Infectious Diseases: An Evidence based Approach [1st ed.] 9781773618371

Table of contents :
Cover......Page 1
Half Title Page......Page 3
Title Page......Page 5
Copyright Page......Page 6
Declaration......Page 7
About the Editor......Page 9
Table of Contents......Page 11
List of Contributors......Page 19
List of Abbreviations......Page 31
Preface......Page 33
SECTION I INFECTION CONTROL AND USE OF ANTIBIOTICS......Page 35
Abstract......Page 37
Background......Page 39
Methods......Page 40
Results......Page 44
Discussion......Page 51
Acknowledgements......Page 56
Authors’ Contributions......Page 57
References......Page 58
Chapter 2 Hospital-and Patient-Related Factors Associated With
Differences in Hospital Antibiotic Use: Analysis of National Surveillance
Results......Page 59
Abstract......Page 60
Background......Page 61
Methods......Page 62
Results......Page 66
Discussion......Page 71
Authors’ Contributions......Page 75
References......Page 76
Chapter 3 Prevention and Control of Antimicrobial Resistant Healthcare-Associated Infections: The Microbiology Laboratory Rocks!......Page 81
Introdution......Page 82
Tasks of The Microbiology Laboratory......Page 83
Antibiotic Stewardship Programs......Page 86
Issues on The Laboratory Efficacy......Page 87
Author Contributions......Page 92
References......Page 93
Chapter 4 Global Increase and Geographic Convergence in Antibiotic
Consumption Between 2000 And 2015......Page 101
Abstract......Page 102
Methods......Page 103
Results......Page 105
Discussion......Page 112
Acknowledgments......Page 117
References......Page 119
Chapter 5 Antibiotic Use and Clinical Outcomes in the Acute Setting Under
Management by an Infectious Diseases Acute Physician Versus
Other Clinical Teams: A Cohort Study......Page 123
Introduction......Page 124
Methods......Page 126
Results......Page 130
Discussion......Page 138
Acknowledgments......Page 141
References......Page 142
Chapter 6 Evaluation of Risk Factors for Antibiotic Resistance In Patients
With Nosocomial Infections Caused Bypseudomonas
Aeruginosa......Page 145
Introduction......Page 146
Material and Methods......Page 148
Results......Page 150
Discussion......Page 155
Authors’ Contributions......Page 160
References......Page 161
Abstract......Page 167
Mechanisms by Which Existing Vaccines Can Address the
Amr Problem......Page 168
Prospects For Gaining Similar Benefits With New or
Improved Vaccines Against Other Amr Pathogens......Page 172
Targeting Vaccines Selectively to Resistant Clones or Directly
Against Factors Mediating Resistance: A Novel Approach
to Controlling AMR......Page 174
Synergy Between Passive or Vaccine-Induced Antibodies and
Antimicrobials in Treating or Preventing
Amr Infections......Page 177
Research and Policy Needs......Page 178
Conclusion......Page 180
References......Page 181
SECTION II TB INFECTION......Page 193
Abstract......Page 195
Introduction......Page 196
History of Antibiotic Development From Penicillin to Streptomycin......Page 197
History of Chemotherapeutic Development From
Arsphenamine to PAS......Page 200
The First Combined Antimicrobial Regimen......Page 202
The Path to Modern Antimicrobial Therapy for Tuberculosis......Page 203
Lessons From The History of Combination Therapy......Page 205
Acknowledgments......Page 208
References......Page 209
Chapter 9 Antibiotic Treatment For Tuberculosis Induces A Profound
Dysbiosis of the Microbiome That Persists Long After
Therapy Is Completed......Page 217
Introduction......Page 218
Results......Page 221
Discussion......Page 234
Methods......Page 236
Acknowledgements......Page 241
References......Page 242
Chapter 10 Genomic Insight Into Mechanisms Of Reversion Of Antibiotic
Resistance In Multidrug Resistant Mycobacterium Tuberculosis
Induced by a Nanomolecular Iodine-Containing Complex FS-1......Page 247
Introduction......Page 248
Materials and Methods......Page 251
Results......Page 256
Discussion......Page 262
Author Contributions......Page 264
Acknowledgments......Page 265
References......Page 266
Chapter 11 Effector Mechanisms of Neutrophils within the Innate Immune System in
Response to Mycobacterium tuberculosis Infection......Page 271
Abstract......Page 272
Neutrophil Extracellular Traps And Its Effector Functions......Page 273
Cytokines Modulating The Functions Of Neutrophils......Page 274
Neutrophils Apoptosis And Phagocytosis Induced Cell Death......Page 275
Genotypic Changes Affecting Neutrophil Functions......Page 276
Neutrophils And Granulomatous Responses Against M. Tb Infection......Page 277
Hiv Infection And Type 2 Diabetes Association With Neutrophil
Immune Responses To M. Tb......Page 278
Concluding Remarks......Page 279
Author Contributions......Page 280
References......Page 281
Chapter 12 Discriminating Active Tuberculosis From Latent Tuberculosis
Infection By Flow Cytometric Measurement of
Cd161-Expressing T Cells......Page 287
Introduction......Page 288
Results......Page 290
Discussion......Page 296
Methods......Page 298
Acknowledgements......Page 299
References......Page 300
Abstract......Page 303
Introduction......Page 304
Establishment And Persistence of Latent M. Tuberculosis infection......Page 305
New Dynamic Model of Latent Tuberculosis Infection......Page 313
Diagnosis of Latent M. Tuberculosis Infection......Page 315
Treatment of Latent M. Tuberculosis Infection......Page 320
Future Prospects......Page 324
Conclusion......Page 325
Acknowledgements......Page 326
References......Page 327
SECTION III PNEUMONIA MANAGEMENT......Page 349
Abstract......Page 351
Introduction......Page 352
Microbial Etiology of Community-Acquired Pneumonia (CAP)......Page 353
Microbial Etiology of Hospital Acquired Pneumonia (HAP)......Page 358
Laboratory Diagnosis of Pneumonia......Page 363
Author Contributions......Page 369
References......Page 370
Abstract......Page 381
Scope Of The Clinical Problem......Page 382
Respiratory Microbiome Research In The ICU......Page 384
A Translational Roadmap Ahead......Page 386
Acknowledgments......Page 388
References......Page 389
Chapter 16 Etiology of Community-Acquired Pneumonia and Diagnostic
Yields of Microbiological Methods: A 3-Year Prospective Study in
Norway......Page 393
Abstract......Page 394
Background......Page 395
Methods......Page 396
Results......Page 399
Discussion......Page 409
Authors’ Contributions......Page 413
References......Page 414
Abstract......Page 419
Introduction......Page 420
C-Reactive Protein (CRP)......Page 421
Procalcitonin (PCT)......Page 423
Soluble Triggering Receptor Expressed on Myeloid Cells-1 (Strem-1)......Page 426
Other Biomarkers......Page 428
Copeptin And Midregional Pro-Atrialnatriuretic Peptide (Mr-Proanp)......Page 429
Cortisol......Page 430
Acknowledgment......Page 431
References......Page 432
Chapter 18 Distribution and Determinants of Pneumonia Diagnosis
Using Integrated Management of Childhood Illness
Guidelines: A Nationally Representative Study in Malawi......Page 441
Abstract......Page 442
Background......Page 443
Methods......Page 444
Results......Page 450
Discussion......Page 456
Conclusions......Page 461
References......Page 462
Index......Page 469

Citation preview

INFECTIOUS DISEASES: AN EVIDENCE BASED APPROACH

INFECTIOUS DISEASES: AN EVIDENCE BASED APPROACH

Edited by: Vikas Mishra

www.delvepublishing.com

Infectious Diseases: An Evidence based Approach Vikas Mishra Delve Publishing 2010 Winston Park Drive, 2nd Floor Oakville, ON L6H 5R7 Canada www.delvepublishing.com Tel: 001-289-291-7705 001-905-616-2116 Fax: 001-289-291-7601 Email: [email protected] e-book Edition 2019 ISBN: 978-1-77361-837-1 (e-book)

This book contains information obtained from highly regarded resources. Reprinted material sources are indicated. Copyright for individual articles remains with the authors as indicated and published under Creative Commons License. A Wide variety of references are listed. Reasonable efforts have been made to publish reliable data and views articulated in the chapters are those of the individual contributors, and not necessarily those of the editors or publishers. Editors or publishers are not responsible for the accuracy of the information in the published chapters or consequences of their use. The publisher assumes no responsibility for any damage or grievance to the persons or property arising out of the use of any materials, instructions, methods or thoughts in the book. The editors and the publisher have attempted to trace the copyright holders of all material reproduced in this publication and apologize to copyright holders if permission has not been obtained. If any copyright holder has not been acknowledged, please write to us so we may rectify. Notice: Registered trademark of products or corporate names are used only for explanation and identification without intent of infringement. © 2019 Delve Publishing ISBN: 978-1-77361-526-4 (Hardcover) Delve Publishing publishes wide variety of books and eBooks. For more information about Delve Publishing and its products, visit our website at www.delvepublishing.com.

DECLARATION Some content or chapters in this book are open access copyright free published research work, which is published under Creative Commons License and are indicated with the citation. We are thankful to the publishers and authors of the content and chapters as without them this book wouldn’t have been possible.

ABOUT THE EDITOR

Vikas obtained his Ph.D. from King George’s Medical University, India in 2010. His interests span from bio-resources, fermentation technology to medical virology and neuroscience research. He has been involved in the research regarding neurological manifestations in various conditions, notably, HIV, malaria, Alzheimer’s and traumatic brain injury.

TABLE OF CONTENTS



List of Contributors...................................................................................... xvii



List of Abbreviations................................................................................... xxix

Preface................................................................................................... ....xxxi SECTION I INFECTION CONTROL AND USE OF ANTIBIOTICS Chapter 1

Antibiotic Control of Antibiotic Resistance in Hospitals: A Simulation Study..................................................................................... 3 Abstract...................................................................................................... 3 Background................................................................................................ 5 Methods..................................................................................................... 6 Results...................................................................................................... 10 Discussion................................................................................................ 17 Appendix: Explanation For The Differences Between Deterministic and Stochastic Simulation Results................................................... 22 Acknowledgements.................................................................................. 22 Authors’ Contributions.............................................................................. 23 References................................................................................................ 24

Chapter 2

Hospital-and Patient-Related Factors Associated With Differences in Hospital Antibiotic Use: Analysis of National Surveillance Results...................................................................................................... 25 Abstract.................................................................................................... 26 Background.............................................................................................. 27 Methods................................................................................................... 28 Results...................................................................................................... 32 Discussion................................................................................................ 37 Conclusions.............................................................................................. 41 Acknowledgements.................................................................................. 41 Authors’ Contributions.............................................................................. 41

References................................................................................................ 42 Chapter 3

Prevention and Control of Antimicrobial Resistant Healthcare-Associated Infections: The Microbiology Laboratory Rocks!.................................................................................... 47 Introdution................................................................................................ 48 Tasks of The Microbiology Laboratory....................................................... 49 Antibiotic Stewardship Programs.............................................................. 52 Issues on The Laboratory Efficacy.............................................................. 53 Conclusion............................................................................................... 58 Author Contributions................................................................................ 58 References................................................................................................ 59

Chapter 4

Global Increase and Geographic Convergence in Antibiotic Consumption Between 2000 And 2015.................................................... 67 Abstract.................................................................................................... 68 Methods................................................................................................... 69 Results...................................................................................................... 71 Discussion................................................................................................ 78 Acknowledgments.................................................................................... 83 References................................................................................................ 85

Chapter 5

Antibiotic Use and Clinical Outcomes in the Acute Setting Under Management by an Infectious Diseases Acute Physician Versus Other Clinical Teams: A Cohort Study..................................................... 89 Abstract.................................................................................................... 90 Introduction.............................................................................................. 90 Methods................................................................................................... 92 Results...................................................................................................... 96 Discussion.............................................................................................. 104 Acknowledgments.................................................................................. 107 References.............................................................................................. 108

Chapter 6

Evaluation of Risk Factors for Antibiotic Resistance In Patients With Nosocomial Infections Caused Bypseudomonas Aeruginosa............................................................................................. 111 Abstract.................................................................................................. 112 Introduction............................................................................................ 112

x

Material and Methods............................................................................. 114 Results.................................................................................................... 116 Discussion.............................................................................................. 121 Conclusion............................................................................................. 126 Authors’ Contributions............................................................................ 126 References.............................................................................................. 127 Chapter 7

How Can Vaccines Contribute to Solving the Antimicrobial Resistance Problem?............................................................................... 133 Abstract.................................................................................................. 133 Minireview............................................................................................. 134 Mechanisms by Which Existing Vaccines Can Address the Amr Problem................................................................................. 134 Prospects For Gaining Similar Benefits With New or Improved Vaccines Against Other Amr Pathogens.......................... 138 Targeting Vaccines Selectively to Resistant Clones or Directly Against Factors Mediating Resistance: A Novel Approach to Controlling AMR....................................................................... 140 Synergy Between Passive or Vaccine-Induced Antibodies and Antimicrobials in Treating or Preventing Amr Infections............................................................................... 143 Research and Policy Needs..................................................................... 144 Conclusion............................................................................................. 146 References.............................................................................................. 147 SECTION II TB INFECTION

Chapter 8

Origins of Combination Therapy For Tuberculosis: Lessons For Future Antimicrobial Development and Application.............................. 161 Abstract.................................................................................................. 161 Introduction............................................................................................ 162 History of Antibiotic Development From Penicillin to Streptomycin........ 163 History of Chemotherapeutic Development From Arsphenamine to PAS.................................................................... 166 The First Combined Antimicrobial Regimen............................................ 168 The Path to Modern Antimicrobial Therapy for Tuberculosis.................... 169 Lessons From The History of Combination Therapy................................. 171 Concluding Remarks............................................................................... 174

xi

Acknowledgments.................................................................................. 174 References.............................................................................................. 175 Chapter 9

Antibiotic Treatment For Tuberculosis Induces A Profound Dysbiosis of the Microbiome That Persists Long After Therapy Is Completed............................................................................ 183 Abstract.................................................................................................. 184 Introduction............................................................................................ 184 Results.................................................................................................... 187 Discussion.............................................................................................. 200 Methods................................................................................................. 202 Acknowledgements................................................................................ 207 References.............................................................................................. 208

Chapter 10 Genomic Insight Into Mechanisms Of Reversion Of Antibiotic Resistance In Multidrug Resistant Mycobacterium Tuberculosis Induced by a Nanomolecular Iodine-Containing Complex FS-1............. 213 Introduction............................................................................................ 214 Materials and Methods........................................................................... 217 Results.................................................................................................... 222 Discussion.............................................................................................. 228 Author Contributions.............................................................................. 230 Acknowledgments.................................................................................. 231 References.............................................................................................. 232 Chapter 11 Effector Mechanisms of Neutrophils within the Innate Immune System in Response to Mycobacterium tuberculosis Infection............................... 237 Abstract.................................................................................................. 238 Introduction............................................................................................ 239 Neutrophil Extracellular Traps And Its Effector Functions......................... 239 Cytokines Modulating The Functions Of Neutrophils.............................. 240 Neutrophils Apoptosis And Phagocytosis Induced Cell Death................. 241 Genotypic Changes Affecting Neutrophil Functions................................ 242 Neutrophil Transcriptional Changes In Chronic Diseases........................ 243 Neutrophils And Granulomatous Responses Against M. Tb Infection...... 243 Hiv Infection And Type 2 Diabetes Association With Neutrophil Immune Responses To M. Tb......................................................... 244 Future Directions: Developing Vaccines That Will Induce xii

Neutrophil-Mediated Favorable Immune Responses Against M. Tb Infection................................................................. 245 Concluding Remarks............................................................................... 245 Author Contributions.............................................................................. 246 References.............................................................................................. 247 Chapter 12 Discriminating Active Tuberculosis From Latent Tuberculosis Infection By Flow Cytometric Measurement of Cd161-Expressing T Cells....................................................................... 253 Abstract.................................................................................................. 254 Introduction............................................................................................ 254 Results.................................................................................................... 256 Discussion.............................................................................................. 262 Methods................................................................................................. 264 Acknowledgements................................................................................ 265 References.............................................................................................. 266 Chapter 13 New Approaches In The Diagnosis And Treatment Of Latent Tuberculosis Infection................... 269 Abstract.................................................................................................. 269 Introduction............................................................................................ 270 Establishment And Persistence of Latent M. Tuberculosis infection.......... 271 New Dynamic Model of Latent Tuberculosis Infection............................ 279 Diagnosis of Latent M. Tuberculosis Infection......................................... 281 Treatment of Latent M. Tuberculosis Infection......................................... 286 Future Prospects..................................................................................... 290 Conclusion............................................................................................. 291 Acknowledgements................................................................................ 292 References.............................................................................................. 293 SECTION III PNEUMONIA MANAGEMENT Chapter 14 Microbial Etiology of Pneumonia: Epidemiology, Diagnosis and Resistance Patterns.......................................................................... 317 Abstract.................................................................................................. 317 Introduction............................................................................................ 318 Microbial Etiology of Community-Acquired Pneumonia (CAP)................ 319 Microbial Etiology of Hospital Acquired Pneumonia (HAP).................... 324 xiii

Laboratory Diagnosis of Pneumonia....................................................... 329 Conclusions............................................................................................ 335 Acknowledgments.................................................................................. 335 Author Contributions.............................................................................. 335 References.............................................................................................. 336 Chapter 15 Translating Lung Microbiome Profiles Into The Next-Generation Diagnostic Gold Standard For Pneumonia: A Clinical Investigator’s Perspective...................................................... 347 Abstract.................................................................................................. 347 Scope Of The Clinical Problem............................................................... 348 Respiratory Microbiome Research In The ICU......................................... 350 Challenges on The Way To Clinical Translation........................................ 352 A Translational Roadmap Ahead............................................................. 352 Conclusions............................................................................................ 354 Acknowledgments.................................................................................. 354 References.............................................................................................. 355 Chapter 16 Etiology of Community-Acquired Pneumonia and Diagnostic Yields of Microbiological Methods: A 3-Year Prospective Study in Norway.................................................................................................. 359 Abstract.................................................................................................. 360 Background............................................................................................ 361 Methods................................................................................................. 362 Results.................................................................................................... 365 Discussion.............................................................................................. 375 Conclusions............................................................................................ 379 Acknowledgments.................................................................................. 379 Authors’ Contributions............................................................................ 379 References.............................................................................................. 380 Chapter 17 Biomarkers: A Definite Plus In Pneumonia............................................. 385 Abstract.................................................................................................. 385 Introduction............................................................................................ 386 C-Reactive Protein (CRP)........................................................................ 387 Procalcitonin (PCT)................................................................................. 389 Soluble Triggering Receptor Expressed on Myeloid Cells-1 (Strem-1)....................................................................................... 392 xiv

Other Biomarkers................................................................................... 394 Endotoxin............................................................................................... 395 Copeptin And Midregional Pro-Atrialnatriuretic Peptide (Mr-Proanp)...... 395 Cortisol................................................................................................... 396 Conclusion............................................................................................. 397 Acknowledgment.................................................................................... 397 References.............................................................................................. 398 Chapter 18 Distribution and Determinants of Pneumonia Diagnosis Using Integrated Management of Childhood Illness Guidelines: A Nationally Representative Study in Malawi...................... 407 Abstract.................................................................................................. 408 Background............................................................................................ 409 Methods................................................................................................. 410 Results.................................................................................................... 416 Discussion.............................................................................................. 422 Conclusions............................................................................................ 427 References.............................................................................................. 428 Index...................................................................................................... 435

xv

LIST OF CONTRIBUTORS Michael Haber Department of Biostatistics and Bioinformatics, Emory University School of Public Health, Atlanta, GA, USA Bruce R Levin Department of Biology, Emory University, Atlanta, GA, USA Piotr Kramarz Pfizer Ltd, Walton Oaks, U.K Current Address: the European Centre for Disease Prevention and Control (ECDC), Stockholm, Sweden Jon Birger Haug Department of Infectious Diseases, Oslo University Hospital Trust, Oslo, Norway Dag Berild Department of Infectious Diseases, Oslo University Hospital Trust, Oslo, Norway Mette Walberg Microbiology Section, Laboratory Centre, Vestre Viken Hospital Trust, Drammen, Norway Åsmund Reikvam Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Oslo, Norway Department of Pharmacology, Oslo University Hospital Trust, Oslo, Norway Alexandra S. Simões Global Health and Tropical Medicine, Instituto de Higiene e Medicina Tropical, Universidade Nova de Lisboa, Lisbon, Portugal

xvii

Isabel Couto Global Health and Tropical Medicine, Instituto de Higiene e Medicina Tropical, Universidade Nova de Lisboa, Lisbon, Portugal Cristina Toscano Laboratório de Microbiologia Clínica e Biologia Molecular, Serviço de Patologia Clínica, Hospital de Egas Moniz, Centro Hospitalar de Lisboa Ocidental, Lisbon, Portugal Centro de Estudos de Doenças Crónicas, NOVA Medical School/Faculdade de Ciências Médicas, Universidade Nova de Lisboa, Lisbon, Portugal Elsa Gonçalves Laboratório de Microbiologia Clínica e Biologia Molecular, Serviço de Patologia Clínica, Hospital de Egas Moniz, Centro Hospitalar de Lisboa Ocidental, Lisbon, Portugal Centro de Estudos de Doenças Crónicas, NOVA Medical School/Faculdade de Ciências Médicas, Universidade Nova de Lisboa, Lisbon, Portugal Pedro Póvoa Centro de Estudos de Doenças Crónicas, NOVA Medical School/Faculdade de Ciências Médicas, Universidade Nova de Lisboa, Lisbon, Portugal Unidade de Cuidados Intensivos Polivalente, Hospital de São Francisco Xavier, Centro Hospitalar de Lisboa Ocidental, Lisbon, Portugal Miguel Viveiros Global Health and Tropical Medicine, Instituto de Higiene e Medicina Tropical, Universidade Nova de Lisboa, Lisbon, Portugal Luís V. Lapão Global Health and Tropical Medicine, Instituto de Higiene e Medicina Tropical, Universidade Nova de Lisboa, Lisbon, Portugal WHO Collaborating Center for Health Workforce Policy and Planning, Instituto de Higiene e Medicina Tropical, Universidade Nova de Lisboa, Lisbon, Portugal Eili Y. Kleina Department of Emergency Medicine, Johns Hopkins School of Medicine, Baltimore, MD 21209 Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205

xviii

Thomas P. Van Boeckel Institute of Integrative Biology, ETH Zürich, CH-8006 Zürich, Switzerland Elena M. Martinez Center for Disease Dynamics, Economics & Policy, Washington, DC 20005 Suraj Pant Center for Disease Dynamics, Economics & Policy, Washington, DC 20005 Sumanth Gandra Center for Disease Dynamics, Economics & Policy, Washington, DC 20005 Simon A. Levine Princeton Environmental Institute, Princeton University, Princeton, NJ 08544 Beijer Institute of Ecological Economics, SE-104 05 Stockholm, Sweden Herman Goossens Laboratory of Medical Microbiology, Vaccine & Infectious Diseases Institute, University of Antwerp, 2610 Antwerp, Belgium Ramanan Laxminarayan Center for Disease Dynamics, Economics & Policy, Washington, DC 20005 Princeton Environmental Institute, Princeton University, Princeton, NJ 08544 Department of Global Health, University of Washington, Seattle, WA 98104 Nicola JK Fawcett Nuffield Department of Medicine, University of Oxford, Oxford, UK Nicola Jones Department of Acute/General Medicine, Oxford University Hospitals NHS Foundation Trust, Oxford, UK T Phuong Quan Nuffield Department of Medicine, NIHR Health Protection Unit in Healthcare Associated Infections and Antimicrobial Resistance, University of Oxford, NIHR Oxford Biomedical Research Centre, Oxford, UK

xix

Vikash Mistry Department of Acute/General Medicine, Oxford University Hospitals NHS Foundation Trust, Oxford, UK Derrick Crook Nuffield Department of Medicine, NIHR Health Protection Unit in Healthcare Associated Infections and Antimicrobial Resistance, University of Oxford, NIHR Oxford Biomedical Research Centre, Oxford, UK Tim Peto Nuffield Department of Medicine, NIHR Health Protection Unit in Healthcare Associated Infections and Antimicrobial Resistance, University of Oxford, NIHR Oxford Biomedical Research Centre, Oxford, UK A Sarah Walker Nuffield Department of Medicine, NIHR Health Protection Unit in Healthcare Associated Infections and Antimicrobial Resistance, University of Oxford, NIHR Oxford Biomedical Research Centre, Oxford, UK Meliha Cagla Sonmezer Department of Clinic of Infectious Diseases and Clinical Microbiology, Ankara Training and Research Hospital, Ankara, Turkey Gunay Ertem Department of Clinic of Infectious Diseases and Clinical Microbiology, Ankara Training and Research Hospital, Ankara, Turkey Fatma Sebnem Erdinc Department of Clinic of Infectious Diseases and Clinical Microbiology, Ankara Training and Research Hospital, Ankara, Turkey Esra Kaya Kilic Department of Clinic of Infectious Diseases and Clinical Microbiology, Ankara Training and Research Hospital, Ankara, Turkey Necla Tulek Department of Clinic of Infectious Diseases and Clinical Microbiology, Ankara Training and Research Hospital, Ankara, Turkey

xx

Ali Adiloglu Department of Microbiology and Clinical Microbiology, Ankara Training and Research Hospital, Ankara, Turkey Cigdem Hatipoglu Department of Clinic of Infectious Diseases and Clinical Microbiology, Ankara Training and Research Hospital, Ankara, Turkey Marc Lipsitch Center for Communicable Disease Dynamics, Department of Epidemiology and Department of Immunology and Infectious Diseases, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA George R. Siber ClearPath Vaccines, Rockville, Maryland, USA Christopher A. Kerantzas Department of Microbiology and Immunology, Albert Einstein College of Medicine, Bronx, New York, USA William R. Jacobs, Jr. Department of Microbiology and Immunology, Albert Einstein College of Medicine, Bronx, New York, USA Howard Hughes Medical Institute, Albert Einstein College of Medicine, Bronx, New York, USA Matthew F. Wipperman Immunology Program, Memorial Sloan Kettering Cancer Center, New York, New York, USA Clinical and Translational Science Center, Weill Cornell Medical College, New York, New York, USA Daniel W. Fitzgerald Weill Cornell Medical College, New York, New York, USA GHESKIO Centers, Port-au-Prince, Haiti Marc Antoine Jean Juste GHESKIO Centers, Port-au-Prince, Haiti

xxi

Ying Taur Infectious Diseases Service, Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, New York, USA Sivaranjani Namasivayam Immunobiology Section, Laboratory of Parasitic Diseases, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, Maryland, USA Alan Sher Immunobiology Section, Laboratory of Parasitic Diseases, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, Maryland, USA James M. Bean Immunology Program, Memorial Sloan Kettering Cancer Center, New York, New York, USA Weill Cornell Medical College, New York, New York, USA Vanni Bucci Department of Biology, Program in Biotechnology and Biomedical Engineering, University of Massachusetts Dartmouth, Dartmouth, Massachusetts, USA Michael S. Glickman Immunology Program, Memorial Sloan Kettering Cancer Center, New York, New York, USA Infectious Diseases Service, Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, New York, USA Weill Cornell Medical College, New York, New York, USA Aleksandr I. Ilin Scientific Center for Anti-Infectious Drugs, Almaty, Kazakhstan Murat E. Kulmanov Scientific Center for Anti-Infectious Drugs, Almaty, Kazakhstan Ilya S. Korotetskiy Scientific Center for Anti-Infectious Drugs, Almaty, Kazakhstan

xxii

Rinat A. Islamov Scientific Center for Anti-Infectious Drugs, Almaty, Kazakhstan Gulshara K. Akhmetova Scientific Center for Anti-Infectious Drugs, Almaty, Kazakhstan Marina V. Lankina Scientific Center for Anti-Infectious Drugs, Almaty, Kazakhstan Oleg N. Reva Department of Biochemistry, Centre for Bioinformatics and Computational Biology, University of Pretoria, Pretoria, South Africa Eric Warren Department of Basic Medical Sciences, College of Osteopathic Medicine of the Pacific, Western University of Health Sciences, 309 East Second Street, Pomona, CA 91766-1854, USA Garrett Teskey Graduate College of Biomedical Sciences, Western University of Health Sciences, Pomona, CA 91766-1854, USA Vishwanath Venketaraman Department of Basic Medical Sciences, College of Osteopathic Medicine of the Pacific, Western University of Health Sciences, 309 East Second Street, Pomona, CA 91766-1854, USA Graduate College of Biomedical Sciences, Western University of Health Sciences, Pomona, CA 91766-1854, USA QiantingYang Guangdong (Shenzhen) Key Laboratory for Diagnosis & Treatment of Emerging Infectious Diseases Shenzhen Key Laboratory of Infection & Immunity, Shenzhen Third People’s Hospital, Guangdong Medical College, China Qian Xu Guangdong (Shenzhen) Key Laboratory for Diagnosis & Treatment of Emerging Infectious Diseases Shenzhen Key Laboratory of Infection & Immunity, Shenzhen Third People’s Hospital, Guangdong Medical College, China xxiii

Institute of Microbiology, Chinese Academy of Sciences, China Qi Chen Shenzhen Key Laboratory of Infection & Immunity, Shenzhen Third People’s Hospital, Guangdong Medical College, China Jin Li Guangdong (Shenzhen) Key Laboratory for Diagnosis & Treatment of Emerging Infectious Diseases Shenzhen Key Laboratory of Infection & Immunity, Shenzhen Third People’s Hospital, Guangdong Medical College, China Mingxia Zhang Guangdong (Shenzhen) Key Laboratory for Diagnosis & Treatment of Emerging Infectious Diseases Shenzhen Key Laboratory of Infection & Immunity, Shenzhen Third People’s Hospital, Guangdong Medical College, China Yi Cai Guangdong (Shenzhen) Key Laboratory for Diagnosis & Treatment of Emerging Infectious Diseases Haiying Liu Institute of Pathogen Biology, Chinese Academy of Medical Sciences, China Yiping Zhou Department of Respiratory Diseases, Shenzhen Futian Hospital, China Guofang Deng Guangdong (Shenzhen) Key Laboratory for Diagnosis & Treatment of Emerging Infectious Diseases Qunyi Deng Shenzhen Key Laboratory of Infection & Immunity, Shenzhen Third People’s Hospital, Guangdong Medical College, China Boping Zhou Guangdong (Shenzhen) Key Laboratory for Diagnosis & Treatment of Emerging Infectious Diseases

xxiv

Hardy Kornfeld Department of Medicine, University of Massachusetts Medical School, USA Xinchun Chen Guangdong (Shenzhen) Key Laboratory for Diagnosis & Treatment of Emerging Infectious Diseases Shenzhen Key Laboratory of Infection & Immunity, Shenzhen Third People’s Hospital, Guangdong Medical College, China Suhail Ahmad Department of Microbiology, Faculty of Medicine, Kuwait University, Kuwait Catia Cilloniz Department of Pneumology, Institut Clinic del Tórax, Hospital Clinic of Barcelona-Institut d’Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), University of Barcelona, Ciber de Enfermedades Respiratorias (CIBERES), 08036 Barcelona, Spain Ignacio Martin-Loeches Department of Clinical Medicine, Trinity Centre for Health Sciences, Multidisciplinary Intensive Care Research Organization (MICRO), Welcome Trust-HRB Clinical Research, St James’s Hospital, St James’s University Hospital, Dublin, Ireland Carolina Garcia-Vidal Department of Infectious Diseases, Hospital Clinic of Barcelona, 08036 Barcelona, Spain Alicia San Jose Department of Pneumology, Institut Clinic del Tórax, Hospital Clinic of Barcelona-Institut d’Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), University of Barcelona, Ciber de Enfermedades Respiratorias (CIBERES), 08036 Barcelona, Spain Antoni Torres Department of Pneumology, Institut Clinic del Tórax, Hospital Clinic of Barcelona-Institut d’Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), University of Barcelona, Ciber de Enfermedades Respiratorias (CIBERES), 08036 Barcelona, Spain

xxv

Georgios D. Kitsios Division of Pulmonary, Allergy and Critical Care Medicine, Department of Medicine, University of Pittsburgh School of Medicine and University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania, USA Center for Medicine and the Microbiome, University of Pittsburgh, Pittsburgh, Pennsylvania, USA Jan C Holter Department of Internal Medicine, Vestre Viken Hospital Trust, Drammen, Norway Research Institute of Internal Medicine, Oslo University Hospital Rikshospitalet, Oslo, Norway Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Oslo, Norway Fredrik Müller Department of Microbiology, Oslo University Hospital Rikshospitalet, Oslo, Norway Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Oslo, Norway Ola Bjørang Department of Medical Microbiology, Vestre Viken Hospital Trust, Drammen, Norway Helvi H Samdal Department of Medical Microbiology, Vestre Viken Hospital Trust, Drammen, Norway Department of Microbiology, Oslo University Hospital Ullevaal, Oslo, Norway Jon B Marthinsen Department of Radiology, Vestre Viken Hospital Trust, Drammen, Norway Department of Radiology, Hospital of Southern Norway HF, Kristiansand, Norway Pål A Jenum Department of Medical Microbiology, Vestre Viken Hospital Trust, Drammen, Norway Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Oslo, Norway xxvi

Thor Ueland Research Institute of Internal Medicine, Oslo University Hospital Rikshospitalet, Oslo, Norway Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Oslo, Norway K.G. Jebsen Inflammatory Research Center, University of Oslo, Oslo, Norway Stig S Frøland Research Institute of Internal Medicine, Oslo University Hospital Rikshospitalet, Oslo, Norway Section of Clinical Immunology and Infectious Diseases, Oslo University Hospital Rikshospitalet, Oslo, Norway Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Oslo, Norway Pål Aukrust Research Institute of Internal Medicine, Oslo University Hospital Rikshospitalet, Oslo, Norway Section of Clinical Immunology and Infectious Diseases, Oslo University Hospital Rikshospitalet, Oslo, Norway Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Oslo, Norway K.G. Jebsen Inflammatory Research Center, University of Oslo, Oslo, Norway Einar Husebye Department of Internal Medicine, Vestre Viken Hospital Trust, Drammen, Norway Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Oslo, Norway Lars Heggelund Department of Internal Medicine, Vestre Viken Hospital Trust, Drammen, Norway Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Oslo, Norway Hanssa Summah Department of Pulmonary Medicine, Huadong Hospital, Fudan University School of Medicine, Shanghai 200040, China xxvii

Jie-Ming Qu Department of Pulmonary Medicine, Huadong Hospital, Fudan University School of Medicine, Shanghai 200040, China Omolara T Uwemedimo Department of Pediatrics and Occupational Medicine, Epidemiology and Prevention, Donald and Barbara Zucker School of Medicine at Hofstra/ Northwell GLOhBAL (Global Learning. Optimizing health. Building Alliances Locally), Hempstead, New York, USA Todd P Lewis Department of Global Health and Population, Harvard TH Chan School of Public Health, Boston, New York, USA Elsie A Essien GLOhBAL (Global Learning. Optimizing health. Building Alliances Locally) at Cohen, Children’s Medical Center, New Hyde Park, New York, USA Grace J Chan Department of Global Health and Population, Harvard TH Chan School of Public Health, Boston, New York, USA Humphreys Nsona Malawi Ministry of Health (IMCI), Lilongwe, Malawi Margaret E Kruk Department of Global Health and Population, Harvard TH Chan School of Public Health, Boston, New York, USA Hannah H Leslie Department of Global Health and Population, Harvard TH Chan School of Public Health, Boston, New York, USA

xxviii

LIST OF ABBREVIATIONS AFB

Acid fast bacilli

AIRR

Adjusted incidence rate ratio

ASPs

Antibiotic Stewardship Programs

ATS

American Thoracic Society

AMR

Antimicrobial resistance

BAL

Bronchoalveolar lavage

CDC’s

Centers for Disease Control and Prevention’s

CNS

Central nervous system

CLSI

Clinical and Laboratory Standards Institute

CAP

Community-acquired pneumonia

CAA

Complex of anti-tuberculosis antibiotics

CRP

C-reactive protein

DOT

Days of therapy

DTH

Delayed-type hypersensitivity

DCA

Detrended coordinate analysis

DA-HAI

Device-associated healthcare associated infections

DRG

Diagnosis-related group

DOT

Directly observed treatment

EVC

Exhaled ventilator condensate

XDR

Extensively drug resistant

FDA

Food Drug Administration

GEE

Generalized estimating equation

GDPPC

Gross domestic product per capita

HAIs

Healthcare-associated infections

HICs

High-income countries

HAART

Highly active antiretroviral therapy

HAP

Hospital-acquired nosocomial pneumonia

IDP

Infectious diseases acute physician

IMCI

Integrated Management of Childhood Illness

ICU

Intensive care unit

ICUs

Intensive care units

IGRA

Interferon Gamma Release Assay

IGRAs

Interferon-gamma Release Assays

IUAT

International Union Against Tuberculosis

IPD

Invasive pneumococcal disease

LTBI

Latent tuberculosis infection

MRC

Medical Research Council

MIC

Minimal inhibitory concentration

NHSN

National Healthcare Safety Network

OTUs

Operational taxonomic units

PBMC

Peripheral blood mononuclear cell

PCV

Pneumococcal conjugate vaccination

PCR

Polymerase chain reaction

RCTs

Randomized controlled trials

RNI

Reactive nitrogen intermediates

RSV

Respiratory syncytial virus

SPA

Service Provision Assessment

SCFAs

Short chain fatty acids

TBRU

Tri-Intuitional Tuberculosis Research Unit

VABP

Ventilated acquired bacterial pneumonia

WCC

White cell counts

WGS

Whole-genome sequencing

WHO

World Health Organization

xxx

PREFACE

Infectious diseases are caused by pathogens, that includes, bacteria, viruses, fungi and parasites. Many of these pathogens are abundantly survive on our bodies or in our surroundings, where they are present harmlessly or even beneficial to the humans. The evidence of infectious diseases have been the center of the research for greater part of 18th to 20th century. Classically, the evidence was gathered around major epidemics like cholera or tuberculosis. These epidemics also were the initiative for the first ever vaccine development by Edward Jenner for smallpox and the development of attenuated vaccine for chicken cholera caused by Pasteurella multocida. Pasteur continued to do research on infectious diseases including anthrax and rabies. In the subsequent decades Pasteur’s work was translated to the development of many live, attenuated vaccines including but not limited to measles, mumps, rubella, varicella, polio and rotavirus. In addition to epidemic outbreaks, the initial population studies were also the evidence based clinical studies that paved the way for most of these studies into the infectious diseases. The broad scope of the infectious diseases research is ranging from causative agents, diagnostic testing, clinical indicators of the disease state, therapeutic interventions and prognosis, for both short term and long term care. This book discusses the causative organism for lung diseases of tuberculosis and pneumonia, as well as the role of antibiotics in infectious diseases. This book is an effort to link the research evidence and clinical outcomes of these lung diseases and to understand the antibiotic regimen required to treat the diseases.

SECTION I INFECTION CONTROL AND USE OF ANTIBIOTICS

CHAPTER 1

ANTIBIOTIC CONTROL OF ANTIBIOTIC RESISTANCE IN HOSPITALS: A SIMULATION STUDY Michael Haber1, Bruce R Levin2 , Piotr Kramarz3,4 Department of Biostatistics and Bioinformatics, Emory University School of Public Health, Atlanta, GA, USA 2 Department of Biology, Emory University, Atlanta, GA, USA 3 Pfizer Ltd, Walton Oaks, U.K 4 Current Address: the European Centre for Disease Prevention and Control (ECDC), Stockholm, Sweden 1

ABSTRACT Background Using mathematical deterministic models of the epidemiology of hospitalacquired infections and antibiotic resistance, it has been shown that the rates Citation Michael Haber, Bruce R Levin, Piotr Kramarz, Antibiotic control of antibiotic resistance in hospitals: a simulation study, https://doi.org/10.1186/1471-2334-10-254. Copyright © Haber et al; licensee BioMed Central Ltd. 2010 This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/ licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Infectious Diseases: An Evidence based Approach

of hospital-acquired bacterial infection and frequency of antibiotic infections can be reduced by (i) restricting the admission of patients colonized with resistant bacteria, (ii) increasing the rate of turnover of patients, (iii) reducing transmission by infection control measures, and (iv) the use of second-line drugs for which there is no resistance. In an effort to explore the generality and robustness of the predictions of these deterministic models to the real world of hospitals, where there is variation in all of the factors contributing to the incidence of infection, we developed and used a stochastic model of the epidemiology of hospital-acquired infections and resistance. In our analysis of the properties of this model we give particular consideration different regimes of using second-line drugs in this process.

Methods We developed a simple model that describes the transmission of drugsensitive and drug-resistant bacteria in a small hospital. Colonized patients may be treated with a standard drug, for which there is some resistance, and with a second-line drug, for which there is no resistance. We then ran deterministic and stochastic simulation programs, based on this model, to predict the effectiveness of various treatment strategies.

Results The results of the analysis using our stochastic model support the predictions of the deterministic models; not only will the implementation of any of the above listed measures substantially reduce the incidences of hospitalacquired infections and the frequency of resistance, the effects of their implementation should be seen in months rather than the years or decades anticipated to control resistance in open communities. How effectively and how rapidly the application of second-line drugs will contribute to the decline in the frequency of resistance to the first-line drugs depends on how these drugs are administered. The earlier the switch to second-line drugs, the more effective this protocol will be. Switching to second-line drugs at random is more effective than switching after a defined period or only after there is direct evidence that the patient is colonized with bacteria resistant to the first antibiotic.

Conclusions The incidence of hospital-acquired bacterial infections and frequencies of antibiotic resistant bacteria can be markedly and rapidly reduced by different

Antibiotic Control of Antibiotic Resistance in Hospitals: A Simulation...

5

readily implemented procedures. The efficacy using second line drugs to achieve these ends depends on the protocol used for their administration. Keywords: Deterministic Model, Stochastic Simulation, Resistant Bacterium, Stochastic Version, Unconditional Probability

BACKGROUND Over the past two decades, antibiotic resistance has become an increasingly grave health problem, serious enough for some to see this not-unanticipated product of evolution as foretelling of the end of the antibiotic era [1]. Because of resistance, bacterial infections that had been readily cleared by antibiotics are lasting longer and are more likely to result in severe morbidity and mortality than they would be if these infecting bacteria were susceptible to the treating antibiotic(s). This resistance problem is particularly serious in hospitals, where patients are commonly compromised by age, illness and treatment with immune suppressing drugs. Invasive procedures and the use of life-support machinery that are likely to be infected by bacteria also contribute to antibiotic-resistant hospital infections [2]. Moreover, patients who enter hospitals for the treatment of resistant bacterial infections or acquire resistant infections while in the hospital are adding to the already too high costs of healthcare [3] and are a source of resistant bacteria and/or resistance-encoding genes. Within this pessimistic framework, however, there is an element of optimism. Hospitals are potentially containable institutions in which the use of antibiotics can be monitored and managed. Unlike the open communities it should be, and in some countries like the Netherlands [4] has been, possible to control the spread of resistance in hospitals. In accord with mathematical models [5], the frequency of colonization and infections with antibiotic-resistant bacteria in hospitals can be reduced in at least five ways by: (1) reducing the rate of use of drugs for which there is resistance, (2) improving infection control and thereby reducing transmission between patients and from hospital care workers to patients, (3) increasing the rate of turnover of patients, (4) reducing the rate of influx of patients with resistant bacteria into hospitals or their intensive care wards, and (5) using additional antibiotics for which there is no resistance [6]. This theory predicts that not

6

Infectious Diseases: An Evidence based Approach

only will these measures individually and collectively reduce the frequency of resistant bacteria in hospitals, they will also reduce the absolute rate of these infections. Moreover, and most importantly, the effects of their implementation should be manifest over a relatively short period of time. Declines in the rates of infection and frequency of resistant bacteria should be seen in months rather than the years or decades anticipated for controlling resistance in open communities. In this report, we consider the contribution of the different factors controlling the incidence of hospital-acquired infections and the spread of antibiotic resistant bacteria considered by [5], giving primary consideration to the use of second-line antibiotics for which there is no resistance. Using a deterministic and a stochastic simulation model, we explore the effects of different regimes employing these second-line antibiotics to successfully treat patients and, at the same time, reduce the frequency of resistance to other drugs. Treatment is initiated with the first-line antibiotic, for which there is resistant bacteria. Patients may be switched to the second-line drug in one of three ways: (i) at random, with a constant probability of switching each day, (ii) after a defined term with the first-line drug, and (iii) directed, where the patient remains on the first-line drug until testing provides evidence that she/he is colonized with bacteria resistant to that drug.

METHODS The Basic Model The model we develop here is an extension of the model presented in a previous publication [5]. The formats of the deterministic and stochastic versions of this model are the same. We assume the hospital is a closed environment into which patients enter and leave. Within the hospital, patients are of different states with respect to colonization and treatment with one of two drugs for the target bacteria (e.g. Staphylococcus or Enterococcus). Patients are either uncolonized, U, colonized with bacteria susceptible to both drugs, S, or colonized with bacteria that are resistant to drug 1 but susceptible to drug 2, R. In this basic model, we assume there is no resistance to the second drug (see the Discussion). On any given day, colonized patients carrying the S bacteria are of three states with respect to treatment, S 0 untreated, S 1 treated

Antibiotic Control of Antibiotic Resistance in Hospitals: A Simulation...

7

with drug 1, S 2 treated with drug 2. Patients colonized with bacteria resistant to drug 1 are of the three corresponding states with respect to treatment with drugs 1 and 2: R 0 , R 1 and R 2 . The letters, U, S, S 1 , ..., are both the designations of these patient states and their numbers (proportions) in the hospital. See Figure 1 for a diagram of this model. The parameters and variables in this basic model are separately defined in Table 1. Table 1: Parameters and their default values Symbol

Description

Value in Simulations

eS

Proportion colonized with sensitive bacteria among patients entering the hospital

0.40

eR

Proportion colonized with resistant bacteria among patients entering the hospital

0.0 - 0.2

β S0

Transmission rate of sensitive bacteria from an untreated patient

0.007

β S1

Transmission rate of sensitive bacteria from a patient treated with drug 1

0.007

β S2

Transmission rate of sensitive bacteria from a patient treated with drug 2

0.007

β R0

Transmission rate of resistant bacteria from an untreated patient

0.007

β R1

Transmission rate of resistant bacteria from a patient treated with drug 1

0.007

β R2

Transmission rate of resistant bacteria from a patient treated with drug 2

0.007

fS

Rate of initiating treatment with drug 1 for a patient in

varies

fR

Rate of initiating treatment with drug 1 for a patient in

varies

sw S

Rate of switching to drug 2 for a patient in

varies

sw R

Rate of switching to drug 2 for a patient in

varies

x

Rate of clearance for patients who are not treated or are treated with an ineffective drug

0.10

v

Additional rate of clearance for patients who are treated with an effective drug

0.50

cU

Rate of exiting hospital for patients in U.

0.20

CS

Rate of exiting hospital for patients in S.

0.10

CR

Rate of exiting hospital for patients in R.

0.10

All the rates are daily rates.

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Infectious Diseases: An Evidence based Approach

Figure 1: Basic hospital model. See the text and Table 1 for the definitions of the parameters and variables and a description of the model and the assumptions behind its construction.

Untreated patients may begin treatment with antibiotic 1 at rates f S , and f R so that on any given day f S ·S 0 and f R ·R 0 untreated patients colonized with bacteria susceptible and resistant to antibiotic 1 begin treatment with that antibiotic and enter the S1 and R1, states respectively. In this basic model, we assume that the rate of treatment of colonized patients is independent of whether they are colonized with sensitive or resistant bacteria, and that antibiotic 2 is a second-line drug which is not used upon first treatment. Colonized patients treated with antibiotic 1, S1 and R1 however, can be switched to antibiotic 2, at rate swS and swR per day. Once a patient is treated with drug 2, treatment will go on until the patient clears the bacteria or until she/he is discharged from the hospital. During their tenure in the hospital, uncolonized patients of the U state may become colonized at rates proportional to the numbers of colonized individuals of the different states. Once colonized, patients enter the untreated class of that state. For example, during the course of a day, (β S0 ·S + β S1 ·S 1 + β S2 ·S 2 )·Uuncolonized patients will be colonized with bacteria 0 that are susceptible to drug 1 and enter the untreated S0 state, and (β R0 ·R + β R1 ·R 1 + β R2 ·R 2 )·U will be colonized by bacteria resistant to drug 1 0 and enter the R0 state. The parameters, βS0 , β S1 , β S2 , β R0 , β R1 , and β R2 are rates of infectious transmission [7] for patients of different states. In this basic model, we assume that patients colonized with susceptible or resistant

Antibiotic Control of Antibiotic Resistance in Hospitals: A Simulation...

9

bacteria, S or R, have to be cleared (enter the U state) before bacteria of the other kind can re-colonize them. Patients may be cleared of the colonized bacteria, either spontaneously or through successful treatment, and enter the U state. The rates of spontaneous and antibiotic-mediated clearance are x and v, respectively. Patients colonized with bacteria resistant to antibiotic and treated with that antibiotic, R 1 , only clear those bacteria spontaneously.

New patients entering the hospital may enter into the states U, S 0 or R 0 at rates eU , e S and e R , respectively. We assume that individuals are not treated before they enter the hospital. Hospitalized patients may be discharged at rates that depend on their colonization status and treatment. The discharge rates are denoted by c Xwhere X denotes the state is which the patient is just prior to discharge. The entrance and discharge rates are chosen so that, on average, the daily number of entering patients equals to the daily number of patients who are discharged.

The Deterministic Model The rates of change in the numbers of the different patient states are given by a set of coupled differential equations. For the numerical analysis of this deterministic model we use Berkeley Madonna™. Copies of the program used can be obtained from http://www.eclf.net/programs.

The Stochastic Model The main difference between the deterministic and stochastic version of this model are that the former considers the transitions at the population level. For example, in the deterministic model on any given day precisely S 0 ·x untreated patients colonized with susceptible bacteria enter the U state. The stochastic version of the model, on the other hand, keeps track of individual patients and in this example, the parameter x is the daily transition probability for an individual patient rather than a rate for the population at large Thus, a patient of the S0 state has a probability of x per day of being spontaneously cleared of the infection. The stochastic version of this model enables us to more precisely simulate processes that include several steps and to investigate the efficacy of different regimes of treatment and switching drugs, e.g. the effectiveness of switching a patient from drug 1 to drug 2 after a fixed number of days of treatment with drug 1 rather than switching at the same rate per day. The stochastic model can also be used to estimate quantities that are related to periods longer than one day. For

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Infectious Diseases: An Evidence based Approach

example, the total number of days a patient was colonized with the resistant bacteria. For the stochastic simulations we use a Monte Carlo protocol where each day we calculate the probability that each patient will move from her/ is current state to each of the other states, or will stay at the same stage. A pseudo random number between 0 and 1 from a uniform distribution is generated and that patient’s new state is determined by comparing this number with the transition probabilities. For example if the random number y is less or equal to x, then a colonized patient will enter the U state that day. If y > x, the patient will remain colonized. Once this has been done for all the patients, the simulation program determines and stores the total number of patients in each state on that day. This simulation program runs for a given number of days (currently 360 days). The outcome for one simulation is the average number of patients in each state over the last 100 days. This entire process is repeated many times (currently 200 times) and the final outcome is the average proportion (over the 200 simulations) of patients uncolonized, colonized with susceptible bacteria and colonized with resistant bacteria. It should be noted that because of their structural differences, and primarily the sequential order of events occurring on the same day (see Appendix), there are often modest quantitative differences between the outcomes predicted by the deterministic model and the average results of the stochastic simulations. This Monte Carlo simulation was programmed in Fortran and can be obtained from [email protected].

RESULTS Deterministic Simulations In an effort to orient the reader on the properties of the basic model and its general predictions, we use numerical solutions to the deterministic version of the model. The values of the parameters used for the numerical solutions were chosen to illustrate the properties of this model. Although we believe they are in a realistic range, they do not reflect estimates made in a specific hospital or treatment situation. In Figure 2(a), we illustrate what we would obtain with and without treatment in the absence of resistance. As long as there is an input of susceptible patients (e S > 0) there will be a stable equilibrium with U and S patients present. As noted in [5], the frequency of colonized patients will depend on the total density of patients in the hospital, the rates at which S 0 and S 1patients are cleared and enter the U state or

Antibiotic Control of Antibiotic Resistance in Hospitals: A Simulation...

11

leave the hospital, their rates of infectious transmission, and rates of input of patients carrying susceptible bacteria.

Figure 2: Deterministic model, numerical solutions: Change in the number of colonized patients or the frequency of resistance for different treatment regimes. (a) Change in the equilibrium frequency of patients colonized with susceptible bacteria for different frequencies of treatment with antibiotic 1. No resistance, and of the patients entering the hospital 60% are uncolonized and 40% are colonized with bacteria susceptible to antibiotic 1. The different curves correspond to different treatment rates which are in parentheses. Clearance rates with and without treatment are × = 0.10, v = 0.50, respectively, all colonized patients leave at the same rate, cS = cR = 0.10, and uncolonized patients leave the hospital at twice that rate, cU = 0.20. (b) Change in the number of patients colonized with bacteria resistant to antibiotic 1 with different frequencies of treatment with antibiotic 1. Parameters are the same as those in (a) but at the start of the simulation 10% of the patients in the hospital are colonized with resistant bacteria. (c) Change in the number of patients colonized with bacteria resistant to antibiotic 1 with different rates of switching to antibiotic 2. 40% of colonized patients are treated with antibiotic 1, fs = fr = 0.40 and antibiotic 1 and antibiotic 2 are equally effective on bacteria that are susceptible to their action. No input of resistant bacteria. Other parameters are the same as in previous figures. (d) Change in the number of patients colonized with bacteria resistant to antibiotic 1 with different rates of switching to antibiotic 2. 10% of the patients entering the hospital carry bacteria resistant to antibiotic 1, 40% are colonized

12

Infectious Diseases: An Evidence based Approach

with bacteria that are susceptible to antibiotic 1, and 50% are uncolonized. Other parameters are the same as in (c).

Our model assumes that treatment augments the rate of clearance of bacteria from colonized patients. Hence, in the absence of resistance the equilibrium frequency of colonized patients will decline with the rate at which they are treated. If patients carrying resistant bacteria never enter the hospital (e R = 0) and resistant bacteria are not at a transmission and/ or removal rate disadvantage (β S = β R and c S = c R ), and in the absence of treatment, the frequency of hospitalized patients colonized with resistant bacteria will decline and the resistant bacteria will be eliminated (Figure 2b). If antibiotic 1 is used alone then there would be a threshold rate of use below which resistance will not ascend and above which the frequency of patients colonized with bacteria resistant to antibiotic 1 will increase. However even under these conditions, as long as patients with susceptible bacteria continue to enter the hospital there will be a polymorphism with uncolonized patients, patients colonized with bacteria resistant to drug 1, and patients colonized with bacteria susceptible to drug 1. The frequency of patients with bacteria resistant to drug 1 will be proportional to the rate at which this antibiotic is used. In Figure 2c we consider the effects of switching to a second antibiotic for which there is no resistance on the frequency of patients colonized with bacteria resistant to antibiotic 1. The greater the rate of switching, the greater the rate and extent of reduction in the frequency of resistance to this first antibiotic. As can be seen in Figure 2d, this optimistic picture of being able to control the frequency and even eliminate resistance to drug 1 by using a second drug for which there is no resistance is thwarted if patients entering the hospital carry bacteria resistant to drug 1. Switching to the second drug is still effective in reducing the frequency of resistance to the first drug, but far less so than in the absence of input of patients colonized with bacteria resistant to the first drug.

Stochastic Simulations The results of the deterministic simulation, like those of the model from whence it was derived [5], illustrates in a general way how switching hospitalized patients colonized with bacteria resistant to one antibiotic to a

Antibiotic Control of Antibiotic Resistance in Hospitals: A Simulation...

13

second one to which there is no resistance can reduce the overall frequency of resistance to the first drug. In the following, we use the stochastic version of this model to evaluate the relative efficacies of three protocols for switching patients to reducing the frequency of resistance to that first drug. (1) Random switching: for each patient there is a constant daily probability of switching to a second drug. In essence, this is a stochastic version of the switching process considered in the deterministic model. (2) Defined term switching: after a pre-determined number of days of treatment with the first drug, the patient is switched to the second drug. (3) Directed switching: The patient remains on the first drug until testing indicates that the bacteria colonizing that individual is resistant to that drug, at which time that patient is switched to the second drug. A list of all the parameters in our model and the values assigned to those parameters that remain fixed are presented in Table 1. Whenever possible the values of the parameters used in the stochastic simulation are the same as those in the deterministic simulations. For all situations we assume, probably realistically, that treatment is initiated before information is available about the resistance status of the colonizing bacteria. In other words, f S = f R .

Random Switching In Figure 3a we consider the relationship between the rate of use of drug 1 and the fraction of the population colonized by bacteria in the absence of resistance. The initial conditions and parameter values in this stochastic model are identical to those in the corresponding deterministic simulations presented in Figure 2. Except for small quantitative differences due to the reasons considered in the Appendix, the results of these stochastic simulations are the same as those of the corresponding deterministic simulations

14

Infectious Diseases: An Evidence based Approach

Figure 3: Stochastic simulations: (a) The effect of different rates of treatment with antibiotic 1 on the frequency of colonized patients in the absence of resistance. (b) The effects of different rates of treatment with antibiotic 1 on the frequency of patients colonized with bacteria resistant to this antibiotic without the use of antibiotic 2. Initially the frequency of patients colonized with bacteria resistant to antibiotic 1 is 10% and no patients enter the hospital carrying resistant bacteria. (c) The effect of different rates of random switching to a second drug on the frequency of resistance to the first antibiotic. Initially 10% of the patients are colonized with bacteria resistant to drug 1 and 40% of colonized patients are treated with drug 1, f = 0.40. No patients carrying bacteria resistant to drug 1 enter the hospital. (d) The effect of different rates of random switching to a second drug on the frequency of resistance to the first antibiotic. The parameters are the same as those in (c) save for the 10% of the patients entering the hospital carrying bacteria resistant to drug 1.

In the absence of resistance, antibiotic treatment will reduce the frequency of colonized patients at a rate proportional to the rate of use (Figure 3a). If the hospital includes patients colonized with bacteria resistant to the antibiotic, the frequency of resistance can increase. As noted with the deterministic model (Figure 2b) the stochastic model predicts a threshold rate of antibiotic use below which resistance will not ascend and above which it will (Figure 3b). In the absence of input of patients colonized with

Antibiotic Control of Antibiotic Resistance in Hospitals: A Simulation...

15

bacteria resistant to antibiotic 1, random switching to a second antibiotic for which there is no resistance can reduce the frequency of patients colonized with bacteria resistant to the first antibiotic, and with a high enough rate of switching this procedure can eliminate resistant bacteria from the hospital (Figure 3c). With the input of patients carrying resistant bacteria however, switching to this second antibiotic can reduce the frequency of resistance but not eliminate resistance from the hospital (Figure 3d).

The Effects of Different Treatment Regimes For the comparison of the impact of different regimes for employing the second-line antibiotic on the total frequency of colonized patients and the frequency of patients colonized with bacteria resistant to the first drug, we consider these frequencies after one year from the initiation of treatment. As in Figures 3, within 100 days these frequencies remain relatively constant. In the absence of patients carrying resistant bacteria entering the hospital, the more rapidly patients are switched to the second drug, the fewer patients are colonized with bacteria, although there is little difference in this frequency if switching occurs within the first five days (Figure 4a). With respect to the fraction of patients colonized, save for late (10 days) switching, there is little difference in whether switching occurs at random, or according to other schemes. While switching to the second drug can clear the hospital of patients carrying resistant bacteria, it is less effective in doing so as the time before switching increases. Moreover, even with delayed switching, the random switching protocol is more effective in reducing the frequency of patients with bacteria resistant to the first drug than directed or defined period switching. The reason for this is that under random switching, there is a good chance that switching will actually occur earlier than the mean time to switching. For example, if the mean time to switching is 5 days then under random switching there is a chance of 59% that the patient will be switched during one of the first 4 days following onset of colonization and will clear infection soon after this. Under directed and defined switching, a patient colonized with bacteria resistant to drug 1 must wait 5 days until s/ he is switched to drug 2, therefore this patient will, on average, stay longer in the ‘resistant’ state.

16

Infectious Diseases: An Evidence based Approach

Figure 4: Stochastic simulations. Effects of different second line drug treatment regimes on the frequency of patients colonized with bacteria and the fraction colonize with bacteria that are resistant to the first line drug. Mean fractions for 200 runs after 1 year under that treatment regime. (a, b) No input of patients with resistant bacteria. (c,d) 10% of the patients entering the hospital carry bacteria resistant to the first-line antibiotic. Save for those related to switching, the values of the parameters of these simulations are the same as those in Figures 2 and 3. The standard errors were less than 1% of the mean for all sets of parameters.

When patients with resistant bacteria enter the hospital, there is a modest increase in the frequency of colonized patients in the hospital relative to that without resistance entering. However, in this situation early switching to the second-line drug has a relatively greater effect on reducing the frequency of colonized patients (Figure 4c). While it’s no longer possible to clear the hospital of patients with bacteria resistant to the first antibiotic, switching to an antibiotic for which there is no resistance reduces the frequency of patients with resistant bacteria. The more rapidly the switching occurs the lower the frequency of patients with resistant bacteria. Once again, random switching is more effective in this regard than directed or defined switching. The reason for this is the same as that described above.

Antibiotic Control of Antibiotic Resistance in Hospitals: A Simulation...

17

DISCUSSION Hospital-acquired infections are a major source of morbidity and mortality in the developed as well as the underdeveloped world, and a significant contributor to the ever-increasing costs of health care. Extrapolating from the data presented in the recent report of the Pennsylvania Health Care Cost Containment Council [8] to the United States at large, under the assumption of the same rates and costs (Pennsylvania is 4.2% of the USA population), we concluded that in 2005 there were more than 456,000 hospital-acquired infections in the USA. The frequencies of mortality of patients with and without these infections were 12.9% and 2.3%, respectively. In other words, in 2005 hospital-acquired infections contributed to approximately 48,000 excess deaths in the US. The term of hospitalization of patients with and without hospital-acquired infections were 20.6 and 4.5 days, respectively, for an excess cost of hospitalization due to hospital-acquired infections in the US at large being on the order of $70 billion in 2005. Needless to say, anything that can be done to reduce the incidence of hospital-acquired infections would be valuable from all perspectives. There is no reason or evidence to suggest that in general the hospital infection problem has abated in the past five years. There is also no reason to assume that unless a concerted effort is made to address this problem that the incidence of hospital-acquired infections and the human and economic costs they engender will wane in the future. There are, however, compelling reasons to believe that unless this effort is made, nosocomial infection will become increasingly difficult to deal with [9]. In this report we have focused primarily on the effects of switching to second-line antibiotics to reduce the incidence and term of hospital-acquired infections, especially infections cased by drug-resistant bacteria. However, before expanding on this pharmaceutical solution to a pharmaceutical problem, we believe it is essential ethically, as well as practically, to illustrate and emphasize the use of protocols that reduce rather than increase the use of drugs to deal with not only the resistance problem, but also to reduce incidence of hospital-acquired infections at large. As noted in our introduction, the antecedent of the model used here [5], as well as this model, predict that the same changes in hospital practices that reduce resistance will also reduce the incidence of hospital-acquired infections in general, a win-win situation. To illustrate this we use the deterministic version of the model in Figure 1 but allow for only three classes of patients, Uncolonized (U), Colonized with susceptible and untreated (S0) and colonized with

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Infectious Diseases: An Evidence based Approach

susceptible and treated with antibiotic 1 (S1). In Table 2 we present the effects of changing specific parameters, one at a time, on the frequency of colonized patients at equilibrium. Table 2: Equilibrium fraction of infected patients with different interventions Intervention (changes in hospital protocol)

Percent of Infected Patients at Equilibrium

Standard Parameters*

52.8

Reduce transmission (βS0) by a factor of two

37.3

Reduce the term of stay of uncolonized patients (1/CU) by a factor of two

60.8

Reduce the term of stay of infected patients (1/CS0) by a factor of two

44.7

Reduce the input of colonized patients entering the hospital (eS) by a factor of two

43.3

Treat 50% of colonized patients with an antibiotic (f = 0.5) - no resistance

21.4

* Standard parameters: βS0 = 2 × 10-3, CU = 0.10, CS0 = 0.05, eS = 0.40, eU = 0.20 x = 0.10 (clearance rate in the absence of treatment - 10 days), v = 0.3333 (clearance rate with treatment - 3 days), βS1 = 2 × 10-3 (transmission rate of treated patients with susceptible bacteria). Total and sustained number of patients N = 100. Note: A number of these parameters are different than those used in simulations presented in the body of this report. Save for reducing the term of stay of uncolonized patients and thereby increasing the fraction of infected patients, all of these interventions reduce the absolute number of infected patients and all but one of these measures does not involve an increase in the use of antibiotics, just the opposite. The absolute magnitude of the effects of these different interventions on the frequency of infected patients depends, of course, on the values of the parameters. We chose these values for illustration rather than on the basis of estimates in real hospital and they are, we would like to think, an extreme on the negative side. At qualitative level, however, these effects of different interventions on the relative frequency of infected patients are, we believe, accurate for parameters in a realistic range. Unfortunately, situations like that considered above, where all bacteria responsible for nosocomial infections are susceptible to the firstline antibiotics traditionally used for treatment are unlikely to be met in many hospitals. The reality that has to be dealt with is how to reduce the

Antibiotic Control of Antibiotic Resistance in Hospitals: A Simulation...

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morbidity and mortality of individual patients who are likely to be infected with bacteria resistant to first-line antibiotics and the frequency of infected patients in the hospital at large. As illustrated in Table 2, even with high frequencies of patients infected with antibiotic resistant bacteria, goals can be achieved without the use of second-line antibiotics by: (i) improving infection control measures, like promoting hand washing and the more effective decontamination of equipment and surfaces and medical devices like catheters and respirators, (ii) quarantining or otherwise preventing patients already colonized with these bacteria from entering intensive care wards or areas of hospitals where patients are particularly prone to bacterial infections and are most likely to manifest serious symptoms, and (iii) increasing the rate of discharge of colonized and infected patients. How many of these interventions can be implemented and the extent to which they can be implemented remains to be seen. It is, however, certain that substantial improvements can be made. This is very clear from the Pennsylvania experience [8]: the incidence of infections varies considerably among hospitals. To be sure, some this variation can be attributed to the variation in the patient population, but not all of it. With respect to the contribution of second-line drugs to reducing the incidence of infections with bacteria resistant to the first-line drugs, the results of the present investigation with a stochastic simulation model are, as would be anticipated, qualitatively consistent with those anticipated from the analysis of the deterministic model [5]. As long as the bacteria remain susceptible to these second-line drugs, switching to those drugs can reduce the frequency of colonized patients at large and the frequency of patients carrying bacteria resistant to these agents. In the absence of patients carrying resistant bacteria entering the hospital, switching to the second-line drug can not only reduce the frequency of patients with bacteria resistant to the firstline drug but actually eliminate those bacteria. If patients carrying resistant bacteria enter the hospital, then the elimination of resistance cannot be achieved and it will not even be possible to reduce the incidence to that which is coming in. However, switching to the second-line drug can still reduce the frequency of patients carrying bacteria resistant to the first-line drug. Whether resistance enters or not, the earlier the switching is done the lower the frequency of patients carrying resistant bacteria. Our results suggest no difference in the contribution of switching to second-line drugs to reducing the frequency of resistance to the first-line

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Infectious Diseases: An Evidence based Approach

drug for two of the three switching regimes considered: defined term and directed switching. In the former regime switching occurs after a specified number of days while under the latter regime patients are switched after being identified as carrying bacteria resistant to the first-line drug. Overall, the third regime considered here, namely stochastic (random) switching, appeared to be the most effective of the three. In this regime, there is a constant daily probability of switching. The reason why random switching is more effective than defined-term switching can be seen in Figure 4b and 4d. The efficacy of switching declines with the time before switching occurs, with early switching contributing more than later switching. With stochastic switching, although the average time to switching may equal to a given number of days, actual switching is more likely to occur earlier than the average time because the distribution of time to switching is very skewed. Since early switching is more effective than late switching, stochastic switching is more effective in reducing resistance than defined term switching when the average number of days to switching are identical in both regimes. In our model, directed switching does not turn out to do better than the other two mechanisms because we did not take costs into account. In general second-line drugs are more expensive than the first-line drugs. Under random and defined switching, many patients who are colonized by bacteria sensitive to the first drug would be unnecessarily switched to the second drug. On the other hand, under directed switching only patients colonized by the resistant bacteria are switched, i.e., the second drug is used only when it is needed. Thus, using a second drug when it is not needed will usually increase the cost of treatment and the likelihood of resistance to that drug. Although much of treatment failure may be due to factors other than inherited antibiotic resistance [10], some failure is indeed due to bacteria being inherently resistant to the treating drug, which should be the primary reason for switching. A thorough analysis of all the costs and benefits is needed in order to decide which of the switching modes is the most costeffective. In this model, we have assumed there is no resistance to the second line drug. Clearly if there is resistance to this drug, its efficacy in controlling the incidence of infections and reducing the incidence of treatment failure due to resistance to first-line drug would be compromised. As the frequency of resistance to this second line drug increases, as it almost certainly will with increasing use of that antibiotic, that proposed switching strategies could lose their effectiveness. Saying this in another way, the use of a second line

Antibiotic Control of Antibiotic Resistance in Hospitals: A Simulation...

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drug to deal with resistance to first line antibiotics is only a transient solution to this problem. We could expand our model to include third and fourthline drugs, as well as multiple switching strategies, but we believe that our conclusions would not be very different. We certainly do not endorse practices that endanger the health and well-being of individual patients by withholding antibiotics because of concerns about resistance. On the other hand, we believe that infection control preventing colonization with pathogenic bacteria remains the most long-term effective measure to reduce both the incidence of hospital-acquired infections in general and the spread of resistance to the antibiotics used for treatment and prophylaxis. How good are the theoretical predictions about the consequences of different interventions on the incidence of hospital-acquired bacterial infections and resistance to first-line antibiotics? Although the stochastic version of this model is more realistic than the deterministic, both models can be described as simplistic caricatures of the complexities of hospitals. Although the parameter values used for the numerical analyses of the properties of these models may be in a ‘realistic range’, they are not estimates obtained from real hospitals and even if they were, they would only represent a small subset of the vast numbers of parameters that govern the dynamics of infectious disease transmission and treatment in hospital settings. While we appreciate and accept the limitations of these models and our analysis of their properties, we believe that at more qualitative rather than quantitative level the results and interpretations we make in this report are correct. In summary, the number of hospital-acquired infections, the excess mortality and costs of these infections, as well as the spread of antibiotic resistance in hospitals can be significantly reduced by: (i) controlling the entry of patients colonized with bacteria (and other microparasites) that can be transmitted within a hospital into the main wards and particularly in the intensive care units, (ii) more intense and strictly enforced measures to reduce transmission of microbes between patients, from health care workers and from catheters and mechanical devices, (iii) reducing the term of hospitalization of infected patients, and (iv) switching to first and secondline drugs for which there is little or no resistance. The strategies for the application of each of these interventions can be, and we of course believe should be, examined with realistic mathematical and computer simulation models analyzed using parameters estimated in hospitals. As well illustrated by the studies [11, 12], what may seem like a good strategy from purely intuitive arguments, like cycling antibiotics, may well not be the optimal.

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Infectious Diseases: An Evidence based Approach

APPENDIX: EXPLANATION FOR THE DIFFERENCES BETWEEN DETERMINISTIC AND STOCHASTIC SIMULATION RESULTS The difference between simulation results obtained from the two methods results from the way they deal with multiple events occurring in the same time interval, which is one day in our case. The transition rates used in deterministic model correspond to probabilities that ignore the possibility of two or more events occurring during the same time interval. Hence these rates correspond to unconditional probabilities. In the stochastic model the probability of an event depends on other events that have happened during the same time interval, hence this model deals with conditional probabilities. For example, consider an uncolonized patient. There are two things that may happen to this patient on a given day. He may exit the hospital (denote this event by E), and he may get colonized (event C). Suppose that the probabilities of E and C are 0.2 and 0.6, respectively. These probabilities determine the transition rates in the deterministic model. The stochastic model, on the other hand, argues that if a patient exits the hospital he cannot be become colonized (and even if he becomes colonized he cannot infect other patients). Thus, the stochastic model considers 0.6 as the conditional probability of the patient becoming colonized if he stays in the hospital, i.e., if the event E has not occurred on the same day. We denote by P(C) the unconditional probability of the event C, and by P(C | E) the conditional probability of the event C when it is known that the event E has occurred. We also denote by Ē the event ‹E has not occurred›, and by P(C | Ē) the probability that C has occurred when it is known that E has not occurred. Then a well-know probability theorem relates the conditional and unconditional probabilities as follows: P(C) = P(C | E) × P(E) + P(C | Ē) × P(Ē). In out example, P(C | E) = 0, P(E) = 0.2, P(C | Ē) = 0.6, P(Ē) = 0.8. Hence the unconditional probability in the stochastic model is 0 × 0.2 × 0.6 × 0.8 = 0.48, rather than the 0.6 used by the deterministic model.

ACKNOWLEDGEMENTS The authors wish to thank the Editor and three reviewers for their helpful comments. This project was partially supported by grants AI40662 and GM091875 from the US National Institutes of Health (BRL). Pfizer Inc.

Antibiotic Control of Antibiotic Resistance in Hospitals: A Simulation...

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also generously provided financial support for this project without imposing review or any other constraints that could possibly be construed as a conflict of interest.

AUTHORS’ CONTRIBUTIONS BRL and MH wrote the manuscript and conducted the deterministic and stochastic simulations, respectively. PK helped with important comments related to the administration of antibiotic-resistant drugs. All authors read and approved the final manuscript.

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Infectious Diseases: An Evidence based Approach

REFERENCES 1.

Levy SB, Marshall B: Antibacterial resistance worldwide: causes, challenges and responses. Nat Med. 2004, 10: S122-S129. 10.1038/ nm1145. 2. Diekema DJ, BootsMiller BJ, Vaughn TE, Woolson RF, Yankey JW, Ernst EJ, Flach SD, Ward MM, Franciscus CL, Pfaller MA, Doebbeling BN: Antimicrobial resistance trends and outbreak frequency in United States hospitals. Clin Infect Dis. 2004, 38: 78-85. 10.1086/380457. 3. Rubin RJ, Harrington CA, Poon A, Dietrich K, Greene JA, Moiduddin A: The economic impact of Staphylococcus aureus infection in New York City hospitals. Emerg Infect Dis. 1999, 5: 9-17. 10.3201/ eid0501.990102. 4. EARSS: Annual Reports. 2004, [http://www.rivm.nl/earss/result/ Monitoring_reports/#tcm:61-25397] 5. Lipsitch M, Bergstrom CT, Levin BR: The epidemiology of antibiotic resistance in hospitals: paradoxes and prescriptions. Proc Natl Acad Sci USA. 2000, 97: 1938-1943. 10.1073/pnas.97.4.1938. 6. Karchmer TB, Durbin LJ, Simonton BM, Farr MB: Cost-effectiveness of active surveillance cultures and contact/droplet precautions for control of methicillin-resistant Staphylococcus aureus. J Hosp Infect. 2002, 51: 126-132. 10.1053/jhin.2002.1200. 7. Anderson RM, May RM: Infectious Diseases of Humans: Dynamics and Control. 1991, Oxford: Oxford University Press 8. PHC4. Hospital Acquired Infections in Pennsylvania. 2006, [http:// www.phc4.org/reports/hai/05/docs/hai2005report.pdf] 9. Torres C: Up against the wall. Nat Med. 2010, 16: 628-631. 10.1038/ nm0610-628. 10. Levin BR, Rozen DE: Non-inherited antibiotic resistance. Nat Rev Microbiol. 2004, 4: 556-562. 10.1038/nrmicro1445. 11. Bergstrom CT, Lo M, Lipsitch M: Ecological theory suggests that antimicrobial cycling will not reduce antimicrobial resistance in hospitals. Proc Natl Acad Sci USA. 2004, 101: 13285-13290. 10.1073/ pnas.0402298101. 12. Bonhoeffer S, Lipsitch M, Levin B: Evaluating treatment protocols to prevent antibiotic resistance. Proc Natl Acad Sci USA. 1997, 94: 12106-12111. 10.1073/pnas.94.22.12106.

CHAPTER 2

HOSPITAL- AND PATIENTRELATED FACTORS ASSOCIATED WITH DIFFERENCES IN HOSPITAL ANTIBIOTIC USE: ANALYSIS OF NATIONAL SURVEILLANCE RESULTS Jon Birger Haug1, Dag Berild1 , Mette Walberg2 and Åsmund Reikvam3,4 1

Department of Infectious Diseases, Oslo University Hospital Trust, Oslo, Norway

2

Microbiology Section, Laboratory Centre, Vestre Viken Hospital Trust, Drammen, Norway

3

Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Oslo, Norway

4

Department of Pharmacology, Oslo University Hospital Trust, Oslo, Norway

Citation Jon Birger Haug, Dag Berild, Mette Walberg and Åsmund Reikvam, Hospitaland patient-related factors associated with differences in hospital antibiotic use: analysis of national surveillance results, DOI 10.1186/s13756-014-0040-5. Copyright © Haug et al.; licensee BioMed Central. 2014 This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/ licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

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Infectious Diseases: An Evidence based Approach

ABSTRACT Background Surveillance data of antibiotic use are increasingly being used for benchmarking purposes, but there is a lack of studies dealing with how hospital- and patient-related factors affect antibiotic utilization in hospitals. Our objective was to identify factors that may contribute to differences in antibiotic use.

Methods Based on pharmacy sales data (2006–2011), use of all antibiotics, all penicillins, and broad-spectrum antibiotics was analysed in 22 Health Enterprises (HEs). Antibiotic utilization was measured in World Health Organisation defined daily doses (DDDs) and hospital-adjusted (ha)DDDs, each related to the number of bed days (BDs) and the number of discharges. For each HE, all clinical specialties were included and the aggregated data at the HE level constituted the basis for the analyses. Fourteen variables potentially associated with the observed antibiotic use – extracted from validated national databases – were examined in 12 multiple linear regression models, with four different measurement units: DDD/100 BDs, DDD/100 discharges, haDDD/100 BDs and haDDD/100 discharges.

Results Six variables were independently associated with antibiotic use, but with a variable pattern depending on the regression model. High levels of nurse staffing, high proportions of short (10 days) hospital stays, infectious diseases being the main ICD-10 diagnostic codes, and surgical diagnosis-related groups were correlated with a high use of all antibiotics. University affiliated HEs had a lower level of antibiotic utilization than other institutions in eight of the 12 models, and carried a high explanatory strength. The use of broad-spectrum antibiotics correlated strongly with short and long hospital stays. There was a residual variance (30%–50% for all antibiotics; 60%–70% for broad-spectrum antibiotics) that our analysis did not explain.

Hospital- and Patient-Related factors Associated with....

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Conclusions The factors associated with hospital antibiotic use were mostly nonmodifiable. By adjusting for these factors, it will be easier to evaluate and understand observed differences in antibiotic use between hospitals. Consequently, the inter-hospital differences can be more confidently acted upon. The residual variation is presumed to largely reflect prescriber-related factors. Keywords: Antibiotic use, Antibiotic surveillance, Hospitals, Risk factors

BACKGROUND In working towards rational use of antibiotics in hospitals, one needs to establish and maintain a suitable system for surveillance of antibiotic use [1]. However, the surveillance commonly applied is hampered by methodological pitfalls that impede the interpretation of the surveillance findings [2]. First, antibiotic utilization measurement using the number of patient bed days (BDs) as denominator may give results and interpretations that differ from those obtained when the number of patient discharges is used. By applying both denominators, a better understanding of the temporal trends in antibiotic use can be gained [3]-[5]. Second, the World Health Organization (WHO)-derived system of defined daily doses (DDDs), although internationally accepted as units of measurement for drug utilization, is not always suitable for showing antibiotic use in hospitalized patients because the WHO doses may differ from the recommended antibiotic doses or the doses that are actually prescribed [6],[7]. Alternative units have been considered [8],[9]. In a recent study, we found a marked difference between WHO defined doses (WHO DDDs) and doses recommended in hospital guidelines, especially for the penicillins [10]. The discrepancy had consequences for the interpretation of the data on antibiotic use and we suggested that WHO DDDs should be supplemented with hospital-adjusted defined daily doses (haDDDs) in the surveillance of antibiotic use. A further challenge in surveillance methodology is to identify factors that affect the use of antibiotics in hospitals. Few studies have addressed this

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Infectious Diseases: An Evidence based Approach

issue. The aim of the present study was to investigate, by use of a national surveillance data set, the extent to which relevant, validated hospital- and patient-related variables can explain differences in antibiotic use.

METHODS Study Hospitals (Health Enterprises, HEs) We registered data on antibiotic use in the period from 2006 to 2011 (six years) for the 19 public HEs (five university-affiliated and 14 large general HEs) and three large private HEs in Norway. Each public HE consists of one to seven hospital units and covers a complete and comparable spectrum of specialties, except specialized units for transplantation, heart surgery, neurosurgery, burns and multitrauma that are established only at the university hospitals. The three private institutions include mainly general internal medicine and surgery and intensive care units. We excluded four private institutions with specialized functions for elective orthopaedics and rheumatology, cardiac surgery and rehabilitation, and all psychiatric and drug abuse institutions. Ideally, analyses of antibiotic use should be performed at the level of hospital units, and the distribution of clinical specialties within each hospital should be known. However, at present administrative and clinical data of this kind are not routinely available from official and validated national sources. The lowest level at which this information may be acquired is the HE. Consequently, data on antibiotic use were analysed for whole HEs.

Antibiotic Use We have previously reported the method for antibiotic data acquisition [10]. Briefly, we acquired data on hospital antibiotic use from a national pharmacy database. The data set was processed in a Microsoft Excel spreadsheet and further analysed in the statistical program Stata version 12 (StataCorp LP, College Station, TX). All systemic antibacterial agents except methenamine included in the Anatomical Therapeutic Chemical (ATC) DDD group J01 were registered. From other ATC DDD groups, we included oral vancomycin, rifampicin and oral metronidazole.

Hospital- and Patient-Related factors Associated with....

29

Data on antibiotic use was expressed in DDDs using the 2011 WHO ATC/ DDD classification [11]. DDDs were related to length of stay, which was measured in BDs, defined as the date of discharge minus the admission date. The number of patient discharges was used as an additional denominator for measure of antibiotic use [12]. In a previous study, we adjusted the WHO DDDs for a number of antibiotic substances [10]. These DDDs, designated haDDDs, were based on dose recommendations outlined in regional and national antibiotic guidelines [13]. The same haDDD values supplemented the WHO DDDs in the current study.

Dependent (Outcome) Variables Total antibiotic use (“all antibiotics”) in the period 2006 to 2011 for the 22 HEs was the main dependent variable in the regression analyses. We also designated two subgroups as dependent variables: use of “broadspectrum antibiotics” (second- and third-generation cephalosporins, fluoroquinolones, carbapenems, and penicillins with enzyme inhibitors) and use of “all penicillins” (penicillinase sensitive, penicillinase resistant and extended-spectrum penicillins). Each of these three antibiotic groups was analysed using the following measurement units: DDD/100 BDs, DDD/100 discharges, haDDD/100 BDs and haDDD/100 discharges.

Independent Variables Administrative data and candidate explanatory variables for each HE were derived from publicly available on-line databases maintained by Statistics Norway [14]. For the regression analyses, we included independent variables that were considered clinically plausible and thus possibly associated with antibiotic use. Moreover, we required the variables to be clearly defined, quality assessed by a recognized national body, and easily accessible. These requirements were set to establish a reproducible, robust data set of optimal quality. The 11 continuous variables (Table 1) were: per cent of hospital stays lasting  10 days, number of physicians per 100 hospital beds, number of registered nurses per 100 hospital beds, per cent of discharges with a cancer ICD-10 main diagnosis, per cent of discharges with an infectious diseases ICD-10 main diagnosis, per cent of discharges with a surgical main diagnosis-related group (DRG), per cent of discharges with a medical main DRG, number of day care

Infectious Diseases: An Evidence based Approach

30

treatments, number of ambulatory consultations for all patients and number of ambulatory consultations for patients with infectious diseases (the last three variables measured per 100 hospital beds). The variables for day care and ambulatory patients were included because these patients were given antibiotics from the same ward stock as the in-patients. Table 1: Measurement units and value ranges for continuous variables entered into 12 linear regression models Continuous variables

Unit

Data pointarange

Mean

Hospital stay  10 days

% of discharges

5.2–17.3

9.7

Number of physiciansb

per 100 hospital beds

38.8–128.3

63.9

Number of nurses

per 100 hospital beds

132.5–300.1

197.6

IDc main ICD-10 diagnosis

% of discharges

1.6–6.0

2.9

Cancer main ICD-10 diagnosisb

% of discharges

3.6–17.1

8.0

Surgical DRGs

% of discharges

18.0–39.8

27.5

Medical DRGsb

% of discharges

46.4–79.6

65.9

All ambulatory consultationsb

per 100 hospital beds

89.9–543.3

327.6

ID ambulatory consultations

per 100 hospital beds

0.5–7.7

2.7

Day-care treatmentsd

per 100 hospital beds

10.1–79.5

35.9

c

d

132 data points: six years of 22 Health Enterprise’s annual data.

a

b

Variable removed from the regression models due to collinearity.

ID: (any) infectious diseases.

c

Variable included in model, but not significantly associated with antibiotic use. d

Three categorical independent variables were also included (Table 2). These were university versus non-university affiliation, size of the HE ( 600 beds) and geographical region (i.e. belonging to one of four Norwegian Health Regions). Table 2: Description of three categorical variables a entered into 12 linear regression models Variables (No. of data points)

% of beds

% of bed days

% of discharges

Average HE stay (d)

University HEsb (30)

40.6

40.7

38.5

4.9

Non-university HEs (102)

59.4

59.3

61.5

4.5

Health Region 1 (60)

54.0

54.2

55.5

4.5

Hospital- and Patient-Related factors Associated with.... Health Region 2 (30)

20.3

20.8

20.5

4.7

Health Region 3 (18)

14.3

14.5

13.8

4.8

Health Region 4 (24)

11.4

10.4

10.2

4.7

HEs  600 beds (47)

60.0

59.7

58.5

4.7

31

132 data points (22 HEsb over 6 years) for each independent variable. Of the three categorical variables, only university affiliation was independently associated with antibiotic use in eight of the 12 regression models. a

b

HE = Health Enterprise.

All HEs used the same DRG version based on the WHO ICD-10 classification (NordDRG, version NOR PR1) during the study period. A surgical main DRG denotes a hospital stay during which a procedure was performed in an operating theatre. A medical main DRG was registered when no such procedure took place.

Statistical Analyses Collection of the annual data on antibiotic use for 22 HEs over six years resulted in data sets containing 132 observations. Analyses were done with Stata statistical software version 12 (StataCorp LP, College Station, TX). For correlations between continuous variables, Pearson correlations (Stata procedure: ‘pwcorr’) was used. Since our data were normally distributed and the dependent variables continuous, we analysed 12 different multiple linear regression models (procedure: ‘regress’). The same 14 independent variables were introduced in all regression models. To account for possible dependence of observations within the individual HEs, that is to say dependence related to repeated and possibly correlated annual measures for the 22 HEs, we performed robust linear regression analyses with HEs as clusters (variance estimator option ‘cluster’). In a stepwise approach, a test for collinearity of the independent variables (i.e. the extent to which the variables are related to each other) was performed to fit the final model. We used the variance inflation factor (vif) which tests for multivariate multicollinearity (procedure: ‘estat vif’). In each regression step, the variable was excluded that had the highest vif, i.e. for which the least amount of its variance was associated with the outcome. This was repeated until no variable had a vif > 5 [15].

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Because of a relatively large number of independent variables, the adjusted R square (aR2) was calculated for each regression model to show how well it fitted the data. For all analyses, aR2 > 0.3 was considered a strong correlation. A two-tailed P- value