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H1N1) Pandemic: a Systematic Review and Meta-Regression Exploring the Influence of Patient, Healthcare System and Study-Specific Factors

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H1N1) Pandemic: a Systematic Review and Meta-Regression Exploring the Influence of Patient, Healthcare System and Study-Specific Factors Heterogeneity in the reporting of mortality in critically ill patients during the 2009-10 Influenza A (H1N1) Pandemic: A systematic review and meta-regression exploring the influence of patient, healthcare system and study-specific factors. by Abhijit Duggal A thesis submitted in conformity with the requirements for the degree of Master of Science, Clinical Epidemiology and Health Care Research Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, ON, Canada. © Copyright by Abhijit Duggal 2015 Heterogeneity in the reporting of mortality in critically ill patients during the 2009-10 Influenza A (H1N1) Pandemic: A systematic review and meta-regression exploring the influence of patient, healthcare system and study-specific factors. Abhijit Duggal Master of Science, Clinical Epidemiology and Health Care Research Institute of Health Policy, Management and Evaluation, University of Toronto 2015 Abstract: Abstract: Introduction: A systematic review with meta-regression to determine heterogeneity in reported mortality associated with critical illness during the 2009-2010 Influenza A (H1N1) pandemic. Results: We identified 219 studies from 50 countries that met our inclusion criteria. There were significant differences in the reported mortality based on the geographic region and economic development of a country. Mortality for the first wave of the H1N1 pandemic was non- significantly higher than wave 2. In our hierarchical model the reported mortality was heavily influenced by the need for mechanical ventilation. Conclusion: While patient-based factors are influential in determining outcomes during outbreaks and pandemics, the region and system of care delivery also influence survival. Outcomes from a relatively small number of patients, early in an outbreak and from specific regions may lead to biased estimates of outcomes on a global scale. This may have important implications for global disease outbreak responses. ii Acknowledgements The research included in this thesis could not have been performed if not for the support of many individuals. I would like to express my sincere gratitude to my thesis mentor Dr. Rob Fowler, for his immense support, patience, motivation. He has helped me through challenging times over the course of the analysis and the writing of the dissertation I sincerely thank him for his confidence in me. I could not have asked for a better mentor and advisor. I would additionally like to thank Dr. Gordon Rubenfeld for his encouragement, insightful comments, and his support in both the research and especially the revision process for this thesis. I would also like to extend my appreciation to Ruxandra Pinto who has been an immense help with the statistics and methodology of this thesis. I would also thank my colleagues both at University of Toronto and Cleveland Clinic who have provided valuable insight, stimulating discussions and have supported me through this process. Finally I would like to extend my deepest gratitude to my family without whose love, support and understanding I could never have completed this degree. iii Table of Contents Acknowledgements………………………………………………………………………...……iii Table of contents……………………………………………………………………………..….iv List of abbreviations……………………………………………………………………………ix List of tables……………………………………………..……………………………...….……xi List of figures………………...………………………………………………………………….xii List of appendices……………………………………..……………………………………….xiii Chapter 1: Thesis overview……………………………..……………………………………….1 1.1 Problem statement……………………………………………………………………….1 1.2 Overview of the thesis………………………………………………………..…………..2 Chapter 2: Introduction…………………….......……………………………………………….3 2.1 Outbreaks, Epidemics and Pandemics……………………………………………..3 2.1.1 Major disease outbreaks during ancient times ………………………….3 2.1.2 Influenza outbreaks and pandemics of the twentieth century ………….4 2.1.3 Influenza outbreaks and pandemics of the twenty-first century….…….4 2.1.3.1 Severe acute respiratory syndrome (SARS)……….. ………….4 2.1.3.2 Influenza A (H1N1) pandemic…………………..………………5 iv 2.1.3.2.1 World Health Organization definitions……...……….5 2.1.3.2.2 Critical illness during the H1N1 pandemic…………..6 2.1.3.2.3 Global disease burden associated with the H1N1…....6 2.1.3.2.4 Waves of the H1N1 pandemic……………...………….7 2.1.3.3 Middle East Respiratory Syndrome (MERS)……… ………….7 2.1.3.4 Influenza A (H5N1) ……………….……………………………..7 2.1.3.5 Influenza A (H7N9)……………… …………….………………..8 2.1.3.6 Ebola ………………….………………………………………….8 2.2 Limitations of reporting outcomes during disease outbreaks and pandemics...…8 Chapter 3: Critical Illness……………………………………….…………………………….10 3.1 Critical illness: A global perspective…………...………………………………….10 3.1.1 Global differences in critical care services…………..………………….10 3.1.2 World-bank economic development………………….………………….11 3.1.3 Geographic regions of the world…………..…………………………….11 3.2 Disease syndromes commonly associated with critical illness……………..…….12 3.2.1 Acute Respiratory Distress Syndrome (ARDS)……. ………………….12 3.2.1.1 Mechanical ventilation……………………………….…………12 v 3.2.1.2 Rescue therapies for acute respiratory distress syndrome…..13 3.2.2 Sepsis, severe sepsis and septic shock……………………………………14 3.2.3 Acute kidney injury………………………………………………………15 Chapter 4: Objectives, and Research questions…………………………………………...….16 4.1 Objectives …………………………………………………...……………………....16 4.2 Research questions…………………………………………...……………………..16 Chapter 5: Material and Methods……………………………………………………………..18 5.1 Search strategy……………………………………...………………………………18 5.2 Study selection and eligibility criteria……………………………………………..18 5.2.1 Inclusion criteria……………………………...………………………….18 5.2.2 Exclusion criteria……………………………..…………………………..19 5.2.3 Eligibility criteria for study sub-groups ………………………….…….19 5.3 Data extraction and study variables………………………………………………21 5.4 Outcomes……………………………………………………………………………22 5.5 Quality assessment………………………………………………………………….22 Chapter 6: Statistical analysis………………………………………………………………….24 6.1 Descriptive statistics…………………………………...……………………………24 vi 6.2 Meta-analysis………………………………………………………………….…….24 6.2.1 Random-effects model ………………………………………….………..24 6.2.2 Tests for statistical heterogeneity……………….……………………….25 6.2.3 Ascertainment of publication bias…………………….…………………25 6.3 Subgroup analysis and meta-regression……………………..……………………26 6.3.1 Time as a factor in the reporting of mortality…………………………..27 6.3.2 Geography and economic development as a factor in the reporting of mortality……………………………………………………………...………….27 6.3.3 Influence of specific ICU populations on the reporting of mortality.…28 6.3.4 Age as a factor in the reporting of mortality…………....………………28 6.3.5 Influence of single center or multicenter studies on the reporting of mortality………………………………………………………………...……….28 6.3.6 Influence of the number of patients in a study on the reporting of mortality………………………...……………………………………………….28 6.3.7 Mortality in specific sub-groups of critically ill patients…..…………...28 6.4 Hierarchical meta-regression………………………………………………………29 Chapter 7: Results…………………………..……………………………………………………………….31 vii 7.1 Description of included studies…………………………………….………………31 7.2 Quality of included studies……………………...……………………………….…35 7.3 Meta-analysis………………………………………………………………………..36 7.4 Meta-regression…………………………….……………………………………….38 7.4.1 Reported mortality over time ……………………………...……………39 7.4.2 Age and reported mortality ………………………..…………………….39 7.4.3 Geographical area of the study and reported mortality………………..39 7.4.4 Economic status of the country and reported mortality………...……..42 7.4.5 Reported mortality in specific ICU populations………………………..44 7.5 Hierarchical meta-regression…………………..…………………………………..46 Chapter 8: Discussion…………………………...…………………….……………………..…51 Chapter 9: Conclusions and suggestions for future research…………………………..……56 Appendix…………….…………………………………………………………………………..78 viii List of Abbreviations: ARDS: Acute Respiratory Distress Syndrome; AKI: Acute Kidney Injury; AIDS: Acquired Immunodeficiency syndrome; CDC: Centers for Disease Control and Prevention; CI: Confidence Interval; CPAP: Continuous positive airway pressure; ECMO: Extracorporeal Membrane Oxygenation; ESRD: end stage renal disease; ETT: Endotracheal tube; FiO2: Fraction of inhaled oxygen; GFR: glomerular filtration rate; HFOV: High frequency oscillatory ventilation; HIV: Human immunodeficiency virus; ICU: Intensive Care Unit; IQR: interquartile range; MAP: mean arterial pressure; ix MeSH: medical subject headings; MERS: Middle East Respiratory Syndrome; NOS: Newcastle-Ottawa Scale; NPPV: Non-invasive positive pressure ventilation; PaO2: Partial Pressure of Oxygen; PEEP: Positive end expiratory pressure; PRISMA: Preferred reporting items for systematic reviews and meta-analyses; RIFLE: Risk, Injury, Failure, Loss, and End-stage renal disease SARS: Severe Acute Respiratory Syndrome; SBP: systolic blood pressure; SCCM: Society for critical care medicine; SD: standard Deviation; WBC: white blood cell; WHO: World Health Organization. x List of Tables: Table 1: System and study based characteristics described in 219 studies from 213 articles. Values are numbers (percentages) unless stated otherwise. Table 2: Description of patient characteristics, intensive care specific interventions and outcomes from included studies compared to the studies selected for the meta-regression and hierarchical model respectively. Table 3: Newcastle-Ottawa Scale describing the mean quality of studies based on different sub- groups used in the meta-regression. Table 4: Tests for evaluation of asymmetry of funnel plot to study
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