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.

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.

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:

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.

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.

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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

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

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

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

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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

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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;

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.

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 subgroups used in the meta-regression.

Table 4: Tests for evaluation of asymmetry of funnel plot to study publication bias Table 5: Meta-analysis comparing the reported mortality from “early enrollment” (the Wave 1 for each individual country) during the H1N1 pandemic with studies describing prolonged enrollment from the same countries. We evaluated the differences in the reporting of mortality among the individual countries by using both a fixed effect and a random effect model. Table 6: Differences in Mortality, Length of Stay in the ICU and duration of Mechanical ventilation based on the World Bank economic development classification. Table 7: Patient characteristics from included studies. Differences in baseline characteristics based on the studies only describing unselected critically ill patients, studies describing patients undergoing mechanical ventilation, and studies describing patients under consideration or actually getting ECMO. Table 8: Hierarchical model with 3 levels (specific patient variables [need for mechanical ventilation are treated as fixed effects] and study and economic development of the country are treated as random effects with studies clustered within the economic development. Table 9: Hierarchical model with two levels specific patient variables [need for mechanical ventilation] is treated as a fixed effect against studies clustered within the economic development of a country Table 10: Hierarchical model with 3 levels specific patient variables [need for mechanical ventilation] is treated as fixed effects and study and economic development of the country are treated as random effects with studies clustered within the economic development.

List of Figures:

Figure1: Preferred reporting items for systematic reviews and meta-analyses (PRISMA) flow diagram. Study identification and selection process.

Figure 2: Funnel plot to assess for risk of publication bias. Figure 3: Funnel Plot with Trim and fill effect revealing missing studies

Figure 4: Reported mortality associated with 2009 Influenza A (H1N1) associated critical illness. We describe the mortality based on temporal (early, late and prolonged enrollment), study (study size, single center compared to multicenter and adults compared to pediatrics), and the geographic location and economic development from the included studies. The black squares represent the point estimate and 95% confidence intervals (CIs) around the mortality for each subgroup. The black diamond is the summary or overall combined estimate of mortality associated with the 2009 Influenza A (H1N1) pandemic.

Figure 5: Differences in reported mortality based on different geographic variables for the included countries (hemisphere, continent and World Bank designated geographical region). The black squares represent the point estimate and 95% confidence intervals (CIs) around the mortality for each subgroup. The black diamond is the summary or overall combined estimate of mortality associated with the 2009 Influenza A (H1N1) pandemic. The use of geographical regions is associated with the best discriminative power to report the differences in mortality in a global context.

Figure 6: Differences in reported mortality based on subgroups of patients with different severity of illness (need for mechanical ventilation), critical illness associated organ failure (ARDS; AKI) or co-presenting conditions (pregnancy). The black squares represent the point estimate and 95% confidence intervals (CIs) around the mortality for each subgroup. The black diamond is the summary or overall combined estimate of mortality associated with the 2009 Influenza A (H1N1) pandemic

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List of Appendices:

Appendix 1: MeSH terms used for the systematic review.

Appendix 2: Figure: Flowchart for the subgroups for analysis for the meta-regression and the hierarchical meta-regression. Appendix 3: Reported mortality associated with 2009 Influenza A (H1N1) associated critical illness for the studies used in the hierarchical meta-regression models. We describe the mortality based on temporal (early, late and prolonged enrollment), study (study size, single center compared to multicenter and adults compared to pediatrics), and the geographic location and economic development from the included studies. The black squares represent the point estimate and 95% confidence intervals (CIs) around the mortality for each subgroup. The black diamond is the summary or overall combined estimate of mortality associated with the 2009 Influenza A (H1N1) pandemic.

Appendix 4: Differences in reported mortality based on different geographic variables for the included countries (hemisphere, continent and World Bank designated geographical region) for the studies used in the hierarchical meta-regression models. The black squares represent the point estimate and 95% confidence intervals (CIs) around the mortality for each subgroup. The black diamond is the summary or overall combined estimate of mortality associated with the 2009 Influenza A (H1N1) pandemic. The use of geographical regions is associated with the best discriminative power to report the differences in mortality in a global context.

Appendix 5: Differences in reported mortality based on subgroups of patients with different severity of illness (need for mechanical ventilation), critical illness associated organ failure (ARDS; AKI) or co-presenting conditions (pregnancy) for the studies used in the hierarchical meta-regression models. The black squares represent the point estimate and 95% confidence intervals (CIs) around the mortality for each subgroup. The black diamond is the summary or overall combined estimate of mortality associated with the 2009 Influenza A (H1N1) pandemic.

Appendix 6: System and study based characteristics described in 219 studies from 213 articles compared to the studies selected for the meta-regression and hierarchical model respectively. Values are numbers (percentages) unless stated otherwise.

Appendix 7: Differences in Mortality, Length of Stay in the ICU and duration of Mechanical ventilation based on the geographic distribution of the different studies. Appendix 8: List of Excluded Studies.

Appendix 9: Case Report Form for Included Studies. Appendix 10: Components of Newcastle-Ottawa Scale

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Chapter 1: Thesis Overview

1.1 Problem Statement

The H1N1 literature that informed the response to the pandemic, focused on the initial waves of influenza. However, the small numbers of patients, the narrow focus of both the clinical questions and physiological parameters studied and both a limited and early pandemic time frame used in these studies may have led to biased estimates of outcomes associated with the

H1N1 pandemic, over a broader time frame. Since the conclusion of the pandemic period, some publications have reported on the second phase or entire time period of the pandemic. However, they have been dominated by experiences from developed countries, leading to a still unclear understanding of the global impact of the H1N1 pandemic. These studies also failed to fully describe the utilization of critical care resources in different geographical settings – developed versus developing or least developed countries - where there may be great differences in capacity and utilization of critical care, and the potential for difference in outcomes.

An accurate global estimate of both burden of illness and outcomes, how these vary across jurisdictions, over time, and patient populations is important to quantify and would aid in understanding the differences between early, selected populations and those representing the entire pandemic period. Exploring the differences in reporting over time, different geographies, and economic development status will help us understand likely differences in critical care resource utilization, to help guide appropriate response and allocation of resources during future pandemics, and to determine which factors are associated with extreme or more accurate estimates of pandemic characteristics.

1.2 Overview of the Thesis

Chapter 1 details the overview of the thesis. Chapter 2 provides a background to the thesis and includes a discussion of the epidemiology of disease outbreaks, with a detailed discussion of the clinical and public health impact of the 2009 Influenza a (H1N1) pandemic at a global level. We also discuss the epidemiologic reporting of the pandemic, and detail the response to critical illness during the H1N1 pandemic. Chapter 3 focuses on critical illness, and the disease syndromes associated with critical care. We also discuss the challenges of reporting on critical illness in a global context. Chapter 4 describes the objectives of this thesis and the research questions addressed. Chapter 5 discusses the methods used for the search strategy, the study selection, the data extraction and the quality assessment tools used in the systematic review.

Descriptions of the study populations are also provided. Chapter 6 provides a detailed description of the methods used in the Meta-analysis and Meta-regression. It also discusses the detailed statistical analysis used in our meta-regression and our hierarchical meta-analyses of studies reporting mortality associated with critical illness due to the 2009 Influenza A (H1N1). Chapter

7 highlights our results. Chapter 8 discusses the major findings and includes a comprehensive discussion of the thesis limitations, and the clinical, policy and global health systems implications of the results. Chapter 9 details the conclusions based on this thesis and also discusses recommendations for future research.

Chapter 2: Introduction

2.1 Outbreaks, Epidemics and Pandemics

A disease outbreak is defined as the occurrence of a new disease or the reporting of a higher number of new cases of a disease than would be normally expected in a defined geographical area or temporal period 1. Disease outbreaks can occur in restricted geographical areas, or can involve several countries. Similarly, outbreaks can last for anywhere from a few days to several years 1. An epidemic is an outbreak that affects a large population in a more expansive geographical area, usually over a relatively short period of time 2. An understanding of the usual prevalence of a disease is important before the determination of an epidemic is made 2.

Propagation of epidemics is dependent on an adequate number of susceptible hosts to an infectious agent. Epidemics that spread over several countries or continents, usually affecting a large population are called Pandemics 3. Pandemics frequently present in multiple waves of infections, where the numbers of infections and deaths can present in well-separated temporal peaks with a separation time-scale of months.

2.1.1 Major disease outbreaks during ancient times

One of the earliest accounts of a recorded pandemic was the plague of Justinian4. Recent studies have revealed that Yersinia pestis likely caused this pandemic. 4 Within two years it affected all the Mediterranean countries. It eventually involved parts of Asia, North Africa, and Went as far north as Ireland4. Historians trace the remnants of this outbreak over the subsequent two centuries, and up to 18 attributed waves.

The “Black Death” is one of the most well-known historical infectious disease outbreaks.

Through genetic sequencing it has also been proven to be caused by Yersinia pestis 5. The

outbreak originated in Central Asia, and various accounts have traced it back to India, China, or even the Russian steppes 6 It engulfed most of continental Europe in less than three years and was responsible for destruction of entire cities 5. Some estimates attribute 50% of all mortality during this time in Europe directly to the plague 5.

2.1.2 Influenza outbreaks and pandemics of the twentieth century

Influenza pandemics occur when a new strain of the influenza virus emerges, usually through antigenic shift, for which there is little or no immunity in the human population7. The twentieth century saw three influenza pandemics beginning in 1918, 1957 and 1968 by different strains 8.

Despite its name the 1918-19 “Spanish Flu” originated in the United States 9. The predominant antigenic subtype was Influenza A (H1N1), and it infected almost one third of the world’s population. It was unusually virulent, and is thought to have caused approximately 50 million deaths within 2 years9. An Influenza A (H2N2) stain outbreak in China in 1957-58 was the second influenza pandemic of the 20th century10. The “Asian Flu” was thought to have caused about 2 million deaths globally10. The 1968-69 "Hong Kong Flu", was an Influenza A (H3N2) outbreak and killed approximately one million people worldwide7.

2.1.3 Influenza outbreaks and pandemics of the twenty-first century

2.1.3.1 SARS

Severe acute respiratory syndrome (SARS) coronavirus was first reported in Southern China and

Hong Kong in early 200311. The illness spread quickly to involve more than 35 countries throughout the world 11 and was notable for substantial nosocomial transmission. SARS had a mortality rate of 9-12% in all confirmed cases, but it went as high as 50% in the critically ill

elderly12. SARS transmission was controlled within a year, and no further outbreaks associated with this viral illness have since been reported

2.1.3.2 2009-10 Influenza A (H1N1) Pandemic

The 2009-2010 Influenza A(H1N1) pandemic was declared due to infections caused by a then variant of the Influenza A virus, that originated from animal influenza viruses and was unrelated to recent human seasonal influenza A(H1N1) viruses. The first cases of disease associated with pandemic H1N1 virus were reported in April 2009 from Mexico and the Southwestern United

States13, 14. The disease spread quickly through the rest of the world and by 11 June 2009, WHO had declared a pandemic phase 6 alert 15. The 2009 H1N1 variant of influenza was the first recognized Pandemic of the 21st Century 15

2.1.3.2.1 World Health Organization definitions of H1N1 Pandemic

WHO and CDC developed specific case definitions for 2009 H1N1 influenza 15-17 a. Confirmed H1N1: An individual with an acute febrile respiratory illness and laboratory- confirmed pandemic (H1N1) 2009 virus infection by one or more of the following tests: real- time (RT)-PCR or viral culture; viral culture; 4-fold rise in pandemic (H1N1) 2009 virus-specific neutralizing antibodies. b. Probable: An individual with an acute febrile respiratory illness who is positive for influenza

A by influenza RT-PCR, but is un-typeable by regents used to detect different strains; or, positive for influenza A by an influenza rapid test or an influenza immunofluorescence assay

(IFA) and meets criteria for a suspected case.

c. Suspected: An individual with acute febrile respiratory illness with onset within 7 days of close contact with a person who is a confirmed case of influenza A (H1N1) virus infection, or within 7 days of travel to a community either locally or internationally where there are one or more confirmed influenza A (H1N1) cases, or resides in a community where there are one or more confirmed influenza A (H1N1) cases.

2.1.3.2.2 Critical Illness during the H1N1 pandemic

The 2009 H1N1 pandemic was associated with a higher rate of critical illness than seasonal influenza. Even though overall mortality was comparable to seasonal influenza, the rates of respiratory failure, requiring ventilator support and the use intensive care resources were much higher in this cohort of patients 18. A large proportion of critically ill patients not only required invasive mechanical ventilation for hypoxemic respiratory failure, but many developed severe

Acute Respiratory Distress Syndrome (ARDS) 19. These patients frequently required the use of

Extracorporeal Membrane Oxygenation (ECMO), and other “rescue” therapies20,21.

2.1.3.2.3 Global Disease burden associated with the H1N1 pandemic

The H1N1 pandemic had a significant impact on the attributable mortality, in particular of young patients, on a global scale22,23. However, most of the studies describing the outcomes associated with H1N1 failed to fully describe the utilization of critical care resources in different geographical settings – developed versus developing or least developed countries. Two large observational studies examining the global impact of H1N1 using administrative databases acknowledged that Asia and Africa were vastly under-represented in their samples 22,23. With almost 40% of the world’s population living in these two continents, it becomes important to examine the impact of the H1N1 pandemic at a global scale.

2.1.3.2.4 Waves of the H1N1 pandemic

The first wave of the 2009 pandemic in the North America began in March 2009 and peaked in late June and early July 2009 13,18. There were markedly fewer cases throughout August, and the second larger wave peaked in late October and, early November. The first wave in the Southern

Hemisphere occurred from May 2009 till August 2009. Also while many countries (e.g. United

States and Canada) experienced at least two waves of infections during the 2009 pandemic, other countries (e.g. China) experienced only a single predominant wave of infection 24.

2.1.3.3 MERS-CoV

Middle East Respiratory Syndrome (MERS) is viral respiratory illness caused by a coronavirus

25.The initial outbreak had a very high reported mortality, but as more detailed epidemiologic data has come forward, the mortality associated with confirmed MERS-CoV infection is thought to be around 30% 26. The first cases were reported from Saudi Arabia in 2012, but as of the beginning of 2015, this outbreak has been reported from 22 countries 27. Almost all cases are linked to the Gulf region 27.

2.1.3.4 Influenza A (H5N1)

The H5N1 avian flu is a highly pathogenic virus that has been reported to have infected humans in small clusters since 2003 28. Most cases have originated in Asia and the Middle East, and the transmission is through poultry. Sustained human-to-human spread has not been reported, but initial epidemiologic surveillance has reported a very high mortality (60%) associated with this virus 28.

2.1.3.5 Influenza A (H7N9)

Initial outbreaks of avian influenza A(H7N9) in humans were reported from China in 2013 29.

Infection due to this virus is associated with severe disease in humans, and most patients develop respiratory failure 30. Reported mortality is close to 30% 30. No evidence of sustained person-to- person spread of H7N9 has been found, though some evidence points to limited person-to-person spread in rare circumstances. 29

2.1.3.6 2014 Ebola Pandemic

Ebola virus disease was first described in 1976, and has been implicated in multiple isolated, but brief outbreaks in sub-Saharan Africa 31. The 2014 outbreak has had a devastating effect on multiple West African countries 32. It is the most widespread epidemic associated with this filovirus 33. The initial reported case fatality rate is 60% 33,34. This outbreak has also been significant due to the impact of secondary infections in health care workers 35. As of February 4,

2015, there have been 22,495 confirmed, probable or suspected cases of Ebola among 9 countries, with an estimated mortality rate of 40% 34.

2.2 Limitations of reporting outcomes during disease outbreaks and pandemics

Many studies discussing the clinical characteristics, possible treatment options, at-risk populations and clinical outcomes are published early in the course of any new disease outbreak.

Initial reports focus on a limited number of very sick patients. There is a high risk of introducing a selection bias in reported outcomes, disease outbreaks are studied over short periods of time, or focus on select groups of patients that are, initially, most readily detected due to severe illness

26,36. This phenomenon has been seen in most of the reported outbreaks over the last decade.

Initial reports of the MERS-CoV outbreak reported extremely high rates of mortality (50% for

MERS-CoV) 26. These reported outcomes were based on case reports and small series associated with these diseases. The H1N1 Pandemic is unique as it is one of the first disease outbreaks that occurred during the modern epidemiologic surveillance times, reported at a global level. The prolonged duration, and multiple waves also made this Pandemic an ideal outbreak for epidemiologic reporting and analysis.

Critical illness associated with outbreaks is difficult to evaluate because of the inherent difficulties with recognition of clinical syndromes such as sepsis and acute respiratory Distress syndrome 37 Moreover there are no gold standards or global benchmarks for standardized treatment of these patients 37. Also most studies also fail to fully describe the utilization of critical care resources in different geographical settings – developed versus developing or least developed countries - where there may be great differences in capacity and utilization of critical care. Similar to other disease outbreaks the reported mortality in critically ill patients during the

H1N1 pandemic was extremely variable - reported to be anywhere from 11% to 48% in different studies 20,38-40. Mortality was much higher in smaller case series based on initial experiences from single centers, 36 and for cohorts of critically ill patients undergoing specific interventions

20,38 or diagnosis 39,40 associated with the H1N1 pandemic. These results may have led to biased estimates of some patient characteristics and the outcomes associated with the H1N1 pandemic, over a broader time frame.

Chapter 3: Critical Illness

There is no single accepted definition of critical illness. However, patients with critical illness often (but not always) have high complexity of disease, associated with actual or a high risk of organ dysfunction. Critical illness syndromes can be difficult to diagnose, often have a short prodrome, and usually are associated with higher mortality than patients with similar spectra of comorbid conditions and acute presentations without critical illness 41. Disease syndromes such as septic shock, and organ dysfunction such as acute respiratory distress syndrome, and acute renal injury are closely associated with the development of critical illness.

3.1 Critical illness: A global perspective

Most chronic diseases including cancers, cardiovascular disease, and infectious outbreaks such as tuberculosis and HIV/ AIDS have reliable global epidemiologic data 42,43. This allows for an attempt at assessment of differences in outcomes, and care delivery among patients all over the world. Unfortunately comparative studies for critical illness syndromes are hampered by a number of factors such as a lack of standardized definitions of disease syndromes, a heavy reliance on resources for critical care services and a lack of trained personnel 37,41.

3.1.1 Global differences in critical care services

Critical care services vary tremendously throughout the world 44. The availability of resources, the overall economic status of a country and its citizens and the systems in place for life- sustaining therapies all impact the use of these services in different countries 45. There are significant challenges in defining and quantifying the capacity to provide critical care among different countries. Studies have evaluated the differences in critical care services based on geographic variables 44. Most studies report on countries or continents when explaining the

differences in care for hospitalized or critically ill patients 44.Socioeconomic status has also been used to describe the differences in resources and outcomes in studies 45. For the purpose of this thesis we decided to use different geographic variables such as continents and hemisphere, as has been used in previous studies. However, we also decided to compare the World Bank geographic regions as they provide a mix of the socioeconomic and geographic variables and can be used a more effective variable to describe differences in resource utilization and outcomes at a global level 46.

3.1.2 World Bank Economic Development

The World Bank classifies the vast majority of the world’s countries into one of four broad categories based on the per capita income: low income economies, lower-middle income economies, upper-middle income economies and high income economies. 46 The composition of these groupings is intended to reflect basic economic country conditions

3.1.3 Geographic regions of the world

Most studies evaluating the global burden of disease describe differences between populations at a country level 44. It is difficult to accurately compare such differences in critical illness because of the inherent differences in patients and resources in different countries 41. A number of studies have described these differences at the level of different regions and continents 44. For this thesis we explored the differences in outcomes at the level of continents, and then based on geographical region of the included countries. We used geographical regions based on the World

Bank classification as follows: North America, Latin America and Caribbean (Mexico is included in Latin America and not North America based on this classification), East Asia and

Pacific, Eastern Europe, Middle East and North Africa, Sub-Saharan Africa, South Asia,

Western Europe, Australia and New Zealand

3.2 Disease Syndromes associated with Critical illness

3.2.1 Acute Respiratory Distress Syndrome (ARDS)

We defined ARDS based on the Berlin definition 47. Even though this definition was formulated after the 2009 Influenza A (H1N1) pandemic, we decided to use this as it is the most appropriate definition for a diagnosis of ARDS. ARDS was defined as: I. Bilateral opacities, unexplained by nodules, atelectasis or effusion on either chest radiograph or CT scan; and II. New or worsening respiratory symptoms or a clinical insult associated with ARDS within 7 days of diagnosis; and

III. Objective assessment of cardiac function with modalities such as echocardiography to exclude cardiogenic pulmonary edema and; IV. Hypoxemia, with a PaO2/FiO2 ≤300 mm Hg despite non-invasive or Invasive mechanical ventilation with a PEEP (Positive End Expiratory

Pressure) or Continuous Positive Airway Pressure (CPAP)≥ 5 cm H2O 47.

3.2.1.1 Mechanical Ventilation

Mechanical ventilation is a method to mechanically assist spontaneous or absent breathing attempts. It is the use of positive pressure to force a predetermined mixture of air into the central airways and alveoli of the lungs. This positive pressure ventilation can be provided either invasively (with the means of an endotracheal tube) or non-invasively (with the use of nasal, or full face masks). For the purpose of this study we defined mechanical ventilation as the use of any device used to provide positive pressure ventilation to the patients. We defined non-invasive mechanical ventilation as the use of facemasks to provide non-Invasive positive pressure ventilation (NPPV), bilevel pressure ventilation, or continuous positive airway pressure (CPAP).

Invasive mechanical ventilation was defined as the use of positive pressure ventilation with any conventional or non-conventional mode of mechanical ventilation with the means of an endotracheal tube (ETT)

3.2.1.2 Rescue therapies

Rescue therapies are defined as the use of adjunctive clinical strategies in patients with severe hypoxemia. Rescue therapies include the following therapeutic interventions (prone position ventilation, high-frequency oscillatory ventilation (HFOV), airway pressure release ventilation

(APRV) and extracorporeal membrane oxygenation (ECMO).

High-frequency oscillatory ventilation (HFOV)

High-frequency oscillatory ventilation (HFOV) provides pressure oscillations around a relatively constant mean airway pressure at very high rates (3–15 breaths per second). As a result very small tidal volumes are achieved with active inspiration and expiration. 48 Although commonly used as a rescue therapy in 2009-2010, with the publication of recent clinical trials demonstrating potential harm, HFOV is no longer widely recommended as a rescue strategy.

Airway pressure release ventilation (APRV)

Airway pressure release ventilation (APRV) is a form of pressure control intermittent mandatory ventilation (PC-IMV) typically used in the setting of ARDS and severe hypoxemia. During

APRV, airway pressure is set at 2 levels, sometimes called for 2 time periods and effectively raises the mean airway pressure, recruits and helps to maintain open alveoli that can then participate in gas exchange. The effect on clinical outcomes of patients with ARDS is uncertain49.

Extracorporeal membrane oxygenation (ECMO)

Extracorporeal membrane oxygenation uses degrees of cardiopulmonary bypass technology to provide gas exchange and to augment blood flow. In patients with severe hypoxemia this modality can increase oxygenation and ventilation while allowing a lung protective ventilation strategy with low tidal volume breaths. With the advent of new technology such as veno-venous circuits and smaller cannulas, the use of ECMO has gained more acceptance as a therapy in patients with ARDS. This trend was seen with the use of ECMO in patients with severe or refractory hypoxemia associated with ARDS during the H1N1 pandemic 50.

Prone Position Ventilation

Prone position ventilation is the use of invasive mechanical ventilation to patients in the prone

(lying on the chest and abdomen as opposed to lying on the back) position 51. The use of this intervention has been associated with a significant risk reduction in mortality in one clinical trial52.

3.2.2 Sepsis /Severe Sepsis and Septic Shock

Sepsis, severe sepsis and septic shock have been defined based on an international consensus statement developed by the Society for Critical Care Medicine (SCCM) Surviving Sepsis.53

Sepsis is defined as the presence (probable or documented) of infection together with systemic inflammatory manifestations. Severe sepsis is defined as sepsis plus sepsis-induced organ dysfunction. Septic shock was defined as sepsis-induced hypotension persisting despite adequate fluid resuscitation, which may be defined as infusion of 30 mL/kg of crystalloids bolus over 10-

15 minutes.53

Vasoactive Medications

Medications that induce vasoconstriction and thereby elevate mean arterial pressure (MAP) are called vasopressors. Inotropes are medications that increase cardiac contractility 54,55. Many drugs have both vasopressor and inotropic effects. For the purpose of our study we defined the use of the common vasopressors (e.g. norepinephrine, vasopressin, epinephrine, dopamine and phenylephrine) or Inotropes (e.g. dobutamine, milrinone) as vasoactive medication use.

3.2.3 Acute Renal Failure

Acute Renal failure is defined as the worsening of serum creatinine and glomerular filtration rate

(GFR), a decrease in the urine output with a risk of progression to chronic renal insufficiency or failure. Recently acute renal failure has been defined based on the Risk, Injury, Failure, Loss, and End stage renal disease (RIFLE) Criteria. The changes in the serum creatinine, urine output and glomerular filtration rate (GFR) help in defining the severity of disease. Worsening kidney dysfunction is labeled as Risk, Injury, and Failure respectively. The RIFLE criterion uses short and long term outcomes to define Loss and ESRD. 56

Chapter 4: Objective and Research Questions

4.1 Objective

The primary objective of this systematic review was to determine the mortality of critically ill patients with Influenza A (H1N1) during the 2009-2010 pandemic.

Our secondary objective was to determine how patient, healthcare system and study-specific, factors influence reported mortality. We examined the differences in outcomes based on the time period of the study, the geographical location (the continent, the geographic region and the specific hemisphere) of the study population, developed or developing country status based on the World Bank designation, and whether the study included unselected critically ill patients, or specific subgroups of critically ill patient populations. We also determined length of stay in ICU and hospital, and the frequency and duration of mechanical ventilation, among appropriate studies.

4.2 Research Questions

The following research questions, organized by topics are addressed by this thesis:

1. What is the most valid estimate of mortality associated with critical illness during the H1N1 pandemic?

2. Are there differences in reported mortality based on the time of enrollment of patients during the H1N1 pandemic?

3. Are there differences in reported mortality based on patients with specific disease syndromes

(ARDS, AKI), therapeutic interventions (mechanical ventilation, ECMO) or co-presenting conditions (pregnancy).

4. Are there differences in reported mortality trends based on different geographic region and socioeconomic status of a country?

5. What is the combined impact of study, system and study level data pertaining to patient- characteristics on the reported mortality during the H1N1 pandemic?

Chapter 5: Materials and Methods

5.1 Search Strategy

We searched Medline (January week 1, 2009 to June week 3, 2013), Embase Classic + Embase

(2009 week 1 to 2013 week 28), LILACS and African Index Medicus for studies that evaluated mortality associated with critical illness in confirmed, probable or suspected cases of 2009-2010

Influenza A (H1N1) infection (For detailed search strategy see Appendix). We reviewed the references of all retrieved studies and review articles to identify any additional studies. We considered full text articles published in any language. We did not consider abstracts or other material presented at medical conferences or unpublished data. The full text of any citation considered potentially relevant was retrieved. The research and ethics committee of our institution waived the need for patient-level consent for this study as only aggregate and previously published data was collected.

5.2 Study Selection and Eligibility Criteria

5.2.1 Inclusion Criteria: We included studies that met the following a priori defined criteria: (1) described confirmed, probable or suspected cases of 2009-2010 influenza A (H1N1) infection; and, (2) described patient(s) who were critically ill. Critical illness was defined by

admission to an adult or pediatric intensive care unit (ICU) or area of the hospital where critically ill patients routinely receive treatment; or, patients receiving invasive or non-invasive mechanical ventilation; or, patients receiving continuous intravenous vasoactive medications; or, another criteria with justification presented in the individual study to designate patients as critically ill.

5.2.2 Exclusion Criteria: We excluded any study that met the following criteria: (1) case series describing fewer than 5 patients; (2) studies that did not report mortality in critically ill patients;

57 studies that only described characteristics of patients who died.

A detailed flowchart based on Preferred reporting items for systematic reviews and meta- analyses (PRISMA) guidelines 58 of the studies included in our systematic review is provided

(Figure 1).

5.2.3 Eligibility criteria for different sub-group of studies

We anticipated that many early and potentially smaller studies would describe patients subsequently included in multicenter or national studies. To prevent non-independent reporting of patient characteristics and outcomes, we included studies only representing unique patient populations for the description of outcomes over different geographical or economic regions and specific ICU populations; however we included studies with potentially duplicated patients for description of outcomes over time, and for single versus multiple centers comparisons. One of the key statistical challenges therefore was to ensure that our estimates were not affected by the duplication of data due to multiple manuscripts describing the same patients. Therefore, we divided all the manuscripts based on the country of enrollment of the patients. We then further evaluated whether the manuscripts were a part of a national database, or not. If they were, we recognized them as being non-duplicate only if they were reporting on cases from different time periods of the pandemic. For studies that were performed in countries without a central data collection mechanism, we reviewed the information on the included medical centers reported in the manuscript, and a study was recognized as being a non-duplicate study only if the centers were different, or if the same centers reported outcomes at different time points. Different

articles were used to describe the effect of system, study and patient based variables, so we have described our methodology for all these groups in detail (Appendix):

Time as a factor in the reporting of mortality: For this analysis we excluded duplicate studies

(both databases, and studies with overlapping patients or reporting a similar time period) and studies with only pediatric patients (as pediatric mortality was low and not comparable to adult patients).

Geography and economic development as factors in the reporting of the mortality: We collected the data on mortality from different countries; we excluded duplicate studies (from similar databases, or reporting on overlapping patients during similar time period). As there was significant heterogeneity in the severity of disease, occurrence of organ failure and the use of

ICU specific therapies in studies from different geographical domains, we also identified the differences in mortality among “unselected” critically ill adults, to examine difference in the reporting of mortality in a homogenous group of studies at a global level and to obtain the most valid estimate of mortality among critically ill patients world-wide.

Influence of specific ICU population on the reporting of mortality: We excluded duplicate studies (any study that might have reported similar patients were screened, and the only studies describing patients over non-overlapping times for each country were included). We excluded studies reporting on only pediatric populations.

Age as a factor in the reporting of mortality: We report mortality from non-duplicate studies for pediatric, adult and both pediatric and adult cohorts.

Influence of single center or multicenter studies on the reporting of mortality: We excluded duplicate studies (any study that could have reported similar patients was screened, and only the

study that reported on the most number of patients for the longest time period were included, specific group of patients reported at different times for each country were included).

Influence of the number of patients in a study on the reporting of mortality: We included all the studies that met our inclusion criteria

Mortality in specific sub-groups of critically ill patients: We selected non-duplicate studies in adults.

Hierarchical meta-regression model: We excluded duplicate studies (any study that could have reported similar patients were screened, and the studies that reported on patients at non- overlapping times for each country were included). We also excluded studies reporting on only pediatric populations.

5.3 Data extraction and study variables

Study characteristics and key results were abstracted by one author (AD) using a standardized study report form. The primary outcome of mortality was abstracted from each study independently by two authors (AD, RF). Disagreements were resolved by consensus. We collected geographic (country, hemisphere, region and continent) variables and economic (World

Bank designation) designation for each country (Country and Lending groups, The World Bank); whether the study included unselected (consecutive) or selective (non-consecutive) critically ill patients, or specific patient populations (e.g. adults or pediatric patients, only mechanically ventilated patients, only patients receiving rescue oxygenation therapy, only those with specific organ injury such as ARDS or acute renal injury); the duration of the study (based on the months and year of inclusion of the first and last patients of the study) and also whether the study period reported on the region-specific first wave, second wave, third wave or more than one wave of the

pandemic. We also collected study level data pertaining to patients: severity of illness using the

Acute Physiology and Chronic Health Evaluation (APACHE) II/III/IV, Pediatric Risk of

Mortality (PRISM) II/III, sequential organ failure assessment (SOFA) or Simplified Acute

Physiology Score (SAPS)II/III; age (overall, and among adults and children <18 years); sex; co- morbidities including obesity, diabetes, congestive heart failure, cerebrovascular disease, neoplastic disorders, chronic liver, or renal diseases; and the presence of immunosuppression.

We collected data on co-presenting conditions such as pregnancy or post-partum status (detailed definitions of all variables provided in Appendix).

5.4 Outcomes

The primary outcome of interest for this systematic review was to determine mortality of critically ill patients with Influenza A (H1N1) during the 2009-2010 pandemic. As mortality was variably reported using different time points in each study, we preferentially used the hospital, then 1 month, then in-ICU mortality, whichever represented the longest period of follow-up.

5.5 Quality Assessment

We used the Newcastle-Ottawa scale (NOS) to assess the quality of included studies. 59,60

Newcastle-Ottawa Scale was developed to assess the quality of non-randomized studies (both cohort and case-control) to help with the interpretation of meta-analytic results 61. Observational studies have specific challenges associated with their implementation and conduct. The NOS is undergoing constant refinement, but its content validity has been established based on critical review of the items by several experts in the field who evaluated its clarity and completeness for the specific task of assessing the quality of studies to be used in a meta-analysis 61. Its content validity and inter-rater reliability have been established 61. Its criterion validity with comparisons

to more comprehensive but cumbersome scales and its intra-rater reliability are currently being examined. The scale allocates up to 9 points to evaluate the risk of bias in cohort or case-control studies in 3 domains: selection of study groups (4 points), comparability of groups (2 points), and ascertainment of either exposure or outcome (3 points). As we were not comparing two distinct groups of patients we evaluated the risk for under- or over-reporting of mortality based on the three domains of the scale. We used a modified NOS to assess the appropriateness of selection, and follow up of these patients and defined the risk as being high for studies with a score of 6 or lower.

Chapter 6: Statistical Analysis

6.1 Descriptive Statistics

We combined data from the included studies to estimate in-hospital mortality associated with the

H1N1 pandemic. Categorical variables are described as frequencies (percentages) and continuous variables are described as median (interquartile range) unless stated otherwise. We described the system based, temporal and geographical characteristics of all studies included in our systematic review. We also described similar variables for studies included in our meta- regression and our hierarchical model. We reported the length of stay and duration of mechanical ventilation as median and interquartile ranges (IQR). The medians reported, are based on combining the reported means or medians (mean of means, or mean of medians) in the included studies

6.2 Meta-Analysis

6.2.1 Random-Effects Model

A fixed effect model assumes that all included studies have a common true effect size and the observed effects are distributed around this value with a standard deviation. A random-effects meta-analysis model allows the true effect to vary among studies. The random effects model thus describes the average of the effects and the degree of heterogeneity among the included studies66. Due to the significant heterogeneity in our included studies we chose the random effects model to incorporate the differences among our studies. We used a random-effects model to obtain summary outcome point estimates and 95% confidence intervals 65. We decided not to use a fixed-effects model for our meta- analysis as there was likely to be significant statistical heterogeneity among our included studies. The statistical heterogeneity in our included studies

was in part due to the clinical differences in the population among the included studies, so we performed further sub-group analysis to explore these concerns 64.

6.2.2 Tests for Statistical Heterogeneity

The heterogeneity among studies should be evaluated using specific statistical tests along with the qualitative assessment of studies 62. We determined statistical heterogeneity among studies by using the using the Cochran Q statistic and I2 index. 63 The Q statistic is calculated as the weighted sum of the square of differences between individual study effects, and their pooled effect across the different studies 62. This measure is a chi-square statistic which is dependent on the number of studies and the corresponding degrees of freedom 64. The Q statistic is a part of the DerSimonian-Laird random effects method, and is useful for evaluating the heterogeneity in meta-analysis with a large number of studies 62. The I2 index is used to describe the variation (in percent) across the studies in a meta-analysis due to heterogeneity 62. The I2 index (I2= 100%x

(Q-df)/Q) is not dependent on the number of studies in the meta-analysis, and is a much simpler expression of inconsistencies among included studies in a meta-analysis 64. Thresholds for the interpretation of I2 can be misleading, since the importance of inconsistency depends on several factors. A rough guide to interpretation is as follows (0% to 40%: might not be important; 30% to 60%: may represent moderate heterogeneity; 50% to 90%: may represent substantial heterogeneity; 75% to 100%: considerable heterogeneity).

6.2.3 Ascertainment of publication bias

A funnel plot is a simple scatter plot of the intervention effect estimates from individual studies against some measure of each study’s size or precision. Effect estimates from small studies will therefore usually scatter more widely at the bottom of the graph, with the spread narrowing

among larger studies. In the absence of bias the plot should approximately resemble a symmetrical (inverted) funnel. Presence of bias usually leads to an asymmetrical appearance of the funnel plot with a gap in one bottom corner of the graph 67. Visual inspection of the funnel plot symmetry provides this information. Egger’s test is most commonly used for the testing of funnel plot asymmetry. Newer contemporary tests such as Begg’s correlation test, Macaskill’s method and Peters’ regression have been described but they are not superior to the Egger test.

All these tests are designed to look at differences in effects in two distinct groups 68 and not at logit proportion for one group, as is the case in our study. Tests for asymmetry should generally be performed only if there are ten or more studies in the meta-analysis

6.3 Subgroup analysis and Meta-Regression

Subgroup analyses and meta-regression are methods to investigate differences between studies.

Statistical significance of the results within separate subgroup analyses should not be compared and we have to be mindful of possible bias through confounding by other study-level characteristics when we consider sub-group analyses. For patient and intervention characteristics, differences in subgroups that are observed within studies are more reliable than analyses of subsets of studies 69. Meta-regression is an extension to subgroup analyses that allows the effect of continuous, as well as categorical, characteristics to be investigated, and in principle allows the effects of multiple factors to be investigated simultaneously. Meta-regression should generally not be considered when there are fewer than ten studies in a meta-analysis 69.

In this thesis we explored clinical heterogeneity by establishing subgroups of studies according to distinct patient populations and conducted subgroup analyses based on different variables extracted from the studies, including specific pandemic time periods (first wave, second wave,

prolonged enrollment), geographical region (country, region, continent, World Bank economic development status), study population characteristics (unselected patients, mechanically ventilated), co-morbidities (pregnancy or post-partum), specific illnesses (ARDS, acute kidney injury) and ICU specific interventions such as receipt of rescue oxygenation therapy (ECMO,

HFOV). Different subgroups are analyzed as follows.

6.3.1 Time as a factor in the reporting of mortality: We divided the pandemic into distinct time-points (based on the enrollment of the patients to the individual studies) and described the mortality associated with Wave I (April 1, 2009 to August 31 2009), Wave II (September 1 2009 to January 31 2010), and for patients enrolled from February 1, 2010. We anticipated a significant overlap of enrollment between these distinct waves of the pandemic. Due to this we also reported on the mortality associated with studies enrolling for between 5 to 9 months of the pandemic and for studies enrolling for more than 9 months of the pandemic (these studies were assessed together regardless of the time period of enrollment). As one of the main hypothesis of our study was to investigate whether early reporting of pandemics was associated with a difference in reported mortality we further performed a paired analysis for all the counties that reported during Wave I of the pandemic with studies from the same countries that enrolled for longer than 9 months. These results were presented as a risk difference, which is defined as the difference between the observed risks in two groups under study. The risk difference describes the estimated difference in the probability of experiencing an event.

6.3.2 Geography and economic development as factors in the reporting of the mortality:

We reported on mortality at three geographical levels: 1. World Bank region; 2. Continent; and,

3. Hemisphere. We also report the mortality using the same cohort of studies as described above

after using the World Bank categorization for high income, upper and lower middle income and low income economy countries.

6.3.3 Influence of specific ICU population on the reporting of mortality: We divided the studies into three distinct categories which we believe signified differing severity of disease in the cohorts that were being evaluated, on the basis of mortality estimates from non-H1N1 populations: unselected critically ill patients; mechanically ventilated patients; and patients undergoing non-conventional mechanical ventilation. We compared the mortality for the three groups, to determine the influence of severity of illness. We also summarized the differences in the duration of mechanical ventilation and length of stay in the ICU for each sub-group.

6.3.4 Age as a factor in the reporting of mortality: Because of the heterogeneity among the pediatric group, we did not include pediatric studies for our comparative analyses and we report a comparison of studies with only adults with studies that describe patients of all ages.

6.3.5 Influence of single center or multicenter studies on the reporting of mortality: We compared reported mortality from multicenter studies compared to single center studies.

6.3.6 Influence of the number of patients in a study on the reporting of mortality: Based on a priori discussion and review of various cohort studies reporting on the Influenza A (H1N1) pandemic we divided the studies into 6 sub-groups. These were based on the number of patients described in each manuscript: 10 or less; 11 to 25; 26-100; 101-250; and >250. We then compared the difference in cumulative mortality in all these sub-groups.

6.3.7 Mortality in specific sub-groups of critically ill patients: We reported the mortality in sub-groups of specific patients. We report mortality associated with co-morbidities or co- presenting conditions (e.g. pregnancy). We also report on studies of that included patients based

upon their receipt of specific therapies such as mechanical ventilation, ECMO, HFOV; and, common organ system failures (ARDS, acute kidney injury)

6.4 Hierarchical meta-regression

Our meta-regression compared mortality rates associated with the H1N1 pandemic in populations with major differences in their access to health care based on the geographical region and economic development of a country. The global prevalence of co-morbid conditions, and underlying health characteristics have stark differences. Moreover the perceptions around critical illness are very different among different cultures and countries and decisions regarding admission to an ICU are dependent on a number of factors, possibly independent from associated patient characteristics. Therefore, it is insufficient to adjust only for the background characteristics of the patients when we compare these studies with respect to their mortality and other outcomes. For the final regression model, we grouped similar predictor variables into hierarchical clusters to investigate their respective and potentially clustered relationships with the primary outcome of mortality 70. We used a three-level hierarchical meta-regression to assess the association between study level data pertaining to patients (age, need for mechanical ventilation, severity of illness) and mortality by considering the variability between the system specific characteristics (either geographical , or socioeconomic status) as well as the variability between studies within the system 69,70. We developed two separate clusters for the system-based variables (socioeconomic status, and geographical region) and the heterogeneity at these two levels was explored with random effects models. We then developed two separate hierarchical models to study the impact of study level data pertaining to patients.

We developed an unconditional three level random effects model with random effects at the level of the system based variables (socioeconomic status of a country, or geographical region of a country) and at the level of studies within those socioeconomic status or geographical region.

We then added a fixed effect at the study level for specific patient characteristics (age, sex and percentage of mechanical ventilation). The patient characteristics that were not significant in the three-level hierarchical meta-regression were removed from the model and we assessed the variance components in the presence of the significant fixed-effects. Similarly if the variance components were not significant were removed from the model. This provided the most precise assessment for the association of study level data pertaining to patients and mortality. We also studied the association between study level data pertaining to patient characteristics and mortality. We took into account the variability at the level of the cluster for system-based variables (we studied variability based on both the socioeconomic status of a country and the geographical region of the country). Finally, we assessed the variability in the reported mortality between studies within a given system both with and without the addition of the cluster of patient related variables.

Chapter 7: Results

7.1 Description of Included studies

Study Flow

Our search strategy yielded 5443 citations after de-duplication. We retrieved 429 articles for a detailed evaluation and included 213 articles for our qualitative assessment. We included 87 articles for our meta-regression (Figure1).

Study Characteristics

We identified 219 studies from 213 articles (6 of the articles compared 2 different time periods of the pandemic and were thus reported separately) from 50 countries that met our inclusion criteria (A detailed description of the study characteristics are described in Table 1). The study characteristics were similar when we evaluated the studies included in the meta-regression, and the hierarchical meta-regression (Table 1). Unselected critically ill patients were described in

69% of the studies, while mechanically ventilated patients were detailed in 47 (21%) of the studies. The included studies were distributed among different geographical regions. Forty

(18%) were from North America, 25 (11%) from Latin America and the Caribbean, 77 (35%) of the studies originated in Europe, and 25 % were from Asia. (Table 1) Only 6 (2%) of the studies were published from African countries. Fifty-six (26%) studies described patients with ARDS, 9

(4%) described patients with acute kidney injury, 20 (9%) of the studies described patients who were evaluated or received ECMO, and only 8 (4%) studies described critically ill pregnant patients. (Table 1) There was no substantial difference in the reporting of demographic, and intervention variables among populations when we evaluated all included studies compared to studies only included in the meta-analyses or hierarchical meta-regression model (Table 2).

Figure 1: Flowchart of studies included in the systematic review using PRISMA guidelines

Records identified through Additional records identified database searching through references of included (Medline-3824 articles and review articles (n = 27) EMBASE-3413)

LILACS-31

African Index Medicus-33)

Records after duplicates removed (n = 5443)

Records excluded (n =5015) Records screened Critically ill patients not (n =5443) described: 2621

No clinical outcomes of interest described: 1546 Conference Abstracts: 461

Reviews: 387

Case reports: 574

Full-text articles assessed for eligibility Full-text articles

(n = 428) excluded, with reasons

(n =215) Outcome of interest not reported: 58

Critically Ill patients not described: 48 Only fatal Cases reported: 21

Articles included in qualitative synthesis Review Article: 19 219 studies from 213 articles Case Report: 14 Other Reasons: 55

Table 1: System and study based characteristics described in 219 studies from 213 articles compared to the studies selected for the meta-regression and hierarchical model respectively.

Study Characteristics All Studies (n-219) Period of Enrollment April 2009-August 2009 50 (23%) September 2009-January 2010 31 (14%) February 2010 until end of pandemic 3 (1%) Studies enrolling through different waves of the pandemic 137 (62%) Multicenter Studies 109 (49%) Study size (number of patients) 5-10 35 (16%) 11-25 74 (34%) 26-100 67 (30%) 101-250 22 (10%) >250 21 (10%) Studies with only adult patients 134 (62%) Studies describing unselected critically ill patients 151(69%) Studies describing specific subgroups ARDS 56 (26%) Acute kidney injury 9 (4%) Pregnant critically ill 8 (4%) Mechanical ventilation 46 (21%) ECMO 20 (9%) Study geographical region Americas North America* 40 (18%) Latin America and Caribbean 25 (11%) Europe Western Europe 67 (31%) Eastern Europe 10 (4%) Asia Middle East 12 (5%) South Asia 12 (5%) East Asia and Pacific 32 (15%) Africa North Africa 3 (1%) Sub-Saharan Africa 3 (1%) Australia/New Zealand 16 (7%) Study country economic status of the country High income economy 155 (71%) Upper middle income economy 50 (22%) Lower middle income economy 13 (7%) Values are numbers (percentages) unless stated otherwise. We describe the system based, temporal and geographical characteristics of countries included in our systematic review. We also describe similar variables for studies included in our meta-regression and our hierarchical model. This table shows that at each level the relative distribution of the variables remained constant

throughout the reported studies.*Mexico is excluded from North America and is considered to be a part of Latin America and Caribbean in the World Bank geographical regions

Characteristics All studies Studies for meta- Studies used in (n=219) regression hierarchical meta- (n=113) regression (n=86) N Median N Median (IQR), N Median (IQR), (IQR), Proportion Proportion Proportion Age 17 40 (33-44) 86 42 (37-46) 69 42 (35-45) 3 Females 17 49% 87 49% 72 49% 0 APACHE II 86 18 (14-20) 43 17 (14-19) 33 17 (15-19) Lung Disease 14 26% 72 25% 57 23% 0 Obesity 98 28% 60 27% 47 24% Pregnancy 10 9% 57 9% 44 8% 1 ICU Course ARDS 12 93% 73 96% 55 96% 8 Acute renal failure 48 35% 25 39% 23 42% Renal replacement 63 17% 35 16% 31 20% therapy Need for Inotropes 97 50% 47 51% 37 59% Antivirals 91 100% 53 100% 43 100% Antibiotics 48 100% 26 100% 24 100% Corticosteroids 69 49% 33 52% 28 56% Outcomes Duration of 69 10 (7-13) 36 10 (7-14) 27 10 (8-13) mechanical ventilation

ICU length of stay 95 11 (8-18) 47 11 (8-20) 40 11 (9-18)

Mortality* 219 28% 113 32% 87 33%

Categorical variables are described as numbers (percentages) and continuous variables are described as median (interquartile range) unless stated otherwise. N Denotes the number of studies that reported on each variable. The reporting of patient level variables remained similar at all levels of our analysis of the reported studies. APACHE II: Acute Physiology and Chronic Health Evaluation II; ARDS: Acute Respiratory Distress Syndrome; ICU: Intensive Care Unit.

7.2 Quality of Included studies

Risk of Bias and quality of evidence assessment

We did not identify any randomized controlled trials; therefore, only observational studies

(cohort, case-series) were included in our analysis. The Newcastle-Ottawa Scale scores for the risk of bias ranged from 4 to 9 out of a maximum of 9 with a median of 7 across studies. Most of the studies were considered to be of high quality. (Table 3) As we were not comparing two distinct groups of patients we evaluated the risk for under- or over-reporting of mortality based on the three domains of the scale. We defined the risk as being high for studies with a score of 6 or lower.

Table 3: The Median (range) of the Newcastle-Ottawa scale for different groups of studies

Overall Selection Comparability Ascertainmen Score of study of groups t of exposure/ groups disease All studies 7 3 2 3 Based on period of Enrollment Wave 1 7 3 2 3 Wave 2 8 3 2 3 Prolonged 7 3 2 3

Geographical Region North America 8 3 2 3 Eastern Europe 7.5 3 2 3 Western Europe 7 2 2 3 Latin America and Caribbean 7 3 2 3 Australia/ New Zealand 7 3 1.5 3 East Asia 7 3 1.5 3 South Asia 8 3 2 3 Mid East and North Africa 8 3 2 3 Sub-Saharan Africa 6 2 1 3 Non-Selected Critically ill 8 3 2 3 patients Newcastle-Ottawa Scale describing the quality of the studies based on different subgroups. We describe the quality of the studies based on the time of enrollment, the geographical regions, and studies just describing non-selected critically ill patients. Most studies were considered to be of high quality based on our scoring criterion (decided a priori)

7.3 Meta-analysis We used a random-effects model for the meta-regression to compare the sub-groups, because of statistically significant heterogeneity among included studies, in addition to substantial clinical heterogeneity among the included studies. (Figure 3)There were differences in the patient characteristics, the interventions provided and the overall severity of illness among many of the studies. The statistical heterogeneity among the included studies was further tested using the I2 statistic. The I2 statistic revealed significant heterogeneity among the studies in all the subgroups.

We also examined the risk of publication bias on non-duplicate studies in adults with a funnel plot and detected a relative paucity of small studies with a large difference in mortality. (Figure

Figure 2: Funnel Plot

Funnel plot examining the risk of publication bias based on the logit proportion of mortality. There is a relative paucity of small studies with a large difference in mortality. Also there are only a few small studies with a small difference in mortality. These represent a specific group (pregnant females) with a very low mortality associated with Influenza A (H1N1) pandemic.

There were only a few small studies with a small difference in mortality. These represent a specific group (pregnant females) with a very low mortality associated with Influenza A (H1N1) pandemic. There are no studies examining the utilization of statistical tests to report publication bias in studies describing outcomes reported in the logit form. Due to this we tested the asymmetry of the funnel plot using 4 different tests: Egger classical; random-effects Egger’s test;

Begg’s Correlation test (Table 4). No asymmetry was detected using the random effects Eggers and Begg’s correlation test. The Egger classic did show a statistical difference, but the regression for this test is unweighted and is thus more unreliable.

Table 4: Tests for evaluation of asymmetry of funnel plot to study publication bias

Method Dependent Independent Weights p-value Variable Variable Treatment/SE 1/SE No weights 0.0263 Egger classical: Egger Weighted regression with multiplicative dispersion Egger: random-effects Treatment SE inverse of (variance+ 0.2827 between-study variance) Begg’s correlation 1 Standardized Variance 0.1579 (Kendall’s tau) treatment Begg’s correlation 2 Standardized Sample size 0.3266 (Kendall’s tau) treatment Trim and fill method on See Figure 3 the random effects model SE: Standard Error

We then used a trim and fill effect on the random effects model and estimated that data from 15 studies was missing. All these studies had a large difference in mortality but they were a mix of both small and large studies (Figure3)

Figure 3: Funnel Plot with Trim and fill effect revealing missing studies

The black dots represent the individual studies with a distribution of the logit of reported mortality. The empty dots represent the potentially missing studies

7.4 Meta-regression

We identified multiple subgroups as detailed above, and performed meta-regression on the reported mortality during the 2009 Influenza A (H1N1) pandemic on all these subgroups. We excluded duplicate studies and studies reporting exclusively on pediatric patients for these subgroups (unless otherwise specified), and used data from 114 studies to report on the mortality associated with specific subgroups of patients.

7.4.1 Reported mortality over time

14 studies reported during the first wave of the pandemic, and 20 studies reported on the second wave. There was a significant overlap between the duration for different studies. Nineteen manuscripts described patients over a prolonged period of time (>9 months) (Figure 3). Overall mortality was 31% among adult patients. Mortality for the first wave of the H1N1 pandemic was

38.7% (95% CI 32.6-45.2) in wave 1, 30.1% (95% CI 22.8-38.6) in wave 2, and 30.5% (95% CI

25.2-36.3) during prolonged enrollment (p=0.66). (Figure 4) There was no difference in mortality when early reports from specific countries were compared with studies reporting on prolonged periods of time. (Table 5)

7.4.2 Age and mortality

There was a significant difference in the mortality based on the age of the population being described. Mortality was significantly lower in the pediatric studies (13.6% (95% CI 9-20.2) compared to the adult studies (29.5% (95 % CI 26.1-33.2) (P<0.0001). 31 studies described both adults and pediatric patients with a reported mortality of 32.7% (95% CI 28.1-36.9), but in all these studies, more than two-thirds of the patients were adults. (Figure 4)

7.4.3 Geographical Area of the study and reported mortality We evaluated the impact of the geographical area on the reporting of mortality in different ways.

As we had a large number of countries we could not report on the individual differences amongst the countries, so we reported the mortality based on the hemisphere, continent and the specific region to which the country belonged. There was no difference in the mortality based on studies from northern hemisphere (30.6% (95% CI 27.9-33.5)) compared to the southern hemisphere

(33% (95% CI 23.6-43.9)) (Figure 5). But when we studied this based on the continents and

Figure 4: Reported mortality associated with 2009 Influenza A (H1N1) associated critical illness.

We describe the mortality based on temporal (early, late and prolonged enrollment), study (study size, single center compared to multicenter and adults compared to pediatrics), and the geographic location and socioeconomic development from the included studies. The black squares represent the point estimate and 95% confidence intervals (CIs) around the mortality for each subgroup. The black diamond is the summary or overall combined estimate of mortality associated with the 2009 Influenza A (H1N1) pandemic.

Table 5: Meta-analysis comparing the reported mortality from “early enrollment” (the Wave 1 for each individual country) during the H1N1 pandemic with studies describing prolonged enrollment from the same countries. We evaluated the differences in the reporting of mortality among the individual countries by using both a fixed-effect and a random-effect model

Country Relative Risk (95% CI) Risk Difference (95% CI) Australia/ New 1.09 (0.87-1.36) 0.01 (-0.01-0.04) Zealand Canada 1.27 (0.89-1.82) 0.04 (-0.01- 0.11) China 1.3 (1.01-1.68) 0.06 (-0.009-0.11) France 2.66 (0.89-7.9) 0.12 (0.03-0.21) Italy 1.003 (0.28-3.53) 0.0006 (-0.25-0.25) Spain 0.84 (0.45-1.55) -0.04 (-0.19-0.11) USA 0.77 (0.51-1.16) -0.06 (-0.17-0.04) Fixed Effect 1.13 (0.98-1.29) p-value 0.07 0.03 (-0.007-0.05) p-value Model 0.009 Random Effect 1.12 (0.93-1.34) p-value 0.21 0.03 (-0.004-0.07) p-value 0.08 Model Quantification of I2=28.1% (0%-69%) I2=45.6% (0%-77%) Heterogeneity

Test of Q=8.34 d f=6 p-value= 0.21 Q=11 d f=6 p-value= 0.08 Heterogeneity The table shows that at an individual country level, the relative risk of death was not statistically significantly different during the duration of the pandemic. The reporting from early case-series gave an approximate estimate of the overall mortality in any given country though the entirety of a pandemic. However, we also found that there were significant intra-country differences in the reported mortality among different countries, and these differences also tended to remain constant when they are studied through the entirety of the pandemic.

geographical regions we found significant differences in the reported mortality among different

continents and geographical regions respectively (Figure 4). Of interest the mortality reported

from Australia (15.1% (95% CI 12.6-17.9) was significantly lower than all other continents.

Studies from Africa reported the highest mortality (41.8% (95% CI 22.9-63.5)), but it was

comparable to studies from Asia (36.9% (95% CI 30.6-43.6)) and South America (36.4% (95%

CI 28.9-44.7)). North America (27.4% (95% CI 23.6-31.6)), and Europe (27.2% (95% CI 23.4-

31.4)) had comparable reported mortality. When we compared the reported mortality based on

the geographical region the reported mortality was the highest in the Sub-Saharan African

(52.7% (95% CI 29.2-75.2)) countries and South Asian (60.9% (95% CI 49.6-71.2)) countries.

Mortality was comparable in North America (24.5% (95% CI 21.9-27.2)); West Europe (25.4%

(95% CI 21.5-29.8)) and East Asia and Pacific 27.6% (95% CI 22.9-32.9)). Reported mortality in Middle Eastern and North African countries (33.8% (95% CI 27.7-40.4)), Eastern European

(35.3% (95% CI 25.5-46.6), and Latin American Countries (38.6% (95% CI 32.2-45.4)) all showed a more pronounced effect when the geographical region rather than the hemisphere or the continent was considered.

7.4.4 Economic status of the country and reported mortality

High income economies had significantly lower reported mortality (26% (95% CI 23.5-28.6) compared to upper middle income Economies (36.7 (95 % CI 31.3-42.4)) and lower middle income economies (57.6% (95% CI 45.8-68.5)) respectively (P<0.0001). (Figure 3) There were clinically relevant differences in the duration of mechanical ventilation among studies from high income economies (11[8-16] days) compared to upper (9 [8-10] days) and lower (8 [6-10] days) middle income economies (Table 6).

Figure 5: Differences in reported mortality based on different geographic variables for the included countries (hemisphere, continent and World Bank designated geographical region).

The Black squares represent the point estimate and 95% confidence intervals (CIs) around the mortality for each subgroup. The black diamond is the summary or overall combined estimate of mortality associated with the 2009 Influenza A (H1N1) pandemic. The use of geographical regions is associated with the best discriminative power to report the differences in mortality in a global context.

Table 6: Differences in Mortality, Length of Stay in the ICU and duration of Mechanical ventilation based on the World Bank economic development classification.

World Bank High income Upper middle Lower middle Economic economy income income development status economy economy Short-term mortality N N N 155 24% 49 35% 13 52% Duration of 49 11 (8-16) 13 9 (8-10) 6 8 (6-10) Mechanical Ventilation, days Length of Stay in ICU, 76 11 (8-20) 14 10 (7-12) 4 10 (7-11) days Variables are described as median (interquartile range) unless stated otherwise. N Denotes the number of studies that reported the specific outcomes. ICU: Intensive Care Unit

7.4.5 Reported mortality in specific ICU populations

Unselected Critically ill patients were described in 71 (63%) of the studies included in our meta- regression, while 36(32%) studies described cohorts with ARDS. We divided the studies based on the severity of illness of patients into multiple sub-groups. Mortality was substantially higher among patients undergoing mechanical ventilation (42.1% [95 % CI 35.8-48.7]) in comparison to unselected critically ill patients, (27.1% [95 % CI 24.4-29.9]) (Figure 5). Mortality in patients with ARDS was 37.4% (95% CI 31.6-43.7) and 43.9% (95% CI 26.1-63.5) among critically ill patients with acute kidney injury, and 9.6% (95% CI 4.5-19.2) among critically ill in pregnant patients.

During the H1N1 pandemic the use of non-conventional therapies for ARDS were extensively reported, so we described the studies in detail at three different levels. Studies with unselected critically ill patients were compared to studies reporting on only mechanically ventilated patients and patients undergoing extracorporeal membrane oxygenation. (Table 7)

Table 7: Differences in baseline characteristics based on the studies only describing unselected critically ill patients, studies describing patients undergoing mechanical ventilation, and studies describing patients under consideration or actually getting ECMO

Characteristics Unselected Mechanical Extracorporeal Critically ill Ventilation membrane (n=151) (n=46) Oxygenation (n=20) n n n Age 119 41 (30-45) 39 41 (35-46) 14 36 (32-40) Females 115 47% 38 50% 16 51% APACHE II 59 18 (14-21) 20 18 (16-21) 6 18 (17-19) Lung Disease 104 28% 25 23% 10 14% Obesity 67 26% 23 27% 7 40% Pregnancy 70 9% 19 9% 12 23% ARDS 75 73% 37 100% 14 100% Acute Renal Failure 37 32% 9 50% 2 49% Renal Replacement Therapy 44 15% 13 17% 6 25% Need for Inotropes 64 44% 24 55% 8 65% Antivirals 59 99% 26 100% 5 100% Antibiotics 35 98% 12 100% 1 100% Corticosteroids 44 48% 17 50% 8 42% Duration of Mechanical 44 9 (7-11) 18 12(9-19) 8 22 (11-27) ventilation

ICU length of stay 68 9 (7-12) 20 12(10-20) 7 22 (18-33)

Short term Mortality* 151 25% 47 36% 20 31%

The patient characteristics, co-morbidities and ICU specific interventions were similar in these sub-groups. Patients who underwent ECMO had longer duration of mechanical ventilation and length of stay in the ICU when compared to mechanically ventilated patients. Patients undergoing ECMO had a lower mortality that patients undergoing mechanical ventilation.

7.5 Hierarchical Meta-Regression

The reported mortality for most subgroups described above was similar when we restricted our analysis to only the studies included in our hierarchical model (Appendix). However, reported

mortality showed a difference based on the number of patients included in the study (24.5 %

[95% CI (20.4%-29%)] in studies with more than 250 patients, and 42% [95% CI (32%-52.7%)] in studies with ≤ 10 patients); (Appendix).

The three-level unconditional meta-analysis model was first fit by taking into account the variability between the economic status and between studies within the economic status. The model had three levels (study, economic development and patient specific variables), but only 2 variance components to estimate though: one at the level of the study and one at the level of economic development. We then introduced the study level data pertaining to patient variables as a fixed effect. Only mechanical ventilation retained any statistical significance when we evaluated the study level data pertaining to patients. So we only used the need for mechanical ventilation as a fixed effect.

When we studied the three level unconditional (no predictors at any level, which helps partition the outcome variation) random effects model with economic stauts of a country and studies within the economic status, the variance at the level of economic status was 0.37 and at the level of the study was 0.22. When we added mechanical ventilation as the fixed effect to the model the variance at the level of the economic status dropped to 0.28 and at the level of the study became

0.17. Therefore need for mechanical ventilation explains 24% (1-0.28/0.37*100) of the variability in reported mortality among the included studies. The variance at the level of the economic status explains 29% (1-0.22/0.17*100) of the variability in reported mortality at the study level within the economic status of countries. (Table 8)

Table 8: Hierarchical model with 3 levels (specific patient variables [need for mechanical ventilation are treated as fixed effects] and study and economic development of the country are treated as random effects with studies clustered within the economic development.

Model 1 Unconditional random effects model with clustering at two levels (study and economic development) Variance Components Estimate Standard Error p-value Second Level: 0.22 0.06 <0.0001 Variance among studies within economic development Third level: 0.36 0.39 0.17 Variance between economic development Model 2 Addition of patient specific variable (mechanical ventilation) as a fixed effect Variance Components Estimate Standard Error p-value Second level: 0.27 0.30 0.18 Variance among studies within economic development Third level: 0.17 0.05 0.0009 Variance between economic development Effect of patient specific variables on reported mortality in a hierarchical model with clustering at three levels Mechanical Ventilation as fixed effect Odds ratio 95% CI p-value <70% 0.55 (0.35 to 0.86) 0.01 70-89% 0.79 (0.49 to 1.28) 0.27 90-99% 0.76 (0.42 to 1.38) 0.30 100% 1 *Odds ratio in comparison to 100% of mechanically ventilated patients. A three level multi- regression model was developed accounting for the variability of study, and the geographic region of the country on the reported mortality during the H1N1 pandemic. When we added the need for mechanical ventilation in critically ill patients to this model it was significantly associated with mortality.

But a part of this variability at the second and third level of our model is explained by the significant heterogeneity existing in the fixed effects variable (mechanical ventilation) in this three level model. Due to this significant variability, the level of the economic status of a country is non- significant. Due to the fact that the variability at the third level is not significant we decided to reduce our economic status model to a 2-level hierarchical model for our final analysis. This two-level hierarchical model showed a significant difference in the mortality based on the addition of the fixed effect variable (mechanical ventilation) (Table 9)

Table 9: Hierarchical model with two levels. The specific patient variables [need for mechanical ventilation] is treated as a fixed effect against studies clustered within the economic development of a country

Unconditional model with clustering at two levels (study and patient) Variance Components Estimate Standard Error p-value Variance among studies within economic 0.29 0.07 <0.0001 development Effect of patient specific variables as affixed effect Mechanical Ventilation as fixed effect Odds ratio 95% CI p-value <70% 0.46 0.30 to 0.69 0.0003 70-89% 0.66 0.66 to 1 0.05 90-99% 0.97 0.56 to 1.69 0.93 100% 1* *Odds ratio in comparison to 100% of mechanically ventilated patients. A two level multi- regression model was developed accounting for the variability at the level of study on the reported mortality during the H1N1 pandemic. The need for mechanical ventilation in critically ill patients was significantly associated with mortality.

We also developed a three-level unconditional meta-analysis model by taking into account the variability between the geographic region and between studies within the geographic region. The model also had three levels (study, geographic region and patient specific variables), but only 2 variance components to estimate though: one at the level of the study and one at the level of geographic region. We then introduced the study level data pertaining to patient variables as a fixed effect. Only mechanical ventilation retained any statistical significance when we evaluated the study level data pertaining to patients. So we only used the need for mechanical ventilation as a fixed effect.

When we studied the three level unconditional (no predictors at any level, which helps partition the outcome variation) random effects model using geographic region of a country and studies within a geographic region the variance at the level of the geographic region was 0.23 and at the level of the study was 0.17. By adding mechanical ventilation as a fixed effect to the model the variance at the level of the geographical region changed to 0.14 and at the level of the study changed to 0.16 signifying that need for mechanical ventilation explains 64% (1-0.23/0.14*100)

of the variability in the reported mortality at the level of the geographic region of a country and only 6% (1-0.17/0.16*100) of the variability at the study level within the geographic regions

(Table 10)

When we evaluated the reported mortality in each three-level hierarchical model, both the study and system based variables were associated with some degree of variability in reported mortality, but the study level data pertaining to patients was strongly associated with the reported mortality.

Table 10: Hierarchical model with 3 levels specific patient variables [need for mechanical ventilation] is treated as fixed effects and study and economic development of the country are treated as random effects with studies clustered within the economic development.

Model 1 Unconditional random effects model with clustering at two levels (study and geographic region) Variance Components Estimate Standard Error p-value Second Level: 0.23 0.15 0.07 Variance among studies within Geographic region Third level: 0.17 0.06 0.001 Variance between geographic region Model 2 Addition of patient specific variable (mechanical ventilation) as a fixed effect Variance Components Estimate Standard Error p-value Second level: 0.14 0.10 0.09 Variance among studies within economic development Third level: 0.16 0.06 0.002 Variance between economic development Effect of patient specific variables on reported mortality in a hierarchical model with clustering at three levels Mechanical Ventilation as fixed effect Odds ratio 95% CI p-value <70% 0.58 (0.38 to 0.88) 0.01 70-89% 0.73 (0.49 to 1.08) 0.11 90-99% 0.79 (0.48 to 1.30) 0.33 100% 1* *Odds ratio in comparison to 100% of mechanically ventilated patients. A three level multi- regression model was developed accounting for the variability at study and geographic region of the country on the reported mortality during the H1N1 pandemic. The need for mechanical ventilation in critically ill patients was significantly associated with mortality

Chapter 8: Discussion

In this systematic review and meta-regression of 219 studies investigating pandemic influenza A

(H1N1) related critical illness from 50 countries, we found that overall mortality for critically ill adults was 31%. Our study highlights significant heterogeneity in the reported mortality among published literature during the Influenza A (H1N1) pandemic. Our systematic review revealed that early in the course of the pandemic there was a tendency to report on selected populations

(i.e. patients requiring mechanical ventilation, severe ARDS etc.), which in turn inflated the early mortality estimates associated with the Influenza A (H1N1) pandemic. Differences in reported mortality were only partly explained by the greater severity of illness of the population under study, and our meta-regression further revealed a significant heterogeneity in the reported mortality according to the global region and the country’s economic development status. When these variables were considered in a hierarchical model, study-based variables (size of the population, single center studies), and system-based variables (geographical region, economic development) were not significantly associated with mortality. In our hierarchical model the reported mortality, instead, was heavily significantly influenced by study based variables pertaining to patient characteristics, most specifically the initial need for mechanical ventilation in the patient population described.

These findings are important because they emphasize that while patient-based factors are most influential in determining outcome, the region and system of care delivery represents a potentially modifiable factor that can lead to improved survival for recoverable infections. These findings also emphasize the limitations of generalizing early reported outcomes from a limited region and among a relatively small number of patients and have relevance for contemporary

outbreaks of seasonal and avian influenza, Middle East Respiratory Syndrome Coronavirus and

Ebola.

We had hypothesized that reporting outcomes from very early phases in a pandemic, when either case definitions are imprecise or treatment protocols or capacity are sub-optimal, may similarly influence reported morbidity and mortality and such estimates might not be generalizable to later stages in a pandemic.14,33,36,71 Our meta-regression showed that the reported mortality was non- significantly higher early in the outbreak (38.8% during the earliest pandemic wave and 29.8% in subsequent waves). However, reported mortality early in the pandemic was heavily influenced by a tendency to report on selected populations (e.g. patients requiring mechanical ventilation, those with severe ARDS). Early reports focusing on these highly selected populations either under-reported mortality (mortality of 13.2% among pediatric studies) or over-report mortality

(e.g. those with severe ARDS, requiring mechanical ventilation, with acute kidney injury, etc.), when compared to the mortality associated with patients afflicted across the entire pandemic.

Early reports during outbreaks and pandemics should ideally describe consecutively enrolled, objectively defined but minimally selected patients to best inform appropriate clinical and policy decisions. Reporting on selected populations is important to identify risk factors for differential outcomes; however, such selected populations should also be clearly defined. This ensures accurate assessment of disease severity at a global scale and allows for early recognition in differences in outcomes over different time periods and geographical regions. Ideally this would be accomplished using prospectively developed, flexible and tiered case report forms that are appropriate for a variety of resource settings 72.

Reporting on differences in regional outcomes associated with critical illness in a global context is challenging. The lack of standardized definitions, and differences in severity of disease that

are vaguely classified as critical illness have been cited as potential barriers.37,41,73 When we compared differences in reported mortality based on early reporting compared to prolonged periods of enrollment for individual countries with available data, there was no intra-country difference in the reported mortality over time, but there were inter-country differences in reported mortality persisted over time in most of the countries (Table 5). 57,74-76 Our also study highlights that the use of geographic variables such as hemispheres or continents is likely less sensitive to differences in outcomes. This may be because differential resources and patient characteristics can exist within broadly defined geographical units. The use of either economic development or geographic regions as defined by World Bank was more sensitive in demonstrating the impact on reported mortality. Recent studies have attempted to describe the burden of critical care and associated utilization of critical care at a global level.44 The economic development of the country might be a surrogate marker for the availability of ICU beds or specific therapies, and the region might give us more information about the similarities or dissimilarities at a system-based and patient-based level in different areas of the world. This was further reaffirmed by our hierarchical meta-regression models, which showed that a patient based variables such as the use of mechanical ventilation was significantly associated with mortality even when we account for the study, geographical or economic variables.

Our study points out that the present mortality reporting for new outbreaks and pandemics are likely heavily influenced by regional and economic variables. The period of enrollment of studies, and the severity of illness are other important factors. These findings highlight the need for standardized reporting of critical illness during outbreaks at a global level. As a number of viral outbreaks are associated with significant respiratory or circulatory failure, initial reports need to make a distinction between reporting of mortality in cohorts of unselected critically ill

patients, and patients with respiratory failure; for instance, those requiring mechanical ventilation and patients requiring rescue therapies. A difference in mortality persisting among countries with similar resources might be a manifestation of the reporting practices in those specific countries.

A number of studies reporting during the H1N1 pandemic used ICU admission and death as a combined outcome.77-79 Critical illness is associated with a 30-40% mortality in many case series and cohort studies. The use of a composite endpoint of mortality and intensive care unit admission is both uneven (mortality and critical illness do not carry the same clinical weighting) and misleading as the main reason for an ICU admission is to minimize the likelihood of death associated with a disease. Future reporting of outcomes associated with critical illness during outbreaks need to consider critical illness as a separate variable from death.

Strengths of this systematic review include a comprehensive search strategy, with duplicate screening and data abstraction that provides the most complete review of pandemic H1N1 outcomes. We used validated strategies to minimize bias in the selection of studies and reporting of outcomes with clinical judgment to decide a priori to combine studies reporting on different time periods of the pandemic, specific sub-groups and clinically important interventions. We further strengthened our results by utilizing multiple meta-regressions to get the most accurate estimate of mortality associated with the H1N1 pandemic. We used random effects models to aggregate data and generate conservative confidence limits for the point estimate of the pooled treatment effect.

However, the quality of our meta-analysis is limited by the quality of included studies, most of which were observational cohorts without a comparison group. In these observational studies, the effect of unidentified confounding factors or residual confounding for known factors cannot be

ruled out. We used the Newcastle-Ottawa scale to ensure we quantified the risk for bias. The majority of our outcomes still focused on the developed regions with capacity to carry out observational and intervention research, and also, the capacity to provide tertiary and quaternary care for these patients. We were able to include some developing countries, but the data from least developed countries is unavailable and therefore missing. The differences in reported mortality based on the economic development of countries highlights that mortality at a global scale may have been higher than had been previously reported. This observation is similar to other recently published studies22,80. Despite an exhaustive review of the literature, we did not collect patient level data, and in the end the estimates on reporting of mortality were based on only study level variables.

Chapter 9: Conclusions and suggestions for future research

In this systematic review of the published literature examining global patient characteristics and outcomes for H1N1-related critical illness during the 2009-2011 pandemic, we provide the most accurate and valid estimates of outcomes, and explore how these outcomes differ according to population, patient and study characteristics. Outcomes associated with new outbreaks may appear very different (usually worse) through reporting on a small, selected group of very ill patients early in the course of an outbreak. Therefore, such reports should consider limiting their reporting to the features associated with the new disease and highlight the serious limitations in predicting true outcome rates. Our analysis also reveals that at a system-based level, the economic development of a country, and the use of geographical regions gives more valid estimation of effect as compared to the traditional use of continents or hemispheres on the reported mortality during disease outbreaks. 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. Differences in mortality in a geographic context are not temporal but reported mortality can be different through the phases of a pandemic in a given country. Our results highlight that a standardized global approach to reporting on outbreaks and pandemics may give us more accurate estimates of morbidity and mortality associated with new diseases. Reported mortality for new outbreaks may be higher or lower depending upon selected patient characteristics, the number of patients described, and the region and economic status of the outbreak location. These findings have relevance for new and ongoing outbreaks. Outbreaks should use case report forms that are prospectively developed, flexible in components, scalable to a variety of resource settings, encompass some measure of severity of illness to allow for risk adjustment across regions, and globally available.72 A standardized global approach to reporting

on outbreaks and pandemics will provide us more accurate estimates of morbidity and mortality associated with new diseases and provide the most valid information upon which to base current and future research, clinical care, and health systems responses.

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Appendix 1: Search strategy and MeSH terms used

MEDLINE search:

Influenza A(H1N1) Virus Search terms: 1. exp Pandemics 2. exp Influenza, Human 3. exp Disease Outbreaks 4. exp Influenza A Virus, H1N1 Subtype 5. exp Influenza A Virus

Critical Illness search terms:

1. exp Critical Care 2. exp Intensive Care Units 3. exp Critical Illness 4. exp Intensive Care 5. exp Mechanical Ventilation 6. exp Artificial ventilation 7. exp Vasopressors 8. exp Inotropes

EMBASE search:

Influenza A(H1N1) Virus Search terms:

1. exp Influenza virus A H1N1/ 2. exp Pandemic influenza/

Critical Illness Search terms:

1. exp Intensive care/ or exp intensive care unit/ 2. exp Critical illness/ 3. exp Critically ill patient/ 4. exp Mechanical Ventilation 5. exp Artificial ventilation 6. exp Vasopressors 7. exp Inotropes

LILACS and African Index Medicus search

1. exp Influenza virus A H1N1/ 2. exp Pandemic influenza/

Appendix 2:

Studies included in qualitative synthesis (n = 213)$

Studies Studies evaluating Studies Comparison of: evaluating time geography/ economic evaluating of enrollment development specific ICU -Number of population patients enrolled (n=107)+ (n=114)* (n=114)* (n=114)*

-Adults vs Pediatrics vs both

(n=131)@

-Single vs Multicenter

(n=114)*

Studies included in

hierarchical meta- regression model (n =60) #

Studies $: 19,21,38-40,75,81-288 289

Studies *: 21,38,40,75,81-85,88,90,92,93,97,99,100,102,105-109,115,120,122,124,127,129,131,133-135,137,144,147-151,153,156- 158,160,161,165-167,169,170,172-174,177,183,185-187,189,190,192-194,196,199,203-205,207,208,215-217,219,221-223,227- 230,234,235,239,241,244,246,248,249,251-253,255-258,260,261,264,267,268,271,272,274,276,277,280,282,288

Studies @:21,38,40,75,81-85,87-90,93,97,99-102,105-109,115,120,122,124,127,129-131,133-135,137,144,147-151,153,156- 158,160,161,165-167,169,170,172-174,177,183,185-187,189,190,192-194,196,198,203-205,207,208,215-217,220-223,227- 230,234,235,239,241,244,246,248,249,251-253,255-258,260,261,264,267,268,270,272,274,276,277,280,282,288 110,123,140,143,171,175,178,180,191,200,213,232,263,266,273,286,287

Studies+: 21,38,40,75,81-85,88,90,93,100,102,105-109,115,122,124,127,129-131,133-135,137,144,148-151,153,156- 158,160,161,165,167,169,170,172-174,183,185-187,189,190,192-194,196,199,201,203-205,207,208,215-217,219,221-223,227- 230,234,235,239,241,244,246,248,249,251-253,255-258,260,261,264,267,268,270,274,276,277,280,282,285,288

Studies#: 21,38,40,81,84,88,93,99,106,111,114,121,135,142,147-149,153,156,158,165-167,174,185,186,190,194,203- 205,208,217,219,221,227-229,233,234,238,239,244,251-253,256,258,260,261,264,267,268,274,277,280,281,283

Appendix 3: Reported mortality associated with 2009 Influenza A (H1N1) associated critical illness for the studies used in the hierarchical meta-regression models

We describe the mortality based on temporal (early, late and prolonged enrollment), study (study size, single center compared to multicenter and adults compared to pediatrics), and the geographic location and economic development from the included studies. The black squares represent the point estimate and 95% confidence intervals (CIs) around the mortality for each subgroup. The black diamond is the summary or overall combined estimate of mortality associated with the 2009 Influenza A (H1N1) pandemic

The black squares represent the point estimate and 95% confidence intervals (CIs) around the mortality for each subgroup. The black diamond is the summary or overall combined estimate of mortality associated with the 2009 Influenza A (H1N1) pandemic. The use of geographical regions is associated with the best discriminative power to report the differences in mortality in a global context.

Appendix 6: Table A1: System and study based characteristics described in 221 studies from 218 articles compared to the studies selected for the meta-regression and hierarchical model respectively.

Study Characteristics All Studies Studies for Studies for (n-219) Meta- hierarchical model regression (n-86) (n-113) Period of Enrollment April 2009- August 2009 50 (23%) 21 (18%) 12 (14%) September 2009-January 2010 31 (14%) 26 (23%) 13(14%) February 2010 till end of pandemic 3 (1%) 1 (1%) 1 (1%) Studies enrolling through different waves of 137 (62%) 66 (58%) 62 (71%) the Pandemic Multicenter Studies 109 (49%) 46 (40%) 42 (48%) Study size (number of patients) 5-10 35 (16%) 23 (20%) 13 (15%) 11-25 74 (34%) 44 (40%) 36 (42%) 26-100 67 (30%) 30 (26%) 20 (23%) 101-250 22 (10%) 6 (5%) 6 (7%) >250 21 (10%) 10 (9%) 11 (13%) Studies with only adult patients 134 (62%) 79 (72%) 56 (66%) Studies describing unselected critically ill 151(69%) 71 (62%) 55 (63%) patients Studies describing specific subgroups ARDS 56 (26%) 36 (32%) 27 (32%) Acute Kidney Injury 9 (4%) 4 (4%) 5 (6%) Pregnant critically ill 8 (4%) 3 (3%) 1 (1%) Mechanical Ventilation 46 (21%) 39 (35%) 30 (36%) ECMO 20 (9%) 8 (7%) 5 (6%) Study geographical region Americas North America* 40 (18%) 12 (11%) 4 (5%) Latin America and Caribbean# 25 (11%) 14 (13%) 15 (18%) Europe Western Europe 67 (31%) 39 (34%) 24 (28%) Eastern Europe 10 (4%) 9 (8%) 9 (10%) Asia Middle East 12 (5%) 6 (5%) 7 (8%) South Asia 12 (5%) 8 (7%) 7 (8%) East Asia and Pacific 32 (15%) 17 (15%) 12 (14%) Africa North Africa 3 (1%) 3 (3%) 3 (4%) Sub-Saharan Africa 3 (1%) 3 (3%) 3 (4%) Australia/New Zealand 16 (7%) 2 (2%) 2(2%) Study country economic status of the country High Income Economy 155 (71%) 73 (64%) 50 (57%) Upper Middle Income Economy 49 (22%) 32 (28%) 28 (32%) Lower Middle Income Economy 13 (7%) 9 (8%) 8 (9%)

Values are numbers (percentages) unless stated otherwise. We describe the system based, temporal and geographical characteristics of countries included in our systematic review. We also describe similar variables for studies included in our meta-regression and our hierarchical model. This table shows that at each level the relative distribution of the variables remained constant throughout the reported studies.

Appendix 7:

Definitions for the Thesis

1. Severity of Illness scores: These are scoring systems used in critically ill patients to assess the severity of disease and provide an estimate of in-hospital mortality. The estimate is based on collection of specific clinical and/or physiologic variables with different weighting. These severity scores are then used to calculate the probability of mortality among patients. Ideal scoring systems should be easy to collect, be well- calibrated, have a high level of discrimination and should be generalizable across various patient populations 290 For the purpose of our study we have collected data on the following severity of illness scores. a. APACHEII/III/IV: The Acute Physiologic and Chronic Health Evaluation (APACHE) scoring system is a severity score used to predict hospital mortality. Age, diagnosis at the time of admission, and numerous acute physiologic and chronic health variables are a part of the APACHE Score 290,291. b. SAPSII/III: Simplified Acute Physiology score (SAPS) is a severity of disease classification system that describes the morbidity in patients based on 12 routine physiologic measurements 292. c. SOFA: The Sequential Organ Failure Assessment (SOFA) uses simple measurements of six major organ functions to calculate a severity score. Serial measurements of this score are predictive of mortality in critically ill patients 293. d. PRISM III: The PRISM III is a scoring system used to predict critical care outcomes for pediatric patients. It describes severity of illness or injury in this population 294.

2. Co-Morbidities Presence of one or more medical conditions that existed in addition to the most significant condition (usually recorded as the "most responsible diagnosis" on hospital discharge abstracts) that caused a patient's stay in the hospital. The number of comorbid conditions is used to provide an indication of the health status (and is also used to help estimate the risk of death) of patients.

a. Heart Disease: Described as the presence of documented any coronary artery disease, congestive heart failure, congenital heart disease, valvular abnormalities and chronic arrhythmias b. Lung Disease: Described as the presence of any asthma, interstitial lung disease, chronic obstructive pulmonary disease (COPD) including chronic bronchitis and emphysema; bronchiectasis, cystic fibrosis, pneumoconiosis and bronchopulmonary dysplasia (BPD) c. Immunosuppression: Immunodeficiency related to use of immunosuppressive drugs (e.g. chemotherapy) or systemic steroids, Human Immunodeficiency Virus infection and Acquired Immune Deficiency Syndrome and autoimmune diseases resulting in systemic immunodeficiency. d. Malignancy: Defined as the presence of any metastatic solid or hematological malignancy. e. Obesity: We used the WHO definition of obesity for our study, defined as a Body Mass Index (BMI) of > 30 kg/m2. BMI is calculated as body weight in kilograms divided by the square of the height in meters (kg/m2). f. Pregnancy: We defined pregnancy as a state for any female who was either pregnant or post-partum (within 6 weeks of delivery) at the time of H1N1 infection.

3. World Bank Classification for geographical regions of the world:

North America (Canada and United States of America); Europe and Central Asia (Albania, Hungary, Romania, Armenia, Kazakhstan, Serbia, Azerbaijan, Kosovo, Tajikistan, Belarus, Kyrgyz Republic, Turkey, Bosnia and Herzegovina, Macedonia, FYR, Turkmenistan, Bulgaria, Moldova, Ukraine, Georgia, Montenegro, Uzbekistan); East Asia and Pacific (American Samoa, Malaysia, Samoa, Cambodia, Marshall Islands, Solomon Islands, China, Micronesia, Fed. Sts, Thailand, Fiji, Mongolia, Timor-Leste, Indonesia, Myanmar, Tuvalu, Kiribati, Palau, Tonga, Dem. Rep. Korea, Papua New Guinea, Vanuatu, Lao PDR, Philippines, Vietnam); South Asia (Afghanistan, India, Pakistan, Bangladesh, Maldives, Sri Lanka, Bhutan, Nepal); Middle East and North Africa(Algeria, Jordan, Tunisia, Djibouti, Lebanon, West Bank and Gaza, Egypt, Libya, Yemen, Iran, Morocco, Iraq, Syrian Arab Republic); Sub-Saharan Africa (Angola, Gambia, Rwanda, Benin, Ghana, São Tomé and Principe, Botswana, Guinea, Senegal, Burkina Faso, Guinea-Bissau, Seychelles, Burundi, Kenya, Sierra Leone, Cameroon, Lesotho, Somalia, Cabo Verde, Liberia, South Africa, Central African Republic, Madagascar, South Sudan, Chad, Malawi, Sudan, Comoros, Mali, Swaziland, Dem. Rep Congo, Mauritania, Tanzania, Congo, Mauritius, Togo, Côte d'Ivoire, Mozambique, Uganda, Eritrea, Namibia, Zambia, Ethiopia, Niger, Zimbabwe, Gabon, Nigeria); Latin America and the Caribbean (Argentina, Ecuador, Nicaragua, Belize, El Salvador, Panama, Bolivia, Grenada, Paraguay, Brazil, Guatemala, Peru, Colombia, Guyana, St. Lucia, Costa Rica, Haiti, St. Vincent and the Grenadines, Cuba, Honduras, Suriname, Dominica, Jamaica, Venezuela, RB, Dominican Republic, Mexico) and Australia and New Zealand

Appendix 8: List of Excluded studies

No. Study Identifier Country of Reason for Exclusion Study 1. Azziz- Baumgartner, Argentina Discusses the burden of disease PLoS One, 2012 and resource utilization associated with H1N1 and does not focus upon patient level variables 2. Palacios, PlosOne, Argentina Severity of illness did not meet 2009 our inclusion criteria 3. Trimarchi, NDT plus Argentina Detailed findings of the same 2009 population described in another manuscript 4. Kusznierz, Influenz Argentina Mortality in critically ill patients and other respir not described viruses, 2013 5. Forrest, Intensive Aus/NZ Discusses only transportation of Care Medicine, 2011 patients requiring ECMO 6. Fitzgerald, Crit Care Aus/NZ Letter to the editor; discusses the and Resuscitation, difficulties with continuous veno- 2012 venous hemodialysis in patients undergoing HFOV 7. Hayashi, Internal Aus/NZ No clear distinction of critically ill Medicine Journal, patients from other patients 2011 8. Ng, American Journal Aus/NZ Fewer than 5 critically ill patients of Transplantation, 2011 9. Bellomo, Australia Outcome variables of interest not Contributions to described Nephrology, 2010 10. Mulrennan, PLoS Aus/NZ Outcome variables of interest not One, 2010 described 11. Hodgson, Crit Care, Australia Described only long term quality 2012 of life in ECMO patients, not outcomes of interest 12. Higgins, Anaesth Aus/ NZ Discusses the economic impact of Intensive Care, 2011 H1N1 Pandemic 13. Hewagama, Clin Aus/ NZ No data describing critically ill Infect Disease, 2010 patients provided 14. Burns, Prehospital Australia Discusses logistics of ECMO Emergency Care, retrieval 2011 15. Pirakalathanan, Australia Only discusses the radiographic Journal of Medical findings in H1N1 patients Imaging and

Radiation Oncology, 2013 16. Lum, Medical Journal Australia Modeling study to examine the of Australia, 2009 demands associated with critical care services during the H1N1 pandemic 17. Khandaker, Australia Neurologic findings associated Neurology 2012 with H1N1 in pediatric patients; no clearly defined parameters for critically ill children 18. Li, Chinese Medical China Describes only histopathological Journal, 2012 findings 19. Capelozzi, Clinics, Brazil Describes only morphological 2010 features associated with ARDS in H1N1 20. Seixas, Brazil Describes histopathology in fatal Histopathology 2010 cases 21. Lorenzoni, Arquivos Brazil Describes muscle biopsy results in de Neuro-Psiquiatria, only fatal cases 2012 22. Lenzi, Revista Da Brazil No outcomes associated with Sociedade Brasileira critical illness reported separately de Medicina Tropical 23. Morris, BMJ Open, Canada No mortality in critically ill 2012 patients provided 24. Muller, PLoS One, Canada Has non-H1N1 data 2010 25. Campbell, CMAJ, Canada Death and ICU admission not 2010 described separately 26. Helferty, CMAJ, Canada No ICU outcomes described 2010 27. Zahariadis, Infect Dis Canada Only two patients described, Med Microbiol, 2010 otherwise a review of microbiology and genetics of H1N1 28. Zhang, Chinese China No clinical outcomes described Medical Journal, 2012 29. Fang, PLos One, China No clinical outcomes described 2012 30. Xu, PLos One, 2013 China Post-pandemic cohort described 31. Yang, Journal of China Separate outcomes of critically ill Infection, 2010 patients not described 32. Yan, Chinese Journal China Critically ill patients not described of Internal Medicine, 2009

33. Chen, Chinese China No clinically relevant outcomes Journal of Radiology discussed 34. Wu, national Medical China Only discusses the features of fatal Journal of China cases 35. Leick-Courtois, France Fewer than 5 critically ill patients Archives de Pediatrie, 2011 36. Luyt, Chest, 2012 France Discusses Long-term outcomes in ARDS patients 37. Annane, Intensive France No outcomes of interest are Care Medicine, 2012 described 38. Fuhrman, France Outcomes in critically ill patients Eurosurveillance, not described separately 2010 39. Wiramus, Annales France Reviews epidemiological data Francaises from different studies throughout d’Anesthesie et de the world, no new data presented Reanimation, 2010 40. Gonzalo-Morales, Chile Characteristics and outcomes Rev Chil Pediatr associated with critical illness not 2011 clearly mentioned 41. Ugarte, Crit Care Chile No patient specific data of interest Med, 2010 provided 42. Gudmundsson, Iceland Editorial Laeknabladid, 2010 43. Prasad, The Journal India Only describes autopsy findings of the association of Physicians of India 44. Bal, Histopathology, India Only describes autopsy findings 2012 45. Sharma, Journal of India No information on critically ill Infect Dev Ctries patients 2010 46. Shelke, Pathology India Only pathological findings International, 2012 described 47. Mishra, PLoSOne, India Does not describe any critically ill 2010 patients separately 48. Kute, Indian Journal India Letter to the editor of Critical Care, 2011 49. Chudasama, Lung India No outcomes in critically ill India, 2011 patients reported

50. Chudasama, J Infect India No outcomes associated with Dev Countries, 2010 critical illness reported 51. Samra, Anaesth, Pain India Fewer than 5 patients and Intensive Care,

2010 52. Samra, Indian J India Letter to the editor, not describing Community Med, variables associated with critical 2011 illness 53. Kinikar, Indian J India No variables associated with Pediatr, 2011 critical illness described 54. Kinikar, Indian J India No variables associated with Pediatr, 2012 critical illness described 55. Jahromi, International Iran Fewer than 5 patients Journal of Obstetric Anesthesia, 2010 56. Gouya, Iranian Red Iran No variables associated with Crescent Medical critical illness were discussed Journal 57. Saleh, Iranian Journal Iran No outcomes associated with of Clinical Infectious critical illness reported Diseases 58. Baldanti, Clin Italy Doesn’t describe specific Microbiol Infect 2011 information in critically ill patients 59. Bellissima, Le Italy Fewer than 5 patients Infezioni in Medicina, 2011 60. NIcolini, Rev Port Italy No clinical outcomes of interest Pneumol, 2012 described in the text 61. Valente, Radiol Med, Italy No clinical outcomes of Interest 2012 described in the text 62. Okumura, Brain and Japan Critically ill population not Development, 2012 defined 63. Nukiwa, Clinical Japan Only fatal cases described Infect Dis, 2010 64. Lopez, Med Spain Case Report Intensiva, 2009 65. Chippiraz, Rev Esp Spain Patients described in the study Quimioter, 2011 have very low APACHE score, so they were excluded 66. Pinilla, Emerg Radiol Spain No clinical outcomes associated 2011 with critical illness reported 67. Martin-Loeches, Spain Describes only fatal cases in Spain Respirology, 2011 68. Peralta, Spain Describes death and ICU Eurosurveillance admission as a combined outcome 2010 without a mechanism to disaggregate 69. Gutierrez-Cuadra, Spain No data associated with critical Revista Espanola de illness provided Quimioterapia

70. Rodriguez, Medicina Spain Describes the outcomes associated Intensiva, 2011 with ICU admissions in the post pandemic period 71. Cardenosa, Human Spain Variables associated with critical Vaccines, 2011 illness not described separately from hospitalized patients 72. Gonzalez, Spain No specific variables associated Enfermedades with critical illness described Infecciosas y separately Microbiologia Clinica, 2011 73. Viasus, Clinical Spain ICU admission and mortality were Microbiology and used as a composite measure for Infection, 2011 severe disease 74. Rodriguez, Archivos Spain Review article de Bronchoneumologia, 2010 75. Bibro, Critical Care USA Case report Nurse, 2011 76. Nickel, Public Health USA Describes death and ICU Reports 2011 admission together with no mechanism to disaggregate 77. Fowlkes, Clinical USA Only describes the epidemiology Infectious Disease, of fatal cases in USA 2011 78. Strouse, Blood, 2010 USA No information on critically ill patients 79. Farooq, J Child USA Outcomes in critically ill patients Neurol, 2012 are not separately reported 80. McKenna, BMC USA Describes death and ICU Infectious Diseases, admission together 2013 81. Mendez-Figueroa, USA Only 3 patients admitted to the Am J Obstet neonatal ICU Gynecol, 2011 82. Jain, Clinical USA Same population was reported in Infectious Diseases article by Bramley et al 2012 83. Skarbinski, Clinical USA Same population was reported in Infectious Diseases, article by Bramley et al 2011 84. Regan, Influenza USA Only describes the epidemiology 2011 of fatal cases in USA 85. Cox, Clinical USA Only has information on pediatric Infectious Diseases, fatalities during the H1N1

2011 pandemic 86. Lee, Clinical USA Only has information on fatal Infectious Diseases, cases in New York 2011 87. Louie, PLoS ONE, USA Only describes fatal cases in 2011 California 88. Nguyen, Crit Care USA No information on mortality in the Medicine, 2012 entire cohort of patients 89. Michaels, American USA Only characteristics of ECMO Journal of Surgery, discussed in this article 2013 90. Miller, Journal of USA No outcomes of interest reported Intensive Care Medicine, 2011 91. Sundar, Journal of USA Variables all divided into short Intensive care term and long term mechanical Medicine, 2011 ventilation 92. Newsome, Birth USA Only outcomes of Infants of Defects Research critically ill pregnant females 93. 94. Li, Journal of Clinical USA Uses all patients infected with Virology, 2009 different strains of influenza 95. Katouzian, Journal of USA No outcomes of interest discussed Investigative Medicine, 2010 96. Pannaraj, Journal of USA No outcomes of interest were Perinatology, 2011 described 97. Jamieson, Lancet, USA Critically ill patients not described 2009 separately 98. Valdes, Rev Cubana Cuba Critically ill patients not described Med Trop, 2011 separately 99. Molbak, Vaccine, Denmark ICU specific outcomes not 2011 described 100. Ahmed, Influenza Egypt Outcomes associated with critical and other respiratory illness not described viruses, 2011 101. Bauernfiend, Germany Influenza A H1N1patients not Infection, 2013 clearly defined as compared to infection due to other viruses 102. Lehners, Emerging Germany ICU admission and mortality were Infectious Diseases, reported together as a marker for 2013 severe disease 103. Stein, Klin Pediatr Germany Reports only on premature 2011 neonates 104. Burkle, Anaesthesist Germany Outcomes associated with critical

2010 illness not described clearly 105. Alb, Dtsch Med Germany Outcomes associated with critical Wochenschr, 2010 illness not described clearly 106. Zarogoulidis, Greece Outcomes associated with critical International Journal illness not reported separately of Internal Medicine, 2013 107. Lee, The Journal of Hong Kong Outcomes associated with critical Infectious Diseases, illness not reported separately 2011 108. Lee, Thorax, 2013 Hong Kong Specific characteristics and outcomes associated with critical illness not reported separately 109. Sigurdsson, Laekna, Iceland Outcomes associated with critical 2010 illness not reported clearly 110. Bayya- Ael, Crit Care Israel No outcomes reported and Resuscitation, 2010 111 Shaham, IMAJ, 2011 Israel No outcomes of interest reported 112. Saidel-Odes, Israel Critically ill population not clearly International Journal delineated of Infectious Diseases, 2011 113. Takeda, Journal of Japan Majority of the patients included Anesthesia, 2012 in the study were in the post pandemic phase 114. Fuchigami, Pediat Japan Critically ill patients not reported Emergency Med separately 2012 115. Okada, J Infect Japan Patients did not meet our Chemother, 2011 definition for critical illness 116. Fujita, Influenza and Japan Letter to the editor other respiratory viruses, 2011 117. Wada, Influenza and Japan ICU admission and mortality were other respiratory described as a composite variable viruses, 2010 with no mechanism to disaggregate 118. Choi, Tuberc Respir South Korea Critically ill specific outcomes not Dis 2010 described 119. Na, Scandinavian South Korea Critically ill specific population Journal of Infectious not defined Diseases, 2011 120. Goong, Infection and South Korea Critically ill specific population Chemotherapy not described 121. Balraj, Malaysian Malaysia Patient characteristics and

Journal of pathology, outcomes not described 2011 122. Chowell, NEJM, Mexico No characteristics or outcomes 2009 associated with critical illness described 123. Echevarria-Zuno, Mexico Critically ill patients not described Lancet, 2009 separately 124. Vazquez- Perez, Mexico Critically ill patients not described Virology Journal, separately 2011 125. Chowell, PLos One, Mexico Critically ill patients not described 2012 separately 126. Silva-Pereya, NEJM, Mexico Only pathological findings 2009 described 127. Rahamat- Netherlands Critically ill patients not described Langendoen, Journal separately of Clinical Virology 2012 128. Pajankar, Oman Oman Patients were not sick enough to Medical Journal, be considered critically ill 2012 129. Rorat, Postepy HIg Poland Only describes fatal cases Med Dosw, 2013 130. Cholewinska, Poland Critically ill patient outcomes not Przeglad described separately Epidemiologiczny, 2010 131. Agha, Mediterranean Saudi Arabia Critically ill patients not described Journal of separately Hematology and Infectious Diseases, 2012 132. Liu, Chin Crit Care China Only risk factors for critical illness Med, 2010 discussed, no outcomes associated with critical illness were described 133. Siau, Singapore Singapore Critically ill patients not described Medical Journal, 2009 134. Wiegand, Wein Klin Switzerland Fewer than 5 patients Wochenschr, 2011 135. Bertisch, Swiss Med Switzerland Critically ill patients not described Wkly, 2010 136. Dede, BJOG, 2011 Turkey Only describes maternal deaths associated with H1N1 137. Ozkan, Pediatric Turkey Critically ill specific cases are not Neurology, 2011 described

138. Gurgun, Tuberkuloz Turkey Patients were not sick enough to ve Toraks Dergisi, qualify to be considered critically 2010 ill 139. Tutuncu, Saudi Med Turkey Discusses risk factors associated J, 2010 with mortality 140. Lucas, Health UK Only discusses fatal cases technology Assessment, 2010 141. Mytton, UK No specific outcomes associated Eurosurveillance, with critical illness reported 2012 142. Campbell, Epidemiol. UK No outcomes associated with Infect 2011 critical illness reported 143. Mytton, Epidemiol. UK No specific characteristics or Infect, 2012 outcomes associated with critical illness described 144. Brett, PLos One 2011 UK ICU admission and death considered as a combined outcome 145. Bewick, Thorax, UK Outcomes associated with critical 2011 illness not reported 146. Myles, PLoS One, UK Outcomes associated with critical 2012 illness not reported 147. Myles, Thorax, 2012 UK ICU admission and death considered as a composite outcome 148. Khan, Anaesthesia, UK Only assesses validity of SOFA 2009 score as a triage tool 149. Fox, PLoS One 2012 Vietnam No separate data on critically ill patients 150. Wang, Chin Crit Care China Only 4 patients described Med, 2010 151. Kato, Nippon Rinsho- Japan Review Article Japanese Journal of Clinical Medicine 152. Guler Ozturk Turkey Describes only 4 patients 153. Dalziel, BMJ 2013 Critical illness and mortality were considered as a composite outcome 154. Jamieson, Lancet, USA Outcomes associated with critical 2009 illness not reported separately 155. Evdokimov, Russia Full text not available Anesteziologiia i Reanimatologiia, 2010 156. Dabnach, Emerging Chile Specific characteristics associated Infectious Diseases, with critical illness not discussed

2011 157. Olga, Anesteziologie Czech Full text article not available e Intenzivni Medicina, 2010 158. Oersted, Clin Denmark No outcomes associated with Microbiology and critical illness discussed Infection, 2012 159. Snacken, Influenza Multiple Outcomes associated with critical and Other Countries illness not described clearly Respiratory Viruses 160. Nakashidze, Georgia Outcomes associated with critical Georgian Medical illness not reported separately News 2012 161. Chowell, NEJM, Mexico Critically ill population not 2009 described clearly 162. Firstenberg, USA Case Report Emerging Infectious Diseases, 2009 163. Gomez, Peru Critically ill population not Eurosurveillance, described 2009 164. Grijalva- Otero, Mexico Describes only fatal cases Archives of Medical Research, 2009 165. Moreno, Intensive NA Review Care Medicine, 2009 166. Oliveira, Brazil Characteristics associated with Eurosurveillance, critical illness not described 2009 separately 167. Fowler, Crit Care NA Review article Med, 2010 168. Patel, Anaesthesia, UK Fewer than 5 patients 2009 169. Peters, Deutsches Germany Editorial Arzteblatt, 2009 170. Smetanin, Canadian Canada Patient level variables and Journal of Infectious outcomes not described Diseases and Medical Microbiology, 2009 171. Presanis, PLoS USA Bayesian Model evaluating Medicine, 2009 severity associated with H1N1 172. Taran, Revista de la Argentina Critically ill patients not described Facultad de Ciencias separately Medicas de Corboda, 2009

173. Webb, Critical care Australia Editorial and Resuscitation 174. Akritidis, American Greece Critically ill patients not described Journal of separately Cardiology, 2010 175. Allard, Diabetes Canada Critically ill patients not described Care, 2010 separately 176. Bellani, Intensive Italy Case Report Care Unit, 2010 177. Berryman, Nursing in UK Case Report Critical Care, 2010 178. Chitnis, WMJ, 2010 USA Critically ill patients not described separately 179. Castilla, Euro Spain Critically ill patients not described Surveillance, 2010 180. Chiumello, Italy Outcomes associated with critical IntensiveCare illness not discussed Medicine, 2010 181. He, Journal of China Outcomes associated with critical Central South illness not described University, 2010 182. Derdak, Crit Care USA No patient data given Medicine, 2010 183. Jaber, Annales France Review Article Francaises d’ Anesthesie et de Reanimation, 2010 184. Jardim, Early Human Portugal Patients did not meet our critically Development, 2010 ill definition 185. Morgan, PLoS ONE, USA Outcomes associated with critical 2010 illness not described 186. Schoub, Expert South Africa Review Review of Respiratory Medicine, 2010 187. 188. Staudinger, Wiener Austria Review article Klinische Wochenschrift, 2010 189. Weiss, Pneumologie, Germany Review Article 2010 190. Bahloul, Trends in Tunisia Review Article Anaesthesia and Critical Care, 2010 191. Charu, CID, 2011 Mexico Only fatal cases discussed 192. Falagas, Argentina Review Article

Epidemiology and Infection, 2011 193. Fezeu, Obesity France Systematic Review reviews, 2011 194. Mosby, American USA Systematic Review Journal of Obstetrics and Gynecology, 2011 195. Presanis, BMJ, 2011 UK Mathematical model of severity 196. Van Kerkhove, Multiple No outcomes associated with Influenza and other Countries critical illness described Respiratory viruses, 2011 197. Van Kerkhove, PLoS Multiple No outcomes associated with ONE, 2011 Countries critical illness reported 198. Wong, Perfusion, NA Review 2011 199. Barai, Australasian India Outcomes associated with critical Medical Journal, illness not described 2012 200. Berdai, Pan African Morocco Outcomes associated with critical Medical Journal, illness not described 2012 201. Dawood, The Lancet Multiple Outcomes associated with critical Infectious Diseases, Countries illness not described 2012 202. Dubrov, Intensive Ukraine Abstract only Care Medicine, 2011 203. Fernandez, Medicina NA Post pandemic report Clinica, 2012 204. Homaira, Bulletin of Bangladesh Variables associated with critical WHO, 2012 illness not described 205. Roll, Infection, 2012 Germany Critically ill patients not described 206. Rolland- Harris, Canada Critically ill patients not Epidemiology and described Infection, 2012 207. Schuck-Paim, PLoS Brazil Critically ill patients not described ONE, 2012 208. Kuchar, Respiratory Poland Critically ill patients not described Physiology and Neurobiology 209. Marzano, Journal of Italy Critically ill patients not described Medical Virology, 2013 210. Golokhvastova, Russia Full text not available Klinicheskaia

Meditsina , 2012 211. Iatyshina, Russia Full text not available Terapevticheskii Arkhiv, 2010 212. Klimova, Russia Full Text Not available Terapevticheskii Arkhiv, 2010 213. Kolobukhina, Russia Full text not available Terapevticheskii Arkhiv, 2011 214. Luzina,Klinicheskaia Russia Full text not available Meditsina, 2011

Appendix 9: CASE REPORT FORM

Number:

Is the Study Data duplicated Yes No

Name of the Author and Study:

Study Variables

1. Year of Publication:

2. Period of Study a. Start: b. Stop:

3. Hemisphere of Study:

4. Country of Study:

5. World Bank Region of the Country

6. Single Center Vs Multicenter :

7. Part of a Database: Yes No a. Name of Database

8. Multiple Countries in the Study: Yes No a. List of countries:

9. World Bank economic status country: Low Income Lower-Middle Income Upper Middle Income High Income

10. Number of Patients in the Study:

11. Patients with: N % Confirmed H1N1 Probable H1N1 Suspected H1N1

12. Population Under Study a. Adults Peds Both

100

b. Unselected Critical Care Population Specific population

Which Kind of Specific population:

13. Demographics Age Mean SD Median IQR Other

Sex (Females) Number Percentage

14. Severity of Illness score ( Day 1) Type Mean SD Median IQR APACHE II/III/IV SOFA PRISM III Other:

15. Major Co-Morbidities N % Lung Disease Heart Disease Renal Disease Neurologic Liver Disease Malignancy Immunosuppressed Diabetes Obesity Smoker Substance Abuse Pregnancy

16. Incidence of Specific diagnosis (At Admission) N % a. Septic Shock b. Acute renal Failure c. ARDS

17. Use of Specific Therapies during ICU stay N % a. Inotropes b. Renal Replacement Therapy c. Mechanical Ventilation i. Invasive ii. Non Invasive iii. Failure of Non Invasive

101

18. Mechanical Ventilation Parameters (Day 1) Mean SD Median IQR a. FIO2 b. PaO2/FiO2 c. PEEP d. Oxygen Index e. Mean Airway Pressure

19. Use of Rescue therapy For Severe Hypoxemia ( At any time in ICU) N % a. Inhaled Nitric Oxide b. Inhaled Prostacyclins c. Neuromuscular Blockade d. High Frequency Oscillation e. Prone Positioning f. ECMO / other ECLS g. APRV h. Recruitment Maneuvers

20. Outcomes Mean SD Median IQR

Duration of Mechanical ventilation Those Dying Those not Dying All

Ventilation free days (of 28 or specify)

ICU Length of Stay Those Dying Those not Dying All ICU free days (of 28 or specify)

Mortality ICU Hospital 28-Day 30-Day 60 Day 90 Day Other (specify):______

102

Appendix 10: Components of Newcastle Ottawa Scale

Cohort Studies

Selection Comparability Outcome Representativeness of cohort Cohorts are comparable on the Assessment of outcome Selection of non-exposed basis of design or analysis cohort Ascertainment of exposure Adequate follow up Outcome absent

Case Control Studies

Selection Comparability Exposure Adequate case definition Comparability of cases and Ascertainment of exposure Representativeness of cases controls on the basis of design Methods similar for cases and Selection of controls or analysis controls Definition of controls Non response rate

103