders, 16, 87–100. http://dx.doi.org/10.1177/ 1087054711416909 Owens, J. A. (2009). A clinical overview of sleep and attention-deficit/hyperactivity disorder in children and adolescents. Journal of the Canadian Academy of Child and Adolescent Psychiatry, 18, 92–102. Owens, J. S., Goldfine, M. E., Evangelista, N. M., Hoza, B., & Kaiser, N. M. (2007). A critical review of selfperceptions and the positive illusory bias in children with ADHD. Clinical Child and Family Psychology Review, 10, 335–351. http://dx.doi.org/10.1007/ s10567-007-0027-3

441

Rooney and Pfiffner

Owens, J. S., & Hoza, B. (2003). The role of inattention and hyperactivity/impulsivity in the positive illusory bias. Journal of Consulting and Clinical Psychology, 71, 680–691. http://dx.doi.org/10.1037/0022006X.71.4.680

Copyright American Psychological Association. Not for further distribution.

Painter, C. A., Prevatt, F., & Welles, T. (2008). Career beliefs and job satisfaction in adults with symptoms of attention-deficit/hyperactivity disorder. Journal of Employment Counseling, 45, 178–188. http:// dx.doi.org/10.1002/j.2161-1920.2008.tb00057.x Pastor, P. N., & Reuben, C. A. (2005). Racial and ethnic differences in ADHD and LD in young school-age children: Parental reports in the National Health Interview Survey. Public Health Reports, 120, 383–392. http://dx.doi.org/10.1177/ 003335490512000405 Patros, C. H. G., Alderson, R. M., Kasper, L. J., Tarle, S. J., Lea, S. E., & Hudec, K. L. (2016). Choice-impulsivity in children and adolescents with attention-deficit/ hyperactivity disorder (ADHD): A meta-analytic review. Clinical Psychology Review, 43, 162–174. http://dx.doi.org/10.1016/j.cpr.2015.11.001 Pennington, B. F., McGrath, L. M., Rosenberg, J., Barnard, H., Smith, S. D., Willcutt, E. G., . . . Olson, R. K. (2009). Gene X environment interactions in reading disability and attention-deficit/hyperactivity disorder. Developmental Psychology, 45, 77–89. http:// dx.doi.org/10.1037/a0014549 Peterson, B. S., Pine, D. S., Cohen, P., & Brook, J. S. (2001). Prospective, longitudinal study of tic, obsessive-compulsive, and attention-deficit/ hyperactivity disorders in an epidemiological sample. Journal of the American Academy of Child and Adolescent Psychiatry, 40, 685–695. http://dx.doi.org/ 10.1097/00004583-200106000-00014 Pettersson, E., Sjölander, A., Almqvist, C., Anckarsäter, H., D’Onofrio, B. M., Lichtenstein, P., & Larsson, H. (2015). Birth weight as an independent predictor of ADHD symptoms: A within-twin pair analysis. Journal of Child Psychology and Psychiatry, 56, 453–459. http://dx.doi.org/10.1111/ jcpp.12299 Pfiffner, L. J., & Haack, L. M. (2014). Behavior management for school-aged children with ADHD. Child and Adolescent Psychiatric Clinics of North America, 23, 731–746. http://dx.doi.org/10.1016/j. chc.2014.05.014 Pfiffner, L. J., & Haack, L. M. (2015). Nonpharmacological treatments for childhood attention-deficit/ hyperactivity disorder and their combination with medication. In P. E. Nathan & J. M. Gorman (Eds.), A guide to treatments that work (4th ed., pp. 55–84). New York, NY: Oxford University Press. Pfiffner, L. J., Hinshaw, S. P., Owens, E., Zalecki, C., Kaiser, N. M., Villodas, M., & McBurnett, K. (2014). 442

A two-site randomized clinical trial of integrated psychosocial treatment for ADHD-inattentive type. Journal of Consulting and Clinical Psychology, 82, 1115–1127. http://dx.doi.org/10.1037/a0036887 Pfiffner, L. J., & McBurnett, K. (2006). Family correlates of comorbid anxiety disorders in children with attention deficit/hyperactivity disorder. Journal of Abnormal Child Psychology, 34, 719–735. http:// dx.doi.org/10.1007/s10802-006-9060-9 Pfiffner, L. J., McBurnett, K., Rathouz, P. J., & Judice, S. (2005). Family correlates of oppositional and conduct disorders in children with attention deficit/ hyperactivity disorder. Journal of Abnormal Child Psychology, 33, 551–563. http://dx.doi.org/10.1007/ s10802-005-6737-4 Pfiffner, L. J., Rooney, M., Haack, L., Villodas, M., Delucchi, K., & McBurnett, K. (2016). A randomized controlled trial of a school-implemented schoolhome intervention for attention-deficit/hyperactivity disorder symptoms and impairment. Journal of the American Academy of Child and Adolescent Psychiatry, 55, 762–770. http://dx.doi.org/10.1016/j. jaac.2016.05.023 Philipsen, A., Feige, B., Hesslinger, B., Scheel, C., Ebert, D., Matthies, S., . . . Lieb, K. (2009). Borderline typical symptoms in adult patients with attention deficit/hyperactivity disorder. ADHD Attention Deficit and Hyperactivity Disorders, 1, 11–18. http:// dx.doi.org/10.1007/s12402-009-0001-7 Philipsen, A., Richter, H., Peters, J., Alm, B., Sobanski, E., Colla, M., . . . Hesslinger, B. (2007). Structured group psychotherapy in adults with attention deficit hyperactivity disorder: Results of an open multicentre study. Journal of Nervous and Mental Disease, 195, 1013–1019. http://dx.doi.org/10.1097/ NMD.0b013e31815c088b Pinto, R., Monzani, B., Leckman, J. F., Rück, C., Serlachius, E., Lichtenstein, P., & Mataix-Cols, D. (2016). Understanding the covariation of tics, attention-deficit/hyperactivity, and obsessivecompulsive symptoms: A population-based adult twin study. American Journal of Medical Genetics Part B: Neuropsychiatric Genetics, 171, 938–947. http:// doi.org/10.1002/ajmg.b.32436 Pliszka, S. (2007). Practice parameter for the assessment and treatment of children and adolescents with attention-deficit/hyperactivity disorder. Journal of the American Academy of Child and Adolescent Psychiatry, 46, 894–921. http://dx.doi.org/10.1097/ chi.0b013e318054e724 Polanczyk, G. V., Willcutt, E. G., Salum, G. A., Kieling, C., & Rohde, L. A. (2014). ADHD prevalence estimates across three decades: An updated systematic review and meta-regression analysis. International Journal of Epidemiology, 43, 434–442. http://dx.doi.org/10.1093/ije/dyt261

Attention-Deficit/Hyperactivity Disorder

Power, T. J., Mautone, J. A., Soffer, S. L., Clarke, A. T., Marshall, S. A., Sharman, J., . . . Jawad, A. F. (2012). A family-school intervention for children with ADHD: Results of a randomized clinical trial. Journal of Consulting and Clinical Psychology, 80, 611–623. http://dx.doi.org/10.1037/a0028188

Copyright American Psychological Association. Not for further distribution.

Reiss, F. (2013). Socioeconomic inequalities and mental health problems in children and adolescents: A systematic review. Social Science and Medicine, 90, 24–31. http://doi.org/10.1016/j. socscimed.2013.04.026 Riddle, M. A., Yershova, K., Lazzaretto, D., Paykina, N., Yenokyan, G., Greenhill, L., . . . Posner, K. (2013). The preschool attention-deficit/hyperactivity disorder treatment study (PATS) 6-year follow-up. Journal of the American Academy of Child and Adolescent Psychiatry, 52, 264–278.e2. http://dx.doi. org/10.1016/j.jaac.2012.12.007 Robison, L. M., Skaer, T. L., Sclar, D. A., & Galin, R. S. (2002). Is attention deficit hyperactivity disorder increasing among girls in the U.S.? Trends in diagnosis and the prescribing of stimulants. CNS Drugs, 16, 129–137. http:// dx.doi.org/10.2165/00023210-200216020-00005 Rohde, P., Clarke, G. N., Lewinsohn, P. M., Seeley, J. R., & Kaufman, N. K. (2001). Impact of comorbidity on a cognitive–behavioral group treatment for adolescent depression. Journal of the American Academy of Child and Adolescent Psychiatry, 40, 795–802. http://dx.doi.org/10.1097/00004583200107000-00014 Rosenberg, J., Pennington, B. F., Willcutt, E. G., & Olson, R. K. (2012). Gene by environment interactions influencing reading disability and the inattentive symptom dimension of attention deficit/hyperactivity disorder. Journal of Child Psychology and Psychiatry, 53, 243–251. http://dx.doi.org/10.1111/j.14697610.2011.02452.x Rosval, L., Steiger, H., Bruce, K., Israël, M., Richardson, J., & Aubut, M. (2006). Impulsivity in women with eating disorders: Problem of response inhibition, planning, or attention? International Journal of Eating Disorders, 39, 590–593. http://dx.doi.org/10.1002/ eat.20296 Rucklidge, J. J., & Tannock, R. (2001). Psychiatric, psychosocial, and cognitive functioning of female adolescents with ADHD. Journal of the American Academy of Child and Adolescent Psychiatry, 40, 530–540. http://dx.doi.org/10.1097/00004583200105000-00012 Russell, A. E., Ford, T., Williams, R., & Russell, G. (2016). The association between socioeconomic disadvantage and attention deficit/hyperactivity disorder (ADHD): A systematic review. Child Psychiatry and Human Development, 47, 440–458. http://dx.doi.org/10.1007/s10578-015-0578-3

Safren, S. A., Sprich, S., Mimiaga, M. J., Surman, C., Knouse, L., Groves, M., & Otto, M. W. (2010). Cognitive behavioral therapy vs. relaxation with educational support for medication-treated adults with ADHD and persistent symptoms: A randomized controlled trial. JAMA: Journal of the American Medical Association, 304, 875–880. http://dx.doi.org/ 10.1001/jama.2010.1192 Sagvolden, T., Johansen, E. B., Aase, H., & Russell, V. A. (2005). A dynamic developmental theory of attention-deficit/hyperactivity disorder (ADHD) predominantly hyperactive/impulsive and combined subtypes. Behavioral and Brain Sciences, 28, 397–419. http://dx.doi.org/10.1017/S0140525X05000075 Sánchez-Mora, C., Richarte, V., Garcia-Martínez, I., Pagerols, M., Corrales, M., Bosch, R., . . . Ribasés, M. (2015). Dopamine receptor DRD4 gene and stressful life events in persistent attention deficit hyperactivity disorder. American Journal of Medical Genetics: Part B. Neuropsychiatric Genetics, 168, 480–491. http:// doi.org/10.1002/ajmg.b.32340 Sanders, A. P., Claus Henn, B., & Wright, R. O. (2015). Perinatal and childhood exposure to cadmium, manganese, and metal mixtures and effects on cognition and behavior: A review of recent literature. Current Environmental Health Reports, 2, 284–294. http://dx.doi.org/10.1007/s40572-015-0058-8 Schneider, H., & Eisenberg, D. (2006). Who receives a diagnosis of attention-deficit/ hyperactivity disorder in the United States elementary school population? Pediatrics, 117, e601–e609. http:// dx.doi.org/10.1542/peds.2005-1308 Sciberras, E., Lycett, K., Efron, D., Mensah, F., Gerner, B., & Hiscock, H. (2014). Anxiety in children with attention-deficit/hyperactivity disorder. Pediatrics, 133, 801–808. http://dx.doi.org/10.1542/peds.2013-3686 Seidman, L. J., Biederman, J., Monuteaux, M. C., Doyle, A. E., & Faraone, S. V. (2001). Learning disabilities and executive dysfunction in boys with attentiondeficit/hyperactivity disorder. Neuropsychology, 15, 544–556. http://dx.doi.org/10.1037/08944105.15.4.544 Semeijn, E. J., Kooij, J. J. S., Comijs, H. C., Michielsen, M., Deeg, D. J. H., & Beekman, A. T. F. (2013). Attention-deficit/hyperactivity disorder, physical health, and lifestyle in older adults. Journal of the American Geriatrics Society, 61, 882–887. http:// dx.doi.org/10.1111/jgs.12261 Shaw, P., Stringaris, A., Nigg, J., & Leibenluft, E. (2014). Emotion dysregulation in attention deficit hyperactivity disorder. American Journal of Psychiatry, 171, 276–293. http://dx.doi.org/10.1176/ appi.ajp.2013.13070966 Sibley, M. H., & Kuriyan, A. B. (2016). DSM–5 changes enhance parent identification of symptoms in 443

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adolescents with ADHD. Psychiatry Research, 242, 180–185. http://dx.doi.org/10.1016/j. psychres.2016.05.036 Sibley, M. H., Pelham, W. E., Jr., Molina, B. S., Gnagy, E. M., Waschbusch, D. A., Garefino, A. C., . . . Karch, K. M. (2012). Diagnosing ADHD in adolescence. Journal of Consulting and Clinical Psychology, 80, 139–150. http://dx.doi.org/10.1037/a0026577

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Siegel, C. E., Laska, E. M., Wanderling, J. A., Hernandez, J. C., & Levenson, R. B. (2016). Prevalence and diagnosis rates of childhood ADHD among racialethnic groups in a public mental health system. Psychiatric Services, 67, 199–205. http://dx.doi.org/ 10.1176/appi.ps.201400364 Silk, T. J., Genc, S., Anderson, V., Efron, D., Hazell, P., Nicholson, J. M., . . . Sciberras, E. (2016). Developmental brain trajectories in children with ADHD and controls: A longitudinal neuroimaging study. BMC Psychiatry, 16, 59. http://dx.doi.org/ 10.1186/s12888-016-0770-4

of tic disorders on ADHD outcome across the life cycle: Findings from a large group of adults with and without ADHD. American Journal of Psychiatry, 158, 611–617. http://dx.doi.org/10.1176/appi. ajp.158.4.611 Spencer, T. J., Faraone, S. V., Tarko, L., McDermott, K., & Biederman, J. (2014). Attention-deficit/ hyperactivity disorder and adverse health outcomes in adults. Journal of Nervous and Mental Disease, 202, 725–731. http://dx.doi.org/10.1097/ NMD.0000000000000191 Staikova, E., Gomes, H., Tartter, V., McCabe, A., & Halperin, J. M. (2013). Pragmatic deficits and social impairment in children with ADHD. Journal of Child Psychology and Psychiatry, 54, 1275–1283. http:// dx.doi.org/10.1111/jcpp.12082 Steadman, K. M., & Knouse, L. E. (2016). Is the relationship between ADHD symptoms and binge eating mediated by impulsivity? Journal of Attention Disorders, 20, 907–912.

Simon, V., Czobor, P., & Bitter, I. (2013). Is ADHD severity in adults associated with the lifetime prevalence of comorbid depressive episodes and anxiety disorders? European Psychiatry, 28, 308–314. http://dx.doi.org/10.1016/j.eurpsy.2012.05.002

Steinberg, L., Graham, S., O’Brien, L., Woolard, J., Cauffman, E., & Banich, M. (2009). Age differences in future orientation and delay discounting. Child Development, 80, 28–44. http://dx.doi.org/10.1111/ j.1467-8624.2008.01244.x

Solanto, M. V., Marks, D. J., Wasserstein, J., Mitchell, K., Abikoff, H., Alvir, J. M. J., & Kofman, M. D. (2010). Efficacy of meta-cognitive therapy for adult ADHD. American Journal of Psychiatry, 167, 958–968. http:// dx.doi.org/10.1176/appi.ajp.2009.09081123

Sucksdorff, M., Lehtonen, L., Chudal, R., Suominen, A., Joelsson, P., Gissler, M., & Sourander, A. (2015). Preterm birth and poor fetal growth as risk factors of attention-deficit/ hyperactivity disorder. Pediatrics, 136, e599–e608. http://dx.doi.org/10.1542/ peds.2015-1043

Solanto, M. V., Pope-Boyd, S. A., Tryon, W. W., & Stepak, B. (2009). Social functioning in predominantly inattentive and combined subtypes of children with ADHD. Journal of Attention Disorders, 13, 27–35. http://dx.doi.org/ 10.1177/1087054708320403 Solomon, C. G., Volkow, N. D., & Swanson, J. M. (2013). Clinical practice: Adult attention-deficit/ hyperactivity disorder. New England Journal of Medicine, 369, 1935–1944. http://dx.doi.org/10.1056/ NEJMcp1212625 Sonuga-Barke, E. J. S. (2005). Causal models of attentiondeficit/hyperactivity disorder: From common simple deficits to multiple developmental pathways. Biological Psychiatry, 57, 1231–1238. http:// dx.doi.org/10.1016/j.biopsych.2004.09.008 Sonuga-Barke, E. J. S., Sergeant, J. A., Nigg, J., & Willcutt, E. (2008). Executive dysfunction and delay aversion in attention deficit hyperactivity disorder: Nosologic and diagnostic implications. Child and Adolescent Psychiatric Clinics of North America, 17, 367–384. http://dx.doi.org/10.1016/j. chc.2007.11.008 Spencer, T. J., Biederman, J., Faraone, S., Mick, E., Coffey, B., Geller, D., . . . Wilens, T. (2001). Impact 444

Sung, V., Hiscock, H., Sciberras, E., & Efron, D. (2008). Sleep problems in children with attention-deficit/ hyperactivity disorder: Prevalence and the effect on the child and family. Archives of Pediatrics and Adolescent Medicine, 162, 336–342. http:// dx.doi.org/10.1001/archpedi.162.4.336 Surman, C. B. H., Biederman, J., Spencer, T., Yorks, D., Miller, C. A., Petty, C. R., & Faraone, S. V. (2011). Deficient emotional self-regulation and adult attention deficit hyperactivity disorder: A family risk analysis. American Journal of Psychiatry, 168, 617–623. http://dx.doi.org/10.1176/appi. ajp.2010.10081172 Taras, H., & Potts-Datema, W. (2005). Chronic health conditions and student performance at school. Journal of School Health, 75, 255–266. http:// dx.doi.org/10.1111/j.1746-1561.2005.00034.x Taylor, E. (2011). Antecedents of ADHD: A historical account of diagnostic concepts. Attention Deficit and Hyperactivity Disorders, 3, 69–75. http:// dx.doi.org/10.1007/s12402-010-0051-x Tucker-Drob, E. M., Johnson, K. E., & Jones, R. N. (2009). The cognitive reserve hypothesis: A longitudinal examination of age-associated declines

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in reasoning and processing speed. Developmental Psychology, 45, 431–446. http://dx.doi.org/10.1037/ a0014012 Uchida, M., Spencer, T. J., Faraone, S. V., & Biederman, J. (2015). Adult outcome of ADHD: An overview of results from the MGH longitudinal family studies of pediatrically and psychiatrically referred youth with and without ADHD of both sexes. Journal of Attention Disorders. Advance online publication. Ullsperger, J. M., Nigg, J. T., & Nikolas, M. A. (2016). Does child temperament play a role in the association between parenting practices and child attention deficit/hyperactivity disorder? Journal of Abnormal Child Psychology, 44, 167–178. http://dx.doi.org/ 10.1007/s10802-015-9982-1 Vaa, T. (2014). ADHD and relative risk of accidents in road traffic: A meta-analysis. Accident Analysis and Prevention, 62, 415–425. http://dx.doi.org/10.1016/j. aap.2013.10.003 Vélez-Galarraga, R., Guillén-Grima, F., Crespo-Eguílaz, N., & Sánchez-Carpintero, R. (2016). Prevalence of sleep disorders and their relationship with core symptoms of inattention and hyperactivity in children with attention-deficit/hyperactivity disorder. European Journal of Paediatric Neurology, 20, 935–937. http://doi.org/10.1016/j.ejpn.2016.07.004 Vile Junod, R. E., DuPaul, G. J., Jitendra, A. K., Volpe, R. J., & Cleary, K. S. (2006). Classroom observations of students with and without ADHD: Differences across types of engagement. Journal of School Psychology, 44, 87–104. http://dx.doi.org/10.1016/j. jsp.2005.12.004 Visser, S. N., Danielson, M. L., Bitsko, R. H., Holbrook, J. R., Kogan, M. D., Ghandour, R. M., . . . Blumberg, S. J. (2014). Trends in the parent-report of health care provider-diagnosed and medicated attention-deficit/ hyperactivity disorder: United States, 2003–2011. Journal of the American Academy of Child and Adolescent

Psychiatry, 53, 34–46. http://dx.doi.org/10.1016/j. jaac.2013.09.001 Weafer, J., Camarillo, D., Fillmore, M. T., Milich, R., & Marczinski, C. A. (2008). Simulated driving performance of adults with ADHD: Comparisons with alcohol intoxication. Experimental and Clinical Psychopharmacology, 16, 251–263. http://dx.doi.org/ 10.1037/1064-1297.16.3.251 Webb, E. (2013). Poverty, maltreatment and attention deficit hyperactivity disorder. Archives of Disease in Childhood, 98, 397–400. http://dx.doi.org/10.1136/ archdischild-2012-303578 Willcutt, E. G. (2012). The prevalence of DSM–IV attention-deficit/hyperactivity disorder: A metaanalytic review. Neurotherapeutics, 9, 490–499. http://dx.doi.org/10.1007/s13311-012-0135-8 Willcutt, E. G. (2015). Theories of ADHD. In R. A. Barkley (Ed.), Attention-deficit hyperactivity disorder: A handbook for diagnosis and treatment (4th ed., ­ pp. 391–404). New York, NY: Guilford Press. Willcutt, E. G., Nigg, J. T., Pennington, B. F., Solanto, M. V., Rohde, L. A., Tannock, R., . . . Lahey, B. B. (2012). Validity of DSM–IV attention deficit/ hyperactivity disorder symptom dimensions and subtypes. Journal of Abnormal Psychology, 121, 991–1010. http://dx.doi.org/10.1037/a0027347 Willcutt, E. G., Sonuga-Barke, E. J. S., Nigg, J. T., & Sergeant, J. A. (2008). Recent developments in neuropsychological models of childhood psychiatric disorders. In T. Banaschewski & L. A. Rohde (Eds.), Advances in biological psychiatry (pp. 195–226). http://dx.doi.org/10.1159/000118526 Yerys, B. E., Wallace, G. L., Sokoloff, J. L., Shook, D. A., James, J. D., & Kenworthy, L. (2009). Attentiondeficit/hyperactivity disorder symptoms moderate cognition and behavior in children with autism spectrum disorders. Autism Research, 2, 322–333.

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Chapter 20

Autism Spectrum Disorder

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Matthew D. Lerner, Carla A. Mazefsky, Susan W. White, and James C. McPartland

Once considered a rare, unitary, intractable diagnosis (Treffert, 1970), the condition now known as autism spectrum disorder (ASD) has evolved into one of the most complex diagnostic categories in modern mental health. ASD is now among the most common neurodevelopmental disorders (with current prevalence at 1:68 children; Centers for Disease Control and Prevention, 2014), and evinces a vast array of presentations across the lifespan, ranging from severe, sometimes genetically discrete entities (Oddi, Crusio, D’Amato, & Pietropaolo, 2013) to subtle, complex manifestations that can be difficult to distinguish from other (often comorbid) conditions, or it can go undetected for decades (Ashwood et al., 2016). Indeed, the current Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM–5; American Psychiatric Association, 2013) has created an umbrella disorder of ASD, consisting of core deficits in social communication, restricted and repetitive behaviors, and atypical response to sensory information. It is increasingly evident that this category encompasses a myriad of largely as-yet-unspecified genetically and environmentally determined conditions and syndromes that are sometimes called “the autisms” (Coleman & Gillberg, 2012); so wide is the array of functioning, that the very concept of dimensionality is reflected in the colloquial reference to ASD as “the spectrum.” Treatment and prognosis evolved in ways consistent with diagnostic changes. A percentage of children with ASD no longer meet diagnostic

criteria in adolescence and young adulthood (socalled “optimal outcomes”; Fein et al., 2013), and there are an increasing number of psychosocial and behavioral interventions that attain “empirically supported” status for treating the core symptoms of ASD (Smith & Iadarola, 2015). In this chapter, we first review the diagnostic entity, history, and practices of ASD. Then, we consider epidemiological and etiological factors related to the condition, including presentation and comorbidity profiles. Finally, we review current interventions for ASD, and discuss future directions for research and practice. Diagnostic Criteria Leo Kanner’s (1943) classic paper, “Autistic Disturbances of Affective Contact,” is widely credited as providing the first description of autism. It included case studies of 11 children with apparent lack of social interest from early in infancy. His use of the term autism was meant to capture their preferred social isolation, but Kanner also described their other characteristics, including difficulty adjusting to nonsocial change and insistence on sameness, as well as unusual self-stimulatory behaviors (e.g., hand flapping), which he interpreted as being a mechanism to cope with change. He also noted that three of the children were nonverbal and described some additional atypical language characteristics, but did not conceputalize them as essential to the disorder.

Matthew D. Lerner was supported by NIMH R01 MH110585, the Simons Foundation (SFARI# 381283), and a NARSAD Young Investigator Award (#24890). Carla A. Mazefsky was supported by NICHD R01 HD079512. Susan W. White was supported by NIMH R33 MH100268, NIMH R34 MH104337, and NICHD R03 HD081070. James C. McPartland was supported by NIH U19 MH108206, NIMH R01 MH107426, and NIMH R01 MH100173. http://dx.doi.org/10.1037/0000065-020 APA Handbook of Psychopathology: Vol. 2. Child and Adolescent Psychopathology, J. N. Butcher (Editor-in-Chief) Copyright © 2018 by the American Psychological Association. All rights reserved. APA Handbook of Psychopathology: Child and Adolescent Psychopathology, edited by J. N. Butcher and P. C. Kendall Copyright © 2018 American Psychological Association. All rights reserved.

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Following Kanner’s (1943) initial report, interest in and research on autism gradually grew. The distinction in phenomenology between autism and schizoprenia was made clearer (E. R. Ritvo & Freeman, 1978; Rutter, 1978) and eventually led to its official recognition as a unique disorder in the DSM–III (American Psychiatric Association, 1980), as infantile autism. It was placed within a new class of disorders called pervasive developmental disorders (PDDs), which focused on early development. To meet criteria for infantile autism, children had to satisfy each criterion, which included pervasive lack of responsivness to other people, language delays, “peculiar” speech patterns, and “bizarre responses” to the environment. Although it also requried an onset prior to 30 months, another category was created to capture children who developed similar symptoms after normal early development called childhood-onset pervasive developmental disorder (COPDD). Infantile autism and COPDD could be diagnosed as full syndrome (meeting all criteria currently) or residual state (met criteria previously). Finally, a diagnosis of atypical persavive developmental disorder (APDD) was included for those who did not meet full criteria for either infantile autism or COPDD. Although inclusion of infantile autism and PDD was an advance for the field, there were several concerns about the approach and criteria. An advisory group was convened before the next iteration of the DSM to address the following primary concerns: “(a) clinical features of [autistic disorder] AD; (b) effects of development on AD symptom presentation; (c) age at onset of AD; (d) validity of COPDD; (e) meaning of APDD; (f) subgroups within PDD; (g) differential diagnosis; (h) IQ and adaptive functioning; and, (i) organic etiology” (Waterhouse, Wing, Spitzer, & Siegel, 1992, p. 529). Decisions were made on the basis of the collective expertise of the group and available empircal evidence. In 1987, DSM–III–R (American Psychiatric Association, 1987) was published and infantile autism was renamed autistic disorder. The final criteria were also informed by a formal field trial (Spitzer & Siegel, 1990) and categorized into three domains, including qualitative impairment in reciprocal social interaction, qualitative impairment in communication, and restricted interests. A polythetic approach was adopted that required eight of 16 criteria to be 448

endorsed, with at least two from the social category and at least one each from the other two categories. Another major change in DSM–III–R was simplification of the categories under the PDD umbrella. The full syndrome versus residual distinctions were eliminated in favor of better specification of the developmental course, COPDD was dropped, and ayptical PDD was replaced with PDD not otherwise specified. Although these changes were felt to be justified, there remained many differences of opinion about nosology and a general feeling that the DSM–III–R approach underspecified potential subgroups (Waterhouse et al., 1992). A particular topic of debate was the possibility of including Asperger’s syndrome as a diagnosis. In 1944, only one year after Kanner’s (1943) description of autism, Asperger had described a group of children who were similar to those described by Kanner’s in many regards, but Asperger focused on their adult-like speech, clumsiness, overly focused interests, and often (but not always) high intellect. However, Asperger’s description went largely unrecognized until it was translated into English decades later (Wing, 1981; see Robison, 2016, for more on this history). Therefore, at the time the DSM–III–R was developed, it was unclear how Asperger’s could be reliably differentiated from autistic disorder, and it was not felt to be useful (Szatmari, 1992). The lack of inclusion of Asperger’s syndrome as a disorder within DSM–III–R was one of the issues in the growing divergence in the approach to PDDs between the DSM and the draft of the tenth edition of the World Health Organization’s (2004) International Statistical Classification of Diseases and Related Health Problems (ICD–10), which did include Asperger’s syndrome (Volkmar, Cicchetti, Bregman, & Cohen, 1992). Results of a series of papers analyzing differences between the ICD and the DSM suggested that the DSM–III–R was more developmentally oriented than the ICD–10, but that it was overly broad (Volkmar et al., 1992). In advance of the publication of the DSM–IV, work groups were convened to review existing research and identify areas of consensus and controversy. The consensus was that, to the extent possible, comparability of the DSM–IV and the ICD–10 was desired (Rutter & Schopler, 1992).

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An international field trial was conducted in an attempt to address these issues and compare the two systems. The results of the field trial suggested that the DSM–III–R differed from the DSM–III (in the “lifetime” diagnostic sense), the ICD–10, and clinician judgment. DSM–III–R criteria resulted in a high rate of false positive cases, particularly if meaningful intellectual disability was present (in such cases, the false positive rate was about 60%). The detailed draft ICD–10 research definition worked well but was, as expected, more detailed than was felt desirable for DSM–IV (which was intended for clinical use and research). Factor analyses were also conducted and supported several solutions including the traditional triad of domains of difficulty (social, communication, and restricted interests). These results informed the final versions of the DSM–IV and ICD–10, which proved reasonably robust, with a good balance of clinical and research utility; differences between the two systems were relatively minor (e.g., the ICD–10 provided various codes for ways in which cases could be subthreshold). Another aspect of the joint field trial was the inclusion of other disorders within the PDD class, including Asperger’s disorder. Although the DSM–IV/ICD–10 categories were highly effective in fostering research (Gupta & Rossignol, 2009), the diagnostic validity of the subtypes (e.g., autistic disorder versus Asperger’s disorder) has been challenged (Mayes, Calhoun, & Crites, 2001). With publication the DSM–5 (American Psychiatric Association, 2013), the diagnostic criteria again underwent nontrivial restructuring. The umbrella of PDDs was transformed into a single class of ASD. This category subsumed previously distinct diagnostic entities. The triad of impairments (social, communication, restricted/ repetitive behavior) was collapsed into two categories: restricted and repetitive behaviors and a merged social–communication domain. This social–communication category was made monothetic, which required that a person demonstrate symptoms across all three clusters to meet criteria for ASD. The restricted and repetitive behaviors domain remained polythetic, requiring presence of symptoms in two of four clusters, and added a symptom cluster reflecting sensory difficulties.

Presence of symptoms in early development was retained, though the capacity for such symptoms to be “masked” by compensatory strategies in some (usually adult) individuals was added. The new separate category of social communication disorder was added as well (Brukner-Wertman, Laor, & Golan, 2016); this distinct class requires pragmatic difficulties and problems in the use of verbal and nonverbal communication in social contexts. Current and historical symptom presentation can be considered in support of a diagnosis of ASD. A single behavioral example should not be used to satisfy multiple criteria. Similarly, it is important to emphasize behaviors that are clearly atypical and present across multiple contexts rather than basing decisions on an isolated single behavior. The symptoms must be present during early development, though a change in the DSM–5 is explicitly noting that they may not be fully evident until social demands increase or may be initially masked by other abilities or learned strategies. In addition, as with all disorders, the symptoms must impair functioning. A reexamination of DSM–IV field trial data suggested that individuals with PDDs other than autism (i.e., Asperger’s syndrome, PDD not otherwise specified) and individuals with normative IQs might be less likely to meet DSM–5 criteria (McPartland, Reichow, & Volkmar, 2012). Other studies suggest that young children (Barton, Robins, Jashar, Brennan, & Fein, 2013) and women (Frazier et al., 2012) may be less likely to meet DSM–5 criteria. However, other studies suggest greater overlap between DSM–IV and DSM–5 coverage (Huerta, Bishop, Duncan, Hus, & Lord, 2012). There are methodological variations among these studies (e.g., reanalysis of historical data versus using proposed criteria; clinical observation versus endorsement on one or more standardized assessment instruments), which influence ascertainment (Mazefsky, McPartland, Gastgeb, & Minshew, 2013). Notably, the DSM–5 contains a “grandfather clause,” which indicates that individuals diagnosed with a PDD under DSM–IV should retain a DSM–5 ASD diagnosis, potentially in response to criticism from stakeholders over concern about losing diagnostic status and related supports. However, recent studies support 449

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the utility and increased reliability of the new criteria, and some have argued that the use of a single dimensional category provides a new opportunity to identify more meaningful subgroups based on underlying biology or clinical features (Grzadzinski, Huerta, & Lord, 2013). In sum, the diagnosis of ASD has evolved considerably over the past 70 years. Although controversy over the specific criteria continues, the criteria are being developed on the basis of a rapidly growing body of scientific evidence, only made possible by outlining a specific approach for the reliable identification of affected individuals. Although continued modification of diagnostic criteria for ASD is foreseeable, there now exists a much deeper understanding of the core characteristics as well as other associated features.

Social–Emotional Aspects of Autism Spectrum Disorder Presentation Above and beyond the DSM-defined symptoms of ASD, there are several notable aspects to the social–emotional presentation of individuals with ASD. First, many individuals are often overwhelmed by massed social stimuli (e.g., crowds, confined spaces, unstructured social settings; Lerner, Haque, Northrup, Lawer, & Bursztajn, 2012; Stein, Klin, & Miller, 2004). Avoidance of such situations is common, which can present similarly to social anxiety disorder (Kerns & Kendall, 2012; Kerns et al., 2014). This reaction is, however, typically distinct from that seen in social anxiety as it is marked less by maladaptive cognitions, and more by basic, often sensory-level reactivity to these environments. Such a response can impact clinical presentation but also intervention, as skills learned in a more structured or predictable setting may be difficult to generalize. Second, many individuals with ASD show difficulties processing not only others’ emotions, but their own emotions as well (Hill, Berthoz, & Frith, 2004). Indeed, several studies suggest that comorbid alexithymia is not only highly common in ASD, but may be more predictive of emotion recognition deficits in this population than ASD status itself (Cook, Brewer, Shah, & Bird, 2013). These challenges can augment social difficulties because of challenges in calibrating one’s response to that of others. 450

Likewise, cognitive–behavioral therapies (CBT) that rely on accurate reflection of emotional response can be hampered by this; additional focus on this stage of therapy may be warranted (Scarpa, White, & Attwood, 2013). Third, many individuals with ASD show difficulties in general self-regulation (i.e., the ability to manage one’s general arousal level; Loveland, 2005), and emotion regulation in particular (Mazefsky, Herrington, et al., 2013). These challenges can manifest in various ways throughout childhood and young adulthood. For instance, many children with ASD may have difficulty engaging in classroom or other learning settings not because they cannot comprehend the given information, but the amount of self-regulation required to do so is quite taxing. Sensory or other self-regulatory techniques (e.g., stress balls, blankets, regular opportunities to run around the room, “destress” corners that children can voluntarily elect to use) are often used to help maximize engagement in these settings (Laurent & Rubin, 2004). Conversely, some individuals with ASD may exhibit flattened or amplified (i.e., “cartoonish”) affect during interactions; such presentation may sometimes be due to self-regulatory challenges, whereas in other individuals it may not vary dramatically by setting or subjective affective differences. Likewise, challenges with emotion regulation can often portend behavioral difficulties in children with ASD, particularly in the face of frustration or disappointment. Although new measures (behavioral and physiological; Mazefsky et al., 2016; Mazefsky & White, 2014; White et al., 2014) have recently been developed in attempts to tap this construct more directly, much practical work on emotion regulation has taken place under the auspices of antecedent management within a behavioral framework. In fact, as with other clinical populations (Aldao, Gee, De Los Reyes, & Seager, 2016), it has been argued that such emotion regulatory challenges may be the core feature of ASD that leads to impairment across settings. Fourth, because of pathognomonic social difficulties, children with ASD tend to exhibit challenges in friendships and other close relationships. Earlier work suggested that friendships were rare among individuals with ASD, but more recent findings

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suggests that most children with ASD do make friends—although they are typically fewer in number, and lesser in subjective quality or closeness, relative to typically developing peers (Mendelson, Gates, & Lerner, 2016). Clinically, a focus on establishing and maintaining such friendships is often vital, given the protective value of even one close friend in typically developing children (Bagwell, Newcomb, & Bukowski, 1998). A final feature of ASD that is less commonly discussed is the emergence of social self-awareness (Lerner, Calhoun, Mikami, & De Los Reyes, 2012; Verhoeven et al., 2012). Children with ASD have traditionally been seen as exhibiting a fundamental inability to view or understand themselves in a social context (D. Williams, 2010), but emerging work suggests that, especially around pre- or early adolescence, children with ASD do often begin to notice differences between themselves and others. This new understanding can be critical, as noticing these differences can coincide with the emergence of increased social challenges, augmenting the so-called “second hit” of adolescence (Picci & Scherf, 2014). It is often essential for clinicians, parents, and teachers to be attuned to such awareness, and to potentially offer evidence of strength domains to counter potentially emergent negative self-perceptions.

Features of Autism Spectrum Disorder That Can Affect Presentation Presentation of individuals with ASD can vary as a function of basic demographic features. Young children with ASD, for instance, may present with higher rates of language abnormalities and selfstimulatory behaviors (Bogdashina, 2016; Tager-Flusberg et al., 2009). However, although such features may become less common with age, they may in fact persist well into adulthood in many individuals. Conversely, some features of ASD may modify with age (e.g., repetitive behaviors manifesting more frequently as internalized perseverative cognitions in adults; Chowdhury, Benson, & Hillier, 2010). This pattern stands in contrast to analog symptoms (e.g., hyperactivity) in other neurodevelopmental disorders, which decrease more markedly with age (Biederman, Mick, & Faraone, 2000).

Marked differences in presentation between men and women with ASD are increasingly appreciated in the current literature (Halladay et al., 2015). There is a 4:1 male to female gender distribution in ASD epidemiologically, but recent work suggests that this gap may overstate the difference somewhat. Girls with ASD may be more likely to go undetected because of subtler presentation of symptoms. For instance, boys with ASD may tend toward more externalizing or visible behaviors, fewer girls with ASD present these behaviors as overtly. Likewise, core features of ASD often differ across genders; boys with ASD may experience perseverative interests associated with clearly atypical topics (e.g., Thomas the Train as an interest for a 14-year-old), whereas girls with ASD may perseverate on more normative topics (e.g., dolls and babies, the social hierarchy of their middle school classroom; Hiller, Young, & Weber, 2016). These features may impact efforts at early detection in girls with ASD, although boys with ASD (especially those with intact cognitive function) may be at greater risk of misdiagnosis with attentiondeficit/hyperactivity disorder (ADHD), anxiety, or oppositional defiant disorder (Van Schalkwyk, Peluso, Qayyum, McPartland, & Volkmar, 2015). Individuals with ASD experience high rates of psychiatric comorbidities (Simonoff et al., 2008). Although such symptoms may increase the burden on an individual patient additively, they can also interact with primary symptoms of ASD in complex ways. For instance, if an individual with ASD experiences social anxiety, it may be difficult to determine whether social withdrawal emerges because of anxiety per se, or more primary ASD-related social deficits (Kerns, Kendall, et al., 2015). On the other hand, many young children with ASD are sometimes also diagnosed with oppositional defiant disorder (Gadow, Devincent, & Drabick, 2008). However, if a child has difficulty processing verbal instructions or is overwhelmed by certain stimuli or settings but cannot articulate this fact, their behaviors may appear oppositional in nature when they are in fact secondary to ASD. Likewise, when these symptoms co-occur, opportunities for normative social engagement may be further curtailed. Therefore, such comorbidities may modify the expression of ASD clinical presentation. 451

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A large portion of the ASD population (30%) exhibits meaningfully limited or no verbal ability (Kasari, Brady, Lord, & Tager-Flusberg, 2013; Tager-Flusberg & Kasari, 2013) and/or intellectual disability. Such impairments directly impact communication in these individuals, augmenting related symptoms in the population. However, limited verbal ability can also impact other symptom expression as well. For instance, minimally verbal children with ASD do not necessarily show any reductions in insistence on sameness or other restricted, repetitive behaviors. However, although a more verbal individual may be able to see such behaviors become more cognitively mediated over time (e.g., as perseverations), minimally verbal individuals may not. Instead, those same behaviors may persist (or even increase) in frequency, and individuals may become reliant on them as stress coping techniques. Finally, it is important to note that cultural variations may also impact symptom expression and response to individuals with ASD. This cultural influence is most evident in the case of eye contact, a normative social behavior in many Western cultures, but less so in some Eastern cultures (Argyle & Cook, 1976). As individuals with ASD tend to show aversion to direct eye contact ( Joseph, Ehrman, McNally, & Keehn, 2008; Tottenham et al., 2013), some ASD-related behaviors may not be as immediately detected (or even viewed as atypical) in cultures where eye contact is generally rare. Conversely, in cultures where use of sensory-aversive clothing or work settings are less prominent, reactivity to these stimuli may be generally less evident. It is worthwhile to consider cultural context when evaluating individuals for potential ASD. Evidence-Based Assessment There are several sets of tools and procedures that comprise an evidence-based assessment process for ASD.

Gold Standard Historically, assessment of ASD has relied on subjective clinical observation as the primary means of diagnosis, leading to relatively low reliability estimates, especially in cases with subtler patterns of presentation (Parks, 1983). However, such practice 452

is no longer preferred and in the late 1990s several research teams converged around a set of instruments that improved the landscape for ASD clinical diagnosis. Specifically, the Autism Diagnostic Observation Schedule (ADOS; Lord et al., 2000) and the Autism Diagnostic Interview—Revised (ADI–R; Le Couteur, Lord, & Rutter, 2003) have become the sine qua non of assessment in research—and, increasingly, clinical—settings. The ADOS is a semistructured play- and interaction-based evaluation that usually takes 30–60 minutes, administered by an individual with existing experience with people with ASD. It involves a highly standardized progression through a sequence of “social press” activities meant to elicit the presence (e.g., restricted/repetitive conversation topics) or absence (e.g., wellmeshed eye contact or nonverbal cue use) of ASD symptoms if present in an individual. The ADOS originally involved four modules based on age and verbal ability, ranging from minimally verbal schoolage children to highly verbal adults. A toddler-based module has recently been added (Luyster et al., 2008), and additional augmentations (e.g., a module for minimally verbal adults) are currently under investigation. The ADOS (recently updated to the ADOS–2; Lord et al., 2012) requires extensive training to administer and score, including reliable blind coding of scores of one’s own administration in comparison with an ADOS trainer. This instrument alone has changed ASD from being one of the least reliably diagnosed childhood disorders to one that is now considered to be assessed reliably (Gotham et al., 2008; Risi et al., 2006). Complementing the ADOS is the ADI–R, a parent-reported semi-structured interview. Taking up to 2 hours, the ADI–R focuses on current and historical symptom presentation, and provides important information about the context and content of symptoms across settings and time (Le Couteur et al., 2003). The ADI–R likewise involves extensive training to administer and score. It is often used in conjunction with the ADOS to verify the extensiveness and impairment of ASD symptoms. The ADOS and the ADI–R are considered the joint “gold standard,” but the Social Communication Questionnaire (SCQ; Rutter, Bailey, & Lord, 2005) is often used as a complementary screener.

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Developed by the same research group, the SCQ is a yes/no questionnaire involving lifetime or current ASD symptoms, and is quick (10 mins) and easy to administer in clinical and research settings. Likewise, it is important to note that, although the ADOS and the ADI–R provide valuable actuarial data for diagnosing ASD, the complexities of clinical cases (in practice and research settings) often necessitate the augmentation of these diagnoses by clinical expert evaluation according to DSM–5 criteria, as well as chart review. Therefore, this is sometimes included in the gold standard assessment designation.

Other Instruments There is a wide array of tools used throughout assessment for children with ASD. Effective screening tools are often part of the diagnostic sequence in this population. Two such tools are the Infant Toddler Checklist for ages 6 to 24 months and the Modified Checklist for Autism in Toddlers for ages 16 to 30 months (Robins, Fein, Barton, & Green, 2001; Wetherby, Brosnan-Maddox, Peace, & Newton, 2008). These high-sensitivity parent-report measures are frequently implemented in pediatric well-child visits and are freely available. Other clinician-administered instruments are often widely used in the community. For instance, the Childhood Autism Rating Scale, Second Edition (CARS2) represents a contemporary update of a longstanding ASD assessment tool (Schopler, Van Bourgondien, Wellman, & Love, 2010). The CARS2 is completed by the clinician after observing the child for a sufficient time to complete the target questions. Along with the clinician form, the CARS2 includes an unscored parent questionnaire that provides a structured way for the clinician to gather the information necessary to complete their ratings. Informant report measures are often used to track progress among children with ASD. For instance, the Autism Behavior Checklist and the Social Responsiveness Scale, Second Edition (SRS-2) offer parent- and teacher-report forms (for ages 2–14 years and 2.5–18 years, respectively) that assess current symptomatology (Frazier et al., 2014; Krug, Arick, & Almond, 2008). Despite some questions about divergent validity (e.g., Hus, Bishop, Gotham,

Huerta, & Lord, 2013), these instruments are often used as outcome measures in intervention studies. Other widely used measures offer complementary strengths, though also some notable psychometric concerns. For instance, the Gilliam Autism Rating Scale is easy to complete and highly comprehensible to parents; however, it tends to have relatively low specificity in clinical settings (Mazefsky & Oswald, 2006). Adults with ASD often complete self-report instruments to aid evaluation. The Autism Quotient (Baron-Cohen, Wheelwright, Skinner, Martin, & Clubley, 2001) is freely available online and provides a highly face-valid assessment of perceived ASD symptoms. However, it sometimes shows poorer convergence with other ASD measures. The SRS-2 contains a self-report measure, which, although subject to the same limitations as the parent-report version, can likewise be used for concurrent symptom assessment and tracking. The Ritvo Autism-Asperger Diagnostic Scale-Revised (R. A. Ritvo et al., 2011) is another self-report diagnostic instrument, which exhibits quite high test–retest reliability, sensitivity, and specificity. Finally, additional observational tools are increasingly used to track symptoms of ASD in naturalistic contexts or over time. For instance, the Social Interaction Observation Scale (Bauminger, 2002; Lerner & Mikami, 2012) provides useful information about the quality and quantity of unstructured peer interactions, and has shown treatment sensitivity over time. Likewise, the Contextual Assessment of Social Skills (Ratto, Turner-Brown, Rupp, Mesibov, & Penn, 2011) involves a standardized role play prompt in which an individual with ASD speaks with a confederate, and their nonverbal and conversational interactions are coded for quality and quantity. Such measures are especially valuable for providing a less subjective assessment of social behavior.

Future of Assessment The ASD assessment field is searching for even more objective measures to assess ASD over time, particularly functional biomarkers (McPartland, 2016). Such biomarkers, if identified, could aid detection of atypical development earlier in life, 453

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empirically defining subgroups, measuring outcomes, and predicting treatment response (Lerner, White, & McPartland, 2012). There are several key features that are needed to maximize utility of these biomarkers. First, such measures must be applicable across a wide range of functioning and development, given the broad presentation of ASD. A measure that is truly generalizable across the population must be feasibly capable of capturing the same construct in minimally verbal, behaviorally challenging children, as well as highly functional, newly diagnosed adults. Second, such measures should be scalable and cheap, such that clinicians can use them readily in the clinic. Third, they must be sensitive not only to behaviorally evident changes over time, but also to subtler changes in neural (or obligatory) indices of ASD symptoms that may not (yet) be observably evident. Finally, such measures should approach objectivity as much as possible, offering consistent data collection in multiple locations, without the need for development and maintenance of clinician reliability. Current developments in electrophysiology and eye tracking show great promise in meeting these goals. Epidemiology, Etiology, and Nosology In recent years, the study of what constitutes ASD, how and why the diagnoses present, and how prevalent it is in the population has advanced considerably.

Prevalence Over the last four decades, prevalence of ASD has increased dramatically. Although in the 1970s there were fewer than 5 diagnosed cases out of every 10,000 children in the United States, this number reached 1 in 150 by the year 2000. This increase has continued with time, with a current rate of 1 in 68, wherein boys are 4.5 times more likely to be diagnosed than girls (Christensen et al., 2016). Internationally, similar increases have been seen in industrialized countries including England, Denmark, Sweden, and Japan (Wing & Potter, 2009), with other studies indicating similar changes 454

in Israel and Iceland (Magnússon & Saemundsen, 2001; Merrick, Kandel, & Morad, 2004). Nonindustrialized countries are more difficult to ascertain, though countries as diverse as Iran, China, Saudi Arabia, and Colombia have seen similar increased prevalence trends when diagnostic tools are available (Elsabbagh et al., 2012; van Meerbeke, TaleroGutierrez, & Gonzalez-Reyes, 2007). A unique total population sample from a suburb of Seoul, South Korea suggests somewhat higher rates (Y. S. Kim et al., 2011). Using DSM–5 criteria, this sample exhibits ASD prevalence of 2.2% (Y. S. Kim et al., 2014), with a more balanced sex ratio (2.7:1). This suggests the striking possibility of continued underdiagnosis in community- and clinic-ascertained samples, suggesting estimates may continue to rise. Indeed, a comprehensive discussion of factors influencing this rise is beyond the scope of this chapter. However, it is generally agreed that there are four factors that could be contributing to this increase: (a) increased awareness and education, (b) changing diagnostic criteria, (c) secular trends on diagnostic patterns among clinicians (Grether, Rosen, Smith, & Croen, 2009), and (d) a true rise in prevalence, even adjusting for population growth. Although there is evidence suggesting each of these has contributed to the rise in prevalence of ASD, the preponderance of evidence suggests the rise appears attributable to the first three factors, and the first two in particular.

Etiology Although specific causes of most manifestations of ASD are largely unknown, there is mounting epidemiological evidence indicating a combination of environmental and genetic factors, and their interactions, as implicated in the etiology of ASD (Newschaffer et al., 2007). Much converging evidence implicates a strong genetic role in ASD. Identical twin diagnostic concordance rates of up to 95% (Bailey et al., 1995), as well as within-family recurrence risk in subsequent siblings of up to 20% (Szatmari et al., 2016) highlight this role. Although a comprehensive summary of the genetics of autism is beyond the scope of this chapter, it is notable that genetic heterogeneity is the norm—not the exception—in ASD (Persico &

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Napolioni, 2013). Indeed, genetic penetrance ranges widely, from fully penetrant de novo point mutations (Krumm, O’Roak, Shendure, & Eichler, 2014) to polygenic manifestations with complex higherorder interactions required to model putative causal pathways. Likewise, many ASD-linked genes are also linked to other neurodevelopmental disorders (e.g., ADHD; Gill, Anney, & Mulligan, 2014), suggesting that the phenotypic complexity of ASD is matched in the genotype. In short, ASD is strongly genetically influenced, but not solely a genetic condition. A comprehensive summary of the environmental factors that influence ASD manifestation is likewise beyond the scope of this chapter. However, converging literature suggests that some of the significant environmental predictors are substantial prematurity (9 weeks), advanced parental (particularly paternal) age, maternal rubella infection, and maternal valproate use (Mandy & Lai, 2016). Indeed, the balance of evidence suggests that perturbation of neurodevelopment (broadly construed) roughly during the second trimester is likely to correspond to initial onset of subsequent ASD. Other potential prenatal predictors include hypothyroidism, thalidomide use, cocaine or alcohol use, and congenital cytomegalovirus infection, with the use of selective serotonin reuptake inhibitors exhibiting somewhat more tenuous effects. ASD is not an exclusively environmentally determined disorder. However, environmental and other risk factors currently explored appear to function via often complex gene  environment interactions.

Theories of Autism Spectrum Disorder There are several leading theories that are often used to explain the core features of ASD. Among the oldest of these is the so-called theory of mind (BaronCohen & Glidden, 2001), which has evolved into a broader consideration of perspective taking. This theory turns on impairments in ability to perceive, comprehend, and share in the cognitive and emotional experiences of others. Individuals with ASD often demonstrate significant deficits in perspective taking relative to peers (Brunsdon & Happé, 2014; Chung, Barch, & Strube, 2014). However, this theory has recently undergone some revision. Specifically, individuals with ASD tend to perform

better on perspective-taking tasks when the social scenario is presented in a more concrete format (Glenwright & Agbayewa, 2012), or when they have more processing time (Kaland, Mortensen, & Smith, 2011), but not when the task requires spontaneous perspective-taking (Senju, Southgate, White, & Frith, 2009), suggesting that there are important individual and developmental differences in perspective taking in ASD. Another theory suggests that social motivation is the key deficit in ASD (Chevallier, Kohls, Troiani, Brodkin, & Schultz, 2012; Kohls, Chevallier, Troiani, & Schultz, 2012). This social motivation theory suggests that symptoms of ASD stem from a failure to find social interactions intrinsically rewarding (Chevallier et al., 2012; Dawson, Webb, & McPartland, 2005). Indeed, many children with ASD exhibit less enjoyment in interactions with peers, and may seek them out less often (Chevallier et al., 2012; Dawson, Webb, & McPartland, 2005). However, many individuals with ASD do seek and value friendships (Calder, Hill, & Pellicano, 2013; Dean et al., 2014) and find social interactions rewarding (Fletcher-Watson, Leekam, & Findlay, 2013). Consistent with this, one study found that higher social motivation may impede processing of social stimuli in children with ASD (Garman et al., 2016). This theory, then, provides a rich venue for hypothesis testing, but does not fully explain the complexity of ASD. Another influential theory of ASD is the weak central coherence theory (Frith, 1989). This theory highlights the detail-oriented focus of ASD (Happé, Briskman, & Frith, 2001), explaining that such a perceptual feature may reflect a failure to integrate more complex (e.g., social) contextual information. However, the relation between weak central coherence and social impairments has weak support (Burnette et al., 2005); to date, this theory has largely provided help to explain repetitive behaviors and rigidity in ASD (Happé & Frith, 2006). A model that considers the relative strengths and weaknesses observed in ASD and focuses on higher order cognitive processes is the complex information processing model (Minshew & Goldstein, 1998). This model posits a generalized deficit in higher-order, complex tasks across domains 455

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(e.g., complex memory, abstract reasoning, problem solving), whereas less complex components of the same domains (e.g., simple attention, rule learning of abstract reasoning) are intact or even enhanced. This is consistent with a prominent social deficit given the complex, multifaceted nature of social interaction. This theory was originally supported by research with extensive neuropsychological batteries among high-functioning adults (Minshew, Muenz, Goldstein, & Payton, 1992) and children (D. L. Williams, Goldstein, & Minshew, 2006), and has been replicated in independent samples and neuroimaging studies, which have supported this pattern of abilities (Just, Keller, Malave, Kana, & Varma, 2012; Libero & Kana, 2013). The enactive mind theory (Klin, Jones, Schultz, & Volkmar, 2003), yet another influential model of ASD, prioritizes the active experience of normative social behavior and interaction as being the necessary, lived catalyst of the developmental cascade of effective social development. Children with ASD, then, experience differences in development of basic social engagement and response that reinforce a lack of salience of social information, as well as excessive salience of nonsocial information. Although less causal in nature, the enactive mind theory nonetheless provides a means of understanding how the canalization of social development in ASD may unfold (and become self-perpetuating) over time. A newer model of ASD is the social information processing speed theory (Mendelson et al., 2016). This theory suggests that systematic inefficiencies in basic (and early) processing of social stimuli act as a sort of gating mechanism that then slows subsequent in vivo processing, decoding, and response to social interactions. Early processes (e.g., electrophysiological responses to faces that emerge within 100 ms–200 ms of seeing a face) may unfold subtly slower in children with ASD (McPartland et al., 2011) and preclude functional engagement with their social context (Lerner, McPartland, & Morris, 2013). This theory has the benefit of being contiguous with most of the previously mentioned theroies while also providing testable hypotheses across multiple levels of analyses; however, it is somewhat less clear how specific it is to ASD, as opposed to 456

reflecting a putative broader transdiagnostic feature of social perception. As assessment methods and clinical nosologies have evolved to match real-world diversity, so too have attempts to deduce a unifying model (with testable hypotheses) of ASD. The complexity of ASD, in etiology and presentation, is reflected in the varying models and theories seeking to explain and understand the disorder.

Comorbidity Until recently, comorbidity among mental health problems was neither a clinical consideration in working with patients with ASD, nor a central topic of scientific inquiry. Rather, symptoms that were non-core to ASD were subsumed under the ASD diagnosis, a practice termed diagnostic overshadowing (Mason & Scior, 2004). There is now exponentially greater appreciation for the existence of comorbidity and the clinical importance of its recognition. For example, secondary disorders (e.g., social anxiety disorder) tend to be life-course persistent (Simonoff et al., 2013; Verheij et al., 2015) and predictive of impairment above and beyond the core ASD symptoms (Chang, Quan, & Wood, 2012; Kaat, Gadow, & Lecavalier, 2013). Rates of comorbidity in mental health disorders are extremely high among children and adolescents with ASD. Indeed, comorbidity is the norm rather than the exception. It has been estimated that one in four children are diagnosed with at least one comorbid disorder (Gadow, Devincent, & Schneider, 2008; Simonoff et al., 2008). When core symptoms of ASD are carefully considered to avoid “double counting” symptoms, comorbidity may be less common. Nevertheless, best estimates suggest it is still the case that approximately half of children with ASD meet stringent criteria for a secondary psychiatric disorder (Mazefsky, Oswald, et al., 2012). ASD commonly co-occurs along with internalizing disorders (e.g., anxiety and mood disorders) as well as externalizing disorders (e.g., ADHD; oppositional defiant disorder). Anxiety disorders, for instance, are among the most common co-occurring mental health conditions seen in children with ASD (White, Oswald, Ollendick, & Scahill, 2009), with approximately 40% of children

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and adolescents having at least one anxiety disorder (van Steensel, Bögels, & Perrin, 2011); interestingly, although children with ASD report symptom patterns that are broadly similar to non-ASD children with anxiety (White et al., 2015), there is also evidence of unique, atypical manifestations of anxiety in ASD (Kerns et al., 2014). Although prior to the publication of the DSM–5, ADHD was disallowed as a co-occurring diagnosis in an individual with ASD, it has long been established that ADHD symptoms are extremely common in autism. It has been estimated that upward of 60% of children with ASD also meet criteria for ADHD (Yoshida & Uchiyama, 2004). Likewise, a large populationbased twin study showed that children with ADHD have elevated levels of ASD traits (Reiersen, Constantino, Volk, & Todd, 2007). Crucially, such comorbidities can themselves be a primary source of impairment among individuals with ASD (Kaat et al., 2013), with sometimes deleterious outcomes: adults with ASD, for example, have recently been shown to be at especially high risk for suicide ­(Cassidy et al., 2014). Comorbidity patterns in ASD are likely an example of multifinality. In other words, it is probable that several variables (e.g., age, ASD severity, gender, developmental level) influence which disorders co-occur. These variables also influence onset, presentation, and course of ASD symptoms and diagnosis (Gotham, Brunwasser, & Lord, 2015; Rosen & Lerner, 2016). Sophisticated research that combines dimensional and categorical conceptualizations across levels of analysis is needed to identify the mechanisms underlying these high comorbidity estimates (Damiano, Mazefsky, White, & Dichter, 2014). It is likely that heightened comorbidity risk is due to either shared pathophysiology (i.e., a process that affects ASD and secondary condition) or that the comorbidity is result of the ASD itself (i.e., result of something core to ASD; Lai & BaronCohen, 2015). Impaired emotion regulation is one such candidate, pathophysiological mechanism (Mazefsky, Pelphrey, & Dahl, 2012; White et al., 2014). Further research on expression and causal pathways is needed to inform effective treatment and prevention efforts.

Evidence-Based Interventions A growing set of clinical activities have come to constitute evidence-based practices for individuals with ASD.

What Constitutes Evidence-Based Practice? The American Psychological Association uses wellestablished criteria for determining whether a given intervention meets criteria for being empirically supported in any given population (Chambless & Hollon, 1998; Silverman & Hinshaw, 2008). In the ASD treatment field, efforts have been made to establish similar standards for empirical support (Wong et al., 2015). However, because of an idiosyncratic history relative to the treatment literature in other fields, intervention studies for ASD have been dominated by a preponderance of single case designs using a strict, rigorous, precision form of behavioral intervention called applied behavior analysis (ABA; for a review and discussion, see Smith, 2014). ABA has indeed become its own field of study, practice, and licensure, and has generated an independent science that grew from, has large overlap with, but is in some ways independent from, the behavior therapy tradition in psychology. As a result, as is sometimes seen in the psychotherapy research literature (Westen, Novotny, & ThompsonBrenner, 2005), there are contingents who advocate for “evidence-based practices” (i.e., intervention approaches that use semi-objective idiographic data collection as core treatment elements) as a superior formulation to empirically supported interventions when answering the question of “what works” in ASD. Nonetheless, recent efforts to reconcile the ASD treatment literature with broader psychotherapy research standards have gained traction (Smith & Iadarola, 2015), and are discussed next. Notably, though, treatment of ASD is quite broad and varied in terms of treatment targets. Many interventions focus on reduction of core ASD symptoms, particularly in the social domain. Relatedly, many interventions aim to build skills within this domain (e.g., effective communication) rather than simply reduce atypical or impairing behaviors. As a result, several interventions see a failure to meet diagnostic criteria as an ultimate treatment goal. Other 457

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approaches aim for broader improvements in quality of life (van Heijst & Geurts, 2014), increased school inclusion (Spaulding, Lerner, & Gadow, 2016), or reductions in comorbid conditions (e.g., reduced anxiety) as goals. In general, the breadth of ASD leaves much on the table in terms of treatment aims and outcomes.

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Early and/or Targeted Intervention Most interventions for individuals with ASD have focused on specific symptom reduction and/or skill acquisition. These interventions are often delivered in the context of early intervention services, provided to children under the age of 3 who meet diagnostic criteria for an ASD diagnosis (Reichow, 2012). However, these same approaches and principles are also offered to individuals with ASD across childhood and adulthood. Generally, these interventions tend to fit into two broad categories: ABA and naturalistic developmental behavioral interventions (NDBI; Schreibman et al., 2015). ABA interventions use operant conditioning approaches to training specific skills or reducing symptoms. Although best known for using discrete trial training approaches, in which tasks are broken down into specific discrete steps and trained sequentially, ABA interventions can also use in vivo aspects (e.g., reinforcing a preferred behavior when at a restaurant), and are widely used in the community. Indeed, the original trials of ABA recommend up to 40 hrs/week of ABA to achieve desired outcomes (Lovaas & Smith, 1988). Other popular interventions also use ABA principles to target more complex behaviors. For instance, pivotal response treatment (Koegel & Koegel, 2006) aims to identify and reinforce “pivotal” behaviors that can lead to a cascade of changes in other social behaviors, including via incorporation of special interests of individuals with ASD. Notably, individually delivered, comprehensive ABA is one of two well-established interventions for children with ASD (Smith & Iadarola, 2015). NDBI interventions tend to emphasize “following the child’s lead” (Greenspan & Wieder, 1998) in their natural settings (e.g., school, home, community) to shape behavior toward developmentally normative skills via existing (rather than 458

contrived) contingencies (Schreibman et al., 2015). Although often viewed as nondirective in nature, NDBI’s tend to instead involve shared control between the interventionist and the child, such that the child’s interests guide the activities, and the interventionist capitalizes on learning opportunities as they appear. Because of this approach, NDBI’s were once considered more difficult to study than ABA-based approaches, despite their parallel emergence and similar recommendations and practices in terms of service intensity and dissemination into communities. This has changed in recent years, with increased use of rigorous assessment designs examining NDBI-based treatment packages. Crucially, several of these innovative newer approaches combine the child-led, naturalistic focus of NDBI with the behavioral precision of ABA. Indeed, several such approaches, including the Early Start Denver Model (Dawson et al., 2010), have demonstrated the most robust singlestudy effects of any intervention for children with ASD. In fact, teacher-implemented interventions that combine ABA and NDBI principles are the second of only two established interventions for ASD (Smith & Iadarola, 2015).

Social-Focused Interventions Social disability is arguably the most prominent diagnostic criterion of ASD. It is pervasive in that it affects multiple areas of functioning and that it tends to not improve with development (Sigman et al., 1999). It is widely established that social interaction impairments are a prime source of distress and debilitation for many with ASD, regardless of cognitive or language ability (Carter, Davis, Klin, & Volkmar, 2005). It is therefore understandable that there has been considerable research on social interventions. There is tremendous variability in focus (or therapeutic target) and approach across these interventions. Additionally, interventions vary by theoretical orientation and conceptualization of ASD-related social deficits. An exhaustive review is beyond the scope of this chapter but selected intervention categories are briefly discussed, with some specific curricular examples and the research base to support their use.

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ABA, as it is applied to treatment of social disability in ASD, requires children to actively engage with their social environment, while providing them with consistent reinforcement for adaptive behaviors and interactive play with peers. In its early applications, ABA was not specifically a social intervention. McEachin, Smith, and Lovaas (1993) conducted a more extensive follow-up to specifically assess social functioning and found that children treated with ABA obtained significantly improved socialization relative to children in the control group. Perhaps the most common format for social intervention in the field of clinical psychology is group-based (Lerner & White, 2015; Lerner, White, et al., 2012; McMahon, Lerner, & Britton, 2013). There are several curricula available for skills training groups. These approaches generally vary in terms of their relative focus on teaching understanding of social rules and norms (social knowledge training) and providing positive reinforcement of experiences of successful social interaction in a context that mitigates factors that may impede enactment of (potentially known) social skills (social performance training; Lerner & Mikami, 2012; Lerner & White, 2015; Mendelson et al., 2016). On the “knowledge focused” end of the continuum, one of the most widely used group-based programs is the Program for the Education and Enrichment of Relational Skills (PEERS) intervention (Laugeson & Frankel, 2010). In an initial randomized control trial (RCT), teens who received the PEERS intervention showed significant improvement relative to peers assigned to the wait list control. Although originally developed and tested as a social intervention for adolescents with ASD, PEERS has been extended to treat adults. In a recent RCT, Laugeson and colleagues (2015) found that young adults who completed the PEERS intervention showed significant improvement across a range of social outcomes, relative to controls in a waiting list condition. One model on the “performance focused” end of the continuum is a program called Sociodramatic Affective Relational Intervention (Lerner, Mikami, & Levine, 2011). This approach uses games and activities designed to promote use and engagement of otherwise challenging aspects of nonverbal social interaction in a rewarding context. This model has

shown promise in community-based and lab-based trials, RCTs, and dismantling studies (Lerner, 2013; Lerner & Mikami, 2012; Lerner & White, 2015). Although not currently considered an evidence-based intervention for social disability in ASD, technology-based interventions are gaining momentum. Some of the common reasons cited for consideration of, and increased research on, technology for social intervention include practical factors like low cost, convenience, and availability as well as user preference for and comfort with the technology (Wieckowski & White, 2017). Within the technologybased interventions, computer-based training programs have been most widely studied, examples of which include the Let’s Face It! program (Tanaka et al., 2010) and the Junior Detective Training Program (Beaumont & Sofronoff, 2008). Collectively, the extant research suggests that these interventions are feasible to deliver and user-friendly. The extant research on the efficacy of social interventions for ASD has been quite mixed. This is likely due, at least in part, to three reasons: (a) the variability present across interventions, (b) variability in response across individuals with ASD, and (c) pervasiveness and the severity of social disability in ASD. Regarding the latter point, if one views ASD as a chronic and biologically based disorder that does not tend to remit, even with best available treatment, it stands to reason that treatment of the core feature of the disorder (i.e., social impairment) is unlikely to show substantial and sustained effects. There are several published meta-analyses and qualitative reviews on social skills interventions for individuals with ASD (Reichow & Volkmar, 2010; Wang, Parrila, & Cui, 2013). The National Standards Project has developed a categorization of interventions (social and otherwise) on the basis of how well-established the intervention’s research base is (Wong et al., 2015). The National Professional Development Center, established by the Office of Special Education Programs to promote use of evidence-based practices, has also identified evidence-based interventions (National Autism Center, 2015). Across these independent reports, there is considerable support for social narratives, peer-mediated training, video modeling, selfmanagement, prompting, and skills training groups. 459

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Cognitive–Behavioral Therapy for Autism Spectrum Disorder Until recent years, CBT for treatment of ASD was relatively rare in the research literature. However, over the last 10 years new research has emerged adapting classic CBT strategies for use among individuals with ASD. Such adaptations typically involve increased use of visual supports, functional analyses, frequent and direct feedback, step-by-step guidance, positive learning experiences, and psychoeducation (Scarpa et al., 2013; White et al., 2010). Notably, these interventions typically focus on core social deficits in individuals with ASD, comorbid anxiety, or both. Two prominent examples of these approaches are the Multimodal Anxiety and Social Skills Intervention (MASSI; White et al., 2010, 2013) and the Behavioral Interventions for Anxiety in Children With Autism (BIACA; Wood et al., 2015). Both of these interventions involve adapting core CBT principles used to treat anxiety in children (e.g., Coping Cat program) to be used with children with ASD; notably, both approaches include a specific parent-training component, following findings that typically developing children with greater ASD symptoms may show differential benefit of parent involvement (Puleo & Kendall, 2011); this contrasts with a lack of benefit for family components in studies of typical child CBT (Kendall, Hudson, Gosch, Flannery-Schroeder, & Suveg, 2008). However, whereas MASSI directly intervenes on social symptoms, BIACA conceptualizes reductions in social symptoms as being downstream effects of focusing on anxiety reduction, given the ways in which anxiety can amplify social challenges in this population (Wood & Gadow, 2010). Overall, the literature on CBT for children with ASD has produced modest effects, with the strongest effects emerging from clinician and informant reports (Weston, Hodgekins, & Langdon, 2016). Future Directions ASD research is moving decisively toward incorporating contemporary advances in psychopathology and intervention research. First, there is ample work focusing on developing improved nosologies for ASD. DSM–5 created a broad umbrella framework 460

for considering the myriad manifestations of ASD, but it remains an open question as to whether the taxonometric systems should reflect a single syndrome that is broadly dimensional in nature, a wide variety of largely as-yet-unidentified discrete syndromes (i.e., the “autisms”), or some combination thereof (H. Kim et al., 2017). Concurrently, recent work has begun to highlight the importance of considering the potential for contextual variation of symptom expression—or behavioral plasticity more broadly—as an underappreciated facet of ASD (Lerner, De Los Reyes, Drabick, Gerber, & Gadow, 2017). That is, rather than relative variations in specific sets of symptoms defining ASD per se, it may be that the capacity of individuals with ASD to vary their behavior in response to diverse social (e.g., school vs. home) and sensory (e.g., overstimulating vs. understimulating) environments may augment current nosologies that tend to presume behavior manifest in one setting represents a “true” expression of children’s symptom profile. Also happening at the same time, ample efforts have aimed to capitalize on clinical neuroscience to inform individual differences within ASD, including highlighting relative differences in amygdala reactivity (Kleinhans et al., 2010), electrophysiological responsiveness to faces (Lerner et al., 2013), and patterns of long-range subcortical-cortical neural connectivity (Mohammad-Rezazadeh, Frohlich, Loo, & Jeste, 2016) as reflecting important markers of variation in severity (or even type) of ASD. In general, improved understanding of etiology—including especially the recent identification of a myriad of risk genes linked to discrete syndromic forms of ASD (Chahrour et al., 2016)—will aid in improving understanding of the wide variety of ways ASD may manifest. Another research target is improved early detection. Thanks in large measure to advances made through research on infant siblings of children with ASD (Szatmari et al., 2016), the age of diagnosis in clinics staffed by experts in ASD is likely to continue to progress even earlier. Recent advances in eye tracking have demonstrated differences early as 6 months of age in children who develop ASD (Jones & Klin, 2013), and investigations into motor behavior and parent–child synchrony are likewise yielding promising results (Ozonoff et al., 2008).

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Autism Spectrum Disorder

Use of in vivo monitoring, such as LENA systems, provides even greater opportunities to leverage large quantities of data to detect early abnormalities in patterns of interaction in these children (Warlaumont, Richards, Gilkerson, & Oller, 2014). Assessment procedures for ASD are improving in their precision and efficiency. There has recently been a push toward more automated, rapid assessments in this population, including observational and behavioral tools for initial diagnostic assessment and treatment monitoring. Questionnaire-based measures continue to become shorter and more efficient, increasing their utility (e.g., Kerns, Maddox, et al., 2015). A continued focus on such measures, as well as biomarkers that provide objective (or at least obligatory) indicators of symptoms at multiple levels of analyses, rather than relying solely on behavior as the final diagnostic arbiter, will be valuable going forward. Interventions for children and adults with ASD are poised to improve as assessment techniques allow for more precise intervention targets (Lerner, White, et al., 2012). For instance, efforts to establish functional profiles (Lerner, Potthoff, & Hunter, 2015) that can aid in specifying treatment targets within the population hold the promise of establishing more efficacious and personalized treatments. Such interventions can also be tailored to changes in deficit domains over time, including shifting from social symptoms to restricted behaviors to cognitive or executive demands over time. Finally, ASD research should prioritize inclusion of individuals with ASD in the design and evaluation process (Pellicano, Dinsmore, & Charman, 2014). Indeed, such efforts have already yielded remarkable insights on the role of perceptual strengths in ASD (Mottron, 2011) and new ways of conceptualizing emotion processing and perspective-taking in the population (Cook et al., 2013). Maximizing involvement of individuals with ASD and other stakeholders will be essential in generating novel insights on the nature of ASD. This can likewise be advanced through a recent push for increased research on—and interventions for—adults with ASD (Bishop-Fitzpatrick, Minshew, & Eack, 2014; Lai & Baron-Cohen, 2015), which itself often provides avenues for stakeholder involvement.

Conclusion Understanding of ASD has changed dramatically over the last several decades. Once characterized by severe, debilitating symptoms exhibited by a small subset of the population, ASD symptoms are now understood to be much more broadly distributed across populations, and to be modified (rather than defined) by other cognitive and demographic features. Diagnostic procedures have evolved dramatically, with some measures of ASD symptoms comparing favorably to those used in almost any mental health condition in terms. Indeed, there is great promise for the advancement of scalable, objective measures of ASD symptoms. Prevalence of ASD itself has risen substantially over the years, with much of this rise attributable to changing diagnostic procedures and increased awareness, but also some evidence of environmental factors playing a smaller—though still significant—role. Likewise, understanding of the role genetics play in ASD prevalence continues to improve. Various etiological, cognitive, and perceptual theories of ASD have advanced over the last decade, provide testable hypotheses that continue to refine understanding of ASD. Patterns of comorbidity continue to be revealed, increasing knowledge of the boundaries of ASD and the varieties of manifestation of the comorbid conditions themselves. Finally, some interventions for children with ASD have recently achieved empirically well-supported status, including operant conditioning-based (ABA) and more naturalistic (NDBI) approaches. Intervention work continues to improve, with additional specificity and efficacy accruing to a broader range of treatments each year. Overall, ASD remains a complex mental health condition, but capacity to understand the experiences of—and improve quality of life for—affected individuals has steadily improved.

References Aldao, A., Gee, D. G., De Los Reyes, A., & Seager, I. (2016). Emotion regulation as a transdiagnostic factor in the development of internalizing and externalizing psychopathology: Current and future directions. Development and Psychopathology, 28, 927–946. http://dx.doi.org/10.1017/ S0954579416000638 461

Lerner et al.

American Psychiatric Association. (1980). Diagnostic and statistical manual of mental disorders (3rd ed.). Washington, DC: Author. American Psychiatric Association. (1987). Diagnostic and statistical manual of mental disorders (3rd ed., rev.). Washington, DC: Author. American Psychiatric Association. (2013). Diagnostic and statistical manual of mental disorders (5th ed.). Washington, DC: Author.

Copyright American Psychological Association. Not for further distribution.

Argyle, M., & Cook, M. (1976). Gaze and mutual gaze. New York, NY: Oxford University Press. Ashwood, K. L., Gillan, N., Horder, J., Hayward, H., Woodhouse, E., McEwen, F. S., . . . Murphy, D. G. (2016). Predicting the diagnosis of autism in adults using the Autism-Spectrum Quotient (AQ) questionnaire. Psychological Medicine, 46, 2595–2604. http://dx.doi.org/10.1017/ S0033291716001082 Bagwell, C. L., Newcomb, A. F., & Bukowski, W. M. (1998). Preadolescent friendship and peer rejection as predictors of adult adjustment. Child Development, 69, 140–153. http:// dx.doi.org/10.1111/j.1467-8624.1998.tb06139.x Bailey, A., Le Couteur, A., Gottesman, I., Bolton, P., Simonoff, E., Yuzda, E., & Rutter, M. (1995). Autism as a strongly genetic disorder: Evidence from a British twin study. Psychological Medicine, 25, 63–77. http://dx.doi.org/10.1017/S0033291700028099 Baron-Cohen, S., & Glidden, L. M. (2001). Theory of mind and autism: A review. In L. M. Glidden (Ed.), International review of research in mental retardation: Autism (pp. 169–184). San Diego, CA: Academic Press. Baron-Cohen, S., Wheelwright, S., Skinner, R., Martin, J., & Clubley, E. (2001). The autism-spectrum quotient (AQ): Evidence from Asperger syndrome/ high-functioning autism, males and females, scientists and mathematicians. Journal of Autism and Developmental Disorders, 31, 5–17. http://dx.doi.org/ 10.1023/A:1005653411471 Barton, M. L., Robins, D. L., Jashar, D., Brennan, L., & Fein, D. (2013). Sensitivity and specificity of proposed DSM–5 criteria for autism spectrum disorder in toddlers. Journal of Autism and Developmental Disorders, 43, 1184–1195. http:// dx.doi.org/10.1007/s10803-013-1817-8 Bauminger, N. (2002). The facilitation of socialemotional understanding and social interaction in high-functioning children with autism: Intervention outcomes. Journal of Autism and Developmental Disorders, 32, 283–298. http:// dx.doi.org/10.1023/A:1016378718278 Beaumont, R., & Sofronoff, K. (2008). A multicomponent social skills intervention for children with Asperger syndrome: The Junior Detective 462

Training Program. Journal of Child Psychology and Psychiatry, 49, 743–753. http://dx.doi.org/10.1111/ j.1469-7610.2008.01920.x Biederman, J., Mick, E., & Faraone, S. V. (2000). Age-dependent decline of symptoms of attention deficit hyperactivity disorder: Impact of remission definition and symptom type. American Journal of Psychiatry, 157, 816–818. http://dx.doi.org/10.1176/ appi.ajp.157.5.816 Bishop-Fitzpatrick, L., Minshew, N. J., & Eack, S. M. (2014). A systematic review of psychosocial interventions for adults with autism spectrum disorders. In F. R. Volkmar, B. Reichow, & J. C. McPartland (Eds.), Adolescents and adults with autism spectrum disorders (pp. 315–327). http://dx.doi.org/ 10.1007/978-1-4939-0506-5_16 Bogdashina, O. (2016). Sensory perceptual issues in autism and Asperger syndrome: Different sensory experiencesdifferent perceptual worlds. Philadelphia, PA: Jessica Kingsley Publishers. Brukner-Wertman, Y., Laor, N., & Golan, O. (2016). Social (pragmatic) communication disorder and its relation to the autism spectrum: Dilemmas arising drom the DSM–5 classification. Journal of Autism and Developmental Disorders, 46, 2821–2829. http:// dx.doi.org/10.1007/s10803-016-2814-5 Brunsdon, V. E. A., & Happé, F. (2014). Exploring the “fractionation” of autism at the cognitive level. Autism, 18, 17–30. Burnette, C. P., Mundy, P. C., Meyer, J. A., Sutton, S. K., Vaughan, A. E., & Charak, D. (2005). Weak central coherence and its relations to theory of mind and anxiety in autism. Journal of Autism and Developmental Disorders, 35, 63–73. http:// dx.doi.org/10.1007/s10803-004-1035-5 Calder, L., Hill, V., & Pellicano, E. (2013). “Sometimes I want to play by myself”: Understanding what friendship means to children with autism in mainstream primary schools. Autism, 17, 296–316. http://dx.doi.org/10.1177/1362361312467866 Carter, A. S., Davis, N. O., Klin, A., & Volkmar, F. R. (2005). Social development in autism. In F. R. Volkmar, R. Paul, A. Klin, & D. Cohen (Eds.), Handbook of autism and pervasive developmental disorders: Vol. 1. Diagnosis, development, neurobiology, and behavior (3rd ed., pp. 312–334). Hoboken, NJ: Wiley. Cassidy, S., Bradley, P., Robinson, J., Allison, C., McHugh, M., & Baron-Cohen, S. (2014). Suicidal ideation and suicide plans or attempts in adults with Asperger’s syndrome attending a specialist diagnostic clinic: A clinical cohort study. Lancet Psychiatry, 1, 142–147. http://dx.doi.org/10.1016/S22150366(14)70248-2

Autism Spectrum Disorder

Centers for Disease Control and Prevention. (2014). Prevalence of autism spectrum disorder among children aged 8 years—Autism and Developmental Disabilities Monitoring Network, 11 sites, United States, 2010. Morbidity and mortality weekly report: Surveillance summaries, 63(2), 1.

Copyright American Psychological Association. Not for further distribution.

Chahrour, M., O’Roak, B. J., Santini, E., Samaco, R. C., Kleiman, R. J., & Manzini, M. C. (2016). Current perspectives in autism spectrum disorder: From genes to therapy. Journal of Neuroscience, 36, 11402–11410. http://dx.doi.org/10.1523/ JNEUROSCI.2335-16.2016 Chambless, D. L., & Hollon, S. D. (1998). Defining empirically supported therapies. Journal of Consulting and Clinical Psychology, 66, 7–18. http://dx.doi.org/ 10.1037/0022-006X.66.1.7 Chang, Y.-C., Quan, J., & Wood, J. J. (2012). Effects of anxiety disorder severity on social functioning in children with autism spectrum disorders. Journal of Developmental and Physical Disabilities, 24, 235–245. http://dx.doi.org/10.1007/s10882-012-9268-2 Chevallier, C., Kohls, G., Troiani, V., Brodkin, E. S., & Schultz, R. T. (2012). The social motivation theory of autism. Trends in Cognitive Sciences, 16, 231–239. http://dx.doi.org/10.1016/j.tics.2012.02.007 Chowdhury, M., Benson, B. A., & Hillier, A. (2010). Changes in restricted repetitive behaviors with age: A study of high-functioning adults with autism spectrum disorders. Research in Autism Spectrum Disorders, 4, 210–216. http://dx.doi.org/10.1016/j. rasd.2009.09.006 Christensen, D. L., Bilder, D. A., Zahorodny, W., Pettygrove, S., Durkin, M. S., Fitzgerald, R. T., . . . Yeargin-Allsopp, M. (2016). Prevalence and characteristics of autism spectrum disorder among 4-year-old children in the Autism and Developmental Disabilities Monitoring Network. Journal of Developmental and Behavioral Pediatrics, 37, 1–8. http://dx.doi.org/10.1097/ DBP.0000000000000235

Child and Adolescent Psychology, 43, 828–843. http:// dx.doi.org/10.1080/15374416.2014.945214 Dawson, G., Rogers, S., Munson, J., Smith, M., Winter, J., Greenson, J., . . . Varley, J. (2010). Randomized, controlled trial of an intervention for toddlers with autism: The Early Start Denver Model. Pediatrics, 125, e17–e23. http://dx.doi.org/10.1542/peds.2009-0958 Dawson, G., Webb, S. J., & McPartland, J. (2005). Understanding the nature of face processing impairment in autism: Insights from behavioral and electrophysiological studies. Developmental Neuropsychology, 27, 403–424. http://dx.doi.org/ 10.1207/s15326942dn2703_6 Dean, M., Kasari, C., Shih, W., Frankel, F., Whitney, R., Landa, R., . . . Harwood, R. (2014). The peer relationships of girls with ASD at school: Comparison to boys and girls with and without ASD. Journal of Child Psychology and Psychiatry, 55, 1218–1225. http://dx.doi.org/10.1111/jcpp.12242 Elsabbagh, M., Divan, G., Koh, Y.-J., Kim, Y. S., Kauchali, S., Marcín, C., . . . Fombonne, E. (2012). Global prevalence of autism and other pervasive developmental disorders. Autism Research, 5, 160–179. http://dx.doi.org/10.1002/aur.239 Fein, D., Barton, M., Eigsti, I.-M., Kelley, E., Naigles, L., Schultz, R. T., . . . Tyson, K. (2013). Optimal outcome in individuals with a history of autism. Journal of Child Psychology and Psychiatry, 54, 195–205. http://dx.doi.org/10.1111/jcpp.12037 Fletcher-Watson, S., Leekam, S. R., & Findlay, J. M. (2013). Social interest in high-functioning adults with autism spectrum disorders. Focus on Autism and Other Developmental Disabilities, 28, 222–229. http:// dx.doi.org/10.1177/1088357613480829 Frazier, T. W., Ratliff, K. R., Gruber, C., Zhang, Y., Law, P. A., & Constantino, J. N. (2014). Confirmatory factor analytic structure and measurement invariance of quantitative autistic traits measured by the Social Responsiveness Scale-2. Autism, 18, 31–44.

Chung, Y. S., Barch, D., & Strube, M. (2014). A metaanalysis of mentalizing impairments in adults with schizophrenia and autism spectrum disorder. Schizophrenia Bulletin, 40, 602–616. http://dx.doi.org/ 10.1093/schbul/sbt048

Frazier, T. W., Youngstrom, E. A., Speer, L., Embacher, R., Law, P., Constantino, J., . . . Eng, C. (2012). Validation of proposed DSM–5 criteria for autism spectrum disorder. Journal of the American Academy of Child and Adolescent Psychiatry, 51, 28–40. http:// dx.doi.org/10.1016/j.jaac.2011.09.021

Coleman, M., & Gillberg, C. (2012). The autisms. New York, NY: Oxford University Press.

Frith, U. (1989). Autism: Explaining the enigma. Oxford, England: Wiley–Blackwell.

Cook, R., Brewer, R., Shah, P., & Bird, G. (2013). Alexithymia, not autism, predicts poor recognition of emotional facial expressions. Psychological Science, 24, 723–732. http:// dx.doi.org/10.1177/0956797612463582

Gadow, K. D., Devincent, C., & Schneider, J. (2008). Predictors of psychiatric symptoms in children with an autism spectrum disorder. Journal of Autism and Developmental Disorders, 38, 1710–1720. http:// dx.doi.org/10.1007/s10803-008-0556-8

Damiano, C. R., Mazefsky, C. A., White, S. W., & Dichter, G. S. (2014). Future directions for research in autism spectrum disorders. Journal of Clinical

Gadow, K. D., Devincent, C. J., & Drabick, D. A. (2008). Oppositional defiant disorder as a clinical phenotype in children with autism spectrum disorder. Journal of 463

Lerner et al.

Autism and Developmental Disorders, 38, 1302–1310. http://dx.doi.org/10.1007/s10803-007-0516-8 Garman, H. D., Spaulding, C. J., Webb, S. J., Mikami, A. Y., Morris, J. P., & Lerner, M. D. (2016). Wanting it too much: An inverse relation between social motivation and facial emotion recognition in autism spectrum disorder. Child Psychiatry and Human Development, 47, 890–902. http://dx.doi.org/10.1007/ s10578-015-0620-5

Copyright American Psychological Association. Not for further distribution.

Gill, M., Anney, R., & Mulligan, A. (2014). Overlap between ADHD and autism—Clinical and genetic evidence. Current Psychiatry Reviews, 10, 143–155. http:// dx.doi.org/10.2174/1573400510666140319235755 Glenwright, M., & Agbayewa, A. S. (2012). Older children and adolescents with high-functioning autism spectrum disorders can comprehend verbal irony in computer-mediated communication. Research in Autism Spectrum Disorders, 6, 628–638. http://dx.doi.org/10.1016/j.rasd.2011.09.013 Gotham, K., Brunwasser, S. M., & Lord, C. (2015). Depressive and anxiety symptom trajectories from school age through young adulthood in samples with autism spectrum disorder and developmental delay. Journal of the American Academy of Child and Adolescent Psychiatry, 54, 369–376. Gotham, K., Risi, S., Dawson, G., Tager-Flusberg, H., Joseph, R., Carter, A., . . . Lord, C. (2008). A replication of the autism diagnostic observation schedule (ADOS) revised algorithms. Journal of the American Academy of Child and Adolescent Psychiatry, 47, 642–651. http://dx.doi.org/10.1097/ CHI.0b013e31816bffb7 Greenspan, S. I., & Wieder, S. (1998). The child with special needs: Intellectual and emotional growth. Reading, MA: Addison-Wesley Longman. Grether, J. K., Rosen, N. J., Smith, K. S., & Croen, L. A. (2009). Investigation of shifts in autism reporting in the California Department of Developmental Services. Journal of Autism and Developmental Disorders, 39, 1412–1419. http://dx.doi.org/10.1007/ s10803-009-0754-z Grzadzinski, R., Huerta, M., & Lord, C. (2013). DSM–5 and autism spectrum disorders (ASDs): An opportunity for identifying ASD subtypes. Molecular Autism, 4, 12. http://dx.doi.org/10.1186/2040-2392-4-12 Gupta, S., & Rossignol, D. A. (2009). Autism spectrum disorders: Rising prevalence drives research on causes and cures. Annals of Clinical Psychiatry, 21, 193. Halladay, A. K., Bishop, S., Constantino, J. N., Daniels, A. M., Koenig, K., Palmer, K., . . . Szatmari, P. (2015). Sex and gender differences in autism spectrum disorder: Summarizing evidence gaps and identifying emerging areas of priority. Molecular Autism, 6, 36. http://dx.doi.org/10.1186/s13229-015-0019-y 464

Happé, F., Briskman, J., & Frith, U. (2001). Exploring the cognitive phenotype of autism: Weak “central coherence” in parents and siblings of children with autism: I. Experimental tests. Journal of Child Psychology and Psychiatry, 42, 299–307. http://dx.doi.org/10.1111/1469-7610.00723 Happé, F., & Frith, U. (2006). The weak coherence account: Detail-focused cognitive style in autism spectrum disorders. Journal of Autism and Developmental Disorders, 36, 5–25. http:// dx.doi.org/10.1007/s10803-005-0039-0 Hill, E., Berthoz, S., & Frith, U. (2004). Brief report: Cognitive processing of own emotions in individuals with autistic spectrum disorder and in their relatives. Journal of Autism and Developmental Disorders, 34, 229–235. http://dx.doi.org/10.1023/ B:JADD.0000022613.41399.14 Hiller, R. M., Young, R. L., & Weber, N. (2016). Sex differences in pre-diagnosis concerns for children later diagnosed with autism spectrum disorder. Autism, 20, 75–84. Huerta, M., Bishop, S. L., Duncan, A., Hus, V., & Lord, C. (2012). Application of DSM–5 criteria for autism spectrum disorder to three samples of children with DSM–IV diagnoses of pervasive developmental disorders. American Journal of Psychiatry, 169, 1056–1064. http://dx.doi.org/10.1176/appi. ajp.2012.12020276 Hus, V., Bishop, S., Gotham, K., Huerta, M., & Lord, C. (2013). Factors influencing scores on the social responsiveness scale. Journal of Child Psychology and Psychiatry, 54, 216–224. http://dx.doi.org/10.1111/ j.1469-7610.2012.02589.x Jones, W., & Klin, A. (2013). Attention to eyes is present but in decline in 2-6-month-old infants later diagnosed with autism. Nature, 504, 427–431. http://dx.doi.org/10.1038/nature12715 Joseph, R. M., Ehrman, K., McNally, R., & Keehn, B. (2008). Affective response to eye contact and face recognition ability in children with ASD. Journal of the International Neuropsychological Society, 14, 947–955. http://dx.doi.org/10.1017/ S1355617708081344 Just, M. A., Keller, T. A., Malave, V. L., Kana, R. K., & Varma, S. (2012). Autism as a neural systems disorder: A theory of frontal-posterior underconnectivity. Neuroscience and Biobehavioral Reviews, 36, 1292–1313. http://dx.doi.org/10.1016/j. neubiorev.2012.02.007 Kaat, A. J., Gadow, K. D., & Lecavalier, L. (2013). Psychiatric symptom impairment in children with autism spectrum disorders. Journal of Abnormal Child Psychology, 41, 959–969. http://dx.doi.org/10.1007/ s10802-013-9739-7

Autism Spectrum Disorder

Kaland, N., Mortensen, E. L., & Smith, L. (2011). Social communication impairments in children and adolescents with Asperger syndrome: Slow response time and the impact of prompting. Research in Autism Spectrum Disorders, 5, 1129–1137. http://dx.doi.org/ 10.1016/j.rasd.2010.12.009

Kim, Y. S., Leventhal, B. L., Koh, Y. J., Fombonne, E., Laska, E., Lim, E. C., . . . Grinker, R. R. (2011). Prevalence of autism spectrum disorders in a total population sample. American Journal of Psychiatry, 168, 904–912. http://dx.doi.org/10.1176/appi. ajp.2011.10101532

Kanner, L. (1943). Autistic disturbances of affective contact. Nervous Child, 2, 217–250.

Kleinhans, N. M., Richards, T., Weaver, K., Johnson, L. C., Greenson, J., Dawson, G., & Aylward, E. (2010). Association between amygdala response to emotional faces and social anxiety in autism spectrum disorders. Neuropsychologia, 48, 3665–3670. http:// dx.doi.org/10.1016/j.neuropsychologia.2010.07.022

Copyright American Psychological Association. Not for further distribution.

Kasari, C., Brady, N., Lord, C., & Tager-Flusberg, H. (2013). Assessing the minimally verbal schoolaged child with autism spectrum disorder. Autism Research, 6, 479–493. http://dx.doi.org/10.1002/ aur.1334 Kendall, P. C., Hudson, J. L., Gosch, E., Flannery-Schroeder, E., & Suveg, C. (2008). Cognitive–behavioral therapy for anxiety disordered youth: A randomized clinical trial evaluating child and family modalities. Journal of Consulting and Clinical Psychology, 76, 282–297. http:// dx.doi.org/10.1037/0022-006X.76.2.282 Kerns, C. M., & Kendall, P. C. (2012). The presentation and classification of anxiety in autism spectrum disorder. Clinical Psychology: Science and Practice, 19, 323–347. http://dx.doi.org/10.1111/cpsp.12009 Kerns, C. M., Kendall, P. C., Berry, L., Souders, M. C., Franklin, M. E., Schultz, R. T., . . . Herrington, J. (2014). Traditional and atypical presentations of anxiety in youth with autism spectrum disorder. Journal of Autism and Developmental Disorders, 44, 2851–2861. http://dx.doi.org/10.1007/s10803-0142141-7 Kerns, C. M., Kendall, P. C., Zickgraf, H., Franklin, M. E., Miller, J., & Herrington, J. (2015). Not to be overshadowed or overlooked: Functional impairments associated with comorbid anxiety disorders in youth with ASD. Behavior Therapy, 46, 29–39. http://dx.doi.org/10.1016/j.beth.2014.03.005 Kerns, C. M., Maddox, B. B., Kendall, P. C., Rump, K., Berry, L., Schultz, R. T., . . . Miller, J. (2015). Brief measures of anxiety in non-treatment-seeking youth with autism spectrum disorder. Autism, 19, 969–979. http://dx.doi.org/10.1177/1362361314558465 Kim, H., Keifer, C. M., Rodriguez-Seijas, C., Eaton, N. R., Lerner, M. D., & Gadow, K. D. (2017). Structural hierarchy of autism spectrum disorder symptoms: An integrative framework. Journal of Child Psychology and Psychiatry. Advance online publication. http:// dx.doi.org/10.1111/jcpp.12698 Kim, Y. S., Fombonne, E., Koh, Y.-J., Kim, S.-J., Cheon, K.-A., & Leventhal, B. L. (2014). A comparison of DSM–IV pervasive developmental disorder and DSM–5 autism spectrum disorder prevalence in an epidemiologic sample. Journal of the American Academy of Child and Adolescent Psychiatry, 53, 500–508. http://dx.doi.org/10.1016/j. jaac.2013.12.021

Klin, A., Jones, W., Schultz, R., & Volkmar, F. (2003). The enactive mind, or from actions to cognition: Lessons from autism. Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences, 358, 345–360. http://dx.doi.org/10.1098/ rstb.2002.1202 Koegel, R. L., & Koegel, L. K. (2006). Pivotal response treatments for autism. Baltimore, MD: Paul H. Brookes. Kohls, G., Chevallier, C., Troiani, V., & Schultz, R. T. (2012). Social “wanting” dysfunction in autism: Neurobiological underpinnings and treatment implications. Journal of Neurodevelopmental Disorders, 4, 1. http://dx.doi.org/10.1186/1866-1955-4-10 Krug, D. A., Arick, J. R., & Almond, P. (2008). Autism screening instrument for educational planning. Austin, TX,: Pro-Ed. Krumm, N., O’Roak, B. J., Shendure, J., & Eichler, E. E. (2014). A de novo convergence of autism genetics and molecular neuroscience. Trends in Neurosciences, 37, 95–105. http://dx.doi.org/10.1016/j. tins.2013.11.005 Lai, M.-C., & Baron-Cohen, S. (2015). Identifying the lost generation of adults with autism spectrum conditions. Lancet Psychiatry, 2, 1013–1027. http:// dx.doi.org/10.1016/S2215-0366(15)00277-1 Laugeson, E. A., & Frankel, F. (2010). Social skills for teenagers with developmental and autism spectrum disorders: The PEERS treatment manual. New York, NY: Routledge. Laugeson, E. A., Gantman, A., Kapp, S. K., Orenski, K., & Ellingsen, R. (2015). A randomized controlled trial to improve social skills in young adults with autism spectrum disorder: The UCLA PEERS Program. Journal of Autism and Developmental Disorders, 45, 3978–3989. http://dx.doi.org/10.1007/s10803-0152504-8 Laurent, A. C., & Rubin, E. (2004). Challenges in emotional regulation in Asperger syndrome and high-functioning autism. Topics in Language Disorders, 24, 286–297. http://dx.doi.org/ 10.1097/00011363-200410000-00006 465

Lerner et al.

Le Couteur, A., Lord, C., & Rutter, M. (2003). The Autism Diagnostic Interview—Revised (ADI–R). Los Angeles, CA: Western Psychological Services. Lerner, M. D. (2013). Knowledge or performance: Why youth with autism experience social problems. Charlottesville: University of Virginia Press.

Copyright American Psychological Association. Not for further distribution.

Lerner, M. D., Calhoun, C. D., Mikami, A. Y., & De Los Reyes, A. (2012). Understanding parent-child social informant discrepancy in youth with high functioning autism spectrum disorders. Journal of Autism and Developmental Disorders, 42, 2680–2692. http://dx.doi.org/10.1007/s10803-012-1525-9 Lerner, M. D., De Los Reyes, A., Drabick, D. A. G., Gerber, A. H., & Gadow, K. D. (2017), Informant discrepancy defines discrete, clinically useful autism spectrum disorder subgroups. Journal of Child Psychology and Psychiatry, 58, 829–839. http:// dx.doi.org/10.1111/jcpp.12730 Lerner, M. D., Haque, O. S., Northrup, E. C., Lawer, L., & Bursztajn, H. J. (2012). Emerging perspectives on adolescents and young adults with high-functioning autism spectrum disorders, violence, and criminal law. Journal of the American Academy of Psychiatry and the Law, 40, 177–190. Lerner, M. D., McPartland, J. C., & Morris, J. P. (2013). Multimodal emotion processing in autism spectrum disorders: An event-related potential study. Developmental Cognitive Neuroscience, 3, 11–21. http://dx.doi.org/10.1016/j.dcn.2012.08.005 Lerner, M. D., & Mikami, A. Y. (2012). A preliminary randomized controlled trial of two social skills interventions for youth with high-functioning autism spectrum disorders. Focus on Autism and Other Developmental Disabilities, 27, 147–157. http:// dx.doi.org/10.1177/1088357612450613 Lerner, M. D., Mikami, A. Y., & Levine, K. (2011). Socio-dramatic affective-relational intervention for adolescents with Asperger syndrome & high functioning autism: Pilot study. Autism, 15, 21–42. http://dx.doi.org/10.1177/1362361309353613 Lerner, M. D., Potthoff, L. M., & Hunter, S. J. (2015). Optimizing cross-sectional prediction of social functioning in youth referred for neuropsychological testing. PLOS ONE, 10, e0128303. http://dx.doi.org/ 10.1371/journal.pone.0128303 Lerner, M. D., & White, S. W. (2015). Moderators and mediators of treatments for youth with autism spectrum disorders. In M. Maric, P. J. M. Prins, & T. H. Ollendick (Eds.), Moderators and mediators of youth treatment outcomes (pp. 146–174). New York, NY: Oxford University Press. Lerner, M. D., White, S. W., & McPartland, J. C. (2012). Mechanisms of change in psychosocial interventions for autism spectrum disorders. Dialogues in Clinical Neuroscience, 14, 307–318. 466

Libero, L. E., & Kana, R. K. (2013). Advancing our understanding of the brain in autism: Contribution of functional MRI and diffusion tensor imaging. Imaging, 5, 453–465. http://dx.doi.org/10.2217/ iim.13.46 Lord, C., Risi, S., Lambrecht, L., Cook, E. H., Jr., Leventhal, B. L., DiLavore, P. C., . . . Rutter, M. (2000). The autism diagnostic observation schedule-generic: A standard measure of social and communication deficits associated with the spectrum of autism. Journal of Autism and Developmental Disorders, 30, 205–223. http:// dx.doi.org/10.1023/A:1005592401947 Lord, C., Rutter, M., DiLavore, P. C., Risi, S., Gotham, K., & Bishop, S. L. (2012). Autism diagnostic observation schedule, second edition (ADOS–2) manual (part 1): Modules 1–4. Torrance, CA: Western Psychological Services. Lovaas, O. I., & Smith, T. (1988). Intensive behavioral treatment for young autistic children In B. B. Lahey (Ed.), Advances in clinical child psychology (pp. 285–324). New York, NY: Springer. Loveland, K. A. (2005). Social-emotional impairment and self-regulation in autism spectrum disorders. In J. Nadel & D. Muir (Eds.), Typical and impaired emotional development, (pp. 365–382). New York, NY: Oxford University Press. Luyster, R., Guthrie, W., Gotham, K., Risi, S., DiLavore, P., & Lord, C. (2008, May). The Autism Diagnostic Observation Schedule–Toddler module: Preliminary findings using a modified version of the ADOS. Paper presented at the International Meeting for Autism Research, London, England. Magnússon, P., & Saemundsen, E. (2001). Prevalence of autism in Iceland. Journal of Autism and Developmental Disorders, 31, 153–163. http:// dx.doi.org/10.1023/A:1010795014548 Mandy, W., & Lai, M.-C. (2016). Annual research review: The role of the environment in the developmental psychopathology of autism spectrum condition. Journal of Child Psychology and Psychiatry, 57, 271–292. http://dx.doi.org/10.1111/jcpp.12501 Mason, J., & Scior, K. (2004). “Diagnostic overshadowing” amongst clinicians working with people with intellectual disabilities in the UK. Journal of Applied Research in Intellectual Disabilities, 17, 85–90. http://dx.doi.org/10.1111/j.13602322.2004.00184.x Mayes, S. D., Calhoun, S. L., & Crites, D. L. (2001). Does DSM–IV Asperger’s disorder exist? Journal of Abnormal Child Psychology, 29, 263–271. http:// dx.doi.org/10.1023/A:1010337916636 Mazefsky, C. A., Day, T. N., Siegel, M., White, S. W., Yu, L., & Pilkonis, P. A. (2016). Development of the emotion dysregulation inventory: A promising

Autism Spectrum Disorder

method for creating sensitive and unbiased questionnaires for autism spectrum disorder. Journal of Autism and Developmental Disorders. Advance online publication.

Copyright American Psychological Association. Not for further distribution.

Mazefsky, C. A., Herrington, J., Siegel, M., Scarpa, A., Maddox, B. B., Scahill, L., & White, S. W. (2013). The role of emotion regulation in autism spectrum disorder. Journal of the American Academy of Child and Adolescent Psychiatry, 52, 679–688. http:// dx.doi.org/10.1016/j.jaac.2013.05.006 Mazefsky, C. A., McPartland, J. C., Gastgeb, H. Z., & Minshew, N. J. (2013). Brief report: Comparability of DSM–IV and DSM–5 ASD research samples. Journal of Autism and Developmental Disorders, 43, 1236–1242. http://dx.doi.org/10.1007/s10803-0121665-y Mazefsky, C. A., & Oswald, D. P. (2006). The discriminative ability and diagnostic utility of the ADOS–G, ADI–R, and GARS for children in a clinical setting. Autism, 10, 533–549. http:// dx.doi.org/10.1177/1362361306068505 Mazefsky, C. A., Oswald, D. P., Day, T. N., Eack, S. M., Minshew, N. J., & Lainhart, J. E. (2012). ASD, a psychiatric disorder, or both? Psychiatric diagnoses in adolescents with high-functioning ASD. Journal of Clinical Child and Adolescent Psychology, 41, 516–523. http://dx.doi.org/10.1080/15374416. 2012.686102 Mazefsky, C. A., Pelphrey, K. A., & Dahl, R. E. (2012). The need for a broader approach to emotion regulation research in autism. Child Development Perspectives, 6, 92–97. http://dx.doi.org/10.1111/ j.1750-8606.2011.00229.x Mazefsky, C. A., & White, S. W. (2014). Emotion regulation: Concepts & practice in autism spectrum disorder. Child and Adolescent Psychiatric Clinics of North America, 23, 15–24. http://dx.doi.org/ 10.1016/j.chc.2013.07.002 McEachin, J. J., Smith, T., & Lovaas, O. I. (1993). Longterm outcome for children with autism who received early intensive behavioral treatment. American Journal on Mental Retardation, 97, 359–372.

Journal of the American Academy of Child and Adolescent Psychiatry, 51, 368–383. http://dx.doi.org/ 10.1016/j.jaac.2012.01.007 McPartland, J. C., Wu, J., Bailey, C. A., Mayes, L. C., Schultz, R. T., & Klin, A. (2011). Atypical neural specialization for social percepts in autism spectrum disorder. Social Neuroscience, 6, 436–451. http:// dx.doi.org/10.1080/17470919.2011.586880 Mendelson, J. L., Gates, J. A., & Lerner, M. D. (2016). Friendship in school-age boys with autism spectrum disorders: A meta-analytic summary and developmental, process-based model. Psychological Bulletin, 142, 601–622. http://dx.doi.org/10.1037/ bul0000041 Merrick, J., Kandel, I., & Morad, M. (2004). Trends in autism. International Journal of Adolescent Medicine and Health, 16, 75–78. http://dx.doi.org/10.1515/ IJAMH.2004.16.1.75 Minshew, N. J., & Goldstein, G. (1998). Autism as a disorder of complex information processing. Mental Retardation and Developmental Disabilities Research Reviews, 4, 129–136. http://dx.doi. org/10.1002/(SICI)1098-2779(1998)4:2129::AIDMRDD103.0.CO;2-X Minshew, N. J., Muenz, L. R., Goldstein, G., & Payton, J. B. (1992). Neuropsychological functioning in nonmentally retarded autistic individuals. Journal of Clinical and Experimental Neuropsychology, 14, 749–761. http://dx.doi.org/ 10.1080/01688639208402860 Mohammad-Rezazadeh, I., Frohlich, J., Loo, S. K., & Jeste, S. S. (2016). Brain connectivity in autism spectrum disorder. Current Opinion in Neurology, 29, 137–147. http://dx.doi.org/10.1097/ WCO.0000000000000301 Mottron, L. (2011). Changing perceptions: The power of autism. Nature, 479, 33–35. http://dx.doi.org/ 10.1038/479033a National Autism Center. (2015). Findings and conclusions: National standards project, phase 2. Randolph, MA: Author.

McMahon, C. M., Lerner, M. D., & Britton, N. (2013). Group-based social skills interventions for adolescents with higher-functioning autism spectrum disorder: A review and looking to the future. Adolescent Health, Medicine and Therapeutics, 2013, 23–28.

Newschaffer, C. J., Croen, L. A., Daniels, J., Giarelli, E., Grether, J. K., Levy, S. E., . . . Windham, G. C. (2007). The epidemiology of autism spectrum disorders. Annual Review of Public Health, 28, 235–258. http://dx.doi.org/10.1146/annurev. publhealth.28.021406.144007

McPartland, J. C. (2016). Considerations in biomarker development for neurodevelopmental disorders. Current Opinion in Neurology, 29, 118–122. http:// dx.doi.org/10.1097/WCO.0000000000000300

Oddi, D., Crusio, W. E., D’Amato, F. R., & Pietropaolo, S. (2013). Monogenic mouse models of social dysfunction: Implications for autism. Behavioural Brain Research, 251, 75–84. http://dx.doi.org/ 10.1016/j.bbr.2013.01.002

McPartland, J. C., Reichow, B., & Volkmar, F. R. (2012). Sensitivity and specificity of proposed DSM–5 diagnostic criteria for autism spectrum disorder.

Ozonoff, S., Young, G. S., Goldring, S., Greiss-Hess, L., Herrera, A. M., Steele, J., . . . Rogers, S. J. (2008). 467

Lerner et al.

Gross motor development, movement abnormalities, and early identification of autism. Journal of Autism and Developmental Disorders, 38, 644–656. http:// dx.doi.org/10.1007/s10803-007-0430-0 Parks, S. L. (1983). The assessment of autistic children: A selective review of available instruments. Journal of Autism and Developmental Disorders, 13, 255–267. http://dx.doi.org/10.1007/BF01531565

Copyright American Psychological Association. Not for further distribution.

Pellicano, E., Dinsmore, A., & Charman, T. (2014). What should autism research focus upon? Community views and priorities from the United Kingdom. Autism, 18, 756–770. http://dx.doi.org/ 10.1177/1362361314529627 Persico, A. M., & Napolioni, V. (2013). Autism genetics. Behavioural Brain Research, 251, 95–112. http:// dx.doi.org/10.1016/j.bbr.2013.06.012 Picci, G., & Scherf, K. S. (2015). A two-hit model of autism: Adolescence as the second hit. Clinical Psychological Science, 3, 349–371. Puleo, C. M., & Kendall, P. C. (2011). Anxiety disorders in typically developing youth: Autism spectrum symptoms as a predictor of cognitive–behavioral treatment. Journal of Autism and Developmental Disorders, 41, 275–286. http://dx.doi.org/10.1007/ s10803-010-1047-2 Ratto, A. B., Turner-Brown, L., Rupp, B. M., Mesibov, G. B., & Penn, D. L. (2011). Development of the Contextual Assessment of Social Skills (CASS): A role play measure of social skill for individuals with high-functioning autism. Journal of Autism and Developmental Disorders, 41, 1277–1286. http:// dx.doi.org/10.1007/s10803-010-1147-z Reichow, B. (2012). Overview of meta-analyses on early intensive behavioral intervention for young children with autism spectrum disorders. Journal of Autism and Developmental Disorders, 42, 512–520. http:// dx.doi.org/10.1007/s10803-011-1218-9 Reichow, B., & Volkmar, F. R. (2010). Social skills interventions for individuals with autism: Evaluation for evidence-based practices within a best evidence synthesis framework. Journal of Autism and Developmental Disorders, 40, 149–166. http:// dx.doi.org/10.1007/s10803-009-0842-0 Reiersen, A. M., Constantino, J. N., Volk, H. E., & Todd, R. D. (2007). Autistic traits in a population-based ADHD twin sample. Journal of Child Psychology and Psychiatry, 48, 464–472. http://dx.doi.org/10.1111/ j.1469-7610.2006.01720.x Risi, S., Lord, C., Gotham, K., Corsello, C., Chrysler, C., Szatmari, P., . . . Pickles, A. (2006). Combining information from multiple sources in the diagnosis of autism spectrum disorders. Journal of the American Academy of Child and Adolescent Psychiatry, 45, 1094–1103. http://dx.doi.org/10.1097/01. chi.0000227880.42780.0e 468

Ritvo, E. R., & Freeman, B. J. (1978). Current research on the syndrome of autism: Introduction. The National Society for Autistic Children’s definition of the syndrome of autism. Journal of the American Academy of Child Psychiatry, 17, 565–575. http:// dx.doi.org/10.1016/S0002-7138(09)61011-6 Ritvo, R. A., Ritvo, E. R., Guthrie, D., Ritvo, M. J., Hufnagel, D. H., McMahon, W., . . . Eloff, J. (2011). The Ritvo Autism Asperger Diagnostic Scale—Revised (RAADS–R): A scale to assist the diagnosis of autism spectrum disorder in adults: An international validation study. Journal of Autism and Developmental Disorders, 41, 1076–1089. http://dx.doi.org/10.1007/ s10803-010-1133-5 Robins, D. L., Fein, D., Barton, M. L., & Green, J. A. (2001). The Modified Checklist for Autism in Toddlers: An initial study investigating the early detection of autism and pervasive developmental disorders. Journal of Autism and Developmental Disorders, 31, 131–144. http:// dx.doi.org/10.1023/A:1010738829569 Robison, J. E. (2016). Kanner, Asperger, and Frankl: A third man at the genesis of the autism diagnosis. Autism. Advanced online publication. Rosen, T. E., & Lerner, M. D. (2016). Externalizing and internalizing symptoms moderate longitudinal patterns of facial emotion recognition in autism spectrum disorder. Journal of Autism and Developmental Disorders, 46, 2621–2634. http:// dx.doi.org/10.1007/s10803-016-2800-y Rutter, M. (1978). Diagnosis and definition of childhood autism. Journal of Autism and Childhood Schizophrenia, 8, 139–161. http://dx.doi.org/10.1007/ BF01537863 Rutter, M., Bailey, A., & Lord, C. (2005). SCQ: The Social Communication Questionnaire manual. Los Angeles, CA: Western Psychological Services. Rutter, M., & Schopler, E. (1992). Classification of pervasive developmental disorders: Some concepts and practical considerations. Journal of Autism and Developmental Disorders, 22, 459–482. http://dx.doi.org/10.1007/ BF01046322 Scarpa, A., White, S. W., & Attwood, T. (2013). CBT for children and adolescents with high-functioning autism spectrum disorders. New York, NY: Guilford Press. Schopler, E., Van Bourgondien, M., Wellman, J., & Love, S. (2010). Childhood autism rating scale—Second edition (CARS2): Manual. Los Angeles, CA: Western Psychological Services. Schreibman, L., Dawson, G., Stahmer, A. C., Landa, R., Rogers, S. J., McGee, G. G., . . . Halladay, A. (2015). Naturalistic developmental behavioral interventions: Empirically validated treatments for autism spectrum disorder. Journal of Autism and Developmental

Autism Spectrum Disorder

Disorders, 45, 2411–2428. http://dx.doi.org/10.1007/ s10803-015-2407-8 Senju, A., Southgate, V., White, S., & Frith, U. (2009). Mindblind eyes: An absence of spontaneous theory of mind in Asperger syndrome. Science, 325, 883–885. http://dx.doi.org/10.1126/science.1176170

Copyright American Psychological Association. Not for further distribution.

Sigman, M., Ruskin, E., Arbeile, S., Corona, R., Dissanayake, C., Espinosa, M., . . . Zierhut, C. (1999). Continuity and change in the social competence of children with autism, Down syndrome, and developmental delays. Monographs of the Society for Research in Child Development, 64, 1–10. http://dx.doi.org/10.1111/1540-5834.00002 Silverman, W. K., & Hinshaw, S. P. (2008). The second special issue on evidence-based psychosocial treatments for children and adolescents: A 10-year update. Journal of Clinical Child and Adolescent Psychology, 37, 1–7. http://dx.doi.org/ 10.1080/15374410701817725 Simonoff, E., Jones, C. R., Baird, G., Pickles, A., Happé, F., & Charman, T. (2013). The persistence and stability of psychiatric problems in adolescents with autism spectrum disorders. Journal of Child Psychology and Psychiatry, 54, 186–194. http:// dx.doi.org/10.1111/j.1469-7610.2012.02606.x Simonoff, E., Pickles, A., Charman, T., Chandler, S., Loucas, T., & Baird, G. (2008). Psychiatric disorders in children with autism spectrum disorders: Prevalence, comorbidity, and associated factors in a population-derived sample. Journal of the American Academy of Child and Adolescent Psychiatry, 47, 921–929. http://dx.doi.org/10.1097/ CHI.0b013e318179964f Smith, T. (2014). Behavior analysts can be interdisciplinary too: A review of Durand’s Autism Spectrum Disorder. New York, NY: Wiley. Smith, T., & Iadarola, S. (2015). Evidence base update for autism spectrum disorder. Journal of Clinical Child and Adolescent Psychology, 44, 897–922. http://dx.doi.org/10.1080/15374416.2015.1077448 Spaulding, C. J., Lerner, M. D., & Gadow, K. D. (2016). Trajectories and correlates of special education supports for youth with autism spectrum disorder and psychiatric comparisons. Autism, 21, 423–435. Spitzer, R. L., & Siegel, B. (1990). The DSM–III–R field trial of pervasive developmental disorders. Journal of the American Academy of Child and Adolescent Psychiatry, 29, 855–862. http:// dx.doi.org/10.1097/00004583-199011000-00003 Stein, M. T., Klin, A., & Miller, K. (2004). When Asperger’s syndrome and a nonverbal learning disability look alike. Pediatrics, 114(Suppl. 6), 1458–1463. http://dx.doi.org/10.1542/peds.20041721L

Szatmari, P. (1992). The validity of autistic spectrum disorders: A literature review. Journal of Autism and Developmental Disorders, 22, 583–600. http:// dx.doi.org/10.1007/BF01046329 Szatmari, P., Chawarska, K., Dawson, G., Georgiades, S., Landa, R., Lord, C., . . . Halladay, A. (2016). Prospective longitudinal studies of infant siblings of children with autism: lessons learned and future directions. Journal of the American Academy of Child and Adolescent Psychiatry, 55, 179–187. http:// dx.doi.org/10.1016/j.jaac.2015.12.014 Tager-Flusberg, H., & Kasari, C. (2013). Minimally verbal school-aged children with autism spectrum disorder: The neglected end of the spectrum. Autism Research, 6, 468–478. http://dx.doi.org/10.1002/ aur.1329 Tager-Flusberg, H., Rogers, S., Cooper, J., Landa, R., Lord, C., Paul, R., . . . Yoder, P. (2009). Defining spoken language benchmarks and selecting measures of expressive language development for young children with autism spectrum disorders. Journal of Speech, Language, and Hearing Research, 52, 643–652. http://dx.doi.org/10.1044/10924388(2009/08-0136) Tanaka, J. W., Wolf, J. M., Klaiman, C., Koenig, K., Cockburn, J., Herlihy, L., . . . Schultz, R. T. (2010). Using computerized games to teach face recognition skills to children with autism spectrum disorder: The Let’s Face It! program. Journal of Child Psychology and Psychiatry, 51, 944–952. http://dx.doi.org/ 10.1111/j.1469-7610.2010.02258.x Tottenham, N., Hertzig, M. E., Gillespie-Lynch, K., Gilhooly, T., Millner, A. J, & Casey, B. J. (2013). Elevated amygdala response to faces and gaze aversion in autism spectrum disorder. Social Cognitive and Affective Neuroscience, 9, 106–117. Treffert, D. A. (1970). Epidemiology of infantile autism. Archives of General Psychiatry, 22, 431–438. http:// dx.doi.org/10.1001/archpsyc.1970.01740290047006 van Heijst, B. F. C., & Geurts, H. M. (2014). Quality of life in autism across the lifespan: A meta-analysis. Autism, 19, 158–167. van Meerbeke, A. V., Talero-Gutierrez, C., & Gonzalez-Reyes, R. (2007). Prevalence of delayed neurodevelopment in children from Bogotá, Colombia, South America. Neuroepidemiology, 29, 74–77. http://dx.doi.org/10.1159/000109499 Van Schalkwyk, G. I., Peluso, F., Qayyum, Z., McPartland, J. C., & Volkmar, F. R. (2015). Varieties of misdiagnosis in ASD: An illustrative case series. Journal of Autism and Developmental Disorders, 45, 911–918. http://dx.doi.org/10.1007/ s10803-014-2239-y van Steensel, F. J., Bögels, S. M., & Perrin, S. (2011). Anxiety disorders in children and adolescents with 469

Lerner et al.

autistic spectrum disorders: A meta-analysis. Clinical Child and Family Psychology Review, 14, 302–317. http://dx.doi.org/10.1007/s10567-011-0097-0

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Verheij, C., Louwerse, A., van der Ende, J., Eussen, M. L. J. M., Van Gool, A. R., Verheij, F., . . . Greaves-Lord, K. (2015). The stability of comorbid psychiatric disorders: A 7 year follow up of children with pervasive developmental disorder-not otherwise specified. Journal of Autism and Developmental Disorders, 45, 3939–3948. http://dx.doi.org/10.1007/ s10803-015-2592-5 Verhoeven, E. W. M., Marijnissen, N., Berger, H. J. C., Oudshoorn, J., van der Sijde, A., & Teunisse, J. P. (2012). Brief report: Relationship between selfawareness of real-world behavior and treatment outcome in autism spectrum disorders. Journal of Autism and Developmental Disorders, 42, 889–894. http://dx.doi.org/10.1007/s10803-011-1311-0 Volkmar, F. R., Cicchetti, D. V., Bregman, J., & Cohen, D. J. (1992). Three diagnostic systems for autism: DSM–III, DSM–III–R, and ICD–10. Journal of Autism and Developmental Disorders, 22, 483–492. http:// dx.doi.org/10.1007/BF01046323 Wang, S.-Y., Parrila, R., & Cui, Y. (2013). Meta-analysis of social skills interventions of single-case research for individuals with autism spectrum disorders: Results from three-level HLM. Journal of Autism and Developmental Disorders, 43, 1701–1716. http:// dx.doi.org/10.1007/s10803-012-1726-2 Warlaumont, A. S., Richards, J. A., Gilkerson, J., & Oller, D. K. (2014). A social feedback loop for speech development and its reduction in autism. Psychological Science, 25, 1314–1324. http:// dx.doi.org/10.1177/0956797614531023 Waterhouse, L., Wing, L., Spitzer, R., & Siegel, B. (1992). Pervasive developmental disorders: From DSM–III to DSM–III–R. Journal of Autism and Developmental Disorders, 22, 525–549. http://dx.doi.org/10.1007/ BF01046326 Westen, D., Novotny, C. M., & Thompson-Brenner, H. (2005). EBP≠ EST: Reply to Crits-Christoph et al. (2005) and Weisz et al. (2005). Psychological Bulletin, 131, 427–433. Weston, L., Hodgekins, J., & Langdon, P. E. (2016). Effectiveness of cognitive behavioural therapy with people who have autistic spectrum disorders: A systematic review and meta-analysis. Clinical Psychology Review, 49, 41–54. http://dx.doi.org/ 10.1016/j.cpr.2016.08.001 Wetherby, A. M., Brosnan-Maddox, S., Peace, V., & Newton, L. (2008). Validation of the Infant-Toddler Checklist as a broadband screener for autism spectrum disorders from 9 to 24 months of age. Autism, 12, 487–511. http://dx.doi.org/10.1177/ 1362361308094501

470

White, S. W., Albano, A. M., Johnson, C. R., Kasari, C., Ollendick, T., Klin, A., . . . Scahill, L. (2010). Development of a cognitive–behavioral intervention program to treat anxiety and social deficits in teens with high-functioning autism. Clinical Child and Family Psychology Review, 13, 77–90. http:// dx.doi.org/10.1007/s10567-009-0062-3 White, S. W., Lerner, M. D., McLeod, B. D., Wood, J. J., Ginsburg, G. S., Kerns, C. M., . . . Walkup, J. (2015). Anxiety in youth with and without autism spectrum disorder: Examination of factorial equivalence. Behavior Therapy, 46, 40–53. White, S. W., Mazefsky, C. A., Dichter, G. S., Chiu, P. H., Richey, J. A., & Ollendick, T. H. (2014). Socialcognitive, physiological, and neural mechanisms underlying emotion regulation impairments: Understanding anxiety in autism spectrum disorder. International Journal of Developmental Neuroscience, 39, 22–36. http://dx.doi.org/10.1016/j.ijdevneu.2014.05.012 White, S. W., Ollendick, T., Albano, A. M., Oswald, D., Johnson, C., Southam-Gerow, M. A., . . . Scahill, L. (2013). Randomized controlled trial: Multimodal anxiety and social skill intervention for adolescents with autism spectrum disorder. Journal of Autism and Developmental Disorders, 43, 382–394. http://dx.doi.org/ 10.1007/s10803-012-1577-x White, S. W., Oswald, D., Ollendick, T., & Scahill, L. (2009). Anxiety in children and adolescents with autism spectrum disorders. Clinical Psychology Review, 29, 216–229. http://dx.doi.org/10.1016/j. cpr.2009.01.003 Wieckowski, A. T., & White, S.W. (2017). Eye-gaze analysis of facial emotion recognition and expression in adolescents with ASD. Journal of Clinical Child and Adolescent Psychology, 46, 110–124. Williams, D. (2010). Theory of own mind in autism: Evidence of a specific deficit in self-awareness? Autism, 14, 474–494. http://dx.doi.org/10.1177/1362361310366314 Williams, D. L., Goldstein, G., & Minshew, N. J. (2006). Neuropsychologic functioning in children with autism: Further evidence for disordered complex information-processing. Child Neuropsychology, 12, 279–298. http://dx.doi.org/10.1080/ 09297040600681190 Wing, L. (1981). Asperger’s syndrome: A clinical account. Psychological Medicine, 11, 115–129. http://dx.doi.org/ 10.1017/S0033291700053332 Wing, L., & Potter, D. (2009). The epidemiology of autism spectrum disorders: Is the prevalence rising. In S. Goldstein, J. A. Naglieri, & S. Ozonoff (Eds.), Assessment of autism spectrum disorders (pp. 18–54). New York, NY: Guilford Press. Wong, C., Odom, S. L., Hume, K. A., Cox, A. W., Fettig, A., Kucharczyk, S., . . . Schultz, T. R. (2015).

Autism Spectrum Disorder

Evidence-based practices for children, youth, and young adults with autism spectrum disorder: A comprehensive review. Journal of Autism and Developmental Disorders, 45, 1951–1966. http:// dx.doi.org/10.1007/s10803-014-2351-z Wood, J. J., Ehrenreich-May, J., Alessandri, M., Fujii, C., Renno, P., Laugeson, E., . . . Storch, E. A. (2015). Cognitive behavioral therapy for early adolescents with autism spectrum disorders and clinical anxiety: A randomized, controlled trial. Behavior Therapy, 46, 7–19. http://dx.doi.org/10.1016/j.beth.2014.01.002

World Health Organization. (2004). International statistical classification of diseases and health related problems (10th rev.). Geneva, Switzerland: Author. Yoshida, Y., & Uchiyama, T. (2004). The clinical necessity for assessing attention-deficit/hyperactivity disorder (ADHD) symptoms in children with highfunctioning pervasive developmental disorder (PDD). European Child and Adolescent Psychiatry, 13, 307–314. http://dx.doi.org/10.1007/s00787-0040391-1

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Wood, J. J., & Gadow, K. D. (2010). Exploring the nature and function of anxiety in youth with autism

spectrum disorders. Clinical Psychology: Science and Practice, 17, 281–292. http://dx.doi.org/10.1111/ j.1468-2850.2010.01220.x

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Chapter 21

Psychological Treatment of Adolescents

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Stephen R. Shirk, Allison A. Stiles, and Skyler Leonard

Occupying much of the second decade of life, adolescence is typically framed by the onset of puberty and the completion of secondary education. Adolescence is a period of rapid physical, psychological, social, and neural development (Lerner & Steinberg, 2009), and given this cascade of changes, adolescence has been characterized as a phase of normative turmoil (Elmen & Offer, 1993). Perhaps it should not be surprising, then, that results from the National Comorbidity Survey—Adolescent Supplement showed that 23.4% of adolescents met criteria for a mental health disorder in the prior 30-day period and a remarkable 79.5% met criteria for at least one disorder over the course of adolescence (Kessler et al., 2012). Some disorders represent continuations from childhood, including attention-deficit/hyperactivity disorder (ADHD), generalized anxiety, and oppositional defiant disorder, whereas others tend to be emergent in adolescence, including major depressive disorder, social anxiety disorder, and eating disorders (Kessler et al., 2012). And across disorders, new clinical features that are infrequently encountered in childhood (e.g., suicidal behavior, nonsuicidal self-injury [NSSI], substance use) complicate clinical presentations during adolescence. Adolescent development also impacts the character and content of psychological treatments (Holmbeck & Kendall, 2002; Shirk, 2001). On the positive side, the growth of self-reflection associated with adolescent cognitive development supports therapeutic strategies that draw on self-observation and self-monitoring skills (Shirk, 2001). Similarly, the emerging capacity to think about thinking

(i.e., recursive thinking) supports cognitive strategies that focus on maladaptive thoughts and associated feelings (Shirk, 2001). On the other hand, the press toward autonomy can increase reactance to therapeutic efforts to promote change by adults, including therapists (Meeks, 1971). The rise in parent–child conflict associated with the movement toward increasing adolescent independence can dampen the effects of robust childhood treatments (e.g., parent management training), in part, because of increased peer influence and reductions in parent leverage. In brief, adolescent development prompts meaningful changes in the clinical presentation of youth and reshapes effective therapeutic interventions. Therefore, the treatment of adolescents deserves separate consideration from the treatment of children on one hand and the treatment of adults on the other (Kendall & Williams, 1986). The aims of this chapter are threefold. First, we review the treatment outcome literature for three of the most prevalent adolescent mental health disorders; major depressive disorder (MDD), social anxiety disorder (SAD), and oppositional defiant disorder (ODD). Epidemiological evidence (Kessler et al., 2012) indicates that these three disorders are among the most common in adolescence and, therefore, likely to be encountered by practicing clinicians. As part of this review, we identify and describe core treatment components of evidence-based treatments for these disorders. Where multiple evidence-based approaches exist, we distill common elements of efficacious treatments (Chorpita & Daleiden, 2009). Our second

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aim is to consider clinical problems that emerge during adolescence and complicate treatment. These problems cut across prevalent adolescent disorders but may not be full comorbid disorders themselves. Specifically, we focus on the clinical management of suicidal thinking/behavior, NSSI, and collateral substance use in the context of treating MDD, SAD, and ODD. Our third and final aim is to consider the challenge of engaging adolescents in evidence-based therapy. Specifically, we examine alliance formation with adolescents. Treatment of Prevalent Adolescent Mental Health Disorders Epidemiological evidence (Kessler et al., 2012) indicates that MDD, SAD, and ODD are among the most common in adolescence. In fact, it is not uncommon for these disorders to co-occur during adolescence. Clinicians who treat adolescents are likely to encounter these three disorders.

Treatment of Major Depressive Disorder in Adolescence MDD is a prevalent, recurrent, and impairing disorder that often has its initial onset during adolescence (Hankin et al., 2015). Although depression is found among children, there is a sharp rise in depressive episodes during middle and late adolescence (Hankin et al., 2015). The National Comorbidity Survey—Adolescent Supplement (Kessler et al., 2012) revealed a 7% prevalence rate for a 30-day period. Lifetime prevalence over the course of adolescence is estimated to be between 20% and 25% (Kessler et al., 2012). Gender-related differences emerge during adolescence with girls being twice as likely as boys to experience a major depressive episode (Hankin et al., 2015). Chronic and recurrent depression is associated with impairment in social relationships, interference with academic performance, and increased risk for substance abuse, early childbearing, and suicide (Beardslee et al., 2013). Several psychological treatments have been developed and evaluated for adolescent depression, including cognitive–behavioral therapy (CBT), interpersonal therapy for adolescents (IPT-A), 476

behavioral activation (BA), and attachment-based family therapy. We focus on two treatments that have been evaluated in multiple clinical trials, CBT and IPT. Individual cognitive–behavioral therapy for adolescent depression.  At the heart of CBT is the view that thoughts, behaviors, and emotions are reciprocally linked (Kendall, 1991). To leverage change in depressed mood, CBT therapists target changes in associated cognitions (e.g., cognitive distortions, negative automatic thoughts) and maladaptive behaviors (e.g., withdrawal, avoidance). Cognitive procedures focus on restructuring depressogenic cognitions and enhancing active coping. Behavioral procedures aim to increase exposure to social reinforcement and decrease passivity and disengagement (Weersing & Brent, 2010). Of all the psychological treatments for adolescent depression, CBT has received the greatest empirical scrutiny (Weisz, McCarty, & Valeri, 2006). Initial estimates of CBT effects were among the most robust in the youth treatment literature; Reinecke, Ryan, and DuBois (1998) reported a large overall effect size of 1.02. As is often the case, with the completion of additional large-scale, clinical trials, the initial estimate has been revised downward (Klein, Jacobs, & Reinecke, 2007). Although evidence strongly supports CBT as an effective, stand-alone treatment for subthreshold depressive symptoms and mild-to-moderate MDD, CBT appears to be more helpful when combined with antidepressant medication for severe MDD (Curry, 2014). Some of the strongest evidence for the efficacy of CBT for MDD comes from a comparative outcome study that evaluated three, active treatments (Brent et al., 1997). In this study, core features of cognitive therapy, specifically thought monitoring and cognitive restructuring, were adapted for adolescents. Although cognitive procedures were emphasized, BA and problem-solving components were included as well. Following 12 to 16 weeks of treatment, remission rates (defined as loss of diagnosis and two consecutive subclinical depression scores) were 60%, 39%, and 38% for CBT, supportive therapy, and family therapy, respectively. At posttreatment, CBT outperformed supportive and family therapy.

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Results from the largest clinical trial for adolescent depression, the Treatment of Adolescent Depression Study (TADS) raised concerns about the efficacy of CBT. In this study (March et al., 2004), 439 adolescents diagnosed with MDD were randomized to one of four conditions; fluoxetine alone, CBT alone, CBT + fluoxetine (combined), or pill placebo. Assessment of treatment response (an independent clinician’s judgment about extent of improvement) at 12 weeks showed CBT to be no better than pill placebo. It is likely that the underperformance of CBT stemmed from two factors, the overall severity of MDD in the TADS sample and the use of an untested CBT protocol that was a novel combination of multiple empirically supported treatments (Hollon, Garber, & Shelton, 2005). Although results from the acute phase of TADS are concerning, two other findings should be remembered. First, response rates for CBT were better after the continuation phase of treatment (18 weeks) and comparable to combination therapy and fluoxetine alone (Kennard, Silva, et al., 2009). It is possible that skill acquisition, consolidation, and application require more sessions (greater dose) to produce remission among severely, and chronically depressed adolescents. Second, the best outcomes in TADS were attained with combination therapy. CBT plus fluoxetine outperformed fluoxetine alone during the acute phase of treatment and were maintained over time (Kennard, Silva, et al., 2009; Kratochvil et al., 2006; March et al., 2004). Consistent with these results, the addition of CBT to antidepressant medication has been shown to be especially effective for adolescents who failed to respond to medication alone (Brent et al., 2008). Overall, then, current evidence indicates that CBT, as a stand-alone treatment, is effective for mild to moderate adolescent depression, including MDD. For adolescents with severe depressive symptoms of long duration and high levels of impairment, the combination of CBT and antidepressant medication is likely to be more effective than either monotherapy. It is worth noting that several studies have indicated that depressed adolescents exposed to interpersonal trauma show poorer response to CBT than their nonexposed counterparts (Nanni, Uher, & Danese, 2012). Treatment modification

for this sizable subgroup might require inclusion of treatment components not typically found in CBT for adolescent depression (DePrince & Shirk, 2013). CBT for adolescent depression is a “family” of treatments with partially overlapping components (Weersing, Rozenman, & Gonzalez, 2009). A sample of components from different protocols includes mood monitoring, cognitive restructuring, BA, goalsetting, coping skills training, and problem-solving training. CBT protocols vary in their inclusion of and emphasis on specific components (Weersing et al., 2009). Common components are found among “close relatives” in this family of treatments, particularly cognitive restructuring and BA. However, given substantial variation in outcomes across protocols that include these common components, “distillation” of active ingredients is challenging (Chorpita & Daleiden, 2009). Remarkably, there have been no dismantling studies of CBT for adolescent depression (Shirk, Jungbluth, & Karver, 2012). Only a few additive designs testing the contribution of parent components or booster sessions have been conducted. Therefore, other forms of evidence must be considered to evaluate active ingredients. One fruitful approach has been to examine covariation between component use and treatment outcomes. In the Treatment of SSRI Resistant Depression in Adolescents study (TORDIA; Brent et al., 2008), therapists delivered a flexible, modularized form of CBT by selecting relevant components from a menu of CBT options. This approach resulted in substantial variation in component use across patients (Kennard, Clarke, et al., 2009). Analyses indicated that use of two components, social skills and problem-solving training, was associated with better treatment response. These two components are not consistently included in adolescent depression protocols, though they are often part of CBT for anxiety and disruptive disorders. Of equal interest, use of some of the most common CBT components, cognitive restructuring and BA, were not related to outcome. This pattern of findings is provocative insofar as it suggests that less frequently used components like social skills training might promote better outcomes than more common components. Of course, adolescents were not randomly assigned to receive specific sets of 477

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components, and evidence indicated that adolescent and family characteristics were associated with component use (Kennard, Clarke, et al., 2009). However, when these characteristics were controlled statistically, use of social skills and problem-solving training continued to predict better outcomes. Cognitive restructuring is a central component of many CBT protocols, yet no study has examined the specific contribution of this component to outcome. Research on mechanisms of change indicate that modification of maladaptive cognitions is associated with symptom reduction (Kaufman et al., 2005; Shirk, Crisostomo, Jungbluth, & Gudmundsen, 2013), and findings from a comparative outcome study indicate that cognitive changes are specific to CBT (Kolko, Brent, Baugher, Bridge, & Birmaher, 2000). However, Shirk et al. (2013) failed to find an association between adolescents’ in-session involvement in cognitive restructuring tasks or their completion of related homework to be predictive of changes in cognitive distortions. Given developmental concerns about adolescents’ capacity to use cognitive restructuring (Silvers et al., 2012) identification of treatment components that contribute to cognitive change could strengthen CBT. In addition to cognitive components, many CBT protocols include some form of BA (Weersing et al., 2009). Research indicates that BA is an efficacious treatment for depressed adults (Dobson et al., 2008), and a recent open trial for depressed adolescents produced promising results with very high rates of remission among treatment completers (Ritschel, Ramirez, Jones, & Craighead, 2011). A recent RCT (McCauley et al., 2016) showed significant reductions in depressive symptoms and increases in global functioning among adolescents treated with BA, but these changes did not exceed those obtained in the treatment as usual (TAU) group. In both studies BA went well beyond the usual activity scheduling found in many CBT protocols. Instead, BA as a stand-alone treatment targets activation and avoidance. To this end, the treatment includes goalsetting, problem solving, and avoidance modification as part of targeted activation. The active, core components of CBT for adolescent depression have not been fully isolated. Distillation of core components is difficult because 478

protocols with shared components (e.g., cognitive restructuring) have produced varied outcomes. Evidence also suggests that less commonly used components like social skills training might be especially useful with some depressed adolescents. Emerging evidence indicates that adolescent depression can be reduced through BA strategies that do not target maladaptive cognitions. It is certainly possible that adolescents with different depression profiles (e.g., self-critical versus withdrawn) might respond differently to cognitive- or behavior-focused treatments. In this respect, common components might be less relevant than personalized matching of components to depression profiles. Interpersonal therapy for adolescent depression.  From an IPT perspective depression is associated with interpersonal problems and functioning (Mufson, Dorta, Moreau, & Weissman, 2005). To be clear, IPT does not assume that all depression is caused by interpersonal factors, but rather interpersonal problems are linked to depression and often maintain it. The focus of IPT-A is on interpersonal problems, usually in one of four areas: loss or grief, role transition, interpersonal dispute, or interpersonal skill deficit. Examples include a break-up with a romantic partner, a change in responsibilities because of parental separation, parent–adolescent conflict around family rules, and isolation because of difficulties with making friends. A fifth area (living with a single parent) was added for adolescent therapy. The first phase of treatment involves a close evaluation of the adolescent’s relationships including specific interpersonal conflicts or problems. Cases are formulated in terms of one of the main problem areas. Emphasis is placed on identifying and understanding emotions that are experienced and communicated in problematic interactions. As the adolescent’s goals for problematic interactions are more fully understood, and the communication patterns closely examined, alternative strategies for attaining interpersonal goals are developed and tried. Two randomized clinical trials were conducted by the IPT-A developers. In the initial feasibility study, 48 adolescents diagnosed with MDD were randomly allocated to 12 sessions of IPT-A or to

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clinical monitoring (Mufson, Weissman, Moreau, & Garfinkel, 1999). Results showed greater reduction in depressive symptoms in the IPT-A group compared with clinical monitoring. Results also revealed better self-reported interpersonal functioning and social problem-solving among adolescents treated with IPT-A than those who were monitored. Remission rates were high for IPT-A with approximately three quarters no longer meeting diagnostic criteria at posttreatment. Relatively high rates of remission in the monitoring condition suggest that depression chronicity might not have been high though the sample included a substantial number of youth with suicidal ideation and a smaller percentage with prior suicide attempts. In a second trial conducted in school-based clinics, Mufson et al. (2004) compared IPT-A with an active control condition, TAU. Adolescents were referred to five school-based clinics and therapy was delivered by school social workers and psychologists. All adolescents met diagnostic criteria for a depressive disorder, but not MDD exclusively. Adolescents treated with IPT-A showed a greater reduction in depressive symptoms compared with those receiving TAU. Treatment effects were moderated by initial severity such that adolescents with more severe symptoms benefited more from IPT-A than TAU. As in the prior trial, adolescents treated with IPT-A reported greater improvement in overall social functioning compared with adolescents treated with TAU. Across multiple outcomes, moderate effect sizes, between .40 and .50, were obtained, sizable effects when compared with an active therapy condition. Two additional randomized trials evaluated a variant of IPT adapted for Puerto Rican adolescents (Rosselló & Bernal, 1999). In the first, 71 adolescents who met criteria for a depressive disorder were randomized to one of three conditions, CBT, IPT, or wait-list. At posttreatment, adolescents treated with either CBT or IPT showed greater reductions in depressive symptoms than wait-list controls. Selfreported, but not parent reported, social adjustment improved in the IPT condition relative to a waitlist control. Based on an index of clinically significant change, 52% and 87% of CBT and IPT treated adolescents were returned to normative levels of

depressive symptoms at posttreatment; however, this difference was not statistically significant given the small sample size. In a second randomized trial evaluating culturally-adapted IPT and CBT (Rosselló, Bernal, & Rivera-Medina, 2008) 112 Puerto Rican adolescents who either met diagnostic criteria for MDD or had elevated depressive symptoms and were judged to be impaired were randomized to one of four conditions: individual CBT, individual IPT, group CBT, or group IPT. At posttreatment all four groups showed marked reductions in depressive symptoms. Format (group vs. individual) did not produce significantly different depression outcomes but across formats, CBT showed a moderate advantage (ES = .43) over IPT. Therapist adherence was lower for IPT than CBT, consequently the relatively poorer showing of IPT might be due to challenges in delivery. Across four clinical trials, then, two variants of IPT have been shown to reduce depressive symptoms. It is noteworthy that IPT has a strong track record with depressed Latino adolescents, either as originally developed or as a culturally adapted therapy. IPT-A trials have included a large percentage of women. It is possible that the focus on relationships fits with critical stressors encountered by adolescent girls. Direct comparisons of IPT and CBT have produced mixed results. Because of small sample sizes, treatment differences have been difficult to detect and no study has examined predictors of differential response. It is tempting to conclude that treatment selection should be based on whether adolescents primarily present with dysfunctional cognitions or interpersonal problems. Of course, many depressed adolescents present with both. There have been no dismantling or process-outcome studies of IPT-A. However, results from prior clinical trials show that trajectories of symptom change in IPT-A and control conditions significantly separate during the middle phase of treatment. In this phase, therapist and adolescent are focused on an interpersonal problem that is linked to depressed mood (Mufson et al., 2005). Two techniques are central to this process. The first is communication analysis: The therapist examines the “play-by-play” of problematic interactions at a granular level of verbal and affective analysis. A central task of this work 479

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is to help adolescents understand links between their emotions and expectations and their communication patterns. The second is decision analysis. Decision analysis is not unique to IPT; instead it overlaps with other approaches to interpersonal problem solving: The therapist works with the adolescent to develop alternative communication strategies that will enable the adolescent to negotiate specific interpersonal difficulties. To a significant extent, therapist and adolescent engage in collaborative problem solving to address the targeted interpersonal problem. Expression and clarification of feelings related to alternative strategies are emphasized. Importantly, new strategies are role-played in session and then tried out in problem situations. Evidence indicating improved interpersonal functioning following IPT is consistent with identifying these processes as potentially active ingredients of treatment. Although CBT and IPT-A are distinct therapies with the former focused on cognitions and the latter on interpersonal relationships, both focus on the development of adaptive alternatives to routinized patterns, either cognitive or interpersonal. To this end, both treatments attempt to increase flexible responding by encouraging adolescents to “step back” and examine thoughts or behaviors that are often automatic. Importantly, both approaches press adolescents to consider alternatives and to apply them in daily living. It is noteworthy that IPT-A involves features of social skills and problem-solving training, components found in some CBT protocols. Evidence indicating that use of these components is predictive of better outcomes in CBT for treatment refractive adolescents (Kennard, Clarke, et al., 2009) suggests possible shared active ingredients across CBT and IPT-A. Distillation of shared components across these empirically supported treatments points to the importance of targeting social and problem-solving skills in the treatment of adolescent depression.

Treatment of Social Anxiety Disorder in Adolescence Adolescence is often conceptualized as a period of social awkwardness and insecurity. Worries about the perceptions of peers, shyness, and selfconsciousness are common experiences during 480

this stage of development. For some adolescents, worries about their own competence and physical appearance, as well as the scrutiny of others, reach maladaptive levels of anxiety and limit social functioning. SAD (also known as social phobia) is characterized by persistent fear or anxiety regarding social situations in which an individual fears that their behavior will elicit negative evaluations from others (American Psychiatric Association, 2013). This fear of social situations and interactions is out of proportion to the actual level of threat, causes significant distress or impairment, and results in avoidance of social situations. Results from the National Comorbidity Survey—Adolescent Supplement reveal that the lifetime prevalence of SAD among 13- to 18-year-olds is 9.1%; with 1.3% of adolescents experiencing severe impairment as a result of the disorder (Merikangas et al., 2010). Like MDD, the prevalence of SAD rises sharply in adolescence. In a survey of over 13,000 adults, the mean age of onset of SAD was found to be 15.5 (Schneier, Johnson, Hornig, Liebowitz, & Weissman, 1992). Research shows that social anxiety has a unique presentation in adolescence (Kashdan & Herbert, 2001). Adolescents experiencing social phobia demonstrate crying, tantrums, freezing (Albano, 1995), somatic symptoms (Faust & Forehand, 1994), antisocial behavior, and impaired school performance (Davidson, Hughes, George, & Blazer, 1993). Social anxiety often presents along with depression during adolescence (Wittchen, Stein, & Kessler, 1999), and treatment can be complicated by the presence of substance misuse (Essau, Conradt, & Petermann, 1999) and suicidal thinking (Schneier, Johnson, Hornig, Liebowitz, & Weissman, 1992). Evidence-based treatments for adolescent SAD largely are adaptations of adult treatments and come primarily from behavioral and cognitive–behavioral traditions. We review the evidence from clinical trials for three major treatments for SAD; group CBT for SADs in adolescents (CBGT-A; Albano, Marten, & Holt, 1991), social effectiveness therapy for children (SET-C; Beidel, Turner, & Morris, 1998), and skills for academic and social success (SASS; Masia et al., 1999). In addition, we review individual CBT for anxiety (Coping Cat; Kendall, 1989) as adolescents with SAD have been included in study samples.

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Psychological Treatment of Adolescents

Cognitive–behavioral group therapy for adolescents.  CBGT-A (Albano, Marten, & Holt, 1991) was designed for adolescents between the ages of 13 and 17 years with the goal of teaching adolescents to overcome avoidance and reduce social anxiety through coping and social skills development and exposure to anxiety-producing social situations (Albano, 1995). The treatment includes psychoeducation, skill building, cognitive restructuring, and imagined or in vivo exposure to anxiety-provoking situations. CBGT-A typically includes some level of parent involvement and is conducted over 16 weeks. CBGT-A consists of two phases of treatment: The initial phase (first eight sessions) is focused on psychoeducation and skill building, and the second phase is focused on behavioral exposure. In the first phase, adolescents are provided with information about the nature of social anxiety and how it is maintained by avoidance. The therapist emphasizes that anxiety and worry are natural and expected responses, but with practice and exposure to feared situations, adolescents can learn that the source of their worries is actually harmless, but by avoiding feared situations, they limit their opportunities to habituate to anxiety and overcome it. Treatment then moves into the skill building, including cognitive restructuring of negative, anticipatory thoughts, problem solving, and social skill building (e.g., joining a conversation). These strategies are taught and role-played in session, and practiced as homework assignments. The second phase of treatment focuses on exposure to anxiety provoking situations. Each group member works through their individual fear and anxiety hierarchy via in-session role-plays and invivo exposure assignments. The therapist helps the adolescent outline goals for the exposure, identify automatic thoughts during the exposure, and replace them with rational responses. Group members then participate in 10-min exposures and give subjective units of distress ratings at each minute. Other group members assist in the role plays, as well as other confederates for role plays involving social rejection, and then the group participates in the processing of the individual’s performance and experience. The experience of anxiety is normalized and tolerance of these feelings is reinforced.

Evaluations of CBGT-A are promising. In an initial pilot study, Albano, Marten, Holt, Heimberg, and Barlow (1995) treated five adolescents with social phobia. Improvement was found on self-report measures of anxiety and depression, and behavioral measures of subjective anxiety ratings throughout treatment and at a 1-year follow up. At 1-year follow up, four of the five participants were in full remission, and one was in partial remission. Hayward et al. (2000) evaluated CBGT-A by randomly assigning 35 adolescent girls to CBGT-A or a no-treatment control group. Assessments were conducted at baseline, after treatment, and at a 1-year follow up. Results indicated a significant reduction in the symptoms of social anxiety and symptom interference in the treatment group compared with the control group. Those in the treatment group were also significantly more likely to no longer meet criteria for social phobia in the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (American Psychiatric Association, 2013), but no significant differences between groups were found at the 1-year follow up. Although a large scale clinical trial evaluating the effects of CBGT-A has not been conducted, results from these small-scale studies are promising. Comparison with an active control condition is noticeably missing. Social effectiveness therapy for children and adolescents.  Based on evidence indicating that children with social anxiety often have social skills deficits, Beidel, Turner, and Morris (2000) developed SET-C to enhance social skills as a means of reducing social anxiety (Beidel, Turner, & Morris, 1999). SET-C is designed to target social phobia by reducing fear, improving interpersonal skills, and increasing social involvement. The program involves an initial educational session followed by 12 weeks of group social skills training, group peer activities (outings), and individual in-vivo exposure sessions. Social-skills training focuses on skills like starting conversations, joining groups, and asserting oneself. These skills are taught via instruction, modeling, behavioral rehearsal, and constructive feedback. Initial empirical support for SET-C was established with preadolescent children (ages 8–12). Subsequent research has focused on adapting SET-C 481

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for adolescents (Baer & Garland, 2005). Twelve adolescents (ages 13–18 years; M = 15.5 years) with a diagnosis of social phobia were randomized to SET or waitlist control groups. Treatment consisted of twelve 90-minute group sessions. The first session included psychoeducation about social phobia and starting social skills training. The subsequent 11 sessions were equally divided between social skills training (or one session of cognitive strategies to manage anxiety) and behavioral exposure. Participants were split into smaller groups to work on individually tailored exposures, which often included use of peer volunteers, while rating their anxiety on a fear thermometer. Participants were encouraged to practice between sessions and to identify a “coach” who they would inform about their homework assignments. Adolescents were assessed at pre- and posttreatment. Results indicated that relative to the control group, the treatment group experienced significant decreases in symptoms of social anxiety by examiner evaluation (as measured by the Anxiety Disorders Interview Schedule for Children [ADIS]; Silverman & Albano, 1996) and self-report (as measured by the Social Phobia and Anxiety Inventory for Children; Beidel, Turner, & Morris, 1995). In a randomized controlled trial (RCT) comparing SET-C, fluoxetine, and pill placebo in the treatment of child and adolescent social phobia, 122 youth ages 7 to 17 years were randomized into one of the three treatment groups (Beidel et al., 2007). Participants were assessed using diagnostic interviews (ADIS; Silverman & Albano, 1996), independent evaluator ratings, self-report questionnaires, parent reports, and behavioral assessments at pretreatment, posttreatment, and 3-, 6-, and 12-month follow-ups. SET-C and fluoxetine both demonstrated superior results over pill placebo regarding reducing social distress and avoidance. Participants in the SET-C group also demonstrated significant gains in social skills, managing anxiety in specific social interactions, and overall social competence. Those in the SET-C group also experienced superior gains at 12-month follow-up and lower rates of relapse compared with those in the fluoxetine treatment group. Although age was controlled for as a covariate in these analyses, the study does not report whether differences were found by age group, it is 482

unknown if SET-C worked equally well for children versus adolescents. Skills for academic and social success.  SASS (Masia et al., 1999) was originally designed as a school-based intervention comprised of 12 group sessions and includes social-skills training and exposure components modeled largely on SET-C, group structure from CBGT-A, and realistic thinking and relapse prevention modules from Overcoming Shyness and Social Phobia (Rapee, 1998). The program includes one educational session, one session on realistic thinking, four sessions on social skills, five sessions of exposure, and one session on relapse prevention. Exposure tasks are completed in session and between-session exposures are assigned to participants. The updated version of SASS includes the original 12 sessions, booster sessions 2 months after termination, two 15-minutes individual meetings to discuss treatment goals, parent meetings, teacher meetings, and four weekend social events which provide additional opportunities for practice of skills learned in other sessions (Fisher, Masia-Warner, & Klein, 2004). This integrated treatment recognizes the limitations, realties, and advantages of schoolbased interventions (Masia-Warner et al., 2005). Sessions are completed in short times (approximately 40 minutes) to allow for completion within a single class period. SASS recruits teachers and outgoing peers to aid in exposure tasks and social interactions, and uses other group members to assist in the facilitation of exposures. Primary support for SASS comes from two RCTs. In the first, 35 adolescents were randomized to SASS or waitlist control (Masia-Warner et al., 2005). At posttreatment, 67% of adolescents treated with SASS no longer met criteria for social phobia, compared to only 6% of waitlist controls. Those in the treatment condition also reported reduced symptoms of social phobia and avoidance, and increased overall functioning compared with waitlist controls. In a second study, Masia-Warner, Fisher, Shrout, Rathor, and Klein (2007) randomized 36 adolescents with SAD to SASS or an active control that was equal to SASS in terms of professional attention and format, but without the therapeutic elements of social skills training, cognitive restructuring, and exposure,

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but including relaxation strategies. Results showed significant symptom reduction in the SASS group relative to the active control condition. All adolescents in the control group still met criteria for SAD, whereas 59% of the SASS group no longer met criteria. Significant differences were maintained 6-months posttreatment. SASS has been shown to be effective in decreasing symptoms of anxiety, depression, and avoidance when implemented by trained teachers and adolescent peer counselors (L. D. Miller et al., 2011). Although no large-scale trial has been completed, replication of results across multiple studies and with active controls suggests that SASS is an efficacious, school-based intervention for social anxiety. Individual cognitive–behavioral therapy—Coping Cat.  Coping Cat (Kendall, 1994) has been identified as an exemplary treatment for adolescent anxiety disorders. Coping Cat targets a set of cognitive and behavioral mechanisms that are common to a variety of youth anxiety disorders, including social anxiety. The treatment can be customized to address different types of anxiety disorders. Although originally developed for children ages 7 to 13, Coping Cat has been extended for use with adolescents. The adolescent version is titled The C.A.T. Project and includes its own manual for therapists and workbook for adolescents (Kendall, Choudhury, Hudson, & Webb, 2002a, 2002b). Although the content and goals of the C.A.T. project and Coping Cat are largely the same, the adolescent version includes age-appropriate adaptations (e.g., different names for different components of the treatment). The primary aim of this approach is to develop coping skills, including cognitive, problem-solving, and relaxation skills that facilitate approach and reduce avoidance in anxietyproducing situations, and to teach children to recognize cues of anxious arousal and then use anxiety management strategies. Treatment strategies include role plays, developing awareness of bodily reactions to anxiety, identifying and modifying anxious selftalk, exposure tasks, and use of rewards for facing anxiety provoking situations. Following an initial skill-building phase, adolescents are encouraged to use newly acquired coping skills during exposure tasks. Coping Cat, as the name implies,

emphasizes the use of coping strategies in exposure sessions as a means of overcoming avoidance and reducing anxiety. In initial RCTs, Coping Cat demonstrated efficacy in the treatment of childhood anxiety disorders (Kendall, 1994; Kendall et al., 1997). Importantly, results were comparable across different types of anxiety disorders, including social phobia (Kendall et al., 1997). The efficacy of Coping Cat has been replicated in multiple clinical trials in several countries though most studies have focused on children and young adolescents (Albano & Kendall, 2002). In a large-scale, multisite RCT (The Child/ Adolescent Anxiety Multimodal Study [CAMS]), Coping Cat was evaluated relative to sertraline, combined treatment with sertraline and Coping Cat, and pill placebo (Compton et al., 2010) for the treatment of social anxiety, separation anxiety, and generalized anxiety disorder in youth ages 7 to 17. The C.A.T. project was implemented with adolescent participants. Four hundred and eighty-eight youth were randomized to one of four treatment conditions. Symptoms of anxiety, impairment, and functioning were assessed at baseline and at four week intervals until 12 weeks with the ADIS, the Clinical Global Impression-Improvement Scale, the Pediatric Anxiety Rating Scale, the Children’s Global Assessment Scale, and the Clinical Global Impressions Severity Scale. The percent of youth were rated as very improved or much improved varied from 23.7% for the pill placebo group, 54.9% for the sertraline only group, 59.7% for the Coping Cat only group, and 80.7% for the combination treatment group (Walkup et al., 2008). Over 80% of acute responders assessed at 12 weeks maintained positive outcomes at 24- and 36-week follow-ups. With the Coping Cat and sertraline continued to be equivalent and combined treatment continued to be superior to both (Piacentini et al., 2014). Coping Cat was highly efficacious relative to pill placebo, comparable to medication, and part of the enhanced effect of combined treatment. Unfortunately, as results were not reported separately for children and adolescents, or for specific types of anxiety disorders, it is difficult to discern the specific impact of Coping Cat on adolescent social phobia. A recent meta-analysis of studies using Coping Cat as a treatment for anxiety 483

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shows that studies include children and adolescents with varied anxiety disorders (Ishikawa, Okajima, Matsuoka, & Sakano, 2007). Importantly, a recent review of the literature found that adolescent and child outcomes for CBT for anxiety are comparable (Kendall & Peterman, 2015). Given the high level of co-occurrence of anxiety disorders, inclusion of children and adolescents with varied combinations of anxiety disorders makes sense, but it also makes it difficult to determine the specific effect of Coping Cat on social anxiety among adolescents. Each of the reviewed treatments has accrued empirical support for its efficacy though not in equal measure. Treatments evaluated with relatively large samples and the strongest evidence have included varied types of anxiety disorders, including social anxiety (Coping Cat) or have included children and adolescents in evaluations (Coping Cat, SETC). Treatments specifically targeting adolescent social anxiety are limited by relatively small sample sizes (GCBT-A, SASS). Consequently, it is difficult to declare a “horse-race” winner when it comes to treating adolescent social anxiety. Fortunately, the foregoing treatments share several core components that are likely to be essential for treating adolescent social anxiety. Broadly, all share a common structure; an initial phase of skill training is followed by graded exposure tasks. Treatments appear to divide in terms of whether they focus on building social or coping skills during this initial phase. The absence of evidence favoring the targeting one set of skills over the other suggests both are viable and that outcomes might be optimized by selection based on assessment of primary skill deficits. Alternatively, social and coping skills might be modularized and combined in the treatment of adolescent social anxiety. Graded exposure is clearly the most common component across treatments and likely to be the single most important active ingredient in all successful treatments of anxiety disorders (Kendall et al., 2005). All reviewed treatments provide opportunities for adolescents to face anxiety-provoking social situations in gradual manner. Although it is not known if exposure without initial skill-building might work for adolescent social anxiety, it seems highly unlikely that skill building without exposure 484

would be effective. In fact, findings from CAMS demonstrate that although the impact of relaxation training had a limited impact on the rate of progress in treatment, cognitive restructuring and exposure tasks significantly accelerated the rate of progress (Peris et al., 2015). It also is noteworthy that some of the treatments use a group format. The group format provides ample opportunity to practice social skills, to conduct evocative role-plays, and to carry out exposure tasks in session. Clearly, effective exposures can be conducted in individual therapy, but it requires skillful and creative implementation of role-plays and the use of confederates for in-session practice as well as heavier reliance on between-session assignments. Group therapy has many of these features built into its social context. Research supports several cognitive–behavioral approaches to the treatment of adolescent social anxiety. At the heart of all treatments for anxiety is the use of exposure, either in group or individual format. Exposure can be challenging for adolescents, consequently the use of contingent reinforcement for completion of exposures should not be overlooked. Similarly, the function of the skill-building phase might go beyond skill-acquisition per se by increasing self-efficacy and preparing the adolescent to engage in the challenges of exposure tasks.

Treatment of Oppositional Defiant Disorder in Adolescence Representing one of the most common clinical problems among adolescents in the United States, ODD typically emerges in childhood and continues into adolescence. Lifetime prevalence rates during adolescence are estimated at approximately 13% (Merikangas et al., 2010). Children who show an earlier onset of symptoms face a poorer prognosis, including a greater likelihood of developing comorbid conduct disorder (CD) than those with a later onset (Connor, 2012). Although male predominance exists in school-age children, the gender gap for ODD appears to decline in adolescence (for a review, see Loeber, Burke, Lahey, Winters, & Zera, 2000). It is not uncommon for adolescents with ODD to also present with comorbid ADHD, CD, anxiety, or mood disorders (Lavigne et al., 2001);

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indeed, the familiar axiom that comorbidity is “the rule, not the exception” holds true for these adolescents. Although some overlap may exist, there is a consensus in the field that ODD, ADHD, and CD are distinct disorders (Frick et al., 1993; for a review, see Frick & Nigg, 2012). ODD has been identified as a risk factor for adjustment problems through adulthood, including antisocial behavior, substance abuse, and difficulties with impulse control (Loeber et al., 2009; R. Rowe, Costello, Angold, Copeland, & Maughan, 2010), as well as academic difficulties and lower cognitive functioning (Merikangas et al., 2010). Despite decades of research and the development of several evidence-based treatments for ODD, a serious and troubling question remains unanswered. If the majority of empirical support is based on preadolescent children with ODD, how relevant and effective are these treatments for adolescents? Indeed, it seems problematic to assume treatment generalizability given the unique developmental, cognitive, and social experiences of adolescents. Keeping this in mind, we review several key treatment approaches for ODD in children and adolescents, highlighting those that have not yet been established for adolescents, and synthesizing common components that may be most effective for adolescents. In particular, evidence-based treatments for ODD fall into two domains: (a) family-based parent management training (PMT) and (b) individual CBT. Parent management training for oppositional defiant disorder.  Established as one of the most widely supported evidence-based treatments for ODD, PMT has been shown to be highly effective in decreasing oppositional, defiant, and aggressive behaviors in preadolescent children (Dretzke et al., 2005; for a review, see Eyberg, Nelson, & Boggs, 2008). PMT, which draws on social learning and behavioral principles, focuses on helping parents build contingency-based management techniques to change child behavior problems. It operates on the basis of understanding that maladaptive parent–child interaction patterns create a social environment that reinforces and maintains child problem behaviors. PMT typically involves instructing parents on how to define and track target problem behaviors. With the guidance of a

therapist, parents are trained in understanding social learning principles such as positive reinforcement, selective mild punishment (e.g., response-cost or time-out) and the use of contingencies. Additional instruction teaches parents to deliver clear directives and to practice adaptive problem-solving skills. Individualized behavior management programs are developed during therapy sessions and implemented in the home by the parents. Though several PMT programs have been developed, Parent Management Training Oregon Model (PMTO; Patterson, Reid, Jones, & Conger, 1975) was a clear forerunner and continues to have a strong presence in the field. Other evidence-based psychological therapies that stem from the foundation of PMTO include, Parent Management Training (Kazdin, 2010), the Incredible Years (Webster-Stratton, 2015), Parent–Child Interaction Therapy (Zisser & Eyberg, 2010), Triple P Positive Parenting (Sanders, 2012), Helping the Noncompliant Child (McMahon & Forehand, 2003), and Defiant Children (Barkley, 1987). An abundance of outcome studies substantiates the efficacy of PMT for preadolescent children in a variety of contexts, including homes, schools, day-care centers, and hospitals. Despite extensive research on the efficacy of PMT, the majority of studies focus on children ages 3 through 10 years, and very few studies include children older than 13 years (Kazdin, 2010). Findings from the limited research on PMT effectiveness among adolescents point to a decline in targeted outcome effects (Barkley, Guevremont, Anastopoulos, & Fletcher, 1992). In one example, Barkley et al. (1992) compared a PMT variant modified for adolescents (Barkley, 1987), a problem-solving communication training intervention (PSCT; Robin & Foster, 1989), and structural family therapy (SFT). PMT was modified from the treatment manual (Barkley, 1987) and included changes like converting timeouts to brief grounding periods and teaching parents to anticipate and plan for potential problem behaviors. PSCT involves working with parents and adolescent on collaborative problemsolving skills and communication skills aimed at supporting problem-solving negotiations. 64 adolescents (12–17 years old) with ADHD and 485

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disruptive behaviors, including ODD, were randomly assigned to 8 to 10 weekly sessions of one of the three treatments: PMT, PSCT, or SFT. At posttreatment, all three groups showed improvements in parent–adolescent communication and in the number and intensity of parent–adolescent conflict, as rated by mothers and adolescents. All three treatments also reduced externalizing and internalizing symptoms. Families in PSCT demonstrated somewhat higher rates of clinically significant change than those in PMT or SFT, though group differences were not significant. Despite these positive findings, only 5% to 30% of families across all conditions experienced clinically significant change to a reliable degree and only 5% to 20% of adolescents demonstrated clinical recovery. These percentages are particularly discouraging compared with the percentage of families with preadolescent children reporting significantly improved outcomes after PMT (60% to 75%; Barkley & Robin, 2014). To replicate and expand the previously described investigation, Barkley, Edwards, Laneri, Fletcher, and Metevia, (2001) compared PSCT (Robin & Foster, 1989) and a combined PMT–PSCT intervention. PMT was the same modified treatment program used in the previous study (Barkley et al., 1992). Ninety-seven adolescents diagnosed with ADHD and ODD (ages 12–18), were randomly assigned to receive either 18 PSCT sessions or 9 PMT and 9 PSCT sessions. No control condition was included in the study. Group level analysis demonstrated that both treatments showed improvement on parent–adolescent conflict ratings and observations but did not differ from each other. Of some concern, less than a quarter of families showed reliable change, bringing to question whether change was due to unreliable assessment or the interventions themselves (Barkley et al., 2001). In another RCT, Bank and colleagues (1991) investigated the effects of a version of PMTO modified for delinquent adolescents. Specific diagnoses were not provided but it is likely that most adolescents were severely ODD or conduct disordered. Nevertheless, PMT was targeted to adolescents. PMT modifications included having the adolescents present with parents during sessions, targeting behaviors that placed adolescents at risk for later 486

delinquency (e.g., class attendance, defiance toward teachers or adults), active monitoring, and close communication with school. The study included 60 boys referred by juvenile court who were randomly assigned to PMT or TAU. Families in the PMT group received about 21.5 hours of face-to-face treatment and 23.3 hours of phone contact, leading to about 40 hours of direct treatment. Families in the TAU group received approximately 5 months of weekly 90-minute family therapy sessions, with about half the group also receiving 2 hours of drug counseling per week, totaling around 50 hours of direct treatment. Delinquency was measured using official offense records, which were examined 1 year prior to treatment, during the treatment year, and for 3 years posttreatment. Both groups showed significant reductions in offense rates during the treatment year, with PMT families achieving faster results and relying on significantly fewer incarcerations than TAU families. Reductions in offense rates were maintained at 1-year and 3-year follow-up, though group differences disappeared. Home observations did not demonstrate improvements in negative or defiant adolescent behaviors nor in negative parent behaviors directed toward the adolescent. On the positive side, results suggest that PMT can help parents gain control over serious delinquent behaviors (i.e., adolescent offending) faster than TAU. However, the lack of observed change around negative, defiant behaviors at home brings to question whether this PMT variant would be effective in improving typical ODD symptoms like defiant, argumentative behavior. Several other evidenced-based treatment interventions that are within this family of treatment options but are not reviewed here include functional family therapy (Barton & Alexander, 1981), multisystemic therapy (Henggeler, Schoenwald, Borduin, Rowland, & Cunningham, 2009), and treatment foster care, Oregon–adolescents (Dishion, Forgatch, Chamberlain, & Pelham, 2016). Though demonstrated to be effective in treating delinquent adolescents, these time-intensive, family-based treatments were developed primarily for adolescents with CD and serious antisocial behavior. Although it is tempting to conclude that treatments developed for severe externalizing problems will be effective

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with less severe problems like adolescent defiance and parent–adolescent conflict, typical in ODD, these have not been the focus of prior clinical trials. Research on the efficacy of parent-training programs in treating adolescents with ODD is surprisingly limited. Component analyses of PMT programs for preadolescents have identified core treatment elements (e.g., time-out training, increasing emotional communication skills, parent consistency), yet it is unclear whether these matter to the same degree for treating defiant adolescents. In fact, the research findings demonstrating attenuated PMT effects among adolescents (Barkley et al., 1992) suggest that PMT components that are central to younger children’s success may be less effective in changing adolescents’ behaviors. For example, strategies like the use of praise, attention, and privileges may be less salient for adolescents who spend more time with peers, less time in the home, and are less dependent on parents (Chronis, Jones, & Raggi, 2006). Despite the dearth of research on treating adolescents with ODD, promising active ingredients can be identified among existing treatments. Findings from Barkley and colleagues (1992, 2001) suggest that including adolescents in the family problemsolving process may be especially important. Results supporting the efficacy of PSCT indicate that the inclusion of additional treatment elements like communication skills training and collaborative problem-solving may also be important. From a developmental perspective, the active involvement of adolescents in treatment, as well as the additional skills training component, may be particularly relevant as adolescents transition toward developing and relying on their own skills. Indeed, considering the typical decrease in parent influence during adolescence, it may be necessary for programs to establish active adolescent involvement to maintain treatment efficacy. Treatment approaches using modified PMT programs have also demonstrated some success. Across these programs, an overlapping feature is the modification of contingency plans that include age-appropriate elements (e.g., brief grounding periods in place of timeouts). Involving the adolescent in the process of establishing the reward system may also be central to ensuring motivation

and investment in the contingency plan. Another common feature among PMT programs modified for adolescents involves teaching parents to proactively identify behaviors and situations that are at risk for later delinquency and planning how to prevent or deal with these scenarios. Age-appropriate treatment modifications and the active involvement of adolescents and parents in the treatment process represent promising core components. Additionally, although the efficacy of PMT and PSCT has been demonstrated, studies on PMT treatments for adolescents with ODD are noticeably limited and outcome effects remain lower than desired. Further research is there for needed to evaluate and improve PMT approaches to effectively treat adolescents with ODD. Cognitive–behavioral therapy for adolescent oppositional defiant disorder.  In addition to parent-focused interventions, adolescent-focused CBTs have been investigated as potential treatments for ODD. CBTs place an emphasis on altering the cognitive deficits and distortions, particularly social–cognitive distortions, that are characteristic among adolescents with ODD (Barrett & Ollendick, 2004). CBTs for adolescents with ODD also typically teach coping skills, including social problem solving, perspective taking, emotional awareness, relaxation or calming techniques, and self-instruction (Lochman, Powell, Whidby, & Fitzgerald, 2012). As with PMT, research on the efficacy of CBTs for ODD has focused largely on preadolescent children. We highlight several of the more wellestablished programs and discuss common characteristics across them. Anger Control Training (ACT; Feindler, Marriott, & Iwata, 1984; Schlichter & Horan, 1981) is one of several CBTs targeting cognitive deficits linked to anger arousal, which often precede aggressive outbursts and are common in children and adolescents with ODD. Aimed at reducing angry arousal, ACT teaches adolescents to recognize anger triggers and then use adaptive coping strategies (e.g., relaxation techniques, use of positive imagery). In an initial outcome study (Schlichter & Horan, 1981), a group of 38 incarcerated delinquent adolescents (ages 13–18) were randomly assigned 487

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to ACT, an abbreviated ACT, or no treatment. Both active treatment conditions produced significantly improved self-reported anger and aggression compared with no treatment, and full ACT also produced enhanced scores on verbal aggression role-plays. An additional study (Feindler et al., 1984) found support for a group version of ACT among 36 delinquent high school students (12–16 years old). Participants were randomly assigned to treatment or no treatment, though pretreatment differences were found that may have attenuated treatment effects. Results showed that severe aggressive behavior was relatively lower over time than the control group. Additionally, teacher-reported self-control and problem-solving ability were greater for adolescents in ACT. Although these results are promising they have not been replicated in a large RCT. And although adolescents were included in the original studies, the samples did not specifically target ODD. Like ACT, the Anger Coping Program (ACP; Lochman, Lampron, Gemmer, & Harris, 1987) and the Coping Power Program (CCP; Lochman & Wells, 1996) focus on anger arousal and subsequent aggression through a social–cognitive lens. Treatment components include emotional awareness, recognition of anger triggers, coping strategies (e.g., self-talk), and interpersonal problem-solving skills (Lochman et al., 1987). CPP also includes a parent training component. The efficacy of ACP and CCP on reducing aggressive, disruptive behaviors has been substantiated in several RCT’s over the past two decades (e.g., Lochman, Burch, Curry, & Lampron, 1984; Lochman, Curry, Dane, & Ellis, 2001). Follow-up studies have also revealed long-lasting preventive effects on deviant behaviors (e.g., substance use; Lochman & Wells, 2004). Though these programs enjoy a more extensive evidence-base than ACT, it is noteworthy that they have only been tested with pre- and early adolescents (9–13 years); studies have not been conducted to evaluate the programs’ generalizability to middle and older adolescents. Several CBTs for ODD focus on addressing deficits in problem-solving skills, particularly around interpersonal difficulties. One such approach, Collaborative and Proactive Solutions (CPS; Greene, 488

Ablon, & Goring, 2003) has been investigated as a specific treatment for ODD among pre- and early adolescents. A distinctive feature of CPS is that problem solving is a collaborative process between adults and children. In a preliminary RCT, Greene et al. (2003) randomly assigned 47 children with ODD and affective dysregulation (4–13 years old) in an outpatient setting to CPS or PMT. Results indicated that oppositional behavior and parent stress declined in both groups. Clinical improvement, as rated by the therapist and parents was significantly higher in the CPS group. A second, larger RCT (Ollendick et al., 2016) compared CPS, PMT, and a waitlist control in a sample of 134 children who met criteria for ODD (ages 7–14). Though not statistically different from one another, both treatment conditions produced significant reductions in clinical severity scores and ODD symptoms, which were maintained at a 6-month follow-up. CPS and PMT were superior to the waitlist control, with 50% of children reaching improvement levels deemed “very much improved” or diagnosis free, compared with 0% of children in the waitlist control. Interestingly, younger age predicted enhanced treatment outcomes for CPS and PMT, with younger children improving more than older children. This finding suggests that although these approaches generate improvement in older children (up to 14 years old), continued modifications may be necessary to maximize treatment efficacy for adolescents. Problem-solving skills training (PSST; Kazdin, 2010) is another evidence-based program that targets children with disruptive behavior and anger regulation problems. The program includes problem-solving techniques like identifying the problem, considering different solutions, choosing one, and evaluating its effectiveness. Outcome effects of PSST have been examined in multiple RCTs. In an initial RCT, Kazdin, Bass, Siegel, and Thomas (1989) compared two versions of PSST and PSST with an additional practice component (PSST + P) with a relationship therapy (RT) comparison group. One hundred twelve children (ages 7–13) were randomly assigned to a group and behaviors were assessed using parent, teacher, and child measures. Results demonstrated that outcomes improved across all three groups, with PSST and PSST + P yielding

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superior improvements in problem behavior and prosocial behavior than RT. Effect sizes on the basis of the Child Behavior Checklist were 0.69 (PSST vs. RT) and 0.85 (PSST + P vs. RT). Superiority in outcomes for PSST and PSST + P over RT were maintained 1 year later. PSST + P demonstrated better outcomes than PSST at posttreatment but not follow-up, suggesting that extra practice may be useful in jumpstarting the change process. As is the case with most of the other treatments reviewed, PSST appears to be an efficacious treatment for ODD but has only been evaluated in young (age 13) adolescents and, the efficacy among adolescents remains largely uncertain. Kazdin, Siegel, and Bass (1992) also evaluated the effectiveness of combined PSST and PMT treatment (PMT + PSST), PMT, and PSST in an RCT of 97 children (7–13 years old) who had previously been referred to an outpatient child conduct clinic. Results showed that all groups produced improved externalizing symptoms and prosocial behavior; though, interestingly, the combined treatment had the greatest improvement on several outcomes (e.g., delinquent/antisocial behavior, depression, and family functioning). PMT + PSST and PSST also demonstrated additional improvements 1 year later. Behavior change based on the Child Behavior Checklist revealed medium effect sizes: 0.45 (combined vs. PSST) and 0.39 (combined vs. PMT). Overall, PSST has a strong empirical foundation though prior studies have not included adolescents over the age of 14. Although several CBT programs for ODD have been evaluated, there appear to be several shared components across treatments. A critical common target across these interventions is the regulation of anger, a core problem among children and adolescents with ODD. Common features include teaching adolescents to recognize anger triggers and signals of anger arousal as a prelude to adaptive coping. Though specific coping strategies vary across treatments the use of relaxation, self-talk, and positive imagery are typically taught. Although the effectiveness of these strategies has been tested primarily with preadolescents, it is noteworthy that they are included in adult anger management programs (Kassinove & Tafrate, 2002). Consequently, they

are likely to be useful for adolescents with ODD who often struggle with anger control. Another notable commonality across treatments is the inclusion of problem-solving skills training, particularly around interpersonal difficulties. Variants of problem-solving training occupy a central or complementary role in all the CBTs for disruptive behavior and anger regulation. A promising approach includes parents and adolescents in collaborative problem solving (Greene et al., 2003). This approach overlaps to some degree with family problem-solving and communication training (Barkley et al., 1992; Robin & Foster, 1989), and could provide a useful framework for reducing anger and resolving parent–adolescent conflict common in ODD. One major advantage of including parents in problem-solving training is the opportunity for direct coaching during “live” problem discussions. Along a similar vein, results from several randomized trials suggest that combining parent management training and problem-solving skills training may result in better treatment outcomes than individual problem solving or PMT alone. Joint problemsolving training could be a useful way of linking individually focused interventions for adolescent anger control with parent-focused contingency management. Although it is possible to distill a set of core components from treatments with demonstrated efficacy, a critical caveat remains. Virtually all evidence comes from research with samples of preadolescent children. Given that components of anger management training and problem-solving skills training originated from adult therapies, it is likely that they could be extended to the treatment of middle and late adolescents. Nevertheless, research on ODD in this age group has been overshadowed by treatment trials for delinquency and conduct disorder. Given the prevalence of ODD in adolescence it is surprising that this gap exists. A comparison of treatments, or the evaluation of a modular treatment composed of common components, for adolescent defiance, negativity, and anger dysregulation is sorely needed. Management of Emergent Problems in Adolescent Treatment Consideration of clinical problems that emerge during adolescence and may complicate therapy 489

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outcomes is an important element of adolescent treatment. These problems co-occur across a range of adolescent disorders and may not meet criteria for full comorbid diagnoses. In particular, we focus on several common emergent problems, including suicidal thinking/behavior, nonsuicidal self-injury, and collateral substance use, and discuss the clinical management of such challenges within the context of treating MDD, SAD, and ODD.

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Suicidal Ideation and Behavior Epidemiological evidence shows a sharp rise in suicidal ideation during the transition to adolescence (around age 13) and a corresponding increase in suicide attempts shortly thereafter (Nock et al., 2013). The cumulative lifetime prevalence rates for suicidal ideation and suicide attempts during adolescence are 12.1% and 4.1%, respectively. Most adolescents, over 90%, with suicidal ideation or behavior met criteria for a lifetime psychiatric disorder (Nock et al., 2013). All three of the disorders discussed in this chapter, MDD, SAD, and ODD, are associated with increased risk for suicidal behavior (Nock et al., 2013). MDD and ODD are among the most common disorders with suicidal ideation and behavior among adolescents. It is noteworthy that two other emergent adolescent problems, substance abuse and NSSI significantly contribute to risk for adolescent suicide (Brent, 2011; Spirito, Mehlenbeck, Barnett, Lewander, & Voss, 2003). In addition to specific disorders, several psychological variables contribute to risk for adolescent suicide including suicidal ideation, depression, hopelessness, impulsive-aggression, borderline traits (e.g., high emotional reactivity), history of trauma, recurrent self-injury, and prior suicide attempt (Bridge, Goldstein, & Brent, 2006). Adolescents who are sexual minorities are at elevated risk compared with heterosexual peers (Marshal et al., 2011). Gender is also associated with suicide risk with girls reporting higher rates of suicidal ideation and attempts, and boys showing higher rates of completed suicide (Bridge et al., 2006). There is some evidence that adolescents with a prior attempt are at increased risk for re-attempt shortly after starting treatment (Brent et al., 2009). Given this pattern, it is essential that clinicians 490

“front-load” safety planning, including family monitoring, reduction of unstructured time, and a plan for emergency mental health access. Although “safety contracting” is widely used, it has not been systematically evaluated; nevertheless, such contracts provide additional information about the adolescent’s willingness to cooperate with treatment. Removal of firearms and other potential means of suicide should be part of the safety plan. Similar actions should be taken with adolescents who have not made a prior attempt but who present with depressed mood and high levels of suicidal ideation. Although the presence of a suicide plan is often used as a marker of risk, it is important to note that 40% of adolescent suicide attempts are unplanned (Nock et al., 2013). Despite challenges, several clinical trials have been conducted with adolescents who have attempted suicide. A meta-analysis of interventions aimed at preventing reattempts yielded a nonsignificant, overall treatment effect (Ougrin, Tranah, Stahl, Moran, & Asarnow, 2015). However, results revealed variation in treatment effects with several approaches showing promise. A recent RCT compared dialectical behavior therapy (DBT) and enhanced usual care (EUC) for the treatment of 77 adolescents with recurrent self-harm behaviors with or without suicidal intent (Mehlum et al., 2014). Approximately one quarter of this largely female sample had attempted suicide in the previous 4 months. All had a history of recurrent self-harm. Adolescents in the DBT condition received 19 weeks of weekly individual DBT, weekly multifamily, group skill-training sessions and family therapy, and telephone coaching as needed. The EUC condition consisted of 19 weeks of weekly individual psychodynamic or CBT, group therapy, family therapy, and pharmacotherapy as needed. EUC was not manualized or monitored for fidelity. The only significant difference in dose was the number of group sessions attended with DBT greater than EUC. The primary outcome measure at posttreatment was number of self-reported self-harm episodes or suicide attempts per week and level of suicidal ideation. Results showed a significant treatment effect with DBT treated adolescents reporting a substantial reduction in self-harm/suicidal behaviors

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and EUC treated adolescents reporting a more modest decrease. Both groups showed similar patterns of reduction in suicidal ideation over the first 15 weeks of treatment but the DBT group continued to show reductions through the end of treatment (19 weeks), whereas the EUC group did not. The difference in this pattern was statistically significant. At 1-year follow-up, DBT continued to be superior to EUC regarding weekly episodes of self-harm/suicidal behavior, though the difference in suicidal ideation was no longer significant (Mehlum et al., 2014). The combination of self-harm and suicidal behaviors as a single outcome obscures the impact of this treatment on recurrent suicide attempts. The overall complexity of this multifaceted treatment makes it difficult to isolate active ingredients. Instead, this promising intervention may need to be delivered as a package to be effective. Replication and dismantling studies would be valuable especially with differentiation of suicide attempts and NSSI as distinct outcomes. A second promising intervention is attachmentbased family therapy (ABFT; Ewing, Diamond, & Levy, 2015). The focus of ABFT is on the repair of fractured parent–adolescent relationships through the identification of core relationship themes, associated unresolved emotions, and the development of adaptive parent–adolescent interactions (G. S. Diamond, Siqueland, & Diamond, 2003). In an RCT, 66 largely African American adolescents with clinically elevated levels of suicidal ideation were allocated to either ABFT or brief clinical management (G. S. Diamond et al., 2010). At posttreatment adolescents in the ABFT condition showed significantly lower levels of suicidal ideation than those in the management group. Although this outcome is promising, adolescents treated with ABFT received substantially more clinical contact than those monitored in the management condition. Further, changes in actual suicidal behavior were not examined. In a subsequent study, ABFT was examined in an open trial with 10 suicidal, adolescents who were sexual minorities (G. M. Diamond et al. 2012). Following 12 weeks of treatment with ABFT, there was a significant reduction in depressive symptoms and suicidal ideation relative to baseline. Replication of findings from both studies

with a comparison treatment of similar dosage is clearly needed. Given the strong association between suicidal behavior and substance use (Esposito-Smythers, Spirito, Kahler, Hunt, & Monti, 2011), interventions that address these co-occurring problems are critical. An augmented form of CBT was developed and evaluated with 36 adolescents who attempted suicide and who abused alcohol or other substances (Esposito-Symthers et al., 2011). The enhanced CBT protocol included “standard” CBT components augmented with motivational interviewing (MI) for addressing substance misuse and a substantial family component that focused on parent–adolescent communication, contingency management, and problem solving. Adolescents were randomized to integrated CBT (iCBT) or to TAU that included CBT but without the augmented components. At an 18-month follow-up, the proportion of adolescents who reattempted suicide differed markedly across groups, 5.3% versus 35.3% for iCBT and TAU, respectively. A similar pattern emerged for rehospitalizations. The two groups differed in terms of number of treatment sessions; consequently it is possible that the treatment effect is a dose effect. Nevertheless, this integrated CBT shows promise as a method for addressing the co-occurrence of suicidal behavior and substance abuse. Given the range of interventions, uneven results, and lack of replication, it is very difficult to identify core components for reducing suicidal ideation or preventing reattempts. Across the three promising treatments, family involvement or therapy is a common feature. However, the content of these family components appear to vary substantially and include family skill training, resolution of critical parent–adolescent conflicts, and communication and problem-solving training. It is possible that these varied interventions reduce conflict and increase support in the parent–adolescent relationship. In turn, this could lead to better communication and enhanced monitoring of suicidal risk. Although two promising treatments targeted skill acquisition as part of their overall packages, individual, skill-based interventions for suicidal adolescents have produced weak or mixed results (see Donaldson, Spirito, & Esposito-Smythers, 2005). 491

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Several skills seem highly relevant as intervention targets for suicidal adolescents, including distress tolerance, emotion regulation, and problem-solving skills. However, the promotion of these skills might not be sufficient, particularly without a strong family component, to reduce suicidal behavior. Finally, it is important to note that successful treatment of depression and anxiety disorders can reduce the risk of suicide. For example, successful treatment of anxiety in children ages 7 to 13 predicts lower suicidality 16 years later (Wolk, Kendall, & Beidas, 2015).

Nonsuicidal Self-Injury NSSI, defined as deliberate destruction of one’s body tissue without suicidal intentions, is a prevalent, dangerous behavior among adolescents (for a review, see Nock, 2010). Self-injurious behaviors include burning, scratching, scraping, or cutting the skin with a sharp object. Often emerging between 12 and 14 years of age (Jacobson & Gould, 2007), lifetime prevalence of NSSI among adolescents range from 12.7% to 46.5% in community samples (Barrocas, Hankin, Young, & Abela, 2012; Giletta, Scholte, Engels, Ciairano, & Prinstein, 2012; Lloyd-Richardson, Perrine, Dierker, & Kelley, 2007) and 40% to 60% in clinical samples (Darche, 1990; DiClemente, Ponton, & Hartley, 1991). NSSI co-occurs with mental disorders at a high rate, although it is not specific to one disorder (Nock, 2010). Of note, the three disorders highlighted in this chapter (MDD, SAD, and ODD) all place adolescents at an elevated risk for NSSI, with ODD and MDD having particularly strong links to self-injurious behaviors (Chartrand, Sareen, Toews, & Bolton, 2012; Nock, Joiner, Gordon, Lloyd-Richardson, & Prinstein, 2006). It is worth mentioning that borderline personality disorder (BPD) is another disorder that is highly associated with NSSI, although many individuals who engage in NSSI do not have BPD. Additionally, the onset of NSSI during early adolescence coincides with the rise of suicidal ideation and behavior, conferring even greater risk for NSSI to evolve into suicidal behavior (Nock, 2009a, 2009b). Adolescents engage in self-injury to manage emotions, deal with stress, and control social experiences (Nock, 2010). Pain analgesia, which refers to experiencing minimal to 492

no pain, is commonly reported among adolescents who engage in self-injury and may represent another risk factor (Nock & Prinstein, 2005). Adolescents who are sexual minorities are also at an increased risk for self-injury (Batejan, Jarvi, & Swenson, 2015). Gender and ethnicity have not been linked with differential rates of NSSI (e.g., Hilt, Nock, Lloyd-Richardson, & Prinstein, 2008). Despite increased attention to self-injury over the past decade, evidence-based interventions for adolescent NSSI have yet to be established (Nock, 2010). Initial treatment efficacy studies have yielded somewhat inconsistent results (Hazell et al., 2009; Wood, Trainor, Rothwell, Moore, & Harrington, 2001) and most studies have not found significant differences in treatment outcomes compared with TAU (e.g., Rathus & Miller, 2002). A further roadblock is that many research investigations have failed to differentiate between suicidal injury and NSSI, making it difficult to pinpoint treatment efficacy for NSSI specifically. Notwithstanding these limitations, several interventions have been developed or modified for adolescent NSSI and recently published RCTs provide support for promising treatments. One treatment, DBT, involves an intensive treatment approach that includes weekly individual therapy and group skills training, and teaches adaptive coping strategies for dealing with emotion dysregulation and interpersonal difficulties. Evidence supporting the efficacy of DBT in treating self-injury is based largely on RCTs with adult samples (e.g., Koons et al., 2001; Linehan et al., 2006). Research on DBT for adolescent NSSI is more limited, with nearly all results stemming from nonrandomized trials or pilot studies (e.g., James, Taylor, Winmill, & Alfoadari, 2008; Rathus & Miller, 2002). Despite this, existing findings among these studies provide initial evidence that DBT may be effective in decreasing NSSI in adolescents. For example, Katz, Cox, Gunasekara, and Miller (2004) compared an adapted version of DBT for adolescents (DBT-A) to TAU in a sample of 62 inpatient adolescents (83% women) with suicidal ideation or behavior. DBT-A adaptations included incorporating parents into treatment, teaching family-focused skills such as understanding common adolescent–family

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dilemmas (A. L. Miller, Rathus, DuBose, DexterMazza, & Goldklang, 2007), and shortening treatment length (Brausch & Girresch, 2012). Results showed that the DBT-A condition experienced significant decreases in suicidal injury or NSSI at posttreatment. However, consistent with most prior studies, treatment effects did not differ from TAU. In the first RCT on DBT-A for NSSI, Mehlum et al. (2014) found DBT-A to be superior to EUC in reducing self-harm in outpatient adolescents. The results of this investigation are promising and provide further evidence that DBT-A may be a beneficial approach to treating NSSI. As with previous studies, however, the lack of distinction between suicidal and non-suicidal self-harm makes it difficult to draw conclusions about the efficacy of DBT-A for NSSI specifically. Another treatment approach that has demonstrated preliminary efficacy for reducing self-injury in adolescents is Mentalization-Based Treatment for Adolescents (MBT-A; Rossouw & Fonagy, 2012). Grounded in psychodynamic theory, MBT-A focuses on helping adolescents understand how their own behaviors are linked to internal thoughts and emotions, particularly those related to interpersonal difficulties. Rossouw and Fonagy (2012) evaluated the efficacy of MBT-A versus TAU with a sample of 80 adolescents (85% girls; ages 12–17). Results indicated that adolescents in both groups reported significant reductions in self-injury over the course of treatment. Importantly, assessment at posttreatment revealed that adolescents in the MBT-A group reported significantly less self-injury compared with TAU. Findings should be interpreted with some caution, however, given the high attrition rates across both groups and the addition of family therapy sessions to DBT-A and not to TAU. As is the case with other treatments for NSSI, further research is needed, though initial results suggest MBT-A may be a useful treatment for adolescent self-injury. Aligned with the interpersonal skills deficit that is common among adolescents engaging in NSSI, IPT-A has been adapted to address self-injurious behavior. Although IPT-A Self Injury (IPT-ASI) focuses mainly on improving social skills and relationships, it also includes additional components like assessment of NSSI, psychoeducation on NSSI,

and increased safety monitoring (Jacobson & Mufson, 2012). An initial randomized trial evaluating a version of IPT-ASI found significant reductions in depression and suicidal ideation compared with TAU; unfortunately, NSSI was not assessed. The impact of IPT-ASI on self-injury remains unknown. Further research is necessary to establish evidence-based approaches to treating self-injury in adolescents. Despite the limited number of studies, a few common features can be identified. First, all reviewed treatments include a component focused on addressing problems with interpersonal functioning. The importance of this component in treatment is corroborated by previous research indicating that interpersonal factors play a major role in adult and adolescent self-injury (e.g., Muehlenkamp, Brausch, Quigley, & Whitlock, 2013). Across treatment approaches for adolescent NSSI, another shared feature is the involvement of family in therapy. Family support has been closely linked to onset, continuation, and cessation of NSSI among adolescents (Tatnell, Kelada, Hasking, & Martin, 2014) and it is not surprising that it may be a core component. It is noteworthy that TAU produced reductions in adolescent self-injury in some studies. It is not clear what interventions constitute TAU, or if it primarily represents a supportive form of therapy. A critical question for future investigations is the importance of directly targeting the cognitions and emotions associated with self-injury, and the degree to which change hinges on the development of adaptive interpersonal and emotion regulation skills. Despite co-occurrence with depressive, anxiety, and externalizing disorders, existing research has mainly focused on addressing NSSI as a distinct and separate problem. Consequently, clinicians who are treating MDD, SAD, or ODD have few guidelines for modifying treatment to address self-injury as part of an integrated therapy. For example, might there be a set of interventions in DBT (e.g., chain analysis) that could be integrated into ongoing CBT for depression, anxiety, or externalizing problems and could effectively address self-injurious behavior? Of course, identifying potentially effective components requires a research strategy that focuses on specific interventions rather than complex treatment packages. Alternatively, components of some treatments 493

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might effectively address specific disorders and collateral self-injury. For example, one study by Nelson-Gray and colleagues (2006) demonstrated that modified DBT skill training improves disruptive behaviors in adolescents with ODD. DBT also appears to be promising for NSSI, this approach might effectively address externalizing problems and self-injury. Similarly, Mindfulness Based Cognitive Therapy (MBCT) may be useful in the treatment of recurrent depression. In a recent study (Rees, Hasking, Breen, Lipp, & Mamotte, 2015), MCBT was found to be superior to supportive therapy in reducing the frequency and severity of NSSI among young adults. It is possible that MCBT might be effective as a treatment for depressed adolescents who engage in self-injury. A potentially important feature of MBCT is that it targets specific mechanisms linked to NSSI (i.e., rumination, emotion dysregulation, overly negative thoughts), allowing it to be used across the treatment of a wide range of disorders. However, given the metacognitive demands of MCBT, it is not evident that it will be as effective with adolescents, especially younger adolescents. In sum, research has revealed several promising intervention programs for treating NSSI among adolescents; however, additional research is sorely needed. Treatment components like interpersonal skills and family involvement are shared features across interventions that appear promising in the treatment of NSSI. Further, given that NSSI often occurs in the context of varied psychological disorders, the development and evaluation of integrative treatment approaches or the identification of effective core components that can be modularized is a high priority.

Collateral Alcohol and Substance Use Problems Substance use disorders (SUDs) involving alcohol or other illicit drugs are prevalent among adolescents (Merikangas et al., 2010), and a high percentage of adolescents with SUDs meet criteria for one or more comorbid mental health disorders, especially disruptive behavior and depressive disorders (Armstrong & Costello, 2002). Conversely, adolescents with some of the most common forms of adolescent psychopathology, including MDD, SAD, and ODD, 494

are at risk for comorbid alcohol and substance use problems. Youth with ODD are at increased risk of SUDs (American Psychiatric Association, 2013), but precise estimates of comorbidity are difficult to determine given potential underreporting of alcohol and substance use by adolescents (Angold, Costello, & Erkanli, 1999). Similarly, the link between adolescent depression and substance use problems has been well-established. Among adolescents diagnosed with severe depression 48% meet criteria for an SUD (Greenbaum, Prange, Friedman, & Silver, 1991). Among adolescents with SAD, one study revealed that nearly one in four (23.7%) met criteria for a comorbid SUD (Essau, Conradt, & Petermann, 1999). Therefore, those treating adolescents with MDD, ODD, and SAD are likely to encounter co-occurring substance use problems. Is there evidence that collateral substance use problems interfere with the treatment of MDD, SAD, or ODD? Surprisingly, this question is difficult to answer, in large part because SUDs have been an exclusion criterion in some clinical trials. Clearly, by including SUD as an exclusion criterion, investigators have assumed that substance use problems would complicate treatment of primary mental health disorders. But, actual empirical evidence is thin. For example, in one study, it was shown that a history of SUD is associated with slower time to recovery in group CBT for adolescent depression (Rohde, Clarke, Lewinsohn, Seeley, & Kaufman, 2001). Similarly, a secondary analysis of data from the Treatment of SSRI Resistant Depression in Adolescents study (Goldstein et al., 2009) showed that depressed adolescents with lower 12-week substance-related impairment responded better across all conditions, including the addition of CBT to medication, than those with higher levels. Evidence suggests that substance-related problems slow or dampen recovery from MDD. Although the association between SAD, ODD, and SUDs is well-established, and there are good reasons to assume that substance-related problems will impact treatment of both, studies are scarce to nonexistent. Instead, most research has focused on the impact of mental health disorders like MDD or SAD on substance abuse recovery. Here the evidence is clearer. For instance, research has demonstrated that adolescents

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being treated for SUDs with comorbid mental health disorders are more likely to relapse (Cornelius et al., 2004) and to return to initial levels of substance use (C. L. Rowe, Liddle, Greenbaum, & Henderson, 2004). Clearly, mental health problems complicate the treatment of SUDs, but less is known about the impact of SUDs on the treatment of psychiatric disorders. Given the frequent co-occurrence of mental health disorders and SUDs in adolescence, are there examples of efficacious, integrated treatments? Curry, Wells, Lochman, Craighead, and Nagy (2003) developed a treatment for adolescent depression and collateral substance use. In their study, 13 adolescents with comorbid depression and substance abuse participated in an integrated group CBT and family therapy intervention called Family and Coping Skills (FACS) therapy. The 3 months of treatment consisted of bi-weekly adolescent group sessions, weekly family therapy sessions, random drug screens, monthly parent psychoeducational group meetings, and crisis intervention as needed. Youth sessions included skills training in problemsolving, expressing emotions, assertiveness for refusing drugs, and relapse prevention. Family sessions included topics like monitoring adolescents and appropriately applying consequences. Posttreatment parent interviews indicated a decrease in symptoms of substance abuse, but not in symptoms of depression or substance dependence. Posttreatment adolescent interviews indicated a decrease in both symptoms of depression and substance abuse, but not in substance dependence. FACS therapy has not yet been replicated on a large scale and cannot be considered an empirically supported treatment. Despite the promise of such integrated approaches, little progress has been made in this area. Although there is limited evidence for integrated treatments, there is substantially greater evidence for efficacious treatments of adolescent SUDs as the primary target of intervention. Research has demonstrated the efficacy of several treatments. In a meta-analysis of randomized evaluations of outpatient treatments targeting elevated substance use or diagnosis of a SUD for adolescents between the ages of 13 and 18, Becker and Curry (2008) identified CBT, brief motivational interventions, and

ecological family therapy as the three treatments with high-quality evidence. In a more recent review of the literature, Hogue, Henderson, Ozechowski, and Robbins (2014) examined outpatient psychological treatment evidence for adolescent substance use and identified ecological family-based treatment, group CBT, and individual CBT as well-established treatments. Additionally, behavioral family-based therapy and motivational interviewing were rated as probably efficacious. CBT appears to be the treatment with the most support for comorbid psychopathology and substance use/abuse/dependence among individuals 10 to 25 years old, especially when treatment is integrated to consider both problems simultaneously (Hides, Lubman, Kay-Lambkin, & Baker, 2007). CBT acknowledges the role of cognitive, behavioral, social, and developmental factors. CBT aims to increase the ability of the client to recognize situations that lead to substance use and incorporates coping skills training with the goals of changing behavior and perceptions that maintain substance use (Deas & Thomas, 2001). MI is a client-centered approach aimed at resolving client ambivalence about behavior change, including substance use behaviors (W. R. Miller & Rollnick, 2013). Alhough findings are somewhat mixed across types of substances and contexts, MI has been shown to be effective as a component of treatment or as a brief, stand-alone treatment for adolescent substance use problems. (Hettema, Steele, & Miller, 2005). Meta-analytic findings support the use of MI as an effective treatment for adolescent substance use (Jensen et al., 2011). In addition to adolescent-focused treatments for substance use problems, ecological family therapy has emerged as an efficacious treatment. At the core of this approach is the view that adolescent substance use is nested within multiple contexts that can maintain or reduce substance use behavior. Not only is the family included in treatment, but other systems (e.g., peer groups, school, neighborhood) are considered as potential targets for leveraging change. Individualized strategies are developed that take these multiple contexts into consideration. A major question, then, is whether clinicians treating MDD, SAD, or ODD with collateral 495

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substance abuse should refer their adolescent patients for parallel substance abuse treatment. It has been argued that parallel treatment of mental health and SUDs is unlikely to produce optimal outcomes for either condition (Riggs & Davies, 2002). Results of research with adults has shown that integrated treatments yield better outcomes than sequential or parallel treatments when treating individuals with severe forms of mental disorders (e.g., psychosis) and comorbid substance and alcohol problems (Kavanagh, Mueser, & Baker, 2003). In their review of the literature on SUDs and unipolar depression, Kaminer, Connor, and Curry (2008) recommend that interventions for both disorders be implemented by the same provider or simultaneously by two experts who keep each other closely informed. Whether an individual provider conducts integrated therapy or coordinates with another provider depends on the clinician’s training in substance abuse treatment and the severity of the adolescent’s substance-related problems. Of course, the development of integrated treatments requires evidence for which components to integrate and how to sequence them. Effective treatments for adolescent SUDs include multiple components. It seems that a reasonable starting point would be to distill overlapping components from mental health interventions and SUD interventions (e.g., training coping skills in MDD and SUD). Other components, like MI, can be delivered in a single session might be readily integrated into CBT, IPT, or problem-solving skills training. However, the field currently lacks sufficient evidence for specific integrated protocols. Progress in this area hinges on overcoming the long history of segregating mental health and SUDs in clinical training and research. Engagement and Alliance in Adolescent Treatment Effective psychological treatments for adolescent disorders and co-occurring problems rely on the participation and involvement of adolescents in therapy. Across CBT and IPT for depression, exposure-based therapies for social anxiety, and anger management or problem-solving skills training for 496

ODD, therapists are faced with engaging adolescents in treatment tasks. In fact, engagement is the first and foremost task of adolescent therapy, and failure to develop a working alliance can portend poor outcomes. Traditionally, adolescents have been portrayed as “challenging” or at high risk for treatment resistance. In his seminal book The Fragile Alliance, Meeks (1971) detailed the potential challenges of alliance formation and maintenance with adolescents. Based on his clinical observations, Meeks offered multiple variants, virtually a typology, of adolescent resistance, traps, and therapeutic quagmires. The Fragile Alliance set expectations about alliance formation with adolescents; it is difficult. This sentiment has been echoed by Castro-Blanco and Karver (2010) in their volume, Elusive Alliance: Treatment Engagement Strategies With High-Risk Adolescents. Adolescent alliance difficulties stem from two sources. The first is developmental. In brief, therapy with its emphasis on change represents a potential challenge to emerging adolescent autonomy and identity. The second is diagnostic. Depression can undermine energy for therapeutic work; social anxiety can make it difficult to talk with a therapist; and ODD can include defiance or hostility toward the therapist. And given that adolescents often are referred by others, rather than treatment seeking, circumstances are ripe for reactance. Clinical accounts of adolescents falling asleep, berating therapists, or simply refusing to talk about anything aside from fashion, music, or sports reinforce the view that the alliance can be elusive and fragile with adolescents. How, then, does this clinical perspective comport with two decades of research on engagement and alliance with adolescents? Meta-analytic evidence indicates that the alliance is associated with treatment outcomes in individual adolescent therapy (Shirk, Karver, & Brown, 2011), but this effect appears to be slightly diluted when group, family, or other modalities of treatment are included in analyses (McLeod, 2011). Although relatively robust across types of therapy and disorders, alliance effects are small to moderate in magnitude (mean r = .22). Interestingly, alliance–outcome relations were not stronger for adolescent therapy than child therapy (Shirk et al., 2011).

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However, strength of association with outcome does not directly address potential alliance difficulties with adolescents. If the adolescent alliance is elusive and fragile, then one might expect relatively low alliance scores, especially early in treatment, and substantial instability over the course of treatment. A review of adolescent alliance studies included in the most recent meta-analysis (Shirk et al., 2011) reveals a different picture. Across the seven published studies with 13 alliance measurements, mean alliance was at or above the midpoint of scales in all cases. Overall, scores were 17% above the midpoint of alliance scales or at 67% of the highest possible score. This indicates that, on average, adolescents evince relatively positive alliances across self-reported, therapist-reported, and observercoded alliance measures. Only two studies included observational measures of alliance, a source potentially less susceptible to response bias, and these ratings were similarly positive at 13% above the midpoint. Unfortunately, few studies evaluated the alliance over time, and those tended to measure alliance at two points, early and late in treatment. No study reported session-by-session changes in alliance scores; as a result, it was not possible to assess alliance instability (fragility) with adolescents. It is instructive to note that this set of studies included inpatients, suicide attempters, and adolescents with CD, depression, or maltreatment histories, and yet, alliance scores were relatively positive. Only a sample of substance abusing adolescents showed a slightly different pattern and their mean score was at the midpoint of the scale. Of course, across studies there was variability in alliance scores and a portion of the distribution evinced relatively negative alliance scores. But, this group is a subsample of adolescents. A critical question, then, is which adolescents are difficult to engage and likely to develop poor or weak alliances? Research on this issue is limited. Associations between early alliance and pretreatment symptom severity are mixed with some studies showing a positive relation (Levin, Henderson, & Ehrenreich-May, 2012), some a negative relation (Zaitsoff, Doyle, Hoste, & le Grange, 2008), and still others no relation (Ormhaug, Jensen, Wentzel-Larsen, & Shirk, 2014). One might expect

adolescents with higher levels of internalizing symptoms to present with more positive alliances, because of the experience of distress, and adolescents with more externalizing symptoms to present with relatively negative alliances because of behaviors related to defiance, noncompliance, and aggression. Unfortunately, findings are so sparse that this intuitive pattern lacks support. A few studies indicate that interpersonal factors like attachment security, family and peer social support, interpersonal expectations, and interpersonal functioning are associated with early alliance (Eltz, Shirk, & Sarlin, 1995; Garner, Godley, & Funk, 2008; Levin et al., 2012), but these associations depend on source of alliance rating and other measurement considerations. Given the alliance involves relationship formation, interpersonal patterns and expectations developed with parents and peers could impact early therapeutic interactions. Other more proximal variables, like problem acknowledgement (Garner et al. 2008) and readiness for change, are likely to influence early alliance formation. And situational variables, like whether treatment was mandated by court or school, coerced or encouraged by parents, or sought out by the adolescent, are likely to affect alliance quality. What does the research literature suggest about effective strategies for engaging reluctant or resistant adolescents? A few studies have examined specific therapist behaviors in the context of CBT and family-based therapy with adolescents. G. M. Diamond, Liddle, Hogue, and Dakof (1999) examined therapist behaviors in initial family sessions that distinguished improved versus unimproved alliance among substance-abusing adolescents. Relative to the unimproved alliance group, therapists who treated adolescents with improved alliances attended to the adolescent’s experience (“Tell me more about your connection with your older brother”), formulated personal goals (“Perhaps getting your mother to stop yelling at you in front of your friends is something we could work on”), and presented themselves as allies to a greater degree (“I might be able to help your mother understand how your feel”). The strength of this study was that it focused on adolescents with poor initial alliances; its weakness was its very small sample size. 497

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Shirk and colleagues (e.g., Jungbluth & Shirk, 2009; Karver et al., 2008; Russell, Shirk, & Jungbluth, 2008) examined therapist engagement strategies in early sessions of individual CBT as predictors of alliance and adolescent involvement (active participation) in therapy. In an initial study, Karver et al. (2008) evaluated three domains of therapist strategies (socialization, rapport-building, and lapses) in the first two sessions of CBT and nondirective, supportive therapy for adolescents who attempted suicide. Although there was some evidence that early rapport-building behaviors (exploring adolescent’s reactions, offering supportive statements) were related to alliance in Session 3, the most robust predictor was therapist lapses (misunderstood adolescent’s statement, failed to acknowledge expressed emotion, criticized). These behaviors were negatively related to alliance at Session 3. Unfortunately, initial alliance or resistance was not assessed in this study so it is not possible to isolate strategies that contributed to improved alliance. To consider therapist and adolescent contributions to treatment involvement, Jungbluth and Shirk (2009) evaluated therapist engagement strategies in CBT for depressed adolescents. Initial level of adolescent resistance at the start of therapy was considered. After controlling for initial resistance, two therapist behaviors were uniquely associated with greater adolescent involvement (participation) in cognitive tasks in Session 2: therapist explores motivation, and therapist provides relatively limited structure. Nearly 40% of the variance in adolescent involvement in Sessions 4 and 8 were explained by therapist behaviors in Session 1: namely, therapist elicits adolescent experiences, therapist explores motivation, and therapist provides lower levels of structure. The negative association between initial therapist structuring and subsequent adolescent involvement in sessions is surprising. The pattern of correlations, however, suggests that low structure involves higher levels of eliciting adolescent experiences, providing adolescents with ample opportunities to talk. It is possible that attempting to “get to know” the adolescent prior to embarking on treatment tasks provides a more personalized context for a manual-guided therapy. This interpretation 498

is supported by findings obtained by Russell et al. (2008) showing that therapists who linked their exploration of adolescent experiences with descriptions of the treatment mode in the first session, a kind of personalized socialization to treatment, promoted stronger subsequent alliances than those who did not. Some of the foregoing findings are consistent with principles of motivational interviewing (W. R. Miller & Rollnick, 2013), especially eliciting client experiences and exploring motivation for change. MI has been described as “a collaborative conversation style for strengthening a person’s own motivation and commitment to change” (W. R. Miller & Rollnick, 2013, p. 12). MI draws heavily from the clientcentered approach (Rogers, 1951) and emphasizes reflective listening and an attitude of acceptance. The therapist does not “push” or advocate for change, but attempts to maximize motivation for change from the client’s perspective. Instead, the therapist selectively reflects on client statements that represent discrepancies between the problem behavior and broader sets of values. MI is hypothesized to have its effect by resolving client ambivalence about change by altering client “change talk” (i.e., increases in change statements and reductions in sustain statements) portend increased motivation and greater behavioral change (Magill et al., 2014). MI was originally developed as an alternative approach to alcohol use; over the years it has been expanded to other addictive behaviors, healthrelated behaviors, and as an adjunct to other interventions like CBT (Burke, Arkowitz, & Menchola, 2003). Meta-analytic reviews support the efficacy of MI for adult alcohol and substance abuse, diet, and exercise, but less effective for smoking cessation (Burke et al., 2003). Most studies of MI have been conducted with adults, but emerging evidence indicates that it can be useful with adolescents. For example, a meta-analysis of controlled trials of MI for adolescent health behaviors (e.g., sexual risk, physical activity) showed that MI has a small but significant effect that is maintained through follow-up, on average 8 months later (Cushing, Jensen, Miller, & Leffingwell, 2014). MI as a standalone treatment for adolescent substance abuse has gathered mixed support (Hogue, Henderson,

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Ozechowski, & Robbins, 2014). Although superior to no treatment and waitlist controls, MI did not outperform educational feedback and TAU in some studies. In fact, MI appears to fare better as a component of a broader CBT approach to adolescent substance abuse (Hogue et al., 2014). An important question is whether the inclusion of MI strategies in early sessions of varied psychological treatments for adolescents might diminish resistance and enhance engagement. Although MI has been included in modular forms of adolescent depression treatments (Kennard, Clarke, et al., 2009), and research suggests that readiness for change predicts adolescent depression outcomes (Lewis et al., 2009), the contribution of MI to improving alliance and outcome in adolescent therapy has not been examined. Nevertheless, the therapist strategies outlined in MI appear to hold promise for engaging adolescents who are reluctant to participate in therapy. It appears that most adolescents enter therapy ready and able to form a working alliance. Conversely, the fragile and elusive alliance seems to characterize a subset of adolescents not clearly distinguished by a specific type of psychopathology. Although pretreatment interpersonal variables appear to be associated with engagement and alliance formation, these processes are poorly understood with adolescents. In contrast, an emerging set of studies suggests that therapist behaviors early in treatment can facilitate or undermine adolescent alliance formation. Given the importance of alliance and involvement in evidence-based treatments for adolescent disorders, these processes merit further consideration. Conclusion Adolescents are often treated for three highly prevalent disorders, MDD, SAD, and ODD. These disorders frequently present together in varied combinations and are complicated by emerging adolescent problems (e.g., suicidal ideation and behavior, NSSI, collateral substance misuse). No doubt, comorbidity and collateral problems increase the difficulty of treating many adolescents. Research largely has tested treatment protocols that target a

single or cluster of related disorders (e.g., anxiety disorders), and many protocols provide limited direction for dealing with collateral problems like substance use. In fact, suicidal and substance abusing adolescents are frequently excluded from clinical trials for specific disorders. Consequently, it can be difficult for empirically oriented clinicians to draw on the evidence base. Does comorbidity require the use of multiple stand-alone treatments, and if so, should they be delivered sequentially or integrated in some manner? This is one of the central challenges of bringing existing research to clinical practice. Our effort to identify core components of evidence-based treatments for prevalent adolescent disorders represents a response to this challenge. Namely, if we are to integrate evidence-based treatments for complex cases, what should we integrate? The identification and distillation of common treatment elements from efficacious treatments represents a promising approach (Chorpita & Daleiden, 2009). For many disorders, there exist comparably efficacious treatment packages. Fortunately, different brands typically include common components. However, most work to this point has focused on distilling components from preadolescent treatment. This chapter provided an initial survey of the territory of adolescent treatments for prevalent disorders and emergent problems. Potential common components were discovered, even for emergent problems like NSSI that complicate adolescent treatment. But gaps in existing research also were discovered. For some problems (e.g., ODD), much of the evidence base must be extrapolated from treatment research with children. We must proceed with caution. Just as treatments “downloaded” from adults might not fit the treatment of children or adolescents, treatments evaluated primarily with children might not produce the same results with adolescents. Instead, the psychological treatment of adolescents requires its own evidence base.

References Albano, A. M. (1995). Treatment of social anxiety in adolescents. Cognitive and Behavioral Practice, 2, 271–298. http://dx.doi.org/10.1016/S10777229(95)80014-X 499

Shirk, Stiles, and Leonard

Albano, A. M., & Kendall, P. C. (2002). Cognitive behavioral therapy for children and adolescents with anxiety disorders: Clinical research advances. International Review of Psychiatry, 14, 129–134. http://dx.doi.org/10.1080/09540260220132644 Albano, A. M., Marten, P. A., & Holt, C. S. (1991). Therapist’s manual for cognitive–behavioral group therapy for adolescent social phobia. Unpublished manuscript.

Copyright American Psychological Association. Not for further distribution.

Albano, A. M., Marten, P. A., Holt, C. S., Heimberg, R. G., & Barlow, D. H. (1995). Cognitive–behavioral group treatment for social phobia in adolescents. A preliminary study. Journal of Nervous and Mental Disease, 183, 649–656. http://dx.doi.org/ 10.1097/00005053-199510000-00006 American Psychiatric Association. (2013). Diagnostic and statistical manual of mental disorders (5th ed.). Washington, DC: Author. Angold, A., Costello, E. J., & Erkanli, A. (1999). Comorbidity. Journal of Child Psychology and Psychiatry, 40, 57–87. http://dx.doi.org/10.1111/1469-7610.00424 Armstrong, T. D., & Costello, E. J. (2002). Community studies on adolescent substance use, abuse, or dependence and psychiatric comorbidity. Journal of Consulting and Clinical Psychology, 70, 1224–1239. http://dx.doi.org/10.1037/0022-006X.70.6.1224 Baer, S., & Garland, E. J. (2005). Pilot study of community-based cognitive behavioral group therapy for adolescents with social phobia. Journal of the American Academy of Child and Adolescent Psychiatry, 44, 258–264. http://dx.doi.org/10.1097/00004583200503000-00010 Bank, L., Marlowe, J. H., Reid, J. B., Patterson, G. R., & Weinrott, M. R. (1991). A comparative evaluation of parent-training interventions for families of chronic delinquents. Journal of Abnormal Child Psychology, 19, 15–33. http://dx.doi.org/10.1007/BF00910562 Barkley, R. A. (1987). Defiant children (pp. 96–108). New York, NY: Guilford Press. Barkley, R. A., Edwards, G., Laneri, M., Fletcher, K., & Metevia, L. (2001). The efficacy of problemsolving communication training alone, behavior management training alone, and their combination for parent–adolescent conflict in teenagers with ADHD and ODD. Journal of Consulting and Clinical Psychology, 69, 926–941. http://dx.doi.org/10.1037/ 0022-006X.69.6.926 Barkley, R. A., Guevremont, D. C., Anastopoulos, A. D., & Fletcher, K. E. (1992). A comparison of three family therapy programs for treating family conflicts in adolescents with attention-deficit/hyperactivity disorder. Journal of Consulting and Clinical Psychology, 60, 450–462. http://dx.doi.org/ 10.1037/0022-006X.60.3.450

500

Barkley, R. A., & Robin, A. L. (2014). Defiant teens: A clinician’s manual for assessment and family intervention. New York, NY: Guilford Press. Barrett, P. M., & Ollendick, T. H. (Eds.). (2004). Handbook of interventions that work with children and adolescents: Prevention and treatment. http://dx.doi.org/ 10.1002/9780470753385 Barrocas, A. L., Hankin, B. L., Young, J. F., & Abela, J. R. (2012). Rates of nonsuicidal self-injury in youth: Age, sex, and behavioral methods in a community sample. Pediatrics, 130, 39–45. http://dx.doi.org/ 10.1542/peds.2011-2094 Barton, C., & Alexander, J. F. (1981). Functional family therapy. In A. S. Gurman & D. P. Kniskern (Eds.), Handbook of family therapy (Vol. 1, pp. 403–443). New York, NY: Routledge. Batejan, K. L., Jarvi, S. M., & Swenson, L. P. (2015). Sexual orientation and non-suicidal self-injury: A meta-analytic review. Archives of Suicide Research, 19, 131–150. http://dx.doi.org/10.1080/13811118. 2014.957450 Beardslee, W. R., Brent, D. A., Weersing, V. R., Clarke, G. N., Porta, G., Hollon, S. D.,. . .Garber, J. (2013). Prevention of depression in at-risk adolescents: Longer-term effects. JAMA Psychiatry, 70, 1161–1170. http://dx.doi.org/10.1001/ jamapsychiatry.2013.295 Becker, S. J., & Curry, J. F. (2008). Outpatient interventions for adolescent substance abuse: A quality of evidence review. Journal of Consulting and Clinical Psychology, 76, 531–543. http://dx.doi.org/ 10.1037/0022-006X.76.4.531 Beidel, D. C., Turner, S. M., & Morris, T. L. (1995). A new inventory to assess childhood social anxiety and phobia: The Social Phobia and Anxiety Inventory for Children. Psychological Assessment, 7, 73–79. http:// dx.doi.org/10.1037/1040-3590.7.1.73 Beidel, D. C., Turner, S. M., & Morris, T. L. (1998). Social effectiveness therapy for children: A treatment manual. Unpublished manuscript, Medical University of South Carolina, Charleston. Beidel, D. C., Turner, S. M., & Morris, T. L. (1999). Psychopathology of childhood social phobia. Journal of the American Academy of Child and Adolescent Psychiatry, 38, 643–650. http://dx.doi.org/10.1097/ 00004583-199906000-00010 Beidel, D. C., Turner, S. M., & Morris, T. L. (2000). Behavioral treatment of childhood social phobia. Journal of Consulting and Clinical Psychology, 68, 1072–1080. http://dx.doi.org/10.1037/0022-006X.68.6.1072 Beidel, D. C., Turner, S. M., Sallee, F. R., Ammerman, R. T., Crosby, L. A., & Pathak, S. (2007). SET-C versus fluoxetine in the treatment of childhood social

Psychological Treatment of Adolescents

Copyright American Psychological Association. Not for further distribution.

phobia. Journal of the American Academy of Child and Adolescent Psychiatry, 46, 1622–1632. http://dx.doi.org/ 10.1097/chi.0b013e318154bb57

randomized trials. Journal of Consulting and Clinical Psychology, 77, 566–579. http://dx.doi.org/10.1037/ a0014565

Brausch, A. M., & Girresch, S. K. (2012). A review of empirical treatment studies for adolescent nonsuicidal self-injury. Journal of Cognitive Psychotherapy, 26, 3–18. http://dx.doi.org/ 10.1891/0889-8391.26.1.3

Chronis, A. M., Jones, H. A., & Raggi, V. L. (2006). Evidence-based psychosocial treatments for children and adolescents with attention-deficit/hyperactivity disorder. Clinical Psychology Review, 26, 486–502. http://dx.doi.org/10.1016/j.cpr.2006.01.002

Brent, D., Emslie, G., Clarke, G., Wagner, K., Asarnow, J., Keller, M.,. . .Birmaher, B. (2008). Switching to another SSRI or to venlafaxine with or without cognitive behavioral therapy for adolescents with SSRI-resistant depression: The TORDIA randomized controlled trial. JAMA, 299, 901–913. http://dx.doi.org/ 10.1001/jama.299.8.901

Compton, S. N., Walkup, J. T., Albano, A. M., Piacentini, J. C., Birmaher, B., Sherrill, J. T.,. . .March, J. S. (2010). Child/Adolescent Anxiety Multimodal Study (CAMS): Rationale, design, and methods. Child and Adolescent Psychiatry and Mental Health, 4, 1–15. http://dx.doi.org/10.1186/1753-2000-4-1

Brent, D. A. (2011). Preventing youth suicide: Time to ask how. Journal of the American Academy of Child and Adolescent Psychiatry, 50, 738–740. http:// dx.doi.org/10.1016/j.jaac.2010.09.017 Brent, D. A., Greenhill, L. L., Compton, S., Emslie, G., Wells, K., Walkup, J. T.,. . .Turner, J. B. (2009). The Treatment of Adolescent Suicide Attempters study (TASA): Predictors of suicidal events in an open treatment trial. Journal of the American Academy of Child and Adolescent Psychiatry, 48, 987–996. http:// dx.doi.org/10.1097/CHI.0b013e3181b5dbe4 Brent, D. A., Holder, D., Kolko, D., Birmaher, B., Baugher, M., Roth, C.,. . .Johnson, B. A. (1997). A clinical psychotherapy trial for adolescent depression comparing cognitive, family, and supportive therapy. Archives of General Psychiatry, 54, 877–885. http:// dx.doi.org/10.1001/archpsyc.1997.01830210125017 Bridge, J. A., Goldstein, T. R., & Brent, D. A. (2006). Adolescent suicide and suicidal behavior. Journal of Child Psychology and Psychiatry, 47, 372–394. http:// dx.doi.org/10.1111/j.1469-7610.2006.01615.x

Connor, D. F. (2012). Aggression and antisocial behavior in children and adolescents: Research and treatment. New York, NY: Guilford Press. Cornelius, J. R., Maisto, S. A., Martin, C. S., Bukstein, O. G., Salloum, I. M., Daley, D. C.,. . .Clark, D. B. (2004). Major depression associated with earlier alcohol relapse in treated teens with AUD. Addictive Behaviors, 29, 1035–1038. http://dx.doi.org/ 10.1016/j.addbeh.2004.02.056 Curry, J. F. (2014). Future directions in research on psychotherapy for adolescent depression. Journal of Clinical Child and Adolescent Psychology, 43, 510–526. http://dx.doi.org/10.1080/15374416. 2014.904233 Curry, J. F., Wells, K. C., Lochman, J. E., Craighead, W. E., & Nagy, P. D. (2003). Cognitive–behavioral intervention for depressed, substance-abusing adolescents: Development and pilot testing. Journal of the American Academy of Child and Adolescent Psychiatry, 42, 656–665. http://dx.doi.org/ 10.1097/01.CHI.0000046861.56865.6C

Burke, B. L., Arkowitz, H., & Menchola, M. (2003). The efficacy of motivational interviewing: A meta-analysis of controlled clinical trials. Journal of Consulting and Clinical Psychology, 71, 843–861. http://dx.doi.org/ 10.1037/0022-006X.71.5.843

Cushing, C. C., Jensen, C. D., Miller, M. B., & Leffingwell, T. R. (2014). Meta-analysis of motivational interviewing for adolescent health behavior: Efficacy beyond substance use. Journal of Consulting and Clinical Psychology, 82, 1212–1218. http://dx.doi.org/10.1037/a0036912

Castro-Blanco, D. E., & Karver, M. S. (Eds.). (2010). Elusive alliance: Treatment engagement strategies with high-risk adolescents. http://dx.doi.org/10.1037/ 12139-000

Darche, M. A. (1990). Psychological factors differentiating self-mutilating and non-selfmutilating adolescent inpatient females. Psychiatric Hospital, 21, 31–35.

Chartrand, H., Sareen, J., Toews, M., & Bolton, J. M. (2012). Suicide attempts versus nonsuicidal selfinjury among individuals with anxiety disorders in a nationally representative sample. Depression and Anxiety, 29, 172–179. http://dx.doi.org/10.1002/ da.20882

Davidson, J. R., Hughes, D. L., George, L. K., & Blazer, D. G. (1993). The epidemiology of social phobia: Findings from the Duke Epidemiological Catchment Area Study. Psychological Medicine, 23, 709–718. http://dx.doi.org/10.1017/S0033291700025484

Chorpita, B. F., & Daleiden, E. L. (2009). Mapping evidence-based treatments for children and adolescents: Application of the distillation and matching model to 615 treatments from 322

Deas, D., & Thomas, S. E. (2001). An overview of controlled studies of adolescent substance abuse treatment. American Journal on Addictions, 10, 178–189. http://dx.doi.org/10.1080/ 105504901750227822 501

Shirk, Stiles, and Leonard

DePrince, A. P., & Shirk, S. R. (2013). Adapting cognitive–behavioral therapy for depressed adolescents exposed to interpersonal trauma: A case study with two teens. Cognitive and Behavioral Practice, 20, 189–201. http://dx.doi.org/10.1016/ j.cbpra.2012.07.001

Copyright American Psychological Association. Not for further distribution.

Diamond, G. M., Diamond, G. S., Levy, S., Closs, C., Ladipo, T., & Siqueland, L. (2012). Attachmentbased family therapy for suicidal lesbian, gay, and bisexual adolescents: A treatment development study and open trial with preliminary findings. Psychotherapy, 49, 62–71. http://dx.doi.org/10.1037/a0026247 Diamond, G. M., Liddle, H. A., Hogue, A., & Dakof, G. A. (1999). Alliance-building interventions with adolescents in family therapy: A process study. Psychotherapy: Theory, Research, Practice, Training, 36, 355–368. http://dx.doi.org/10.1037/ h0087729 Diamond, G. S., Siqueland, L., & Diamond, G. M. (2003). Attachment-based family therapy for depressed adolescents: Programmatic treatment development Clinical Child and Family Psychology Review, 6, 107–127. http://dx.doi.org/10.1023/ A:1023782510786 Diamond, G. S., Wintersteen, M. B., Brown, G. K., Diamond, G. M., Gallop, R., Shelef, K., & Levy, S. (2010). Attachment-based family therapy for adolescents with suicidal ideation: A randomized controlled trial. Journal of the American Academy of Child and Adolescent Psychiatry, 49, 122–131. DiClemente, R. J., Ponton, L. E., & Hartley, D. (1991). Prevalence and correlates of cutting behavior: Risk for HIV transmission. Journal of the American Academy of Child and Adolescent Psychiatry, 30, 735–739. Dishion, T., Forgatch, M., Chamberlain, P., & Pelham, W. E., III. (2016). The Oregon model of behavior family therapy: From intervention design to promoting large-scale system change. Behavior Therapy, 47, 812–837. http://dx.doi.org/10.1016/ j.beth.2016.02.002 Dobson, K. S., Hollon, S. D., Dimidjian, S., Schmaling, K. B., Kohlenberg, R. J., Gallop, R. J.,. . .Jacobson, N. S. (2008). Randomized trial of behavioral activation, cognitive therapy, and antidepressant medication in the prevention of relapse and recurrence in major depression. Journal of Consulting and Clinical Psychology, 76, 468–477. http://dx.doi.org/ 10.1037/0022-006X.76.3.468 Donaldson, D., Spirito, A., & Esposito-Smythers, C. (2005). Treatment for adolescents following a suicide attempt: Results of a pilot trial. Journal of the American Academy of Child and Adolescent Psychiatry, 44, 113–120. http://dx.doi.org/10.1097/00004583200502000-00003 502

Dretzke, J., Frew, E., Davenport, C., Barlow, J., StewartBrown, S., Sandercock, J.,. . .Taylor, R. (2005). The effectiveness and cost-effectiveness of parent training/education programmes for the treatment of conduct disorder, including oppositional defiant disorder, in children. Health Technology Assessment, 9(50), 1–233. http://dx.doi.org/10.3310/hta9500 Elmen, J., & Offer, D. (1993). Normality, turmoil, and adolescence. In P. H. Tolan & B. J. Cohler (Eds.), Handbook of clinical research and practice with adolescents (pp. 5–19). Oxford, England: Wiley. Eltz, M. J., Shirk, S. R., & Sarlin, N. (1995). Alliance formation and treatment outcome among maltreated adolescents. Child Abuse and Neglect, 19, 419–431. http://dx.doi.org/10.1016/0145-2134(95)00008-V Esposito-Smythers, C., Spirito, A., Kahler, C. W., Hunt, J., & Monti, P. (2011). Treatment of co-occurring substance abuse and suicidality among adolescents: A randomized trial. Journal of Consulting and Clinical Psychology, 79, 728–739. http://dx.doi.org/10.1037/ a0026074 Essau, C. A., Conradt, J., & Petermann, F. (1999). Frequency and comorbidity of social phobia and social fears in adolescents. Behaviour Research and Therapy, 37, 831–843. http://dx.doi.org/10.1016/ S0005-7967(98)00179-X Ewing, E. S. K., Diamond, G., & Levy, S. (2015). Attachment-based family therapy for depressed and suicidal adolescents: Theory, clinical model and empirical support. Attachment and Human Development, 17, 136–156. http://dx.doi.org/10.1080/ 14616734.2015.1006384 Eyberg, S. M., Nelson, M. M., & Boggs, S. R. (2008). Evidence-based psychosocial treatments for children and adolescents with disruptive behavior. Journal of Clinical Child and Adolescent Psychology, 37, 215–237. http://dx.doi.org/10.1080/15374410701820117 Faust, J., & Forehand, R. (1994). Adolescents’ physical complaints as a function of anxiety due to familial and peer stress: A causal model. Journal of Anxiety Disorders, 8, 139–153. http://dx.doi.org/ 10.1016/0887-6185(94)90012-4 Feindler, E. L., Marriott, S. A., & Iwata, M. (1984). Group anger control training for junior high school delinquents. Cognitive Therapy and Research, 8, 299–311. http://dx.doi.org/10.1007/BF01173000 Fisher, P. H., Masia-Warner, C., & Klein, R. G. (2004). Skills for social and academic success: A schoolbased intervention for social anxiety disorder in adolescents. Clinical Child and Family Psychology Review, 7, 241–249. http://dx.doi.org/10.1007/ s10567-004-6088-7 Frick, P. J., Lahey, B. B., Loeber, R., Tannenbaum, L., Van Horn, Y., Christ, M. A. G.,. . .Hanson, K. (1993). Oppositional defiant disorder and conduct disorder:

Psychological Treatment of Adolescents

Copyright American Psychological Association. Not for further distribution.

A meta-analytic review of factor analyses and crossvalidation in a clinic sample. Clinical Psychology Review, 13, 319–340. http://dx.doi.org/10.1016/02727358(93)90016-F

for repeated deliberate self-harm in adolescents: Failure of replication of a randomized trial. Journal of the American Academy of Child and Adolescent Psychiatry, 48, 662–670.

Frick, P. J., & Nigg, J. T. (2012). Current issues in the diagnosis of attention deficit hyperactivity disorder, oppositional defiant disorder, and conduct disorder. Annual Review of Clinical Psychology, 8, 77–107. http:// dx.doi.org/10.1146/annurev-clinpsy-032511-143150

Henggeler, S. W., Schoenwald, S. K., Borduin, C. M., Rowland, M. D., & Cunningham, P. B. (2009). Multisystemic therapy for antisocial behavior in children and adolescents. New York, NY: Guilford Press.

Garner, B. R., Godley, S. H., & Funk, R. R. (2008). Predictors of early therapeutic alliance among adolescents in substance abuse treatment. Journal of Psychoactive Drugs, 40, 55–65. http://dx.doi.org/ 10.1080/02791072.2008.10399761

Hettema, J., Steele, J., & Miller, W. R. (2005). Motivational interviewing. Annual Review of Clinical Psychology, 1, 91–111. http://dx.doi.org/10.1146/ annurev.clinpsy.1.102803.143833

Giletta, M., Scholte, R. H., Engels, R. C., Ciairano, S., & Prinstein, M. J. (2012). Adolescent non-suicidal selfinjury: A cross-national study of community samples from Italy, the Netherlands and the United States. Psychiatry Research, 197, 66–72. http://dx.doi.org/ 10.1016/j.psychres.2012.02.009 Goldstein, B. I., Shamseddeen, W., Spirito, A., Emslie, G., Clarke, G., Wagner, K. D.,. . .Brent, D. A. (2009). Substance use and the treatment of resistant depression in adolescents. Journal of the American Academy of Child and Adolescent Psychiatry, 48, 1182–1192. http:// dx.doi.org/10.1097/CHI.0b013e3181bef6e8 Greenbaum, P. E., Prange, M. E., Friedman, R. M., & Silver, S. E. (1991). Substance abuse prevalence and comorbidity with other psychiatric disorders among adolescents with severe emotional disturbances. Journal of the American Academy of Child and Adolescent Psychiatry, 30, 575–583. http://dx.doi.org/ 10.1097/00004583-199107000-00008 Greene, R. W., Ablon, J. S., & Goring, J. C. (2003). A transactional model of oppositional behavior: Underpinnings of the Collaborative Problem Solving approach. Journal of Psychosomatic Research, 55, 67–75. http://dx.doi.org/10.1016/S00223999(02)00585-8 Hankin, B. L., Young, J. F., Abela, J. R., Smolen, A., Jenness, J. L., Gulley, L. D.,. . .Oppenheimer, C. W. (2015). Depression from childhood into late adolescence: Influence of gender, development, genetic susceptibility, and peer stress. Journal of Abnormal Psychology, 124, 803–816. http://dx.doi.org/ 10.1037/abn0000089 Hayward, C., Varady, S., Albano, A. M., Thienemann, M., Henderson, L., & Schatzberg, A. F. (2000). Cognitive–behavioral group therapy for social phobia in female adolescents: Results of a pilot study. Journal of the American Academy of Child and Adolescent Psychiatry, 39, 721–726. http://dx.doi.org/ 10.1097/00004583-200006000-00010 Hazell, P. L., Martin, G., McGill, K., Kay, T., Wood, A., Trainor, G., & Harrington, R. (2009). Group therapy

Hides, L., Lubman, D. I., Kay-Lambkin, F. J., & Baker, A. (2007). Young people with co-existing mental health and drug and alcohol problems. In A. Baker & R. Velleman (Eds.), Clinical handbook of co-existing mental health and drug and alcohol problems (pp. 132–158). East Sussex, England: Routledge. Hilt, L. M., Nock, M. K., Lloyd-Richardson, E. E., & Prinstein, M. J. (2008). Longitudinal study of nonsuicidal self-injury among young adolescents: Rates, correlates, and preliminary test of an interpersonal model. Journal of Early Adolescence, 28, 455–469. http:// dx.doi.org/10.1177/0272431608316604 Hogue, A., Henderson, C. E., Ozechowski, T. J., & Robbins, M. S. (2014). Evidence base on outpatient behavioral treatments for adolescent substance use: Updates and recommendations, 2007–2013. Journal of Clinical Child and Adolescent Psychology, 43, 695–720. http://dx.doi.org/10.1080/15374416. 2014.915550 Hollon, S. D., Garber, J., & Shelton, R. C. (2005). Treatment of depression in adolescents with cognitive behavior therapy and medications: A commentary on the TADS project. Cognitive and Behavioral Practice, 12, 149–155. http://dx.doi.org/ 10.1016/S1077-7229(05)80019-7 Holmbeck, G. N., & Kendall, P. C. (2002). Introduction to the special section on clinical adolescent psychology: Developmental psychopathology and treatment. Journal of Consulting and Clinical Psychology, 70, 3–5. http://dx.doi.org/10.1037/ 0022-006X.70.1.3 Ishikawa, S. I., Okajima, I., Matsuoka, H., & Sakano, Y. (2007). Cognitive behavioural therapy for anxiety disorders in children and adolescents: A meta-analysis. Child and Adolescent Mental Health, 12, 164–172. http://dx.doi.org/10.1111/j.14753588.2006.00433.x Jacobson, C. M., & Gould, M. (2007). The epidemiology and phenomenology of non-suicidal self-injurious behavior among adolescents: A critical review of the literature. Archives of Suicide Research, 11, 129–147. http://dx.doi.org/10.1080/13811110701247602 503

Shirk, Stiles, and Leonard

Jacobson, C. M., & Mufson, L. (2012). Interpersonal psychotherapy for depressed adolescents adapted for self-injury (IPT-ASI): Rationale, overview, and case summary. American Journal of Psychotherapy, 66, 349–374.

Copyright American Psychological Association. Not for further distribution.

James, A. C., Taylor, A., Winmill, L., & Alfoadari, K. (2008). A preliminary community study of dialectical behaviour therapy (DBT) with adolescent females demonstrating persistent, deliberate selfharm (DSH). Child and Adolescent Mental Health, 13, 148–152. http://dx.doi.org/10.1111/j.14753588.2007.00470.x Jensen, C. D., Cushing, C. C., Aylward, B. S., Craig, J. T., Sorell, D. M., & Steele, R. G. (2011). Effectiveness of motivational interviewing interventions for adolescent substance use behavior change: A meta-analytic review. Journal of Consulting and Clinical Psychology, 79, 433–440. http://dx.doi.org/10.1037/a0023992 Jungbluth, N. J., & Shirk, S. R. (2009). Therapist strategies for building involvement in cognitive–behavioral therapy for adolescent depression. Journal of Consulting and Clinical Psychology, 77, 1179–1184. http://dx.doi.org/10.1037/a0017325 Karver, M., Shirk, S., Handelsman, J. B., Fields, S., Crisp, H., Gudmundsen, G., & McMakin, D. (2008). Relationship processes in youth psychotherapy: Measuring alliance, alliance-building behaviors, and client involvement. Journal of Emotional and Behavioral Disorders, 16, 15–28. http://dx.doi.org/ 10.1177/1063426607312536 Kashdan, T. B., & Herbert, J. D. (2001). Social anxiety disorder in childhood and adolescence: Current status and future directions Clinical Child and Family Psychology Review, 4, 37–61. http://dx.doi.org/ 10.1023/A:1009576610507 Kassinove, H., & Tafrate, R. C. (2002). Anger management: The complete treatment guidebook for practitioners. Oakland, CA: Impact. Katz, L. Y., Cox, B. J., Gunasekara, S., & Miller, A. L. (2004). Feasibility of dialectical behavior therapy for suicidal adolescent inpatients. Journal of the American Academy of Child and Adolescent Psychiatry, 43, 276–282. http://dx.doi.org/10.1097/00004583200403000-00008 Kaufman, N. K., Rohde, P., Seeley, J. R., Clarke, G. N., & Stice, E. (2005). Potential mediators of cognitive–behavioral therapy for adolescents with comorbid major depression and conduct disorder. Journal of Consulting and Clinical Psychology, 73, 38–46. http://dx.doi.org/10.1037/0022-006X.73.1.38 Kavanagh, D. J., Mueser, K. T., & Baker, A. (2003). Management of comorbidity. In M. Teesson & H. Proudfoot (Eds.), Comorbid mental disorders and substance use disorders: Epidemiology, prevention, and treatment (pp. 78–120). Sydney, Australia:

504

National Drug and Alcohol Research Centre, University of New South Wales. Kazdin, A. E. (2010). Problem-solving skills training and parent management training for oppositional defiant disorder and conduct disorder J. R. Weisz & A. E. Kadzin (Eds.), Evidence-based psychotherapies for children and adolescents (pp. 211–226). New York, NY: Guilford Press. Kazdin, A. E., Bass, D., Siegel, T., & Thomas, C. (1989). Cognitive–behavioral therapy and relationship therapy in the treatment of children referred for antisocial behavior. Journal of Consulting and Clinical Psychology, 57, 522–535. http://dx.doi.org/ 10.1037/0022-006X.57.4.522 Kazdin, A. E., Siegel, T. C., & Bass, D. (1992). Cognitive problem-solving skills training and parent management training in the treatment of antisocial behavior in children. Journal of Consulting and Clinical Psychology, 60, 733–747. http://dx.doi.org/ 10.1037/0022-006X.60.5.733 Kendall, P. C. (1989). Cognitive–behavioral therapy for anxious children: Treatment manual. Kearney, NE: Workman. Kendall, P. C. (1991). Guiding theory for therapy with children and adolescents. In P. Kendall (Ed.), Child and adolescent therapy: Cognitive–behavioral procedures (pp. 3–21). New York, NY: Guilford Press. Kendall, P. C. (1994). Treating anxiety disorders in children: Results of a randomized clinical trial. Journal of Consulting and Clinical Psychology, 62, 100–110. http://dx.doi.org/10.1037/0022006X.62.1.100 Kendall, P. C., Choudhury, M., Hudson, J., & Webb, A. (2002a). The C.A.T. project therapist manual. Ardmore, PA: Workbook. Kendall, P. C., Choudhury, M., Hudson, J., & Webb, A. (2002b). The C.A.T. project workbook for the cognitive–behavioral therapy for anxious adolescents. Ardmore, PA: Workbook. Kendall, P. C., Flannery-Schroeder, E., PanichelliMindel, S. M., Southam-Gerow, M., Henin, A., & Warman, M. (1997). Therapy for youths with anxiety disorders: A second randomized clinical trial. Journal of Consulting and Clinical Psychology, 65, 366–380. http://dx.doi.org/10.1037/0022006X.65.3.366 Kendall, P. C., & Peterman, J. S. (2015). CBT for adolescents with anxiety: Mature yet still developing. American Journal of Psychiatry, 172, 519–530. http:// dx.doi.org/10.1176/appi.ajp.2015.14081061 Kendall, P. C., Robin, J. A., Hedtke, K. A., Suveg, C., Flannery-Schroeder, E., & Gosch, E. (2005). Considering CBT with anxious youth? Think exposures. Cognitive and Behavioral Practice, 12, 136–148. http://dx.doi.org/10.1016/S10777229(05)80048-3

Psychological Treatment of Adolescents

Kendall, P. C., & Williams, C. L. (1986). Therapy with adolescents: Treating the marginal man. Behavior Therapy, 17, 522–537. http://dx.doi.org/10.1016/ S0005-7894(86)80092-2

Copyright American Psychological Association. Not for further distribution.

Kennard, B. D., Clarke, G. N., Weersing, V. R., Asarnow, J. R., Shamseddeen, W., Porta, G.,. . .Brent, D. A. (2009). Effective components of TORDIA cognitive–behavioral therapy for adolescent depression: Preliminary findings. Journal of Consulting and Clinical Psychology, 77, 1033–1041. http://dx.doi.org/10.1037/a0017411 Kennard, B. D., Silva, S. G., Tonev, S., Rohde, P., Hughes, J. L., Vitiello, B.,. . .March, J. (2009). Remission and recovery in the Treatment for Adolescents With Depression Study (TADS): Acute and long-term outcomes. Journal of the American Academy of Child and Adolescent Psychiatry, 48, 186–195. http://dx.doi.org/ 10.1097/CHI.0b013e31819176f9 Kessler, R. C., Avenevoli, S., Costello, E. J., Georgiades, K., Green, J. G., Gruber, M. J.,. . .Merikangas, K. R. (2012). Prevalence, persistence, and sociodemographic correlates of DSM–IV disorders in the National Comorbidity Survey Replication Adolescent Supplement. Archives of General Psychiatry, 69, 372–380. http://dx.doi.org/10.1001/ archgenpsychiatry.2011.160 Klein, J. B., Jacobs, R. H., & Reinecke, M. A. (2007). Cognitive–behavioral therapy for adolescent depression: A meta-analytic investigation of changes in effect-size estimates. Journal of the American Academy of Child and Adolescent Psychiatry, 46, 1403–1413. http://dx.doi.org/10.1097/ chi.0b013e3180592aaa Kolko, D. J., Brent, D. A., Baugher, M., Bridge, J., & Birmaher, B. (2000). Cognitive and family therapies for adolescent depression: Treatment specificity, mediation, and moderation. Journal of Consulting and Clinical Psychology, 68, 603–614. http://dx.doi.org/ 10.1037/0022-006X.68.4.603 Koons, C. R., Robins, C. J., Tweed, J. L., Lynch, T. R., Gonzalez, A. M., Morse, J. Q.,. . .Bastian, L. A. (2001). Efficacy of dialectical behavior therapy in women veterans with borderline personality disorder. Behavior Therapy, 32, 371–390. http:// dx.doi.org/10.1016/S0005-7894(01)80009-5 Kratochvil, C., Emslie, G., Silva, S., McNulty, S., Walkup, J., Curry, J.,. . .Casat, C. (2006). Acute time to response in the Treatment for Adolescents With Depression Study (TADS). Journal of the American Academy of Child and Adolescent Psychiatry, 45, 1412–1418. http://dx.doi.org/10.1097/01. chi.0000237710.73755.14 Lavigne, J. V., Cicchetti, C., Gibbons, R. D., Binns, H. J., Larsen, L., & DeVito, C. (2001). Oppositional defiant disorder with onset in preschool years: Longitudinal stability and pathways to other

disorders. Journal of the American Academy of Child and Adolescent Psychiatry, 40, 1393–1400. http:// dx.doi.org/10.1097/00004583-200112000-00009 Lerner, R. M., & Steinberg, L. (2009). Handbook of adolescent psychology. Volume 1: Individual bases of adolescent development. New York, NY: Wiley. Levin, L., Henderson, H. A., & Ehrenreich-May, J. (2012). Interpersonal predictors of early therapeutic alliance in a transdiagnostic cognitive–behavioral treatment for adolescents with anxiety and depression. Psychotherapy, 49, 218–230. http:// dx.doi.org/10.1037/a0028265 Lewis, C. C., Simons, A. D., Silva, S. G., Rohde, P., Small, D. M., Murakami, J. L.,. . .March, J. S. (2009). The role of readiness to change in response to treatment of adolescent depression. Journal of Consulting and Clinical Psychology, 77, 422–428. http://dx.doi.org/ 10.1037/a0014154 Linehan, M. M., Comtois, K. A., Murray, A. M., Brown, M. Z., Gallop, R. J., Heard, H. L.,. . .Lindenboim, N. (2006). Two-year randomized controlled trial and follow-up of dialectical behavior therapy vs therapy by experts for suicidal behaviors and borderline personality disorder. Archives of General Psychiatry, 63, 757–766. http://dx.doi.org/10.1001/ archpsyc.63.7.757 Lloyd-Richardson, E. E., Perrine, N., Dierker, L., & Kelley, M. L. (2007). Characteristics and functions of non-suicidal self-injury in a community sample of adolescents. Psychological Medicine, 37, 1183–1192. http://dx.doi.org/10.1017/S003329170700027X Lochman, J. E., Burch, P. R., Curry, J. F., & Lampron, L. B. (1984). Treatment and generalization effects of cognitive–behavioral and goal-setting interventions with aggressive boys. Journal of Consulting and Clinical Psychology, 52, 915–916. http://dx.doi.org/ 10.1037/0022-006X.52.5.915 Lochman, J. E., Curry, J. F., Dane, H., & Ellis, M. (2001). The Anger Coping Program: An empiricallysupported treatment for aggressive children. Residential Treatment for Children and Youth, 18, 63–73. http://dx.doi.org/10.1300/J007v18n03_06 Lochman, J. E., Lampron, L. B., Gemmer, T. C., & Harris, S. R. (1987). Anger coping intervention with aggressive children: A guide to implementation in school settings. In L. VandeCreek (Ed.), Innovations in clinical practice: A source book. Volume 6 (pp. 339–356). Sarasota, FL: Professional Resource Press. Lochman, J. E., Powell, N. R., Whidby, J. M., & Fitzgerald, D. P. (2012). Aggression in children. In P. C. Kendall (Ed.), Child and adolescent therapy: Cognitive–behavioral procedures (pp. 27–60). New York, NY: Guilford Press. Lochman, J. E., & Wells, K. C. (1996). A social-cognitive intervention with aggressive children: prevention effects 505

Shirk, Stiles, and Leonard

and contextual implementation issues In R. D. Peters & R. J. McMahon (Eds.), Preventing childhood disorders, substance abuse, and delinquency (p. 111–143). http:// dx.doi.org/10.4135/9781483327679.n6

Copyright American Psychological Association. Not for further distribution.

Lochman, J. E., & Wells, K. C. (2004). The coping power program for preadolescent aggressive boys and their parents: Outcome effects at the 1-year follow-up. Journal of Consulting and Clinical Psychology, 72, 571–578. http://dx.doi.org/10.1037/0022-006X.72.4.571 Loeber, R., Burke, J. D., Lahey, B. B., Winters, A., & Zera, M. (2000). Oppositional defiant and conduct disorder: A review of the past 10 years, part I. Journal of the American Academy of Child and Adolescent Psychiatry, 39, 1468–1484. http://dx.doi.org/ 10.1097/00004583-200012000-00007 Loeber, R., Burke, J. D., & Pardini, D. A. (2009). Development and etiology of disruptive and delinquent behavior. Annual Review of Clinical Psychology, 5, 291–310. http://dx.doi.org/10.1146/ annurev.clinpsy.032408.153631 Magill, M., Gaume, J., Apodaca, T. R., Walthers, J., Mastroleo, N. R., Borsari, B., & Longabaugh, R. (2014). The technical hypothesis of motivational interviewing: A meta-analysis of MI’s key causal model. Journal of Consulting and Clinical Psychology, 82, 973–983. http://dx.doi.org/10.1037/a0036833 March, J., Silva, S., Petrycki, S., Curry, J., Wells, K., Fairbank, J.,. . .Severe, J. (2004). Fluoxetine, cognitive–behavioral therapy, and their combination for adolescents with depression: Treatment for Adolescents With Depression Study (TADS) randomized controlled trial. JAMA, 292, 807–820. http://dx.doi.org/10.1001/jama.292.7.807 Marshal, M. P., Dietz, L. J., Friedman, M. S., Stall, R., Smith, H. A., McGinley, J.,. . .Brent, D. A. (2011). Suicidality and depression disparities between sexual minority and heterosexual youth: A meta-analytic review. Journal of Adolescent Health, 49, 115–123. http://dx.doi.org/10.1016/j.jadohealth.2011.02.005 Masia, C., Beidel, D. C., Albano, A. M., Rapee, R. M., Turner, S. M., Morris, T. L., & Klein, R. G. (1999). Skills for academic and social success. Available from Carrie Masia Warner, PhD, New York University School of Medicine, Child Study Center. Masia-Warner, C., Fisher, P. H., Shrout, P. E., Rathor, S., & Klein, R. G. (2007). Treating adolescents with social anxiety disorder in school: An attention control trial. Journal of Child Psychology and Psychiatry, 48, 676–686. http://dx.doi.org/10.1111/j.1469-7610.2007.01737.x Masia-Warner, C., Klein, R. G., Dent, H. C., Fisher, P. H., Alvir, J., Albano, A. M., & Guardino, M. (2005). School-based intervention for adolescents with social anxiety disorder: Results of a controlled study. Journal of Abnormal Child Psychology, 33, 707–722. http://dx.doi.org/10.1007/s10802-005-7649-z

506

McCauley, E., Gudmundsen, G., Schloredt, K., Martell, C., Rhew, I., Hubley, S., & Dimidjian, S. (2016). The adolescent behavioral activation program: Adapting behavioral activation as a treatment for depression in adolescence. Journal of Clinical Child and Adolescent Psychology, 45, 291–304. http:// dx.doi.org/10.1080/15374416.2014.979933 McLeod, B. D. (2011). Relation of the alliance with outcomes in youth psychotherapy: A meta-analysis. Clinical Psychology Review, 31, 603–616. http:// dx.doi.org/10.1016/j.cpr.2011.02.001 McMahon, R. J., & Forehand, R. L. (2003). Helping the noncompliant child. New York, NY: Guilford Press. Meeks, J. E. (1971). The fragile alliance: An orientation to the outpatient psychotherapy of the adolescent. Philadelphia, PA: Lippincott Williams & Wilkins. Mehlum, L., Tørmoen, A. J., Ramberg, M., Haga, E., Diep, L. M., Laberg, S.,. . .Grøholt, B. (2014). Dialectical behavior therapy for adolescents with repeated suicidal and self-harming behavior: A randomized trial. Journal of the American Academy of Child and Adolescent Psychiatry, 53, 1082–1091. http:// dx.doi.org/10.1016/j.jaac.2014.07.003 Merikangas, K. R., He, J. P., Burstein, M., Swanson, S. A., Avenevoli, S., Cui, L.,. . .Swendsen, J. (2010). Lifetime prevalence of mental disorders in U.S. adolescents: Results from the National Comorbidity Survey Replication—Adolescent Supplement (NCS-A). Journal of the American Academy of Child and Adolescent Psychiatry, 49, 980–989. http://dx.doi.org/ 10.1016/j.jaac.2010.05.017 Miller, A. L., Rathus, J. H., DuBose, A. P., Dexter-Mazza, E. T., & Goldklang, A. R. (2007). Dialectical behavior therapy for adolescents. In L. A. Dimeff & K. Koerner (Eds.), Dialectical behavior therapy in clinical practice: Applications across disorders and settings (pp. 245–263). New York, NY: Guilford Press. Miller, L. D., Gold, S., Laye-Gindhu, A., Martinez, Y. J., Yu, C. M., & Waechtler, V. (2011). Transporting a school-based intervention for social anxiety in Canadian adolescents. Canadian Journal of Behavioural Science/Revue Canadienne des Sciences du Comportement, 43, 287. Miller, W. R., & Rollnick, S. (2013). Motivational interviewing: Helping people change (3rd ed.). New York, NY: Guilford Press. Muehlenkamp, J., Brausch, A., Quigley, K., & Whitlock, J. (2013). Interpersonal features and functions of nonsuicidal self-injury. Suicide and Life-Threatening Behavior, 43, 67–80. http://dx.doi.org/10.1111/ j.1943-278X.2012.00128.x Mufson, L., Dorta, K. P., Moreau, D., & Weissman, M. (2005). Efficacy to effectiveness: Adaptations

Psychological Treatment of Adolescents

of interpersonal psychotherapy for adolescent depression. In E. D. Hibbs & P. S. Jensen (Eds.), Psychosocial treatments for child and adolescent disorders: Empirically based strategies for clinical practice (2nd ed., pp. 165–186). Washington, DC: American Psychological Association.

Copyright American Psychological Association. Not for further distribution.

Mufson, L., Dorta, K. P., Wickramaratne, P., Nomura, Y., Olfson, M., & Weissman, M. M. (2004). A randomized effectiveness trial of interpersonal psychotherapy for depressed adolescents. Archives of General Psychiatry, 61, 577–584. http://dx.doi.org/ 10.1001/archpsyc.61.6.577 Mufson, L., Weissman, M. M., Moreau, D., & Garfinkel, R. (1999). Efficacy of interpersonal psychotherapy for depressed adolescents. Archives of General Psychiatry, 56, 573–579. http://dx.doi.org/10.1001/ archpsyc.56.6.573 Nanni, V., Uher, R., & Danese, A. (2012). Childhood maltreatment predicts unfavorable course of illness and treatment outcome in depression: A meta-analysis. American Journal of Psychiatry, 169, 141–151. http://dx.doi.org/10.1176/appi. ajp.2011.11020335 Nelson-Gray, R. O., Keane, S. P., Hurst, R. M., Mitchell, J. T., Warburton, J. B., Chok, J. T., & Cobb, A. R. (2006). A modified DBT skills training program for oppositional defiant adolescents: Promising preliminary findings. Behaviour Research and Therapy, 44, 1811–1820. http://dx.doi.org/10.1016/ j.brat.2006.01.004 Nock, M. K. (Ed.). (2009a). Understanding nonsuicidal self-injury: Origins, assessment, and treatment. http:// dx.doi.org/10.1037/11875-000 Nock, M. K. (2009b). Why do people hurt themselves? New insights into the nature and functions of selfinjury. Current Directions in Psychological Science, 18, 78–83. http://dx.doi.org/10.1111/j.14678721.2009.01613.x Nock, M. K. (2010). Self-injury. Annual Review of Clinical Psychology, 6, 339–363. http://dx.doi.org/10.1146/ annurev.clinpsy.121208.131258 Nock, M. K., Green, J. G., Hwang, I., McLaughlin, K. A., Sampson, N. A., Zaslavsky, A. M., & Kessler, R. C. (2013). Prevalence, correlates, and treatment of lifetime suicidal behavior among adolescents: Results from the National Comorbidity Survey Replication Adolescent Supplement. JAMA Psychiatry, 70, 300–310. http://dx.doi.org/10.1001/2013. jamapsychiatry.55 Nock, M. K., Joiner, T. E., Jr., Gordon, K. H., LloydRichardson, E., & Prinstein, M. J. (2006). Nonsuicidal self-injury among adolescents: Diagnostic correlates and relation to suicide attempts. Psychiatry Research, 144, 65–72. http://dx.doi.org/10.1016/ j.psychres.2006.05.010

Nock, M. K., & Prinstein, M. J. (2005). Contextual features and behavioral functions of self-mutilation among adolescents. Journal of Abnormal Psychology, 114, 140–146. http://dx.doi.org/10.1037/0021843X.114.1.140 Ollendick, T. H., Greene, R. W., Austin, K. E., Fraire, M. G., Halldorsdottir, T., Allen, K. B., . . .Noguchi, R. J. (2016). Parent management training and collaborative & proactive solutions: A randomized control trial for oppositional youth. Journal of Clinical Child and Adolescent Psychology, 45, 591–604. Ormhaug, S. M., Jensen, T. K., Wentzel-Larsen, T., & Shirk, S. R. (2014). The therapeutic alliance in treatment of traumatized youths: Relation to outcome in a randomized clinical trial. Journal of Consulting and Clinical Psychology, 82, 52–64. http:// dx.doi.org/10.1037/a0033884 Ougrin, D., Tranah, T., Stahl, D., Moran, P., & Asarnow, J. R. (2015). Therapeutic interventions for suicide attempts and self-harm in adolescents: Systematic review and meta-analysis. Journal of the American Academy of Child and Adolescent Psychiatry, 54, 97–107.e2. http://dx.doi.org/10.1016/j.jaac.2014.10.009 Patterson, G. R., Reid, J. B., Jones, R. R., & Conger, R. E. (1975). A social learning approach to family intervention. Kouvola, Finland: Castalia House. Peris, T. S., Compton, S. N., Kendall, P. C., Birmaher, B., Sherrill, J., March, J., . . . Piacentini, J. (2015). Trajectories of change in youth anxiety during cognitive–behavior therapy. Journal of Consulting and Clinical Psychology, 83, 239–252. http://dx.doi.org/ 10.1037/a0038402 Piacentini, J., Bennett, S., Compton, S. N., Kendall, P. C., Birmaher, B., Albano, A. M.,. . .Walkup, J. (2014). 24- and 36-week outcomes for the Child/Adolescent Anxiety Multimodal Study (CAMS). Journal of the American Academy of Child and Adolescent Psychiatry, 53, 297–310. http://dx.doi.org/10.1016/ j.jaac.2013.11.010 Rapee, R. M. (1998). Overcoming shyness and social phobia: A step-by-step guide. Lanham, MD: Jason Aronson. Rathus, J. H., & Miller, A. L. (2002). Dialectical behavior therapy adapted for suicidal adolescents. Suicide and Life-Threatening Behavior, 32, 146–157. http:// dx.doi.org/10.1521/suli.32.2.146.24399 Rees, C. S., Hasking, P., Breen, L. J., Lipp, O. V., & Mamotte, C. (2015). Group mindfulness based cognitive therapy vs group support for selfinjury among young people: Study protocol for a randomised controlled trial. BMC Psychiatry, 15, 154. http://dx.doi.org/10.1186/s12888-015-0527-5 Reinecke, M. A., Ryan, N. E., & DuBois, D. L. (1998). Cognitive–behavioral therapy of depression and

507

Shirk, Stiles, and Leonard

depressive symptoms during adolescence: A review and meta-analysis. Journal of the American Academy of Child and Adolescent Psychiatry, 37, 26–34. http:// dx.doi.org/10.1097/00004583-199801000-00013

Copyright American Psychological Association. Not for further distribution.

Riggs, P. D., & Davies, R. D. (2002). A clinical approach to integrating treatment for adolescent depression and substance abuse. Journal of the American Academy of Child and Adolescent Psychiatry, 41, 1253–1255. http://dx.doi.org/10.1097/00004583200210000-00016 Ritschel, L. A., Ramirez, C. L., Jones, M., & Craighead, W. E. (2011). Behavioral activation for depressed teens: A pilot study. Cognitive and Behavioral Practice, 18, 281–299. http://dx.doi.org/10.1016/ j.cbpra.2010.07.002 Robin, A. L., & Foster, S. (1989). Negotiating parent–adolescent conflict. New York, NY: Guilford Press. Rogers, C. R. (1951). Client-centered counseling. London, England: Constable. Rohde, P., Clarke, G. N., Lewinsohn, P. M., Seeley, J. R., & Kaufman, N. K. (2001). Impact of comorbidity on a cognitive–behavioral group treatment for adolescent depression. Journal of the American Academy of Child and Adolescent Psychiatry, 40, 795–802. http:// dx.doi.org/10.1097/00004583-200107000-00014 Rosselló, J., & Bernal, G. (1999). The efficacy of cognitive–behavioral and interpersonal treatments for depression in Puerto Rican adolescents. Journal of Consulting and Clinical Psychology, 67, 734–745. http://dx.doi.org/10.1037/0022-006X.67.5.734 Rosselló, J., Bernal, G., & Rivera-Medina, C. (2008). Individual and group CBT and IPT for Puerto Rican adolescents with depressive symptoms. Cultural Diversity and Ethnic Minority Psychology, 14, 234–245. http://dx.doi.org/10.1037/10999809.14.3.234 Rossouw, T. I., & Fonagy, P. (2012). Mentalization-based treatment for self-harm in adolescents: A randomized controlled trial. Journal of the American Academy of Child and Adolescent Psychiatry, 51, 1304–1313.e3. http://dx.doi.org/10.1016/j.jaac.2012.09.018 Rowe, C. L., Liddle, H. A., Greenbaum, P. E., & Henderson, C. E. (2004). Impact of psychiatric comorbidity on treatment of adolescent drug abusers. Journal of Substance Abuse Treatment, 26, 129–140. http://dx.doi.org/10.1016/S0740-5472(03)00166-1 Rowe, R., Costello, E. J., Angold, A., Copeland, W. E., & Maughan, B. (2010). Developmental pathways in oppositional defiant disorder and conduct disorder. Journal of Abnormal Psychology, 119, 726–738. http:// dx.doi.org/10.1037/a0020798 Russell, R., Shirk, S., & Jungbluth, N. (2008). Firstsession pathways to the working alliance in

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cognitive–behavioral therapy for adolescent depression. Psychotherapy Research, 18, 15–27. http://dx.doi.org/10.1080/10503300701697513 Sanders, M. R. (2012). Development, evaluation, and multinational dissemination of the triple P-Positive Parenting Program. Annual Review of Clinical Psychology, 8, 345–379. http://dx.doi.org/10.1146/ annurev-clinpsy-032511-143104 Schlichter, K. J., & Horan, J. J. (1981). Effects of stress inoculation on the anger and aggression management skills of institutionalized juvenile delinquents. Cognitive Therapy and Research, 5, 359–365. http:// dx.doi.org/10.1007/BF01173687 Schneier, F. R., Johnson, J., Hornig, C. D., Liebowitz, M. R., & Weissman, M. M. (1992). Social phobia: Comorbidity and morbidity in an epidemiologic sample. Archives of General Psychiatry, 49, 282–288. http://dx.doi.org/10.1001/ archpsyc.1992.01820040034004 Shirk, S., Jungbluth, N., & Karver, M. (2012). Change processes and active components P. C. Kendall (Ed.), Child and adolescent therapy: Cognitive–behavioral procedures (pp. 471–498). New York, NY: Guilford Press. Shirk, S. R. (2001). Development and cognitive therapy. Journal of Cognitive Psychotherapy, 15, 155. Shirk, S. R., Crisostomo, P. S., Jungbluth, N., & Gudmundsen, G. R. (2013). Cognitive mechanisms of change in CBT for adolescent depression: Associations among client involvement, cognitive distortions, and treatment outcome. International Journal of Cognitive Therapy, 6, 311–324. http:// dx.doi.org/10.1521/ijct.2013.6.4.311 Shirk, S. R., Karver, M. S., & Brown, R. (2011). The alliance in child and adolescent psychotherapy. Psychotherapy, 48, 17–24. http://dx.doi.org/10.1037/ a0022181 Silverman, W. K., & Albano, A. M. (1996). The anxiety disorders interview schedule for children (ADIS-C/P). San Antonio, TX: Psychological Corporation. Silvers, J. A., McRae, K., Gabrieli, J. D., Gross, J. J., Remy, K. A., & Ochsner, K. N. (2012). Age-related differences in emotional reactivity, regulation, and rejection sensitivity in adolescence. Emotion, 12, 1235–1247. http://dx.doi.org/10.1037/a0028297 Spirito, A., Mehlenbeck, R., Barnett, N., Lewander, W., & Voss, A. (2003). The relation of mood and behavior to alcohol use in adolescent suicide attempters. Journal of Child and Adolescent Substance Abuse, 12, 35–53. http://dx.doi.org/10.1300/ J029v12n04_03 Tatnell, R., Kelada, L., Hasking, P., & Martin, G. (2014). Longitudinal analysis of adolescent NSSI: The role of intrapersonal and interpersonal factors. Journal

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of Abnormal Child Psychology, 42, 885–896. http:// dx.doi.org/10.1007/s10802-013-9837-6

Copyright American Psychological Association. Not for further distribution.

Walkup, J. T., Albano, A. M., Piacentini, J., Birmaher, B., Compton, S. N., Sherrill, J. T.,. . .Kendall, P. C. (2008). Cognitive behavioral therapy, sertraline, or a combination in childhood anxiety. New England Journal of Medicine, 359, 2753–2766. http://dx.doi.org/ 10.1056/NEJMoa0804633 Webster-Stratton, C. (2015). The Incredible Years series: A developmental approach In M. J. Van Ryzin, K. L. Kumpfer, G. M. Fosco, & M. T. Greenberg (Eds.), Family-based prevention programs for children and adolescents: Theory, research, and largescale dissemination (pp. 42–67). New York, NY: Psychology Press. Weersing, V. R., & Brent, D. (2010). Treating depression in adolescents: Using individual cognitive–behavioral therapy. In J. Weisz & A. Kazdin (Eds.), Evidencebased psychotherapies for children and adolescents (pp. 126–139). New York, NY: Guilford Press.

Wittchen, H. U., Stein, M. B., & Kessler, R. C. (1999). Social fears and social phobia in a community sample of adolescents and young adults: Prevalence, risk factors, and comorbidity. Psychological Medicine, 29, 309–323. http://dx.doi.org/10.1017/ S0033291798008174 Wolk, C. B., Kendall, P. C., & Beidas, R. S. (2015). Cognitive–behavioral therapy for child anxiety confers long-term protection from suicidality. Journal of the American Academy of Child and Adolescent Psychiatry, 54, 175–179. http://dx.doi.org/10.1016/j. jaac.2014.12.004 Wood, A., Trainor, G., Rothwell, J., Moore, A., & Harrington, R. (2001). Randomized trial of group therapy for repeated deliberate self-harm in adolescents. Journal of the American Academy of Child and Adolescent Psychiatry, 40, 1246–1253. http:// dx.doi.org/10.1097/00004583-200111000-00003

Weersing, V. R., Rozenman, M., & Gonzalez, A. (2009). Core components of therapy in youth: Do we know what to disseminate? Behavior Modification, 33, 24–47. http://dx.doi.org/10.1177/0145445508322629

Zaitsoff, S. L., Doyle, A. C., Hoste, R. R., & le Grange, D. (2008). How do adolescents with bulimia nervosa rate the acceptability and therapeutic relationship in family-based treatment? International Journal of Eating Disorders, 41, 390–398. http://dx.doi.org/ 10.1002/eat.20515

Weisz, J. R., McCarty, C. A., & Valeri, S. M. (2006). Effects of psychotherapy for depression in children and adolescents: A meta-analysis. Psychological Bulletin, 132, 132–149. http://dx.doi.org/ 10.1037/0033-2909.132.1.132

Zisser, A., & Eyberg, S. M. (2010). Parent–child interaction therapy and the treatment of disruptive behavior disorders J. R. Weisz & A. E. Kazdin (Eds.), Evidencebased psychotherapies for children and adolescents (pp. 179–193). New York, NY: Guilford Press.

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Chapter 22

Treatment of Trauma in Children and Adolescents

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Rochelle F. Hanson, Angela D. Moreland, and Rosaura E. Orengo-Aguayo

A traumatic event is defined as a distressing or dangerous experience, occurring outside the range of normal human experience, resulting in intense and overwhelming emotional, physical, and behavioral responses (Weathers & Keane, 2007). Traumatic events may include (a) serious injury or witnessing the serious injury or death of another person, (b) imminent threats of serious injury or death to self or others, and/or (c) a violation of personal physical integrity. Traumatic events may be acute in nature (i.e., short-lived, one-time events), including school or mass shootings, terrorist attacks, natural disasters, motor vehicle crashes, intrusive medical procedures, the sudden or violent loss of a loved one, or a physical or sexual assault (Furr, Comer, Edmunds, & Kendall, 2010). Exposure to trauma also may be chronic in nature (i.e., occurring repeatedly over an extended period of time), including physical and sexual abuse, domestic or interpersonal violence, community violence, or war (National Child Traumatic Stress Network, 2015). Most children and adolescents can recover from exposure to traumatic events over time, eventually returning to their previous levels of functioning (Bonanno & Mancini, 2008). Nevertheless, children who have been exposed to multiple traumatic events, have a history of family adversity, and who have a history of anxiety or other psychological problems are at greater risk of developing traumatic stress (Bonanno & Mancini, 2008). Traumatic stress in children may manifest as an experience of intense

distress and difficulty coping, resulting in disturbed sleep, anger and irritability, behavioral difficulties, difficulty paying attention and concentrating, repeated and intrusive thoughts, and/or extreme distress when faced with trauma reminders. In turn, children may develop several psychological disorders, the most common of which include posttraumatic stress disorder (PTSD), depression, anxiety, and behavioral disorders (Anda et al., 2006; Hanson et al., 2008). Overview of Traumatic Stress An estimated 40% to 80% of children and adolescents experience some type of traumatic event during their lifetime (Finkelhor, Turner, Ormrod, & Hamby, 2009; Finkelhor, Turner, Shattuck, & Hamby, 2013; Kilpatrick, Saunders, & Smith, 2003). Lifetime prevalence rates of physical abuse among children range from 4% to 19%, and from 17% to 71% for physical assault, depending on how abuse and assault are defined, with more stringent thresholds (e.g., required going to the doctor; left bruises, welts, or severe marks; burned, cut, or tied up; assaulted with a weapon; life threatened) showing lower prevalence rates (Finkelhor, Turner, et al., 2009; Finkelhor et al., 2013; Kilpatrick, Saunders, & Smith, 2003; McLaughlin et al., 2012). An estimated 8% to 10% of children have experienced at least one episode of sexual victimization during their lifetime, with adolescent girls having higher prevalence rates

This chapter is partly supported by Substance Abuse and Mental Health Services Administration Grant No. 1U79SM061269-01, National Institute of Mental Health Grant No. 1R34MH104470-01, National Institute of Drug Abuse Grant No. 5K12DA031794-03, and Duke Endowment Grant No. 1790-SP. http://dx.doi.org/10.1037/0000065-022 APA Handbook of Psychopathology: Vol. 2. Child and Adolescent Psychopathology, J. N. Butcher (Editor-in-Chief) Copyright © 2018 by the American Psychological Association. All rights reserved.

APA Handbook of Psychopathology: Child and Adolescent Psychopathology, edited by J. N. Butcher and P. C. Kendall Copyright © 2018 American Psychological Association. All rights reserved.

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(13%–17%) compared with adolescent boys (3%– 5%; Finkelhor et al., 2013; Finkelhor, Turner, Shattuck, & Hamby, 2015; Saunders & Adams, 2014). Data from the National Child Abuse and Neglect Data System indicated that in 2014, there was an estimated 3.6 million referrals to child protective service systems for suspected child maltreatment involving over 6.6 million children (U.S. Department of Health and Human Services, 2016). This resulted in a nationally estimated 702,000 victims of abuse and neglect, with children in their first year of life having the highest rate of victimization (U.S. Department of Health and Human Services, 2016). In addition to direct victimization, an estimated 38% to 70% of adolescents report witnessing one or more serious incidents of community violence, and an estimated 9% to 33% report witnessing violence in the home (Finkelhor et al., 2013, 2015; Zinzow, Ruggiero, Resnick, et al., 2009). Older adolescents report witnessing greater past-year violence, making them a uniquely vulnerable group regarding trauma exposure (Saunders & Adams, 2014). Furthermore, 18% of adolescents have lost a family member or a friend as a result of homicide (Rheingold, Zinzow, Hawkins, et al., 2012); approximately 15% of adolescents have experienced a natural disaster during their lifetime (McLaughlin et al., 2013); and 21% of adolescents endorse being in a serious accident during their lifetime, including motor vehicle crashes (Kilpatrick & Saunders, 1995).

Consequences of Trauma Exposure Trauma exposure has been linked with a range of detrimental outcomes for children and adolescents including PTSD, anxiety, depression, suicidality, self-injury, problematic substance use, delinquency and other risk behaviors, neurological deficits, physical health concerns, and greater risk of future perpetration of child abuse and neglect (Anda et al., 2006; Begle et al., 2011; Danielson et al., 2010; Hanson et al., 2008). A nationally representative sample of adolescents found that an estimated 8% to 21% of trauma-exposed children reported alcohol use, drug use, or delinquent behavior (Begle et al., 2011), and 10% to 15% reported engaging in self-harm or suicidal behavior (Vermeiren et al., 2002). In another national survey, of the 61.8% of adolescents who 512

had experienced a potentially traumatic event during their lifetime, the prevalence of PTSD was 4.7%, with girls having higher prevalence rates (7.3%) than boys (2.2%). The probability of having a PTSD diagnosis was higher for victims of interpersonal violence and for teens with preexisting fear and distress disorders (McLaughlin et al., 2012). Adolescents with trauma exposure and PTSD also are at greater risk of engaging in risky behaviors, including substance use, suicidality, and sexual risky behaviors, which may result in sexually transmitted illnesses and unwanted pregnancy (Danielson et al., 2010). Importantly, from a public health perspective, exposure to interpersonal violence presents a significant concern, as the associated lifetime costs resulting from new cases of child maltreatment in a 1-year period were estimated at $124 billion, which equated to $21,000 per victim (Fang, Brown, Florence, & Mercy, 2012). Studies of nationally representative community samples indicate that approximately 20% to 50% of children report exposure to more than one traumatic event during their lifetime, and over 15% report exposure to six or more different types within a 1-year period (Finkelhor et al., 2013; Ford, Elhai, Connor, & Frueh, 2010; Turner, Finkelhor, & Ormrod, 2010). Among older adolescents (ages 15–18), an estimated 10% have experienced 15 or more types of victimization (Finkelhor, Ormrod, & Turner, 2009). Prevalence rates for exposure to multiple traumatic events (i.e., polyvictimization, which is defined as four or more different types of trauma; Turner et al., 2010) are higher among adolescents referred to clinics for mental health services (48.6%; Adams, Moreland, et al., 2016), and polyvictimization has been associated with higher rates of mental health problems (Finkelhor et al., 2007; Ford et al., 2010). This reflects a dose–response relationship, such that children with greater exposure to traumatic life events are at greater risk for adverse outcomes (e.g., placement in residential or correctional facilities, internalizing and externalizing problems, greater PTSD symptoms, substance use, and suicidal behaviors). For example, polyvictimized adolescents, drawn from a clinic sample, were 2 to 5 times more likely to meet diagnostic criteria for PTSD and 3 to 16 times more likely to engage in drug use than

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Treatment of Trauma in Children and Adolescents

children who experienced one trauma type, underscoring the importance of understanding a child’s complete trauma history (Adams, Moreland, et al., 2016; Álvarez-Lister et al., 2014; Ford, Wasser, & Connor, 2011). Similarly, longitudinal data from a nationally representative sample of adolescents indicated that cumulative trauma exposure was associated with risk for subsequent development of depression, binge drinking, PTSD, and delinquency (Cisler et al., 2012), as well as increased risk of cigarette use (Cisler et al., 2011). Polyvictimization has been associated with older age, single-family household, high community disorder, low family support, and greater exposure to nonvictimization adversity (e.g., poverty; Turner, Shattuck, Finkelhor, & Hamby, 2016).

Developmental Considerations Special considerations in young children.  The most common disorder resulting from traumatic stress in children and adolescents is PTSD (McLaughlin et al., 2012). Research shows that young children are still developing abstract cognitive and verbal expression abilities and do not always manifest their distress in the form of intrusive thoughts. Instead, overt behaviors may be more predictive of distress (De Young, Kenardy, & Cobham, 2011; Scheeringa, Zeanah, Drell, & Larrieu, 1995). To make the PTSD diagnostic criteria more developmentally sensitive, the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM–5; American Psychiatric Association, 2013) incorporated a subtype of PTSD in preschool children. The modifications made to the DSM–5 criteria in young children include (a) removal of the criterion requiring that the child show extreme distress at the time of the traumatic event, (b) acknowledgement that spontaneous or intrusive memories may not necessarily appear distressing to the child, (c) requirement of only one symptom in either the avoidance symptoms or negative alterations in mood and cognition clusters, (d) removal of symptoms like “sense of foreshortened future” and “inability to recall an important aspect of the event,” (e) change in the wording of symptoms to enhance the face validity, (f) inclusion of examples of how certain

symptoms may manifest in young children (e.g., constricted play, social withdrawal), and (g) inclusion of extreme temper tantrums under the arousal symptoms. These modifications in criteria were derived empirically from samples of 2- to 6-year-old preschool children (Scheeringa, Myers, Putnam, & Zeanah, 2012; Scheeringa, Zeanah, Myers, & Putnam, 2003) showing that three to eight times more children met criteria for the modified diagnosis compared with DSM–IV criteria (Scheeringa, Zeanah, & Cohen, 2011). Special considerations in adolescents.  Research specifically focused on adolescents indicates a high prevalence of trauma-related mental health outcomes, including anxiety, PTSD, and depression (Hanson et al., 2008; Kilpatrick, Ruggiero, et al., 2003; Kilpatrick, Saunders, & Smith, 2003). Trauma-exposed adolescents also have an increased likelihood of engaging in risky sexual behavior, substance use, delinquency, self-injurious behaviors, and suicide (Anda et al., 2006; Begle et al., 2011; Danielson et al., 2010), placing them at risk for additional consequences like HIV and other sexually transmitted diseases and substance abuse. Further, high-risk behaviors tend to occur simultaneously (Dembo & Schmeidler, 2002), and evidence supports significant links between trauma exposure and engagement in multiple high-risk behaviors among adolescents (Begle et al., 2011). Results from a nationally representative study of adolescents demonstrated a bidirectional link between high-risk behaviors and trauma exposure, with significant gender differences highlighted within these relationships (Begle et al., 2011). Specifically, when examining adolescent girls exposed to interpersonal violence, results indicated that girls exposed to sexual assault were more likely to engage in subsequent high-risk behaviors than their peers, although high-risk behavior did not predict subsequent sexual assault. Among boys, those who were exposed to physical assault and witnessed violence were at increased risk for subsequent high-risk behaviors, and those who engaged in high-risk behaviors were also at increased risk for subsequent physical assault and witnessed violence. These significant and unique consequences of trauma exposure 513

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among adolescents have important treatment implications that should be considered when working with this population.

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Disparities in Prevalence Rates and Access to Care Unfortunately, racial/ethnic and sexual minorities are more likely to report exposure to multiple traumatic events and to evidence higher rates of related behavioral health problems than their European American peers (Andrews et al., 2015; Breslau et al., 2004; Hatch & Dohrenwend, 2007; Roberts et al., 2011). For instance, African American and Latino adolescents typically report substantially higher rates of violence exposure compared with European Americans (Andrews et al., 2015; Crouch et al., 2000). However, closer examination of these data indicate a complex interplay among race/ethnicity, socioeconomic factors (e.g., household income), and other risk variables, including family background, parental substance use, and neighborhood context (e.g., Crouch et al., 2000; Weist, Acosta, & Youngstrom, 2001; Zimmerman & Messner, 2013). For example, Crouch and colleagues (Crouch et al., 2000) found that rates of violence exposure decreased with higher household income for European American, but not African American or Hispanic children. Additionally, African American and Hispanic children reported significantly higher rates of witnessed violence at each income level compared with their European American peers. In their study of exposure to community violence among adolescents, Weist and colleagues (2001) found that certain risk factors (e.g., parental substance use, number of people in the home, out-of-home placements) were more important predictors of violence exposure than demographics, with life stress making the most significant contribution. Although racial and ethnic disparities in violence exposure are consistently reported, findings highlight the complexity of relations among race/ethnicity, sociodemographics, individual differences, and neighborhood contextual factors (Zimmerman & Messner, 2013). Lesbian, gay, bisexual, and transgender (LGBT) children and adolescents are also at high risk for trauma exposure and victimization by peers, including verbal and physical assault, bullying, and hate 514

crimes (D’Augelli, 2002; Stoddard et al., 2009), as well as social isolation, rejection, and homelessness (Tyler & Cauce, 2002). LGBT adolescents are also particularly vulnerable to trauma-related consequences, including problematic alcohol use (Talley et al., 2014), suicidality (Almazan, Roettger, & Acosta, 2014), and other self-harm behaviors (Batejan, Jarvi, & Swenson, 2015). Compounding these disparities in prevalence rates, racial/ethnic minority and LGBT children and adolescents face numerous barriers to accessing mental health services and are less likely to receive evidence-based, culturally sensitive services when they do access care (Merikangas et al., 2011; Whaley & Davis, 2007). Trauma Screening and Assessment The prevalence of trauma exposure and heightened risk for psychological symptoms and problems make screening and assessment an important priority for mental health treatment providers working with all ages and backgrounds of people. At minimum, a brief screening to determine history of exposure to potentially traumatic events and trauma-related symptoms is recommended for inclusion in all initial evaluation protocols. This initial screening can help to determine whether a more extensive evaluation is warranted. The evaluation should include screening for history of exposure to traumatic events, as well as the use of evidence-based standardized assessment instruments to determine whether children are experiencing trauma-related symptoms, including PTSD, and common comorbid/co-occurring problems (e.g., depression, anxiety, behavioral problems (Schneider, Grilli, & Schneider, 2013). With adolescents, assessment for high-risk behaviors (e.g., substance use, suicidality, risky sexual behaviors) is critical. It is helpful to collect information from multiple sources, including the child, caregiver, and other collateral sources (e.g., teachers, other service providers). If the screening and/or assessment results indicate that treatment is not currently warranted, it may be helpful to repeat periodically, particularly if there are concerns about additional exposure to trauma and/or changes in trauma symptoms (Wherry et al., 2014).

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One important caveat related to psychological assessment involving cases of child abuse is that the purpose is not to determine whether the abuse occurred. This falls under the purview of a forensic evaluator. Rather, the intent of a psychological evaluation is to collect information from multiple sources to arrive at an informed opinion regarding a child’s trauma experiences and related symptoms to determine treatment needs, not the veracity of the abuse disclosure. This distinction is important (Wherry et al., 2014). A second caveat pertains to assessments more broadly: these evaluations cannot predict future behavior. Instead, the goals of the assessment are to (a) determine a child’s treatment needs, (b) assess ongoing progress in treatment, and (c) determine whether treatment goals have been met. With trauma cases, specifically, an additional goal is to determine whether, and to what extent, children’s symptoms are related to their trauma experience(s) to inform treatment decisions. A few summary points related to assessments: In terms of selecting instruments, clinicians should use those with strong psychometric properties and those that are appropriate for the child’s age/developmental level, gender, cultural background, and language when available. Clinicians should use parsimony and judiciously select those measures needed to achieve the goals of the assessment. For example, to determine a child’s need for treatment and develop an informed treatment plan, a more comprehensive initial evaluation may be needed. In contrast, for ongoing repeated assessment to monitor a child’s progress in treatment, a few, brief measures would be indicated. There are several available brief and psychometrically sound measures to assess for PTSD and the most common comorbid/co-occurring symptoms (e.g., depression, anxiety, oppositional defiant disorder, behavior problems; Schneider et al., 2013). Examples include the University of Los Angeles PTSD Reaction Index (Steinberg, Brymer, Decker, & Pynoos, 2004), the Child PTSD Symptom Scale (Foa, Johnson, Feeny, & Treadwell, 2001), the Short Moods and Feelings Questionnaire (Angold et al., 1995), the SCARED (Birmaher et al. 1999), the Pediatric Symptom Scale-17 (Gardner, Murphy, Childs, et al., 1999) and the Strengths and Difficulties Questionnaire (Goodman, 1997; Goodman

et al., 1998). At minimum, clinicians should obtain child self-report and caregiver report whenever possible, but additional collateral reports are helpful, like those from teachers and other service providers. For child trauma cases, there may be several different service providers and service systems (e.g., child welfare, judicial system), all of which would benefit from accessibility to the assessment results. To this end, a concise report that details treatment recommendations can be beneficial in coordinating care for this population. As with all assessments, clinicians should ensure that the respondent understands the instructions and can read independently. Treatments for Traumatic Stress Among Children and Adolescents There are several mental health treatments, clinical interventions, and other trauma-informed service approaches that have been developed to treat trauma-related sequelae in children and their families. However, these approaches vary considerably across several dimensions, including level of research support, target population (e.g., young children, school age-children, adolescents), targeted outcomes (e.g., mental health outcomes, like PTSD or depression; functional impairment; risky behaviors), cultural relevance, mode of delivery (i.e., individual, group, family), and service setting (i.e., outpatient clinic, school, residential treatment center, home). There are several online databases that provide descriptions of interventions available for children and families experiencing traumarelated symptoms, with some including ratings of the level of research support. Examples include the American Academy of Child and Adolescent Psychiatry’s Practice Guidelines for Trauma Treatment (http://www.aacap.org/aacap/resources_for_primary_care/practice_parameters_and_resource_centers/practice_parameters.aspx), the Substance Abuse and Mental Health Service Administration’s National Registry of Evidence-based Programs and Practices (NREPP; http://www.samhsa.gov/nrepp), the California Evidence-Based Clearinghouse for Child Welfare (CEBC; http://www.CEBC4cw.org), and the Child Welfare Information Gateway (https://www.childwelfare.gov). The NREPP and 515

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CEBC sites offer the ability to search for a treatment, intervention and/or program along various dimensions (e.g., age, targeted outcome) and include ratings of the interventions on the basis of available empirical support. Details of the inclusion and review process can be found on their respective websites. Another helpful resource is the National Child Traumatic Stress Network (NCTSN; http://www. nctsn.org), which includes a collaboration of frontline providers, researchers, and families focused on improving quality of care while increasing access to evidence-based services for trauma exposed children and their families. The NCTSN was established by Congress in 2000, and currently has 79 funded organizations, 47 affiliate organizations, and 72 individual affiliates. The NCTSN website provides links to fact sheets that include descriptive summaries of different clinical treatments, mental health interventions, and other traumainformed service approaches that have been developed and/or implemented by the NCTSN and its affiliate centers. As noted on their website, the interventions and treatments “span a continuum of evidence-based and evidence-supported interventions ranging from rigorously evaluated interventions to promising practices and newly emerging practices.” It is important to acknowledge that none of these resources offer complete lists of all the interventions developed for treating children and adolescents who have experienced trauma. However, the CEBC and NREPP offer a resource for determining empirical support for a given intervention, which can be a useful factor in the decision-making process. The NCTSN (2015) also has developed a position statement regarding prerequisite clinical competences for implementing effective, trauma-informed interventions. The position statement reflects the consensus of the NCTSN regarding the minimal clinical competencies needed to deliver traumainformed interventions. In brief, this includes proficiencies in seven core areas, all of which are relevant for delivery of any mental health treatment intervention, but are particularly critical to address trauma and its potential impact: (a) basic assessment to identify presenting problems, resources in the child’s 516

environment, and the availability and strengths of caregivers to support the child; (b) risk assessment to determine the child’s risk of harm to self or others, as well as the risk of harm to the child, and the ability of caregivers and other adults to ensure a safe environment; (c) case conceptualization, which integrates assessment data as a way to frame and understand the child’s key problems and targets for intervention; (d) treatment planning to develop an effective, reasonable and feasible clinical approach for the child and family, with measurable outcome goals; (e) treatment engagement to develop a “working alliance” with the child and family to ensure consistent involvement in the treatment process and increase likelihood of successful outcomes; (f) treatment implementation, which refers to the clinician’s ability to deliver a targeted treatment as intended to meet identified goals; and (g) treatment quality monitoring, which includes the clinician’s abilities to objectively assess treatment progress and to achieve targeted goals. These core competencies align with the practice guidelines developed by the American Academy of Child and Adolescent Psychiatry (AACAP) for treatment of children and adolescents with PTSD (Cohen, 2010). As discussed by Schneider et al. (2013), the AACAP guidelines outline three key recommendations for treatment with these populations: consideration of the severity and degree of PTSD symptoms, use of integrated approaches that can address comorbid conditions when possible, and use of trauma-focused psychotherapies as a first-line treatment. The parameters also highlight the importance of caregiver involvement in treatment, attention to functional impairment as a treatment target, and the use of standardized assessment throughout treatment. Importantly, the AACAP guidelines highlight the importance of using trauma-focused psychotherapies as first-line treatment, emphasizing that supportive, nondirective approaches are contraindicated for this population.

Cross-Cutting Treatment Components As noted in several existing literature reviews, most treatments with empirical support for trauma populations include cognitive–behavioral therapies (CBTs; Chaffin & Friedrich, 2004; Dorsey, Briggs, & Woods, 2011; Kendall, 2012; Silverman et al., 2008;

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Treatment of Trauma in Children and Adolescents

Wethington et al., 2008). Common cross-cutting elements among various CBT approaches include psychoeducation about trauma and its impact (e.g., PTSD), affective modulation skills (e.g., relaxation, controlled breathing), gradual exposure to trauma memories, and cognitive processing to address unhelpful and/or inaccurate cognitions (e.g., guilt, self-blame). Gradual exposure appears to be a particularly important treatment element, given the cumulative evidence regarding its specific impact in reducing PTSD symptoms (Deblinger et al., 2011; Gilboa-Schechtman et al., 2010; Salloum & Overstreet, 2012; Vandervord Nixon, Sterk, & Pearce, 2012). In brief, this involves repeated exposure to details of the trauma to extinguish trauma-related emotional and behavioral responses. This treatment strategy also helps to improve cognitive processing of the traumatic event(s), which has been demonstrated to facilitate recovery. This is commonly accomplished through development of a trauma narrative in which the child provides a detailed account of their traumatic experiences and enables the clinician to identify unhelpful and inaccurate cognitive distortions (e.g., self-blame), which can then be processed in the treatment sessions. As with most child mental health treatment interventions, involvement of a supportive caregiver in trauma-focused treatments can be another important element related to positive outcomes, including reduced drop-out (Chowdhury & Pancha, 2011), increased family engagement (McKay & Bannon, 2004), and improved parent–child relationships (Bernardon & Pernice-Duca, 2010; Lieberman et al., 2011). It is not possible to provide a detailed overview of all existing trauma-focused treatment interventions, so the next section highlights several interventions that were selected to provide examples along several specific dimensions: (a) those targeting trauma broadly vs. a specific type of traumatic event (e.g., physical abuse, grief), (b) those targeting different age groups (young children vs. adolescents), and (c) those delivered in different service systems (e.g., mental health clinics vs. schools). When possible, an emphasis is placed on interventions with demonstrated empirical support. Table 22.1 provides an overview of the included treatment interventions. It is important to emphasize that selection of these

interventions is not intended as an endorsement, but rather to provide an array of interventions, with strong encouragement for readers to examine the AACAP, CEBC, NREPP and NCTSN websites for a more complete list of trauma-focused interventions. Interested readers are also referred to several prior reviews on trauma treatment for children and adolescents (Chaffin & Friedrich, 2004; Dorsey et al., 2011; Feeny, Foa, Treadwell, & March, 2004; Silverman et al., 2008; Taylor & Chemtob, 2004; Wethington et al., 2008).

Targeting Trauma-Related Symptoms Broadly Trauma-focused cognitive–behavioral therapy.  Trauma-focused CBT (TF-CBT; Cohen, Mannarino, & Deblinger, 2006) is highlighted first because of its strong empirical support (15 randomized controlled trials to date) and wide penetration across a variety of children service settings, within the United States (Sigel, Benton, Lynch, & Kramer, 2013; Wonderlich et al., 2011) and outside the United States (Konanur, Muller, Cinamon, Thornback, & Zorzella, 2015; Murray et al., 2013; O’Callaghan, McMullen, Shannon, Rafferty, & Black, 2013). TF-CBT is designed for children and adolescents ages 3 to 18. The typical course of treatment is 12 to 16 sessions, with a longer duration (up to 25 sessions) for complex trauma. It is a multicomponent model that addresses trauma-related symptoms, including PTSD, depression, and moderate behavioral problems. It involves individual parallel sessions with the child and a nonoffending, supportive caregiver, as well as conjoint parent–child sessions. The intent of the conjoint work is to facilitate sharing and open communication about the traumatic event. The model includes eight treatment components that comprise the acronym PRACTICE divided into three phases: Phase 1, stabilization, includes (a) psychoeducation and parenting skills, (b) relaxation, (c) affective expression and modulation, and (d) cognitive coping; Phase 2, trauma narrative (TN), includes (e) TN development and processing; and Phase 3, integration/consolidation, includes (f) in vivo mastery of trauma reminders, (g) conjoint sharing of the TN, and (h) enhancing future safety and development. 517

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Table 22.1 Trauma Treatments Intervention

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Trauma-focused cognitive–behavioral therapy

Author(s)

Primary target

Trauma broadly Cohen, Mannarino, and Deblinger (2006)

Target population

Typical course: 12–16 sessions; 16–25 sessions for complex trauma; parallel and joint child/caregiver sessions Individual and joint child–caregiver sessions; 6–9 months

Components-based (PRACTICE); targets PTSD, depression and behavior problems

Targets families who exhibit or are at risk for problems with anger, aggression and/or child physical abuse Typical course: Treats oppositional, 14 sessions; defiant, and other parent–child dyad externalizing behavior problems Improves symptoms of Typical course: 10 PTSD, depression, sessions; individual/ and traumatic grief group with children, one session with parents Typical course: Goal to support and 50 sessions; strengthen childParent–child dyad caregiver relationship to address traumarelated problems Flexible pacing and Exposure-based order of components; integrated treatment average course is 24 for PTSD and weekly individual and comorbid high-risk child–caregiver joint behaviors sessions 12 sessions; group or Goals to improve individual emotion regulation and reduce PTSD. 7 treatment components (FREEDOM) divided into 3 phases 10 school-based School based group group sessions, 1–3 and individual individual sessions, 2 intervention to parent sessions, and reduce PTSD, a teacher educational depression, and session behavioral problems

Coercive family interactions; physical abuse

5–17

Parent–child interaction Eyberg et al. therapy (2001)

Externalizing behaviors; abuse risk

2–7

Grief and trauma intervention for children

Salloum and Overstreet (2008)

Grief and trauma

7–12

Child–parent psychotherapy

Lieberman, Van Horn, and Ippen (2005)

Young children

0–5

Risk reduction through family therapy

Danielson (2014)

Adolescents; PTSD and high-risk behaviors

12–18

Trauma affect regulation: Guide for education and therapy

Ford and Russo (2006)

Adolescents

10–18

Cognitive–behavioral intervention for trauma in schools

School-based Stein, Elliott, et al. (2003); Stein, Jaycox, et al. (2003)

518

Length/format

3–18

Alternatives for families: Kolko (1996a) A cognitive– behavioral therapy

Brief overview of TF-CBT components.  The psychoeducation component focuses on providing information to the child and caregiver about the index trauma(s) and common trauma reactions. It

Brief description

8–15 (third– eighth grade)

also includes an overview of the course of TF-CBT (e.g., frequency, duration), and the critical role of the caregiver throughout treatment. During the parenting component, behavior management strategies

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Treatment of Trauma in Children and Adolescents

(e.g., use of praise and positive reinforcement, active ignoring, use of timeout and contingency management) are taught to address common trauma-related behavioral problems, and to enhance the caregiver’s general parenting knowledge and skills. The relaxation component includes skills like controlled breathing, progressive muscle relaxation, mindfulness, and meditation to help children manage the physiological manifestations of fear and anxiety (e.g., increased heart rate, hyperarousal, and sleep disturbances). In the affect identification and modulation component, goals are to expand the child’s emotional vocabulary and to teach the child ways to express and regulate emotions (e.g., use of positive self-statements). Cognitive coping helps the child to recognize and identify his or her thoughts and to differentiate between thoughts, feelings, and behaviors. This is facilitated through discussion of the cognitive triangle, in which the child is taught the connections between thoughts, feelings, and behaviors, as well as strategies to address unhelpful or inaccurate cognitions (e.g., all or nothing thinking, focus on the negative or worst possible scenario). The emphasis is to help the child understand that the way an event is perceived (i.e., the thought) directly impacts the emotional response. Furthermore, the thought is the target for change, rather than the feeling (i.e., feelings are neither good nor bad). With this component, it is especially important to focus on non–traumarelated events, as those specifically tied to the trauma will be addressed in later treatment components. Developing and processing the TN is a form of gradual exposure, which is one of the most effective treatment strategies for trauma-related symptoms (e.g., PTSD). In this component, the child is encouraged to create a detailed account of the traumatic event(s) that includes thoughts and feelings, and an attempt to capture fully any trauma-related sensations (e.g., smells, tastes, touches, sounds) that can trigger a fear response. It is important to help the child share as many details as possible; to discuss what happened before, during, and after the event(s); and to put the account in chronological order to provide closure. The TN is an important component of TF-CBT as the “telling” of the child’s story facilitates identification of unhelpful and inaccurate cognitions related to the trauma, which can then be addressed in therapy

sessions. The gradual exposure that occurs during this component also provides the opportunity to reduce the overwhelming or distressing negative emotions that can accompany discussion of the trauma events. The TN development can require multiple sessions, depending on the complexity of the child’s experiences. It is most commonly accomplished through the writing of a book, making drawings, creating a timeline, writing songs, or writing poetry. The specific method is secondary to the core objective, which is to help the child provide a full and detailed account of their trauma experiences. After completion of the TN, the therapist uses cognitive processing techniques (e.g., Socratic questioning, “best friend role plays”) to help identify, explore, and challenge inaccurate or unhelpful trauma-related thoughts and beliefs. In vivo mastery of trauma reminders is necessary as the TN involves imaginal exposure, which may not address trauma-triggers that occur in contexts outside of the therapy setting. Some children will experience trauma-related fears in the “real world,” which adversely impact their functioning. To address these, it is necessary to provide the opportunity for direct exposure in the child’s environment. A common example is the child who was sexually abused in their bedroom and is now extremely fearful of sleeping in his or her own room, which results in disrupted sleep and impaired functioning. In vivo mastery involves a gradual approach, with incremental steps, including the creation of a fear hierarchy, to help the child directly face his or her fears without the opportunity for avoidance. The in vivo component takes place outside of the therapy setting, meaning that involvement of the caregiver is especially critical. An important caveat is that in vivo should not be used if the child’s fear poses a genuine threat to safety (e.g., child lives in a dangerous neighborhood and is afraid to play outside). There are multiple opportunities for conjoint parent–child sessions, and these should be conducted throughout treatment. For example, during the relaxation component, the child can “teach” the caregiver how to do controlled breathing; during psychoeducation, the therapist can share trauma specific information with the child and caregiver together. An especially important goal of conjoint sessions is to facilitate sharing of the TN. This increases 519

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communication between the child and caregiver about the trauma and allows the caregiver to provide additional support, praise, and encouragement to the child. Prior to the actual sharing of the TN, it is essential that sufficient time is spent to prepare the caregiver. Specifically, while the child is working to develop their narrative, parallel caregiver sessions should focus on sharing the child’s developing narrative to ensure that the caregiver has full understanding and details about the trauma, to provide the opportunity for the caregiver to process their own trauma-related distress, to identify and process inaccurate or unhelpful cognitions, and to teach skills to enhance caregiver support of their child. The final treatment component, enhancing safety, addresses strategies to reduce risk of revictimization, to increase the child’s sense of safety and self-efficacy, and plan for the future by teaching skills like safe, healthy sexual practices; body ownership (i.e., okay and not okay touches); assertiveness; clear communication; and the development of safety plans. Because this is the last formal component of TF-CBT, sufficient time and attention should be paid to ending treatment in a positive manner. This includes helping the child to identify what has been learned in therapy; planning for the future, which help contextualize the traumatic event and help the child attain closure; and reviewing with the child and caregiver signs that may indicate need for a future booster session. Empirical support for TF-CBT.  TF-CBT has undergone extensive empirical investigation, including multiple randomized clinical trials (RCTs) demonstrating reductions in trauma-related symptoms for young children (e.g., Konanur et al., 2015; Thornback & Muller, 2015) and adolescents (e.g., Cohen et al., 2016; O’Callaghan et al., 2013), within and outside of the United States. There have been several systematic reviews of interventions for this population, and TF-CBT consistently emerges as the one with the most significant empirical support (e.g., Cary & McMillen, 2012; de Arellano et al., 2014; Dorsey et al., 2011; Silverman et al., 2008; Wethington et al., 2008).

Targeting a Specific Trauma: Child Physical Abuse Alternatives for families: A cognitive–behavioral therapy.  Alternatives for families (AF-CBT; Kolko, 520

1996b; Kolko, Fitzgerald, & Laubach, 2014) is a theory-driven intervention for families with children ages 5 to 17 who exhibit or are at risk for problems with anger, aggression, and/or child physical abuse. AF-CBT seeks to improve family relationships using a comprehensive family-centered approach that targets the risks for and clinical consequences of exposure to conflict and coercion. AF-CBT focuses on decreasing family conflict by reducing hostile, coercive parent–child interactions and targeting child behavior problems and risk for child physical abuse. The intervention also aims to improve child social competence and child safety/welfare. AF-CBT uses individual and joint child–caregiver treatment sessions. Treatment usually occurs weekly for a 6- to 9-month period; however, the duration of treatment and number of sessions vary, due to factors like the complexity of the case and the resources available. AF-CBT has been applied in outpatient clinics, homes, residential treatment programs, hospitals, schools, and other community-based settings. Brief overview of AF-CBT components.  AF-CBT consists of 12 treatment methods or “steps” that are organized in three phases: engagement and psychoeducation, individual skill-building, and family applications. Phase 1 includes alliance building between the therapist and family, psychoeducation, and discussion of family experiences. Phase 2 emphasizes emotion regulation, cognitive restructuring, positive parenting, assertiveness and social skills, behavior management, and imaginal exposure. Phase 3 emphasizes positive family interactions, including communication skills, enhancing safety through clarification, and family problem solving. Empirical support for AF-CBT.  Research has demonstrated that AF-CBT is associated with reductions in child aggression, caregiver use of force, parental anger, parenting distress, family conflict, and improved family cohesion (Kolko, 1996a, 1996b). Additional research supports the sustainability and clinical benefits of AF-CBT in community practice settings (Kolko, Baumann, Herschell, Hart, & Wisniewski, 2012; Kolko, Iselin, & Gully, 2011). Parent–child interaction therapy.  Parent–child interaction therapy (PCIT) is an empirically supported treatment, originally developed for children

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ages 2 to 7 years, to treat oppositional, defiant, and other externalizing behavior problems (Eyberg et al., 2001). It has been adapted and shown to be effective for physically abused and traumatized children ages 4 to 12 whose main presentation is externalizing behavior problems (Chaffin et al., 2004; Hakman et al., 2009). The overall goal of PCIT is to improve the quality of the parent–child relationship, increase child prosocial behaviors while decreasing problematic behaviors, and to enhance parenting skills while decreasing parenting distress. Brief overview of PCIT components.  In the first phase of treatment, parents learn positive parenting skills (known as the PRIDE skills: praise, reflections, imitation, descriptions, and enthusiasm) and are coached live through a transmitter and receiver system as they interact with their child. The goal is to increase the positive interaction and overall warmth in the dyad and to change negative parent–child patterns. During the second phase of treatment, parents are instructed and coached in a positive discipline program focused on effective delivery of commands and appropriate response to child compliance. Daily homework sessions (10 min) are designed to reinforce skills acquired in session. A free web course, entitled PCIT for Traumatized Children, can be accessed via the University of California Davis website (http://pcit.ucdavis.edu/pcit-web-course). Empirical support for PCIT.  PCIT has undergone extensive empirical investigation, including multiple RCTs, on the effectiveness of improving parent and child outcomes following engagement (Eyberg et al., 2001; Hood & Eyberg, 2003; Thomas & ZimmerGembeck, 2007). An RCT has also demonstrated effectiveness of PCIT in trauma-exposed families, as parents assigned to PCIT had fewer subsequent reports of physical abuse following completion of PCIT (Chaffin et al., 2004).

Targeting a Specific Trauma: Grief TF-CBT for traumatic grief.  The sudden and/ or unexpected death of a loved one because of homicide, suicide, or fatal accident can result in a combination of grief and posttraumatic stress symptoms, which extend beyond a typical grief reaction. Child traumatic grief (CTG) is a modification

of TF-CBT, in which each treatment component is augmented with interventions that specifically address loss and grief (http://www.ctg.musc.edu; Cohen, Mannarino, & Knudsen, 2004; Cohen, Mannarino, & Staron, 2006). Techniques (e.g., creating letters to the deceased) can assist with this emotional expression. Preserving positive memories incorporates strategies, including making a memory box, a memory book, a collage, or another type of memorial to help the child focus on positive aspects of the relationship with the loved one following his or her death. Redefining the relationship helps the child to move from relating to their loved one through direct interactions to a relationship of memories. This process supports commitment to present relationships and helps children deal with difficult emotions (e.g., guilt) related to “moving on” or experiencing positive emotions in present relationships. Although there have only been two pilot studies (Cohen et al., 2004, 2006) to provide preliminary effectiveness data for CTG, it is important to note that this is an augmentation of an empirically sound intervention and not meant to be used without significant expertise and knowledge in the delivery of TF-CBT. Grief and trauma intervention for children.  Grief and trauma intervention (GTI) for children (Salloum & Overstreet, 2008, 2012) was specifically designed for children ages 7 to 12 with posttraumatic stress due to witnessing or being a direct victim of one or more types of violence or a disaster, or due to experiencing or witnessing the death of a loved one, including death by homicide. The purpose of the intervention is to improve symptoms of posttraumatic stress, depression, and traumatic grief. The intervention is conducted with children in a group or individual format in 10 sessions of approximately 1 hour each. One session is conducted with parents. Children participating in group sessions attend an additional session conducted one-on-one. The intervention uses developmentally appropriate methods, including art, drama, and play; an ecological perspective; and culturally relevant approaches, especially regarding death rituals, spiritual beliefs, coping strategies, historical occurrences, and the child’s language. Sessions address topics that are 521

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common to children who are experiencing grief and trauma, including dreams (nightmares), questioning, anger, and guilt. Brief overview of GTI components.  The techniques used in the treatment sessions are grounded in CBT and narrative therapy and include narrative exposure to the trauma (through drawing, discussing, and writing), development of an in-depth, coherent narrative while eliciting the child’s thoughts and feelings, development of positive coping strategies, and making meaning of losses. GTI for children has been used in various community-based settings, including schools, afterschool programs, and community centers. Empirical support for GTI.  Data from two clinical trials indicated positive treatment outcome effects for GTI. In the first study, children experiencing posttraumatic stress symptoms following Hurricane Katrina were randomly assigned to individual vs. group GTI. Findings indicated that children in both conditions evidenced significant reductions in symptoms, with no differences because of format (Salloum & Overstreet, 2008). The second study was conducted 3 years after Katrina and involved children who were exposed to hurricane-related stressors and other potentially traumatic events and who were reporting posttraumatic stress symptoms (Salloum & Overstreet, 2012). This involved randomization to one of two group treatment conditions: a standard intervention and one that included the cognitive–behavioral skills only (not the trauma narrative processing). Children in both conditions had significant reductions in posttraumatic stress symptoms from pre- to posttreatment and at two follow up assessments (3 and 12 months). No significant differences were noted in the two treatment conditions. The authors noted it was possible that children may benefit from coping skills training alone, but further research is needed to determine whether children experiencing high levels of trauma-related distress would evidence greater improvement with the inclusion of trauma processing (Salloum & Overstreet, 2012).

Targeting Young Children Child–parent psychotherapy.  Child–parent psychotherapy (CPP; Lieberman, Van Horn, & Ippen, 522

2005) is a relationship-based approach for infants, toddlers, and preschoolers following traumatic event exposure, as well as other concerns including parental mental illness, maladaptive parenting practices, and/or poor parent–child temperamental styles. CPP is based in attachment theory and integrates psychodynamic, developmental, social learning, and cognitive–behavioral theories. Joint child–parent sessions (50 sessions and intensive case management) aim to improve the parent–child relationship via child modulation and integration of affect, supported by the parent’s increased ability to respond in emotionally and developmentally appropriate ways. Specifically, sessions focus on safety, affect regulation, improving the child–parent relationship, normalization of trauma-related responses, and development of a trauma narrative. Brief overview of CPP components.  Goals of CPP include addressing safety (e.g., concerns in the environment, promoting safe behavior, limit setting, child–parent roles), affect regulation (e.g., labeling affective experiences, improving the parent’s ability to respond to emotions in helpful ways, strategies for affect regulation); reciprocity in relationships (e.g., love and understanding for one another, expression of positive and negative feelings); making meaning of the trauma (e.g., the parent acknowledges and understands the child’s traumatic experience, perspectives and memories of the event); developmental guidance; establishing links between past experiences and current thoughts, feelings, and behaviors; developing the joint narrative; and reinforcing behaviors that help recovery; and continuity of daily living (e.g., encouraging prosocial behavior, engaging in appropriate activities, establishing daily routines). These goals are met via techniques such as play, physical contact, and language; unstructured/reflective developmental guidance; modeling protective behaviors; linking the past and present; emotional support; and concrete assistance, case management, and crisis intervention. Empirical support for CPP.  Empirical support for CPP has been established via RCTs (Cicchetti, Rogosch, & Toth, 2006; Lieberman et al., 2005; Toth, Maughan, Manly, Spagnola, & Cicchetti, 2002), as well as studies involving high-risk samples (Cicchetti, Rogosch, & Toth, 2000; Cicchetti,

Treatment of Trauma in Children and Adolescents

Toth, & Rogosch, 1999; Lieberman, Weston, & Pawl, 1991; Toth, Rogosch, Manly, & Cicchetti, 2006). Studies have demonstrated the effectiveness of CPP in improving various child and caregiver outcomes, including child and maternal PTSD symptoms, child behavior problems, attachment, and maternal mental health symptoms.

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Targeting Adolescents Risk reduction through family therapy.  Risk reduction through family therapy (RRFT; Danielson, 2014) is the only exposure-based, integrated treatment, specifically tailored for adolescents to reduce PTSD and comorbid substance abuse problems, with published empirical support. RRFT uses an ecologically based approach to address the multiple and diverse outcomes in adolescents that follow exposure to trauma, including PTSD, substance use problems, risky sexual behavior, nonsuicidal self-injurious behavior (e.g., cutting), and suicidal behavior. Simply stated, the treatment targets multiple systems and the interaction among these systems, including children, family, peer group, school, and community. Goals of treatment are to identify risk and protective factors at each system level, with the aim of reducing the risk factors (e.g., decreasing time spent with substance using peers) and strengthening the protective factors (improving family communication). Assessment, consideration, and incorporation of cultural factors are emphasized in RRFT by examining the ways in which these factors can influence learning of skills and achievement of treatment goals. RRFT includes components from existing empirically supported treatments, including TF-CBT and multisystemic therapy (Henggeler, 1999). Brief overview of RRFT components.  There are seven primary treatment components of RRFT: psychoeducation, coping, family communication, substance abuse, PTSD, healthy sexual decision making, and sexual revictimization risk reduction. Of note, RRFT is meant to be flexible and tailored to meet the individual needs of the child. As such, it is not a manual-driven protocol; therapists are advised to administer the psychoeducation and coping modules prior to the exposure work in the PTSD component, but otherwise components are to

be delivered in a flexible order. Substance use and PTSD symptoms are monitored throughout RRFT using standardized assessment tools to help track treatment progress and guide clinical decision making. The average frequency and duration of RRFT depends on the symptom level of each child, but averages 24 weekly, 60–90 min sessions with periodic check-ins (by phone, text, etc.) between scheduled appointments. RRFT clinicians typically meet with children and caregivers individually, as well as in joint sessions. Empirical support for RRFT.  Published results from an open pilot trial and a RCT support the efficacy of RRFT (Danielson et al., 2010, 2012). A large scale RCT is underway and near completion, with 104 adolescent participants (40% ethnic/ racial minorities). Preliminary results from this trial (Danielson, Adams, Chapman, et al., 2015; Danielson, Adams, de Arellano, et al., 2015) provided additional empirical support of RRFTs effectiveness with regard to substance use problems, child- and caregiver-reported PTSD, risky sexual behaviors, and family-related factors (e.g., inconsistent discipline), in comparison to treatment as usual (i.e., a trauma-focused treatment being delivered in the child advocacy centers in which the RCT is taking place). Research that included feedback from families and clinicians demonstrated a preference for this type of integrated treatment approach for adolescents (Adams, McCauley, et al., 2016).

Targeting Adolescents—Individual vs. Group Sessions Trauma affect regulation: Guide for education and therapy.  Trauma affect regulation: guide for education and therapy (TARGET; Ford, 2015; Ford & Russo, 2006) treats PTSD in children (ages 10–18) and adults by helping them to improve emotion regulation and gain control of reactions to posttraumatic stress. TARGET can be delivered through group or individual therapy in 12 sessions, and has three main goals: to provide education about the biological and behavioral components of PTSD, to improve emotion processing and self-regulation skills, and to develop an autobiographical narrative that describes the trauma and PTSD. 523

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Brief overview of TARGET components.  The model includes 7 treatment components that comprise the acronym FREEDOM (Focus, Recognize, Emotions, Evaluate, Define, Option, and Make a contribution) divided into three phases: stabilization and self-regulation, trauma processing, and extension of therapy. This last phase incorporates the learning from Phases 1 and 2 into lifestyle, values, goals, and plans. In Phase 1, the Focus component aims to reduce anxiety and increase mental alertness. In Phase 2, the Recognize component contains an activity to help individuals recognize specific stress triggers, and the Emotion component includes identification of primary feelings. The Evaluate component involves individuals evaluating main thoughts and self-statements that were identified in previous components. In the Define component, children and adolescents determine and define their main personal goals; and in the Option component, they identify one choice that represents a successful step toward that main goal. Finally, in Phase 3, the Make a contribution component involves children and adolescents recognizing how that option reflects their core values and makes a difference in others’ lives. Empirical support for TARGET.  Empirical support has been established for TARGET in two studies of children. In a 3-year RCT, adolescent girls who received TARGET had reductions in avoidance/ numbing and intrusive/reexperiencing symptoms compared with those in a gender-specific relational therapy (Ford, Steinberg, Hawke, Levine, & Zhang, 2012). In a second study involving children in a juvenile detention facility, TARGET was associated with a decrease in the number of reported disciplinary incidents, decrease in disciplinary sanctions, and lack of recidivism (Ford & Hawke, 2012). In addition, children with severe trauma histories and trauma-related symptoms had 50% greater benefits.

Service Setting—Schools Cognitive–behavioral intervention for trauma in schools.  Cognitive–behavioral intervention for trauma in schools (CBITS; Stein, Elliott, et al., 2003; Stein, Jaycox, et al., 2003) is a school-based group and individual intervention that targets symptoms of PTSD, depression, general anxiety, and behavioral 524

problems; improves peer and parent support; and enhances coping skills among students exposed to traumatic events. Although typically delivered in schools, it also has been used in additional settings, including mental health clinics. Brief overview of CBITS components.  CBITS is a cognitive–behavioral intervention, which uses similar components to that of TF-CBT (i.e., psychoeducation, relaxation, social problem solving, cognitive restructuring, imaginal exposure, exposure to trauma reminders, and development of a trauma narrative). The program includes 10 group sessions and one to three individual sessions for students, two parent psychoeducational sessions, and one teacher educational session. Empirical support for CBITS.  CBITS has undergone examination in a few studies, including a RCT examining PTSD and depression symptoms following engagement in CBITS (Stein, Jaycox, et al., 2003). CBITS has been implemented and found effective with racially and ethnically diverse children, including Latino immigrant children (Kataoka et al., 2003). Most studies have involved children in Grades 3 through 8, although it has been implemented with high school students as well. Additional Treatment Considerations In addition to providing an overview of several selected treatment interventions that target different populations, trauma types, and service settings, there are several additional issues to highlight regarding provision of treatment for children and adolescents who have experienced trauma. First, because of the high prevalence of trauma exposure and the potential severity and longevity of trauma-related symptoms, screening for trauma history and trauma-related symptoms is critical for any child presenting for mental health treatment services. Children and/or caregivers may not spontaneously endorse trauma exposure, nor identify trauma-related symptoms, and research indicates that a significant number of children seen in mental health service settings have experienced a traumatic event, which is not disclosed (Broman-Fulks et al., 2007). Additionally, trauma-related symptoms often

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mirror and/or overlap with other common behavioral health disorders, including attention deficit disorder, conduct disorder, depression, and anxiety (Ford et al., 2000; Kilpatrick, Ruggiero, et al., 2003), which can lead to inaccurate or incomplete diagnoses and subsequent selection of treatment interventions which do not adequately address the child’s needs. Thus, screening and further assessment, as indicated, increase the likelihood for accurate diagnosis and appropriate treatment selection. Relatedly, not all children and adolescents who experience trauma will need mental health treatment, which further emphasizes the need for screening. Research indicates that certain individual level variables (e.g., age, gender, race/ethnicity) and characteristics of the traumatic event can increase the likelihood of trauma-related problems. For example, trauma prevalence generally increases with age, women are more likely than men to experience sexual assault, and rates of trauma exposure vary across racial/ethnic and sexual minority groups (e.g., Andrews et al., 2015; Breslau et al., 2004; Hatch & Dohrenwend, 2007; Roberts et al., 2011). Heightened risk for trauma-related problems is also associated with age, gender, and race/ethnicity, as well as prior or comorbid mental health problems and exposure to multiple traumatic events (e.g., Finkelhor et al., 2015; Hanson et al., 2008; Kilpatrick, Saunders, & Smith, 2003). Incident characteristics like the severity of the event(s), including whether or not the child experienced an injury and/or saw someone else injured; perception of life threat; relationship to the perpetrator; and penetration sexual assault increase risk for mental health problems (Kilpatrick, Saunders, & Smith, 2003). A second issue is the importance of caregiver involvement, which is not specific to this population (Dowell & Ogles, 2010). Studies are somewhat mixed on the role of caregivers as a core factor in treatment outcomes, with some indicating that such involvement may decrease certain negative child outcomes (e.g., anxiety, depression; e.g., BarkerCollo & Read, 2003; Barmish & Kendall, 2005; Cohen, Berliner, & Mannarino, 2010; Deblinger et al., 2011; Lovett, 2004; Manassis et al., 2014; Silverman et al., 2008; Zajac, Ralston, & Smith, 2015), whereas others suggest this may not be necessary

for reducing posttraumatic stress symptoms specifically (Silverman et al., 2008). However, studies do indicate that such involvement has a positive impact on the caregiver’s own well-being (e.g., Deblinger, Mannarino, Cohen, Runyon, & Steer, 2011), that this may be particularly important for young children (e.g., Scheeringa, Weems, et al., 2011) and that it can help to generalize skills (e.g., positive coping strategies; Mendlowitz et al., 1999; Spence, Donovan, & Brechman-Toussaint, 2000) outside of the therapy session. Despite these mixed findings, the involvement of a supportive, nonoffending caregiver may be especially salient with this population, as trauma often impacts the entire family. However, caregiver engagement poses a significant challenge for mental health providers (e.g., Cary & McMillen, 2012; de Arellano et al., 2014; Hanson et al., 2014), making it important to highlight the expectations and role of the caregiver at the onset of treatment. Further, use of empirically supported strategies that address logistical (e.g., transportation, child care) and perceptual (stigma related to mental health, mistrust in the mental health system) barriers can help to improve ongoing engagement (McKay & Bannon, 2004; McKay et al., 2005). A third issue centers on selection and/or tailoring of interventions and treatment materials that are specific to a child’s developmental level and that are culturally sensitive, with respect to factors like race/ethnicity, gender, and sexual orientation. For example, the effectiveness of TF-CBT has been demonstrated across a wide age range (e.g., Cohen et al., 2016; Konanur et al., 2015; O’Callaghan et al., 2013; Scheeringa, Weems, et al., 2011; Thornback & Muller, 2015) and with diverse populations (e.g., Jensen et al., 2014; Murray et al., 2013, 2015; O’Callaghan et al., 2013). The caveat is that components and materials are individually tailored. For young children, play based techniques that are clearly directed by the therapist can help to facilitate development and processing of the child’s traumatic events (Drewes & Cavett, 2012), and caregiver involvement may be especially important for this age group. Adolescents are more likely to present with complex trauma histories and are at higher risk for substance use/abuse, self-harm/self-injurious behaviors, and risky sexual behaviors, all of which should 525

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be assessed for in the initial screening process and integrated into treatment when possible. Evidencebased treatments, such as RRFT and TARGET, have been developed for this population and should be strongly considered as a first line intervention. Data generally indicate that in most cases, rather than continuing to develop new, idiosyncratic treatment interventions, modifications, and enhancements to existing evidence-based treatments result in positive outcomes. Nonetheless, as noted in several recent reviews (e.g., de Arellano et al., 2014; Silverman et al., 2008), research on the effectiveness of trauma-focused interventions across diverse populations is an important area for future research.

which should be assessed for in the initial screening process and integrated into treatment when possible. Screening and Assessment: ■■

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Conclusion We provided an overview of trauma prevalence and its impact, highlighted several interventions as examples of those for specific age groups, trauma types, service settings, and treatment modalities, and outlined pertinent trauma-related assessment and treatment issues. Next, we offer key take home points to maximize positive outcomes for children and adolescents experiencing trauma-related behavioral health problems.

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Treatment Selection: ■■

Prevalence and Characteristics of Traumatic Stress: ■

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An estimated 40% to 80% of children and adolescents experience some type of traumatic event during their lifetime. Trauma prevalence generally increases with age, women are more likely than men to experience sexual assault, and rates of trauma exposure vary across racial/ethnic and sexual minority groups. Heightened risk for trauma-related problems is also associated with factors like age, gender, and race/ethnicity, as well as prior or comorbid mental health problems and exposure to multiple traumatic events. Young children do not always manifest their distress in the form of intrusive thoughts; overt behaviors may be more predictive of distress. Adolescents are more likely to present with complex trauma histories and are at higher risk for substance use/abuse, self-harm/self-injurious behaviors, and risky sexual behaviors, all of

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Screening for trauma history and trauma-related symptoms is critical for any child presenting for mental health treatment services, regardless of presenting problem(s). Children may develop several psychological disorders, the most common of which include PTSD, depression, anxiety, and behavioral disorders, which need to be comprehensively addressed. Ongoing assessment should be conducted throughout the treatment process. Although studies regarding caregiver involvement are mixed, ongoing engagement of the caregiver may be beneficial and often is critical to treatment success, especially for younger children.

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Most treatments with empirical support for trauma populations are CBTs with various cross-cutting elements including psychoeducation about trauma and its impact (e.g., PTSD); affective modulation skills (e.g., relaxation, controlled breathing); gradual exposure to trauma memories; and cognitive processing to address unhelpful and/or inaccurate cognitions (e.g., guilt, self-blame). Specific treatments are available for different presenting types of trauma, for delivery in different settings, and for individual versus group implementation. When selecting a treatment, it is important for the clinician to be familiar with the level of empirical support. Online databases like NREPP, CEBC, and NCTSN provide descriptions of trauma-focused interventions, along with information about their targeted population, and other pertinent details, including cultural relevance, clinician requirements, availability of treatment manuals, and availability of training. Selection and/or tailoring of interventions and treatment materials that are specific to a child’s

Treatment of Trauma in Children and Adolescents

developmental level and that are culturally sensitive with respect to race/ethnicity, gender, and sexual orientation is crucial.

References

Copyright American Psychological Association. Not for further distribution.

Adams, Z. W., McCauley, J. L., Back, S. E., Flanagan, J. C., Hanson, R. F., Killeen, T. K., & Danielson, C. K. (2016). Clinician perspectives on treating adolescents with co-occurring posttraumatic stress disorder, substance use, and other problems. Journal of Child and Adolescent Substance Abuse, 25, 575–583. http://dx.doi.org/10.1080/1067828X.2016.1153555

treatment. Trauma, Violence, and Abuse, 4, 95–111. http://dx.doi.org/10.1177/1524838002250760 Barmish, A. J., & Kendall, P. C. (2005). Should parents be co-clients in cognitive–behavioral therapy for anxious youth? Journal of Clinical Child and Adolescent Psychology, 34, 569–581. http://dx.doi.org/ 10.1207/s15374424jccp3403_12 Batejan, K. L., Jarvi, S. M., & Swenson, L. P. (2015). Sexual orientation and non-suicidal self-injury: A meta-analytic review. Archives of Suicide Research, 19, 131–150. http://dx.doi.org/10.1080/13811118. 2014.957450

Adams, Z. W., Moreland, A., Cohen, J. R., Lee, R. C., Hanson, R. F., Danielson, C. K., . . . Briggs, E. C. (2016). Polyvictimization: Latent profiles and mental health outcomes in a clinical sample of adolescents. Psychology of Violence, 6, 145–155. http://dx.doi.org/ 10.1037/a0039713

Begle, A. M., Hanson, R. F., Danielson, C. K., McCart, M. R., Ruggiero, K. J., Amstadter, A. B., . . . Kilpatrick, D. G. (2011). Longitudinal pathways of victimization, substance use, and delinquency: Findings from the National Survey of Adolescents. Addictive Behaviors, 36, 682–689. http://dx.doi.org/ 10.1016/j.addbeh.2010.12.026

Almazan, E. P., Roettger, M. E., & Acosta, P. S. (2014). Measures of sexual minority status and suicide risk among young adults in the United States. Archives of Suicide Research, 18, 274–281. http://dx.doi.org/ 10.1080/13811118.2013.824832

Bernardon, S., & Pernice-Duca, F. (2010). A family systems perspective to recovery from posttraumatic stress in children. Family Journal, 18, 349–357. http://dx.doi.org/10.1177/1066480710376618

Álvarez-Lister, M. S., Pereda, N., Abad, J., & Guilera, G. (2014). Polyvictimization and its relationship to symptoms of psychopathology in a southern European sample of adolescent outpatients. Child Abuse and Neglect, 38, 747–756. http://dx.doi.org/ 10.1016/j.chiabu.2013.09.005 American Psychiatric Association. (2013). Diagnostic and statistical manual of mental disorders (5th ed.). Washington, DC: Author. Anda, R. F., Felitti, V. J., Bremner, J. D., Walker, J. D., Whitfield, C., Perry, B. D., . . . Giles, W. H. (2006). The enduring effects of abuse and related adverse experiences in childhood. A convergence of evidence from neurobiology and epidemiology. European Archives of Psychiatry and Clinical Neuroscience, 256, 174–186. http://dx.doi.org/10.1007/s00406-005-0624-4 Andrews, A. R., III, Jobe-Shields, L., López, C. M., Metzger, I. W., de Arellano, M. A., Saunders, B., & Kilpatrick, D. G. (2015). Polyvictimization, income, and ethnic differences in trauma-related mental health during adolescence. Social Psychiatry and Psychiatric Epidemiology, 50, 1223–1234. http:// dx.doi.org/10.1007/s00127-015-1077-3 Angold, A., Costello, E. J., Messer, S. C., Pickles, A., Winder, F., & Silver, D. (1995). Development of a short questionnaire for use in epidemiological studies of depression in children and adolescents. International Journal of Methods in Psychiatric Research, 5, 237–249. Barker-Collo, S., & Read, J. (2003). Models of response to childhood sexual abuse: Their implications for

Birmaher, B., Brent, D. A., Chiappetta, L., Bridge, J., Monga, S., & Baugher, M. (1999). Psychometric properties of the Screen for Child Anxiety Related Emotional Disorders (SCARED): A replication study. Journal of the American Academy of Child and Adolescent Psychiatry, 38, 1230–1236. http://dx.doi.org/ 10.1097/00004583-199910000-00011 Bonanno, G. A., & Mancini, A. D. (2008). The human capacity to thrive in the face of potential trauma. Pediatrics, 121, 369–375. http://dx.doi.org/10.1542/ peds.2007-1648 Breslau, N., Wilcox, H. C., Storr, C. L., Lucia, V. C., & Anthony, J. C. (2004). Trauma exposure and posttraumatic stress disorder: A study of youths in urban America. Journal of Urban Health, 81, 530–544. http://dx.doi.org/10.1093/jurban/jth138 Broman-Fulks, J. J., Ruggiero, K. J., Hanson, R. F., Smith, D. W., Resnick, H. S., Kilpatrick, D. G., & Saunders, B. E. (2007). Sexual assault disclosure in relation to adolescent mental health: Results from the National Survey of Adolescents. Journal of Clinical Child and Adolescent Psychology, 36, 260–266. http://dx.doi.org/ 10.1080/15374410701279701 Cary, C. E., & McMillen, J. C. (2012). The data behind dissemination: A systematic review of traumafocused cognitive behavioral therapy for use with children and youth. Children and Youth Services Review, 34, 748–757. http://dx.doi.org/10.1016/ j.childyouth.2012.01.003 Chaffin, M, & Friedrich, B. (2004). Evidence-based treatments in child abuse and neglect. Child Youth Services Review, 26, 1097–1113. 527

Hanson, Moreland, and Orengo-Aguayo

Chaffin, M., Silovsky, J. F., Funderburk, B., Valle, L. A., Brestan, E. V., Balachova, T., . . . Bonner, B. L. (2004). Parent–child interaction therapy with physically abusive parents: Efficacy for reducing future abuse reports. Journal of Consulting and Clinical Psychology, 72, 500–510. http://dx.doi.org/ 10.1037/0022-006X.72.3.500

Copyright American Psychological Association. Not for further distribution.

Chowdhury, U., & Pancha, A. (2011). Post-traumatic stress disorder in children and adolescents. Community Practitioner, 84, 33–35. Cicchetti, D., Rogosch, F. A., & Toth, S. L. (2000). The efficacy of toddler-parent psychotherapy for fostering cognitive development in offspring of depressed mothers. Journal of Abnormal Child Psychology, 28, 135–148. http://dx.doi.org/10.1023/A:1005118713814 Cicchetti, D., Rogosch, F. A., & Toth, S. L. (2006). Fostering secure attachment in infants in maltreating families through preventive interventions. Development and Psychopathology, 18, 623–649. http://dx.doi.org/10.1017/S0954579406060329 Cicchetti, D., Toth, S. L., & Rogosch, F. A. (1999). The efficacy of toddler-parent psychotherapy to increase attachment security in offspring of depressed mothers. Attachment and Human Development, 1, 34–66. http://dx.doi.org/10.1080/ 14616739900134021 Cisler, J. M., Amstadter, A. B., Begle, A. M., Resnick, H. S., Danielson, C. K., Saunders, B. E., & Kilpatrick, D. G. (2011). A prospective examination of the relationships between PTSD, exposure to assaultive violence, and cigarette smoking among a national sample of adolescents. Addictive Behaviors, 36, 994–1000. http:// dx.doi.org/10.1016/j.addbeh.2011.05.014 Cisler, J. M., Begle, A. M., Amstadter, A. B., Resnick, H. S., Danielson, C. K., Saunders, B. E., & Kilpatrick, D. G. (2012). Exposure to interpersonal violence and risk for PTSD, depression, delinquency, and binge drinking among adolescents: Data from the NSA-R. Journal of Traumatic Stress, 25, 33–40. http://dx.doi.org/ 10.1002/jts.21672 Cohen, J. A. (2010). Practice parameter for the assessment and treatment of posttraumatic stress disorder in children and adolescents. Journal of the American Academy of Child and Adolescent Psychiatry, 49, 414–430. http://dx.doi.org/10.1016/j.jaac.2009.12.020 Cohen, J. A., Berliner, L., & Mannarino, A. (2010). Trauma focused CBT for children with co-occurring trauma and behavior problems. Child Abuse and Neglect, 34, 215–224. http://dx.doi.org/10.1016/ j.chiabu.2009.12.003 Cohen, J. A., Mannarino, A. P., & Deblinger, E. (2006). Treating trauma and traumatic grief in children and adolescents. New York, NY: Guilford Press. Cohen, J. A., Mannarino, A. P., Jankowski, K., Rosenberg, S., Kodya, S., & Wolford, G. L., II. (2016). A randomized 528

implementation study of trauma-focused cognitive behavioral therapy for adjudicated teens in residential treatment facilities. Child Maltreatment, 21, 156–167. http://dx.doi.org/10.1177/ 1077559515624775 Cohen, J. A., Mannarino, A. P., & Knudsen, K. (2004). Treating childhood traumatic grief: A pilot study. Journal of the American Academy of Child and Adolescent Psychiatry, 43, 1225–1233. http://dx.doi.org/ 10.1097/01.chi.0000135620.15522.38 Cohen, J. A., Mannarino, A. P., & Staron, V. R. (2006). A pilot study of modified cognitive–behavioral therapy for childhood traumatic grief (CBT-CTG). Journal of the American Academy of Child and Adolescent Psychiatry, 45, 1465–1473. http://dx.doi.org/ 10.1097/01.chi.0000237705.43260.2c Crouch, J. L., Hanson, R. F., Saunders, B. E., Kilpatrick, D. G., & Resnick, H. S. (2000). Income, race/ethnicity, and exposure to violence in youth: Results from the National Survey of Adolescents. Journal of Community Psychology, 28, 625–641. http://dx.doi.org/10.1002/ 1520-6629(200011)28:63.0.CO;2-R Danielson, C. K. (2014). Risk reduction through family therapy. In B. Reece, R. Hanson, & J. Sargent (Eds.), Treatment of child abuse: Common ground for mental health, medical, and legal professionals (2nd ed., pp. 154–167) Baltimore, MD: Johns Hopkins University Press. Danielson, C. K., Adams, Z., Chapman, J., McCart, M., Sheidow, A., & de Arellano, M. A. (2015, November). Characteristics and targets of treatment for adolescents with comorbid posttraumatic stress symptoms and substance use problems. In C. K. Danielson (Chair), Targets of integrated treatment approaches for comorbid mental health and substance use problems in teens and adults: Findings from four NIH-funded clinical trials. Symposium conducted at the meeting for the Association for Behavior and Cognitive Therapies, Chicago, IL. Danielson, C. K., Adams, Z., de Arellano, M., Saunders, B., McGuan, E., & Soltis, K. (2015, November). Tracking posttraumatic stress symptoms and substance use during the course of an integrated, exposure-based treatment with teens: An RRFT case series. In J. Flannigan & S. Back (Chairs), Treatment for comorbid PTSD and substance use disorders. Symposium conducted at the meeting for the Association for Behavior and Cognitive Therapies in Chicago, IL. Danielson, C. K., Macdonald, A., Amstadter, A. B., Hanson, R., de Arellano, M. A., Saunders, B. E., & Kilpatrick, D. G. (2010). Risky behaviors and depression in conjunction with—or in the absence of—lifetime history of PTSD among sexually abused adolescents. Child Maltreatment, 15, 101–107. http:// dx.doi.org/10.1177/1077559509350075

Copyright American Psychological Association. Not for further distribution.

Treatment of Trauma in Children and Adolescents

Danielson, C. K., McCart, M. R., de Arellano, M. A., Macdonald, A., Doherty, L. S., & Resnick, H. S. (2010). Risk reduction for substance use and trauma-related psychopathology in adolescent sexual assault victims: Findings from an open trial. Child Maltreatment, 15, 261–268. http://dx.doi.org/ 10.1177/1077559510367939

Eyberg, S. M., Funderburk, B. W., Hembree-Kigin, T. L., McNeil, C. B., Querido, J. G., & Hood, K. (2001). Parent-child interaction therapy with behavior problem children: One- and two-year maintenance of treatment effects in the family. Child and Family Behavior Therapy, 23, 1–20. http://dx.doi.org/ 10.1300/J019v23n04_01

Danielson, C. K., McCart, M. R., Walsh, K., de Arellano, M. A., White, D., & Resnick, H. S. (2012). Reducing substance use risk and mental health problems among sexually assaulted adolescents: A pilot randomized controlled trial. Journal of Family Psychology, 26, 628–635. http://dx.doi.org/10.1037/ a0028862

Fang, X., Brown, D. S., Florence, C. S., & Mercy, J. A. (2012). The economic burden of child maltreatment in the United States and implications for prevention. Child Abuse and Neglect, 36, 156–165. http:// dx.doi.org/10.1016/j.chiabu.2011.10.006

D’Augelli, A. R. (2002). Mental health problems among lesbian, gay and bisexual youths ages 14 to 21. Clinical Child Psychology and Psychiatry, 7, 433–456. http://dx.doi.org/10.1177/1359104502007003039 de Arellano, M. A., Lyman, D. R., Jobe-Shields, L., George, P., Dougherty, R. H., Daniels, A. S., . . . Delphin-Rittmon, M. E. (2014). Trauma-focused cognitive–behavioral therapy for children and adolescents: Assessing the evidence. Psychiatric Services, 65, 591–602. http://dx.doi.org/10.1176/ appi.ps.201300255 Deblinger, E., Mannarino, A. P., Cohen, J. A., Runyon, M. K., & Steer, R. A. (2011). Trauma-focused cognitive behavioral therapy for children: Impact of the trauma narrative and treatment length. Depression and Anxiety, 28, 67–75. http://dx.doi.org/ 10.1002/da.20744 Dembo, R., & Schmeidler, J. (2002). Family empowerment intervention: An innovative service for high-risk youths and their families. Binghamton, NY: Hawthorne Press. De Young, A. C., Kenardy, J. A., & Cobham, V. E. (2011). Diagnosis of posttraumatic stress disorder in preschool children. Journal of Clinical Child and Adolescent Psychology, 40, 375–384. http://dx.doi.org/ 10.1080/15374416.2011.563474 Dorsey, S., Briggs, E. C., & Woods, B. A. (2011). Cognitive–behavioral treatment for posttraumatic stress disorder in children and adolescents. Child and Adolescent Psychiatry Clinics of North America, 20, 255–269. http://dx.doi.org/10.1016/j.chc.2011.01.006 Dowell, K. A., & Ogles, B. M. (2010). The effects of parent participation on child psychotherapy outcome: A meta-analytic review. Journal of Clinical Child and Adolescent Psychology, 39, 151–162. http:// dx.doi.org/10.1080/15374410903532585 Drewes, A. A., & Cavett, A. M. (2012). Play applications and skills components. In J. A. Cohen, A. P. Mannarino, & E. Deblinger (Eds.), Traumafocused CBT for children and adolescents: Treatment applications (pp. 105–123). New York, NY: Guilford Press.

Feeny, N., Foa, E., Treadwell, K., & March, J. (2004). Posttraumatic stress disorder in youth: A critical review of the cognitive and behavioral treatment outcome literature. Professional Psychology: Research and Practice, 35, 466–476. http://dx.doi.org/ 10.1037/0735-7028.35.5.466 Finkelhor, D., Ormrod, R. K., & Turner, H. A. (2007). Polyvictimization and trauma in a national longitudinal cohort. Development and Psychopathology, 19, 149–166. http://dx.doi.org/ 10.1017/S0954579407070083 Finkelhor, D., Ormrod, R. K., & Turner, H. A. (2009). Lifetime assessment of polyvictimization in a national sample of children and youth. Child Abuse and Neglect, 33, 403–411. http://dx.doi.org/10.1016/ j.chiabu.2008.09.012 Finkelhor, D., Turner, H., Ormrod, R., & Hamby, S. L. (2009). Violence, abuse, and crime exposure in a national sample of children and youth. Pediatrics, 124, 1411–1423. http://dx.doi.org/10.1542/ peds.2009-0467 Finkelhor, D., Turner, H. A., Shattuck, A., & Hamby, S. L. (2013). Violence, crime, and abuse exposure in a national sample of children and youth: An update. JAMA Pediatrics, 167, 614–621. http://dx.doi.org/ 10.1001/jamapediatrics.2013.42 Finkelhor, D., Turner, H. A., Shattuck, A., & Hamby, S. L. (2015). Prevalence of childhood exposure to violence, crime, and abuse: Results from the National Survey of Children’s Exposure to Violence. JAMA Pediatrics, 169, 746–754. http://dx.doi.org/10.1001/ jamapediatrics.2015.0676 Foa, E. B., Johnson, K. M., Feeny, N. C., & Treadwell, K. R. (2001). The child PTSD Symptom Scale: A preliminary examination of its psychometric properties. Journal of Clinical Child Psychology, 30, 376–384. http://dx.doi.org/10.1207/ S15374424JCCP3003_9 Ford, J. (2015). An affective cognitive neurosciencebased approach to PTSD psychotherapy: The TARGET model. Journal of Cognitive Psychotherapy, 29, 68–91. http://dx.doi.org/10.1891/08898391.29.1.68

529

Hanson, Moreland, and Orengo-Aguayo

Ford, J. D., Elhai, J. D., Connor, D. F., & Frueh, B. C. (2010). Polyvictimization and risk of posttraumatic, depressive, and substance use disorders and involvement in delinquency in a national sample of adolescents. Journal of Adolescent Health, 46, 545–552. http://dx.doi.org/10.1016/ j.jadohealth.2009.11.212

Copyright American Psychological Association. Not for further distribution.

Ford, J. D., & Hawke, J. (2012). Trauma affect regulation psychoeducation group and milieu intervention outcomes in juvenile detention facilities. Journal of Aggression, Maltreatment, and Trauma, 21, 365–384. http://dx.doi.org/10.1080/10926771.2012.673538 Ford, J. D., Racusin, R., Ellis, C. G., Daviss, W. B., Reiser, J., Fleischer, A., & Thomas, J. (2000). Child maltreatment, other trauma exposure, and posttraumatic symptomatology among children with oppositional defiant and attention deficit hyperactivity disorders. Child Maltreatment, 5, 205–217. http:// dx.doi.org/10.1177/1077559500005003001 Ford, J. D., & Russo, E. (2006). Trauma-focused, present-centered, emotional self-regulation approach to integrated treatment for posttraumatic stress and addiction: Trauma adaptive recovery group education and therapy (TARGET). American Journal of Psychotherapy, 60, 335–355. Ford, J. D., Steinberg, K. L., Hawke, J., Levine, J., & Zhang, W. (2012). Randomized trial comparison of emotion regulation and relational psychotherapies for PTSD with girls involved in delinquency. Journal of Clinical Child and Adolescent Psychology, 41, 27–37. http://dx.doi.org/10.1080/15374416.2012.632343 Ford, J. D., Wasser, T., & Connor, D. F. (2011). Identifying and determining the symptom severity associated with polyvictimization among psychiatrically impaired children in the outpatient setting. Child Maltreatment, 16, 216–226. http:// dx.doi.org/10.1177/1077559511406109 Furr, J. M., Comer, J. S., Edmunds, J. M., & Kendall, P. C. (2010). Disasters and youth: A meta-analytic examination of posttraumatic stress. Journal of Consulting and Clinical Psychology, 78, 765–780. http://dx.doi.org/10.1037/a0021482 Gardner, W., Murphy, M., Childs, G., Kelleher, K., Pagano, M., Jellinek, M., . . . Sturner, R. (1999). The PSC-17: A brief pediatric symptom checklist with psychosocial problem subscales. A report from PROS and ASPN. Ambulatory Child Health, 5, 225–236. Gilboa-Schechtman, E., Foa, E. B., Shafran, N., Aderka, I. M., Powers, M. B., Rachamim, L., . . . Apter, A. (2010). Prolonged exposure versus dynamic therapy for adolescent PTSD: A pilot randomized controlled trial. Journal of the American Academy of Child and Adolescent Psychiatry, 49, 1034–1042. http://dx.doi.org/ 10.1016/j.jaac.2010.07.014

530

Goodman, R. (1997). The strengths and difficulties questionnaire: A research note. Journal of Child Psychology and Psychiatry, 38, 581–586. http:// dx.doi.org/10.1111/j.1469-7610.1997.tb01545.x Goodman, R., Meltzer, H., & Bailey, V. (1998). The strengths and difficulties questionnaire: A pilot study on the validity of the self-report version. European Child and Adolescent Psychiatry, 7, 125–130. http:// dx.doi.org/10.1007/s007870050057 Hakman, M., Chaffin, M., Funderburk, B., & Silovsky, J. F. (2009). Change trajectories for parent–child interaction sequences during parent–child interaction therapy for child physical abuse. Child Abuse and Neglect, 33, 461–470. http:// dx.doi.org/10.1016/j.chiabu.2008.08.003 Hanson, R. F., Borntrager, C., Self-Brown, S., Kilpatrick, D. G., Saunders, B. E., Resnick, H. S., & Amstadter, A. (2008). Relations among gender, violence exposure, and mental health: The National Survey of Adolescents. American Journal of Orthopsychiatry, 78, 313–321. http://dx.doi.org/10.1037/a0014056 Hanson, R. F., Gros, K. S., Davidson, T., Barr, S., Cohen, J., Deblinger, E., . . . Ruggiero, K. J. (2014). National trainers’ perspectives on challenges to implementation of an empirically supported mental health treatment. Administration and Policy in Mental Health and Mental Health Services Research, 41, 522–534. http://dx.doi.org/10.1007/s10488-013-0492-6 Hatch, S. L., & Dohrenwend, B. P. (2007). Distribution of traumatic and other stressful life events by race/ ethnicity, gender, SES, and age: A review of the research. American Journal of Community Psychology, 40, 313–332. http://dx.doi.org/10.1007/s10464-0079134-z Henggeler, S. W. (1999). Multisystemic therapy: An overview of clinical procedures outcomes, and policy implications. Child Psychology and Psychiatry Review, 4, 2–10. http://dx.doi.org/10.1017/ S1360641798001786 Hood, K. K., & Eyberg, S. M. (2003). Outcomes of parent–child interaction therapy: Mothers’ reports of maintenance three to six years after treatment. Journal of Clinical Child and Adolescent Psychology, 32, 419–429. http://dx.doi.org/10.1207/ S15374424JCCP3203_10 Jensen, T. K., Holt, T., Ormhaug, S. M., Egeland, K., Granly, L., Hoaas, L. C., . . . Wentzel-Larsen, T. (2014). A randomized effectiveness study comparing trauma-focused cognitive behavioral therapy with therapy as usual for youth. Journal of Clinical Child and Adolescent Psychology, 43, 356–369. Kataoka, S. H., Stein, B. D., Jaycox, L. H., Wong, M., Escudero, P., Tu, W., . . . Fink, A. (2003). A schoolbased mental health program for traumatized Latino immigrant children. Journal of the American Academy

Treatment of Trauma in Children and Adolescents

of Child and Adolescent Psychiatry, 42, 311–318. http://dx.doi.org/10.1097/00004583-200303000-00011 Kendall, P. C. (Ed.). (2012). Child and adolescent therapy: Cognitive–behavioral procedures (4th ed.). New York, NY: Guilford Press.

Copyright American Psychological Association. Not for further distribution.

Kilpatrick, D. G., Ruggiero, K. J., Acierno, R., Saunders, B. E., Resnick, H. S., & Best, C. L. (2003). Violence and risk of PTSD, major depression, substance abuse/ dependence, and comorbidity: Results from the National Survey of Adolescents. Journal of Consulting and Clinical Psychology, 71, 692–700. http://dx.doi.org/ 10.1037/0022-006X.71.4.692 Kilpatrick, D. G., & Saunders, B. E. (1995). National Survey of Adolescents in the United States, 1995. Retrieved from http://www.icpsr.umich.edu/ icpsrweb/ICPSR/studies/2833#dev Kilpatrick, D. G., Saunders, B. E., & Smith, D. W. (2003). Research in brief: Youth victimization: Prevalence and implications. Washington, DC: National Institute of Justice, U.S. Department of Justice.

Lieberman, A. F., Chu, A., Van Horn, P., & Harris, W. W. (2011). Trauma in early childhood: Empirical evidence and clinical implications. Development and Psychopathology, 23, 397–410. http://dx.doi.org/ 10.1017/S0954579411000137 Lieberman, A. F., Van Horn, P., & Ippen, C. G. (2005). Toward evidence-based treatment: Child-parent psychotherapy with preschoolers exposed to marital violence. Journal of the American Academy of Child and Adolescent Psychiatry, 44, 1241–1248. http:// dx.doi.org/10.1097/01.chi.0000181047.59702.58 Lieberman, A. F., Weston, D. R., & Pawl, J. H. (1991). Preventive intervention and outcome with anxiously attached dyads. Child Development, 62, 199–209. http://dx.doi.org/10.2307/1130715 Lovett, B. B. (2004). Child sexual abuse disclosure: Maternal response and other variables impacting the victim. Child and Adolescent Social Work Journal, 21, 355–371. http://dx.doi.org/10.1023/ B:CASW.0000035221.78729.d6

Kolko, D. J. (1996a). Clinical monitoring of treatment course in child physical abuse: Psychometric characteristics and treatment comparisons. Child Abuse and Neglect, 20, 23–43. http://dx.doi.org/ 10.1016/0145-2134(95)00113-1

Manassis, K., Lee, T. C., Bennett, K., Zhao, X. Y., Mendlowitz, S., Duda, S., . . . Wood, J. J. (2014). Types of parental involvement in CBT with anxious youth: A preliminary meta-analysis. Journal of Consulting and Clinical Psychology, 82, 1163–1172. http://dx.doi.org/10.1037/a0036969

Kolko, D. J. (1996b). Individual cognitive behavioral treatment and family therapy for physically abused children and their offending parents: A comparison of clinical outcomes. Child Maltreatment, 1, 322–342. http://dx.doi.org/10.1177/1077559596001004004

McKay, M. M., & Bannon, W. M., Jr. (2004). Engaging families in child mental health services. Child and Adolescent Psychiatry Clinics of North America, 13, 905–921. http://dx.doi.org/10.1016/j.chc.2004.04.001

Kolko, D. J., Baumann, B. L., Herschell, A. D., Hart, J. A., & Wisniewski, S. (2012). Implementation of AF-CBT by community practitioners serving child welfare and Mental Health: A randomized trial. Child Maltreatment, 17, 32–46. http://dx.doi.org/ 10.1177/1077559511427346 Kolko, D. J., Fitzgerald, M., & Laubach, J. (2014). Evidence-based practices for working with physically abusive families: Alternatives for families: A cognitive behavioral therapy. In R. M. Reece, R. F. Hanson, & J. Sargent (Eds.), Treatment of child abuse: Common ground for mental health, medical and legal practitioners (2nd ed., pp. 59–66). Baltimore, MD: Johns Hopkins University Press. Kolko, D. J., Iselin, A. M., & Gully, K. J. (2011). Evaluation of the sustainability and clinical outcome of Alternatives for families: A cognitive–behavioral therapy (AF-CBT) in a child protection center. Child Abuse and Neglect, 35, 105–116. http://dx.doi.org/ 10.1016/j.chiabu.2010.09.004 Konanur, S., Muller, R. T., Cinamon, J. S., Thornback, K., & Zorzella, K. P. (2015). Effectiveness of traumafocused cognitive behavioral therapy in a communitybased program. Child Abuse and Neglect, 50, 159–170. http://dx.doi.org/10.1016/j.chiabu.2015.07.013

McKay, M. M., Lynn, C. J., & Bannon, W. M. (2005). Understanding inner city child mental health need and trauma exposure: Implications for preparing urban service providers. American Journal of Orthopsychiatry, 75, 201–210. http://dx.doi.org/ 10.1037/0002-9432.75.2.201 McLaughlin, K. A., Greif Green, J., Gruber, M. J., Sampson, N. A., Zaslavsky, A. M., & Kessler, R. C. (2012). Childhood adversities and first onset of psychiatric disorders in a national sample of U.S. adolescents. JAMA Psychiatry, 69, 1151–1160. http:// dx.doi.org/10.1001/archgenpsychiatry.2011.2277 McLaughlin, K. A., Koenen, K. C., Hill, E. D., Petukhova, M., Sampson, N. A., Zaslavsky, A. M., & Kessler, R. C. (2013). Trauma exposure and posttraumatic stress disorder in a national sample of adolescents. Journal of the American Academy of Child and Adolescent Psychiatry, 52, 815–830.e14. http://dx.doi.org/ 10.1016/j.jaac.2013.05.011 Mendlowitz, S. L., Manassis, K., Bradley, S., Scapillato, D., Miezitis, S., & Shaw, B. E. (1999). Cognitive–behavioral group treatments in childhood anxiety disorders: The role of parental involvement. Journal of the American Academy of Child and Adolescent Psychiatry, 38, 1223–1229. http://dx.doi.org/ 10.1097/00004583-199910000-00010 531

Hanson, Moreland, and Orengo-Aguayo

Merikangas, K. R., He, J. P., Burstein, M., Swendsen, J., Avenevoli, S., Case, B., . . . Olfson, M. (2011). Service utilization for lifetime mental disorders in U.S. adolescents: Results of the National Comorbidity Survey-Adolescent Supplement (NCS-A). Journal of the American Academy of Child and Adolescent Psychiatry, 50, 32–45. http://dx.doi.org/10.1016/ j.jaac.2010.10.006

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Murray, L. K., Familiar, I., Skavenski, S., Jere, E., Cohen, J., Imasiku, M., . . . Bolton, P. (2013). An evaluation of trauma focused cognitive behavioral therapy for children in Zambia. Child Abuse and Neglect, 37, 1175–1185. http://dx.doi.org/10.1016/ j.chiabu.2013.04.017 Murray, L. K., Skavenski, S., Kane, J., Mayeya, J., Dorsey, S., Cohen, J., Michalopoulos, L. T. M., Imasiku, M., & Bolton, P. (2015). Effectiveness of trauma-focused cognitive behavioral therapy among trauma-affected children in Lusaka, Zambia: A randomized clinical trial. JAMA Pediatrics, 169, 761–769. http://dx.doi.org/ 10.1001/jamapediatrics.2015.0580 National Child Traumatic Stress Network. (2015). NCTSN position statement: Prerequisite clinical competencies for implementing effective, traumainformed intervention. Retrieved from http://nctsn. org/sites/default/files/assets/pdfs/nctsn_position_ statement_on_clinical_competency.pdf O’Callaghan, P., McMullen, J., Shannon, C., Rafferty, H., & Black, A. (2013). A randomized controlled trial of trauma-focused cognitive behavioral therapy for sexually exploited, war-affected Congolese girls. Journal of the American Academy of Child and Adolescent Psychiatry, 52, 359–369. http://dx.doi.org/ 10.1016/j.jaac.2013.01.013 Rheingold, A. A., Zinzow, H., Hawkins, A., Saunders, B. E., & Kilpatrick, D. G. (2012). Prevalence and mental health outcomes of homicide survivors in a representative U.S. sample of adolescents: Data from the 2005 National Survey of Adolescents. Journal of Child Psychology and Psychiatry, 53, 687–694. http:// dx.doi.org/10.1111/j.1469-7610.2011.02491.x Roberts, A. L., Gilman, S. E., Breslau, J., Breslau, N., & Koenen, K. C. (2011). Race/ethnic differences in exposure to traumatic events, development of posttraumatic stress disorder, and treatment-seeking for post-traumatic stress disorder in the United States. Psychological Medicine, 41, 71–83. http://dx.doi.org/ 10.1017/S0033291710000401 Salloum, A., & Overstreet, S. (2008). Evaluation of individual and group grief and trauma interventions for children post disaster. Journal of Clinical Child and Adolescent Psychology, 37, 495–507. http:// dx.doi.org/10.1080/15374410802148194 Salloum, A., & Overstreet, S. (2012). Grief and trauma intervention for children after disaster: Exploring coping skills versus trauma narration. Behaviour 532

Research and Therapy, 50, 169–179. http://dx.doi.org/ 10.1016/j.brat.2012.01.001 Saunders, B. E., & Adams, Z. W. (2014). Epidemiology of traumatic experiences in childhood. Child and Adolescent Psychiatry Clinics of North America, 23, 167–184. http://dx.doi.org/10.1016/j.chc.2013.12.003 Scheeringa, M. S., Myers, L., Putnam, F. W., & Zeanah, C. H. (2012). Diagnosing PTSD in early childhood: An empirical assessment of four approaches. Journal of Traumatic Stress, 25, 359–367. http://dx.doi.org/ 10.1002/jts.21723 Scheeringa, M. S., Weems, C. F., Cohen, J. A., AmayaJackson, L., & Guthrie, D. (2011). Trauma-focused cognitive–behavioral therapy for posttraumatic stress disorder in 3-through 6-year-old children: A randomized clinical trial. Journal of Child Psychology and Psychiatry, 52, 853–860. http://dx.doi.org/ 10.1111/j.1469-7610.2010.02354.x Scheeringa, M. S., Zeanah, C. H., & Cohen, J. A. (2011). PTSD in children and adolescents: Toward an empirically based algorithma. Depression and Anxiety, 28, 770–782. http://dx.doi.org/10.1002/da.20736 Scheeringa, M. S., Zeanah, C. H., Drell, M. J., & Larrieu, J. A. (1995). Two approaches to the diagnosis of posttraumatic stress disorder in infancy and early childhood. Journal of the American Academy of Child and Adolescent Psychiatry, 34, 191–200. http:// dx.doi.org/10.1097/00004583-199502000-00014 Scheeringa, M. S., Zeanah, C. H., Myers, L., & Putnam, F. W. (2003). New findings on alternative criteria for PTSD in preschool children. Journal of the American Academy of Child and Adolescent Psychiatry, 42, 561–570. http:// dx.doi.org/10.1097/01.CHI.0000046822.95464.14 Schneider, S. J., Grilli, S. F., & Schneider, J. R. (2013). Evidence-based treatments for traumatized children and adolescents. Current Psychiatry Reports, 15, 332. http://dx.doi.org/10.1007/s11920-012-0332-5 Sigel, B. A., Benton, A. H., Lynch, C. E., & Kramer, T. L. (2013). Characteristics of 17 statewide initiatives to disseminate trauma-focused cognitive–behavioral therapy (TF-CBT). Psychological Trauma: Theory, Research, Practice, and Policy, 5, 323–333. http:// dx.doi.org/10.1037/a0029095 Silverman, W. K., Ortiz, C. D., Viswesvaran, C., Burns, B. J., Kolko, D. J., Putnam, F. W., & Amaya-Jackson, L. (2008). Evidence-based psychosocial treatments for children and adolescents exposed to traumatic events. Journal of Clinical Child and Adolescent Psychology, 37, 156–183. http://dx.doi.org/ 10.1080/15374410701818293 Spence, S. H., Donovan, C., & Brechman-Toussaint, M. (2000). The treatment of childhood social phobia: The effectiveness of a social skills trainingbased, cognitive–behavioural intervention, with and without parental involvement. Journal of Child

Treatment of Trauma in Children and Adolescents

Psychology and Psychiatry and Allied Disciplines, 41, 713–726. http://dx.doi.org/10.1111/14697610.00659 Stein, B. D., Elliott, M. N., Tu, W., Jaycox, L. H., Kataoka, S. H., Fink, A., & Wong, M. (2003). School-based intervention for children exposed to violence [Reply]. JAMA, 290, 2542. http://dx.doi.org/10.1001/ jama.290.19.2542-a

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Stein, B. D., Jaycox, L. H., Kataoka, S. H., Wong, M., Tu, W., Elliott, M. N., & Fink, A. (2003). A mental health intervention for schoolchildren exposed to violence: A randomized controlled trial. JAMA, 290, 603–611. http://dx.doi.org/10.1001/jama.290.5.603 Steinberg, A. M., Brymer, M. J., Decker, K. B., & Pynoos, R. S. (2004). The University of California at Los Angeles post-traumatic stress disorder reaction index. Current Psychiatry Reports, 6, 96–100. http:// dx.doi.org/10.1007/s11920-004-0048-2 Stoddard, J. P., Dibble, S. L., & Fineman, N. (2009). Sexual and physical abuse: A comparison between lesbians and their heterosexual sisters. Journal of Homosexuality, 56, 407–420. http://dx.doi.org/ 10.1080/00918360902821395 Talley, A. E., Hughes, T. L., Aranda, F., Birkett, M., & Marshal, M. P. (2014). Exploring alcoholuse behaviors among heterosexual and sexual minority adolescents: Intersections with sex, age, and race/ethnicity. American Journal of Public Health, 104, 295–303. http://dx.doi.org/10.2105/ AJPH.2013.301627 Taylor, T. L., & Chemtob, C. M. (2004). Efficacy of treatment for child and adolescent traumatic stress. Archives of Pediatrics and Adolescent Medicine, 158, 786–791. http://dx.doi.org/10.1001/ archpedi.158.8.786 Thomas, R., & Zimmer-Gembeck, M. J. (2007). Behavioral outcomes of parent–child interaction therapy and Triple P—Positive Parenting Program: A review and meta-analysis. Journal of Abnormal Child Psychology, 35, 475–495. http://dx.doi.org/10.1007/ s10802-007-9104-9

to reorganize attachment in the young offspring of mothers with major depressive disorder: A randomized preventive trial. Journal of Consulting and Clinical Psychology, 74, 1006–1016. http://dx.doi.org/ 10.1037/0022-006X.74.6.1006 Turner, H. A., Finkelhor, D., & Ormrod, R. (2010). Polyvictimization in a national sample of children and youth. American Journal of Preventive Medicine, 38, 323–330. http://dx.doi.org/10.1016/ j.amepre.2009.11.012 Turner, H. A., Shattuck, A., Finkelhor, D., & Hamby, S. (2016). Polyvictimization and youth violence exposure across contexts. Journal of Adolescent Health, 58, 208–214. http://dx.doi.org/10.1016/ j.jadohealth.2015.09.021 Tyler, K. A., & Cauce, A. M. (2002). Perpetrators of early physical and sexual abuse among homeless and runaway adolescents. Child Abuse and Neglect, 26, 1261–1274. http://dx.doi.org/10.1016/S01452134(02)00413-1 U.S. Department of Health and Human Services. (2016). Child maltreatment, 2014. Retrieved from http://www. acf.hhs.gov/programs/cb/research-data-technology/ statistics-research/child-maltreatment Vandervord Nixon, R. D., Sterk, J., & Pearce, A. (2012). A randomized trial of cognitive behaviour therapy and cognitive therapy for children with posttraumatic stress disorder following singleincident trauma. Journal of Abnormal Child Psychology, 40, 327–337. http://dx.doi.org/10.1007/ s10802-011-9566-7 Vermeiren, R., Ruchkin, V., Leckman, P. E., Deboutte, D., & Schwab-Stone, M. (2002). Exposure to violence and suicide risk in adolescents: A community study. Journal of Abnormal Child Psychology, 30, 529–537. http://dx.doi.org/10.1023/A:1019825132432 Weathers, F. W., & Keane, T. M. (2007). The Criterion A problem revisited: Controversies and challenges in defining and measuring psychological trauma. Journal of Traumatic Stress, 20, 107–121. http:// dx.doi.org/10.1002/jts.20210

Thornback, K., & Muller, R. T. (2015). Relationships among emotion regulation and symptoms during trauma-focused CBT for school-aged children. Child Abuse and Neglect, 50, 182–192. http:// dx.doi.org/10.1016/j.chiabu.2015.09.011

Weist, M. D., Acosta, O. M., & Youngstrom, E. A. (2001). Predictors of violence exposure among inner-city youth. Journal of Clinical Child Psychology, 30, 187–198. http://dx.doi.org/10.1207/ S15374424JCCP3002_6

Toth, S. L., Maughan, A., Manly, J. T., Spagnola, M., & Cicchetti, D. (2002). The relative efficacy of two interventions in altering maltreated preschool children’s representational models: Implications for attachment theory. Development and Psychopathology, 14, 877–908. http://dx.doi.org/10.1017/ S095457940200411X

Wethington, H. R., Hahn, R. A., Fuqua-Whitley, D. S., Sipe, T. A., Crosby, A. E., Johnson, R. L., . . . Chattopadhyay, S. K. (2008). The effectiveness of interventions to reduce psychological harm from traumatic events among children and adolescents: A systematic review. American Journal of Preventive Medicine, 35, 287–313. http://dx.doi.org/10.1016/j.amepre.2008.06.024

Toth, S. L., Rogosch, F. A., Manly, J. T., & Cicchetti, D. (2006). The efficacy of toddler–parent psychotherapy

Whaley, A. L., & Davis, K. E. (2007). Cultural competence and evidence-based practice in mental 533

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health services: A complementary perspective. American Psychologist, 62, 563–574. http://dx.doi.org/ 10.1037/0003-066X.62.6.563

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Wherry, J. M., Briggs-King, E., & Hanson, R. F. (2014). Psychosocial assessment in child maltreatment. In R. M. Reece, R. F. Hanson, & J. Sargent (Eds.), Treatment of child abuse: Common ground for mental health, medical, and legal practitioners (2nd ed., pp. 12–30). Baltimore, MD: Johns Hopkins University Press. Wonderlich, S. A., Simonich, H. K., Myers, T. C., LaMontagne, W., Hoesel, J., Erickson, A. L., . . . Crosby, R. D. (2011). Evidence-based mental health interventions for traumatized youth: A statewide dissemination project. Behaviour Research and Therapy, 49, 579–587. http://dx.doi.org/10.1016/ j.brat.2011.07.003

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Zajac, K., Ralston, M. E., & Smith, D. W. (2015). Maternal support following childhood sexual abuse: Associations with children’s adjustment post-disclosure and at 9-month follow-up. Child Abuse and Neglect, 44, 66–75. http://dx.doi.org/10.1016/j.chiabu.2015.02.011 Zimmerman, G. M., & Messner, S. F. (2013). Individual, family background, and contextual explanations of racial and ethnic disparities in youths’ exposure to violence. American Journal of Public Health, 103, 435–442. http://dx.doi.org/10.2105/AJPH.2012.300931 Zinzow, H. M., Ruggiero, K. J., Resnick, H., Hanson, R., Smith, D., Saunders, B., & Kilpatrick, D., (2009). Prevalence and mental health correlates of witnessed parental and community violence in a national sample of adolescents. Journal of Child Psychology and Psychiatry, 50, 441–450. http://dx.doi.org/ 10.1111/j.1469-7610.2008.02004.x

Chapter 23

Adolescent Offenders With Mental Disorders

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Jeremy Colley, Bipin Subedi, and Richard Rosner

Evolving societal standards and individual legal decisions have shaped the way juvenile offenders have been managed over time. Historically, there has been tension about defining the level of culpability that should be assigned to adolescents as it relates to their age and developmental stage. Under English Common Law, which served as the legal framework for the American colonies and was the initial scaffolding for American criminal law, responsibility was primarily determined by the offender’s ability to understand their actions. “Infants” under the age of 7 were thought to be categorically incapable of developing the requisite mens rea (“guilty mind”) to commit a serious crime, whereas those 15 and older were categorically assumed to possess this ability. Those between the ages of 7 and 15 could potentially be found guilty of a serious offense, including murder, if it could be proven that they understood the difference between right and wrong (Blackstone, 1825). The Common Law approach to the handling of child and adolescent offenders began to shift in the 19th century. Economic recessions in the early 1820s led to an increased number of homeless children and the subsequent opening of “houses of refuge” in New York, Boston, and Philadelphia (Steinberg, 2009). The purpose of these houses was to offer support and address delinquent behavior through targeting underlying behavioral, educational, and vocational issues (Soulier & Scott, 2010). An emphasis on the state as a caretaker for delinquent children, imbedded in the notion of parens patriae (“the parent of

the country”), began to take hold across the country (Binder, 1988). The 1838 Pennsylvania Supreme Court verdict in Ex parte Crouse formalized this role by ruling that the government had the authority and obligation to care and protect children. This decision opened the door for the creation of status offenses: acts that are prohibited for adolescents but not adults, further dividing the way adult and juvenile offenses were perceived and handled. Simultaneously, child and adolescent offenders were increasingly housed in facilities separate from their adult counterparts, and by the mid-1800s almost all major cities had separate facilities for juvenile offenders (Office of Juvenile Justice and Delinquency Prevention, 1999). These changes also coincided with the growing recognition that moral and cognitive development in adolescence was unique and that decision making in this group should be looked at differently than adults (Office of Juvenile Justice and Delinquency Prevention, 1999). The shifting approach to adolescent offenders naturally led to the development of a separate legal system to manage the issues specific to this population. The Illinois Juvenile Court Act of 1899 established first juvenile court in the United States that year (Ash, 2012). The act sought to “regulate the treatment and control of dependent, neglected and delinquent children” and covered those under the age of 16. Under this new legal model, culpability was no longer the focus, and instead, interventions were directed toward providing support and rehabilitation to adolescents in custody

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537

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(Ash, 2012). Judge Julian Mack (1909) said the following about juvenile courts:

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The child who must be brought into the court should, of course, be made to know that he is face to face with the power of the state, but he should at the same time, and more emphatically, be made to feel that he is the object of its care and solicitude. The ordinary trappings of the courtroom are out of place in such hearings. (p. 120) Juvenile courts proliferated over the next several decades and it is thought that the focus on adolescent development, vulnerability, and recovery, also opened the door for the field of child psychiatry (Soulier & Scott, 2010). However, the focus on rehabilitation and the civil nature of the courtroom process gave the courts broad authority and reach, and individuals in juvenile courts were not offered the same constitutional protections provided to their adult counterparts (Scott & Steinberg, 2008). This began to change in the middle of the 20th century, beginning with the decisions in Haley v. Ohio (1948) and Gallegos v. Colorado (1962), which gave constitutional rights to adolescents during confessions. Several subsequent U.S. Supreme Court cases in the 1960s introduced additional protections to adolescents who were processed in juvenile courts. In the following section, we will briefly review some of these decisions (see Appendix 23.1 for a more detailed discussion of landmark cases in juvenile justice and mental health). The first of these rulings, Kent v. United States (1966), established that adolescent offenders were guaranteed due process protections under the Constitution. Morris Allen Kent was 16 years old when he was arrested in 1961 on charges of burglary, robbery, and rape. Kent’s mother and attorney hired mental health experts to evaluate the defendant and requested that the case be transferred to juvenile court. However, the court signed a waiver to have the case transferred to adult court without holding a hearing or providing Kent, his mother, or his attorney an opportunity to present their case against the waiver. He was subsequently found guilty of the robbery and burglary charges and sentenced to 30 to 90 years in prison. The U.S. Supreme Court, on appeal, 538

determined that the waiver of an adolescent to adult court was a “critically important” and that a hearing was required to determine whether a juvenile should be transferred to adult criminal court. The Court also highlighted the importance of counsel in this process. The case was reversed and sent back to the trial court. The Court decision in In re Gault (1967) expanded and more clearly defined the protections afforded to juveniles in legal proceedings. Gerald Gault was 15 years old when he was arrested in 1964 for making a lewd phone call. He was subsequently found guilty and sentenced to a juvenile facility for 5 years. However, Gault did not have representation at his adjudication hearing, his accuser was not present, and there was no transcript of this proceeding. The lower court decision was appealed and eventually heard by the U.S. Supreme Court. The Court reversed the lower court’s ruling on the basis that Gault’s Sixth Amendment rights were violated. This included the right to an attorney and an opportunity to confront his accuser. Juvenile proceedings moved even closer to those of criminal court in In re Winship (1970). Samuel Winship was 12 years old when he was arrested for stealing $112 from a woman’s pocketbook. He was subsequently found guilty based on a preponderance of evidence, the standard for juvenile cases based on the New York Family Court Act. The case was appealed to the U.S. Supreme Court who reversed because juvenile offenders should be afforded the same safeguards and due process protections as their adult counterparts who are being accused of a crime, and that the “reasonable doubt” standard should be applied to adolescent cases as well. These decisions broadened the procedural protections for juveniles being charged with crimes by drawing parallels to adult criminal courts. However, the consequence of doing so was to also blur the lines between the original rehabilitative aims of juvenile proceedings and the punishment-driven adult criminal justice system. Over the next several years, several additional U.S. Supreme Court cases helped define the limits of this relationship. In 1971, in McKeiver v. Pennsylvania, the Court determined that a jury trial was not required in juvenile proceedings, placing limits to the decision in In re Gault. In Breed v. Jones (1975), the Court

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Adolescent Offenders With Mental Disorders

decided that trying a juvenile for the same charge in juvenile and adult criminal court was a constitutional violation of double jeopardy under the Fifth Amendment. In this ruling, the court emphasized the importance of transfer hearings in juvenile court. Lastly, in Schall v. Martin (1984), the Court determined that juveniles could be detained without a probable cause hearing. This ruling was rooted in the idea that juveniles, by nature of their age, were always in some state of custody, eroding the potency of detention centers from a civil liberty standpoint. These decisions balanced the rehabilitative aims of the juvenile court with the constitutional civil liberties of the adolescent. However, the rise of juvenile crime rates in the late 1980s and early 1990s led to a new panic over the juvenile system and heightened concerns that the rehabilitative model was “too soft” and ineffective in controlling juvenile offenders (Soulier & Scott, 2010). Several policy changes during this time led to a dramatic increase in the number of juvenile cases transferred to criminal court. This included lowering the age of transfer to 14 in many states; expanding the types of crimes for which juveniles could be transferred; and the creation of automatic transfer statues which led to the required transfer of juveniles, irrespective of age, to adult court based on the crime that was committed (Scott & Steinberg, 2008). As a result, the number of juveniles in custody (including those in correctional facilities) increased 41% from 1991 to 1999 (Snyder & Sickmund, 2006). These changes led to a focus on deterrence, and opened the door for more severe punishments for juveniles that were previously only reserved for adult offenders. The increase in the number of juveniles exposed to harsher punishments led to challenges to the constitutionality and limits of sentencing in this population. Although the U.S. Supreme Court ruled in 1998 in Thompson v. Oklahoma that the lack of a specified age-limit to the death penalty violated cruel and unusual punishment, the following year it ruled in Stanford v. Kentucky that the death penalty was constitutional in those 16 and older (Soulier & Scott, 2010). It was not until 2005, in the case of Roper v. Simmons, that the Court definitively weighed in on capital punishment for all minors. Christopher Simmons was 17 years old in 1993

when he and an acquaintance killed Shirley Crook by tying her up and throwing her over a bridge. He was convicted and sentenced to the death penalty. Simmons initially appealed this ruling partially on the grounds of ineffectiveness of council, but later sought postconviction relief for this sentence. The case made its way to the U.S. Supreme Court, and in 2005 the Court, based on “evolving standards of decency,” determined that the execution of minors under the age of 18 was a violation of Cruel and Unusual Punishment under the Eighth Amendment. In 2010, in Graham v. Florida, the U.S. Supreme Court determined that sentencing a minor to life without parole for a nonhomicide offense was also a violation of Cruel and Unusual Punishment. Terrance Graham was 16 years old when he pled guilty to attempted robbery. He was arrested again 6 months later in a robbery attempt and was subsequently sentenced to life in prison without parole. The case was heard by the Court on appeal. The Court decided that a life sentence for an adolescent for a nonviolent crime violated the Eighth Amendment. The Court decided in Miller v. Alabama (2012) that sentencing a minor to life without parole for any offense, including murder, violated the Eighth Amendment’s prohibition against Cruel and Unusual Punishment. At present, the U.S. Supreme Court has not ruled whether juvenile offenders have a constitutional right to be fit to proceed, and, therefore, have not opined whether, or how, the legal standards for competency to stand trial established in United States v. Dusky (1960) should apply to adolescents. For this reason, states vary in terms of how, and if, they address competency to stand trial for juvenile offenders, and no single standard exists. Likewise, state laws with regards to who can perform fitness to proceed evaluations, and their qualifications, vary. Among adults, approximately 80% to 90% of defendants found unfit are restored to competence (Pinals, 2005); among juveniles, the rate is lower, around 70%, (McGaha, Otto, McClaren, & Petrila, 2001) likely secondary to the higher rate of mental retardation among juveniles referred for restoration services. In the United States, with the development of the juvenile justice system and its emphasis on rehabilitation rather than punishment, most states found the 539

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Colley, Subedi, and Rosner

insanity defense unnecessary for juveniles; currently 46 states and the federal courts have an insanity defense for adult offenders but only 10 states offer it in their juvenile justice system. (Rogers & Meyers, 2013) However, if a juvenile is waived to adult court, the insanity defense in place for adult offenders applies. As noted previously, the management and approach to adolescent offenders has developed considerably over time, with a tension between the rehabilitative goals of the juvenile justice system and the need to protect society. However, not all juvenile offending leads to contact with law enforcement, let alone arrest, detention, adjudication by juvenile or criminal courts, and sentencing to a juvenile or adult correctional facility. The next section examines the prevalence and nature of juvenile offending at each of these stages, and describes how different municipal systems direct alleged juvenile offenders through the criminal justice system. Juvenile Delinquency A delinquent offense is an act committed by a juvenile for which an adult could be prosecuted in criminal court. There are, however, behaviors that are law violations only for juveniles and/or young adults because of their status. These status offenses may include behaviors like running away from home, truancy, alcohol possession or use, incorrigibility, and curfew violations. The types and frequency of delinquent offenses are difficult to determine with certainty; though official records (e.g., statistics reported by the courts and law enforcement) provide some data, they likely underrepresent juvenile delinquency given that for each act that results in contact with law enforcement, many more do not, given each is not reported, discovered nor substantiated via investigation. Therefore, researchers have also attempted to estimate rates of juvenile delinquency via self-report and collateral information from families and other sources, though these methods also have flaws, as minors may not remember details of offending accurately or may not report certain behaviors. The Office of Juvenile Justice and Delinquency Prevention (Sickmund, 2014) provides a comprehensive data set with regards to juvenile offending, 540

based on multiple sources. A 2011 survey (Centers for Disease Control and Prevention, 2012) found that 33% of high school students reported having been in a physical fight in the past year, with boys reporting a greater frequency (41%) than girls (24%). Only 4% reported needing medical attention following a fight. Among high school students, 26% reported having had property stolen or vandalized in the prior year. About 5% of high school students reported having carried a weapon to school in the past month, and 7.5% reported having been threatened or injured with a weapon. In 2010, nearly half (48%) of high school seniors reported having tried illicit drugs at some point in their lives (Johnston, O’Malley, Bachman, & Schulenberg, 2011); likewise, 37% of 10th graders and 21% of eighth graders responded similarly. About half of these groups responded that they had tried marijuana only, whereas 25% of high school seniors reported having used an illicit drug other than, or in addition to, marijuana. Seventy percent of high school seniors reported having used alcohol, and 23% reported having 5 or more drinks at a time in the prior 2 weeks. For tobacco use, 42% of high school seniors reported having smoked a cigarette, and 19% reported having smoked within the last month. The percentage of high school students reporting that they had been offered, sold, or given an illegal drug while at school in the past year varied by state, with a range of 12% to 35%. Self-report of illicit drug use among high school seniors peaked in the late 1970s, generally declined to a low in the early 1990s, rose until around 2000 and has since plateaued, about midway between the extremes. Alcohol use has shown a more consistent downward trend from the late 1970s, with only a mild rebound in the 1990s. Determining the rates of murder committed by juveniles has proven difficult, as only about 65% of murders result in identification and apprehension of a suspect. (Federal Bureau of Investigation, 2012) However, the FBI estimated that in 2010, 1 out of every 12 murders involved a juvenile. In about half of these murders, the juvenile acted alone, 40% acted with an adult, and 9% acted with other juveniles. Between 1980 and 2010, about half the victims of homicides involving juvenile offenders were between the ages of 14 and 24.

Adolescent Offenders With Mental Disorders

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The timing of offenses by juveniles varies between school and nonschool days; on school days, offenses sharply spike between 1:00pm and 7:00pm, with a peak around 3:00pm, just after the end of the school day (Federal Bureau of Investigation, 2016). On nonschool days, the rate increases gradually during the day peaking around 9:00pm, consistent with the timing of offense among adults. Robberies and offenses involving firearms are exceptions, as both occur in the evening on school days and nonschool days for juveniles. Juvenile Justice Systems Since the first juvenile court was founded in 1899 in Illinois, every state has formalized a juvenile justice system; however, the philosophy and emphasis between rehabilitation and punishment varies, in practice and in the detail in which these philosophies are articulated explicitly in state statute. Most states have adopted the principles of Balanced and Restorative Justice, a model for juvenile justice systems that consists of three core components: public safety, individual accountability to victims and the community, and development of skills to help offenders live law abiding lives. Other states model the Standard Juvenile Court Act, which states that the purpose of juvenile justice systems is as follows: each child coming within the jurisdiction of the court shall receive . . . the care, guidance and control that will conduce to his welfare and the best interest of the state, and that when he is removed from the control of his parents the court shall secure for him care as nearly as possible equivalent to that which they should have given him. (Sickmund, 2014, p. 87) Other states follow the Guide for Drafting Family and Juvenile Court Acts, published in the late 1960s, which emphasizes care, protection and wholesome mental and physical development of children involved with the juvenile court; to remove from children committing delinquent acts the consequences of criminal

behavior and to substitute therefore a program of supervision, care and rehabilitation; to remove a child from the home only when necessary for his welfare or in the interests of public safety; and to assure all parties their constitutional and other legal rights. (Sickmund, 2014, p. 89) Tables 23.1 and 23.2 summarize the different emphasis among the states’ juvenile justice systems. States also vary in how they define juveniles with regards to criminal justice. For delinquency matters, as opposed to status offenses, most states have an upper limit of 17. New York and North Carolina are outliers, with an upper age limit of 15, meaning 16- and 17-year-olds are prosecuted as adults. Some states define a lower limit for inclusion, but most do not and follow case or common law. How referrals to juvenile justice systems are made varies substantially between the states, and even between counties or cities within a state, so a detailed description of these mechanisms is beyond the scope of this chapter. However, some general observations are worth noting. Most referrals to juvenile court come from law enforcement, but parents, victims, schools, and probation officers can refer independent of law enforcement. In 2010, of cases referred to juvenile courts, 83% came from law enforcement. That said, 23% of arrests do not result in referral for prosecution. Once a referral is made, intake departments screen cases for merit. Almost half of referrals are handled informally, meaning they are dismissed or the accused juvenile agrees to do, or refrain from doing, specific things for a given period (e.g., attend school, seek counselling, make restitution to the victim). Probation officers monitor compliance. If the intake department decides to handle a case formally, it petitions that either the juvenile court conduct a hearing to make the delinquent a ward of the court, or it petitions that the case be waived by juvenile court for prosecution in adult criminal court, which results in conviction and sentencing. Juveniles may be held in custody pending a delinquency hearing, but this requires a distinct hearing that usually occurs between 24 and 48 hours following arrest, where the state must prove that detention is in the best interest of the community and/or the 541

Table 23.1 Mechanisms by Which States May Transfer Juvenile Delinquency Hearings to Adult Criminal Court

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State

Alabama Alaska Arizona Arkansas California Colorado Connecticut Delaware DC Florida Georgia Hawaii Idaho Illinois Indiana Iowa Kansas Kentucky Louisiana Maine Maryland Massachusetts Michigan Minnesota Mississippi Missouri Montana Nebraska Nevada New Hampshire New Jersey New Mexico New York North Carolina North Dakota Ohio Oklahoma Oregon Pennsylvania Rhode Island South Carolina South Dakota Tennessee Texas Utah Vermont Virginia Washington West Virginia Wisconsin Wyoming

Judicial

Prosecutorial

Statutory

Reverse

Blended

Blended

waiver

discretion

exclusion

waiver

sentencing

sentencing

juvenile

criminal

x

x x x

x x x x x x x x x x x x x x x x x x x x x

x x x x x x

x x x x

x

x x x x

x x x x x

x x x

x x x x x x

x

x x

x x

x x

x

x x

x x

x

x x x x x

x x x x

x x

x x

x x x x x

x x x x x x x x x x x x x x x x x x

x

x x

x x x

x x

x

x x x

x x x

x x

x

x

x x

x

x x x

x x x

x x

x x

x x

x

x

x x

x

x

x x

Note. Adapted from Juvenile Offenders and Victims: 2014 National Report (p. 100), by M. A. Sickmund, 2014, Pittsburgh, PA: National Center for Juvenile Justice. In the public domain.

Table 23.2 Emphasis and Precedent for States’ Juvenile Justice Codes and Oldest Age for Juvenile Justice Jurisdiction

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State

Alabama Alaska Arizona Arkansas California Colorado Connecticut Delaware DC Florida Georgia Hawaii Idaho Illinois Indiana Iowa Kansas Kentucky Louisiana Maine Maryland Massachusetts Michigan Minnesota Mississippi Missouri Montana Nebraska Nevada New Hampshire New Jersey New Mexico New York North Carolina North Dakota Ohio Oklahoma Oregon Pennsylvania Rhode Island South Carolina South Dakota Tennessee Texas Utah Vermont Virginia Washington West Virginia Wisconsin Wyoming

BARJ

Juvenile

Legislative

Accountability/

court

guide

protection

welfare

age for

age for

act

language

emphasis

emphasis

jurisdiction

jurisdiction

17 17 17 17 17 17 16 17 17 17 16 17 17 16 17 17 17 17 16 17 17 16 16 17 17 16 17 17 17 16 17 17 15 15 17 17 17 17 17 17 16 17 17 16 17 17 17 17 17 16 17

Case law Case law 8 10 Case law 10 Case law Case law Case law Case law Case law Case law Case law Case law Case law Case law 10 Case law 10 Case law 7 7 Case law 10 10 Case law Case law Case law Case law Case law Case law Case law 7 6 Case law Case law Case law Case law 10 Case law Case law 10 Case law 10 Case law 10 Case law Case law Case law 10 Case law

Child

x x x x x

x x

x

x x x x

x x x

x x x

x x

x x x x

x

x

x

x x x x x

x x

x x

x

x

x x x

x x x x x x x x x x x x

x x

x x x x x x

x

Oldest

Youngest

Note. BARJ = Balanced and restorative justice. Adapted from Juvenile Offenders and Victims: 2014 National Report (p. 88), by M. A. Sickmund, 2014, Pittsburgh, PA: National Center for Juvenile Justice. In the public domain.

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Colley, Subedi, and Rosner

child. About 21% of arrests result in the juvenile being retained in custody (Sickmund, 2014). For cases that remain in juvenile court, formal probation is the most severe sanction for most cases; only 26% are ordered for residential placement in a private or public youth correctional facility (Sickmund, 2014). Probation most often includes more than just supervision, based on assessment of the offender’s needs, and may include mental health or drug treatment and weekend stays in a local detention center. Moreover, probation lasts for a specified amount of time, after which, if the delinquent has complied with its terms, the judge can dismiss the case. In considering a waiver to adult criminal court, the judge typically focuses on the amenability of the juvenile to treatment, which often rests on how well, or poorly, the juvenile has done when granted chances at rehabilitation via juvenile court in the past, and the severity of the crime. Of cases considered for adult court, only about 1% are waived (Sickmund, 2014). Other mechanisms allow transfer from juvenile court to adult court, via state statute for certain crimes—usually also factoring in age of the accused—or via prosecutorial discretion. Some states also allow a “reverse waiver,” which allows a juvenile defendant whose case has been assigned to adult criminal court to challenge that decision for removal to juvenile court. Moreover, many states allow for “blended sentencing,” which is assigning a sentence in the juvenile justice system even if the conviction is in adult criminal court, or, assigning a sentence in the adult criminal justice system because of a hearing in juvenile court, which is usually suspended unless the juvenile fails to comply with conditions set down by the juvenile court. Mental Health Disorders Among Adolescent Offenders This section examines the prevalence of different mental disorders among juvenile offenders at various stages, and how mental disorders correlate with different types of offending and responses from the criminal justice system. Table 23.3 compares the relative prevalence of mental disorders between adults and juveniles who have been arrested and/or detained. The Northwestern Juvenile Project is an ongoing, longitudinal research initiative that has followed a 544

Table 23.3 Psychiatric Disorders Among Arrested and Detained Processed in Adult or Juvenile Court Disorder Any disorder Any disorder except conduct disorder Any affective disorder Major depression Dysthymia Mania Hypomania Any anxiety disorder Panic disorder Separation anxiety disorder Overanxious disorder Generalized anxiety disorder Obsessive-compulsive disorder Posttraumatic stress disorder Psychotic disorder Any disruptive behavior disorder Attention-deficit/hyperactivity disorder Oppositional defiant disorder Conduct disorder Any substance use disorder Alcohol use disorder Marijuana use disorder Other substance use disorder Alcohol and drug use disorder

Adult (%) Juvenile (%) 66 64 22 16 12 3 1 24 0 16 9 8 10 8 2 41 9 15 37 55 29 49 2 24

68 62 20 14 13 2 2 22