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Handbook of early childhood development programs, practices, and policies
 9781118937297, 1118937295, 9781118937310, 1118937317, 9781118937334

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The Wiley Handbook of Early Childhood Development Programs, Practices, and Policies

The Wiley Handbooks of Developmental Psychology This outstanding series of handbooks provides a cutting‐edge overview of classic research, current research, and future trends in developmental psychology. • Each handbook draws together newly commissioned chapters to provide a comprehensive overview of a sub‐discipline of developmental psychology. • The international team of contributors to each handbook has been specially chosen for its expertise and knowledge of each field. • Each handbook is introduced and contextualized by leading figures in the field, l­ending coherence and authority to each volume. The Wiley Handbooks of Developmental Psychology will provide an invaluable overview for advanced students of developmental psychology and for researchers as an authoritative definition of their chosen field. Blackwell Handbook of Adolescence Edited by Gerald R. Adams and Michael D. Berzonsky The Science of Reading: A Handbook Edited by Margaret J. Snowling and Charles Hulme Blackwell Handbook of Early Childhood Development Edited by Kathleen McCartney and Deborah A. Phillips Blackwell Handbook of Language Development Edited by Erika Hoff and Marilyn Shatz The Wiley Blackwell Handbook of Childhood Cognitive Development, 2nd edition Edited by Usha Goswami The Wiley Blackwell Handbook of Adulthood and Aging Edited by Susan Krauss Whitbourne and Martin Sliwinski The Wiley Blackwell Handbook of Infant Development, 2nd Edition Edited by Gavin Bremner and Theodore D. Wachs The Wiley Blackwell Handbook of Childhood Social Development Edited by Peter K. Smith and Craig H. Hart The Wiley Handbook of Developmental Psychology in Practice: Implementation and Impact Edited by Kevin Durkin and H. Rudolph Schaffer The Wiley Handbook of Early Childhood Development Programs, Practices, and Policies Edited by Elizabeth Votruba‐Drzal and Eric Dearing The Wiley Handbook of Group Processes in Children and Adolescents Edited by Adam Rutland, Drew Nesdale, and Christia Spears Brown

The Wiley Handbook of Early Childhood Development Programs, Practices, and Policies Edited by Elizabeth Votruba‐Drzal and Eric Dearing

This edition first published 2017 © 2017 John Wiley & Sons, Inc. Registered Office John Wiley & Sons Ltd, The Atrium, Southern Gate, Chichester, West Sussex, PO19 8SQ, UK Editorial Offices 350 Main Street, Malden, MA 02148‐5020, USA 9600 Garsington Road, Oxford, OX4 2DQ, UK The Atrium, Southern Gate, Chichester, West Sussex, PO19 8SQ, UK For details of our global editorial offices, for customer services, and for information about how to apply for permission to reuse the copyright material in this book please see our website at www.wiley.com/wiley‐blackwell. The right of Elizabeth Votruba‐Drzal and Eric Dearing to be identified as the authors of the editorial material in this work has been asserted in accordance with the UK Copyright, Designs and Patents Act 1988. All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted, in any form or by any means, electronic, mechanical, photocopying, recording or otherwise, except as permitted by the UK Copyright, Designs and Patents Act 1988, without the prior permission of the publisher. Wiley also publishes its books in a variety of electronic formats. Some content that appears in print may not be available in electronic books. Designations used by companies to distinguish their products are often claimed as trademarks. All brand names and product names used in this book are trade names, service marks, trademarks or registered trademarks of their respective owners. The publisher is not associated with any p­roduct or vendor mentioned in this book. Limit of Liability/Disclaimer of Warranty: While the publisher and authors have used their best efforts in preparing this book, they make no representations or warranties with respect to the accuracy or completeness of the contents of this book and specifically disclaim any implied w­arranties of merchantability or fitness for a particular purpose. It is sold on the understanding that the publisher is not engaged in rendering professional services and neither the publisher nor the author shall be liable for damages arising herefrom. If professional advice or other expert a­ssistance is required, the services of a competent professional should be sought. Library of Congress Cataloging‐in‐Publication Data Names: Votruba-Drzal, Elizabeth. Title: The Wiley handbook of early childhood development programs, practices, and policies / edited by Elizabeth Votruba-Drzal and Eric Dearing. Description: Hoboken, New Jersey : John Wiley & Sons, 2017. | Includes bibliographical references and index. Identifiers: LCCN 2016041895| ISBN 9781118937297 (cloth) | ISBN 9781118937327 (epub) | ISBN 9781118937310 (ePDF) Subjects: LCSH: Early childhood education. Classification: LCC LB1139.23 .H36 2017 | DDC 372.21–dc23 LC record available at https://lccn.loc.gov/2016041895 A catalogue record for this book is available from the British Library. Cover image: vnosokin/Gettyimages Cover design by Wiley Set in 10.5/12.5pt Adobe Garamond by SPi Global, Pondicherry, India 10 9 8 7 6 5 4 3 2 1


Notes on Contributors viii Forewordxx Prefacexxv Part I  The State of Young Children in the United States


1 The State of Young Children in the United States: School Readiness Terri J. Sabol and Robert C. Pianta


2 The State of Young Children in the United States: A Developmental Psychopathology Perspective on the Mental Health of Preschool Children Susan B. Campbell


3 Early Childhood Health Disparities, Biological Embedding, and Life‐Course Health Daniel Berry


4 Social and Contextual Risks Robert H. Bradley Part II Theoretical and Empirical Contexts of Applied Developmental Science of Early Childhood 5 From the Lab to the Contexts in which Children Live and Grow: Historical Perspectives on the Field Pamela A. Morris and Maia C. Connors


97 99

vi Contents   6 What Does it Mean to be Evidence‐based? Margaret R. Burchinal and Nina E. Forestieri


  7 Neural Development in Context: Differences in Neural Structure and Function Associated with Adverse Childhood Experiences Emily C. Merz and Kimberly G. Noble


Part III  Early Childhood Education and Care


  8 Publicly Supported Early Care and Education Programs W. Steven Barnett, Elizabeth Votruba‐Drzal, Eric Dearing, and Megan E. Carolan


  9 Early Childhood Education and Care for Dual Language Learners Lianna Pizzo and Mariela Páez


10 Early Childhood Education and Care for Children with Disabilities Penny Hauser‐Cram, Miriam Heyman, and Kristen Bottema‐Beutel


11 Classroom‐based Early Childhood Interventions Stephanie Jones, Dana Charles McCoy and Lauren Hay


12 Child Care and Child Development in the United States: Where Have We Come From, What Do We Know Now, and Where Are We Going? Anna D. Johnson


Part IV  Parenting, Family, and Dual‐generation Programs


13 Family‐School Partnerships in Early Childhood Susan M. Sheridan, Amanda L. Moen, and Lisa L. Knoche


14 Parenting and Home Visiting Interventions Nancy Donelan‐McCall


15 The Two‐Generation Approach to Building Human Capital: Past, Present, and Future330 Margo Gardner, Jeanne Brooks‐Gunn, and P. Lindsay Chase‐Lansdale Part V  Public Policy and Young Children


16 Immigration Policy and Early Childhood Development Soojin Oh Park and Hirokazu Yoshikawa


Contents  vii 17 Marriage Policy and Early Childhood Development Rebekah Levine Coley


18 Child Welfare Policy Kristen S. Slack and June Paul


19 Effects of United States Income and Work Supports Policies on Children in Low‐Income Families Aletha C. Huston


20 The Role of Conditional Cash Transfer Programs in Promoting Early Childhood Development in the United States Sharon Wolf, Juliette Berg, Pamela A. Morris, and J. Lawrence Aber


21 Work‐Family Policies Anna Gassman‐Pines and Rachel Goldstein



Notes on Contributors

J. Lawrence Aber, Ph.D., is the Willner Family Professor of Psychology and Public Policy at the Steinhardt School of Culture, Education, and Human Development, and University Professor at New York University. He received his Ph.D. in Clinical‐Community and Developmental Psychology from Yale University. His basic research examines the i­nfluence of poverty and violence, at the family and community levels, on the social, emotional, behavioral, cognitive, and academic development of children and youth. Currently, he conducts research on the impact of poverty and HIV/AIDS on children’s development in South Africa (in collaboration with the Human Sciences Research Council), the impact of preschool teacher training quality and children’s learning and development in Ghana (in collaboration with Innovations for Poverty Action) and on school‐ and community‐ based interventions in the Democratic Republic of Congo, Niger, Sierra Leone, and Lebanon (in collaboration with the International Rescue Committee). W. Steven Barnett, Ph.D., is Board of Governors Professor of Education and Director of the National Institute for Early Education Research (NIEER) at Rutgers University. His research includes wide‐ranging studies on early childhood policy and economics including: research on long‐term effects of early education programs; benefit‐cost analyses of early childhood programs; educating bilingual/migrant populations; the effects of curriculum on executive functions, attitudes, and social behavior; and the series of State Preschool Yearbooks providing annual state‐by‐state analyses of progress in public pre‐k. Dr. Barnett published a benefit‐cost analysis of lifetime effects of the Perry Preschool in 1985 based on adult follow‐up finding a $7 to $1 return. Nearly 30 years later he summarized what has been learned about producing such results on a large scale in the journal Science. Dr. Barnett earned his Ph.D. in economics from the University of Michigan. Juliette Berg, Ph.D., is a researcher at American Institutes for Research. Dr. Berg has extensive experience conducting applied child development research in a variety of settings, including K–12 schools and urban communities, and for several countries including the

Notes on Contributors  ix United States, France, South Africa, and India. Dr. Berg has collaborated on several large‐ scale randomized control trials of social and emotional learning, instructional improvement, and school climate interventions in K–12 schools, and a conditional cash transfer program in New York City. She has methodological expertise in research design, program evaluation, implementation science, and advanced quantitative methods, and content expertise in social and emotional learning and school climate. Dr. Berg earned her Ph.D. in Applied Developmental Psychology from New York University. She completed her post‐doctoral work at the University of Virginia. Daniel Berry is an Assistant Professor at the Institute of Child Development, University of Minnesota. His research is concerned with the bioecology of children’s self‐regulation development across early and middle childhood – in particular, the developmental dynamics underlying early experience, self‐regulation, and the organization of children’s physiological stress systems. Kristen Bottema‐Beutel is an Assistant Professor of Special Education in The Lynch School of Education at Boston College. She received her Ph.D. from the joint doctoral program in special education at the University of California at Berkeley and San Francisco State University. Following her graduate work, she completed a post‐ d­octoral fellowship in special education at Vanderbilt University. Dr. Bottema‐Beutel’s areas of research include social interaction dynamics in children and adolescents with  autism spectrum disorders (ASDs), educational interventions to promote peer interaction and social development in children with ASDs, social‐communication development, and decision‐making processes regarding peer inclusion in social contexts. Robert H. Bradley, Ph.D., is Director of the Family and Human Dynamics Research Institute at ASU. He is a member of the HHS/HRSA Advisory Committee on Maternal, Infant and Early Childhood Home Visitation Program Evaluation and on the editorial boards of Parenting: Science and Practice, Journal of Developmental and Behavioral Pediatrics, and Early Childhood Research Quarterly and was associate editor for both Child Development and Early Childhood Research Quarterly. He has more than 350 publications dealing with parenting, early education, fathers, child care, and the relation between home environments and children’s health and development. Dr. Bradley is one of the developers of the HOME Inventory. Dr. Jeanne Brooks‐Gunn is the Virginia and Leonard Marx Professor Child Development at Teachers College and the College of Physicians and Surgeons at Columbia University. She is also the co‐director of the National Center for Children and Families (www.policy forchildren.org). Dr. Brooks‐Gunn is a developmental psychologist who studies children, youth, and families over time. She is interested in the family and neighborhood conditions that influence how children and youth thrive, or do not, and how conditions at different ages influence development. She also does policy work as well as designing and evaluating interventions for children and families (home visiting clinic‐based programs, early childhood education programs, and after school programs).

x  Notes on Contributors Margaret R. Burchinal, Ph.D., Senior Scientist and Director of the Data Management and Analysis Core at the FPG Child Development Institute and Research Professor of Psychology at the University of North Carolina at Chapel Hill. Burchinal has extensive experience in managing the data management and statistical analyses for large multi‐site studies, serving as the lead statistician for projects such as the NIH Family Life Project, NICHD Study of Early Care and Youth Development, the IES National Center for Early Development and Learning, and for center grants and program p­rojects funded by NIA, NIDA, NICHD, and IES. Her research interests include growth curve methodology and the short‐ and long‐term impacts of early care and education, especially for children at risk due to poverty. She has authored over 150 peer‐reviewed papers and several chapters, including the most recent chapter on early care and education in the Handbook of Child Psychology and Developmental Science. She has served as: an associate editor for Child Development and Early Childhood Research Quarterly; a panel member of grant review committees for MCH, IES, and NICHD; a member for several National Research Council committees and several Head Start research and evaluation committees; and is currently a trustee for the W.T. Grant Foundation. Dr. Susan B. Campbell is Professor Emerita of Psychology at the University of Pittsburgh. Her earlier research focused on emerging behavior problems in young children emphasizing child, parenting, and family risk factors, including maternal depression, that predict the onset and persistence of adjustment difficulties. Dr. Campbell was the PI at the Pittsburgh Site of the NICHD Study of Early Child Care and Youth Development from 1995 through 2009 and co‐PI when the study began in 1990. She is currently completing a study on the social development of toddlers at genetic risk for Autism Spectrum Disorder. Her work has appeared in leading developmental and clinical child journals and she is the author of Behavior Problems in Preschool Children: Clinical and Developmental Issues (Guilford, 2002). Dr. Campbell is a Past President of the Section on Clinical Child Psychology (now Division 53) of the American Psychological Association and a founding member and the first secretary of the International Society for Research in Child and Adolescent Psychopathology. From 1998 until 2005 Campbell was the editor of the Journal of Abnormal Child Psychology; she continues to serve on several editorial boards. Megan E. Carolan is the Associate Director for Policy Research at the Institute for Child Success, where she helps direct research on early childhood policy as well as supporting its technical assistance for Pay for Success jurisdictions. Megan was previously the Policy Research Coordinator at the National Institute for Early Education Research at Rutgers University, where she managed the State Preschool Yearbook, NIEER’s annual report on state‐funded pre‐k policy. She also provided technical assistance to states through the Center on Enhancing Early Learning Outcomes, a federally funded technical assistance center focused on improving outcomes for children from birth to age 8. Megan holds a Master of Public Policy from the Edward J. Bloustein School at Rutgers University, and graduated magna cum laude from Fairfield University, majoring in s­ociology and politics.

Notes on Contributors  xi P. Lindsay Chase‐Lansdale is the Frances Willard Professor of Human Development and Social Policy at the School of Education and Social Policy, a Faculty Fellow in the Institute for Policy Research (IPR), and Associate Provost for Faculty, Northwestern University. Much of her work addresses family strengths as well as programs and policies that lead to children’s positive social and educational outcomes in the context of economic hardship. Chase‐Lansdale is an elected member of the Harvard Board of Overseers and of the National Academy of Education, a fellow in the American Psychological Association, the Association for Psychological Science, and the Aspen Institute’s Ascend Program, Two‐ Generations, One Future. She is the recipient of the Society for Research in Child Development (SRCD) Award for Distinguished Contributions to Public Policy for Children as well as the Society for Research on Adolescence (SRA) Social Policy Award. Rebekah Levine Coley, Ph.D., is a Professor of Applied Developmental and Educational Psychology at Boston College’s Lynch School of Education. Coley’s research seeks to delineate the key family, school, and community processes which transmit economic and social inequality to children’s development from infancy through adolescence. Her work seeks to connect rigorous developmental science research to practice and policy at the local, state, and federal level. Professor Coley’s research has been published in dozens of leading journals and edited volumes, and has received funding from the National Institutes of Health, the Australian Research Council, and numerous private foundations. She holds leadership positions in the Society for Research in Child Development, the Society for Research on Adolescence, the Child Care and Early Education Policy Research Consortium, and the University‐based Child and Family Policy Consortium. Her research excellence has been recognized through receipt of a Fulbright Senior Scholar Award and a Social Policy Award from the Society for Research in Adolescence. Maia C. Connors is Senior Research Associate, Research & Policy Initiatives at the Ounce of Prevention Fund. Her research focuses on early childhood care and education policy, systems’ support of high quality early education and professional learning, and adults’ support of young children’s development. Dr. Connors draws on interdisciplinary theory and rigorous quantitative methods to answer pragmatic research questions and translate findings into policy and practice. Her recent work has explored sources of variation in the impacts of Head Start; identified promising policy levers for improving preschool quality at scale; and informed the decisions of city and state departments of education regarding early childhood school accountability, expansion, and improvement. Dr. Connors received an A.B. in Sociology and Education Studies from Brown University, and Ph.D. in Applied Psychology from New York University. Eric Dearing is a Professor of Applied Developmental Psychology in the Lynch School of Education at Boston College and a Senior Researcher at the Norwegian Center for Child Behavioral Development. He is also a fellow in the Center for Optimized Student Support at Boston College. Eric received his PhD in Psychology from the University of New Hampshire in 2001. From 2001 to 2003, he completed postdoctoral training in clinical research at Harvard University. Eric’s work is focused on the consequences of children’s lives outside of school for their performance in school, with special attention to the power

xii  Notes on Contributors of families, early education and care, and neighborhood supports to bolster achievement for children growing up poor. Presently, as a member of the Development and Research in Early Math Learning (DREME) Network being funded by the Heising‐Simons Foundation, much of his research is focused on the roles of parents and early educators in low‐income children’s math learning. Nina E. Forestieri, is a research analyst at the Frank Porter Graham Child Development Institute at the University of North Carolina at Chapel Hill. Forestieri has a master’s degree in Maternal and Child Health from University of North Carolina at Chapel Hill. She has experience working on complex analyses with numerous early childhood data sets on studies funded by NICHD, IES, and MCHB. Her research interests include risk and protective factors associated with developmental outcomes among diverse populations, and family and caregiver characteristics contributing to development throughout childhood. Dr. Margo Gardner is a Senior Research Scientist at the National Center for Children and Families at Teachers College, Columbia University. She earned her Ph.D. in developmental psychology at Temple University, and her B.A. in psychology at Duquesne University. Dr. Gardner’s work is aimed at exploring child and youth development in low‐income and otherwise at‐risk populations. Her past research has focused on a­dolescent risk‐taking, the development of juvenile offending, and the consequences of  youths’ exposure to neighborhood and family violence. Currently, Dr. Gardner is  working on projects related to young adult development, postsecondary access and  credentialing, and postsecondary gains among low‐income mothers of young children. Anna Gassman‐Pines is Associate Professor of Public Policy and Psychology and Neuroscience at the Sanford School of Public Policy at Duke University and a Faculty Fellow of Duke’s Center for Child and Family Policy. Gassman‐Pines received her B.A. with distinction in psychology from Yale University, where she was an Affiliate of the Bush Center for Child Development and Social Policy, and her PhD in Community and Developmental Psychology from New York University. Her research focuses on the effects of low‐wage work and anti‐poverty policies on low‐income children and families’ well‐ being. She has received awards for both research and teaching, including a Changing Faces of American Young Scholars Award from Foundation for Child Development and the Sanford School of Public Policy’s Richard A. Stubbing Teacher Mentor Award. Her research has been funded by the National Science Foundation, National Institute of Mental Health, and the American Psychological Foundation. Rachel Goldstein is a Federal Healthcare Consultant with Deloitte Consulting LLP, based out of Washington, DC. She currently works on identifying clinical data sharing needs for electronic health record interoperability between two major Federal Departments. Rachel has a Master of Public Policy degree, with a concentration in Health Care Policy, from the Sanford School of Public Policy at Duke University and a B.A. from University of California – Davis.

Notes on Contributors  xiii Penny Hauser‐Cram is Professor of Applied Developmental and Educational Psychology at the Lynch School of Education at Boston College. She has written extensively on s­ervices for young children with developmental disabilities. Her current research focuses on longitudinal studies of children with developmental disabilities. In particular, she investigates the ways that the family, social services, and educational systems support c­hildren’s development and learning. Lauren Hay is currently a client coach at Joyable. Prior to that, she worked in several research labs focusing on mindset, emotion regulation, the neural bases of autism spectrum disorder, mental health issues in incarcerated populations, and social and emotional learning in youth. She graduated from Stanford University with a B.A. in Human Biology and Psychology and from the Harvard Graduate School of Education with an Ed.M. in Mind, Brain, and Education. Miriam Heyman is a Postdoctoral Associate at the University of Massachusetts Medical School. Her research focuses on factors that promote positive development of executive function and social skills, both for typically developing children and for children with developmental disabilities. Miriam earned her doctorate in Applied Developmental and Educational Psychology from the Boston College Lynch School of Education. Prior to her doctoral studies, Miriam worked as a special education elementary school teacher in the New York City Public Schools. She earned her master’s degree in special education from the City University of New York, and her master’s degree in Applied Developmental and Educational Psychology from Boston College. Aletha C. Huston is the Priscilla Pond Flawn Regents Professor Emerita of Child Development at the University of Texas at Austin. She specializes in understanding the effects of poverty on children and the impact of child care and income support policies on children’s development. Her books include Higher Ground: New Hope for the Working Poor and their Children (2007) (with Duncan and Weisner), Developmental Contexts of Middle Childhood: Bridges to Adolescence and Adulthood (2006), and Children in Poverty: Child Development and Public Policy (1991). She is Past President of the Society for Research in Child Development, the Developmental Psychology Division of the American Psychological Association, and the Consortium of Social Science Associations, and the recipient of the Urie Bronfenbrenner Award for Lifetime Contributions to Developmental Psychology in the Service of Science and Society. Anna D. Johnson is an Assistant Professor in the Psychology Department at Georgetown University. Dr. Johnson’s primary research focus has been on the potential of publicly funded early childhood education and care programs to reduce school readiness gaps between low‐income children and their more advantaged peers. To this end, she has extensively studied the use of the federal child care subsidy program and its effects on child care quality, type, and child development. In additional lines of work, Dr. Johnson is investigating associations between other threats to child well‐being, including food insecurity and maternal depression, and child and family outcomes. She is also extending her research

xiv  Notes on Contributors on predictors and consequences of child care subsidy receipt to explore participation in and effects of public food assistance programs. Dr. Johnson holds a Ph.D. in Developmental Psychology (with distinction) and a Masters in Public Administration, both from Columbia University. Stephanie Jones is the Marie and Max Kargman Associate Professor in Human Development and Urban Education at the Harvard Graduate School of Education. Her research, anchored in prevention science, focuses on the effects of poverty and exposure to violence on children and youth’s social, emotional, and behavioral development. Specifically, her work focuses on the causes and consequences of social‐emotional problems and competencies; strategies for altering the pathways that shape children’s social‐emotional development; and programs, interventions, and pedagogy that foster social‐emotional competencies among children, adults, and environments. Over the last 10 years her work has included both evaluation research addressing the impact of preschool and elementary focused social‐ emotional learning interventions on child and classroom outcomes; as well as new curriculum development, implementation, and testing. Jones is a recipient of the Grawemeyer Award in Education for her work on A Vision for Universal Preschool Education (Cambridge University Press, 2006) and the Joseph E. Zins Early‐Career Distinguished Contribution Award for Action Research in Social and Emotional Learning. Lisa L. Knoche is a Research Associate Professor and Director of the Nebraska Early Childhood Research Academy in the Nebraska Center for Research on Children, Youth, Families and Schools at the University of Nebraska–Lincoln. Dr. Knoche is an applied developmental psychologist with expertise in the design, development, and evaluation of early childhood intervention and prevention programs to support both healthy development in young children and family engagement in early learning. Dr. Knoche is particularly experienced in issues of implementation science, including measurement of fidelity across systemic levels, and she is interested in identifying and supporting effective professional development strategies for early childhood professionals. She has extensive experience in implementing collaborative research programs with community partners. Dr.  Knoche has authored publications to advance understanding of issues r­elevant to young children and families and has provided numerous local, national, and international presentations to advance early childhood science, and improve practice and policy. Nancy Donelan‐McCall, Ph.D., is a developmental psychologist, Associate Professor of Pediatrics at the University of Colorado, and Director of the DANCE (Dyadic Assessment of Naturalistic Caregiver‐child Experiences) program. She has over 25 years’ experience working to improve the lives of vulnerable children and families through research and applied practice. She has spent the past 10 years conducting programmatic quality improvement initiatives for the Nurse‐Family Partnership® with the goal of improving the tools and resources nurse home visitors use to support caregiving for low‐income, first‐ time mothers. She teaches courses on early childhood development, intervention, and observational measures as well as developed courses for early childhood educators on child development and the importance of early caregiver‐child relationships.

Notes on Contributors  xv Kathleen McCartney received her Ph.D. from Yale University in developmental psychology in 1982. Her research focuses on childcare and early childhood experience, education policy, and parenting. She has authored more than 150 articles and book chapters and was a principal researcher for a 20‐year study of the effects of child care on child development funded by the National Institute of Child Health and Human Development. She is a fellow of the American Academy of Arts and Sciences, the National Academy of Education, the American Educational Research Association, the American Psychological Association, and the American Psychological Society. In 2009, she was the recipient of the Distinguished Contribution Award from the Society for Research in Child Development. Currently, she is the president of Smith College. Dana Charles McCoy is an Assistant Professor at the Harvard Graduate School of Education. Her work focuses on understanding the ways that poverty‐related risk factors in children’s home, school, and neighborhood environments affect the development of their cognitive and socioemotional skills in early childhood. She is also interested in the development, refinement, and evaluation of early intervention programs designed to promote positive development and resilience in young children, particularly in terms of their self‐regulation and executive function. Before joining the HGSE faculty, Dr. McCoy served as an NICHD National Research Service Award post‐doctoral fellow at the Harvard Center on the Developing Child. She graduated with an A.B. in Psychological and Brain Sciences from Dartmouth College and received her Ph.D. in Applied Psychology from New York University. Emily C. Merz is a postdoctoral fellow in the Psychiatric Epidemiology Training program at Columbia University. She received her Ph.D. in clinical and developmental psychology from the University of Pittsburgh in 2012. Her research uses multiple levels of analysis to investigate the effects of early contextual risk and parental care on the development of top‐down control processes and the prefrontal cortex during childhood. Amanda L. Moen is a doctoral candidate in School Psychology and a graduate research assistant in the Nebraska Center for Research on Children, Youth, Families and Schools at the University of Nebraska  –  Lincoln. She is interested in family engagement in early childhood and the promotion positive outcomes for young children and their families. In particular, Ms. Moen is interested in understanding classroom, teacher, and administrative factors that may play a role in effective partnership between the teachers and parents of young children. Ms. Moen has co‐authored publications related to family‐school partnership, and has a number of regional and national presentations that inform the family engagement literature in early childhood. Pamela A. Morris is a Professor of Applied Psychology and the Vice Dean for Research and Faculty Affairs at NYU’s Steinhardt School of Culture, Education, and Human Development. Morris’s work lies at the intersection of social policy and developmental psychology. Examples of her current research include a study of income volatility, large‐ scale randomized experiments of enhancements to preschool, work with NYC’s Department of Education and the Mayor’s office to strengthen the research architecture in

xvi  Notes on Contributors the context of NYCs historic expansion of Universal Pre‐K, and the study of an integrated primary/secondary parenting intervention within the population‐scalable pediatric care platform. A former William T. Grant scholar, Morris currently serves as a lead editor of the Journal of Research on Educational Effectiveness and has served on a number of boards and review groups, including the National Academy of Science’s Board on Children, Youth, and Families and the Institute of Education Sciences’ Early Intervention and Early Childhood Education Panel. Kimberly G. Noble, M.D., Ph.D., is an Associate Professor of Neuroscience and Education at Teachers College, Columbia University. Trained as a cognitive neuroscientist and pediatrician, she studies socio‐economic disparities in children’s cognitive and brain development. Her work examines both brain structure and function across infancy, childhood, and adolescence. She is particularly interested in understanding how early in childhood such disparities develop, the modifiable environmental differences that account for these disparities, and the ways we might harness this research to inform the design of interventions. Dr. Mariela Páez is an Associate Professor at the Lynch School of Education, Boston College. She has a doctorate in Human Development and Psychology from the Graduate School of Education at Harvard University. Her primary research interests include bilingualism, children’s language and early literacy development, and early childhood education. Dr. Páez has conducted several longitudinal studies with young bilingual children with funding from the National Institute of Child Health and Human Development (NICHD) and the Office for Educational Research and Improvement, Department of Education. She is author of numerous articles and co‐editor of Latinos: Remaking America (with Marcelo Suárez‐Orozco, 2008). Soojin Oh Park is an Assistant Professor in Early Childhood and Family Studies, and an affiliate faculty of the West Coast Poverty Center at the University of Washington (UW). She is a core faculty member of the Learning Sciences and Human Development, and the Education, Equity, and Society programs. Prior to joining UW, she completed a research fellowship at the National Research Center on Hispanic Children and Families. Drawing on transdisciplinary perspectives in psychology, sociology, and public policy, she studies the effects of public policies, immigration, and poverty on parenting and children’s development, particularly among ethnically diverse, immigrant‐origin children. As a former editor of the Harvard Educational Review, she co‐chaired a special issue, Immigration, Youth, and Education. She holds a doctorate in Human Development and Education and an Ed.M. in Education Policy and Management from Harvard University, and a B.A. in psychology, summa cum laude, from the University of Pennsylvania. June Paul is currently a doctoral student in the School of Social Work at the University of Wisconsin‐Madison and a Graduate Research Fellow at the Institute for Research on Poverty studying children, youth, and families in the child welfare system; intersectionality and disproportionality among dimensions of race, class, sexual orientation and gender identity in child welfare; youth aging out of foster care; strategies for providing effective

Notes on Contributors  xvii services to lesbian, gay, bisexual, transgender, and questioning (LGBTQ) youth involved in social service systems; and policy and program evaluation. Prior to returning to graduate school, June managed state‐wide child welfare programming and policies for over 15 years. Deborah Phillips is Professor of Psychology and Associated Faculty in the McCourt School of Public Policy Institute at Georgetown University. She was the first Executive Director of the Board on Children, Youth, and Families at the National Academies and served as Study Director for the Board’s report: From Neurons to Neighborhoods: The Science of Early Child Development. She has also served as President of the Foundation for Child Development, Director of Child Care Information Services at the National Association for the Education of Young Children, and Congressional Science Fellow (Society for Research in Child Development). Her research focuses on the developmental effects of early childhood programs for both typically developing children and those with special needs, including research on child care, Head Start, and state pre‐kindergarten programs. Dr. Phillips currently serves on the National Board for Education Sciences for the US  Department of Education. She is a Fellow of the American Psychological Society and  the American Psychological Association. In 2011, she received the Distinguished Contributions to Education in Child Development Award from the Society for Research in Child Development. Robert C. Pianta is Dean of the Curry School of Education at the University of Virginia. He also holds positions as the Novartis Professor of Education, Founding Director of the Curry School’s Center for Advanced Study of Teaching and Learning (CASTL), Professor of Psychology at the UVa College of Arts & Sciences, and Director of the National Center for Research in Early Childhood Education. Pianta’s research and policy interests focus on teacher‐student interactions and relationships and on the improvement of teachers’ contributions to students’ learning and development. Pianta is the creator of an observational assessment of teacher‐student interactions known as the Classroom Assessment Scoring System™ with versions for use with infants through 12th grade students. He has also created professional development supports to improve teachers’ effectiveness called MyTeachingPartner™. Pianta began his career as a special education teacher. Upon completing a Ph.D. in Psychology from the University of Minnesota, he joined the University of Virginia faculty in 1986. He is a nationally recognized expert in both early childhood education and K–12 teaching and learning. Lianna Pizzo is an Assistant Professor at the University of Massachusetts Boston. Dr. Pizzo has worked in the field of early education and care for over 15 years as a school psychologist, family literacy teacher, program director, and educational researcher. Her scholarship focuses on the curriculum, instruction, and assessment for bilingual and multilingual populations. Her work has been with both spoken language bilingual populations as well as American Sign Language (ASL)‐English bilingual learners. Dr. Pizzo’s research includes an emphasis on linguistically responsive assessment practices of teachers in an urban public school with a high percentage of bilingual learners, ASL vocabulary instruction in ASL‐English bilingual classrooms, and fidelity of implementation of assessment in early childhood settings.

xviii  Notes on Contributors Her most recent work connects research on spoken language bilingualism and ASL‐English bilingualism to address the needs of deaf ASL users who come from homes where a language other than English is present, or deaf multilingual learners (DMLs). Terri J. Sabol is an Assistant Professor in the School of Education and Social Policy and Faculty Associate for the Institute for Policy Research at Northwestern University. She received her Ph.D. in Applied Development Science from the University of Virginia. Her research focuses on the individual and environmental factors that lead to healthy child development, with a particular emphasis on schools and families. Kristen S. Slack is a Professor of Social Work at the University of Wisconsin‐Madison. Her research focuses on understanding the role of poverty and economic hardship in the etiology of child maltreatment, with a particular emphasis on child neglect. She is also interested in the caseload dynamics of child welfare systems in relation to other public benefit systems, and in community‐based programs designed to prevent child maltreatment. Her work advances approaches to better coordinating services and benefits to effectively address the economic needs of families at risk for child maltreatment, and improved assessment strategies for identifying risks and protective factors related to child neglect. Susan M. Sheridan is Director of the Nebraska Center for Research on Children, Youth, Families and Schools (CYFS), and a George Holmes University Professor of Educational Psychology at the University of Nebraska – Lincoln. Dr. Sheridan’s research is focused on early childhood education and interventions; parent‐teacher relationships; the development of meaningful home‐school partnerships; and interventions promoting children’s social skills, social‐emotional development and behavioral competencies. Sheridan has published more than 100 books, chapters, and refereed journal articles on early childhood, family‐school partnerships, rural education, social‐emotional skills and development, and behavioral interventions. The American Psychological Association’s Division 16 (School Psychology) recognized her research excellence with the Lightner Witmer Award (1993) for early career accomplishments and the Senior Scientist Award (2015) for distinguished career‐long scholarship. She also received the 2005 Presidential Award from the National Association of School Psychologists, and the 2014 University of Nebraska’s Outstanding Research and Creativity Award. Elizabeth Votruba‐Drzal is an Associate Professor of Psychology at the University of Pittsburgh and a faculty affiliate of the Center on Race and Social Problems and the Learning Research and Development Center. Elizabeth received her Ph.D. in Human Development and Social Policy from the School of Education and Social Policy at Northwestern University in 2004. Her research aims to strengthen understanding of the influences of socio‐economic status, early childhood education and care, schools, families and communities on child development, with a particular focus on the lives of children from economically disadvantaged and immigrant families. Sharon Wolf is an Assistant Professor in the Graduate School of Education at the University of Pennsylvania. Her work focuses on the social and environmental determinants

Notes on Contributors  xix of child development and inequalities, with a focus on disadvantaged populations in the US, and in low‐income and conflict‐affected countries. After receiving her Ph.D., Sharon was a National Poverty Fellow with the Institute for Research on Poverty, where she was in residence at the US Department of Health and Human Services conducting poverty‐ related research and analysis. She received her Ph.D. in Psychology and Social Intervention with a concentration in Quantitative Analysis from New York University. Hirokazu Yoshikawa is the Courtney Sale Ross Professor of Globalization and Education at the Steinhardt School of New York University, and a University Professor there. He studies the effects of public policies and programs related to immigration, early childhood development, and poverty reduction, on child and youth development. He conducts research in the United States and in low‐ and middle‐income countries. He co‐directs, with Larry Aber, the Global TIES for Children (Transforming Intervention Effectiveness and Scale) Center at New York University, a center devoted to research on programs and policies for children in low‐income and conflict‐affected countries. He also currently serves on the Leadership Council and as the Co‐Chair of the ECD and education workgroup of the UN Sustainable Development Solutions Network, the research and technical group advising the Secretary‐General on the 2015–2030 Sustainable Development Goals. He serves on the boards of the Foundation for Child Development and the Russell Sage Foundation.


A decade ago, Blackwell published its first Handbook of Early Childhood Development (McCartney & Phillips, 2006). This second edition (Votruba‐Drzal & Dearing, 2016) offers much more than an update of the earlier volume. As with developmental growth, the chapters in this new volume capture both gradual advancements and dramatic leaps in the field’s attention and capacity to address pressing national issues facing young children, their families, and the social institutions that serve them. The trajectory of knowledge represented in this volume is characterized by both continuities and discontinuities. And, while sometimes linear, the chapters fully demonstrate how the field’s course towards greater understanding of development and how to best to support lifelong well‐being takes unexpected turns and even experiences set‐backs en route to continued growth. What binds the two volumes is the field’s enduring fascination with the extraordinarily complex, adaptive capacity of the young child‐in‐context and the associated optimism of those who seek – through programs, practices, and policies – to direct this capacity towards promising lifelong development. The driving questions in the field continue to be: How do we account for individual differences in development, given the complex interplay between nature and nurture? When is there continuity and when is there change – or what is malleable? What are the mediating processes – genetic, biological, environmental – that support healthy development? How can we translate scientific knowledge into effective intervention strategies? And, how can we most responsibly move policy and practice forward with imperfect knowledge? This volume is replete with new insights into these questions. Below, we highlight five insights that cut across the volume’s chapters. Notably, as indicated by this volume’s title – Handbook of Early Childhood Programs, Practices, and Policies – it is more applied in focus than was its predecessor. Far from implying that the evidentiary base on early development is fully mature and set to support a science of application and intervention, the authors cycle between evidence and its application and back to evidence, thus taking seriously Bronfenbrenner’s charge to the field that, “if you want to understand something, try to change it” (Bronfenbrenner, 1977, cited in Morris & Connors, this volume, pg. 20 manuscript). Indeed, virtually every set of

Foreword  xxi authors in this volume acknowledges their intellectual debt to bioecological theory. Moreover, like young children, science develops in a context. Today, this context includes pressures from policymakers to identify “what works” and elevate approaches that have met the test of rigorous evaluation (see Jones et al., this volume). These pressures have fueled urgency within the field to deploy our empirical knowledge in the service of designing and documenting successful strategies for improving early developmental outcomes and life trajectories. Indeed, policymakers are increasingly mandating and scripting the specifics of program and policy evaluation initiatives, such as the National Head Start Impact Study, the Parents and Children Together evaluation, and the Maternal, Infant, and Early Childhood Home Visiting Program passed as part of the Patient Protection and Affordable Care Act. This Handbook’s focus on practices, programs, and policies reflects the field’s own positive adaption to this shifting policy context. The first volume of the Handbook marked a period when the explosion of neurobiological and epigenetic evidence solidly identified the early years of life as a period when contexts and experiences have an especially profound impact on lifelong well‐being. In this volume, authors highlight advancements over the past decade in the field’s understanding of the mechanisms that underlie early malleability and their exquisite sensitivity to environmental variation, including variation within the normal range of what most young children experience. As such, all early environments  –  not just those explicitly designed as interventions for children designated “at risk” – constitute “interventions” that shape development (explicitly or implicitly, for better or for worse) and are, in turn, amenable to being shaped by programs, practices and policies. This point of view is reflected in the wide swath of typical contexts covered in this volume. Families (e.g., parenting, marriage, working families) and early childhood care and education environments (e.g., child care, preschool education, classroom curricula) receive extensive attention – greater attention than in the 2006 Handbook. Morris and Connors (this volume) even call on the field to observe children in school lunchrooms and during recess. But, the authors also summarize advances in services and policies that are deliberately and explicitly designed to intervene in young children’s lives, e.g., poverty programs, special education, dual language instruction, and the child welfare system. This volume also updates the evidence base on several targets for efforts to redirect the course of development that have long commanded the field’s attention: the quality of the proximal adult‐child interactions that children experience, the adequacy of a family’s economic and social resources, and the supports for early learning that young children receive. But, it notably advances this evidence base with its attention to adult‐focused interventions as an essential component of child or family‐focused interventions. Classroom‐based interventions now place a much stronger emphasis on professional support in the form of personalized coaching and mentoring, which go far beyond simply training teachers to implement instructional strategies (see Jones, McCoy, & Hay, Chapter 11). Taking this one step further, Johnson calls for explicit attention to the economic and psychological well‐being of child care teachers as essential to the success of quality improvement initiatives (Chapter 12). The focus on family‐centered practices in the special education field has led to a growing emphasis on family capacity‐building approaches that address directly the high levels of parenting stress and poor self‐efficacy that characterize families of children with disabilities (see Hauser‐Cram, Heyman, & Bottema‐Beutel, Chapter 10).

xxii Foreword The inclusion of Coley’s chapter on marriage policy and Gassman‐Pines’ and Goldstein’s chapter on work‐family policies explicitly acknowledges the power of adults’ exosystems to either support or undermine interventions focused on young children, and the importance therefore of directing intervention strategies towards these more child‐distal systems as essential to facilitating positive child development. This heightened emphasis on exo‐level contexts is accompanied by far greater attention to the barriers that confront efforts to intervene in the lives of young children and the essential need for highly strategic and sustained approaches to supporting change efforts. The designers of the new generation of home visiting (see Chapter 14) and two‐generation approaches (see Chapter 15), for example, have displayed a highly sensitive understanding of the need to address directly parents’ motivation and capacity to participate in these interventions, including experimentation with cohort models. “Community health promoters,” trusted community members who serve as outreach agents, are being used in immigrant neighborhoods to connect families to services and benefits (Park & Yoshikawa, Chapter 16). The era of “build it and they will come” approaches to family‐focused interventions has come to an end. Today’s 2‐G programs have also adopted localized human capital‐building approaches tailored to the job markets in each program site and the pertinent credentials that employment in these markets requires. Similarly, those who design classroom‐based interventions have arrived at a much greater appreciation for the need to  offer teachers tools and strategies that are directly focused on desired outcomes (e.g., improved social skills, self‐regulatory capacities) and can be inserted into ongoing classroom interactions to promote flexibility (Jones et al., Chapter 11). Attention to the supports necessary for sustained progress is leading 2‐G programs to deploy “workforce intermediaries” who help parents bridge educational and job settings, child care programs to institutionalize mentors as ongoing participants in quality improvement efforts (see Johnson, Chapter 12, and Morris and Connors, Chapter 5), and child welfare agencies to turn to intensive family preservation services and Family Group Decision‐making approaches to address recurring referrals and promote sustained reunification plans. These new approaches and intermediary roles, in turn, offer new opportunities in which to study child development in context. The macrosystem of public policies affecting children and families figures prominently in both editions of the Handbook. What is new and exciting in this second edition, in addition to its more explicit attention to this layer of the ecological model (six chapters devoted to public policies), is the extent to which many of the chapters not in this section integrate pertinent federal and state public policy actions into their discussion (e.g., the Affordable Care Act in the chapter on home visiting, the federal Race to the Top Early Learning Challenge initiative in the chapters on classroom interventions and on family‐ school partnerships, and the Individuals with Disabilities Education Act in the chapter on children with disabilities). In the child care chapter, Johnson summarizes research that examines the developmental impacts of the federal Child Care and Development Fund and state Quality Rating and Improvement Systems. Issues not mentioned in the 2006 Handbook such as food insecurity, income volatility and chaos, domestic violence, linkages between special education and child welfare policy, household physical conditions and environmental toxins, and conditional cash transfer programs have now entered into empirical discourse and are shaping the research agendas of scientists to an unprecedented

Foreword  xxiii extent. There is no stronger evidence that Bronfenbrenner’s wish that “policy will guide science as much as science will guide policy” has come true. By including chapters devoted to immigrant families, dual language learners (DLLs), and children with disabilities, this second edition of the Handbook carries the potential to foster much‐needed integration of the research literatures on these rapidly growing populations within our nation, and our early childhood programs, into the core science of early development. These children, for example, too often remain as afterthoughts rather than central foci of efforts to examine heterogeneity of developmental processes and program impacts, yet the opportunities to bring new perspectives, constructs, measures, and intervention approaches to research on all children and families are vast. For example, the rapidly growing number of DLLs and children with disabilities in child care and pre‐k programs has led to the development of specialized assessments of program quality. These assessments include attention to issues of inclusion and peer acceptance, incorporation of a child’s home language and culture, and parent engagement that are not included in most commonly used assessments of the quality of early care and education settings, yet are important for all children’s happiness, comfort, and well‐being in these environments. A  stronger cross‐walk between the special education and early education fields could advance a more nuanced and appropriate use of special education placement as an indicator of mid‐term persistence or fade‐out of early education impacts – one that, rather than assuming that all special education placements represent a failure of early education, d­ifferentiates appropriate and beneficial placements from those that truly signal lack of support for the early learning of a young child. The work on immigrant families has brought the developmental significance of experiences of racialized discrimination and stigmatization, parental detention (real and feared), and community acceptance to the field’s attention. Yet these issues are not restricted to immigrant children and their parents, and, as such, need to be incorporated into the mainstream of developmental science as potential risk and protective factors ‐‐ and targets for programs and policies – especially as we become a non‐majority nation, a trend for which young children are the harbingers. The authors in this volume also identify some of the thorny questions that will guide the next decade of developmental science aimed at informing effective programs, practices, and policies for young children. Many of these questions revolve around methods and assessment. First, we know, and have known for years, that the “transforming experiment,” as Bronfenbrenner labeled it, provides the best causal evidence for change efforts, whether a parenting program or an immigration policy. Yet, as Burchinal and Forestieri (Chapter  6) have noted, “behavioral treatment,” whether naturally occurring, like m­altreatment, or a specified intervention, like special education, poses both ethical and pragmatic challenges to evaluation via randomized, controlled trials. Further, rigorous replication is rare in developmental science, and small impacts of change efforts remain the norm. How then do we think about translating an imperfect knowledge base into programs and policies? We have only begun to answer that question; however, as Burchinal and Forestieri conclude, we must rely on judgment, while appreciating the tension that necessarily exists between judgment and evidence, because real children depend on our action today. Second, many of our measures lack psychometric study, especially with respect to validity. The urgency of this work is compounded by the fact that many of the field’s

xxiv Foreword constructs and measures inadequately reflect issues raised by the growing diversity of the early childhood population. Third, examinations of longer term impacts are rare, disappointing, and lack strong theories of change that incorporate mediating processes, presumably because they are costly and difficult to implement. Yet, follow‐up studies are necessary to help the field refine its understanding of how the impacts of early environments are re‐shaped by children’s subsequent experiences, and how to capture these influences. Developmentalists have theorized that early experiences have cascading reciprocal effects on a wide variety of outcomes (Berry, Chapter  3). In order to understand the mechanisms to explain these effects, especially biological mechanisms, we need to approach developmental science as the study of development. Finally, several of the authors here highlight important findings from new epigenetic research. For example, Berry notes, it is likely that early childhood is a “critical developmental ‘switch point’ in the biological embedding of experience” (p. 37 manuscript). Merz and Noble (Chapter 7) summarize existing research that is consistent with the view that early adversity plays a causal role in shaping brain development, providing some of the best evidence in support of early interventions, whether parent‐ or child‐focused. Collaborations between behavioral and biological scientists are likely to transform the field. The challenges of developmental science can be viewed through the lens of opportunity – to advance the field through studies that enable stronger causal inferences and identify the most powerful malleable factors for next‐stage intervention efforts, to promote advances in measurement including those necessary for studying our increasingly diverse young child population, to advocate for funding for longitudinal research, and to advance work on the biological embedding of experience. By doing so, we will advance science as well as our ability to serve as a voice of knowledge and conscience for societal investments in improved life circumstances for young children. Although young children are “complex creatures living in a complex world,” we have produced a robust knowledge base on early experience and development (Bradley, Chapter 4, p. 3). With increasing public support for early childhood investments as smart economic and societal policy, scientists and practitioners alike bear increasing responsibility for advancing knowledge about how to design, implement, evaluate, and improve upon effective strategies for ensuring positive early developmental outcomes. The editors and authors have our enthusiastic congratulations, as well as our admiration and gratitude, for contributing substantially to this essential goal. They have produced what will surely prove to be the essential guidebook for the next decade of applied research on early childhood programs, practices, and policies. Deborah Phillips Kathleen McCartney


The cumulative scientific knowledge on early childhood is perhaps greater than for any other time in the life span, with a foundational theme in the study of human development being the formative role of early experience. In turn, attempts to apply this knowledge to early childhood programs, practices, and policies are defining features of the zeitgeist, with the study of applied early childhood development now a thriving interdisciplinary field. Moreover, ideological divides in the United States over the roles of government in the lives of children, aside, attention to early childhood from stakeholders outside of the sciences has never been greater; families, communities, businesses, and policy decision makers across the political spectrum have increasingly recognized the importance of a healthy and thriving childhood for the future of the country (Shonkoff, 2010; Tseng, 2012). Yet, this Handbook also comes at a critical demographic nexus for the United States. Rapidly rising economic inequality is opening a radical divide between the lives of those with exceptional wealth and the many more in exceptional poverty. The large number of young children on the losing end of this divide face disadvantage in their homes, early learning centers, and communities that translates into meaningful differences in early childhood development. For example, our own analysis of nationally representative data from the Early Childhood Longitudinal Study Kindergarten Cohort of 2010 shows that children from low‐ income households score between 0.80 and 1 full standard deviations lower than their high‐ income counter parts when it comes to reading, math, and science achievement at kindergarten entry. These gaps tend to persist or even grow as children develop and give rise to large differences in educational achievement and attainment, as well as adult employment and earnings (Duncan, Magnuson, & Votruba‐Drzal, 2015; Jäntti, 2009). Socio‐economic gaps in behavioral skills that are essential for success in school, including attention and conduct skills, begin in early childhood as well and continue into adulthood (Duncan et al., 2015). Although narrowing modestly very recently, economic gaps in children’s development have grown in recent decades: the income‐achievement gap is 30–40% larger for children born in 2001 than it was for children born in the late 1970s and is now nearly twice the size of the Black–White test score gap (Reardon, 2011; Reardon & Portilla, 2016).

xxvi Preface At the same time that economic inequality has grown, the United States has also been dramatically reshaped by immigration across the last two decades, no more so than for the population of young children in the country. In particular, the population of first‐ and second‐generation immigrant children in the US grew by more than 50% between 1995 and 2014. Today, children of immigrants make up one fourth of all children in the US (Child Trends Databank, 2014). The implications of the increased cultural and linguistic diversity have become a pressing topic of interest for all professionals who are working with, shaping policies for, or studying young children. In addition, the typical family structures of young children have shifted, particularly for young children of color and those growing up poor. Births to unmarried mothers grew from 32% of all births in 1960 to 40% in 2014 and are particularly prevalent for ethnic minority women. In 2013, 72% of all births to Black women, 53% of births to Hispanic women, and 66% of births to Indian or Alaskan native women were to unmarried women (Child Trends Databank, 2015). Discouragingly, young children have also, for several years, been overrepresented among those whose families are involved with child welfare services, including m­altreatment cases. There does not exist, however, a comprehensive assessment of applied developmental science on early childhood in this new national context. To meet this need, the present Handbook provides a thorough compiling of knowledge – theory and empirical work – on contemporary early childhood development programs, practices, and policies. Bringing together the cumulative expertise, the Handbook provides guidance for future scientific inquiry, and intends to guide the design and implementation of future policies and programs for young children and their families. To do so, the Handbook is organized in four sections. In the first section, four chapters capture the state of young children in the United States, focusing on their achievement (Chapter  1, Sabol & Pianta), mental health (Chapter 2, Campbell), physical health (Chapter 3, Berry), and exposure to risk (Chapter 4, Bradley). Together, these chapters offer a wide‐angle view of children’s lives, today, including opportunities for and challenges to improving developmental outcomes. In turn, the next three chapters help contextualize the state of applied developmental science on early childhood. Specifically, this section starts (Chapter 5, Morris & Connors) with a historical and forward‐looking perspective on the foundation of our field in bioecological systems theory, taking stock of how far we have come and how far we have yet to go to realize Urie Bronfenbrenner’s vision. From there two empirical advances in the field are summarized and assessed: the movement toward empirically based programs, practices, and policies (Chapter 6, Burchinal & Forestieri) and dramatic neuroscience advances in our understanding of early growth and development (Chapter  7, Merz & Noble). These three chapters help frame the conceptual and scientific relevance for each of the following chapters in the Handbook and, more generally, the critical need to focus empirical work on early childhood. The remaining sections of the Handbook directly address programs, practices, and policies relevant for young children’s development. In these sections authors review existing theoretical, empirical, and applied issues most relevant for the topics at hand. As a final step in their evaluation of the state of the empirical evidence, we have asked authors to reflect on the principles laid out in Chapter 5, concerning internal and external validity and practical importance of findings. To do so, most chapters in the following sections

Preface  xxvii provide brief summary tables of empirical benchmarks for the program, practice, and policy topics at hand. These tables are efficient complements to the rich, precise evaluation provided in the text of the chapters. Two sections in the Handbook are focused on programs and practices. One section addresses early childhood education and care, including public preschool (Chapter  8, Barnett, Votruba‐Drzal, Dearing, & Carolan) and ECEC considerations for dual language learners (Chapter 9, Pizzo & Páez) and children with developmental disabilities (Chapter 10, Hauser‐Cram, Heyman, & Bottema‐Beutel). This section also covers classroom‐based intervention models (Chapter  11, Jones, McCoy, & Hay) and early child care (Chapter  12, Johnson). Together, these chapters provide a thorough review of early care and learning contexts that young children experience outside of their homes as well as a careful assessment of the empirical knowledge on salient programmatic, practice, and policy features that may maximize the benefits of these contexts for select populations and US children, at large. As a closely connected follow‐up to the focus on ECEC, three chapters cover family‐ school partnerships in early childhood (Chapter 13, Sheridan, Moen, & Knoche), parenting and home‐visiting interventions (Chapter 14, Donelan‐McCall), and dual‐generation interventions (Chapter 15, Gardner, Brooks‐Gunn, & Chase‐Lansdale). Each addresses the cross‐context links that characterize children’s early lives and the relevance of thriving connections between home, school, and community as well as the relevance of supporting parents’ social, emotional, and cognitive functioning and capacities as a route toward supporting their parenting and their children’s growth. To conclude, the final section of the Handbook covers policies with serious implications for young children and their families. These policy chapters are purposefully focused on the new demographic reality of the nation’s children with special attention to immigration (Chapter 16, Park & Yoshikawa), marriage (Chapter 17, Levine Coley), child welfare (Chapter 18, Slack & Paul), income (Chapter 19, Huston) and cash‐supports (Chapter 20, Wolf, Berg, Morris, & Aber), and work‐family policies (Chapter 21, Gassman‐Pines and Goldstein). Collectively, these chapters address policies at local, state, and federal levels that have consequences for children and their families. In so doing, they provide coverage of a thorough array of public approaches to supporting children and families. As a collection, this Handbook identifies strategies that are most effective for promoting early childhood development, with a focus on early childhood development in context – family, school and community, and society – laying out the present landscape of young children’s lives in the United States, offering calls to critical theoretical and empirical concerns for guiding science, and synthesizing scientifically rigorous applied research. Bringing together this knowledge and expertise, we hope to direct the early childhood science disciplines toward the next generation of cutting edge empirical questions. Moreover, we hope this Handbook serves as a resource for stakeholders in the lives of young children from the worlds of policy and practice, our political and professional leaders who must choose how to best support young children and their families. For these audiences, the Handbook authors have carefully scrutinized our cumulative knowledge and have clarified which strategies hold the greatest promise for improving the lives of young children and their families, particularly those who are presently vulnerable and marginalized. As many of the authors eloquently note, making use of this knowledge will have lasting positive consequences for the nation’s social and economic future.

xxviii Preface

References Child Trends Databank. (2015). Births to unmarried women. Available at: http://childtrends. org/?indicators=births‐to‐unmarried‐women – See more at: http://childtrends.org/?indicators= births‐to‐unmarried‐women#sthash.QTB7UBlJ.dpuf Child Trends Databank. (2014). Immigrant children. Available at: http://childtrends. org/?indicators=immigrant‐children ‐ See more at: http://childtrends.org/?indicators=immigrant‐ children#sthash.YSiHX16J.dpuf Duncan, G. J., Magnuson, K. A., & Votruba‐Drzal, E. (2015). Socioeconomic status and child development. In M. H. Bornstein, T. Leventhal, & R. Lerner (Eds.), Handbook of child psychology, Vol. 3: Ecological settings and processes (7th ed.). Hoboken, NJ: John Wiley & Sons, Inc. Jäntti, M. (2009). Mobility in the United States in comparative perspectives. In S. Danziger & M. Cancian (Eds.), Changing poverty, changing policies (pp. 180–200). New York, NY: Russell Sage Foundation. Reardon, S. F. (2011). The widening academic achievement gap between the rich and the poor: New evidence and possible explanations. In G. J. Duncan & R. J. Murnane (Eds.), Whither opportunity: Rising inequality, schools, and children’s life chances (pp. 91–116). New York, NY: Russell Sage Foundation. Reardon, S. F., & Portilla, X. A. (2016). Recent trends in income, racial, and ethnic school readiness gaps at kindergarten entry. AERA Open, 2(3), 1–18. doi: 10.1177/2332858416657343 Shonkoff, J. P. (2010). Building a new biodevelopmental framework to guide the future of early childhood policy. Child Development, 81, 357–367. Tseng, V. (2012). The uses of research in policy and practice. Social Policy Report, Vol. 26(2). Ann Arbor, MI: Society for Research on Child Development.

Part I The State of Young Children in the United States

CHAPTER ONE The State of Young Children in the United States: School Readiness Terri J. Sabol and Robert C. Pianta

School readiness refers to the set of foundational skills, behaviors, and knowledge children display as they enter school that enable them to achieve academic success in elementary school, graduate from high school, and eventually thrive in the workforce and beyond (La Paro & Pianta, 2000; Pianta, Cox, & Snow, 2007; Zaslow, Tout, Halle, Whittaker, & Lavelle, 2010). Children prepared to adapt to the school environment when they enter kindergarten are more likely to meet academic and social demands of the classroom and succeed in school. Although there is no clear consensus on the exact definition of school readiness, it is generally agreed to include a combination of cognitive, language, executive functioning, socioemotional, behavioral, and health characteristics that cooperate to p­romote children’s functioning in a school setting (Boivin & Bierman, 2013; Sabol & Pianta, 2012). In the United States, kindergarten teachers report that children on average are not fully prepared to meet the demands of the classroom environment, particularly in terms of academic skills. In 2010–2011, teachers reported that only 27% of children were proficient in reading and math at school entry based on a nationally representative sample of newly entering kindergarteners (Bernstein, West, Newsham, & Reid, 2014). Moreover, the United States has large disparities in school readiness based on children’s family backgrounds. Children from low‐income backgrounds are almost a year behind at school entry The Wiley Handbook of Early Childhood Development Programs, Practices, and Policies, First Edition. Edited by Elizabeth Votruba-Drzal and Eric Dearing. © 2017 John Wiley & Sons, Inc. Published 2017 by John Wiley & Sons, Inc.

4  Sabol and Pianta in terms of their academic and language skills compared to children from higher income families (Denton Flanagan & McPhee, 2009; Halle et al., 2009). To promote children’s school readiness, there is a large and growing movement to invest in high quality education and care of young children before they enter school. The largest share of this investment is spent on early childhood education for 3‐ and 4‐year‐old children, which includes the federally funded Head Start program as well as state‐funded preschool programs. In particular, there is increasing momentum to expand access to high quality early childhood education programs (Barnett, Votruba‐Drzal, Dearing, & Carolan, in this volume). At the federal level, over $6 billion dollars is spent annually for Head Start and states spend approximately $5 billion annually on public pre‐kindergarten programs. This collective investment represents an increase of almost $4 billion in early education programs compared to a decade ago (Barnett, Carolan, Squires, & Clarke Brown, 2013; US Department of Health and Human Services, 2014). The substantial investment in young children will only lead to lasting change if the early childhood interventions target the skills that matter most for children’s short‐ and long‐term development (Pianta, Barnett, Burchinal, & Thornburg, 2009). This is predicated on the assumption that the field has a clear definition (and accompanying assessments) of school readiness that serves two critical functions: (a) it consistently predicts children’s performance over time; and (b) it accurately highlights children’s performance as well as inequalities in children’s outcomes. Without a definition and assessment that addresses these two key aims, any early childhood education intervention may only target a portion of the skills that are important for later school success. The broad aim of this chapter is to describe: (1) school readiness in the United States in the 21st century; (2) the current framework for assessing school readiness and how this may be strengthened; (3) gaps in school readiness based on this framework; and (4) the importance of aligning early childhood interventions and policies to more comprehensive definitions of school readiness. We pay particular attention to ways in which our measurement of and policies targeting school readiness can work together to improve the life chances of children.

School Readiness in the United States in the 21st Century Definition of school readiness Researchers, educators, and policymakers generally agree that school readiness is a multidimensional concept that includes cognitive, executive functioning, language, socioemotional, behavioral, and health characteristics that contribute to children’s ability to adapt and thrive in school settings (Boivin & Bierman, 2013). These performance domains are correlated but typically are assessed and studied as independent indicators of school readiness and predictors of later achievement. Importantly, the guiding definitions of school readiness typically include skills and behaviors that are related to learning processes as well as learning outcomes, as opposed to the K–12 system, which often only emphasizes student outcomes based on children’s performance on academic achievement tests.

School Readiness  5 In the area of cognition, school readiness includes both acquired knowledge or skills in particular content area (such as knowing a certain number of letters) as well as learning/ processing skills or how fast children acquire knowledge. In particular, there has been a growing emphasis on executive functioning skills and how these skills interact with other domains to promote learning in preschool classrooms. Executive functioning typically is defined as the set of skills and behaviors required to attain a goal, including working memory, attention control, attention shifting, and response inhibition. For young children, this means being able to resist distractions (e.g., pay attention to a teacher rather than talk with peers), inhibit dominant responses in emotional contexts (e.g., raise hand instead of talking while the teacher is reading a book), and prioritize and sequence information and hold onto it in memory (e.g., plan and carry out the series of steps required to line up for lunch; Diamond, 2006; Jacob & Parkinson, 2015). In addition, school readiness includes children’s language skills, including their receptive language (i.e., the ability to listen and understand language) and expressive language (i.e., the ability to communicate with others using verbal language). Children’s socioemotional skills are also an important component of school readiness and include behaviors such as cooperation with teachers and peers and developing social relationships, as well behavior problems, including aggression or poor regulation. There are also a set of skills referred to as approaches to learning, which reflect children’s curiosity, flexibility, attention, persistence, and engagement. The physical health domain includes motor development, such as development of fine and gross motor skills, and healthy behavior practices. Collectively, all of these skills are theorized to affect children’s learning opportunities and their acquisition of new skills and behaviors in the classroom setting (Diamond, 2006; Jacob & Parkinson, 2015). Most early childhood education policies recognize the importance of children’s skills across these multiple domains. The Race‐to‐the‐Top Early Learning Challenge, a grant competition that was part of the American Recovery and Reinvestment Act and designed to close the achievement gap, delineated the key components of school readiness that generally align to the field’s multidimensional, comprehensive definition. These “essential domains of school readiness,” based strongly on the framework from the National Education Goals Panel, include language and literacy development, cognition, general knowledge (e.g., early mathematics and early scientific development), and physical well‐ being, as well as children’s approaches to learning and executive functioning skills, and socioemotional development (Kagan, Moore, & Bradekamp, 1995; US Department of Education, 2014).

Children’s readiness for school in the United States In the United States, children vary in terms of their readiness for school entry across domains. Results from a nationally representative study of over 8,000 newly entering kindergarten children from most recent cohort of the Early Childhood Longitudinal Study‐ Kindergarten Class of 2010–2011 (ECLS‐K: 2011) indicate that only a quarter of children were deemed “proficient” or ready for school in reading and math based on teacher reports. Although this suggests that the majority of children were not ready for school from

6  Sabol and Pianta teachers’ perspectives, this percentage has increased over the past 10 years when compared to the same measures from the Early Childhood Longitudinal Study‐Kindergarten Class of 1998–1999 (ECLS‐K: 1999; Bassok & Latham, 2014). In the ECLS‐K: 2011, teachers did report that, compared to reading and math skills, children have higher levels of p­roficiency in terms of motivation, engagement, and socio‐emotional competency at school entry. More specifically, teachers reported that 48% of children had proficient l­evels of motivation and engagement in school, and 52% of children were proficient in socioemotional and executive functioning skills. In addition, there are large disparities in achievement at school entry based on family background. In particular, trends from a nationally representative longitudinal study of approximately 14,000 children in the United States (the Early Childhood Longitudinal Study Birth Cohort [ECLS‐B]) demonstrate large gaps in achievement between higher income and lower income children. At age 4, children from families in the bottom income quintile performed over a standard deviation behind their peers in the top quintile on literacy and math assessments. In addition, at‐risk children (defined by low‐maternal e­ducation or low‐income) performed lower on assessments of learning and processing of information, including working memory and cognitive flexibility, performing .85 and .76 of a standard deviation behind their more advantaged peers (Bernstein et al., 2014). Low‐ income children also had more behavioral problems, although the gaps were not quite as large (Bradbury, Corak, Waldfogel & Washbrook, 2011). These income‐based achievement gaps in the United States, particularly in terms of cognitive and language skills, are larger than many other OECD countries, such as Australia and Canada (Bradbury et al., 2011). In addition, the income‐based achievement gap is now larger than the race‐based achievement gap, which in fact narrowed over the same period (Fryer & Leavitt, 2004; Reardon, 2011; Reardon, Robinson‐Cimpian, & Weathers, 2014). Currently, at kindergarten entry, the income‐based achievement gap (top quartile versus bottom quartile) is almost twice the size of the Black‐White achievement gap (1.39 versus 0.73 standard deviation gap; Reardon, 2011). There are also differences in achievement between first‐ and second‐generation immigrant children and non‐immigrant children. Immigrant families have been expanding more rapidly than any other demographic group, representing close to one‐fourth of the national population (Hernandez & Napierala, 2012). Although there is an increasing number of children of immigrants who come from middle‐ or high‐income backgrounds with well‐educated parents, a significant portion of immigrant children are low‐income (Hernandez, Denton, Macartney, & Blanchard, 2012). Differences in cognitive and language development between low‐income children of immigrants versus native families emerge early in life and this gap grows over time. Using the Fragile Families and Child Wellbeing Study, a birth cohort study of over 5,000 children born in large US cities to predominately unwed mothers, Yiu (2011) found that children of immigrants at age 3 were about half of a standard deviation behind on a standardized measure of language compared to children of native families (defined as third generation or greater). Two years later, the gap had doubled, growing to over a standard deviation in language scores. DeFeyter and Winsler (2009) found a similar pattern in the early language and c­ognitive skills of immigrant children among a sample of low‐income children receiving subsidies to

School Readiness  7 attend childcare centers in Miami, Florida. However, they also found that children from immigrant families had a number of strengths in terms of their social skills. Preschool‐aged children of immigrants (first and second generation) lagged behind their non‐immigrant peers in terms of their cognitive and language skills. Yet, children from immigrant families were rated by teachers as demonstrating more initiative, self‐control, and fewer behavior problems than children in non‐immigrant families.

Current approaches to measuring school readiness One challenge to characterizing school readiness and identifying disparities in skills is that school readiness is a multidimensional construct. Current approaches to assessing school readiness skills are structured around a framework of readiness‐related constructs and the particular methods for measuring them. As a result, the prototypic approaches for measuring school readiness constructs often involve a multi‐informant, multi‐ assessment approach, including a combination of teacher report, parent report, and direct assessment. For example, the ECLS‐K: 2011 included direct assessments, parent reports, and teacher reports across a range of outcomes. Direct assessments were used to assess c­hildren’s reading, mathematics, and science skills. Children’s fluid cognition or executive functioning was assessed through physical and computerized tests of cognitive flexibility and working memory. Children’s height and weight were also measured in person during data collection rounds. To complement the direct assessments, teachers evaluated children’s academic performance on language and literacy, science, and mathematical thinking. These rating scales were designed to assess both learning processes and outcomes (whereas the direct assessments were only to assess outcomes). In addition, teachers reported on  the  quality of teacher‐child relationships and teachers and parents also reported on c­ hildren’s social interactions, attentional focus, self‐control, problem behaviors, and approaches to learning. The ECLS‐K: 2011’s measurement approach is common in most other large‐scale studies of early childhood development in the United States, such as the National Institute of Child Helath and Human Development Study of Early Child Care and Youth Development (NICHD SECCYD), the Head Start Family and Child Experiences Survey (Head Start FACES), the Fragile Families and Child Wellbeing Study, and the Panel Study of Income Dynamics‐ Child Development Supplement. In addition, many local and state initiatives that screen and evaluate children’s school readiness assess children across a n­umber of domains (although a few states that require kindergarten assessments at school entry only require measures of literacy performance; Stedron & Berger, 2010). These studies and initiatives contribute to a body of knowledge on a set of well‐validated tools that can be used to assess school readiness and have provided important knowledge on the ways in which indicators of performance across multiple domains lead to varying conclusions about rates and nature of school readiness gaps in the US population. There have been recent efforts to develop relatively simple measures that can provide population‐based portraits of kindergarten readiness to inform the design of interventions and policies. The Early Development Instrument (EDI; Janus & Offord, 2007) is perhaps

8  Sabol and Pianta the best example of this approach. The EDI is a teacher‐report questionnaire that focuses on five key areas of school readiness: physical health and well‐being, social competence, emotional maturity, language and cognitive development, and communication skills and general knowledge. Teachers complete the EDI for individual children in a class, but the scores are then aggregated to the population of interest, including school, neighborhood, state, or country level. This measure has established validity at the community level and has been used to identify disparities in school readiness based on family background characteristics (Janus, Brinkman, & Duku, 2011). Recent advances in measurement techniques have led to a set of low‐cost, reliable measures of children’s skills across a range of multiple domains. For example, the National Institutes of Health Toolbox, developed by experts in neurological and behavioral health measurement, includes a number of short assessments of cognition, emotion, motor function, and sensation explicitly designed for large‐scale studies (Weintraub et al., 2013). The measures are all computer‐based, relatively quick and easy to administer (approximately 10 minutes each), and can be used from age 3 through late adulthood. These measures present opportunities to assess children’s development across a range of domains that are important but often missing from child assessment batteries used at‐scale in community or school‐based applications. There have also been advances in using observational tools within early learning settings to assess children’s early learning processes. The methods typically involve a trained observer entering the classroom and using standardized procedures for coding children’s behavior based on actual demonstration of those behaviors in the context of the c­lassroom setting. Observational tools can be used within early childhood education programs to assess motivation (Berhenke, Miller, Brown, Seifer, & Dickstein, 2011), social behaviors with peers (Bierman et  al., 2008), and engagement with teachers, peers, and tasks (Downer, Booren, Lima, Luckner, & Pianta, 2010). These observational tools typically focus less on outcomes (e.g., literacy skills) but rather the ways in which children learn and develop when they are young. This approach to assessment opens up opportunities beyond parent‐ or teacher‐report surveys and direct standardized assessments of children’s skills and behaviors, and provides real‐time observational data about how children express those skills and behaviors in a given moment. Because children’s interactions and engagement with their peers, teachers, and families are a key contributor to their development, observing these interactions provides complementary information to other assessment methods and could lead to new understanding of how children learn and develop (Downer et al., 2010). For example, a recently developed observational tool, the Individualized Classroom Assessment Scoring System (inCLASS) uses observational methods to measure children’s engagement within the classroom setting. Modeling the multiple components of children’s engagement with teachers, peers, and tasks simultaneously, researchers found that children’s positive engagement with teachers and peers was related to improved language and literacy skills, and that children’s negative engagement in the classroom was associated with lower language, literacy, and self‐regulatory skills. The findings hold even after c­ontrolling for previous performance and classroom quality (Sabol, Bohlmann, & Downer, 2013), suggesting that children’s own engagement may play an important role in early skill accrual and preparedness for school.

School Readiness  9

Relation among current measures of school readiness and long‐term development The interest in measuring and assessing school readiness derives in part from the goal of understanding the developmental roots of later behaviors and skills. Fundamental to the concept of “readiness” is the assumption that these skills, whatever they may be, collectively forecast success and failure over the life course, including graduating from high school, attaining a postsecondary degree, becoming productive adults, and being well‐ informed, lawful citizens. Longitudinal studies that examine these predictive relations p­rovide an important check on our current conceptualization and measurement of school readiness, particularly as we attempt to strategically align interventions to early skills with strong predictive power. The “skill beget skill” approach to understanding children’s development suggests that a number of children’s early skills or behaviors are indeed related to later skills or outcomes (Duncan et al., 2007; Fischer, 1980; Flavell, Miller, & Miller, 1985; Heckman & Mosso, 2014; Rogoff, 1990). Feinstein (2003) used the 1970 Birth Cohort Study of over 19,000 children in the United Kingdom and found that children with low achievement in early childhood were particularly at risk for poor adult outcomes. The prediction to adult outcomes was more pronounced at school entry compared to in toddlerhood. Children in the bottom quartile on achievement at 22 months (measured by items such as cube stacking and drawing a straight line) were much less likely to receive advanced degrees compared to children in top quartile of achievement (32% versus 43%). By 5 years old, the difference was almost 10 times greater, with only 18% of children in bottom quartile of achievement at age 5 attaining an advanced degree compared to 58% in the top quartile. In terms of school performance, Duncan et  al. (2007) conducted one of the most c­omprehensive studies on how children’s reading and math achievement, attention, and behavior predict elementary school achievement using six longitudinal studies of early childhood (e.g. ECLS‐K: 1999, NICHD SECCYD). Results indicated that early math, reading, and attention skills were predictive of elementary school achievement, but socioemotional skills were not. Surprisingly, the effect sizes for early math skills on reading achievement were similar in magnitude to the effect of early reading skills. These f­indings suggest the importance of early academic and attention skills for success in e­lementary school. In an even larger meta‐analysis of over 3,500 studies that included over 380,000 s­tudents, Hattie (2008) found a similar pattern in which prior achievement was a strong predictor of later achievement (.67), Notably the age range at both time points was considerably larger than in Duncan et al. (2007). Yet, as the author notes, there was still a significant portion of later functioning and performance that was not explained by earlier performance (upwards of 50%). With a smaller sample of studies (n = 234), Hattie found that self‐efficacy, self‐concept, motivation, persistence, and conscientiousness were highly correlated with achievement, and thus may represent another important element of school readiness that has predictive power. On the other hand, in a meta‐analysis on the relation among executive functioning and achievement, Jacob and Parkinson (2015) found that very few studies rigorously control for family background, suggesting that much more work is needed to establish the causal relationship between executive function and long‐term achievement outcomes.

10  Sabol and Pianta

Using a child‐oriented perspective to characterize school readiness Fundamentally, “readiness” is a descriptor applied to a child. Clearly we need to better understand how early foundational skills are developed, but there is also a need to understand how skills interact over time to support lifelong success of that child. Importantly, although readiness skills do not exist or operate independently of one another within a particular child or group of children, most research and applications of readiness assessments treat individual skills and behaviors as the central unit of analysis and examine prediction in terms of stability and change, rather than understanding the child as the central unit of analysis (Bergman & Magnuson, 1997; Sabol & Pianta, 2012). This conceptualization of children’s performance as a discrete set of skills could lead to a number of gaps in our understanding of school readiness. For example, we may know that early mathematical skills predict later mathematical skills, but it does little to explain how a child’s early math skills operate simultaneously with working memory, language, and social skills to foster development over time. A child‐oriented approach could help elucidate how specific skills work together to shape development over time. This approach has implications for understanding the systemic nature of development by capturing the nonlinear combinations of early skills within children and how these patterns predict later achievement. Several studies have used cluster‐based profiles of children to characterize school readiness (Cooper et al., 2014; Hair, Hall, Terry‐Humen, Lavelle, & Calkins, 2006; Konold & Pianta, 2005; McWayne & Bulotsky‐Shearer, 2013, Quirk, Nylund‐Gibson, & Furlong, 2013). For example, McWayne, Cheung, Wright, & Hahs‐Vaughn (2012) used the Head Start FACES 2000 to examine patterns of school readiness for low‐income children attending Head Start and how they related to children’s demographic characteristics. Latent classifications revealed three profiles based on teacher report and direct assessment of children’s cognitive skills, behavior problems, and cooperative classroom behaviors. The first profile, high average social and academic skills, was marked by high ratings and performance across social and cognitive domains. Children in this profile were more likely to be older, White, and girls. The second profile was characterized by high behavior p­roblems and low to low‐average scores across cognitive measures, with children that were younger, disabled, Black or Latino, and boys. The third profile was distinguished by a­verage performance across all domains, with children who were more likely to be English language learners, girls, and Black or Latino. Results suggest that patterns of school readiness map onto to certain demographic subgroups. Quirk and colleagues (2013) took a similar analytic approach, but focused on social‐ emotional, physical, and cognitive domains among a sample of almost 800 Latino kindergartens in California. Using teacher report of kindergarten readiness across 16 measures, they identified five profiles through latent class analysis. Three profiles were characterized by high, average, or low performance across all domains. There were also two profiles that were distinguished by a combination of high, average, and low social, cognitive, and physical skills: (1) moderate social‐emotional, low cognitive, and average physical skills; and (2) low social‐emotional, moderate‐low cognitive. These two profiles and the low performance across all domains profile had lower academic performance in 2nd grade compared to the higher performing school readiness profiles.

School Readiness  11 The ultimate strength of child‐level school readiness profiles may be in forecasting later achievement. The profiles allow researchers to measure school readiness as a multifaceted construct for a given child, with peaks and dips across different domains within individuals, and then examine within‐ and cross‐domain associations over time. Konold & Pianta (2005) used the NICHD SECCYD to identify six profiles of functioning in typically developing 54‐month‐old children using an assessment battery that consisted of measures of executive functioning (both working memory and attention) and social functioning. Children were classified into six groups reflecting various peaks and valleys of relative performance, such as (a) attention problems, average social functioning, and average working memory, (b) high social competence and average working memory, and (c) high working memory and mild externalizing problems. Sabol & Pianta (2012) then used these same school readiness profiles to forecast achievement in 5th grade. Of note were the cross‐domain associations that a traditional variable‐oriented approach may have missed. For example, a group of preschool‐aged children with low attention, but without socioemotional problems, had strong social skills and academic performance in 5th grade in comparison to more traditional profiles that had low skills in all areas. In addition, patterns in which high social competence or high working memory were prominent, predicted high 5th‐grade achievement. Results indicate that profiles that capture cross‐domain interactions in performance may be articularly well suited to identifying multiple pathways to later performance and p­ s­ocioemotional skills. In addition, several studies have examined associations among domains of functioning and how child‐level school readiness profiles may predict later performance using a nationally representative sample (Cooper et  al., 2013; Hair et  al., 2006; Halle, Hair, Burchinal, Anderson, & Zaslow, 2012). Duncan and Magnuson (2011) used the ECLS‐K: 1999 and examined the basic correlations among reading, math, attention, externalizing and internalizing behavior problems at the start of kindergarten. Not surprisingly, the highest correlation was between reading and math skills (.69). Yet, higher performing students were as likely to exhibit behavior problems as children who were low‐performing. Children’s attention skills were also highly correlated with higher performance (.29–.41) and lower behavior problems (.36–.51). By 5th grade the correlations all grew in magnitude, suggesting that students’ academic and social skills become more connected over time. Hair and colleagues (2006) took a person‐oriented approach to examining school readiness in a nationally representative study (ECLS‐K: 1999). They used five dimensions of school readiness that align to the National Education Goals Panel and Race‐to‐the‐Top Early Learning Challenge “essential domains of school readiness”, – physical well‐being, social‐emotional development, language, cognition, and approaches to learning. Cluster analysis discriminated four profiles at kindergarten entry: positive development (above average performance on health, social‐emotional, language, and cognition; 30%), social/ emotional and health strengths (with low‐moderate language and cognitive skills; 34%), social/emotional risk (with below average health, social‐emotional, and cognitive skills; 13%), and health risk (with low language and cognitive skills and above average social‐ emotional skills; 22.5%). Children from the risk profiles were more likely to come from economically or socially disadvantaged families.

12  Sabol and Pianta By 1st grade, children with positive development had the highest academic performance and highest rated social skills. Children with high social skills and average cognitive skills also performed well. Interestingly, children with health risk profile had low academic p­erformance in elementary school. Similar to Sabol & Pianta (2012), this study identifies alternative pathways to positive outcomes, particularly for children characterized with high cognitive skills or high social skills in early childhood. These results also suggest the importance of measuring health outcomes in early childhood as well, which may place children at‐risk for later academic development. Halle et al. (2012) then examined the long‐term associations between school readiness profiles and academic achievement and socioemotional skills achievement in elementary school and middle school using the ECLS‐K: 1999. They used similar measures to Hair et al. (2006) and uncovered four empirically validated school readiness profiles that were distinguished by high, above average, average, or low performance on all school readiness domains. None of the profiles were characterized by high performance on some school readiness domains and low on others. Latent growth analysis indicated that children in the highest performing school readiness profile continued to outperform children in other profiles. However, children in the bottom profiles demonstrated greater improvement over time and had more rapid growth from 1st through 8th grade. These findings may be somewhat limited by measurement concerns in terms of capturing growth over time, but do demonstrate that children arrive at school with different patterns of school readiness competencies. It is hard to draw comparisons across the studies of school readiness that use a person‐ oriented approach given the variation in measures included and the population studied. However, results suggest that person‐oriented approaches may elucidate patterns of school readiness that would not be identified in traditional variable‐oriented approaches. Moreover, results suggest that children’s performance in elementary school and middle school can be predicted by children’s patterns of school readiness, again confirming the importance of early skills for later development and outcomes. The importance capturing children’s skills across multiple dimensions has significant implications for how we define and measure gaps across a diverse range of children. Typical approaches to identifying achievement gaps typically do not measure children’s skills across multiple domains simultaneously and instead focus on a set of early skills, such as achievement tests, that may only tell part of the story in terms of forecasting long‐term outcomes. Thus, there is the potential that we are missing important strengths and weaknesses in children’s early skills and behaviors from a diverse range of outcomes. Future work may attempt to characterize children’s school readiness gaps using a more parsimonious, holistic approach. In addition, school readiness profiles need to be replicated across multiple datasets that represent the range of children’s skills and backgrounds, in order to determine the utility and validity of this approach.

Measuring School Readiness in a Policy Context Defining and measuring school readiness is challenging. Yet, the increased emphasis on school readiness within policy contexts highlights the critical need of getting the definition “right” particularly when public preschool for low‐income children is promoted as a

School Readiness  13 gap‐closing investment and when school districts are considering widespread implementation of kindergarten assessments. In the coming years, it will become even more important to expand the conceptualization and strengthen the measurement to include the components of children’s early skills and functioning that accurately depict disparities as well as forecasting later performance. As local, state, and federal policies attempt to mitigate disparities through various policies and programs, measures of children’s school readiness may be increasingly used as benchmark of the success of those policies. The K–12 system provides an example of how policy can lead to a reductionist view of student achievement and development, but can also spur action. The No Child Left Behind legislation mandated the improvement of student achievement as measured through test score gains. All states were required to develop state tests to be used in annual testing, focused primarily on 3rd through 8th grade student performance. The emphasis on student achievement tests led to a somewhat myopic focus from schools on improving the skills that directly related to student test scores and less of a focus on other subjects or skills that may promote later outcomes. On the other hand, it provided a common metric and language to track and monitor student progress and also provided an opportunity to examine the schools that have lower or higher achievement. The focus on student achievement has trickled down to kindergartners. In the Race‐to‐ the‐Top Early Learning Challenge (RTT‐ELC), states were judged on the basis of whether they collected data on school readiness at kindergarten entry. The RTT‐ELC delineated that the kindergarten assessment tool should cover all of the essential domains of school readiness. The goal of the kindergarten assessments was to help states understand the status of children’s learning at kindergarten entry, how early learning programs such as state‐ funded pre‐kindergarten programs may be improved to strengthen children’s early skills, and plan for how best to serve children in the K–12 system (Scott‐Little, Bruner, Schultz & Maxwell, 2013). As a result of this funding, more states than ever use kindergarten assessment tools. In 2010, 25 states had established a kindergarten entry assessment and 21 required universal assessment of kindergarten students (Stedron & Berger, 2010). Thus far, school readiness assessments have been used primarily for descriptive purposes and have only played a small to moderate role in higher stakes contexts, such as states’ preschool accountability systems (separate from NCLB), Quality Rating and Improvement Systems (QRIS), but that may change in the near future. To date, QRIS have primarily focused on measuring classroom level inputs, such as the global classroom environment and class size, to rate preschool quality across states. However, children’s performance is increasingly being integrated into preschool ratings. For example, in 2010 only 11 states (out of 25; 44%) used child assessments as a way to determine preschool program quality. By 2014, that number had almost doubled to 21 states (out of 38 states; 55%), suggesting that states are moving in the direction of using school readiness as one indicator of program quality (QRIS Compendium, 2014). School readiness may also increasingly be used in even more high stakes settings. For  example, in Chicago, the Mayor recently proposed funding pre‐kindergarten programs using $17 million in social‐impact bonds. Lenders will only be repaid if students demonstrate an increase in school readiness for kindergarten, among other outcomes, such as lowering the need for special‐education programs. Children’s school readiness will be used to determine in part how much money the city pays for pre‐kindergarten and whether private lenders reclaim their investment. This approach has been used in Utah as well,

14  Sabol and Pianta and could represent the growing interest in using school readiness as a marker of the e­ffectiveness of early childhood education programs. Future work focused on strong, consistent measurement will help to build conceptual clarity and provide a common language that can be used by researchers, teachers, parents, and the general public to track children’s progress. There are a number of efforts underway to address this goal, such as the application of the Early Development Instrument (EDI; a population‐based school readiness measurement tool), creating a common kindergarten entry assessment, and accompanying K‐3 formative assessments. In addition, researchers and educators are currently developing common early learning and development standards from birth to kindergarten entry that are aligned to the Common Core State Standards. Conceptual clarity on school readiness is predicated on the development of measurement tools that are culturally relevant for children from diverse backgrounds that capture the nonlinear relationships of multiple domains of functioning. Recent advances in measurement provide opportunities to incorporate easy‐to‐use tools across multiple domains, which is exemplified in the ECLS‐K: 2011. However, much more work needs to be done to examine the predictive validity of individual measures, as well as how patterns of school readiness across multiple domains are associated with longer term outcomes. Overall, policy should drive better assessment. For instance, if programs do not assess how kids are performing in math, it is challenging to know how to align programming to those skills. Improved assessment of school readiness can also highlight strengths in c­hildren’s early skills, as well as disparities. This in turn can inform program evaluation and planning, as well as mobilize communities around the needs of their children. When children enter kindergarten, the whole child enters, not just individual behaviors and skills. The field has made impressive gains around characterizing and measuring c­hildren’s development across multiple domains and how these skills operate in concert over time to produce learning gains in the short term. Further work may examine the long‐term predictive validity of this child‐oriented approach and how policies and p­rograms can target these skills to support the lifelong success of children.

References Barnett, W. S., Carolan, M. E., Squires, J. H., & Clarke Brown, K. (2013). The state of preschool 2013: State preschool yearbook. New Brunswick, NJ: National Institute for Early Education Research. Bassok, D., & Latham, S. (2014). Kids today: Changes in school‐readiness in an early childhood era. Paper presented at the meeting for the Association for Public Policy Analysis and Management, Albuquerque, New Mexico. Bergman, L. R., & Magnusson, D. (1997). A person‐oriented approach in research on developmental psychopathology. Development and Psychopathology, 9(02), 291–319. Berhenke, A., Miller, A. L., Brown, E., Seifer, R., & Dickstein, S. (2011). Observed emotional and behavioral indicators of motivation predict school readiness in Head Start graduates. Early Childhood Research Quarterly, 26(4), 430–441. Bernstein S., West, J., Newsham, R., & Reid, M. (2014). Kindergartners’ skills at school entry: An analysis of the ECLS‐K. New York, NY: Mathematica Policy Research.

School Readiness  15 Bierman, K. L., Domitrovich, C. E., Nix, R. L., Gest, S. D., Welsh, J. A., Greenberg, M. T., … Gill, S. (2008). Promoting academic and social‐emotional school readiness: The Head Start REDI Program. Child Development, 79(6), 1802–1817. doi:10.1111/j.1467‐8624.2008.01227.x Boivin, M., & Bierman, K. L. (Eds.). (2013). Promoting school readiness and early learning: Implications of developmental research for practice. New York, NY: Guilford Publications. Bradbury, B., Corak, M., Waldfogel, J., & Washbrook, E. (2011). Inequality during the early years: Child outcomes and readiness to learn in Australia, Canada, United Kingdom, and United States (No. 6120). Retrieved from http://nbn‐resolving.de/urn:nbn:de:101:1‐201111303525 Cooper, B. R., Moore, J. E., Powers, C. J., Cleveland, M., & Greenberg, M. T. (2014). Patterns of  early reading and social skills associated with academic success in elementary school. Early Education and Development, 25(8), 1248–1264. De Feyter, J. J., & Winsler, A. (2009). The early developmental competencies and school readiness of low‐income, immigrant children: Influences of generation, race/ethnicity, and national o­rigins. Early Childhood Research Quarterly, 24(4), 411–431. Denton Flanagan, K., & McPhee, C. (2009). The children born in 2001 at kindergarten entry: First findings from the kindergarten data collections of the Early Childhood Longitudinal Study, Birth Cohort (ECLS‐B) (NCES 2010‐005).Washington, DC: US Department of Education. Diamond, A. (2006). The early development of executive functions. In E. Bialystok & F. I. M. Craik (Eds.), Lifespan cognition: Mechanisms of change (pp. 70–96). New York, NY: Oxford University Press. Downer, J. T., Booren, L. M., Lima, O. K., Luckner, A. E., & Pianta, R. C. (2010). The Individualized Classroom Assessment Scoring System (inCLASS): Preliminary reliability and validity of a system for observing preschoolers’ competence in classroom interactions. Early Childhood Research Quarterly, 25(1), 1–16. doi:10.1016/j.ecresq.2009.08.004 Duncan, G. J., Dowsett, C. J., Claessens, A., Magnuson, K., Huston, A. C., Klebanov, P., … Japel, C. (2007). School readiness and later achievement. Developmental Psychology, 43(6), 1428. Duncan, G. J., & Magnuson, K. (2011). The nature and impact of early achievement skills, a­ttention skills, and behavior problems. In G. J. Duncan & R. J. Murnane (Eds.), Whither opportunity? Rising inequality, schools, and children’s life chances (pp. 47–69). New York, NY: Russell Sage Foundation. Feinstein, L. (2003). Inequality in the early cognitive development of British children in the 1970 cohort. Economica, 70, 73–97. doi: 10.1111/1468‐0335.t01‐1‐00272 Fischer, K. W. (1980). A theory of cognitive development: The control and construction of h­ierarchies of skills. Psychological Review, 87(6), 477–531. Flavell, J. H., Miller, P. H., & Miller, S. A. (1985). Cognitive development. Englewood Cliffs, NJ: Prentice‐Hall. Fryer Jr., R. G., & Levitt, S. D. (2004). Understanding the black‐white test score gap in the first two years of school. Review of Economics and Statistics, 86(2), 447–464. Hair, E., Halle, T., Terry‐Humen, E., Lavelle, B., & Calkins, J. (2006). Children’s school readiness in the ECLS‐K: Predictions to academic, health, and social outcomes in first grade. Early Childhood Research Quarterly, 21(4), 431–454. Halle, T., Forry, N., Hair, E., Perper, K., Wandner, L., Wessel, J., & Vick, J. (2009). Disparities in early learning and development: Lessons from the Early Childhood Longitudinal Study–Birth Cohort (ECLS‐B). Washington, DC: Child Trends. Halle, T. G., Hair, E. C., Burchinal, M., Anderson, R. & Zaslow, M. (2012) In the Running for Successful Outcomes: Exploring the evidence for thresholds of school readiness. Washington, DC: US Department of Health and Human Services, Office of the Assistant Secretary for Planning and Evaluation. Retrieved from http://aspe.hhs.gov/hsp/13/IntheRunningtechnicalreport/ rpt.pdf

16  Sabol and Pianta Hattie, J. (2008). Visible learning: A synthesis of meta‐analyses relating to achievement. New York, NY: Routledge. Heckman, J. J., & Mosso, S. (2014). The Economics of Human Development and Social Mobility (No. w19925). Cambridge, MA: National Bureau of Economic Research. Hernandez, D. J., Denton, N. A., Macartney, S., & Blanchard, V. L. (2012). Children in immigrant families: Demography, policy, and evidence for the immigrant paradox. In C. G. Coll & A. Marks (Eds.) The immigrant paradox in children and adolescents: Is becoming American a developmental risk? (pp. 17–36). Washington, DC: American Psychological Association. Hernandez, D. J., & Napierala, J. S. (2012). Children of immigrant families: Essential to America’s future. New York, NY: Foundation for Child Development: Jacob, R., & Parkinson, J. (2015). The potential for school‐based interventions that target executive function to improve academic achievement: A review. Report submitted to US Department of Education, Washington, DC. Janus, M., Brinkman, S. A., & Duku, E. K. (2011). The school entry gap: Socioeconomic, family, and health factors associated with children’s school readiness to learn. Early Education and Development, 18(3), 375–403. Janus, M., & Offord, D. R. (2007). Development and psychometric properties of the Early Development Instrument (EDI): A measure of children’s school readiness. Canadian Journal of Behavioural Science, 39(1), 1–12. Kagan, S. L., Moore, E., & Bradekamp, S. (Eds.) (1995). Reconsidering children’s early development and learning: Toward common views and vocabulary. Washington, DC: National Education Goals Panel Goal 1 Technical Planning Group. Konold, T. R., & Pianta, R. C. (2005). Empirically‐derived, person‐oriented patterns of school readiness in typically‐developing children: Description and prediction to first‐grade achievement. Applied Developmental Science, 9(4), 174–187. La Paro, K. M., & Pianta, R. C. (2000). Predicting children’s competence in the early school years: A meta‐analytic review. Review of Educational Research, 70(4), 443–484. McWayne, C. M., & Bulotsky‐Shearer, R. J. (2013). Identifying family and classroom practices associated with stability and change of social‐emotional readiness for a national sample of low‐income children. Research in Human Development, 10(2), 116–140. McWayne, C. M., Cheung, K., Wright, L. E. G., & Hahs‐Vaughn, D. L. (2012). Patterns of school readiness among head start children: Meaningful within‐group variability during the transition to kindergarten. Journal of Educational Psychology, 104(3), 862–878. Pianta, R. C., Barnett, W. S., Burchinal, M., & Thornburg, K. R. (2009). The effects of preschool education: What we know, how public policy is or is not aligned with the evidence base, and what we need to know. Psychological Science in the Public Interest, 10(2), 49–88. Pianta, R. C., Cox, M. J., & Snow, K. L. (2007). School readiness and the transition to kindergarten in the era of accountability. Baltimore, MD: Paul H. Brookes Publishing. QRIS Compendium. (2014). A catalog and comparison of quality rating and improvement systems. Retrieved from http://qriscompendium.org/ Quirk, M., Nylund‐Gibson, K., & Furlong, M. (2013). Exploring patterns of Latino/a children’s school readiness at kindergarten entry and their relations with grade 2 achievement. Early Childhood Research Quarterly, 28(2), 437–449. Reardon, S. F. (2011). The widening academic achievement gap between the rich and the poor: New evidence and possible explanations. In R. Murnane & G. Duncan (Eds.), Whither opportunity? Rising inequality and the uncertain life chances of low‐income children (pp. 91–116). New York, NY: Russell Sage Foundation. Reardon, S. F., Robinson‐Cimpian, J. P., & Weathers, E. S. (2014). Patterns and trends in racial/ ethnic and socioeconomic academic achievement gaps. In H. A. Ladd & M. E. Goertz (Eds.), Handbook of research in education finance and policy (pp. 497–516). New York, NY: Routledge.

School Readiness  17 Rogoff, B. (1990). Apprenticeship in thinking: Cognitive development in social context. Oxford, UK: Oxford University Press. Sabol, T. J., Bohlmann, N., & Downer, J. (2013). The Role of Observed Children’s Engagement in Preschool Classrooms on School Readiness. Paper presented at the Society for Research in Child Development Biennial Research Conference, Seattle, WA. Sabol, T. J., & Pianta, R. C. (2012). Patterns of school readiness forecast achievement and s­ocioemotional development at the end of elementary school. Child Development, 83(1), 282–299. Scott‐Little, C., Bruner, C., Schultz, T., & Maxwell, K. (2013). Kindergarten Entry Assessment Discussion Guide 2013. Retrieved from http://www.buildinitiative.org/WhatsNew/ViewArticle/ tabid/96/ArticleId/662/Kindergarten‐Entry‐Assessment‐Discussion‐Guide‐2013.aspx Stedron, J. M., & Berger, A. (2010). State approaches to school readiness assessment. Washington, DC: National Conference of State Legislatures. US Department of Education. (2014, November). Race to the Top – Early Learning Challenge (RTT‐ ELC) Program. Retrieved from http://www.ed.gov/early‐learning/elc‐draft‐summary/definitions US Department of Health and Human Services. (2014, November). Head Start fact sheet: Head Start program facts fiscal year 2013. Retrieved from http://eclkc.ohs.acf.hhs.gov/hslc/data/ factsheets/2013‐hs‐program‐factsheet.html Weintraub, S., Dikmen, S. S., Heaton, R. K., Tulsky, D. S., Zelazo, P. D., Bauer, P. J., … Gershon, R. C. (2013). Cognition assessment using the NIH Toolbox. Neurology, 80, S54–S64. Yiu, J. (2011). The school readiness of the children of immigrants in the United States: The role of families, childcare and neighborhoods (Fragile Families Working Paper No. WP‐11‐11‐FF). Princeton, NJ: Bendheim‐Thoman Center for Research on Child Wellbeing. Zaslow, M., Tout, K., Halle, T., Whittaker, J. V., & Lavelle, B. (2010). Toward the identification of  features of effective professional development for early childhood educators. Washington DC: Office of Planning, Evaluation and Policy Development, US Department of Education.

chapter TWO The State of Young Children in the United States: A Developmental Psychopathology Perspective on the Mental Health of Preschool Children Susan B. Campbell

Over the last several decades there has been a growing interest in identifying problem behaviors in young children and understanding how child characteristics interact with risk and protective factors in the child’s environment to facilitate or undermine optimal devel­ opment (Campbell, 2002; Sameroff, 2009; Shonkoff, 2010). One important goal of this work, both theoretical and empirical, is to inform prevention and early intervention p­rograms meant to forestall the emergence of long‐term and potentially serious emotional and behavior disorders of middle childhood and adolescence (e.g., Shaw, Dishion, Supplee, Gardner, & Arnds, 2006), many of which have their roots or precursors in early childhood (e.g., Moffitt, Caspi, Dickson, Silva, & Stanton, 1996; Rutter, Kim‐Cohen, & Maughan, 2006). This emphasis on developmental process and the early identification of problem behaviors has stimulated several important and developmentally‐informed longitudinal studies of community (e.g., Lavigne, LeBailly, Hopkins, Gouze, & Binns, 2009; NICHD Early Child Care Research Network [ECCRN], 2002) and high risk samples (e.g., Campbell, Pierce, March, Ewing, & Szumowski, 1994; Olson, Sameroff, Kerr, Lopez, & Wellman, 2005; Shaw et al., 2006; Shaw et al., 1998) that begin in infancy or the p­reschool period. These studies have added to our understanding of factors associated with the onset and developmental course of problem behaviors first identified in early childhood.

The Wiley Handbook of Early Childhood Development Programs, Practices, and Policies, First Edition. Edited by Elizabeth Votruba-Drzal and Eric Dearing. © 2017 John Wiley & Sons, Inc. Published 2017 by John Wiley & Sons, Inc.

The Mental Health of Preschool Children  19 The move to identify problems earlier and earlier, however, has also had some negative consequences. When children’s difficult behaviors are viewed from an “adevelopmental” and decontextualized lens, explosive or withdrawn behaviors that often signal age appro­ priate struggles with developmental tasks or young children’s attempts to adapt to challenging environmental circumstances may be mislabeled as “disorders” (Campbell, 2002; Sroufe, 1990, 1997) by parents, teachers, or medical professionals without a back­ ground in early development. An especially worrisome side effect of this trend is the alarming increase in the use of psychoactive medications prescribed to very young children (Fontanella, Hiance, Phillips, Bridge, & Campo, 2014; Zito et al., 2000; Zito et al., 2007) who are rarely given a mental health or developmental assessment or offered psychosocial interventions. For example, Fontanella et al. (2014) reviewed Medicaid claims in Ohio from 2002–2008 and reported that young children (ages 2–5) who were treated with psychotropic medications for behavioral and emotional problems were likely to be boys, to be living in poverty, many in rural areas, and to have received treatment from non‐ specialty personnel in primary care settings, rather than from a child psychiatrist; moreover, the majority of these children (over 70%) did not receive a mental health assessment prior to receiving a prescription for medication. Zito and colleagues (2000; 2007) have consist­ ently raised concerns about the “off‐label” use of medications for young children who are at high risk for adverse drug effects. This situation is likely to reflect the attempts of d­esperate parents and overworked professionals to cope with behavior problems in an era of shrinking resources. Parents living in poverty and primary care providers in rural and inner‐city areas lacking services may be unaware of or unable to access more age‐appropriate psychosocial interventions to address family needs. Problem behaviors in young children, from roughly 18 months to 5 years, are often signs of developmental perturbations and the emergence of new skills that involve the reorganization of child and parent behavior and expectations. The emergence of skilled unsupported walking and the development of language early in the second year are two obvious examples of new skills that dramatically change the child’s ability to interact with the world and consequently the demands on parents. Once children begin to walk and talk, they are more likely to “get into things” as they move around independently and explore their environment, and they are more likely to say “no” to parent requests. Most parents will welcome and support these developmental advances, but parents of a very active or demanding toddler, especially if they themselves are highly stressed and over­ whelmed, may attempt to restrict exploration and mastery, leading to early parent–child conflict. Thus, common problem behaviors may arise when young children show age‐ related and transient adjustment difficulties associated with developmental advances and challenges (e.g., autonomy‐seeking) or with a stressful (even normative) life event such as the birth of a sibling (e.g., tantrums, regressive behavior) or entry into child care (e.g., separation anxiety, noncompliance). For most children, these problem behaviors will abate, especially in the context of supportive and child‐centered parenting (Campbell, 2002; Laible & Thompson, 2007). However, some parents may react to these develop­ mental advances with harsh control, inappropriate or inconsistent limits, and anger, potentially inaugurating a cycle of coercive toddler–parent interaction that is a risk factor for persistent problems (Smith et al., 2014). Thus, in a small proportion of cases, problems that emerge in toddlerhood or the preschool period may become entrenched and even

20 Campbell worsen with development, presaging more chronic adjustment difficulties (e.g., Campbell, Shaw, & Gilliom, 2000; Moffitt et  al., 1996; Pierce, Ewing, & Campbell, 1999; Shaw et al., 1998). A developmental psychopathology framework places children’s problems into a deve­ lopmental, family, and ecological context (Bronfenbrenner, 1977; Cicchetti & Cohen, 1995; Cummings, Davies, & Campbell, 2000; Shonkoff & Phillips, 2000), while also considering the ongoing transactions between the child, parents, and the wider social environment (Sameroff, 2009). This means that young children’s problems must be ­considered from the perspective of the child’s functioning across domains including lan­ guage and cognitive development, and social and emotional competence. Parenting quality and family functioning are also central to understanding young children’s develop­ mental accomplishments and adjustment to changing expectations. Finally, community resources and cultural norms place children’s adjustment into a broader ecological context (Bronfenbrenner, 1977). A transactional model underscores the constantly shifting bidirectional influences between the developing child, whose needs and competencies are changing rapidly during infancy and early childhood, and the caregiving environment that also changes and is changed by the developing child. Within this framework, attachment theory specifically addresses the quality of the relationship between the young child and primary caregivers (Ainsworth, Blehar, Waters, & Wall, 1978). Children who receive responsive and sensitive parenting in the first year of life are likely to be securely attached to at least one parent, providing them with a secure base for exploration, and a sense of well‐being; across the second year, with the emergence of a goal‐corrected partnership between the toddler and the parent who is sensitive to the child’s changing needs, the well‐functioning child is likely to develop internal representations of and expectations for positive, supportive rela­ tionships that carry forward to new settings and people. These in turn will help to shape the child’s emerging sense of self, social competence, ability to regulate emotion, and internalization of parental standards (Kochanska & Kim, 2012; Laible &Thompson, 2007). Secure attachment is also considered protective, potentially inoculating young c­hildren to stress and scaffolding the regulatory skills and sense of self that facilitate adjustment to normative change, such as entry into child care or adapting to family stress. More recent conceptualizations of young children’s social‐emotional development emphasize the interplay between children’s biological (e.g., specific genetic polymor­ phisms) and personality characteristics (e.g., temperamental negative emotionality) and the caregiving environment, suggesting that some children are more sensitive than others to childrearing practices (e.g., harsh or nurturant parenting) and social context (marital conflict, parental depression, poverty and family stress, the quality of child care) (Belsky & Pluess, 2009; Boyce & Ellis, 2005). The ecological model, in turn, places these dynamic developmental, parent‐child, and family processes into the broader neighborhood, c­ommunity, socio‐political and cultural context in which the child and family reside. A developmental perspective on emerging behavior problems must first take into account the profound changes in cognitive, social, emotional, and communicative devel­ opment that occur over the first five years of life. During infancy these include establishing routines, regulating physical (e.g., hunger, fatigue) and emotional (e.g., distress) states with adult support, communicating wants and needs, initiating and responding to social

The Mental Health of Preschool Children  21 interactions, and establishing attachment relationships with primary and secondary c­aregivers. Individual differences in sociability and positive mood, irritability and fussi­ ness, and soothability and adaptability may make early parenting more or less challenging for stressed or first‐time parents, depending upon these child characteristics and family resources. Thus, stressed parents and irritable, difficult to soothe infants may start out f­acing challenges that set the stage for later problems (Bates & Pettit, 2007). Once infants become mobile and can begin to communicate with words and gestures, they play a more active role in initiating and maintaining social interaction and exploring and mastering the environment. At the same time, young children’s limited self‐regulatory skills may tax parents’ resources and they may become concerned about their young child’s activity level and impulse control. In the second year, typically‐developing children become fully mobile and curious about the world around them. After the second birthday, children’s cognitive, language, and social behavior shows increasingly rapid developmental change. Language improves dramatically over the next two years as children begin to talk in sentences and engage in reciprocal conversations, seek information, and verbally express their wants and opinions (Shatz, 2007). Social cognitive advances are also evident as tod­ dlers begin to show awareness of the self as an independent being and of their own and others’ mental states; perspective‐taking, empathy, and pretend play also emerge during this period (Brownell & Kopp, 2007). In the second year, children also begin to internalize standards for behavior and are aware when they have violated parental expectations (Kochanska & Kim, 2012). Toddlers also become increasingly interested in peers as p­laymates and companions (Hughes & Dunn, 2007). Between ages 3 and 4, children’s play shifts from the parallel play of toddlerhood, becoming more complex, as children engage in shared pretend play scenarios, turn‐taking, role assignments, and emerging friendships. Thus, as children become more independent, self‐aware, and competent, their behaviors may also become more challenging for parents and other adult caregivers. Struggles over autonomy versus dependence, the need for (but resistance to) more consistent limit‐setting, and interest in peers who are going through these same developmental changes, may be reflected in a range of short‐term problem behaviors (Campbell, 2002). These include aggression toward playmates and siblings, d­ifficulty sharing toys or taking turns, poor regulation of anger and frustration, defiance of adult requests, and temper tantrums. Eating and sleeping problems are also common. Some children also go through time‐limited phases of fearfulness, shyness, and separation anxiety. These same behaviors also define clinically significant problems underscoring the  question of how to differentiate between an age‐related adjustment reaction and a potentially more serious problem requiring intervention.

Dimensions and Categories of Problem Behaviors in Young Children Decades of research have confirmed that problem behaviors across the age range from t­oddlerhood to adolescence fall into the two broad albeit correlated dimensions of externalizing and internalizing behavior (Achenbach & Rescorla, 2000, 2001; Pickles & Angold, 2003). The externalizing dimension is characterized by aggressive, destructive,

22 Campbell noncompliant, disruptive, and impulsive behavior. In terms of psychiatric classification, disorders in early childhood that reflect externalizing symptoms include oppositional d­efiant disorder (ODD) and attention deficit hyperactivity disorder (ADHD) (Egger & Angold, 2006). In contrast, internalizing problems are characterized by sadness, anxiety, worry, fearfulness, and social withdrawal. At the diagnostic level, internalizing problems are captured by anxiety and depressive disorders. Of particular relevance to preschool children is separation anxiety disorder. Debates about the relative merits of categorical and dimensional approaches to the clas­ sification of problems in young children are beyond the scope of this chapter (but see Egger & Angold, 2006; Sonuga‐Barke & Halperin, 2010). Suffice it to say that when children are at the extremes on dimensional measures of problem behaviors (i.e., the top 10%) or are experiencing difficulties that are severe enough to meet diagnostic criteria for a disorder on developmentally sensitive assessment measures, children and families are likely to need help. The challenge of differentiating between transient and more chronic problems still remains, as not all young children with highly elevated symptoms will c­ontinue to have problems and not all children with only minor problems will outgrow early difficulties. This raises the question of what constitutes a disorder. The Diagnostic and Statistical Manual of Mental Disorders (DSM) provides extensive guidelines for the diagnosis of psychiatric disorders (American Psychiatric Association, 2000; 2013), although the emphasis is largely on adult disorders, with inadequate discussion of developmental issues, especially for younger children. However, criteria for both ODD and ADHD include the requirement that symptoms must persist for at least six months, thereby addressing the issue of time‐limited developmental transitions to some degree. I have discussed the pros and cons of using the DSM‐IV criteria for young children elsewhere (Campbell, 2002). In this context I have also proposed a definition of disorder that includes not only a clear constellation of symptoms that are both frequent and severe, but that also persist beyond the period needed to adjust to a life transition (e.g., birth of a sibling) or developmental challenge (e.g., autonomy struggles, extreme shyness with strangers). Importantly, symp­ toms of a full‐blown disorder should also interfere with social and cognitive development, and be evident across situations (home, child care) and relationships with responsible adults (parents, caregivers, preschool teachers). Problems that are situation or relationship specific are likely to require a different intervention strategy from problems that are e­vident across settings and relationships. Studies of the prevalence of specific problem behaviors and diagnoses in early child­ hood are the first step in identifying patterns of dysfunction that warrant concern. However, these studies are hampered by the lack of consensus on diagnostic criteria (Egger & Angold, 2006) and the limited attention to developmental issues as noted above (Campbell, 2002; Wakschlag, Tolan, & Leventhal, 2010). Thus, it may not be surprising that few studies have examined the prevalence of specific diagnoses in preschool age chil­ dren; when they have, samples have been small and/or unrepresentative of the population and older versions of the diagnostic criteria have been employed. Several recent studies suggest that between 9% (Lavigne et  al., 1996) and 12% (Egger & Angold, 2006) of c­hildren in the 2–5‐year age range meet diagnostic criteria for a serious emotional (internalizing) and/or behavior (externalizing) disorder, meaning that their parents report

The Mental Health of Preschool Children  23 enough specific symptoms to meet the diagnostic criteria for at least one disorder, that these have persisted over time, and the child shows impairment in functioning that is impeding cognitive, social, and/or emotional development. A more recent study using a structured diagnostic interview with parents of 541 3‐year‐olds reported that 27% met criteria for at least one diagnosis (Bufferd, Dougherty, Carlson, & Klein, 2011). Samples in all of these studies have been skewed toward more advantaged families. Despite this, the marked differences in rates of disorder are likely due to variations in sample composition, assessment measures, age of child, and whether or not a clinically significant level of impairment in functioning was required for a diagnosis. Indeed, it is likely that the focus on 3‐year‐olds in the Bufferd et al. study accounts for the elevated prevalence, especially of oppositional and anxiety disorders (specific phobia, separation anxiety), many of which are likely age‐related and transient at this age.

Externalizing problems The studies noted above also provide prevalence data for specific disorders. Studies gener­ ally indicate that ODD is more common than ADHD, although rates vary across studies. For example, rates of ODD range from 6.6% in the Egger and Angold data to 16% in the study by Lavigne et al. (1996). Bufferd et al. reported a prevalence of 9.4% for ODD and a more recent study of 796 4‐year‐olds in the Chicago area (Lavigne et al., 2009) found that 13.4% met criteria for ODD symptoms, but the rate dropped to 8.3% when severe impairment was required. It seems likely that these rates (especially of younger children or when impairment is not considered) include both children with time‐limited symptoms who are going through the “terrible twos” and “trying threes” and children with more serious and chronic problems. Consistent with this, in the Lavigne et  al. (1996) study, the prevalence of ODD peaked in the 2–3‐year range and declined between ages 4 and 5. In these studies, the prevalence of ADHD ranged from 2% (Bufferd et  al., 2011; Lavigne et al., 1996) to 3.3% (Egger & Angold, 2006). However, Lavigne et al. (2009) report a rate of 8.8%, even with impairment criteria included. In addition, the Centers for Disease Control (CDC; Perou et al., 2013) conducts regular surveys of children’s mental health, asking parents whether their child was “ever” given a diagnosis by a professional. The CDC prevalence estimate for 2–5‐year‐olds who ever received a diagnosis of ADHD was 1.5% based on a survey conducted in 2007. In general, while rates of ODD decline from preschool age to middle childhood, rates of ADHD increase, as expectations for self‐regulation, including the control of attention, activity level, and impulsivity rise with the transition to school. For example, in the CDC survey, by ages 6–11 the prevalence of ADHD had jumped to 9.1%. In addition, prevalence rates varied widely by geographic region, ethnicity, educational level, and income in the CDC data. Sex differences are u­sually evident, with both ADHD and ODD more common in boys, but this also varies with age and sample composition. As already noted, one major issue in the identification of behavior problems in young children concerns the overlap between typical behaviors that are age‐related, even when annoying to adults, and behaviors that at an extreme define a disorder.

24 Campbell Oppositional defiant disorder is characterized by high levels of dysregulated, disruptive, and uncooperative behavior including temper tantrums, defiance, angry mood, argu­ ing, blaming others, and deliberately annoying others at rates that would be considered “more f­requent than observed in children of the same age and developmental level.” These behaviors are extremely common in the general population of preschoolers and in the absence of age‐specific criteria, this disorder is likely to be vastly over‐diagnosed (Campbell, 2002; Wakschlag et al., 2007). However, questionnaire measures that allow parents to report behaviors that are “sometimes” and “very often” a problem indicate that whereas most children may “sometimes” disobey, annoy others, or throw a temper tantrum, relatively few children receive ratings of “very often” on multiple behaviors that define a syndrome (Achenbach & Rescorla, 2000). For example, Wakschlag and colleagues (2012) demonstrated that most preschoolers in the 3–5‐year age range were reported to have the o­ccasional temper tantrum, but fewer than 10% had them almost daily. Further, tantrums that occurred with adults other than a parent, without an obvi­ ous trigger, or included destructive behavior were more likely to be associated with a diverse set of other behavior problems including aggression. Thus, in line with the diagnostic criteria for ODD, c­hildren who show a constellation of four or more symp­ toms at an extreme level that persists for at least six months and interferes with cogni­ tive and social development are likely to be experiencing a clinically significant problem that merits intervention. This points to the importance of longitudinal studies that assess both categorical disorders and dimensional measures of problem behaviors, as it is often not possible to differentiate between time‐limited albeit difficult behaviors and more chronic problems on the basis of cross‐sectional data. Studies to be discussed later in the chapter address the c­orrelates and risk factors associated with persistent problems in children followed longitudinally. Although questionnaire measures of externalizing problem behaviors such as the Child Behavior Checklist (Achenbach & Rescorla, 2000) include items that assess physical aggression toward peers and adults, aggression is not included in the diagnostic criteria for ODD, but instead is contained in the criteria for conduct disorder. However, numerous studies have identified aggressive behavior toward peers as a serious concern of parents and caregivers, as a strong correlate of ODD symptoms, and as the reason that some young children have difficulties in preschool and child care settings. When children are followed longitudinally it is evident that physical aggression generally peaks by age 3 and then declines dramatically over the period between 3 and 5 (NICHD ECCRN, 2004; Tremblay, 2000) as children develop better language and self‐regulatory skills that allow them to deal with peer conflict in more acceptable and mature ways. In the NICHD Study, aggression was high and stable in only 3% of the sample of over 1,100 children whose aggressive behavior was tracked from age 2 to age 9 using maternal reports. Other studies have reported similar results (Broidy et al., 2003), suggesting that the prevalence of high and stable physical aggression is similar to rates of ODD and ADHD. This also underscores the need for longitudinal studies, as the children with elevated and chronic aggression are at highest risk for adjustment problems that also include symptoms of ODD and ADHD as well as problems with academic functioning and peer relationships (Campbell, Spieker, Burchinal, Poe, and the NICHD Early Child Care Research Network, 2006; NICHD ECCRN, 2004).

The Mental Health of Preschool Children  25 Similarly, ADHD is defined by many symptoms that are age‐related and would be expected to change over the preschool years. These include restlessness, excessive running or climbing, difficulty sitting still, excessive talking, difficulty paying attention, listening, and following instructions, and difficulty awaiting turns. Clearly these are often typical behaviors in 2 and 3‐year‐olds, especially boys, although when children enter child care or preschool, they are expected to conform to classroom rules and to learn to regulate many of these behaviors. Indeed, Egger and Angold (2006) reported that 40% of the preschool­ ers in their study who met diagnostic criteria for ADHD had been suspended from pre­ school or child care because they were unmanageable. In addition, longitudinal studies of preschoolers showing clearly elevated symptoms of impulsivity, overactivity, and inatten­ tion at age 4 (Campbell et al., 1994; Campbell, Pierce, Moore, Marakovitz, & Newby, 1996) or meeting DSM criteria for ADHD (Lee, Lahey, Owens, & Hinshaw, 2008) indi­ cate that problems are likely to persist in from 50–75% of cases. Whereas these behaviors may be more time‐limited in toddlerhood, by age 4 or 5 when most children are learning to conform to the demands of the preschool classroom or child care setting and to func­ tion cooperatively in the peer group, extreme levels of activity, impulsivity, and inattention are likely to forecast more serious problems as children enter formal schooling (Campbell et al., 1996; Lee et al., 2008). Thus, we return later to the question of how best to differ­ entiate between transient, age‐related problems and the early onset of problem behaviors that are likely to persist and worsen with development.

Internalizing problems Internalizing problems encompass anxiety, fearfulness, sad mood, and social withdrawal. Most children at some point experience some degree of sadness and anxiety. Furthermore, concerns about separation from parents, and fear of animals, the dark, and loud noises are extremely common in the first five years of life (Bell‐Dolan, Last, & Strauss, 1990; Klein & Last, 1989). The many anxiety disorders listed in the DSM, the lack of attention to devel­ opmental issues for most anxiety disorders, and the fact that many fears are normative in early childhood, make it especially challenging to decide when a specific fear or worry is of serious clinical concern. It is also generally agreed that it is difficult to diagnose depression in preschoolers, although sad mood may be a correlate of other problems. In addition, young children’s sadness is often expressed as irritability and negative affect, meaning that adults may mistake depressed mood for noncompliance. Thus, internalizing disorders in preschool children are poorly understood. Clearly young children showing high and p­ersistent levels of anxiety and/or sadness (or irritability) are a cause for concern and it is important to be aware of the family and other issues that are likely to co‐occur with inter­ nalizing symptoms in young children, independent of whether they fit into a diagnostic category. Several recent studies indicate that symptoms of anxiety and depression are highly correlated in preschool children (Bufferd et al., 2011; Lavigne, Hopkins, Gouze, & Bryant, 2014). In the cross‐sectional data reported by Egger and Angold (2006), 9.4% of children met symptom criteria for an anxiety disorder, including separation anxiety (2.4%), generalized anxiety (6.5%), and social phobia (2.1%). In the CDC data set, 2.6% of parents responded

26 Campbell that their preschool child had experienced fears and phobias over the past 12 months. Both the Lavigne et al. studies (1996; 2009) likewise reported very low levels of anxiety disorders and depression. In contrast, Bufferd et al. reported that 19.6% of 3‐year‐olds met symptom criteria for an anxiety disorder (9% specific phobia, 5.4% separation a­nxiety). These discrepancies are likely to reflect confusion among parents and health care providers about what constitutes common fears and worries in young children and what constitutes a clinically significant problem. Separation anxiety is the one DSM anxiety disorder that is specific to childhood and that includes developmental guidelines. Children must show “developmentally inappro­ priate and excessive anxiety concerning separation from home or from major attachment figures” as reflected in three symptoms that cause significant distress and last for at least four weeks (American Psychiatric Association, 2000). Symptoms include distress about anticipated separation, fear of being alone or sleeping alone, fears about losing or harm befalling an attachment figure, nightmares, and physical complaints when separation is anticipated. Some of these symptoms are clearly age‐related and may even be adaptive under certain conditions of family stress and disruption. Moreover, the DSM notes that separation anxiety may emerge after a stressful event that involves separation or loss. Therefore, the events surrounding the onset of symptoms, the way that caregivers are handling the situation, and the degree of impairment in functioning are all relevant to understanding how to conceptualize and manage severe separation anxiety. It is notewor­ thy that Egger and Angold reported that 8.6% of preschool children in their sample met symptom criteria (i.e., at least three symptoms for four weeks), but when they included impairment in functioning as an additional criterion, the rate fell to 2.4%. Longitudinal studies of separation anxiety are needed to determine the predictors and correlates of a long‐standing problem with separation and to differentiate clinically significant separa­ tion anxiety from a possibly appropriate response to a stressful life event that resolves with parental support and understanding. In conclusion, although several problem syndromes can be identified in young chil­ dren, most notably ODD, ADHD, and separation anxiety disorder, it is important to distinguish between time‐limited, albeit annoying or worrisome, behavior and disorders that are associated with impaired functioning and that compromise development. Moreover, some behaviors that are likely to interfere with children’s social and cognitive development, including high levels of aggression and destructive behavior and sad mood and irritability, do not fit neatly into the diagnostic nomenclature for young children, although elevated rates of these behaviors are likely to be associated with serious family problems. Finally, it is well‐documented that across age, children who have one disorder at a diagnosable level are likely to have more than one (Bufferd et  al., 2011; Egger & Angold, 2006; Lavigne et al., 2009). Thus, young children who meet criteria for ADHD are also much more likely than children who do not to also meet criteria for ODD. The children with co‐occurring ODD and ADHD symptoms are most at risk for continuing problems into elementary school and beyond (Lee et al., 2008). Finally, there is increasing evidence that preschool children with externalizing problems are also likely to experience anxiety and depressive symptoms, possibly because common processes including poorly regulated negative emotion and poor impulse control underlie all of these expressions of distress in young children (Egger & Angold, 2006; Lavigne et al., 2009).

The Mental Health of Preschool Children  27

Differentiating transient from persistent problems As already noted longitudinal data are the first step in differentiating between age‐related and transient problems and emerging disorders. A snap shot at one point in time may indeed indicate that a child is at the extremes on one or more symptom dimensions such as aggression, oppositional behavior, or hyperactivity. However, only longitudinal data will clearly indicate which children exhibiting externalizing problems in early childhood are reacting to a stressful event with time‐limited negative behavior and which children will continue on a pathway of more intractable problems even several months later. The probability that problems will persist, however, clearly increases as a function of co‐occurring child, family, and contextual risk factors. The likelihood of persistent prob­ lems increases with the number and severity of problem behaviors identified early. Studies also indicate that roughly 50% of children with elevated symptoms at preschool age will meet diagnostic criteria for a disorder in middle childhood (Campbell et al., 1996; Lavigne et al., 2001), but the other 50% will not. This means that we need to identify markers of  risk that will add to the prediction of persistent in contrast to more time‐limited problems. The correlates and risk factors for persistent problems in young children have been delineated in numerous studies. These consist of child characteristics, the quality of parent­ ing, family functioning, and more distal aspects of the child’s social and community c­ontext. Most of these studies examine young children with elevated externalizing prob­ lems as defined by parent ratings, rather than clinical diagnoses of disorder. That said, there is converging evidence from studies that explore both dimensional and categorical defini­ tions of problem behavior (e.g., Lavigne et al., 2001), that have followed preschool children with diagnoses of ODD or ADHD to school entry (e.g., Lee et al., 2008), or have exam­ ined diagnostic outcomes of young children with elevated symptom ratings (e.g., Campbell et al., 1996; Shaw, Hyde, & Brennan, 2012) to draw conclusions about what constitute risk factors for ongoing externalizing problems. In general, these studies have not examined internalizing problems independent of their co‐occurrence with externalizing difficulties. Results, however, indicate that risk factors are relatively non‐specific (McMahon, Grant, Compas, Thurm, & Ey, 2003) and cumulative, meaning that children experiencing the most risk factors across domains of risk are also more likely to have persistent problems (Deater‐Deckard, Dodge, Bates, & Pettit, 1998; NICHD ECCRN, 2004). Child characteristics.  As the discussion above suggests, when children meet diagnostic criteria for a disorder, especially at ages 4 or 5, including more severe externalizing prob­ lems that appear to interfere with developmental progressions, they may be on a pathway to continuing problems. This is especially the case when severe ODD is paired with another disorder (Lavigne et al., 2001). Moreover, when externalizing problems evident in early childhood persist through school entry (i.e., from ages 4 to 6), children are likely to continue to have difficulties both in school and with peers through middle childhood and early adolescence (Pierce et al., 1999). Other child characteristics are also risk factors for continuing problems, including pregnancy and birth complications, infant temperamental difficultness, and cognitive and language delays (Blenner, Hironaka, Vanderbilt, & Frank, 2014). These also may co‐occur as children who have experienced early birth complications

28 Campbell and health problems are more likely than those without complications also to be irritable, fussy, and difficult to soothe and to show some developmental delays. Thus, a constellation of early child difficulties across domains of functioning place children at risk for chronic behavioral and emotional problems (e.g., Poehlmann et al., 2011). Parenting.  Beginning with Patterson’s (1982) early writings on coercive parenting, Baumrind’s (1967) discussion of authoritative and authoritarian parenting, and Ainsworth’s demonstration of the importance of maternal sensitivity and responsiveness for the devel­ opment of a secure attachment relationship (Ainsworth, Blehar, Waters, & Wall, 1978), theory and research have emphasized the important role that parents play in children’s early social and emotional development. It is well‐documented that when parents are sensitive and tuned into their infants’ needs and communications, and also able to modify their parenting practices in ways that are responsive to their child’s changing developmen­ tal needs, young children are likely to thrive. By toddlerhood, parents must balance their responsiveness and support with their child’s need for limit‐setting, thereby helping the child to learn to regulate impulses and emotions, and develop an awareness of expectations for appropriate social behavior (Campbell, 2002). Parents who have difficulty setting l­imits, either by being rigid and harsh or overly lax or inconsistent may be setting the stage for emerging problems. However, this naïve parent effects model has given way to more complex models that include bidirectional influences (including child effects on parents) and complex transactions between child characteristics and parenting styles (e.g., Choe, Olson, & Sameroff, 2013; Lee, Altschul, & Gershoff, 2013; Smith et al., 2014). Both diathesis‐stress and differential susceptibility models (Belsky & Pleuss, 2009) posit that irritable, explosive, or hard‐to‐manage toddlers who receive harsh parenting that includes physical punishment, yelling and threatening, and lack of warmth are at high risk for serious behavior problems. Numerous studies link harsh parenting to early and continuing aggression and other externalizing problems (e.g., Campbell et al, 1996; Kochanska & Kim, 2012; Lee et al., 2013; Shaw et al., 1996). Whether this pattern partly stems from parents’ difficulties responding to a difficult and fussy infant who is not easily soothed or reflects the child’s negative reaction to insensitive parenting, an escalating cycle of coercive parent–child interactions may ensue (Smith et al., 2014), wherein the parent withdraws and the child learns to protest limits. Alternatively, the parent may lash out, modeling aggression and poor emotion regulation. These transactional processes may also be part of the family climate, reflecting patterns of negative interaction between parents and between parents and siblings, as well as the siblings themselves. Diathesis‐stress or dual risk models identify the combination of a difficult infant or toddler and a parent with less investment in or ability to engage in child‐centered, warm, and developmentally appropriate parenting as strong predictors of emerging behavior problems. The differential susceptibility model also posits a positive or protective effect that is amplified when difficult toddlers receive warm, supportive parenting with appro­ priate limit‐setting (Pluess & Belsky, 2010). Kochanska and Kim (2012) discuss the role that secure attachment and warm, sensitive, but firm parenting play in helping young children learn self‐regulation, the internalization of standards, and the willingness to c­omply with adult requests. Overall, converging evidence points to the importance of early relationship quality, parent‐child mutuality, and appropriate limit‐setting in fostering

The Mental Health of Preschool Children  29 young children’s self‐regulation and willing adherence to parental standards and requests (e.g., Choe et al., 2013; Halligan et al., 2013; Kochanska & Kim, 2012) and the differen­ tial susceptibility model suggests that these effects are especially strong when children are difficult to manage. Family functioning and parent mental health.  Decades of research have confirmed that parental mental health is a strong predictor of children’s outcomes, with most research focusing on maternal depressive symptoms. Although maternal depression may have direct effects on the child, most models focus on parenting as the mediator of links between maternal depression and children’s functioning (Cummings et  al., 2000; Goodman & Gotlib, 1999). Moreover, maternal depression evident in infancy, toddler­ hood, and the preschool years is of particular concern as young children are especially dependent on parents. Evidence indicates that mothers who report more chronic symp­ toms of depression are less warm and engaged with their children (NICHD ECCRN, 1999) and also show more harsh control (Lovejoy, Graczyk, O’Hare, & Neuman, 2000). These patterns of parenting in turn mediate effects of maternal depression on children’s language, self‐regulation, attachment security, and behavior problems (e.g., Campbell et al., 2004; Choe et al., 2013; NICHD ECCRN, 1999). In addition, maternal depres­ sion may co‐occur with other risk factors including marital distress (Cummings et  al., 2000; Downey & Coyne, 1990), low social support, single parent status, and financial stress (NICHD ECCRN, 1999). These factors may exacerbate the depressive symptoms and also contribute to even more marital distress or lower social support. Studies also confirm that marital distress and family conflict are strong correlates of problem behaviors in young children, partly reflecting a sense of anxiety and insecurity engendered in the child by parental arguing and aggression (Cummings et  al., 2000; Davies & Cummings, 1994; Davies, Winter, & Cicchetti, 2006). Models that include inter‐parental conflict indicate both direct and indirect effects of conflict on children’s internalizing and externalizing symptoms (e.g., Lavigne, Gouze, Hopkins, Bryant, & LeBailly, 2012). Direct effects are likely to be partly a function of genetic factors, but they also suggest the modeling of negative interaction styles and anger that cascades into aggres­ sion and defiance. Fearfulness and withdrawal are also logical direct effects of marital hostility and high levels of conflict. In the Lavigne et al. (2012) study, indirect effects of family conflict were mediated via parental hostility, maternal depression, and low social support. Taken together, a large body of data indicates that parental mental health, the quality of the relationship between adult partners, and overall family conflict or cohesion are strong predictors of adjustment in young children. Moreover, these effects may be especially strong when young children are already irritable or poorly regulated. Distal factors.  A range of distal factors including low parent education and financial stress, low social support, family chaos, and neighborhood violence have been included in studies of emerging problems in young children (Lavigne et al., 2012; Lee et al., 2013; Moffitt et al., 1996; NICHD ECCRN, 2004; Shelleby et al., 2014). In general, the effects of these distal factors on children’s adjustment are largely indirect, at least in the preschool years, mediated by aspects of parenting such as harshness, low parental warmth and sensi­ tivity, and parental distress. Other studies suggest that high quality child care can serve as

30 Campbell a protective factor (Pluess & Belsky, 2010), mitigating to some extent the effects of family disorganization and disengaged or harsh parenting on young children’s self‐regulation.

Summary and Implications for Social Policy – Prevention and Early Intervention In summary, it is well‐established that sociodemographic, parenting, and family risk are associated with the onset and persistence of behavior and emotional problems in preschool age children, with harsh parenting, lack of parental warmth and engagement, parental depression, and family stress predicting ongoing problems via complex direct and indirect pathways. Comprehensive programs that target high risk families early, in infancy or toddlerhood, and provide a range of services focused on the parents’ mental health and well‐being, on the early parent‐child relationship, and on enhancing young children’s self‐ regulation, cognitive, and language development have the potential to prevent early diffi­ culties in many, but not all children. Numerous interventions have targeted parental warmth and limit‐setting in an attempt to address early onset oppositional and defiant behavior as well as hyperactivity in preschool‐age children (e.g., Bor, Sanders, & Markie‐ Dadds, 2002; Reid, Webster‐Stratton, & Baydar, 2004) with positive effects. However, for intervention effects to be long‐lasting or for problems to be prevented before they emerge, more comprehensive programs that target the complexity of risk factors in the child’s fam­ ily and community are more likely to be effective (see Shonkoff, 2010). The extensive data base on behavior problems that emerge in the preschool years underscores who is most at risk for problems by kindergarten entry and where and when to intervene. At the same time, it is important to remember that many young children show aggressive, noncompli­ ant, or dysregulated behavior that is time‐limited, reflecting a difficult developmental transition or challenge. Helping parents, preschool teachers, and other caregivers under­ stand how to support young children as they adapt to environmental changes and negoti­ ate developmental challenges may be adequate in the absence of more serious problems.

References Achenbach, T. M., & Rescorla, L. (2000). Manual of the ASEBA Preschool Forms and Profiles. Burlington, VT: University of Vermont, Research Center for Children, Youth, and Families. Achenbach, T. M., & Rescorla, L. (2001). Manual of the ASEBA School‐Age Forms and Profiles. Burlington, VT: University of Vermont, Research Center for Children, Youth, and Families. Ainsworth, M.D.S., Blehar, M. C., Waters, E., & Wall, S. (1978). Patterns of attachment: A psychological study of the Strange Situation. Hillsdale, NJ: Erlbaum. American Psychiatric Association (2000). Diagnostic and Statistical Manual of Mental Disorders, text revision (4th ed.). Washington, DC: Author. American Psychiatric Association (2013). Diagnostic and Statistical Manual of Mental Disorders (5th ed.). Washington, DC: Author. Bates, J., & Pettit, G. S. (2007). Temperament, parenting, and socialization. In J. Grusec & P. Hastings (Eds.), Handbook of Socialization: Research and Theory. (pp. 153–177). New York, NY: Guilford Press.

The Mental Health of Preschool Children  31 Baumrind, D. (1967). Child care practices anteceding three patterns of preschool behavior. Genetic Psychology Monographs, 75, 43–88. Bell‐Dolan, D.J., Last, C. G., & Strauss, C. C. (1990). Symptoms of anxiety disorders in normal children. Journal of the American Academy of Child and Adolescent Psychiatry, 29, 759–765. Belsky, J., & Pluess, M. (2009). The nature (and nurture?) of plasticity in early human development. Perspectives in Psychological Science, 4, 345–351. Blenner, S., Hironaka, L. K., Vanderbilt, D. L., & Frank, D. A. (2014). Prematurity and failure to thrive: The interplay of medical conditions and development. In M. Lewis & K. D. Rudolph (Eds.), Handbook of developmental psychopathology (3rd ed.). (pp. 389–408). New York, NY: Springer. Bor, W., Sanders, M. R., & Markie‐Dadds, C. (2002). The effects of the Triple P Positive Parenting Program on preschool children with co‐occurring disruptive behavior and attentional‐hyperactive difficulties. Journal of Abnormal Child Psychology, 30, 571–587. 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. Broidy, L. M., Nagin, D. S., Tremblay, R. E., Bates, J. E., Brame, B., Dodge, K. A. … Vitaro, F. (2003). Developmental trajectories of childhood disruptive behaviors and adolescent delinquency: A six‐site, cross‐national study. Developmental Psychology, 39, 222–245. Bronfenbrenner, U. (1977). Toward an experimental ecology of human development. American Psychologist, 32, 513–531. Brownell, C. A., & Kopp, C.B. (2007). Transitions in toddler socioemotional development. In  C.  A. Brownell and C. B. Kopp (Eds.), Socioemotional development in the toddler years: Transitions and transformations (pp. 1–40). New York, NY: Guilford Press. Bufferd, S. J., Dougherty, L. R., Carlson, G. A., & Klein, D. N. (2011). Parent‐reported mental health in preschoolers: findings using a diagnostic interview. Comprehensive Psychiatry, 52, 359–369. Campbell, S. B. (2002). Behavior problems in preschool children: Clinical and developmental issues (2nd ed.). New York, NY: Guilford Press. Campbell, S. B., Brownell, C. A., Hungerford, A., Spieker, S., Mohan, R., & Blessing, J. S. (2004). The course of maternal depressive symptoms and maternal sensitivity as predictors of preschool attachment security at 36 months. Development and Psychopathology, 16, 231–252. Campbell, S. B., Pierce, E. W., March, C. L., Ewing, L. J., & Szumowski, E. K. (1994). Hard‐to‐ manage preschool boys: symptomatic behavior across contexts and time. Child Development, 65, 836–851. Campbell, S. B., Pierce, E. W., Moore, G., Marakovitz, S., & Newby, K. (1996). Boys’ externalizing problems at elementary school: Pathways from early behavior problems, maternal control, and family stress. Development and Psychopathology, 8, 701–720. Campbell, S. B., Shaw, D. S., & Gilliom, M. (2000). Early externalizing behavior problems: Toddlers and preschoolers at risk for later maladjustment. Development and Psychopathology, 12, 467–488. Campbell, S. B., Spieker, S., Burchinal, M., Poe, M., and the NICHD Early Child Care Research Network (2006). Trajectories of aggression from toddlerhood to age 9 predict academic and social functioning through age 12. Journal of Child Psychology and Psychiatry, 47, 791–800. Cicchetti, D., & Cohen, D. J. (1995). Perspectives on developmental psychopathology. In D. Cicchetti and D. J. Cohen (Eds.). Developmental psychopathology: Vol. I. Theory and methods (pp. 3–20). New York, NY: John Wiley & Sons, Inc. Choe, D. E., Olson, S. L., & Sameroff, A. J. (2013). Effects of early maternal distress and parenting on the development of children’s self‐regulation and externalizing behavior. Development and Psychopathology, 25, 437–453.

32 Campbell Cummings, E. M., Davies, P., & Campbell, S. B. (2000). Developmental psychopathology and family process: Research, theory, and clinical implications. New York, NY: Guilford Press. Davies, P. T., & Cummings, E. M. (1994). Marital conflict and child adjustment: An emotional security hypothesis. Psychological Bulletin, 116, 367–411. Davies, P. T., Winter, M., & Cicchetti, D. (2006). The implications of emotional security theory for understanding and treating childhood psychopathology. Development and Psychopathology, 18, 707–735. Deater‐Deckard, K., Dodge, K. A., Bates, J. E., & Pettit, G. S. (1998). Multiple risk factors in the development of externalizing behavior problems. Group and individual differences. Development and Psychopathology, 10, 469–493. Downey, G., & Coyne, J. C. (1990). Children of depressed parents: An integrative review. Psychological Bulletin, 108, 50–76. 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. Fontanella, C. A., Hiance, D. L., Phillips, G. S., Bridge, J. A., & Campo, J. V. (2014). Trends in psychotropic medication use for Medicaid‐enrolled preschool children. Journal of Child and Family Studies, 23, 617–631. Goodman, S., & Gotlib, I. (1999). Risk for psychopathology in the children of depressed mothers: A developmental model for understanding mechanisms of transmission. Psychological Review, 106, 458–490. Halligan, S. L., Cooper, P. J., Fearon, P., Wheeler, S. L., Crosby, M., & Murray, L. (2013). The longitudinal development of emotion regulation capacities in children at risk for externalizing disorders. Development and Psychopathology, 25, 391–406. Hughes, C., & Dunn, J. (2007). Children’s relationships with other children. In C. A. Brownell and C. B. Kopp (Eds.), Socioemotional development in the toddler years: Transitions and transformations (pp. 177–200). New York, NY: Guilford Press. Klein, R. G., & Last, C. G. (1989). Anxiety disorders in children. Newbury Park, CA: Sage Publications. Kochanska, G., & Kim, S. (2012). Toward a new understanding of the legacy of early attachment for future antisocial trajectories: Evidence from two longitudinal studies. Development and Psychopathology, 24, 783–806. Laible, D., & Thompson, R. A. (2007). Early socialization: A relationship perspective. In J. Grusec & P. Hastings (Eds.). Handbook of socialization: Theory and research (pp. 181–207). New York, NY: Guilford Press. Lavigne, J. V., Cicchetti, C., Gibbons, R. D., Binns, H. J., Larson, L., & DeVito, C. (2001). Oppositional Defiant Disorder with onset in the preschool years: Longitudinal stability and pathways to other disorders. Journal of the American Academy of Child and Adolescent Psychiatry, 35, 204–214. Lavigne, J. V., Gibbons, R.D., Christoffel, K. K., Arend, R., Rosenbaum, D., Binns, H. J., … Isaacs, C. (1996). Prevalence rates and risk factors for psychiatric disorders among preschool children. Journal of the American Academy of Child and Adolescent Psychiatry, 35, 204–214. Lavigne, J. V., Gouze, K. R., Hopkins, J., Bryant, F. B., & LeBailly, S. A. (2012). A multi‐domain model of risk factors for ODD symptoms in a community sample of 4‐year‐olds. Journal of Abnormal Child Psychology, 40, 741–757. Lavigne, J. V., Hopkins, J., Gouze, K. R., & Bryant, F. B. (2014). Bidirectional influences of anxiety and depression in young children. Journal of Abnormal Child Psychology, 42, 937–951. Lavigne, J. V., LeBailly, S. A., Hopkins, J., Gouze, K. R., & Binns, H. J. (2009). The prevalence of ADHD, ODD, depression and anxiety in a community sample of 4‐year‐olds. Journal of Clinical Child and Adolescent Psychology, 38, 315–328.

The Mental Health of Preschool Children  33 Lee, S. J., Altschul, I., & Gershoff, E. T. (2013). Does warmth moderate longitudinal associations between maternal spanking and child aggression in early childhood? Developmental Psychology, 49, 2017–2028. Lee, S. S., Lahey, B. B., Owens, E. B., & Hinshaw, S. P. (2008). Few preschool boys and girls with ADHD are well‐adjusted during adolescence. Journal of Abnormal Child Psychology, 36, 373–383. Lovejoy, M. C., Graczyk, P. A., O’Hare, E., & Neuman, G. (2000). Maternal depression and p­arenting: A meta‐analytic review. Clinical Psychology Review, 20, 561–592. McMahon, S. D., Grant, K. E., Compas, B. E., Thurm, A. E., & Ey, S. (2003). Stress and psycho­ pathology in children and adolescents: is there evidence of specificity? Journal of Child Psychology and Psychiatry, 44, 107–133. Moffitt, T. E., Caspi, A., Dickson, N., Silva, P., & Stanton, W. (1996). Childhood‐onset versus adolescent‐onset antisocial conduct problems in males: Natural history from ages 3 to 18. Development and Psychopathology, 8, 399–424. NICHD Early Child Care Research Network (1999). The course of maternal depressive symptoms, maternal sensitivity, and child outcomes at 36 months. Developmental Psychology, 35, 1297–1310. NICHD Early Child Care Research Network. (2002). Non‐maternal care and family factors in early development: An Overview of the NICHD Study of Early Child Care. Journal of Applied Developmental Psychology, 22, 457–492. NICHD Early Child Care Research Network. (2004). Trajectories of aggression from toddlerhood to middle childhood: Predictors, correlates, and outcomes. Monographs of the Society for Research in Child Development, 69, whole no. 4. Olson, S. L., Sameroff, A. J., Kerr, D. C., Lopez, N. L., & Wellman, H. M. (2005). Developmental foundations of externalizing problems in young children: The role of effortful control. Development and Psychopathology, 17, 25–45. Patterson, G. R. (1982). Coercive family process. Eugene, OR: Castalia Publishing. Perou, R., Bitsko, R. H., Blumberg, S.J., Pastor, P., Ghandour, R. M., Gfroerer J. C., … Huang, L. N. (2013) Mental health surveillance among children – United States, 2005–2011. Morbidity and Mortality Weekly Reports, Supplement (May 17, 2013), 62, 1–35. Pickles, A., & Angold, A. (2003). Natural categories or fundamental dimensions: On carving nature at the joints and the re‐articulation of psychopathology. Development and Psychopathology, 15, 613–640. Pierce, E. W., Ewing, L. J., & Campbell, S. B. (1999). Diagnostic status and symptomatic behavior of hard‐to‐manage preschool children in middle childhood and early adolescence. Journal of Clinical Child Psychology, 28, 44–57. Pluess, M., & Belsky, J. (2010). Differential susceptibility to parenting and quality child care. Developmental Psychology, 46, 379–390. Poehlmann, J., Schwichtenberg, A. J., Shlafer, R. J., Hahn, E., Bianchi, J. P., & Warner, R. (2011). Emerging self‐regulation in toddlers born preterm or low birth weight: Differential susceptibility to parenting? Development and Psychopathology, 23, 177–193. Reid, M. J., Webster‐Stratton, C., & Baydar, N. (2004). Halting the development of conduct prob­ lems in Head Start children: The effects of parent training. Journal of Clinical Child and Adolescent Psychology, 33, 279–291. Rutter, M., Kim‐Cohen, J., & Maughan, B. (2006). Continuities and discontinuities in psychopa­ thology between childhood and adult life. Journal of Child Psychology and Psychiatry, 47, 262–275. Sameroff, A. J. (2009). The transactional model of development: How children and contexts shape each other. Washington, DC: American Psychological Association.

34 Campbell Shatz, M. (2007). Revisiting A toddler’s life for the toddler years: Conversational participation as a tool for learning across knowledge domains. In C. A. Brownell and C. B. Kopp (Eds.), Socioemotional development in the toddler years: Transitions and transformations (pp. 241–257). New York, NY: Guilford Press. Shaw, D. S., Dishion, T. J., Supplee, L., Gardner, F., & Arnds, K. (2006). Randomized trial of a family‐centered approach to the prevention of early conduct problems: 2‐year effects of the f­amily check‐up in early childhood. Journal of Consulting and Clinical Psychology, 74, 1–9. Shaw, D. S., Hyde, L. W., & Brennan, L. M. (2012). Early predictors of boys’ antisocial trajectories. Development and Psychopathology, 24, 871–888. Shaw, D. S., Winslow, E. B., Owens, E., Vondra, J. L., Cohn, J. F., & Bell, R. Q. (1998). The  development of early externalizing problems among children from low income families: A transformational perspective. Journal of Abnormal Child Psychology, 26, 95–107. Shelleby, E. C., Votruba‐Drzal, E., Shaw, D. S., Dishion, T. J., Wilson, M. N., & Gardner, F. (2014). Income and children’s behavioral functioning: A sequential mediation analysis. Journal of Family Psychology, 28, 936–946. Shonkoff, J. P. (2010). Building a new bio‐developmental framework to guide the future of early childhood policy. Child Development, 81, 357–367. Shonkoff, J., & Phillips, D. (2000). From neurons to neighborhoods. Washington, DC: National Academy Press. Smith, J. D., Dishion, T. J., Shaw, D. S., Wilson, M. N., Winter, C. C., & Patterson, G. R. (2014). Coercive family process and early‐onset conduct problems from age 2 to school entry. Development and Psychopathology, 26, 917–932. Sonuga‐Barke, E., & Halperin, J. (2010). Developmental phenotypes and causal pathways in a­ttention deficit/hyperactivity disorder: potential targets for early intervention? Journal of Child Psychology and Psychiatry, 51, 368–398. Sroufe, L. A. (1990). Considering normal and abnormal together: The essence of developmental psychopathology. Development and Psychopathology, 2, 335–347. Sroufe, L. A. (1997). Psychopathology as an outcome of development. Development and Psychopathology, 9, 251–268. Tremblay, R. E. (2000). The development of aggressive behavior during childhood: What have we learned in the past century? International Journal of Behavioral Development, 24, 129–141. Wakschlag, L. S., Briggs‐Gowan, M. J., Carter, A. S., Hill, C., Danis, B., Keenan, K., … Leventhal, B. L. (2007). A developmental framework for distinguishing disruptive behavior from normative m­isbehavior in preschool children. Journal of Child Psychology and Psychiatry, 48, 976–987. Wakschlag, L., Choi, S. W., Carter, A. S., Hullsiek, H., Burns, J., McCarthy, K., … Briggs‐Gowan, M. J. (2012). Defining the developmental parameters of temper loss in early childhood: Implications for developmental psychopathology. Journal of Child Psychology and Psychiatry, 53, 1099–1108. Wakschlag, L. S., Tolan, P. H., & Leventhal, B. L. (2010). Ain’t misbehavin’: Towards a d­evelopmentally‐specified nosology for preschool disruptive behavior. Journal of Child Psychology and Psychiatry, 51, 3–22. Zito, J. M., Safer, D. J., dosReis, S., Gardner, J. F., Boles, M., & Lynch, F. (2000). Trends in the prescribing of psychotropic medications to preschoolers. Journal of the American Medical Association, 283, 1025–1030. Zito, J. M., Safer, D. J., Valluri, S., Gardner, J. F., Korelitz, J. J., & Mattison, D. R. (2007). Psychotherapeutic medication prevalence in Medicaid‐insured preschoolers. Journal of Child and Adolescent Psychopharmacology, 17, 195–203.

chapter THREE Early Childhood Health Disparities, Biological Embedding, and Life‐Course Health Daniel Berry

The longevity of a vibrant and productive society depends upon the health of its children. Over the last two decades, the United States (US) has made gains in some important indicators of child health. Sparked, in part, by the Children’s Health Insurance Program (CHIP) established in 1997 and the Affordable Care Act’s (ACA) extension of CHIP through 2015, the percentage of children in the US covered by health insurance has increased – particularly for families struggling with economic adversity (Rosenbaum & Kenney, 2014). Fewer children are exposed to tobacco smoke, unsafe drinking water, and dangerous levels of lead. Mortality rates in infancy and early childhood have declined (Bloom, Cohen, & Freeman, 2013). This is good news. Nonetheless, in absolute terms, the US lags behind most industrialized countries on many critical health indicators. Of the 34 Organisation for Economic Co‐Operation and Development (OECD) countries, the US is ranked last with respect to health care coverage. In fact, the US is one of only two OECD countries in which fewer than 80% of its total population is covered by health insurance (Mexico is the other). Although the obesity epidemic is evident across industrialized countries, the US ranks highest with respect to adult and childhood obesity (OECD, 2015). In addition, whereas the obesity rate has recently stabilized in many of the OECD countries, it remains comparatively more positive in the US (19% for children ages 3–17 in 2011, up from 17% in 2001 and 12% in 1991). The Wiley Handbook of Early Childhood Development Programs, Practices, and Policies, First Edition. Edited by Elizabeth Votruba-Drzal and Eric Dearing. © 2017 John Wiley & Sons, Inc. Published 2017 by John Wiley & Sons, Inc.

36 Berry Perhaps most strikingly, infant and childhood mortality rates  –  our most profound i­ndicator of child health – are higher in the US than almost all other industrialized c­ountries (more than 60 infant and 9 child deaths per 10,000), ranking 30th and 31st respectively out of the 34 OECD countries (OECD, 2015). Within the US, there are marked disparities in child health across the income d­istribution – children from low‐income contexts fare worse on virtually all key health indicators available from nationally representative data. Children from poor families are at greater risk for a variety of negative health conditions, ranging from low birth weight to chronic conditions such as asthma and hearing, speech and vision problems – with 33% of poor children compared to 27% of nonpoor children experiencing any of these chronic health conditions (Case, Lee, & Paxson, 2008; Case, Lubotsky, & Paxson, 2002; Currie & Lin, 2007). This health disparity – or the “income: health gradient” – is evident even in the very first years of life. To put this in context, based on the number of children below the US poverty threshold in 2013 (Jiang, Ekono, & Skinner, 2015), approximately 5.7 million infants, toddlers, and preschoolers begin life at heightened risk for chronic health problems that could plague them throughout their lives. Moreover, these disparities are unevenly distributed across children, with racial/ethnic minority children facing the dual burdens of poverty and ill‐health at far higher rates than their white peers (Olshansky et al., 2012; Williams, Mohammed, Leavell, & Collins, 2010). Accumulating evidence across several literatures – ranging from health economics to psychoneuroimmunology  –  indicates that social and economic adversity across the p­renatal to early childhood years1 may have especially salient and far‐reaching impacts on life‐course health. In this chapter, I introduce theory and empirical findings from across these literatures. I first describe some of most well‐documented early childhood health disparities and then highlight recent theoretical and empirical work concerning the b­iological mechanisms through which early adversity may “get under the skin” to affect children’s longer term health trajectories. To close, I briefly discuss the implications of this growing literature for early childhood programs and policy.

Health Disparities in Early Childhood Income/SES The income: health gradient is evident in the earliest days of development. Infants born to poor mothers are more likely to be born pre‐term and/or low birth weight (see Blumenshine, Egerter, Barclay, Cubbin, & Braveman, 2010)  –  a known risk factor for subsequent physical and cognitive problems (Boulet, Schieve, & Boyle, 2011). Infant mortality rates are highest for those born to mothers with low incomes (e.g., 100% poverty threshold) and/or low levels of education (e.g., < high school; Gage, Fang, O’Neill, & Di Rienzo, 2013). After infancy, children from low‐income contexts are at greater risk for unintended accidents (e.g., falls, poisonings; Gielen, McDonald, & Shields, 2015) – the leading cause of mortality in early childhood  –  and are more likely to suffer from chronic  conditions, such as asthma, obesity, and hearing, speech and vision problems

Early Childhood Health Disparities  37 (Braveman, Cubbin, Egerter, Williams, & Pamuk, 2010; Case, Lee, & Paxson, 2008; Case, Lubotsky, & Paxson, 2002; Currie & Lin, 2007). Indeed, nationally representative data indicate that children from low‐income families tend to show worse health, broadly conceived. For instance, based on National Health Interview (NHIS) data (1997–2003), Case and colleagues (2008) find that less affluent families are less likely than their more affluent peers to rate their children’s health as “very good or excellent” – reflections presumably informed by the finding that these same low‐income children were more likely to suffer from an array of chronic health problems (e.g., asthma, bronchitis, sinusitis, diabetes; Case, Lubotsky, & Paxson, 2002). Some work suggests that these early disparities increase as children age. Even with controls for parental education level, Case and colleagues (Case, Fertig, & Paxson, 2005; Case, Lubotsky, & Paxson, 2002) found that a doubling of family income increases the probability that a child is in excellent or very good health by 4% for ages 0–3, 4.9% for ages 4–8, 5.9% for ages 9–12, and 7.2% for ages 13–17. Most strikingly, despite long‐term, secular improvements in child health over the last several decades, the rates of improvement have been considerably slower for children growing up in low‐income families and communities – yielding increases in socio‐economic health disparities over time. For example, using somewhat broad indices of cumulative risk comprising county‐level indicators (e.g., income, education, poverty rate, occupation), Singh and Kogan (2007) reported that, despite overall population‐level decreases in infant mortality between 1969 and 2000, the disparity in childhood mortality (ages 1–14) between the top and bottom SES quintile actually increased over this period. Adjusting for age and race, the relative risk of all-cause childhood mortality between the bottom and top SES quintile increased from 38% to 72%, between 1969 and 2000. Remarkably, these increases were quite consistent across mortality attributed to more common experiential causes (e.g., unintentional accidents), as well as comparatively rarer biological causes (e.g., cardiovascular disease, cancer).

Race/Ethnicity Disparities across adverse birth outcomes, infant mortality, and more general early‐childhood health are also evident across racial/ethnics lines (Olshansky et al., 2012; Williams et al., 2010). For example, non‐Hispanic Black mothers are more likely to give birth to preterm and/or low birth weight infants than are White mothers (20% versus 12% for pre‐term births and 16% versus 8% for low birth weight infants; Martin, Hamilton, Osterman, Curtin, & Mathews, 2015). In infancy, despite some important historical improvements, the infant mortality rates for Black infants remain approximately twice that of White infants (13 versus 5 per 1000 live births; Mathews & MacDorman, 2012). As they age, Black children are at greater risk for chronic conditions, such as asthma (14% asthma prevalence rate for Black children versus 7% for White children; CDC, 2011; McDaniel, Paxson, & Waldfogel, 2006); and both Black and Hispanic children are more likely to show the types of dramatic early childhood Body Mass Index (BMI) increases that are thought to underlie chronic obesity (27% of young Black children and 25% of Hispanic children are at the 95th percentile or above, compared to 7% of White children; Taveras, Gillman, Kleinman, Rich‐Edwards, & Rifas‐Shiman, 2013).

38 Berry Notably, the complex intersection of race and social and economic inequality paints a multi‐factorial and less well‐understood etiological picture. For instance, due to far‐reaching socio‐historical inequalities, race and income are strongly inter‐twined. Approximately 69% of non‐Hispanic Black and 66% of Hispanic children under the age of six live in  families earning less than 200% of the poverty line, compared with 30% of White children (Jiang et al., 2015). As expected, these socio‐economic differences partially explain racial disparities in health and mortality in childhood (Woolf & Braveman, 2011). However, the mechanisms underlying racial/ethnic health disparities are seemingly more complex than SES alone. In the broadest statistical sense, racial/ethnic health differences are evident across wide‐ranging short‐ and long‐term health outcomes (see Woolf & Braveman, 2011), even after adjusting for socio‐economic indicators like income and education. Further, some have proposed that – due to the massive social inequalities experienced by minority families in the US  –  gains in broad markers of socio‐economic status (e.g., income, e­ducation) will yield less-pronounced health benefits for minority compared with majority individuals (i.e., the “diminishing returns” hypothesis; Farmer & Ferraro, 2005). Indeed, this idea has garnered some empirical support. For example, using data from a representative population‐based study of recent mothers in California, Braveman and colleagues (2015) found that the Black‐White difference in the preterm (i.e., < 37 weeks) birth rate was evident only in families at the high end of the SES distribution (i.e.. here, a composite of family and neighbor income, parental education, and occupational p­restige). Specifically, consistent with the “diminishing returns” hypothesis, they found that the relation between higher SES and lower risk of pre‐term birth was evident for White but not Black mothers. The driving forces behind such conditional racial health disparities are unclear; h­owever, they likely reflect the complex intersection of cumulative social and environmental inequalities. For instance, Black children (and increasingly Hispanic children) face substantially greater residential segregation than do their White peers (Logan & Stults, 2011)  –  segregation linked to a “geographic accumulation” (Acevedo‐Garcia, Osypuk, McArdle, & Williams, 2008, p. 322) of physical and social health risks. Black and Hispanic children are more likely to grow up in contexts with higher concentrations of neighborhood poverty and low levels of educational attainment (Lichter, Parisi, & Taquino, 2012). Minority children growing up in low‐income contexts tend to live farther away from resources like supermarkets (i.e., “food deserts”) than do their majority white peers (see Walker, Keane, & Burke, 2010). And, although the findings with respect to racial differences in physical access to green space and health‐supportive environments (e.g., public playgrounds) have been somewhat mixed (Franzini et  al., 2010; Macintyre, 2007), the quality and safety of these environments tend to be worse in neighborhoods comprising largely poor, minority children (Franzini et  al., 2010). Minority children are also at greater risk for being exposed to risky health behaviors (e.g., smoking; Quinto et al., 2013), as well as broad levels of environmental toxicity. For example, Currie (2011) found that  –  holding zipcode, income, education, and s­everal other potential confounds constant  –  non‐white mothers were more likely to live within 2,000 meters of a Superfund or Toxic Release Inventory (TRI) site during pregnancy.

Early Childhood Health Disparities  39

Rurality Although we often think of these broad‐ranging environmental health risks as occurring in “inner‐city” centers, increasing evidence suggests that growing up in rural contexts may carry its own health risks. For instance, in their work considering birth trends in Alabama, Kent, McClure, Zaitchik, & Gohlke (2013) found that – unlike the secular decreases in adverse birth outcomes (e.g., pre‐term birth, low birth weight) seen typically between 2005 and 2010 for mothers from more urban contexts – mothers in rural contexts tended to maintain historic highs in these adverse birth outcomes over this span. National‐level trends remain undocumented; however, similar findings have been noted in other southern states (e.g., Georgia; Markley & Tu, 2015). At the national level, death rates due to unintentional injuries are substantially greater for children in rural areas. For instance, between birth and age 14, the mortality rate due to motor‐vehicle accidents for those growing up in the most rural counties is more than double that of those in the most urban (28 vs. 11 per 100,000, respectively; Myers et al., 2013). Albeit attenuated, these estimates were robust after adjusting for an array of county‐level socio‐demographic covariates. Data with respect to rurality and more normative health outcomes are only beginning to emerge. However, descriptive findings at both the state (Crooks, 2000; Davy, Harrell, Stewart, & King, 2004) and national levels (Davis, Bennett, Befort, & Nollen, 2011; Lutfiyya, Lipksy, Wisdom‐Behounek, Inpanbutr‐Martinkus, 2007) suggest that children in rural/nonmetropolitan counties tend to show higher obesity rates (22%) than do their peers in more urban counties (17%). Interestingly, some of this work suggests that these relations are evident, despite the fact that children in rural and urban contexts tend to show very similar patterns of physical activity, screen time, and dietary intake (Davis et al., 2011). Overall, the links between rurality and obesity found in childhood align with those established with adults. Indeed, in addition to showing higher rates of obesity (Slack, Myers, Martin, & Heymsfield, 2014), adults living in rural contexts are more likely to suffer from chronic health problems (e.g., hypertension, diabetes, cancer; Meit et  al., 2014; Wallace, Grindeanu, & Cirillo, 2004), and higher mortality rates (Singh & Siahpush, 2014) than do those living in more urban contexts. Like the effects of income and race/ethnicity, health disparities across rural and more urban contexts reflect a complex integration of economic, cultural, and social forces. For instance, rurality is strongly intertwined with poverty and race. Of the 708 US counties showing persistent childhood poverty in 2015, nearly 80% were rural (US Department of Agriculture, 2015). The over‐representation of racial/ethnic minority families in rural poverty is also striking. Nearly 60% of all rural Black families and 32% of all rural Hispanic families live in counties with high concentrations of poverty (Lichter et al., 2012). Many of the health risks associated with poverty and race introduced earlier in the chapter are also likely at play in rural contexts and may well be amplified when considered in tandem with risk factors that are particular to rural areas. For example, in addition to the accumulation of physical and social health risks borne of concentrated poverty, c­hildren growing up in rural contexts also often face “institutional” risk factors, such as comparatively more limited access to high‐quality health care. In the most basic sense, there are fewer medical specialists available to children and families living in rural areas. Nationally, the number of general pediatricians per 100,000 residents is approximately six

40 Berry times larger in metropolitan (20–25 per 100,000) counties than it is in the most rural counties (4 per 100,000; Meit et al., 2014). Furthermore, accessing the health care that is available may be more difficult for those in more rural contexts. At the national level, the representation of uninsured children is nearly 50% higher for children in the most rural (15%) compared to the most urban regions (10%; O’Hare, 2009). As such, children growing up in rural poverty may face double or triple jeopardy – the cumulative physical and psychosocial stressors of poverty and/or racial/ethnic marginalization, combined with less access to high‐quality preventative and compensatory health care (Continelli, McGinnis, & Holmes, 2010; Shi, Lebrun, & Tsai, 2010). However, given the dearth of empirical work to date, understanding the complexities of rurality and health for young children remains a pressing area of study. In summary, early childhood health disparities are unevenly distributed across socio‐ economic, racial, and geographic lines. Although the complex intersection of these factors in the etiology of these disparities is not fully understood, as introduced later in the chapter, a convergent empirical and theoretical literature indicates that these early experiences and health disparities likely have far‐reaching impacts on life‐course health.

Early Adversity and Long‐Term Health: Empirical Findings Findings from both epidemiological studies of adult mortality and prospective longitudinal studies of life‐course health suggest that socioeconomic adversities experienced in early childhood likely play unique and important roles in long‐term health. For instance, Galobardes, Lynch, and Smith’s (2004, 2006, 2008) systematic reviews of the literature illustrate well‐replicated links between childhood socio‐economic status and adult mortality and disease morbidity. Of the 33 studies concerning childhood a­dversity and all‐cause adult mortality (US and Europe) surveyed by these authors, 28 indicated that adult mortality rates were elevated for those experiencing socio‐economic risk factors in childhood. Although childhood socio‐economic risk was typically measured using broad‐scale indices such as paternal education, similar relations were evident for other indicators (e.g., household crowding, poor ventilation). Critically, across several studies there was evidence that these relations were specific to socio‐economic risk experienced in childhood – adjusting for socio‐economic (e.g., education, income) and even health risks (e.g. diastolic blood pressure, cholesterol, body mass index) in adulthood typically only attenuated the long‐term associations. Across studies, the all‐cause mortality risk was approximately 20–40% higher for those who experienced socio‐ economic risks as children. These relations were remarkably consistent across different indices of risk, geographic locations, and historical periods in which the c­hildhood risk occurred. Moreover, there was some evidence of disease specificity. For example, childhood socio‐ economic risk showed rather robust associations with cardiovascular disease morbidity and mortality in adulthood (e.g., coronary heart disease, stroke)  –  outcomes often the result of cumulative and chronic cardiovascular problems. Yet, there was little evidence that these early risks were associated with overall cancer mortality. Rather, relations

Early Childhood Health Disparities  41 between early socio‐economic risk and cancer mortality were typically limited to cancers linked to health lifestyles (e.g., smoking). The methodological limitations of this literature notwithstanding (e.g., retrospective accounts, broad risk indices, potential endogeneity), collectively, this work suggests both direct and indirect connections between childhood socio‐economic risk and disease morbidity and mortality in adulthood. However, these reviews left several important questions unanswered  –  in particular: (a) whether early childhood serves as an especially salient developmental span, and (b) whether similar relations extend to more normative health outcomes. More recent longitudinal work provides positive support for both possibilities. For instance, using data from the Panel Study of Income Dynamics (PSID), Ziol‐Guest, Duncan, and Kalil (2009) found that – for low‐income families – lower income in early childhood was predictive of higher body mass index (BMI) upwards of 30 to 37 years later. Similar to prior findings for children’s academic outcomes, the relation was nonlinear, such that the income effect was most pronounced for the lowest income families. Quite similar relations were evident for other health outcomes (Ziol‐Guest, Duncan, Kalil, & Boyce, 2012). Lower income in early childhood was predictive of higher rates of arthritis and hypertension between the ages of 30 and 41. The magnitudes of these relations were non‐trivial. In adulthood, the hypertension and arthritis rates for those who experienced low‐income in early childhood were nearly twice that of those who did not experience this early risk factor. The timing of these outcomes is also noteworthy. Arthritis and hypertension are typically conditions of old age. Here, the differences were evident when the p­articipants were still quite young, perhaps indicating the early onset of conditions known to have origins in chronic inflammatory immune response (see later). With respect to developmental timing, there was an indication that the income effect was most salient when low income was experienced between gestation and the first one or two years of life (see also Duncan, Ziol‐Guest, & Kalil, 2010). Because data regarding timing effects are quite scant, these findings should be considered preliminary. Collectively, though, this work suggests that socio‐economic risk experienced in early childhood may have long‐term consequences for adult health  –  ranging from more normative (e.g., obesity, hypertension) to more profound (e.g., cardiovascular disease, premature death) health outcomes.

Early Adversity and Long‐Term Health: (Some) Biological Mechanisms The effects of early childhood experiences on children’s long‐term health can be explained by a myriad of plausible, intersecting development mechanisms. Muenning (2014) and Conti (2013) have provided thoughtful accounts of the ways through which early experiences (e.g., better income and resources, lower stress, better early health) likely bolster cognitive and social abilities that, in turn, have cascading reciprocal effects on health‐ supportive environments (e.g., education, earnings, peers) and behaviors (e.g., diet, exercise, abstaining from drugs and alcohol) underlying better life‐course health. Rather than r­eiterate this work, in this section my aim is to extend it by highlighting some plausible

42 Berry biological mechanisms through which children’s early experience may impact long‐term health. Poverty is used as an example of early childhood adversity throughout, given that it is a broad, well‐studied stressor in the lives of families and young children. However, there is every reason to suspect that the described processes function quite similarly with respect to experiential stressors caused by the diverse array of experiences introduced previously (e.g., social and institutional). We first take a look at the “big picture,” laying out an evolutionary rationale for why one might expect links between early experience and long‐term health. I then introduce some of the specific biological mechanisms through which these long‐term effects likely manifest – the biological embedding of early experience through: (1) the early experiential coordination of young children’s physiological stress and immunological systems, and (2) epigenetic alteration.

Evolution, “Fetal Origins,” and Pre-/Perinatal Stress Drawing from wide‐ranging areas of study – from evolutionary ecology to developmental psychoneuroimmunology  –  a convergent literature indicates that children’s experiences very early in life can become biologically “embedded” in ways that have pronounced effects on life‐course health. Much of this work is either directly or indirectly informed by contemporary evolutionary theories of developmental plasticity (West‐Eberhard, 1989; 2003). The basic idea of developmental plasticity is that we (like many organisms) have evolved mechanisms through which early experiences tune our biological, psychological, and behavioral processes in ways that maximize the probability of early survival and subsequent reproductive success in that given environment. That is, in the most fundamental way, we are built to adapt to our experiences. In evolutionary ecology the instantiations of these inter‐related processes are often referred to as Life History (LH) Strategies (Belsky, Steinberg, & Draper, 1991; Ellis, Figueredo, Brumbach, & Schlomer, 2009). In the context of environments that cue threats to reproductive success, organisms are theorized to adopt “fast” LH strategies. Fast LH strategies entail metabolic, physiological, and psychological shifts that assure early survival in the context of physical (e.g., nutrition) and psychological (e.g., stress) risks, as well as quickly paced maturation and sexual debut. With respect to selection, fast LH strategies are thought to be adaptive because they maximize early survival and subsequent fecundity when threats to reproductive success and offspring survival are high. However, central to Life History Theory, such strategies typically come with tradeoffs. For instance, fast LH strategies are energetically costly and, therefore, come at the expense of longevity (Belsky et al., 1991; Ellis et al., 2009). Slow LH strategies, in contrast, support metabolic, physiological, and psychological shifts suited to contexts in which threats to early survival and eventual reproductive success are minimal, such as lower metabolism, slower maturation, and richer physiological and psychological tools to negotiate the environment. Here, energetic costs are expended over a protracted developmental span and, thus, exert far less of a toll on longevity. Yet, given that absolute numbers of offspring are comparatively diminished, slow LH strategies nonetheless

Early Childhood Health Disparities  43 also incur a cost. Indeed, slow LH strategies would incur particularly strong costs, were environments to become unpredictable, competitive, or dangerous after slow strategies have been largely established. In the simplest terms “fast” LH strategies are thought to reflect short‐term investments in offspring quantity – sometimes at the cost of long‐term morbidity; whereas, “slow” LH strategies are thought to reflect longer‐term investments in offspring quality (Belsky et al., 1991; Del Giudice, Ellis, & Shirtcliff, 2011; Ellis et al., 2009). What does any of this have to do with early experiences and long‐term health? Given these tradeoffs, the biological machinery that evolved to allow one to maximize early survival and subsequent reproductive success in the context of physical and psychological stress (i.e., fast LH strategies) may well be the same biological machinery that underlies long‐term health problems (i.e., morbidity, premature senescence). That is, if the stressors faced by children in early childhood act upon this same vestigial biological machinery, one would expect clear connections between early experience and life‐course health – because that is the way the machinery was selected to work. There is good reason to suspect that the early life experiences faced by children growing up in poverty may often be functionally similar to those experienced on a more evolutionary time‐scale. That is, although we no longer face the same predator–prey, survival, and reproductive pressures of our ancestral past, the biological mechanisms presumably selected in these ancestral contexts likely extend to the physical and psychosocial risk f­actors experienced by low‐income children today (Ellis et al., 2009). Early work in this area was concerned with the potential long‐term impacts of pre‐ and perinatal exposure to malnutrition and stress on children’s health – risk factors that are considerably more common for pregnant women in poverty. For instance, pregnant women in poverty are more likely to show unhealthy weight gain during pregnancy, with some under‐gaining and others over‐gaining (Laraia, Epel, & Siega‐Riz, 2013). Both are associated with greater risk of preterm birth (Nagahawatte & Goldenberg, 2008). Pregnant women in poverty are also more likely to face a confluence of physical and psychosocial stressors known to affect the hormonal milieu of the inter‐uterine environment and infant health (DiPietro, 2004; Schetter, 2011). In particular, Barker’s (1998) seminal “fetal‐origins” hypothesis raised the generative idea that exposure to early physical (e.g., nutrition) and physiological stressors (e.g., stress hormones) alter the biological set‐points that regulate physiological and metabolic responses to changing internal and external demands (Gluckman, Hanson, Cooper, & Thornburg, 2008). In turn, these early adaptations were posited to have long‐term cascading effects on health. For instance, malnutrition and physiological stress in utero are theorized to initiate metabolic changes in the developing fetus in ways that maximize the body’s ability to store fatty acids and insulin (Barker, 2004). From an evolutionary perspective, this biological plasticity to early nutritional or physiological stress is thought to confer adaptive advantages because it essentially calibrates these nascent systems to function in ways that are optimally aligned with survival in the c­urrent environment. In this case, these metabolic changes are critical for supporting the immense energetic demands of the developing prenatal central nervous system (Gluckman et al., 2008). To the extent to which nutritional quality was also compromised in the post‐natal environment, these prenatal compensatory adaptations also confer secondary benefits – for example, postnatal growth in the context of limited caloric availability. However, this same

44 Berry adaptation may also confer risks. For instance, such early metabolic changes also place young children at risk for early insulin resistance and obesity (Stout, Espel, Sandman, Glynn, & Davis, 2015; Vickers, Breier, Cutfield, Hofman, & Gluckman, 2000). This is likely particularly the case for children growing up in present‐day poverty, whose diets often comprise low‐cost, high‐energy foods (Drewnowski & Darmon, 2005). At face value, one would typically consider insulin resistance and obesity to be negative health outcomes. Indeed, with regard to modern‐day longevity they are undeniably n­egative (Park, Falconer, Viner, & Kinra, 2012). Yet, from a Life History perspective, early nutritional or physiological threats experienced early in development may trigger a range of biological and behavioral changes that are – in an evolutionary sense – quite adaptive, despite their negative impacts on long‐term health (Del Guidice et al., 2011; Ellis et al., 2009). For instance, early adaptations underlying heightened adiposity/obesity are also known to trigger earlier sexual maturation (e.g., Ong et al., 2009; Sloboda, Hart, Doherty, Pennell, & Hickey, 2007; Terry, Ferris, Tehranifar, Wei, & Flom, 2009). Early sexual maturation is a critical component of fast LH strategies that maximize the likelihood of fecundity in the context of limited or threatening environments. As such, an adaptation that once carried substantial fitness value can nonetheless be maladaptive when considered in the context of present‐day lifespans. Using low birth/infant weight as a proxy for early biological adaptation, considerable epidemiological evidence shows long‐term associations between birth/infant weight and long‐term health (Barker, 1998; 2004; Hanson & Gluckman, 2014). Similar effects are also evident in “natural experiments” (Almond & Mazumder, 2005; Roseboom et  al., 2001; Stein, Zybert, Van der Pal‐de Bruin, & Lumey, 2006). For example, between 1944 and 1945 the Nazi regime cut food and fuel supplies from reaching a highly populated region of western Netherlands. The ensuing famine led to catastrophic declines in daily caloric intake in the region during this span (~400–800 calories; Roseboom et al., 2001). Researchers have leveraged this malevolent action as a plausibly exogenous shock, studying the causal impacts of prenatal caloric restriction and physiological stress on long‐term health. For instance, comparing those who experienced this nutritional (and presumably stressful) event in utero to those who were in utero just prior to or just after the event, Roseboom and colleagues (2001) found that by the age of 30 those who experienced the famine prenatally fared worse on several physiological health indicators (e.g., glucose tolerance, lipid profiles, BMI). Using sibling‐fixed effects methods, Stein and colleagues (Stein et al., 2006) found that the famine had similar effects on weight and adiposity in women nearly 60 years after exposure. Although the mechanisms remain unclear, this work suggests that health insults occurring very early in life may have far‐reaching effects on life‐course health.

Poverty and Stress in Early Childhood More recent theoretical and empirical work highlights the idea that the relation between early experience and long‐term health extends beyond the prenatal period and into early childhood. Several evolutionarily‐informed models propose that experiential cues – such as the less predictable, responsive, and emotionally warm social relations that tend to occur in the context of poverty – play a direct role in organizing young children’s nascent

Early Childhood Health Disparities  45 physiological, affective, and cognitive systems (Blair & Raver, 2012; Boyce & Ellis, 2005; Miller, Chen, & Parker, 2011; Del Guidice et al., 2011; Karatoreos & McEwen, 2013; Parker, Buckmaster, Sundlass, Schatzberg, & Lyons, 2006; Porges, 2011). In particular, early experiences comprising chronically unsupportive, unpredictable, or challenging social experiences are proposed to “tune” children’s developing autonomic (parasympathetic and sympathetic) and adrenocortical (e.g., HPA‐axis) systems, such that they are particularly “vigilant” and responsive to environmental change (e.g., perceived threat). These systemic physiological shifts toward vigilance are thought to confer (short‐term) adaptive advantages by supporting the organism to effectively regulate environmental and psychological challenges. Given the vestiges of their evolutionary origins, they are also theorized to underlie physiological, metabolic and behavioral profiles reflecting “fast” life‐ history strategies that would have carried fitness benefits in our ancestral past – yet, as described earlier, accrue health risks in the long term. There is good evidence that the physical and psychosocial environments faced by young children in poverty play an important role in their developing physiological stress systems. For instance, the cumulative stress of poverty can undermine parents’ abilities to e­ffectively and consistently read, interpret, and respond to their children’s affective needs (Blair & Raver, 2012; Conger, Conger, & Martin, 2010). In turn, both experimental work with animals and observational studies with children suggest that compromised parenting can impact autonomic nervous system (ANS) and hypothalamic pituitary adrenal (HPA) axis functioning – the two main human physiological stress systems (Calkins, Propper, & Mills‐Koonce, 2013; Del Guidice et  al., 2011; Gunnar & Herrera, 2013; Hostinar & Gunnar, 2013). Lower quality parenting is commonly predictive of atypical (i.e., over‐ or under‐responsive) physiological profiles. Indeed, there is some direct support for such cascading effects in early childhood. For example, Blair and colleagues (2011) found that low family income experienced early in life was predictive of subsequently elevated cortisol levels  –  the end‐product hormone produced by the HPA axis  –  in early childhood. Moreover, they showed that this relation was explained partially by the way low income “trickled down” to negatively affect parenting quality. Beyond psychosocial risks, the physical environments experienced by children in poverty have been implicated in shaping children’s developing physiological stress systems (Evans, 2004; Evans & Wachs, 2010). For example, children in low‐income families are more likely to be exposed to environments that are more densely populated, noisy, disorganized, and unpredictable (Evans & English, 2002; Evans, 2004). Collectively, these aspects of the environment are often considered under the umbrella term, household chaos. A growing literature suggests that chaotic environments may alter children’s ANS (Evans & English, 2002) and HPA axis functioning (Evans & English, 2002; Berry, Blair, Vernon‐Feagans, Willoughby, & Granger, 2015).

Stress, Inflammation, and Health in Early Childhood The idea that chronic physiological stress has negative impacts on health is not new (e.g., Seyle, 1950). However, plausible biological accounts for how stress exposure early in life translates to health effects that manifest several decades later have only recently begun

46 Berry to emerge (e.g., Danese & McEwen, 2012; Miller et al., 2011). Miller and colleagues’ (2011) model of biological embedding provides a particularly compelling theoretical framework for clarifying these developmental processes. Aligned with the evolutionarily‐informed models introduced earlier in the chapter, they argue that the physiological stress systems (e.g., ANS, HPA axis) and innate immune systems are intimately linked, given the functional selection advantages borne of pairing metabolic adjustments and tissue repair to physiological cues of caloric need and physical danger (see also Sapolsky, Romero, & Munck, 2000). However, in the long term, they posit that the v­igilant/reactive physiological and behavioral profiles that often manifest in the context of chronic stress also initiate immunological changes that put one at heightened risk for health problems later in life. As described earlier, this is simply another tradeoff of fast LH strategies. Specifically, these authors propose that exposure to chronic physiological stress in early childhood leads to cumulative changes in the way the innate immune system regulates inflammation. In the context of normative (i.e., modest/moderate) levels of physiological stress, inflammation plays a key role in supporting cellular health. When tissues are damaged, inflammatory processes coordinate cells of the innate immune system to remove pathogens and repair or remove damaged tissue (Koh & DiPietro, 2011). A key component of this process entails the recruitment of monocytes  –  white blood cells that a­ccumulate in damaged tissue. Upon reaching tissue, monocytes subsequently divide and differentiate into macrophages and dendritic cells that are critical to effective cellular repair (Koh & DiPietro, 2011; Miller et al., 2011). These complex cellular processes are driven by molecules called pro‐inflammatory cytokines (e.g., interleukin‐1β [IL‐1 β]; interleukin‐6 [IL‐6], and tumor necrosis factor‐α [TNF]), which, in turn, transact with proteins (e.g., C Reactive Protein) and white blood cells associated with the acquired immune system (e.g., T Cells) to support effective acute responses to infection and injury. In the short term, acute inflammatory responses are critical for survival. In contrast, inflammation can play a more sinister role when activated chronically. A  well‐developed literature shows that chronic inflammation underlies wide‐ranging m­etabolic problems, including insulin resistance, type II diabetes, obesity, atherosclerosis, hypertension, and stroke (see Gregor & Hotamisligil, 2011; Hotamisligil, 2006; Libby, DiCarli, & Weissleder 2010). Recent work has also implicated inflammation as a key mechanism in the hypothalamic regulation of these metabolic processes (Valdearcos, Xu, & Koliwad, 2015). Chronic inflammation is thought to play a role in premature aging (e.g., weakness, immobility, declining immunological and endocrine functioning; Chung et al., 2009; Jenny, 2012). Further, although inflammation has been found to show both pro‐ and anti‐tumor effects in cancer pathology, increasing evidence suggests that inflammation often promotes the initiation and progression of tumor growth (Galdiero & Mantovani, 2015). Indeed, recent estimates suggest that approximately 25% of cancers may be associated with chronic inflammation (Brennecke, Allavena, Laface, Mantovani, Bottazzi, 2015; Mantovani, Allavena, Sica, Balkwill, 2008). Informed by well‐developed neuroimmunological literature (see Irwin & Cole, 2011), Miller and colleagues (2011) propose that the chronic activation of the two major physiological stress system – the ANS and the HPA axis – early in life initiate cellular modifications that lead to elevated inflammatory response and weakened inhibitory regulation of

Early Childhood Health Disparities  47 these inflammation processes. That is, owing to the complex feed‐forward and feedback processes linking the ANS, HPA axis, and immunological systems, the chronic release of catecholamines (e.g., epinephrine, norepinephrine) from the sympathetic branch of the ANS and cortisol from the HPA axis create a biological milieu that promotes the pro‐ inflammatory tendencies of macrophages and other white blood cells that drive innate immune response. Over time, these processes are theorized to culminate in increasingly chronic states of inflammation. Although longitudinal data are scarce, evidence from the Dunedin Multidisciplinary Health and Development Study (DMHDS) is consistent with this model. The DMHDS comprises data from a birth cohort of children sampled in from Dunedin, New Zealand in 1972–1973, who were then subsequently followed prospectively through adulthood. Based on these data, Danese and colleagues (2009) found that several indicators of experiential stress in childhood  –  child maltreatment, low SES, and social isolation  –  were independently predictive of high levels of a C Reactive Protein (a biomarker of heightened inflammation) as well as heightened risk of cumulative metabolic problems (i.e., high blood pressure, high total cholesterol, low high‐density lipoprotein cholesterol, high g­lycated hemoglobin, and low maximum oxygen consumption) at age 32. These effects were robust after adjusting for familial health risk, as well as contemporaneous SES and heath behaviors. Similar findings are evident from cross‐sectional, retrospective studies. For instance, Coehlo and colleagues’ (Coelho, Viola, Walss‐Bass, Brietzke, & Grassi‐Oliveira, 2014) recent systematic review of this literature (~20 studies, after exclusion) suggests a rather robust positive relation between child maltreatment and concentrations of biological indicators of inflammation in adulthood. Other work indicates that these relations do not appear to be restricted to the more “extreme” stress of maltreatment. For example, Miller and colleagues (Miller et  al., 2009) sampled 25–40‐year‐olds who  –  based on parental occupational prestige  –  were rated as being low versus high SES in early childhood. Adjusting for contemporaneous SES and other potential confounds, they found that those from low‐SES families tended to output higher levels of cortisol across the day. In addition, when they exposed the participants’ extracted white blood cells to viral and bacterial stimuli known to evoke immune response (i.e., ex vivo), they found that the pro‐inflammatory response was stronger for those from low‐SES backgrounds. Of course, one should keep the considerable caveats with respect to potential endogeneity problems in mind (e.g., unobserved confounds; inaccurate retrospective accounts). Collectively, though, as part of a growing literature (see Danese & McEwan, 2012; Miller et al., 2011), this work is consistent with the following ideas: (1) early life stress may have long‐term effects on physiological stress systems, (2) perhaps, by virtue of this internal physiological milieu – these early experiences are associated with heighted inflammatory immune response at a cellular level, and (3) these processes collectively manifest in disease states much later in life. Notably, a critical question remains: How do early states of physiological stress and chronic inflammation become embedded biologically? What early biological changes might catalyze developmental effects on health that may be rather minimal early in life, yet become substantial with age? Although this area of study is largely in its infancy, several possibilities have been advanced, including cumulative structural changes to chromosomes

48 Berry (e.g., telomere erosion; Cohen et al. 2013; Haussmann & Marchetto, 2010) and systematic, long‐term changes to tissues within the stress systems and brain (e.g., tissue remodeling; Miller et al., 2011). As introduced next, however, one of the most developed literatures concerns the plausible role of epigenetic alteration – (largely) stable modifications to genetic expression that occur without changes to the actual DNA sequence.

Epigenetic Mechanisms of Early Adversity and Life‐Course Health Epigenetic mechanisms play a role in an array of biological processes, such as cellular d­ifferentiation, imprinting parental origin of DNA, X chromosome silencing, as well as cellular pathologies (e.g., cancer). Importantly, as sparked by Meaney and colleagues’ (see Meaney & Szyf, 2005) seminal research program with rodents, increasing evidence indicates that epigenetic modification may serve as an important molecular mechanism through which experience impacts long‐term developmental outcomes. Indeed, although evidence suggests epigenetic modifications can be pharmacologically reversed (Weaver et al., 2004; Weaver, Meaney, & Szyf., 2006), epigenetic changes are often stable over long developmental spans and are heritable across generations (Heard & Martienssen, 2014; Szyf, 2015). Thus, epigenetic processes serve as a particularly plausible mechanism through which the effects of early adversity can manifest much later in life – and, perhaps, across generations (Heard & Martienssen, 2014). Epigenetic modifications can be driven by several molecular events (see Gräff, Kim, Dobbin, & Tsai, 2011). Many involve altering the availability of the particular DNA sequences that can be read (i.e., transcribed) and subsequently expressed as RNA, amino acids, and (ultimately) proteins. When we think of DNA, we often think of the ladder‐like structure of the double‐helix. Notably, this double‐helix is wrapped tightly around p­roteins called histones, which help to provide structure to the DNA strands. In turn, individual histones are clustered in tight 8‐histone (octamer) groupings called nucleosomes. The nucleosomes are then bound together to form a macromolecule called chromatin – the physical packaging of the DNA within a chromosome. This compact binding of DNA is necessary because it allows the DNA to fit within the nucleus of eukaryotic cells, where it is housed. However, the compactness of chromatin structure is dynamic and flexible and affected by the chemical signals it receives. Changes in the density of the chromatin structure (i.e., tight to loose) have pronounced effects on genetic expression – that is, the degree to which the gene is turned on or off – because the tightness or looseness of the c­hromatin affects the extent to which DNA are visible for transcription and, thereby, the extent to which the gene is expressed. One epigenetic mechanism, histone modification, affects genetic expression by chemically altering the “tails” of histones in ways that impact the chromatin structure. The specific process by which this occurs (e.g., acetylation, methylation, phosphorylation, ubiquitination) and the extent to which it up‐ or down‐regulates expression is complex (Gräff et al., 2011). However, as one example, acetylation of the histone tail often initiates a molecular c­ascade that relaxes the chromatin structure, such that it can be more effectively t­ranscribed and ultimately expressed.

Early Childhood Health Disparities  49 To date, the most relevant work with respect to children’s early experiences and epigenetic alteration has largely concerned the methylation of the DNA itself. Methylation occurs when a methyl group binds with a cytosine DNA nucleotide. This typically occurs in sites called CpG islands, which comprise rich segments of adjacent cytosine‐phosphate‐ guanine dinucleotides (hence, CpG). CpG islands are commonly located in or near the promoter regions of a gene  –  non‐coding segments of DNA that help regulate gene t­ranscription (Christensen et  al., 2009). Methylation of the promotor typically blocks transcription factors from being able to initiate transcription. It also often sets off a series of events that lead to the deacetylation of histone tails (Fuks, 2005). Deacetylation is the inverse of acetylation; it leads to the compaction of the chromatin structure. As such, methylation typically down‐regulates genetic expression by virtue of multiple chemical processes. It may also serve as an important biochemical mechanism through which early experience impacts long‐term outcomes.

Emerging Developmental Evidence from Non‐Human Animal Studies There is good evidence that epigenetic processes, such as methylation, serve as important chemical mechanisms through which information about the caregiving environment is transmitted to offspring. Meaney and colleagues’ (Meaney & Szyf, 2005) elegant cross‐ fostering experiments with rodents provided much of the groundwork in this area of study. In their work, they have shown that normative differences in maternal behavior, such as licking and grooming (LG) by rat dams, can have long‐lasting effects on the infant rat pups’ developing physiological stress systems (Caldji, Diorio, & Meaney, 2000; Caldji et al., 1998; Weaver et al., 2004) and, in turn, behavior (Weaver et al., 2006). Critically, these cascading effects of parenting on stress physiology and behavior were explained partially by the fact that maternal LG behavior manifested in different methylation patterns in glucocorticoid receptor (GR) genes (e.g., NR3C1) extracted from the hippocampal brain cells of the cross‐fostered rat pups. Rat pups cross‐fostered to low LG dams showed highly methylated GR receptors in the hippocampal area of the brain; whereas those of rat pups cross‐fostered to high LG dams were hypomethylated (McGowan et  al., 2011; Weaver et  al., 2004). The fact that rat pups were cross‐fostered is non‐trivial because it rules out the possibility that any parenting effects are actually explained by genetic (i.e., DNA) differences and/or DNA‐environment correlations. With respect to long‐term health, these epigenetic changes in GR‐related genes may be critical. Glucocorticoid receptors provide the main pathways through which cortisol – the end‐product hormone of the HPA axis  –  impacts the brain. Specifically, when cortisol binds to GRs it initiates a negative feedback loop between the GR and the hypothalamus, which tells the hypothalamus to stop the HPA‐axis cascade (i.e., end the stress response; Gunnar & Quevedo, 2007). As such, with high LG caregiving hypomethylation of GRs leads to greater GR expression and, in turn, more efficient negative feedback messaging within the HPA axis. In contrast, low LG caregiving methylates the GR promoter, which leads to low levels of GR expression, and less efficient negative feedback signaling within the HPA axis. As introduced earlier, this likely plays an important role in long‐term health

50 Berry because the over activation of the HPA axis is thought to underlie the chronic inflammatory states known to cause wide‐ranging negative effects on health. Notably, because cortisol also typically shows similar negative feedback effects on inflammation (i.e., more cortisol, less inflammation), the effect of chronic cortisol on heightened inflammation likely manifests by desensitizing of this acute inhibitory effect of cortisol on inflammation (Miller et al., 2011; see ex vivo findings later in the chapter). Increasing evidence also suggests that life stress may affect the expression of genes a­ffiliated with both stress and immune responses. Experimental work with non‐human primates, for example, has shown that exposure to early life stress is predictive of systematic differences in the methylation patterns in cell‐lines known to underlie stress and immune response (Cole et al., 2012; Provencal et al., 2012; Tung et al. 2012). And, there is evidence suggesting that the effects of early rearing on these epigenetic alterations are direct, as opposed to a downstream effect occurring later in development (Provencal et al., 2012). The distal impacts of these epigenetic effects on genetic expression and, ultimately, stress and immune function are unclear from these data. Collectively, nonetheless, the evidence from studies of non‐human animals provides support for the idea that early experiential stress may leave a biological signature on the developing stress and immune systems – perhaps, even in very first few weeks of life.

Emerging Developmental Evidence from Human Studies Developmental studies of epigenetic processes are beginning to extend into human populations. Indeed, some work suggests that such embedding processes begin prenatally. Based on (virtually) the same sample of surviving adults from the 1944 Dutch famine described previously, Tobi and colleagues (2009) found that individuals (~60 years old) who were exposed to the famine around the time of their conception (or to a lesser degree in late gestation) showed higher methylation levels across several candidate genes linked with immunological functioning (e.g., inflammation, ATP, leptin) compared to their own siblings who were not exposed to the famine. Interestingly, some (but not all) of the methylation patterns were similar to those found with genetic homologues evidenced in studies of non‐human primates (e.g., Provencal et al., 2012). Other work has used mixed PBMCs from newborns’ umbilical cords as a proxy measure for epigenetic changes that occurred in utero. For example, infants born to mothers suffering from depression in their third trimester have been found to show more highly methylated regions of the glucocorticoid receptor (NR3C1) than those of non‐depressed mothers (Oberlander et al., 2008). This is strikingly consistent with the early NR3C1findings from studies of rat pups fostered by low LG mothers (Meaney & Szyf, 2005), particularly given the cell‐line differences between the studies (i.e., blood cell versus hippocampal brain cell). Indeed, the extent to which one expects meaningful epigenetic consistencies across cell‐types is still largely unclear (though, Provencal et al., 2012; also see later). An increasingly convergent literature suggests that related epigenetic effects extend to children’s experiences later in childhood. For example, retrospective accounts of childhood maltreatment and parental loss have been linked to NR3C1 methylation patterns in

Early Childhood Health Disparities  51 adulthood that are quite similar to those found perinatally (Tyrka, Price, Marsit, Walters, & Carpenter, 2012). Related findings are also evident when considered at a genome‐wide level. Naumova and colleagues (2012) compared the genome‐wide methylation profiles between a sample of Russian children (7–10 years old) raised in institutionalized care – which entails substantial early deprivation and social isolation – with those from an income‐matched comparison group of Russian children raised by their biological parents. Based on data from mixed PMBCs they found noteworthy differences in the methylation patterns between groups. Children raised in institutionalized care typically showed methylation levels that were much higher than the matched comparison group. Bioinformatic analyses suggested the methylated genetic loci tended to cluster in genes known to impact physiological stress system functioning (e.g., arginine/vasopressin, glucocorticoid and steroid biosynthesis), the neural transmission of stress‐ and reward‐relevant information (e.g,, the dopaminergic system, serotonin biosynthesis and receptor activity), and immune functioning (e.g., inflammatory response, cytokine activity, antigen processing, toll‐like receptor signaling). With respect to this clustering of stress and immunologically relevant genes, remarkably similar trends have been shown with samples of foster children in the US. Based on T‐Cells extracted from mixed PMBCs  –  blood cells that are particularly relevant to acquired immune‐system functioning – Bick and colleagues (2012) compared the genome‐ wide methylation patterns of children exposed to foster care in childhood with those who had not. As young adults, those who were exposed to the foster care system in childhood showed higher methylation patterns across 72 genes and lower methylation across 101 genes. Although the exact methylation pattern varied somewhat from the findings with the Russian sample, their bioinformatic analysis, again, showed that the largest methylation differences across the foster versus non‐foster groups typically occurred in genes underlying immunological functioning (e.g., ubiquitin‐mediated modulation of inflammation response, antibody activity). Using a candidate‐gene approach with these same data, these authors found that mother‐reported ratings of affection and warmth toward the child measured 5 to10 years prior to the collection of the blood samples was strongly negatively (‐.81) correlated with methylation of the GR (NR3C1) gene, as well as the immunologically‐relevant gene (MIF) known to mitigate the link between glucocorticoids on inflammation (‐.80). This likely means that (like the early findings with rodents) warm parenting is predictive of more efficient down‐regulation of the HPA axis (via GR ­expression/function), as well as, perhaps, immunological mechanisms that regulate connections between the HPA axis and inflammation. Importantly, these findings do not appear to be limited to children experiencing extreme levels of social adversity (e.g., maltreatment, social isolation). Longitudinal work using the 1958 British Birth Cohort Study showed long‐term associations between broader measures of SES between birth and 7 and the genome‐wide methylation patterns found in the children’s mixed PMBCs as adults (~45 years old; Borghol et al., 2012). Methylation differences between children experiencing low and high SES in early childhood were evident in genes linked to a broad array of functions – ranging from intra‐ and extra‐cellular signaling to sensation and perception. Notably, there was a sizable representation of genes thought to underlie immunological functioning (e.g., inflammation, cytokines), including some of the same processes noted by others (e.g., ubiquitin‐mediated modulation of

52 Berry inflammation; Bick et al., 2012). Further, this work indicated that these relations were: (1) evident after adjusting for levels of contemporaneous SES in adulthood, and (2) that there was very little overlap in the methylation patterns associated with early versus later SES. These findings raise the provocative idea that epigenetic effects that emerge early in life may be quite distinct and, perhaps, considerably more salient than those occurring later in life. Indeed, the findings from a growing number of retrospective studies similarly highlight the importance of environmental stress experienced in early childhood. In particular, a comprehensive series of studies by Chen, Cole, Kobor, and Miller provide compelling evidence that early‐life SES is predictive of differences in epigenetic methylation and genetic expression in adulthood, after adjusting for contemporaneous SES. For example, based on mixed PMBC samples from adults (~25–40) selected to have experienced either high or low SES (operationalized as parental occupational prestige) between birth and the age of 5, these authors reported differences in expression across 140 genes between the two early‐life SES groups (73 up‐regulated, 67 down‐regulated). Bioinformatic analyses revealed that these expression differences were associated with the up‐regulation of genes linked to the adrenergic functioning and immunological signaling, as well as the down‐ regulation of genes linked with glucocorticoid receptor functioning (though, not NR3C1, specifically; Miller et al., 2009). Again, this is consistent with the idea that chronic experiential stress is predictive of  over‐active physiological and immune response, as well as the less‐efficient down‐ regulation of these systems. These inferences were also evident empirically. On average, those from low‐SES contexts in early childhood were found to show higher levels of overall cortisol output over the course of the day as adults. Further, ex vivo analyses of the response of these cells to toll‐like receptor stimulation  –  a chemical trigger that initiates cellular immune response to pathogen/injury  –  showed that the cells from adults from the low‐SES group showed a considerably more substantial immunological response (i.e., interleukin‐6 cytokine). In subsequent work with this same sample, this group found rather weak correlations between methylation and genetic expression (Lam et al., 2012). This is somewhat c­ ounter‐ intuitive, given the common interpretation that methylation down‐regulates genetic expression. However, it likely underscores that fact that these processes are far more complex, heterogeneous, and dynamic across individuals, cell‐types, and loci than considered typically (Gräff et al., 2014). Nonetheless, the findings with respect to methylation are strikingly similar to those for expression. On average, low SES in early childhood, but not adulthood, was predictive of higher levels of CpG methylation as adults and higher levels of methylation were, in turn, predictive of higher levels of diurnal cortisol output and self‐reported levels of stress in adulthood. Further, ex vivo analyses of cellular function showed that higher levels of methylation were associated with more pronounced cellular immune response (i.e., cytokine interleukin 6) to toll‐receptor stimulation – quite similar to the relation already noted for genetic expression. Indeed, more recent work from this group has provided provocative cross‐species e­vidence of the close tie between the sympathetic branch of the ANS and immunological function (Powell et  al., 2013). Based on findings from a repeated social defeat stress p­aradigm conducted with mice, they show that the effect of experiential stress on immune

Early Childhood Health Disparities  53 function is driven, in part, by the effects of sympathetic stress response on the expression of genes in bone marrow cells that produce monocytes and other immune‐relevant white blood cells (myelopoiesis). Recall that monocytes are the critical workhorse leukocytes that help orchestrate a cascade of cellular immunological changes (e.g., inflammation) that drive the immune response. That is, in addition the regulatory cross‐talk between the physiological stress systems and the immune systems, sympathetic innervation of myelopoiesis may also lead to a baseline over‐production of the leukocytes that drive the chronic states of inflammation. Critically, they show that the genetic expression differences between stressed and non‐stressed mice (in monocyte cells) show notable overlap (~32%) with the genetic expression differences (in mixed RBNCs) found between adults who experienced either low versus high levels of early‐life SES. As such, it seems plausible that part of the effects of early stress on long‐term health may manifest via the effects of stress on the very biological machinery that undergirds the production of white blood cells – the backbone of inflammatory response of the innate immune system.

Putting the Pieces Together The first section of this chapter briefly reviewed the considerable literature suggesting that health disparities are evident across socio‐economic, racial, and geographic lines from the earliest stages of development – prenatally, perinatally, and throughout early childhood. These disparities persist  –  and may grow stronger  –  into adulthood. The etiologies of these short‐ and long‐term disparities are complex and multifactorial. As posited by “s­ystems” (e.g., Gottlieb, Wahlsten, & Lickliter, 1998) and “cascade” models of development (Masten & Cicchetti, 2010), as well as econometric models of “dynamic complementarity” (Conti & Heckman, 2014), they likely reflect the ongoing, reciprocal, and self‐organizing transaction of one’s health strengths/liabilities (biologically and behaviorally) and health‐related experiences (opportunities and choices) over time. Rather than attempt to articulate these complex dynamics in full, the aim was to introduce the idea that these long‐term developmental dynamics are likely explained, in part, by the way early experience shapes the organization of several biological systems underlying life‐course health. Specifically, I highlighted contemporary evolutionary thinking behind much of this work, recent theory implicating the roles of stress and chronic inflammation as critical mechanisms explaining these long‐term relations, and epigenetic alteration as one possible biological process through which these inflammation‐mediated effects may emerge. My intent was not to privilege biological mechanisms over experiential and psychological mechanisms in any substantive way. Well‐rounded coverage of the latter has recently been provided by others (Muennig, 2015; Conti, 2013). Indeed, I hope that the brief foray into epigenetics accentuates the inextricable connection between experience and biology. With respect to developmental processes, the epigenetic literature is arguably the most well‐developed and provides a compelling biological explanation for how early experience can feasibly show far‐reaching developmental effects. However, several other processes – such as telomere erosion and tissue remodeling  –  are garnering increased interest and

54 Berry theoretical and empirical support (Cohen et  al. 2013; Miller et  al., 2011) and will undoubtedly add to a more comprehensive understanding of these long‐term developmental processes. In addition, it is worth noting that epigenetic research is still in its infancy. As noted, some consistencies are beginning to emerge across studies (sometimes even species); however, there are also notable inconsistencies across studies. Indeed, inconsistencies are going to be common. Genome‐wide expression/methylation analyses typically make comparisons across tens of thousands to millions of loci (e.g., expression quantitative loci [eQTLs]), and bioinformatic analyses cross‐reference increasingly large collections of data. The fact that there is often a substantial lack of overlap between studies and samples is likely partially due to the reality that many of the findings are probably false‐positives. Like molecular genetics (i.e., DNA) and fMRI, epigenetics fights an uphill battle against Type I error  –  disentangling meaningful heterogeneity across predictors, outcomes, samples, cell‐lines, methods and development from noise and false‐positives remains a challenge across these areas of inquiry. More substantively, the field is only beginning to get a handle on how best to interpret epigenetic modifications. A common concern is the extent to which epigenetic alterations are specific to cell‐type. For instance, is there good reason to expect that early experiences will be reflected similarly in the methylation patterns of GR genes that are extracted from brain cells affiliated with different regions of the limbic‐HPA axis (e.g., frontal cortex, hypothalamus, hippocampus)? Moreover, should we expect commonalities across these brain cells and, say, blood cells or buccal mucosa cells extracted from cheek swabs – common cell types used in studies of human epigenetics? Although there seems to be some overlap across cell‐types (Provencal et al., 2012; Smith et al., 2015), the intricacies and ultimate implication of differences remain somewhat unclear. This is an obvious limitation for studies of humans, for whom brain tissue is not readily available (though Alt et al., 2010; McGowan et al., 2008). Indeed, even in the context of immune‐system functioning in which one has ready access to the cells that are doing the actual biological legwork (i.e., white blood cells), increasing findings suggest that methylation/expression profiles may show important differences across different types of white blood cells (e.g., CD3+ T Cells, CD14 + monocytes; Lam et al., 2012). In addition, as illustrated earlier, there is no 1‐to‐1 correspondence between methylation and expression – which is arguably one of the most clearly articulated epigenetic alterations. This is not terribly surprising given the complex, dynamic connections likely occurring within and across genes, specific loci, cells, and individuals. However, it does suggest that – despite the exciting new contributions that this area of research has made to developmental science – there is much left to learn.

Does Any of This Matter for Early Childhood Policy? There is credible evidence that children’s early experiences can have long‐term impacts on health. This has non‐trivial implications for early childhood policy. There is increasingly good reason to suspect that these long‐term effects are driven, in part, by dynamic d­evelopmental connections between children’s emerging physiological stress and immunological systems (see Miller et al., 2011) – a partially vestigial remnant of our ancestral

Early Childhood Health Disparities  55 past (Del Giudice et al., 2011). Although the theorized evolutionary back‐story may seem tangential with respect to “real-world” applications, it is likely quite relevant. Theory s­uggests that chronic stress doesn’t function like a bull in an immunological china shop, leaving random destruction in its wake. Rather, chronic stress may well calibrate cross‐ s­ystem integration in internally consistent and predictable ways. On one hand, this has troubling implications. If this is accurate, then the present‐day link between early adversity and later health problems is likely partially driven by the fact that normative connections between stress and immune functioning are, in fact, normative because of their remarkably efficient capacity to maximize short‐term success at the cost of long‐term health. In other words, in evolutionary terms, these systems are working as intended (i.e., selected) – contemporary humans just live too long. On the other, it suggests that under conditions of moderate, surmountable, and intermittent stress – such as those occurring in the context of warm, responsive, and stable adult‐child relationships and consistent and predictable home environments – these same mechanisms can be leveraged to initiate stress‐immune processes underlying “slow” life history strategies thought to reflect effective emotional and behavioral self‐regulatory control, the perpetuation of positive parenting, and better life‐course health. Consistent with economically-minded perspectives (e.g., Conti & Heckman, 2014) the biological data also point to the possibility that experiences occurring in early childhood may have particularly salient developmental effects on children’s long‐term outcomes. For instance, although some of the methodological limitations are noteworthy (e.g., retrospective, observational data; small, non‐representative samples), several findings indicated that the long‐term relations evident between early childhood adversity and genetic expression, methylation, and immunological functioning were robust after adjusting for contemporaneous levels of economic adversity in adulthood. The findings from experimental studies of rodents and non‐human primates indicate that these experiential effects can occur very early in development and remain stable across the lifespan (Cole et al., 2012; Meaney & Szyf, 2005; Provencal et al., 2012). Indeed, epigenetic changes can be heritable and transmitted across generations (Szyf, 2015). It is also worth noting, however, that increasing evidence suggests that epigenetic modification can be reversed (at least, pharmacologically with rodents; Weaver et al., 2004; 2006). Even more importantly, epigenetic effects on physiology (e.g., HPA axis) and broader behavioral phenotypes (e.g., anxiety, cognition) can be reversed in the context of subsequently supportive environments, even without affecting the epigenetic modification itself. That is, just like the effects of genetic (i.e., DNA) variation, the long‐term effects of the epigenetic variation are subject to experiential modification. Nonetheless, questions regarding developmental timing of particularly sensitive periods or “thresholds” underlying links between experience and children’s subsequent health remain largely unanswered. With some recent exceptions (e.g., Miller & Wherry, 2014; Ziol‐Gest et al., 2009; 2012), very few studies of humans have adopted designs that allow the disaggregation of potentially meaningful developmental thresholds. This is often a thorny design problem, given the typical within‐child stability in broad indicators of SES over time. On one hand there is every reason to suspect that good health begets good health (Conti & Heckman, 2014). Thus, children’s experiences in early childhood likely play a particularly salient role in life‐course health. This certainly aligns with biological

56 Berry perspectives, which tend to view early childhood as a critical developmental “switch point” in the biological embedding of experience. On the other hand, it is also worth noting that contemporary theory tends to view early childhood as one of multiple salient developmental switch points in which the complex cross‐system dynamics underlying experience and health are especially plastic to re‐organization and change – developmental apertures that change but don’t close (e.g. see Del Giudice et al., 2011). A final implication of this work concerns the estimation of policy costs and benefits. The efficacy of public policy is commonly measured as the benefits produced by the policy relative to cost. The evidence introduced in this chapter would suggest that the validity of these analyses with respect to early childhood policy and health will be contingent upon the accurate assessment of long‐term health impacts. Benefit‐to‐cost ratios will likely be negatively biased, to the extent to which long‐term health impacts are excluded. This is noteworthy; the benefits of good health (and the costs of bad health) accrue at far greater rates in adulthood because health problems tend to be comparatively more chronic and costly than earlier in life. On average, in the US we tend to incur about 8% of our lifetime average health costs in childhood (0–19), whereas we incur almost a third of our total lifetime health costs in middle age (40–64; Alemayehu & Warner, 2004). In addition, health disparities have benefit‐relevant effects on productivity that manifest only upon joining the workforce. For example, type 2 diabetes – a chronic and largely preventable health condition linked to chronic inflammation – is predictive of unemployment and lower wages (Minor, 2013). Such health effects carry both personal and social costs. For  instance, in the US, diabetes‐related (combined type 1 and 2) absenteeism and p­roductivity losses were estimated to cost approximately $5 billion and $20.8 billion, respectively, in 2012. By virtue of their potential organizing effects on children’s nascent stress and immune systems (e.g. inflammation), health‐relevant programs/policies (e.g., Earned Income Tax Credit [EITC], State Child Health Insurance Program [SCHIP], Women Infants and Children [WIC]; Head Start, etc.) that evince even seemingly modest and/or transient effects on health in early childhood may well have clinically and economically meaningful impacts in the long term. Indeed, when considered in light of the presumably massive positive externalities garnered by long‐term health benefits (e.g., Cawley & Meyerhoefer, 2012; Minor, 2013), these programs quite likely pay for themselves. Of course, this remains a conjecture to be tested directly. However, it has certainly been the case for early childhood interventions such as the Perry Preschool and Abecederian Programs, which have shown marked social returns per dollar spent (e.g., Barnett & Masse, 2007; Heckman, Moon, Pinto, Savelyev, & Yavitz, 2010). Indeed, these social returns are themselves likely downwardly biased, given that (to my knowledge) they have yet to account for the impacts of these programs on long‐term health (Campbell et al., 2014; Muennig, Schweinhart, Montie, & Neidell, 2009). In sum, our growing understanding of the psychoneuroimmunology of early childhood suggests that children’s early experiences play a critical role in the organization and dynamics of their nascent autonomic, adrenocortical, and immune systems. In particular, exposures to ongoing stressors – such as those experienced by children facing socio‐economic adversity – have been theorized to create an internal hormonal milieu leading to states of chronic inflammation. In the short term, inflammation plays a vital role in the body’s

Early Childhood Health Disparities  57 ability to support effective acute responses to infection and injury. However, when chronic inflammation accumulates over time, it is known to underlie a litany of debilitating long‐ term health problems, ranging from type 2 diabetes and cardiovascular disease to tumor growth. As such, the seeds of long‐term health are sewn early. From a policy perspective, this work suggests that the true impacts of health‐relevant early childhood policies will require a life‐course approach; the most dramatic health impacts likely manifest in the long term. Given the extreme personal and social costs of ill‐health in adulthood, there is good reason to suspect that the long‐term benefits may well exceed short‐ and long‐term costs. Collectively, this work suggests that clarifying the biopsychosocial processes underlying early experience and health are vital to understanding the long‐term implications of early childhood policy.

Note 1. “Early childhood” is used throughout as a catch‐all term for this period, unless stated otherwise.

References Alemayehu, B., & Warner, K. E. (2004). The lifetime distribution of health care costs. Health Services Research, 39(3), 627–642. Almond, D., & Mazumder, B. (2005). The 1918 influenza pandemic and subsequent health o­utcomes: an analysis of SIPP data. American Economic Review, 258–262. Alt, S. R., Turner, J. D., Klok, M. D., Meijer, O. C., Lakke, E. A., DeRijk, R. H., & Muller, C. P. (2010). Differential expression of glucocorticoid receptor transcripts in major depressive disorder is not epigenetically programmed. Psychoneuroendocrinology, 35(4), 544–556. Acevedo‐Garcia, D., Osypuk, T. L., McArdle, N., & Williams, D. R. (2008). Toward a policy‐ r­elevant analysis of geographic and racial/ethnic disparities in child health. Health Affairs, 27(2), 321–333. Barker, D. J. (1998). In utero programming of chronic disease. Clinical Science, 95(2), 115–128. Barker, D. J. (2004). The developmental origins of insulin resistance. Hormone Research, 64, 2–7. Barnett, W. S., & Masse, L. N. (2007). Comparative benefit–cost analysis of the Abecedarian p­rogram and its policy implications. Economics of Education Review, 26(1), 113–125. Belsky, J., Steinberg, L., & Draper, P. (1991). Childhood experience, interpersonal development, and reproductive strategy: An evolutionary theory of socialization. Child Development, 62(4), 647–670. Berry, D., Blair, C., Vernon‐Feagans, E., Willoughby, M., & Granger, D. (March, 2015). Household chaos and child physiology across infancy and toddlerhood: A child‐fixed effect approach. Presented at Society for Research in Child Development Biennial Meeting, Philadelphia, PA. 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 candidate‐gene associations. Development and Psychopathology, 24(04), 1417–1425. Blair, C., Granger, D. A., Willoughby, M., Mills‐Koonce, R., Cox, M., Greenberg, M. T., … Fortunato, C. K. (2011). Salivary cortisol mediates effects of poverty and parenting on executive functions in early childhood. Child Development, 82(6), 1970–1984.

58 Berry Blair, C., & Raver, C. C. (2012). Child development in the context of adversity: experiential c­analization of brain and behavior. American Psychologist, 67(4), 309–318. Bloom, B., Cohen, R. A., & Freeman, G. (2013). Summary health statistics for US children: National Health Interview Survey, 2012. Vital and health statistics. Series 10, Data from the National Health Survey. Blumenshine, P., Egerter, S., Barclay, C. J., Cubbin, C., & Braveman, P. A. (2010). Socioeconomic disparities in adverse birth outcomes: a systematic review. American Journal of Preventive Medicine, 39(3), 263–272. Borghol, N., Suderman, M., McArdle, W., Racine, A., Hallett, M., Pembrey, M., … Szyf, M. (2012). Associations with early‐life socio‐economic position in adult DNA methylation. International Journal of Epidemiology, 41(1), 62–74. Boulet, S. L., Schieve, L. A., & Boyle, C. A. (2011). Birth weight and health and developmental outcomes in US children, 1997–2005. Maternal and Child Health Journal, 15(7), 836–844. 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(02), 271–301. Braveman, P. A., Cubbin, C., Egerter, S., Williams, D. R., & Pamuk, E. (2010). Socioeconomic Disparities in Health in the United States: What the Patterns Tell Us. American Journal of Public Health, 100(Suppl 1), S186–S196. Braveman, P. A., Heck, K., Egerter, S., Marchi, K. S., Dominguez, R. P., Cubbin, C., … Curtis, M. (2015). The role of socioeconomic factors in Black‐White disparities in preterm birth. American Journal of Public Health, 105(4), 694–702. Brennecke, P., Allavena, P., Laface, I., Mantovani, A., & Bottazzi, B. (2015). Inflammatory and innate immune cells in cancer microenvironment and progression. In Cancer Immunology (pp. 9–28). Springer Berlin Heidelberg. Caldji, C., Diorio, J., & Meaney, M. J. (2000). Variations in maternal care in infancy regulate the development of stress reactivity. Biological Psychiatry, 48(12), 1164–1174. Caldji, C., Tannenbaum, B., Sharma, S., Francis, D., Plotsky, P. M., & Meaney, M. J. (1998). Maternal care during infancy regulates the development of neural systems mediating the expression of fearfulness in the rat. Proceedings of the National Academy of Sciences, 95(9), 5335–5340. Calkins, S. D., Propper, C., & Mills‐Koonce, W. R. (2013). A biopsychosocial perspective on parenting and developmental psychopathology. Development and Psychopathology, 25, 1399–1414. 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(6178), 1478–1485. Case, A., Fertig, A., & Paxson, C. (2005). The lasting impact of childhood health and circumstance. Journal of Health Economics, 24(2), 365–389. Case, A., Lee, D., & Paxson, C. (2008). The income gradient in children’s health: A comment on Currie, Shields and Wheatley Price. Journal of Health Economics, 27(3), 801–807. Case, A., Lubotsky, D., & Paxson, C. (2002). Economic status and health in childhood: The origins of the gradient (No. w8344). National Bureau of Economic Research. Cawley, J., & Meyerhoefer, C. (2012). The medical care costs of obesity: an instrumental variables approach. Journal of Health Economics, 31(1), 219–230. Centers for Disease Control and Prevention (CDC). (2011). Vital signs: asthma prevalence, disease characteristics, and self‐management education: United States, 2001–2009. MMWR. Morbidity and Mortality Weekly Report, 60(17), 547. Christensen, B. C., Houseman, E. A., Marsit, C. J., Zheng, S., Wrensch, M. R., Wiemels, J. L., … Kelsey, K. T. (2009). Aging and environmental exposures alter tissue‐specific DNA methylation dependent upon CpG island context. PLoS Genetics, 5(8), e1000602.

Early Childhood Health Disparities  59 Chung, H. Y., Cesari, M., Anton, S., Marzetti, E., Giovannini, S., Seo, A. Y., … Leeuwenburgh, C. (2009). Molecular inflammation: underpinnings of aging and age‐related diseases. Ageing Research Reviews, 8(1), 18–30. Coelho, R., Viola, T. W., Walss‐Bass, C., Brietzke, E., & Grassi‐Oliveira, R. (2014). Childhood maltreatment and inflammatory markers: a systematic review. Acta Psychiatrica Scandinavica, 129(3), 180–192. Cohen, S., Janicki‐Deverts, D., Turner, R. B., Marsland, A. L., Casselbrant, M. L., Li‐Korotky, H. S., … Doyle, W. J. (2013). Childhood socioeconomic status, telomere length, and susceptibility to upper respiratory infection. Brain, Behavior, and Immunity, 34, 31–38. Cole, S. W., Conti, G., Arevalo, J. M., Ruggiero, A. M., Heckman, J. J., & Suomi, S. J. (2012). Transcriptional modulation of the developing immune system by early life social adversity. Proceedings of the National Academy of Sciences, 109(50), 20578–20583. Conger, R. D., Conger, K. J., & Martin, M. J. (2010). Socioeconomic status, family processes, and individual development. Journal of Marriage and Family, 72(3), 685–704. Conti, G. (2013), The Developmental Origins of Health Inequality. In P. R Dias & O. O’Donnell (Eds.) Health and inequality: Research on economic inequality, volume 21. (pp. 285–309). Bingley, UK: Emerald Group. Conti, G., & Heckman, J. J. (2014). Economics of Child Well‐Being (pp. 363–401). Springer Netherlands. Continelli, T., McGinnis, S., & Holmes, T. (2010). The effect of local primary care physician s­upply on the utilization of preventive health services in the United States. Health & place, 16(5), 942–951. Crooks, D. L. (2000). Food consumption, activity, and overweight among elementary school c­hildren in an Appalachian Kentucky community. American Journal of Physical Anthropology, 112(2), 159–170. Currie, J. (2011). Inequality at birth: some causes and consequences (No. w16798). National Bureau of Economic Research. Currie, J., & Lin, W. (2007). Chipping away at health: more on the relationship between income and child health. Health Affairs, 26, 331–344. Danese, A., & McEwen, B. S. (2012). Adverse childhood experiences, allostasis, allostatic load, and age‐related disease. Physiology & Behavior, 106(1), 29–39. Danese, A., Moffitt, T. E., Harrington, H., Milne, B. J., Polanczyk, G., Pariante, C. M., … Caspi, A. (2009). Adverse childhood experiences and adult risk factors for age‐related disease: depression, inflammation, and clustering of metabolic risk markers. Archives of Pediatrics & Adolescent Medicine, 163(12), 1135–1143. Davis, A. M., Bennett, K. J., Befort, C., & Nollen, N. (2011). Obesity and related health behaviors among urban and rural children in the United States: data from the National Health and Nutrition Examination Survey 2003–2004 and 2005–2006. Journal of Pediatric Psychology, 36(6), 669–676. Davy, B. M., Harrell, K., Stewart, J., & King, D. S. (2004). Body weight status, dietary habits, and physical activity levels of middle school‐aged children in rural Mississippi. Southern Medical Journal, 97(6), 571–577. Del Giudice, M., Ellis, B. J., & Shirtcliff, E. A. (2011). The adaptive calibration model of stress responsivity. Neuroscience & Biobehavioral Reviews, 35(7), 1562–1592. DiPietro, J. A. (2004). The role of prenatal maternal stress in child development. Current Directions in Psychological Science, 13(2), 71–74. Drewnowski, A., & Darmon, N. (2005). The economics of obesity: dietary energy density and energy cost. The American Journal of Clinical Nutrition, 82(1), 265S–273S.

60 Berry Duncan, G. J., Ziol‐Guest, K. M., & Kalil, A. (2010). Early‐Childhood Poverty and Adult Attainment, Behavior, and Health. Child Development, 81(1), 306–325. Ellis, B. J., Figueredo, A. J., Brumbach, B. H., & Schlomer, G. L. (2009). Fundamental dimensions of environmental risk. Human Nature, 20(2), 204–26X. Evans, G. W. (2004). The environment of childhood poverty. American Psychologist, 59(2), 77. Evans, G. W., & English, K. (2002). The environment of poverty: Multiple stressor exposure, psychophysiological stress, and socioemotional adjustment. Child Development, 73(4), 1238–1248. Evans, G. W., & Wachs, T. D. (2010). Chaos and its influence on children’s development. Washington, DC: American Psychological Association. Farmer, M. M., & Ferraro, K. F. (2005). Are racial disparities in health conditional on socioeconomic status? Social science & medicine, 60(1), 191–204. Franzini, L., Taylor, W., Elliott, M. N., Cuccaro, P., Tortolero, S. R., Gilliland, M. J., … Schuster, M. A. (2010). Neighborhood characteristics favorable to outdoor physical activity: disparities by s­ocioeconomic and racial/ethnic composition. Health & Place, 16(2), 267–274. Fuks, F. (2005). DNA methylation and histone modifications: teaming up to silence genes. Current Opinion in Genetics & Development, 15(5), 490–495. Gage, T. B., Fang, F., O’Neill, E., & DiRienzo, G. (2013). Maternal education, birth weight, and infant mortality in the United States. Demography, 50(2), 615–635. Galobardes, B., Lynch, J. W., & Smith, G. D. (2004). Childhood socioeconomic circumstances and cause‐specific mortality in adulthood: systematic review and interpretation. Epidemiologic Reviews, 26(1), 7–21. Galobardes, B., Lynch, J. W., & Smith, G. D. (2008). Is the association between childhood socioeconomic circumstances and cause‐specific mortality established? Update of a systematic review. Journal of Epidemiology and Community Health, 62(5), 387–390. Galobardes, B., Smith, G. D., & Lynch, J. W. (2006). Systematic review of the influence of c­hildhood socioeconomic circumstances on risk for cardiovascular disease in adulthood. Annals of Epidemiology, 16(2), 91–104. Galdiero, M. R., & Mantovani, A. (2015). Tumor‐associated macrophages in tumor progression: From Bench to Bedside. In Multi‐Targeted Approach to Treatment of Cancer (pp. 99–111). Springer International Publishing. Gielen, A. C., McDonald, E. M., & Shields, W. (2015). Unintentional home injuries across the life span: problems and solutions. Annual Review of Public Health, 36, 231–253. Gluckman, P. D., Hanson, M. A., Cooper, C., & Thornburg, K. L. (2008). Effect of in utero and early‐life conditions on adult health and disease. New England Journal of Medicine, 359(1), 61–73. Gottlieb, G., Wahlsten, D., & Lickliter, R. (1998). The significance of biology for human development: A developmental psychobiological systems view. Handbook of child psychology. Hoboken NJ: Wiley. Gräff, J., Joseph, N. F., Horn, M. E., Samiei, A., Meng, J., Seo, J., … Tsai, L. H. (2014). Epigenetic priming of memory updating during reconsolidation to attenuate remote fear memories. Cell, 156(1), 261–276. Gräff, J., Kim, D., Dobbin, M. M., & Tsai, L. H. (2011). Epigenetic regulation of gene expression in physiological and pathological brain processes. Physiological Reviews, 91(2), 603–649. Gregor, M. F., & Hotamisligil, G. S. (2011). Inflammatory mechanisms in obesity. Annual Review of Immunology, 29, 415–445. Gunnar, M. R., & Herrera, A. M. (2013). The development of stress reactivity: A neurobiological perspective. The Oxford handbook of developmental psychology, 2, 45–80. Gunnar, M., & Quevedo, K. (2007). The neurobiology of stress and development. Annual Review of Psychology, 58, 145–173.

Early Childhood Health Disparities  61 Hanson, M. A., & Gluckman, P. D. (2014). Early developmental conditioning of later health and disease: physiology or pathophysiology? Physiological Reviews, 94(4), 1027–1076. Haussmann, M. F., & Marchetto, N. M. (2010). Telomeres: linking stress and survival, ecology and evolution. Current Zoology, 56(6), 714–727. Heard, E., & Martienssen, R. A. (2014). Transgenerational epigenetic inheritance: myths and mechanisms. Cell, 157(1), 95–109. Heckman, J. J., Moon, S. H., Pinto, R., Savelyev, P. A., & Yavitz, A. (2010). The rate of return to the HighScope Perry Preschool Program. Journal of Public Economics, 94(1), 114–128. Hostinar, C. E., & Gunnar, M. R. (2013). The developmental psychobiology of stress and emotion in childhood. In I. B. Weiner, D. K. Freedheim, & R. M. Lerner (Eds.), Handbook of psychology (2nd ed.). (pp. 121–141). Hoboken, NJ: Wiley. Hotamisligil, G. S. (2006). Inflammation and metabolic disorders. Nature, 444(7121), 860–867. Irwin, M. R., & Cole, S. W. (2011). Reciprocal regulation of the neural and innate immune s­ystems. Nature Reviews Immunology, 11(9), 625–632. Jenny, N. S. (2012). Inflammation in aging: cause, effect, or both? Discovery Medicine, 13(73), 451–460. Jiang, Y., Ekono, M. M., & Skinner, C. (2015). Basic facts about low‐income children, children under 6 Years, 2013. New York, NY: National Center for Children in Poverty. 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, 54(4), 337–347. Kent, S. T., McClure, L. A., Zaitchik, B. F., & Gohlke, J. M. (2013). Area‐level risk factors for adverse birth outcomes: trends in urban and rural settings. BMC Pregnancy and Childbirth, 13(1), 129. Koh, T. J., & DiPietro, L. A. (2011). Inflammation and wound healing: the role of the macrophage. Expert Reviews in Molecular Medicine, 13, e23. Lam, L. L., Emberly, E., Fraser, H. B., Neumann, S. M., Chen, E., Miller, G. E., & Kobor, M. S. (2012). Factors underlying variable DNA methylation in a human community cohort. Proceedings of the National Academy of Sciences, 109(Supplement 2), 17253–17260. Laraia, B., Epel, E., & Siega‐Riz, A. M. (2013). Food insecurity with past experience of restrained eating is a recipe for increased gestational weight gain. Appetite, 65, 178–184. Libby, P., DiCarli, M., & Weissleder, R. (2010). The vascular biology of atherosclerosis and imaging targets. Journal of Nuclear Medicine, 51(Supplement 1), 33S–37S. Lichter, D. T., Parisi, D., & Taquino, M. C. (2012). The geography of exclusion: Race, segregation, and concentrated poverty. Social Problems, 59(3), 364–388. Logan, J. R., & Stults, B. (2011). The persistence of segregation in the metropolis: New findings from the 2010 census. Census Brief prepared for Project US2010. Lutfiyya, M. N., Lipsky, M. S., Wisdom‐Behounek, J., & Inpanbutr‐Martinkus, M. (2007). Is rural residency a risk factor for overweight and obesity for US children? Obesity, 15(9), 2348–2356. Macintyre, S. (2007). Deprivation amplification revisited; or, is it always true that poorer places have poorer access to resources for healthy diets and physical activity? International Journal of Behavioral Nutrition and Physical Activity, 4(1), 32. Mantovani, A., Allavena, P., Sica, A., & Balkwill, F. (2008). Cancer‐related inflammation. Nature, 454(7203), 436–444. Markley, S., & Tu, W. (2015). Regional and Racial Disparity of Preterm Birth Prevalence in Georgia, 1995–2012. Papers in Applied Geography, 1(2), 168–175. Martin, J. A., Hamilton, B. E., Osterman, M. J., Curtin, S. C., & Mathews, T. J. (2015). Births: final data for 2013. National Vital Statistics Reports, 64(1), 1–65. Masten, A. S., & Cicchetti, D. (2010). Developmental cascades. Development and Psychopathology, 22(03), 491–495.

62 Berry Mathews, T. J., & MacDorman, M. F. (2012). Infant mortality statistics from the 2008 period linked birth/infant death data set. National Vital Statistics Reports, 60(5). McDaniel, M., Paxson, C., & Waldfogel, J. (2006). Racial disparities in childhood asthma in the United States: evidence from the National Health Interview Survey, 1997 to 2003. Pediatrics, 117(5), e868–e877. McGowan, P. O., Sasaki, A., Huang, T. C., Unterberger, A., Suderman, M., Ernst, C., … Szyf, M. (2008). Promoter‐wide hypermethylation of the ribosomal RNA gene promoter in the suicide brain. PloS one, 3(5), e2085. McGowan, P. O., Suderman, M., Sasaki, A., Huang, T. C., Hallett, M., Meaney, M. J., & Szyf, M. (2011). Broad epigenetic signature of maternal care in the brain of adult rats. PLoS One, 6(2), e14739. Meaney, M. J., & Szyf, M. (2005). Maternal care as a model for experience‐dependent chromatin plasticity? Trends in Neurosciences, 28(9), 456–463. Meit, M., Knudson, A., Gilbert, T., Yu, A. T. C., Tanenbaum, E., Ormson, E., … Popat, M. S. (2014). The 2014 Update of the Rural‐Urban Chartbook. Rural Health Reform Policy Research Center. Miller, G. E., Chen, E., Fok, A. K., Walker, H., Lim, A., Nicholls, E. F., … Kobor, M. S. (2009). Low early‐life social class leaves a biological residue manifested by decreased glucocorticoid and increased proinflammatory signaling. Proceedings of the National Academy of Sciences, 106(34), 14716–14721. Miller, G. E., Chen, E., & Parker, K. J. (2011). Psychological stress in childhood and susceptibility to the chronic diseases of aging: moving toward a model of behavioral and biological m­echanisms. Psychological Bulletin, 137(6), 959. Miller, S. M., & Wherry, L. R. (2014). The long‐term health effects of early life Medicaid coverage. Available at SSRN 2466691. Minor, T. (2013). An investigation into the effect of type I and type II diabetes duration on e­mployment and wages. Economics & Human Biology, 11(4), 534–544. Muennig, P. (2015). Can universal pre‐kindergarten programs improve population health and l­ongevity? Mechanisms, evidence, and policy implications. Social Science & Medicine, 127, 116–123. Muennig, P., Schweinhart, L., Montie, J., & Neidell, M. (2009). Effects of a prekindergarten e­ducational intervention on adult health: 37‐year follow‐up results of a randomized controlled trial. American Journal of Public Health, 99(8), 1431–1437. Myers, S. R., Branas, C. C., French, B. C., Nance, M. L., Kallan, M. J., Wiebe, D. J., & Carr, B. G. (2013). Safety in numbers: are major cities the safest places in the United States? Annals of Emergency Medicine, 62(4), 408–418. Nagahawatte, N., & Goldenberg, R. L. (2008). Poverty, maternal health, and adverse pregnancy outcomes. Annals of the New York Academy of Sciences, 1136(1), 80–85. Naumova, O. Y., Lee, M., Koposov, R., Szyf, M., Dozier, M., & Grigorenko, E. L. (2012). Differential patterns of whole‐genome DNA methylation in institutionalized children and c­hildren raised by their biological parents. Development and Psychopathology, 24(01), 143–155. Oberlander, T. F., Weinberg, J., Papsdorf, M., Grunau, R., Misri, S., & Devlin, A. M. (2008). Prenatal exposure to maternal depression, neonatal methylation of human glucocorticoid r­eceptor gene (NR3C1) and infant cortisol stress responses. Epigenetics, 3(2), 97–106. O’Hare, W.P., (2009, September 17). The forgotten fifth: child poverty in rural America. The Carsey School of Public Policy retrieved from http://scholars.unh.edu/carsey/76 Olshansky, S. J., Antonucci, T., Berkman, L., Binstock, R. H., Boersch‐Supan, A., Cacioppo, J. T., … Rowe, J. (2012). Differences in life expectancy due to race and educational differences are widening, and many may not catch up. Health Affairs, 31(8), 1803–1813.

Early Childhood Health Disparities  63 Ong, K. K., Emmett, P., Northstone, K., Golding, J., Rogers, I., Ness, A. R., … Dunger, D. B. (2009). Infancy weight gain predicts childhood body fat and age at menarche in girls. The Journal of Clinical Endocrinology & Metabolism, 94(5), 1527–1532. Organization for Economic and Co‐Operation and Development (2015, March 16). Families and Children Database. Retrieved from http://www.oecd.org/els/family/. Park, M. H., Falconer, C., Viner, R. M., & Kinra, S. (2012). The impact of childhood obesity on morbidity and mortality in adulthood: a systematic review. Obesity Reviews, 13(11), 985–1000. Parker, K. J., Buckmaster, C. L., Sundlass, K., Schatzberg, A. F., & Lyons, D. M. (2006). Maternal mediation, stress inoculation, and the development of neuroendocrine stress resistance in primates. Proceedings of the National Academy of Sciences of the United States of America, 103(8), 3000–3005. Porges, S. W. (2011). The Polyvagal Theory: Neurophysiological Foundations of Emotions, Attachment, Communication, and Self‐regulation (Norton Series on Interpersonal Neurobiology). WW Norton & Company. Powell, N. D., Sloan, E. K., Bailey, M. T., Arevalo, J. M., Miller, G. E., Chen, E., … Cole, S. W. (2013). Social stress up‐regulates inflammatory gene expression in the leukocyte transcriptome via β‐adrenergic induction of myelopoiesis. Proceedings of the National Academy of Sciences, 110(41), 16574–16579. Provençal, N., Suderman, M. J., Guillemin, C., Massart, R., Ruggiero, A., Wang, D., … Szyf, M. (2012). The signature of maternal rearing in the methylome in rhesus macaque prefrontal cortex and T cells. The Journal of Neuroscience, 32(44), 15626–15642. Quinto, K. B., Kit, B. K., Lukacs, S. L., & Akinbami, L. J. (2013). Environmental tobacco smoke exposure in children aged 3–19 years with and without asthma in the United States, 1999–2010. NCHS data brief, 126, 1–8. Roseboom, T. J., Van Der Meulen, J. H., Ravelli, A. C., Osmond, C., Barker, D. J., & Bleker, O. P. (2001). Effects of prenatal exposure to the Dutch famine on adult disease in later life: an o­verview. Molecular and Cellular Endocrinology, 185(1), 93–98. Rosenbaum, S., & Kenney, G. M. (2014). The Search for a National Child Health Coverage Policy. Health Affairs, 33(12), 2125–2135. Sapolsky, R. M., Romero, L. M., & Munck, A. U. (2000). How do glucocorticoids influence stress responses? Integrating permissive, suppressive, stimulatory, and preparative actions 1. Endocrine Reviews, 21(1), 55–89. Schetter, C. D. (2011). Psychological science on pregnancy: stress processes, biopsychosocial m­odels, and emerging research issues. Annual Review of Psychology, 62, 531–558. Selye, H. (1950). Stress and the general adaptation syndrome. British Medical Journal, 1(4667), 1383. Shi, L., Lebrun, L. A., & Tsai, J. (2010). Access to medical care, dental care, and prescription drugs: the roles of race/ethnicity, health insurance, and income. Southern Medical Journal, 103(6), 509. Singh, G. K., & Kogan, M. D. (2007). Persistent socioeconomic disparities in infant, neonatal, and postneonatal mortality rates in the United States, 1969–2001. Pediatrics, 119(4), e928–e939. Singh, G. K., & Siahpush, M. (2014). Widening rural–urban disparities in life expectancy, US, 1969–2009. American Journal of Preventive Medicine, 46(2), e19–e29. Slack, T., Myers, C. A., Martin, C. K., & Heymsfield, S. B. (2014). The geographic concentration of US adult obesity prevalence and associated social, economic, and environmental factors. Obesity, 22(3), 868–874. Sloboda, D. M., Hart, R., Doherty, D. A., Pennell, C. E., & Hickey, M. (2007). Age at menarche: influences of prenatal and postnatal growth. The Journal of Clinical Endocrinology & Metabolism, 92(1), 46–50.

64 Berry Smith, A. K., Kilaru, V., Klengel, T., Mercer, K. B., Bradley, B., Conneely, K. N., … Binder, E. B. (2015). DNA extracted from saliva for methylation studies of psychiatric traits: Evidence tissue specificity and relatedness to brain. American Journal of Medical Genetics Part B: Neuropsychiatric Genetics, 168(1), 36–44. Stein, A. D., Zybert, P. A., Van der Pal‐de Bruin, K., & Lumey, L. H. (2006). Exposure to famine during gestation, size at birth, and blood pressure at age 59 y: evidence from the Dutch Famine. European Journal of Epidemiology, 21(10), 759–765. Stout, S. A., Espel, E. V., Sandman, C. A., Glynn, L. M., & Davis, E. P. (2015). Fetal programming of children’s obesity risk. Psychoneuroendocrinology, 53, 29–39. Szyf, M. (2015). Nongenetic inheritance and transgenerational epigenetics. Trends in Molecular Medicine, 20, 1–11. Taveras, E. M., Gillman, M. W., Kleinman, K. P., Rich‐Edwards, J. W., & Rifas‐Shiman, S. L. (2013). Reducing racial/ethnic disparities in childhood obesity: the role of early life risk factors. JAMA Pediatrics, 167(8), 731–738. Terry, M. B., Ferris, J. S., Tehranifar, P., Wei, Y., & Flom, J. D. (2009). Birth weight, postnatal growth, and age at menarche. American Journal of Epidemiology, kwp095. Tobi, E. W., Lumey, L. H., Talens, R. P., Kremer, D., Putter, H., Stein, A. D., … Heijmans, B. T. (2009). DNA methylation differences after exposure to prenatal famine are common and t­iming‐ and sex‐specific. Human Molecular Genetics, 18(21), 4046–4053. Tung, J., Barreiro, L. B., Johnson, Z. P., Hansen, K. D., Michopoulos, V., Toufexis, D., … Gilad, Y. (2012). Social environment is associated with gene regulatory variation in the rhesus macaque immune system. Proceedings of the National Academy of Sciences, 109(17), 6490–6495. Tyrka, A. R., Price, L. H., Marsit, C., Walters, O. C., & Carpenter, L. L. (2012). Childhood adversity and epigenetic modulation of the leukocyte glucocorticoid receptor: preliminary findings in healthy adults. PloS one, 7(1), e30148. US Department of Agriculture (2015, September 17). Rural Poverty and Well‐Being. Retrieved from http://www.ers.usda.gov/topics/rural‐economy‐population/rural‐poverty‐well‐being/ child‐poverty.aspx Valdearcos, M., Xu, A. W., & Koliwad, S. K. (2015). Hypothalamic Inflammation in the Control of Metabolic Function. Physiology, 77(1), 131. Vickers, M. H., Breier, B. H., Cutfield, W. S., Hofman, P. L., & Gluckman, P. D. (2000). Fetal origins of hyperphagia, obesity, and hypertension and postnatal amplification by hypercaloric nutrition. American Journal of Physiology‐Endocrinology and Metabolism, 279(1), E83–E87. Walker, R. E., Keane, C. R., & Burke, J. G. (2010). Disparities and access to healthy food in the United States: a review of food deserts literature. Health & Place, 16(5), 876–884. Wallace, R. B., Grindeanu, L. A., & Cirillo, D. J. (2004). Rural/urban contrasts in population morbidity status. Critical Issues in Rural Health. (pp. 15–26). Danvers, MA: Blackwell Publishing. Weaver, I. C., Cervoni, N., Champagne, F. A., D’Alessio, A. C., Sharma, S., Seckl, J. R., … Meaney, M. J. (2004). Epigenetic programming by maternal behavior. Nature Neuroscience, 7(8), 847–854. Weaver, I. C., Meaney, M. J., & Szyf, M. (2006). Maternal care effects on the hippocampal transcriptome and anxiety‐mediated behaviors in the offspring that are reversible in adulthood. Proceedings of the National Academy of Sciences of the United States of America, 103(9), 3480–3485. West‐Eberhard, M. J. (1989). Phenotypic plasticity and the origins of diversity. Annual Review of Ecology and Systematics, 249–278. West‐Eberhard, M. J. (2003). Developmental plasticity and evolution. New York, NY: Oxford University Press.

Early Childhood Health Disparities  65 Williams, D. R., Mohammed, S. A., Leavell, J., & Collins, C. (2010). Race, socioeconomic status, and health: complexities, ongoing challenges, and research opportunities. Annals of the New York Academy of Sciences, 1186(1), 69–101. Woolf, S. H., & Braveman, P. (2011). Where health disparities begin: the role of social and e­conomic determinants  –  and why current policies may make matters worse. Health affairs, 30(10), 1852–1859. Ziol‐Guest, K. M., Duncan, G. J., & Kalil, A. (2009). Early childhood poverty and adult body mass index. American Journal of Public Health, 99(3), 527. Ziol‐Guest, K. M., Duncan, G. J., Kalil, A., & Boyce, W. T. (2012). Early childhood poverty, immune‐mediated disease processes, and adult productivity. Proceedings of the National Academy of Sciences, 109(Supplement 2), 17289–17293.

chapter FOUR Social and Contextual Risks Robert H. Bradley

Over the past half century, scientists and policymakers have given increased attention to early childhood as a period of life that has major implications for long‐term health and productivity. The interest has been driven by four factors: (1) the changing world of work – more women in the workforce and the rapid decline of “blue collar” jobs (i.e., jobs requiring physical labor); (2) the changing landscape of family structure – most notably the decline in children who live in stable 2‐parent households throughout the course of childhood; (3) advances in brain sciences showing details about brain growth from early infancy; and (4) accumulating evidence that many children enter formal schooling lacking the competencies and proclivities needed to maximally benefit from school. Side by side with the growing attention to early education has been attention to child, family, community, and societal factors that increase the risk that a child will not be ready to enter formal schooling prepared to benefit from what formal education offers and that they will, therefore, be on a path that decreases their own long‐term well‐being and the overall well‐being of society. The focus of this chapter is on social and contextual conditions that increase such risks. What is a risk factor? In a certain sense, the definition is somewhat circular: any condition that increases the likelihood of a negative outcome. Broadly this suggests that a risk condition leads to the turning on of processes that are detrimental to well‐being or the The Wiley Handbook of Early Childhood Development Programs, Practices, and Policies, First Edition. Edited by Elizabeth Votruba-Drzal and Eric Dearing. © 2017 John Wiley & Sons, Inc. Published 2017 by John Wiley & Sons, Inc.

Social and Contextual Risks  67 turning off of processes that are supportive of well‐being. But such a definition is far from precise. It leaves vague who is at risk and what type of outcome is at issue (Moore, 2006). As Evans, Li, and Whipple (2013) have argued, there is no tidy way of categorizing risks and no consensus on how risk factors function to shape individual trajectories. The science pertaining to social and contextual risk has just not developed to that level yet. Thus, I cannot resolve the uncertainties scientists and practitioners often face in trying to address risk exposure. Toward the end of increased understanding about the role of social and contextual risks, I like to put the processes that presumably impact developmental course into three buckets: (1) those that damage critical developmental systems, (2) those that disrupt system actions needed for smooth system functioning, and (3) those that delay growth of system capacities (i.e., there is essentially an insufficient amount of the process on‐going) – the 3‐Ds. An example of the first would be exposure to teratogens or exposure to social conditions that create trauma or fear. An example of the second would be exposure to noise or destabilizing events. An example of the third would be having limited access to learning materials, nutritious food, or responsive parents. Clearly, some of these conditions could lead to more than one of the Ds, especially over time, but recognizing that some risks lead to system damage, some to system disruption, and some to delays in system growth would seem useful in organizing information about social and contextual risks and perhaps in addressing them. In this chapter, I give a sense of how the social and physical conditions children encounter create risks for their well‐being. The literature is so vast that it is not easy to provide a simple summary of what we know (or what we think we know). Rather, I will offer some broad framing on how context matters for early development. Then I will give attention to risk conditions associated with home life and in the broader community (even societal) context that seem especially relevant for children. Finally, I will focus on aspects of parenting and household material resources that often function as pathways between risk conditions and child well‐being. The chapter will end with some recommendations about research and policies that might improve the prospects of children living in conditions of risk. My perspective is more that of a social scientist than the perspective of an economist, a biologist, a geographer, an architect, or a physician. However, aspects of these alterative perspectives will be integrated into the text from time to time in hopes of offering a more comprehensive view of how risk conditions insinuate themselves into children’s lives.

Complex Creatures Living in a Complex World Complementary developmental systems When considering how social and contextual risks operate to affect early learning and development, it is critical to bear in mind that humans are complex creatures living in complex, dynamic environments (Wachs, 2000). We are composed of numerous subsystems that are mutually influential. Cunha and Heckman (2008) provide evidence that various skills (a term which includes health, adaptive functioning, and competence) o­perate in complementary ways to increase overall skill development (or well‐being more

68  Robert H. Bradley generally)  –  what they refer to as dynamic complementarity. They also show that the actual dynamic is partially determined by various types of investments by parents, organizations, and the like. In a number of ways, the ideas promulgated by Cunha and Heckman are extensions of notions about skill development described by other theorists, Kurt Fischer (1980) being prominent among them. Fischer discusses the interplay among component systems of skills that lead to transformations and reorganization of competencies to ever more complex skill sets in humans. A key precept in dynamic skills theory is that skills are context dependent and thus require appropriate contextual supports. Under optimal conditions, multiple complementary skills (characteristics) are supported such that they can function in a self‐productive manner with each component skill helping to support related component skills. However, when something goes wrong (damage or disruption) in one system or there simply isn’t sufficient support for normal development of one s­ystem (delay), it can have impacts on connected systems. If a risk factor damages, disrupts, or delays growth in any of these component systems, it reduces the likelihood that the connected subsystems can function to maximally support achievement as well.

Affordances of place To appreciate how environmental risks function to decrease skill formation and adaptive functioning requires an understanding of the dynamic interplay of environmental forces as well. Heft (1993) argued that the affordances of the environment are its functionally significant properties, considered in relation to a particular individual. Central to the concept of affordances is the notion that affordances are not exactly properties of the environment per se (Chemero, 2003). Rather, affordances pertain to relations between features of the environment and the capacities and proclivities of humans that encounter them. The exact same physical feature or arrangement of features may provide a different set of opportunities or challenges to different children, depending on age, gender, health status, culture, personal history, or the other features present, including the others with whom they interact. The same object in two different settings may induce quite different social exchanges between parents and children and different exchanges between a child and the object (e.g., a toy train in a supermarket versus a museum). The same object in the same setting may induce quite different exchanges between two different parent‐child dyads (e.g., a toy train in the mall where one parent is a civil engineer and the second parent is a high‐school drop‐out). There is, in effect, a dynamic interplay between people, objects, and physical conditions in every setting; whether an encounter (or history of encounters) in a particular setting leads to damage, delay, or disruption depends on the interplay of the child and conditions present. This fact is captured in two key propositions from general systems theory: (1) multifinality – the idea that the same condition can lead to different outcomes or end states; and (2) equifinality – the idea that different conditions can lead to the same outcome or end states (Bertalanffy, 1968). As will be shown in the review which follows certain negative conditions (e. g., low parental education, parental depression, household instability) can have diverse negative consequences for children. Likewise, diverse negative conditions can be implicated in the same bad outcome for children.

Social and Contextual Risks  69

Engaging the environment The affordances of a child’s surroundings at any given moment do not fully determine what that child derives from those surroundings. How a child engages the environment is critical. Optimal engagement means that the structures and resources in a setting tightly fit a child’s capacities and predilections (Holland, 1992). For young children, that means not only having the needed physical and material accouterments, but often means having caregivers (and playmates) that offer the kinds of stimulation, structure, and socio‐emotional support needed for full engagement. According to self‐determination theory, humans need environments that promote competence, relatedness, and a sense of autonomy (Ryan & Deci, 2000). Such environments maximize the expression of intrinsic motivational tendencies; and in so doing promote task persistence, subjective well‐being, and better assimilation of the individual into critical social networks – the ingredients for self‐productivity mentioned earlier. When settings do not afford the resources and structures that allow a child to engage intrinsic motivations, they may contribute to poor self‐regulation and behavioral adjustment, lower competence, and alienation. In the stress and coping literature, there is also general agreement that when stressful situations persist and individuals are unable to engage sufficiently powerful internal and external supports to overcome the challenges presented, there are likely to be negative psychological and biological consequences (Repetti, Robles, & Reynolds, 2011). When a child concludes that there is no relation between anything he or she can do and the outcome, an acquired expectancy of “helplessness” emerges. This sense of helplessness can generalize to other situations and settings to the detriment of the individual. By contrast, if a child believes he or she can exercise some manner of personal control to produce a desired outcome, a sense of agency emerges. A positive expectancy, “coping,” arises when a child concludes that he or she can handle the situation with positive results. Settings vary not only in the extent to which they present threats and challenges but also in the extent to which they contain features that increase the likelihood of adaptive and effective responses from the individual.

Ecology and dynamic system of family life Young children have limited capacity to determine what their surroundings afford by way of supports for maximum engagement and positive benefit. Much is a function of what caregivers bring to the mix; albeit, not all is a function of the human and social capital connected with caregivers (Parcel, Dufur, & Zito, 2010). Bio‐ecological systems theories speak to the wide diversity of conditions that have a bearing on what children experience in their surroundings, both in and through time (Bronfenbrenner, 1995). The systems include microsystems such as family life, child care, and preschool. They also include mesosystems (i.e., the interaction between microsystems), exosystems (i. e., microsystems in which the focal individual is not involved but in which key social actors connected to the focal individual are), and macrosytems (i.e., the broad social, political, and cultural milieu in which the microsystem functions). For young children, life in key microsystems such as home and child care tend to be more instrumental in determining developmental course. However, critical to appreciate is how many elements from all the various systems

70  Robert H. Bradley come into play in determining a child’s developmental course; thus, how many types of potential risks for development may be present in a child’s life. Further increasing the complexity, one of the most significant implications of dynamic systems principles for understanding risk exposure is that relations between risk conditions and particular outcomes are likely to be nonlinear, with the possibility of threshold effects (Thelen, 2005). In dynamic systems moderator effects are also quite likely; that is, the impact of a particular risk may depend on the presence of a second risk factor or a protective factor. The literature is full of examples of both (e.g., the many co‐factors of poverty, and the protective value of social support when families face stressful life events). Yet, as scholars utilize broad theoretical frameworks, such as Bronfenbrenner’s, they tend to focus only on those aspects of key systems that are central to their own work (e.g., what leads to effective parenting, what contributes to poor health status, what increases the likelihood of maladaptive behavior). Even those who take a cumulative risk perspective typically only include a subset of risk factors from all the different systems in the indices they use to evaluate how risk exposure is implicated in early development (Flouri, Tzavidis, & Kallis, 2010; Guttman, Sameroff, & Cole, 2003). It is, for all intents and purposes, simply not feasible to do so. I am no less challenged in this regard, so will not attempt to construct a fully inclusive model of risk for young children. What follows will be selective in its focus, with attention to elements from various systems that seem to have salient consequences for early development and for which pathways of influence have, in some cases, been characterized adequately enough to inform policy, programs, and practices for young children. Specifically, I offer examples of risk conditions that o­perate according to three broad concepts from ecological‐developmental theory: (1) there are both proximal (microsystem) and distal (macrosystem, mesosystem, exosystem) factors that pose risks to children’s development; (2) risk conditions can operate through a multiplicity of interconnected processes to influence developmental course; and (3) some influences are immediately detectable while others may be detectable only after a considerable period of time (including cross‐generationally).

Risk Conditions and Child Well‐Being Household socio‐economic status Access to human, financial and social capital connected to family socio‐economic status is important for human development at every point in the life course, including prior to birth (Boyle et al., 2006; Bradley & Corwyn, 2002; Gershoff, Aber, Raver, & Lennon, 2007; Huston & Bentley, 2010). With advances in biological assessments, it has become increasingly clear that adversities connected to low SES become biologically embedded and can even extend to subsequent generations (Blair & Raver, 2012; Johnson, Riley, Grander, & Riis, 2013). SES and child health.  Children from low‐SES families are more likely to show inadequate neurobehavioral development in utero, to be premature, and to have a birth defect, fetal alcohol syndrome, or AIDS (Borders, Grobman, Amsden, & Holl, 2007;

Social and Contextual Risks  71 Carmichael et al., 2007; Kramer, 1987; US Department of Health & Human Services, 2000a; Vrijheid, Dolk, Stone, Alberman, & Scott, 2000). Low‐SES children are also more likely to suffer illnesses and injuries and to die (Centers for Disease Control, 2013; Cohen, 1999; Howe, Huttly, & Abramsky, 2006; Johnston‐Brooks, Lewis, Evans, & Whalen, 1998; Keall, Baker, Ormandy, & Baker, 2011; Overpeck, Brenner, Trumble, Trifiletti, & Berendes, 1998; Rosenbaum, 1992). They have increased likelihood of higher blood lead levels (Starfield, 1982), iron deficiency (Skalicky et al., 2006; US Department of Health & Human Services, 2000a), and obesity (National Center for Health Statistics, 2011) as well. These health problems derive from greater exposure to hazards like pollution, crowding, second‐hand smoke, and dilapidated housing (Evans, 2004). Poor nutrition and lack of access to good medical and dental care, and lifestyle choices such as limited physical activity are also factors (Adler & Ostrove, 1999; Cook & Frank, 2008; Federal Interagency Forum on Child and Family Statistics, 2013; Galea, Nanadi, & Viahov, 2004). Finally, there is mounting evidence that chronic stress related to low SES leads to damage in m­ultiple biological systems (McEwen & Gianaros, 2010; Miller & Chen, 2013) and that stress exposure early in life (when biological systems are rapidly developing) may be particularly harmful (Shonkoff et al., 2012). SES and child competence.  There is a long history of research documenting relations between household SES and the development of competence. Growing up in a low‐SES environment leads to decrements in neural development (Blair & Raver, 2012). Notable are impacts on language development and early cognitive processing (Dearing, McCartney, & Taylor, 2001; Hart & Risley, 1995; Hoff, 2003). The pathways linking SES to competence development appear to be multiple (Bradley & Corwyn, 2002; Conger & Donnellan, 2007). McLoyd (1998) pointed to high levels of stress and poor monitoring as vehicles that help explain lower achievement for poor children; and there is a deep literature on the adverse consequences to early exposures to heavy metals and other teratogens (Mason, Harp, & Han, 2014). Low‐SES children have less opportunity of attending high quality schools and of affiliating with academically competent peers (Sirin, 2005). Far and away the most studied pathways linking SES with child competence are those involving children’s access to various forms of stimulation, as ­competence development in every domain requires adequate exposure to appropriate forms of materials and social experiences and careful structuring of those experiences. Engagement in complex communications with others, access to books and toys that encourage exploration and learning, efforts on the part of others to teach particular concepts and skills, access to enriching out‐of‐home facilities (e.g., parks, museums) have all been explicated as means of promoting competence (Bradley & Corwyn, 2005; Davis‐Kean, 2005; Dearing et al., 2012). Such experiences also appear to foster motivational tendencies that increase the likelihood that children will access opportunities for learning on their own (Brody, Flor, & Gibson, 1999; Cunha & Heckman, 2008; Farah et al., 2008). It is also the case that some of the same factors that seem to be operative as regards SES‐competence relations are operative as regards SES‐health and SES‐adaptive behavior relations (Corwyn & Bradley, 2005). Specifically, part may pertain to conditions like parent mental illness, household crowding, inadequate nutrition, or exposure to teratogens.

72  Robert H. Bradley SES and child adaptive behavior.  The relation between low SES and maladaptive behavior is well established (Davis, Sawyer, Lo, Priest, & Wake, 2010; Earls, 1980; Kiernan & Huerta, 2008). Research (consistent with theory on stress induction) shows that living in a low‐SES home impacts physiological arousal and decreases self‐regulatory competence beginning quite early in life (Blair & Raver, 2012; Evans et al., 2013). Adversity increases the likelihood of negative forms of parenting that can give rise to maladaptive behavior (e.g., hostility, harsh discipline, inconsistency) and that it decreases the likelihood of positive forms of parenting that protect against maladaptive behavior and that promote socioemotional competence (e.g., warmth, responsiveness, encouragement) (Bradley et  al., 2001a; Gershoff et al., 2007; Kiernan & Huerta, 2008). It is also the case that low SES is connected with parental characteristics such a depression and low intelligence that may contribute to the likelihood of poor adaptive functioning (Kiernan & Huerta, 2008; Bradley, 2012a); thus, part of the connection may be genetically mediated (Miller & Chen, 2013). Relatedly, research shows that children with highly reactive temperaments are more likely to manifest behavioral maladjustment if their homes are chaotic or their parents exhibit poor quality caregiving (Ellis, Boyce, Belsky, Bakermans‐Kranenburg, & van Ijzendoorn, 2011).

Other selected salient risk conditions Neighborhood and country level deprivation.  There is growing evidence that low levels of human, social, and material capital in one’s surroundings can have major consequences for well‐being (Adler & Ostrove, 1999; Leventhal & Brooks‐Gunn, 2000; Mohnen, Groenewegen, Volker, & Flap, 2011; UNICEF Innocenti Research Centre, 2012). The effects on health begin prior to birth (Bosma, van de Mheen, Borsboom, & Machenbach, 2001; Sargent et al., 1995; Vrijheid et al., 2000; Walker et al., 2007; Wasserman et al., 1998), with evidence indicating that poor nutrition, poor sanitation and hygiene practices, and exposure to teratogens are major contributory factors (Grantham‐McGregor et al., 2007). There is also evidence that living in a poor and dangerous neighborhood or a low‐HDI country can negatively affect cognitive development and school readiness (Leventhal & Brooks‐Gunn, 2000; United Nations Development Programme, 2013; World Bank, 2015). In countries ranked low on the Human Development Index, parents engaged in fewer cognitively stimulating activities with their children (Bornstein & Putnick, 2012). There is also some evidence that living in deprived neighborhoods or low‐income countries can contribute to poor psychosocial development (Dercon & Krishnan, 2009); but the mechanisms responsible have not been fully identified (Leventhal & Brooks‐Gunn, 2000). Moreover, children in disadvantaged neighborhoods also tend to be exposed to greater risk conditions at home and at school (Attar, Guerra, & Tolan, 1994); thus, it can be difficult to determine how much neighborhood risk contributes to poor outcomes. For example, there tends to be a fairly high level of residential mobility among the poor (Higgitt, 1996); thus, it is hard to disaggregate the negative effects of living in a poor or dangerous neighborhood from the constellation of other risks family members face (Murphey, Bandy, & Moore, 2012). Interestingly, when Anderson and colleagues (2014) examined the issue, they found that living in a neighborhood with

Social and Contextual Risks  73 a concentration of high‐SES families provided some protection against internalizing and externalizing problems but that living in a neighborhood with a concentration of poor families did not increase risk of maladaptive behavior. Geographic isolation.  Living in a rural area often means that families have to travel longer distances to access key services (Bradley, 2012b). Rural families in the US are less likely to have ready availability of center‐based child care and preschool (Smith, 2010; Swenson, 2008; Temple, 2009). On average, children living in rural areas perform less well on m­easures of school readiness (Williams & Mann, 2011); albeit, the latter finding may reflect that a high percentage of families living in rural areas are poor and that rural parents tend to be less well educated (Temple, 2009). Critically, families are less likely to have easy access to health care, particularly specialists important for prenatal care and serious illnesses and injuries. As it happens, mortality rates for infants and young children are higher in rural areas (Cherry, Huggins, & Gilmore, 2007) and children with special health care needs are less likely to be seen by a pediatrician (Skinner & Slifkin, 2007). Household chaos and  instability.  According to theory, positive adaptation in children requires predictability and controllability in the settings where they spend time (Evans, 2004). Studies show fairly consistent associations between household chaos and poor social, emotional, and cognitive functioning (Dumas et al., 2005; Petrill, Pike, Price, & Plomin, 2004; Vernon‐Feagans et al., 2012). Chronic exposure to high levels of noise and chaotic housing conditions are associated with poor attention focusing, problems with cognitive processing and lower achievement (Belojevic, Evans, Paunovic, & Jakovljevic, 2012; Deater‐Deckard et  al., 2009). Such conditions contribute to a sense of learned helplessness and eventual withdrawal from academic challenge (Hanscombe, Haworth, Davis, Jaffee, & Plomin, 2011). Dush, Schmeer, & Taylor (2013) found that crowded, noisy, and cluttered household conditions contributed to poor health in preschoolers, even controlling for key confounders. Relations between household chaos and less optimal child functioning seem to occur because chaos produces stress in children and because it degrades the care provided children (Coldwell, Pike, & Dunn, 2006; Dumas et al., 2005; Jaffee, Hanscombe, Haworth, Davis, & Plomin, 2012). The pathway through parenting seems especially likely in many households characterized by disorder in that maternal depression is also common in such households. Interestingly, consistent with ecological developmental theory, Dush et al. (2013) found that the impact of chaos was not limited just to the household environment; rather, chaos measured at the meso‐system level (chaos produced by work conditions and child care arrangements) was also associated with decreases in child health. Sometimes disorder is a consequence of moving from one house to another (i.e., residential mobility). According to the National Survey of Children’s Health (Centers for Disease Control and Prevention, 2013), the majority of young children have moved at least once, but less than 10% have moved 4 or more times. In general, residential mobility itself does not appear to be a major cause of poor development in preschool children (Murphey, Bandy, & Moore, 2012); but it often co‐occurs with other risk factors (single‐parent status, family dissolution, parental substance abuse, parental mental illness). Thus, moving can result in disruptions in access to health care, child care, and preschool (Jelleyman & Spencer, 2008).

74  Robert H. Bradley Family conflict.  Adverse distal conditions, such as poverty and chaos, can not only reduce the likelihood caregivers will engage in tasks that tend to benefit young children but they can also contribute to a milieu that directly inhibits well‐being of both parents and c­hildren (Bradley & Corwyn, 2002; Robila & Krishnakumar, 2005). Among the most consequential is family conflict, which impedes social competence and fosters mental and physical health problems in children (Repetti, Taylor, & Seeman, 2002; van Ijzendoorn, Schuengel, & Bakermans‐Kranenburg, 1999). There is also evidence that paternal alcohol abuse is associated with marital conflict, which can lead to lower parental warmth and to greater behavior problems in children. Maternal drinking problems were also associated with behavior problems in offspring, but not through the same processes, showing the complexity of relations with family factors. Children continuously exposed to conflict within the family can develop a sense of “learned helplessness” (Evans et al., 2013); and there is mounting evidence that social information from continued encounters with c­onflict changes brain function (Fernald & Maruska, 2012). Family dissolution and structural changes.  Children who experience divorce tend to have more adjustment problems: albeit, the impact varies depending on the age at which separation occurs and the extent to which divorce leads to substantial reductions in the resources available to caregiving parents and the kind of caregiving children receive from both biological and step‐parents thereafter (Lansford, 2009; Ryan & Claessens, 2015; Strohschein, 2007; Wallerstein & Lewis, 2007). Lower parental sensitivity and reduced quality of the home environment are commonplace after divorce (Cavanagh & Huston, 2006). A critical factor in children’s adjustment is the extent to which children are exposed to marital conflict., the quality of parenting children receive post‐divorce, and the quality of parent‐child relationships (Amato & Cheadle, 2005; Lansford, 2009). A complicating factor is whether children who experience divorce have continued contact with fathers (McClanahan, 1999). Children can easily feel lonely and anxious in the period immediately following divorce, particularly young children who may have difficulties interpreting what family dissolution means (Lansford, 2009). Among the factors that determine children’s response to divorce is the number and types of transitions that follow (e.g., moves, step families, custody arrangements) (Cavanagh & Huston, 2006; Kelly & Emery, 2003). Fomby and Cherlin (2007) executed a study with 10 to 14‐year‐olds designed to determine the extent to which the negative impacts associated with structural instability result from stresses directly connected to change and the extent to which the impacts result from “selection effects” (gene‐environment correlations). Their results suggested that cognitive outcomes were more genetically mediated while behavioral outcomes were related to environmental circumstances. Analysis of data from the National Longitudinal Survey of Youth showed that the impact of changes in family structure on children from higher‐income families was muted (Ryan & Claessens, 2015; see also, Coley, this volume). Single parenthood.  Generally speaking, children growing up with a single parent fare less well on a number of markers of well‐being, but it appears that factors such as less supervision, lower income, and poorer connections to potentially supportive social networks may be key to actual impacts (Barajas, 2011). Residential instability and changes in family structure are more common for children born to single‐parent mothers, with greater

Social and Contextual Risks  75 instability showing adverse consequences for both mother and child (Cavanagh & Huston, 2006). A key problem in trying to determine just how much single‐parenthood per se is implicated in poor health and developmental outcomes is that most single‐parent households are headed by mothers (82%), which means that fathers are absent from many children’s daily lives and many of those families are poor (US Bureau of the Census, 2011). There is some evidence that children living with single‐parent mothers show less maladaptive behavior if they have continued meaningful contact with their fathers and there is minimal conflict between parents during the time they live with a single parent (Davies & Cummings, 1994). Single‐parent mothers living in poverty often experience stress and tend to spank their children more, provide them less supervision, and lower quality communication (Jackson, Preston, & Franke, 2010). A major limitation of the research on single parenthood is that it is dominated by studies of single‐parent mothers. As it happens there has been a 9‐fold increase in the number of children reared by single‐ parent fathers in the past half century. Demographically they are similar to single‐parent moms; however, there are less likely to be living in poverty (Livingston, 2013). At present there is almost no information on how living with a single‐parent father during early childhood affects children’s well‐being. In one of the few studies extant, Leininger and Ziol‐Guest (2008) found that children’s access to health care was lower in single‐father households. A study of single‐parent households in Denmark indicated that fathers were better off, had few psychological problems, and were less severe with their children (Christoffersen, 1998). Parental substance abuse.  The American Academy of Child and Adolescent Psychiatry (2011) lists an array of negative outcomes for children who grow up with caregivers who are alcoholic parents: from anxiety and anger and somaticizing behavior to failure to form secure relationships to others to poor school performance to engagement in risky behavior to the development of addiction themselves (Solis, Shadur, Burns, & Hussong, 2012). Burke, Schmied, & Montrose (2006) argue that it is difficult to pin child problems directly on parental alcohol misuse in view of the fact that alcohol misuse often accompanies parent mental illness, family conflict, household chaos, and poor parenting. Parents with serious addiction problems often neglect children, do not monitor or communicate with them adequately, do not spend enough time engaging them in stimulating activities, and are unable to provide sensitive care due to the stresses connected to addiction (Hayward, Depanfilis, & Woodruff, 2010; Magura & Laudet, 1996). A review by the World Health Organization (2006) points to direct acts of violence children experience as a consequence of the loss of self‐control in parents who are alcoholics. Osborne and Berger (2009) found that negative consequences for preschoolers in the areas of health and adaptive behavior were more likely if both parents had problems with addiction. One of the difficulties in living with mothers who abuse alcohol and drugs is that the many imbibe substances while pregnant, thus substantially increases the odds of adverse impacts on child well‐being. There is substantial evidence that prenatal exposure to alcohol and various classes of drugs increase risk for problems in cognitive, language, and visual‐spatial functioning; defects in morphology; hyperactivity; and maladaptive behavior (Behnke, Smith, & Committes on Substance Abuse and on Fetus and Newborn, 2013; Sood et al., 2001; Testa, Quigley, & EIden, 2003). In some studies, negative impacts persisted even with controls on family

76  Robert H. Bradley SES or the quality of the home environment; however, problems were worse if caregivers showed signs of postnatal stress (Lewis et  al., 2004; McLaughlin et  al., 2011; Semple, Strathdee, Zians, & Patterson, 2011). Parent mental illness.  Psychiatric illness in parents predicts the likelihood of abuse and neglect, with negative impacts notable even controlling for SES and substance abuse (Chaffin, Kelleher, & Hollenberg, 1997). Based on their review of literature, the Canadian Paediatric Society (2004) concluded that children whose mothers were depressed showed both more anger and withdrawal, less mature levels of self‐regulation and autonomy, were less creative, manifested lower quality interactions with others, and did less well on measures of cognitive competence. Less is known about paternal depression, but a fairly recent longitudinal study showed the depression in fathers was associated with adverse emotional and behavioral outcomes in children ages 3 to 5 (Ramchandani, Stein, Evans, & O’Connor, 2005). Other studies have shown that depressed fathers are more likely to spank their children and less likely to read to them or engage with them in stimulating activities (Davis, Davis, Freed, & Clark, 2011). Not surprisingly, parental depression is more likely in a number of key demographics (single parents, low income families, parents with low levels of education) (Child Trends, 2014). Mothers who are psychotic also tend to have poor social relationships and struggle with parenthood (Hans, Auerback, Styr, & Marcus, 2004; Howard, Kumar, & Thornicroft,, 2001). They manifest difficulties interacting with their children (Hipwell & Kumar, 1996) and they tend to engage in less play and learning stimulation with their children, leading to problems in children’s development (Goodman & Brumley, 1990). Cognitive deficits in offspring of schizophrenics are common, and sometimes foreshadow the development of schizophrenia later in life (Ross, Wagner, Heinlein, & Zerbe, 2008). At present, there is difficulty determining whether it is the parent’s mental illness per se or the various other risks that tend to accompany the illness. For example, women with schizophrenia are likely to abuse alcohol during pregnancy and fail to get adequate prenatal care (Solari, Dickson, & Miller, 2009). It is also a fact that schizophrenic mothers frequently lose custody of their children (Rasic, Hajek, Alda, & Uber, 2013). In general, young children whose parents are mentally ill are at increased risk of injury and medical problems; as well they show higher rates of emotional problems (Beardslee, Versage, & Gladstone, 1998). Unfortunately, poor mental health on the part of one parent increases the likelihood the second parent will also have poor mental health. In such cases, it is even more likely children will manifest adjustment problems. On the other hand, when the second parent is mentally healthy, there is decreased likelihood offspring will have behavior problems (Dietz, Jennings, Kelley, & Marshal, 2009; Kahn, Brandt, & Whitaker, 2004). Household physical conditions.  Officials from the United Nations (2012) have long expressed worries about the physical conditions present in children’s homes. When homes are poorly constructed or in disrepair there is increased likelihood of disease exposure from rats, roaches, mosquitoes and the like (Bradman et al., 2005). Asthma rates also appear to be higher in homes with high levels of deterioration (Suglia, Duarte, Sandel, & Wright, 2010). In third‐world countries many households do not have sources of clean water or

Social and Contextual Risks  77 provisions for adequate sanitation, leaving young children at particularly high risk of bacterial and parasitic illnesses (Lindskog & Lundqvist, 1998). Not having proper facilities to deal with waste contributes to childhood illness and mortality (Baltazar & Solon, 1989; Podewils, Mintz, Nataro, & Parashar, 2004). There is a synergistic relation between these infections and undernutrition that undermines growth and immune function (Calder & Jackson, 2000). Another source of illness is food contamination, a situation that is common in households with no refrigeration (Bartlett, 1999). Sometimes homes lack refrigeration because there is no reliable source of electricity, a circumstance that has been connected to lower levels of literacy in developing countries (Kanagawa & Nakata, 2008). Some homes also have high concentrations of mold, which increase respiratory tract infections in young children (Kosikinen, Husman, Meklin, & Nevalainen, 1999). Respiratory problems in both children and adults are also more likely in homes with poor ventilation, a circumstance that is more common in poor areas where families use open stoves to cook (Gauderman et al., 2004). The latter circumstance increases the likelihood of childhood burns as well (Mirkazemi & Kar, 2009). The physical affordances of settings in which children spend time can pose direct risks for children’s health and adaptive functioning (Evans, 2006). They can also create barriers to optimal caregiving, given that poor housing conditions are  often associated with parental depression and illness (McCracken, Smith, Diaz, Mittleman, & Schwartz, 2007; Wells & Harris, 2007). Exposure to teratogens.  When children reside in dilapidated housing or a deteriorating neighborhood, they are likely to be exposed to pollutants or contaminants that pose consequential risk to health and adaptive functioning (Stein, Schettler, Wallinga, & Valenti, 2002; World Health Organization, 2005). However, children living in rural areas where pesticides and herbicides are used and children living in areas near mines or where traffic is heavily concentrated can be at high risk for exposure to teratogens as well (Weiss, Amler, & Ambler, 2004). Research on exposure to neurotoxins (e.g., lead, arsenic, mercury, manganese, PCBs) is particularly compelling as regards negative impacts on children’s health, cognitive functioning, and behavior (Kahn et  al., 2012; Vreugdenhil, Slijper, Mulder, & Weisglas‐Kuperlus, 2002; World Health Organization, 2005). In general, there is support for the proposition that exposure to environmental chemicals, such as PCBs and pesticides, damages the endocrine system (Chevrier, Eskenazi, Bradman, Fenster, & Barr, 2007; Rogan & Ragan, 2003). There is emerging evidence that exposure to heavy metals such as lead, mercury, nickel, arsenic, chromium, and cadmium can modify DNA methylation and ncRNA expression, leading to numerous downstream consequences for brain development and disease susceptibility (Senut et  al., 2012). Mercury can come from consuming fish, runoffs from mines, light bulbs, and airborne pollutants (Ronchetti et al., 2006). Lead can come from paint, cosmetics, runoff from certain industries, gasoline, etc. (Horton, Mortensen, Iossifova, Wald, & Burgess, 2013). The impact of specific neurotoxins depends on the level, timing, and duration of exposure (Boucher et al., 2014; Tyler & Allan, 2014; White et al., 2007). There is a massive literature on the consequences of airborne particulates like nitrogen dioxide, sulfur dioxide, phthalates, formaldehyde, carbon monoxide, hydrogen sulfide, benzene, and ozone, consequences that can begin prenatally (e.g., growth retardation, birth defects) (Aguilera et al., 2013; van den Hooven et al., 2012). Such pollutants can have negative impacts on

78  Robert H. Bradley lung (Aguilera et  al., 2013; Gauderman et  al., 2004) and immune system functions (World Health Organization, 2005). Phthalates, which mostly derive from polyvinyl chloride, have been associated with growth problems, thyroid problems, ADHD, and lower IQ (Cho et  al., 2010; Kim et  al., 2009). Formaldehyde, which can come from particleboard, insulation, and certain types of carpets and furniture, has been associated with reduced pulmonary function (McGwin. Lienert, & Kennedy, 2010; Roda et  al., 2011). Living in homes with serious structural defects increases the likelihood of infestation by roaches, mites, rats, and other pests that pose health risks (Rauh, Chew, & Garfinkel, 2002; Salam, Li, Langholz, & Gilliland, 2003). Asthma and respiratory illness is also more common in households where parents smoke (Cook & Strachan, 1999). The consequences of exposure to toxins are only partially characterized, partly because exposures are often coincident with living in poverty and with non‐optimal parenting. However, studies that have controlled for some of these social and economic conditions still indicate that exposure to high levels of most toxins creates risk for poor development (Boucher et al., 2014; Horton et al., 2013).

Caregiving Pathways by Which Adverse Distal Conditions Undermine Children’s Well‐Being Many risk conditions described in this chapter are distal to the child. For distal risk conditions, negative impacts derive from various psycho‐biological processes (i.e., pathways) that influence the functioning of particular psychological and biological systems, directly (Evans et al., 2013; Shonkoff et al., 2012; Wachs, 2000). The sheer number of those processes (i.e., pathways) is too vast to fully cover them all. Using select exemplars from the literature on the distal risk conditions covered in this chapter, the following section focuses on key psychological experiences that can directly influence child functioning, namely the proximal role of caregiving and interactions between family members. When they are young, children are highly dependent on caregivers to survive and adapt to their surroundings. I developed a framework for organizing the key tasks of c­aregiving (Bradley, 2006). Central to the framework is the notion that optimal caregiving (a facilitative home environment) is best understood as a set of regulatory acts and conditions aimed at helping children successfully adapt to the affordances of the settings they inhabit and successfully exploit the resources those settings contain. It is consistent with the idea that humans are phylogentically‐advanced organisms that consciously engage their environments and with the idea that maximum adaptation entails building personnel assets (Ford & Lerner, 1992; Lewis, 1997; Scales & Leffert, 1999). Starting with this set of premises, I identified 7 primary tasks that parents (or other caregivers) need to perform on behalf of children: (1) provide sustenance, (2) assure safety, (3) provide stimulation, (4)  generate socioemotional support, (5) provide structure, (6) engage in surveillance, and (7) encourage social integration – the 7 Ss of effective caregiving. The literature makes clear that all 7 are essential to children’s well‐being; and the literature also makes clear that various types of risks reduce the likelihood that caregivers can o­ptimally perform these 7 tasks.

Social and Contextual Risks  79

Sustenance It is essentially a sine qua non that not being well nourished or having access to decent health care can lead to poor health and even death (Pollitt, 1996). And, as the literature reviewed in this chapter indicates, sustenance is a critical mechanism relaying the harm of distal risks such as low household SES to young children. Poverty at household, community, and country levels has been identified as a risk for inadequate nutrition, with evidence showing impacts on growth, survival, and the overall burden of disease (Caulfield, Huffman, & Piwoz, 1999; Darmon & Drewnowski, 2007; Ezzati et al., 2002; Janevic, Petrivoc, Bjelic, & Kubera, 2010). However, poverty is not the only risk condition that increases the likelihood children will be poorly nourished. Low maternal education and parental substance abuse are also implicated (Janevic et al., 2010; Magura and Laudet, 1996; Miller & Chen, 2013). By contrast, families with stronger social networks are less likely to experience food insecurity (Martin, Rogers, Cook, & Joseph, 2004); and neighborhood disorganization can sometimes lead to having non‐nutritious diets (Lee & Cubbin, 2002). Some such neighborhoods have been characterized as “food deserts” due to lack of access to fresh fruits and vegetables, making it more likely that they will feed children meals that contain inadequate amounts of key nutrients and too much sugar and fat (American Nutrition Association, 2014; Darmon & Drewnowski, 2007). For low income families, lack of good health care facilities nearby is but one of an associated set of factors that reduce the likelihood they can provide adequate health care for their children, including lack of insurance and the simple cost of health services (DeVoe et al., 2007; Hogue, 2005; Kirby, 2008). In addition, living in remote rural areas can make it challenging to get adequate health care, particularly for children with special needs (Skinner & Slifkin, 2007).

Safety Given the limited capabilities of young children, they are highly vulnerable to injury and exploitation. Consequently, it is incumbent upon caregivers to proactively address potential safety issues. In a study of safety hazards within home settings, Glik, Greaves, Kronenfeld, & Jackson (1993) found that maternal supervisory style was instrumental in reducing hazard exposure. Careful supervision and preemptory management of hazard exposure is more difficult for single parents; thus, it is not surprising that accidents are more common in single‐parent households (Freisthler, Gruenewald, Ring, & LaScala, 2008) and when caregivers are depressed (Howe et al., 2006). Moreover, as noted earlier, low‐SES children are more likely to suffer injuries and to die (Howe et al., 2006; Keall et al., 2011; Overpeck et al., 1998), as their homes often have safety hazards (e.g., water heaters set too high in temperature) and lack safety protections (e.g., smoke alarms) (Bradley et al., 2001b; Gielen et al., 2012; Evans, 2006; Gauderman et al., 2004). Mothers with depression are less likely to use car seats, safety latches, or covers for outlets (McLearn, Minkovitz, Strobino, Marks, & Hou, 2005). Residential instability can make it challenging for parents to address potential safety hazards (Cohen & Wardrip, 2011), particularly since it is more likely that families will move if parents have mental illness, are involved

80  Robert H. Bradley with drugs, or have experienced conflict within the family (Freisthler et al., 2008; Magura & Laudet, 1996). As children get a little older, poor conditions in the immediate vicinity increase the likelihood of injury as well (Bradley et al., 2001b; Dercon & Krishnan, 2009). Living in rural areas can also presents notable safety risks. For example, preschoolers in rural areas are more likely to die from burns, drowning, and motor vehicle accidents than are their urban counterparts, as the outdoor affordances and access to health care differ in the two types of environments (Hwang, Stallones, & Keefe, 1997). Exposure to toxins is also exceptionally likely for children who live with parents who use and/or manufacture drugs (e.g., methamphetamines; Hayward et al., 2010).

Stimulation There is substantial support for the idea that stimulation from people, objects, and events is important for cognitive, social, and psychomotor development; and it begins at the neural level (Bradley et al., 2001a; Crosnoe et al., 2010; Ryan & Deci, 2000; Warsito, Khomsan, Hernawati, & Anwar, 2012). Children from low‐SES families generally receive less productive stimulation, from less parent talk to less access to stimulating materials to fewer exposures to out‐of‐home enriching experiences (Bradley et  al., 2001b; Hart & Risley, 1995; Hoff, 2003), sometimes as a consequence of maternal depression (Kiernan & Huerta, 2008). Noll, Zuker, Fitzgerald, & Curtis (1992) found that preschool‐aged children of alcoholics had poor language and reasoning skills as a consequence of inadequate stimulation at home. When there are chaotic conditions in the home, certain forms of stimulation (e.g., instruction on the part of parents) can be reduced whereas other forms of stimulation (e.g., noise) can be increased (Evans, 2006). In both cases, the impacts are negative. Area of residence can also be a factor in how likely it is that parents will engage their children in outdoor physical activity; thereby avoiding the tendency to be overweight. According to a review conducted by the National Recreation and Park Association (2012), low income neighborhoods are less likely to have parks and other outdoor accouterments that encourage such stimulation. By contrast, remote rural areas are less likely to have major historical and art museums that provide enriching experiences for children; nor are rural areas as likely to have facilities that promote specialized skill development.

Socio‐emotional support To be socially competent and emotionally healthy it is critical that young children receive adequate socioemotional support from caregivers and live in environments that afford them protection from forces that would undermine adaptive functioning (Egger & Angold, 2006). Studies conducted throughout the world show that parental warmth helps support positive adjustment, good health, and competence (Rohner, 1986). When caregivers are unresponsive to children or treat them harshly, it undermines the development of self‐regulation (Blair & Raver, 2012). Family SES may have implications for these processes in multiple ways. Luster, Rhoades, & Hass (1989), for example, found that the

Social and Contextual Risks  81 values of showing toughness and conforming to adult directives held by some low‐SES parents increased the likelihood they would spank a child and decreased the likelihood they would be responsive or show warmth. Moreover, stress connected with a­ dversity – economic or otherwise – increases the likelihood of negative forms of parenting (hostility, harsh discipline, inconsistency, poor monitoring) and decreases the likelihood of positive forms of parenting (warmth, sensitivity, encouragement, stimulation) (Bradley et  al., 2001b; Conger & Donnellan, 2007; Dodge, Pettit, & Bates, 1994; Felner et al., 1995; Grant et al., 2003; Gutman, Sameroff, & Cole, 2003; Kiernan & Huerta, 2008), which can make it harder for children to control their emotions and inhibit behavior (Lansford & Deater‐Deckard, 2012; McLoyd, 1998). Mental illness also decreases the likelihood parents will be emotionally responsive to their children (Howard et al., 2001; Wilson & Durbin, 2010), which can lead to disorganized attachment (van Ijzendoorn et al., 1999) and have long‐term impacts on stress physiology and cooperative behavior (Doherty, Tolep, Smith, & Rose, 2013; Koblinsky, Kuvalanka, & Randolph, 2006). When depression accompanies divorce, there is a tendency of mothers to withdraw from their children, leading to more behavioral maladjustment (Wood, Repetti, & Roesch, 2004). Ridener and Thurman (1994) reported that when mothers had a cocaine problem they were less responsive to their children’s needs. Similarly, Eiden, Edwards, & Leonard (2007) found that paternal alcoholism predicted low warmth and sensitivity for both fathers and ­mothers, which in turn predicted poor self‐regulation and behavior problems in their offspring. Moreover, when Dodge, Pettit, & Bates (1994) included both low SES and single parenthood as predictors of children’s externalizing problems and aggression, a socialization composite that included harsh discipline, parental warmth, cognitive s­timulation, and exposure to violence indirectly linked both household SES and single parenthood to the child outcomes.

Surveillance To effectively manage children’s lives, caregivers must keep track of their activities and whereabouts. Injuries are more common when parents fail to adequately supervise young children (Garbarino, 1988; Greaves et al., 1994; Morrongiello et al., 2011). The Centers for Disease Control (2012) identified parent level of education, household income, single‐ parent status, and household crowding as risk factors for childhood injury. Parental SES, the child’s previous history of injury, and the condition of the house were found to be associated with the number of hazards present in homes and the likelihood of monitoring a child (Bradley et al., 2001b; Glik et al., 1993). Greaves, Glik, Kronenfeld, & Jackson (1994) found that poor mothers felt higher levels of stress, which made it more difficult for them to cope generally, leading to lower engagement in safety practices needed for young children. Although there is evidence that parents adjust the extent to which the supervise children as children age, a major reason that parents leave children inadequately supervised is that they tend to overestimate children’s capacity to understand the dangers present in situations or control their proclivities to interact with things in the environment (Karstad, Kvello, Wichstrom, & Berg‐Nielsen, 2014). Having a more permissive parenting style also contributes to lower levels of supervision (Morrongiello, Kane, & Zdzieborski, 2011).

82  Robert H. Bradley

Structure Optimal development requires order, consistency, and careful arrangement of social and physical inputs so that it is easy for a child to uptake the positive inputs available and cope with challenges. In effect, to do well daily life needs structure (Spagnola & Fiese, 2007). Zolkoski and Bullock (2012) found that children fare better when parents were able to foster a continuing sense of family cohesion and a general sense that there is still coherence and manageability in life. Further, Bradley and colleagues (1994) showed that children are more resilient when families are able to provide structure and organization during times of instability. Yet, low‐SES homes are frequently characterized as having high levels of chaos and disorganization (Britto, Fuligni, & Brooks‐Gunn, 2002; Muniz, Silver, & Stein, 2014), with evidence showing that the chaos affects stress‐related cortisol trajectories in children and lack of productive routines predicted poor achievement (Chen, Cohen, & Miller, 2010; Muniz et  al., 2014). In addition, one of the most common reactions to events like divorce, becoming homeless, residential moves, incarceration, and trauma is the perception of lack of control over the situation and the loss of comfortable routines (Kelly & Emery, 2003; Mayberry, Shinn, Benton, & Wise, 2014; Tennen & Affleck, 1990), and the likelihood a parent can provide the needed structure and sense of coherence is diminished when the parent is struggling with mental illness or addiction (Solari et al., 2009). Also note that McLoyd, Toyokawa, & Kaplan (2008) observed that work demands increased depression and work–family conflict for single‐parent mothers, resulting in decreased family routines, which gave rise to an increase in child maladaptive behavior (see also Koblinsky et  al., 2006). Anderson and Whitaker (2010) likewise found that o­besity was lower for children who had regular meal times, sleep, and TV watching routines and that having such routines was less likely in low‐SES and single‐parent homes. But the need for structure goes beyond having a generally organized daily life with consistent routines to the management of daily encounters with people, objects, and events. In middle‐ class families there is a often a pattern of “concerted cultivation” of children’s social and cognitive development through on‐going, structured encounters with people and things and through deliberate involvement in enriching activities (Lareau, 2003).

Social integration For children to succeed in any society, it is important that parents connect them with the social fabric of the society as it is through such connections that children receive the opportunities and supports they need to live productively (Weisner, 2002). Connecting to extended social networks can be particularly important for children living in adverse circumstances, such as poverty or family instability (Horvat, Weininger, & Lareau, 2003). It is through connections with social networks (e.g., extended family, other adults in the community, work associates, community agencies) that parents can access potential resources for children (Runyan et al., 1998). Single‐parent homes generally have less access to extended family and the resources they can provide; and, as a consequence, children’s achievement tends to be lower (Amato, 1995). There is evidence that social integration within neighborhood and community can have benefits for both parents and children.

Social and Contextual Risks  83 Rivara and Mueller (1997) found that in tight‐knit communities, there is greater monitoring of children leading to fewer injuries. As it happens, parents with mental illness or problems with addiction are often in a difficult position to connect their children to useful social networks because they themselves are socially isolated (Howard et al., 2001; Semple et al., 2011). Likewise, when mothers in low‐income countries felt that they were not part of the community and that they had few people in the community they could call upon in times of need, it tended to contribute to chronic depression (De Silva, Huttly, Harpham, & Kenward, 2007). Parents can connect young children to potentially useful peers and adults by enrolling them in high quality childcare and preschools. However, studies show that lower class parents are less able to connect with teachers in behalf of their children (Horvat et al., 2003), whereas high income parents have more involvement with schools, and this helps promote academic achievement (Lee & Bowen, 2006). There are limitations in research pertaining to social capital; even so, it appears that (a) children facing adversity are likely to have less social capital but (b) higher family social capital produces advantages in multiple areas of well‐being.

Conclusions For decades evidence has accumulated showing that exposure to social and contextual risks can compromise children’s development (Evans et al., 2013). Negative impacts can begin at conception and continue through the life course, with impacts manifest in many bio‐ psychological systems (Shonkoff et al., 2012). Adverse distal conditions can degrade the capacity of caregivers to perform tasks that children require. They can also reduce the likelihood parents can utilize social connections that can provide needed resources for children. Adverse conditions can even lead to an overall social milieu or family environment that is inimical to child well‐being. Because humans are complex creatures living in dynamic environments, scientists have struggled to delineate all the pathways through which child development is compromised. Part of the difficulty is that a given action could take on different meanings and have different functions depending on the circumstances. There is evidence that negative impacts can derive through multiple paths simultaneously and through different paths for different children. The pathways can also shift through time; and the effects can be moderated by other environmental conditions and via the child’s own characteristics, some of which can increase the likelihood of negative impact and some of which are protective. For this review, the focus has been on how adverse distal conditions (alone and in combination) damage children’s health, competence, and adaptive behavior; but it is clear that children are active in their own development and that there is reciprocal interplay between what children bring to their environments and what the affordances of those environments do to influence developmental course. Future research will need to include a greater diversity of measures and samples to capture the synergistic interplay of these multiple factors and they will need to involve the use of genetically sensitive designs. It is likely the theories will need to be expanded and that better measures will need to be constructed to fully determine how adverse conditions are implicated in the course of development as well. At present there is

84  Robert H. Bradley sufficient understanding of how some processes operate to direct the course of children’s development so that productive policies and programs can be implemented. As the science of contextual effects improves, so can policies and practices.

References Adler, N. E., & Ostrove, J. M. (1999). Socioeconomic status and health: What we know and what we don’t. Annals of the New York Academy of Sciences, 896, 3–15. Aguilera, I., Pedersen, M., Garcia‐Esteban, R., Ballester, F., Basterrechea, J., Esplugues, A. …, Sunyer, J. (2013). Early life exposure to outdoor air pollution and respiratory health, ear i­nfections, and eczema in infants from the INMA study. Environmental Health Perspectives, 121, 387–392. Amato, P. (1995). Single‐parent households as settings for children’s development, well‐being and attainment: A social network/resources perspective. In A. Ambert (Ed.). Sociological studies of children, vol. 7 (pp. 19–47). Greenwich, London: JAI Press. Amato, P. R., & Cheadle, J. (2005). The long reach of divorce: Divorce and child well‐being across three generations. Journal of Marriage and Family, 67, 191–206. American Academy of Child and Adolescent Psychiatry. (2011, December). Facts for families. Children of alcoholics. Available from http://www.aacap.org/App_Themes/AACAP/docs/facts_ for_families/17_children_of_alcoholics.pdf American Nutrition Association. (2014). USDA defines food desert. Nutrition Digest, 37(1). Available from http://www.americannutritionassociation.org/newletter/usda‐defines‐food‐deserts Anderson, S., Leventhal, T., & Dupere, V. (2014). Exposure to neighborhood affluence and poverty in childhood and adolescence and academic achievement and behavior. Applied Developmental Science, 18, 123–138. Anderson, S. E., & Whitaker, R. C. (2010). Household routines and obesity in US preschool‐aged children. Pediatrics, 125, 420–428. Attar, B. K., Guerra, N. G., & Tolan, P. H. (1994). Neighborhood disadvantage, stressful life events, and adjustment in urban elementary‐school children. Journal of Clinical Child Psychology, 23, 391–400. Baltazar, J. C., & Solon, F. S. (1989). Disposal of faeces of children under two years old and diarrhoea incidence: A case‐control study. International Journal of Epidemiology 18(4), S16–S19. Barajas, M. S. (2011). Academic achievement of children in single parent homes: A critical review. The Hilltop Review, 5, 13–21. Bartlett, S. (1999). Children’s experience of the physical environment in poor urban settlements and the implications for policy and practice. Environment and Urbanization, 11, 63–73. Beardslee, W., Versage, E., & Gladstone, T. (1998). Children of affectively ill parents: A review of the past 10 years. Journal of the American Academy of Child and Adolescent Psychiatry, 37, 1134–1141. Behnke, M., Smith, V. C., & Committee on Substance Abuse, and Committee on Fetus and Newborn. (2013). Prenatal substance abuse: Short‐ and long‐term effects on the exposed fetus. Pediatrics, 131, 1009–1024. Belojevic, G., Evans, G. W., Paunovic, K., & Jakovljevic, B. (2012). Traffic noise and executive functioning in urban primary school children: The moderating role of gender. Journal of Environmental Psychology, 32, 337–341. Bertalanffy, L. von. (1968). General system theory. Foundations, development, applications. New York, NY: George Braziller.

Social and Contextual Risks  85 Blair, C., & Raver, C. C. (2012). Child development in the context of adversity. American Psychologist, 67, 309–318. Borders, A. E. B., Grobman, W. A., Amsden, L. B., & Holl, J. L. (2007). Chronic stress and how low birth weight neonates in a low‐income population of women. Obstetrics and Gynecology, 109, 331–338. Bornstein, M. H., & Putnick, D. L. (2012). Cognitive and socioemotional caregiving in developing countries. Child Development, 83, 46–61. Bosma, H., van de Mheen, D., Borsboom G. J., & Machenbach, J. P. (2001). Neighborhood s­ocioeconomic status and all‐cause mortality. American Journal of Epidemiology, 153, 863–871. Boucher, O., Muckle, G, Jacobson, J., Carter, R. C., Kaplan‐Estrin, M., Ayotte, … Jacobson, S. (2014). Domain‐specific effects of prenatal exposure ot PCBs, mercury, and lead in infant cognition: Results from the environmental contaminants and child development study in Nunavik. Environmental Health Perspectives, 122, 310–316. Boyle, M. H., Racine, Y., Georgiades, K., Snelling, D., Hong, S., Omariba, W. … Rao‐Melacini, P. (2006). The influence of economic development level, household wealth and maternal education in the developing world. Social Science and Medicine, 63, 2242–2254. Bradley, R. H. (2006). Home environment. In N. Watt, C. Ayoub, R. H. Bradley, J. Puma & W. LaBoeuf (Eds.), The crisis in youth mental health, vol. 4: Early intervention programs and policies (pp. 89–120). Westport, CN: Greenwood Publishing Group. Bradley, R. H. (2012a). The HOME Inventory. In L. C. Mayes & M. Lewis (Eds.), A developmental environment measurement handbook (pp. 568–589). New York, NY: Cambridge University Press. Bradley, R. H. (2012b). Rural versus urban environments. In L. C. Mayes & M. Lewis (Eds.), A developmental environment measurement handbook (pp. 330–346) New York, NY: Cambridge University Press. Bradley, R. H., & Corwyn, R. F. (2002). Socioeconomic status and child development. Annual Review of Psychology, 53, 371–399. Bradley, R. H., & Corwyn, R. F. (2005). Caring for children around the world: A view from HOME. International Journal of Behavioral Development, 26, 468–478. Bradley, R. H., Corwyn, R. F., Burchinal, M., McAdoo, H. P., & Garcia Coll, C. (2001a). The home environments of children in the United States. Part 2: Relations with behavioral development through age 13. Child Development, 72, 1868–1886. Bradley, R. H., Corwyn, R. F., McAdoo, H. P., & Garcia Coll, C. (2001b). The home environments of children in the United States. Part 1: Variations by age, ethnicity, and poverty status. Child Development, 72, 1844–1867. Bradley, R. H., Whiteside, L., Mundfrom, D. J., Casey, P. H., Kelleher, K. J., & Pope, S. K. (1994). Early indications of resilience and their relation to living in poverty. Child Development, 65, 246–260. Bradman, A., Chevrier, J., Tager, I., Lipsett, M., Dedgwick, J., Macher, J., … Eskenazi, B. (2005). Association of housing disrepair indicators with cockroach and rodent infestations in a cohort of  pregnant Latina women and their children. Environmental Health Perspectives, 113, 1795–1801. Britto, P., Fuligni, A. S., & Brooks‐Gunn, J. (2002). Reading, rhymes, and routines: American parents and their young children. In N. Halfvon & K. T. McLearn (Eds.), Child rearing in America: Challenges facing parents with young children (pp. 117–145). New York, NY: Cambridge University Press. Brody, G. H., Flor, D. L., & Gibson, N. M. (1999). Linking maternal efficacy beliefs, developmental goals, parenting practices, and child competence in rural single‐parent African American families. Child Development, 70, 1197–1208.

86  Robert H. Bradley Bronfenbrenner, U. (1995). The bioecological model from a life course perspective: Reflections of a participant observer. In P. Moen, G. H. Elder, & K. Luscher (Eds.), Examining lives in context (pp. 619–647). Washington, DC: American Psychological Association. Burke, S., Schmied, V., & Montrose, M. (2006, July). Parental alcohol misuse and the impact on children. Research Report, New South Wales Department of Community Services. Ashfield, NSW, Australia. Available from http://www.community.nsw.gov.au Calder, P. C., & Jackson, A. A. (2000). Undernutrition, infection and immune function. Nutrition Research Reviews, 13, 3–29. Canadian Paediatric Society. (2004). Maternal depression and child development. Paediatrics and Child Health, 9, 575–583. Carmichael, S. L., Yang, W., Herring, A., Abrams, B., & Shaw, G. M. (2007). Maternal food insecurity is associated with increased risk for certain birth defects. Journal of Nutrition, 137, 2087–2092. Caulfield, L., Huffman, S., & Piwoz, C. (1999). Interventions to improve intake of complementary foods by infants 6 to 12 months of age in developing countries: Impact on growth and on the prevalence of malnutrition and potential contribution to child survival. Food and Nutrition Bulletin, 20, 183–200. Cavanagh, S. E., & Huston, A. C. (2006). Family instability and children’s early problem behavior. Social Forces, 85, 551–582. Centers for Disease Control. (2012). National action plan for child injury prevention. Available from http://www.cdc.gov/injury Centers for Disease Control. (2013). National Survey of Children’s Health. Available from http:// www.cdc.gov/nchs/slaits/nsch.htm Chaffin, M., Kelleher, K., & Hollenberg, J. (1997). Onset of physical abuse and neglect: Psychiatric, substance abuse, and social risk factors from prospective community data. Child Abuse and Neglect, 20, 191–203 Chemero, A. (2003). An outline of a theory of affordances. Ecological Psychology, 15, 181–195. Chen, E., Cohen, S., & Miller, G. E. (2010). How low socioeconomic status affects 2‐year hormonal trajectories in children. Psychological Science, 21, 31–37. Cherry, D. C., Huggins, B., & Gilmore, K. (2007). Children’s health in the rural environment. Pediatric clinics of North America, 54, 121–133. Chevrier, J., Eskenazi, B., Bradman, A., Fenster, L., & Barr, D. (2007). Associations between p­renatal exposure to polychlorinated biphenyls and neonatal thyroid‐stimulating hormone levels in a Mexican‐American population, Salinas Valley, California. Environmental Health Perspectives, 115, 1490–1496. Child Trends. (2014, August). Parental depression. Available from http://www.childtrends.org/ wp‐content/uploads/2014/08/54_Parental_Depression1.pdf Cho, S‐C., Bhang, S‐Y., Hong, Y‐C., Shin, M‐S., Kim, B‐N., Kim, Kim H‐W. (2010). Relationship between environmental pthtalate exposure and the intelligence of school‐age c­hildren. Environmental Health Perspectives, 118, 1027–1032. Christoffersen, M. (1998). Growing up with dad: A comparison of children aged 3 to 5 years old living with their mothers or their fathers. Childhood: A Global Journal of Child Research, 5, 41–54. Cohen, R., & Wardrip, K. (2011, February). Should I stay or should I go? Exploring the effects of housing instability and mobility on children. Available from http://www.nhc.org/media/files/ HsginstabiltyandMobility.pdf Cohen, S. (1999). Social status and susceptibility to respiratory infections. In N. Adler, M. Marmot, B. McEwen, & J. Stewart (Eds.), Socioeconomic status and health in industrial nations (pp. 246–253). New York, NY: The New York Academy of Sciences.

Social and Contextual Risks  87 Coldwell, J., Pike, A., & Dunn, J. (2006). Household chaos  –  links with parenting and child b­ehavior. Journal of Child Psychology and Psychiatry, 47, 1116–1122. Conger, R. D., & Donnellan, M. B. (2007). An interactionist perspective on the socioeconomic context of human development. Annual Review of Psychology, 58, 175–199. Cook, D. G., & Strachan, D. P. (1999). Summary of effects of parental smoking on the respiratory health of children and implications of research. Thorax, 54, 357–366. Cook, J. T., & Frank, D. A. (2008). Food security, poverty, and human development in the United States. Annals of the New York Academy of Science, 1136, 193–209. Corwyn, R. F., & Bradley, R. H. (2005). Socioeconomic status and childhood externalizing behavior: A structural equation modeling framework. In V. L. Bengston, A. C. Acock, K. R. Allen, P. Dillworth‐Anderson, & D. M. Klein (Eds.), Sourcebook on family theory and research (pp. 469–483). Thousand Oaks, CA: Sage. Crosnoe, R., Leventhal, T., Wirth, R., Pierce, K., Pianta, R., & NICHD Early Child Care Research Network. (2010). Family socioeconomic status and consistent stimulation in early childhood. Child Development, 81, 972–987. Cunha, F., & Heckman, J. J. (2008). Formulating, identifying and estimating the technology of cognitive and noncognitive skill formation. Journal of Human Resources, 43, 741–782. Darmon, N., & Drewnowski, A. (2007. Does social class predict diet quality? American Journal of Clinical Nutrition, 87, 1107–1117. Davies, P. T., & Cummings, E. M. (1994). Marital conflict and child adjustment: An emotional security hypothesis. Psychological Bulletin, 116, 387–411. Davis, E., Sawyer, M. G., Lo, S. K., Priest, N., & Wake, M. (2010). Socioeconomic risk factors for mental health problems in 4 and 5‐year‐old children: Australian population study. Academic Pediatrics, 10, 41e–47e. Davis, R. N., Davis, M. M., Freed, G. L., & Clark, S. J. (2011) Fathers’ depression related to positive and negative parenting behaviors with 1‐year‐old children. Pediatrics, 127, 612–618. Davis‐Kean, P. E. (2005). The influence of parent education and family income on child achievement: The indirect role of parental expectations and the home environment. Journal of Family Psychology, 19, 294–304. Dearing, E., Casey, B., Ganley, C., Tillinger, M., Laski, E., & Monntecillo, C. (2012). Young girls’ arithmetic and spatial skills: The distal and proximal roles of socioeconomics and home learning experiences. Early Childhood Research Quarterly, 27, 458–470. Dearing, E., McCartney, K.., & Taylor, B. (2001). Change in family income‐to‐needs matters more for children with less. Child Development, 72, 1779–1783. Deater‐Deckard, K., Mullinex, P. Beekman, C., Petroll, S. A., Schatschneider, C., & Thompson, L. A. (2009). Conduct problems, IQ, and household chaos: A longitudinal multi‐informant design. Journal of Child Psychology and Psychiatry, 50, 1301–1308. Dercon, S., & Krishnan, P. (2009). Poverty and the psychosocial competencies of children: Evidence from the Young Lives sample in four developing countries. Children, Youth and Environments, 19, 138–163. De Silva, M., Huttly, S., Harpham, T., & Kenward, M. (2007). Social capital and mental health: A comparative analysis of four low‐income countries. Social Science and Medicine, 64, 5–20. DeVoe. J., Baez, A., Angier, H., Krois, L., Edlund, C., & Carney, P. (2007). Insurance plus access does not equal health care: Typology of barriers to health care access for low‐income families. Annals of Family Medicine, 5, 511–516. Dietz, L., Jennings, K., Kelley, S., & Marshal, M. (2009). Maternal depression, paternal psychopathology, and toddlers’ behavior problems. Journal of Clinical Child and Adolescent Psychology, 38, 48–61.

88  Robert H. Bradley Dodge, K. A., Pettit, G. S., & Bates, J. E. (1994). Socialization mediators of the relation between socioeconomic status and child conduct problems. Child Development, 65, 649–665. Doherty, L., Tolep, M., Smith, V., & Rose, S. (2013). Early exposure to parental depression and parenting: Association with young offspring’s stress physiology and oppositional behavior. Journal of Abnormal Child Psychology, 41, 1299–1310. Dumas, J., Nissley, J., Nordstrom, A., Smith, E., Prinz, R., & Levine, D. (2005). Home chaos: Sociodemographic, parenting, interactional, and child correlates. Journal of Child and Adolescent Psychology, 34, 93–104. Dush, C. M., Schmeer, K. K., & Taylor, J. (2013). Chaos as a social determinant of child health: Reciprocal associations? Social Science and Medicine, 95, 69–75. Earls, F. (1980). Prevalence of behavior problems in 3‐year‐old children: a cross‐national replication. Archives of General Psychiatry, 37, 1153–1157. Egger, H. L., & Angold, A. (2006). Common emotional and behavioural disorders in preschool children: Presentation, nosology, and epidemiology. Journal of Child Psychology and Psychiatry, 47, 313–337. Eiden, R. D., Edwards, & Leonard, K. (2007). A conceptual model for the development of externalizing behavior problems among kindergarten children of alcoholic families: Role of parenting and children’s self regulation. Developmental Psychology, 43, 1187–1201. 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. Evans, G. W. (2004). The environment of childhood poverty. American Psychologist, 59, 77–92. Evans, G. W. (2006). Child development and the physical environment Annual Review of Psychology, 57, 423–451. Evans, G. W., Li, D., & Whipple, S. S. (2013). Cumulative risk and child development. Psychological Bulletin, 139, 1342–1396. Ezzati, M., Lopez, A. D., Rodgers, A., Hoorn, S. V., Murray, C. J., & the Comparative Risk Assessment Collaborating Group. (2002). Selected major risk factors and global and regional burden of disease. Lancet, 360(9343), 1342–1343. Farah, M. J., Betancourt, L., Shera, D. M., Savage, J. H., Giannetta, J. M., Brodsky, M. L, … Hurn H. (2008). Environmental stimulation, parental nurturance and cognitive development in humans. Developmental Science, 11, 793–801. Federal Interagency Forum on Child and Family Statistics. (2013). America’s children: Key national indicators of well‐being, 2013. Washington, DC: U. S. Government Printing Office. Felner, R. D., Brand, S., DuBois, D. L., Adan, A. M., Mulhall, P. F., & Evans, E. G. (1995). Socioeconomic disadvantage, proximal environmental experiences, and socioemotional and a­cademic adjustment in early adolescence: Investigation of a mediated effects model. Child Development, 65, 296–318. Fernald, R. D., & Maruska, K. P. (2012). Social information changes the brain. PNAS, 109 (suppl. 2), 17194–17199. Fischer, K. W. (1980). A theory of cognitive development: The control and construction of h­ierarchies of skill. Psychological Review, 87, 477–531. Flouri, E., Tzavidis, N., & Kallis, C. (2010). Adverse life events, area socioeconomic advantage, and psychopathology and resilience in young children: The importance of risk factors’ accumulation and protective factors’ specificity. European Journal of Child and Adolescent Psychiatry, 19, 535–546. Fomby, P., & Cherlin, A. J. (2007). Family instability and child well‐being. American Sociological Review, 72, 181–204.

Social and Contextual Risks  89 Ford, D. H., & Lerner, R. M. (1992). Developmental systems theory: An integrative approach. Newbury Park, CA: Sage. Freisthler, B., Gruenewald, P., Ring, L., & LaScala, E. (2008). An ecological assessment of the population and environmental correlates of childhood accident, assault and child abuse injuries. Alcohol and Clinical Experimental Research, 32, 1969–1975. Galea, S., Nanadi, A., & Viahov, D. (2004). The social epidemiology of substance use. Epidemiologic Reviews, 26, 36–52. Garbarino, J. (1988). Preventing childhood injury: Developmental and mental health issues. American Journal of Orthopsychiatry, 58, 25–45. Gauderman, W. J., Avol, E., Gilliland F., Vora, H., Thomas, D., Berhane, K. … Peters, J. (2004). The effect of air pollution on lung development from 10 to 18 years of age. New England Journal of Medicine, 351, 1057–1067. Gershoff, E. T., Aber, J. L., Raver, C. C., & Lennon, M. C. (2007). Income is not enough: Incorporating material hardship into models of income associations with parenting and child development. Child Development, 78, 70–95. Gielen, A., Shields, W., McDonald, E., Frattaroli, S., Bishai, D., & Ma, X. (2012). Home safety and low‐income housing quality. Pediatrics, 130, 1053–1059. Glik, D., Greaves, P., Kronenfeld, J., & Jackson, K. (1993). Safety hazards in households with young children. Journal of Pediatric Psychology, 18, 115–131. Goodman, S. H., & Brumley, H. E. (1990). Schizophrenic and depressed mothers: Relational deficits in parenting. Development Psychology, 26, 31–39. Grant, K. E., Compas, B. E., Stuhlmacher, A., Thurm, A., McMahon, S., & Halpert, J. (2003). Stressors and child and adolescent psychopathology: Moving from markers to mechanisms of risk. Psychological Bulletin, 129, 447–466. Grantham‐McGregor, S., Cheung, Y. B., Dueto, S., Glewwe, P, Richter, L., Strupp, B., & the International Child Development Steering Group. (2007). Developmental potential in the first 5 years for children in developing countries. Lancet, 369, 60–70. Greaves, P., Glik, D. C., Kronenfeld, J. J., & Jackson, K. (1994). Determinants of controllable in‐ home child safety hazards. Health Education Research, 9, 307–315. Gutman, L. M., Sameroff, A. J., & Cole, R. (2003). Academic growth trajectories from 1st grade to 12th grade: Effects of multiple social risk factors and preschool child factors. Developmental Psychology, 39, 777–790. Hans, S. L., Auerback, J. G., Styr, B., & Marcus, J. (2004). Offspring of parents with schizophrenia: Mental disorders during childhood and adolescence. Schizophrenia Bulletin, 30, 303–315. Hanscombe, K., Haworth, C., Davis, O., Jaffee, S., & Plomin, R. (2011). Chaotic homes and  school achievement: A twin study. Journal of Child Psychology and Psychiatry, 52, 1212–1220. Hart, B., & Risley, R. T. (1995). Meaningful differences in the everyday experience of young American children. Baltimore, MD: Paul H. Brookes. Hasbrouck. Hayward, R. A., Depanfilis, D., & Woodruff, K. (2010). Parental methamphetamine use and implications for child welfare intervention: A review of the literature. Journal of Public Child Welfare, 4, 25–60. Heft, J. (1993). A methodological note on overestimates of reaching distance: Distinguishing between perceptual and analytic judgments. Ecological Psychology, 5, 255–271. Higgitt, N. (1996). Toward a conceptual model: Residential mobility among low‐income, inner‐city families. Housing and Society, 23, 47–60. Hipwell, A. E., & Kumar, R. (1996). Maternal psychopathology and prediction of outcome based on mother‐infant interaction ratings (BMIS). British Journal of Psychiatry, 169, 655–661.

90  Robert H. Bradley Hoff, E. (2003). The specificity of environmental influence: Socioeconomic status affects early vocabulary development via maternal speech. Child Development, 74, 1368–1378. Hogue, E. (2005). Dwelling disparities, how poor housing leads to poor health. Environmental Health Perspectives, 113, A311–A319. Holland, J. H. (1992). Complex adaptive systems. Daedalus, 121, 17–30. Horton, L., Mortensen, M., Iossifova, Y, Wald, M., & Burgess, P. (2013). What do we know of childhood exposures to metals (Arsenic, cadium, lead, and mercury) in emerging market c­ountries? International Journal of Pediatrics, ID 872596. Horvat, E., Weininger, E., & Lareau, A. (2003). From social ties to social capital: Class differences in the relations between schools and parent networks. American Educational Research Journal, 40, 319–351. Howard, L. M., Kumar, R., & Thornicroft, G. (2001). Psychosocial characteristics and needs of mothers with psychotic disorders. British Journal of Psychiatry, 178, 427–432. Howe, L., Huttly, S., & Abramsky, T. (2006). Risk factors for injuries in young children in four developing countries: The Young Lives Study. Tropical Medicine and International Health, 11, 1557–1566. Huston, A., & Bentley, A. (2010). Human development in societal context. Annual Review of Psychology, 61, 411–437. Hwang, H‐C., Stallones, L., & Keefe, T. (1997). Childhood injury deaths: Rural and urban differences, Colorado 1980–88. Injury Prevention, 3, 35–37. Jackson, A. P., Preston, K., & Franke. T. M. (2010). Single parenting and child behavior problems in kindergarten. Race and Social Problems, 2, 50–58. Jaffee, S., Hanscombe, B., Haworth, C., Davis, O., & Plomin, R. (2012). Chaotic homes and children’s disruptive behavior: A longitudinal cross‐lagged twin study. Psychological Science, 23, 643–650. Janevic, T., Petrivoc, O., Bjelic, I., & Kubera, A. (2010). Risk factors for childhood malnutrition in Roma settlements in Serbia. BMC Public Health, 10, 509. Jelleyman, T., & Spencer, N. (2008). Residential mobility in childhood and health outcomes: A systematic review. Journal of Epidemiology and Community Health, 62, 584–592. Johnson, S. B., Riley, A. W., Grander, D. A., & Riis, J. (2013). The science of early life toxic stress for pediatric practice and advocacy. Pediatrics, 131, 319–327. Johnston‐Brooks, C. H., Lewis, M. A., Evans, G. W., & Whalen, C. K. (1998). Chronic stress and illness in children: The role of allostatic load. Psychosomatic Medicine, 60, 597–603. Kahn K., Wasserman, G., Liu, X., Admed, E., Parvez, F., Slavkovich V. … Factor‐Litvak, P. (2012). Manganese exposure from drinking water and children’s academic achievement. Neurotoxicology, 33, 91–97. Kahn, R., Brandt, D., & Whitaker, R. C., (2004). Combined effect of mothers’ and fathers’ mental health symptoms on children’s behavioral and emotional well‐being. Archives of Pediatrics and Adolescent Medicine, 158, 721–729. Kanagawa, M., & Nakata, T. (2008). Assessment of access to electricity and the socio‐economic impacts in rural areas of developing countries. Energy Policy, 36, 2016–2029. Karstad, S. B., Kvello, O., Wichstrom, L., & Berg‐Nielsen, T. S. (2014). What do parents know about their children’s comprehension of emotions? Accuracy of parental estimates in a community sample of pre‐schoolers. Child: Care Health and Development, 40, 346–353. Keall, M., Baker, M., Ormandy, D., & Baker, M. (2011). Injuries associated with housing conditions in Europe: A burden of disease study based on 2004 injury data. Environmental Health, 10, 98. Kelly, J. B., & Emery, E. E. (2003). Children’s adjustment following divorce: Risk and resilience perspectives. Family Relations, 52, 352–362.

Social and Contextual Risks  91 Kiernan, K., & Huerta. M. C. (2008). Economic deprivation, maternal depression, parenting and children’s cognitive and emotional development in early childhood. The British Journal of Sociology, 59, 783–806. Kim, B. N., Cho, S. C., Kim, Y., Shin, M. S., Yoo, H. J., Kim J. W. … Kim, H. W. (2009). Phthalates exposure and attention‐deficit/hyperactivity disorder in school‐age children. Biological Psychiatry, 66, 958–963. Kirby, J. (2008). Poor people, poor places and access to health care in the United States. Social Forces, 87, 325–355. Koblinsky, S., Kuvalanka, K., & Randolph, S. (2006). Social skills and behavior problems of urban, African American preschoolers: Role of parenting practices, family conflict, and maternal d­epression. American Journal of Orthopsychiatry, 76, 554–563. Koskinen, O., Husman, T., Meklin, T., & Nevalainen, A. (1999). The relationship between moisture or mold observations in houses and the state of health of their occupants. European Respiratory Journal, 14, 1363–1367. Kramer, M. S. (1987). Determinants of low birthweight: Methodological assessment and m­eta‐analysis. Bulletin of the World Health Organization, 65, 663–737. Lansford, J. E. (2009). Parental divorce and children’s adjustment. Perspectives on Psychological Science, 4, 140–152. Lansford, J. E., & Deater‐Deckard, K. (2012). Childrearing discipline and violence in developing countries. Child Development, 83, 62–75. Lareau, A. (2003). Unequal childhoods: Class, race, and family life. Berkeley: University of California Press, 35, 66–67. Lee, J‐S., & Bowen, N. K., (2006). Parent involvement, cultural capital, and the achievement gap among elementary school children. American Educational Research Journal, 43, 193–218. Lee, R., & Cubbin, C. (2002) Neighborhood context and youth cardiovascular health behaviors. American Journal of Public Health, 92, 428–436. Leininger, L. J., & Ziol‐Guest, K. M. (2008). Reexamining the effects of family structure on c­hildren’s access to care: The single‐father family. Health Research and Trust, 43, 117–133. Leventhal, T., & Brooks‐Gunn, J. (2000). The neighborhoods they live in: The effect of neighborhood residence on child and adolescent outcomes. Psychological Bulletin, 126, 309–337. Lewis, B. A., Singer, L. T., Short, E. J., Minnes, S., Arendt, R., Weishampel, P. … Min, M. O. (2004). Four‐year language outcomes of children exposed to cocaine in utero. Neurotoxicology and Teratology, 26, 617–627. Lewis, M. D. (1997). Altering fate. New York, NY: Guilford Press. Lindskog, P., & Lundqvist, J. (1998). Why poor children stay sick: The human ecology of child health and welfare in rural Malawi. Uppsala: Scandinavian Institute of African Studies. Livingston, B. (2013, July). The rise of single fathers. Pew Research Center. Luster, T., Rhoades, K., & Hass, B. (1989). The relation between parental values and parenting behavior: A test of the Kohn hypothesis. Journal of Marriage and Family, 51, 139–147. Magura, S., & Laudet, A. (1996). Parental substance abuse and child maltreatment: Review and implications for intervention. Children and Youth Services Review, 18, 193–220. Martin, K. S., Rogers, B. L, Cook, J. T., & Joseph, H. M. (2004). Social capital is associated with decreased risk of hunger. Social Science and Medicine, 58, 2645–2654. Mason, L., Jarp, J., & Han, D. (2014). Pb Neurotoxicitiy: Neuropsychological effects of lead t­oxicity. BioMed Research International, 2014, 840547. Mayberry, L., Shinn, M., Benton, J., & Wise, J. (2014). Families experiencing housing instability: The effects of housing programs on family routines and rituals. American Journal of Orthopsychiatry, 84, 95–109.

92  Robert H. Bradley McCracken, J. P., Smith, K. R., Diaz, A., Mittleman, M. A., & Schwartz, J. (2007). Chimney stove intervention to reduce long‐term wood smoke exposure lowers blood pressure among Guatemalan women. Environmental Health Perspectives, 115, 996–1001. McEwen, B. S., & Gianaros, P. J. (2010). Central role of the brain in stress and adaptation: Links to socioeconomic status, health, and disease. Annals of the New York Academy of Sciences, 1186, 190–222. McGwin, G., Lienert, J., & Kennedy, J. I. (2010). Formaldehyde exposure and asthma in children: A systematic review. Environmental Health Perspectives 118, 313–317. McLanahan, S. (1999). Father absence and children’s welfare. In E. M. Hetherington (Ed.), Coping with divorce, single parenting, and remarriage: A risk and resiliency perspective. Mahway, NJ: Erlbaum. McLaughlin, A. A., Minnes, S., Singer, L. T., Min, M., Short, E. J., Scott, T. L., & Satayathum, S. (2011). Caregiver and self‐report of mental health symptoms in 9‐year‐old children with prenatal cocaine exposure. Neurotoxicology and Teratology, 33, 582–591. McLearn, K. T., Minkovitz, C., Strobino, D., Marks, E., & Hou, W. (2005). The timing of maternal depressive symptoms and mothers’ parenting practices with young children: Implications for pediatric practice. Pediatrics, 118, e174–e182. McLoyd, V. C. (1998). Socioeconomic disadvantage and child development. American Psychologist, 53, 185–204. McLoyd, V. C., Toyokawa, T., & Kaplan, R. (2008). Work demands, work‐family conflict, and child adjustment in African American families. Journal of Family Issues, 29, 1247–1267. Miller, G. E., & Chen, E. (2013). The biological residue of childhood poverty. Child Development Perspectives, 2, 67–73. Mirkazemi, R., & Kar, A. (2009). Injury related unsafe behavior among households from different socioeconomic strata in Pune City. Indian Journal of Community Medicine, 34, 301–305. Mohnen, S. M., Groenewegen, P. P., Volker, B., & Flap, H. (2011). Neighborhood social capital and individual health. Social Science and Medicine, 72, 660–667. Moore, K. A., (2006). Defining the term “at risk”. Child Trends, Research‐to‐Results Brief (#2006‐12). Morrongiello, B., Kane, A., & Zdzieborski, D. (2011). “I think he is in his room playing a video game”: Parental supervision of young elementary‐school children at home. Journal of Pediatric Psychology, 36, 708–717. Muniz, E., Silver, E., & Stein, R. (2014). Family routines and social‐emotional school readiness among preschool‐age children. Journal of Developmental and Behavioral Pediatrics, 35, 93–99. Murphey, E., Bandy, T., & Moore, K. A. (2012, January). Frequent residential mobility and young children’s well‐being. Child Trends Research Brief (#2012‐02). Available from http://www. childtrends.org National Center for Health Statistics. (2011). Health, United States, 2011: With special feature on socioeconomic status. Washington, DC: US Government Printing Office. National Recreation and Park Association. (2012). Parks & recreation in underserved areas. A public health perspective. Available from http://www.nrpa.org/uploadedFiles/nrpa.org/Publications_ and_Research/Research/Papers/Parks‐Rec‐Underserved‐Areas.pdf Noll, R., B., Zuker, R. A., Fitzgerald, H. E., & Curtis, W. (1992). Cognitive and motor achievement of sons of alcoholic fathers and controls: The early childhood years. Developmental Psychology, 28, 665–675. Osborne, C., & Berger, L. M. (2009). Parental substance abuse and child well‐being. Journal of Family Issues, 30, 341–370. Overpeck, M D., Brenner, R. A., Trumble, A. C., Trifiletti, L. B., & Berendes, H. W. (1998). Risk factors for infant homicide in the United States. New England Journal of Medicine, 339, 1211–1216.

Social and Contextual Risks  93 Parcel, T. L., Dufur, M.J., & Zito, R. C. (2010). Capital and home and school: A review and s­ynthesis. Journal of Marriage and Family, 72, 828–846. Petrill, S. A., Pike, A., Price, T., & Plomin, R. (2004). Chaos in the home and socioeconomic status are associated with cognitive development in early childhood: Environmental mediators i­dentified in a genetic design. Intelligence, 32, 445–460. Podewils, L., Mintz, E., Nataro, J., & Parashar, U. (2004). Acute, infectious diarrhea among c­hildren in developing countries. Seminars in Pediatric Infectious Diseases, 15, 155–168. Pollitt, E. (1996). Timing and vulnerability in research on malnutrition and cognition. Nutrition News, 54 (Supplement 1), S49–S55. Ramachandani, P. Stein, A., Evans, J., & O’Connor, T. (2005), Paternal depression in the postnatal period and child development: A prospective population study. Lancet, 365, 2201–2205. Rasic, D., Hajek, T., Alda, M., & Uber, R. (2013). Risk of mental illness in offspring of parents with schizophrenia, bipolar disorder, and major depressive disorder: A meta‐analysis of family high‐risk studies. Schizophrenia Bulletin, 40, 28–38. Rauh, V. A., Chew, G., & Garfinkel, R. S. (2002). Deteriorated housing contributes to high cockroach allergen levels in inner‐city households. Environmental Health Perspectives, 110, 303–310. Repetti, R. L., Robles, T. F., & Reynolds, B. (2011). Allostatic processes in the family. Development and Psychopathology, 23, 921–938. Repetti, R., Taylor, S., & Seeman, T. (2002). Risky families: Family social environments and the mental and physical health of offspring. Psychological Bulletin, 128, 330–366. Ridener, G. S., & Thurman, S. K. (1994). The effects of prenatal cocaine exposure on mother‐ infant interaction and infant arousal in the newborn period. Topics in Early Childhood Special Education, 14, 217–231. Rivara, F., & Mueller, B. (1987). The epidemiology of childhood injuries. Journal of Social Issues, 43, 13–31. Robila, M., & Krishnakumar, A. (2005). Effects of economic pressure on marital conflict in Romania. Journal of Family Psychology, 19, 246–251. Roda, C., Kousignian, I., Guihenneuc‐Jouyaux, C., Dassonville, C., Nicolis, I, Just, J., & Momas, I. (2011). Formaldehyde exposure and lower respiratory infections in infants: Findings from the PARIS cohort study. Environmental Health Perspectives, 119, 1653–1658. Rogan, W. J., & Ragan, N.B. (2003). Evidence of effects of environmental chemicals on the e­ndocrine system in children. Pediatrics 112, 247–252. Rohner, R. (1986). The warmth dimension. Beverly Hills, CA: Sage. Ronchetti, R., Zuurbier, M., Jesenak, M., Koppe, J., Ahmed U., Ceccatelli, S., & Villa, M. (2006). Children’s health and mercury exposure. Acta Pediatrica, 95 (Supplement 453), 36–44. Rosenbaum, S. (1992). Child health and poor children. American Behavioral Scientist, 35, 275–289. Ross, R., Wagner, B., Heinlein, S., & Zerbe, G. (2008). The stability of inhibitory and working memory deficits in children and adolescent who are children of parents with schizophrenia. Schizophrenia Bulletin, 34, 47–51. Runyan, D., Hunter, W., Socolar, R., Amaya‐Jackson, L., Enclish, D., Landsverk J. … Mathew, R. (1998). Children who prosper in unfavorable circumstances: The relationship to social capital. Pediatrics, 101, 12–18. Ryan, R. M., & Claessens, A. (2015). Associations between family structure change and child behavior problems: The moderating effect of family income. Child Development, 86, 112–127. Ryan, R. M., & Deci, E. L. (2000). Instrinsic and extrinsic motiations: Classic definitions and new directions. Contemporary Educational Psychology, 25, 54–67.

94  Robert H. Bradley Salam, M. H., Li, Y‐F., Langholz, B., & Gilliland, B. D. (2003). Early life environmental risk f­actors for asthma: Findings from the Children’s Health Study. Environmental Health Perspectives, 112, 760–765. Sargent, J. D., Brown, M. J., Freeman, A., Bailey, D., Goodman, D., & Freeman, D. H. (1995). Childhood lead poisoning in Massachusetts’s communities: Its association with sociodemographic and housing characteristics. American Journal of Public Health, 85, 528–534. Scales, P., & Leffert, N. (1999). Developmental assets. Minneapolis, MN: Search Institute. Semple, S., Strathdee, S., Zians, J., & Patterson, T. L. (2011). Methamphetamine‐using parents: The relationships between parental role strain and depressive symptoms. Journal of Studies on Alcohol and Drugs, 72, 954–964. Senut, M‐C., Cingolani, P., Sen, A., Kruger, A., Shaik, A., Hirsch, H. … Ruden, D. (2012). Epigenetics of early‐life lead exposure and effects on brain development. Epigenomics, 4, 665–674. Shonkoff, J. P., Garner, A. S., Siegel, B. S., Dobbins, M. I., Earls, M. F., McGuinn, L., … Wood, D. L. (2012). The lifelong effects of early childhood adversity and toxic stress. Pediatrics, 129, e232–e246. Sirin. S. R. (2005). Socioeconomic status and academic achievement: A meta‐analytic review of research. Review of Educational Research, 75, 417–453. Skalicky, A., Meyers, A. F., Adams, W., Yang, Z., Cook, J., & Frank, D. (2006). Child food insecurity and iron deficiency anemia in low‐income infants and toddlers in the United States. Maternal and Child Health Journal, 10, 177–185. Skinner, A. C., & Slifkin, R. T. (2007). Rural/urban differences in barriers to and burden of care for children with special health care needs. The Journal of Rural Health, 23, 150–157. Smith, L. K. (2010). Child care in rural areas: Top challenges. Available from National Association of Child Care Resource and Referral Agencies, Arlington, VA. Available from http://www. naccrra.org/sites/default_site_pages/2010/rural_topconcerns_070910.pdf Solari, H., Dickson, K., & Miller, L. (2009). Understanding and treating women with schizophrenia during pregnancy and postpartum. Canadian Journal of Clinical Pharmacology, 16, e23–e32. Solis, J., Shadur, J., Burns, A., & Hussong, A. (2012). Understanding diverse needs of children whose parents abuse substances. Current Drug Abuse Review, 5, 135–147. Sood, B., Delaney‐Black, V., Covington, C., Nordstrom‐Klee, B., Templin, T., Janisse, J. … Sokol, R. J. (2001). Prenatal alcohol exposure and childhood behavior at age 6 to 7 years: I. Dose‐ response effect. Pediatrics, 108, e34. Spagnola, M., & Fiese, B. H. (2007). Family routines and rituals, A context for development in the lives of young children. Infants and Young Children, 20, 284–299. Starfield, B. (1982). Family income, ill health and medical care of U.S. children. Journal of Public Healthy Policy, 3, 244–259. Stein, J., Schettler, T., Wallinga, D., & Valenti, M. (2002). In harm’s way: Toxic threats to child development. Journal of Developmental and Behavioral Pediatrtcs, 23, S13–S22. Strohschein, L. (2007). Challenging the presumption of diminished capacity to parent: Does divorce really change parenting practices? Family Relations, 56, 358–368. Suglia, S.F., Duarte, C. S., Sandel, M. T., & Wright, R. J. (2010). Social and environmental s­tressors in the home and childhood asthma. Journal of Epidemiology and Community Health, 64, 636–642. Swenson, K. (2008). Child care arrangement in urban and rural areas. PB2009102944. Washington, DC: Department of Health and Human Services, Office of the Assistant Secretary for Planning and Evaluation. Temple, J. A. (2009). Rural gaps in participation in early childhood education. Journal of Agriculture and Applied Economics, 41, 403–410.

Social and Contextual Risks  95 Tennen, H., & Affleck, G. (1990). Blaming others for threatening events. Psychological Bulletin, 108, 209–232. Testa, M., Quigley, B. M., & Eiden, R. D. (2003). Effects of prenatal alcohol exposure in infant mental development: A meta‐analytic review. Alcohol and Alcoholism, 38, 295–304. Thelen, E. (2005). Dynamic systems theory and the complexity of change. Psychoanalytic Dialogues, 15, 255–283. Tyler, C., & Allan, A., (2014) The effects of arsenic exposure on neurological and cognitive d­ysfunction in human and rodent studies: A review. Current Environmental Health Report, 1, 132–147. UNICEF Innocenti Research Centre (2012). Measuring child poverty: New league tables of child poverty in the world’s rich countries. Innocenti Report Card 10, UNICEF Innocenti Research Centre, Florence, Italy. United Nations (2012). The Millennium development goals report 2012. United Nations Development Programme. (2013). Human development report 2013, The rise of the south. Available from hdr.undp.org/en/reports/global/hdr2013 US Bureau of Census (2011). Children’s living arrangements and characteristics: March 2011, Table C8. Washington DC. US Department of Health and Human Services. (2000). Child health USA 2000. Washington, DC: US Government Printing Office. Van den Hooven, E., Pierik, F., de Kluizenaar, Y., Hofman, A., van Ratingen, S. … Jaddoe, V. (2012). Air pollution exposure and markers of placental growth and function: The Generation R study. Environmental Health Perspectives, 120, 1753–1759. van Ijzendoorn, M. H., Schuengel, C., & Bakermans‐Kranenburg. M. J. (1999). Disorganized attachment in early childhood: Meta‐analysis of precursors, concomitants, and sequelae. Development and Psychopathology, 11, 225–249. Vernon‐Feagans, L., Garrett‐Peters, P., Willoughby, M., Mills‐Koonce, R., & the Family Life Project Key Investigators (2012). Chaos, poverty, and parenting: Predictors of early language development. Early Childhood Research Quarterly, 27, 330–351. Vreugdenhil, H. J., Slijper, F., Mulder, P., & Weisglas‐Kuperus, N. (2002). Effects of perinatal exposure to PCBs and dioxins on play behavior in Dutch children at school age. Environmental Health Perspectives, 110, A593–A598. Vrijheid, M., Dolk, H., Stone, D., Alberman, E., & Scott, J. (2000). Socioeconomic inequalities in risk of congenital anomaly. Archives of Diseases in Children, 82, 349–352. Wachs, T. D. (2000). Necessary but not sufficient. Washington, DC. American Psychological Associaation. Walker, S., Wachs, T., Gardner, J., Lozoff, B., Wasserman, G., Politt, E. … the International Child Development Steering Group. (2007). Child development: Risk factors for adverse outcomes in developing countries. Lancet, 369, 145–157. Wallerstein, J., & Lewis, J. (2007). Disparate parenting and step‐parenting with siblings in the post‐divorce family: Report from a 10‐year longitudinal study. Journal of Family Studies, 13, 224–235. Warsito, O., Khomsan, A., Hernawati, H. B., & Anwar, F. (2012). Relationship between nutritional status, psychosocial development in preschool children in Indonesia. Nutrition Research and Practice, 6, 451–457. Weisner, T. S. (2002). Ecocultural understandings of children’s developmental pathways. Human Development, 45, 275–281. Weiss, B., Amler, S., & Ambler, R. W. (2004). Pesticides. Pediatrics, 113, 1030–1036. Wells, N. M., & Harris, J. D. (2007). Housing quality, psychological distress, and the mediating role of social withdrawal: A longitudinal study of low‐income women. Journal of Environmental Psychology, 27, 69–78.

96  Robert H. Bradley White, L. D., Cory‐Slechta, D. A., Gilbert, M. E., Tiffany‐Castiglioni, E., Zawia, N. H., Virgolini, M. … Basha, M. R. (2007). New and evolving concepts in the neurotoxicology of lead. Toxicology and Applied Pharmacology, 225, 1–27. Williams, T., & Mann, T. L. (2011). Early childhood education in rural communities: Access and q­uality issues. Fairfax, VA: UNCF/Frederick D. Paterson Research Institute. Available from http://www.ruraledu.org/user_uploads/file/EarlyChildhood.pdf Wilson, S., & Durbin, C. E. (2010). Effects of paternal depression on fathers’ parenting behaviors: A meta‐analytic review. Clinical Psychological Review, 30, 167–180. Wood, J., Repetti, R., & Roesch, S. (2004). Divorce and children’s adjustment problems at home  and school: The role of depressive/withdrawn parenting. Child Psychiatry and Human Development, 35, 121–142. World Bank. (2015). World development report 2015: Mind, society, and behavior. Washington, DC: World Bank. World Health Organization. (2005). Effects of air pollution on children’s health and development. Available [email protected] World Health Organization. (2006). Child maltreatment and alcohol. Available from http://www. who.int/substance_abuse Zolkoski, S. M., & Bullock, L. M. (2012). Resilience in children and youth: A review. Children and Youth Services Review, 34, 2295–2303.

PART II Theoretical and Empirical Contexts of Applied Developmental Science of Early Childhood

chapter FIVE From the Lab to the Contexts in which Children Live and Grow: Historical Perspectives on the Field Pamela A. Morris and Maia C. Connors

Introduction Since the mid to late 1970s, the field has made tremendous strides in research in the area of applied early childhood development. While the growth of the scientific knowledge in the field certainly cannot be tied to a single researcher or team of researchers, there is one scholar whose repeated calls for improvements in research on human development were undeniably a central force in the development of the field. That scholar was Urie Bronfenbrenner. Urie Bronfenbrenner changed the way in which scientists theorize about and conduct work in human development. In his formulation of the bioecological theory of human development (Bronfenbrenner, 1979), he developed a theoretical paradigm that emphasized the dynamic relations between the individual and a multi‐leveled ecological context. His emphasis on theory, multidisciplinary scholarship, research designs that both test as well as formulate hypotheses, and the interplay between science and policy has powerfully transformed research in human development and its application in programs serving children and families.

The Wiley Handbook of Early Childhood Development Programs, Practices, and Policies, First Edition. Edited by Elizabeth Votruba-Drzal and Eric Dearing. © 2017 John Wiley & Sons, Inc. Published 2017 by John Wiley & Sons, Inc.

100  Morris and Connors Nowhere is his contribution more pronounced than in the field of early child development. Bronfenbrenner’s strong commitment to improving the human condition, and his special role in the development of the Head Start program, resulted in careful attention to early childhood development, in general, and preschool programs and policy, in particular. Bronfenbrenner’s theories moved the field from the study of “the science of the strange behavior of children in strange situations with strange adults for the briefest possible periods of time” (Bronfenbrenner, 1977, p. 513) to the study of individuals in the natural contexts in which development occurs. In this chapter, we reflect on the scientific history at the time that Bronfenbrenner made his famous call for research outside of the laboratory, review progress that has been made since that initial call, and discuss areas that still require further development. We focus on five key areas that were a central focus of Bronfenbrenner’s “calls” for the scientific community: (1) the move from the lab to the field; (2) the study of context; (3) the “bio” component of the bio‐ecological model; (4) the measurement of proximal processes; and (5) the transforming experiment. In reflecting on progress in each of these areas, our central (but not exclusive) focus is on research on children’s development in the  preschool context. We do this given Bronfenbrenner’s strong commitment to this particular area of research and policy (indeed, he devoted an entire chapter in the Ecology of Human Development to research on preschool). Our review suggests that while there has been substantial progress since the time of Bronfenbrenner’s major contributions, we still have a long way to go to fully meet the challenges that Bronfenbrenner raised to the field over four decades ago.

From the Lab to the Field In the 1960s and 1970s, just before Bronfenbrenner published his famous statement about the focus of developmental psychology on the “strange behavior of children in strange situations,” much (although certainly not all) of contemporary developmental psychology research took place in the lab setting. Children’s behavior was observed – often through a one‐way mirror – and their interaction with objects, their parent, or a stranger, was carefully and reliably observed, recorded, analyzed, and interpreted. These contexts in which children engaged were highly scripted and organized, so as to “standardize” all but the child’s behavior. Examples of popular assessments of the time include the Strange Situation paradigm, which was developed in 1969 to study attachment behavior (Ainsworth & Wittig, 1969), and Piagetian tasks conducted in the 1970s and 80s to study children’s cognitive development (Bremner, 1985; Field, 1987). The theory volume of the 1983 Handbook of Child Psychology, in which Bronfenbrenner published his first “ecological model,” also included chapters by Piaget, Siegler, and Stevenson (Mussen, 1983). The volume placed a heavy focus on the development of cognitive processes and learning and relied heavily on lab‐based experiments to build that knowledge. We would be mistaken to assume that his call meant that observational studies in the field did not exist during this time. Indeed, his call surely did not imply that lab‐based

Historical Perspectives on the Field  101 research was unimportant in building our knowledge of child development. In fact, there are some important examples of observational studies in the field, especially in research on preschool contexts. Much of the preschool research at this time was focused on understanding the relative merits of curricular approaches with the goal of identifying key ingredients of successful preschool programs (Weikart, 1969), and thus was necessarily conducted in the preschool settings themselves. Several studies conducted in the late 1960s found that highly structured or “task‐oriented” curriculum models were most strongly associated with children’s learning (Curtis & Berzonsky, 1967; Deutsch, 1968; Di Lorenzo & Salter, 1968; Hodges, McCandless, & Spicker, 1967; Karnes, Teska, & Hodgins, 1969; Klaus & Gray, 1968; Weikart, 1967). A comparison of three theoretically‐distinct curricular models conducted by Weikart in 1969 hypothesized that any such model would be effective within a program in which teachers collaborated with mothers, were “creatively involved” in program operation, and were supported to “intensively think about each child” (Weikart, 1969, p. 16). Today, we might reference these same concepts in discussing the importance of program implementation, including family involvement, teacher buy‐in and decision‐making, individualization of the curriculum, and use of child observation and assessment data. Of course, perhaps the most notable example of an experimental study of children (in Bronfenbrenner‐like terminology) in relevant situations (preschool programs) with familiar adults (teachers and parents) for extended periods of time (two years, initially) is the randomized controlled trial of the Perry Preschool Program in the early 1960s (Weikart, 1967; Weikart, Deloria, Lawser, & Weigerink, 1970). Given that these efforts did exist, and certainly Bronfenbrenner knew about them, why did he lament the lack of ecological validity in research in child development? Why did he not highlight the research just cited, but instead raise concerns about studies of children in strange situations with strange adults for the briefest periods of time? Our best guess is that while naturalistic studies were conducted in the field, these studies did not drive the field’s understanding of human development. That is, while studies existed “in the field,” the key contributions about developmental phenomena came from research in which children’s behavior and learning was observed in highly controlled environments. Research “in the field” is now commonplace, and perhaps as importantly, it is being utilized to understand development. Children are now routinely observed in the settings in which they engage, such as in their homes and in preschool settings. This is particularly important, as Bronfenbrenner’s early writings stressed the need for greater “relevance” of the work that would lead to greater “ecological validity.” Indeed, he writes that by “… removing the child from the environment in which he ordinarily finds himself and placing him in another setting which is typically unfamiliar, short‐lived, and devoid of the persons, objects, and experiences that have been central in his life, we are getting only a partial picture of both the child and his environment” (Bronfenbrenner, 1974, pp. 3–4). To Bronfenbrenner, this was not a small exercise in ensuring ecological “validity,” but a much larger issue: by only making predictions about human development from the lab context, we had dramatically and unequivocally misrepresented the potential of human development. From his perspective, a small study of children’s behavior in context was much more powerful for building an understanding of development than a large, well‐controlled lab experiment could ever be.

102  Morris and Connors Given some movement, then, on research “from the lab to the field,” what about our progress in acknowledging an ecological framework, unarguably what Bronfenbrenner is most famous for? That is, how have we fared in making progress on addressing the role of context in our more “ecologically valid” studies?

The Study of Context Bronfenbrenner conceived of the “ecology of human development” as encompassing both human behavior and the environment as inextricably entwined components of one system (Bronfenbrenner, 1977). In 1979, he described the ecological environment as “a set of nested structures, each inside the next like a set of Russian dolls” (Bronfenbrenner, 2005, p. 50). The full iterations of this theory, as first described in his influential book The Ecology of Human Development: Experiments by Nature and Design (1979), encompassed not only the most proximal contexts and interactions, but also ever more distal layers of relationships, activities, physical settings, politics, economy, and cultures surrounding a developing person. More specifically, Bronfenbrenner described four interrelated ecological levels: (1) the microsystem, which contains the developing individual; (2) the mesosystem, the interrelationships between microsystems; (3) the exosystem, those contexts that do not directly involve the developing person, but have an influence on the microsystem; and (4) macrosystems, the highest level of the ecological model, involving culture, policy, and other macro‐institutions that create consistency in the underlying systems. In doing so, he incorporated the contexts that were typically the domain of other social science disciplines, and, in linking them to the individual, invited interdisciplinary scholarship on human development across the life course. Bronfenbrenner pointed out that, in order to study such complex contexts and systems, it would be necessary to expand the field’s scientific methods beyond the common practice of the time. He argued that research must reflect the multifaceted, nested structure of the ecological environment: “the understanding of human development demands going beyond direct observation of behavior on the part of one or two persons in the same place; it requires examination of multiperson systems of interaction not limited to a single setting and must take into account aspects of the environment beyond the immediate situation containing the subject” (Bronfenbrenner, 2005, p. 514). So the problem wasn’t just the “strangeness” of the situation, it was the extent to which we rarely assessed all the relevant ecological levels of the system, even when we succeeded at studying children in context. Bronfenbrenner cautioned that it is essential that components of the ecological environment are (1) assumed to be interdependent and (2) analyzed as such. Importantly, simple regression analyses, in which the main effect of one variable on an outcome variable is the primary coefficient of interest, are likely not up to the task. As such, he posited that “in ecological research, the principal main effects are likely to be interactions” (Bronfenbrenner, 2005, p. 518). Toward this end, Bronfenbrenner proposes that experimental designs explicitly take into account the realities of the multiperson systems (N + 2 systems) in which people grow and develop, noting that “such larger systems must be

Historical Perspectives on the Field  103 analyzed in terms of all possible subsystems (i.e., dyads, triads, etc.) and the potential second‐ and higher‐order effects associated with them” (Bronfenbrenner, 1977, p. 520). Yet as the number of people increases to one that may reasonably approximate most developmental settings (e.g., a family with multiple children or a classroom), this becomes extraordinarily difficult to do well. In fact, in 1977 Bronfenbrenner could not find any examples of studies that had even attempted to explore such questions in systems including more than three people. Moreover, examining these interactions across multiple settings increases the complexity even further. And thus the question arises: How much progress have we made since Bronfenbrenner’s 1979 seminal book? That is, do we examine each interconnected layer of the environment (i.e., Russian dolls of every size)? In this area the field has made relatively uneven progress. A great deal of recent work has focused on either the most proximal micro‐level contexts for young children or the most distal macro‐level contexts (Bradley, 1995; Brooks‐Gunn, Duncan, Klebanov, & Sealand, 1993; Dubow & Ippolito, 1994; Duncan, Brooks‐Gunn, & Klebanov, 1994; Korenman, Miller, & Sjaastad, 1995). At the micro level, there is a large body of literature on measuring classroom quality and understanding its relationship with children’s learning and development; similar progress has been made in the home environment. And at the macro level, there have been many studies of key policies and funding streams as well as neighborhood level effects. Overall, the micro‐level context literature has found small but consistent associations between high quality preschool classrooms and improved child outcomes, although there is some disagreement about the magnitude of these associations and which elements of quality are most important. For example, some correlational research suggests that classroom process quality – or the quality of the interactions, relationships, conversations, and other social processes between teachers and children – may be particularly important for children’s development (e.g., Burchinal, Vandergrift, Pianta, & Mashburn, 2010). This empirical evidence supports Bronfenbrenner’s assertion that proximal processes are the “engines of development” (Bronfenbrenner & Morris, 2006). And, a separate body of research has focused on a different proximal environment for children’s learning: the quality of the home environment. This literature finds that the quality of learning materials and physical environment in the home predict children’s learning and development (Bradley, 1995; Brooks‐Gunn et al., 1993; Dubow & Ippolito, 1994; Duncan et al., 1994; Korenman, Miller, & Sjaastad, 1995). Yet another set of research has thoroughly explored classroom‐level interventions – such as coaching or education for teachers or new curricula – that can improve classroom quality (Boller, Atkins‐Burnett, Malone, Baxter, & West, 2010; Coburn & Russell, 2008; Neuman & Cunningham, 2009; Pianta, Mashburn, Downer, Hamre, & Justice, 2008; Ramey et al., 2011; Raver et al., 2008). These studies exemplify those that thoroughly examine the proximal environment surrounding a young child, but fail to account for more distal layers of the ecological system. Although these more distal layers are sometimes controlled for in order to isolate classroom‐level factors (e.g., Pianta et al., 2005), they typically are not examined explicitly in studies focused on the classroom environment. However, they are likely to play a substantial role in the quality of a classroom and in the success or failure of quality improvement interventions. For example, how often have we seen studies of classrooms that don’t look

104  Morris and Connors at what is happening in the lunchroom, at recess, in the neighborhood on the way to school? How often do we ignore what is happening in the afterschool program that the child is also in? In contrast, other research, such as the Head Start Impact Study or studies of federal child care subsidies (Herbst & Tekin, 2010; Puma et al., 2010; Ryan, Johnson, Rigby, & Brooks‐Gunn, 2011), focus instead on the macro policy context within which young children grow and learn. In these studies, policies – such as the early childhood funding stream – are treated as the primary context in which early childhood care and education programs exist. The assumption is that these policies will influence not only children’s access to these programs, but also programs’ internal structure, quality, and practices (Bronfenbrenner & Morris, 2006; Elmore & Burney, 1999; Fullan, 2000). Thus studies of these macro‐level policy contexts have treated a wide variety of variables as outcomes: including children’s access to formal early childhood education, classroom quality, and children’s learning and development (Herbst & Tekin, 2010; Hotz & Xiao, 2011; Puma et al., 2010; Ryan et al., 2011). In this way, the early childhood field has begun to explore the ways in which these macro and micro contexts are related to one another. Of course, a substantial body of work exists on how macro contexts affect families and children, especially as exemplified in the work on neighborhood effects that exists across the psychological, sociological, and economic literatures (Brooks‐Gunn, Duncan, & Aber, 1997; Caughy, Hayslett‐McCall, & O’Campo, 2007; Leventhal & Brooks‐Gunn, 2000). And in the area of early care and education that was Bronfenbrenner’s focus, much policy focuses on improving classroom quality as an important lever for improving child outcomes (Barnett, Carolan, Squires, & Clarke Brown, 2013; NACCRRA, 2011; Tout et al., 2010). In practice, this has manifested as a recent proliferation of regulations regarding classroom quality across a wide range of federal programs such as Head Start (Improving Head Start for School Readiness Act, 2007) and the Child Care and Development Block Grant (2014) and state initiatives such as child care licensing regulations (Payne, 2011) and quality rating and improvement systems (Tout et al., 2010). This approach is born from the wealth of correlational research that suggests that (1) high quality classrooms are linked to positive outcomes for young children (discussed earlier), and (2) that classroom quality can be regulated via macro‐level policies (Puma et al., 2010; Ryan et al., 2011). One corollary of this line of work is the assumption that all classrooms subject to the same policy‐imposed standards, or funded through the same funding mechanism, can thus be thought of as belonging to one holistic “program” despite being spread across hundreds or thousands of different program sites. This assumption is particularly important for studying the impacts of such policy interventions because it suggests that there will be less variation in classroom quality within these “programs” than between “p­rograms” that are subject to different policy standards and regulations. While this may be true to a degree, an infinite number of other interrelated elements at all levels of the ecological environment – especially those that fall between the distal policy context and the micro context of the classroom  –  vary across local program sites within these macro‐level “programs.” Bronfenbrenner’s ecological model reminds us that all settings are embedded within a broader context, and that distal as well as proximal mechanisms influence development

Historical Perspectives on the Field  105 and learning (Bronfenbrenner, 1979; Bronfenbrenner & Morris, 1998, 2006). Within an early childhood program, the classroom is the most proximal context for children’s development, but when it is the quality of that context that policy aims to change, it must account not only for children’s development, but for teachers’ as well. The most proximal context for teachers’ learning and development is not the classroom, but rather the broader professional environment of the preschool program (Connors, 2016). Thus it follows that in order to improve classroom quality and help even the most qualified teachers reach their full potential, policy may also need to target factors outside of the classroom. Small bodies of research exploring the importance of (1) site‐level factors (Gerber, Whitebook, & Weinstein, 2007; Phillips, Mekos, Scarr, McCartney, & Abott‐Shim, 2000) and (2) neighborhood‐level factors (Dupéré, Leventhal, Crosnoe, & Dion, 2010; McCoy, Connors, Morris, Yoshikawa, & Friedman‐Krauss, 2015; Phillips, Voran, Kisker, Howes, & Whitebook, 1994), provide empirical evidence that these “exo‐level” factors are instrumental in shaping the most proximal contexts in which children live, and ultimately children’s learning and development as well. For example, observational studies have found that program characteristics, such as teachers’ wages (Phillips et al., 2000) and program size (Gerber et  al., 2007) are strongly associated with observed classroom quality and teachers’ sensitivity with the children in their classrooms. As a field, early childhood researchers have historically paid little attention to measuring and investigating these exo‐level school contexts. Similarly, many current policies are still crafted based on a theory of change that assumes that classroom quality must be regulated directly, and thus do not address elements that are only slightly more distal to children: those in the professional environment of the early childhood program site or local program that are likely instrumental in effecting change. Yet the recent introduction of several innovative tools for measuring the quality of the professional environment (e.g., Program & Business Administration Scales; Talan & Bloom, 2009, 2011; Early Childhood Work Environment Survey; Bloom, 2010; and Supportive Environmental Quality Underlying Adult Learning; Whitebook & Ryan, 2012) have facilitated increased focus on these topics, and hopefully will lead to their inclusion in many more studies in the near future. And so we must conclude that we are doing a better job of studying the micro and macro levels than we are in the intervening levels. It is not that we cannot find important examples of such work, but that these critical studies are not yet commonplace in the field. But Bronfenbernner’s contribution wasn’t only about the micro, meso, exo, and macro‐ levels of an ecological model that he introduced to the field in his 1979 book (Bronfenbrenner, 1979) and 1983 chapter in the Handbook of Child Psychology (Bronfenbrenner & Crouter, 1983). His work continued to evolve through the 1980s and 90s and in the 1998 Handbook, he expanded two key aspects of his theory: First, he expanded the discussion of the “person” characteristics, and thus renamed his model the “bio‐ecological model.” Second, he argued that the “center of gravity” of the model had shifted to “proximal processes,” which, he argued, were key drivers of developmental change. Indeed, the model he develops nests the individual in an ecological framework, but considers change in development as a function of the exchanges between the individual and an actively changing context. Thus we next address developments in the bodies of research within each of these areas to gauge our progress as a field.

106  Morris and Connors

The Bioecological Model In regards to the bio component of the bioecological model, Bronfenbrenner argued that we had not focused enough on the characteristics of developing individuals, suggesting that “existing developmental studies have provided far more knowledge about the nature of developmentally relevant environments, near and far, than about the characteristics of developing individuals, then and now” (Bronfenbrenner, 1989, p. 188). This is perhaps the area of research that has seen the greatest development over the last decade. We now understand much more than we previously did about the “biological” system of the developing person, especially through recent advances in neuroscience. Moreover, this work has not only expanded our understanding of all “levels” of the person, from the biological to the behavioral, but also has allowed us to re‐conceptualize the way we think about the interaction between person and environment. These developments have perhaps been most pronounced in the area of stress‐sensitivity to the environment, and thus we turn our attention to that research next (for a review, see Ganzel, Morris, & Wethington, 2010). This work, drawn primarily from animal models, has shown the power of maternal behavior in shaping both the behavior, as well as underlying neurobiology, of developing offspring. For example, research on rats has shown that during the initial period of a rat pup’s life, gradients in levels of maternal care (e.g., the extent of licking, grooming, and arched‐back nursing that mothers offer to their pups; maternal “responsiveness” in Bronfenbrenner’s language) are associated with changes in offsprings’ stress physiology and behavior that extend into adulthood (for reviews, see Diorio & Meaney, 2007; Levine, 2000). That is, rat pups who receive high levels of maternal care during the first postnatal week grow up to show lower hypothalamic‐pituitary‐adrenal (HPA) axis response and less fear of novelty as adults, relative to adult pups of mothers with low levels of maternal care (e.g., Caldji, Diorio, & Meaney, 2000; Liu et al., 1997). Notably, although low‐responsive mothers are more fearful and have more reactive HPA axis responses (and the converse is true for high‐responsive mothers), the distinct biobehavioral traits of their pups are not a function of genomic inheritance – but instead a direct consequence of maternal caregiving during a sensitive period of development. When the offspring of low‐responsive mothers are fostered by high‐responsive mothers, they are less fearful as adults and show lower HPA response – the opposite is true when pups of high‐responsive mothers are fostered by low‐responsive dams (Francis, Diorio, Liu, & Meaney, 1999). Together, these data provide compelling evidence that, in rodents, mothers’ behavior (or proximal processes within the micro‐system, as Bronfenbrenner would have described it) within a narrow developmental window (i.e., timing matters) is responsible for organizing lasting differences in the offsprings’ underlying neurobiology that is manifest in behavioral correlates of the pup. What is even more interesting, perhaps, is that Weaver and colleagues (2004) have shown that these effects carry forward to the next generation through epigenetic processes. That is, these processes occur through an interaction between the person and the environment that is conceptually distinct from that originally described by Bronfenbrenner in his writings. More specifically, environmental conditions (in this case maternal behavior in the form of responsive caregiving among the rat mothers toward their pups) are “remembered”

Historical Perspectives on the Field  107 in terms of their behavioral and physiological responses in the next, subsequent, generation through the regulation of gene expression (rather than by changes in DNA sequence; Harper, 2005). More specifically, stable alterations in the DNA methylation patterns of the exon 17 glucocorticoid receptor promotor affect the hippocampus’ ability to regulate glucocorticoid negative feedback on the HPA axis, which produce the increases in HPA‐ axis response discussed above, for pups who experience low responsive maternal care (Weaver et al., 2004). Notably, severe food shortages during pregnancy have been shown to have similar lasting effects on later health (physical growth; Susser & Stein, 1994), suggesting that parallel processes may take place among humans. In this way, epigenetics serves as the process by which early experiences carry forward lasting modifications to individuals’ responses to stressors that arise later in development. But how has the interaction between biology and environment been conceptualized among humans? Building from these findings in rats, as well as other research, Boyce and Ellis (2005) developed the biological sensitivity to context theory, which posits that some individuals are more “sensitive” to environmental influences than others. As such, they argue that stress reactivity has the potential for negative effects under conditions of adversity and positive effects under conditions of supportive environments. That is, individuals with highly reactive phenotypes are highly responsive to both the threats and dangers in high stress environments, as well as to the social supports in highly supportive environments. It is this increased vigilance and awareness of environmental influence that results in their highly divergent outcomes across contexts. Moreover, these biological predispositions are not a given at birth – they are, in part, the result of adaptations of stress‐response systems to the environment through phenotypic plasticity (Boyce & Ellis, 2005). Thus, in Bronfenbrenner’s terminology, proximal processes can produce changes in a child’s development through changes in neurobiology. But the “interaction” between genes and environment is not at all as Bronfenbrenner (or the field) understood at the time of his development of the bio‐ecological model in 1998. Rather than a person characteristic – a force, resource, or demand characteristic of the person (Bronfenbrenner & Morris, 1998) – that interacts (statistically) with the environment, promoting development in some contexts and impinging on it in others, these advances show how the environment can actually shape biology. In the case of rat pups, alterations in the methylation of this glucocorticoid receptor gene set off a chain of neural and hormonal processes that result in increases in HPA‐axis response and its behavioral correlates in rats exposed to suboptimal care. In the case of humans, environments may lead to changes in responsivity through phenotypic plasticity. Bronfenbrenner’s writings never imagined the nature of environmental interaction with biology that these findings have revealed. But he (as well as others) certainly encouraged this work with a call to focus on the person – the person not just as an outcome, but as a shaper of experience (Bronfenbrenner & Morris, 1998).

Proximal Processes According to Bronfenbrenner, perhaps the most critical experiences that shape development are proximal processes. Thus, we now turn to the second area of development of the bioecological model, and a key contribution of this model to developmental theory.

108  Morris and Connors Proximal processes became central in Bronfenbrenner’s model during the time he was working on the 1998 Handbook chapter (Bronfenbrenner & Morris, 1998). Bronfenbrenner termed these proximal processes the “primary engines of development” – and defined them as “progressively more complex reciprocal interaction between an active, evolving biopsychosocial human organism and the persons, objects, and symbols in its immediate external environment … [that] occur on a fairly regular basis over extended periods of time” (Bronfenbrenner & Morris, 2006, p. 797). The key component of this definition is that the developing child who is benefiting from the proximal processes is not passive, but rather active and engaged. As such, proximal processes are, at their very core, reciprocal interactions. This idea of reciprocity in proximal processes has important implications not only for best practice in early childhood education (e.g., engaging children in conversations and cooperative play), but also for research. In 1977, Bronfenbrenner reminded us that “… an ecological experiment must allow for reciprocal processes; that is not only the effect of A on B, but also the effect of B on A” (p. 519). Nearly four decades ago he lamented that “while the thesis that most behavior in social situations is reciprocal is generally accepted in principle, it is often disregarded in practice” (Bronfenbrenner, 1977, p. 519). Save for a few notable exceptions, this is still very true of current early childhood research. We rarely study preschool classrooms with strong measures of reciprocal processes that capture this dynamic micro‐system in the way Bronfenbrenner would have described it. Indeed, while preschool classrooms are proximal contexts filled with reciprocal interactions between and among teachers and children, our measures of classroom quality – even of what has come to be termed process quality – are often best described as measures of individual teacher behavior. For example, the Caregiver Interaction Scale (Arnett, 1989) is a self‐proclaimed measure of a teacher’s interactions with children, yet it measures only the teachers’ behavior and not that of the children. Conversely, there are also many examples of measures of children’s behavior, including some completed by a teacher about children’s behavior in a classroom, that are attributed only to the child, and not to his or her teacher (e.g., Child Behavior Checklist, Achenbach & Rescorla, 2000; Adjustment Scales for Preschool Intervention, Lutz, Fantuzzo, & McDermott, 2002). Moreover, these two groups of measures are most often analyzed within the classic framework of examining the effects of A (teacher’s behavior) on B (children’s behavior) (Burchinal & Cryer, 2004; Dunn, 1993; Peisner‐Feinberg et al., 2001). Rarely is the analysis reversed, although a few notable exceptions do exist (e.g., Friedman‐Krauss, Raver, Morris, & Jones, 2014). Perhaps the biggest strides towards measuring adult and child behaviors as the reciprocal processes that they are have been made by Pianta and colleagues, in their development of the well‐known Classroom Assessment Scoring System (CLASS; Pianta, La Paro, & Hamre, 2008). This measure is designed to assess the quality of classroom processes based explicitly on both teachers and children and their reciprocal interactions. Scoring of CLASS indicators is based not only the behavior of adults, but also of the children in the classroom. For example, one indicator of high quality positive climate specifies that “[t]he teacher and students consistently demonstrate respect for one another” (Pianta et  al., 2008, p. 23). Other indicators explicitly address the reciprocal nature of teacher‐student interactions: “There are frequent feedback loops – back‐and‐forth exchanges – between teachers and students” (Pianta et al., 2008, p. 69). As such, the CLASS is probably the best

Historical Perspectives on the Field  109 and most complete example of a true measure of the “proximal processes” in the education context; it allows us to examine the effect of “good processes” on developmental outcomes. Another notable example of a measure that is explicitly transactional in nature is Pianta’s Student‐Teacher Relationship Scale (STRS; Pianta, 2001). The STRS is a measure of a relationship, rather than any one individual’s behavior or attributes. The scale is completed by the teacher regarding his or her relationship with one focal child, and nearly every item includes both the words “this child” and “I” or “me” (e.g., “If upset, this child will seek comfort from me”; Pianta, 2001). Both of these examples represent a substantial step forward from work on children of low birthweight conducted by Drillien (1964) and cited by Bronfenbrenner (Bronfenbrenner & Morris, 2006), which measured mother‐infant interaction based only on mother’s responsiveness. They are also a substantial improvement on prior measures of process quality in early care settings, which focus only on teachers’ behavior. However, these measures still may not be as dynamic as is required to answer Bronfenbrenner’s call to study the dynamic interplay of teachers and children in preschool settings. The measures discussed earlier are most often employed in the same constrained way as are more traditional tools – ascribed to either adults’ or children’s behavior and used to predict one from the other (e.g., Mashburn et  al., 2008; Rimm‐Kaufman, La Paro, Downer, & Pianta, 2005; Rimm‐Kaufman, Curby, Grimm, Nathanson, & Brock, 2009). In fact, CLASS scores have often been used to assess teacher “quality” without recognition of the fact that such scores do change with new cohorts of children (Friedman‐Krauss et al., 2014; Horn, Mancini, Mattera, & Morris, 2015) and thus are better measures of classroom quality than teacher quality. Moreover, the back‐and‐forth of teacher and child behavior is lost with the molar measures of the CLASS. That is, imagine what we might learn if we examined teachers with the same precision that we have studied parent‐child interactions. In the parent‐child literature, molecular study of children with behavior problems has shown the ways in which parents and children can become caught in coercive cycles of negative behavior (Patterson, 1982; Dishion, French, & Patterson, 1995): adults inadvertently exacerbating children’s aggressive behavior through harsh and ineffective limit‐setting techniques, children responding with increasingly aversive behavior, and adults, exasperated, stopping their own attempts at controlling behavior, thus reinforcing the children’s negative behavior (Dishion, French, & Patterson, 1995). Studying this coercive interactional pattern helped the field to learn about parent‐child interactions and to address questions about their dynamic relationships, and similarly, there is much to be learned from the analogous study of children’s interactions with teachers and other adults in the preschool context.

The Transforming Experiment One final aspect of Bronfenbrenner’s call is less well known than his bioecological model, but perhaps as important, and certainly critical to the field of early childhood education. Bronfenbrenner asked not only for more studies of the components of the bioecological system, but also for “tests” of the system as a whole. To do this, he proposed what is now

110  Morris and Connors generally regarded as “policy experimentation.” He writes: “To the extent that we include ecological contexts in our research, we select and treat them as sociological givens rather than as evolving social systems susceptible to significant and novel transformation” (1977, p. 528). In the 1970s, at the time of his writings, “experiments” of new contexts were rare, especially in the area of preschool programming (although Perry Preschool is a notable exception). In his 1977 article, he recalled advice he received from Walter Fenno Dearborn, his first doctoral mentor, who told him: “Bronfenbrenner, if you want to understand something, try to change it” (p. 517). Or, as he later wrote in his own words: “If you wish to understand the relationship between the developing person and some aspect of his or her environment, try to budge the one, and see what happens to the other” (1977, p. 518). Are we now engaged in the use of social experimentation as a means to test the relation of individual to contextual factors? Well, yes. But perhaps not exactly in the way Bronfenbrenner would have liked, and, unfortunately, not in the way that will advance our understanding of human development most effectively. In fact, perhaps influenced in large part by economics, developmental psychologists have increasingly gravitated to the use of randomized experiments in the field to gain understanding of causal processes. For example, randomized studies of parents’ income and employment have tested whether income matters (Duncan, Morris, & Rodrigues, 2011); randomized studies of parenting programs have helped us understand how changes in parenting behavior can lead to changes in outcomes for children (Dishion & Stormshak, 2007; Spoth, Kavanagh, & Dishion, 2002); randomized experiments in education have explored the “impact of curricula” and the effects of macro‐level policies (see later). Indeed, several experimental or quasi‐experimental studies have been conducted over the past several decades to test place‐ specific preschool programs. These include studies of the Abecedarian Project in the 1970s (Ramey & Smith, 1977), Chicago Child‐Parent Centers in the 1980s (Reynolds, 2000a; 2000b), New Jersey’s Abbott Preschool Program (Frede, Jung, Barnett, Lamy, & Figueras, 2007) and Tulsa’s Pre‐K Program (Gormley, Gayer, Phillips, & Dawson, 2005) in the early 2000s, and most recently Boston’s public pre‐kindergarten program (Weiland & Yoshikawa, 2013). Other studies have also estimated the impacts of federal early childhood funding streams  –  such as Head Start (Puma et  al., 2010) and Early Head Start (Vogel, Xue, Moiduddin, Carlson, & Kisker, 2010) – on children’s outcomes. But when you examine this body of work, which leverages experiments to answer questions of relevance to developmental psychology, it becomes apparent that it is more focused on using randomization to get at causal inference than it is on using randomization to understand the bioecological model and the process of development. That is, we rarely test in such research the kinds of small behavioral changes that we would study from a developmental perspective. Because these studies come from the fields of prevention science, policy research, and education science, they tend to be large, complex, multicomponent interventions, rather than the small “budges to some aspect of the environment” that Bronfenbrenner described. Curiously, when we do examine effects on children in these large randomized experiments, we generally pull children out of their typical environments to assess their skills in small, separate assessment rooms. Although observations of children’s interactions with adults often occur in naturalistic settings, assessments of their cognition and behavior are

Historical Perspectives on the Field  111 rarely collected in these same settings at the same time. Instead, we typically use highly scripted and controlled direct assessment tools to measure, for example, children’s cognitive performance on standardized tests (Woodcock‐Johnson Tests of Achievement; Woodcock, McGrew, & Mather, 2001; or Peabody Picture Vocabulary Test; Dunn, & Dunn, 2012), their emerging understanding of emotions and social interactions (Facial Emotions Identification Task; Garner, Jones, & Miner, 1994; Challenging Situation Task; Denham, Bouril, & Belouad, 1994), and executive function skills (Hearts & Flowers; Diamond, Barnett, Thomas, & Munro, 2007; Fish Flanker, Rueda, Posner, Rothbart, & Davis‐Stober, 2004). A notable exception to this is the Individualized Classroom Assessment Scoring System measure (inCLASS; Downer, Booren, Lima, Luckner, & Pianta, 2010), which allows for the assessment of children’s engagement in classroom activities and interactions with peers and teachers. But this measure is rarely used. The unfortunate result of this practice is that we generally do not observe children within their changed contexts. If we think that context matters, then why assess children outside of the context we have worked so hard to budge? We are relying on the old lab experiments that Bronfenbrenner lamented in his early writings, even as we have developed more ecologically valid research. Indeed, it is as if we are trying to bring the lab itself out into the field. If we really want to understand whether a given preschool program allows children to better engage in the classroom activities and material taught, we miss an opportunity to understand how the context affects that engagement by only assessing the child in a separate space. An example from Stephanie Jones’ pilot study of a program called SECURe (Jones, Bailey, & Jacob, 2014) is quite a propos of this discussion: In this effort, Jones developed a school‐based social‐emotional learning program designed to support children’s regulation in the classroom context. Information was collected in the “typical” manner – examining the effects of the program on standard directly‐assessed measures of children’s cognitive performance. But she also assessed children’s behavior during these tasks, as assessed by the interviewer. And what did she find? That the pilot data did not show improvements in children’s performance on cognitive tasks assessed in a separate room as a result of their exposure to the program; instead, it did show improvements in children’s ability to change their behavior during this performance. And, of course, this makes sense – the program effectively taught children to manage their attention, and thus the effects are strongest for measures that tap that. Of course, now those early findings need to be paired with assessments of children in the classroom. Without assessments of behavior in the same context that we “budge,” we risk strongly underestimating the effects of our programming efforts. When Bronfenbrenner talked about the transforming experiment, he explicitly stated that he wanted it used not as a means to test hypotheses, which one would typically attempt after conducting a study that examined associations, but for “heuristic ­purposes” – as he says, “to analyze systematically the nature of the existing accommodation between the person and the surrounding milieu” (1977, p. 517). To him, the advantage of the transforming experiment is that it allowed us to truly “separate out” the person effects from the ecological effects, and, perhaps even more importantly, to test the dynamic nature of the system (to understand for example, how much a classroom‐based intervention can change child behavior and how much those same changes in behavior can, in turn, lead to changes in the classroom and the teacher).

112  Morris and Connors Early childhood researchers are doing a good job of rigorously testing new programs in the field. But we are still doing a relatively poor job of using these approaches to test the bioecological model and to effectively learn about human development. We would be remiss not to mention the substantial methodological hurdles in conducting such work, but even simple changes in our approach may begin to get closer to these lofty goals. In short, our randomized experiments are a missed opportunity to make progress on the study of developmental processes.

Conclusion So where does all this leave us? Unfortunately, Bronfenbrenner’s statement from the mid‐ 1980s is still highly relevant today; he would still be “harangu[ing us] for a surfeit of studies on context without development” (1986, p. 286). We now have more studies of context and in context, and we now do many more randomized experiments, with especially increased rigor in preschool education science. But because we do not test small behavioral changes and because we do not test changes in the person within those contexts, our understanding of developmental processes is woefully incomplete. Bronfenbrenner always urged us to be citizens and change agents as well as scientists. And so, we conclude with a quote from the end of the 2006, his final Handbook chapter: “Thus, we have arrived at a point where the concerns of basic developmental science are converging with the most critical problems we face as a nation. That convergence c­onfronts us, both as scientists and as citizens, with new challenges and opportunities” (Bronfenbrenner  & Morris, 2006, p. 824). Our hope is that reminding ourselves of Bronfenbrenner’s call to action will inspire and guide us to rise above the challenges in order to conduct research that gives deserved attention to both context and development.

References Achenbach, T. M., & Rescorla, L. A. (2000). Manual for the ASEBA Preschool Forms & Profiles: An Integrated System of Multi‐informant Assessment; Child Behavior Checklist for Ages 1 1/2‐5; Language Development Survey; Caregiver‐Teacher Report Form. Burlington, VT: University of Vermont. Ainsworth, M. D. S., & Wittig, B. A. (1969). Attachment and exploratory behavior of one‐year‐ olds in a strange situation. In B. M. Foss (Ed.), Determinants of Infant Behavior, 4 (pp. 111–136). London: Methuen. Arnett, J. (1989). Caregivers in day‐care centers: Does training matter? Journal of Applied Developmental Psychology, 10(4), 541–552. Barnett, W.S., Carolan, M.E., Squires, J.H., & Clarke Brown, K. (2013). The state of preschool 2013: State preschool yearbook. New Brunswick, NJ: National Institute for Early Education Research. Bloom P. J. (2010). Measuring work attitudes in the early childhood setting: Technical manual for the Early Childhood Job Satisfaction Survey and Early Childhood Work Environment Survey (2nd ed.). Wheeling, IL: McCormick Center for Early Childhood Leadership, National‐Louis University.

Historical Perspectives on the Field  113 Boller, K., Atkins‐Burnett, S., Malone, E. M., Baxter, G. P., & West, J. (2010). Compendium of Student, Teacher, and Classroom Measures Used in NCEE Evaluations of Educational Interventions. Volume I: Measures Selection Approaches and Compendium Development Methods (No. 6568). Washington, DC: Mathematica Policy Research. Boyce, W. T., & Ellis, B. J. (2005). Biological sensitivity to context: An evolutionary‐developmental theory of the origins and functions of stress reactivity. Development and Psychopathology, 17, 271–301. Bradley, R. H. (1995). Home environment and parenting. In M. Borstein (Ed.), Handbook of p­arenting. Hillsdale, NJ: Erlbaum. Bremner, J. G. (1985). Object tracking and search in infancy: A review of data and a theoretical evaluation. Developmental Review, 5(4), 371–396. Bronfenbrenner, U. (1974). Developmental research, public policy, and the ecology of childhood. Child Development, 45(1), 1–5. Bronfenbrenner, U. (1977). Toward an experimental ecology of human development. American Psychologist, 32(7), 513. Bronfenbrenner, U. (1979). The ecology of human development: Experiments by nature and design. Cambridge, MA: Harvard University Press. Bronfenbrenner, U. (1986). Ecology of the family as a context for human development: Research perspectives. Developmental Psychology, 22(6), 723. Bronfenbrenner, U. (1989). Ecological systems theory. In R. Vasta (Ed.), Annals of child development. Six theories of child development: Revised formulations and current issues (pp. 187–249). London: JAI Press. Bronfenbrenner, U. (Ed.). (2005). Making human beings human: Bioecological perspectives on human development. Thousand Oaks, CA: Sage. Bronfenbrenner, U., & Crouter, A. C. (1983). The evolution of environmental models in developmental research. In W. Kessen & P. H. Mussen, Handbook of child psychology: Vol. 1. History, theory, and methods (4th ed., pp. 357–414). New York, NY: John Wiley & Sons, Inc. Bronfenbrenner, U., & Morris, P. (1998). The ecology of developmental processes. In W. Damon & R. M. Lerner (Eds.), Handbook of child psychology: Vol. 1. Theoretical models of human development (5th ed., pp. 993–1028). New York, NY: John Wiley & Sons, Inc. Bronfrenbrenner, U., & Morris, P.A. (2006). The biological 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: John Wiley & Sons, Inc. Brooks‐Gunn, J., Duncan, G., & Aber, J. L. (Eds.). (1997). Neighborhood poverty, Volume 1: Context and consequences for children. New York, NY: Russell Sage Foundation. Brooks‐Gunn, J., Duncan, G. J., Klebanov, P. K., & Sealand, N. (1993). Do neighborhoods i­ nfluence child and adolescent development? American Journal of Sociology, 99(2), 353–395. Burchinal, M. R., & Cryer, D. (2004). Diversity, child care quality, and developmental outcomes. Early Childhood Research Quarterly, 18(4), 401–426. Burchinal, M., Vandergrift, N., Pianta, R., & Mashburn, A. (2010). Threshold analysis of association between child care quality and child outcomes for low‐income children in pre‐kindergarten programs. Early Childhood Research Quarterly, 25(2), 166–176. Caldji, C., Diorio, J., & Meaney, M. J. (2000). Variations in maternal care in infancy regulate the development of stress reactivity. Biological Psychiatry, 48, 1164–1174. Caughy, M. O. B., Hayslett‐McCall, K. L., & O’Campo, P. J. (2007). No neighborhood is an island: incorporating distal neighborhood effects into multilevel studies of child developmental competence. Health & Place, 13(4), 788–798. Child Care and Development Block Grant Act of 2014, P.L. 113‐76, 42 Stat. 9858.

114  Morris and Connors Coburn, C. E., & Russell, J. L. (2008). District policy and teachers’ social networks. Educational Evaluation and Policy Analysis, 30(3), 203–235. Connors, M. (2016). Creating cultures of learning: A theoretical model of effective early care and education policy. Early Childhood Research Quarterly, 36, 32–45. Curtis, C. A., & Berzonsky, M. D. (1967). Preschool and primary education project. Harrisburg, PA: Council for Human Services Commonwealth of Pennsylvania. Denham, S. A., Bouril, B., & Belouad, F. (1994). Preschoolers’ affect and cognition about challenging peer situations. Child Study Journal, 24, 1–21. Deutsch, C.P. (1968). Environment and perception. In M. Deutsch, I. Katz, & A. Jensen (Eds.), Social class, race, and psychological development. New York, NY: Holt, Rinehart, and Winston. Di Lorenzo, L. T., & Salter, R. (1968). An evaluative study of prekindergarten programs for educationally disadvantaged children: follow up and replication. Exceptional Children, 35(2), 111–119. Diamond A., Barnett, W. S., Thomas, J., & Munro, S. (2007). Preschool program improves c­ognitive control. Science, 318, 1387–1388. Diorio, J., & Meaney, M.J. (2007). Maternal programming of defensive responses through s­ustained effects on gene expression. Journal of Psychiatry Neuroscience, 32, 275–284. Dishion, T. J., French, D. C., & Patterson, G. R. (1995). The development and ecology of antisocial behavior. New York, NY: John Wiley & Sons, Inc. Dishion, T. J., & Stormshak, E. A. (2007). Intervening in children’s lives: An ecological, family‐centered approach to mental health care. American Psychological Association. Downer, J. T., Booren, L. M., Lima, O. K., Luckner, A. E., & Pianta, R. C. (2010). The Individualized Classroom Assessment Scoring System (inCLASS): Preliminary reliability and validity of a system for observing preschoolers’ competence in classroom interactions. Early Childhood Research Quarterly, 25(1), 1–16. Drillien, C.M. (1964). The growth and development of the prematurely born infant. Edinburgh: Livingston. Dubow, E. F., & Ippolito, M. F. (1994). Effects of poverty and quality of the home environment on changes in the academic and behavioral adjustment of elementary school‐age children. Journal of Clinical Child Psychology, 23(4), 401–412. Duncan, G., Brooks‐Gunn, J., & Klebanov, F. (1994). Economic deprivation and early‐childhood development. Child Development, 65(2), 296–318. Duncan, G. J., Morris, P. A., & Rodrigues, C. (2011). Does money really matter? Estimating impacts of family income on young children’s achievement with data from random‐assignment experiments. Developmental Psychology, 47(5), 1263. Dunn, L. (1993). Proximal and distal features of day care quality and children’s development. Early Childhood Research Quarterly, 8(2), 167–192. Dunn, L. M., & Dunn, D. M. (2012). Peabody Picture Vocabulary Test, (PPVT‐4). Johannesburg: Pearson Education Inc. Dupéré, V., Leventhal, T., Crosnoe, R., & Dion, E. (2010). Understanding the positive role of neighborhood socioeconomic advantage in achievement: The contribution of the home, child care, and school environments. Developmental Psychology, 46(5), 1227–1244. Elmore, R., & Burney, D. (1999). Investing in teacher learning: Staff development and instructional improvement in Community School District #2, New York City. In L. Darling‐Hammond & G. Sykes (Eds.), Handbook of Policy and Practice (pp. 263–291). San Francisco: Jossey‐Bass. Field, D. (1987). A review of preschool conservation training: An analysis of analyses. Developmental Review, 7(3), 210–251. Francis, D., Diorio, J., Liu, D., & Meaney, M. J. (1999). Nongenomic transmission across generations of maternal behavior and stress responses in the rat. Science, 286(5442), 1155–1158.

Historical Perspectives on the Field  115 Frede, E., Jung, K., Barnett, W. S., Lamy, C. E., & Figueras, A. (2007). The Abbott preschool program longitudinal effects study (APPLES). New Brunswick, NJ: National Institute for Early Education Research. Friedman‐Krauss, A. H., Raver, C. C., Morris, P. A., & Jones, S. M. (2014). The role of classroom‐ level child behavior problems in predicting preschool teacher stress and classroom emotional climate. Early Education and Development, 25(4), 530–552. Fullan, M. (2000). The three stories of education reform. Phi Delta Kappan, 81, 581–584. Ganzel, B. L., Morris, P. A., & Wethington, E. (2010). Allostasis and the human brain: Integrating models of stress from the social life sciences. Psychological Review, 117(1), 134–174. Garner, P. W., Jones, D. C., & Miner, J. L. (1994). Social competence among low‐income p­reschoolers: Emotion socialization practices and social cognitive correlates. Child Development, 65(2), 622–637. Gerber, E. B., Whitebook, M., & Weinstein, R. S. (2007). At the heart of child care: Predictors of teacher sensitivity in center‐based child care. Early Childhood Research Quarterly, 22(3), 327–346. Gormley Jr, W. T., Gayer, T., Phillips, D., & Dawson, B. (2005). The effects of universal pre‐K on cognitive development. Developmental Psychology, 41(6), 872. Harper, L. (2005). Epigenetic inheritance and the intergenerational transfer of experience. Psychological Bulletin, 131, 340–360. Herbst, C. M., & Tekin, E. (2010). Child care subsidies and child development. Economics of Education Review, 29(4), 618–638. Hodges, W. L., McCandless, B. R., & Spicker, H. H. (1967). The development and analysis of a diagnostically based curriculum for psycho‐socially disadvantaged children. Final Report, Indiana University. Contract No. OEG‐32‐24‐021C‐1011, US Office of Education. Horn, E. P, Mancini, P., Mattera, S. K., & Morris, P. A. (2015). The stability of CLASS scores across academic school years in a large‐scale randomized control trial. Poster presented at the Society for Research in Child Development Biennial Meeting. Hotz, V. J., & Xiao, M. (2011). The impact of regulations on the supply and quality of care in child care markets. The American Economic Review, 101(5), 1775. Improving Head Start for School Readiness Act of 2007, P.L. 110‐134, 121 Stat. 1363. Jones, S. M., Bailey, R., & Jacob, R. (2014). Social‐emotional learning is essential to classroom management. Phi Delta Kappan, 96(2), 19–24. Karnes, M. B., Teska, J. A., & Hodgins, A. S. (1969). A longitudinal study of disadvantaged children who participated in three different preschool programs. Paper presented at the annual meeting of the American Educational Research Association, Los Angeles, California. Klaus, R. A., & Gray, S. W. (1968). The early training project for disadvantaged children: a report after five years. Monographs of the Society for Research in Child Development, No. 120. Korenman, S., Miller, J. E., & Sjaastad, J. E. (1995). Long‐term poverty and child development in the United States: Results from the NLSY. Children and Youth Services Review, 17(1), 127–155. Leventhal, T., & Brooks‐Gunn, J. (2000). The neighborhoods they live in: the effects of neighborhood residence on child and adolescent outcomes. Psychological Bulletin, 126(2), 309. Levine, S. (2000). Influence of psychological variables on the activity of the hypothalamic–pituitary– adrenal axis. European Journal of Pharmacology, 405(1), 149–160. Liu, D., Diorio, J., Tannenbaum, B., Caldji, C., Francis, D., Freedman, A., … Meaney, M. J. (1997). Maternal care, hippocampal glucocorticoid receptors, and hypothalamic‐pituitary‐adrenal responses to stress. Science, 277(5332), 1659–1662. Lutz, M. N., Fantuzzo, J., & McDermott, P. (2002). Multidimensional assessment of emotional and behavioral adjustment problems of low‐income preschool children: Development and initial validation. Early Childhood Research Quarterly, 17(3), 338–355.

116  Morris and Connors Mashburn, A. J., Pianta, R. C., Hamre, B. K., Downer, J. T., Barbarin, O., Bryant, D., … Howes, C. (2008). Measures of classroom quality in prekindergarten and children’s development of a­cademic, language, and social skills. Child Development, 79, 732–749. McCoy, D. C., Connors, M. C., Morris, P. A., Yoshikawa, H., & Friedman‐Krauss, A. H. (2015). Neighborhood economic disadvantage and children’s cognitive and social‐emotional development: Exploring Head Start classroom quality as a mediating mechanism. Early childhood research quarterly, 32, 150–159. Mussen, P. (Ed.). (1983). Handbook of child psychology (4th ed.) (Vol. 1.) New York, NY: John Wiley & Sons, Inc. National Association of Child Care Resource & Referral Agencies (NACCRRA). (2011). We can do better: 2011 update. NACCRRA’s ranking of state child care regulation and oversight. Retrieved from: http://www.naccrra.org/about‐child‐care/state‐child‐care‐licensing/we‐can‐do‐better‐state‐child‐ care‐center‐licensing Neuman, S. B., & Cunningham, L. (2009). The impact of professional development and coaching on early language and literacy instructional practices. American Educational Research Journal, 46(2), 532–566. Patterson, G. R. (1982). Coercive family process (Vol. 3). Eugene, OR: Castalia Publishing Company. Payne, A. L. (2011). Strong licensing: The foundation for a quality early care and education system. Lexington, KY: National Association for Regulatory Administration. Peisner‐Feinberg, E. S., Burchinal, M. R., Clifford, R. M., Culkin, M. L., Howes, C., Kagan, S. L., & Yazejian, N. (2001). The relation of preschool child‐care quality to children’s cognitive and social developmental trajectories through second grade. Child Development, 72(5), 1534–1553. Pianta, R. C. (2001). STRS: Student‐teacher Relationship Scale: Professional manual. Psychological Assessment Resources. Pianta, R., Howes, C., Burchinal, M., Bryant, D., Clifford, R., Early, D., & Barbarin, O. (2005). Features of pre‐kindergarten programs, classrooms, and teachers: Do they predict observed classroom quality and child‐teacher interactions? Applied Developmental Science, 9(3), 144–159. Pianta, R. C., La Paro, K. M., & Hamre, B. K. (2008). Classroom Assessment Scoring System (CLASS) Manual: K‐3. Baltimore, MD: Paul H. Brookes Publishing Company. Pianta, R. C., Mashburn, A. J., Downer, J. T., Hamre, B. K., & Justice, L. (2008). Effects of web‐ mediated professional development resources on teacher–child interactions in pre‐kindergarten classrooms. Early Childhood Research Quarterly, 23(4), 431–451. Phillips, D., Mekos, D., Scarr, S., McCartney, K., & Abott‐Shim, M. (2000). Within and beyond the classroom door: Assessing quality in child care centers. Early Childhood Research Quarterly, 15, 475–496. Phillips, D. A., Voran, M., Kisker, E., Howes, C., & Whitebook, M. (1994). Child care for children in poverty: Opportunity or inequity? Child Development, 65(2), 472–492. Puma, M., Bell, S., Cook, R., Heid, C., Shapiro, G., Broene, P., … Spier, E. (2010). Head Start Impact Study. Final Report. Washington, DC: US Department of Health and Human Services, Administration for Children and Families. Ramey, S. L., Crowell, N. A., Ramey, C. T., Grace, C., Timraz, N., & Davis, L. (2011). The dosage of professional development for early childhood professionals: How the amount and density of professional development may influence its effectiveness. The Early Childhood Educator Professional Development Grant: Research and Practice Advances in Early Education and Day Care, 15, 11–32. Ramey, C. T., & Smith, B. J. (1977). Assessing the intellectual consequences of early intervention with high‐risk infants. American Journal of Mental Deficiency, 81, 318–324. Raver, C. C., Jones, S. M., Li‐Grining, C. P., Metzger, M., Champion, K. M., & Sardin, L. (2008). Improving preschool classroom processes: Preliminary findings from a randomized trial i­mplemented in Head Start settings. Early Childhood Research Quarterly, 23, 10–26.

Historical Perspectives on the Field  117 Reynolds, A. J. (2000a). Success in early intervention: The Chicago child parent centers. University of Nebraska Press. Reynolds, A. J. (2000b). Educational success in high‐risk settings: Contributions of the Chicago Longitudinal Study. Journal of School Psychology, 37(4), 345–354. Rimm‐Kaufman, S. E., Curby, T. W., Grimm, K. J., Nathanson, L., & Brock, L. L. (2009). The contribution of children’s self‐regulation and classroom quality to children’s adaptive behaviors in the kindergarten classroom. Developmental Psychology, 45(4), 958. Rimm‐Kaufman, S. E., La Paro, K. M., Downer, J. T., & Pianta, R. C. (2005). The contribution of classroom setting and quality of instruction to children’s behavior in kindergarten classrooms. The Elementary School Journal, 105(4), 377–394. Rueda, M. R., Posner, M. I., Rothbart, M. K., & Davis‐Stober, C. P. (2004). Development of the time course for processing conflict: an event‐related potentials study with 4‐year‐olds and adults. BMC Neuroscience, 5(1), 39. Ryan, R. M., Johnson, A., Rigby, E., & Brooks‐Gunn, J. (2011). The impact of child care subsidy use on child care quality. Early Childhood Research Quarterly, 26(3), 320–331. Spoth, R. L., Kavanagh, K. A., & Dishion, T. J. (2002). Family‐centered preventive intervention science: Toward benefits to larger populations of children, youth, and families. Prevention Science, 3(3), 145–152. Susser, M., & Stein, Z. (1994). Timing in prenatal nutrition: A reprise of the Dutch Famine Study. Nutrition Reviews, 52, 84–94. Talan, T. N., & Bloom, P.J. (2009). Business Administration Scale for Family Child Care. New York, NY: Teachers College Press. Talan T. N., & Bloom P. J. (2011). Program Administration Scale: Measuring early childhood leadership and management (2nd ed.). New York, NY: Teachers College Press. Tout, K., Starr, R., Soli, M., Moodie, S., Kirby, G., & Boller, K. (2010). Compendium of Quality Rating Systems and Evaluations. Prepared for the US Department of Health and Human Services, Administration for Children and Families, Office of Planning, Research and Evaluation. Washington, DC: Child Trends. Vogel, C. A., Xue, Y., Moiduddin, E. M., Carlson, B. L., & Kisker, E. E. (2010). Early Head Start Children in Grade 5: Long‐Term Followup of the Early Head Start Research and Evaluation Project Study Sample (No. 4e81525b2ed848808383ce904c03ba2b). Mathematica Policy Research. Weaver, I. C. G., Cervoni, N., Champagne, F. A., D’Alessio, A. C., Sharma, S., Seckl, J. R. … Meaney, M. J. (2004). Epigenetic programming by maternal behavior. Nature Neuroscience, 7, 847–854. Weikart, D. P. (1967). Preschool intervention: A preliminary report of the Perry Preschool Project. Ann Arbor, MI: Campus Publishers. Weikart, D. P. (1969). A comparative study of three preschool curricula. A paper presented at the biennial meeting of the Society for Research in Child Development, Santa Monica, California, March, 1969. In Frost, J. (Ed.) Disadvantaged child. (2nd ed.). New York, NY: Houghton Mifflin. Weikart, D. P., Deloria, D. J., Lawser, S. A., & Weigerink, R. (1970). Longitudinal results of the Ypsilanti Perry Preschool Project. Ypsilanti, MI: High/Scope Press. Weiland, C., & Yoshikawa, H. (2013). Impacts of a prekindergarten program on children’s mathematics, language, literacy, executive function, and emotional skills. Child Development, 84(6), 2112–2130. Whitebook, M., & Ryan, S. 2012. Supportive Environmental Quality Underlying Adult Learning (SEQUAL). Berkeley, CA: Center for the Study of Child Care Employment, University of California, Berkeley. Woodcock, R. W., McGrew, K. S., & Mather, N. (2001). Woodcock‐Johnson tests of achievement. Itasca, IL: Riverside Publishing.

chapter SIX What Does it Mean to be Evidence‐based? Margaret R. Burchinal and Nina E. Forestieri

Millions of families with young children in United States are affected by the early childhood policies and programs addressed in this Handbook. And many of these policies and programs involve major investments of tax dollars. For example, $7,968,544,000 was spent on Head Start in 2012 (Office of Head Start, 2013) and $5,556,840,884 was spent on state pre‐kindergarten programs in 2014 (Barnett, Carolan, Squires, Clarke Brown, & Horowitz, 2015). In addition to accountability issues when programs are funded publicly, there are ethical responsibilities for policymakers and practitioners to ensure that children are receiving services that are helpful. In the end, everyone – parents, practitioners, and policymakers – cares deeply about the extent to which early childhood programs and policies are providing services that improve the lives of young children and their families. Accordingly, most funding of programs for young children now requires that the services be “evidence‐based.” However, reaching the lofty goal of determining whether services are effective is complicated by many factors, one of which involves determining what it means to be evidence‐based. This chapter identifies key methodological considerations for evaluating evidence in the field of early childhood, providing a roadmap for the Handbook chapters that follow, each of which offers a careful review and critique of the most current empirical knowledge on strategies for promoting development during early childhood. In doing so, the chapter The Wiley Handbook of Early Childhood Development Programs, Practices, and Policies, First Edition. Edited by Elizabeth Votruba-Drzal and Eric Dearing. © 2017 John Wiley & Sons, Inc. Published 2017 by John Wiley & Sons, Inc.

What Does it Mean to be Evidence-based?  119 calls attention to the importance of internal validity, external validity, and practical significance in the search for evidence‐based early childhood programs, practices, and policies. Following a summary of the evidence‐based practices (EBP) movement and some of the criticisms, this chapter provides definitions of key constructs and a theoretical context for understanding the evidence in the field of child development. The chapter closes with a call for placing a greater focus on research rigor to ensure that publicly funded programs have the greatest likelihood of providing children with additional opportunities to succeed, especially for the most vulnerable children.

Contemporary and Historical Context of Prioritizing Evidence‐based Programs, Practices, and Policies Across practice and policy sectors, early childhood stakeholders and decision‐makers desire an evidence base that can inform their efforts, with evidence on extent to which programs and practices produce promised outcomes being one of their primary concerns (Murnane & Willett, 2011; National Association for the Education of Young Children, 2015). These audiences increasingly rely on early childhood research to determine which of the program, practice, and policy options at their disposal do, in fact, improve the growth of young children in meaningful and lasting ways. In their now classic guide to empirical methods in the social sciences, Campbell and colleagues (e.g., Cook & Campbell, 1979; Shadish, Cook, & Campbell, 2002) laid out the critical importance of two domains of validity for answering such questions. The first, internal validity, concerns the validity of cause and effect inferences in empirical studies. Studies with strong internal validity can isolate the benefits of a particular program or policy from other potentially confounding factors. Randomized controlled trials are typically considered the most rigorous design for establishing strong internal validity. The second, external validity, concerns the generalizability of findings from empirical studies, or the degree to which you can expect consistent outcomes across different samples within the population and over time. In studies of early childhood programs, practices, and policies, these complementary validity domains require attention to: (a) whether documented relations between programs, practices, and policies and child outcomes are, in fact, causal and (b) whether any such causal effects will generalize to children and families beyond those in the study samples. There is a long‐standing consensus among methodologists regarding the ideal design for maximizing internal validity: well‐designed experiments in which children are randomly assigned to treatment and control conditions. Yet, there are also increasing calls for researchers to be more rigorous in probing causality within non‐experimental designs (Duncan, Magnuson, & Ludwig, 2004; Foster, 2010; McCartney, Burchinal, & Bub, 2006). Moreover, the importance or robustness of effects across variations in design, methods, and measurement is now a priority for the field, with implications for both internal and external validity (Duncan, Engel, Claessens, & Dowsett, 2014; Murnane & Willett, 2011; Shadish et al., 2002). Beyond questions of internal and external validity, researchers and other stakeholders must also be concerned with the practical significance of the effects of early childhood programs, practices, and, policies (Burchinal, Magnuson, Powell, & Hong, 2015; Duncan,

120  Burchinal and Forestieri Ludwig, & Magnuson, 2007; McCartney & Rosenthal, 2000). In short, practical significance addresses how meaningful the findings are. Several questions must be answered to determine the practical significance of a study such as: (a) how large are the improvements in child outcomes; (b) how important is the outcome for promoting overall child well‐being and life chances; and (c) how large are benefits for children and society relative to the costs of programs and policies (Duncan et al., 2007; McCartney & Rosenthal, 2000). In addition, effective policy and practice requires empirical attention to issues of bringing programs and practices to scale and sustainability at scale (Durlak & DuPre, 2008; Halle, Metz, & Martinez‐Beck, 2013). This research must inform, for example, how evidence‐based programs can be successfully adapted to disparate real world contexts, while maintaining program fidelity (Franks & Schroeder, 2013). Thus, EBP involves identifying effective practices, services, programs, or policies based on research evidence and evaluating that evidence in the context of practitioner expertise and the characteristics of the individuals for whom the practices are intended (Thomas & Pring, 2004). Evidence‐based criteria involve determining whether the evidence regarding the effective of a program, service, or practice indicates that its effects are large enough to make a practical difference for individual children and/or for society in general, and whether children from different cultural backgrounds benefit similarly from them (Duncan & Murnane, 2014). Not surprisingly, the criteria used to determine what c­onstitutes evidence‐based policy vary across discipline and philosophical perspectives. For example, the weight that is given to professional expertise, research rigor, and cultural diversity differs in assessing the evidence base in research across disciplines. The priority of evidence‐based decision making in the field of medicine was espoused in 1992 – “Evidence‐based medicine de‐emphasizes intuition, unsystematic clinical experience, and pathophysiologic rationale as sufficient grounds for clinical decision making and stresses the examination of evidence from clinical research” (Evidence‐Based Medicine Working Group, 1992) – and use of EB practice in medicine has been routine for some time (Sackett, Straus, Richardson, Rosenberg, & Haynes, 2000). More recently, the focus on EB decisions has spread to other health fields such as dentistry and nursing and beyond to psychology (Spring & Hitchcock, 2009), education (Thomas & Pring, 2004), and social work (Social Work Policy Institute, 2008). Yet, best practice for the use of research evidence includes its integration with practitioner expertise and individual characteristics of individuals receiving care. According to a NIH‐funded multi‐disciplinary council on EB policies in health fields, broadly defined, this approach Entails making decisions about how to promote health or provide care by integrating the best available evidence with practitioner expertise and other resources, and with the characteristics, state, needs, values and preferences of those who will be affected. This is done in a manner that is compatible with the environmental and organizational context. Evidence is research findings derived from the systematic collection of data through observation and experiment and the formulation of questions and testing of hypotheses. (Evidence‐Based Behavioral Practice Consortium, 2015)

There is no widely accepted standard for defining what constitutes evidence‐based in the fields of child and family services according to the NIH‐funded Promising Practices

What Does it Mean to be Evidence-based?  121 Network (Mattox & Kilburn, 2013), perhaps because of the focus on integrating research evidence, clinician expertise, and client characteristics. On one hand, there is a strong research tradition that has clear standards on determining whether treatments are effective. This tradition involves conducting experiments in which the treatment (e.g., service or program) is randomly assigned to individuals, and the results provide empirically‐based evidence about whether and for whom the treatment worked and how great the effects are. On the other hand, for clinicians and other practice professionals, there is a strong tradition encouraging their reliance on professional training, experience, and judgment to determine what is most effective for children and families. This reliance of experience and judgment to determine what are effective practices for children and families is often reflected in policy initiatives even when expert clinicians may not be involved in delivering the services. Balancing these research and practice traditions can make it difficult to determine what is evidence‐based (Buysse & Wesley, 2006). In fact, large variations exist in: (a) the definitions of EBP; (b) general developmental theories and frameworks that underlie the research evidence; and (c) thr amount and types of evidence needed to determine whether there is evidence of efficacy. Each of these issues is discussed briefly in this chapter. In turn, we offer recommendations that clarify the importance of evidence‐based approaches and offer guidance on howbest to employ an evidence‐based approach within the current research, practice, and policy landscape.

Definitions of Evidence‐Based A basic tenet of the evidence‐based approach is that the best evidence should be used in all policies and programs – including medical care, programs designed to assist families and children, and the teaching and caregiving of young children (Mattox & Kilburn, 2013). Nonetheless, there are large differences in how EB is defined and the extent to which the quality of the research evidence is viewed as the most important component in selecting practices. For example, many states require an evidence‐based curriculum in child care programs that receive the highest star ratings within state Quality Rating and Improvement Systems (QRIS Compendium, 2014) and within state pre‐kindergarten programs (Barnett et  al., 2015). Careful examination of the curricula that are considered to be evidence‐ based, however, reveal that some curricula have been subject to the most rigorous standards of evaluation, but most were deemed “evidence-based” without any direct evaluation because they are based on conceptual models that are indirectly linked with a body of evidence and, as such, viewed as important for preschool children (e.g., whole‐child focused rather than focusing on a given content area). In this case, the conceptual model, not the empirical evidence per se, is viewed as the necessary criterion for viewing a curriculum as evidence‐based. There is agreement that the focus on research evidence in the EBP approach is an improvement over selecting practices based on rules of thumb, folklore, or tradition alone. In addition, there is considerable agreement that methodologically strong research p­rovides critical evidence for selecting policy and practice strategies, with the following caveats appearing (and emphasized) to different degrees: (1) the value of the evidence may be

122  Burchinal and Forestieri limited exclusively to instances in which the policies, programs, and practice strategies are applied in precisely the same manner and with the same population of individuals as they were studied (Kazdin, 2008); and (2) policies, programs, and practices may require tailoring to individual characteristics, values, and community settings, and thus programs and services may need to be flexible in response to the individuals served (Kazdin, 2008; March et al., 2005). Creating some tension, there seems to be less agreement in the field about the extent to which factors such as practitioner expertise should be weighted relative to quality and quantity of research evidence in making decisions about practices. This tension can be seen in the definitions provided by two different professional organizations. The What Works Clearinghouse (WWC) is an arm of the US Department of Education that provides information of EBP in education (WWC, 2015). It is modeled on the m­edical model as articulated by the Federal Drug Agency (FDA) for evaluating drugs or medical devices. Like the FDA, there is a very strict and clearly articulated focus on quality of research and magnitude of effect sizes, with attention to whether research was available for the designated population. As they state on their website: For nearly a decade, the WWC has been a central and trusted source of scientific evidence for what works in education to improve student outcomes. What does that mean? Central. We want to be the place you turn to when you want to know about education research. We have reviewed thousands of studies on hundreds of education programs, products, practices, and policies. Trusted. We strive to provide accurate information on education research. All of our procedures and policies are publicly available, and our goal is to provide transparent reviews of the research literature. Scientific evidence. In order to tell you what works, we conduct thorough reviews of the research literature and critically assess the evidence presented. (WWC, 2015)

The WWC has very stringent criteria in terms of the rigor of the research study and places a heavy emphasis on the magnitude of effects of educational interventions. Greater priority weight is given to studies that involve random assignment or other rigorous quasi‐ experimental designs such as regression discontinuity, and to the clean random assignment to treatment or control groups in ways that generate unbiased treatment effects. While there is some attention paid to the study population in drawing conclusions and determining for whom programs and policies are likely to work, there is much less attention paid to the characteristics of the teachers or the children included in the study than to the rigor of the design and analysis. Perhaps most concerning is the lack of attention to the need for the service organization to modify programs, services, and practices when they are applied in the real world (Tseng, 2012). As an example, the Department of Health and Human Services and the Department of Education (2016) recently noted the lack of d­istrict and state attention to cultural and linguistic moderators as a primary obstacle to effective implementation of federal requirements to engage families’ publicly funded e­ducation efforts from Head Start through Elementary and Secondary education. The past 10 years of educational research have demonstrated that model programs can provide important evidence of “proof of concept” by demonstrating that a program can be successful – especially when the developer is a researcher and is actively engaged in implementing that program – but these programs often need to adapted to meet local needs when implemented by practitioners or policymakers (Tseng, 2012). For example, the most recent at‐scale evaluation of Teach for America (TFA) found smaller, often not statistically

What Does it Mean to be Evidence-based?  123 significant, impacts across grade level, whereas the original rigorous evaluation in a smaller study did find positive impacts for TFA at all grade levels (Clark, Isenberg, Liou, Makowsky, & Zukiewicz, 2015). A possible explanation for this discrepancy involved the use of more experienced teachers as the comparison teachers in the scale‐up study and more novice teachers in the original research. In contrast, professional organizations such as the American Psychological Association (APA) equate research, clinical expertise, and contextual factors in determining what c­onstitutes evidence (APA, 2015). According to APA, EBP is the integration of the best available research with clinical expertise in the context of patient characteristics, culture, and preferences, saying: “The purpose of EBP is to promote effective psychological p­ractice and enhance public health by applying empirically supported principles of p­sychological assessment, case, formulation, therapeutic relationship, and intervention” (APA, 2005). They state that research evidence should involve a body of studies that i­deally includes systematic reviews of the randomized clinical trial, but they also recognize that gaps in the literature will almost certainly exist when seeking evidence for any specific application. Many question whether the individuals in randomized clinical trials are similar to most patients, and whether the outcomes in the carefully controlled clinical trials are relevant in the real world (Kazdin, 2008; March et al., 2005). Whereas there is a focus on empirical evidence, there is also an emphasis on integrating research evidence from both a wider range of studies (e.g., qualitative and quantitative observational and experimental studies) with the clinician’s expertise and the recipient’s characteristics, culture, and preferences (APA, 2005). They state on their webpage: Clinical decisions should be made in collaboration with the patient, based on the best clinically relevant evidence, and with consideration for the probable costs, benefits, and available resources and options. It is the treating psychologist who makes the ultimate judgment regarding a particular intervention or treatment plan. The involvement of an active, informed patient is generally crucial to the success of psychological services. Treatment decisions should never be made by untrained persons unfamiliar with the specifics of the case. The treating psychologist determines the applicability of research conclusions to a particular patient. Individual patients may require decisions and interventions not directly addressed by the available research. The application of research evidence to a given patient always involves probabilistic inferences. Therefore, ongoing monitoring of patient progress and adjustment of treatment as needed are essential to EBP. (APA, 2005)

This tension between the focus on relative contribution of clinical judgment and evidence from rigorous experimental studies is the source of deep tensions in early childhood fields such as education and psychology. Based on differences in theoretical frameworks and corresponding approaches to research, it is not surprising that such tensions could contribute to contradictions in how to determine evidence‐based practices.

Developmental science theory and the evidence‐based approach Within developmental science, there are salient differences in research scientists’ approaches to questions about what practices work for whom (Hjørland, 2011). These differences in part reflect the differences seen in the definitions of EBP. And these differences tend to fall

124  Burchinal and Forestieri on a continuum of emphasis favoring either internal or external validity. Theoretical frameworks within developmental psychology such as broad socio‐ecological and transactional frameworks incorporate multidimensional and bi‐directional influences of both children and their environment in shaping developmental processes (Bronfenbrenner & Morris, 2006; Overton, 2015; Sameroff, 2009). For example, Overton (2015) argues that living organisms actively create, organize, and regulate their concepts of the world, and this develops through activities and actions within their social milieu. In addition, positive and negative feedback loops with their social context lead to increasing complexity over time. For applied research, the primary focus in determining the quality of the evidence is viewed within this socio‐ecological tradition, attending primarily to whether the research generating the evidence matches the people, circumstances, and contexts to which it is being applied. Research in this tradition explores these issues using a wide variety of methods including ethnographic studies of person‐context interactions, descriptive studies of populations, and even experimental studies. There is an implicit assumption that evidence in one context, or for one subgroup, is not likely to generalize, and even what works for a subgroup in one context might not generalize to another context. These are concerns over external validity, concerns that exclusively focusing on internal validity in experimental studies comes at the cost of ignoring characteristics, circumstances, and contexts that determine the generalizability of findings (Kazdin, 2008). Other researchers, often less theoretical or invoking broad socio‐ecological theory with more focus on policy and practice questions (e.g., economists and some psychological and education scientists), have honed in on the original definition of EBP by embracing statistical models for determining whether, and the extent to which, practices are empirically based (Holland, 1986). This framework, an extension to some degree of the reductionist philosophic point of view, focuses on obtaining the strongest unbiased evidence of e­ffectiveness by using study designs and statistical approaches that reduce or eliminate plausible alternative explanation for findings in studies (McCartney et al., 2006). Moving away from a strictly philosophic framework, the scientific method is based on the belief that one can use empirical studies to test hypotheses that are able to provide support or not for the use of specific programs or practices. The focus is on the careful articulation of the conditions under which the impacts of a given practice can be estimated, and identification of threats to impact estimation. This involves using statistical approaches that increase the likelihood that causal inferences can be drawn about the efficacy of a program, and the magnitude of the program impact can be estimated. These methods pay close attention to whether the treated and comparison groups are comparable at recruitment, using designs such as random assignment and paying close attention to baseline equivalence and attrition (Winship & Morgan, 1999). Some flexibility in research methods is allowed when those methods can be shown to be equivalent – in terms of internal validity – to random‐assignment experiments under specific circumstances (e.g., using econometric methods such as regression discontinuity), but only if it can be proven that their use does not introduce the potential of bias in estimating treatment effect. There is some concern about generalization (does it work for everyone?), but the primary focus is on ensuring that the evidence that a practice is effective is unbiased – that alternative explanations can be ruled out. The evidence of efficacy often

What Does it Mean to be Evidence-based?  125 starts with a small carefully controlled clinical trial that, ideally, eventually results in a large study of the EBP at scale being administered in the field, not by the researchers, and e­xamines the degree to which the effectiveness of EBP is sustained even without the extra support of the research study (Kazdin, 2008). This framework was embraced by the US Department of Education’s Institute of Education Science (IES). IES designed a framework for developing EBP (Coalition for Evidence‐Based Policy, 2003). The first stage involved developing the treatment and collecting pilot data. The second stage involved a carefully controlled clinical trial of the treatment that addressed questions about the size of the impacts and whether they varied for different groups of students. The final stage involved a very large multi‐site clinical trial in which schools implemented the treatment at scale, typically in multiple places, to see the extent to which the impacts obtained when implementation is controlled by the practitioner and whether the treatment is sustained without the researcher’s extra support. This tension between the focus on understanding the multiple influences of context and on obtaining unbiased impact estimates is the source of deep tensions in many fields such as education and psychology. Some compromises exist, however. For example, many developmental scholars now use economic methods to examine important policy q­uestions (Burchinal et al., 2015), and there is growing recognition that treatments, even those with the strongest evidence, are going to be adapted when implemented due to local policies, politics, and beliefs (Tseng, 2012). Nevertheless, misunderstandings and controversy over “evidence‐based” standards persist.

Variations in the level of evidence needed There are large variations in the standards of evidence that are needed to determine whether there is evidence that practices are effective. These variations include the s­tandards for determining efficacy, the consistency and magnitude of the evidence needed to establish a practice as an EBP, and the level of evidence that the practice can be implemented at scale. While almost everyone agrees that randomized clinical trials provide the strongest evidence for program impacts or the efficacy of practices, there is much less agreement about whether evidence from less rigorous studies should be viewed as research evidence. Strict adherents of the medical model of EBP such as the WWC or FDA will only classify programs or practices as effective if there were statistically significant treatment effects from randomized clinical trial or other rigorous quasi‐experimental designs (Winship & Morgan, 1999). In addition to the use of these designs, a strict protocol involves demonstrating that treatment and control groups did not differ at baseline or that subsequent analyses accounted for those differences and possible impacts of attrition (WWC, 2015). Other organizations clearly privilege findings from such studies, but do not require such evidence when determining which practices are evidence‐based. For example, the guidelines for the North Carolina pre‐kindergarten program require that they be e­ vidence‐ based and provide a list of curricula that it views as evidence‐based. Of the five curricula listed, none of them met the strict standards of the WWC and most of the e­ vidence suggested the curricula were efficacious based on observational studies performed by the

126  Burchinal and Forestieri curriculum developer. Similarly, Head Start guidelines list 13 components that should be considered in selecting a curriculum, and being evidence‐based is only one. Guidance provided in selecting curricula indicates that selecting an evidence‐based curriculum might be important only if your program has teachers who are not experienced in teaching preschool (Head Start National Center on Teaching and Learning, 2015). There are also large differences in the extent to which there is a focus on the consistency and magnitude of the impacts in the research. Ideally, the best evidence would involve large and significant program impacts from multiple studies that include large number of individuals whose characteristics are similar to those you are trying to serve. Unfortunately, rigorous replication is rare. If rigorous studies are available with similar participants, then those studies likely provide the best evidence for selecting programs and practices. Focusing on less rigorous observational or quasi‐experimental studies (e.g., pre‐post comparisons) should be the last resort. This, however, leads to a tension between focusing on less rigorous studies that may have involved the most similar individuals and more rigorous studies that included less similar individuals. In this case, it is likely to be the more rigorous studies that provide the best evidence unless there is compelling evidence that the program or practice has variable effects for different children. Interactions between treatment and either child or family characteristics are not common in studies of early care and education (Burchinal et al., 2015). Unfortunately, the existence of any research conducted by anyone is sometimes viewed as providing sufficient evidence for determining EBP. In the example described previously, it appears that both Head Start and NC pre‐kindergarten programs were willing to accept research studies conducted by the developer that were not available to the public. The claim that a research study had been conducted seemed to be sufficient to warrant labeling the curriculum as evidence‐based. Finally, there needs to be attention paid to how the treatments and practices were implemented in the research studies. Ideally, there is evidence from rigorous studies such as randomized clinical trials demonstrating the program is effective when implemented at scale by practitioners without extra support from the developers. The IES has a hierarchy for developing interventions that begins with exploratory research, studies to develop the intervention, efficacy studies in which the developer is engaged in ensuring the fidelity of implementation of the treatment, and effectiveness studies in which the treatment is tested at scale as delivered in the real world. This progression allows for the careful development of a program and demonstration that it is effective under ideal circumstances and under real circumstances. There is a real need for many more studies of the widely used programs as they are delivered at scale by practitioners. Too many advocates assume that treatment effects from less rigorous studies or even from very controlled rigorous studies provide estimated impacts of those programs when delivered at scale – whereas some dilution in those impacts should be expected in those circumstances.

Recommendations for using an evidence‐based approach In light of these important variations in definitions of EBP, theoretical orientations, and standards of evidence, determining what constitutes evidence‐based might seem less than straightforward. Nonetheless, clarity and guidance on which programs, practices, and

What Does it Mean to be Evidence-based?  127 policies are most likely to support and better children’s lives are in high demand from a wide range of stakeholders (Murnane & Willett, 2011). Everyone’s goal is to improve the life circumstances of children and families. For this reason, it seems to us that it is important to use the most reliable information to create these programs or services and requirements to use such treatments provide some protection against always seeking the cheapest alternative when funding decisions are made. Addressing concerns about internal validity and causality should be the foundation. Ideally, there would be evidence from multiple randomized clinical studies (or their quasi‐ experimental equivalent) that the treatment is effective in meaningful ways (e.g., large enough effects on outcomes that have practical significance). Such evidence would provide some confidence that the treatment really does what it purports to do. This foundation is not sufficient, because there should also be evidence that the treatment worked for children from different backgrounds and contexts. Ideally, the randomized clinical trials would have included such children and analyses would demonstrate efficacy for important subgroup. In reality, this type of evidence often does not exist and decisions still must be made. For my part, I would rather rely on a treatment that had proven to be effective for children, even from a different context, because most programs and services for young children are based on relatively universal principles (e.g., children respond to sensitive stimulating care). For example, high quality center‐based early care and education is thought to enhance language, academic, and social development especially for children from low‐income families (Burchinal et  al., 2015; Yoshikawa et  al., 2013). This example of a moderating effect does not suggest different practices for different children – only that some children may benefit more. This has been the case in most of the many studies of early development in which I, as the statistician, looked for moderators (see Burchinal, Magnuson, Powell, & Hong, 2015 for review of mixed evidence regarding ethnic, gender, and SES differences in associations between child care experiences and early child development). For example, we found that all children seemed to benefit from attending high quality child care, but that children from more disadvantaged backgrounds (e.g., low‐income, low maternal education) appeared to benefit more (Burchinal et al., 2000, 2008; McCartney, Dearing, & Taylor, 2003; Minervino, 2014; Peisner‐Feinberg & Burchinal, 1997; Pungello et al., 2010). The practical implications must also be considered. It is important to pay attention to whether the evidence suggests that treatment impacts have practical implications (Kazdin, 2007) and that their benefits outweigh their costs. It is just as important that the outcomes in the clinical trials have real‐world implications as it is to show there are treatment impacts. For example, showing that the language and literacy skills that are the outcome in a study of early literacy instruction are linked to later reading skills is as important as demonstrating treatment impacts on those skills. Issues of cost are important as well because programs will not be implemented if they are too expensive, no matter how effective they are. Many randomized clinical trials are now being asked to collect cost estimates so that this information, as well as treatment impacts, is available to policymakers and practitioners as they make decisions. Finally, requirements that there be evidence to support services, practices, and programs for young children could be really important in ensuring that decisions are made, at least in part, based on evidence and not just cost. Funders – government, insurance

128  Burchinal and Forestieri companies, and families  –  are keen to spread their funds as widely as possible, and to choose the cheapest solution. By requiring strong evidence of effectiveness, it is possible that funders may be forced to choose more expensive and more effective options for these vulnerable children and their families.

Example of EBP Implemented in Early Childhood – Boston Pre‐kindergarten The Boston Pre‐kindergarten program provides one of the most successful examples of implementing EBP at scale (Weiland & Yoshikawa, 2013). Boston revamped its pre‐­ kindergarten (pre‐K) program after an internal evaluation raised questions about its quality and impact on children. They developed a model to change the pre‐kindergarten program that used evidence at each stage. Their theory of change focused on implementing explicit, intentional, and uniform curricula across classrooms with professional development s­upport to improve and maintain the quality of supports provided to teachers, based on a large early childhood literature that indicated that teachers with college degrees were more effective than teachers without college degrees (Barnett, 2013). They capitalized on research that showed that focused, sequenced curricula were more effective than the ­activity‐ based curricula typically used in preschools (Burchinal et al., 2015), and that intensive professional development linked to the curricula and which promoted higher quality teacher‐child interactions was more effective than typical professional development (Akers & Aiken, 2011; Bierman et al., 2008; Neuman & Cunningham, 2009; Powell, Diamond, Burchinal, & Koehler, 2010; Raver et al., 2011). Accordingly, they required teachers to have a college degree, selected a reading and math curriculum with strong evidence of being effective, and provided teachers with extensive professional development. The reading curriculum was Opening the World of Learning (OWL; Schickedanz & Dickinson, 2005) and the mathematics curriculum was Building Blocks (Clements & Sarama, 2007). Both OWL and Building Blocks have been shown to have positive effects on child outcomes, especially when implemented by teachers with college degrees (Ashe, Reed, Dickinson, Morse, & Wilson, 2009; Clements & Sarama, 2007, 2008, 2011). The Boston Pre‐K also provided aligned intensive professional development based on prior evidence of the efficacy of such practices on child outcomes (Pianta, Barnett, Burchinal, & Thornburg, 2009). The Boston Pre‐K offered workshops prior to the beginning of the school year, coaches that were well trained in the curricula, and communities of learners to support the implementation of the curricula within a framework that also focused on the emotional support in teacher‐child interactions and classroom organization. Any child within the city of Boston who turned four by September 1 could apply for the program. Not only was the pre‐K program designed using EBP with regard to teacher education, curricula, and professional development, the evaluation was also designed as a clinical trial to test the effectiveness of the overall model. The evaluation was conducted in 2008–09 in the program’s second year by an independent team from the Harvard Graduate School of Education. The program was evaluated when 2,045 children in 69 elementary schools were enrolled. Random assignment was not possible for the program that was already

What Does it Mean to be Evidence-based?  129 implemented in neighborhood, so a regression discontinuity design was used which has been shown to generate results equivalent to a randomized experiment. The regression discontinuity design compared students born close to the age cut‐off for enrollment who were leaving the program with those who were entering. A propensity score analysis was included to reduce possible pre‐existing differences in the children entering and leaving the program. Results indicated substantial differences in language (d = .34), early reading (d = .62), early math (d = .58), executive function inhibitory control (d = .20) and attention shifting (d = .27), and emotional recognition (d = .28). While the program appeared to be beneficial for all children, larger impacts were observed for Hispanic and Black children than for white children and for low‐income than for middle‐income children. Thus, while there were subgroup differences, there were also beneficial effects for all children on average. A follow‐up study using a less rigorous design (propensity score matching) indicated that differences were maintained on reading and math tests into third grade (Minervino, 2014).

Moving Forward: Translating Evidence to Policy and Practice Today, almost all publicly funded programs are expected to attend to empirical evidence on effectiveness (Haskins & Baron, 2011). Yet, it is clear that the wide scope of current definitions has resulted in uneven use of research evidence in determining best practices. To some extent, variations in definitions reflect differences in the emphasis placed on differential developmental trajectories or on rigor of study design and analysis for drawing causal inference. In addition, translating research into policy and practice depends on factors beyond the quality of the evidence, including social justice orientation of those doing the translation (Tseng, 2012). Although a major goal of most researchers, practitioners, and policymakers is to reduce the gaps between the more and less advantaged children, one of the factors involved in translating research into policy and practice involves balancing a goal of promoting social justice and a goal of focusing on empirical evidence. There is a concern that relying too heavily on having the highest quality research available to validate the programs and p­ractices used to achieve this goal may be too slow and can be disrespectful of the family values and culture (Learner, 2015). According to some, a social justice perspective informs judgment calls regarding practices that have some evidence of efficacy but have not been proven to be effective at scale in that specific context. Many proponents of the social j­ustice approach argue that we cannot wait for a meta‐analysis of dozens of experimental trials to determine the best practices because that would take too long and almost never focus on the specific context in question. Often framed within a Relational‐Developmental‐Systems model of development, the social justice perspective also argues that some programs of research have excluded the very children and families for whom policies and programs are being developed and implemented. Further, they tend to be concerned that validated programs and practices focus more on deficits (i.e., the skills the child may need to learn to catch up with the typical child) rather than on strengths (i.e., the skills, culture, values, and practices of that child and his/her family). Accordingly, they prefer to rely on practitioner expertise to make decisions. Other researchers, however, are willing to assume that research conducted on children

130  Burchinal and Forestieri who are somewhat similar to the target population is likely to also be effective for that target population. They argue that there is limited evidence that early childhood practices have markedly different impacts across contexts and populations. In summary, it appears the different views of evidence‐based decision making may result in an uncomfortable tradeoff. On one hand, focusing more on the empirical evidence almost certainly results in generalizing findings beyond the populations included in the evaluation, and, perhaps, in the process being inconsiderate of family contexts and value. On the other hand, focusing more on the context and the practitioner’s expertise may involve implementing practices that are ineffective or possibly even detrimental to the child and family. Based on my experience as a statistician in many early childhood research studies, I would recommend relying more on empirical evidence because I have never found that early childhood programs and practices that show a positive impact for some children and families are harmful for other children and families. In contrast, it is clear that evaluations have indicated that many widely‐used programs and practices are not effective when evaluated as evidenced by findings from evaluations of educational curricula, practices, and programs as reported in the WWC.

Conclusion In conclusion, evidence‐based approaches are essential for developing and delivering effective early childhood programs, practices, and policies to all children and families. And for policy and practice decision‐makers, a careful understanding of what constitutes evidence‐based is critical for those programs and policies to achieve their goals: Internal validity, external validity, and the practical significance of findings are key ingredients to evidence‐based approaches. Consistent with this sentiment, authors in the present Handbook of Early Childhood Development Programs, Practices, and Policies conclude their chapters with empirical progress charts. These progress charts concisely identify the state of evidence on the topic at hand. In doing so, the authors classify the evidence base from strong to weak, addressing the extent to which: (a) study designs meet scientific standards for causal inference, (b) there is evidence that programs and practices can be brought up to a national implementation scale with fidelity, and (c) the benefits to children and families are of practical significance. While political and social realities may mean that research is rarely the primary determinant of policy and practice (Tseng, 2012), attention to results from rigorous scientific evaluation will increase the likelihood that public programs are effective in promoting early childhood development.

References Akers, N., & Aikens, L. (2011). Background review of existing literature on coaching. No. c83feeedd 6494b7891ed25a04b0df255. Mathematica Policy Research. American Psychological Association (2005). Policy statement on evidence‐based practice in psychology. Washington, DC: Author. Retrieved from http://www.apa.org/practice/guidelines/evidence‐ based‐statement.aspx

What Does it Mean to be Evidence-based?  131 Ashe, M. K., Reed, S., Dickinson, D. K., Morse, A. B., & Wilson, S. J. (2009). Opening the World of Learning: Features, effectiveness, and implementation strategies. Early Childhood Services, 3, 179–191. Barnett, W. S. (2013). Getting the facts right on pre‐K and the president’s pre‐K proposal. New Brunswick, NJ: National Institute for Early Education Research. Barnett, W. S., Carolan, M. E., Squires, J. H., Clarke Brown, K., & Horowitz, M. (2015). The state of preschool 2014: State preschool yearbook. New Brunswick, NJ: National Institute for Early Education Research. Bierman, B. L., Domitrovich, C. E., Nix, R. L., Gest, S. D., Welsh, J. A., Greenberg, M. T., & Gill, S. (2008). Promoting academic and social‐emotional school readiness: The Head Start REDI Program. Child Development, 79, 1802–1817. Bronfenbrenner, U., & Morris, P. A. (2006). The bioecological model of human development. In W. Damon & R. M. Lerner (Eds.), The Handbook of child psychology. Vol. 1. Theoretical models of human development (6th ed.). (pp. 793–828). Hoboken, NJ: John Wiley & Sons, Inc. Burchinal, M., Magnuson, K., Powell, D., & Hong, S. S. (2015). Early child care and education and child development. In M. Bornstein, R. Lerner, & T. Leventhal (Eds.), Handbook of Child Psychology and Developmental Science. Vol. 4. Ecological Settings and Processes. Hoboken, NJ: John Wiley & Sons, Inc. Burchinal, M. R., Peisner‐Feinberg, E., Bryant, D. M., & Clifford, R. (2000). Children’s social and cognitive development and child‐care quality: Testing for differential associations related to p­overty, gender, or ethnicity. Applied Developmental Science, 4(3), 149–165. Burchinal, M. R., Roberts, J. E., Zeisel, S. A., & Rowley, S. J. (2008). Social risk and protective factors for African American children’s academic achievement and adjustment during the transition to middle school. Developmental Psychology, 44, 286–292. Buysse, V., & Wesley, P. W. (Eds.) (2006). Evidence‐based practice in the early childhood field. Washington, DC: Zero to Three. Clark, M. A., Isenberg, E., Liou, A. Y., Makowsky, L., & Zukiewicz, M. (2015). In focus: Assessing the Effectiveness of Teach for America’s Investing in Innovation Scale‐up. Princeton, NJ: Mathematica Policy Research. Clements, D. H., & Sarama, J. (2007). Building blocks—SRA real math, grade preK. Columbus, OH: SRA/McGraw‐Hill. Clements, D. H., & Sarama, J. (2008). Experimental evaluation of the effects of a research‐based preschool mathematics curriculum. American Educational Research Journal, 45, 443–494. Clements, D. H., & Sarama, J. (2011). Early childhood mathematics intervention. Science, 333(6045), 968–970. Coalition for Evidence‐Based Policy. (2003). Identifying and implementing educational practices supported by rigorous evidence: A user friendly guide. Washington, DC: Council for Excellence in Government. Retrieved from https://www2.ed.gov/rschstat/research/pubs/rigorousevid/ rigorousevid.pdf Cook, T. D., & Campbell, D. T. (1979). Quasi‐experimentation: Design and analysis for field setting. Boston, MA: Houghton Mifflin. Duncan, G. J., Engel, M., Claessens, A., & Dowsett, C. J. (2014). Replication and robustness in developmental research. Developmental Psychology, 50(11), 2417. Duncan, G. J., Ludwig, J., & Magnuson, K. A. (2007). Reducing poverty through preschool i­nterventions. The Future of Children, 17(2), 143–160. Duncan, G. J., Magnuson, K. A., & Ludwig, J. (2004). The endogeneity problem in developmental studies. Research in Human Development, 1(1–2), 59–80. Duncan, G. J., & Murnane, R. J. (2014). Restoring opportunity: The crisis of inequality and the c­hallenge for American education. Cambridge, MA: Harvard University Press.

132  Burchinal and Forestieri Durlak, J. A., & DuPre, E. P. (2008). Implementation matters: A review of research on the i­nfluence of implementation on program outcomes and the factors affecting implementation. American Journal of Community Psychology, 41(3–4), 327–350. Evidence‐Based Behavioral Practice Consortium (2015). Evidence‐Based Behavioral Practice. Retrieved from www.ebbp.org/ Evidence‐Based Medicine Working Group. (1992). Evidence‐based medicine: A new approach to  teaching the practice of medicine. Journal of the American Medical Association, 268, 2420–2425. Foster, E. M. (2010). Causal inference and developmental psychology. Developmental Psychology, 46(6), 1454. Franks, R. P., & Schroeder, J. (2013). Implementation science: What do we know and where do we go from here. In T. Halle, A. Metz, & Martinez‐Beck (Eds.), Applying implementation science in early childhood programs and systems (pp. 5–20). Baltimore, MD: Paul H. Brookes Publishing. Halle, T., Metz, A., & Martinez‐Beck, I. (Eds.). (2013). Applying implementation science in early childhood programs and systems. Baltimore, MD: Paul H. Brookes Publishing. Haskins, R., & Baron, J. (2011). Building the connection between policy and evidence. London, UK: NESTA. Head Start National Center on Teach and Learning. (2015). Planning and curriculum. http://eclkc. ohs.acf.hhs.gov/hslc/tta‐system/teaching/eecd/Curriculum Hjørland, B. (2011). Evidence‐based practice: An analysis based on the philosophy of science. Journal of the American Society for Information Science and Technology, 62, 1301–1310. doi:10.1002/asi.21523 Holland, P. W. (1986). Statistics and causal inference. Journal of the American Statistical Association, 81(396), 945–960. Kazdin, A. E. (2008). Evidence‐based treatment and practice: new opportunities to bridge clinical research and practice, enhance the knowledge base, and improve patient care. American Psychologist, 63(3), 146. Learner, R. M. (2015). Promoting positive human development and social justice: Integrating t­heory, research and application in contemporary developmental science. International Journal of Psychology, 50(3), 155–163. doi: 10.1002/ijop.1216 March, J., Silva, S.G., Compton, S., Shapiro, M., Califf, R., & Krishnan, R. (2005). The case for practical clinical trials in psychiatry. American Journal of Psychiatry, 162, 836–846. Mattox, T., & Kilburn, M. R. (2013). What is evidence‐based practice? Washington, DC: Promising Practices Network. Retrieved from http://www.promisingpractices.net/briefs/briefs_evidence_ based_practices.asp McCartney, K., Burchinal, M. R., & Bub, K. L. (2006). Selection, detection, and reflection. Monographs of the Society for Research in Child Development, 71(3), 105–126. McCartney, K., Dearing, E., & Taylor, B. A. (2003). Is higher‐quality child care an intervention for children from low‐income families? Paper presented at the Biennial Meeting of the Society for Research in Child Development, Tampa, FL. McCartney, K., & Rosenthal, R. (2000). Effect size, practical importance, and social policy for children. Child Development, 173–180. Minervino, J. (2014). Lessons from research and the classroom: Implementing high‐quality pre‐K that makes a difference for young children. Seattle, WA: Bill & Melinda Gates Foundation. Murnane, R., & Willett, J. (2011). Methods matter: Improving causal inference in educational and social science research. New York, NY: Oxford University Press. National Association for the Education of Young Children. (2015). Using research evidence. Retrieved from https://www.naeyc.org/research/using

What Does it Mean to be Evidence-based?  133 Neuman, S. B., & Cunningham, L. (2009). The impact of professional development and coaching on early language and literacy instructional practices. American Educational Research Journal, 46, 532–566. Office of Head Start. (2013). Fiscal year 2012 Head Start program fact sheet. [Fact Sheet] Retrieved from http://eclkc.ohs.acf.hhs.gov/hslc/data/factsheets/2012‐hs‐program‐factsheet.html Overton, W. F. (2015). Processes, relations and relational‐developmental‐systems. In W. F. Overton & P. C. M. Molenaar (Eds.), Handbook of child psychology and developmental science. Vol. 1. Theory and method. (7th ed.) (pp. 9–62). Hoboken, NJ: John Wiley & Sons, Inc. Peisner‐Feinberg, E., & Burchinal, M. (1997). Relations between preschool children’s child‐care experiences and concurrent development: The cost, quality, and outcomes study. Merrill‐Palmer Quarterly, 43, 451–477. Pianta, R. C., Barnett, W. S., Burchinal, M., & Thornburg, K. R. (2009). The effects of preschool education: what we know, how public policy is or is not aligned with the evidence base, and what we need to know. Psychological Science in the Public Interest, 10(2), 49–88. Powell, D. R., Diamond, K. E., Burchinal, M. R., & Koehler, M. J. (2010). Effects of an early l­iteracy professional development intervention on Head Start teachers and children. Journal of Educational Psychology, 102, 299–312. Pungello, E. P., Kainz, K., Burchinal, M., Wasik, B. H., Sparling, J., Ramey, C. T., & Campbell, F. (2010). Early educational intervention, early cumulative risk, and the early home environment as predictors of young adult outcomes within a high‐risk sample. Child Development, 81, 410–426. QRIS Compendium (2014). A catalog and comparison of quality rating and improvement systems. Retrieved from qriscompendium.org/ Raver, C. C., Jones, S. M., Li‐Grining, C., Zhai, F., Bub, K., & Pressler, E. (2011). CSRP’s impact on low‐income preschoolers’ preacademic skills: Self‐regulation as a mediating mechanism. Child Development, 82, 362–378. Sackett, D. L., Straus, S. E., Richardson, W. S., Rosenberg, W., & Haynes, R. B. (2000). Evidence based medicine: How to practice and teach EBM. (2nd ed.). London, UK: Churchill Livingstone. Sameroff, A. J. (Ed.). (2009). The transactional model of development: How children and contexts shape each other. Washington, DC: American Psychological Association. Schickedanz, J. A., & Dickinson, D. K. (2005). Opening the world of learning: A comprehensive early literacy program. Parsippany, NJ: Pearson Early Learning. Shadish, W., Cook, T. D., & Campbell, D. T. (2002). Experimental and quasi‐experimental designs for generalized causal inference. Boston, MA: Houghton Mifflin Company. Social Work Policy Institute. (2008). Evidence‐based practice. Retrieved from http://www. socialworkpolicy.org/research/evidence‐based‐practice.html Spring, B., & Hitchcock, K. (2009). Evidence‐based practice in psychology. In I. B. Weiner & W. E. Craighead (Eds.), Corsini’s encyclopedia of psychology (4th ed.) (pp. 603–607). New York, NY: John Wiley & Sons, Inc. Tseng, V. (2012). The uses of research in policy and practice. Ann Arbor, MI: Society for Research in Child Development. Thomas, G., & Pring, R. (Eds.). (2004). Evidence‐based practice in education. Buckingham, UK: Open University. US Department of Health and Human Services & US Department of Education (2016). Family engagement from early years to early grades: Draft policy statement. Retrieved from https://www.acf. hhs.gov/sites/default/files/ecd/draft_hhs_ed_family_engagement.pdf

134  Burchinal and Forestieri Weiland, C., & Yoshikawa, H. (2013). Impacts of a prekindergarten program on children’s m­athematics, language, literacy, executive function, and emotional skills. Child Development, 84(6), 2112–2130. What Works Clearinghouse. (2015). About the WWC. Retrieved from http://ies.ed.gov/ncee/wwc/ aboutus.aspx Winship, C., & Morgan, S. L. (1999). The estimation of causal effects from observational data. Annual Review of Sociology, 25, 659–706. Yoshikawa, H., Weiland, C., Brooks‐Gunn, J., Burchinal, M. R., Espinosa, L. M., Gormley, W. T., … Zaslow, M. J. (2013). Investing in our future: The evidence base on preschool education. Ann Arbor, Michigan: Society for Research in Child Development.

chapter SEVEN Neural Development in Context: Differences in Neural Structure and Function Associated with Adverse Childhood Experiences Emily C. Merz and Kimberly G. Noble

Adversity refers to hardship or negative circumstances that threaten an individual’s typical functioning. A wide variety of adverse experiences can occur during childhood, potentially posing challenges to development across multiple domains and producing lasting impacts on mental health and academic achievement. Indeed, evidence from epidemiological studies suggests a dose‐response association in which greater exposure to childhood adversity is linked with a higher risk of negative long‐term outcomes (Felitti et al., 1998). New research is extending this work by investigating the ways in which early adversity influences children’s brain development. In this chapter, we focus on one of the most prevalent types of early adversity, poverty or socioeconomic disadvantage, which is estimated to affect more than 1 in 5 children from birth to 5 years of age in the United States (US Census Bureau, 2015). Low socioeconomic status (SES), which is typically indicated by low parental income, education, or occupational status, is a distal marker indicating a  higher risk of more stressful and less cognitively enriching proximal environments (Evans, 2004). We also discuss several examples of more severe types of early adversity, including child maltreatment (i.e., abuse or neglect on the part of a caregiver) and early institutionalization (i.e., orphanage rearing). Children with these backgrounds tend to experience extreme adversity such as trauma or profound cognitive and social‐emotional deprivation (Smyke et al., 2007). The Wiley Handbook of Early Childhood Development Programs, Practices, and Policies, First Edition. Edited by Elizabeth Votruba-Drzal and Eric Dearing. © 2017 John Wiley & Sons, Inc. Published 2017 by John Wiley & Sons, Inc.

136  Merz and Noble Extensive evidence has indicated that exposure to adverse experiences early in life is associated with poor health and achievement outcomes throughout the lifespan. Studies of broad‐based outcomes indicate associations between socioeconomic disadvantage and lower academic achievement from early childhood through adolescence (Bradley & Corwyn, 2002; McLoyd, 1998; Reardon, 2011). Children and adolescents from low‐SES backgrounds also show higher rates and levels of internalizing (e.g., depression, anxiety) and externalizing problems (e.g., conduct disorders) compared to those from higher SES backgrounds, and the risk of mental health problems increases with the duration of socioeconomic disadvantage (Goodman, Slap, & Huang, 2003; Merikangas et al., 2010; NICHD ECCRN, 2005; Shanahan, Copeland, Costello, & Angold, 2008; Tracy, Zimmerman, Galea, McCauley, & Vander Stoep, 2008; Wadsworth & Achenbach, 2005). Similarly, children who have been maltreated or adopted from institutions are at elevated risk for multiple forms of psychopathology that persists into adulthood (Bos et al., 2011; Cohen, Brown, & Smailes, 2001; McLaughlin et al., 2012; Green et al., 2010). This research has generated considerable interest in identifying the more specific underlying neural mechanisms that link adverse childhood experiences with a higher risk of negative long‐term outcomes. Understanding these mechanisms is important in terms of informing the design of effective targeted interventions for at‐risk children and their families and shaping policy that dictates the allocation of funding and resources for prevention and intervention programs. Describing the effects of adverse childhood experiences on neural development is also important for a basic scientific understanding of the role of experience in brain development. As such, in this chapter we review the research on associations between adverse childhood experiences and brain development. We first present an overview of normative brain development and briefly describe theoretical perspectives linking early experience with brain development. We then review research in humans showing links between childhood adversity and neural structure and function. This section is focused on exposure to socio‐ economic disadvantage during childhood but also highlights studies of child maltreatment and early institutionalization. We conclude by discussing topics and future directions relevant to researchers and practitioners interested in early experience.

Normative Patterns of Brain Development In general, brain development consists of progressive and regressive changes, with neurons, glial cells, and synapses being initially overproduced and then pruned. Prenatally, neurons are produced through the process of neurogenesis and then migrate to their final position (Stiles & Jernigan, 2010). Neurons then decrease in number as they undergo apoptosis or programmed cell loss, prior to birth. Once neurons have migrated, their differentiation, including the development of dendrites and axons that allow them to communicate with other neurons, begins prenatally but continues postnatally. In contrast to neurons, glial cell production and migration continues for an extended period after birth, glial cell differentiation continues throughout childhood, and glial cells undergo naturally occurring cell death postnatally (Brown & Jernigan, 2012).

Neural Development in Context  137 Postnatally, the brain develops most rapidly during early childhood, continues to develop through adolescence, with plateauing of some structures and circuits but ongoing age‐related changes in others across the lifespan. The pruning of neuronal processes such as axons and dendrites occurs postnatally throughout childhood and adolescence. Myelin, which is an insulating layer or sheath (made out of glial cells), begins to form around axons in the third trimester, allowing signals to be transmitted quickly and efficiently. Although myelination is mostly complete by the end of the second postnatal year, it c­ontinues into adulthood in some cortical areas. Synaptogenesis refers to the formation of synapses which allow electrochemical signals to be transmitted from one neuron to another. It begins during the third trimester, peaks about three months after birth, and ends before the second year of life (Huttenlocher & Dabholkar, 1997; Petanjek et al., 2011). During this early period of synaptic growth, there are rapid increases in synaptic density and the total number of synapses, to a final number that exceeds adult levels (Huttenlocher & de Courten, 1987; Innocenti & Price, 2005; Rakic, Bourgeois, Eckenhoff, Zecevic, & Goldman‐Rakic, 1986). Synaptic pruning/elimination begins in early childhood and continues for an extended period through childhood and adolescence. The process of pruning is regulated by competitive interactions between neuronal connections (Courchesne, Chisum, & Townsend, 1994). As two neurons co‐ activate, the association between the neurons strengthens, and it becomes more likely that this synaptic connection will persist. In contrast, synaptic connections that are infrequently activated become weaker over time and are likely to be pruned (Greenough, Black, & Wallace, 1987; Purves & Lichtman, 1980), leading to the common aphorism, “cells that fire together, wire together.” These processes at the cellular level underlie developmental changes in gray matter (neuron cell bodies, dendrites) and white matter (axons, glia) volumes. Total gray matter volume follows a nonlinear trend in which it initially increases in early life and then begins to decrease in middle childhood (Durston et al., 2001; Giedd et al., 1996a; Giedd et al., 1996b). However, cortical volume (gray matter volume) represents a composite of cortical surface area and cortical thickness, which show different developmental trajectories. Cortical thickness decreases rapidly in childhood and early adolescence, followed by a more gradual thinning, and ultimately plateauing in early adulthood (Giedd & Rapoport, 2010; Gogtay et al., 2004; Raznahan et al., 2011; Schnack et al., 2015; Sowell et al., 2004; Sowell et al., 2003; Sowell et al., 2007). This is likely due to both synaptic pruning and increases in white matter myelination. In contrast, cortical surface area expands through childhood and early adolescence and then shrinks in adulthood (Schnack et al., 2015). This is likely due to synaptic pruning and pressure from increased myelination expanding the brain surface outward. White matter volume increases rapidly during early childhood then continues to increase over childhood into adulthood (Durston et al., 2001). The specific timing of these developmental processes varies by brain region. In general, subcortical structures (e.g., amygdala, hippocampus) develop earlier than cortical structures (Payne, Machado, Bliwise, & Bachevalier, 2010). Furthermore, primary sensory and motor cortices mature earlier than association cortices, such as prefrontal cortex (PFC); (Giedd & Rapoport, 2010; Gogtay et al., 2004; Gogtay & Thompson, 2010; Shaw et al., 2008). For example, synaptic elimination occurs earlier for primary sensory cortex and later for association cortex (Huttenlocher & Dabholkar, 1997; Huttenlocher & de Courten, 1987).

138  Merz and Noble

Theoretical Framework Linking Early Experience with Brain Development Environmental experience is thought to play an important role in shaping brain development. Experience‐expectant models of development propose that expected environmental input (e.g., species‐typical care, such as adequate nutrition, social and linguistic stimulation, and the presence of an attachment figure) must be provided during certain time frames or sensitive periods for typical neural development to proceed (Greenough, Black, & Wallace, 1987; Marshall & Kenney, 2009; Rutter & O’Connor, 2004). Thus, the lack of species‐typical care during sensitive periods would be expected to lead to lasting alterations in brain development. In contrast, experience‐dependent models posit that variation in experience shapes brain development regardless of the timing of the experience (Greenough et al., 1987). These processes are more relevant to explaining individual differences in neural development due to variability in experience on a continuous scale closer to the normal range of experience. Experience‐dependent processes also emphasize the adaptation of neural development to an individual’s particular circumstances. Experience‐adaptive programming is a third concept linking experience and brain development which shares features with both experience‐expectant and experience‐ dependent models (Marshall & Kenney, 2009). Experience‐adaptive models specify that variability in experience during certain time frames will lead to persistent individual differences in neural development, with reduced plasticity outside of these time frames. Neural functioning is expected to adapt to the specific characteristics of the early environment. Experience‐expectant and experience‐adaptive models are closely associated with the notion of “sensitive periods” in development, namely, times when neural systems exhibit increased plasticity and therefore vulnerability to environmental influences. Sensitive periods are thought to coincide with periods when the brain is rapidly developing, and thus early childhood may be a time of maximal neural plasticity and vulnerability to environmental effects (Lupien, McEwen, Gunnar, & Heim, 2009). Given that brain regions vary in the timing of their normative developmental trajectories, they likely vary in their sensitive periods. It has been argued that those with a more protracted period of postnatal development, such as the PFC, may be particularly vulnerable to postnatal experience.

Differences in Neural Structure Associated with Adverse Childhood Experiences Exposure to adverse childhood experiences is associated with structural and functional alterations in the brain. Neuroimaging studies of brain structure (e.g., using magnetic resonance imaging [MRI] and diffusion tensor imaging [DTI]) have shown effects of early adversity on a number of brain regions and circuits underlying language, executive f­unction (EF), memory, and social‐emotional processing. These regions include left hemisphere

Neural Development in Context  139 language cortex, PFC, hippocampus, and amygdala as well as white matter pathways c­onnecting these structures (Brito & Noble, 2014). Although we focus here on these regions, which have the broadest empirical support, we note that differences associated with adverse childhood experiences have also been observed in other areas of the brain. Lower SES is associated with structural differences in the left temporal, temporo‐occipital, and frontal cortices, which support language skills (Hanson et al., 2013; Jednoróg et al., 2012). In a study of SES effects with the largest sample size to date, lower family income and parental education were associated with reduced surface area of left hemisphere language areas (Noble et  al., 2015). In another study, an interaction between parental education and age was found for the left superior temporal gyrus and left inferior frontal gyrus, such that SES differences increased with age in these regions. Specifically, at older ages, lower parental education was associated with smaller volume of these regions (Noble, Houston, Kan, & Sowell, 2012). Furthermore, a marginally significant correlation between SES and inferior frontal gyrus volume was observed in a small sample of 5‐year‐ old children (Raizada, Richards, Meltzoff, & Kuhl, 2008). The PFC, the anterior portion of the frontal lobe, is associated with EF (which includes inhibitory control, working memory, and cognitive flexibility) and emotion regulation. The anterior cingulate cortex (ACC) and lateral PFC areas, such as dorsolateral PFC (dlPFC), support EF and attention regulation (Bunge & Crone, 2009). Effortful emotion regulation processes also rely on the dorsal ACC and regions in the lateral PFC, which modulate amygdala activity (Buhle et al., 2014; Ochsner et al., 2004; Urry et al., 2006). Medial PFC areas, including the orbitofrontal cortex (OFC), modulate amygdala activity to support implicit or automatic emotion regulation processes (Milad & Quirk, 2012; Weinberg, Johnson, Bhatt, & Spencer, 2010; Etkin, Egner, Peraza, Kandel, & Hirsch, 2006). Neuroimaging studies have shown that childhood socioeconomic disadvantage is associated with changes in PFC structure. Specifically, lower parental education has been associated with reduced prefrontal cortical thickness in the left superior frontal gyrus and right anterior cingulate gyrus (Lawson, Duda, Avants, Wu, & Farah, 2013). Similarly, lower scores on an SES composite were associated with reduced volume of the superior and middle frontal gyri in a small sample of 8–10‐year‐old children (Jednoróg et al., 2012). In a study of over 1,000 children and adolescents, lower family income and parental education were associated with reduced surface area of prefrontal cortical regions, including the middle and superior frontal gyri. Differences in total cortical surface area were found to mediate links between family income and children’s performance on certain EF tasks (Noble et al., 2015). Another study found SES‐related differences in cortical thickness in areas that included PFC regions (Mackey et al., 2015), and these brain structural differences partially accounted for socioeconomic disparities in school achievement test scores. Another study reported that infants from lower income families had reduced frontal lobe volume, suggesting that SES‐related differences in PFC structure may emerge early in life. Children from lower income families also had slower trajectories of brain growth during infancy and early childhood (Hanson et  al., 2013). In addition, children from lower income families displayed structural differences in the frontal lobe, which partially explained their lower academic achievement (Hair, Hanson, Wolfe, & Pollak, 2015). Childhood maltreatment and early institutionalization have also been associated with decreased PFC volume (De Brito et al., 2013; Kelly et al., 2013). For instance, children

140  Merz and Noble exposed to physical abuse had smaller OFC volume, which was associated with their behavioral functioning (Hanson et al., 2010), and 12–14‐year‐old post‐institutionalized children had smaller prefrontal cortical volume (attributable to differences in surface area) compared to non‐adopted children reared in their biological families (Hodel et al., 2015). The hippocampus is part of the limbic system and is required for learning and memory (Jarrard, 1993). Lower family income (Hair, Hanson, Wolfe, & Pollak, 2015; Hanson, Chandra, Wolfe, & Pollak, 2011; Noble, Houston, Kan, & Sowell, 2012; Luby et al., 2013), lower parental education (Hanson et  al., 2011; Noble et  al., 2015), and lower scores on SES composites (Hanson et al., 2015; Jednoróg et al., 2012) have been associated with smaller hippocampal volumes in children. In one study of adults, childhood SES was positively associated with hippocampal volume, even after adjusting for adulthood SES, suggesting that early experience may have an effect on structural brain development over and above later experience (Staff et  al., 2012). Maltreatment and early institutionalization have also been associated with differences in hippocampal volume, but findings are more mixed. For instance, childhood exposure to maltreatment is associated with reduced hippocampal volume in adulthood but not when measured in childhood, p­ossibly because effects on the hippocampus emerge later in life even when exposure to adversity occurred early (Hart & Rubia, 2012; Tottenham & Sheridan, 2010). Similarly, previously institutionalized children have not been found to differ from control groups in the volume of the hippocampus (Mehta et al., 2009; Tottenham et al., 2010; Sheridan, Fox, Zeanah, McLaughlin, & Nelson, 2012; McLaughlin, Sheridan, Winter, Fox, Zeanah, & Nelson, 2013). The amygdala is part of the limbic system that supports emotional and social information processing (Adolphs, Tranel, Damasio, & Damasio, 1995). Findings for effects on the amygdala of childhood socioeconomic disadvantage, maltreatment, and early institutionalization have been inconsistent. In one study of SES, lower parental education (but not family income) was associated with larger amygdala volume (Noble, Houston, Kan, & Sowell, 2012), while in another study, lower family income was associated with smaller amygdala volume (Luby et al., 2013). One recent study found that socioeconomic disadvantage, physical abuse, and early institutionalization were each linked to smaller amygdala volumes (Hanson et al., 2015). However, other studies have failed to find significant SES‐related differences in amygdala structure (Hanson et al., 2011; Noble et al., 2015). Findings among adolescents exposed to childhood maltreatment have been similarly mixed (Hart & Rubia, 2012). Although two studies of children exposed to early institutionalization found larger amygdala volume (Mehta et al., 2009; Tottenham et al., 2010), other studies of this population have not found group differences (Sheridan, Fox, Zeanah, McLaughlin, & Nelson, 2012; McLaughlin, Sheridan, Winter, Fox, Zeanah, & Nelson, 2014). One possibility is that adverse childhood experiences result in an initial increase in amygdala volume, along with increased reactivity during emotion processing, which over time leads to “burnout” and diminished amygdala volume (Tottenham & Sheridan, 2010). Studies using diffusion tensor imaging (DTI) to measure white matter tracts have also reported structural differences linked to early childhood adversity. Maltreated children have decreased volume of the corpus callosum, which connects the right and left hemispheres and is the largest white matter tract in the brain (Hart & Rubia, 2012). Early institutional rearing is associated with alterations in white matter integrity, including in limbic and

Neural Development in Context  141 paralimbic pathways (e.g., uncinate fasciculus, which connects the frontal lobe (OFC) to the amygdala; Eluvathingal et al., 2006; Hanson et al., 2013; Kumar et al., 2014) and frontostriatal circuitry (Behen et al., 2009; Govindan, Behen, Helder, Makki, & Chugani, 2010; Hanson et al., 2013). In one longitudinal study, early institutional rearing was associated with alterations in white matter microstructure throughout the brain, but institutionalized children randomly assigned to high quality foster care did not significantly differ from the control group for most white matter tracts, suggesting improvement in white matter integrity following removal from the depriving environment (Bick et  al., 2015). Similarly, children randomly assigned to foster care did not differ from the control group in total white matter volume (Sheridan, Fox, Zeanah, McLaughlin, & Nelson, 2012). In sum, the evidence to date from studies using structural neuroimaging techniques suggests that adverse childhood experiences are associated with reduced volume in a number of brain regions including left hemisphere language cortex, PFC, and hippocampus, and differences in white matter integrity. Results for the amygdala have been inconsistent, but one possibility is that early adversity affects amygdala structure differently at different points in development. Similar effects are found across different types of adversity, which range in severity. Although many studies report on cortical volume, some studies have distinguished between surface area and cortical thickness, and effects of early adversity on both have been found across studies (Brito & Noble, 2014). Further research is needed to more fully elucidate the differences in neural structure associated with the type, timing, and duration of exposure to adversity.

Differences in Neural Function Associated with Adverse Childhood Experiences Adverse childhood experiences have also been linked with differences in brain function in studies using functional MRI (fMRI) and electrophysiological methods. Studies of childhood SES have revealed differences in neural activation during language and EF tasks, and a few studies have examined SES‐related disparities in neural function with regard to memory and social‐emotional processing (Ursache & Noble, 2016). In contrast, studies of neural function in maltreated and post‐institutionalized children have had more of a focus on social‐emotional processing and EF (Hart & Rubia, 2012).

Language In one fMRI study, SES was associated with the degree of hemispheric specialization in the left inferior frontal gyrus in 5‐year‐olds during an early literacy (phonological awareness) task. Higher SES was associated with higher left lateralization of language processing, which has been found to reflect the maturation of language‐processing areas of the brain (Raizada et al., 2008). In another fMRI study, SES moderated the relation of neural function in the left fusiform gyrus to phonological skill (Noble, Wolmetz, Ochs, Farah, & McCandliss, 2006). Specifically, among lower SES struggling readers, phonological skill

142  Merz and Noble differences were associated with large differences in brain activation during a reading task, primarily in the left fusiform gyrus region, an area of the brain that has been shown to be important for visual‐orthographic aspects of reading. However, this brain‐behavior relationship weakened as SES increased. One possible interpretation is that, among children who struggle with reading in the context of limited access to resources, difficulty reading might occur despite a typical underlying neurobiological profile. In contrast, among c­hildren who struggle with reading despite plentiful access to resources, reading difficulties might suggest an atypical neurobiological profile. In one study of 6–9‐month‐old infants, recordings of baseline EEG activity showed that lower SES infants had lower frontal gamma power, which may indicate early risk for language problems (Tomalski et al., 2013). In adults, childhood SES has been associated with larger amplitude negativity to syntactic violations. Specifically, in response to syntactic violations, adults who were raised in lower SES families exhibited smaller negative event‐related potential (ERP) responses in left anterior sites than did those who had grown up in higher SES environments (Pakulak & Neville, 2010). This effect was independent of adult education level.

Executive function Several studies have reported evidence of SES‐related disparities in EF using both fMRI and ERP methods. In one fMRI study, lower SES 8–12‐year‐old children performed worse than higher SES children on a nonverbal stimulus‐response learning task. During completion of this EF task, low‐SES children showed greater recruitment of the right m­iddle frontal gyrus compared to higher SES children, which may reflect inefficient recruitment of neural resources during the task because this increased brain activation was not associated with improved task performance (Sheridan, Sarsour, Jutte, D’Esposito, & Boyce, 2012). In female adolescents, lower SES was associated with decreased inhibitory control and increased ACC activation (but no differences in dlPFC activation) over a two‐year period. Female adolescents with lower SES may develop less efficient inhibitory control, requiring greater and relatively unsuccessful compensatory recruitment of ACC (Spielberg et al., 2015). Several studies have also demonstrated SES‐related differences in ERP activity during selective attention tasks associated with the PFC (D’Angiulli, Herdman, Stapells, & Hertzman, 2008; Kishiyama, Boyce, Jimenez, Perry, & Knight, 2009). For example, one study investigated SES‐related disparities in neural indices of auditory selective attention in children. This study found that ERP responses to attended versus unattended auditory stimuli were reduced in lower SES compared to higher SES children (Stevens, Lauinger, & Neville, 2009). Maltreatment and early institutionalization have also been associated with differences in neural function during EF tasks. An fMRI study showed that maltreated adolescents did not differ in their performance on an inhibitory control task from control adolescents. However, they had increased activation in ACC and mPFC and decreased activation of dlPFC while completing the task (Carrion, Garrett, Menon, Weems, & Reiss, 2008). Also, adolescents who were adopted following early maltreatment or institutionalization

Neural Development in Context  143 (i.e., adopted from US foster care or international orphanages) showed greater activation in PFC regions (left inferior frontal cortex and dorsal ACC) and the striatum while p­erforming an inhibitory control task; yet they displayed worse performance on the task (Mueller et al., 2010).

Memory Few studies have investigated socioeconomic disparities in the neural correlates of m­emory performance. One study found that maternal reports of higher subjective social status were related to greater hippocampal activation in children during a relational m­emory task, but subjective social status was unrelated to behavioral performance (Sheridan, How, Araujo, Schamberg, & Nelson, 2013).

Social‐emotional processing Functional neuroimaging studies measuring neural activity associated with emotional stimuli (e.g., happy, sad, or angry faces) have been conducted with adults exposed to socioeconomic disadvantage during childhood. For example, lower perceived parental social standing has been associated with greater amygdala reactivity to angry faces in a sample of college students (Gianaros et  al., 2008). Similarly, adults with lower family income at age 9 had more difficulty suppressing amygdala activation and had reduced PFC activity during a task in which they had to use cognitive reappraisal to regulate their emotional responses to negative stimuli (Kim et al., 2013). Interestingly, these associations were specific to childhood SES as adulthood SES was not associated with brain activity. In a study of adults, lower parental education was related to activation in and connectivity among corticostriatal brain systems during a reward processing task. These associations remained significant even after controlling for participants’ own levels of education and household income (Gianaros et al., 2011). Child maltreatment is also associated with differences in neural responses during social‐ emotional processing tasks. An fMRI study of 8–18‐year‐olds adopted following maltreatment or institutionalization (i.e., from US foster care or orphanages abroad) measured activation in the hippocampus and amygdala while subjects viewed emotional faces (Maheu et al., 2010). Compared to control children, maltreated children had faster reaction times to angry faces and they showed significantly greater amygdala and hippocampus activation when viewing angry or fearful faces. In another fMRI study, maltreated children exhibited significantly greater activation of the insula and amygdala in response to angry faces (McCrory et al., 2011; see also McCrory et al., 2013). Increased amygdala activation to negative facial cues has also been reported in an fMRI study investigating the impact of early institutionalization (Tottenham et al., 2011). Children with a history of physical abuse or neglect also generated altered ERP response amplitudes compared to non‐maltreated children when presented with angry or fearful stimuli compared to happy or neutral targets (Cicchetti & Curtis, 2005; Curtis & Cicchetti, 2011; Parker & Nelson, 2005; Pollak, Klorman, Thatcher, & Cicchetti, 2001; Shackman,

144  Merz and Noble Shackman, & Pollak, 2007). Together, these results suggest that exposure to maltreatment is associated with a pattern of altered brain activity to threatening stimuli. Maltreated children may be hyper‐vigilant to perceived threat in the form of angry stimuli, which may be adaptive in the context of an abusive environment but maladaptive outside of this context. In sum, there is a small but growing literature on neural function in children exposed to adverse childhood experiences. Studies of SES have found differences in neural activation associated with language, EF, memory, and social‐emotional processing. In addition, maltreatment and early institutionalization have been linked with differences in the neural correlates of EF and social‐emotional processing. Early adversity may alter patterns of neural activity used to complete these tasks. For certain domains, such as EF, this may mean greater neural activation despite equal or worse task performance. Studies of SES, maltreatment, and early institutionalization have reported greater amygdala reactivity to negative emotional stimuli, which may persist into adulthood.

Explaining Differences in Academic Achievement and Mental Health Outcomes In this chapter, we have covered emerging neuroscience research providing insight into the links between early adversity and brain development. Results from this review indicate significant associations between adverse childhood experiences and changes to the neural systems supporting language, memory, EF, and social‐emotional processing, including left hemisphere language cortex, PFC, hippocampus, and amygdala, and white matter tracts connecting these structures. Importantly, the findings of this review are consistent with neurocognitive studies showing links between childhood adversity and decreased performance on specific neurocognitive tasks, including language, memory, EF, and social‐emotional processing tasks (Hart & Rubia, 2012; Merz, McCall, Wright, & Luna, 2013; Noble, McCandliss, & Farah, 2007; Noble, Norman, & Farah, 2005). One limitation of this literature is that most studies have been correlational with cross‐ sectional designs, which does not allow for any inference into causality or the direction of associations. Future research is needed that builds on these findings using longitudinal designs in which early adversity and brain development are assessed over time. Another way to strengthen causal inference is through randomized control trials in which some families receive an intervention whereas others do not, and children’s neural development is measured. For example, in terms of SES, such interventions can occur at the level of SES (e.g., income supplements), putative mediating factors (e.g., increasing linguistic stimulation in the home environment), or outcomes of interest (e.g., educational interventions). If children in the intervention group demonstrate improved brain development relative to those in the control group, then evidence for a causal role of experience is strengthened. Although interventions at these levels have been conducted to examine effects on behavioral outcomes (Costello, Compton, Keeler, & Angold, 2003; Raver et  al., 2011), few studies have included neuroimaging measures (see later in the chapter for further discussion of this topic). Indeed, studies of behavioral outcomes have provided evidence of a causal

Neural Development in Context  145 role for SES in children’s academic and mental health outcomes (Hackman, Farah, & Meaney, 2010), suggesting that SES may impact the neural mechanisms underlying these outcomes. The studies in this review have also elucidated the neural mechanisms that potentially underlie associations between adverse childhood experiences and higher risk of persistent mental health problems and lower academic achievement. Differences in the neural c­ircuitry underlying language, memory, and EF have been associated with academic achievement. Some evidence has supported a mediation model in which early adversity affects academic achievement via differences in neural structure. For example, lower f­amily income was associated with widespread reductions in cortical thickness, which in turn were associated with lower performance on standardized academic tests (Mackey et al., 2015), and structural differences in the frontal and temporal lobes were found to partially explain socioeconomic disparities in academic achievement (Hair, Hanson, Wolfe, & Pollak, 2015). In addition, neural systems underlying social‐emotional processing and EF have been associated with mental health. There is some empirical support for a mediation model in which early adversity influences mental health via differences in neural structure. For example, reduced prefrontal cortical thickness partially mediated the association of institutionalization with inattention and impulsivity, suggesting that atypical PFC structure may be partially responsible for the markedly elevated rates of ADHD found among post‐institutionalized children (McLaughlin et  al., 2014). However, further work, especially using longitudinal designs, is needed that examines mediation models which include associations between these neural measures and behavioral functioning.

Mechanisms Linking Adverse Childhood Experiences and Neural Development Here we provide an overview of two pathways by which adverse childhood experiences have been hypothesized to influence neural development: stress and low levels of cognitive and linguistic stimulation.

Stress Across separate literatures focused on childhood socioeconomic disadvantage, maltreatment, and early institutionalization, chronic stress is thought to be a primary mediator of effects on brain development. Indeed, these adverse environments represent stressful experiences for children and are often described as types of “early life stress” (McLaughlin, Sheridan, & Lambert, 2014; Pechtel & Pizzagalli, 2011). Although the specific experiences differ, children exposed to any of these types of adversity often endure high levels of stress in their lives. Specific stressors that often characterize disadvantaged environments include crowding, dangerous neighborhoods, household chaos and unpredictability, and lower parental nurturance (Evans, 2004; Evans & Kim, 2013; Hackman, Farah, & Meaney,

146  Merz and Noble 2010). Maltreated children experience extremely high levels of stress from trauma or attachment insecurity in the parent‐child relationship (Shackman, Shackman, & Pollak, 2007). Institutionalized children frequently experience stress early in their lives from the lack of an attachment figure, exposure to frequent changes in caregivers, and low levels of responsive caregiving (Smyke et al., 2007). Neuroendocrine stress system reactivity is hypothesized to be a primary neurobiological mechanism through which early adversity affects neural development. One of the most extensively studied systems is the hypothalamic‐pituitary‐adrenal (HPA) axis. The HPA axis response to a stressor comprises a cascade of events which prepare the body to deal with a threat. In brief, the hypothalamus secretes corticotrophin‐releasing hormone (CRH), which signals the pituitary to release adrenocorticotropic hormone (ACTH). ACTH then binds to receptors in the adrenal glands leading to the release of cortisol. Circulating c­ortisol exerts negative feedback inhibition to shut down its own release when cortisol levels are high by acting on glucocorticoid receptors in the hippocampus. Adverse childhood experiences are associated with disrupted development and functioning of the HPA axis (Gunnar & Quevedo, 2007). Higher basal cortisol levels and cortisol reactivity have been found following early exposure to socioeconomic disadvantage (Blair et al., 2011; Chen, Cohen, & Miller, 2010; Evans & Schamberg, 2009; Lupien, King, Meaney, & McEwen, 2001), maltreatment (McCrory, De Brito, & Viding, 2010), and institutionalization (Hostinar, Sullivan, & Gunnar, 2014). However, exposure to these adverse experiences has also been associated with hypocortisolism, which is characterized by low cortisol, flat daytime production patterns, and blunted responses to stressors (Badanes, Watamura, & Hankin, 2011; Bruce, Fisher, Pears, & Levine, 2009; Fisher, Stoolmiller, Gunnar, & Burraston, 2007; Kraft & Luecken, 2009; Ouellet‐Morin et al., 2011). Differences in the duration, timing, and type of adversity are thought to explain these discrepancies. In particular, extreme or chronic exposure to stressors may lead to higher cortisol reactivity in the short term but blunted cortisol reactivity in the long term (Carpenter, Shattuck, Tyrka, Geracioti, & Price, 2011; MacMillan et al., 2009; Trickett, Gordis, Peckins, & Susman, 2014). In turn, HPA axis dysregulation has been associated with changes to neural structure and function, especially in the hippocampus, amygdala, and PFC (Arnsten, 2009). These areas of the brain have been shown to be particularly vulnerable to stress‐response system dysregulation because they play a role in mediating the HPA axis stress response and have high concentrations of glucocorticoid receptors (McEwen & Gianaros, 2010; Tottenham & Sheridan, 2010). For example, in rodent studies, chronic stress reduced the size of PFC dendrites, the parts of the neuron that receive input from neighboring brain cells. These structural differences in turn correlated with impaired performance on PFC‐dependent EF tasks (Arnsten, 2009; Hains et  al., 2009; Liston et  al., 2006; Liston, McEwen, & Casey, 2009; McEwen & Morrison, 2013). Parenting quality early in life is associated with variation in children’s HPA axis reactivity. Extensive research has shown that variations in licking and grooming of rat pups are associated with enduring differences in HPA axis reactivity (Gunnar & Quevedo, 2007; Liu et al., 1997; Weaver et al., 2004). Specifically, offspring of low licking and

Neural Development in Context  147 grooming mothers show higher HPA responses to stress compared with the offspring of high licking and grooming mothers. Low licking and grooming has been found to cause a reduction in the number of glucocorticoid receptors in the hippocampus and thus disrupt negative feedback regulation of the HPA axis (Hackman, Farah, & Meaney, 2010). Parental buffering of HPA axis reactivity has also been observed in human research (Blair et  al., 2011; Tottenham, 2012). For instance, in the presence of the attachment figure, toddlers who are in secure attachment relationships do not show elevations in cortisol to distress‐e­liciting events, whereas toddlers in insecure attachment relationships do (Gunnar & Quevedo, 2007; Hostinar, Sullivan, & Gunnar, 2014). Thus, lower parenting or caregiving quality whether occurring in the context of socioeconomic disadvantage, maltreatment, or institutionalization, may lead to HPA axis dysregulation, which in turn influences neural structure and function (Belsky & de Haan, 2011).

Low levels of cognitive and linguistic stimulation Low levels of cognitive and linguistic stimulation are another hypothesized mechanism by which adverse childhood experiences may affect neural development (Brito & Noble, 2014; Hackman, Farah, & Meaney, 2010; McLaughlin, Sheridan, & Lambert, 2014). Children from disadvantaged families often experience lower quality and quantity of cognitive and linguistic input in both their home and school environments compared to their peers from more advantaged families (Bradley & Corwyn, 2002; Hart & Risley, 1995; Hoff, 2003; Rowe & Goldin‐Meadow, 2009). They also experience lower exposure to enriching cognitive experiences in the home and school environments, including reduced access to books and extracurricular activities (Linver, Brooks‐Gunn, & Kohen, 2002; NICHD ECCRN, 2005; Sirin, 2005). In more extreme situations, children who are neglected by their parents or caregivers are not provided with adequate social interactions and physical resources, including books and toys (Hildyard & Wolfe, 2002). Children raised in institutions experience markedly lower exposure to interactions with adults, variation in daily routines and activities, and novel and age‐appropriate enriching cognitive stimuli (Smyke et al., 2007). Animal models have shown effects of variability in cognitive stimulation on brain development. Rodents assigned to rearing in “impoverished” environments (i.e., standard lab cage, without toys or littermates) have been compared to those reared in “enriched” environments (i.e., large cages with interesting and changing objects and multiple littermates). Such environmental “enrichment” is associated with increased cortical thickness due to greater dendritic branching, increased dendritic spine density, and more synapses per neuron in a number of brain areas (Davidson & McEwen, 2012; Kempermann, Kuhn, & Gage, 1997; Markham & Greenough, 2004; Sale, Berardi, & Maffei, 2009; van Praag, Kempermann, & Gage, 2000) as well as larger volume and greater myelination in the corpus callosum (Juraska & Kopcik, 1988; Sánchez, Hearn, Do, Rilling, & Herndon, 1998; though it has been pointed out that even such “enriched” environments are likely far less cognitively stimulating than the experience of growing

148  Merz and Noble up in the wild). Regardless, it is notable that these effects follow differences in early experience and have not been observed following variation in later experiences (Markham, Herting, Luszpak, Juraska, & Greenough, 2009). Lower levels of cognitive and linguistic stimulation may underlie differences in the development of left hemisphere language‐supporting cortical regions associated with adverse childhood experiences. For example, differences in the quality and quantity of linguistic stimulation in the home have been associated with variability in the development of left hemisphere language cortex (Perkins, Finegood, & Swain, 2013). Although we have emphasized their similarities, it is important to reiterate that d­ifferent types of environmental experiences may influence brain development through different underlying mechanisms. In general, maltreatment and institutionalization are more severe forms of early adversity compared to socioeconomic disadvantage, and it is important to  recognize that many socioeconomically disadvantaged families provide warm and n­urturing homes for their children. By definition, maltreatment and early institutionalization also more narrowly refer to disruptions in the parenting or caregiving environment. In contrast, low SES often indicates lower quality of a broad range of environments including home, school, and neighborhood settings. Given that these different adverse experiences often co‐occur, measuring the degree to which children face various mediating factors (e.g., stress, cognitive deprivation) is important for future research. It is likely that m­ediating mechanisms are better able to explain neural outcomes than are categorical characterizations of adverse experiences (McLaughlin, Sheridan, & Lambert, 2014).

Developmental Timing of Adversity Adversity likely has the most deleterious effects when it occurs during sensitive periods of development. Early childhood may be one such period of heightened vulnerability to environmental effects on neural development. Animal studies suggest that early exposure to adversity may have enduring effects on neural development even when circumstances improve later in life, whereas later exposure to adversity is less likely to produce lasting effects (Tottenham & Sheridan, 2010). Studies of early institutionalization, in which the timing and duration of extreme adversity can be clearly delineated, allow insight into sensitive periods in humans. As reviewed earlier, children adopted from institutions who were exposed to a circumscribed period of early deprivation exhibit differences in neural structure and function even many years after removal from an institution (Hodel et al., 2015; Mehta et al., 2009; Tottenham et al., 2010). In studies of this population, older age at adoption has been associated with poorer behavioral outcomes, often in a step‐like or threshold pattern rather than a linear association. Although age at adoption cut‐offs vary widely across studies, children adopted from institutions before 6 months of age tend to not differ from non‐adopted children reared in their birth families (Rutter et al., 2007) whereas those adopted after 18–24 months are generally at higher risk for a range of negative outcomes (Merz & McCall, 2010). Studies of SES have addressed timing effects as well and similarly found that childhood SES predicts neural

Neural Development in Context  149 structure above and beyond adulthood SES (Gianaros et  al., 2011; Kim et  al., 2013; Staff et al., 2012). Given that neural systems differ in their developmental trajectories, the specific timing of sensitive periods likely varies across neural systems. Therefore, depending on the timing of exposure, some neural processes may exhibit greater effects than others. For example, in the Bucharest Early Intervention Project (BEIP), institutionalized children who were randomly assigned to high‐quality foster care did not differ from control children who were raised in their birth families in total white matter volume or white matter integrity of most circuits (Bick et al., 2015; Sheridan et al., 2012), whereas they did have reduced cortical thickness across prefrontal, parietal, and temporal regions compared to the control group (McLaughlin et  al., 2014). These findings are consistent with the idea that neural processes vary in their plasticity and sensitive periods (Andersen et al., 2008) and underscore the need for future research to examine how neural plasticity varies across brain regions.

Leveraging Neuroimaging Tools in Prevention and Intervention Research The studies reviewed here highlight a set of specific neurocognitive skills (e.g., language, EF, memory, and social‐emotional processing) that early interventions should target in order to improve mental health and academic achievement trajectories in at‐risk children. Indeed, early interventions targeting and improving EF skills have found subsequent gains in academic achievement (Bierman, Nix, Greenberg, Blair, & Domitrovich, 2008; Diamond, Barnett, Thomas, & Munro, 2007; Raver et al., 2011). These findings suggest that targeting specific neurocognitive skills may be important in reversing the negative effects of early adversity on the underlying neural systems. However, few p­revention or intervention studies have integrated measures of neural development into their research designs. Integrating neuroimaging measures into prevention and intervention research is important for a variety of reasons (Beauchaine, Neuhaus, Brenner, & Gatzke‐Kopp, 2008; Bryck & Fisher, 2012; Cicchetti & Gunnar, 2008). In particular, differences at the neural level can be found even in the absence of differences at the behavioral level. More specifically, interventions may be found to enhance neural functioning even when behavioral improvement is not immediately observed (Raizada & Kishiyama, 2010). Thus, without examining outcomes at the ­neural level, positive intervention effects may be missed. Neural data may also be especially important when examining the maintenance of intervention gains over time. While the positive behavioral effects of many interventions seem to decrease over time, it is p­ossible that changes to neural systems persist. These changes could explain instances when the positive behavioral effects of interventions are seen years after the intervention has ­concluded (Raizada & Kishiyama, 2010). Neuroimaging tools can also be used to measure the neural mechanisms by which interventions improve behavioral outcomes and thus elucidate the mechanisms linking experience and behavioral outcomes in an experimental context that strengthens causal

150  Merz and Noble inferences. Only a few studies to date have examined the effects of interventions on neural development in at‐risk children. In one such study, a combination of parent training s­essions, which emphasized strategies to support children’s attention and reduce family stress, and child attention training sessions enhanced ERP correlates of selective attention and cognitive development in preschoolers from lower SES backgrounds (Neville et al., 2013). In another ERP study, an intervention involving parent training and a therapeutic playgroup produced increased feedback‐related ERP amplitudes in 5–7‐year‐old children in foster care (Bruce, McDermott, Fisher, & Fox, 2009). These studies suggest that neural development may be malleable to positive interventions following early adversity and extend findings from intervention studies showing improvements on attention and EF tasks by showing that the neural correlates of these cognitive processes are also amenable to improvement. Further research is needed to link changes in the neural underpinnings of neurocognitive skills (e.g., selective attention, EF) with improvement in children’s mental health and academic achievement over time.

Conclusion Consistent with theoretical perspectives linking experience with neural development, emerging neuroscience research has shown early adversity to be associated with changes to the structure and function of neural systems supporting language, EF, memory, and social‐emotional processing. These findings provide insight into the neural mechanisms potentially underlying associations between adverse childhood experiences and a higher risk of persistent mental health problems and lower academic achievement. Although existing research hints that early adversity plays a causal role in shaping brain development, future research is needed to strengthen causal inference. For example, large, prospective and longitudinal studies can help rule out threats to validity. Stronger still are randomized control trials which examine whether children who experience an intervention demonstrate changes in brain development, thereby ruling out selection bias and providing evidence of a causal role for early adversity. Prevention and intervention services that are provided early in life and target the neural systems identified in this review may be the most e­ffective in terms of reversing the effects of early adversity and promoting positive academic and mental health outcomes.

References Adolphs, R., Tranel, D., Damasio, H., & Damasio, A. R. (1995). Fear and the human amygdala. The Journal of Neuroscience, 15(9), 5879–5891. Andersen, S. L., Tomada, A., Vincow, E. S., Valente, E., Polcari, A., & Teicher, M. H. (2008). Preliminary evidence for sensitive periods in the effect of childhood sexual abuse on regional brain development. The Journal of Neuropsychiatry and Clinical Neurosciences, 20(3), 292–301.

Neural Development in Context  151 Arnsten, A. F. (2009). Stress signalling pathways that impair prefrontal cortex structure and f­unction. Nature Reviews Neuroscience, 10(6), 410–422. Badanes, L. S., Watamura, S. E., & Hankin, B. L. (2011). Hypocortisolism as a potential marker of allostatic load in children: Associations with family risk and internalizing disorders. Development and Psychopathology, 23(03), 881–896. 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(3), 745–774. Behen, M. E., Muzik, O., Saporta, A. S., Wilson, B. J., Pai, D., Hua, J., & Chugani, H. T. (2009). Abnormal fronto‐striatal connectivity in children with histories of early deprivation: A diffusion tensor imaging study. Brain Imaging and Behavior, 3(3), 292–297. Belsky, J., & de Haan, M. (2011). Annual research review: Parenting and children’s brain development: The end of the beginning. Journal of Child Psychology and Psychiatry, 52(4), 409–428. Bick, J., Zhu, T., Stamoulis, C., Fox, N. A., Zeanah, C., & Nelson, C. A. (2015). Effect of early institutionalization and foster care on long‐term white matter development: A randomized clinical trial. JAMA Pediatrics, 169(3), 211–219. Bierman, K. L., Nix, R. L., Greenberg, M. T., Blair, C., & Domitrovich, C. E. (2008). Executive functions and school readiness intervention: Impact, moderation, and mediation in the Head Start REDI program. Development and Psychopathology, 20(3), 821–843. Blair, C., Granger, D. A., Willoughby, M., Mills‐Koonce, R., Cox, M., Greenberg, M. T., … Fortunato, C. K. (2011). Salivary cortisol mediates effects of poverty and parenting on executive functions in early childhood. Child Development, 82(6), 1970–1984. Bos, K., Zeanah, C. H., Fox, N. A., Drury, S. S., McLaughlin, K. A., & Nelson, C. A. (2011). Psychiatric outcomes in young children with a history of institutionalization. Harvard Review of Psychiatry, 19(1), 15–24. Bradley, R. H., & Corwyn, R. F. (2002). Socioeconomic status and child development. Annual Review of Psychology, 53(1), 371–399. Brito, N. H., & Noble, K. G. (2014). Socioeconomic status and structural brain development. Frontiers in Neuroscience, 8, 276. Brown, T. T., & Jernigan, T. L. (2012). Brain development during the preschool years. Neuropsychology Review, 22(4), 313–333. Bruce, J., Fisher, P. A., Pears, K. C., & Levine, S. (2009). Morning cortisol levels in preschool‐aged foster children: Differential effects of maltreatment type. Developmental Psychobiology, 51(1), 14–23. Bruce, J., McDermott, J. M., Fisher, P. A., & Fox, N. A. (2009). Using behavioral and electrophysiological measures to assess the effects of a preventive intervention: A preliminary study with preschool‐aged foster children. Prevention Science, 10, 129–140. Bryck, R. L., & Fisher, P. A. (2012). Training the brain: Practical applications of neural plasticity from the intersection of cognitive neuroscience, developmental psychology, and prevention s­cience. American Psychologist, 67(2), 87–100. Buhle, J. T., Silvers, J. A., Wager, T. D., Lopez, R., Onyemekwu, C., Kober, H., … Ochsner, K. N. (2014). Cognitive reappraisal of emotion: A meta‐analysis of human neuroimaging studies. Cerebral Cortex, 24(11), 2981–2990. Bunge, S. A., & Crone, E. A. (2009). Neural correlates of the development of cognitive control. In  J. M. Rumsey & M. Ernst (Eds.), Neuroimaging in Developmental Clinical Neuroscience. (pp. 22–37). New York, NY: Cambridge University Press. Carpenter, L. L., Shattuck, T. T., Tyrka, A. R., Geracioti, T. D., & Price, L. H. (2011). Effect of childhood physical abuse on cortisol stress response. Psychopharmacology, 214(1), 367–375.

152  Merz and Noble Carrion, V. G., Garrett, A., Menon, V., Weems, C. F., & Reiss, A. L. (2008). Posttraumatic stress symptoms and brain function during a response‐inhibition task: An fMRI study in youth. Depression and Anxiety, 25(6), 514–526. Chen, E., Cohen, S., & Miller, G. E. (2010). How low socioeconomic status affects 2‐year hormonal trajectories in children. Psychological Science, 21(1), 31–37. Cicchetti, D., & Curtis, W. (2005). An event‐related potential study of the processing of affective facial expressions in young children who experienced maltreatment during the first year of life. Development and Psychopathology, 17(3), 641–677. Cicchetti, D., & Gunnar, M. R. (2008). Integrating biological measures into the design and evaluation of preventive interventions. Development and Psychopathology, 20(3), 737–743. Cohen, P., Brown, J., & Smailes, E. (2001). Child abuse and neglect and the development of  mental disorders in the general population. Development and Psychopathology, 13(4), 981–999. Conger, R. D., & Donnellan, M. B. (2007). An interactionist perspective on the socioeconomic context of human development. Annual Review of Psychology, 58, 175–199. Costello, E. J., Compton, S. N., Keeler, G., & Angold, A. (2003). Relationships between poverty and psychopathology: A natural experiment. JAMA, 290(15), 2023–2029. Courchesne, E., Chisum, H., & Townsend, J. (1994). Neural activity‐dependent brain changes in  development: Implications for psychopathology. Development and Psychopathology, 6(04), 697–722. Curtis, W. J., & Cicchetti, D. (2011). Affective facial expression processing in young children who have experienced maltreatment during the first year of life: An event‐related potential study. Development and Psychopathology, 23(2), 373–395. D’Angiulli, A., Herdman, A., Stapells, D., & Hertzman, C. (2008). Children’s event‐related p­otentials of auditory selective attention vary with their socioeconomic status. Neuropsychology, 22(3), 293–300. Davidson, R. J., & McEwen, B. S. (2012). Social influences on neuroplasticity: Stress and interventions to promote well‐being. Nature Neuroscience, 15(5), 689–695. De Brito, S. A., Viding, E., Sebastian, C. L., Kelly, P. A., Mechelli, A., Maris, H., & McCrory, E. J. (2013). Reduced orbitofrontal and temporal grey matter in a community sample of maltreated children. Journal of Child Psychology and Psychiatry, 54(1), 105–112. Diamond, A., Barnett, W. S., Thomas, J., & Munro, S. (2007). Preschool program improves c­ognitive control. Science, 318(5855), 1387–1388. Durston, S., Pol, H. E. H., Casey, B. J., Giedd, J. N., Buitelaar, J. K., & Van Engeland, H. (2001). Anatomical MRI of the developing human brain: What have we learned? Journal of the American Academy of Child & Adolescent Psychiatry, 40(9), 1012–1020. Eluvathingal, T. J., Chugani, H. T., Behen, M. E., Juhász, C., Muzik, O., Maqbool, M., … Makki, M. (2006). Abnormal brain connectivity in children after early severe socioemotional deprivation: A diffusion tensor imaging study. Pediatrics, 117(6), 2093–2100. Etkin, A., Egner, T., Peraza, D. M., Kandel, E. R., & Hirsch, J. (2006). Resolving emotional c­onflict: A role for the rostral anterior cingulate cortex in modulating activity in the amygdala. Neuron, 51(6), 871–882. Evans, G. W. (2004). The environment of childhood poverty. American Psychologist, 59(2), 77–92. Evans, G. W., & Kim, P. (2013). Childhood poverty, chronic stress, self‐regulation, and coping. Child Development Perspectives, 7(1), 43–48. Evans, G. W., & Schamberg, M. A. (2009). Childhood poverty, chronic stress, and adult working memory. Proceedings of the National Academy of Sciences, 106(16), 6545–6549.

Neural Development in Context  153 Felitti, V. J., Anda, R. F., Nordenberg, D., Williamson, D. F., Spitz, A. M., Edwards, V., … Marks, J. S. (1998). Relationship of childhood abuse and household dysfunction to many of the leading causes of death in adults: The Adverse Childhood Experiences (ACE) Study. American Journal of Preventive Medicine, 14(4), 245–258. 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(8), 892–905. Gianaros, P. J., Horenstein, J. A., Hariri, A. R., Sheu, L. K., Manuck, S. B., Matthews, K. A., & Cohen, S. (2008). Potential neural embedding of parental social standing. Social Cognitive and Affective Neuroscience, 3(2), 91–96. Gianaros, P. J., Manuck, S. B., Sheu, L. K., Kuan, D. C., Votruba‐Drzal, E., Craig, A. E., & Hariri, A. R. (2011). Parental education predicts corticostriatal functionality in adulthood. Cerebral Cortex, 21(4), 896–910. Giedd, J. N., & Rapoport, J. L. (2010). Structural MRI of pediatric brain development: What have we learned and where are we going? Neuron, 67(5), 728–734. Giedd, J. N., Snell, J. W., Lange, N., Rajapakse, J. C., Casey, B. J., Kozuch, P. L., … Rapoport, J. L. (1996a). Quantitative magnetic resonance imaging of human brain development: ages 4–18. Cerebral Cortex, 6(4), 551–559. Giedd, J. N., Vaituzis, A. C., Hamburger, S. D., Lange, N., Rajapakse, J. C., Kaysen, D., … Rapoport, J. L. (1996b). Quantitative MRI of the temporal lobe, amygdala, and hippocampus in normal human development: ages 4–18 years. Journal of Comparative Neurology, 366(2), 223–230. Gogtay, N., Giedd, J. N., Lusk, L., Hayashi, K. M., Greenstein, D., Vaituzis, A. C., … Thompson, P. M. (2004). Dynamic mapping of human cortical development during childhood through early adulthood. Proceedings of the National Academy of Sciences of the United States of America, 101(21), 8174–8179. Gogtay, N., & Thompson, P. M. (2010). Mapping gray matter development: Implications for  t­ypical development and vulnerability to psychopathology. Brain and Cognition, 72(1), 6–15. Goodman, E., Slap, G. B., & Huang, B. (2003). The public health impact of socioeconomic status on adolescent depression and obesity. American Journal of Public Health, 93, 1844–1850. Govindan, R. M., Behen, M. E., Helder, E., Makki, M. I., & Chugani, H. T. (2009). Altered water diffusivity in cortical association tracts in children with early deprivation identified with tract‐ based spatial statistics (TBSS). Cerebral Cortex, bhp122. 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(2), 113–123. Greenough, W. T., Black, J. E., & Wallace, C. S. (1987). Experience and brain development. Child Development, 58(3), 539–559. Grolnick, W. S., Gurland, S. T., DeCourcey, W., & Jacob, K. (2002). Antecedents and consequences of mothers’ autonomy support: An experimental investigation. Developmental Psychology, 38(1), 143–155. Gunnar, M., & Quevedo, K. (2007). The neurobiology of stress and development. Annual Review of Psychology, 58, 145–173. Hackman, D. A., Farah, M. J., & Meaney, M. J. (2010). Socioeconomic status and the brain: Mechanistic insights from human and animal research. Nature Reviews Neuroscience, 11(9), 651–659.

154  Merz and Noble Hains, A. B., Vu, M. A. T., Maciejewski, P. K., van Dyck, C. H., Gottron, M., & Arnsten, A. F. (2009). Inhibition of protein kinase C signaling protects prefrontal cortex dendritic spines and cognition from the effects of chronic stress. Proceedings of the National Academy of Sciences, 106(42), 17957–17962. Hair, N. L., Hanson, J. L., Wolfe, B. L., & Pollak, S. D. (2015). Association of child poverty, brain development, and academic achievement. JAMA Pediatrics, 169(9), 822–829. Hanson, J. L., Adluru, N., Chung, M. K., Alexander, A. L., Davidson, R. J., & Pollak, S. D. (2013). Early neglect is associated with alterations in white matter integrity and cognitive functioning. Child Development, 84(5), 1566–1578. Hanson, J. L., Chandra, A., Wolfe, B. L., & Pollak, S.D. (2011). Association between income and the hippocampus. PLoS ONE, 6:e18712. Hanson, J. L., Chung, M. K., Avants, B. B., Shirtcliff, E. A., Gee, J. C., Davidson, R. J., & Pollak, S. D. (2010). Early stress is associated with alterations in the orbitofrontal cortex: A tensor‐ based morphometry investigation of brain structure and behavioral risk. The Journal of Neuroscience, 30(22), 7466–7472. Hanson, J. L., Hair, N., Shen, D. G., Shi, F., Gilmore, J. H., Wolfe, B. L., & Pollak, S. D. (2013). Family poverty affects the rate of human infant brain growth. PLoS ONE, 8:e80954. Hanson, J. L., Nacewicz, B. M., Sutterer, M. J., Cayo, A. A., Schaefer, S. M., Rudolph, K. D., … Davidson, R. J. (2015). Behavioral problems after early life stress: Contributions of the hippocampus and amygdala. Biological Psychiatry, 77(4), 314–323. Hart, B., & Risley, T. R. (1995). Meaningful differences in the everyday experience of young American children. Baltimore, MD: Paul H Brookes Publishing. Hart, H., & Rubia, K. (2012). Neuroimaging of child abuse: A critical review. Frontiers in Human Neuroscience, 6, 1–24. Hildyard, K. L., & Wolfe, D. A. (2002). Child neglect: Developmental issues and outcomes. Child Abuse & Neglect, 26(6), 679–695. Hodel, A. S., Hunt, R. H., Cowell, R. A., Van Den Heuvel, S. E., Gunnar, M. R., & Thomas, K. M. (2015). Duration of early adversity and structural brain development in post‐institutionalized adolescents. NeuroImage, 105, 112–119. Hoff, E. (2003). The specificity of environmental influence: Socioeconomic status affects early vocabulary development via maternal speech. Child Development, 74(5), 1368–1378. Hostinar, C. E., Sullivan, R. M., & Gunnar, M. R. (2014). Psychobiological mechanisms underlying the social buffering of the hypothalamic–pituitary–adrenocortical axis: A review of animal models and human studies across development. Psychological Bulletin, 140(1), 256–282. Huttenlocher, P. R., & Dabholkar, A. S. (1997). Regional differences in synaptogenesis in human cerebral cortex. Journal of Comparative Neurology, 387(2), 167–178. Huttenlocher, P. R., & De Courten, C. (1987). The development of synapses in striate cortex of man. Human Neurobiology, 6(1), 1–9. Innocenti, G. M., & Price, D. J. (2005). Exuberance in the development of cortical networks. Nature Reviews Neuroscience, 6(12), 955–965. Jarrard, L. E. (1993). On the role of the hippocampus in learning and memory in the rat. Behavioral and Neural Biology, 60(1), 9–26. Jednoróg, K., Altarelli, I., Monzalvo, K., Fluss, J., Dubois, J., Billard, C., … Ramus, F. (2012). The influence of socioeconomic status on children’s brain structure. PLoS One, 7(8), e42486. Juraska, J. M., & Kopcik, J. R. (1988). Sex and environmental influences on the size and ultrastructure of the rat corpus callosum. Brain Research, 450(1), 1–8. Kelly, P. A., Viding, E., Wallace, G. L., Schaer, M., De Brito, S. A., Robustelli, B., & McCrory, E. J. (2013). Cortical thickness, surface area, and gyrification abnormalities in children exposed to maltreatment: Neural markers of vulnerability? Biological Psychiatry, 74(11), 845–852.

Neural Development in Context  155 Kempermann, G., Kuhn, H. G., & Gage, F. H. (1997). More hippocampal neurons in adult mice living in an enriched environment. Nature, 386(6624), 493–495. Kim, P., Evans, G. W., Angstadt, M., Ho, S. S., Sripada, C. S., Swain, J. E., … Phan, K. L. (2013). Effects of childhood poverty and chronic stress on emotion regulatory brain function in adulthood. Proceedings of the National Academy of Sciences, 110(46), 18442–18447. Kishiyama, M. M., Boyce, W. T., Jimenez, A. M., Perry, L. M., & Knight, R. T. (2009). Socioeconomic disparities affect prefrontal function in children. Journal of Cognitive Neuroscience, 21(6), 1106–1115. Kraft, A. J., & Luecken, L. J. (2009). Childhood parental divorce and cortisol in young adulthood: Evidence for mediation by family income. Psychoneuroendocrinology, 34(9), 1363–1369. Kumar, A., Behen, M. E., Singsoonsud, P., Veenstra, A. L., Wolfe‐Christensen, C., Helder, E., & Chugani, H. T. (2013). Microstructural abnormalities in language and limbic pathways in orphanage‐reared children: A diffusion tensor imaging study. Journal of Child Neurology, 29(3), 318–325. doi: 10.1177/0883073812474098 Lawson, G. M., Duda, J. T., Avants, B. B., Wu, J., & Farah, M. J. (2013). Associations between children’s socioeconomic status and prefrontal cortical thickness. Developmental Science, 16(5), 641–652. Linver, M. R., Brooks‐Gunn, J., & Kohen, D. E. (2002). Family processes as pathways from income to young children’s development. Developmental Psychology, 38(5), 719–734. Liston, C., McEwen, B. S., & Casey, B. J. (2009). Psychosocial stress reversibly disrupts prefrontal processing and attentional control. Proceedings of the National Academy of Sciences, 106(3), 912–917. Liston, C., Miller, M. M., Goldwater, D. S., Radley, J. J., Rocher, A. B., Hof, P. R., … McEwen, B. S. (2006). Stress‐induced alterations in prefrontal cortical dendritic morphology predict selective impairments in perceptual attentional set‐shifting. The Journal of Neuroscience, 26(30), 7870–7874. Liu, D., Diorio, J., Tannenbaum, B., Caldji, C., Francis, D., Freedman, A., … Meaney, M. J. (1997). Maternal care, hippocampal glucocorticoid receptors, and hypothalamic‐pituitary‐ a­drenal responses to stress. Science, 277(5332), 1659–1662. Luby, J., Belden, A., Botteron, K., Marrus, N., Harms, M.P., Babb, C., … Barch, D. (2013). The effects of poverty on childhood brain development: The mediating effect of caregiving and stressful life events. JAMA Pediatrics, 167, 1135–1142. Lupien, S. J., King, S., Meaney, M. J., & McEwen, B. S. (2001). Can poverty get under your skin? Basal cortisol levels and cognitive function in children from low and high socioeconomic status. Development and Psychopathology, 13(03), 653–676. Lupien, S. J., McEwen, B. S., Gunnar, M. R., & Heim, C. (2009). Effects of stress throughout the lifespan on the brain, behaviour and cognition. Nature Reviews Neuroscience, 10(6), 434–445. Mackey, A. P., Finn, A. S., Leonard, J. A., Jacoby‐Senghor, D. S., West, M. R., Gabrieli, C. F., & Gabrieli, J. D. (2015). Neuroanatomical correlates of the income‐achievement gap. Psychological Science, 26, 925–933. MacMillan, H. L., Georgiades, K., Duku, E. K., Shea, A., Steiner, M., Niec, A., … Schmidt, L. A. (2009). Cortisol response to stress in female youths exposed to childhood maltreatment: Results of the youth mood project. Biological Psychiatry, 66(1), 62–68. Maheu, F. S., Dozier, M., Guyer, A. E., Mandell, D., Peloso, E., Poeth, K., … Ernst, M. (2010). A  preliminary study of medial temporal lobe function in youths with a history of caregiver d­eprivation and emotional neglect. Cognitive, Affective, & Behavioral Neuroscience, 10(1), 34–49. Markham, J. A., & Greenough, W. T. (2004). Experience‐driven brain plasticity: Beyond the s­ynapse. Neuron Glia Biology, 1(4), 351–363.

156  Merz and Noble Markham, J. A., Herting, M. M., Luszpak, A. E., Juraska, J. M., & Greenough, W. T. (2009). Myelination of the corpus callosum in male and female rats following complex environment housing during adulthood. Brain Research, 1288, 9–17. Marshall, P. J., & Kenney, J. W. (2009). Biological perspectives on the effects of early psychosocial experience. Developmental Review, 29(2), 96–119. McCrory, E. J., De Brito, S. A., Kelly, P. A., Bird, G., Sebastian, C. L., Mechelli, A., … Viding, E. (2013). Amygdala activation in maltreated children during pre‐attentive emotional processing. The British Journal of Psychiatry, 202(4), 269–276. McCrory, E. J., De Brito, S. A., Sebastian, C. L., Mechelli, A., Bird, G., Kelly, P. A., & Viding, E. (2011). Heightened neural reactivity to threat in child victims of family violence. Current Biology, 21(23), R947–R948. McCrory, E., De Brito, S. A., & Viding, E. (2010). Research review: The neurobiology and genetics of maltreatment and adversity. Journal of Child Psychology and Psychiatry, 51(10), 1079–1095. McEwen, B. S., & Gianaros, P. J. (2010). Central role of the brain in stress and adaptation: Links to socioeconomic status, health, and disease. Annals of the New York Academy of Sciences, 1186(1), 190–222. McEwen, B. S., & Morrison, J. H. (2013). The brain on stress: Vulnerability and plasticity of the prefrontal cortex over the life course. Neuron, 79(1), 16–29. McLaughlin, K. A., Green, J. G., 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 US adolescents. Archives of General Psychiatry, 69(11), 1151–1160. McLaughlin, K. A., Sheridan, M. A., & Lambert, H. K. (2014). Childhood adversity and neural development: Deprivation and threat as distinct dimensions of early experience. Neuroscience & Biobehavioral Reviews, 47, 578–591. McLaughlin, K. A., Sheridan, M. A., Winter, W., Fox, N. A., Zeanah, C. H., & Nelson, C. A. (2013). Widespread reductions in cortical thickness following severe early‐life deprivation: A neurodevelopmental pathway to attention‐deficit/hyperactivity disorder. Biological Psychiatry, 76(8), 629–638. McLoyd, V. C. (1990). The impact of economic hardship on black families and children: Psychological distress, parenting, and socioemotional development. Child Development, 61(2), 311–346. McLoyd, V. C. (1998). Socioeconomic disadvantage and child development. American Psychologist, 53(2), 185–204. Mehta, M. A., Golembo, N. I., Nosarti, C., Colvert, E., Mota, A., Williams, S. C., … Sonuga‐ Barke, E. J. (2009). Amygdala, hippocampal and corpus callosum size following severe early institutional deprivation: The English and Romanian Adoptees study pilot. Journal of Child Psychology and Psychiatry, 50(8), 943–951. Merikangas, K. R., He, J. P., Brody, D., Fisher, P. W., Bourdon, K., & Koretz, D. S. (2010). Prevalence and treatment of mental disorders among US children in the 2001–2004 NHANES. Pediatrics, 125(1), 75–81. Merz, E. C., & McCall, R. B. (2010). Behavior problems in children adopted from psychosocially depriving institutions. Journal of Abnormal Child Psychology, 38(4), 459–470. Merz, E. C., McCall, R. B., Wright, A. J., & Luna, B. (2013). Inhibitory control and working memory in post‐institutionalized children. Journal of Abnormal Child Psychology, 41(6), 879–890. Milad, M. R., & Quirk, G. J. (2012). Fear extinction as a model for translational neuroscience: Ten years of progress. Annual Review of Psychology, 63, 129–151. Mueller, S. C., Maheu, F. S., Dozier, M., Peloso, E., Mandell, D., Leibenluft, E., … Ernst, M. (2010). Early‐life stress is associated with impairment in cognitive control in adolescence: An fMRI study. Neuropsychologia, 48(10), 3037–3044.

Neural Development in Context  157 National Institute of Child Health and Human Development Early Child Care Research Network. (2005). Duration and developmental timing of poverty and children’s cognitive and social development from birth through third grade. Child Development, 76, 795–810. Neville, H. J., Stevens, C., Pakulak, E., Bell, T. A., Fanning, J., Klein, S., & Isbell, E. (2013). Family‐based training program improves brain function, cognition, and behavior in lower s­ocioeconomic status preschoolers. Proceedings of the National Academy of Sciences, 110(29), 12138–12143. Noble, K. G., Houston, S. M., Brito, N. H., Bartsch, H., Kan, E., Kuperman, J. M., … Sowell, E. R. (2015). Family income, parental education and brain structure in children and adolescents. Nature Neuroscience, 18(5), 773–778. Noble, K. G., Houston, S. M., Kan, E., & Sowell, E. R. (2012). Neural correlates of socioeconomic status in the developing human brain. Developmental Science, 15(4), 516–527. Noble, K. G., McCandliss, B. D., & Farah, M. J. (2007). Socioeconomic gradients predict individual differences in neurocognitive abilities. Developmental Science, 10(4), 464–480. Noble, K. G., Norman, M. F., & Farah, M. J. (2005). Neurocognitive correlates of socioeconomic status in kindergarten children. Developmental Science, 8(1), 74–87. Noble, K. G., Wolmetz, M. E., Ochs, L. G., Farah, M. J., & McCandliss, B. D. (2006). Brain– behavior relationships in reading acquisition are modulated by socioeconomic factors. Developmental Science, 9(6), 642–654. Ochsner, K. N., Ray, R. D., Cooper, J. C., Robertson, E. R., Chopra, S., Gabrieli, J. D., & Gross, J. J. (2004). For better or for worse: Neural systems supporting the cognitive down‐and up‐regulation of negative emotion. Neuroimage, 23(2), 483–499. Ouellet‐Morin, I., Odgers, C. L., Danese, A., Bowes, L., Shakoor, S., Papadopoulos, A. S., … Arseneault, L. (2011). Blunted cortisol responses to stress signal social and behavioral problems among maltreated/bullied 12‐year‐old children. Biological Psychiatry, 70(11), 1016–1023. Pakulak, E., & Neville, H. J. (2010). Proficiency differences in syntactic processing of monolingual native speakers indexed by event‐related potentials. Journal of Cognitive Neuroscience, 22(12), 2728–2744. Parker, S. W., & Nelson, C. A. (2005). The impact of early institutional rearing on the ability to discriminate facial expressions of emotion: An event‐related potential study. Child Development, 76(1), 54–72. Payne, C., Machado, C. J., Bliwise, N. G., & Bachevalier, J. (2010). Maturation of the hippocampal formation and amygdala in Macaca mulatta: A volumetric magnetic resonance imaging study. Hippocampus, 20(8), 922–935. Pechtel, P., & Pizzagalli, D. A. (2011). Effects of early life stress on cognitive and affective function: an integrated review of human literature. Psychopharmacology, 214(1), 55–70. Perkins, S. C., Finegood, E. D., & Swain, J. E. (2013). Poverty and language development: Roles of parenting and stress. Innovations in Clinical Neuroscience, 10(4), 10–19. Petanjek, Z., Judaš, M., Šimić, G., Rašin, M. R., Uylings, H. B., Rakic, P., & Kostović, I. (2011). Extraordinary neoteny of synaptic spines in the human prefrontal cortex. Proceedings of the National Academy of Sciences, 108(32), 13281–13286. Pollak, S. D., Klorman, R., Thatcher, J. E., & Cicchetti, D. (2001). P3b reflects maltreated children’s reactions to facial displays of emotion. Psychophysiology, 38(2), 267–274. Purves, D., & Lichtman, J. W. (1980). Elimination of synapses in the developing nervous system. Science, 210(4466), 153–157. Raizada, R. D., & Kishiyama, M. M. (2010). Effects of socioeconomic status on brain development, and how cognitive neuroscience may contribute to levelling the playing field. Frontiers in Human Neuroscience, 4(3), 1–11.

158  Merz and Noble Raizada, R. D., Richards, T. L., Meltzoff, A., & Kuhl, P. K. (2008). Socioeconomic status predicts hemispheric specialisation of the left inferior frontal gyrus in young children. Neuroimage, 40(3), 1392–1401. Rakic, P., Bourgeois, J. P., Eckenhoff, M. F., Zecevic, N., & Goldman‐Rakic, P. S. (1986). Concurrent overproduction of synapses in diverse regions of the primate cerebral cortex. Science, 232(4747), 232–235. Raver, C. C., Jones, S. M., Li‐Grining, C., Zhai, F., Bub, K., & Pressler, E. (2011). CSRP’s impact on low‐income preschoolers’ preacademic skills: self‐regulation as a mediating mechanism. Child Development, 82(1), 362–378. Raznahan, A., Shaw, P., Lalonde, F., Stockman, M., Wallace, G. L., Greenstein, D., … Giedd, J. N. (2011). How does your cortex grow? The Journal of Neuroscience, 31(19), 7174–7177. Reardon, S. F. (2011). The widening socioeconomic status achievement gap: New evidence and possible explanations. In G. J. Duncan & R. Murnane (Eds.), Whither Opportunity? Rising Inequality, Schools, and Children’s Life Chances (pp. 91–115). New York, NY: Russell Sage Foundation. Repetti, R. L., Taylor, S. E., & Seeman, T. E. (2002). Risky families: Family social environments and the mental and physical health of offspring. Psychological Bulletin, 128(2), 330–366. Rowe, M. L., & Goldin‐Meadow, S. (2009). Differences in early gesture explain SES disparities in child vocabulary size at school entry. Science, 323(5916), 951–953. Rutter, M., Beckett, C., Castle, J., Colvert, E., Kreppner, J., Mehta, M., … Sonuga‐Barke, E. (2007). Effects of profound early institutional deprivation: An overview of findings from a UK longitudinal study of Romanian adoptees. European Journal of Developmental Psychology, 4(3), 332–350. Rutter, M., & O’Connor, T. G. (2004). Are there biological programming effects for psychological development? Findings from a study of Romanian adoptees. Developmental Psychology, 40(1), 81–94. Sale, A., Berardi, N., & Maffei, L. (2009). Enrich the environment to empower the brain. Trends in Neurosciences, 32(4), 233–239. Sánchez, M. M., Hearn, E. F., Do, D., Rilling, J. K., & Herndon, J. G. (1998). Differential rearing affects corpus callosum size and cognitive function of rhesus monkeys. Brain Research, 812(1), 38–49. Schnack, H. G., van Haren, N. E., Brouwer, R. M., Evans, A., Durston, S., Boomsma, D. I., … Pol, H. E. H. (2015). Changes in thickness and surface area of the human cortex and their r­elationship with intelligence. Cerebral Cortex, 25(6), 1608–1617. Shackman, J. E., Shackman, A. J., & Pollak, S. D. (2007). Physical abuse amplifies attention to threat and increases anxiety in children. Emotion, 7(4), 838–852. Shanahan, L., Copeland, W., Costello, E.J., & Angold, A. (2008). Specificity of putative psychosocial risk factors for psychiatric disorders in children and adolescents. Journal of Child Psychology and Psychiatry, 49, 34–42. Shaw, P., Kabani, N. J., Lerch, J. P., Eckstrand, K., Lenroot, R., Gogtay, N., … Wise, S. P. (2008). Neurodevelopmental trajectories of the human cerebral cortex. The Journal of Neuroscience, 28(14), 3586–3594. Sheridan, M. A., Fox, N. A., Zeanah, C. H., McLaughlin, K. A., & Nelson, C. A. (2012). Variation in neural development as a result of exposure to institutionalization early in childhood. Proceedings of the National Academy of Sciences, 109(32), 12927–12932. Sheridan, M. A., How, J., Araujo, M., Schamberg, M. A., & Nelson, C. A. (2013). What are the links between maternal social status, hippocampal function, and HPA axis function in children? Developmental Science, 16(5), 665–675.

Neural Development in Context  159 Sheridan, M. A., Sarsour, K., Jutte, D., D’Esposito, M., & Boyce, W. T. (2012). The impact of social disparity on prefrontal function in childhood. PLoS One, 7(4), e35744. Sirin, S.R. (2005). Socioeconomic status and academic achievement: A meta‐analytic review of research. Review Educational Research, 75, 417–453. Smyke, A. T., Koga, S. F., Johnson, D. E., Fox, N. A., Marshall, P. J., Nelson, C. A., & Zeanah, C. H. (2007). The caregiving context in institution‐reared and family‐reared infants and toddlers in Romania. Journal of Child Psychology and Psychiatry, 48(2), 210–218. Sowell, E.R., Peterson, B. S., Kan, E., Woods, R. P., Yoshii, J., Bansal, R., … Toga, A. W. (2007). Sex differences in cortical thickness mapped in 176 healthy individuals between 7 and 87 years of age. Cerebral Cortex, 17, 1550–1560. Sowell, E.R., Peterson, B. S., Thompson, P. M., Welcome, S. E., Henkenius, A. L., & Toga, A. W. (2003). Mapping cortical change across the human life span. Nature Neuroscience, 6, 309–315. Sowell, E.R., Thompson, P. M., Leonard, C. M., Welcome, S. E., Kan, E. & Toga, A. W. (2004). Longitudinal mapping of cortical thickness and brain growth in normal children. The Journal of Neuroscience, 24, 8223–8231. Spielberg, J. M., Galarce, E. M., Ladouceur, C. D., McMakin, D. L., Olino, T. M., Forbes, E. E., … Dahl, R. E. (2015). Adolescent development of inhibition as a function of SES and gender: Converging evidence from behavior and fMRI. Human Brain Mapping, 36(8), 3076–3086. Staff, R. T., Murray, A. D., Ahearn, T. S., Mustafa, N., Fox, H. C., & Whalley, L. J. (2012). Childhood socioeconomic status and adult brain size: Childhood socioeconomic status influences adult hippocampal size. Annals of Neurology, 71(5), 653–660. Stevens, C., Lauinger, B., & Neville, H. (2009). Differences in the neural mechanisms of selective attention in children from different socioeconomic backgrounds: An event‐related brain potential study. Developmental Science, 12(4), 634–646. Stiles, J., & Jernigan, T. L. (2010). The basics of brain development. Neuropsychology Review, 20(4), 327–348. Tomalski, P., Moore, D. G., Ribeiro, H., Axelsson, E. L., Murphy, E., Karmiloff‐Smith, A., … Kushnerenko, E. (2013). Socioeconomic status and functional brain development–associations in early infancy. Developmental Science, 16(5), 676–687. Tottenham, N. (2012). Human amygdala development in the absence of species‐expected caregiving. Developmental Psychobiology, 54(6), 598–611. Tottenham, N., Hare, T. A., Millner, A., Gilhooly, T., Zevin, J. D., & Casey, B. J. (2011). Elevated amygdala response to faces following early deprivation. Developmental Science, 14(2), 190–204. Tottenham, N., Hare, T. A., Quinn, B. T., McCarry, T. W., Nurse, M., Gilhooly, T., … Casey, B. J. (2010). Prolonged institutional rearing is associated with atypically large amygdala volume and difficulties in emotion regulation. Developmental Science, 13(1), 46–61. Tottenham, N., & Sheridan, M. A. (2010). A review of adversity, the amygdala and the hippocampus: A consideration of developmental timing. Frontiers in Human Neuroscience, 3, 68. Tracy, M., Zimmerman, F. J., Galea, S., McCauley, E., & Vander Stoep, A. (2008). What explains the relation between family poverty and childhood depressive symptoms? Journal of Psychiatry Research, 42, 1163–1175. Trickett, P. K., Gordis, E., Peckins, M. K., & Susman, E. J. (2014). Stress reactivity in maltreated and comparison male and female young adolescents. Child Maltreatment, 19(1), 27–37. Urry, H. L., Van Reekum, C. M., Johnstone, T., Kalin, N. H., Thurow, M. E., Schaefer, H. S., … Davidson, R. J. (2006). Amygdala and ventromedial prefrontal cortex are inversely coupled d­uring regulation of negative affect and predict the diurnal pattern of cortisol secretion among older adults. The Journal of Neuroscience, 26(16), 4415–4425.

160  Merz and Noble Ursache, A., & Noble, K. G. (2016). Neurocognitive development in socioeconomic context: Multiple mechanisms and implications for measuring socioeconomic status. Psychophysiology, 53(1), 71–82. US Census Bureau. (2015). Retrieved from http://datacenter.kidscount.org/data/tables/8447‐ children‐in‐poverty‐100‐by‐age‐group‐and‐race‐and‐ethnicity?loc=1&loct=2#detailed/1/any/ false/869,36/2757,4087,3654,3301,2322,3307,2664|/17079,17080 Van Praag, H., Kempermann, G., & Gage, F. H. (2000). Neural consequences of environmental enrichment. Nature Reviews Neuroscience, 1(3), 191–198. Wadsworth, M. E., & Achenbach, T. M. (2005). Explaining the link between low socioeconomic status and psychopathology: Testing two mechanisms of the social causation hypothesis. Journal of Consulting and Clinical Psychology, 73, 1146–1153. Weaver, I. C., Cervoni, N., Champagne, F. A., D’Alessio, A. C., Sharma, S., Seckl, J. R., … Meaney, M. J. (2004). Epigenetic programming by maternal behavior. Nature Neuroscience, 7(8), 847–854. Weinberg, M. S., Johnson, D. C., Bhatt, A. P., & Spencer, R. L. (2010). Medial prefrontal c­ortex  activity can disrupt the expression of stress response habituation. Neuroscience, 168(3), 744–756.

PART III Early Childhood Education and Care

chapter EIGHT Publicly Supported Early Care and Education Programs W. Steven Barnett, Elizabeth Votruba‐Drzal, Eric Dearing, and Megan E. Carolan

Introduction The last 50 years have witnessed substantial expansion of preschool education in the United States. In 1965 only about 11% of 3 and 4‐year‐olds attended a preschool classroom. By 2000 enrollment was five times higher. However, preschool participation p­lateaued shortly thereafter, and today it remains about 55% with nearly 70% of 4‐year‐ olds and 40% of 3‐year‐olds regularly in a classroom (National Center for Education Statistics, 2012). As public support for preschool programs has grown over the past 15 years, the percentage of children in public programs has increased, but overall enrollment in public and private programs as a percentage of the population has changed little. The increase in the relative importance of public funding for preschool programs c­reated at least the possibility of increased hours and improved quality, particularly for the most disadvantaged. The National Institute for Early Education Research (NIEER) estimates that in the 2013–14 school year about 1.3 million children were enrolled in state‐funded preschool programs and Head Start serves another 700,000 3‐ and 4‐year‐olds (Barnett, Carolan, Squires, Clarke Brown, & Horowitz, 2015). Of those enrolled in state pre‐K, the vast majority have been 4‐year‐olds, and Head Start served somewhat more The Wiley Handbook of Early Childhood Development Programs, Practices, and Policies, First Edition. Edited by Elizabeth Votruba-Drzal and Eric Dearing. © 2017 John Wiley & Sons, Inc. Published 2017 by John Wiley & Sons, Inc.

164  Barnett, Votruba-Drzal, Dearing, and Carolan children at age 4 than at age 3. In addition, some school districts and cities operate public preschool programs that serve primarily 4‐year‐olds. Overall, about 2.3 million of the roughly 8 million children ages 3 and 4 are served by public programs with additional children receiving a public subsidy for early care and education at this age (National Center for Education Statistics, 2012; Glynn, 2012). This chapter aims to carefully review developmental theory, research, and policy initiatives related to children’s early publicly funded preschool experiences in the United States. Throughout this chapter, the term pre‐K is used to refer to center‐ or school‐based early childhood classrooms that children attend during the year or two before entering kindergarten regardless of whether it is called child care, preschool, or something else. We wish to differentiate pre‐K from other early childhood education services that are delivered one‐on‐one or in more informal relative or home‐based settings and from those serving younger children. The majority of pre‐K programs target 4‐year‐old children, though significant numbers of 3‐year‐olds are also enrolled. Pre‐K programs exist in the private and public sectors, and share a focus on early childhood education, which differentiates pre‐K from programs that emphasize providing long hours of safe and nurturing care for children so their parents can work. We begin the chapter with a discussion of the central developmental theories that inform research on pre‐K. Then we review the extensive research literature addressing associations between pre‐K participation and children’s development, with attention to evidence emerging from model programs, Head Start, state pre‐K evaluations, and other studies. Next we highlight several important features found to enhance the e­ffectiveness of pre‐K programs. We end with a discussion of the current pre‐K policy landscape in the US, major challenges faced by policymakers, and considerations for future research.

Theoretical Foundations of Pre‐k Research With the expansion of public pre‐K programs, the need for descriptive, evaluative, and formative research that informs policy and practice has grown. While a complete review of relevant theory guiding this empirical work is beyond the scope of this single chapter, we provide a brief overview and point readers to recent comprehensive reviews. Much of the initial work on early education (e.g., Weikart, 1969; Zigler & Butterfield, 1968) was guided more by findings of burgeoning research on the harm of deprivation and the importance of early experience (e.g., Skeels, 1942; Skodak & Skeels, 1949; Hunt, 1961) than by theory, though the influences of Piaget (1954), Skinner (1954), and Gagne (1963) are evident in the various curricula used. The empirical research has continued to inform policy and practice at a broad level as modern neuroscience has provided greater precision and depth of evidence regarding the importance of early brain development (e.g., Center on the Developing Child, 2007; regarding its limitations see Bowers, 2016).  Yet, meta‐theory on child development, such as ecological systems theory (Bronfenbrenner  & Morris, 1998), and theory more specifically addressing children’s cognitive, emotional, and social well‐being are each critical for guiding the development

Publicly Supported Programs  165 and interpretation of pre‐K research and, in turn, improving practice and policy (Anderson, Moffat, McTavish, & Shapiro, 2014; Baroody, Lai, & Mix, 2006; Denham, Zinnser, & Brown, 2014; Seifert, 2014). Fundamental to ecological systems theory is the proposition that contexts directly containing the child have a unique potential to influence development through the child’s proximal interactions (Bronfenbrenner & Morris, 1998). Moreover, when children’s growth is embedded within multiple proximal milieux, such as home and preschool, there is the potential for dynamic consequences whereby contexts operate on development in a complementary, compensatory, or even a contaminating fashion (Bronfenbrenner & Morris, 1998; Lerner & Benson, 2003). In this respect, stimulating and supportive preschool is expected to operate as a complement to enriched and supportive home contexts and as compensation for deprived and unsupportive home contexts (Phillips, Vorran, Kisker, Howes, & Whitebook, 1994). Within this framework one set of scholars has emphasized that the theoretical underpinnings of what represents high quality preschool closely parallel theory on high quality early home environments, namely with regard to the primary importance of child safety as well as the importance of warmth, responsiveness, and consistent caregiving that scaffold and promote learning, and opportunities for the child to independently explore an enriched environment (Bowlby, 1988; Vygotsky, 1978; Piaget, 1954). In addition, theory on early learning contexts emphasizes the value of developmentally appropriate supports that simultaneously address the connected developmental domains of children’s self‐r­egulatory, social‐emotional, and cognitive growth (Blair, 2002). And, in turn, researchers have argued that high quality preschool containing these supports can promote behavioral regulation, academic engagement, and foundational learning skills, thereby improving children’s readiness for school (Magnuson, Meyers, Ruhm, & Waldfogel, 2004; Reynolds, 2000). However, other scholars have continued to emphasize the design of domain specific intentional teaching practices (including, but not limited to, direct instruction) regardless of the child’s socio‐ e­conomic background in light of consistent findings that this is associated with larger increases in academic skills and knowledge (e.g., Camilli, Vargas, Ryan, & Barnett, 2010; Clements, Sarama, Spitler, Lange, & Wolfe, 2011; Justice, Mashburn, Hamre, & Pianta, 2008). Theorists also emphasize the need for relational perspectives on all aspects of children’s growth, including their cognitive development, when considering the inherently social environments of preschool classrooms (Seifert, 2014). For emotional development and social competence, processes of modeling, adult and peer reactions to a child’s behavior, and teaching (e.g., about emotions, emotional regulation, and social skills) each become central aspects of children’s learning and growth in the pre‐K classroom relationships (Denham, Zinnser, & Brown, 2014). Moreover, as Seifert (2014, p. 10) notes in his comprehensive review of cognitive development in early education settings, “seen in broader context, even a child working alone is still a partner with teachers, peers, and … unseen others in learning and thinking.” From its origins, the field has intensely debated whether and how the education of young children should differ by socio‐economic background (Beatty, 2012). Farran and colleagues (e.g., Farran, 2000; Farran & Son‐Yarbrough, 2001) are among the more recent

166  Barnett, Votruba-Drzal, Dearing, and Carolan to argue that aiming to employ preschools as interventions for children from disadvantaged homes may require learning and behavioral supports that qualitatively differ from what is considered high quality education and care in the preschool or homes of middle‐ and higher‐income children. That is, living in a deprived and chaotic home environment has the potential to alter the types of supports and early learning practices that will best prepare children for success in school and later in life. Keeping in mind the symbiotic relation between theory and empirical work, the potential moderating role of deprivation and disadvantage for optimal early learning supports is an area in which debates can be expected to continue in the theoretical literature, and to which the increasing evaluation work on quality early education that we review in this chapter offers promise for informing.

Empirical Evidence Decades of study indicate that some early childhood education programs have produced enduring positive outcomes for children, their families, and larger society (Barnett, 1995, 2008, 2011b). Studies of program participants reveal that preschool programs can produce: lower rates of grade retention and special education; higher cognitive test scores years later; higher levels of educational attainment; and lower rates of delinquency and crime in both childhood and adulthood (Aos, Lieb, Mayfield, Miller & Pennucci, 2004; Camilli et  al., 2010). A meta‐analytic review of 123 preschool studies found positive impacts on children’s social skills and school progress, with the largest effect sizes for cognitive outcomes (Camilli et  al., 2010). On average, cognitive effects decline over time, with long‐term effects half the size of initial effects (Camilli, et  al., 2010). The average cognitive impact across meta‐analyses in the last 25 years was about half a standard deviation, the equivalent of moving from 30th to the 50th percentile for achievement test scores (Barnett, 2008). Turning to social and emotional development, average effect sizes are smaller (.33 SD), but fewer programs have focused intently on these outcomes, and specificity of program design matters (Camilli et al., 2010). An even more recent meta‐analysis focusing on the value of pre‐K for reducing externalizing behavior p­roblems finds that effect sizes increase with the specificity and intensity of the program (Schindler et al., 2015).

Model programs Some of the strongest evidence regarding the long‐term efficacy of pre‐K programs and the features that comprise “high quality” comes from longitudinal analyses of model programs, including several randomized trials. Frede (1998) summarized their common features as: curriculum content and learning processes that cultivate school‐related skills and knowledge, with a heavy focus on language development; qualified teaching staff who use reflective teaching practices aided by highly qualified supervisors; low teacher–child ratio and small class sizes; intense and coherent programming; and collaborative relationships

Publicly Supported Programs  167 with parents. We briefly provide additional detail from the three most commonly cited studies. The three preschool programs in these studies were developed to enhance the cognitive development and school success of children from low‐income families in disadvantaged communities. The two small‐scale randomized trials offer high internal validity, but generalization presents problems: public programs have been much less intensive and populations and their contexts have changed. The third has weaker internal validity, but is much more generalizable to today’s public programs. Synthesizing findings across these interventions, pre‐K demonstrated long‐term benefits into adulthood and the investments in preschool were cost‐effective, and triangulation across the three provides greater confidence than each alone that intensive pre‐K on a large scale can produce these results (Barnett & Masse, 2007). The earliest of the three, the High/Scope Perry Preschool program, launched in 1962 in Ypsilanti, Michigan. The High/Scope Perry Preschool provided 3 hours per day in the classroom and weekly home visits for 1:1 tutoring to disadvantaged Black preschool‐age children. In the experimental evaluation of the program, 128 children were selected based on low IQ scores and randomly assigned to treatment and control groups with minor exceptions (e.g., for siblings). Of the 123 who completed the treatment period, 58 were assigned to the treatment group and 65 – who were indistinguishable in terms of socio‐ economics and IQ from those assigned to treatment – to the control group (Schweinhart et al., 2005). Most children in the treatment group received 2 years of pre‐K, beginning at age 3, in small class sizes of 12 or 13 students with 2 highly qualified teachers. The program was operated by the public schools, and teachers often had MA degrees and were paid for their superior qualifications. The curriculum emphasized “verbal bombardment,” socio‐dramatic play, and broad cognitive development in contrast to what was considered a traditional nursery school’s less structured approach emphasizing social‐emotional and physical development (Weikart, 1969). Researchers found impressive initial effects on language and cognitive abilities (Berrueta‐Clement, Schweinhart, Barnett, Epstein, & Weikart, 1984). Moreover, following these students well into adulthood provided significant evidence of the long‐term benefits of the preschool program, despite some initial fading of results. While the initial advantage on IQ tests faded as the control group caught up, beginning in kindergarten, the children who had received preschool had higher achievement on tests through middle school. Teachers also reported that these students had better classroom behavior in the early grades. Later, the treatment group had lower delinquency and crime rates, fewer special education placements, and were more likely to graduate from high school. As adults, the intervention group also had greater economic success as measured by employment, earnings, self‐sufficiency, and asset accumulation (Barnett, 1996; Schweinhart et al., 2005). A second model program, the Abecedarian project, started in North Carolina in 1972. Abecedarian provided a high quality educational intervention in a full‐day child care program to a high‐risk population of children (Ramey & Campbell, 1984). Children in the intervention group were enrolled in a full‐day, year‐round center‐based program from 4 months of age through kindergarten entry. Researchers have followed 104 of the original 112 participants randomly assigned to treatment and control groups through age 30, p­roviding information on long‐term outcomes.

168  Barnett, Votruba-Drzal, Dearing, and Carolan Although large initial IQ gains for the Abecedarian treatment group decreased over time, some IQ gain remained and effects on achievement test scores averaged about 0.40 standard deviations from ages 8 to 21. Moreover, the program was particularly effective in reducing grade retention and special education placements (interventions which are p­articularly costly to school districts), reducing each by 23 percentage points (Campbell, Ramey, Pungello, Sparling, & Miller‐Johnson, 2002). A significant impact was found on 4‐year college attendance, with 36% of the program group enrolling, compared to 14% of the control group. Benefits of the intervention extended beyond academic outcomes. Members of the treatment group were less likely to become teen parents or smoke marijuana, and were more likely to have a skilled job (Campbell et al., 2002). Most recently positive effects have been reported for adult health (Campbell et al., 2014). While the intensive experience offered by the Abecedarian program was much more expensive than typical public programs even today, the long‐term results stand as a testament to the potential of early childhood education. The Child Parent Centers (CPC), which began in 4 centers in 1967 and expanded to 25 centers over the next decade, are also typically categorized as a “model” program but served thousands of children and were operated by the Chicago Public Schools for decades. The CPC program provided low‐income children with a half‐day of pre‐K, a year of k­indergarten, and in the primary grades offered reduced class size, individualized instruction, and ongoing family support services in elementary school (Reynolds, 2000; Reynolds, Temple, & Ou, 2010). More than half of the study’s participants attended 2 years of high quality preschool, starting at age 3. Preschool classes of 18 students were staffed by a lead and assistant teacher, and the program had a strong parent outreach component. Variations in participation allowed separate estimation of the effects of preschool and school‐age components. The large scale of the CPC program made it possible to include a large sample (1,539) in an evaluation of children who attended during the 1980s (Reynolds et  al., 2007). Children who attended the program were compared to similar children in similar neighborhoods in Chicago that did not have access to the CPC program. Evaluations of the program have found positive effects on children’s learning, including program impacts on test scores through middle school, lower special education placement rates, increased high school graduation rates, and reductions in delinquency and crime (Arteaga, Humpage, Reynolds, & Temple, 2014; Reynolds et  al., 2010; Reynolds, Temple, White, Ou, & Robertson, 2011). Although the pattern of effects is remarkably similar to that in the Perry Preschool study, the impacts from this less expensive, less intensive program tended to be more modest in size.

Head Start Additional evidence about the benefits of pre‐K participation comes from the federally funded Head Start program. Head Start began in 1965 during the War on Poverty as an 8‐week summer program serving more than 560,000 low‐income children and families through community‐based pre‐K programs. The program has grown since then and works with children throughout the school year. During the 2012–13 school year the program

Publicly Supported Programs  169 served 903,679 children across the country and in the US territories (Office of Head Start, 2014). Through the federal Department of Health and Human Services, Administration for Children and Families, Head Start provides grants to local organizations that promote school readiness through early childhood education and the provision of comprehensive services and opportunities for family engagement. Head Start services vary based on the needs of their communities, but all centers are subject to the same federal performance standards. All programs include early childhood education, but programs can be half‐ or full‐day and may operate less than 5 days per year. Some provide home‐based services as well whereas others do not. Studies of the program present a mixed view of short‐ and long‐term outcomes that seems to vary historically as well as depending on whether studies focus on specific providers or on a nationally representative sample of providers and on the extent to which the comparison group attends another type of preschool program (Shager et al., 2013). The Head Start National Impact Study (NIS), a large‐scale randomized trial (Puma et  al., 2005; US Department of Health and Human Services, Administration for Children and Families, 2010) of children who attended the program for one year in 2002–03 has particularly strong internal and external validity. The estimated initial effects of one year of Head Start in the NIS were modest, .15 to .35 SD depending on the outcome. Effects tended to be smaller in broad domains such as language and mathematics, but larger for narrow, more readily mastered literacy such as letter identification. Confirmation comes from a purely correlational study of Head Start effects using nationally representative data for children who entered kindergarten in 1999 that found a similar pattern of modest initial effects and no meaningful long‐term impacts (Magnuson, Ruhm, & Waldfogel, 2007). In contrast, some studies of Head Start in specific locations and in earlier times have produced evidence of larger and longer lasting effects (Abbott‐Shim, Lambert, & McCarty, 2003; Gormley, Phillips, & Gayer, 2008: Ludwig & Miller, 2007). Multiple explanations have been put forward for the lack of apparent long‐term gains from Head Start in the NIS, including control children attending other preschools and the impacts of subsequent schooling (e.g., Gibbs, Ludwig, & Miller, 2011). Most studies of Head Start’s impacts were conducted prior to significant reforms that include the 2007 Improving Head Start for School Readiness Act (P.L. 110‐134). Among other things, this Act increased requirements for Head Start teacher qualifications and professional development. Head Start may have become somewhat more effective due to these reforms. Analyses of achievement test score gains by ethnicity for children attending Head Start found a pattern of increasingly larger gains across cohorts from 2003, 2006, and 2009 for language and literacy, though not for mathematics (Barnett, 2013).

State and city (school district) pre‐K Forty states and the District of Columbia have funded public pre‐K programs for some time, albeit with considerable variation across states with regard to per pupil expenditure, program extent (half‐ vs. full‐day), the extent to which programs were operated by the public schools or private providers, and whether programs targeted disadvantaged children or were available to families regardless of income (Barnett et al., 2015). These programs

170  Barnett, Votruba-Drzal, Dearing, and Carolan are so heterogeneous that they cannot be expected to have uniform or even similar o­ utcomes. However, the growing literature evaluating these programs complements e­vidence from “model” programs in finding that intensive, quality pre‐K programs can be scaled up with both short‐ and long‐term benefits for children (Karoly & Auger, 2016; Kay & Pennucci, 2014). In order to evaluate the potential impact of large‐scale public programs, it is useful to review the existing literature of the state‐funded programs that have grown so notably in the last 15 years. These evaluations vary in scope and methodological rigor, and not all can be characterized as high quality studies. However, several quality studies in the last decade have provided evidence of success in state‐funded programs. Here, we briefly review this evidence. A multi‐year study of Louisiana’s LA4 program, which provided high quality, full‐day pre‐K to an economically heterogeneous group of children in public schools, found gains in language, literacy, and mathematics that lasted at least through kindergarten, as well as reductions in grade retention and special education. Gains in the first year of the LA4 program, when it was only part‐day, were much smaller than subsequent years, when the program was scaled up to full‐day, thereby suggesting that program extent may be important in examining differences in program impacts across states (Ramey, Landesman Ramey  & Stokes, 2009). Research on Michigan’s School Readiness Program, which is targeted at the most disadvantaged 4‐year‐olds and is administered by the intermediate school district, which then subcontracts out to school and local for‐profit and non‐profit community organizations, indicates that it increased pass rates on the state’s literacy and math tests and decreased grade repetition in the fourth and eighth grades (Malofeeva, Daniel‐Echol, & Xiang, 2007). Similarly, an evaluation of Arkansas’s program found positive impacts on language, literacy, and math persisting into the primary grades (Hustedt, Jung, Barnett, & Williams, 2015). An evaluation of North Carolina’s program at third grade found increased test scores and decreased special education placements (Muschkin, Ladd, & Dodge, 2015; Ladd, Muschkin, & Dodge, 2014). Finally, a follow‐up study of Tulsa’s program impacts at grade three finds some evidence of modest impact in math (Hill, Gormley, & Adelstein, 2015). Not all of the evidence coming from state pre‐K programs is positive, however. Most recently, a randomized control evaluation of Tennessee’s Voluntary Prekindergarten program (TN‐VPK), which gives priority to disadvantaged children and is administered through the public schools, showed more mixed results. More specifically, modest achievement gains that were evident at the end of pre‐K were not sustained into kindergarten or first grade and reversed in second grade. Negative effects of TN‐VPK were evident for behavior in first grade as well, with teachers reporting that TN‐VPK participants were less well prepared for school (Lipsey, Farran, & Hofer, 2015). Low quality offers one explanation for the disappointing outcomes as 85% of the classrooms scored less than good on the Early Childhood Environment Rating Scale (Farran, Hofer, Lipsey, & Bilbrey, 2014), but this pattern of findings is unique in the entire body of research on preschool education and deserves further investigation. Further evidence of the impacts of quality pre‐K comes from an evaluation of the City of Boston universal pre‐K program. Using a regression discontinuity design, Weiland and Yoshikawa (2013) found greater benefits in Boston than some other public programs,

Publicly Supported Programs  171 with large effects on language, reading, and math skills and small impacts on emotion recognition and executive functioning. It has been speculated that this may in part be due to the careful implementation of evidence‐based reading (Opening the World of Learning; Schickedanz & Dickinson, 2005) and math curricula (Building Blocks; Clements & Sarama, 2007) in the Boston pre‐K classrooms, again drawing attention to the importance of program quality including the curriculum. Beyond these individual studies, a 2008 evaluation using a regression‐discontinuity design explored the impact of five state pre‐K programs on children’s receptive vocabulary, math, and print awareness skills (Wong, Cook, Barnett, Jung, 2008). Researchers looked at Michigan, New Jersey, Oklahoma, South Carolina, and West Virginia. The programs in New Jersey and Oklahoma had significant standardized impacts on receptive vocabulary. Coefficients were positive in all programs for math, though Michigan and New Jersey were the only states whose studies yielded reliable results. Print awareness had the most impressive results: all five programs yielded positive coefficients, and these were reliable for all programs except Oklahoma. While each of these five programs has generally higher standards than are seen across the spectrum of state‐funded pre‐K programs, overall the results are an encouraging sign that pre‐K can work in a variety of contexts. Concerns that high quality programs can never be brought to scale should be assuaged by the findings of such studies and of those reviewed earlier. However, even within these five there were notable differences in outcomes, and in the context of the larger literature there is ample caution that highly effective programs are neither cheap nor easily i­mplemented at scale.

Additional evidence from preschools in nationally representative samples Complementing evaluations of preschool interventions and public programs, nationally representative data from the Early Childhood Longitudinal Study – Kindergarten Class of 1998–1999 (ECLS‐K) and Early Childhood Longitudinal Study  –  Birth Cohort (ECLS‐B) have also been used to study early childhood education and care during the preschool years, including preschool programs and other center‐based child care. Although relying on correlational methods and limited data on program quality, one advantage of this line of work is that it provides a national portrait of associations between a variety of pre‐K experiences and child outcomes. It is important to note, however, that these studies have difficulty differentiating various public pre‐K programs from each other and from private programs and sometimes have collapsed all into a single category. Using ECLS‐K data, for example, Magnuson et  al. (2004) found that children who attended a center or school‐based pre‐K program had better scores on reading and math assessments at kindergarten entry than their peers who had experienced only parental care, after controlling for family background and other factors that may have affected their enrollment in such programs. Children were also less likely to be retained in kindergarten, and some effects persisted at least into first grade. Effects for the most disadvantaged groups were largest. Using the ECLS‐B, however, negative behavioral outcomes associated with time in preschool have been documented; Coley, Votruba‐Drzal, Miller, & Koury (2013) uncovered

172  Barnett, Votruba-Drzal, Dearing, and Carolan consistent evidence of elevations in children’s behavior problems in kindergarten for c­hildren who attended formal preschool and center‐based child care (more than 25 hours per week) when compared to children in home‐based settings during the preschool years. Yet, in follow‐up work with these data, Votruba‐Drzal, Coley, Collins, & Miller (2015) determined that time in center‐based preschool was more likely to predict improved reading, math, and expressive language skills, and lower externalizing behaviors (as rated by parents) for children of immigrants than for children of native‐born parents. Moreover, using the twin sample from ECLS‐B, Tucker‐Drob (2012) estimated the extent to which preschool enrollment reduced between‐family variation in achievement at age five, finding preschool improved math and reading abilities, especially for children from minority and lower socio‐economic status families.

Developmentally meaningful features of public pre‐K programs Quality.  As evident in the varying levels of success of public preschool, program quality is a large part of what determines the effects of preschool on children. Quality is generally defined as consisting of process quality (including quality of the teaching, curriculum, classroom activities, and teacher‐student relationships) that is influenced by such structural features as physical space, class size, teacher qualifications, professional development, and use of formative assessment (Mashburn et al., 2008; Pianta, Barnett, Burchinal, & Thornburg, 2009). Consistent with theory on the importance of proximal processes, most research indicates that high process quality related to learning enrichment (e.g., intentional interactions aimed at learning; “serve‐and‐return” interactions between adults and child, variety of age‐appropriate activities) is most clearly related to children’s learning and development (Yoshikawa, et al., 2013). In turn, structural features such as class size can help create the right conditions for high process quality, but do not guarantee it will occur (Early et al., 2007; Francis, 2014; Yoshikawa, et al., 2013). Process quality in a pre‐K program is more difficult to measure and shape with policy than structural indicators, but several research‐informed methods have been widely used to do so. The Early Childhood Environmental Rating Scale – Revised (ECERS‐R), for example, is a widely employed observational tool specifically intended for center‐based early childhood care. Trained observers score the program on 49 indicators in seven s­ubscales on key areas of development and interaction (Spaces and furnishings; Personal care routines; Language‐reasoning; Activities; Interaction; Program structure; Parents and staff ) that combine into a total composite score. Each item is scored on a scale of 1 to 7, with “5” usually reported as “good” quality and 7 as “high” (Harms & Clifford, 1980; Clifford, Reszka, & Rossbach, 2010). The Classroom Assessment Scoring System (CLASS), is another observational instrument that assesses quality of teacher‐child i­nteractions in center‐based preschool in three domains: emotional support, classroom organization, and instructional support. Observers rate each dimension on a 1–7 scale: 1–2 indicates low quality interaction; 3–5 is mid‐range; 6–7 indicates consistent effective interactions (Murray, 2014). Numerous studies have found that ECERS‐R and CLASS ratings are associated with  positive impacts on child outcomes on various measures of math and literacy

Publicly Supported Programs  173 (Burchinal, et al., 2008; Clifford, Reszka, & Rossbach, 2010; Mashburn, et al., 2008; Peisner‐Feinberg, Burchinal, Clifford, Culkin, Howes, Kagan, & Yazejiam, 2001). Yet, high ratings on process quality metrics are not related with child outcomes in a one‐to‐ one fashion. Some studies have found that differences in quality are most strongly related to differences in low‐income children’s social competence and achievement at the high end of the quality spectrum (Burchinal, Vandergrift, Pianta, & Mashburn, 2010). If this is true, “good enough” preschool quality – high enough quality to produce positive substantive improvements in learning and development – may, in fact, require fairly high‐ end process quality. More generally, recent research has raised questions about the efficacy of quality rating and improvement systems as a regulatory approach to raising process quality (Sabol, Hong, Pianta, & Burchinal, 2013). An alternative approach to continuous improvement instituted in a public education system has demonstrated effectiveness in transforming quality and improving children’s educational outcomes (Barnett & Frede, in press). Dosage.  One key feature of interest regarding children’s preschool experiences is the amount of time they spend in early education settings, with questions regarding the length of each day, number of days per year, and number of years. In general, the evidence is consistent with the view that, if quality is high, more is better with respect to hours and days per year (Robin, Frede, & Barnett, 2006; Reynolds et al., 2014). However, there are other ways to increase program intensity than by increasing hours, and much of the time in a longer day can be wasted (Farran et al., 2014). Also to be considered is that additional hours provide child care that can increase parental labor supply while families may find drop‐off and pick‐up arrangements in half‐day programs difficult or impossible (Nores & Barnett, 2014). Finally, a caveat to the positive effects for achievement outcomes is needed. There is evidence that a large number of hours in preschool is associated with heightened risk of behavior problems. This evidence comes from non‐experimental studies, however, and the findings are mixed when econometric methods (e.g., instrumental variable models and fixed‐effects models) are used to isolate the exogenous components of dosage: some find no effect, some find negative effects of dosage on behavior, and some find positive effects of dosage on behavior (e.g., Crosby, Dowsett, Gennetian, & Huston, 2010; Jaffee, Van Hulle, & Rodgers, 2011). Regarding the number of years, or how young children should start preschool, there is little experimental evidence, but correlational research seems to indicate positive, but declining, impacts with each additional year (Yoshikawa, et al., 2013). Nevertheless, even the benefits from high quality preschool prior to age 3 can be substantial (Duncan & Sojurner, 2013; Li, Farkas, Duncan, Burchinal, & Vandell, 2013). For starting at age 3 rather than age 4, studies finding substantial long‐term gains include evaluations in the Harrisburg schools and of New Jersey’s Abbott ­preschool program; both find higher achievement for children who began at age 3 (Barnett & Frede, in press; Domitrovich et al., 2013). A study of the Chicago Child‐Parent Centers found that compared to children who attended for only one year, children attending for 2 years were significantly less likely to receive special e­ducation, be abused or neglected, or to commit crimes (Artega, Humpage, Reynolds, & Temple, 2014).

174  Barnett, Votruba-Drzal, Dearing, and Carolan

Cost‐Benefit Analyses Providing high quality pre‐K is not an inexpensive public endeavor. However, some evidence indicates potential for strong returns – financial and otherwise – through the long‐ run improvements to children’s life chances. Cost‐benefit analyses have, in fact, consistently demonstrated accrued benefits from preschool investments, both for individuals who participate and for society. Individual participants benefit from improved academic outcomes, lower grade retention and special education placement rates, higher earnings, and higher levels of educational attainment. Benefits also accrue to the public through savings from reduced special education and grade retention, reduced need for social services, reduced health care costs, increased tax payments, and decreased costs of crime to the criminal justice system, and savings from reduced special education and grade retention (Barnett, 1996; Temple & Reynolds, 2007). The Perry Preschool program yielded benefit‐cost ratio estimates of 7 to 1 or higher, for every dollar spent (Barnett, 1996; Schweinhart et al., 2005). The Abecedarian program, which was more expensive as it provided more years of service, accrued benefits of roughly 2.5 to 1 (Barnett & Masse, 2007). Estimates based on participants who have not yet reached adulthood in universal programs range from a 3 to 5 to 1 ratio (Bartik, Gormley, & Adelstein, 2012; Karoly & Bigelow, 2005). For the Chicago Child Parent Centers, benefit‐cost ratios ranged from about 6 to 1 to as high as 10 to 1 (Temple & Reynolds, 2007). An analysis of state and district public preschool programs has estimated an expected net present value of $22,236 per child, with an estimated 4 to 1 return on dollars invested and 91% chance that public financial benefits relative to cost will be at least 1 to 1 (Kay & Pennucci, 2014). The same analysis estimated that Head Start has a net expected present value of $13,888 per child, with a $2.63 return for every dollar invested and an 89% chance of no net loss on the investment (Kay & Pennucci, 2014). Although cost‐benefit analysis provides a compelling argument in favor of expanding programs, the findings discussed here do not readily generalize to most public programs, which are of lower quality and intensity. The cost of quality education must be paid up front, while benefits and cost savings will be accrued in future years (Barnett, 2013). This delayed gratification proves a difficult hurdle for policymakers and the public to overcome (Minervino, 2014). The disappointing results in a number of studies underscore the risks of underfunding and poorly implementing programs at the cost of quality, with only a small number of local or state programs identified as producing results that could be expected to yield strong positive economic returns (Minervino, 2014).

Pre‐K Policy Landscape in the US Policymakers across the country seeking to design and implement pre‐K programs that yield positive economic returns confront a variety of difficult challenges when it comes to making decisions about key program features. Three critical questions are discussed next. Who should be served? How much should be spent on quality, perhaps at the cost of c­overage? Whether and how to use a mixed delivery system?

Publicly Supported Programs  175

Who should be served: targeted v. universal programs Early childhood education programs are typically operated either as targeted, serving a particular subset of children based on income or a risk factor, or universal, based only on age eligibility. Although 20 states and the District of Columbia have no means test for at least one statewide pre‐K program, only DC, Florida, Oklahoma, West Virginia, and Vermont effectively offer preschool to all children regardless of income. The others limit access in practice due to restricted funding and 19 states have only means‐tested pre‐K (Barnett et al., 2015). Thus, in practice, access to the vast majority of state pre‐K programs is limited to children from low‐income families, as is access to Head Start and to state child care subsidies. Of course, the income requirement is not nearly as restrictive as limits on funding. About a quarter of all children under 6 live in poverty and nearly half (47%) are below 200% of the poverty line, a cutoff at about the level required for families to meet basic needs (Jiang, Ekono, & Skinner, 2016). Several common arguments are used to support means‐tested (targeted) programs, including the idea that public resources should only be spent for children in low‐income families who benefit most and cannot afford pre‐K on their own, with some arguing that only children in poverty benefit (e.g., Fuller, 2008; Rolnick & Grunewald, 2011). Studies overwhelmingly show that economically disadvantaged children benefit long term from preschool, but there is evidence that children from other socio‐economic backgrounds benefit as well (Barnett, 2008; Votruba‐Drzal, Coley, Koury, & Miller, 2013). The school readiness and failure problem isn’t a dichotomous problem that afflicts children in poverty alone. In fact, the relationship between income and achievement is better characterized as linear and continuous, with middle‐income children as far behind the high income as low‐income children are behind the middle (Nores & Barnett, 2014). As resources are limited, there is an appeal to limiting public funding for preschool to the children most at risk of school failure (and other problems) as this should yield the highest payoff, other things being equal. There are two problems with this argument. The first is that even if returns are higher from a targeted program, the returns to a universal program may be high enough to make it sound policy. The second is that other things clearly are not equal. Practical problems result in lower coverage, higher costs, and lower effectiveness in real world targeted programs than is assumed in the theoretical arguments. The notion that children from middle‐income families already access high quality pre‐K without government assistance and would not benefit from expanded access is simply incorrect. Only a quarter of children from middle‐income families attended preschool programs scoring good or better on the ECERS‐R in 2005, according to a national study (NCES, 2012). There is also evidence that they can benefit from increased access. Two recent studies of universal programs – in Tulsa and Boston – have found substantial positive effects on math, literacy, and language for both low‐ and middle‐income students. While impacts were larger for those children eligible for free and reduced‐price lunch (185% of the federal poverty level), benefits were still notable for middle‐income students (Yoshikawa, et al., 2013). The Tulsa program was found to have positive impacts on literacy, mathematics, and socio‐emotional development for all children, though benefits were larger for more disadvantaged children (Gormley, Phillips, & Gayer, 2008).

176  Barnett, Votruba-Drzal, Dearing, and Carolan Inferences regarding the relative effectiveness of targeted and universal programs will be best drawn from a very broad review of research on early development, program participation and selection, and preschool program impacts. Few studies have directly compared the impacts of targeted and universal programs, a comparison made more difficult by differences in program design between, for example, Head Start and universal state‐funded pre‐K. Among the studies that have, the results are inconclusive with one study showing no differences when it comes to gains in student achievement made by children in targeted versus universal programs (Dotterer, Burchinal, Bryant, Early, & Pianta, 2013) and another showing larger gains for children enrolled in a universal program when compared to a targeted program (Henry, Gordon, & Rickman, 2006). How do real world practical problems affect the choice between a universal or targeted approach? Targeted programs must spend money to identify and recruit eligible children, encouraging those who might decline to participate because of the stigma associated with programs for the poor and screening out those who might seek entry despite being over income (Hustedt & Barnett, 2011). In addition, family income is not a constant and due to fluctuations even perfect compliance with eligibility requirements at program recruitment will leave some eligible children excluded while noneligibles are served. Studies also find benefits to children from interacting with peers from different backgrounds that are lost if programs serve only children from low‐income families (Barnett, 2011a). Finally, it may also be easier to sustain strong long‐term political support for quality in a universal program that benefits all families, than for programs targeting specific populations (Gelbach & Pritchett, 2002). One option that seeks to obtain the advantages of both approaches is to implement high quality, intensive universal programs in geographic areas with high concentrations of poverty, as in New Jersey’s Abbott districts and South Carolina’s CDEPP (Carolan & Connors‐Tadros, 2015). This hybrid approach still targets funding for pre‐K based on need, but it allows for some socioeconomic diversity in programs. Within a c­ommunity it simplifies enrollment, reduces the cost of determining eligibility, and reaches every child who will go on to kindergarten in the community potentially producing larger changes in school climate and teaching as children move through the local schools. It also does not stigmatize participation or create disincentives to increase earnings, two problems that can afflict any income‐tested program.

Investing in quality While many American preschoolers have access to some form of preschool program, there is no guarantee that children receive high quality education, and most do not. The Early Childhood Longitudinal Study‐Birth cohort study (ECLS‐B) found that only about a third of preschool classrooms rated good or better (5 or higher) on the ECERS‐R. About 10% of preschool classrooms were rated as low quality (below 3). Family home care scored even more poorly with just 10% good or better and more than 40% low quality. Clearly, the majority of services are not of high enough quality to obtain the benefits research has indicated are possible (Nores & Barnett, 2014). Although public support for pre‐K has improved the provision of high quality p­rograms to children in low‐income families, unfortunate inequalities remain (Nores & Barnett, 2014).

Publicly Supported Programs  177 At age 4, just 57% of low‐income children are enrolled in center‐based pre‐K, as opposed to 77% of high‐income children. Access to the best programs remains unequal, as well, with 29% of children in the top income quintile in pre‐K rated good or better on the ECERS‐R compared to only 18% of those in low‐income families. Children from m­iddle‐ income families in many states face the “squeeze” of being over income eligibility for publicly funded programs, but unable to afford a space in high quality private programs so that while their access to preschool is better, its average quality is not. Inadequate public funding is not the only explanation for the quality problem, but it surely is one major problem. Florida, for example, spends less than $2500 per child enrolled in state‐funded pre‐K, and there is no required local match (Barnett et al., 2015). Nationally, state average per child expenditure on pre‐K falls far short of spending per child on K–12, as does federal spending for the Head Start program (Barnett et al., 2015). When expenditure levels are set too low to permit programs to replicate the features of those found to be successful, entire systems are essentially set up for failure and policymakers are overpromising benefits based on much more expensive models. In setting funding levels, program planning should begin with specification of program features needed to provide the level of intensity and quality that can be expected to produce the desired results. The public preschool programs that have been identified as highly effective are noticeably more expensive than the average (Minervino, 2014; Weiland & Yoshikawa, 2013). An additional complication is that when programs seek to simultaneously meet the needs for long hours of child care and provide high levels of educational quality the cost is much higher than when either goal is pursued alone. Policymakers should be cautioned not to sacrifice child development benefits in order to extend child care to more families at low cost. Such an approach appears to characterize the current child care subsidy system to such an extent that it may not just squander potential benefits, but harm child development, albeit modestly (Herbst & Tekin, 2016). Neither adequate funding nor high standards for program structure guarantee high process quality; additional policies are required for strong implementation (Connors & Morris, 2015; Minervino, 2014; Pianta et al., 2009). Foremost is a continuous improvement system that begins with high standards for learning and teaching and employs a cycle of planning, self‐assessment, evaluation, and individualized feedback at all levels (Barnett & Frede, in press). At the teacher level, reflection and individualized coaching that provide actionable guidance are particularly valuable. At higher levels, systems simply must not permit low quality classrooms to continue if they cannot improve with assistance. Detailed guidance is available from current large‐scale examples (Barnett & Frede, in press; Minervino, 2014).

Mixed delivery systems Unlike K–12 programs, most state‐funded pre‐K programs provide considerable opportunity to deliver services through private organizations and not just public schools. Many state‐funded pre‐K programs allow contracting and sub‐contracting with a variety of local providers, including private child care providers, Head Start centers, faith‐based settings, and family child care providers (Barnett & Hustedt, 2011). Similarly, Head Start is

178  Barnett, Votruba-Drzal, Dearing, and Carolan delivered primarily through private organizations rather than the public schools. This approach has some advantages as it allows programs to utilize the existing talent and infrastructure in the field, and it has the potential to lower costs, though some apparent cost savings can be due to lower quality staff and facilities. It also can facilitate braiding funding from public education, Head Start, and the child care subsidy system to support services of higher quality and greater duration. However, the inclusion of private providers can c­omplicate governance and regulation as each funding stream has its own regulations and standards and private providers are accustomed to substantially more autonomy than p­ublic education typically allows. Nevertheless, some mixed delivery systems have successfully provided high quality early education at scale (Minervino, 2014). New Jersey’s Abbott preschool program offers one example. New Jersey began the Abbott system in 1999–2000 using the existing mix of public school preschools, Head Start, and private child care programs to rapidly expand to deliver high quality education to 50,000 3 and 4‐year‐olds in 31 communities. In 1999– 2000, less than 15% of the classrooms scored good or better (5 or higher) on the ECERS‐R while nearly 1 in 4 scored below minimal quality (under 3); the average program scored 3.9 (Frede, Jung, Barnett, & Figueras, 2009). By 2006–07, the average classroom scored above 5 and scores continued to rise with the average above 5.4 in 2014–15 (Francis, 2015). Most programs score in the good to excellent range and scores below 3 are rare. This level of quality system‐wide is higher than is found generally and demonstrates that quality can be transformed through a mixed delivery system, though it requires sustained attention to quality over many years.

Conclusions There is strong evidence that high quality early childhood education programs can produce large benefits for children, families, and communities that extend well beyond a student’s time in the preschool classroom. Less well understood are the program features and broader circumstances, concurrently and as children pass through the public schools, sufficient for long‐term substantive improvements in learning and development and to make public programs a sound investment. Yet, if we have much to learn about the least that is required, we know enough to design effective programs and p­olicies that can produce the desired results on a large scale even if this is not the only or most efficient way to produce results. When this is pointed out one typical reaction is that programs in which we can have confidence are unrealistically expensive. We have to ask, by what standard? Society spends more than $30,000 per inmate for federal prison (Bureau of Prisons, 2015). The cost per deployed soldier in Iraq in 2015 was about $4 million (Belasco, 2014). Moreover, what matters for sound policy is the net return, the difference between costs and benefits, not cost per se. Fundamentally, society lacks the will to effectively invest in young children rather than the knowledge or financial ability. Yet, a resistance to the cost of high quality is not the only impediment to sound policy. Implementation at scale is difficult, and there is much to be learned that could improve policy.

Publicly Supported Programs  179 As a whole, the research base on the effects of preschool education is remarkable and robust with the whole stronger than the parts. Small, intensive programs that have been studied for decades using randomized trials find lifelong benefits from preschool. Evaluations of large public programs have more mixed findings. We do not fully understand why some programs and not others have produced observable long‐term gains, but we can say with some confidence that intensive high quality preschool programs attended for multiple years tend to produce larger lasting gains. From meta‐analysis we conclude that variations in outcomes have more to do with differences in programs and the available alternative preschool programs than with differences in research design. Nevertheless, the limits of our knowledge dictate that any large‐scale program should regularly assess its quality and fidelity of implementation as well as outcomes for children and use this information for continuous improvement. There is always room for further research with respect to policy and practice including such topics as: duration, curriculum, the mix of activities during the day, and structural features including class size and staffing patterns (e.g., would 2 fully qualified teachers with a somewhat larger class outperform a teacher and aide with 15 students?), and how best to support continued gains as children progress from one year to the next. In general, new studies might be more productive if they moved away from “horse race” designs that compare Head Start to state pre‐K or one curriculum to another and instead focused on testing theoretical principles. In the long run it is not particularly helpful to know that Brand A is more effective than Brand B if one does not know why, particularly because what constitutes Brands A and B is likely to change over time. In addition, the field could benefit from systems‐level studies that investigated, for example, the effects of providing every child with high quality pre‐K (rather than serving relatively few children in a cohort) on subsequent peer experiences, classroom climate, and what and how teachers teach in kindergarten and the later grades.

Empirical Progress Chart The breadth and diversity of research on preschool programs and the need to interpret each study in the context of the research as a whole makes it difficult to report the strengths of the literature as simply as Table 8.1 requires. For example, small‐scale studies of intensive programs have strong internal validity but weak external validity. Large‐scale studies of public programs have sometimes used designs associated with strong internal validity, but their treatments (relative to the counterfactual) have been less clearly defined, which weakens internal validity. External validity is greater for programs operating at scale and serving diverse populations. Practical impact is obvious when a policy or practice is on average associated with large gains – as is the case for the provision of intensive high quality preschool per se, or when a particular practice is focused, intensive, and sustained as, for example, with interventions to reduce externalizing behavior or intentional i­ndividualized teaching in literacy or mathematics. It is less obvious for features of program and policy that may be necessary but not sufficient to produce strong outcomes for children as their average association can be weak even though it is difficult or impossible

180  Barnett, Votruba-Drzal, Dearing, and Carolan Table 8.1  Empirical progress chart for public pre‐K Factor

Internal Validity

External Validity

Practical Significance

Overall effects of preschool on child outcomes Pre‐K Process Quality Pre‐K Structural Quality (e.g., effect of teacher training and class size) Dosage effects (i.e., length of day, number of hours, and number of years)

Strong Moderate Moderate & Mixed Moderate & Mixed

Moderate Moderate Moderate

Strong Moderate Not in isolation



to obtain desired results without them (Pianta et al., 2009). Small average results in that case do not indicate a lack of import. To inform some policy questions we must rely more on comparisons across studies than on comparisons within studies, and on such demonstrations as the Abbott Preschool program where the “proof ” depends on a large‐scale policy change producing its intended systemic changes. With these caveats in mind, we have provided overall assessments of the progress of research on preschool programs to date in Table 8.1.

References Abbott‐Shim, M., Lambert, R., & McCarty, F. (2003). A comparison of school readiness outcomes for children randomly assigned to a Head Start program and program’s waiting list. Journal of Education for Students Placed at Risk, 8(2), 191–214. Anderson, J., Moffat, L., McTavish, M., & Shapiro, J. (2014). Rethinking language education in early childhood: Sociocultural perspectives. In O. N. Saracho & B. Spodek (Eds.), Handbook of research on the education of young children (pp. 117–134). New York, NY: Taylor & Francis. Aos, S., Lieb, R., Mayfield, J., Miller, M., & Pennucci, A. (2004). Benefits and costs of prevention and early intervention programs for youth (No. 04‐07, p. 3901). Olympia, WA: Washington State Institute for Public Policy. Artega, I., Humpage, S., Reynolds, A. J., & Temple, J. A. (2014). One year of preschool or two: Is it important for adult outcomes? Economics of Education Review, 40, 221–237. Barnett, W. S. (1995). Long‐term effects of early childhood programs. The Future of Children, 5(3), 25–50. Barnett, W. S. (1996). Lives in the balance: Age‐27 benefit‐cost analysis of the High/Scope Perry Preschool program. Ypsilanti, MI: HighScope. Barnett, W. S. (2008). Preschool education and its lasting effects: Research and policy implications. Boulder and Tempe: Education and the Public Interest Center & Education Policy Research Unit. Retrieved from http://nieer.org/resources/research/PreschoolLastingEffects.pdf Barnett, W. S. (2011a). Four reasons the United States should offer every child a preschool education. In E. Zigler, W. Gilliam, & W. S. Barnett (Eds.), The pre‐k debates: current controversies and issues (pp. 34–39). Baltimore, MD: Brookes Publishing. Barnett, W. S. (2011b). Effectiveness of early educational intervention. Science, 333, 975–978. Barnett, W. S. (2013). Expanding Access to Quality Pre‐K is Sound Public Policy. National Institute for Early Education Research, New Brunswick: NJ. Retrieved from http://nieer.org/sites/nieer/ files/Why%20expanding%20quality%20PreK%20is%20a%20sound%20public%20policy.pdf.

Publicly Supported Programs  181 Barnett, W. S., Carolan, M. E., Squires, J. H., Clarke Brown, K., & Horowitz, M. (2015). The state of preschool 2014: State preschool yearbook. New Brunswick, NJ: National Institute for Early Education Research. Barnett, W. S., & Frede, E. C. (in press). Long‐term effects of a system of high‐quality universal preschool education. In H. P. Blossfeld (Ed.), Child care arrangemets and social inequalites: a cross‐ country comparison. Cheltenham, UK: Edward Elgar. Barnett, W. S. & Hustedt, J. T. (2011). Improving public financing for early learning programs. New Brunswick, NJ: NIEER. Barnett, W. S., & Masse, L. (2007). Comparative benefit‐cost analysis of the Abecedarian program and its policy implications. Economics of Education Review, 26, 113–25 Baroody, A. J., Lai, M., & Mix, K. S. (2006). The development of young children’s early number and operation sense and its implications for early childhood education. In B. Spodek & O. N. Saracho (Eds.), Handbook of research on the education of young children (2nd ed.) (pp. 187–221). Mahwah, NJ: Lawrence Erlbaum Bartik, T., Gormley, W. T., & Adelstein, S. (2012). Earnings benefits of Tulsa’s Pre‐K program for different income groups. Economics of Education Review, 31, 1143–1161. Beatty, B. (2012). The debate of the young “disadvantaged child”: preschool intervention, developmental psychology, and compensatory education in the 1960s and early 1970s. Teachers College Record, 114, 1–36. Belasco, A. (2014). The cost of Iraq, Afghanistan, and other global war on terror operations since 9/11. CRS Report. Washington, DC: Congressional Research Service. Berrueta‐Clement, J. R., Scwheinhart, L. L., Barnett, W. S., Epstein, A. S., & Weikart, D. P. (1984). Changed lives: The effects of the Perry Preschool program on youths through age 19. Ypsilanti, MI: High/Scope Press. Blair, C. (2002). School readiness: Integrating cognition and emotion in a neurobiological conceptualization of children’s functioning at school entry. American Psychologist, 57, 111–127. Bowers, J. S. (2016). The practical and principled problems with educational neuroscience. Psychological Science. Advance online downloaded March 3, 2016 from 10.1037/rev0000025 Bowlby, J. (1988). A secure base: Parent‐child attachment and healthy human development. London: Routledge. Bronfenbrenner, U., & Morris, P. A. (1998). The ecology of developmental processes. In W. Damon & R. M. Lerner (Eds.), Handbook of child psychology: Vol. 1. Theoretical modes of human development (5th ed.) (pp. 993–1028). New York, NY: John Wiley & Sons, Inc. Burchinal, M., Howes, C., Pianta, R., Bryant, D., Early, D., Clifford, R., & Barbarin, O. (2008). Predicting child outcomes at the end of kindergarten from the quality of pre‐kindergarten teacher‐child interactions and instruction. Applied Developmental Science, 12(3), 140–153. Burchinal, M., Vandergrift, N., Pianta, R., & Mashburn, A. (2010). Threshold analysis of association between child care quality and child outcomes for low‐income children in pre‐kindergarten programs. Early Childhood Research Quarterly, 25(2), 166–176. Bureau of Prisons. (2015). Annual determination of average cost of incarceration. Retrieved from https://www.gpo.gov/fdsys/pkg/FR‐2015‐03‐09/pdf/2015‐05437.pdf Camilli, G., Vargas, S., Ryan, S., & Barnett, W.S. (2010). Meta‐analysis of the effects of early education interventions on cognitive and social development. Teachers College Record, 112(3), 579–620. 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(6178), 1478–1485. Campbell, F. A., Ramey, C. T., Pungello, E. P., Sparling, J., & Miller‐Johnson, S. (2002). Early Childhood Education: Young Adult Outcomes from the Abecedarian Project. Applied Developmental Science, 6, 42–57.

182  Barnett, Votruba-Drzal, Dearing, and Carolan Carolan, M. & Connors‐Tadros, L. (2015). Eligibility Policy for State Pre‐K Programs: Research on Risk Factors and Considerations for Revising State Policy to Increase Access and Reduce Burden on Programs and Families. (CEELO Policy Report). New Brunswick, NJ: Center on Enhancing Early Learning Outcomes. Center on the Developing Child. The Science of Early Childhood Development. (2007). National Scientific Council on the Developing Child. Retrieved from www.developingchild.net Clements, D. H., & Sarama, J. (2007). Building blocks – SRA real math, grade preK. Columbus, OH: SRA/McGraw‐Hill. Clements, D. H., Sarama, J., Spitler, M. E., Lange, A. A., & Wolfe, C. B. (2011). Mathematics learned by young children in an intervention based on learning trajectories: A large‐scale cluster randomized trial. Journal for Research in Mathematics Education, 42(2), 127–166. Clifford, R.M., Reszka, S.S., & Rossbach, H. (2010) Reliability and validity of the Early Childhood Environment Rating Scale. Working paper. UNC. Coley, R. L., Votruba‐Drzal, E., Miller, P. L., & Koury, A. (2013). Timing, extent, and type of child care and children’s behavioral functioning in kindergarten. Developmental Psychology, 49(10), 1859. Crosby, D. A., Dowsett, C. J., Gennetian, L. A., & Huston, A. C. (2010). A tale of two methods: Comparing regression and instrumental variable estimates of the effects of the effects of p­reschool child care type on the subsequent externalizing behavior of children in low‐income families. Developmental Psychology, 46, 1030–1048. Denham, S. A., Zinnser, K. M., & Brown, C. A. (2014). The emotional basis of learning and d­evelopment in early childhood education. In O. N. Saracho & B. Spodek (Eds.), Handbook of research on the education of young children (pp. 67–88). New York, NY: Taylor & Francis. Domitrovich, C. E., Morgan, N. R., Moore, J. E., Cooper, B. R., Shah, H. K., Jacobson, L., & Greenberg, M. T. (2013). One versus two years: Does length of exposure to an enhanced p­reschool program impact the academic functioning of disadvantaged children in kindergarten? Early Childhood Research Quarterly, 28(4), 704–713. Dotterer, A. M., Burchinal, M., Bryant, D., Early, D., & Pianta, R. C. (2013). Universal and t­argeted pre‐kindergarten programmes: a comparison of classroom characteristics and child o­utcomes. Early Child Development and Care, 183(7), 931–950. Duncan, G. J., & Sojourner, A. J. (2013). Can intensive early childhood intervention programs eliminate income‐based cognitive and achievement gaps? Journal of Human Resources, 48(4), 945–968. Early, D. M., Maxwell, K. L., Burchinal, M., Alva, S., Bender, R. H., Bryant, D., … Zill, N. (2007). Teachers’ education, classroom quality, and young children’s academic skills: Results from seven studies of preschool programs. Child Development, 78(2), 558–580. Farran, D. C. (2000). Another decade of intervention for children who are low income or disabled: What do we know now? In J. P. Shonkoff & S. J. Meisels (Eds.), Handbook of early childhood intervention (2nd ed.) (pp. 510–548). New York, NY: Cambridge University Press Farran, D. C., Hofer, K., Lipsey, M., & Bilbrey, C. (2014). Variations in the quality of TN‐VPK classrooms. Paper presented at the Society for Research on Educational Effectiveness, March 8, Washington, DC. Farran, D. C. & Son‐Yarbrough, W. (2001). Title I funded preschools as a developmental context for children’s play and verbal behaviors. Early Childhood Research Quarterly, 16, 245–262. Francis, J. (2014). Relating Preschool Class Size to Classroom Life and Student Achievement (Unpublished Dissertation). Retrieved from: http://ecommons.luc.edu/luc_diss/894 Francis, J. (2015). New Jersey Preschool Quality EvaluSation Study Spring 2015 Summary Report. New Brunswick, NJ: NIEER, Rutgers University. Retrieved from http://www.nj.gov/education/ ece/research/PreschoolQualityEvaluationStudy15.pdf

Publicly Supported Programs  183 Frede, E. C. (1998). Preschool program quality in programs for children in poverty. In W. S. Barnett and S. S. Boocock (Eds.) Early care and education for children in poverty: promises, programs, and long‐term outcomes (pp. 77–98). Buffalo, NY: State University of New York Press. Frede, E. C., Jung, K., Barnett, W. S., & Figueras, A. (2009). The APPLES blossom: Abbott Preschool  Program Longitudinal Effects Study (APPLES), preliminary results through 2nd grade. New Brunswick, NJ: NIEER. Fuller, B. (2008). Standardized childhood: The political and cultural struggle over early education. Palo Alto, CA: Stanford University Press. Gagne, R. M. (1963). Military training and principles of learning. American Psychologist, 17, 83–91. Gelbach, J., & Pritchett, L. (2002). Is more for the poor less for the poor? The politics of means tested targeting. Topics in Economic Analysis and Policy, 2(1), 26. Retrieved from http://​www.​ bepress.​com/​cgi/​viewcontent.​cgi?​article  = ​1027&​context  = ​bejeap Gibbs, C., Ludwig, J., & Miller, D. L. (2011). Does Head Start do any lasting good? (No. w17452). National Bureau of Economic Research. Glynn, S. J. (2012). Child care: families need more help to care for their children. Washington, DC: Center for American Progress. Gormley, W. T., Phillips, D., & Gayer, T. (2008). Preschool programs can boost school readiness. Science, 320, 1723–1724. Retrieved from: http://nieer.org/resources/research/Gormley062708.pdf Harms, T., & Clifford, R. (1980). Early Childhood Environment Rating Scale (ECERS). New York, NY: Teachers College Press. Henry, G. T., Gordon, C. S., & Rickman, D. K. (2006). Early education policy alternatives: Comparing quality and outcomes of Head Start and state prekindergarten. Educational Evaluation and Policy Analysis, 28(1), 77–99. Herbst, C. & Tekin, E. (2016). The impact of child‐care subsidies on child development: Evidence from geographic variation in the distance to social service agencies. Journal of Policy Analysis and Management, 35(1), 94–116. Hill, C. J., Gormley, W. T., & Adelstein, S. (2015). Do the short‐term effects of a high‐quality preschool program persist? Early Childhood Research Quarterly, 32, 60–79. Hunt, L. McV. (1961). Intelligence and experience. NY: Ronald Press. Hustedt, J. T. & Barnett, W. S. (2011). Financing early childhood education programs: State, f­ederal and local issues. Educational Policy, 25(1), 167–192. Hustedt, J. T., Jung, K., Barnett, W. S., & Williams, T. (2015). Kindergarten Readiness Impacts of the Arkansas Better Chance State Prekindergarten Initiative. The Elementary School Journal, 116(2), 198–216. Jaffee, S. R., Van Hulle, C. V., & Rodgers, J. L. (2011). Effects of nonmaternal care in the first 3 years on children’s academic skills and behavioral functioning in childhood and early adolescence: A sibling comparison study. Child Development, 82, 1076–1091. Jiang, Y., Ekonao, M., & Skinner, C. (2016). Basic facts about low‐income children: Children under 6 years, 2014. NY: National Center for Children in Poverty, Columbia University. Justice, L. M., Mashburn, A. J., Hamre, B. K., & Pianta, R. C. (2008). Quality of language and literacy instruction in preschool classrooms serving at‐risk pupils. Early Childhood Research Quarterly, 23(1), 51–68. Karoly, L. A., & Auger, A. (2016). Informing investments in preschool quality and access in cincinnati: Evidence of impacts and economic returns from national, state, and local preschool programs. Santa Monica, CA: RAND Corporation. Retrieved from http://www.rand.org/pubs/research_reports/ RR1461.html Karoly, L., & Bigelow, J. (2005). The economics of investing in universal preschool education in California. Santa Monica, CA: RAND Corporation.

184  Barnett, Votruba-Drzal, Dearing, and Carolan Kay, N., & Pennucci, A. (2014). Early childhood education for low‐income students: A review of the evidence and benefit‐ cost analysis (Doc. No. 14‐01‐2201). Olympia: Washington State Institute for Public Policy. Ladd, H. F., Muschkin, C. G. & Dodge, K. A. (2014). From birth to school: Early childhood i­nitiatives and third‐grade outcomes in North Carolina. Journal of Policy Analysis Management, 33: 162–187. doi: 10.1002/pam.21734 Lerner, R. M. & Benson, P. L. (Eds.) (2003). Developmental assets and asset building communities: Implications for research, policy, and practice. New York, NY: Kluwer Academic. Li, W., Farkas, G., Duncan, G. J., Burchinal, M. R., & Vandell, D. L. (2013). Timing of high‐ quality child care and cognitive, language, and preacademic development. Developmental Psychology, 49(8), 1440. Lipsey, M. W., Farran, D. C., & Hofer, K. G., (2015). A Randomized Control Trial of the Effects of a Statewide Voluntary Prekindergarten Program on Children’s Skills and Behaviors through Third Grade (Research Report). Nashville, TN: Vanderbilt University, Peabody Research Institute. Ludwig, J., & Miller, D. L. (2007). Does Head Start improve children’s life chances? Evidence from a regression discontinuity design. Quarterly Journal of Economics, 122, 159–208. Magnuson, K. A., Meyers, M. K., Ruhm, C. J., & Waldfogel, J. (2004) Inequality in Preschool Education and School Readiness. American Educational Research Journal, 41, 115. doi: 10.3102/00028312041001115 Magnuson, K. A., Ruhm, C. & Waldfogel, J. (2007). Does prekindergarten improve school preparation and performance? Economics of Education Review, 26(1), 33–51. Mashburn, A. J., Pianta, R., Hamre, B. K., Downer, J. T., Barbarin, O., Bryant, D., … Howes, C. (2008). Measures of classroom quality in pre‐kindergarten and children’s development of academic, language and social skills. Child Development, 79, 732–749. doi: 10.1111/ j.1467‐8624.2008.01154 Malofeeva, E., Daniel‐Echol, M., & Xiang, Zongping (2007). Findings from the Michigan School Readiness Program 6 to 8 Follow‐up Study. Yspsilanti, MI: HighScope. Minervino, J. (2014). Lessons from research and the classroom: implementing high‐quality pre‐k that makes a difference for young children. Seattle: Bill and Melinda Gates Foundation. Murray, P. (2014). Attachment A: Overview of ECERS‐R and CLASS. Retrieved from http:// m­urray.seattle.gov/wp‐content/uploads/2014/05/AttachmentAthruE.pdf Muschkin, C. G., Ladd, H. F., & Dodge, K. A. (2015). Impact of North Carolina’s Early Childhood Initiatives on Special Education Placements in Third Grade. Educational Evaluation and Policy Analysis, 37(4), 478–500. National Center for Education Statistics. (2012). Digest of Education Statistics, Table 61. Percentage distribution of quality rating of child care arrangements of children at about 4 years of age, by type of arrangement and selected child and family characteristics: 2005‐06. Retrieved from http://nces.ed.gov/programs/digest/d12/tables/dt12_061.asp Nores, M., & Barnett, W. S. (2014). Access to High Quality Early Care and Education: Readiness and Opportunity Gaps in America (CEELO Policy Report). New Brunswick, NJ: Center on Enhancing Early Learning Outcomes. Office of Head Start (OHS). (2014). Head Start Program Facts Fiscal Year 2013. Retrieved from https://eclkc.ohs.acf.hhs.gov/hslc/data/factsheets/docs/hs‐program‐fact‐sheet‐2013.pdf Peisner‐Feinberg, E. S., Burchinal, M. R., Clifford, R. M., Culkin, M. L., Howes, C., Kagan, S. L., & Yazejian, N. (2001). The relation of preschool child‐care quality to children’s cognitive and social developmental trajectories through second grade. Child Development, 72(5), 1534–1553. Phillips, D. A., Vorran, M., Kisker, E., Howes, C., & Whitebook, M. (1994). Childcare for children in poverty: Opportunity or inequity? Child Development, 65, 472–492.

Publicly Supported Programs  185 Piaget, J. (1954). The construction of reality in the child. New York, NY: Basic Books. Pianta, R. C., Barnett, W. S., Burchinal, M., & Thornburg, K. (2009). The effects of preschool education: What we know, how public policy is or is not aligned with the evidence base, and what we need to know. Psychological Science in the Public Interest, 10(2), 49–88. Puma, M., Bell, S., Cook, R., Heid, C., Lopez, M., Zill, N., … Bernstein, H. (2005). Head Start impact study: First year findings. Washington, DC: US Department of Health and Human Services, Administration for Children and Families. Ramey, C. T., & Campbell, F. A. (1984). Preventive education for high‐risk children: cognitive consequences of the Carolina Abecedarian Project. American Journal of Mental Deficiency, 88(5), 515–523. Ramey, C. T., Landesman Ramsey, S., & Stokes, B. R. (2009). Research evidence about program dosage and student achievement: Effective public prekindergarten programs in Maryland and Louisiana. In R. C. Pianta & C. Howes (Eds.), The promise of pre‐k (pp. 79–105). Baltimore, MD: Paul H. Brooks Publishing Co. Reynolds, A. J. (2000). Success in early intervention: The Chicago Child‐Parent Centers. Lincoln, NE: University of Nebraska Press. Reynolds, A. J., Richardson, B. A., Hayakawa, M., Lease, E. M., Warner‐Richter, M., Englund, M. M., … Sullivan, M. (2014). Association of a full‐day vs part‐day preschool intervention with school readiness, attendance, and parent involvement. JAMA, 312(20), 2126–2134. doi:10.1001/jama.2014.15376. Reynolds, A. J. Temple, J. A., & Ou, S. (2010). Preschool education, educational attainment, and crime: Contributions of cognitive and non‐cognitive skills. Children and Youth Services Review, 32(8), 1054–1063. Reynolds, A. J., Temple, J. A., Ou, S. R., Robertson, D. L., Mersky, J. P., Topitzes, J. W., & Niles, M. D. (2007). Effects of a school‐based, early childhood intervention on adult health and well‐being: A 19‐year follow‐up of low‐income families. Archives of Pediatrics & Adolescent Medicine, 161(8), 730–739. 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(1), 379–404. Robin, K. B., Frede, E. C., & Barnett, W. S. (2006). Is more better? The effects of full‐day vs. half‐day preschool on early school achievement (NIEER Working paper). New Brunswick, NJ: NIEER. Rolnick, A., & Grunewald, R. (2011). The economic case for targeted preschool programs. In E. Zigler, W. Gilliam, & W. S. Barnett (Eds.), The pre‐k debates: current controversies and issues (pp. 22–27). Baltimore, M D: Brookes Publishing. Sabol, T. J., Hong, S. L. S., Pianta, R. C., & Burchinal, M. R. (2013). Can rating pre‐K programs predict children’s learning. Science, 341(6148), 845–846. Schickedanz, J. A., & Dickinson, D. K. (2005). Opening the world of learning: A comprehensive early literacy program. Parsippany, NJ: Pearson Early Learning. Schindler, H. S., Kholoptseva, J., Oh, S. S., Yoshikawa, H., Duncan, G. J., Magnuson, K. A., & Shonkoff, J. P. (2015). Maximizing the potential of early childhood education to prevent externalizing behavior problems: A meta‐analysis. Journal of School Psychology, 53(3), 243–263. Schweinhart, L. J., Montie, J., Xiang, Z., Barnett, W. S., Belfield, C. R., & Nores, M. (2005). Lifetime effects: The HighScope Perry Preschool study through age 40. (Monographs of the HighScope Educational Research Foundation, 14). Ypsilanti, MI: HighScope Press. Seifert, K. L. (2014). Cognitive development and the education of young children. In O. N. Saracho & B. Spodek (Eds.), Handbook of research on the education of young children (pp. 19–32). New York, NY: Taylor & Francis.

186  Barnett, Votruba-Drzal, Dearing, and Carolan Shager, H. M., Schindler, H. S., Magnuson, K. A., Duncan, G. J., Yoshikawa, H., & Hart, C. M. (2013). Can research design explain variation in Head Start research results? A meta‐analysis of cognitive and achievement outcomes. Educational Evaluation and Policy Analysis, 35(1), 76–95. Skeels, H. M. (1942). A study of the effects of differential stimulation on mentally retarded c­hildren. American Journal of Mental Deficiency, 44, 114–136. Skinner, B. F. (1954). The science of learning and the art of teaching. Harvard Educational Review, 24, 86–97. Skodak, M., & Skeels, H. M. (1949). A final follow‐up study of one hundred adopted children. Journal of Genetic Psychology, 75, 85–125. Temple, J., & Reynolds, A. (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. Tucker‐Drob, E. M. (2012). Preschools reduce early academic‐achievement gaps: A longitudinal twin approach. Psychological Science, 23(3), 310–319. US Department of Health and Human Services, Administration for Children and Families (2010). Head Start Impact Study. Final Report. Washington, DC. Votruba‐Drzal, E., Coley, R. L., Collins, M., & Miller, P. (2015). Center‐Based Preschool and School Readiness Skills of Children from Immigrant Families. Early Education and Development, 26(4), 549–573. Votruba‐Drzal, E., Coley, R. L., Koury, A. S., & Miller, P. (2013). Center‐based child care and cognitive skills development: Importance of timing and household resources. Journal of Educational Psychology, 105(3), 821. Vygotsky, L. S. (1978). Mind in society: The development of higher psychological processes. Cambridge, MA: Harvard University Press. Weikart, D. P. (1969). Comparative Study of Three Preschool Curricula. Ithaca, NY: Cornell University, Regional Training Office, Project Head Start, Upstate New York. Weiland, C., & Yoshikawa, H. (2013). Impacts of a prekindergarten program on children’s mathematics, language, literacy, executive function, and emotional skills. Child Development, 84: 2112–2130. doi: 10.1111/cdev.12099 Wong, V. C., Cook, T. D. Barnett, W. S., & Jung, K. (2008). An effectiveness‐based evaluation of five state pre‐kindergarten programs. Journal of Policy Analysis and Management, 27(1), 122–154. Yoshikawa, H., Weiland, C., Brooks‐Gunn, J. Burchinal, M. R., Espinosa, L. M., Gormley, W. T., … Zaslow, M. J. (2013). Investing in our future: The evidence base on preschool education. Ann Arbor, MI: Society for Research in Child Development. Retrieved from http://www.srcd.org/sites/ default/files/documents/washington/mb_2013_10_16_investing_in_children.pdf Zigler, E. F., & Butterfield, E. C. (1968). Motivational aspects of changes in IQ test performance of culturally deprived nursery school children. Child Development, 39, 1–14.

CHAPTER NINE Early Childhood Education and Care for Dual Language Learners Lianna Pizzo and Mariela Páez

Dual language learners (DLLs), or children who speak a language other than English at home, are the fastest growing group of the US public school population (Castro, García, & Markos, 2013). In 2013, the number of school‐aged (ages 5–17) DLLs was 11.7 million, or 22% of the school population (The Annie E. Casey Foundation, 2015). This is up from 4.7 million or 10% in 1980 (Aud et al., 2011), indicating an increase of 12% of DLLs in schools since that time. While immigration patterns have contributed to this DLL population growth, the majority of DLLs (75%) are not immigrants; rather they are US born (Child Trends, 2014; Espinosa, 2013). As language skills are a necessary resource for the next generation of children to “thrive in a global multilingual world” (Espinosa, 2006, p. 2), the increase in the DLL population could be seen as an opportunity for this country to compete in a global economy. However, today’s educational system has yet to fully foster the distinct linguistic, cognitive, and cultural benefits that result from learning more than one language. As such, there has been a persistent gap in educational achievement between DLL and monolingual students, which has raised significant concerns for educators, scholars, and policymakers. Specifically, national achievement data indicate that monolingual students have consistently outperformed DLLs on measures of literacy and math across different grade levels (Kena et al., 2014). In fact, the National Assessment of The Wiley Handbook of Early Childhood Development Programs, Practices, and Policies, First Edition. Edited by Elizabeth Votruba-Drzal and Eric Dearing. © 2017 John Wiley & Sons, Inc. Published 2017 by John Wiley & Sons, Inc.

188  Pizzo and Mariela Páez Educational Progress (NAEP) has shown large differences in reading between DLL and monolingual students, with an average gap of 38 points at 4th grade and 45 points at 8th grade favoring monolinguals (Kena et al., 2014). Consequently, the number of research studies with DLL students has been increasing in an effort to better understand and improve their language and literacy skills. The purpose of this chapter is to discuss the current knowledge base on promoting early childhood development for DLLs, including current program and policy approaches to address the needs of this population. Research shows that early childhood programs for infants/toddlers, preschool, and kindergarten through 3rd grade education all contribute to the continuum of learning that promotes academic achievement. Although there is much to be discussed in regard to infant/toddler programs and kindergarten through 3rd grade education, this chapter will focus on early childhood education and care at the preschool level. More specifically, the information presented builds upon themes in the developmental research for DLLs across the early childhood period, and emphasizes the unique programs and policies that directly affect preschool experiences of DLL children in the United States. Therefore, this chapter will be organized around three key questions important to understanding early childhood education and care for young DLLs in the US: 1. Who are the young DLLs in early childhood settings? 2. What are the important developmental and language learning theories guiding practice with young DLLs? 3. What are important considerations for programs, practices, and policies in designing and implementing educational services for DLLs?

A Portrait of Young DLLs In order to understand the educational and policy considerations for DLLs, it is first important to understand the population characteristics of this group. DLLs, or dual language learners, can be defined as children learning two or more languages at the same time, as well as those learning a second language while continuing to develop their first (or home) language (US DHHS, 2008). While various terms have been employed to describe this group of children (e.g., English Language Learners  –  ELLs, Limited English Proficient – LEP, English Learners – ELs), DLL has been selected for this chapter as it acknowledges and respects the roles that both language systems play in the development of young learners of two languages. Furthermore, by valuing the contribution of the first language, the use of DLL avoids employing a deficit perspective that may ignore key strengths that young DLLs possess and bring with them to the educational experience. Finally, the term DLL encompasses all children that are developing two languages, not only those who are experiencing difficulty in acquiring English and are receiving English language services in school; as a result, this term also recognizes the cultural and linguistic diversity of these young language learners. There are four key population characteristics that are essential to understand before reviewing the theory, research, and practice on educating young DLLs. First, among the

Education and Care for Dual Language Learners  189 DLLs, Spanish‐speaking children make up the largest percentage of children; however, many other languages are represented. Second, DLLs are more likely to live below both poverty and low‐income thresholds than monolingual children (Jiang, Ekono, & Skinner, 2015). Third, DLLs are less likely to have access to high quality early childhood care and education (NCELA, 2011). Finally, it is important to note that there is still much unknown about young DLLs with regard to a national portrait, as inconsistent data collection across states and districts forces the field to use proxy measures to estimate population characteristics with unknown accuracy.

Language According to the US Census Bureau population data for 2011, there are currently more than 300 languages represented across the US (Ryan, 2013). For people who speak a language other than English, Spanish‐speakers comprise the largest percentage of the group at 62%, demonstrating a 232% change since 1980 (Ryan, 2013). The next largest group consists of Chinese speakers (approximately 5%), which is a significantly smaller percentage of the DLL population. The remaining top 10 languages other than English spoken in the home include (Ryan, 2013): Tagalog (2.6%); Vietnamese (2.3%); French (2.1%); Korean (1.9%); German (1.8%); Arabic (1.6%); Russian (1.5%); African Languages (1.5%). The category of African languages comprises 19 languages native to Africa, with the largest of these language sub‐categories being Kru, Ibo, and Yoruba. The language patterns within the school‐age population are similar to those of the nation at large. In addition, there is great diversity across regions and metropolitan areas. According to data from the 2012–13 school year, five states accounted for 62% of the DLL students enrolled in the K–12 public school system: California, Texas, Florida, New York, and Illinois. Although Spanish was the top language represented in each of these states, the remaining top five languages for each state varied. For example, In New York the second most prevalent language was Chinese, while in Florida it was Haitian, both representing 10% of the population for DLLs in their state (Ruiz Soto, Hooker, & Batalova, 2015). Among Spanish‐speakers, important distinctions among country of origin, dialect, and geographical location must also be noted. The three largest Hispanic origin groups in the US consist of Mexicans (65%), Puerto Ricans (9.2%), and Cubans (3.7%). Furthermore, various Hispanic origin groups are concentrated in different geographical locations within the US. For example, Mexicans, El Salvadorians, and Guatemalans are concentrated in the Western states; Cubans, Colombians, Hondurans, and Peruvians are in the South; and Puerto Ricans, Dominicans, and Ecuadorians are in the Northeast (Motel & Patten, 2012).

Family income In regard to family income, data from 2013 shows that 48% of children under the age of 6 (11.1 million) are currently living in low‐income1 households, with 25% of those children living in poverty. In addition, racial groups with higher numbers of DLLs are

190  Pizzo and Mariela Páez more likely than White children to fall below the low‐income and poverty threshold (Jiang et al., 2015). For example, 35% of Hispanic children, 13% of Asian children, and 41% of American Indian children as compared to 15% of White children under the age of 6 live in poverty (Jiang et al., 2015). Furthermore, the numbers are even greater for children in low‐income households; as 66% of Hispanic children (4.0 million), 30% of Asian children (0.3 million), and 6% of American Indian children (0.1 million) as compared to 34% of White children under the age of 6 meet the criteria for low‐income status (Jiang et  al., 2015). Finally, it is also important to note that 56% of children under the age of 6 from immigrant families, or 3.3 million, also meet these criteria (Jiang, et al., 2015).

Early childhood education enrollment In addition to higher levels of poverty, young DLLs are also less likely to attend high quality center‐based preschool programs than other groups of children (Espinosa, 2013). Recent data indicates that Hispanic children are less likely than any other ethnic group to attend early care and education programs (National Task Force on Early Childhood Education for Hispanics, 2007; Figueras‐Daniel & Barnett, 2013). Furthermore, the lack of participation by DLLs in early childhood education and care is often misunderstood. There is a general misconception that cultural values and beliefs plays a role in the low enrollment of particular groups in early childhood programs; however, the real impediment to early childhood participation is due to access and affordability issues (Espinosa, 2008; Figueras‐Daniel & Barnett, 2013). Research indicates that immigrant populations, in particular, might not be aware of the types of early childhood programs available (Espinosa, 2013). Furthermore, the availability of programs with non‐English speaking care providers may also impact the enrollment of children in early childhood education and care for immigrant populations, as the cultural match provided by those settings might be an important contextual factor for families (Miller, Votruba‐Drzal, & Coley, 2013). As a result, DLLs have had less opportunity to engage in high quality preschool experiences that have long‐term educational and social impacts (Yoshikawa et al., 2013). The need to increase access for this population is even more urgent as research has shown that these kinds of educational experiences have an even greater impact for promoting academic achievement on Hispanic children than any other group (Yoshikawa et al., 2013). Although estimates of the DLL population within early childhood education and care are available, there are no precise data regarding the size and characteristics of the population under the age of 5. Finding accurate and detailed demographic information is challenging given the multiple sources of data that are collected across different types of programs and settings. In fact, many of the numbers used to understand the early childhood DLL population use proxy measures, as the actual demographic and language information is not systematically collected across programs for these children (NCELA, 2011). Data collected through the national Head Start and Early Head Start programs are exceptions in this regard, as they pay heightened attention to understanding the diverse populations served in their programs. In other arenas, data on the characteristics of DLLs are

Education and Care for Dual Language Learners  191 often extrapolated from data on race, ethnicity, and/or immigration status; however, there is not a one‐to‐one correspondence among these groups. For example, not all Hispanic children are DLLs in the same way that not all DLLs are children of immigrants. Therefore, while these statistics are the best data currently available, they remain limited in providing the field with the information that is truly needed.

Developmental and Language Learning Theories Developmental and language learning theories have important implications for programs, policies, and practices in early childhood education and care. Not only must educators consider the developmentally appropriate practices for educating young children, they must also consider the unique language learning characteristics for DLLs and how developing more than one language can impact children’s cognitive, language, early literacy, and social emotional development (Castro, Espinoza, & Páez, 2011). Although the research knowledge base for DLLs has been growing in recent years, there are still crucial gaps in the research literature that limit our understanding of this population. First, most of the research has been conducted with Spanish‐speaking, low‐income populations; thus there is less known about speakers of other languages (McCabe et al., 2013). In addition, there is a critical need for research that can disentangle effects of confounding variables such as family income and other important demographic characteristics (e.g., country of origin, geography, immigration status, parental resources and cultural capital, etc.). Second, there is a critical need for more DLL research with children under the age of 5, especially from birth through age three (Castro et al., 2013). Third, while a number of methodologies have been employed to address developmental research with DLLs, one of the most powerful types of investigation, longitudinal studies, is underrepresented in the literature. Therefore, claims regarding developmental trajectories and individual differences among young DLLs are limited by the current status of the research, which results in gaps in knowledge to inform developmentally appropriate practices and policies. Regardless of the limitations in current research, there are important themes in language learning and developmental theories that can be used to support and guide the field of early childhood education and care for young DLLs. In this section, the developmental and language learning theories that prevail in the research literature will be presented. Specifically, a review of the three major areas related to bilingual development and pre‐­academic learning will be addressed: bilingual language development, promoting oral l­anguage skills, and the importance of developing first language skills.

Bilingual language development: multiple pathways in language learning As with all language learning there are universal dimensions, or principles that can be extrapolated to most DLLs, and unique dimensions, or aspects of development due to individual circumstances (Bialystok, 2001). The universal dimensions key to understanding the language learning of young DLLs pertain to the documented

192  Pizzo and Mariela Páez developmental considerations and stages that are representative of this population. The first important consideration is related to the sequence and rate of exposure of each language. Simultaneous bilinguals are exposed to two languages from birth (Paradis, Genesee, & Crago, 2011). Sequential bilinguals are exposed to one language almost exclusively until a second language is introduced, which is typically after the age of three (Paradis et al., 2011). It is most common for young DLLs to be sequential bilinguals, as they are primarily exposed to one language until the time of enrolling in a formal early childhood program (Tabors, 2008). Within this pattern of language exposure, Tabors found that children generally go through a four‐stage developmental sequence of language. When children are exposed to their second language environment for the first time, they continue to speak their home language as they acquire the skills to distinguish their first and new languages. Then children typically progress into a nonverbal period where children use other forms of communication including use of gesture and paralinguisitic communicative strategies while they develop their receptive language skills in their second language. Next, children use telegraphic and formulaic language, which involves using common words and phrases to engage in routine classroom activities and to sound like other members of their new language group. Finally, children enter the stage that allows them to productively use the new language independently to meet their needs in the classroom setting. While this developmental progression is typical of young bilinguals, the process of learning two languages is not the same for all children (Bialystok, 2011). Young DLLs’ rate of progression through each stage varies significantly due to unique dimensions such as familial, societal, and individual factors (August & Hakuta, 1997; August & Shanahan, 2006; Brisk, 2006). Familial factors can include race, culture, socio‐­economic status, parent participation in early childhood programs, and home language environment (Brisk, 2006; Goldenberg, Rueda, & August, 2006). Societal factors may include the dominant ideologies regarding bilingualism, the community’s cultural and language environment, influence of policies on society, and access to local and national resources (Brisk, 2006; Goldenberg, Rueda, & August, 2006). Individual factors that may affect how efficiently a child acquires a second language include motivation, exposure, age, and personality (Tabors & Snow, 2002). Finally, research has shown that early learning experiences and cognitive abilities also play key roles in moving children along their language and literacy trajectory (August & Shanahan, 2006; Bialystok, 2001). As such, DLLs in early childhood settings remain a very heterogeneous population in regard to how and when they develop not only their first and second languages, but also early literacy skills. Through understanding these developmental processes and by engaging in efforts to learn about the individual, familial, and other important contextual factors, early childhood educators can promote language and early literacy in young DLLs. Furthermore, it is important to make these factors related to developmental trajectories of language learning part of the central tenets and data collection within early childhood programs. By providing structures to programs for gathering information using valid and reliable assessments in English and the home language, programs can facilitate collection of key information needed to make informed instructional choices for the children in their care.

Education and Care for Dual Language Learners  193

Oral language skills: building the foundation for learning in young DLLs In addition to developing their first and second language, DLLs in early childhood ­settings are also tasked with learning early literacy skills that are precursors to later reading and connect to overall academic development of DLLs across subject areas. Several language and early literacy skills are considered foundational for high quality literacy programs: phonological and phonemic awareness, book and print concepts, conceptual knowledge, and oral language and vocabulary (Dickinson, McCabe, Anastasopoulous, Peisner‐Feinberg, & Poe, 2003; Dickinson & Tabors, 2001). Furthermore, the National Early Literacy Panel (2008) found that there are additional key predictors of later reading and writing outcomes including: alphabet knowledge, phonological awareness, rapid auto‐naming of letters or digits and objects or colors, name writing, and phonological memory. Research on young DLLs also confirms the importance of these components in early childhood instruction, but finds the typical dosage and pattern of instruction insufficient to meet the needs of young DLLs (August & Shanahan, 2006). Specifically, the National Literacy Panel on Language Minority Youth (August & Shanahan, 2006) reported that additional attention should be placed on oral English skills. Oral language skills, such as vocabulary, have been linked to both phonological awareness and word recognition in the early grades (Nagy, 2005), with those children who have larger English vocabularies demonstrating higher levels of phonological awareness and word recognition. In addition, English vocabulary is also one of the strongest predictors of reading comprehension in the later grades, with children who demonstrate larger vocabularies in early childhood demonstrating higher reading comprehension levels at the 4th, 7th, and 11th grade level (August, Carlo, Dressler, & Snow, 2005; Cunningham & Stanovich, 1997; Scarborough, 1998; Tabors, Snow, & Dickinson, 2001). Given the strong evidence supporting the importance of vocabulary in predicting reading success, researchers have paid increased attention to the oral language skills and vocabulary for DLLs (Hammer et al., 2014; Páez, Tabors, & López, 2007; Páez, Bock, & Pizzo, 2011). In particular, longitudinal research has shown that Spanish‐English bilinguals from low socio‐economic backgrounds demonstrate sufficient word‐reading abilities (e.g., letter knowledge and phonemic skills), but continue to lag behind monolingual peers on skills related to vocabulary, concept development, and meaning‐making skills (Lesaux, Crosson, Kiefer, Pierce, 2010). In fact, the relationship between socio‐economic background and language has been well established in the research literature (Hoff, 2006) and is also a confounding factor when considering the language development of young DLLs (Howard et al., 2014). These effects are even more profound for young bilinguals, as SES has been found to have greater educational impact in the early years in comparison to elementary grades (Howard et al., 2014). Therefore, in order to provide a strong foundation for future reading skills, the education of oral language skills needs to begin in early childhood educational settings that are systematically and explicitly targeting these skills through instruction. Although the need for instructional emphasis on oral language and vocabulary has been repeatedly recognized in the literature, the reality is that these skills are often overlooked in instruction (August & Shanahan, 2006). This lack of oral language and vocabulary instruction is even more pronounced within the early childhood setting, as recent research

194  Pizzo and Mariela Páez indicated that formal vocabulary instruction was nearly absent from four prevalent early childhood curricula and classroom instruction over the course of 660 hours of observation (Neuman & Dwyer, 2009; Neuman, 2011). Similar findings have also been made when considering educational settings that have large percentages of DLLs. For example, a study of kindergarten and first‐grade classrooms in urban settings in California and Texas found that vocabulary instruction was prevalent in less than 7% of the total instructional time (Saunders, Foorman, & Carlson, 2006). Moreover, instruction needs to be embedded in high quality language environments that promote strong linguistic interactions with teachers, as shown by research demonstrating that the complexity and variety of teacher language accounted for children’s language levels above and beyond other contextual variables in the children’s lives (Huttenlocher, Vasilyeva, Cymerman, & Levine, 2002). Early childhood settings compliment the language learning that takes place in the home environment, but are even more important for DLLs, as these settings may be the primary venue for learning English language skills (Snow & Páez, 2004). Therefore, early childhood settings serving DLLs need not only to provide high quality language environments and interactions, but also to provide intentional and targeted instruction on developing strong oral language skills. Furthermore, policy efforts need to address these specific needs of DLLs while providing avenues for teacher development and curriculum quality in early childhood settings.

First language skills: capitalizing on strengths and knowledge of young dlls The importance of oral language skills extends to the first language, as first language skills have been shown to contribute to developing English proficiency (August & Shanahan, 2006). DLLs acquire proficiency in a second language through one of two processes: additive and subtractive bilingualism (Paradis et al., 2011). Additive bilingualism is when a person acquires a second language (L2) while maintaining or developing their first language (L1). Subtractive bilingualism is when a person loses their first language as a result of learning a second one; this is differentiated from language attrition, in which the first language is not lost, but does not develop at the expected rate (Anderson, 2012). Unfortunately, the larger educational field has historically ignored the advantages and resiliency that many bilingual learners possess, resulting in subtractive bilingualism. However, emerging evidence supports our capitalizing on first language skills as a strength and vehicle for educating young DLLs. Linguistic transfer.  Theory on second language acquisition has proposed that first language skills transfer to support the learning of a second language through the process of interdependence (Cummins, 1979, 1991; Royer & Carlo, 1991). Although a limited number of studies have been conducted with young DLLs to investigate this theory, research supporting the interdependence of first and second languages has been slowly growing (August & Shanahan, 2006). This expanding evidence base includes support for cross‐linguistic transfer for Spanish‐English bilinguals in vocabulary (Ordóñez, Carlo, Snow, & McLaughlin, 2002; Snow 1990), phonological awareness (Dickinson, McCabe, Clark‐Chiarelli, & Wolf, 2004; Lindsey, Manis, & Bailey, 2003; López &

Education and Care for Dual Language Learners  195 Greenfield, 2004), and English reading and comprehension skills (Manis, Lindsey, & Bailey, 2004; Proctor, August, Carlo, & Snow, 2006). Furthermore, longitudinal research has shown that preschoolers’ first language skills in Spanish contribute to later reading skills in English (Hammer, Lawrence, & Miccio, 2007; Páez & Rinaldi, 2006; Rinaldi & Páez, 2008). For first languages other than Spanish, there are fewer studies investigating aspects of cross‐linguistic transfer. This small body of evidence suggests that the nature of transfer may be related to the linguistic similarities and differences among the first and second languages (August & Shanahan, 2006). For example, findings from one study investigating transfer in first‐grade Cantonese‐English, Spanish‐English, and Hebrew‐English bilinguals indicated that evidence of cross‐linguistic transfer was connected to both the relationship among the languages and the writing systems of these languages (Bialystok, Luk, & Kwan, 2005). Even with the growing number of research studies on this topic, there is still much to learn about the factors and conditions influencing transfer and the extent that transfer can be leveraged for second language acquisition (Snow, 2006). Furthermore, some have noted that the correlational methodology employed in these studies makes these findings vulnerable to alternative explanations, including lack of specificity in the direction of these relationships and the notion that there may be unknown underlying competencies driving the results currently attributed to transfer (e.g., more general cognitive skills which can be involved in language learning processes) (Bialystok, 2001). As such, this is an area of research that is ripe for further investigation. Advantages of bilingualism.  The influence of first language skills and knowledge extends beyond interdependence theory and notions of transfer, as research has also shown cognitive, social, and personal benefits of being bilingual (Espinosa, 2013; McCabe et  al., 2013). The cognitive benefits of bilingualism have indicated that bilinguals exhibit increased executive control over their monolingual peers (Bialystok, Craik, Green, & Gollan, 2009). These findings have spanned the ages from infancy through older age (e.g., Kovács & Mehler, 2009; Poulin‐Dubois, Blaye, Courtya, & Bialystok, 2011; Costa, Hernández, & Sebastián‐Gallés, 2008; Bialystok, Craik, Klein, & Viswanathan, 2004). In particular, research has shown cognitive advantages such as higher levels of performance on selective attention, theory of mind, and cognitive conflict resolution tasks (Bialystok, 2009). Social, cultural, and personal benefits of bilingualism include social competence skills, sense of self, and identity (Espinosa, 2006, 2008). By developing proficiency in their first language, children “establish a strong cultural identity” that facilitates the abilities of bilingual children to be successful in an increasingly diverse and multilingual society (Espinosa, 2006). In fact, one study showed that children from Mexican immigrant families had fewer externalizing and internalizing behaviors at kindergarten entry than their White or African American counterparts (Espinosa, 2006). Unfortunately, the field has not acknowledged the advantages and resiliency that many young bilinguals possess, as this population is often viewed from an at‐risk perspective. However, utilizing a strengths‐based approach to early childhood education would afford “a potential source of resilience that school personnel should recognize, support, and enhance” (Espinosa, 2008, p. 8).

196  Pizzo and Mariela Páez Subtractive bilingualism.  Despite the distinct advantages of bilingualism, many young DLLs in the United States are victims of subtractive bilingualism, or the loss of the first language as they are immersed in second language educational environments (Tabors, 2008). Indeed, the course of second language acquisition and first language loss can vary according to many factors including exposure and use of these languages and individual factors such as motivation, age, and personality. Yet, there is a real possibility for decreases in first language abilities when young children who speak a language other than English at home enter early childhood settings that are completely in English and do not support the home language. As such, the loss and/or attrition of first language skills may increase the risk of incurring negative impacts on children’s thinking and reasoning skills (Bialystok, 2001). Furthermore, these children may lose their ability to communicate fully with their immediate and extended family members, in turn potentially affecting their sense of self and other social‐emotional competencies (Anderson, 2012; Wong Fillmore, 1991). Although the field has identified this as an important consideration for DLLs, more empirical research is needed to investigate the consequences and prevalence of language loss and/or attrition. These findings regarding the importance of first language skills for DLLs’ development have two important implications for early childhood education. The first is that center and school‐based approaches supporting and advancing first language skills may have key advantages for young bilinguals. By employing targeted linguistic strategies to capitalize on transfer, as well as recognizing the assets that young DLLs bring with them, educators can capitalize on the strengths and knowledge that come from dual language development. For example, by providing books in the children’s first language and making the L1 to L2 connections explicit including use of cognates and background knowledge, educators of DLLs can maximize first language as a resource (Páez et al., 2011). The second implication is the increased emphasis on promoting high quality first language use in the home through targeted family engagement and programming in early care and education. By empowering families to continue using their most comfortable and most advanced language, children are able to receive the richest and most robust linguistic input in the early years providing strong first language skills that lay the foundation for future language development.

Early Childhood Landscape and Programs In regard to young DLLs, research has been primarily aimed at investigating the effects of various interventions including programmatic, professional, and curricular approaches that impact learning for these students (Buysse, Peisner‐Feinberg, Paez, Scheffner Hammer, & Knowles, 2014). While these research endeavors are important to our understanding of early childhood education and care for young DLLs, the number of studies still pale in comparison to the number of investigations on the larger population (Burger, 2010). Of the programmatic efforts, Head Start and Early Head Start have received the most attention followed by public preschool. It is important to note that these programs are not specifically designed for DLLs, rather they examine differential effects of learning within

Education and Care for Dual Language Learners  197 the traditional program (Buysse, Castro, & Peisner‐Feinberg, 2010). In addition, although some of the research includes assessment measures in the home language, the research is focused on English outcomes and fails to truly address issues of language maintenance or potential negative consequences of home language loss in an English‐based education program (Anderson, 2012). In this section, findings regarding program effects for DLLs will be presented for Head Start, public schools, and private and other types of care settings. Note that while the findings have been organized in this manner, they are less than optimal as mixed funding streams rely on multiple sources of federal, state and local funding. Thus, the variation in funding and complexity of public preschool programs makes it difficult to discuss research for this particular type of program. It is possible, however, to present estimates of enrollment and discuss outcomes for DLLs as presented in the research literature.

Head Start Head Start is a federally funded, comprehensive educational program targeting preschool age children from low‐income households. It was created in 1964 as a way to promote success for children who may be disadvantaged when beginning school. Early Head Start was created in 1994 to extend the work to pregnant women, infants, and toddlers. Given the high percentage of young DLLs who meet the criteria for low‐income status, it is unsurprising that approximately 30% of participants in Head Start and Early Head Start are DLLs. DLLs participating in Head Start are mostly Spanish‐speakers (84%), followed by Asian languages (5%) and African languages (3%), respectively. The majority of young DLLs in Head Start and Early Head Start were born in the United States (92%); however, the majority of their parents were immigrants (86% of mothers and 90% of fathers). Monolinguals were more likely to be enrolled in full‐day programs, while two‐thirds of DLLs in Head Start were enrolled in part‐day programs, a finding that illustrates the aforementioned issue of access to high quality preschool programs for young DLLs (US Department of Health and Human Services [US DHHS], 2013). Of the DLLs enrolled, the majority had some instruction in their home language within Head Start (60%) and Early Head Start (85%) as part of the program (US DHHS, 2013). Research on the outcomes of Head Start and Early Head Start indicate that overall these programs have positive effects on DLLs’ learning when compared to DLLs who are not participating in these programs (US DHHS, 2013). The Head Start Impact Study (HSIS) final report concluded that DLLs appear to benefit from Head Start participation as these children’s oral English language skills were positively impacted through the program (US DHHS, 2010). Furthermore, DLL children participating in Head Start programming had positive impacts over children in control conditions in measures of vocabulary, phonological awareness, letter identification, and early math skills (US DHHS, 2010). These positive effects for DLLs in Head Start programs are impressive and even more so when considering that this population attends programs of lower quality in terms of classroom environment and instructional support. Global ratings of classroom quality indicate that, on average, Head Start classrooms of DLLs provide minimal to good

198  Pizzo and Mariela Páez quality care and low instructional support (US DHHS, 2013); while reports for the ­general population noted that the majority of Head Start children (about 70%) attended centers that were rated as good or higher quality (US DHHS, 2010). In addition to overall program impacts, there have been factors associated with Head Start that have shown differential effects on children in Head Start classrooms. For example, immigrant children in Head Start demonstrated higher outcomes compared to those not enrolled in Head Start; and immigrant children who had mothers with lower maternal education demonstrated the greatest gains (Magnuson, Ruhm, & Waldfogel, 2004). In another study, Hammer et al. (2007) found that DLLs who participated in Head Start showed long‐term progress in oral language abilities and reading when tested at the end of kindergarten. Also, the quality of classroom language was found to be significantly linked to vocabulary outcomes in English and Spanish for young DLLs (Hindman & Wasik, 2015). Finally, a recent review on the effects of early education programs and practices on DLLs confirms these findings regarding Head Start and other early childhood programs (Buysse et al., 2014).

Public school There are currently 41 states with state funded preschool; however these programs only serve approximately 28% of the 4‐year‐old children in those states (Barnett, Carolan, Squires, & Clarke Brown, 2013). Of those states, 24 collect and examine the data for DLLs as a separate subgroup. In addition to state funding, public preschools can also be funded by districts. Within these programs, 11% are currently DLLs (Barnett et al., 2013). The evidence for public school impacts on DLLs has been growing in recent years, however, there are some mixed results in the literature. For example, positive effects for DLLs were found in pre‐reading, pre‐writing, and early math skills compared to DLLs not enrolled in the public school program (Gormley, 2008). In addition, positive effects in regard to the reduction of challenging behaviors were also found for DLLs in public school programs. While these results are encouraging, one study did not find programmatic effects for English and Spanish language development, English literacy, and math skills (Stipek, Ryan, & Alarcón, 2001). Recent data from universal preschool efforts perhaps provide the strongest evidence for the impact of publicly funded preschool on DLL children. For example, in Tulsa, Oklahoma researchers found substantial benefits for Latino students especially those who came from a home where Spanish is the primary language (Gormley, 2008). These Spanish‐speaking students were found to experience greater benefits when compared to other groups in measures of English‐language skills and other cognitive abilities including prewriting and premath skills (Gormley, 2008). The programs in Tulsa might be unique in that they attract a high number of Hispanic families and also, their classrooms score very high on measures of instructional support and quality. In another example, in Boston, Massachusetts, researchers used regression discontinuity design and found the largest impact on cognitive outcomes for Latino children (Weiland & Yoshikawa, 2013). Indeed, there is a convergence of evidence suggesting that Latino, and in particular DLL, children are likely to benefit from high quality early childhood programs.

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Private school and other care settings There are the least amount of programming data on DLLs attending private schools and other care settings, include family child care. This challenge is not specific to DLLs, as the data are often not systematically collected or aggregated for any children in regard to these settings. Recently states and districts have been working to improve reporting systems across these contexts and it is likely that new information regarding DLLs will emerge from these efforts. However, there is very little information regarding children’s participation in these programs. Along with the data issues for private school and other care settings, research is also not as robust in regard to these programs. In one investigation with a slightly larger sample size (Winter, Zurcher, Hernández, & Yin, 2007), a community‐based educational program was investigated over 2 years. The program was conducted in English and included community involvement, education of parents and families, educator professional development, assessment of program quality, and transition planning. This program demonstrated positive effects for English language development in year 1 over the control group; however, there were no differences in year 2 for English. Another line of research has investigated the effects of subsidies on children of immigrants’ school readiness skills using a nationally representative database (ECLS‐B; Johnson, Han, Ruhm, & Waldfogel, 2014). Results indicated that subsidized community‐based care was positively associated with reading skills and demonstrated an advantage over unsubsidized centers, contrary to previous findings with other populations (Johnson et al., 2014). Although research evidence suggests modest effects for programs other than Head Start and public preschool, the lack of substantial evidence investigating private or other care settings for DLLs makes it difficult to draw conclusions about this population based on this information. While there are studies that investigate the impact of various early childhood settings on learning, lack of direct evidence regarding DLLs or use of proxy variables such as immigration status limit the inferences that can be made for this population. Furthermore, the diversity in regard to the types of programs within this group leaves much unknown about the program components, curricula, and child outcomes.

Conclusion While the evidence regarding programmatic approaches to educating young DLLs is modest and larger conclusions cannot be made, Head Start, Early Head Start, and public preschool programs have shown promise for this population. Specifically, DLLs who begin school with lower English language abilities or have lower maternal education levels may show increased benefits from participation in these programs. Despite these favorable findings, barriers to access continue to be a significant issue for DLLs in the early years. Moreover, there is a definite need for more research and better systems for tracking participation, development, and learning for DLL children across these different types of programs.

200  Pizzo and Mariela Páez

Cross Program Themes: Considering DLLs There are important considerations for programs, practices, and policies regarding the young DLL population. In order for programs to be more successful with this population, every aspect of early childhood education and care needs to consider the diverse profiles among DLL children such as language, culture, race, ethnicity, family background, economic background, educational experiences, and immigration experience. Specifically, this diversity needs to be considered in: (1) developing curricula and selecting instructional practices; (2) working directly with families through school‐family partnerships; (3) developing Early Childhood professionals who are culturally competent to work with this population.

Curriculum and instruction DLLs benefit from a strong core curriculum (Buysse et  al., 2014; LaForett, Peisner‐ Feinberg, & Buysse, 2013; Orosco & Klingner, 2010) that takes into account the whole child (Barnett, Yarosz, Thomas, Jung, & Blanco, 2007; Durán, Roseth, & Hoffman, 2010) and emphasizes DLLs’ oral language skills in addition to other key components of reading (August & Shanahan, 2006). These instructional components must recognize the importance of building oral language skills as a foundation for learning. In addition to oral language skills, language of instruction and quality of language in the classroom may be important factors in DLLs’ learning. Programs that involve the use of two languages in instruction (i.e., Two‐Way and transitional bilingual programs) have demonstrated positive effects when compared to English‐only classrooms (e.g., Barnett et al., 2007; Slavin, Madden, Calderson, Chamberlain, & Hennessy, 2011). Furthermore, researchers have concluded that the quality of the language environment in the classroom is also a crucial factor for DLLs’ language outcomes (Cheung & Slavin, 2012). Currently, efforts are increasing to address the unique needs of the DLL population; however, their focus is primarily on K–12 schooling, which leaves an urgent and pressing need to expand the availability of linguistically and culturally appropriate curricula for DLLs from birth to age 5 in early childhood settings.

Family engagement Strong partnerships between schools and families have been an essential component of high quality early education and care (NAEYC, 2009). Furthermore, research has repeatedly demonstrated the positive effects of family‐school relationships on academic and language outcomes of young linguistically diverse children (e.g., Delgado‐Gaitán, 2004; Goldenberg, Reese, & Gallimore, 1992). School‐family partnerships range in their purpose and foci; however, they are all based on the premise that “involving families in [their] children’s education has social and academic benefits” (Páez et al., 2011, p. 144). Many early childhood programs adopt a range of family involvement and engagement strategies

Education and Care for Dual Language Learners  201 such as monthly parental meetings, parent education workshops, parent conferences, and home‐school communications/newsletters. Some programs are even more targeted towards the families of DLLs such as family literacy programs and intervention programs that are focused on educating parents in high quality language and early literacy practices in the home (e.g., using games, songs, storybooks, and conversation to support young DLLs language development). For example, these programs have demonstrated effects in empowering parents (Paratore, Krol‐Sinclair, Páez, & Bock, 2010), increased use of language strategies in the home (Shanahan, Mulhern, & Rodríguez‐Brown, 1995), and vocabulary outcomes for young DLL children (Páez et al., 2011). Lessons from these programs align with the adoption of a strengths‐based approach in working with families to capitalize on their first language skills to promote children’s language and early literacy skills and should be applied widely to early childhood programs to address the unique needs of DLL children and families.

Creating culturally competent professionals In order to implement strategies like those discussed previously, early childhood professionals need to recognize the specific needs of diverse student populations in their practice (ACEI, 2006; Hyson, 2003; NAEYC, 2009; NCATE, 2008). Researchers and professional organizations alike have concluded that educators need to be knowledgeable on high quality practice for DLLs (Castro et al., 2011; Espinosa, 2013; Zepeda, Castro, & Cronin, 2011). Specifically, recent professional development frameworks have stressed the importance of developing cross‐cultural competence for working with cultural and linguistically diverse children and their families. For example, Lynch and Hanson (2011) defined the goal of cultural competency for professionals working with families as gaining knowledge of the diverse sociocultural and sociopolitical contexts of children and their families in order to create and maintain a climate of respect, acceptance, and caring that supports all students. Furthermore, knowledge of first and second language development, effective instructional practices for young DLLs, and implementing appropriate assessment strategies are also key components in creating a knowledgeable workforce. This is exactly the type of competence and knowledge that draws upon the foundation of dual language developmental research that increases the preparation of professionals for supporting DLL students in their classrooms.

Research and Policy: Discord and Dissonance In this chapter, we have presented a profile of young DLLs, reviewed important developmental and language learning theories, and discussed important considerations for programs, practices, and policies in addressing the needs of DLLs. Important themes have been identified from the developmental and educational research that needs to be considered when examining the effects of early childhood education and care for young DLLs. These themes include the different paths for second language acquisition, the importance

Internal Validity

• A few randomized, experimental designs • A few longitudinal designs

• A few experimental design studies investigating curricula with DLLs

• Limited information on instructional adjustment within quasi‐experimental designs • Limited descriptive data on the students and their context within experimental studies • Few studies used randomized control experiments


DLL children are likely to benefit from access to high quality early childhood programs

DLLs may benefit from a strong core curriculum that takes into account the whole child

DLL children are likely to benefit from systematically and explicitly targeting oral language skills in English and the home language

Table 9.1  Empirical progress chart for major themes in DLL research

• Few studies examining each oral strategy, intervention, or curricular component • Studies are predominantly with Spanish‐speaking, low‐income populations • Few studies with DLLs from other languages

• Most DLLs are educated in private or other care settings, but very limited information is available on the effectiveness of those settings • The variation in funding and varying configuration of components in preschool programs makes scalability difficult • Studies are predominantly with Spanish‐speaking, low‐income populations • Evidence is based on a small number of studies

External Validity

Barriers to implementing formal oral language instruction in early childhood education: • Lack of curricular resources that include focus on oral language and vocabulary Lack of professional development to address oral language teaching

Barriers to DLLs’ participation in center‐based early childhood education and care include: • Access to programs • Affordability of programs • Availability of programs • Awareness of available and affordable programs Barriers to implementing a high quality curriculum: • Lack of availability of core curricula that account for both DLLs and have consequences for health, well‐being, and/or life chances. Lack of research investigating early childhood curricula and curricular models for young DLLs

Practical Significance

• Predominantly correlational research • A few experimental design studies investigating two‐way language programs

• Meta‐analysis that does not disaggregate for language status • Correlational and quasi‐experimental studies with modest sample sizes • Lack of randomized studies

DLLs benefit from leveraging first language skills to promote second language development

Family‐school relationships have positive impacts on academic and language outcomes of young DLLs • Limited number of programmatic studies • Limited number and descriptions of non‐programmatic investigations • Studies are mostly with Spanish‐ speaking, low‐income populations

• Studies are mostly with Spanish‐ speaking, low‐income populations • Few studies with DLLs from other languages

Barriers to implementing first language strategies in early childhood programs: • Political ideology is privileged over empirical evidence • Lack of professional development to address the use of first language strategies Barriers to enacting family‐school partnerships: • Cultural conflict between school and home • Lack of professional development on culturally competent practice • Lack of school resources for formal programming • Within classroom randomized studies are not always practical or ethical

204  Pizzo and Mariela Páez of supporting first language skills, and the need for a focus on oral language skills. Based on these themes and our review of the empirical evidence (see Table 9.1), three major policy implications are recommended. First, we need to take these themes into account when establishing high quality center‐ based programs. Second, the field needs to consider the needs of DLLs when developing new instruments and revising existing widely used quality measures to measure the effectiveness of these programs. Third, considerable efforts should be made for developing professional expertise for effective instruction and connecting with families to establish reciprocal relationships even when faced with the challenge of cultural and linguistic diversity. The current body of research supports consideration of the unique developmental profiles of DLLs, as “the type of education schools provide ultimately determines the educational success of bilingual students, but knowledge of such contextual and individual factors can help school staff better support bilingual learners and their families, and understand how these factors are influencing school policy and practice” (Brisk, 2006, p. 58). Furthermore, DLLs need to be included as a separate subgroup within national research endeavors; there also need to be additional targeted research investigations, interventions, and longitudinal studies of language development with young DLLs, particularly targeted to those under the age of 5. Despite this knowledge, the education of DLLs often remains an afterthought for both programs and policies. For example, the continued existence of an English‐only policy in some states, lack of curricula with emphasis on oral language and vocabulary skills, and resulting disregard for the wealth of knowledge that young DLLs bring into the classroom are indicative of a political environment unable to recognize and support the needs of young DLLs. This lack of consideration of DLLs is concerning, as this population has grown and continues to grow at rapid rates. Therefore, it is no longer acceptable to disregard young DLLs when designing research, programs, and policy, as they are central to the future of our country. Thus it is imperative that our efforts in early childhood education and care make explicit provision to meet the needs of DLLs.

Note 1. Poverty is defined by the US Census Bureau as less than 100% of the poverty threshold and low income is defined as less than 200% of the poverty threshold (Jiang et al., 2015).

References Anderson, R. (2012). First language loss in Spanish‐speaking children: Patterns of loss and implications for clinical practice. In B.A. Goldstein (Ed.), Bilingual language development and disorders in Spanish‐English speakers (2nd ed.) (pp. 193–212). Baltimore, MD: Paul H. Brookes Publishing. The Annie E. Casey Foundation. (2015). Children who speak a language other than English at home. Kids Count Data Center. Retrieved from http://datacenter.kidscount.org/data/tables/81‐ children‐who‐spe#detailed/1/any/false/36,868,867,133,38/any/396,397

Education and Care for Dual Language Learners  205 Association for Childhood Education International (ACEI). (2006). Global guidelines for early childhood education and care in the 21st century. Retrieved on March 15, 2015 from http://www. acei.org/sites/default/files/global‐guidelines/gguidelines.doc. Aud, D., Hussar, W., Kena, G., Bianco, K., Frohlich, L., Kemp, J., & Tahan, K. (2011). The condition of education 2011. National Center for Education Statistics, Institute of Education Sciences, US Department of Education. Washington, DC: US Government Printing Office. August, D., Carlo, M., Dressler, C., & Snow, C. E. (2005). The critical role of vocabulary development for English language learners. Learning Disabilities Research and Practice, 20(1), 50–57. August, D., & Hakuta, K. (Eds.). (1997). Improving schooling for language‐minority children: A research agenda. Washington, DC: National Academies Press. August, D., & Shanahan, T. (Eds.). (2006). Developing literacy in second‐language learners: Report of the National Literacy Panel on Language‐Minority Children and Youth. Mahwah, NJ: Erlbaum. Barnett, W. S., Carolan, M. E., Squires, J. H., & Clarke Brown, K. (2013). The state of preschool 2013: State preschool yearbook. New Brunswick, NJ: National Institute for Early Education Research. Barnett, W. S., Yarosz, D. J., Thomas, J., Jung, K., & Blanco, D. (2007). Two‐way and monolingual English immersion in preschool education: An experimental comparison. Early Childhood Research Quarterly, 22(3), 277–293. Bialystok, E. (2001). Bilingualism in development: Language, literacy & cognition. Cambridge, UK: Cambridge University Press. Bialystok, E. (2009). Claiming evidence from non‐evidence: A reply to Morton and Harper. Developmental Science, 12(4), 499–450. Bialystok, E. (2011). Reshaping the mind: The benefits of bilingualism. Canadian Journal of Educational Psychology, 65(4), 229–235. Bialystok, E., Craik, F. I. M., Green, D. W., & Gollan, T. H. (2009). Bilingual minds. Psychological Science in the Public Interest, 10, 89–129. Bialystok, E., Craik, F. I. M., Klein, R., & Viswanathan, M. (2004). Bilingualism, aging, and cognitive control: Evidence from the Simon task. Psychology and Aging, 19, 290–303. Bialystok, E., Luk, G., & Kwan, E. (2005). Bilingualism, biliteracy, and learning to read: Interactions among languages and writing systems. Scientific Studies of Reading, 9(1), 43–61. Brisk, M. E. (2006). Bilingual education: From compensatory to quality schooling (2nd ed.). New York, NY: Lawrence Erlbaum Associates, Inc. Burger, K. (2010). How does early childhood care and education affect cognitive development? An  international review of the effects of early interventions for children from different social backgrounds. Early Childhood Research Quarterly, 25(2), 140–165. Buysse, V., Castro, D. C., & Peisner‐Feinberg, E. (2010). Effects of a professional development program on classroom practices and outcomes for Latino English language learners. Early Childhood Research Quarterly, 25(2), 194–206. Buysse, V., Peisner‐Feinberg, E., Paez, M., Scheffner Hammer C., & Knowles M. (2014). Effects of early education programs and practices on development of dual language learners: A review of the literature. Early Childhood Research Quarterly, 29(4), 765–785. Castro, D. C., Espinosa, L., & Páez, M. (2011). Defining and measuring quality early childhood practices that promote dual language learners’ development and learning. In M. Zaslow, I.  Martinez‐Beck, K. Tout, & T. Halle (Eds.), Quality measurement in early childhood settings (pp. 257–280). Baltimore, MD: Paul H. Brookes Publishing. Castro, D. C., García, E. E., & Markos, A. M. (2013). Dual language learners: Research informing policy. Chapel Hill, NC: Frank Porter Graham Child Development Institute, Center for Early Care and Education.

206  Pizzo and Mariela Páez Cheung, A. C. K., & Slavin, R. E. (2012). Effective reading programs for Spanish‐dominant English language learners (ELLs) in the elementary grades: A synthesis of research. Review of Educational Research, 82(4), 351–395. Child Trends. (2014). Immigrant children and youth: Indicators on children and youth. Bethesda, MD: Author. Costa A., Hernández M., & Sebastián‐Gallés N. (2008). Bilingualism aids conflict resolution: Evidence from the ANT task. Cognition, 106, 59–86. Cummins, J. (1979). Linguistic interdependence and the educational development of bilingual children. Review of Educational Research, 49(2), 222–251. Cummins, J. (1991). The development of bilingual proficiency from home to school: A longitudinal study of Portuguese‐speaking children. Journal of Education, 173, 85–98. Cunningham, A. E., & Stanovich, K. E. (1997). Early reading acquisition and its relation to reading experience and ability 10 years later. Developmental Psychology, 33(6), 934–945. Delgado‐Gaitán, C. (2004). Involving Latino families in schools: Raising student achievement through home‐school partnerships. Thousand Oaks, CA: Corwin Press. Dickinson, D. K., McCabe, A., Anastasopoulos, L., Peisner‐Feinberg, E., & Poe, M. D. (2003). The comprehensive language approach to early literacy: The interrelationships among vocabulary, phonological sensitivity, and print knowledge among preschool‐aged children. Journal of Educational Psychology, 95, 465–481. Dickinson, D. K., McCabe, A., Clark‐Chiarelli, N., & Wolf, A. (2004). Cross‐language transfer of phonological awareness in low‐income Spanish and English bilingual preschool children. Applied Psycholinguistics, 25, 323–347. Dickinson, D. K., & Tabors, P. O. (Eds.). (2001). Beginning literacy with language: Young children learning at home and school. Baltimore, MD: Paul H. Brookes Publishing. Durán, L., Roseth, C., & Hoffman, P. (2010). An experimental study comparing English‐only and transitional bilingual education on Spanish‐speaking preschoolers’ early literacy development. Early Childhood Research Quarterly, 25(2), 207–217. Espinosa, L. M. (2006). Challenging myths of young English language learners. New York, NY: Foundation for Child Development. Espinosa, L. M. (2008). Early literacy for English language learners. In A. Bruin‐Parecki (Ed.), Effective early literacy practice: Here’s how, here’s why (pp. 71–86). Baltimore: Paul H. Brookes Publishing. Espinosa, L. M. (2013). Challenging common myths about teaching young English Language Learners: An update to the seminal 2008 report. New York, NY: Foundation for Child Development. Figueras‐Daniel A., & Barnett S. W. (2013). Preparing young Hispanic dual‐language learners for a knowledge economy. New Brunswick, NJ: National Institute for Early Education Research. Goldenberg, C., Reese, L., & Gallimore, R. (1992). Effects of literacy materials from school on Latino children’s home experiences and early reading achievement. American Journal of Education, 100(4), 497–536. Goldenberg, C., Rueda, R., & August, D. (2006). Sociocultural influences on the literacy attainment of language‐minority children and youth. In D. August & T. Shanahan (Eds.), Developing literacy in second‐language learners (pp. 269–318). Mahwah, NJ: Lawrence Erlbaum Associates, Inc. Gormley, W. T. (2008). The effects of Oklahoma’s pre‐K program on Hispanic children. Social Science Quarterly, 89(4), 916–936. Hammer, C. S., Hoff, E., Uchikoshi, Y., Gillanders, C., Castro, D., & Sandilos, L. (2014). The language and literacy development of young dual language learners: A critical review. Early Childhood Research Quarterly, 29(4), 715–733.

Education and Care for Dual Language Learners  207 Hammer, C. S., Lawrence, F. R., & Miccio, A. W. (2007). Bilingual children’s language abilities and early reading outcomes in Head Start and kindergarten. Language, Speech, and Hearing Services in Schools, 38, 237–248. Hindman, A., & Wasik, B. (2015). Building vocabulary in two languages: An examination of Spanish‐speaking dual language learners in head start. Early Childhood Research Quarterly, 31(2), 19–33. Hoff, E. (2006). How social contexts support and shape language development. Development Review, 26, 55–88. Howard, E., Páez, M., August, D., Barr, C., Kenyon, D., & Malabonga, V. (2014). The importance of SES, home and school language and literacy practices, and oral vocabulary in bilingual children’s English reading development. Bilingual Research Journal, 37(2), 120–141. Huttenlocher, J., Vasilyeva, M., Cymerman, E., & Levine, S. (2002). Language input and child syntax. Cognitive Psychology, 45(3), 337–374. Hyson, M. (2003). Preparing early childhood professionals: NAEYC’s standards for programs. Washington, DC: NAEYC. Jiang, Y., Ekono, M., & Skinner, C. (2015). Basic facts about low‐income children: Children under 6 years, 2013. New York, NY: National Center for Children in Poverty. Johnson, A., Han, W., Ruhm, C., & Waldfogel, J. (2014). Child care subsidies and the school readiness of children of immigrants. Child Development, 85(6), 2140–2150. Kena, G., Aud, S., Johnson, F., Wang, X., Zhang, J., Rathbun, A., … Kristapovich, P. (2014). The Condition of Education 2014 (NCES 2014‐083). US Department of Education, National Center for Education Statistics. Washington, DC. Kovács Á. M., & Mehler J. (2009). Cognitive gains in 7‐month‐old bilingual infants. Proceedings of the National Academy of Sciences, 106, 6556–6560. LaForett, D. R., Peisner‐Feinberg, E. S., & Buysse, V. (2013). Recognition and response for dual language learners. In V. Buysse, & E. S. Peisner‐Feinberg (Eds.), Handbook for response to intervention in early childhood (pp. 355–369). Baltimore, MD: Paul H. Brookes Publishing. Lesaux, N. K., Crosson, A. C., Kieffer, M. J., & Pierce, M. (2010). Uneven profiles: Language minority learners’ word reading, vocabulary, and reading comprehension skills. Journal of Applied Developmental Psychology, 31(6), 475–483. Lindsey. K. A., Manis, F. R., & Bailey, C. E. (2003). Prediction of first‐grade reading in Spanish‐ speaking English language learners. Journal of Educational Psychology, 95(3), 482–494. López, L. M., & Greenfield, D. B. (2004). The cross‐language transfer of phonological skills of Hispanic Head Start children. Bilingual Research Journal, 28(1), 1–18. Lynch, E., & Hanson, M. (2011). Developing cross‐cultural competence (4th ed.). Baltimore, MD: Paul H. Brookes Publishing. Magnuson, K., Ruhm, C., & Waldfogel, J. (2004). Does prekindergarten improve school preparation and performance? (NBER Working Paper No. 10452). National Bureau of Economic Research. Manis, F.R., Lindsey, K.A., & Bailey, C.E. (2004). Development of reading in grades K–2 in Spanish‐speaking English Language learners. Learning Disabilities Research and Practice, 19(4), 214–224. McCabe, A., Tamis‐LeMonda, C., Bornstein M. H., Cates, C. B., Golinkoff, R., Hirsch‐Pasik, K., … Guerra, A. W. (2013). Multilingual children: Beyond myths and toward best practices. Social Policy Report, 27(4), 2–37. Miller, P., Votruba‐Drzal, E., & Coley, R.L. (2013). Predictors of early care and education type among preschool‐aged children in immigrant families: The role of region of origin and characteristics of the immigrant experience. Children and Youth Services Review, 35, 1342–1355.

208  Pizzo and Mariela Páez Motel, S., & Patten, E. (2012). The 10 largest Hispanic origin groups: Characteristics, rankings, top counties. Pew Research Center. Retrieved from http://www.pewhispanic.org/2012/06/27/ the‐10‐largest‐hispanic‐origin‐groups‐characteristics‐rankings‐top‐counties/ Nagy, W. E. (2005). Why vocabulary instruction needs to be long term and comprehensive. In E. H. Hiebert & M. L. Kamil (Eds.), Teaching and learning vocabulary: Bringing research to practice (pp. 27–44). Mahwah, NJ: Erlbaum. National Association for the Education of Young Children (NAEYC). (2009). Where we stand on responding to linguistic and cultural diversity. Washington, DC: Author. National Clearinghouse for English Language Acquisition and Language Instruction Education Programs (2011). Key demographics and practice recommendations for young English learners. Washington, DC: NCELA. National Early Literacy Panel, & National Center for Family Literacy. (2008). Developing early literacy: Report of the National Early Literacy Panel. Washington, DC: National Institute for Literacy. National Task Force on Early Childhood Education for Hispanics. (2007). Para nuestros niños: Expanding and improving early childhood education for Hispanics. Tempe, AZ: Author. NCATE. (2008). Professional standards for the accreditation of teacher preparation instruction. Washington DC: Author. Neuman, S. (2011). The challenges of teaching vocabulary in early education. Handbook of early literacy research, 3, 358–372. Neuman, S., and Dwyer, J. (2009). Missing in action: Vocabulary instruction in pre‐K. The Reading Teacher, 62, 384–392. Ordóñez, C. L., Carlo, M. S., Snow, C. E., & McLaughlin, B. (2002). Depth and breadth of vocabulary in two languages: Which vocabulary skills transfer? Journal of Educational Psychology, 94(4), 719–728. Orosco, M. J., & Klingner, J. (2010). One school’s implementation of RTI with English language learners: Referring into RTI. Journal of Learning Disabilities, 43(3), 269–288. Páez, M., Bock, K., & Pizzo, L. (2011). Supporting the language and early literacy skills of English language learners: Effective practices and future directions. Handbook of early literacy research, 3, 136–152. Páez, M., & Rinaldi, C. (2006). Predicting English word reading skills for Spanish‐speaking students in first grade. Topics in Language Disorders, 26(4), 338–350. Páez, M., Tabors, P. O., & López, L. M. (2007). Dual language and literacy development of Spanish‐speaking preschool children. Journal of Applied Developmental Psychology, 28(2), 85–102. Paradis, J., Genesee, F., & Crago, M. B. (2011). Dual language development and disorders: A handbook on bilingualism and second language learning (2nd ed.). Baltimore, MD: Paul H. Brookes Publishing. Paratore, J., Krol‐Sinclair, B., Páez, M., & Bock, K. (2010). Supporting literacy learning in families for whom English is an additional language. In G. Li & P. Edwards (Eds.), Best practices in ELL instruction (pp. 299–327). New York, NY: Guilford. Poulin–Dubois, D., Blaye, A., Coutya, J., & Bialystok, E. (2011). The effects of bilingualism on toddlers’ executive functioning. Journal of Experimental Child Psychology, 108, 567–579. Proctor, C. P., August, D., Carlo, M., & Snow, C. E. (2006). The intriguing role of Spanish language vocabulary knowledge in predicting English reading comprehension. Journal of Educational Psychology, 98(1), 159–169. Rinaldi, C., & Páez, M. (2008). Preschool matters: Predicting reading difficulties for Spanish‐ speaking students in first grade. Learning Disabilities: A Contemporary Journal, 6(1), 71–84.

Education and Care for Dual Language Learners  209 Royer, J. M., & Carlo, M. S. (1991). Transfer of comprensión skills from native to second language. Journal of Reading, 34(6), 450–455. Ruiz Soto, A., Hooker, S., & Batalova, J. (2015). Top languages spoken by English language learners nationally and by state. Washington, DC: Migration Policy Institute. Ryan, C. (2013). Language use in the United State: 2011. US Census Bureau, Washington, DC. Saunders, W. M., Foorman, B. R., & Carlson, C. D. (2006). Is a separate block of time for oral English language development in programs for English learners needed? The Elementary School Journal, 107, 181–198. Scarborough, H. S. (1998). Early identification of children at risk for reading disabilities: Phonological awareness and some other promising predictors. In B. K. Shapiro, P. J. Accardo, & A. J. Capute (Eds.), Specific reading disability: A view of the spectrum (pp. 75–119). Timonium, MD: York Press. Shanahan, T., Mulhern, M., & Rodríguez‐Brown, F. (1995). Project FLAME: Lessons learned form a family literacy program for linguistic minority families. The Reading Teacher, 48(7), 586–593. Slavin, R. E., Madden, N., Calderón, M., Chamberlain, A., & Hennessy, M. (2011). Reading and language outcomes of a multiyear randomized evaluation of transitional bilingual education. Educational Evaluation and Policy Analysis, 33(1), 47–58. Snow, C. E. (1990). The development of definitional skill. Journal of Child Language, 17(3), 697–710. Snow, C. E. (2006). Cross‐cutting themes and future research directions. In D. August & T. Shanahan (Eds.), Developing literacy in second‐language learners: Report of the National Literacy Panel on Language‐Minority Children and Youth. Mahwah, NJ: Erlbaum. Snow, C. E., & Páez, M. (2004). The Head Start classroom as an oral language environment: What should the performance standards be? In E. Zigler & S. Styfco (Eds.), The Head Start Debates (pp. 113–128). Baltimore, MD: Paul H. Brookes Publishing. Stipek, D., Ryan, R., & Alarcón, R. (2001). Bridging research and practice to develop a two‐way bilingual program. Early Childhood Research Quarterly, 16(1), 133–149. Tabors, P. O. (2008). One child, two languages: A guide for early childhood educators of children learning English as a second language (2nd ed.). Baltimore, MD: Paul H. Brookes Publishing Co. Tabors, P. O., & Snow, C. E. (2002). Young bilingual children and early literacy development. In S. B. Neuman & D. K. Dickinson (Eds.), Handbook of Early Literacy Research (pp. 159–178). New York: NY: Guilford. Tabors, P., Snow, C., & Dickinson. D. (2001). Home and schools together: Supporting language and literacy development. In D. Dickinson, and P. Tabors (Eds.), Beginning literacy with language: Young children learning at home and school (pp. 313–334). Baltimore, MD: Paul H. Brookes Publishing. US Department of Health and Human Services, Administration for Children and Families. (2008). Dual language learning: What does it take? Washington, DC: Author. US Department of Health and Human Services, Administration for Children and Families. (2010). Head Start Impact Study: Final report. Washington, DC: Author. US Department of Health and Human Services, Administration for Children and Families. (2013). Report to Congress on Dual Language Learners in Head Start and Early Head Start Programs. Washington, DC: Author. Weiland, C., & Yoshikawa, H. (2013). Impacts of a prekindergarten program on children’s mathematics, language, literacy, executive function, and emotional skills. Child Development, 84(6), 2112–2130. Winter, S. M., Zurcher, R., Hernández, A., & Yin, Z. (2007). The Early ON School Readiness Project: A preliminary report. Journal of Research in Childhood Education, 22(1), 55.

210  Pizzo and Mariela Páez Wong Fillmore, L. (1991). When learning a second language means losing the first. Early Childhood Research Quarterly, 6, 323–347. Yoshikawa, H., Weiland, C., Brooks‐Gunn, J., Burchinal, M. R., Espinosa, L.M., Gormley, W., … Zaslow, M.J. (2013). Investing in our future: The evidence base on preschool education. Ann Arbor, MI: Society for Research in Child Development. Zepeda, M., Castro, D., & Cronin, S. (2011). Preparing early childhood teachers to work with young dual language learners. Child Development Perspectives, 4(3), 190–194.

chapter TEN Early Childhood Education and Care for Children with Disabilities Penny Hauser‐Cram, Miriam Heyman, and Kristen Bottema‐Beutel

Public policy has been integral to the construction of systems of early childhood education and care for children with disabilities in the United States. Stimulated by the Civil Rights movement, the rights of children with disabilities have gained momentum over the last three decades. Federal legislation, beginning with the passage of P.L. 99‐457 in 1986, included a provision to provide intervention services to children with disabilities younger than school age. This was a remarkable change in national policy as this legislation required school systems to serve preschool‐aged children for the first time. At that time, Part H of the law, later renamed Part C as part of the Individuals with Disabilities Education Act  (IDEA), encouraged states to provide early intervention services to children with d­isabilities under age 3 and their families. A major focus of the Part C legislation is the emphasis on the inclusion of families in service provision. Young children with developmental disabilities are defined in the federal legislation as those with a diagnosed physical or mental condition that has a high probability of resulting in a developmental delay. This includes children with chromosomal abnormalities, such as Down syndrome, genetic or congenital disorders, sensory impairment, inborn errors of metabolism, congenital infections, severe attachment disorders, and disorders secondary to exposure to toxic substances, including fetal alcohol syndrome. Firm estimates of the prevalence of childhood disability are hard to establish, however, as definitions vary. The Wiley Handbook of Early Childhood Development Programs, Practices, and Policies, First Edition. Edited by Elizabeth Votruba-Drzal and Eric Dearing. © 2017 John Wiley & Sons, Inc. Published 2017 by John Wiley & Sons, Inc.

212  Hauser-Cram, Heyman, and Bottema-Beutel For example, reports from the Centers for Disease Control and Prevention indicate that approximately 16–17% of children have a developmental disability (Boyle et  al., 2011) whereas results from the National Health Interview Surveys suggest that approximately 8% of children under age 18 have a disability (Currie & Kahn, 2012). An increase of approximately 16% in the percentage of children with developmental disabilities over the last decade has been reported, with children with neurodevelopmental disabilities, especially autism spectrum disorder (ASD) accounting for most of that change (Houtrow, Larson, Olson, Newacheck, & Halfon, 2014). Children with developmental delays (i.e., children who do not have a diagnosis but are not displaying appropriate developmental milestones in areas of cognitive, physical, communication, social or emotional development, and/or adaptive behavior) may be served through Part C legislation, although large state variation exists in relation to defining such delays for the purpose of providing services. For example, some states (e.g., Alabama, Indiana) will serve children if they have a delay of 25% or more in one or more areas of development whereas others (e.g., Alaska, Arizona, Nevada) will only serve those with delays of 50% or more (National Early Childhood Technical Assistance System, 2015). Notwithstanding such discrepancies in state definitions, all states have elected to provide early intervention Part C services, and all states are required under IDEA (2004) to provide preschool services, termed Part B in the legislation. Despite substantial differences in these two service systems, one focused on the infant‐toddler years and one focused on the preschool years, they are similar in their goal of “improving educational results and functional outcomes for all children with disabilities” (20 U.S.C. SS1416). Head Start, which largely serves young children living in low‐income families, also provides services to children with disabilities. Head Start is required to maintain at least 10% of its enrollment for children with disabilities at both the preschool (Head Start) and infant‐toddler (Early Head Start) levels (Office of Head Start Administration for Children and Families, 2015). In this chapter, we review the evidence on the effectiveness of services for young children with disabilities in these various service systems. Because these systems are substantially separate, although required to work collaboratively, we consider them sequentially. We begin with a discussion of the theoretical models guiding the development and implementation of services for young children with disabilities. We then examine evidence on the effectiveness of each of these service systems, including information about transitions from one service sector to another. Because of the steep increase in the number of young children with ASD included in early childhood services over the past decade (Centers for Disease Control and Prevention, 2014), we give separate focus to interventions that serve this population and the complexities this increase entails. Throughout the chapter we discuss the challenges inherent in such investigations. We conclude with sections that highlight future directions and summarize the current issues regarding the efficacy of services for young children with disabilities.

Theoretical Models A central strength of much research on young children with developmental disabilities is the ability of scholars in this field to draw on developmental systems models. Historically, such research was guided by mechanistic models of development that guided behavioral

Early Childhood Education for Children with Disabilities  213 interventions (Wolery, 2000) and by medical models in which the biological etiology of disability was emphasized (Shonkoff & Marshall, 2000). More recently, however, scholars have recognized the value of core developmental theories in understanding how children with disabilities develop and how their families adapt and support them (Hauser‐Cram, Cannarella, Tillinger, & Woodman, 2013; Shonkoff & Phillips, 2000). In 2007 the World Health Organization (WHO) released the International Classification of Functioning, Disability and Health for Children and Youth (ICF‐CY) which emphasizes both specific and unique aspects of disability during the childhood years. The ICF classification melds together the medical model with a more social model of disabilities, which recognizes the social and environmental factors that affect functioning (Halfon, Houtrow, Larson, & Newacheck, 2012). In 2010 a new definition of disability was adopted based on work by the UN Convention on the Rights of Persons with Disabilities. This definition, contained in Article 1 of the UN Convention, states that “persons with disabilities include those who have long‐term physical, mental, intellectual or sensory impairments which in interaction with various barriers may hinder their full and effective participation in society on an equal basis with others.” (United Nations, Article 1, Convention on the Rights of Persons with Disabilities). In the United States, models guiding early childhood programs and practices for c­hildren with disabilities also derive from the ecological model proposed by Bronfenbrenner and Morris (2006), biopsychosocial models of health care (Engel, 1977), and developmental systems perspectives (Guralnick, 2001; Lerner & Callina, 2014). Based on such models core principles have served as the foundation for guiding practice in programs for children with disabilities (Guralnick, 2007). One central principle is the value of family‐ centered practices that include awareness of cultural perspectives (García Coll & Magnusson, 2000), a focus on the importance of social exchanges between a child and a parent (Shonkoff, 2010), and a complex understanding of the family system (Moore, Pérez‐Méndez, & Kaczmarek, 2011). A second principle, the emphasis on inclusion, serves to promote the participation of children with disabilities and their families in c­ommunity settings that serve typically developing children. The third principle focuses on the need to integrate and coordinate services, including the transition from one service system to another (Kaczmarek, 2011). The emphasis on the family system in developmental systems perspectives is vital because it embraces the role of families, especially parents, in promoting child development. Families of children with disabilities, however, often face unique challenges. Although family researchers have moved beyond views that parents necessarily exhibit “chronic sorrow” in relation to having a child with a disability (Solnit & Stark, 1961), evidence indicates that parents of children with disabilities often report high levels of parenting stress especially in relation to children’s problem behaviors (Woodman & Hauser‐ Cram, 2013). In particular, parents of children with ASD report higher levels of stress than do parents of children with other disabilities (Eisenhower, Baker, & Blacher, 2005), and such stress is associated with low levels of quality of life (Reed, Sejunaite, & Osborne, 2016). Moreover, parenting stress and child behavior problems form a longitudinal interacting system, including both child‐driven and family‐driven processes (Norona & Baker, 2014; Woodman, Mawdsley, & Hauser‐Cram, 2015). Parents’ levels of stress are often compounded by children’s medical challenges, necessitating an accumulation of daily tasks, such as preparing particular diets and arranging for medical appointments

214  Hauser-Cram, Heyman, and Bottema-Beutel (Perry, 2004), as well as requiring parents and children to face major medical events, such as surgery for orthopedic and cardiovascular complications (American Academy of Pediatrics, Medical Home Initiatives for Children with Special Needs Project Advisory Committee, 2002). Therefore, parenting a child with a disability requires a range of internal and external resources, including access to high quality and effective services for early education and care during the early childhood years.

Characteristics of Children with Disabilities Receiving Special Services Early intervention is by law available to any child under the age of 3 years who meets the general qualifications of having a diagnosed disability or significant developmental delay. Less than 2% of the infant‐toddler population receives early intervention services (Bailey, Hebbeler, Scarborough, Spiker, & Mallik, 2004), although estimates indicate that 13% have delays that would deem them eligible (Rosenberg, Zhang, & Robinson, 2008). The gap between children who are eligible and those who are served in early intervention is large and, according to Rosenberg et al. (2008), is at least partially explained by race/ ethnicity as White children with delays were twice as likely as Black children to receive services. State eligibility definitions may also explain this gap, however. All states serve children with a diagnosed disability such as Down syndrome, but large state variation exists in serving children considered to be “at biological risk” (such as low birth weight) or to have “environmental risk” (such as being likely to be abused or neglected). States have flexibility in determining criteria for delay, including the extent of delay a child must have to qualify for services. As a result of state determinations, enrollment in early intervention services varies considerably from a low of less than 1% in Nevada to a high of 9.4% of infants and toddlers in Hawaii (McManus, McCormick, Acevedo‐Garcia, Ganz, & Hauser‐Cram, 2009). States also vary as to whether they have specific mandates for insurance to provide coverage for intensive services for children with ASD (Autism Speaks, 2015). Even though one national study found that compared to national statistics a higher proportion of children from low‐income families are served in early intervention (Hebbeler et  al., 2007), children from low socio‐economic groups are less likely to receive early intervention services if they are in states that have strict eligibility requirements (McManus et al., 2009). Another issue is the age at which certain disabilities can be reliably diagnosed. For example, ASD cannot be reliably diagnosed until around 24 months, unlike disorders in which detectible genetic variations may make diagnosis possible shortly after or even before birth. For children with ASD, the average age of diagnosis is over age 4 (CDC, 2014). Research efforts have been focused on making more reliable diagnoses earlier in children with ASD, and at reducing disparities in the age of diagnosis that exist across race/e­thnicity and SES groups (Fountain, King, & Bearman, 2011; Zuckerman, Mattox, Donelan, Batbayar, Baghaee, & Bethel, 2013). In relation to the preschool age group, demographic analyses of children in preschool and kindergarten special education indicate that a higher percentage of boys and of s­tudents from low‐income families receive these services (National Center for Education

Early Childhood Education for Children with Disabilities  215 Statistics (NCES), 2007). Also, a slightly higher percentage of White, non‐Hispanic c­hildren (4.6%) than those of other racial or ethnic backgrounds receive special education services (NCES, 2007). Only a few studies have focused on children with disabilities in Head Start programs in particular. Using data from the Head Start Family and Child Experiences Survey (FACES), a nationally representative sample of children in Head Start, Barton, Spiker, and Williamson (2012) reported that children with disabilities were substantially underidentified in Head Start. They indicated that only 8% of children in Head Start had an Individualized Education Program (IEP), whereas almost one third of children in the sample met one or more criteria for a disability or delay. In relation to demographic differences of children on IEPs in Head Start, Barton et al. (2012) reported that identified children were more likely to be male and less likely to be African American compared to children who had not received an IEP. Underidentification of very young children with disabilities was also a finding in a study of children from low‐income families enrolled in Early Head Start (Peterson et al., 2004). The investigators reported that although almost 90% of the children in their sample displayed indicators of potential disabilities, only 4.7% were receiving early intervention Part C services. Those children who did receive early intervention tended to be from less educated families and families of color as well as those with a primary language other than English. Across these different age groups and service systems, the demographics indicate that a low proportion of eligible children are being served in early education and care programs. There are many possible reasons for this. State variation is one (McManus et al., 2009), with funding of services an important but often overlooked priority. The gap between developmental screening by health care providers and the provision of child services is another (King et al., 2010; Marshall & Mendez, 2014). Further, there are mixed messages directed toward medical professionals regarding the screening of young children. In the case of ASD, some professional organizations maintain that there is insufficient evidence regarding the impact of early screening on long‐term outcomes to recommend universal screening (United States Preventive Services Task Force, 2015). Others view universal screening as a critical means of ensuring that all children who qualify will receive early intervention services (American Academy of Pediatrics, 2006; Robins et  al., 2016). Underlying beliefs about the need to “wait and see” how children develop as well as reluctance by both pediatricians and parents to identify children as having a disability at a young age also likely affect such gaps. Additionally, culturally situated beliefs and perspectives regarding what constitutes developmental delays as well as what should serve as appropriate services for young children may limit some parents’ predilection for entering early intervention services (García Coll & Magnusson, 2000; Moore et  al., 2011). For example, Cohen (2013) suggests that in some Latino cultures, parents value añoñar (nurturing) which leads them to prioritize social‐emotional support of their children with disabilities and this may contrast with the emphasis by early intervention service providers on the development of independence, including the acquisition of self‐help skills such as feeding and dressing. Also, in some American Indian cultures there is no word for disability; instead of focusing on interventions adults attend to the ways an individual can participate in society (Marshall & Largo, 1999). Discrepancies in belief systems might discourage families from seeking or accepting early intervention services as the goals of these services do not align with their own perspectives and priorities.

216  Hauser-Cram, Heyman, and Bottema-Beutel

The Effectiveness of Early Childhood Education and Care Services for Children with Disabilities Ever since Urie Bronfenbrenner (1974) published his classic article entitled “Is early intervention effective?” the question of program effectiveness has been asked repeatedly. Bronfenbrenner’s original focus was on services for children living in poverty, but over time this central question has been raised in relation to services for young children with disabilities and their families. Despite decades of inquiry, however, we only have scant information on such effectiveness. One possible explanation for this is that neither early intervention nor early childhood special education represents a delineated program model (Hebbeler & Spiker, 2011). Instead, each provides a collection of services, combined in different ways, often driven by local and state budgets, availability of service providers, and local belief systems about effective services, as well as by the goal of providing individualized services based on children’s and families’ needs. As a whole, these complex constellations make summaries of a general service system like early childhood special education e­specially challenging. The use of randomized controlled trial (RCT) studies with a no‐treatment group for children in early intervention and early childhood special education services has rarely been endorsed for ethical reasons, as children and families are entitled to these services. Some attempts have been made, however, to randomly assign different aspects of services. Over two d­ecades ago Taylor, White, & Kusmierek (1993) conducted a randomized study on increasing hours of service in early intervention. In a comparison of measures of family functioning in those families assigned to three hours of services a week (n=33) versus those receiving the usual one hour of service (n=39), the researchers found only small advantages (an overall mean effect size of .13) associated with the increased hours of s­ervice. Families in the expanded program reported greater levels of social support and lower levels of stress, but many other effects were not significant. Given these small effects, Taylor and colleagues concluded that increasing the intensity of early intervention services is likely to have negligible effects. Some researchers, however, have criticized this study because it does not represent current views of best practices in which hours of service are based on the needs of children and families rather than on a fixed amount (Hebbeler & Spiker, 2011). Although seldom employed in studies of early intervention services per se, randomized designs in which children and families have been randomly assigned to receive services or be in a control group have been employed in studies of early developmental interventions for children born preterm who are likely to have neurodevelopmental disabilities (Orton, Spittle, Doyle, Anderson, & Boyd, 2009). The Infant Health and Development Program (IHDP) (Brooks‐Gunn et al., 1994; Hill, Brooks‐Gunn, & Waldfogel, 2003), a multi‐site randomized controlled trial, investigated the efficacy of a 36‐month intensive early childhood intervention in improving the developmental outcomes of approximately 1,000 low birth weight infants (less than 2,500g). Both intervention and control infants received high‐risk follow up care with at least annual developmental testing and referral to all available services in the community. The intervention consisted of three components: home visits, attendance at high quality child care centers, and monthly parent meetings.

Early Childhood Education for Children with Disabilities  217 Results indicated that at age 3 children in the intervention group had higher IQ scores by almost 10 points as well as fewer behavior problems compared to those in the control group. At the follow‐ups at ages 5 and 8, the “heavier” of the low birth weight group (2,001–2,499 grams) had persistent IQ advantages (Orton et al., 2009). Because services included daycare as well as home visits, this was a more comprehensive and intensive intervention than either state supported community‐based early intervention services or most home visit programs (see Donelan-McCall, this volume). In recent years, there have also been several randomized controlled trials (RCTs) of developmentally based interventions for young children with ASD (e.g., Dawson et al., 2010; Kasari, Paparella, Freeman, & Jahromi, 2008; Green et al., 2010). One study examined language outcomes 5 years after completion of the intervention, and indicated that later language ability was moderated by important treatment goals at study entry, including joint attention and play levels (Kasari, Gulsrud, Freeman, Paparella, & Hellemann, 2012). This follow‐up study, however, did not involve examination of the control group across a similar time frame. These interventions show promise, but further study is needed to determine the extent to which these treatments impact well‐being into later childhood and adulthood. The largest and most representative study of children and families in early intervention services was undertaken in the late 1990s. The aim of the National Early Intervention Longitudinal Study (NEILS) was to systematically examine patterns of experiences of children and families receiving early intervention services (Hebbeler et al., 2007). In contrast to focusing on a particular population of children with special needs, this study employed a stratified sampling procedure to enroll a total of 3,338 children and parents receiving early intervention services in 20 states in 1997–98. Data on children’s development were collected from parents through telephone interviews using survey methodology rather than by direct assessment. Parents reported that at the termination of early intervention services at age 3, 46% of children could communicate well, although wide variability was evident as 23% continued to have no spoken vocabulary. Parents reported large impacts on themselves with 95% indicating that they knew how to provide basic care for their child and 65% that they knew how to help their child develop and learn, yet only 11% expressed confidence about dealing with their child’s behavior. Most (82%) expressed belief that their family was better off as a result of early intervention services (Bailey et al., 2005). The NEILS study provides important descriptive information on the children and families receiving early intervention services in the United States. Nevertheless, this was not developed as a study of the effectiveness of such services on children and families. Bailey, Nelson, Hebbeler, and Spiker (2007) reported that parents’ views of the quality of early intervention services related to perceived child outcomes as well as to perceived family outcomes. Satisfaction patterns indicate that families identifying as ethnic minorities, families in which mothers reported lower levels of education, and families with low levels of income were less satisfied with early intervention services than other families. Several investigations have employed meta‐analyses to compare the effectiveness of early intervention services. Early meta‐analyses were termed “first generation” because of their overall emphasis on efficacy (Guralnick, 1993). These analyses, conducted largely on studies implemented before early intervention services were routinely available, which

218  Hauser-Cram, Heyman, and Bottema-Beutel allowed some comparisons of children receiving services to similar children not receiving them, indicated that early intervention services produced an overall effect size of .62 (Shonkoff & Hauser‐Cram, 1987) or .68 (Casto & Mastropieri, 1986). The researchers noted variations in effects based on center‐based versus home‐based services and child age of entry (Casto & Mastropieri, 1986) as well as by child type of disability and parent involvement (Shonkoff & Hauser‐Cram, 1987). In a comparison of meta‐analyses on special education and related services, Forness (2001) noted that the effects of early intervention services are smaller than those found for the use of behavior modification but larger than those found for the benefits of psychotropic drugs. The moderate effects found in these studies appear to be compelling, yet some have raised the concern that they are based on studies employing didactic child‐centered approaches rather than on current family‐centered approaches (Guralnick, 1993). In contrast to studies on overall effectiveness of early intervention services, “second generation” studies focus on the nuances of what works for whom. Employing this approach, studies have been conducted on specific interventions, such as parent‐implemented language interventions for children with language delays (Roberts & Kaiser, 2011). Many second generation studies have focused on specific interventions for children with ASD. Several meta‐analyses have been conducted on studies of programs providing Early Intensive Behavioral Intervention (EIBI) for children with ASD, and positive effects are reported in almost all of these. Reichow (2012) reviewed five meta‐analyses of EIBI in which interventions were provided for at least 2 years for children with ASD. Four of the five meta‐analyses concluded that EIBI is an effective approach for children with ASD, with overall mean effect sizes ranging from .38 to 1.19 for IQ and .30 to 1.09 for adaptive behavior. Camargo et al. (2015) conducted a meta‐analysis on 19 intervention studies that met specific high quality standards of single‐case designs and focused predominately on the preschool age group. The interventions involved behavioral components such as prompting, modeling, reinforcement, and imitation to target social skills. The researchers reported high effect sizes for most of the studies, with an overall Tau‐U effect size of .88. In a meta‐analysis of a set of 9 studies using controlled designs of EIBI, Eldevik et  al. (2009) computed effects sizes for dependent groups of 1.10 and .66 for changes in full scale IQ and adaptive behavior respectively. Virués‐Ortega (2010) conducted a meta‐­ analysis of 22 studies on applied behavior analysis (ABA) that met specified standards of ­criteria related to study quality and type of measurement. He reported that intensive intervention (20–40 hours a week) led to positive medium to large effects in intellectual functioning, language development, acquisition of daily skills, and social functioning, with higher effects noted (a pooled effect size of 1.48) for language and communication skills than for other aspects of behavior. In addition, Virués‐Ortega indicated that effect sizes were dose‐dependent for both language and adaptive behavior outcomes, suggesting that more intensive interventions resulted in larger effects. Systematic reviews have also been conducted on specific curricular approaches. Smith and Iadarola (2015) reviewed interventions that had been manualized for children aged 5 or younger and included an evaluation component. They grouped the original articles according to whether the interventions followed theoretical principles of either applied behavior analysis (ABA), developmental social‐pragmatic models, or both. In contrast to ABA interventions which generally follow operant conditioning strategies to lead to

Early Childhood Education for Children with Disabilities  219 desired outcomes, developmental social‐pragmatic interventions aim to promote social communication through expanding on child‐initiated play activities. Employing a defined set of methodologically rigorous criteria, the authors concluded that two intervention approaches were “well‐established”: individual, comprehensive ABA and teacher‐ implemented‐focused ABA plus developmental social‐pragmatic models. They point out that little information is available on mediators and moderators of the outcomes of such studies, however. In contrast to controlled studies specializing in EIBI under highly specified conditions, Rivard, Terroux, and Mercier (2014) examined the success of EIBI in a community‐based publicly funded program in Canada. They found that preschool‐aged children with ASD who had received 16–20 hours per week of intensive intervention for one year improved by approximately one half a standard deviation in adaptive behavior and cognitive performance scores. The authors note that these effects are lower than those reported for many of the controlled treatment studies in the literature but may be indicative of improvements exhibited in the less resource‐intensive and more ecologically valid community‐based settings. For children with ASD, social communication impairments are primary targets for early intervention, as they represent a core deficit (American Psychiatric Association, 2013). In terms of the effectiveness of early intervention on social communication outcomes, it is unclear whether EIBIs produce robust effects that are generalizable to settings other than the intervention context, and are maintained over time. In a recent research synthesis, Yoder, Bottema‐Beutel, Woynaroski, Chandrasekhar, & Sandbank and colleagues (2014) found that EIBI interventions generally produced “proximal” and “context‐bound” outcomes, meaning that the social communication skills learned in these interventions did not extend beyond what was directly taught, and were not measured outside of the intervention context. Functionally, this means that the social communication gains made in intervention are likely to be lost soon after the intervention has ceased. Only interventions that took a developmental approach produced gains in social communication that generalized across contexts, and went beyond the skills directly targeted by the intervention. The following were identified as potentially “key ingredients,” as they recurred across the interventions determined to be promising: naturalistic interactions, child‐centered programming, adult‐organized learning opportunities, play routines, parent and family involvement, a developmental orientation, and consideration of the child’s physiological regulation (Bottema‐Beutel, Yoder, Woynaroski, & Sandbank, 2014). Several well‐designed RCTs using these approaches have been published in recent years, and show promising outcomes and continued improvement well after the intervention has ceased (e.g., Kasari, Paparella, Freeman, & Jahromi, 2008). Despite the promise of these interventions, there is little infrastructure to ensure that families of young children with ASD will have access to these services, or that they will translate to non‐clinical settings. A possible reason for poor uptake in non‐clinical settings is the lack of fit between interventions that were initially developed in a clinic, and the social context in which they are meant to be adopted (Dingfelder & Mandell, 2011). There is some emerging research to indicate that these approaches can be adapted and implemented with efficacy in diverse community settings (e.g., Vivanti et al., 2014), but policy shifts will be needed to ensure that these services become more widely available, and that they are routinely recommended by primary care providers.

220  Hauser-Cram, Heyman, and Bottema-Beutel Another approach to understanding program efficacy for young children with disabilities involves cost analyses. Ideally, such analyses provide information about both the resources (costs) and the results (outcomes). Parenting a child with disabilities often engenders many costs in addition to the expenses of various interventions, including travel to appointments and lost employment productivity (Horlin, Falkmer, Parsons, Albrecht, & Falkmer, 2014), The authors of one study conducted in Britain estimated that the cost of caring for an individual with ASD extends to more than 2.5 million euros (approximately $2.7 million) over a lifetime (Järbrink & Knapp, 2001). Therefore, studies are needed to indicate which services are most cost‐effective and whether services can assist families in reducing the costs of caring for their child with a disability. In relation to early intervention services in general a few cost studies were conducted in the 1980s and 1990s (Tarr & Barnett, 2001; Warfield, 1994). Those studies indicated that large ranges in the cost of services exist, with home visits generally more expensive than center‐based visits. More recently, costs associated with different intervention services were analyzed by researchers in the NEILS investigation (Hebbeler, Levin, Perez, Lam, & Chambers, 2009; Levin, Perez, Lam, Chambers, & Hebbeler, 2004). They reported an estimated mean cost per child of $916 per month, with variation based on the child’s type of disability; the greatest expenditures occurred for those families where a child had a diagnosed condition, such as Down syndrome or fragile X syndrome. In cost‐benefit analyses, however, costs are only one part of the examination; such costs need to be compared to outcomes. Greenberg and Martinez (2008) conducted a cost‐benefit analysis of young children enrolled in an ABA program in which they received intervention for 10 hours each week. They reported cost estimates of approximately twice that reported by NEILS. They also indicated, however, that 95% of the enrolled children moved into “least restrictive” settings (generally, those settings primarily serving typically developing children) the following year, suggesting future cost savings. Indeed, most of the cost‐benefit studies on early intervention services have focused on the predicted cost savings over time of providing EIBI for young children with ASD. For example, Jacobson, Mulick, and Green (1998) conducted a cost‐benefit analysis of EIBI for children with ASD in the state of Pennsylvania. They gathered data on the cost of providing EIBI during the first 3 years of life and estimated that long‐term cost savings would be substantial (from $187,000 to $203,000 per child for ages 3–22, in 1998 ­dollars). Such savings, they reasoned, would be due to improved functioning of a large proportion of such children, resulting in the need for less intensive special education services during the school years. The ability to compare costs to outcomes in early intervention services in the United States has been limited, however. The NEILS investigation, for example, examined general cost data but lacked data on independent child outcome assessments to provide a sufficient analysis of cost‐benefit. The ability to conduct cost‐benefit analyses in the future might be improved by new federal data collection mechanisms and requirements that Part C and Part B programs submit data on child (and possibly, family) outcomes. A recent report, however, indicates that states vary greatly in data quality, with a total of 6 states receiving the lowest possible rating for data quality (Early Childhood Technical Assistance Center, 2014). With data improvement, analyses on the relation between costs and o­utcomes can be undertaken.

Early Childhood Education for Children with Disabilities  221 In contrast to cost‐benefit analyses, cost‐effectiveness analyses focus on the costs and outcomes of one intervention compared to those of another. The cost savings of applying targeted approaches for specific types of disabilities, such as EIBI for children with ASD, have been examined in some studies. Motiwala, Gupta, & Hon (2006) conducted a cost‐ effectiveness study on expanding EIBI to all young children with ASD in Ontario. They estimated that the provision of EIBI (defined as 20 hours or more a week of intervention) during early childhood would result in a substantial increase in “dependency‐free years” (i.e., time not requiring special education and other public services) and produce substantial cost savings for the government (CAD45 million) over the course of participants’ lifetimes (ages 2–65). A cost comparison study conducted in the Netherlands (Peters‐Scheffer, Didden, Korzilius, & Matson, 2012) compared the costs of children receiving EIBI to those receiving “eclectic treatment or treatment as usual.” The authors reported that despite the high costs of administering EIBI, the estimated cost savings to society as a result of EIBI would be substantial, largely due to reduced need for special services and care. Few such studies have focused on community‐based low intensity early intervention services for children with a range of disabilities. One example is a series of studies conducted by Warfield (1994, 1995) in Massachusetts. She found that children with mild delays who entered early intervention before 12 months of age made greater gains in both adaptive behavior and mother‐child interaction per $1,000 of investment than did those who entered after 12 months of age. She also compared the effectiveness of home visits versus center‐based visits in early intervention services and reported that home visits were associated with greater reductions in maternal reports of parenting stress. Replications of these studies in other states and employing utilization of current data are greatly needed.

Transition from One Service System to Another An important consideration of the effectiveness of early childhood services for children with disabilities involves the successful transition of children from one service system to another. Young children with disabilities make at least two critical service transitions during their first few years of life. First, they make a transition from early intervention (Part C) to preschool (Part B), followed by the transition from preschool, including Head Start, to public school kindergarten programs. In each of these transitions, services and the way they are delivered often change, expectations of children’s behaviors and skills often accelerate, and relationships of families to service systems undergo fundamental alterations. Ideally, transitions are not disruptive to children and families but instead propel children onto positive developmental paths (Rimm‐Kaufman & Pianta, 2000).

The transition to preschool When children in early intervention services reach age 3, if they continue to require s­ervices, they must transition from receiving services under Part C of IDEA to Part B. Part C services are implemented by a designated agency in each state, commonly a health

222  Hauser-Cram, Heyman, and Bottema-Beutel agency (Malone & Gallagher, 2009) whereas Part B services, which serve children in p­reschool through age 21, are provided by the state education authority. In general, the quality of the transition is dependent on multiple factors, including utilization of family‐ centered practices (Pang, 2010). The transition process is complex, with necessary steps required by Federal Law including referral, screening, and evaluations (Malone & Gallagher, 2008). Only about 66% of children who qualified for Part C, however, have been found to be eligible to receive services under Part B (United States Department of Education, 2014). This is often because the developmental delays of some children are not significant enough to warrant Part B service receipt in their respective states. For these children, transition involves identifying other sources of support and services, such as a preschool program with the capacity to continually assess children and recommend additional services as needed (Lillie & Vakil, 2002). Federal monitoring reports indicate problems with the implementation of these legal guidelines. Such problems include the failure to determine eligibility for Part B by the third birthday, failure to implement an IEP by the third birthday, and lack of attendance and participation from relevant agency staff at transition planning meetings (Malone & Gallagher, 2009). While Part B preschool programs serve a larger number of children than Part C programs (approximately 750,000 children and 334,000 children respectively), the process for determining eligibility differs between the two service systems (Malone & Gallagher, 2009; United States Department of Education, 2014). For example, children deemed to be “at risk” for developmental disabilities may be served in Part C programs but not in Part B programs. Factors such as this require families to complete lengthy evaluation and eligibility processes, which contribute to the problems noted in the federal monitoring reports. In addition to logistical challenges associated with completing required steps, transition can be inherently stressful. Families must move from a service system that emphasizes family‐centeredness through Individualized Family Service Plans (IFSPs), to one that is  largely focused on the child, as reflected by the replacement of IFSPs with IEPs (Mawdsley  & Hauser‐Cram, 2013). Research indicates that families are often disappointed or frustrated with service options provided by the school systems (Hanson et al., 2000). Furthermore, many families feel insufficiently prepared for the transition process, and this lack of preparation negatively affects their participation in decision making (Hanson et al., 2000). Professionals also report a lack of training that explicitly addresses the transition process (Myers, 2007).

The transition to kindergarten Although there is a large amount of information, including recommendations, regarding the transition from IDEA Part C services to Part B, there is relatively little about the transition from preschool to kindergarten for children with developmental disabilities. This might be due to the fact that the logistics of the latter transition are relatively straightforward; the same agency, the state education authority, is the responsible party both before and after the transition occurs. Beginning kindergarten is a milestone for children and families, however, and therefore the transition warrants attention.

Early Childhood Education for Children with Disabilities  223 Although only a few studies have investigated the transition to kindergarten of children with disabilities, one instructive study involved follow‐up analyses of children enrolled in the NEILS investigation when the children were in kindergarten. Ninety percent of children entered kindergarten at the expected age, and only 6% attended a school designated exclusively for children with disabilities, indicating that most children were in an inclusive setting for at least part of the day. Slightly more than one half of the children who had been in early intervention were receiving special education services (58%), primarily for disabilities related to speech and language skills. Almost all parents (88%) reported that the transition to kindergarten was “easy” or “very easy.” Preschool teachers reported that they have more overall concerns regarding transition for children with ASD as compared to children with other developmental disabilities, which suggests that there may be barriers to successful transition that are specific to children with ASD (Quintero & McIntyre, 2011). More research is needed to identify these barriers, and to design transition p­rogramming that accommodates the unique needs of children with ASD. In relation to children’s school experiences, 58% of children in the NEILS sample received special education services in kindergarten. Employing survival analysis to examine the achievement of developmental milestones of children in this follow‐up of the NEILS participants, Scarborough, Hebbeler, Spiker, and Simeonsson (2011) reported differences based on children’s disability characteristics. Considering the expected developmental milestones of children in kindergarten (e.g., tells a simple story, gives both first and last names, knows meaning of half, prints 4 or 5 letters) only 18% of children with diagnosed conditions (e.g., Down syndrome) achieved the expected milestones. In comparison 31% of those considered “developmentally delayed” at entry to early intervention, 40% of those “at risk of delay” (e.g., low birth weight), and 50% of those with exclusive speech and language delays reached those milestones. Although within group heterogeneity was large, these results indicate that the prognosis for early intervention recipients in achieving expected developmental tasks in kindergarten is most positive for children with speech and language delays or for those with “risk” conditions. Even so, these findings further demonstrate that the majority of children who had received early intervention services continue to lag in developmentally appropriate skill acquisition in kindergarten. As part of a longitudinal study of children with intellectual disabilities compared to those developing typically, McIntyre, Blacher, & Baker (2006) analyzed children’s transition to kindergarten. They found that teachers rated the children with intellectual disabilities as 3 times more likely than other children to have behavior problems, especially externalizing behavior problems and attentional difficulties. In addition, children with intellectual disabilities were reported to have lower levels of social skills. The importance of social skills for kindergarten success has been noted by many researchers (Robinson & Diamond, 2014). For example, analyses of children in the Pre‐elementary Education Longitudinal Study (PEELS), a nationally representative sample of over 3,000 preschool children with disabilities receiving early childhood special education, indicated that c­hildren with higher levels of social skills were more likely to no longer need special education (Carlson et al., 2009). In contrast, the combination of behavior problems, attention difficulties, and social skill delays increases the probability of a poor school adaptation for children with developmental disabilities.

224  Hauser-Cram, Heyman, and Bottema-Beutel Despite the importance of the transition to kindergarten for all children, including those in Head Start, this process has been rarely studied for children with documented special needs. Studies on transition of children in Head Start to kindergarten settings have either excluded children with special needs or not analyzed this group separately. This is an enormous information gap as evidence points to the challenges children in Head Start often face as they advance through kindergarten programs (Robinson & Diamond, 2014).

The Effectiveness of Early Childhood Special Education Over the last several decades the nature of early childhood special education has changed considerably. Many children with disabilities now enter preschool having received several years of early intervention services, and most enter into inclusive classroom settings. In the 1990s, studies were conducted to compare children’s outcomes based on whether they were enrolled in inclusive versus substantially separate classrooms, with advantages noted for those in inclusive settings (Buysse & Bailey, 1993). Given the current interpretation of federal legislation, which emphasizes the importance of children being served in inclusive settings, recent questions regarding evidence on the effectiveness of early childhood special education seldom focus on the issue of inclusion per se. Instead, studies now test the potential benefits of specific types of interventions, such as social skills training, psychotropic medications, and direct instruction (Forness, 2001). In a “mega‐analysis” of 24 meta‐analyses of the effectiveness of such interventions, Forness (2001) concluded that an overall effect size of special education could be estimated to be .55, a moderate effect. He cautioned, however, that this grand mean masks much of the variability. Only a few recent studies have been conducted on the effectiveness of early childhood special education. Mahoney, Wheeden, & Perales (2004) studied 70 children in 41 early childhood special education programs. They found no differences for children in different program models nor did they find change in rate of children’s development based on the model. Additionally, they indicated, however, that dimensions of parent interaction predicted children’s outcomes. In a larger and nationally representative study, researchers in the PEELS measured progress in emerging literacy, mathematics, motor skills, and social behavior of young children receiving special education services over a several‐year period (Carlson, Jenkins, Bitterman, & Keller, 2011). They reported that after one year of service provision, these children improved in mathematics, social skills, and some aspects of emerging literacy. Progress in these areas continued through the elementary school years, yet the rate of progress decreased over time. For example, children’s scores on receptive vocabulary at age 3 grew 12.9 points whereas at age 10 they grew only 1.4 points. Two recent studies using propensity score matching on nationally representative samples of children have findings that contrast considerably with the earlier meta‐analyses and studies on special education services. In both investigations, the researchers approximated a randomization process by creating treated and untreated groups using measured confounding variables to create groups with equivalent expectations of receiving special e­ducation services. In one investigation, researchers examined the effect of preschool special education services on the attainment of kindergarten academic skills (Sullivan & Field, 2013). Using data from the Early Childhood Longitudinal Study‐Birth Cohort, the researchers created

Early Childhood Education for Children with Disabilities  225 two groups of children who generally had mild to moderate delays and few m­edical risks; one group had received special education services in preschool and one had not. They found that children who had received these services, in comparison to similar ­children who had not, had lower means in reading and mathematics scores in kindergarten. The authors suggest that such services as are currently offered do not advance young children’s academic skills and may, in fact, be detrimental for this group of children. A similar investigation, with somewhat mixed results, was conducted using data on children in the Early Childhood Longitudinal Study, Kindergarten Class of 1989–99 (Morgan, Frisco, Farkas, & Hibel, 2010). The researchers found that children receiving special e­ducation services scored about one standard deviation lower on a test of reading but also demonstrated greater gains over a 2‐year period than similar children not receiving special e­ ducation. In relation to mathematics, children who received special education services had lower scores in mathematics but did not show greater gains than similar children not receiving these services. Finally, those children receiving special education services did not  show improved behavior regulation as reported by their teachers. The researchers interpret these findings as indicating that special education services may not be of sufficient strength to advance children’s outcomes. Despite the federal regulations that Head Start programs serve children with disabilities, studies on the effectiveness of Head Start on this group of children are extremely limited. In one of the few such investigations Lee and Rispoli (2016) conducted a secondary analysis of children with disabilities in a nationally representative sample of children in Head Start compared to those in a randomly assigned control group. They found no overall effect of Head Start on cognitive scores of children with disabilities but reported subgroup effects. In particular, children in Head Start with multiple physician diagnosed disabilities had higher scores on language, literacy and math skills than similar children in the control group. Lee, Calkin, and Shin (2015) reported on the same sample of children and found that Head Start had no effect on the social-emotional outcomes of those with disabilities. Such studies indicate the need for a greater examination of the effectiveness of Head Start programs on children with disabilities. Overall findings of the limited effectiveness of special education services in general reported in these investigations certainly give pause to those interested in promoting p­ositive outcomes for young children with disabilities. Although the studies employing propensity score matching are limited to only one segment of the population of children in special education, those with milder and less extensive disabilities, the negative and null findings are noteworthy. Such findings should provide additional momentum for future work on the effectiveness of services for the full range of young children including those receiving Head Start services.

Future Directions Future studies on the effectiveness of different interventions for young children with developmental disabilities will likely investigate the use of Response to Intervention (RTI). The reauthorization of IDEA in 2004 changed the law about the identification of children with learning and related disabilities and pointed to a way of identifying them through RTI (Aron & Loprest, 2012). RTI is a system of multi‐tiered combinations of assessment and instruction for all students that aims to prevent small, remediable learning difficulties

226  Hauser-Cram, Heyman, and Bottema-Beutel from becoming large, intractable ones, with an overall goal of reducing inappropriate placements of children into special education (Council for Exceptional Children, 2008). Generally, RTI models propose tiers of services and assessment; both intensity and individualization increase as tier numbers increase. The lowest tier commonly represents evidence‐based teaching practices available to all students whereas the second tier involves small group instruction, and the third tier incorporates highly intense interventions, often provided to students individually. Placement of children in tiers is based on data‐based decision making, allowing students to move between tiers as appropriate (Peisner‐Feinberg, Buysse, Benshoff, & Soukaky, 2011). The majority of research studies have focused on the use of RTI for students of school age with specific learning disabilities (Lindstrom, 2013), and its evaluation remains limited (Kovaleski & Black, 2010). The implementation of RTI in early childhood programs is relatively nascent. Scholars have articulated a definition of RTI in early childhood, emphasizing the central role of families in promoting development in young children (DECCEC et al., 2014; Lieberman‐ Betz, Vail, & Chai, 2013; Odom & Wolery, 2003). This definition of RTI in early childhood places equal emphases on both caregiving and teaching, and therefore pertains strongly to both socio‐emotional and academic outcomes. Although there are different models for early childhood programs that can be considered RTI, there are four common features: (1) multiple tiers of teaching and caregiving practices, with intensity of supports and individualization increasing as tiers increase, (2) a high quality, culturally and developmentally appropriate curriculum reflecting family and professional input, (3) ongoing assessment and progress monitoring, and (4) a collaborative problem‐solving process that involves input from family members and professionals (DECCEC et al., 2014). Initial work on RTI for young children has focused on academic outcomes such as math skills (Clarke et  al., 2011) and reading skills (Greenwood et  al., 2013; O’Connor, Bocian, Sanchez, & Beach, 2014). Future research will likely focus on increasing evidence of the effectiveness of this approach implemented during the early childhood years.

Big Issues, Broad Policies, Mixed Effects The federal initiatives for young children with disabilities are remarkable in their influence on the everyday lives of children with disabilities and their families. Unlike any other group in our society children with disabilities are offered services from the time they are very young; for many children diagnosis occurs in the first years of life. In contrast, as a society we do not provide services to all infants whose families live in poverty, all toddlers who are dual language learners, or all preschoolers who are overweight. In comparison to other groups of children with needs in our society, young children with disabilities g­enerally receive more services and from a younger age. As indicated in Table 10.1, the evaluation of the effectiveness of early intervention and preschool services for children with disabilities is exceedingly limited, however. With the exception of services for children with ASD, especially those living in one of the 43 states that mandate insurance coverage for intensive interventions for those with ASD (Autism Speaks, 2015), early intervention services are meager in intensity, with an average of 1–2 hours per week provided to most children and families (Hebbeler et al., 2007). Moreover,

Early Childhood Education for Children with Disabilities  227 service intensity has been diminishing over the past few years (Belcher, Hairston‐Fuller, & McFadden, 2011). Despite the lack of intense service interventions for most infants and toddlers with disabilities, as a society we expect a great deal from these services. Early meta‐ analyses suggest that, in general, such services are associated with moderate positive effects on children and parents, especially if parents are directly involved in the services. In contrast, moderate to large effects on children’s specific developmental skills have been reported for those with ASD who have received EIBI or interventions that target core deficits, but more constrained effects are reported for social communication outcomes (Yoder et al., 2014). Current efforts are directed at determining effective service models and understanding how these might differ for children and families with distinct needs. Despite such efforts, those investigating the results of early intervention services are challenged with ways to determine effectiveness given the individualized nature of services, the variations in populations served, the changing needs of children and families, and the rights of children and families to receive these services. Importantly, the effectiveness of early intervention s­ervices likely depends in part on the quality of these services, with indicators of quality including the competencies of professionals and the use of family‐centered practices (Trivette, Dunst, & Hamby, 2010). Although researchers have developed scales to assess service quality, quality measurement has been problematic for many reasons, including the individualized nature of services (Kontos & Diamond, 2002). Additionally, many children receiving early intervention services are also participating in child care arrangements. For example, 41% of children in the NEILS study also received child care services for at least 10 hours a week (Hebbeler et al., 2007). We have no information on how effective such services are for infants and toddlers with disabilities; nor do we know how well child care and early intervention services work together to support optimal child development. Special education services for preschoolers are more intensive than early intervention services but also vary greatly in quality. Despite a compendium of recommended practices from the Division of Early Childhood (DEC) for serving young children with disabilities and their families (Sandall, Hemmeter, McLean, & Smith, 2004), little information exists on the effectiveness of these services for children with a range of disabilities. Moreover, the few reported studies vary considerably in their findings about such effectiveness, except for the consistently positive findings regarding services targeted directly at specific outcomes for children with ASD. Although some studies indicate that children receiving special education services show small but significant advantages in cognitive and academic skills, other studies suggest that such services are not effective, at least for children who have mild and less extensive disabilities. Finally, despite decades of studies on Head Start, we  know little about the effectiveness of this program for children with disabilities in p­articular. Rather than conducting studies on effectiveness, much evaluation work has focused on more general issues of compliance (Maude & DeStefano, 2011). Future work clearly needs to be directed at issues of service effectiveness for young c­hildren with disabilities across all the service sectors. That work needs to consider how services work together as children with a range of disabilities are often served in more than one type of early childhood service and also make transitions from one sector to another. The future task – despite being able to draw upon meager empirical evidence – has a rich theoretical foundation to anchor it. When the effectiveness of low intensity, community‐ based services for children with a range of disabilities has been investigated, effects vary but

228  Hauser-Cram, Heyman, and Bottema-Beutel tend to be small. Even small effects, however, can be critical ones for the young child. Small negative effects can place children on deleterious and cascading trajectories (Shaw, Gilliom, Ingoldsby, & Nagin, 2003) whereas small positive effects can place children on positive developmental pathways (Hauser‐Cram, Warfield, Shonkoff, & Krauss, 2001). The state of empirical evidence on ways to enhance the effectiveness of such services needs to grow substantially so children with a range of disabilities will develop optimally through their early childhood years and productively meet the challenges of schooling. Table 10.1  Empirical progress chart for studies of effects of early intervention and special education for young children with disabilities Services for Children with Disabilities → Child Development

Internal Validity

External Validity

Practical Significance

Early intervention for children with a range of disabilities (ages 0–3)→ Child skills, parent stress reduction Specific interventions: intensive early interventions for low birth‐weight infants (0–3)→ Child IQ, behavior regulation Specific interventions: Early Intensive Behavioral Interventions (EIBI) for children with autism

Meta‐analyses: Mix of quasi‐ experiments and non‐randomized designs Strong: RCT

Many small n, non‐ representative samples

Meta‐analyses: mix of quasi‐ experiments, experiments, and pre‐post designs

Head Start (0–5) for children with disabilities→ Child skills

Moderate: RCT but determination of disabilities based on parent report

Fewer community‐ based studies than treatment controlled studies. Generalization beyond study contexts limited. Moderate: nationally representative sample but insufficient studies

Early Childhood Special Education (3–5)→ Child academic skills

Moderately strong: propensity score matching

Moderate effect sizes for child skills Insufficient data for parent stress reduction Moderate effects for cognitive skills of heavier low‐birth‐ weight group; modest effects on behavior regulation Moderate to strong effects for IQ and adaptive skills but more constrained effects for social communication outcomes No overall effects of Head Start on children with IEPs; small positive effects on academic skills of those with multiple disabilities Small negative and null effect sizes

Strong empirical support Moderately strong empirical support Moderate empirical support Moderately weak empirical support Weak empirical support

Moderate: samples from several locations in US

Weak: only included those with mild disabilities

Early Childhood Education for Children with Disabilities  229

References American Academy of Pediatrics, Council on Children with Disabilities, Section on Developmental and Behavioral Pediatrics, Bright Futures Steering Committee, Medical Home Initiatives for Children With Special Needs Project Advisory Committee. (2006). Identifying infants and young children with developmental disorders in the medical home: An algorithm for developmental surveillance and screening [published correction appears in Pediatrics. 2006;119:1808–1809]. Pediatrics, 118, 405–420. American Academy of Pediatrics, Medical Home Initiatives for Children with Special Needs Project Advisory Committee. (2002). The medical home. Pediatrics, 110, 184–186. doi: 10.1542/ peds.110.1.184 American Psychiatric Association. (2013). Diagnostic and Statistical Manual of Mental Disorders: DSM‐5. Washington, D.C: American Psychiatric Association. Aron, L., & Loprest, P. (2012). Disability and the education system. The Future of Children, 22(1), 97–122. Autism Speaks (2015). North Carolina Gov. McCrory signs autism insurance reform legislation. Retrieved from https://www.autismspeaks.org/news‐item. Bailey, D., Hebbeler, K., Scarborough, A., Spiker, D., & Mallik, S. (2004). First experiences with early intervention: A national perspective. Pediatrics, 113(4), 887–896. doi: 10.1542/ peds.113.4.887 Bailey, D. B., Jr., Hebbeler, K., Spiker, D., Scarborough, A., Mallik, S., & Nelson, L. (2005). Thirty‐six‐month outcomes for families of children who have disabilities and participated in early intervention. Pediatrics, 116(6), 1346–1352. doi: 10.1542/peds.2004‐1239 Bailey, D. B., Jr., Nelson, L., Hebbeler, K., & Spiker, D. (2007). Modeling the impact of formal and informal supports for young children with disabilities and their families. Pediatrics, 120(4), e992–e1001. doi: 10.1542/peds.2006‐2775 Barton, L. R., Spiker, D., & Williamson, C. (2012). Characterizing disability in Head Start p­rograms: Not so clearcut. Early Childhood Research Quarterly, 27(4), 596–612. doi: 10.1016/ j.ecresq.2012.04.002 Belcher, H. M. E., Hairston‐Fuller, T. C., & McFadden, J. (2011). How do we assess family supports and fairness in early intervention? Developmental Disabilities Research Reviews, 17, 36–43. doi:10.1002/ddrr.137 Bottema‐Beutel, K., Yoder, P., Woynoroski, T., & Sandbank, M. (2014). Targeted intervention for social‐communication symptoms in preschoolers. In F. R. Volkmar, R. Paul, S. J. Rogers, and K. A. Pelphrey (Eds.), Handbook of autism and pervasive developmental disorders. Hoboken, NJ: John Wiley & Sons, Inc. Boyle, C. A., Boulet, S., Schieve, L. A., Cohen, R. A., Blumberg, S. J., Yeargin‐Allsopp, M., … Kogan, M. D. (2011). Trends in the prevalence of developmental disabilities in US children, 1997–2008. Pediatrics, 127(6), 1034–1042. doi: 10.1542/peds.2010‐2989 Bronfenbrenner, U. (1974). Is early intervention effective? Teachers College Record, 76(2), 279–303. 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: John Wiley & Sons, Inc. Brooks‐Gunn, J., McCarton, C. M., Casey, P. H., McCormick, M. C., Bauer, C. R., Bernbaum, J.  C., … Meinert, C. L. (1994). Early intervention in low‐birth‐weight premature infants: Results through age 5 years from the infant health and development program. JAMA: Journal of the American Medical Association, 272(16), 1257–1262. doi: 10.1001/jama.272.16.1257

230  Hauser-Cram, Heyman, and Bottema-Beutel Buysse, V., & Bailey, D. B. (1993). Behavioral and developmental outcomes in young children with disabilities in integrated and segregated settings: A review of comparative studies. The Journal of Special Education, 26(4), 434–461. doi: 10.1177/002246699302600407 Camargo, S., Rispoli, M., Ganz, J., Hong, E. R., Davis, H., & Mason, R. (2015). Behaviorally based interventions for teaching social interaction skills to children with ASD in inclusive s­e ttings: A meta‐analysis. Journal of Behavioral Education, doi: 10.1007/ s10864‐015‐9240‐1 Carlson, E., Daley, T., Bitterman, A., Heinzen, H., Keller, B., Markowitz, J., & Riley, J. (2009). Early School Transitions and the Social Behavior of Children with Disabilities: Selected Findings From the Pre‐Elementary Education Longitudinal Study. Rockville, MD: Westat. Available at www. peels.org. Carlson, E., Jenkins, F. Bitterman, A., & Keller, B. (2011). A longitudinal view of the receptive vocabulary and math achievement of young children with disabilities. Rockville, MD: Westat. Retrieved from http:ies.ed.gov/ncser/pubs/20113006. Casto, G., & Mastropieri, M. A. (1986). The efficacy of early intervention programs: A meta‐analysis. Exceptional Children, 52(5), 417–424. Centers for Disease Control and Prevention (CDC). (2014, March 28). Prevalence of autism spectrum disorder among children aged 8 years: Autism and developmental disabilities monitoring network, 11 sites, United States, 2010. MMWR. Morbidity and Mortality Weekly Reports. Retrieved from http://www.cdc.gov/mmwr/preview/mmwrhtml/ss6302a1.htm?s_cid= ss6302a1_w Clarke, B., Smolkowski, K., Baker, S. K., Fien, H., Doabler, C. T., & Chard, D. J. (2011). The impact of a comprehensive tier I kindergarten program on the achievement of students at risk in mathematics. The Elementary School Journal, 111(4), 561–584. Cohen, S. R. (2013). Advocacy for the “abandonados”: Harnessing cultural beliefs for Latino families and their children with intellectual disabilities. Journal of Policy and Practice in Intellectual Disabilities, 10(1), 71–78. doi: 10.1111/jppi.12021 Convention on the Rights of Persons with Disabilities, United Nations, March 30, 2007. Final Report of the Ad Hoc Committee on a Comprehensive and Integral International Convention on the Protection and Promotion of the Rights and Dignity of Persons with Disabilities [A/61/611 ‐ PDF, 117KB] Council for Exceptional Children, 2008, CEC’s Position on Response to Intervention (RTI): The Unique Role of Special Education and Special Educators. Retrieved from http://www.cec.sped. org/~/media/Files/Policy/CEC%20Professional%20Policies%20and%20Positions/RTI.pdf Currie, J., & Kahn, R. (2012). Children with disabilities: Introducing the issue. The Future of Children, 22 (1), 3–11. 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(1), e17–e23. doi: 10.1542/peds.2009‐0958 Dingfelder, H. E., & Mandell, D. S. (2011). Bridging the research‐to‐practice gap: An application of diffusion of innovation theory. Journal of Autism and Developmental Disorders, 41, 597–609. doi: 10.1007/s10803‐010‐1081‐0 Division for Early Childhood of the Council for Exceptional Children (DECCEC), National Association for the Education of Young Children (NAEYC), & National Head Start Association  (NHSA). (2014). Frameworks for Response to Intervention in early childhood: Description and implications. Communication Disorders Quarterly, 35(2), 108–119. doi: 10.1177/ 1525740113514111 Early Childhood Technical Assistance Center (2014). Using child outcomes data for determination: A proposal. Retrieved from http://www2.ed.gov/about/offices/list/osers/osep/rda/ idea‐part‐c‐results‐in‐determinations.pdf

Early Childhood Education for Children with Disabilities  231 Eisenhower, A. S., Baker, B. L., & Blacher, J. (2005). Preschool children with intellectual disability: Syndrome specificity, behaviour problems, and maternal well‐being. Journal of Intellectual Disability Research, 49(9), 657–671. doi: 10.1111/j.1365‐2788.2005.00699.x Eldevik, S., Hastings, R. P., Hughes, J. C., Jahr, E., Eikeseth, S., & Cross, S. (2009). Meta‐analysis of early intensive behavioral intervention for children with autism. Journal of Clinical Child and Adolescent Psychology, 38(3), 439–450. doi: 10.1080/15374410902851739 DOI:10.1080/ 15374410902851739#_blank Engel, G. L. (1977). The need for a new medical model: A challenge for biomedicine. Science, 196(4286), 129–136. doi: 10.1126/science.847460 Forness, S. R. (2001). Special education and related services: What have we learned from meta‐ analysis? Exceptionality, 9(4), 185–197. doi: 10.1207/S15327035EX0904_3 Fountain, C., King, M. D., & Bearman, P. S. (2011). Age of diagnosis for autism: Individual and community factors across 10 birth cohorts. Journal of Epidemiology and Community Health, 65(6), 503–510. García Coll, C. T., & Magnuson, K. (2000). Cultural differences as sources of developmental v­ulnerabilities and resources: a view from developmental research. In S. J. Meisels & J. P. Shonkoff (Eds.), Handbook of Early Childhood Intervention (pp. 94–111). Cambridge, UK: Cambridge University Press. Green, J., Charman, T., McConachie, H., Aldred, C., Slonims, V., Howlin, P…. Pickles, A. (2010). Parent‐mediated communication‐focused treatment in children with autism (PACT): A r­ andomized controlled trial. The Lancet, 375(9732), 2152–2160. doi: 10.1016/S0140‐ 6736(10)60587‐9 Greenberg, J. H., & Martinez, R. C. (2008). Starting off on the right foot: One year of behavior analysis in practice and relative cost. International Journal of Behavioral Consultation and Therapy, 4(2), 212–226. doi: 10.1037/h0100844 Greenwood, C. R., Carta, J. J., Atwater, J., Goldstein, H., Kaminski, R., & McConnell, S. (2013). Is a response to intervention (RTI) approach to preschool language and early literacy instruction needed? Topics in Early Childhood Special Education, 33(1), 48–64. doi: 10.1177/ 0271121412455438 Guralnick, M. J. (1993). Second generation research on the effectiveness of early intervention. Early Education and Development, 4(4), 366–378. doi: 10.1207/s15566935eed0404_11 Guralnick, M. J. (2001). A developmental systems model for early intervention. Infants & Young Children,14(2), 1–18. Guralnick, M. J. (2007). The system of early intervention for children with developmental disabilities: Current status and challenges for the future. In J. W. Jacobson, J. A. Mulick, & J. Rojahn (Eds.) Handbook of intellectual and developmental disabilities, (pp. 465–480). New York, NY: Springer. Halfon, N., Houtrow, A., Larson, K., & Newacheck, P. W. (2012). The changing landscape of d­isability in childhood. The Future of Children, 22(1), 13–42. doi: 10.1353/foc.2012.0004 Hanson, M. J., Beckman, P. J., Horn, E., Marquart, J., Sandall, S. R., Greig, D., & Brennan, E. (2000). Entering preschool: Family and professional experiences in this transition process. Journal of Early Intervention, 23(4), 279–293. doi: 10.1177/10538151000230040701 Hauser‐Cram, P., Cannarella, A., Tillinger, M., & Woodman, A. (2013). Disabilities and development. In R. M. Lerner, A. Easterbrooks, & J. Mistry (Vol. Eds.), Handbook of psychology, Vol. 6, Developmental psychology (2nd ed.) (pp. 547–569). Hoboken, NJ: Wiley. Hauser‐Cram, P., Warfield, M. E., Shonkoff, J. P., & Krauss, M. W. (2001). Children with disabilities: A longitudinal study of child development and parent well‐being. Monographs of the Society for Research in Child Development, 66(3), 1–131. doi: 10.1111/1540‐5834.00151 Hebbeler, K., Levin, J., Perez, M., Lam, I., & Chambers, J. G. (2009). Expenditures for early intervention services. Infants & Young Children, 22(2), 76–86. doi: 10.1097/IYC.0b013e3181a02f30

232  Hauser-Cram, Heyman, and Bottema-Beutel Hebbeler, K., & Spiker, D. (2011). Cost‐effectiveness and efficacy of programs. In C. Groark, S. Eidelman, L. A. Kaczmarek, & S. Maude, Early childhood intervention: Shaping the future for children with special needs and their families, Vols. 1–3 (pp. 173–207). Santa Barbara, CA: Praeger/ ABC‐CLIO. Hebbeler, K., Spiker, D., Bailey, D., Scarborough, A., Mallik, S., Simeonsson, R., … Nelson, L. (2007). Early intervention for infants and toddlers with disabilities and their families: Participants, services, and outcomes. Menlo Park, CA: SRI International. Hill, J. L., Brooks‐Gunn, J., & Waldfogel, J. (2003). Sustained effects of high participation in an early intervention for low‐birth‐weight premature infants. Developmental Psychology, 39(4), 730–744. doi: 10.1037/0012‐1649.39.4.730 Horlin, C., Falkmer, M., Parsons, R., Albrecht, M. A., & Falkmer, T. (2014). The cost of autism spectrum disorders. PLoS ONE, 9(9). Houtrow, A. J., Larson, K., Olson, L. M., Newacheck, P. W., & Halfon, N. (2014). Changing trends of childhood disability, 2001–2011. Pediatrics, 134(3), 530–538. doi: 10.1542/peds.2014‐0594 Individuals with Disabilities Education Act, 20 U.S.C. § 1400 (2004). Jacobson, J. W., Mulick, J. A., & Green, G. (1998). Cost‐benefit estimates for early intensive behavioral intervention for young children with autism – general model and single state case. Behavioral Interventions, 13(4), 201–226. doi: 10.1002(SICI)1099‐078X(199811)13:4 200); one study’s sample was small (n