Measuring the Short-Term Effect of Ambient Air Pollution on Acute Health Service Use in Ontarians Living with Chronic Obstructive Pulmonary Disease

Richard George Foty

A thesis submitted in conformity with the requirements for the degree of Doctor of Philosophy Institute of Medical Science University of Toronto

Measuring the Short-Term Effect of Ambient Air Pollution on Health Service Use in Ontarians Living with Chronic Obstructive Pulmonary Disease

Richard George Foty

Doctor of Philosophy

Institute of Medical Science University of Toronto

2017

Abstract

Short-term air pollution exposure has been associated with increased morbidity, especially amongst vulnerable populations like those with Chronic Obstructive Pulmonary Disease

(COPD). The Air Quality Health Index (AQHI) is an aggregate measure of overall ambient air pollution. It was designed to communicate to the public in an understandable way air quality levels, associated health risks, and strategies to reduce exposure. Its formulation was based on relative mortality and little is known about the association between the AQHI and morbidity amongst individuals with COPD. The objective of my dissertation is to determine the association between ambient air pollution and acute health service use (HSU - hospitalization and emergency department [ED] visits) amongst individuals with COPD. My first original research study determined the perception and knowledge of individuals with COPD, regarding air pollution health risks, assessment, and exposure reduction strategies. The second quantified the association between the AQHI and acute HSU, and the final study examined whether temperature modifies this association. While Canadians with COPD believed that air pollution affects health, their level of knowledge regarding air quality assessment, health risks, and strategies to reduce exposure was lacking. The AQHI was associated with increased risk of acute HSU for non- accidental, COPD and cardiovascular (CVD) causes. A statistically significant association

ii between the AQHI and CVD hospitalization was observed at all temperatures, whereas the association with COPD hospitalization was only significant at higher temperatures. Results also suggested individuals with prior history of hospitalization for COPD, lower respiratory tract infection, or acute myocardial infarction may be more vulnerable. These findings provide important insights for policy and public health strategy. Reducing the negative health effects of air pollution amongst individuals with COPD requires patient and health care provider education, real-time dissemination of air quality levels regardless of temperature, and specific recommendations on how to reduce exposure.

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Acknowledgments

I would like to express my deepest gratitude to Dr. Teresa To. In addition to being my PhD supervisor, Teresa has been my friend and mentor for many years, even before the start of my doctoral degree. Teresa has always been incredibly supportive and encouraged me to pursue my passions.

I would also like to thank my thesis advisory committee: Dr. Sharon Dell, Dr. Andrea Gershon, and Dr. Hong Chen. This dissertation would not have been possible without their unwavering support, and their clinical, epidemiological and methodological insights. Sharon has been my friend and role model for over a decade. She has always inspired me to perform at my best, while giving me the freedom to learn from my mistakes. Andrea’s limitless curiosity dared me to embrace the deeper questions generated by my research, and explore the topics that are most relevant to patient health. Hong’s eagerness to develop and apply novel methodologies has taught me the importance of iteration, and challenged me to produce the highest quality research.

To my friends, mentors and partners in translational research in Toronto and internationally through the Eureka Institute. You have challenged me to define “success” in my own mind.

Thank you for igniting this insatiable passion to pursue the things I love, and giving me the freedom to cross the deepest valleys in the most imaginative ways possible.

Not a single one of my successes would have been possible without the unconditional love of my family and friends. To my lovely wife, Nikki. You have been my life support through these last few years. My heart cannot wait to start the rest of our lives together. To my loving parents and sister. You are the best parts of me and I hope I will always make you proud. To my cousin Dr. iv

Ramsey Foty who has always been more of an older brother. You inspire, even without meaning to. I would have never achieved my goals without your love and friendship. To my friends

Derek, Brittany and Katie, thank you for all our adventures and misadventures that keep me laughing and remind me to breathe. Finally, thank you to my nephews Alexandre, Nicholas and

Sebastien who remind me that all my frustrations, late nights and dedication is inspiring them to pursue their passions, in the same way others have inspired me.

Contributions

I, Richard Foty, am the sole author of this thesis. I was involved in all aspects of this work, including the study design, data analysis and preparation of all original research manuscripts.

The following contributions were made by other individuals who I formally acknowledge below:

Dr. Teresa To (Primary Supervisor): Mentorship, and guidance to obtain research ethics board approval, and data from the Institute for Clinical Evaluative Sciences (ICES), the Ontario

Ministry of the Environment and Climate Change, and Environment and Climate Change

Canada. Epidemiological and statistical expertise, and assistance in the planning and execution of all data analyses, as well as manuscript and dissertation preparation.

Dr. Sharon Dell (Thesis Advisory Committee member): Mentorship, respiratory disease content expertise, and guidance on clinical and epidemiological aspects of the original research, as well as manuscript and thesis preparation.

Dr. Andrea Gershon (Thesis Advisory Committee member): Mentorship, COPD content expertise, and guidance on clinical and epidemiological aspects of the original research, as well as manuscript and thesis preparation.

Dr. Hong Chen (Thesis Advisory Committee member): Mentorship, statistical and epidemiological content expertise, and guidance training on all methodological aspects of the original research, as well as manuscript and thesis preparation.

Jingqin Zhu (ICES analyst): Preparation of all ICES raw data, data content expertise, and review of study proposal.

Dr. Kristian Larson: Preparation of air pollution monitoring maps (Figure 1-2). Geospatial content expertise.

Dr. Dave Stieb (Health Canada): Acquisition of Health Canada data for study 1. Health effects of air pollution content expertise.

Dr. Wolfgang Viechtbauer (Maastricht University): Statistical expertise, and guidance on meta- analytic methodologies.

Data for study 1 of this dissertation (Chapter 3) was provided by Christina Daly, Sharon Jeffers and Dr. Dave Stieb of Health Canada. Data for studies 2 and 3 (Chapters 4 and 5), were provided by ICES, which is funded by an annual grant from the Ontario Ministry of Health and Long-

Term Care (MOHLTC).

The opinions, results and conclusions reported in this paper are those of the authors and are independent from the funding sources. No endorsement by Health Canada, ICES or the Ontario

MOHLTC is intended or should be inferred. Parts of this material are based on data and information compiled and provided by the Canadian Institute for Health Information (CIHI) and

Health Canada. However, the analyses, conclusions, opinions and statements expressed herein are those of the authors, and not necessarily those of CIHI.

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Doctoral funding was provided by: Dr. Teresa To; Dr. Sharon Dell; the Hospital for Sick

Children Restracomp graduate scholarship; the Canadian Respiratory Research Network

Studentship Award, and; the Institute of Medical Science, Doctoral Completion Award.

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Table of Contents

ACKNOWLEDGMENTS ...... IV

CONTRIBUTIONS ...... VI

TABLE OF CONTENTS ...... IX

LIST OF TABLES ...... XIV

LIST OF FIGURES ...... XVI

LIST OF ABBREVIATIONS ...... XVII

LIST OF APPENDICES ...... XIX

CHAPTER 1: INTRODUCTION ...... 1

1.1 ORGANIZATION OF THE DISSERTATION ...... 3

1.2 THE BURDEN OF CHRONIC OBSTRUCTIVE PULMONARY DISEASE (COPD) IN CANADA ...... 4

1.3 CHRONIC OBSTRUCTIVE PULMONARY DISEASE ...... 6 1.3.1 Definition and Pathophysiology ...... 6 1.3.2 Diagnosis ...... 9 1.3.3 Clinical Presentation ...... 10 1.3.4 Management ...... 11 1.3.5 Risk Factors for the Development and Progression of COPD, as well as the Exacerbation of Symptoms ...... 13 1.3.6 Major Comorbidities ...... 17 1.3.6.1 Asthma-COPD Overlap Syndrome (ACOS) ...... 18 1.3.6.2 Cardiovascular Disease (CVD) ...... 19 1.3.6.3 Lower Respiratory Infection ...... 20 1.3.6.4 Lung Cancer ...... 20 1.4 AIR POLLUTION IN ONTARIO ...... 21 1.4.1 Air Quality Monitoring ...... 23

1.4.2 Nitrogen Dioxide (NO2) ...... 24

1.4.3 Ozone (O3) ...... 25

1.4.4 Fine Particulate Matter (PM2.5) ...... 26 1.4.5 Recommended Levels ...... 26 1.4.6 The Air Quality Health Index (AQHI) ...... 27 1.4.6.1 Background: The Air Quality Index (AQI) ...... 29 1.4.6.2 Creation of the AQHI ...... 30 ix

1.4.6.3 The Numeric Formulation ...... 31 1.4.6.4 Communication Materials ...... 33 1.4.6.5 Implementation of the AQHI across Ontario ...... 35 1.5 SHORT-TERM AIR POLLUTION EXPOSURE AS A RISK FACTOR FOR COPD MORTALITY AND MORBIDITY ...... 36 1.5.1 Commonly Used Methodologies ...... 37 1.5.1.1 Study Design ...... 37 1.5.1.2 Lag Structure ...... 39 1.5.1.3 Confounders and Effect Modifiers ...... 39 1.5.2 Literature Review ...... 41 1.5.2.1 Multi-City Studies ...... 42 1.5.2.2 Meta-Analyses of Published Literature to Date ...... 47 1.5.2.3 Studies Examining Seasonal and Temperature Effects ...... 51 1.5.2.4 Studies Using the Air Quality Health Index ...... 55 1.6 BIOLOGICAL MECHANISMS ...... 57 1.6.1 Oxidative stress ...... 57 1.6.2 Inflammation ...... 58 1.6.3 Mucus ...... 59 1.6.4 Thermoregulatory response to heat ...... 59

CHAPTER 2: THESIS OBJECTIVES, HYPOTHESES & METHODOLOGICAL APPROACH ...... 61

2.1 THESIS OBJECTIVES AND HYPOTHESES ...... 62 2.1.1 Overall Thesis Objective ...... 62 2.1.2 Study 1: Perceptions and Behaviours of Canadians with COPD towards the Health Effects of Air Pollution ...... 63 2.1.3 Study 2: The Seasonal Health Risks of Air Pollution Exposure on People Living With COPD: A Population-Based Study ...... 64 2.1.4 Study 3: The Combined Effects Of Air Pollution And Temperature On The Health Of Persons Living With COPD: A Population-Based Study ...... 65

2.2 DESIGN OF STUDY 1 ...... 67 2.2.1 Data Source ...... 67 2.2.2 Sample Size Calculation ...... 69

2.3 STUDY DESIGN OF STUDIES 2 AND 3 ...... 70 2.3.1 Data Sources ...... 70 2.3.1.1 Outcome Data ...... 70 2.3.1.2 Environmental Exposure Data ...... 71 2.3.1.3 Study population ...... 73 x

2.3.2 Sample Size Calculation ...... 74 2.3.3 Analytic Methods ...... 74 2.3.3.1 Case-Crossover Analysis ...... 74 2.3.3.2 Conditional Logistic Regression ...... 77 2.3.3.3 Akaike’s Information Criterion ...... 78 2.3.3.4 Meta-Analysis ...... 79 2.3.3.5 Research Ethics ...... 80

CHAPTER 3: PERCEPTIONS AND BEHAVIOURS OF CANADIANS WITH COPD TOWARDS THE HEALTH EFFECTS OF AIR POLLUTION ...... 81

3.1 ABSTRACT ...... 82

3.2 BACKGROUND ...... 83

3.3 OBJECTIVES ...... 84

3.4 METHODS ...... 84 3.4.1 Data Source ...... 84 3.4.2 Study Population ...... 85 3.4.3 Study Variables ...... 86 3.4.4 Statistical Analysis ...... 87 3.4.5 Research Ethics Approval ...... 88

3.5 RESULTS ...... 88 3.5.1 Population Characteristics ...... 88 3.5.2 Reported Knowledge of the health effects of air pollution ...... 90 3.5.3 Reported Local Air Quality Assessment ...... 94 3.5.4 Reported Health Effects of Air Pollution and Behavioral Change to Reduce Exposure ...... 94

3.6 DISCUSSION ...... 98

3.7 CONCLUSIONS ...... 103

CHAPTER 4: THE SEASONAL HEALTH RISKS OF AIR POLLUTION EXPOSURE ON PEOPLE LIVING WITH COPD: A POPULATION-BASED STUDY ...... 105

4.1 ABSTRACT ...... 106

4.2 BACKGROUND ...... 108

4.3 OBJECTIVES ...... 110

4.4 METHODS ...... 110 4.4.1 Study Design ...... 110 4.4.2 Data Sources ...... 111

4.4.3 Study Population ...... 112 4.4.4 Outcome Measures ...... 113 4.4.5 Exposure Measures ...... 113 4.4.6 Covariates and Effect Modifiers ...... 116 4.4.7 Statistical Analysis ...... 117 4.4.8 Research Ethics Approval ...... 119

4.5 RESULTS ...... 119 4.5.1 Baseline Population ...... 119 4.5.2 Distribution of Acute Health Service Use ...... 122 4.5.3 Environmental Exposures ...... 125 4.5.4 Main Effect – Hospitalizations ...... 129 4.5.5 Main Effect – ED Visits ...... 129 4.5.6 Sensitivity Analyses ...... 131 4.5.7 Stratified Analyses ...... 132 4.5.8 Seasonal Effect ...... 140

4.6 DISCUSSION ...... 140

4.7 CONCLUSIONS ...... 146

CHAPTER 5: THE COMBINED EFFECTS OF AIR POLLUTION AND TEMPERATURE ON THE HEALTH OF PERSONS LIVING WITH COPD: A POPULATION-BASED STUDY ...... 148

5.1 ABSTRACT ...... 149

5.2 BACKGROUND ...... 151

5.3 OBJECTIVES & HYPOTHESES ...... 153

5.4 METHODS ...... 154 5.4.1 Study Design ...... 154 5.4.2 Data Source ...... 154 5.4.3 Study Population ...... 155 5.4.4 Outcome Measures ...... 155 5.4.5 Exposure Measure ...... 156 5.4.6 Covariates and Effect Modifiers ...... 158 5.4.7 Statistical Analysis ...... 158 5.4.8 Research Ethics Approval ...... 161

5.5 RESULTS ...... 161 5.5.1 Baseline Population ...... 161 xii

5.5.2 Distribution of Exposures and Outcomes Hospitalizations ...... 161 5.5.3 Analysis by temperature quartile ...... 166 5.5.4 Analysis by °C ...... 168 5.5.5 Sensitivity analyses ...... 169

5.6 DISCUSSION ...... 172

5.7 CONCLUSIONS ...... 179

CHAPTER 6: SUMMARY OF MAIN FINDINGS & GENERAL DISCUSSION ...... 181

6.1 SUMMARY OF FINDINGS ...... 181

6.2 HEALTH EFFECTS OF AIR POLLUTION AMONGST INDIVIDUALS WITH COPD ...... 185

6.3 UTILITY OF THE AQHI IN AIR POLLUTION RESEARCH ...... 186

6.4 USE OF THE AQHI IN THE POPULATION ...... 189 6.4.1 AQHI Awareness and Adoption ...... 189 6.4.2 Interpretation of the AQHI ...... 190 6.4.3 Active Dissemination of Real-Time AQHI data ...... 190

6.5 TAKING ACTION ...... 191 6.5.1 Education ...... 192 6.5.2 Government Strategies ...... 193

6.6 STRENGTHS ...... 196

6.7 LIMITATIONS ...... 198

6.8 CONCLUSIONS ...... 202

CHAPTER 7: FUTURE DIRECTIONS ...... 204

REFERENCES ...... 209

APPENDIX: SUPPLEMENTAL TABLES ...... 235

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List of Tables

Table 1-1: The Air Quality Health Index Categories and Messages ...... 34

Table 1-2: Summary of Notable Multi-City Studies Examining the Association Between Air Pollution and Cardiorespiratory Outcomes ...... 46

Table 1-3: Summary of Recently (2013-2016) Published Meta-Analyses Examining the Association Between Air Pollution and COPD Outcomes ...... 49

Table 3-1: Study population characteristics ...... 89

Table 3-2: Reported knowledge of air pollution, method of air quality assessment, health outcomes, and behaviour modifications by reported chronic disease status...... 91

Table 3-3: Associations between reported health problems, behaviour modifications and reported chronic disease status...... 96

Table 3-4: Association between method of air quality assessment and health outcomes amongst the total study population, and stratified by disease group...... 97

Table 4-1: International classification of disease (ICD-9 and ICD-10) codes for chronic disease outcomes and comorbidities...... 115

Table 4-2: Demographics of Study Population with Chronic Obstructive Pulmonary Disease in Ontario (2003-2013) ...... 121

Table 4-3: Distribution of Acute Health Service Use Across Ontario Census Divisions (2003- 2013) ...... 123

Table 4-4: Distribution of Average Annual Environmental Exposures Across Ontario Census Divisions (2003-2013) ...... 126

Table 4-5: Pearson Correlations Between Environmental Exposures ...... 127

Table 5-1. Distribution of environmental exposures and hospitalizations by Ontario Census Divisions between 2003 and 2013 ...... 163

Table 5-2. Pooled Results of the association between the AQHI and hospitalization amongst persons in Ontario living with COPD between 2003 and 2013, by ambient temperature level, age and sex...... 167

List of Figures

Figure 1-1: Normal vs. Emphysematous Alveoli...... 8

Figure 1-2: Map of AQHI Monitoring Sites in Ontario 2003-2013 ...... 22

Figure 1-3: The Air Quality Health Index...... 28

Figure 5-1a. Average monthly AQHI values in Ontario between 2003 and 2013 ...... 164

Figure 5-1b. Time series of average monthly ambient temperature and relative humidity values in Ontario between 2003 and 2013 ...... 165

Figures 5-2a-c. Pooled estimates of the risk of (a) Non-accidental; (b) COPD; (c) CVD hospitalization amongst individuals with COPD ...... 170

Figures 5-3a-b. Pooled estimates of the risk of COPD hospitalization amongst individuals with COPD aged < 65 years (a) and 65+ years (b) ...... 171

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List of Abbreviations

95% CI 95% Confidence Interval AAQC Ambient Air Quality Criteria AATD Alpha-1 Antitrypsin Deficiency AECOPD Acute Exacerbation of COPD AIC Akaike Information Criterion APHEA Air Pollution and Health, a European Approach AQHI Air Quality Health Index AQI Air Quality Index CCHS Canadian Community Health Survey CHMS Canadian Health Measures Survey CIHI Canadian Institute for Health Information CMA Canadian Medical Association CO Carbon Monoxide COH Coefficient of Haze COPD Chronic Obstructive Pulmonary Disease CRRN Canadian Respiratory Research Network ED Emergency Department EMRB Environmental Monitoring and Reporting Branch

FEV1 Forced Expiratory Volume in 1 Second FVC Forced Vital Capacity HSU Health Service Use ICD-10 International Classification of Diseases 10th Revision ICD-9 International Classification of Diseases 9th Revision ICES Institute of Clinical Evaluative Sciences LOESS Locally Weighted Smoothing Function LRTI Lower Respiratory Tract Infections MOECC Ministry of the Environment and Climate Change

N2 Nitrogen Gas NAPS National Air Pollution Surveillance NMMAPS National Morbidity, Mortality, and Air Pollution Study NO Nitric Oxide

NO2 Nitrogen Dioxide O Oxygen Atom

O2 Oxygen Gas

O3 Ozone OHIP Ontario Health Insurance Plan OR Odds Ratio

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PCP Primary Physician Practices

PM2.5 Particulate Matter less than 2.5 Micrometres in Diameter ppb Parts Per Billion QA/QC Quality Assurance and Quality Control RR Relative Risk SLCDC Survey on Living with Chronic Diseases in Canada

SO2 Sulfur Dioxide WHO World Health Organization

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List of Appendices

Table S.3-1: Study population characteristics ...... 236

Table S.3-2: Reported knowledge of air pollution, method of air quality assessment, health outcomes, and behaviour modifications by reported chronic disease status...... 237

Table S.3-3: Associations between health outcomes, behaviour modifications and reported chronic disease status...... 241

Table S.3-4: Association between method of air quality assessment and health outcomes amongst the Heart Disease, or Diabetes without respiratory disease group...... 242

Table S.4-6a: Results of Main Analyses: Non-Accidental and Disease Specific Pooled Estimates of the Risk of Acute Health Service Use by Air Pollutant (Lag03) Across Ontario Census Divisions – During the Full Year ...... 243

Table S.4-6b: Results of Main Analyses: Non-Accidental and Disease Specific Pooled Estimates of the Risk of Acute Health Service Use by Air Pollutant (Lag03) Across Ontario Census Divisions – During the Warm Season (Jun-Aug) ...... 244

Table S.4-6c: Results of Main Analyses: Non-Accidental and Disease Specific Pooled Estimates of the Risk of Acute Health Service Use by Air Pollutant (Lag03) Across Ontario Census Divisions – During the Cold Season (Dec-Feb) ...... 245

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xx 1

Chapter 1:

Introduction

Preface

Chronic Obstructive Pulmonary Disease (COPD) is a progressive life-threatening lung disease characterized by irreversible airflow limitation and chronic inflammation of the lungs. It is an increasingly prevalent disease in both developed and developing countries(Global Initiative for

Chronic Obstructive Lung Disease 2017) with an estimated 64 million people affected worldwide(1). COPD is the 3rd leading cause of mortality globally, responsible for the deaths of over 3.1 million people in 2015.(World Health Organization 2014)

In Canada, COPD is a leading cause of morbidity and mortality(Chapman, Bourbeau et al. 2003,

Public Health Agency of Canada 2007), and represents an important preventable and treatable public health challenge. Compelling evidence exists that short-term exposure to air pollution, may be associated with Acute Exacerbation of COPD (AECOPD). Even at levels within current air quality standards, COPD symptoms may be exacerbated by air pollution and result in acute health service use (HSU) including emergency department visits, and hospitalizations.(Anderson,

Spix et al. 1997, Medina-Ramon, Zanobetti et al. 2006, Stieb, Szyszkowicz et al. 2009)

To properly understand the association between ambient air pollution and acute HSU amongst persons with COPD, we must begin by understanding the disease. Chapter 1 of this dissertation will discuss the health burden of COPD and describe its pathology, diagnosis, clinical manifestations, and risk factors for the onset and exacerbation of the disease. The chapter will then present an overview of the current knowledge regarding the health effects of air pollution amongst individuals with COPD, and describe the monitoring and dissemination of air pollution information in Ontario.

1.1 Organization of the Dissertation

In the following sections of Chapter 1, I describe the burden, pathophysiology, clinical

presentation, diagnosis and risk factors for the onset and exacerbation of COPD. I then review

the current knowledge in the literature as it pertains to the health effects of ambient air pollution

exposure, and describe the monitoring and dissemination of air quality information in Ontario. In

Chapter 2, I give an overview of the methodologies used in my original research that is presented

in the three subsequent chapters. Chapter 3 examines the impact of ambient air pollution on the

lives of individuals with COPD, as well as their perceptions and level of knowledge as it pertains

to air pollution health effects, assessment, and exposure-reduction strategies. Results of this

study serve as a contextual framework for the subsequent original research studies in this

dissertation, and can assist in their interpretation of their findings. In Chapter 4, I quantify the

magnitude of the association between ambient air pollution and acute health service use (HSU),

including potential seasonal effects, and the identification of potentially vulnerable

subpopulations. Building on the results of the previous study, Chapter 5 examines the potential

modifying role of temperature in the association between ambient air pollution and acute HSU

amongst individuals with COPD. Finally, Chapters 6 and 7 summarize the key findings of my

original research, and discuss future directions that have been motivated by the knowledge

generated from this dissertation.

1.2 The Burden of Chronic Obstructive Pulmonary Disease (COPD) in Canada

The Canadian Community Health Survey (CCHS) estimated that in 2008, 4.6% of Canadians

aged 35 years or older, and 4.7% of individuals living in Ontario reported having been diagnosed

with COPD by a health professional.(Statistics Canada 2016) These estimates may however, be

largely underestimating the true proportion of Canadians living with COPD. Studies using

administrative data sources and validated algorithms estimate prevalence in Ontario amongst

those aged 35 years or older to be closer to 9.5% in 2007,(Gershon, Wang et al. 2010) while that

same year, the Canadian Health Measures Survey (CHMS) estimated a national prevalence of

nearly 17% based on direct spirometric (pulmonary function) measures amongst those aged 35

years or older.(Evans, Chen et al. 2014)

Given the high proportion of Canadians living with COPD, the substantial number of

hospitalizations is not surprising. The Canadian Institute for Health Information (CIHI) reported

that between 2006 and 2007, COPD accounted for the highest rate of hospital admission amongst

all major chronic diseases in Canada.(Canadian Institute for Health Information 2008) At an

estimated average cost of $10,000 per hospitalization, the national economic burden for COPD

hospitalizations alone has ranged from $646 million to $736 million each year.(Mittmann,

Kuramoto et al. 2008)

Compelling evidence exists that even at levels within current air quality standards, short-term

exposure to ambient (outdoor) air pollution may exacerbate COPD and result in emergency

department visits, hospitalizations, or death.(Anderson, Spix et al. 1997, Medina-Ramon,

Zanobetti et al. 2006, Stieb, Szyszkowicz et al. 2009). Approximately, 21,000 deaths, 11,000 hospitalizations and 92,000 Emergency Department (ED) visits in 2008 were associated with air pollution exposure in Canada.(Canadian Medical Association 2008). As will be discussed later in section 6.5.2, the Canadian government continues to devise strategies to minimize air pollution at all levels (government, industries, community and individuals). Despite these efforts exposure to some level of ambient air pollution will likely continue, and air pollution is projected to pose serious health risk for the foreseeable future.(OECD 2012) Between 2008 and 2031, the

Canadian Medical Association projects that premature overall mortality associated with air pollution to increase by 83% to 39,000 and hospitalizations and ED visits by over 60% to 18,000 and 152,000 respectively. Nearly 40% of the overall morbidity and mortality associated with air pollution are due to respiratory illness, and the largest increases will be seen amongst those aged over 65 years.(Canadian Medical Association 2008)

To effectively reduce exposure to ambient air pollution amongst individuals with COPD and consequently reduce its impact on their health, it is important to have a deep understanding of the magnitude of the association between air pollution and morbidity when risk of acute HSU is the highest, and which subpopulations may be most vulnerable. Only then can knowledge be most effectively translated to inform the public of these risks and how to make behavioural changes to minimize exposures while maximizing quality of life.

1.3 Chronic Obstructive Pulmonary Disease

1.3.1 Definition and Pathophysiology

COPD is a blanket term for a chronic respiratory condition, characterized by persistent and

irreversible airflow limitation.(Global Initiative for Chronic Obstructive Lung Disease 2017) The

airflow limitation is caused by combination of small airway disease such as obstructive

bronchiolitis, and parenchymal destruction as in emphysema. The complex relationships between

these related disorders can result in substantial heterogeneity of clinical presentation and

pathophysiologic variation amongst patients.(Friedlander, Lynch et al. 2007) Efforts have been

made to characterize phenotypes of COPD in order to allow for the classification of patients into

different prognostic and therapeutic subgroups associated with clinically meaningful

outcomes.(Han, Agusti et al. 2010) These phenotypes have been broadly classified into 3 groups:

radiographic, physiologic, and clinical. However, these categories do not take into account the

full heterogeneity of COPD and apart from smoking, risk factors that determine these phenotypes

are not well understood.(Friedlander, Lynch et al. 2007) For instance, is the phenotype of

patients with both asthma and COPD (Asthma-COPD Overlap Syndrome [ACOS]) different than

that of those with COPD alone? Is the phenotype of COPD in never-smokers different than that

of smokers? Studies of Mexican woman exposed to smoke from biomass fuels have been shown

to have similar clinical characteristics, quality of life, and mortality as those with COPD from

tobacco smoking.(Ramírez-Venegas, Sansores et al. 2006) However, a study of US patients with

COPD showed life expectancy to be longer amongst those who had never smoked, compared to

smokers.(Shavelle, Paculdo et al. 2009) A better understanding of COPD phenotypes may

7 improve diagnoses, disease management, symptom control, and reduce the frequency and severity of exacerbations to ultimately improve health status and quality of life.

Exposure to noxious particles and gases can result in chronic inflammation which may result in airway remodeling, progressive airflow limitation and declining lung function. This neutrophilic inflammation is fundamental to the pathogenesis of COPD and can contribute to the hypersecretion of mucus, narrowing of the peripheral airways, tissue destruction and interfere in the normal repair and defence mechanisms of the lung.(Quint and Wedzicha 2007) Neutrophils are the first line of immune defense. In COPD however, they play a role in the destructive processes that characterize the disease. Their primary role in health is to destroy invading microorganisms and foreign particles like pollutants through phagocytosis

(ingestion).(Hoenderdos and Condliffe 2013) During this process, proteases (enzymes) and oxidants are released into the adjacent lung tissue. The most important of these proteases is neutrophil elastase, which has a high potential to cause significant damage to healthy lung tissue when it accumulates at sites of inflammation, as it does in COPD.(Köhnlein and Welte 2008)

Emphysema affects the lung parenchyma, the parts of the lungs involved in gas transfer. The parenchyma consists of the acinus (alveolar ducts, sacs, and alveoli), as well as the associated capillaries and interstitium. In an emphysematous lung, the alveoli at the distal end of the terminal bronchioles suffer permanent enlargement and the destruction of the airspace walls

(Figure 1-1).(Rennard 1998) As a result, the alveoli lose their natural elasticity which impairs their ability to ventilate, as well as absorb oxygen and expel carbon dioxide, resulting laboured breathing.

Figure 1-1: Normal vs. Emphysematous Alveoli. Reproduced with permission from Taraseviciene-Stewart and Voelkel, 2008.(Taraseviciene-Stewart and Voelkel 2008)

Several subtypes of emphysema exist. They are defined according to the affected parts of the

acinus. Proximal, or centriacinar emphysema is most commonly associated with cigarette

smoking and refers to damage and enlargement of the respiratory bronchioles, the central part of

the acinus.(Thurlbeck and Müller 1994) Panacinar emphysema is commonly associated with a

hereditary autosomal disorder called alpha-1 antitrypsin deficiency (AATD) and affects all parts

of the acinus.(Köhnlein and Welte 2008) Finally, distal acinar or paraseptal emphysema, affects

the alveolar ducts closest to the lung septa.

Chronic bronchitis is a progressive lung disease included under the umbrella of COPD and is

characterized by the inflammation of the lining of bronchiole tubes that carry air to and from the

alveoli. This inflammation is often triggered by exposure to noxious particles from air pollution

and cigarette smoke and causes an increase in mucus production that results in chronic

productive cough. Clinically, chronic bronchitis is diagnosed when a chronic productive cough

lasts for more than 3 months in a year, for at least 2 consecutive years, and when other causes of

chronic cough have been excluded.(Medical Research Council 1965)

1.3.2 Diagnosis

According to the current Global Initiative for Chronic Obstructive Lung Disease (GOLD)

guidelines,(Global Initiative for Chronic Obstructive Lung Disease 2017) a clinical diagnosis of

COPD should be considered when a patient exhibits chronic symptoms of cough, dyspnea

(difficult, or labored breathing), or sputum production, and has a history of exposure to known

risk factors for COPD (see details in section 1.3.5). The diagnosis and severity of COPD is

confirmed through spirometry. As part of pulmonary function testing, spirometry is used to

measure forced vital capacity (FVC) and forced expiratory volume in 1 second (FEV1). During

the testing, a patient is asked to take the deepest breath possible and exhale as hard and long as

they can onto a sensor. FVC measures the total amount of air that can be exhaled during this

forced breath, while FEV1 measures the amount of air that can be exhaled during the first

second.(Moore 2012) A post bronchodilator ratio of FEV1/FVC less than 0.7 is indicative of air

flow limitation. There is evidence that the use of this fixed ratio results in more frequent

diagnosis of COPD in the elderly,(Hardie, Buist et al. 2002) and less frequent in those aged less

than 45 years of age, compared to a cut-off based on the lower limit of normal (LLN) values for

FEV1/FVC. This is especially true in the cases of mild disease.(Cerveri, Corsico et al. 2008) The

LLN values are classified as the bottom 5% of FEV1/FVC based on a normal distribution of the

healthy population. However, to date there is no consensus agreement on which method is best.

COPD is then classified into one of four GOLD levels of severity from mild disease (Gold 1) to

very severe (Gold 4) using percent predicted FEV1 and history of exacerbation.(Global Initiative

for Chronic Obstructive Lung Disease 2017)

1.3.3 Clinical Presentation

The definition of COPD is based on airflow limitation. In practice however, a diagnosis can only

be made if an affected individual actively seeks medical care, which usually occurs when chronic

symptoms or AECOPD affect daily life.(Global Initiative for Chronic Obstructive Lung Disease

2017) When the condition (cough, shortness of breath, sputum production) of an individual with

COPD acutely worsens from the stable state, beyond normal day-to-day variation, and requires a

change in regular medication, an AECOPD is said to occur.(Rodriguez-Roisin 2000) The 3

characteristic symptoms of COPD are chronic and progressive dyspnea, chronic cough, and

sputum production. Other symptoms may also be present including wheezing and chest

tightness; fatigue; weight gain due to inactivity; depression; anxiety, as well as; weight loss and

anorexia with more severe disease.

Patients generally underestimate the extent of their symptoms. An international survey in the

year 2000 showed that there was a significant disparity between the reported perception of

patient symptoms and the degree of severity of disease measured by an objective breathlessness

scale.(Rennard, Decramer et al. 2002). Symptoms can also vary over the course of a day, or

week. In a 2011 study, approximately 62% of participants with moderate to severe COPD

reported their symptoms were worse in the morning.(Kessler, Partridge et al. 2011)

1.3.4 Management

Although there is currently no cure for COPD, through a combination of lifestyle changes and

use of effective available treatments to control symptoms and reduce the risk of AECOPD,

individuals with COPD can live active lives. The single most important change that can be made

is smoking cessation. This intervention has the greatest capacity to decrease the progression of

COPD. Albeit, there is no single treatment that works for everyone, many effective treatments

for tobacco dependence exist including nicotine replacement products such as nicotine gum, and

transdermal patches. Pharmacologic products have also been shown to improve long-term

cessation,(Jorenby, Leischow et al. 1999, Lancaster, Stead et al. 2000) however it is

recommended that these be used as part of a supportive intervention program and not on their

12 own.(Global Initiative for Chronic Obstructive Lung Disease 2017) While smoking cessation is important, changing such an addictive behaviour is also extremely challenging. Studies indicate that even with dedication of resources and time, long-term quit rates are only in the neighbourhood of 25%-35%.(Anthonisen, Connett et al. 1994, Tønnesen 2013)

A number of pharmacologic options are also able to reduce symptoms, frequency and severity of

AECOPD, improve exercise tolerance, and consequently reduce acute HSU. These include short- and long-acting bronchodilators, inhaled steroids, combination inhalers (containing a steroid medication and either a short-acting or a long-acting beta-agonist), oral steroids, phosphodiesterase-4 inhibitors, and theophylline.(The Mayo Clinic 2016) To date, none of the pharmaceutical options has been conclusively shown to modify the long-term decline in lung function.(Global Initiative for Chronic Obstructive Lung Disease 2017) The choice of pharmaceutical treatment must be patient specific and guided by the severity of symptoms, risk of AECOPD, availability of the medication, patient response to the medication, tolerance to medication, and cost.

Respiratory infections, such as influenza and pneumonia, can aggravate symptoms of COPD and cause serious illness including lower respiratory tract infections that may lead to hospitalizations,(Wongsurakiat, Maranetra et al. 2004) or death. While antibiotics can be useful to treat AECOPD, annual influenza vaccination is recommended for prevention. All individuals with COPD should be vaccinated, however the vaccines have been shown to be particularly important and effective for older individuals.(Woodhead, Blasi et al. 2005)

Canadian and international guidelines for the management of COPD(O’Donnell, Hernandez et al. 2008, Global Initiative for Chronic Obstructive Lung Disease (GOLD) 2014) also recommend

a range of non-pharmacologic treatments such as education, self-management, oxygen therapy

and pulmonary rehabilitation, which aims to reduce symptoms, improve quality of life, optimize

functional status and increase physical and emotional participation in everyday activities.(Nici,

Donner et al. 2006, Ries, Bauldoff et al. 2007) Pulmonary rehabilitation consists of a range of

intervention strategies to be integrated into the daily management of a patient’s disease. These

strategies also cover non-pulmonary problems which medical therapy was unable to address,

such as relative social isolation, altered mood states, exercise de-conditioning, and muscle

wasting. Guidelines also emphasize the importance of patient self-management education, which

is essential to the discussion of air pollution exposure reduction and involves teaching,

counseling, as well as behavior modification techniques.(Bodenheimer, Lorig et al. 2002, Nici,

Donner et al. 2006)

1.3.5 Risk Factors for the Development and Progression of COPD, as well as the

Exacerbation of Symptoms

COPD is the result of complex gene-environment interactions. A number of risk factors exist that

effect the onset and progression of the disease, as well as the exacerbation of symptoms. Such

exacerbations can result in declines in lung function, emergency department visits, or

hospitalizations.(Anderson, Spix et al. 1997, Medina-Ramon, Zanobetti et al. 2006, Stieb,

Szyszkowicz et al. 2009) Increased frequency of AECOPD has also been shown to lead to an

even greater decline in lung function and longer length of stay in hospital.(Donaldson,

Seemungal et al. 2002) Cigarette smoking is the primary and most studied risk factor for the

onset of COPD.(Kohansal, Martinez-Camblor et al. 2009, Global Initiative for Chronic

Obstructive Lung Disease 2017) However, it is far from being the only one. There is evidence that non-smokers are also at risk of developing COPD(Behrendt 2005, Celli, Halbert et al. 2005,

Tan, Sin et al. 2015). Much of the knowledge regarding COPD risk factors is generated from cross-sectional epidemiological studies which tend to quantify associations rather than prove causality. Understanding these risk factors and their interactions is key to developing strategies to reduce exposures.

Genetic predispositions may increase a person’s susceptibility to certain risk factors. For instance, among people with the same smoking history, not all will develop COPD. It has been estimated that 73% of individuals with COPD are classified as ever-smoker.(Tan, Sin et al. 2015)

In rare cases, an inherited genetic disorder called alpha-1 antitrypsin deficiency can predispose individuals who smoke to developing emphysema.(Kalsheker 2009) Other genetic factors such as maternal and paternal asthma have also been linked increased susceptibility to developing

COPD(Svanes, Sunyer et al. 2010). Recently, genome-wide association analyses have also identified loci that may be associated with decreased lung function and COPD.(Wain, Shrine et al. 2017) Age and sex may also play a role. Studies have shown women to be more susceptible to the effects of cigarette smoke than men, (Kamil, Pinzon et al. 2013) and others have shown

COPD to increase with age. Although, it is unclear if the risk is increased because of the effect of age itself, or if it is the result of cumulative years of exposure. (Global Initiative for Chronic

Obstructive Lung Disease 2017) Additionally, there is evidence that COPD is inversely related to socioeconomic status (SES).(Prescott, Lange et al. 1999) Those with lower SES have been shown to be at higher risk of COPD. However, it is not clear whether this is due to differential exposure to air pollutants, smoking status, infections, poor nutrition, or some other factor related to low SES.

Risk factors for COPD may also be related in more complex ways. For example, gender may influence whether a person takes up smoking or experiences certain occupational or environmental exposures; socioeconomic status may be linked to a child’s birth weight which may in turn impact lung growth and development, increasing susceptibility to develop the disease; and longer life expectancy will allow greater lifetime exposure to risk factors.

Understanding the relationships among risk factors requires further investigation.

Factors that decrease lung function may increase the risk of COPD. Increased airway responsiveness to allergens or other external triggers has been shown to be a risk factor for the accelerated decline of FEV1(Rijcken, Schouten et al. 1995, Tracey, Villar et al. 1995).

Prematurity, early childhood respiratory infection can cause atypical lung growth in gestation and childhood, which has also been associated with decreased FEV1.(Svanes, Sunyer et al. 2010)

(Barker, Godfrey et al. 1991)

A variety of environmental exposures have also been linked to COPD. As previously mentioned, smoking is the most important risk factor for COPD. Compared to non-smokers, those who smoke tobacco (including cigarettes, pipes, cigars, water pipes,(Raad, Gaddam et al. 2011) and marijuana (Tan, Lo et al. 2009)) have more respiratory symptoms, more lung function abnormalities including decreased FEV1, and a higher COPD mortality rate.(Kohansal, Martinez-

Camblor et al. 2009) Even cumulative exposure to second hand or environmental smoke has been shown to possibly contribute to COPD. (Eisner, Balmes et al. 2005)

Occupational exposures, including particulate matter, organic and inorganic dusts, gases, fumes and chemical agents have been associated with COPD prevalence.(Korn, Dockery et al. 1987,

Mehta, Miedinger et al. 2012, Guillien, Puyraveau et al. 2016) The odds of having COPD have been estimated to be more than 2 times higher in those reporting these occupational exposures

(OR 2.5; 95% CO 1.9, 3.4).(Blanc, Eisner et al. 2009) Biomass fuels are used worldwide as a main source of energy for heat and cooking. However, the burning of these fuels in the form of wood, crops residues, coal, and animal dung in poorly functioning stoves with inadequate ventilation can lead to high levels of indoor air pollution which has also been associated with increased risk of COPD.(Smith, Mehta et al. 2004, Ezzati 2005, Assad, Balmes et al. 2015)

Finally, ambient air pollution from fossil fuel combustion has been associated with decreased

FEV1.(Abbey, Burchette et al. 1998) Major sources of ambient air pollution include, industrial processes and utilities such as coal-fired power plants. The primary source however, is vehicular traffic. There is evidence that long-term exposure to traffic-related air pollution may contribute to the development of COPD (Andersen, Hvidberg et al. 2011)

While the role of ambient air pollution as a risk factor in the onset of COPD is not fully understood, there is evidence that long-term exposure to traffic related air pollution may cause a chronic low-grade inflammatory response that may contribute to airway remodelling, airway obstruction, and decreased lung function.(Hogg and Van Eeden 2009, Ling and van Eeden 2009)

There is also compelling evidence that even at levels below recommended air quality guidelines, short-term exposure to air pollution is associated COPD mortality and morbidity.(Delfino,

Murphy-Moulton et al. 1998, Chen, Hong et al. 2004, Chen and Copes 2013)

Short-term air pollution exposure may serve as a trigger for AECOPD. Other common triggers include smoking, and viral and bacterial infections.(Sapey and Stockley 2006, Mohan, Chandra

et al. 2010) The health effects of short-term air pollution including potential biological

mechanisms will be discussed at length in sections 1.5 and 1.6.

1.3.6 Major Comorbidities

Comorbidity amongst individuals with COPD has been well documented.(Nici and ZuWallack

2011, Decramer and Janssens 2013, Gershon, Mecredy et al. 2015) The presence of

comorbidities and their combined health effects with those of COPD may increase sensitivity to

risk factors for exacerbation and have significant impact on the health of individuals. Leading

causes of death amongst individuals with COPD have been documented as cardiovascular

disease (CVD), lung cancer and respiratory failure.(Anthonisen, Skeans et al. 2005, McGarvey,

John et al. 2007) Comorbid conditions also place a heavy burden on healthcare systems.(Feary,

Rodrigues et al. 2010, Nici and ZuWallack 2011, Burgel, Escamilla et al. 2013, Gershon,

Mecredy et al. 2015)

Comorbidities may share risk factors, or common mechanistic links with COPD. Still others may

arise completely independently. Identification of comorbidity and understanding whether they

modify the association between ambient air pollution and acute HSU amongst individuals with

COPD is important to patient management and treatment, and may consequently improve overall

health outcomes.(Decramer and Janssens 2013)

1.3.6.1 Asthma-COPD Overlap Syndrome (ACOS)

A significant proportion of individuals with COPD also exhibit symptoms that are consistent with asthma. Both COPD and asthma are characterized by airway and lung tissue inflammation,

However, the nature of the inflammation and the specific immune cells involved differ between these 2 conditions. Bronchial hyperreactivity in response to inhaled stimuli is considered to be the hallmark of inflammation in asthma and can lead to episodes of wheezing, breathlessness, chest tightness and coughing, particularly at night or in the early morning.(Global Initiative for

Asthma 2016) Unlike COPD where airway obstruction is largely irreversible, the airway obstruction in asthma is reversible through the use of pharmacologic interventions including inhaled corticosteroids and long-acting bronchodilators.

Epidemiological studies have shown that individuals with asthma are at significantly higher risk of developing COPD.(Vonk, Jongepier et al. 2003, Silva, Sherrill et al. 2004) Recently, the

Global Initiative for Asthma (GINA) and the GOLD released a joint document that recognizes this condition as Asthma-COPD Overlap Syndrome (ACOS). (Global Initiative for Asthma

2014) Individuals with ACOS have a higher frequency of exacerbations, a more rapid decline in lung function, and a decreased quality of life as compared to those with asthma or COPD alone.(Menezes, de Oca et al. 2014, Papaiwannou, Zarogoulidis et al. 2014) A 2016 study of

6,040 individuals with incident asthma found the risk of developing ACOS was nearly three times higher in those exposed to higher levels of cumulative air pollution.(To, Zhu et al. 2016)

However, whether the presence of asthma as a comorbidity modifies the association between air pollution and health outcomes amongst persons with COPD has yet to be determined.

1.3.6.2 Cardiovascular Disease (CVD)

Studies have shown that COPD and cardiovascular disease (CVD) often coexist.(Global

Initiative for Chronic Obstructive Lung Disease 2017) In Ontario Canada, Gershon et al found that individuals with COPD used twice as many health services CVD than people without

COPD.(Gershon, Mecredy et al. 2015) In the United Kingdom, administrative data on more than

1.2 million patients showed that the risk of having CVD was nearly five times higher amongst patients with COPD compared to those without COPD (OR 4.98; 95% CI 4.85, 5.81).(Feary,

Rodrigues et al. 2010)

The most prevalent CVD comorbidities amongst persons with COPD are acute myocardial infarction (AMI), arrhythmia, chronic heart failure, peripheral vascular disease, and stroke.(Nici,

Donner et al. 2006) Evidence suggests that individuals with more severe COPD (lower FEV1) are at higher risk of having CVD. (Tockman, Pearson et al. 1995, Engström, Hedblad et al. 2000)

This may in part be due to the presence of pro-inflammatory mediators in the lung tissue and air spaces that are released as a result of the introduction airborne irritants (e.g. air pollution, or cigarette smoke) into the lungs. These mediators have the ability to spill over into the blood stream causing systemic inflammation which may then lead to vascular inflammation and a cardiac event.(Van Eeden, Leipsic et al. 2012) The National Health and Nutrition Examination

Survey (NHANES) I study, reported that the risk of death from CVD was more than twice as high for individuals in the lowest quintile of FEV1 compared to the highest, independent of smoking status.(Sin, Wu et al. 2005) However, the NHANES I failed to correct for air pollution exposure. While the association between COPD and CVD is well quantified, the effect of air

20 pollution on CVD outcomes amongst those the COPD has yet to be determined. Whether comorbid CVD increases the vulnerability of individuals with COPD to the effects of air pollution, also requires investigation.

1.3.6.3 Lower Respiratory Infection

Acute and chronic lower respiratory tract infections (LRTI) are significant comorbidities in

COPD. Exposure to noxious chemicals or gases impairs lung defence in COPD and as a result, microbial pathogens are able to establish themselves more easily in the lower respiratory tract.

The presence of these pathogens can induce inflammation, increased mucus secretion and disruption of ciliary activity, leading to further impairment of lung defence and progressive loss of lung function.(Sethi 2010)

In Ontario it is estimated that individuals with COPD use twice as many health services for lower respiratory tract infections compared to those without COPD.(Gershon, Mecredy et al. 2015)

Whether the presence of LRTI makes an individual with COPD more susceptible to the health effects of air pollution has yet to be determined.

1.3.6.4 Lung Cancer

Studies have shown that lung cancer is one of the leading causes of death amongst individuals with COPD and a significant comorbidity.(Anthonisen, Skeans et al. 2005) In Ontario, over half

of all lung cancer hospitalizations and ED visits were used by individuals with COPD.(Gershon,

Mecredy et al. 2015) Although smoking is a common risk factor for both conditions, the

increased risk of lung cancer amongst those with COPD is independent of smoking.(Turner,

Chen et al. 2007) The pathophysiologic mechanism responsible for the increased risk remains

unclear. Studies have proposed chromosomal loci and candidate genes that may be associated

with both COPD and lung cancer,(Cohen, Graves et al. 1977, Pillai, Ge et al. 2009, Young and

Hopkins 2011) ciliary dysfunction, or local or systemic inflammation.(Nici and ZuWallack

2011) Although the coexistence of COPD and lung cancer, as well as the increased burden place

on health systems is well documented, it is unclear if individuals with COPD and lung cancer are

more susceptible to the health effects of air pollution.

1.4 Air Pollution in Ontario

The National Air Pollution Surveillance (NAPS) program was established in 1969 to provide a

uniform standard of accurate air quality data across Canada. NAPS is composed of a system of

provincial monitors that play an important role in the monitoring and assessment of ambient air

quality trends and impacts, as well providing data for strategic programs to reduce air emissions

and manage air quality. In Ontario, Air Quality Health Index (AQHI) monitoring sites are

operated by the Environmental Monitoring and Reporting Branch (EMRB) of the Ontario

Ministry of the Environment and Climate Change (MOECC).(Environment and Climate Change

Canada 2015) While public implementation of the AQHI in Ontario did not begin until 2007,

data from January 1, 2003 to March 31, 2013 is available from 42 AQHI monitoring sites across

the province (Figure 1-2).

Figure 1-2: Map of AQHI Monitoring Sites in Ontario 2003-2013

1.4.1 Air Quality Monitoring

Air quality monitoring stations are positioned to best represent the areas where the majority of

the population people live, work and play. There are also some stations positioned to characterize

the long range movement of transboundary and transport related air pollution.(Environment and

Climate Change Canada 2016)

Continuous hourly data were collected using Thermo Scientific TE42C/I monitors for Nitrogen

dioxide (NO2), TE49C/I for Ozone (O3), and tapered element oscillating microbalance (TEOM)

monitors for particulate matter less than 2.5 micrometres in diameter (PM2.5). However, in

January of 2013, Ontario upgraded the network to employ the more accurate Synchronized

Hybrid Ambient Real-time Particulate (SHARP) 5030 to replace the TEOM monitors. These

new monitors report higher PM2.5 concentrations in colder weather, resulting in an increase in

overall annual mean values.

The air quality monitoring network is continuously maintained to ensure data is valid, high

quality, complete, comparable, and representative. Day-to-day maintenance and support of the

instruments is administered by EMRB staff to ensure instrument precision. Quarterly quality

assurance and quality control (QA/QC) reviews are performed to identify and correct potential

abnormalities in the data. Instruments are calibrated during mandatory bi-monthly onsite visits.

All instrumentation is standardized to Thermo Electron Corporation analyzers to streamline parts

inventory and facilitate the use of common hardware across the monitoring network. Finally, all

station activity is documented using FieldWorker Inc. software, and transferred directly to the

MOECC database.

Section 1.4 will describe the AQHI, and its air pollutant components in further detail. The health

effects of air pollution exposure and potential biological mechanisms will be discussed in

sections 1.5 and 1.6.

1.4.2 Nitrogen Dioxide (NO2)

Nitrogen dioxide (NO2) is a pungent, reddish-brown gas which exists in naturally low

concentrations in ambient air as nitrogen gas (N2) and oxygen gas (O2). At normal temperatures,

these chemical components do not react. However, in the heat of internal combustion engines,

nitrogen and oxygen can combine to form nitric oxide (NO).

&'() (1) !" + %" 2 !%

The NO formed from combustion reacts spontaneously with O2 in air to form NO2.

2 !% + %" → 2 !%" (2)

Major sources of NOX emissions include vehicular traffic and the transportation sector in

general, as well as industrial processes and utilities including coal-fired power plants and natural

gas processing.

1.4.3 Ozone (O3)

Tropospheric ozone (O3) or ground-level O3, is a colourless and odourless gas at typical ambient

concentrations. It is a major component of smog (a noxious mixture of particles and gases) and

poses significant environmental and health risks, in contrast to stratospheric (upper atmosphere,

15-55 km above Earth’s surface) O3 which protects the earth from the sun’s ultraviolet radiation.

In Ontario, ground-level O3 (herein referred to as O3) levels are typically highest on hot and

sunny days (generally from May to September) between noon and early evening, and lowest

during the colder months.(Ministry of the Environment and Climate Change 2015)

O3 is not generally emitted directly into the atmosphere. Rather, it is formed when nitrogen

oxides (NOX) and volatile organic compounds (VOCs – man made, or natural organic

compounds with low boiling points that evaporate readily into the air) react in the presence of

bright sunlight. The process below uses NO2 as an example, but similar reactions occur with

VOCs as well.

./012 (3) NO" NO + O

The resulting single oxygen atom is extremely reactive and readily attaches to O2 to form ozone.

O + O" → O3 (4)

Major sources of VOCs responsible for ground-level O3 include the transportation and industrial

sectors.(Ministry of the Environment and Climate Change 2015)

1.4.4 Fine Particulate Matter (PM2.5)

Airborne particulate matter is a mixture of solids particles and liquid droplets suspended in air.

PM2.5 specifically refers to respirable particles that are less than 2.5 micrometres in diameter. In

Ontario, PM2.5 is typically composed of nitrates, sulphates, organic matter and particle-bound

water, and is another major component of smog. Major emission sources include motor vehicles,

dust emissions from fields and roads, wood burning fireplaces and stoves, agricultural burning

and forest fires, smelters, coal-burning power plants, and industrial facilities.(Environmental

Monitoring and Reporting Branch of the Ontario Ministry of the Environment 2014)

1.4.5 Recommended Levels

The Ontario Ambient Air Quality Criteria (AAQC) are developed by the Ontario Ministry of the

Environment and Climate Change (MOECC). AAQC are meant to represent a desirable

concentration of individual contaminants in the air, and are based on the protection against

adverse environmental and health effects.(Ontario Ministry of the Environment: Standards

Development Branch 2012) However, there is evidence that negative health effects of air

pollution exist even below these levels.(Stieb, Szyszkowicz et al. 2009, Schwartz, Bind et al.

2017)

The AAQC in Ontario for NO2 is 200 ppb averaged over 1 hour; 80 ppb averaged over 1 hour for

3 O3, and; 28 µg/m averaged over 24 hours for PM2.5. Pollutant concentrations differ over time

and across regions. Changing weather patterns and emission sources can contribute to these

differences. For instance, south-western Ontario on the north shore of Lake Erie is heavily

influenced by transboundary pollution, mainly from the United States. High levels of

transboundary O3 can exceed the AAQC. Generally, air pollutants in Ontario stay within the

recommended standards, with exceedances being the exception to the rule. For the whole of 2012

for example, 24-hour max NO2 was not exceeded in any region. In that same year however,

across all regions PM2.5 reference level was exceeded a maximum of 3 times in downtown

Hamilton, and Grand Bend exceeded the maximum 1 hour O3 AAQC criterion a total of 109

times, while areas out of south-western Ontario experienced much fewer

exceedances.(Environmental Monitoring and Reporting Branch of the Ontario Ministry of the

Environment 2014)

1.4.6 The Air Quality Health Index (AQHI)

The AQHI is a single aggregate measure of air pollution developed by Health Canada and

Environment and Climate Change Canada. It was designed specifically as a means to

communicate to the public in an understandable way, the current level of overall air quality, the

associated health risks, and specific exposure reduction strategies.(Environment and Climate

Change Canada 2015) The AQHI is the current standard used by the Ministry of the

Environment and Climate Change to communicate air quality levels in the province of Ontario. It

ranges from 1 to 10+ (Figure 1-3) and is divided into 4 risk categories: low health risk (AQHI: 1-

3) moderate (4 to 6); high (7 to 10), and; very high (10+).

Figure 1-3: The Air Quality Health Index. Adapted from the Ontario Ministry of the Environment and Climate Change.(Ministry of the Environment and Climate Change 2010)

It is calculated based on the relative mortality risk of the combined impacts NO2, O3, and PM2.5.

However, the purpose of the AQHI is not to quantify the precise risk of health outcomes attributable to individual air pollutants. Rather it is a relative scale that represents the concentration of the overall mixture of ambient air pollution. It is intended to be used as a guide for the public to reduce their exposure to whichever elements of the air pollution mix may be responsible for adverse health effects.

The AQHI has been validated risk communication tool based on mortality data. Additional studies are required to determine its use in quantifying risk of morbidity. The methodology for its design was published by Stieb et al. in 2008 and is described in the following sections.(Stieb,

Burnett et al. 2008)

1.4.6.1 Background: The Air Quality Index (AQI)

The AQHI was designed to replace the Air Quality Index (AQI). AQIs are used internationally to reflect both the amount of air pollution present and its health significance at a given point in time. Most existing AQIs compare each of the included pollutants to a reference standard. The reported index value is then based on the single pollutant that is highest relative to its standard.(Hewings 2001) In Canada, the AQI reported daily maximum values for NO2, O3,

PM2.5, Sulfur Dioxide (SO2), Carbon Monoxide (CO) in a point scale from 0 to 100. If air pollution was expected to be widespread, persistent, and elevated beyond a threshold, smog advisories were issued which alerted the population to modify their behaviour. While there were health messages associated with AQI levels, these messages varied by pollutant and may have

30 caused confusion amongst the public. In fact, there was a lack of empirical evidence to support a quantitative relationship between the AQI and health effects.(Chen and Copes 2013)

Health Canada and Environment and Climate Change Canada began to review the AQI in 2001 based on concerns that the index was out of date. Three main areas of concern were identified.(Health Canada 2010) First, the validity of the science used to create the AQI was called into question. The use of thresholds suggests health effects are only seen in extreme conditions. This did not reflect current knowledge that suggested there is no safe level for exposure, and that the relationship between air pollution and health is more linear.(Daniels,

Dominici et al. 2004, World Health Organization (Regional Office for Europe) 2006) A 2001 study in Toronto, Canada(Toronto Public Health 2001) concluded that the AQI system in use at the time was not a good indicator of the health impact of air quality. Over 92% of premature mortality and hospitalization occurred when the AQI was in the “Very Good”, or “Good” range.

Second, the methodologies used to determine the threshold levels of the AQI and smog advisories were not standardized across the country, and so there was concern regarding inconsistency in calculations. Finally, the lack of consistent and clear health messaging and suggested behaviour modification to reduce exposure was of concern. The development of the

AQHI was thus commissioned to address these issues.

1.4.6.2 Creation of the AQHI

The development of the AQHI was a multi-stakeholder process and included health and environmental officials from multiple levels of government, and nongovernmental organizations, as well as health professionals and members of the media. In order for the index to effectively

31 convey health risk to the public, it was necessary to develop both the numeric formulation, as well as the associated communication materials.

1.4.6.3 The Numeric Formulation

Exposure data (1981-2000) was obtained from the NAPS for NO2, O3, PM2.5, CO, SO2, and particulate matter with a median aerodynamic diameter less than 10 microns (PM10). Non- accidental mortality data (ICD-9: <800; ICD-10 A00-R00) were obtained from the national mortality database maintained by Statistics Canada. A time-series analysis using generalized linear models generated risk estimates for non-accidental mortality for each pollutant. The analyses were first conducted at the city level for cities with available daily data for PM2.5 and at least 3 of the 4 gasses (Calgary, Edmonton, Halifax, Hamilton, Montreal, Ottawa, Quebec, Saint

John, Toronto, Vancouver, Windsor, and Winnipeg) In cases when more than 1 monitor was available in a given city, the average exposure was taken. In order for the index to be responsive to short-term changes in air pollution, a decision was made a priori to calculate the AQHI based on rolling 3-hour average concentrations. All models were adjusted for city-specific seasonal cycles, temporal trends and temperature. Potential lag effects (delayed effects of exposure on outcome), were accounted for by examining single day lags for air pollution from 0-2 days.

Further details regarding lag structure can be found in section 1.5.1.2.

City-level regression estimates for each lag were then pooled for individual pollutants using a random effects meta-analytic model.(Burnett, Ross et al. 1995) This produced average risk estimates that could be used across multiple cities. All possible combinations of 2-, 3-, 4-, and 5-

32 pollutant models were used to evaluate whether the risk associated with any single pollutant was sensitive to the inclusion of other pollutants in the model. Risk estimates were also examined by time period (1981–1990 vs. 1991–2000).

The daily maximum percent excess mortality estimate was then selected for each city. This percent was averaged across all available cities for each day and weighted by the average number of deaths per day by city during this period. The estimate was then scaled and rounded to the nearest integer to create an index from 1 to 10. Multiple versions of the index were created using coefficients from single and multipollutant models, as well as for the different time- periods. The correlation between each index version and individual pollutant concentrations was then compared in each city. The version which demonstrated consistently high associations across all pollutants was deemed to best represent the overall mix of ambient air pollution.

Results of the study indicated that NO2 O3, and PM2.5 were important predictors of the effect of the overall air pollution mix on non-accidental mortality. Whereas, the associations with CO and

SO2 were small and not statistically significant in multipollutant models. Although some

confounding exists between them, the AQHI version based on NO2 O3, and PM2.5 from single- pollutant models (1991-2000) was most consistently correlated with the individual pollutants and therefore best represented the overall air pollutant mixture. The final AQHI model was calculated using the following formula:

10 (5) AQHI = × (100 × exp (0.000871 × NO − 1 + exp 0.000537 × % − 1 10.4 " 3

+ exp 0.000487 × GH".I − 1))

where NO2 and O3 are measured in parts per billion (ppb) and PM2.5 in micrograms per cubic meter (µg/m3).(Stieb, Burnett et al. 2008)

1.4.6.4 Communication Materials

Stakeholders were involved in the development of the communication tools from the outset to determine target audiences and information needs, developing and testing communication materials and planning program evaluation.(Stieb, Burnett et al. 2008) A series of telephone interviews were conducted on a nationally representative sample to evaluate how people perceive the health risks of air pollution and identify misconceptions and potential obstacles to effective risk communication. Telephone interviews were also conducted on a nationally representative sample to assess knowledge, attitudes, and behaviours as they pertain to air pollution and to air quality indices.(Group 2005) Additional telephone interviews were conducted within 48 hours of an issued air quality advisory to measure awareness and response to the advisory. Multi- stakeholder workshops were held to draft new communication materials which were then evaluated in focus groups. The final tool is depicted in Table 1-1.

Table 1-1: The Air Quality Health Index Categories and Messages Health Messages Health AQHI At-Risk Population General Population Risk Category Low 1 to 3 Enjoy your usual outdoor activities. Ideal air quality for outdoor activities. Moderate 4 to 6 Consider reducing or rescheduling No need to modify your usual outdoor strenuous activities outdoors if you activities unless you experience symptoms are experiencing symptoms. such as coughing and throat irritation. High 7 to 10 Reduce or reschedule strenuous Consider reducing or rescheduling activities outdoors. Children and the strenuous activities outdoors if you elderly should also take it easy. experience symptoms such as coughing and throat irritation. Very High > 10 Avoid strenuous activities outdoors. Reduce or reschedule strenuous activities Children and the elderly should also outdoors, especially if you experience avoid outdoor physical exertion. symptoms such as coughing and throat irritation. People with heart or breathing problems are at greater risk. Follow your doctor's usual advice about exercising and managing your condition. Adapted from the Ontario Ministry of the Environment and Climate Change.(Ministry of the Environment and Climate Change 2010)

1.4.6.5 Implementation of the AQHI across Ontario

The first phase of the AQHI pilot project was initiated by Toronto Public Health in 2007. Phase 2 expanded the pilot to the Greater Toronto Area and included health units in Peel, York, Durham, and Halton Regions. At the meeting of the Association of Local Public Health Agencies in June

2010, resolution A10-6 was passed: Provincial Adoption and Promotion of the Air Quality

Health Index.(Ontario Public Health Association 2010) This resolution called for the province of

Ontario to: 1) make the AQHI available to all health units; 2) replace the AQI with the AQHI, and; 3) partner with health units and other stakeholders to promote the adoption and use of the

AQHI across the province. The AQHI officially replaced the AQI in June 2015.(Ministry of the

Environment and Climate Change 2016)

The adoption of the AQHI and consequently its utility in informing the public and reducing exposures and negative health outcomes, is in part dependent on public awareness of its availability, its relevance to their daily living, as well as the belief that air pollution negatively affects health. While studies have aimed to identify and explain factors influencing AQHI adoption,(Radisic, Newbold et al. 2016) little is known about the knowledge level of Canadians with COPD regarding the health effects of air pollution, its assessment, and exposure-reduction strategies.

1.5 Short-term Air Pollution Exposure as a Risk Factor for COPD Mortality

and Morbidity

There has been a substantial amount of evidence generated to demonstrate the negative health

effects of short-term exposure to ambient air pollution. Single day and cumulative day exposures

to air pollution have been found to be associated with a range health outcomes including

hospitalization, emergency room visits and mortality, especially among individuals with existing

respiratory conditions like COPD.(Anderson, Spix et al. 1997, Samet, Dominici et al. 2000,

Katsouyanni 2003, Peng, Dominici et al. 2005, Analitis, Katsouyanni et al. 2006, Medina-

Ramon, Zanobetti et al. 2006, Wong, Vichit-Vadakan et al. 2008, Stieb, Szyszkowicz et al. 2009)

Air pollutant exposures in these studies generally include measures of various ambient gasses

and particles. The introduction of these pollutants to the respiratory airways can irritate the lungs

and cause inflammation, as well as lower resistance to respiratory infection and cause a range a

symptoms. Individuals with pre-existing respiratory disorders, such as COPD are at particular

risk.(Zanobetti and Schwartz 2009, Ministry of the Environment and Climate Change 2015)

In the following sections I will summarize the knowledge in the literature as it pertains to the

short-term health effects of air pollution. I will begin by discussing some of the most commonly

used methodologies and landmark mortality studies before summarizing the literature

surrounding health morbidity and mortality.

1.5.1 Commonly Used Methodologies

1.5.1.1 Study Design

Two of the more common statistical approaches to the health effects of air pollution are the

ecological time-series analyses and individual level case-crossover studies. A time-series is a

chronological sequence of values spaced at equal time intervals. Time-series analysis makes use

of all available data, using aggregated daily event counts as the outcome and is therefore useful

when individual level data is not available.(Goldberg, Gasparrini et al. 2011) In air pollution

health effect research these values are typically daily measures of air pollution and counts of

health outcomes such as morbidity, hospitalizations, or ED visits.(Yang, Chen et al. 2004)

Poisson (log-linear) regression models are used to express the expected total number of events on

each day as a function of the exposure level and potential confounding variables. (Dominici,

McDermott et al. 2002) Observation are close together in time and there is therefore some

potential measure of autocorrelation between them. It is important to carefully interpret results

from such an ecological design as the lack of information about individual characteristics such as

smoking status or age may introduce bias. Time-series analysis requires sophisticated techniques

such as locally weighted smoothing functions (LOESS) to adjust for annual and seasonal trends

in the data, as well as monthly, weekly and daily variation.(Greenland and Robins 1994,

Dominici, McDermott et al. 2005) In this way the time-series analysis is flexible and does not

assume linearity, but allows the user to input a smoothing parameter to better fit the data.

However, time-series analysis has been shown to be sensitive the parameters and user choices

added to the model.(Li and Roth 1995)

More recently an increasing number of studies are using the case-crossover method (Levy et al) as an alternative to time-series analysis. This method is ideally suited to examine short-term exposures with acute outcomes. A more detailed description of the case-crossover method can be found in section 2.3.3.1. Briefly, each person serves as their own control such that both known and unknown individual level time-invariant characteristics (e.g. age, sex) are controlled for by design,(Fung, Krewski et al. 2003). Such control is not possible in time-series analyses. With proper selection of control periods, case-crossover studies are also able to adjust for time-trends and minimize the potential for autocorrelation in the exposure measures by design,(Sunyer,

Schwartz et al. 2000, Janes, Sheppard et al. 2005) compared to the complex modelling necessary in time-series analysis. Conditional logistic regression is typically used to compare the pollution during a hazard/case period (i.e. date of an acute health event) to that during control periods (i.e. periods with no health events). In this way, the association between the exposure and outcomes can be quantified.

Because the case-crossover method uses individual level data, it also allows the examination of potential effect modification by personal characteristics such as age, sex, and the presence of comorbidities. These may be important in identifying elevated risks in sub-populations that may be otherwise washed out in analyses conducted on whole populations.

Case-crossover and time-series methods are often considered competing methodologies, however when there is a common exposure such as in air pollution studies, these methods have been shown to be equivalent.(Lu and Zeger 2007) While the Poisson regression used in time-series analyses has been shown to be more precise than the conditional logistic model of the case-

39 crossover design,(Lu and Zeger 2007) studies that have examined the same data using both designs have produced methods yield similar results for both methods.(Lee and Schwartz 1999,

Neas, Schwartz et al. 1999, Kan and Chen 2004)

1.5.1.2 Lag Structure

Short-term health effects associated with air pollution may occur, immediately, or hours after exposure, and may even persist for several days. Unlike mortality or the onset of symptoms,

HSU involves at least in part, a decision to seek care. Even if symptom onset is immediate, other factors such as symptom tolerance, access to care, or even busy personal schedules may influence when someone decides to seek care. Studies therefore, often select several lags a priori to consider in the analyses. Many have shown that lagged values of pollutant exposure are more highly associated with human health than unlagged values. (Samet, Zeger et al. 2000,

Atkinson, Anderson et al. 2001, Stieb, Szyszkowicz et al. 2009, To, Feldman et al. 2015) Herein, single day lags (lag0, lag1, lag2…lagn) refer to exposures on the day of an event to n days prior to the event. Cumulative lags (lag01, lag02, lag03, lag0n) refer to daily exposures averaged from the day of the event to n days prior to the event.

1.5.1.3 Confounders and Effect Modifiers

As in most scientific investigation, evaluation of potential confounding is critical in studies of air pollution health effects. Time-series studies examining the daily number of hospitalizations and

40 variation in daily ambient air pollution concentrations for instance, are interested in how these events are associated and co-vary over time. Both hospitalizations and air pollution have strong temporal cycles influenced by weather (ambient temperature and relative humidity), and/or daily, seasonal, or long-term trends that must be taken into account.(Institute 2003)

The composition of the lung microbiome has also been shown to differ between healthy lungs, those of smokers, and those with varying severity of COPD.(Dy and Sethi 2016) Compromised mucociliary and barrier functions may in COPD increase susceptibility to viral infections,

(Sajjan 2013) and regular use of certain antibiotics has been shown to reduce the rate of symptom exacerbation.(Seemungal, Wilkinson et al. 2008, Ni, Shao et al. 2015) While the role of the lung microbiome in COPD exacerbation requires further research, many air pollution studies have controlled for influenza epidemics as they have been shown to contribute to morbidity and mortality(Thompson, Shay et al. 2003, Thompson, Shay et al. 2004) and modify the association between air pollution and hospitalization.(Touloumi, Samoli et al. 2005, Wong,

Yang et al. 2009)

Personal characteristics such as smoking, diet, age, sex, socio-demographic factors, or presence of comorbidities are not likely to vary with pollution over short periods of time. However, many studies examine these as potential effect modifiers, that may help identify sub-populations of individuals more susceptible to the health effects of air pollution.(Chen, Villeneuve et al. 2014)

(Zanobetti, Schwartz et al. 2000, Stafoggia, Forastiere et al. 2010, Samoli, Nastos et al. 2011,

Cakmak, Kauri et al. 2014) Additional research is required to further elucidate the heterogeneity in COPD and determine if the health effects of air pollution differ according to COPD potential phenotype.

Ecological factors have also often been considered as confounders in air pollution health effects

research. In cases where the decision to seek medical care is associated with some component of

choice, as with acute HSU, statutory holidays may affect when care is sought. (Anderson, Spix et

al. 1997, Stieb, Szyszkowicz et al. 2009, Samoli, Nastos et al. 2011, Samoli, Atkinson et al.

2016)

Adjustment for confounders and effect modifiers is dependent on available data and analytical

approach. Use of personal characteristics to examine potential effect modification requires access

to individual level data. Case-crossover analyses are able to account for time-trends by design,

whereas time-series analyses require the use of more sophisticated statistical methods such as

Generalized Additive Models to adjust for temporal variation. Regardless of the methodology

used, it is important to adequately account for effects of these factors in order to minimize

spurious results and better elucidate the association between air pollution and health outcomes.

1.5.2 Literature Review

It has been observed that multi-city estimates of air pollution health effects are more stable and

precise than those based on individual cities.(Dominici, Samet et al. 2000) Results from these

studies have been used in the development of air quality standards and guidelines.(World Health

Organization 2006) The following will describe some of the more notable multi-city studies as

well as results from additional Canadian studies examining the non-accidental and disease

specific health effects of air pollution. I will then present results from recently published meta-

analyses that have attempted to quantify the collective knowledge regarding air pollution health

42 effects from literature to date. Finally, I will discuss studies that have used the AQHI as their primary exposure measure.

1.5.2.1 Multi-City Studies

The Air Pollution and Health, a European Approach (APHEA)

The Air Pollution and Health, a European Approach (APHEA) project began in 1993. The

APHEA project was a multicentre study using aggregated data from 15 European cities across 10 countries with a total sample size of 25 million.(Anderson, Spix et al. 1997) Its primary objectives were to quantify the short-term health effects of air pollution and develop a standardize the methodology to do so using epidemiological time-series data.

As part of phase 1 of the project, Anderson et al (1997) examined the short-term effects of air pollution on hospital admissions for COPD using a 2-stage hierarchical approach.(Anderson,

Spix et al. 1997) Adjusted Poisson regressions were first used to estimate city-level associations between daily pollutant levels (NO2, O3, SO2, Total Suspended Particles [TSP], and Black

Smoke [BS]) and hospital admissions data, individually from Amsterdam (1977-, Barcelona,

London, Milan, Paris and Rotterdam. Statistically significant relative risks were found for NO2

(RR 1.02; 95% CI 1.00, 1.05); O3 (RR 1.04; 95% CI 1.02, 1.07); TSP (RR 1.02; 95% CI 1.00,

1.05), and; BS (RR 1.04; 95% CI 1.01, 1.06) per 50 µg/m3 increase in daily mean level of pollutant lagged from 1 to 3 days before the hospitalization (Table 1-2). These results suggest

43 there are small, but significant increased risks of COPD hospitalizations associated with exposure to various air pollutants exposure across European cities with widely varying climates.

As part of phase 2 of the project Analitis et al. (2006) estimated the effects of ambient airborne particle concentration (PM10 and BS) on respiratory and CVD mortality, using data from 29

European cities.(Analitis, Katsouyanni et al. 2006) A similar 2-stage hierarchical approach estimated a 0.58% (95% CI 0.21%, 0.95%) increase in respiratory deaths, and a 0.76% (95% CI

3 0.47%, 1.05%) increase in CVD deaths for each 10 µg/m increase in PM10 exposure. For a similar increase in concentration of BS, respiratory and CVD deaths increased by 0.84% (95%

CI 0.11%, 1.57%), and 0.62% (95% CI 0.35%, 0.90%) respectively.

The National Morbidity, Mortality, and Air Pollution Study (NMMAPS)

In 1996, Heath Effects Institute initiated the National Morbidity, Mortality, and Air Pollution

Study (NMMAPS) to examine the health effects of air pollution across large number of cities in the United states, in a consistent way. Primarily, to determine if the effects of air pollution on

mortality are due to solely to particulate air pollution (PM10), or if this effect was due, in part or completely, to other air pollutants (O3, NO2, SO2 and CO). (Samet, Dominici et al. 2000)

The study used a hierarchical modeling approach, first estimating associations at the city level, then combining these to generate overall summary estimates. Cities were selected for inclusion based on the availability of air pollution data and their population size. The mortality analyses were first conducted using data from the 20 largest cities, then later extended to include 90 cities

(including the original 20). The morbidity analyses used hospitalization data from the Health

Care Financing Administration for individuals aged 65 years and older, enrolled in the Medicare

44 program, across 14 cities.(Samet, Zeger et al. 2000)

Results indicated an increased risk of 0.51% (95% CI 0.07%, 0.93%) in total non-accidental

3 mortality per 10 µg/m increase in PM10 at lag1.(Samet, Dominici et al. 2000) Risk of CVD and respiratory mortality was slightly higher at 0.68% (95% CI 0.20%, 1.16%). Risk estimates were consistent when other pollutants were added to the model, and the lag day of exposure was changed. Risk of mortality due to increases in other pollutants were statistically significant for

3 some pollutants (NO2 and CO) but not robust to the inclusion of PM10 to the models. A 10µg/m

increase in PM10 concentration at lag01 was also associated with increased risk of 1.98% (95%

CI 1.49%, 2.47%) for COPD hospitalization, and 1.17% (95% CI 1.01%, 1.33%) and 1.98 (95%

CI 1.65%, 2.31%) for CVD and pneumonia hospitalizations respectively.(Samet, Zeger et al.

2000) These risks were not confounded by the effects of other pollutants, nor were they sensitive to demographic census measures including unemployment, poverty, or education.

Air Pollution and Health: A Combined European and North American Approach Project

(APHENA)

In 2008 results of the Air Pollution and Health: A Combined European and North American

Approach (APHENA) project was published. The APHENA project used a common methodology to reanalyze multi-city time-series data from the APHEA project in Europe, the

NNMAPS data in the United States and multi-city studies in Canada and compare the results across regions. Poisson regression models were used to estimate risk in each city and region using a using a two-stage hierarchical approach.

After adjusting for O3, the risk of non-accidental mortality was shown to increase with each

3 10µg/m increase in PM2.5 (lag1). This risk was similar in Europe (0.33%; 95% CI 0.22, 0.44) and the United States (0.29%; 95% CI 0.18, 0.40), and higher in Canada (0.48%; 95% CI 0.30,

1.40), although this difference was not statistically significant.(Samoli, Peng et al. 2008) A

3 similar pattern was observed when examining the risk of mortality with 10 µg/m increases in O3

(lag1). After adjusting for PM2.5, the risk of non-accidental mortality was shown to increase due to O3 exposure in the United States (0.13%; 95% CI -0.18, 0.44). However, the risks in in Europe

(0.19%; 95% CI 0.10, 0.28) and Canada (0.48; 95% CI -0.18, 1.20) were no longer statistically significant.(Peng, Samoli et al. 2013)

Canada: 7-City Study of Air Pollution and Emergency Department Visits for Cardiac and

Respiratory Conditions

In 2009, Stieb et al.(Stieb, Szyszkowicz et al. 2009) published results of a time-series analysis conducted on nearly 400,000 ED visits to 14 hospitals in seven Canadian cities (Montreal,

Ottawa, Edmonton, St John, Halifax, Toronto, Vancouver) during the 1990s and early 2000s.

The objective of the study was to quantify the association between air pollution and emergency department visits for cardiac and respiratory conditions. Generalized linear models were used to estimate the association between air pollution and daily number of ED visits. Results indicated that O3 (lag1-2) was most consistently associated with respiratory visits. Risks increased by 3.7%

(95% CI -0.5, 7.9) for COPD visits, and 3.2% (95% CI, 0.3, 6.2) for asthma visits with 18.4 ppb increase in O3. Associations tended to be of greater magnitude during the warm season for the months of April to September.

Table 1-2: Summary of Notable Multi-City Studies Examining the Association Between Air Pollution and Cardiorespiratory Outcomes Cardio- Region respiratory Summary risk estimate Author, Year Exposure Outcome (95% CI) Air Pollution and Health, a European Approach (APHEA) Anderson, 1997(Anderson, Spix et al. 1997) Europe NO2 COPD - H RR 1.02 (1.00, 1.05)*

Europe O3 COPD - H RR 1.04 (1.02, 1.07)* Europe TSP COPD - H RR 1.02 (1.00, 1.05)* Europe BS COPD - H RR 1.04 (1.01, 1.06)* Analitis, 2006(Analitis, Katsouyanni et al. 2006) Europe PM10 RD - M %D 0.58 (0.21, 0.95)**

Europe PM10 CVD - M %D 0.76 (0.47, 1.05)** Europe BS COPD - M %D 0.84 (0.11, 1.57)** Europe BS CVD - M %D 0.62 (0.35, 0.90)** National Morbidity, Mortality, and Air Pollution Study (NMMAPS) Samet, 2000(Samet, Dominici et al. 2000) United States PM10 COPD - H %D 1.98 (1.49, 2.47)†

United States PM10 NA – M %D 0.51 (0.07, 0.93)†

United States PM10 RD/CVD - M %D 0.68 (0.20, 1.16)†

United States PM10 CVD – H %D 1.17 (1.01, 1.33)†

United States PM10 PN - H %D 1.98 (1.65, 2.31)† Air Pollution and Health: A Combined European and North American Approach Project (APHENA)

Europe, PM10 NA - H %D 0.33 (0.22, 0.44)**

United States PM10 NA - H %D 0.29 (0.18, 0.40)**

Canada PM10 NA - H %D 0.48 (0.30, 1.40)**

Europe, O3 NA - H %D 0.19 (0.10, 0.28)**

United States O3 NA - H %D 0.13 (-0.18, 0.44)**

Canada O3 NA - H %D 0.48 (-0.18, 1.20)** 7-City Study of Air Pollution and Emergency Department Visits for Cardiac and Respiratory Conditions Stieb, 2009(Stieb, Szyszkowicz et al. 2009) Canada O3 COPD - ED %D 3.7 (-0.5, 7.9)‡

Canada O3 CVD - ED %D 3.2 (0.3, 6.2)‡ COPD = Chronic Obstructive Pulmonary Disease; NA = Non-accidental; CVD = Cardiovascular Disease; RD = Respiratory Disease; PN = Pneumonia; H = Hospitalization; ED = Emergency Department Visits; M = Mortality; RR = Relative Risk; OR = Odds Ratio; %D = Percent

Change in Risk; NO2 = Nitrogen Dioxide’ O3 = Ozone; PM2.5 = Particulate Matter < 2.5 micrometers; BS = Black Smoke; TSP = Total Suspended Particles. *Lag1-3; **Lag01; †Lag1; ‡Lag1-2

1.5.2.2 Meta-Analyses of Published Literature to Date

In the last 30 years, there have been a substantial number of studies conducted internationally examining the effect of air pollution on both morbidity and mortality. In Canada, studies have examined the associations between ambient particulate matter (Lippmann, Ito et al. 2000, Stieb,

Beveridge et al. 2000, Brauer, Ebelt et al. 2001, Chen, Yang et al. 2004, Gan, FitzGerald et al.

2013, Weichenthal, Lavigne et al. 2016) and gases(Burnett, Dales et al. 1994, Stieb, Beveridge et al. 2000, Yang, Chen et al. 2005, Gan, FitzGerald et al. 2013) and COPD mortality(Lippmann,

Ito et al. 2000, Gan, FitzGerald et al. 2013), and morbidity, including hospitalization (Burnett,

Dales et al. 1994, Lippmann, Ito et al. 2000, Chen, Yang et al. 2004, Yang, Chen et al. 2005,

Gan, FitzGerald et al. 2013), emergency department visits (Stieb, Beveridge et al. 2000,

Weichenthal, Lavigne et al. 2016), and lung function.(Brauer, Ebelt et al. 2001)

The overall evidence worldwide strongly suggests the existence of small and consistent associations between short-term exposure to ambient air pollution and increases in morbidity and mortality.

In recent years several meta-analyses have attempted to summarize this considerable body of literature in order to quantify overall risk estimates for the effect of air pollution on COPD outcomes. A summary of the results of these meta-analyses are presented in Table 1-3. Percent increased risk (calculated as: (exp(regression coefficient) – 1) ´ 100%) of COPD mortality ranged from 1.0% (95% CI 0.0, 1.0) due to O3 exposure in China, to 7.0% (95% CI 4.0%,

11.0%) due to PM10 exposure in Europe. Increased risk of COPD hospitalization ranged from

1.0% (95%CI 1.0, 2.0) due to PM10 exposure in China, to 3.0% (95% CI 2.0, 5.0) due to PM2.5 exposure in studies across Canada, the United States, Europe, China and Australia. Finally,

48 increases in risk of COPD hospitalization or ED visits ranged from 1.0% (95% CI 0.0, 2.0%) due to SO2 to 4% (95% CI 3.0, 6.0%) due to NO2 exposure across 9 international studies.

Table 1-3: Summary of Recently (2013-2016) Published Meta-Analyses Examining the Association Between Air Pollution and COPD Outcomes Region COPD Author (Included Studies) Outcome Summary risk estimate

NO2 Lai, Tsang et al. 2013(Lai, Tsang et al. 2013) China (26) M OR 1.02 (1.01, 1.03) DeVries, Kriebel et al. 2016(DeVries, Kriebel et al. 2016) International (9) M RR 1.03 (1.02, 1.05)

Zhang, 2016(Zhang, Li et al. 2016) East Asia (8) H RR 1.02 (1.02, 1.02) DeVries, Kriebel et al. 2016(DeVries, Kriebel et al. 2016) International (15) H or ED RR 1.02 (1.01, 1.03) DeVries, Kriebel et al. 2016(DeVries, Kriebel et al. 2016) International (9) H or ED RR 1.04 (1.03, 1.06)*

O3 Lai, Tsang et al. 2013(Lai, Tsang et al. 2013) China (26) M OR 1.01 (1.00, 1.01)

Zhang, 2016(Zhang, Li et al. 2016) East Asia (6) H RR 1.03 (1.02, 1.04)

PM2.5

Li, Fan et al. 2016(Li, Fan et al. 2016) International (6) M OR 1.03 (1.02, 1.04) DeVries, Kriebel et al. 2016(DeVries, Kriebel et al. 2016) International (10) M RR 1.05 (1.02, 1.08)

Li, Fan et al. 2016(Li, Fan et al. 2016) International (12) H OR 1.03 (1.02, 1.05)

Zhang, 2016(Zhang, Li et al. 2016) East Asia (3) H RR 1.02 (1.01, 1.03) DeVries, Kriebel et al. 2016(DeVries, Kriebel et al. 2016) International (9) H or ED RR 1.01 (1.01, 1.02) DeVries, Kriebel et al. 2016(DeVries, Kriebel et al. 2016) International (10) H or ED RR 1.03 (1.02, 1.03)*

PM10 Song, Christiani et al. 2014(Song, Christiani et al. 2014) United States (4) M OR 1.03 (1.00, 1.06) Song, Christiani et al. 2014(Song, Christiani et al. 2014) Europe (4) M OR 1.07 (1.04, 1.11) Zhu, Chen et al. 2013(Zhu, Chen et al. 2013) China (18) M OR 1.01 (1.01, 1.01)

Lai, Tsang et al. 2013(Lai, Tsang et al. 2013) China (26) M OR 1.01(1.00, 1.01) Song, Christiani et al. 2014(Song, Christiani et al. 2014) China (4) M OR 1.01 (1.00, 1.01)

Song, Christiani et al. 2014(Song, Christiani et al. 2014) International (14) M OR 1.03 (1.02, 1.05)

Zhang, 2016(Zhang, Li et al. 2016) East Asia (6) H RR 1.01 (1.01, 1.02) Song, Christiani et al. 2014(Song, Christiani et al. 2014) United States (10) H OR 1.02 (1.01, 1.03) Song, Christiani et al. 2014(Song, Christiani et al. 2014) Europe (8) H OR 1.02 (1.02, 1.04) Song, Christiani et al. 2014(Song, Christiani et al. 2014) China (5) H OR 1.01 (1.01, 1.02) Zhu, Chen et al. 2013(Zhu, Chen et al. 2013) China (13) H OR 1.03 (1.02, 1.04) Song, Christiani et al. 2014(Song, Christiani et al. 2014) International (27) H OR 1.02 (1.01, 1.02)

SO2 Lai, Tsang et al. 2013(Lai, Tsang et al. 2013) China (26) M OR 1.01 (1.00, 1.02)

Zhang, 2016(Zhang, Li et al. 2016) East Asia (6) H RR 1.01 (1.01, 1.01) DeVries, Kriebel et al. 2016(DeVries, Kriebel et al. 2016) International (11) H or ED RR 1.02 (1.01, 1.04) DeVries, Kriebel et al. 2016(DeVries, Kriebel et al. 2016) International (9) H or ED RR 1.01 (1.00, 1.02)* H = Hospitalization; ED = Emergency Department Visits; M = Mortality; RR = Relative Risk; OR = Odds Ratio; *Multi-day lag

The lack of standardized methodologies across international studies examining the health effects of air pollution presents can be problematic for meta-analyses. Studies may differ in study sample, data collection, exposure assessment, and/or statistical methodologies. They may also report associations for a variety of exposures and units of exposure, outcomes, seasons, or lag times. Some studies report all results, and others will report only the results with the largest effect estimates. Meta-analyses must therefore determine a priori which estimates will be included. For instance, from each study DeVries and colleagues extracted specific single- and multi-day lag results, and selected to use the strongest result when multiple estimates were available.(DeVries, Kriebel et al. 2016) Despite many air pollution health effect studies differing in their methodologies and reporting of outcomes, the consistency in the results clearly demonstrate air pollution affects health, and that observed increased risks are robust.(Chen and

Copes 2013)

1.5.2.3 Studies Examining Seasonal and Temperature Effects

The association between ambient temperature and respiratory health has been well established.(Hajat, Armstrong et al. 2006, Analitis, Katsouyanni et al. 2008, Anderson and Bell

2009, Anderson, Dominici et al. 2013, Chen, Wang et al. 2016) It is also recognized that there exists a correlation between ambient temperature and air pollution, and therefore studies that examine the risk of one of these exposures, also generally control for the other in order to quantify their independent effects.(Anderson, Spix et al. 1997, Samet, Zeger et al. 2000, Chen,

Wang et al. 2016)

Studies also adjust for seasonal trends in their statistical models,(Yang, Chen et al. 2004,

Dominici, Peng et al. 2006, Samoli, Stafoggia et al. 2014, To, Feldman et al. 2015) but few examine season as a potential effect modifier. Studies in Europe(Anderson, Spix et al. 1997,

Sunyer, Atkinson et al. 2003) and Canada(Burnett, Dales et al. 1994, Stieb, Szyszkowicz et al.

2009) have found the association between air pollution and morbidity to be higher in the warm season. As part of the APHEA study in Europe, Anderson et al.(Anderson, Spix et al. 1997) found risk of COPD hospitalization due to NO2 (warm season: RR 1.03; 95% CI 1.00, 1.06; cold season: RR 1.01; 95% CI 0.99, 1.03) and O3 (warm season: RR 1.03; 95% CI 1.01, 1.05; cold season: RR 1.01; 95% CI 0.98, 1.05) exposure were higher during the warm season than the cold. The seasonal effect however was not statistically significant. Canadian multi-city studies have found similar associations between O3 and respiratory hospitalizations(Burnett, Dales et al.

1994) and ED visits(Stieb, Szyszkowicz et al. 2009) in the summer, but not in the winter.

Several studies have examined seasonal effects in tropical and sub-tropical climates.(Lee, Tsai et al. 2007, Tsai, Chiu et al. 2014, Lin, Liu et al. 2016) Tsai et al. (Tsai, Chiu et al. 2014) found the association between PM2.5 exposure and respiratory hospitalization (including COPD) was only significant in the cold season (OR 1.46; 95% CI 1.36, 1.57) compared to the warm (OR 1.01;

95% CI 0.94, 1.10). However, it is important to note that in a tropical climate, the cold season includes all temperatures up to 25°C.

While studies that examine the association between individual pollutants and health outcomes have provided some insight into seasonality, use of an aggregate exposure measure such as the

AQHI may provide a better indication of how season affects the association between the overall mix of ambient air pollution and health outcomes.

The presence of these observed seasonal effects may be suggestive of a more complex interaction between air pollution and temperature. A limited number of studies have examined the combined effects of air pollution and temperature on respiratory morbidity.(Ren, Williams et al. 2006, Ren, Williams et al. 2008, Stafoggia, Forastiere et al. 2008, Analitis, Michelozzi et al.

2014, McCormack, Belli et al. 2016) However, results are conflicting results and this interaction has yet to be examined in a climate that experiences such extreme cold and hot temperatures as

Canada. These studies will be discussed further in section 5.3.

Global climate models for the next 100 years predict that warming will be associated with more frequent, severe and longer lasting heat-waves (Meehl and Tebaldi 2004) These changes in climate are expected to further aggravate the health effects of air pollution. Results of a 2004 study in the Netherlands suggest that a significant proportion of deaths attributed to extreme heat may have actually been caused by increased ambient O3 and particulate air pollution present at these temperatures. (Fischer, Brunekreef et al. 2004) A study of 22 cities in Europe and the Mediterranean found evidence on an interaction between extreme heat and high O3 and PM10 concentrations. The heat-wave effect on mortality was estimated to be 54% higher on days with high amounts of O3. Significant heat-wave effects were also observed for persons over the age of 75 during days with high PM10 concentrations.

Increased frequency and intensity of heat-waves may also give way to increasing frequency of wild fires. Smoke from forest fires, or burning of agricultural fields is a complex composition of

54 particles and gasses. Pollution resulting from these extremes has also been shown to negatively affect health. Increases in respiratory symptoms, physician visits, and emergency department visits for COPD due to exposures to forest fires have been observed in the US(Sutherland, Make et al. 2005, Moore, Copes et al. 2006) , internationally,(Mott, Mannino et al. 2005) and in

Canada.(Moore, Copes et al. 2006) A 2011 study in British Columbia(Henderson, Brauer et al.

2011) found significant increases in risk of physician visits (OR 1.02, 95% CI 1.01, 1.03) and hospital admissions (OR 1.05, 95% 1.00, 1.10) for respiratory diseases with increases in PM10.

Surface temperature inversions have also been shown to be responsible for trapping air pollution at ground level and causing high pollution concentrations over several days. Typically, air temperature decreases at higher altitudes. This creates a convection effect whereby warm air at the planet’s surface rises, taking with it air pollutants from ground level. A temperature inversion occurs when the surface temperature of the earth is cooler than that of the atmosphere, decreasing the effect of convection. These inversions can occur frequently during winter months when the sun is low on the horizon. A 2010 study by Wallace and Nair conducted in Hamilton,

Ontario used satellite data to show the effect of temperature inversion on sputum cell counts of patients with respiratory disease. Results showed counts of neutrophils and macrophages increased by 12.6% and 2.5%, respectively, on inversion days.

In response, the American Thoracic Society and the European Respiratory Society have expressed their concern about the threat posed by air pollution and climate change on respiratory disease.(Pinkerton KE, Rom WN, Akpinar-Elci M, et al. An official American Thoracic

Society workshop report: climate change and human health. Proc Am Thorac Soc 2012; 9: 3–8.)

Use of an aggregate exposure measure such as the AQHI that provides an indication of the overall mix of ambient air pollution, may prove to be a useful tool for further elucidating the combined effects of air pollution and temperature on health outcomes.

1.5.2.4 Studies Using the Air Quality Health Index

Examining health effects using individual pollutants may not adequately estimate the true risk, as people are exposed to a mixture of hundreds of substances present in ambient air pollution which may act in combination or synergistically.(Katsouyanni 2003, Chen and Copes 2013) When designing a study to examine the health effects of air pollution, it is therefore critical to determine its purpose a priori. If the objective is to determine which single pollutant is most responsible for negative health outcomes in order to inform regulatory bodies and industries for example, then use of individual pollutant exposures is necessary. If however, as in the case of this dissertation, the objective is to quantify the association between the overall mix of ambient air pollution and health outcomes, or identify characteristics of the population that may make them more susceptible to health risk given ambient air pollution exposure, then use of an aggregate measure such as the AQHI, may be more appropriate.

In recent years, studies have used the AQHI to examine the association between air pollution and health conditions including asthma(To, Shen et al. 2013) (Szyszkowicz and Kousha 2014), otitis media (an inflammatory disease of the middle ear)(Kousha and Castner 2016), urticaria (a skin

56 pathology), as well as cardiac diseases and stroke (Cakmak, Kauri et al. 2014, Chen, Villeneuve et al. 2014). Our previous work(To, Shen et al. 2013, To, Feldman et al. 2015) is the only study to my knowledge to use the AQHI to quantify HSU amongst individuals with COPD. We used aggregate administrative data from the Ontario, Canada and time-series analyses to quantify the health impact of exposure to poor air quality. Results showed COPD hospitalization (Rate Ratio

1.03; 95% CI 1.03, 1.04), and ED visits (Rate Ratio 1.00; 95% CI 1.00, 1.01) were associated with each unit increase in the AQHI, demonstrating that the AQHI could be used to communicate risk of morbidity amongst individuals with chronic diseases, including COPD.

To fully understand the extent of the association between the AQHI and acute HSU amongst individuals with COPD requires further investigation. Questions remain as to whether health risks are limited to warm summer months when air pollution is typically higher(Halonen, Lanki et al. 2009, Stieb, Szyszkowicz et al. 2009) and air quality advisories are actively disseminated by popular media outlets; or, if similar risks exist during cold months. Studies have also shown that COPD and cardiovascular disease (CVD) often coexist. (Global Initiative for Chronic

Obstructive Lung Disease 2017) In Ontario Canada, Gershon et al found that individuals with

COPD used twice as many health services for lower respiratory tract infections and CVD than people without COPD.(Gershon, Mecredy et al. 2015) In the United Kingdom, administrative data on more than 1.2 million patients showed that the risk of having CVD was nearly five times higher amongst patients with COPD compared to those without COPD (OR 4.98; 95% CI 4.85,

5.81).(Feary, Rodrigues et al. 2010) While this risk is well quantified, the effect of air pollution on CVD outcomes amongst those the COPD has yet to be determined. Furthermore, the role of

CVD and other comorbidities(Gershon, Mecredy et al. 2015) in the modification of this association is unclear.

1.6 Biological Mechanisms

A number of potential mechanisms have been proposed to explain the pathophysiological

pathways responsible for the effects of air pollution on health, and the combined effects with

temperature.

1.6.1 Oxidative stress

Oxidative stress is a state that occurs when the body is unable to balance the generation of free

radicals and their regulation by antioxidants.(Lobo, Patil et al. 2010). Free radicals are

electronically unstable atoms or molecules that attempt to achieve stability by stripping

(oxidizing) electrons from neighbouring molecules. In turn, this process creates even more

unstable molecules in a domino-like chain reaction, causing potential damage to a range of

molecular species including proteins, lipids, and nucleic acids.(Young and Woodside 2001) Free

radicals are generated in the process of normal metabolism, but they can also be produced from

exogenous environmental sources including cigarette smoke and air pollutants, (Young and

Woodside 2001) or endogenous sources released as a by-product of immune response from

activated inflammatory cells such as macrophages and neutrophils.(Domej, Oettl et al. 2014,

Global Initiative for Chronic Obstructive Lung Disease 2017)

The body defends against free radicals by using antioxidants. These act to prevent tissue damage

by impeding the formation of free radicals, scavenging them, or acting as catalysts to their

breakdown. This balance between free radicals and antioxidants is critical to proper

physiological function.

It has also been suggested that oxidative stress may reduce neutrophil deformability, which can

lead to lung tissue damage. (MacNee and Rahman 2001) Polymorphonuclear neutrophils (PMN)

are present in higher numbers in COPD affected lungs, and are believed to play an important role

in COPD pathogenesis. When activated, PMN release harmful substances that can cause lung

damage. Under normal circumstances, when neutrophils enter the microvasculature of the lungs,

they tend to get sequestered (trapped). Because of their size, they are forced to deform in order to

negotiate their way through the smaller capillary segments. However, if neutrophils are no longer

able to deform themselves due to oxidative stress, they can no longer escape the

microvasculature which may in turn lead to further tissue damage of a COPD affected lung.

(MacNee and Donaldson 2000)

1.6.2 Inflammation

When pollutants are inhaled into the lungs, they are blocked by airway epithelial cells,(Driscoll,

Carter et al. 1997) which in turn release inflammatory mediators to trigger

inflammation.(Devalia, Bayram et al. 1997) Inflammation is the body’s immune response to the

introduction of foreign species. Neutrophils and macrophages are white blood cells present in the

airways that can engulf foreign particles through phagocytosis. However, they can also release

inflammatory mediators such as tumor necrosis factor (TNF) that are capable of causing tissue

damage. Neutrophil elastase is an enzyme involved in phagocytosis which may also attack

proteins outside of the neutrophil and cause damage to the lung tissue.(Doring 1994, Köhnlein

and Welte 2008)

Airway inflammation caused in part by exposure to ambient air pollution or smoking, can also

decrease the deformability of the neutrophils and increase sequestration in the pulmonary

microvasculature. This can in turn lead to further damage of lung tissue.(MacNee and Donaldson

2000) In COPD, the number of neutrophils and macrophages are increased(Cross, van der Vliet

et al. 1994) and consequently, so are levels of inflammatory mediators.(Keatings, Collins et al.

1996) Exposure to inhaled air pollutants may trigger this inflammatory process and lead to an

exacerbation of COPD symptoms.

1.6.3 Mucus

Although in many circumstances mucus aids in the protection of the lung by trapping foreign

particles, air pollutants have been shown to increase mucus secretion which may contribute to

the exacerbation of COPD symptoms.(Jany, Gallup et al. 1991) The excess mucus increases

resistance in the larger airways and may completely block the smaller airways.(Siafakas,

Vermeire et al. 1995) Patients with COPD often also have damaged cilia, which in addition to

the excess mucus may overwhelm the mucociliary escalator and reduce the ability of the lungs to

effectively manage the inhaled particles.

1.6.4 Thermoregulatory response to heat

Several mechanisms have been proposed to explain why temperature may modify the effect of

air pollution on health outcomes. Biologically, the synergy between temperature and air pollution

may be explained in part by the body’s thermoregulatory response to heat. When internal

60 temperature increases as a result of ambient temperature or exercise, the body activates systems to dissipate excess heat. Together with the potential change in air pollution composition that occurs in warmer weather(Ministry of the Environment and Climate Change 2005, Kavouras and

Chalbot 2017) (discussed in section 5.7), these systems can have direct effects on the introduction of toxicants to the body. The primary thermoregulatory response to heat stress is a combination of an increase in skin temperature due to increased skin blood flow through peripheral vasodilation, and the body beginning to sweat.(Blatteis 1998) The body’s overall sensitivity to toxicants has been shown to increase due to higher ambient temperature.(Doull

1972, Gordon, Mohler et al. 1988, Gordon 2003) When the body is under heat stress, respiration rate also increases to reduce heat through evaporation, potentially increasing total intake of air borne pollutants. This may also increase the total intake of airborne pollutants which can directly affect the airways.(Gordon 2003, Mautz 2003)

Chapter 2:

Thesis Objectives, Hypotheses & Methodological Approach

Preface

Chapters 3, 4, and 5 of this dissertation present the manuscripts based on my original research studies. I present here my study specific and overall thesis objectives and hypotheses. While each of these chapters contains information regarding study methodology, in Chapter 2 I present further details regarding the methods which were too lengthy to include in the manuscripts.

2.1 Thesis Objectives and Hypotheses

2.1.1 Overall Thesis Objective

The overall objective of my dissertation is to determine the association between ambient air

pollution and acute HSU amongst individuals with COPD. I hypothesize that there will be a

statistically significant association between ambient air pollution and acute HSU, and that this

association will be stronger at higher temperatures and amongst individuals with comorbid

conditions.

I selected the AQHI as my primary exposure of interest as it is the current standard for

disseminating air quality information in Ontario, and was specifically designed to represent the

overall mix of ambient air pollution individuals are exposed to daily. Further details have been

discussed in section 1.4.6.2.

To examine this association, I conducted 3 original research studies described in Chapters 3, 4,

and 5 of this dissertation. In Chapter 3, I examined the perceptions and behaviours of Canadians

with COPD towards the health effects of air pollution. In Chapter 4, I quantified the association

between the ambient air pollution and acute HSU, examined the potential for seasonal effects,

and identified subpopulations that may be more vulnerable to the effects of air pollution. Finally

in Chapter 5, I examined the potential combined effect of air pollution and temperature on

hospitalization amongst individuals with COPD.

Results of my work provide important insights and may be used to inform health policy and

public health strategy regarding: the measurement of ambient air pollution; the dissemination of

air quality levels, and; targeted education of health care providers and vulnerable populations

including individuals with COPD. This research also provides a comprehensive methodological

framework which can be used to examine the association between ambient air pollution and

acute HSU for other conditions potentially susceptible to the effects of poor air quality.

2.1.2 Study 1: Perceptions and Behaviours of Canadians with COPD towards the Health

Effects of Air Pollution

Canadian and international guidelines for the management of COPD(O’Donnell, Hernandez et

al. 2008, Global Initiative for Chronic Obstructive Lung Disease (GOLD) 2014) recommend

self-management education(Nici, Donner et al. 2006). However, little is known about the current

level of knowledge of Canadians with COPD with regard to air pollution health effects,

assessment and strategies to reduce personal exposure.

The objectives of my first original research study were:

1. To examine the perception and knowledge of health risks associated with air pollution

exposure amongst individuals living with COPD;

2. To determine the process by which these individuals assess air quality and;

3. To determine if, and how they modify their behaviours to reduce exposure.

I hypothesize the majority of Canadians with COPD will believe that air pollution negatively

affects health. However, only a small percentage will be aware of air quality forecasting tools

including the AQHI, and their assessment of air pollution will be primarily based on subjective

experience. Few will modify their behaviour to reduce exposure to air pollution, and actions

taken may not translate to meaningful reductions.

2.1.3 Study 2: The Seasonal Health Risks of Air Pollution Exposure on People Living With

COPD: A Population-Based Study

Previous research(To, Shen et al. 2013, To, Feldman et al. 2015) has quantified the impact of air

pollution and shown an association between the AQHI and health service use (HSU) amongst

individuals with COPD. However, to fully understand the extent of this risk requires further

investigation. Questions remain as to whether these health risks are limited to warm summer

months when air pollution is typically higher(Halonen, Lanki et al. 2009, Stieb, Szyszkowicz et

al. 2009) and air quality levels are actively disseminated by popular media outlets, or if similar

risks exist during colder months. Furthermore, it remains unclear if there are sub-populations

amongst those with COPD who are more vulnerable to the effects of air pollution.

The objectives of my second original research study are to:

1. Use individual level data to provide further evidence of the association between the

AQHI and acute (hospitalizations and emergency department [ED] Visits) HSU amongst

individuals with physician-diagnosed COPD;

2. Quantify potential seasonal effects of air quality on non–accidental and disease-specific

acute HSU, and

3. Determine if the association between air quality and acute HSU is modified by the

presence of comorbidity, or other personal characteristics.

I hypothesize that the use of AQHI to communicate health risk can be extended to include acute

HSU amongst individuals with COPD, and that results will show an increased risk of acute HSU

per unit increase in the AQHI. This risk will also be higher in warmer seasons, and amongst

those with a history of hospitalization for COPD and CVD.

2.1.4 Study 3: The Combined Effects Of Air Pollution And Temperature On The Health Of

Persons Living With COPD: A Population-Based Study

As our global climate continues to change, unseasonal variations of temperature may become

more common. It is therefore important to understand if the health risks of air pollution exposure

vary across temperatures rather than across seasons, or if temperature modifies the association

between air pollution and health. Currently, there is no clear consensus regarding the existence

of a combined effect between air pollution and temperature. Such knowledge would have

important public health implications to exposure reduction strategies and minimizing the health

consequences of air pollution.

The objectives of my third original research study are to:

1. Determine the potential modifying effects of ambient air temperature on the association

between air pollution and risk of hospitalization amongst persons with COPD, and

2. Identify any potential temperature thresholds above which the risk of acute HSU due to

air pollution exposure becomes more pronounced.

I hypothesize that the association between air pollution and acute HSU amongst individuals with

COPD will be significantly higher at warmer compared to colder temperatures. I also hypothesize that as temperatures get warmer there will be a threshold where the risk of acute

HSU due to air pollution exposure becomes more pronounced.

2.2 Design of Study 1

2.2.1 Data Source

Chapter 3 of this dissertation describes the first of 3 original research studies. Study data for

study 1 were taken from the Environics 2010 Survey of Knowledge and Attitudes about Air

Quality and Air Quality Indices in Canada.(Health Canada - Environics Research Group 2010)

This survey was commissioned by Health Canada to evaluate communication programs designed

to raise awareness of air pollution health risks, and educate the public about the value of the

AQHI. The original objectives of the survey were to: 1) determine the views of Canadians on the

health impact of ambient air pollution; 2) assess whether Canadians are concerned about the

potential health effects of air pollution exposure; determine if Canadians are aware of, or familiar

with any available air quality indices; 3) determine if individuals who lived in areas where the

AQHI had been implemented were aware of its availability; 4) determine the extent to which

individuals who lived in areas where the AQHI had been implemented use the index, and; 5)

determine if individuals living in areas where the AQHI had been implemented take appropriate

actions based on the index’s health messaging to reduce their exposure to air pollution. The

following is a description of the methodology Environics used to collect the original data. Details

regarding the inclusion criteria for study 1 are described in section 3.5.1.

The Environics survey randomly selected Canadians over the age of 18, living within households

across the 10 provinces and 3 territories. It also included an oversample of adults who self-

identified as having at least one risk factor of interest: 1) age 65 or over; 2) diagnosis by a

physician for lung disease (including emphysema, chronic bronchitis, COPD, pneumonia),

68 asthma, cancer, skin rashes heart disease, or diabetes, and; 3) spending more than 6.5 hours outside on a typical summer day.

The survey was administered using a cross-sectional design with a random-digit dial telephone interview questionnaire. Sampling framework used a database of active phone ranges, which were composed of 100 contiguous working blocks. A block is a set of 100 sequential numbers defined by the first 8 digits of a phone number. For instance, a block numbered 55529133 would contain all 100 numbers from 555-291-3300 to 555-291-3399. Blocks are then merged with a directory of all households in the country with a registered phone number. They are determined to be working if at least 1 number in the block is a registered phone number.(Donsbach and

Traugott 2007). Each generated phone number is checked against an electronic phone book database to confirm geographic location, “do not call” status, and a business indicator. The adult aged 18 or over with the most recent birthday was then selected for participation.(Salmon and

Nichols 1983).

The questionnaire was developed by Environics in consultation with Health Canada.

Interviewers asked a combination of multiple-choice and open-ended questions that allowed multiple answers to be recorded. Data were collected between June 10 and 29, 2010, from a national sample of 1,405 Canadians aged 18 years or older and an oversample of 396 targeted at risk individuals from across Canada’s 10 provinces and three territories. Given the relatively low response rate of the original survey (9%: number of completed interviews divided by the total number of approached), I consider the study sample as one of convenience rather than a representation of the greater population.

2.2.2 Sample Size Calculation

When conducting scientific research, it is critical to have an appropriate sample size in order to

draw precise and accurate conclusions. A sample that is too small may generate a result which is

not sufficiently powered to detect a difference between groups, leading to a false negative result

(type II error).(Grimes and Schulz 2002)

Study 1 examines the knowledge of Canadians, including individuals with COPD regarding air

pollution health effects, assessment, and exposure-reduction strategies in sample collected

through random digit dialing. As this data was originally collected as part of a separate study, I

had no control over the sampling. However, it is possible to calculate the sample size required to

be able to estimate for instance, that 50% of respondents believe that air pollution at least

somewhat affects the health of Canadians. In this case I would require 384 participants to have

95% confidence that the true frequency lies between 45% and 55%. This sample size was

calculated with the OpenEpi open source calculator, Version 3(Dean AG, Sullivan KM et al.)

using the sample size for frequency in a population formula:

[MNOO ∗ !Q 1 − Q ] (6) K = " " (S TUVW " ∗ ! − 1 + Q ∗ (1 − Q)]

Where DEFF is the design effect (for random samples DEFF = 1), N is the source population size

(approximately 34,005,3000 Canadians in 2010), p is the hypothesized frequency of the outcome

(50%), d is the percent confidence limit (5%), and Z represents the Z-score of the corresponding

confidence level (95%).

2.3 Study Design of Studies 2 and 3

Studies 2 and 3 are described in chapters 3 and 4 of this dissertation respectively. While they

have different objectives, they share many of the same data sources and methodologies which

will be described in further detail in the following sections. |

2.3.1 Data Sources

2.3.1.1 Outcome Data

All Ontario residents with a valid health card are entitled to access a wide range of provincial

health-care services including visits to a family physician, as well as emergency and preventive

care services including surgery and hospital stays, under the single-payer, universal Ontario

Health Insurance Plan (OHIP). The plan is funded by the Ontario health premium which is paid

for by residents through the provincial personal income tax system. To be eligible for OHIP an

individual must: 1) be a Canadian citizen, permanent resident or a part of one of the select

“newcomer to Canada” groups as outlined in the Ontario’s Health Insurance Act; 2) make

Ontario their primary place of residence; 3) be physically present in the province for at least 153

days in any 12 month period, and for at least 153 days of the first 183 days after establishing

permanent residency in the province.(Ontario Ministry of Health and Long Term Care 2016)

Tourists, transients and visitors to Ontario are not covered by OHIP.

Service details are captured in health administrative databases that can be linked at the individual

level using a resident’s unique encrypted health card number, and are housed at the Institute for

Clinical Evaluative Sciences (ICES). Data for studies 2 and 3 described in chapters 4 and 5 of this dissertation respectively, were obtained for the time between January 1, 2003 and March 31,

2013 (most recent available data) from the following ICES databases: 1) The Canadian Institute for Health Information Discharge Abstract Database (CIHI-DAD) records the primary and secondary diagnoses for all hospitalizations; 2) the National Ambulatory Care Reporting System database (NACRS) records data of all emergency department (ED) visits and day surgeries; demographic data, including date of birth, sex, and residential postal code were obtained from the Ontario Registered Persons Database (RPDB), and; ecological data on neighbourhood income and education were obtained from the 2006 Canada census.

Events were excluded from the statistical analysis if: 1) they were listed as day procedures in the

CIHI-DAD database; 2) they occurred out of the province of Ontario; 3) the census division

(CD) in which they occurred could not be ascertained; 4) had incomplete exposure data, or; 5) they had fewer than 3 control days with complete exposure data. Further description of control days can be found in section 2.3.3.1. Hospitalizations were restricted to 1 per person, per chronic condition, per day. A similar restriction was applied to ED events.

2.3.1.2 Environmental Exposure Data

Air quality data were obtained from the Ontario Ministry of the Environment and Climate

Change. Hourly Air Quality Health Index (AQHI), nitrogen dioxide (NO2), ozone (O3), and fine particulate matter less than 2.5 microns (PM2.5) were collected by 42 fixed-site air quality monitors, across 25 of the 49 CDs in Ontario. Further details regarding air quality collection and

72 calculation of the AQHI can be found in section 1.4. If less than 75% of the daily hourly data was available from a given station, the daily AQHI value was considered unreliable and set to missing(Stieb, Burnett et al. 2008). All missing daily values were then imputed by averaging the values from the year before, and year after for the specific station and date in question. I calculated daily maximum values of the AQHI, NO2, O3, and PM2.5 for each monitoring station, then averaged these data across all monitoring stations within each census division. Individuals with COPD included in the study were assigned exposure based on their census division of residence at the time of each event. In January 2013, the province of Ontario upgraded its air quality monitoring network and replaced the TEOM PM2.5 monitors with the SHARP 5030.

These new monitors were more accurate and reported a higher PM2.5 concentrations in colder weather. However, this change in equipment should have little effect on my results. The study window for studies 2 and 3 ends March 31, 2013. Thus, the change in monitoring equipment would only affect the last 3 months of data. Furthermore, as is discussed in section 2.3.3.1, studies 2 and 3 employ a case-crossover study design in which individuals serve as their own controls. For each individual, air quality on a day of an acute HSU event is compared to the exposure on a day with no event. Therefore, use of the SHARP 5030 or the TEOM monitors should have little effect on the estimated association, providing the exposure on both days is measured using the same monitoring equipment. The only event and control day exposures affected by this change would be those occurring on the first 3 days of January 2013, for whom exposures may have been taken in the final days of 2012.

Mean daily ambient temperature and relative humidity data were derived using the same methodology and data obtained from Environment and Climate Change Canada for all weather stations in Ontario.

2.3.1.3 Study population

As COPD does not typically occur in younger individuals, the included study population was composed of Ontario residents aged 35 years and older who were included in the Ontario COPD database from January 1, 2003 to March 3, 2013, and who had experienced at least 1 acute HSU claim (ED claims also included same day surgeries) after entry into the cohort. This database uses an algorithm to identify those with COPD from health administrative databases. The algorithm has been previously validated in a 2009 population based study by Gershon et al.(Gershon, Wang et al. 2009) This study used 442 charts abstracted from adults aged 35 years with COPD, asthma, other respiratory conditions as well as controls with non-respiratory conditions (hypertension and musculoskeletal problems). Patient charts were then reviewed by a panel of expert pulmonologists and compared to create the reference standard for the study. A patient’s reference standard diagnosis was then linked to health administrative data stored at

ICES, using their unique encrypted health insurance number. The ability of predefined COPD health administrative algorithms to correctly identify patients with COPD was then compared to the reference standard diagnosis.

For the purposes of research, or when the identification of patients with only definite COPD is required, it is reasonable to use an algorithm that offers high specificity (low false positives) to ensure that few people with COPD are missed, low sensitivity to ensure few people with other diseases are incorrectly classified.(Gershon, Wang et al. 2009) Results of this study concluded that an algorithm of 3 or more physician billing claims within 2 consecutive years, or one or more hospital discharges with COPD diagnostic codes has a specificity of 96.9% (95% CI: 94.4,

98.5) and a sensitivity of 57.3% (47.8, 66.4) to identify persons who have COPD. This high

specificity algorithm was used to identify individuals with COPD from health administrative

databases for studies 2 and 3.

2.3.2 Sample Size Calculation

Formulas for calculating sample size for case-crossover designs (described in section 2.3.3.1) do

not yet exist.(Carracedo-Martínez, Taracido et al. 2010) Air pollution health effect studies are

typically large and those which use this design do not report a calculation of sample size, but

rather refer to the ability of their study to detect significant results as an indication of having a

sufficiently large sample and power. The power to detect small effects using the case-crossover

approach is generally lower than that of other widely used methods in air pollution research

methods.(Fung, Krewski et al. 2003) Accordingly, sample size would therefore need to be larger

to compensate. The study population included in original research studies 2 and 3 of this

dissertation constitutes the entire population of individuals in Ontario living with COPD that are

captured in the ICES COPD database. The sample size is substantial, cannot be any larger, and

should provide ample power to detect small effects.

2.3.3 Analytic Methods

2.3.3.1 Case-Crossover Analysis

The case-crossover method, is an established approach developed by Maclure(Maclure 1991) in

the early 1990s to examine short-term exposures with acute outcomes. In recent years, it has

75 been used to study the short-term effects of air pollution on health.(Zanobetti and Schwartz 2005,

Chen, Villeneuve et al. 2014, Szyszkowicz and Kousha 2014) All participants in a case- crossover study are inherently cases, in that they have all experienced the outcome of interest.

This method takes elements of both a classic crossover study and a matched case-control study.

Each subject serves as their own control as in a crossover analysis, and similar to a matched case-control study, the estimate is based on a comparison of exposure rather than the risk of disease.(Jaakkola 2003) In the case of analyses conducted in studies 2 and 3 of this dissertation, for each individual included in the study, air pollution exposure during the day of an acute HSU event (a hazard period) is compared to exposures during days when the event did not occur

(control periods). In this way, each subject serves as their own control.

Choosing control periods that are close to the time of the event can eliminate potential confounding by constant, or slow-varying personal characteristics as well as sources of selection and overlap bias.(Levy, Lumley et al. 2001, Janes, Sheppard et al. 2005) However, the position of the control periods relative to the event must be carefully considered. The case-crossover design assumes the exposure time-series (the hazard) to be stationary (constant for each individual) over time.(Lumley and Levy 2000) The selection of control periods proximal in time to the case period is sufficient to adjust for temporal trends that may arise from a nonstationary exposure time-series. Conversely, if control periods are too close to the case date, there will exist some autocorrelation with the event exposure and consequently less power to detect risk of the outcome. It has been suggested that a difference of 6 days between event and control periods is sufficient to avoid any autocorrelation while still adjusting for temporal trends.(Janes, Sheppard et al. 2005)

Study designs which restrict all control periods to occurring either before or after the event

(unidirectional), or where the event occurs in the center of the control periods (bidirectional), have been shown to introduce selection bias.(Levy, Lumley et al. 2001) Overlap bias may occur if the position of control periods is dependent on that of the hazard period. In this case the control periods selected are not disjoint (i.e. independent) and the analysis may be subject to overlap bias.(Levy, Lumley et al. 2001, Janes, Sheppard et al. 2005) The time stratified design has been proposed as a means to overcome such bias. This method divides time a priori into fixed strata and uses the remaining days as control periods for a case that falls within the stratum.

For instance, the exposure of a case occurring on Monday, January 9, would be compared to the exposure on all other Mondays in January (i.e. January 2, 16, 23, and 30). Because the predefined strata are fixed and disjoint, there is no threat of overlap bias. In studies 2 and 3 of this dissertation I followed a time-stratified approach and conditional logistic regression to quantify the association between air pollution and acute HSU amongst individuals with COPD.

Any bias introduced by this method has been shown to be small if the event is rare.(Lumley and

Levy 2000)

All regression analyses adjusted for ambient temperature, and humidity. In addition, I created an indicator variable to account for statutory holidays, as these may affect the timing of seeking health care. To account for any possible confounding by influenza,(Wong, Yang et al. 2009) I also adjusted for daily influenza rate. Influenza rate was defined as the number of daily OHIP visits for influenza.(Chen, Wang et al. 2016) This data was obtained from the OHIP claims database (code 487) for each CD over the complete study period. The same code is also used by physicians when administering influenza vaccinations. As such, I excluded events with OHIP fee-codes G538, G539, G590, G591, Q003, Q130, which are typically associated with

77 vaccinations. All census division level analyses were conducted with SAS v9.4.(SAS Institute

Inc. 2013)

2.3.3.2 Conditional Logistic Regression

The goal of any regression analysis is to estimate the model that best describes the association between an outcome and a set of exposures.(Hosmer Jr and Lemeshow 2004) The distinguishing feature of binomial logistic regression compared to linear regression is that the outcome is dichotomous (binary) rather than linear. As such the assumption of linear regression, specifically that the residual values are normally distributed, are violated. Therefore, the ordinary least squares method used to estimate parameters in linear regression cannot be used without first converting the binary outcome to a continuous value. This is accomplished through a logit, or log-odds transformation which estimates the odds of the outcome given various levels of the exposure, takes the ratio of those odds, then takes the logarithm of that ratio. Once the outcome has been converted to a continuous value, it can be fit to the exposures using linear regression.

The resulting estimate is then exponentiated to obtain the predicted odds.

Basic assumptions must be met before using logistic regression.(Stoltzfus 2011) First, all outcomes should be independent with no duplicate responses. Second, there should be a linear relationship between any continuous independent variates and the logit-transformed outcome.

Third, there should be no multicollinearity, or redundancy amongst the independent variables.

Finally, there should an absence of influential outliers.

In case-crossover analyses individuals serve as their own control and so it is necessary to account

78 for this dependency. Conditional logistic regression is a special case of logistic regression that allows matching to be taken into consideration.(Breslow, Day et al. 1978) It is also capable of creating models using varying numbers of control periods.(Jaakkola 2003) Careful selection of these control periods is critical to minimizing bias, as discussed above. (Lumley and Levy 2000,

Janes, Sheppard et al. 2005)

2.3.3.3 Akaike’s Information Criterion

Akaike’s Information Criterion (AIC) is used to assess which models best approximate the available data.(Akaike 1973) The AIC is based on the log-likelihood, which reflects the overall fit of the model, but does not account for the number of variables included in the model. The

AIC adds a penalty for each variable added to the model, thus the best model is one that fits the data well, but has the minimum number of parameters. The AIC is defined as follows:

AIC = -2(log-likelihood) + 2K (7)

where K is the number of parameters in the model (i.e., the number of variables + the intercept).(Burnham and Anderson 2004) Values of the AIC in and of themselves have no meaning. Instead, the lowest AIC value indicates which of the a priori defined models best fits the available data. The utility of the AIC is dependent on the quality of the a priori defined candidate models. Candidate models must be carefully considered and based on previous research and content knowledge. If all the candidate models are poor, the AIC will select the best of the poor models.

The Bayesian information criterion (BIC) was also considered as a means to assess model fit.

(Schwarz 1978) However, while the AIC and BIC may appear to be similar, they serve different purposes and so the decision of which criterion to use is dependent on the objective of the analysis. The BIC assumes that the “true” model is part of the candidate set. Whereas the AIC selects the model with the least mean squared error, under the assumption that the “true” model is not in the candidate set.(Aho, Derryberry et al. 2014) In studies 2 and 3, information criterion is used to determine which model best approximates the available data, and includes variables that previous research has suggested are important confounders in the association between ambient air pollution and acute HSU. For this purpose, the AIC is the more appropriate measure.

The difference in AIC (D) values is an simple way to rank the remaining candidate models. The model that best approximates the data has the minimum AIC (D = 0). All other models have a positive D value. Models with a D of less than 2 are considered equivalent to the best model.

Those with a D between 3 and 7 do not approximate the data as well, and those with a D of greater than 10 do not approximate the data well.(Burnham and Anderson 2004)

2.3.3.4 Meta-Analysis

Meta-analysis techniques are used to summarize quantitative results from related studies.(Egger and Smith 1997) Health effects of air pollution research often use this technique to pool results of similar regressions performed at regional levels to produce a weighted summary estimate of the association between air pollution exposure and a health outcome.(Anderson, Spix et al. 1997,

Sunyer, Atkinson et al. 2003, Dominici, McDermott et al. 2005, Stieb, Szyszkowicz et al. 2009)

Meta-analyses use either fixed-, or random-effect statistical models.(Riley, Higgins et al. 2011).

A fixed-effect analysis assumes all studies, or regions are estimating the same true effect and there is no heterogeneity in the true treatment effect between them. A random-effects analysis on the other hand, makes no such assumption and allows the treatment effect to vary. In instances where there is a risk of regional heterogeneity (variation), random-effect models are more appropriate.

In original research studies 2 and 3 in Chapters 4 and 5 of this dissertation, an a priori decision was made to use maximum likelihood (ML) estimation and random-effects models. In this way I can account for potential regional variation between census divisions, allowing the true risk estimate to vary across regions.(DerSimonian and Laird 1986, Riley, Higgins et al. 2011) While

ML estimation has been shown to underestimate variance, this bias becomes negligible in very large samples such as I am using.(Nemes, Jonasson et al. 2009). Heterogeneity was assessed using the Cochrane Q statistic.(Petitti 1999) P-values of less than 0.05 suggested heterogeneity between regions. Regional risk estimates are weighted by the inverse of their variance to reflect differences in population size. All meta-analyses were conducted using R statistical software(R

Studio Team 2015) and the METAFOR(Viechtbauer 2010) package.

2.3.3.5 Research Ethics

Research ethics for all original research in this dissertation was granted by the Research Ethics

Boards at SickKids: The Hospital for Sick Children (REB#:1000051010) and the University of

Toronto.

Chapter 3:

Perceptions and Behaviours of Canadians with COPD towards the Health Effects of Air Pollution

Preface

When interpreting results studies that quantify the health effects of air pollution, it is important to consider the potential impact of the adoption and use of air quality indices such as the AQHI within the study population. A highly informed COPD population for instance, may be more likely to engage in self-correcting behaviours that reduce exposure on high pollution days, which would in turn make it more difficult to detect associations between air pollution and acute HSU.

This first original research study examined the knowledge of Canadians, including those with

COPD regarding air pollution health effects, assessment and exposure reduction strategies.

Results of this study were then used as a contextual framework, and by extension assisted in the interpretation of my studies 2 and 3, quantifying the association between air pollution and acute

HSU (hospitalization and ED visits) amongst those with COPD.

3.1 Abstract

Objectives: Short-term air pollution exposure has been associated with increased morbidity,

especially amongst vulnerable populations like those with Chronic Obstructive Pulmonary

Disease (COPD). This study examines the knowledge of individuals with COPD regarding air

pollution health effects, assessment, and exposure-reduction strategies.

Methods: Data were obtained from a 2010 Health Canada commissioned survey of the Canadian

general population. Respondents were restricted to those aged over 35 years and grouped by self-

reported physician-diagnosed: COPD; asthma without COPD; heart disease without respiratory

disease; diabetes without respiratory disease, and; a reference population. Chi-squared tests

compared knowledge across disease groups and adjusted logistic regressions estimated

associations between disease group, health outcomes, and exposure-reduction behaviours.

Results: Over 95% of the 1167 respondents (11% COPD) believed air pollution affects health.

Amongst respondents with COPD, 61% believed effects occur even at low levels, and 13%

believed they could be immediate. Over 50% reported health problems related to air pollution.

Of these, 69% acted to reduce exposure, although strategies were impractical. Only 43% used

forecasts to assess air quality. Compared to exclusively using forecasts, use of senses to assess

air quality resulted in 3 times higher odds of reporting health problems. Results were similar

amongst the reference population.

Conclusions: While Canadians with COPD believe air pollution affects health, knowledge

regarding risk, assessment, and exposure-reduction strategies is lacking, and no better than that

of those free of chronic disease. Further education and public health messaging targeted

specifically at Canadians with COPD should be considered to mitigate adverse health effects and

improve quality of life.

3.2 Background

Chronic obstructive pulmonary disease (COPD) is a leading cause of morbidity and mortality in

Canada.(Chapman, Bourbeau et al. 2003, Public Health Agency of Canada 2007) Individuals

with COPD often experience breathlessness, chronic cough and physical limitations, which

affect their daily living, occupation, relationships, self-perception and overall quality of

life.(Kelly and Lynes 2007, Hernandez, Balter et al. 2009) There is mounting evidence that

short-term exposure to air pollution may be associated with severe exacerbation of COPD

resulting in emergency department visits, or hospitalization.(Anderson, Spix et al. 1997, Medina-

Ramon, Zanobetti et al. 2006) In an effort to educate the public of these risks, Health Canada and

Environment Canada jointly developed the Air Quality Health Index (AQHI) in 2001. The AQHI

is a composite measure that was designed to quantify air pollution, as well as communicate

health significance and specific exposure reduction strategies to Canadians in an understandable

way.(Environment and Climate Change Canada 2015) It is based on the combined impacts of

nitrogen dioxide (NO2), ozone (O3), and fine particulate matter less than 2.5 microns

(PM2.5).(Stieb, Burnett et al. 2008)

For an individual to protect their health against the effects of air pollution, there must first be an

understanding, or belief that air pollution negatively effects health. It is also important for

individuals to have a reliable way of assessing air pollution in real time, and to have strategies to

effectively reduce their exposure. Canadian and international guidelines for the management of

COPD(O’Donnell, Hernandez et al. 2008, Global Initiative for Chronic Obstructive Lung

Disease (GOLD) 2014) recommend patient self-management education(Nici, Donner et al.

2006). While some studies have examined public perception of air quality(Simone, Eyles et al.

2012), its influence on behavior,(Semenza, Hall et al. 2008, Simone, Eyles et al. 2012) and

methods of air quality assessment,(Bickerstaff and Walker 2001, Wakefield, Elliott et al. 2001)

few studies have examined the influence of air quality information on health behavior.(Radisic,

Newbold et al. 2016) Furthermore, little is known about the level of knowledge of Canadians

with COPD with regards to air pollution health effects, or how air pollution exposure affects

their daily lives. Such insights can be used to assist in the interpretation of studies examining the

health effects of air pollution, as well as to inform patient care and public health policy.

3.3 Objectives

The objectives of the current study are to examine the perception and knowledge of health risks

associated with air pollution exposure amongst individuals living with COPD; to determine the

process by which these individuals assess air quality and; to determine if, and how they modify

their behaviours to reduce exposure.

3.4 Methods

3.4.1 Data Source

Study data were taken from the Environics 2010 Survey of Knowledge and Attitudes about Air

Quality and Air Quality Indices in Canada,(Health Canada - Environics Research Group 2010)

which was commissioned by Health Canada. The survey evaluated communication programs that

were designed to raise awareness of air pollution health risks, and educate the public about the

value of the AQHI. Health information was obtained using a cross-sectional design with a

random-digit dial telephone interview questionnaire. The sampling framework used a database of

active phone ranges composed of 100 contiguous working blocks. The adult aged 18 or over with

the most recent birthday was selected for participation in the study.(Salmon and Nichols 1983)

Data were collected between June 10 and 29, 2010, from a national sample of 1,405 Canadians

aged 18 years or older across Canada’s 10 provinces and 3 territories. The study also

oversampled an additional 396 Canadians thought to be at risk of negative health effects due to

air pollution exposure. These included seniors, individuals who self-report of one more chronic

conditions, diagnosed by a health professional (asthma, lung disease, heart disease, allergies, and

diabetes), and those who spend six or more hours outdoors during an average week day in the

summer. The questionnaire was developed by Environics in consultation with Health Canada.

Interviewers asked a combination of multiple-choice and open-ended questions, and allowed

multiple answers to be recorded. Given the relatively low response rate of the original survey

(9%: number of completed interviews divided by the total number of approached), I considered

the study sample as one of convenience rather than a representation of the greater population.

3.4.2 Study Population

As the prevalence of COPD is most common in adults aged 35 and older,(Buist, McBurnie et al.

2007) and for consistency with other Canadian studies,(Gershon, Wang et al. 2010) I limited

inclusion in the current study to only those aged 35 years and older. The study sample was

divided into groups according to the presence of the following self-reported physician-diagnosed

chronic diseases: 1) COPD (lung disease: including emphysema, chronic bronchitis, COPD,

pneumonia); 2) asthma without COPD; 3) heart disease without COPD, or asthma; 4) diabetes

without COPD, or asthma and; 5) a reference population (no self-report of any of the

aforementioned chronic diseases). Herein these are referred to as the COPD, asthma, heart

disease, diabetes and reference populations respectively. These populations were also aggregated

into respiratory (COPD and asthma), and non-respiratory (heart disease and diabetes) disease

groups for select analyses as described below.

3.4.3 Study Variables

Questions included in this study are divided into four categories (Tables 1 and 2): 1) population

characteristics; 2) knowledge of the health effects of air pollution; 3) individual assessment of air

quality, and; 4) health effects due to air pollution and behaviour modification to reduce exposure.

Interviewers coded open ended questions into lists of a priori defined responses. I estimated self-

perceived health using the question “Compared to other people your age, would you say your

health is generally: Excellent/Very Good, Good, Only Fair/Poor”. I determined how respondents

assessed air quality using the question “How do you know when the air quality in your area is

poor?”. This open-ended question allowed multiple responses which were grouped as follows:

“Using only senses” (affects lungs, aggravates allergies, can feel, see, smell, or taste it); “Using

only forecasts” (air quality advisories, AQI, AQHI), or; “Using a combination of senses and

forecasts”.

3.4.4 Statistical Analysis

The distribution of responses of the COPD population was compared to the population reference

group. Calculated proportions of responses and associated 95% confidence intervals (CI) took

into account missing data. Chi-square tests of significance were used to compare the statistical

difference of responses between the COPD and the reference populations. Sensitivity analyses

were conducted comparing responses from each of the remaining chronic disease groups to the

reference population. Logistic regression was used to estimate the associations between disease

status and: self-report of a health problem attributable to air pollution in the 2 years preceding the

survey, and; behavioural modification to reduce exposure. An a priori decision was made to

combine the heart disease and diabetes population into a single non-respiratory disease group.

Additional regressions were then conducted to determine if the use of forecasting tools was

associated with lower risk of health problems attributable to air pollution. For this final set of

regressions, an a priori decision was made to combine respondents with COPD, or asthma into a

single respiratory disease group. All regression models were adjusted for age, sex, and level of

education. Level of self-perceived health may influence an individual’s motivation to improve

their knowledge regarding the health effects of air pollution and strategies to avoid

exposure.(Glanz, Rimer et al. 2008) This variable was therefore was examined as a possible

confounder and included in the final adjusted model in instances when its addition changed the

magnitude of association between the primary exposure and the outcome by at least

10%.(Greenland, Pischon et al. 2008) All analyses were conducted using SAS version 9.3 (SAS

Inc, USA).

3.4.5 Research Ethics Approval

Ethics approval was obtained from the Hospital for Sick Children (REB#:1000051010) and the

University of Toronto.

3.5 Results

3.5.1 Population Characteristics

A total of 1,167 respondents were eligible for inclusion into the current study.(Table 3-1). Of

these, 39% were male, 11% reported a diagnosis of COPD, 8% of respondents reported a

diagnosis of asthma without COPD, 6% heart disease, 8% diabetes (Supplemental Table S.3-1),

and 68% reported no diagnosis for any of the aforementioned chronic diseases and were

considered the reference population. The average age was highest amongst those with heart

disease (68 years), followed by diabetes (63 years), COPD (60 years), the reference population

(56 years), and those with asthma (52 years). A statistically higher proportion of each of the

chronic disease populations reported their health as being “only fair/poor” compared to

references.

Table 3-1: Study population characteristics % (95% Confidence Interval) Reference Asthma Total Population Population COPD without COPD (n=1167) (n = 793) (n = 123) (n = 95) Sex Female 60.8 (57.9, 63.6) 58.8 (55.3, 62.2) 66.7 (58.3, 75) 80 (71.9, 88.1)‡ Male 39.2 (36.4, 42.1) 41.2 (37.8, 44.7) 33.3 (25, 41.7) 20 (11.9, 28.1)‡

Age Group 35 - 44 19.5 (17.2, 21.8) 21.7 (18.8, 24.6) 8.4 (3.4, 13.4)† 38.3 (28.5, 48.1)* 45 - 54 25.2 (22.7, 27.7) 26.9 (23.8, 30) 29.4 (21.2, 37.6) 21.3 (13, 29.6) 55 - 64 25.9 (23.4, 28.5) 26.0 (22.9, 29.1) 25.2 (17.4, 33) 20.2 (12.1, 28.3) 65 - 74 19.4 (17.1, 21.6) 18.1 (15.4, 20.8) 26.1 (18.1, 34) 13.8 (6.8, 20.8) 75 - 84 10 (8.2, 11.7) 7.3 (5.5, 9.1) 10.9 (5.3, 16.5) 6.4 (1.4, 11.3)

What is the highest level of education you have completed? Without high school diploma 7.1 (5.6, 8.6) 4.9 (3.4, 6.4) 10.9 (5.3, 16.5)† 9.7 (3.7, 15.7) High School and some post-secondary 31.3 (28.7, 34) 29.8 (26.6, 33) 37.0 (28.3, 45.7)† 28.0 (18.8, 37.1) Post-secondary certification** 61.6 (58.7, 64.4) 65.3 (62, 68.7) 52.1 (43.1, 61.1)† 62.4 (52.5, 72.2)

How many hours would you likely spend outdoors on a typical weekday during the summer months Less than one hour 5.8 (4.5, 7.2) 4.5 (3.1, 6) 7.3 (2.7, 11.9) 8.4 (2.8, 14) 1 to 3 hours 42.8 (39.9, 45.6) 41.7 (38.3, 45.2) 43.1 (34.3, 51.9) 45.3 (35.2, 55.3) 3 to 6 hours 31.1 (28.4, 33.8) 31.3 (28, 34.5) 28.5 (20.5, 36.4) 33.7 (24.2, 43.2) More than 6 hours 18.3 (16, 20.5) 20.1 (17.3, 22.8) 19.5 (12.5, 26.5) 12.6 (5.9, 19.3) DEPENDS 0.9 (0.4, 1.5) 0.9 (0.2, 1.5) 1.6 (0, 3.9) 0 (0, 0) DK/NA 1.1 (0.5, 1.7) 1.5 (0.7, 2.4) 0 (0, 0) 0 (0, 0)

Compared to other people your age, would you say your health is generally: Excellent/ Very good 43.3 (39.6, 47) 54.4 (49.9, 59) 13.5 (5.7, 21.3) 44.4 (31.1, 57.7) Good 42.9 (39.2, 46.6) 38.3 (33.9, 42.7) 56.8 (45.4, 68.1) 38.9 (25.8, 51.9) Only fair/Poor 13.8 (11.2, 16.4) 7.2 (4.9, 9.5) 29.7 (19.3, 40.2)‡ 16.7 (6.7, 26.6)* *p<0.05 compared to the reference population; † p<0.01 compared to the reference population; ‡ p<0.0001 compared to the reference population. DK/NA: Don’t know/Not Applicable

3.5.2 Reported Knowledge of the health effects of air pollution

The majority of respondents reported that they believed air pollution somewhat affects the health

of Canadians, and likely contributes to respiratory illness (Table 3-2). Results of the non-

respiratory disease groups can be found in the appendix (Tables S.3-2). Approximately 60% of

respondents in each of the COPD and asthma populations believed air pollution affects health

even at low levels, compared to 50% of the reference group, 48% of heart disease, and 44% of

diabetes the populations. Regardless of disease, no more than 29% of respondents believed that

the health effects of air pollution tended to be more immediate. However, after further probing,

84% of those who had initially considered effects were only long-term agreed that there may also

be immediate effects.

Table 3-2: Reported knowledge of air pollution, method of air quality assessment, health outcomes, and behaviour modifications by reported chronic disease status. Reference Asthma Population COPD without COPD (n = 793) (n = 123) (n = 95) % (95% Confidence Interval) Knowledge of the Health Effects of Air Pollution

In your view, to what extent does air pollution affect the health of Canadians? At Least Somewhat 87.1 (84.8, 89.5) 95.9 (92.4, 99.4)* 93.7 (88.8, 98.6) Not very much 7.7 (5.8, 9.6) 2.4 (0, 5.2) 4.2 (0.2, 8.3) Not at all 1.8 (0.8, 2.7) 0 (0, 0) 0 (0, 0) DK/NA 3.4 (2.1, 4.7) 1.6 (0, 3.9) 2.1 (0, 5.0)

Do you think air pollution contributes to respiratory illnesses, such as bronchiolitis? Definitely contributes 72.5 (69.3, 75.7) 82.6 (75.9, 89.4)* 86 (79.0, 93.1) Likely contributes 24.5 (21.4, 27.5) 14.9 (8.5, 21.2) 14 (6.9, 21.0) Likely does not contribute 1.6 (0.7, 2.5) 0 (0, 0) 0 (0, 0) Definitely does not contribute 0.7 (0.1, 1.2) 0 (0, 0) 0 (0, 0) Depends (e.g. on type of individual) 0.1 (0, 0.4) 0.8 (0, 2.4) 0 (0, 0) DK/NA 0.7 (0.1, 1.2) 1.7 (0, 3.9) 0 (0, 0)

Do you think that air pollution affects people's health at any level? Even at low levels 50 (46.4, 53.6) 61.2 (52.5, 69.9) 61.3 (51.4, 71.2) Only when it reaches a certain level 42.7 (39.1, 46.2) 31.4 (23.1, 39.7) 34.4 (24.7, 44.1) Depends (e.g. on type of person) 3.5 (2.1, 4.8) 3.3 (0.1, 6.5) 4.3 (0.2, 8.4) DK/NA 3.9 (2.5, 5.2) 4.1 (0.6, 7.7) 0 (0, 0)

Do you think the health effects of air pollution tend to be more immediate ones that people notice right away, or more longer-term problems that won't be evident for some time? More Immediate 11.7 (9.4, 14) 13.2 (7.2, 19.3) 12.9 (6.1, 19.7) More Long Term 72.5 (69.3, 75.7) 75.2 (67.5, 82.9) 69.9 (60.6, 79.2) Both Equally 14.1 (11.6, 16.6) 10.7 (5.2, 16.3) 16.1 (8.6, 23.6) DK/NA 1.7 (0.8, 2.7) 0.8 (0, 2.4) 1.1 (0, 3.2)

Do you think there are any immediate health effects that people in Canada might experience as a result of air pollution?§ Yes 70.1 (66.3, 73.9) 83.7 (76.1, 91.3) * 84.8 (76.2, 93.5) * No 26.0 (22.3, 29.6) 12.0 (5.3, 18.6) 12.1 (4.2, 20) DK/NA 3.9 (2.3, 5.6) 4.3 (0.2, 8.5) 3 (0, 7.2)

Are you familiar with air quality information available in your area? At Least Somewhat Familiar 52.6 (49.1, 56.1) 56.1 (47.3, 64.9) 67.4 (57.9, 76.8)† Not very familiar 16.8 (14.2, 19.4) 15.4 (9, 21.8) 18.9 (11.1, 26.8) Not at all familiar 29.1 (26, 32.3) 25.2 (17.5, 32.9) 13.7 (6.8, 20.6)

DK/NA 1.5 (0.7, 2.4) 3.3 (0.1, 6.4) 0 (0, 0)

Are you aware of / heard of the Air Quality Health Index (AQHI) Aware of the AQHI 43.3 (38.3, 48.2) 50 (38.9, 61.1) 55.2 (43.3, 67.2)

Individual Assessment of Air Quality

How do you know when the air quality in your area is poor?|| Using a combination of senses and forecasts 23.9 (20.5, 27.3) 30.1 (21.2, 39) † 24.4 (15.3, 33.5) Only using senses 48.4 (44.4, 52.4) 57.3 (47.7, 66.9) 59.3 (48.9, 69.7) Only by forecast 27.7 (24.1, 31.2) 12.6 (6.2, 19.1) 16.3 (8.5, 24.1)

Without the benefit of a local air quality or weather forecast, would you be able to tell on your own that the air quality is poor as soon as you step out of doors? Yes 61.3 (56.1, 66.4) 75.4 (64.6, 86.3)* 76.2 (65.6, 86.8)*

How often do you personally use this type of air quality information? Frequently 22.2 (18.7, 25.7) 45.5 (35, 55.9) ‡ 34.1 (23.9, 44.4)* Occasionally 47.8 (43.6, 52.0) 44.3 (33.9, 54.7) 47.6 (36.7, 58.4) Never 28.7 (24.9, 32.5) 9.1 (3.1, 15.1) 18.3 (9.9, 26.7) DK/NA 1.3 (0.3, 2.2) 1.1 (0, 3.4) 0 (0, 0)

Health Effects of Air pollution and Behaviour Modification to Reduce Exposure

Have you or someone else in your household experienced any type of physical or health problems over the past two years that might have been attributed to air pollution at the time? Yes 19.3 (16.5, 22) 55.3 (46.5, 64.1) ‡ 50.5 (40.5, 60.6) ‡

At any point in the past year have you, or another member of the household, taken any steps or made changes in your daily routine as a result of the air quality forecast information you noticed or found? ¶ Yes 22.8 (16.3, 29.2) 30.8 (16.2, 45.4) 35.14 (19.6, 50.6)

Have you or others in your household taken specific actions to reduce your exposure to air pollution because of the impact it has had on your health?** Yes 57.9 (50, 65.8) 68.7 (57.5, 79.9) 59.6 (45.4, 73.7)

What, if anything, do you believe people can do to reduce their exposure to air pollution and its harmful health effects? § Stay Indoors/Keep Windows Closed 34.6 (31.2, 37.9) 27.6 (19.7, 35.6) 35.8 (26.1, 45.4) Avoid High Traffic Areas/Exposure at Certain Times of Day 12.6 (10.3, 14.9) 17.9 (11.1, 24.7) 17.9 (10.2, 25.6) Wear A Mask 16 (13.5, 18.6) 10.6 (5.1, 16.0) 12.6 (5.9, 19.3) Buy/Use Air Filters 6.8 (5.1, 8.6) 10.6 (5.1, 16) 9.5 (3.6, 15.4) Move to Country/Rural Area 8.3 (6.4, 10.2) 9.8 (4.5, 15.0) 2.1 (0, 5.0)* None/No Way to Limit Exposure 5.5 (4, 7.1) 8.1 (3.3, 13.0) 5.3 (0.8, 9.8)

Quit Smoking/Avoid Second- hand Smoke 4.3 (2.9, 5.7) 4.1 (0.6, 7.6) 4.2 (0.2, 8.3) Avoid/Reduce Strenuous Exercise/Physical Exertion 3.8 (2.5, 5.1) 2.4 (0, 5.2) 4.2 (0.2, 8.3) Take Medication/Oxygen 1.0 (0.3, 1.7) 2.4 (0, 5.2) 1.1 (0, 3.1) Reschedule Strenuous Exercise Until Air Quality Improves 2.1 (1.1, 3.2) 1.6 (0, 3.9) 3.2 (0, 6.7) Protect Self from The Sun 0.4 (0, 0.8) 0 (0, 0) 1.1 (0, 3.1)

If Yes to having had a health event attributable to air pollution, what steps have you taken to reduce your exposure? § No, Did Nothing 47.7 (37.1, 58.4) 50.0 (35.4, 64.6) 32.1 (14.6, 49.7) Reduced Time Spent Outdoors 31.8 (21.9, 41.7) 47.8 (33.2, 62.4) 50.0 (31.2, 68.8) Purchased/Installed/Used Air Purifier/Hepafilter/Humidifier 21.6 (12.8, 30.4) 32.6 (18.9, 46.3) 25 (8.7, 41.3) Take Medication/Oxygen 8 (2.2, 13.7) 13 (3.2, 22.9) 3.6 (0, 10.5) Get Out of The City/Away from Polluted Area 17.0 (9.0, 25.1) 10.9 (1.8, 20) 14.3 (1.1, 27.4) Close/Open Windows at Certain Times 10.2 (3.8, 16.7) 8.7 (0.4, 16.9) 25.0 (8.7, 41.3)* Use Air Conditioner 4.5 (0.1, 9.0) 8.7 (0.4, 16.9) 10.7 (0, 22.3) Wear A Mask 3.4 (0, 7.3) 0 (0, 0) 7.1 (0, 16.8) Cut Down on Strenuous Activity/Aerobic Exercise 4.5 (0.1, 9.0) 0 (0, 0) 3.6 (0, 10.5) Saw Doctor/Health Professional 2.3 (0, 5.4) 0 (0, 0) 0 (0, 0) Sought Out More Information on Advisory/Air Quality 1.1 (0, 3.4) 0 (0, 0) 0 (0, 0) Avoid Second-Hand Smoke 5.7 (0.7, 10.6) 8.7 (0.4, 16.9) 7.1 (0, 16.8) Protect Self from The Sun 1.1 (0, 3.4) 2.2 (0, 6.4) 0 (0, 0) Drive Car Less/Take Transit/Do Not Idle in Car/Bike 14.8 (7.2, 22.3) 2.2 (0, 6.4)* 7.1 (0, 16.8) *p<0.05; †p<0.01; ‡p<0.0001; §If only believe air pollution effects are only long term, or don't know; ||Unprompted, multiple responses; ¶If familiar with air quality information available in their area; **If reported ‘yes’ to “Have you, or someone else in your household experienced any type of physical or health problems over the past two years that might have been attributed to air pollution at the time?”

3.5.3 Reported Local Air Quality Assessment

Awareness of local air quality information was highest amongst the asthma and heart disease

populations. When asked specifically about the AQHI, nearly half of respondents in each of the

COPD and asthma populations indicated being aware of the index, but this was no higher than

the reference population(Table 3-2, S.3-2).

When assessing local air quality, approximately 60% of respondents across all chronic disease

groups reported relying exclusively on their senses, compared to 48% of the reference population

(p > 0.05). In fact, over 75% of respondents in each of the COPD and asthma populations (p <

0.05 for each compared to the reference population) believed they would be able to detect poor

air quality without the aid of forecasts, by simply stepping outside. Roughly 40% of respondents

across all chronic disease groups, reported using air quality forecasts, either exclusively or in

combination with their senses, compared to 52% of the reference population. Respondents in

each of the COPD and asthma populations who also reported being familiar with their local air

quality information, also indicated more frequent use of forecasts than the reference population

(Table 3-2).

3.5.4 Reported Health Effects of Air Pollution and Behavioral Change to Reduce Exposure

Over 50% of respondents with COPD, or asthma reported they themselves, or someone in their

household had had a health problem that might have been attributable to air pollution within 2

years of completing the survey, compared to 19% of the reference population (p< 0.001 for both

COPD and asthma populations), 20% of heart disease and 13% of diabetes populations (Table 3-

2). The adjusted odds of reporting a health problem were 4 times higher amongst respondents in the COPD (odds ratio [OR] 4.3; 95% CI: 2.5, 7.6), and asthma (OR 3.9, 95% CI: 2.1, 7.0, Table

3-3) populations, compared to the reference population. The reporting of health problems attributable to air pollution was not statistically different in the heart disease, or diabetes populations compared to the reference population (Supplemental Tables S.3-3). The odds of reporting a health problem in the respiratory disease population were nearly 3 times higher amongst those who reported using their senses exclusively (OR 2.6, 95% CI 1.0, 6.9), or in combination with forecasts (OR 2.9, 95% CI 1.0, 8.3), compared to using forecasts alone (Table

3-4). Odds of reporting a health problem were also statistically higher amongst those in the reference population who used only their senses to determine air quality (OR 1.7, 95% CI 1.1,

2.8). No such association was seen amongst those with non-respiratory disease (Supplemental

Table S.3-4).

Of those who were aware of the AQHI, 31% of respondents in the COPD, and 35% of those in the asthma populations indicated that in year preceding the survey, they, or someone else in their household had modified their daily routine as a result of air quality forecasts, compared to 23% of the reference population (Table 3-2). When this analysis was restricted to those who had also reported health problems, the proportions increased to 69% of the COPD, and 60% of asthma populations. Adjusted ORs showed no statistically significant differences between any of the chronic disease groups and the reference population with regards to behavioural modification

(Table 3-3).

Table 3-3: Associations between reported health problems, behaviour modifications and reported chronic disease status. Odds ratio (95% Confidence Interval) Reference Population COPD Asthma without COPD (n = 793) (n = 123) (n = 95)

Outcome Unadjusted Adjusted* Unadjusted Adjusted* Unadjusted Adjusted*

Health problem attributed to air pollution in the last 2 years: Yes, self, or someone else in the home. REF REF 5.2 (3.5, 7.7) 4.3 (2.5, 7.6) 4.3 (2.8, 6.6) 3.9 (2.1, 7.0) Have you or others in your household taken specific actions to reduce your exposure to air pollution because of the impact it has had on your health?* REF REF 1.6 (0.9, 2.9) 0.9 (0.4, 2.2) 1.1 (0.6, 2.1) 1.7 (0.6, 4.6) At any point in the past year have you, or another member of your household, taken any steps or made changes in your daily routine as a result of air quality forecast information you noticed or found? REF REF 1.5 (0.7, 3.3) 2.1 (0.6, 6.9) 1.8 (0.9, 4.0) 1.6 (0.5, 5.0) *Adjusted for age, sex, education and level of self-perceived health

Table 3-4: Association between method of air quality assessment and health outcomes amongst the total study population, and stratified by disease group. Odds ratio (95% Confidence Interval) Total Population Reference Population Respiratory Disease (n = 1167) (n = 793) (COPD, or Asthma, n = 218) Method of Air Quality Analysis Unadjusted Adjusted* Unadjusted Adjusted* Unadjusted Adjusted*

Exclusive use of senses† 2.2 (1.5, 3.2) 2.1 (1.4, 3.2) 1.7 (1.1, 2.8) 1.7 (1.1, 2.8) 3.0 (1.2, 7.3) 2.6 (1.0, 6.9)

Use of forecasts and senses† 1.8 (1.1, 2.8) 1.7 (1.1, 2.7) 1.3 (0.7, 2.3) 1.2 (0.7, 2.2) 2.7 (1.0, 7.2) 2.9 (1.0, 8.3) *Adjusted for age, sex, and education; † Compared to exclusive use of forecasts

When asked what they believe people can do to reduce their exposure to air pollution, the most

common response irrespective of disease was “stay indoors/keep windows closed” (~30%),

followed by: “avoid high traffic areas/exposures at certain times of day”; “wear a mask”,

“buy/use air filters”; and “move to the country/rural area” (Table 3-2). Fewer than 10% of

respondents believed there was “no way to limit exposure”. When those who had suffered a

health problem attributable to air pollution were asked what steps they had taken to reduce their

exposure, the most common answer was “did nothing” on at least 1 occasion. Other common

answers included “reduced time spent outdoors”; “used an air purification device”; “took

medication”; “got out of the city”; “closed/opened windows at certain times”; “used an air

conditioner” and; “wore a mask”.

3.6 Discussion

This study takes an important first step in determining how knowledgeable Canadians with

COPD are regarding the health effects of air pollution, its assessment, and strategies to reduce

exposure. My results suggest that within my study population, knowledge is generally lacking,

and respondents with COPD are no more knowledgeable, nor likely to modify their behaviours

to reduce exposure than my reference population.

Although the current literature suggests there is no safe level of air pollution exposure,(World

Health Organization (Regional Office for Europe) 2006, Stieb, Burnett et al. 2008, Abelsohn and

Stieb 2011) and health effects can be both immediate(Goldberg, Bailar 3rd et al. 2000) and long

term,(Andersen, Hvidberg et al. 2011) this knowledge appeared to be lacking across all disease

99 groups and the reference population. Without proper knowledge of health risks, and an accurate and reliable method of assessing air quality levels, it is difficult to avoid exposure.

My findings are consistent with results of other published research. In a convenience sample of

707 subjects aged 18 and over, living in Hamilton, Ontario (25% with a pre-existing respiratory condition), Radisic et al.(Radisic, Newbold et al. 2016) found that only 35% of participants understood the meaning of a high AQHI and where to check for daily values.(Radisic, Newbold et al. 2016) Studies have also found that the use of sensory cues (sight, touch, smell…etc.) prevail over established forecasts to assess outdoor air quality.(Bickerstaff and Walker 2001,

Radisic, Newbold et al. 2016) There is currently a lack of research evidence to suggest that the use of human senses alone is a reliable means of assessing air quality, and while senses may detect high levels of air pollution, such days are rare. For example, the annual average number of smog advisory days in Ontario (the province with largest population) from 2003 to 2014 was only 18.6.(Ontario Ministry of the Environment and Climate Change 2015) Air quality forecasts are more accurate and reliable tools of assessment. Amongst those with respiratory disease in my study, use of senses alone or in combination with forecasts to determine air quality was associated with nearly 3 times higher odds of reporting a health problem, as compared to exclusively using forecasts. Respondents with COPD who were aware of local air quality forecasts, also reported frequent use as compared to the reference population, suggesting education may increase forecast use amongst such vulnerable populations.

Consistent with other Canadian(Stieb, Paola et al. 1995) and U.S.,(Wen, Balluz et al. 2009) studies, only one third of my survey respondents with respiratory disease who were aware of local forecasts, took action to reduce exposure to air pollution. Furthermore, despite reported poorer health, neither the COPD nor asthma populations were more likely than the reference

100 population to take action to reduce exposure. It has been suggested that the marginal effectiveness of air quality alerts in promoting behavioural change is in part due to the lack of knowledge regarding protective actions.(Stieb, Paola et al. 1995) However, education alone may not be sufficient to change behaviour. The extent to which an individual action is perceived to make a meaningful difference, as well as a person’s own self-rated health and self-efficacy, play important roles in initiating and maintaining behavioural change.(Hochbaum 1958, Bourbeau,

Nault et al. 2004) This may explain why behaviour modification was twice as common amongst persons who had reported health problems attributable to air pollution in my study.

While the protective actions reported in my study may be effective, they are rather impractical.

As in other studies, many simpler actions were reported in only a few.(Evans, Fullilove et al.

2002) For instance, only a small proportion of the COPD population reported avoiding strenuous activity, or rescheduling activity until the air quality improved. Public Health advisories and related educational initiatives need to clearly delineate the increased risk associated with air pollution exposure in vulnerable populations, provide evidence that action makes a measurable difference, and more clearly suggest simple strategies that would result in meaningful reductions in exposure.

Air pollution is projected to continue to pose a serious risk to human health for the foreseeable future.(OECD 2012) Education plays a crucial role in learning to live with air pollution, while maintaining a high quality of life. In February of 2015, the Ontario Ministry of Health and Long

Term Care put forth its Patient’s First Action Plan for Health Care, which includes providing education, information and transparency for patients to make informed decisions about their health.(Ontario Ministry of Health and Long Term Care 2015) While advances in mobile health

101 technologies, will increase the opportunity for patients to play active roles in their self- management, the role of health care professionals as educators will continue to be critical.

Studies have shown that when education is provided by a health care professional, patients pay closer attention to air quality alerts and make better risk-reduction decisions.(Wen, Balluz et al.

2009) A Canadian study found that a short session on the use and application of the AQHI was easily implemented into a respiratory disease education plan and resulted in significant improvements in patient knowledge.(Abelsohn and Stieb 2011) However, access to pulmonary rehabilitation programs which include patient education must be improved as current programs have capacity to serve only 1% Canada’s COPD population,(Brooks, Sottana et al. 2007) and there is evidence that only a minority of patients with COPD ever see a lung health educator, or receive an action plan.(Hernandez, Balter et al. 2009)

There may also be room to improve the knowledge of health care professionals regarding air quality health effects. In 2014, the American Thoracic Society conducted a survey to determine its member’s attitudes concerning climate change and related effects on patients.(Sarfaty,

Bloodhart et al. 2015) Results showed a clear consensus that climate change is occurring, and having a direct impact on patient health. Roughly 60% believed physicians should be involved in advocacy pertaining to the health effects of climate change, including patient education.

However, only 7% reported being very knowledgeable and 31% moderately knowledgeable about the association between climate change and health.

To the best of my knowledge, this study is the first of its kind in Canada. It is one of few COPD studies to focus on individual reported outcomes, and compare their responses to those of other chronic disease and reference groups of similar age. Although previous studies have examined

102 the ability of pollution indices to predict morbidity and mortality,(To, Shen et al. 2013, Chen,

Villeneuve et al. 2014) or their effectiveness in communicating air pollution health effects and exposure reduction strategies,(Stieb, Paola et al. 1995) to the best of my knowledge, this is the first study to examine the effectiveness of pollution forecast use to successfully reduce morbidity. These results can be used to inform patient care and policy, assist in generating hypotheses, and the further development of tools like the AQHI. Furthermore, an informed population may be more likely to engage in self-correcting behaviours that reduce exposure on high pollution days, and in turn make the detection of associations between air pollution and health more difficult. In this way, my results may also be used to assist in the interpretation of studies that aim to quantify such associations.

Data used in my analyses were obtained from a survey originally commissioned by Health

Canada for a separate study. As such there exist several limitations that should be considered in the interpretation of my results, and addressed in future studies. The low response rate of the original questionnaire challenges the generalizability of my results and I therefore considered my study sample one of convenience. Nevertheless, many of my results are supported by the findings of previous literature. Furthermore, as survey respondents are likely individuals who are more engaged in self-management strategies, any non-response bias present would have generated more conservative results, showing a higher level of overall knowledge. Self-report of

COPD diagnosis may have led to misclassification bias. A 2013 study in Ontario Canada compared patient self-report of COPD diagnosis to a validated algorithm based on health administrative data.(Muggah, Graves et al. 2013) While results of this study showed poor agreement between the two methods (kappa = 0.29), self-report of COPD diagnosis was highly specific (0.97). Therefore, any misclassification in this study would place persons with COPD in

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the reference population, attenuating the statistical differences between the groups and leading to

more conservative results. The survey definition of COPD also included emphysema, chronic

bronchitis, and pneumonia. My COPD population may therefore include a fraction of

respondents who had suffered from acute pneumonia, but were otherwise free of chronic

respiratory disease. While the oversampling of high risk respondents increased the total sample

of those with chronic disease, results may have been affected by degree of selection bias. The

survey did not ask questions regarding environmental tobacco smoke exposure. Although

questions asked about health problems specifically attributable to air pollution, I cannot rule out

the possibility that some reported health effects may have been the result of smoke, or other

exposures. Questions regarding health problems and actions taken to reduce exposure, included

other members of the household that were affected, and were not restricted to the individual

respondent. Finally, the survey collected only limited demographic information, and did not ask

questions regarding socioeconomic status or access to health services that may have assisted in

further explaining the differences in knowledge and behaviour between my COPD and reference

populations.

3.7 Conclusions

My study results suggest that while Canadians with COPD believe strongly that air pollution

affects health, their current level of knowledge regarding the assessment of air quality, and risks

of exposure is lacking, does not translate to meaningful behavioural modification, and is no

better than those who are free of chronic disease. COPD patients represent a large group of

Canadians who have increased susceptibility to the adverse health effects of air pollution.

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Education on the use of available air quality indices and behavioural strategies to mitigate adverse effects should be targeted specifically to this group. Further education of both patients and health care professionals is a critical component in limiting exposure to air pollution, improving respiratory health, and the quality of life of Canadians with COPD.

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Chapter 4:

The Seasonal Health Risks of Air Pollution Exposure on People Living With COPD: A Population-Based Study

Preface

The results of original research study 1 suggest that individuals with COPD are not well informed about the health risks of air pollution exposure and few employ accurate and reliable tools to assess air quality such as the AQHI, or engage in exposure reduction strategies. These findings provide insights to assist in the interpretation of the results of the following study which quantified the association between the AQHI and acute HSU amongst individuals with COPD.

More specifically, it is unlikely that the results of study 2 are influenced by self-correcting behaviours to reduce exposure on days with high concentrations of ambient air pollution.

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4.1 Abstract

Background: Compelling evidence exists that short-term exposure to air pollution may be

associated with severe acute exacerbation of chronic obstructive pulmonary disease (COPD)

symptoms. The Air Quality Health Index (AQHI) is an aggregate measure of overall ambient air

pollution. The AQHI has previously been shown to be associated with health service use (HSU)

amongst individuals with COPD, but questions remain.

Objectives: This study aims to verify the association between the AQHI and acute

(hospitalizations and emergency department [ED] Visits) HSU amongst individuals with COPD;

quantify potential seasonal effects, and determine if the association between air quality and acute

HSU is modified by the presence of comorbidity, or other personal characteristics.

Methods: Individuals with COPD aged 35-99 were identified from administrative datasets in

Ontario, Canada using a validated algorithm. I used a time-stratified case-crossover analysis to

estimate the association between the AQHI and acute HSU for all non-accidental, COPD and

cardiovascular (CVD) causes during the full-year as well as the cold (December-February) and

warm (June-August) seasons. Risk estimates were obtained for each census division, and

provincial estimates generated using random-effects meta-analytic techniques.

Results: Statistically significant associations were found between the AQHI and hospital

admissions for all outcomes and suggested the presence of a seasonal effect. The AQHI was

significantly associated with non-accidental hospitalization through the full-year (OR 1.006;

95% CI 1.001, 1.011), with more marked effects during the warm (OR 1.018; 95% CI 1.006,

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1.031) and cold seasons (OR 1.012; 95% CI 1.001, 1.022). The association between the AQHI and COPD hospitalization was higher during the warm season (OR 1.042; 95% CI 1.022, 1.063), while with CVD hospitalization it was strongest during the cold season (OR 1.043; 95% CI

1.017, 1.070). Similarly, associations between the AQHI and non-accidental (OR 1.012; 95% CI

1.002, 1.021) and COPD ED visits (OR 1.033; 95% CI 1.014, 1.052) were higher during the warm season, while CVD ED visits showed strongest effects during the cold season (OR 1.031;

95% CI 1.006, 1.057). Individuals with a history of hospitalization for lower respiratory tract infections, and AMI were more vulnerable to the effects of air pollution.

Conclusions: My study provides further evidence that there is an association between the AQHI and acute HSU amongst individuals with COPD, and identifies comorbidities that increase vulnerability to the effects of air pollution. The presence of this seasonal effect may be suggestive of a more complex interaction between air pollution and temperature. Further investigation is required to elucidate this relationship.

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4.2 Background

Chronic obstructive pulmonary disease (COPD) is a leading cause of morbidity and mortality in

Canada.(Chapman, Bourbeau et al. 2003, Public Health Agency of Canada 2007) Compelling

evidence exists that short-term exposure to air pollution may be associated with severe acute

exacerbation of COPD symptoms (AECOPD). Even at levels below current air quality standards,

air pollution exposure can result in a decline in lung function, emergency department visits, or

hospitalizations.(Anderson, Spix et al. 1997, Medina-Ramon, Zanobetti et al. 2006, Stieb,

Szyszkowicz et al. 2009) Increased frequency of AECOPD has also been shown to lead to a

greater decline in lung function and longer length of stay in hospital.(Donaldson, Seemungal et

al. 2002) Despite this considerable body of literature there remains an underappreciation of these

risks by patients and clinicians.(Nowka, Bard et al. 2011, Foty, Dell et al. 2015) This may be in

part attributable to the complexity of the interpretation of air quality measures and

communication of their associated health risks.

The Air Quality Health Index (AQHI) is a single aggregate measure of air pollution developed

by Health Canada and Environment Canada. It is currently the primary index by which the

province of Ontario measures air quality.(Environment and Change 2016) The index ranges from

1 (low health risk) to 10+ (very high health risk) and is calculated based on the relative mortality

risk of the combined impacts of nitrogen dioxide (NO2), ozone (O3), and fine particulate matter

less than 2.5 microns in size (PM2.5).(Stieb, Burnett et al. 2008) This formulation has been shown

to best represent the overall mix of air pollution in the ambient air. The AQHI was designed to

communicate the level of overall ambient air pollution, the associated health risks, and specific

exposure reduction strategies to the public in an understandable way.(Environment Canada 2015)

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Our previous work(To, Shen et al. 2013, To, Feldman et al. 2015) using aggregate data and time series analyses quantified the impact of exposure to poor air quality, and showed an association between the AQHI and health service use (HSU) amongst individuals with chronic diseases, including COPD. However, to fully understand the extent of this risk requires further investigation. Questions remain as to whether these health risks are limited to warm summer months when air pollution is typically higher(Halonen, Lanki et al. 2009, Stieb, Szyszkowicz et al. 2009) and air quality levels are more regularly disseminated by popular media outlets; or, if similar risks exist during cold months when air quality levels must be actively sought. Studies have also shown that COPD and cardiovascular disease (CVD) often coexist.(Global Initiative for Chronic Obstructive Lung Disease (GOLD) 2016) In Ontario Canada, Gershon et al found that individuals with COPD used twice as many health services for lower respiratory tract infections and CVD than people without COPD.(Gershon, Mecredy et al. 2015) In the United

Kingdom, administrative data on more than 1.2 million patients showed that the risk of having

CVD was nearly five times higher amongst patients with COPD compared to those without

COPD (OR 4.98; 95% CI 4.85, 5.81).(Feary, Rodrigues et al. 2010) While this risk is well quantified, the effect of air pollution on CVD outcomes amongst those the COPD has yet to be determined. Furthermore, the role of CVD and other comorbidities(Gershon, Mecredy et al.

2015) in the modification of these associations is unclear. Such knowledge would allow the quantification of this risk amongst individuals with COPD, as well as the identification of populations that are potentially more vulnerable to the health effects of air pollution.

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4.3 Objectives

The objectives of the current study are to: 1) use individual level data determine if there is an

association between the AQHI and acute (hospitalizations and emergency department [ED]

Visits) HSU amongst individuals with physician-diagnosed COPD; 2) quantify potential seasonal

effects of air quality on non–accidental and disease-specific (COPD and CVD) acute HSU, and;

3) determine if the association between air quality and acute HSU is modified by the presence of

comorbidity, or other personal characteristics.

4.4 Methods

4.4.1 Study Design

I used a time-stratified case-crossover analysis(Maclure 1991) to estimate the association

between the AQHI and acute HSU. The case-crossover method, is an established approach to

examine short-term exposures with acute outcomes. Briefly, for each individual who experienced

an acute HSU event, their exposure to air pollution on the day of the event (a hazard period) was

compared to exposures during days when the event had not occurred (control periods). In this

way, each subject served as their own control. The position of the control periods relative to the

event was also carefully considered. Choosing control periods close to the time of the event can

eliminate potential confounding by constant, or slow-varying personal characteristics. However,

study designs which restrict all control periods to occurring either before or after the event

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(unidirectional), or where the event occurs in the center of the control periods (bidirectional),

have been shown to introduce selection bias.(Levy, Lumley et al. 2001) Instead, ambidirectional

models in which an event can occur at any point in the control window are preferred. In the

current study, I followed a time-stratified approach that divided control periods a priori into

fixed strata around each event, and allowed the relative position of the event to vary. Any bias

introduced by this method has been shown to be small if the event is rare.(Lumley and Levy

2000) I therefore defined control periods as the same days of the week within the same month

and year of each event, thus controlling for annual, seasonal, monthly, and day-of-the-week

trends by design.(Bateson and Schwartz 1999, Bateson and Schwartz 2001)

4.4.2 Data Sources

My study used health administrative data from Ontario, Canada. Both large and culturally

diverse, Ontario’s population of over 13 million is the largest of the Canadian provinces. All

Ontario residents with a valid health card are entitled to access provincial health-care services

including emergency and preventive care under the single-payer universal Ontario Health

Insurance Plan (OHIP), with no personal cost. Tourists, transients and visitors to Ontario are not

covered by OHIP. Service details are captured in health administrative databases housed at the

Institute for Clinical Evaluative Sciences (ICES), and can be linked at the individual level using

a resident’s unique encrypted health card number. The Canadian Institute for Health Information

Discharge Abstract Database (CIHI-DAD) records the primary and secondary diagnoses for all

hospitalizations; the National Ambulatory Care Reporting System database (NACRS) records

data of all emergency department (ED) visits and day surgeries; and the OHIP Database contains

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information on fee-for-service billings for physician services. Demographic data, including date

of birth, sex, and residential postal code were obtained from the Ontario Registered Persons

Database (RPDB). Ecological data on neighbourhood income and education were obtained from

the 2006 Canada census.

4.4.3 Study Population

I identified individuals aged 35-99 from the Ontario COPD database for whom an acute HSU

claim was recorded in Ontario between January 1, 2003 and March 31, 2013. This database

contains information on all residents of Ontario with physician-diagnosed COPD, identified by

having 3 or more physician billing claims within 2 consecutive years, or one or more hospital

discharges with COPD diagnostic codes (International Classification of Disease [ICD]-9: 49,

492, 496; ICD-10: J41, J42, J43, J44). This algorithm has been previously validated in a

population-based study and was found to have a specificity of 96.9% (95% CI: 94.4, 98.5) and a

sensitivity of 57.3% (47.8, 66.4) to identify persons who have COPD, when compared with real-

world clinical evaluation by a physician.(Gershon, Wang et al. 2009) This high specificity

algorithm was determined to be most useful for research purposes, and instances when the

identification of only patients with definite COPD is desired. Baseline was defined as date of

entry into the COPD cohort, or January 1, 2003 for those who entered the cohort prior to the start

of the study window. Subjects were excluded if they had died before the start of the study

window, or if their death date was the same as their date of entry into the COPD cohort.

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4.4.4 Outcome Measures

Acute HSU claims occurring between Jan 1, 2003 and March 31, 2013 were identified from

health administrative databases. Primary diagnosis codes were used to identify non-accidental

events (Table 4-1), and disease-specific outcomes for COPD, and cardiovascular disease

(CVD).(Gershon, Mecredy et al. 2015) To avoid dependencies in repeated admissions resulting

from a single exposure event, claims were restricted to only one per physician per individual per

day per disease code. Events with identical classification codes that recurred within 30 days of an

initial claim were excluded.(Chen, Yang et al. 2005) I also excluded events for which an

exposure measure could not be assigned either because the individual’s census division of

residence at the time of the event did not contain an AQHI monitor, or it could not otherwise be

ascertained.

4.4.5 Exposure Measures

Air quality data were obtained from the Ontario Ministry of the Environment and Climate

Change. Hourly AQHI, NO2, O3, and PM2.5 were collected by 42 fixed-site monitors, across 25

of the 49 Census Divisions in Ontario. Further details regarding air quality collection can be

found in section 1.4. The AQHI was calculated using the following formula:

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10 (1) AQHI = × (100 × exp (0.000871 × NO − 1 + exp 0.000537 × % − 1 10.4 " 3

+ exp 0.000487 × GH".I − 1))

where NO2 and O3 are measured in parts per billion (ppb) and PM2.5 in micrograms per cubic meter (µg/m3).(Stieb, Burnett et al. 2008) If less than 75% of the daily hourly data was available from a given station, the daily AQHI value was considered unreliable and set to missing.(Stieb,

Burnett et al. 2008) All missing daily values were then imputed by averaging the values from the year before, and year after for the specific station and date in question. I calculated daily maximum values of the AQHI, NO2, O3, and PM2.5 for each monitoring station, then averaged these data across all monitoring stations within each census division. Individuals with COPD included in the study were assigned exposure based on their census division of residence at the time of each event.

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Table 4-1: International classification of disease (ICD-9 and ICD-10) codes for chronic disease outcomes and comorbidities. ICD-9 ICD-10 All Non-accidental <800 A00-R99 Acute myocardial infarction (AMI) 410 I21 Angina 413 I20 Asthma 493 J45, J46 COPD 491, 492, 496 J41, J43, J44 Respiratory Related Conditions 460, 461, 464, 466, 473, 476, J10-J18, J20, J22, J40 477, 480-487, 494, 530, 536, 786, 787 Congestive heart failure (CHF) 428 I500, I501, I509 Cardiovascular Disease (CVD) 410, 412, 413, 427, 428, 432, G45, G46, H34.0, I06, I20-I25, 435, 436, 437 I47.1, I47.2, I48-I50

Diabetes 250 E10, E11, E13, E14 Hypertension 401, 402, 403, 404, 405 I10, I11, I12, I13, I15 Ischemic heart disease (IHD) 411, 414 I24, I251, I258, I259 Lower Respiratory Infection (LRI) 466, 480-487 J10-J20, J22, J40, J44 Lung Cancer 162, 163 C34.0, C38.4, C45.0 Cancers (Non-Lung) 140-239 (excluding 162, 163, C00-C96 (excluding C34.0-C34.9, 210-234) C38.4, C45.0) Stroke 433, 434, 435, 436 G45, G46, I63, I64

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4.4.6 Covariates and Effect Modifiers

Daily mean ambient temperature has been shown to be associated with mortality in a number of

countries.(Gasparrini, Guo et al. 2015) I obtained daily mean ambient temperature and relative

humidity from Environment Canada for all weather stations in Ontario.(Chen, Wang et al. 2016)

Days in which less than 75% of the hourly data was available were set as missing. Studies

suggest there may be a combined effect of air pollution and influenza on respiratory outcomes.

To account for any potential confounding by influenza,(Wong, Yang et al. 2009) I obtained daily

rate of physician visits for influenza per 100,000 people in each CD. I also created an indicator

variable for statutory holidays(Chen, Wang et al. 2016) as these may affect the timing of seeking

health care.

The effects of air pollution on acute HSU may be modified by personal characteristics, or the

presence of comorbidities previously shown to be associated with COPD.(Gershon, Mecredy et

al. 2015, To, Feldman et al. 2015, Chen, Wang et al. 2016) I included the following effect

modifiers in my analysis (Table 4-2): 1) personal characteristics: age, sex, rurality of residence,

death during the study window; 2) history of hospitalization for comorbid disease (since 1991-

earliest records) as a proxy for the following comorbidities (COPD, COPD-related conditions,

asthma cardiovascular disease (CVD), acute myocardial infarction (AMI), angina, congestive

heart failure (CHF), hypertension, ischemic heart disease, lower respiratory tract infection, lung

cancer, non-lung cancers, stroke, and diabetes) 3) income and education as proxies for

socioeconomic status.(Forastiere, Stafoggia et al. 2007) Income and education were ecological

variables examined using data from the 2006 Canada census and assigned at the dissemination

area (neighbourhood) level based on residential postal code. Income was defined as median

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family income for a census economic family (a group of two or more persons who live in the

same dwelling and are related to each other by blood, marriage, common-law, or adoption).

Education was defined as the percentage of the dissemination area population aged 15+ having

less than a high school diploma. All ecological variables were assigned as quartiles of exposure

based on area of residence. Rural status was defined as living in towns, or municipalities with

populations of less than 10,000 people.(Plessis V, Beshiri R et al. 2002) Subject characteristics

and ecological variables were assigned at baseline which I defined as the date of entry into the

COPD cohort, or January 1st, 2003 for those who entered the cohort prior to the start of the

study.

4.4.7 Statistical Analysis

I first calculated the annual, and seasonal distributions of all exposures and outcomes for CDs

with available AQHI data. I then conducted a 2-stage case-crossover analysis. In the first stage, I

developed conditional logistic regression models to estimate the association between air

pollution and each of non-accidental, COPD and CVD acute HSU, for each CD across Ontario. I

used maximum daily AQHI values,(To, Feldman et al. 2015) and adjusted for mean daily

ambient temperature, mean daily relative humidity, daily influenza rate and statutory holidays. I

examined the influence of lag structures by fitting single-day air pollution exposures from the

day of the event (lag0) to 3 days before the event (lag3), and cumulative exposures which

averaged air pollution for lags 0-1 (lag01), 0-2 (lag02), and 0-3 (lag03).(Stafoggia, Forastiere et

al. 2010) If the event occurred at the beginning of the month, exposure lags were selected from

the last week of the previous month. To account for the non-linear association between

118 temperature and HSU in the full-year analysis, both a quadratic and linear term of daily ambient temperature at lag0 were added to the models.(Tsai, Chang et al. 2013) In the season-specific analysis, I chose a priori to include only a linear term for ambient temperature at lag0 during the warm season (June–August), and lag06 (December-February) during the cold season.(Braga,

Zanobetti et al. 2002, Chen, Wang et al. 2016)

In the second phase, I estimated the provincial average effect of air pollution exposure on acute

HSU by pooling census division level estimates using random-effect techniques, which allow the true risk estimate to vary across regions.(DerSimonian and Laird 1986, Riley, Higgins et al.

2011) I assessed heterogeneity between census-divisions using the Cochran Q statistic.(Petitti

1999)

I conducted sensitivity analyses by comparing the Akaike Information Criterion (AIC) of the adjusted AQHI models to those produced using adjusted multipollutant models that included

NO2, O3, and PM2.5. I also compared the effect of ambient temperature using a cubic spline term with 3, 4, or 5 equally spaced knots for the full-year analysis.

To examine potential effect modification by personal characteristics, comorbidity and SES, stratified analyses were conducted for each census division, and provincial estimates generate by meta-analyses. Effect modification was assessed by identifying risks across strata with non- overlapping 95% confidence intervals. In census divisions with few acute HSU counts, quasi or complete separation in the data in the stratified analyses may have occurred, whereby the predictors nearly perfectly, or perfectly predicted the outcome. Such separation produces unreliable effect estimates and variances, preventing the estimation of provincial effects by meta-

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analysis. To avoid such situations, stratified analyses with variances above the 95th percentile

were excluded.

To determine the presence of a seasonal effect, I compared results of the main and stratified

analyses across warm (June-August) and cold (December-February) seasons. Effects were

reported as odds ratios (OR) and 95% confidence intervals (95% CI) for the risk of each outcome

associated with every unit increase in AQHI value, every 10 ppb of NO2 and O3, and every 10

3 µg/m of PM2.5. All census division level analyses were conducted with SAS v9.4,(SAS Institute

Inc. 2013) and meta-analyses using R statistical software(R Studio Team 2015) and the

METAFOR(Viechtbauer 2010) package.

4.4.8 Research Ethics Approval

Ethics approval was obtained from the Hospital for Sick Children (REB#:1000051010) and the

University of Toronto.

4.5 Results

4.5.1 Baseline Population

My analyses included 420,211 individuals (51.81% males) with COPD across 25 census

divisions with available exposure data. At baseline, 64.8% of the study population were aged

over 65 years, 9.8% lived in rural areas, 33.6% lived in neighbourhoods with the lowest median

120 quartile of income, and 28.6% lived in neighbourhoods with the highest percentage of persons aged over 15 years with less than a high school education (Table 4-2). History of hospitalizations for CVD (34.8%), lower respiratory tract infection (27.8%) and COPD (27.8%) were most common, and by the end of my study window 41.5% of my population had died.

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Table 4-2: Demographics of Study Population with Chronic Obstructive Pulmonary Disease in Ontario (2003-2013) Overall Female Male N (%) N (%) N (%) Total 420,211 (100.0) 202,674 (48.2)‡ 202,674 (51.8)‡ Age Group* 35-64 147,829 (35.2) 70,378 (34.7) 77,451 (35.6) 65-74 112,283 (26.7) 50,728 (25.0) 61,555 (28.3) 75-84 113,686 (27.1) 54,668 (27.0) 59,018 (27.1) 85+ 46,413 (11.1) 26,900 (13.3) 19,513 (9.0)

Died** 174,352 (41.5) 80,502 (39.7) 93,850 (43.1)

Residence in a Rural Area*† 41,272 (9.8) 18,953 (9.4) 22,319 (10.3)

Neighbourhood Median Income* Quartile 1 140,120 (33.6) 70,051 (21.6) 70,069 (32.5) Quartile 2 107,963 (25.9) 51,544 (20.7) 56,419 (26.2) Quartile 3 91,682 (22.0) 43,049 (24.8) 48,633 (22.6) Quartile 4 76,943 (18.5) 36,399 (33.0) 40,544 (18.8)

Percentage of Neighbourhood aged 15+ with < High School Education* Quartile 1 87,616 (21.0) 42,791 (21.3) 44,825 (20.8) Quartile 2 100,237 (24.1) 48,284 (24.0) 51,953 (24.1) Quartile 3 109,571 (26.3) 52,725 (26.2) 56,846 (26.4) Quartile 4 119,284 (28.6) 57,243 (28.5) 62,941 (28.8)

History of Hospitalization** Non-Accidental 371,208 (100.0) 180,805 (100.0) 190,403 (100.0) Angina 23,193 (6.3) 9,869 (5.5) 13,324 (7.0) Asthma 16,259 (4.4) 11,287 (6.2) 4,972 (2.6) Congestive Heart Failure (CHF) 51,214 (13.8) 24,196 (13.4) 27,018 (14.2) COPD 103,006 (27.8) 52,399 (28.9) 50,607 (26.6) COPD Related Conditions 84,057 (22.6) 42,043 (23.3) 42,014 (22.1) Cardiovascular Disease 129,281 (34.8) 55,710 (30.8) 73,571 (38.6) Diabetes 16,422 (4.4) 7,105 (3.9) 9,317 (4.9) Hypertension 5,672 (1.5) 3,219 (1.8) 2,453 (1.3) Ischaemic Heart Disease 32,242 (8.7) 11,658 (6.5) 20,584 (10.8) Lower Respiratory Infection 103,190 (27.8) 50,792 (28.1) 52,398 (27.5) Lung Cancer 18,950 (5.1) 8,429 (4.7) 10,521 (5.5) Non-Lung Cancers 58,601 (15.8) 25,203 (13.9) 33,398 (17.5) Stroke 33,999 (9.2) 15,464 (8.6) 18,535 (9.7) *At baseline; **Throughout Study Period; †Defined as living in towns, or municipalities with populations of <10,000 people; ‡Percent of total population

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4.5.2 Distribution of Acute Health Service Use

Table 4-3 shows the overall and seasonal distribution of acute HSU for non-accidental and

disease-specific events by census division. Between 2003-2013, the average annual non-

accidental hospitalization rate was 155.1 per 1000 persons (32.0 and 24.3 per 1000 persons for

COPD and CVD respectively), and the non-accidental ED visit rate was 331.8 per 1000 persons

(38.7 and 27.1 per 1000 persons for COPD and CVD respectively). HSU rates were consistently

statistically higher during the cold season than the warm. The Toronto census division had the

highest rate of acute HSU, with higher rates during the cold compared to the warm season.

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Table 4-3: Distribution of Acute Health Service Use Across Ontario Census Divisions (2003-2013) Census Divisions Population Size (n) Non-Accidental (n) COPD (n) CVD (n) Full-Year Warm Cold Full-Year Warm Cold Full-Year Warm Cold Season Season Season Season Season Season Hospitalizations Algoma 9,113 17,476 4,451 4,253 3655 808 936 3062 811 770 Bruce 4,138 3,216 689 798 608 113 176 576 111 144 Chatham-Kent 8,042 9,409 2,136 2,414 1869 331 584 1622 358 442 Durham 22,524 29,474 6,664 7,702 6057 1182 1706 4725 1054 1306 Essex 22,924 31,029 7,236 8,328 5260 1069 1551 5244 1263 1396 Frontenac 6,176 6,944 1,471 1,838 1848 333 522 1024 230 268 Halton 13,977 26,876 6,159 7,441 5448 1076 1738 3992 913 1094 Hamilton 22,293 45,638 10,512 12,410 9501 1869 2807 7577 1751 2060 Hastings 8,875 15,056 3,360 4,075 4020 759 1126 2524 597 684 Huron 4,018 604 156 78 129 30 14 93 23 11 Lambton 8,699 11,980 2,983 2,926 2062 479 567 2195 549 542 Middlesex 16,562 21,759 4,959 5,835 4506 869 1307 3053 707 819 Niagara 23,751 33,061 7,585 8,615 7762 1517 2248 5150 1239 1314 Nipissing 6,608 15,381 3,475 4,063 3018 566 927 2613 608 654 Ottawa 27,899 46,019 10,231 12,641 11119 2110 3380 6817 1533 1843 Parry Sound 3,034 2,001 461 513 384 79 103 287 73 68 Peel 25,004 45,141 10,239 12,108 9574 1846 2894 6729 1514 1788 Peterborough 9,095 14,016 3,164 3,602 3542 678 1050 1911 425 478 Simcoe 21,396 43,222 9,764 11,501 10241 2014 2975 7001 1565 1826 Stormont/Dundas 8,497 12,123 2,717 3,130 3049 586 836 2130 442 560 and Glengarry Thunder Bay 9,283 19,385 4,678 4,684 3930 805 999 3478 877 830 Toronto 95,940 161,140 36,853 43,543 28536 5856 8606 24526 5734 6512 Waterloo 15,347 18,980 4,299 4,831 4458 882 1234 2726 615 726 Wellington 7,425 4,863 1,043 1,451 1235 199 421 708 161 203 York 19,591 33,307 7,809 8,398 6036 1339 1662 4973 1162 1292 Ontario 420,211 668,100 153,094 177,178 137,847 27,395 40,369 104,736 24,315 27,630 Ontario Annual Average Rate* 155.1 35.5 41.1* 32.0 6.4 9.4** 24.3 5.6 6.4**

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Non-Accidental (n) COPD (n) CVD (n) Census Divisions Population Size (n) Full-Year Warm Cold Full-Year Warm Cold Full-Year Warm Cold Season Season Season Season Season Season Emergency Department Visits Algoma 9,113 41,090 10,684 9,572 4785 1072 1142 2907 757 743 Bruce 4,138 10,773 2,249 2,643 1274 205 327 693 136 174 Chatham-Kent 8,042 21,126 4,839 5,378 2738 529 808 1831 375 482 Durham 22,524 65,832 15,429 15,955 7727 1490 2128 5629 1242 1419 Essex 22,924 64,751 15,844 16,995 6516 1363 1880 6223 1503 1649 Frontenac 6,176 18,056 4,316 4,508 3108 620 862 1233 283 305 Halton 13,977 46,014 11,072 12,519 4991 1016 1555 3771 902 1011 Hamilton 22,293 87,309 20,881 23,083 10615 2109 3122 7924 1883 2132 Hastings 8,875 39,686 9,711 9,812 5537 1143 1481 2872 689 728 Huron 4,018 1,627 467 169 252 61 24 106 37 11 Lambton 8,699 29,130 7,620 7,052 3176 744 839 2425 621 593 Middlesex 16,562 45,538 11,101 11,383 5505 1208 1448 3503 798 862 Niagara 23,751 79,644 19,766 20,454 9027 1935 2503 5517 1350 1477 Nipissing 6,608 37,686 8,893 9,412 4632 943 1315 2998 712 741 Ottawa 27,899 100,027 24,096 26,017 13053 2648 3881 8751 1982 2373 Parry Sound 3,034 4,483 1,058 1,031 638 126 151 270 66 54 Peel 25,004 81,370 19,548 21,005 9829 2035 2827 7133 1643 1854 Peterborough 9,095 31,641 7,761 7,670 4736 958 1307 2034 507 470 Simcoe 21,396 97,740 23,727 24,397 13297 2740 3628 8004 1913 1981 Stormont/ 8,497 34,304 8,404 7,760 4535 889 1137 2412 549 587 Dundas and Glengarry Thunder Bay 9,283 44,810 11,298 10,113 4803 1010 1164 3481 867 809 Toronto 95,940 336,692 81,515 88,365 32290 6943 9420 27785 6453 7367 Waterloo 15,347 38,589 8,918 9,570 5502 1134 1453 3154 702 845 Wellington 7,425 10,227 2,131 3,000 1552 249 513 733 162 203 York 19,591 61,093 15,468 14,017 6383 1506 1623 5410 1365 1245 Ontario 420,211 1,429,238 346,796 361,880 166,501 34,676 46,538 116,799 27,497 30,115 Ontario Annual Average Rate† 331.8 80.5 84.0 38.7 8.1 10.8** 27.1 6.4 7.0* *p<0.01 for difference between warm and cold season; **p<0.001 for difference between warm and cold season; †Per 1000 individuals with COPD

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4.5.3 Environmental Exposures

Table 4-4 describes environmental exposures for each census division. The provincial maximum

daily AQHI value, averaged between 2003-2013 was 3.3 (warm season: 3.6; cold season: 3.1; p

< 0.0001) and ranged from a minimum of 2.6 to a maximum of 4.3. Provincial maximum daily

NO2 values averaged 21.3 ppb (warm season: 17.0 ppb; cold season: 24.9 ppb; p < 0.0001),

maximum daily O3 averaged 40.6 ppb (warm season: 49.4; cold season: 32.2; p < 0.0001) and

3 3 3 PM2.5 values averaged 13.9 !g/m (warm season: 17.8 !g/m ; cold season 11.4 !g/m ; p <

0.0001). Average mean daily temperature was 7.2°C (warm season: 19.5°C; cold season: -5.4°C;

p < 0.0001), and average mean daily relative humidity was 74.7% (warm season: 73.6%; cold

season: 78.1%; p < 0.0001).

Pearson correlations between mean daily AQHI, air pollutants, temperature and relative humidity

in full-year and by seasons are presented in Table 4-5. Stronger correlations were observed

between the AQHI and NO2 during the cold season, and the AQHI and O3, and PM2.5 during the

warm seasons.

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Table 4-4: Distribution of Average Annual Environmental Exposures Across Ontario Census Divisions (2003-2013)

Census Division Maximum Daily AQHI Maximum Daily NO2 (ppb) Maximum Daily O3 (ppb) Mean (SD) Mean (SD) Mean (SD) Full-Year Warm Cold Full-Year Warm Cold Full-Year Warm Cold Season Season Season Season Season Season York 3.2 (1.1) 3.6 (1.3) 3.0 (0.9) 19.0 (11.2) 13.6 (6.7) 23.9 (13.1) 42.7 (13.8) 51.4 (15.6) 34.7 (6.7) Halton 3.7 (1.2) 4.2 (1.3) 3.4 (0.9) 29.9 (11.6) 27.6 (10.1) 30.6 (11.3) 42.1 (14.7) 54.3 (15.1) 32.0 (7.0) Peel 3.7 (1.2) 4.0 (1.3) 3.5 (1.0) 28.7 (13.4) 23.4 (11.0) 31.3 (13.0) 41.2 (14.4) 51.5 (15.1) 32.3 (7.6) Toronto 4.3 (1.3) 4.6 (1.4) 4.0 (1.1) 38.6 (12.3) 36.2 (11.4) 39.8 (11.4) 41.7 (15.3) 54.8 (16.4) 31.3 (6.8) Frontenac 3.0 (1.0) 3.5 (1.3) 2.6 (0.6) 11.4 (7.4) 8.2 (4.6) 14.4 (8.4) 42.1 (13.5) 51.7 (15.5) 34.5 (5.4) Niagara 3.3 (1.0) 3.8 (1.1) 3.0 (0.7) 21.6 (10.3) 18.2 (7.9) 22.6 (9.8) 41.7 (14.3) 53.4 (13.7) 31.3 (6.7) Essex 4.1 (1.2) 4.7 (1.3) 3.7 (0.9) 33.4 (11.7) 30.9 (10.4) 35.7 (11.2) 42.1 (18.3) 59.1 (16.6) 26.7 (7.9) Ottawa 3.1 (1.1) 3.0 (1.1) 3.3 (1.0) 21.1 (11.6) 14.6 (7.0) 27.8 (12.1) 36.9 (11.9) 42.1 (12.8) 31.2 (7.1) Algoma 2.8 (0.9) 3.0 (1.2) 2.6 (0.7) 13.6 (8.3) 11.3 (6.5) 16.6 (9.3) 38.7 (11.9) 42.9 (14.1) 33.4 (5.5) Peterborough 2.8 (1.0) 3.2 (1.2) 2.6 (0.8) 12.8 (9.3) 8.7 (4.9) 16.9 (11.4) 39.9 (12.7) 46.6 (15.2) 33.2 (6.0) Middlesex 3.5 (1.1) 3.8 (1.2) 3.1 (0.9) 23.2 (11.1) 19.0 (9.0) 25.8 (11.3) 39.3 (14.3) 49.6 (14.0) 28.7 (7.2) Durham 3.0 (1.0) 3.4 (1.2) 2.7 (0.7) 15.7 (8.5) 11.8 (5.4) 19.4 (9.8) 39.6 (13.5) 48.5 (15.8) 31.9 (6.5) Hastings 3.0 (1.1) 3.5 (1.3) 2.7 (0.8) 16.9 (10.2) 13.9 (8.1) 19.6 (10.8) 42.1 (15.1) 52.3 (17.4) 32.7 (6.8) Nipissing 2.9 (1.0) 2.8 (1.1) 3.1 (0.9) 20.2 (12.7) 13.1 (6.1) 27.1 (14.0) 39.0 (11.8) 43.1 (13.9) 33.7 (5.4) Lambton 3.7 (1.3) 4.3 (1.6) 3.2 (0.9) 22.6 (9.9) 20.5 (8.8) 24.3 (10.3) 41.7 (15.8) 52.3 (17.1) 31.1 (8.6) Thunder Bay 2.9 (0.9) 2.6 (0.8) 3.1 (0.8) 20.3 (10.8) 13.6 (6.0) 26.2 (11.9) 36.7 (9.4) 37.3 (9.9) 33.6 (5.8) Hamilton 3.8 (1.2) 4.3 (1.3) 3.4 (0.9) 28.7 (11.7) 24.5 (9.9) 30.2 (11.3) 41.0 (14.8) 52.7 (14.9) 30.8 (7.3) Stormont/Dundas and Glengarry 2.9 (0.9) 3.0 (1.0) 2.9 (0.9) 15.6 (10.4) 10.1 (5.8) 20.5 (11.9) 38.6 (12.1) 43.6 (12.5) 32.2 (6.2) Simcoe 3.3 (1.1) 3.3 (1.2) 3.3 (1.1) 24.3 (13.1) 19.0 (9.1) 29.2 (14.9) 38.7 (12.5) 44.9 (13.9) 32.0 (6.7) Chatham-Kent 3.3 (1.1) 3.9 (1.2) 2.9 (0.8) 16.6 (9.2) 12.0 (5.8) 21.5 (10.0) 44.3 (15.8) 57.4 (15.3) 31.6 (7.1) Huron 3.1 (1.2) 3.8 (1.3) 2.4 (0.5) 6.8 (4.5) 5.3 (2.0) 8.8 (8.1) 48.1 (17.0) 59.2 (18.6) 36.0 (6.0) Waterloo 3.4 (1.1) 3.6 (1.2) 3.2 (1.1) 21.2 (12.4) 14.2 (7.5) 25.7 (13.5) 41.2 (13.9) 49.0 (13.9) 31.9 (7.2) Parry Sound 2.6 (0.9) 2.9 (1.1) 2.3 (0.6) 10.0 (7.1) 8.3 (5.2) 11.5 (8.0) 40.4 (11.9) 44.8 (14.5) 34.8 (5.5) Bruce 2.6 (1.0) 2.9 (1.2) 2.3 (0.5) 5.7 (4.8) 3.7 (2.4) 8.1 (6.6) 41.2 (13.3) 45.5 (16.0) 35.4 (6.2) Wellington 3.0 (0.9) 3.4 (1.0) 2.8 (0.8) 15.4 (9.3) 10.4 (4.9) 18.8 (10.9) 42.3 (12.9) 51.4 (13.7) 34.6 (6.8) Ontario 3.3 (1.2) 3.6 (1.3) 3.1 (1.0)* 21.3 (13.1) 17.0 (11.0) 24.9 (13.4)* 40.6 (14.0) 49.4 (15.7) 32.2 (7.0)*

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3 Census Division Maximum Daily PM2.5 (!g/m ) Maximum Daily Temperature (C) Maximum Daily Relative Humidity (%) Mean (SD) Mean (SD) Mean (SD) Full-Year Warm Cold Full-Year Warm Cold Season Full-Year Warm Season Cold Season Season Season Season York 12.5 (8.9) 16.3 (10.8) 10.5 (6.8) 8.2 (10.7) 20.7 (3.4) -4.3 (5.7) 69.9 (11.9) 66.9 (10.1) 73.3 (9.6) Halton 15.3 (9.8) 19.8 (11.2) 12.5 (7.4) 9.2 (9.9) 20.7 (3.5) -2.0 (5.2) 70.3 (11.2) 68.6 (10.9) 72.0 (9.5) Peel 14.4 (9.4) 18.2 (10.5) 11.8 (7.1) 8.8 (10.6) 21.1 (3.4) -3.5 (5.5) 70.5 (11.8) 68.0 (10.4) 74.7 (8.9) Toronto 17.3 (10.6) 22.2 (11.9) 14.0 (8.0) 9.0 (9.9) 20.6 (3.1) -2.6 (5.2) 72.3 (11.4) 72.7 (10.2) 73.7 (9.7) Frontenac 12.9 (8.5) 18.1 (10.8) 10.2 (5.1) 7.9 (10.8) 20.4 (3.0) -4.9 (6.2) 72.4 (11.9) 73.3 (10.2) 73.7 (11.3) Niagara 14.3 (8.9) 18.9 (10.1) 11.1 (6.0) 9.2 (10.0) 21.0 (3.1) -2.3 (5.1) 72.7 (9.9) 71.3 (8.9) 75.3 (8.1) Essex 18.5 (11.2) 24.9 (13.8) 14.0 (7.0) 9.4 (10.2) 21.3 (3.2) -2.6 (5.0) 73.7 (10.0) 71.8 (8.5) 77.9 (9.1) Ottawa 12.3 (8.6) 14.5 (9.6) 12.6 (8.5) 6.7 (11.8) 20.1 (3.3) -7.7 (6.5) 73.7 (13.2) 73.1 (10.4) 77.6 (11.3) Algoma 11.7 (8.5) 15.2 (10.2) 9.5 (5.9) 4.5 (11.5) 17.3 (3.2) -9.3 (6.5) 73.9 (11.5) 72.6 (9.8) 78.7 (8.6) Peterborough 11.5 (8.0) 14.5 (9.5) 9.6 (5.8) 7.0 (10.8) 19.3 (3.2) -5.7 (6.3) 73.9 (11.1) 74.5 (9.0) 75.6 (10.1) Middlesex 15.9 (10.2) 20.5 (11.5) 12.8 (7.5) 8.1 (10.4) 20.1 (3.2) -4.1 (5.4) 74.3 (10.4) 71.9 (8.8) 79.7 (7.2) Durham 12.1 (8.3) 16.0 (9.7) 10.0 (5.8) 8.1 (10.4) 20.2 (3.0) -3.8 (5.5) 74.4 (9.7) 72.8 (8.1) 77.6 (9.0) Hastings 11.7 (8.0) 15.4 (9.9) 9.8 (5.8) 6.7 (11.0) 19.2 (3.2) -6.3 (6.5) 74.6 (10.6) 74.5 (8.2) 76.3 (9.6) Nipissing 9.9 (7.1) 12.2 (8.9) 9.1 (5.3) 4.3 (11.8) 17.7 (3.3) -9.9 (6.8) 75.4 (12.5) 73.9 (9.6) 79.8 (10.3) Lambton 21.0 (12.5) 26.0 (13.8) 17.6 (11.0) 8.9 (10.3) 20.8 (3.5) -3.0 (5.2) 75.4 (10.6) 74.7 (9.3) 78.3 (9.4) Thunder Bay 10.4 (6.9) 12.4 (8.5) 9.2 (5.6) 2.4 (12.2) 16.1 (3.1) -12.6 (6.9) 75.8 (10.3) 75.5 (8.4) 77.8 (8.9) Hamilton 18.2 (12.4) 22.9 (12.7) 13.1 (8.0) 8.5 (10.2) 20.4 (3.3) -3.2 (5.5) 75.9 (10.8) 74.2 (9.2) 78.9 (8.8) Stormont/Dundas and Glengarry 12.4 (8.4) 15.8 (9.2) 10.9 (7.1) 6.2 (11.8) 19.9 (3.3) -7.6 (6.8) 76.1 (12.1) 74.7 (10.5) 79.5 (10.2) Simcoe 13.2 (9.0) 16.6 (10.2) 10.6 (6.6) 7.0 (10.7) 19.1 (3.3) -5.5 (6.1) 76.4 (10.0) 74.9 (8.1) 80.4 (7.6) Chatham-Kent 14.4 (9.2) 19.6 (10.7) 11.4 (6.3) 9.0 (10.0) 20.7 (3.1) -2.7 (5.1) 76.8 (9.5) 76.3 (8.5) 80.1 (8.3) Huron 12.6 (8.3) 18.1 (9.1) 7.8 (6.4) 7.9 (10.0) 19.1 (3.5) -3.6 (5.1) 77.4 (10.7) 77.3 (9.7) 81.1 (7.8) Waterloo 15.2 (10.3) 19.3 (11.4) 12.1 (8.0) 7.3 (10.5) 19.3 (3.4) -4.9 (5.6) 77.5 (10.9) 76.1 (9.3) 81.2 (7.7) Parry Sound 9.9 (7.1) 12.5 (8.1) 9.1 (7.2) 6.1 (11.4) 18.9 (3.1) -7.5 (6.7) 77.8 (11.4) 77.7 (9.4) 81.3 (9.1) Bruce 9.6 (7.5) 13.0 (9.3) 7.2 (4.5) 6.7 (10.0) 17.9 (3.4) -4.7 (5.3) 80.2 (10.7) 79.2 (9.2) 84.9 (8.7) Wellington 12.1 (7.7) 16.2 (8.2) 10.7 (7.5) 6.8 (10.5) 18.7 (3.4) -5.5 (5.6) 81.7 (12.0) 79.2 (9.6) 88.3 (8.1) Ontario 13.9 (9.7) 17.8 (11.3) 11.4 (7.3)* 7.2 (10.9) 19.5 (3.5) -5.4 (6.5)* 74.7 (11.5) 73.6 (10.0) 78.1 (9.9)* *p<0.001 for difference between warm and cold season; Warm season (Jun-Aug); Cold Season (Dec-Feb)

Table 4-5: Pearson Correlations Between Environmental Exposures

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AQHI NO2 (ppb) O3 (ppb) PM2.5 (!g/m3) Ambient Relative Temperature (C) Humidity (%) Full-Year AQHI 1.00 0.61 0.65 0.76 0.23 -0.15 NO2 (ppb) 1.00 0.06 0.37 -0.17 -0.08 O3 (ppb) 1.00 0.48 0.48 -0.34 PM2.5 (!g/m3) 1.00 0.37 0.06 Ambient Temperature (C) 1.00 -0.06 Relative Humidity (%) 1.00 Warm Temperature Season AQHI 1.00 0.52 0.87 0.81 0.55 -0.03 NO2 (ppb) 1.00 0.32 0.37 0.18 -0.10 O3 (ppb) 1.00 0.66 0.57 -0.11 PM2.5 (!g/m3) 1.00 0.53 0.10 Ambient Temperature (C) 1.00 -0.14 Relative Humidity (%) 1.00 Cold Temperature Season AQHI 1.00 0.85 0.07 0.65 -0.09 -0.11 NO2 (ppb) 1.00 -0.08 0.54 -0.06 -0.07 O3 (ppb) 1.00 -0.20 -0.33 -0.40 PM2.5 (!g/m3) 1.00 0.15 0.14 Ambient Temperature (C) 1.00 0.41 Relative Humidity (%) 1.00 Based on maximum daily values for all air pollution measures, and mean daily values for ambient temperature and relative humidity

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4.5.4 Main Effect – Hospitalizations

Table 4-6 reports the results of the meta-analyses comparing the pooled provincial effects across

census divisions for the single day and cumulative lags during the full-year, and the warm and

cold seasons. Statistically significant associations were found between hospital admissions for all

non-accidental, COPD and CVD outcomes with every unit increase in the AQHI. The strongest

effects were generally seen at lag03 and are reported here. Non-accidental hospitalization was

significantly associated with increases in the AQHI through the full-year (OR 1.006; 95% CI

1.001, 1.011), with more marked effects during the warm (OR 1.018; 95% CI 1.006, 1.031) and

cold seasons (OR 1.012; 95% CI 1.001, 1.022). The risk of COPD hospitalization with increases

in the AQHI was higher in the warm season (OR 1.042; 95% CI 1.022, 1.063), while CVD

outcomes showed the strongest effects during the cold season (OR 1.043; 95% CI 1.017, 1.070).

4.5.5 Main Effect – ED Visits

Emergency department visits for non-accidental, COPD and CVD also showed significant

associations with increases in the AQHI. Again, the highest magnitude of effect was generally

seen at lag03 (Table 4-6). Non-accidental (OR 1.012; 95% CI 1.002, 1.021) and COPD outcomes

(OR 1.033; 95% CI 1.014, 1.052) were higher during the warm season, while CVD outcomes

showed strongest effects during the cold season (OR 1.031; 95% CI 1.006, 1.057).

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Table 4-6: Results of Main Analyses: Non-Accidental and Disease Specific Pooled Estimates of the Risk of Acute Health Service Use with Unit Increases in Daily Maximum Air Quality Health Index (Lag03) Across Ontario Census Divisions Hospitalizations Emergency Department Visits Full-Year Warm Season Cold Season Full-Year Warm Season Cold Season OR 95% CI OR 95% CI OR 95% CI OR 95% CI OR 95% CI OR 95% CI Non-Accidental Lag0 1.002 (0.999, 1.005) 1.009 (1.003, 1.015) 1.005 (0.998, 1.012) 1.001 (0.998, 1.003) 1.007 (1.003, 1.011) 1.006 (1.001, 1.012) Lag1 1.004 (1.001, 1.008) 1.009 (1.001, 1.017) 1.011 (1.004, 1.019) 0.999 (0.997, 1.002) 1.006 (1.002, 1.010) 1.007 (1.001, 1.013) Lag2 1.002 (0.998, 1.005) 1.008 (0.999, 1.016) 1.003 (0.996, 1.010) 0.999 (0.996, 1.002) 1.005 (1.001, 1.009) 0.996 (0.991, 1.001) Lag3 1.002 (0.999, 1.005) 1.006 (1.000, 1.012) 1.001 (0.993, 1.008) 1.000 (0.997, 1.002) 1.004 (0.998, 1.011) 0.997 (0.991, 1.003) Lag01 1.004 (1.000, 1.008) 1.011 (1.005, 1.018) 1.011 (1.003, 1.020) 1.000 (0.997, 1.003) 1.008 (1.003, 1.013) 1.009 (1.003, 1.016) Lag02 1.005 (1.000, 1.009) 1.015 (1.003, 1.027) 1.011 (1.002, 1.021) 1.000 (0.997, 1.004) 1.010 (1.005, 1.015) 1.006 (0.999, 1.013) Lag03 1.006 (1.001, 1.011) 1.018 (1.006, 1.031) 1.012 (1.001, 1.022) 1.001 (0.997, 1.004) 1.012 (1.002, 1.021) 1.005 (0.997, 1.012) Chronic Obstructive Pulmonary Disease Lag0 0.997 (0.990, 1.005) 1.017 (1.003, 1.032) 1.004 (0.989, 1.019) 0.999 (0.992, 1.006) 1.020 (1.002, 1.039) 1.006 (0.991, 1.021) Lag1 1.006 (0.998, 1.014) 1.021 (1.007, 1.036) 1.001 (0.986, 1.016) 1.001 (0.994, 1.008) 1.025 (1.010, 1.040) 1.002 (0.987, 1.016) Lag2 1.004 (0.996, 1.011) 1.022 (1.008, 1.036) 0.991 (0.975, 1.008)* 1.002 (0.995, 1.009) 1.018 (1.005, 1.031) 0.991 (0.977, 1.006) Lag3 1.002 (0.995, 1.009) 1.020 (1.006, 1.034) 0.982 (0.967, 0.997)* 0.994 (0.986, 1.003) 1.004 (0.991, 1.017) 0.984 (0.966, 1.002) Lag01 1.002 (0.993, 1.011) 1.025 (1.009, 1.042) 1.004 (0.986, 1.022) 1.000 (0.991, 1.008) 1.028 (1.011, 1.047) 1.006 (0.989, 1.023) Lag02 1.004 (0.994, 1.014) 1.034 (1.015, 1.053) 0.998 (0.978, 1.018) 1.001 (0.992, 1.011) 1.033 (1.016, 1.050) 1.000 (0.981, 1.019) Lag03 1.005 (0.994, 1.016) 1.042 (1.022, 1.063) 0.991 (0.969, 1.012)* 1.000 (0.990, 1.010) 1.033 (1.014, 1.052) 0.994 (0.974, 1.015) Cardiovascular Disease Lag0 1.010 (1.001, 1.019) 1.016 (1.001, 1.031) 1.012 (0.993, 1.031) 1.011 (1.003, 1.019) 1.014 (1.000, 1.028) 1.008 (0.991, 1.026) Lag1 1.014 (1.005, 1.023) 1.014 (0.999, 1.029) 1.032 (1.014, 1.051) 1.005 (0.993, 1.016) 0.998 (0.973, 1.023) 1.018 (1.000, 1.035) Lag2 1.005 (0.997, 1.013) 1.003 (0.989, 1.018) 1.018 (1.000, 1.037) 1.004 (0.996, 1.011) 1.000 (0.981, 1.019) 1.013 (0.996, 1.031) Lag3 1.005 (0.997, 1.013) 1.009 (0.989, 1.030) 1.019 (1.001, 1.037) 1.007 (0.999, 1.014) 1.015 (0.994, 1.037) 1.019 (1.001, 1.036) Lag01 1.017 (1.006, 1.027) 1.019 (1.002, 1.036) 1.031 (1.009, 1.053) 1.011 (1.001, 1.020) 1.005 (0.981, 1.030) 1.017 (0.997, 1.038) Lag02 1.017 (1.006, 1.029) 1.018 (0.999, 1.037) 1.036 (1.011, 1.061) 1.012 (1.001, 1.022) 1.004 (0.974, 1.036) 1.024 (1.001, 1.047) Lag03 1.018 (1.006, 1.031) 1.026 (0.992, 1.061) 1.043 (1.017, 1.070) 1.014 (1.003, 1.026) 1.011 (0.977, 1.047) 1.031 (1.006, 1.057) OR: Odds Ratio; 95% CI: 95% Confidence Interval; Warm Season (Jun-Aug); Cold Season (Dec-Feb); *Statistically significant seasonal effect; Bolded values indicate statistical significance; All models adjusted for ambient temperature (Full-year quadratic and linear term at specified lag; warm and cold seasons linear term at specified lag), relative humidity (full-year, warm season and cold season at specified lag), influenza events and holidays. *Seasonal Effect

131

4.5.6 Sensitivity Analyses

Results of meta-analyses modelling the association between acute HSU and increases in NO2,

O3, and PM2.5 can be found in the appendix (Supplemental Tables S.4-6a, b, c). Overall, the

individual pollutants exhibited associations that trended towards, but few reached statistical

significance. The magnitude of risk was also not as consistently seen at lag03 as with the AQHI.

During the full-year, COPD hospitalizations were associated with increases in O3 (lag1: 1.013;

95% CI 1.004, 1.022) and PM2.5 (lag2: 1.011; 95% CI 1.000, 1.021), while CVD hospitalizations

were associated with increases in NO2 (lag03: 1.011; 95% CI 1.000, 1.021). In the warm season,

COPD hospitalizations were associated with increases in NO2 (lag01: OR 1.017; 95% CI 1.000,

1.034) and O3 (lag03: OR 1.022; 95% CI 1.002, 1.042), while non-accidental hospitalizations

were associated with increases in PM2.5 (lag03: OR 1.011; 95% CI 1.002, 1.019). During the cold

season increases in risk of COPD hospitalization was associated with PM2.5 (lag02: OR 1.042;

95% CI 1.022, 1.062), while non-accidental and CVD hospitalizations were associated with

increases in PM2.5 (lag02: 1.013; 95% CI 1.001, 1.025 and lag01: OR 1.037; 95% CI 1.012,

1.062 respectively). Increases in NO2 were associated with a reduction in risk of COPD

hospitalization (lag03: OR 0.965; 95% CI 0.948, 0.982).

During the full-year, ED visits for non-accidental and COPD causes were associated with

increases in O3 (lag03: OR 1.003; 95% CI 1.000, 1.006 and lag02: OR 1.013; 95% CI 1.004,

1.022 respectively), and PM2.5 (lag1: OR 1.008 95% CI 1.005, 1.011 and lag1: OR 1.017; 95%

CI 1.007, 1.027 respectively). During the warm season non-accidental and COPD ED were

associated with increases in O3 (lag01: OR 1.007; 95% CI 1.002, 1.012 and lag02: OR 1.018;

95% CI 1.002, 1.034 respectively). During the cold season, non-accidental and COPD ED visits

132

were associated with O3 (lag03: OR 1.016; 95% CI 1.002, 1.030 and lag03: OR 1.033; 95% CI

1.000, 1.067 respectively), while PM2.5 was associated with all outcomes (Non-accidental ED

lag1: OR 1.031; 95% CI 1.022, 1.040; COPD ED lag1: OR 1.052; 95% CI 1.030, 1.074; CVD

ED lag1: OR 1.051; 95% CI 1.025, 1.078).

Models using the AQHI produced AIC values similar to those produced using multipollutant

models, which suggested both models fit the data equally well.(Burnham and Anderson 2004)

For each outcome, the average difference in AIC values across all CDs was 3 or less. Models

produced using a quadratic temperature function produced AIC values lower than those using

cubic spline functions with 3, 4, or 5 knots.

4.5.7 Stratified Analyses

Tables 4-7a and 4-7b show the results of the stratified meta-analyses, comparing the magnitude

of effect across strata for the full-year analysis and within the warm and cold seasons. I report

here the results for lag03, which I observed to have generally the highest effect estimates in the

main analysis. Although I observed statistically significant associations between air pollution

exposure and acute HSU in many strata, significant effect modification was only present in a few

situations (Table 4-7a). During the warm season, the risk of non-accidental hospitalization per

unit increase in the AQHI was statistically higher amongst individuals with a history of COPD

hospitalization (OR 1.037; 95% CI 1.020, 1.056), as well as those with a history of

hospitalizations for lower respiratory tract infections (OR 1.042; 95% CI 1.020, 1.064) compared

to those with no history (OR 1.005; 95% CI 0.992, 1.018, and OR 1.004; 95% CI 0.991, 1.018

133 respectively). The risk of COPD hospitalization per unit increase in the AQHI was higher amongst those with no history of AMI hospitalization (OR 1.058; 95% CI 1.031, 1.086), as compared to those who had a history (OR 0.996; 95% CI 0.905, 1.031). During the cold season, those without a history of hospitalization for lower respiratory tract infection were at higher risk of CVD hospitalization per unit increase in the AQHI (OR 1.075; 95% CI 1.041, 1.110) compared to those who had been hospitalized (OR 0.989; 95% CI 0.947, 1.033). Finally, for each unit increase in the AQHI, those who lived in rural areas were less likely to visit the ED for non- accidental causes (OR 0.980; 95% CI 0.965, 0.997 – Table 4-7b) compared to those who lived in urban centres (OR 1.002; 95% CI 0.998, 1.005).

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Table 4-7a: Results of Stratified Analyses: Risk Non-Accidental and Disease Specific Hospitalization Per Unit Increase in Daily Maximum Air Quality Health Index (Lag03) Across Ontario Census Divisions Full-Year Warm Season Cold Season OR 95% CI OR 95% CI OR 95% CI Non-Accidental Age ≥65 years 1.006 (1.000, 1.011) 1.017 (0.999, 1.036) 1.010 (0.998, 1.022) Age <65 years 1.007 (0.997, 1.016) 1.024 (1.005, 1.044) 1.017 (0.996, 1.038) Did not die during study window 1.007 (0.998, 1.015) 1.011 (0.994, 1.028) 1.022 (1.004, 1.039) Died during study window 1.006 (1.000, 1.012) 1.021 (1.008, 1.034) 1.007 (0.994, 1.020) Female 1.006 (0.999, 1.013) 1.019 (1.000, 1.039) 1.015 (1.000, 1.031) Male 1.006 (1.000, 1.013) 1.019 (1.006, 1.033) 1.008 (0.994, 1.023) Urban Area 1.007 (1.002, 1.012) 1.018 (1.005, 1.032) 1.013 (1.002, 1.023) Rural Area 0.998 (0.969, 1.028) 1.040 (0.974, 1.110) 1.003 (0.951, 1.057) Percent < high school education - Q1 1.005 (0.994, 1.017) 1.027 (0.994, 1.060) 1.012 (0.990, 1.034) Percent < high school education - Q2 1.007 (0.997, 1.017) 1.013 (0.993, 1.033) 1.025 (1.004, 1.046) Percent < high school education - Q3 1.005 (0.995, 1.015) 1.011 (0.989, 1.034) 1.003 (0.976, 1.031) Percent < high school education - Q4 1.007 (0.998, 1.017) 1.029 (1.009, 1.050) 1.007 (0.987, 1.027) Median family income - Q1 1.006 (0.998, 1.015) 1.022 (1.005, 1.040) 1.027 (1.010, 1.046) Median family income - Q2 1.001 (0.991, 1.011) 1.004 (0.984, 1.025) 0.987 (0.967, 1.008) Median family income - Q3 1.008 (0.998, 1.018) 1.015 (0.994, 1.037) 1.022 (1.000, 1.045) Median family income - Q4 1.009 (0.998, 1.021) 1.026 (1.002, 1.049) 1.010 (0.986, 1.034) No PH for COPD 1.002 (0.992, 1.011) 1.005 (0.992, 1.018) 1.020 (1.005, 1.034) PH for COPD 1.010 (1.003, 1.017) 1.037 (1.020, 1.056) 1.003 (0.988, 1.018)* No PH for a COPD related condition 1.003 (0.997, 1.009) 1.012 (0.999, 1.024) 1.010 (0.995, 1.026) PH for COPD related condition 1.013 (1.003, 1.023) 1.029 (1.011, 1.047) 1.010 (0.993, 1.028) No PH for asthma 1.005 (1.000, 1.010) 1.018 (1.008, 1.029) 1.010 (0.999, 1.021) PH for asthma 1.011 (0.990, 1.032) 1.010 (0.975, 1.047) 1.029 (0.993, 1.067) No PH for a lower respiratory tract infection 1.004 (0.998, 1.011) 1.004 (0.991, 1.018) 1.021 (1.002, 1.040) PH for a lower respiratory tract infection 1.008 (1.001, 1.016) 1.042 (1.020, 1.064) 0.997 (0.982, 1.012)* No PH for lung-cancer 1.005 (1.000, 1.010) 1.016 (1.005, 1.026) 1.012 (1.001, 1.023) PH for lung-cancer 1.015 (0.992, 1.038) 1.041 (1.000, 1.084) 1.009 (0.968, 1.051) No PH for a cardiovascular condition 1.009 (1.002, 1.016) 1.031 (1.017, 1.045) 1.013 (0.999, 1.028) PH for a cardiovascular condition 1.003 (0.996, 1.010) 1.003 (0.988, 1.017) 1.010 (0.995, 1.025) No PH for acute myocardial infarction 1.007 (1.001, 1.012) 1.024 (1.007, 1.040) 1.014 (1.003, 1.026) PH for acute myocardial infarction 1.004 (0.993, 1.015) 1.001 (0.973, 1.029) 1.001 (0.978, 1.024) No PH for angina 1.005 (1.000, 1.010) 1.019 (1.009, 1.030) 1.012 (1.001, 1.023) PH for angina 1.011 (0.996, 1.026) 0.998 (0.967, 1.030) 1.011 (0.976, 1.049) No PH congestive heart failure 1.006 (1.000, 1.012) 1.023 (1.011, 1.035) 1.011 (0.999, 1.023) PH congestive heart failure 1.006 (0.996, 1.016) 1.001 (0.981, 1.021) 1.013 (0.993, 1.034) No PH for hypertension 1.006 (1.001, 1.011) 1.021 (1.007, 1.034) 1.012 (1.002, 1.023) PH for hypertension 1.013 (0.982, 1.045) 0.972 (0.904, 1.044) 0.992 (0.929, 1.061) No PH for ischemic heart disease 1.005 (1.000, 1.010) 1.021 (1.007, 1.035) 1.010 (0.998, 1.021) PH for ischemic heart disease 1.014 (1.001, 1.028) 1.006 (0.978, 1.034) 1.027 (0.997, 1.057) No PH for diabetes 1.006 (1.001, 1.011) 1.018 (1.007, 1.029) 1.011 (1.001, 1.022) PH for diabetes 1.007 (0.990, 1.024) 1.012 (0.977, 1.048) 1.007 (0.967, 1.050) No PH for stroke 1.006 (1.001, 1.011) 1.020 (1.007, 1.034) 1.015 (1.004, 1.026) PH for stroke 1.006 (0.992, 1.019) 1.014 (0.986, 1.043) 0.989 (0.960, 1.019) No PH for cancer (non-lung) 1.005 (1.000, 1.011) 1.016 (1.001, 1.031) 1.009 (0.997, 1.020) PH for cancer (non-lung) 1.008 (0.997, 1.019) 1.030 (1.007, 1.053) 1.024 (1.000, 1.049)

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Full-Year Warm Season Cold Season OR 95% CI OR 95% CI OR 95% CI Chronic Obstructive Pulmonary Disease Age ≥65 years 1.003 (0.991, 1.016) 1.031 (1.003, 1.060) 0.991 (0.967, 1.016) Age <65 years 1.012 (0.990, 1.034) 1.089 (1.039, 1.142) 0.994 (0.951, 1.038)* Did not die during study window 1.018 (0.998, 1.038) 1.087 (1.031, 1.145) 1.008 (0.971, 1.046) Died during study window 0.999 (0.986, 1.012) 1.031 (1.002, 1.062) 0.986 (0.960, 1.012) Female 1.006 (0.990, 1.021) 1.070 (1.026, 1.115) 0.984 (0.954, 1.014)* Male 1.004 (0.989, 1.020) 1.030 (0.997, 1.065) 0.998 (0.968, 1.029) Urban Area 1.006 (0.995, 1.017) 1.047 (1.021, 1.073) 0.991 (0.970, 1.014)* Rural Area 0.999 (0.937, 1.065) 0.997 (0.791, 1.256) NA NA NA Percent < high school education - Q1 0.997 (0.975, 1.021) 1.039 (0.981, 1.100) 0.975 (0.931, 1.022) Percent < high school education - Q2 1.012 (0.990, 1.035) 1.053 (1.003, 1.105) 0.992 (0.951, 1.036) Percent < high school education - Q3 1.006 (0.983, 1.028) 1.050 (1.001, 1.101) 1.000 (0.959, 1.044) Percent < high school education - Q4 1.003 (0.982, 1.025) 1.047 (0.997, 1.100) 0.995 (0.954, 1.037) Median family income - Q1 1.009 (0.991, 1.028) 1.079 (1.031, 1.129) 0.981 (0.946, 1.018)* Median family income - Q2 0.998 (0.973, 1.025) 1.028 (0.977, 1.081) 0.991 (0.943, 1.041) Median family income - Q3 1.017 (0.994, 1.041) 1.029 (0.978, 1.082) 1.023 (0.977, 1.072) Median family income - Q4 0.987 (0.962, 1.013) 1.029 (0.973, 1.089) 0.973 (0.924, 1.024) No PH for COPD NA NA NA NA NA NA NA NA NA PH for COPD 1.005 (0.994, 1.016) 1.045 (1.020, 1.070) 0.991 (0.969, 1.012) No PH for a COPD related condition 1.001 (0.988, 1.014) 1.033 (1.003, 1.063) 0.982 (0.957, 1.008) PH for COPD related condition 1.015 (0.996, 1.034) 1.070 (1.026, 1.116) 1.013 (0.975, 1.052) No PH for asthma 1.003 (0.991, 1.014) 1.048 (1.021, 1.075) 0.984 (0.962, 1.007)* PH for asthma 1.025 (0.992, 1.058) 1.016 (0.947, 1.090) 1.041 (0.977, 1.110) No PH for a lower respiratory tract infection 1.000 (0.979, 1.021) 1.032 (0.984, 1.083) 0.990 (0.950, 1.032) PH for a lower respiratory tract infection 1.007 (0.994, 1.020) 1.050 (1.021, 1.079) 0.992 (0.967, 1.017)* No PH for lung-cancer 1.004 (0.993, 1.015) 1.043 (1.018, 1.069) 0.990 (0.968, 1.012)* PH for lung-cancer 1.031 (0.976, 1.089) 1.071 (0.933, 1.228) 0.989 (0.873, 1.121) No PH for a cardiovascular condition 1.008 (0.994, 1.022) 1.063 (1.032, 1.096) 0.996 (0.970, 1.024)* PH for a cardiovascular condition 1.001 (0.984, 1.019) 1.014 (0.974, 1.054) 0.982 (0.948, 1.017) No PH for acute myocardial infarction 1.009 (0.997, 1.020) 1.058 (1.031, 1.086) 0.996 (0.973, 1.019)* PH for acute myocardial infarction 0.983 (0.955, 1.012) 0.966 (0.905, 1.031) 0.962 (0.907, 1.020) No PH for angina 1.007 (0.995, 1.018) 1.050 (1.024, 1.076) 0.994 (0.972, 1.017)* PH for angina 0.985 (0.945, 1.028) 0.967 (0.875, 1.069) 0.941 (0.864, 1.025) No PH congestive heart failure 1.005 (0.993, 1.017) 1.050 (1.022, 1.078) 0.994 (0.969, 1.020)* PH congestive heart failure 1.006 (0.981, 1.032) 1.023 (0.967, 1.083) 0.969 (0.921, 1.019) No PH for hypertension 1.004 (0.993, 1.015) 1.045 (1.021, 1.071) 0.991 (0.970, 1.013)* PH for hypertension 1.094 (0.996, 1.201) 0.982 (0.768, 1.256) 0.945 (0.782, 1.141) No PH for ischemic heart disease 1.005 (0.994, 1.017) 1.049 (1.023, 1.075) 0.992 (0.970, 1.014)* PH for ischemic heart disease 1.004 (0.968, 1.042) 1.000 (0.920, 1.088) 0.981 (0.911, 1.056) No PH for diabetes 1.005 (0.994, 1.016) 1.046 (1.019, 1.073) 0.991 (0.969, 1.013)* PH for diabetes 1.005 (0.955, 1.058) 1.063 (0.947, 1.193) 0.985 (0.890, 1.090) No PH for stroke 1.004 (0.993, 1.015) 1.041 (1.016, 1.068) 0.996 (0.974, 1.018) PH for stroke 1.015 (0.978, 1.053) 1.079 (0.990, 1.176) 0.944 (0.878, 1.016) No PH for cancer (non-lung) 1.002 (0.991, 1.014) 1.042 (1.016, 1.069) 0.982 (0.956, 1.009)* PH for cancer (non-lung) 1.033 (0.994, 1.073) 1.060 (0.988, 1.138) 1.045 (0.957, 1.142)

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Full-Year Warm Season Cold Season OR 95% CI OR 95% CI OR 95% CI Cardiovascular Disease Age ≥65 years 1.022 (1.008, 1.036) 1.035 (0.996, 1.075) 1.040 (1.011, 1.070) Age <65 years 1.003 (0.975, 1.031) 0.971 (0.907, 1.039) 1.083 (0.995, 1.178) Did not die during study window 1.026 (1.003, 1.048) 1.011 (0.962, 1.063) 1.032 (0.985, 1.081) Died during study window 1.015 (1.000, 1.030) 1.024 (0.993, 1.056) 1.050 (1.018, 1.083) Female 1.015 (0.996, 1.034) 1.013 (0.974, 1.053) 1.050 (1.009, 1.092) Male 1.021 (1.005, 1.038) 1.026 (0.967, 1.089) 1.039 (1.004, 1.075) Urban Area 1.017 (1.004, 1.030) 1.021 (0.995, 1.048) 1.039 (1.012, 1.067) Rural Area 1.054 (0.991, 1.121) 1.005 (0.862, 1.173) 1.139 (1.006, 1.291) Percent < high school education - Q1 1.036 (1.001, 1.072) 1.067 (0.991, 1.148) 1.059 (1.002, 1.119) Percent < high school education - Q2 1.028 (1.003, 1.054) 1.040 (0.988, 1.094) 1.072 (1.018, 1.129) Percent < high school education - Q3 1.018 (0.993, 1.043) 0.983 (0.934, 1.035) 1.037 (0.963, 1.117) Percent < high school education - Q4 1.006 (0.983, 1.030) 1.014 (0.936, 1.099) 1.004 (0.955, 1.056) Median family income - Q1 1.009 (0.988, 1.031) 1.012 (0.969, 1.058) 1.055 (1.009, 1.103) Median family income - Q2 1.023 (0.999, 1.049) 1.011 (0.959, 1.065) 1.013 (0.963, 1.066) Median family income - Q3 1.016 (0.990, 1.043) 1.011 (0.934, 1.095) 1.079 (1.020, 1.141) Median family income - Q4 1.031 (1.001, 1.061) 1.049 (0.990, 1.113) 1.035 (0.973, 1.101) No PH for COPD 1.021 (1.005, 1.036) 1.015 (0.984, 1.047) 1.051 (1.018, 1.085) PH for COPD 1.015 (0.994, 1.037) 1.029 (0.985, 1.074) 1.030 (0.985, 1.078) No PH for a COPD related condition 1.022 (1.006, 1.037) 1.011 (0.980, 1.043) 1.070 (1.037, 1.104) PH for COPD related condition 1.012 (0.991, 1.034) 1.040 (0.995, 1.087) 0.991 (0.946, 1.037) No PH for asthma 1.018 (1.005, 1.031) 1.018 (0.992, 1.045) 1.046 (1.019, 1.074) PH for asthma 1.023 (0.970, 1.079) 1.061 (0.937, 1.200) 0.979 (0.870, 1.101) No PH for a lower respiratory tract infection 1.027 (1.011, 1.043) 1.014 (0.982, 1.047) 1.075 (1.041, 1.110) PH for a lower respiratory tract infection 1.005 (0.985, 1.025) 1.033 (0.991, 1.076) 0.989 (0.947, 1.033) No PH for lung-cancer 1.019 (1.006, 1.032) 1.017 (0.991, 1.044) 1.044 (1.017, 1.072) PH for lung-cancer 1.000 (0.922, 1.084) 1.107 (0.875, 1.400) 1.011 (0.858, 1.190) No PH for a cardiovascular condition NA NA NA NA NA NA NA NA NA PH for a cardiovascular condition 1.018 (1.006, 1.031) 1.021 (0.995, 1.047) 1.043 (1.017, 1.070) No PH for acute myocardial infarction 1.009 (0.992, 1.026) 1.008 (0.961, 1.057) 1.045 (1.008, 1.083) PH for acute myocardial infarction 1.030 (1.011, 1.048) 1.041 (0.990, 1.095) 1.043 (1.005, 1.083) No PH for angina 1.015 (1.001, 1.030) 1.024 (0.994, 1.054) 1.049 (1.018, 1.080) PH for angina 1.029 (1.004, 1.055) 1.012 (0.961, 1.067) 1.025 (0.973, 1.081) No PH congestive heart failure 1.030 (1.010, 1.051) 1.013 (0.954, 1.075) 1.055 (1.012, 1.100) PH congestive heart failure 1.012 (0.996, 1.027) 1.018 (0.986, 1.052) 1.038 (1.005, 1.073) No PH for hypertension 1.018 (1.005, 1.030) 1.024 (0.998, 1.050) 1.042 (1.015, 1.070) PH for hypertension 1.037 (0.968, 1.110) 0.931 (0.792, 1.094) 1.094 (0.938, 1.275) No PH for ischemic heart disease 1.015 (1.001, 1.029) 1.012 (0.982, 1.042) 1.040 (1.010, 1.071) PH for ischemic heart disease 1.029 (1.004, 1.055) 1.051 (0.998, 1.106) 1.055 (1.000, 1.113) No PH for diabetes 1.017 (1.004, 1.030) 1.023 (0.996, 1.051) 1.044 (1.016, 1.073) PH for diabetes 1.035 (0.995, 1.076) 1.001 (0.923, 1.086) 1.043 (0.956, 1.138) No PH for stroke 1.018 (1.005, 1.032) 1.024 (0.996, 1.052) 1.043 (1.014, 1.072) PH for stroke 1.021 (0.989, 1.054) 1.002 (0.938, 1.071) 1.049 (0.981, 1.122) No PH for cancer (non-lung) 1.015 (1.001, 1.028) 1.021 (0.994, 1.049) 1.043 (1.014, 1.072) PH for cancer (non-lung) 1.043 (1.008, 1.079) 1.022 (0.952, 1.098) 1.037 (0.933, 1.152) OR: Odds Ratio; 95% CI: 95% Confidence Interval; NA: Did not converge; PH: Previous Hospitalization; Italics indicate statistically significant difference in risk between strata; *Statistically significant seasonal effect within strata; All models adjusted for ambient temperature (Full-year quadratic and linear term at lag0; warm season linear term at lag0; cold season linear term at lag06), relative humidity (full-year at lag0; warm season at lag0; cold season at lag06), influenza events and holidays.

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Table 4-7b: Results of Stratified Analyses: Risk Non-Accidental and Disease Specific Emergency Department Visits Per Unit Increase in Daily Maximum Air Quality Health Index (Lag03) Across Ontario Census Divisions Full-Year Warm Season Cold Season OR 95% CI OR 95% CI OR 95% CI Non-Accidental Age ≥65 years 0.998 (0.991, 1.004) 1.005 (0.994, 1.015) 1.004 (0.995, 1.013) Age <65 years 1.004 (0.998, 1.010) 1.024 (1.012, 1.035) 1.004 (0.991, 1.016) Did not die during study window 0.998 (0.993, 1.004) 1.005 (0.995, 1.015) 0.994 (0.983, 1.005) Died during study window 1.003 (0.998, 1.007) 1.018 (1.006, 1.030) 1.012 (1.002, 1.022) Female 0.999 (0.992, 1.005) 1.010 (1.000, 1.020) 1.002 (0.992, 1.012) Male 1.002 (0.997, 1.007) 1.014 (1.000, 1.028) 1.005 (0.995, 1.016) Urban Area 1.002 (0.998, 1.005) 1.011 (1.004, 1.018) 1.005 (0.998, 1.013) Rural Area 0.980 (0.965, 0.997) 1.008 (0.970, 1.047) 0.981 (0.949, 1.014) Percent < high school education - Q1 0.999 (0.992, 1.007) 1.026 (1.005, 1.048) 0.999 (0.983, 1.014) Percent < high school education - Q2 1.004 (0.997, 1.011) 1.009 (0.995, 1.023) 1.012 (0.997, 1.027) Percent < high school education - Q3 1.005 (0.998, 1.012) 1.005 (0.991, 1.019) 1.015 (1.000, 1.030) Percent < high school education - Q4 0.994 (0.986, 1.001) 1.012 (0.998, 1.025) 0.992 (0.979, 1.006) Median family income - Q1 1.001 (0.994, 1.008) 1.008 (0.997, 1.020) 1.010 (0.998, 1.023) Median family income - Q2 0.997 (0.990, 1.004) 1.005 (0.990, 1.020) 0.992 (0.977, 1.007) Median family income - Q3 0.998 (0.991, 1.006) 1.015 (1.000, 1.030) 1.007 (0.991, 1.023) Median family income - Q4 1.005 (0.997, 1.014) 1.022 (1.004, 1.041) 1.005 (0.988, 1.023) No PH for COPD 1.001 (0.996, 1.005) 1.005 (0.996, 1.014) 1.005 (0.995, 1.015) PH for COPD 1.002 (0.997, 1.008) 1.024 (1.009, 1.040) 1.005 (0.994, 1.017) No PH for a COPD related condition 1.000 (0.996, 1.004) 1.008 (1.000, 1.017) 1.001 (0.992, 1.010) PH for COPD related condition 1.005 (0.999, 1.010) 1.017 (1.005, 1.029) 1.012 (0.999, 1.025) No PH for asthma 1.001 (0.997, 1.005) 1.010 (1.003, 1.017) 1.005 (0.997, 1.013) PH for asthma 0.998 (0.981, 1.015) 1.024 (1.001, 1.047) 1.006 (0.982, 1.030) No PH for a lower respiratory tract infection 0.999 (0.995, 1.004) 1.006 (0.997, 1.016) 1.002 (0.992, 1.012) PH for a lower respiratory tract infection 1.005 (0.999, 1.010) 1.021 (1.003, 1.038) 1.009 (0.998, 1.020) No PH for lung-cancer 1.001 (0.998, 1.005) 1.010 (1.003, 1.018) 1.005 (0.997, 1.013) PH for lung-cancer 1.009 (0.994, 1.025) 1.030 (0.998, 1.063) 1.004 (0.971, 1.038) No PH for a cardiovascular condition 1.004 (0.999, 1.009) 1.019 (1.010, 1.029) 1.007 (0.996, 1.017) PH for a cardiovascular condition 0.998 (0.992, 1.003) 1.003 (0.986, 1.020) 1.003 (0.992, 1.014) No PH for acute myocardial infarction 1.002 (0.998, 1.006) 1.014 (1.001, 1.026) 1.007 (0.998, 1.015) PH for acute myocardial infarction 1.001 (0.992, 1.009) 1.000 (0.984, 1.017) 0.997 (0.980, 1.015) No PH for angina 1.002 (0.998, 1.005) 1.012 (1.005, 1.019) 1.005 (0.997, 1.013) PH for angina 0.999 (0.989, 1.010) 1.005 (0.983, 1.027) 1.001 (0.979, 1.024) No PH congestive heart failure 1.001 (0.997, 1.005) 1.012 (1.004, 1.020) 1.003 (0.995, 1.012) PH congestive heart failure 1.003 (0.996, 1.011) 1.008 (0.993, 1.023) 1.011 (0.995, 1.028) No PH for hypertension 1.002 (0.998, 1.005) 1.012 (1.005, 1.019) 1.005 (0.997, 1.012) PH for hypertension 1.000 (0.977, 1.022) 0.973 (0.928, 1.020) 1.008 (0.960, 1.059) No PH for ischemic heart disease 1.002 (0.998, 1.005) 1.012 (1.001, 1.024) 1.004 (0.996, 1.012) PH for ischemic heart disease 1.000 (0.990, 1.009) 1.003 (0.984, 1.023) 1.012 (0.991, 1.033) No PH for diabetes 1.001 (0.997, 1.005) 1.012 (1.005, 1.019) 1.003 (0.995, 1.011) PH for diabetes 1.008 (0.995, 1.021) 1.004 (0.978, 1.029) 1.023 (0.988, 1.059) No PH for stroke 1.003 (0.999, 1.006) 1.013 (1.005, 1.020) 1.005 (0.997, 1.013) PH for stroke 0.993 (0.983, 1.003) 0.995 (0.970, 1.020) 1.005 (0.983, 1.027) No PH for cancer (non-lung) 1.000 (0.995, 1.005) 1.009 (0.997, 1.021) 1.005 (0.997, 1.013) PH for cancer (non-lung) 1.003 (0.994, 1.012) 1.020 (0.997, 1.044) 1.004 (0.986, 1.023)

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Full-Year Warm Season Cold Season OR 95% CI OR 95% CI OR 95% CI Chronic Obstructive Pulmonary Disease Age ≥65 years 0.999 (0.986, 1.012) 1.021 (0.994, 1.048) 0.995 (0.971, 1.020) Age <65 years 0.997 (0.974, 1.021) 1.060 (1.019, 1.103) 0.994 (0.957, 1.032) Did not die during study window 0.996 (0.980, 1.013) 1.023 (0.987, 1.061) 0.990 (0.955, 1.027) Died during study window 1.002 (0.989, 1.015) 1.037 (1.009, 1.066) 0.999 (0.972, 1.027) Female 0.994 (0.979, 1.008) 1.015 (0.983, 1.048) 0.994 (0.959, 1.031) Male 1.006 (0.992, 1.020) 1.051 (1.009, 1.095) 0.987 (0.959, 1.016) Urban Area 1.001 (0.990, 1.011) 1.032 (1.009, 1.055) 0.995 (0.974, 1.016) Rural Area 0.986 (0.941, 1.032) 1.035 (0.919, 1.167) 0.991 (0.907, 1.081) Percent < high school education - Q1 1.005 (0.983, 1.028) 1.087 (1.020, 1.157) 0.991 (0.948, 1.036) Percent < high school education - Q2 1.008 (0.987, 1.029) 1.067 (0.991, 1.150) 1.004 (0.963, 1.046) Percent < high school education - Q3 1.004 (0.984, 1.025) 1.011 (0.967, 1.057) 1.000 (0.961, 1.042) Percent < high school education - Q4 0.986 (0.967, 1.005) 1.039 (0.996, 1.085) 0.989 (0.951, 1.028) Median family income - Q1 0.998 (0.982, 1.015) 1.026 (0.990, 1.065) 0.994 (0.957, 1.033) Median family income - Q2 1.007 (0.981, 1.032) 1.028 (0.983, 1.076) 0.969 (0.926, 1.014) Median family income - Q3 0.991 (0.970, 1.014) 1.027 (0.978, 1.077) 1.020 (0.976, 1.067) Median family income - Q4 1.010 (0.985, 1.036) 1.076 (1.021, 1.134) 1.007 (0.957, 1.058) No PH for COPD 0.988 (0.964, 1.014) 0.977 (0.924, 1.033) 0.998 (0.949, 1.050) PH for COPD 1.002 (0.991, 1.014) 1.042 (1.017, 1.067) 0.994 (0.972, 1.017) No PH for a COPD related condition 1.003 (0.991, 1.016) 1.025 (0.998, 1.053) 0.993 (0.968, 1.018) PH for COPD related condition 0.994 (0.976, 1.012) 1.060 (1.006, 1.118) 1.000 (0.964, 1.037) No PH for asthma 0.999 (0.988, 1.010) 1.031 (1.007, 1.056) 0.991 (0.969, 1.013) PH for asthma 1.007 (0.976, 1.038) 1.037 (0.976, 1.101) 1.023 (0.965, 1.084) No PH for a lower respiratory tract infection 0.997 (0.980, 1.014) 1.025 (0.988, 1.063) 0.999 (0.966, 1.033) PH for a lower respiratory tract infection 1.002 (0.989, 1.015) 1.053 (1.004, 1.104) 0.992 (0.966, 1.019) No PH for lung-cancer 1.000 (0.990, 1.011) 1.028 (1.005, 1.052) 0.995 (0.974, 1.017) PH for lung-cancer 0.996 (0.952, 1.043) 1.082 (0.975, 1.201) 0.983 (0.895, 1.079) No PH for a cardiovascular condition 1.001 (0.988, 1.014) 1.035 (1.006, 1.064) 0.997 (0.971, 1.023) PH for a cardiovascular condition 0.999 (0.983, 1.016) 1.025 (0.989, 1.063) 0.992 (0.959, 1.026) No PH for acute myocardial infarction 1.002 (0.990, 1.013) 1.039 (1.014, 1.064) 0.998 (0.976, 1.021) PH for acute myocardial infarction 0.992 (0.961, 1.024) 1.026 (0.906, 1.161) 0.975 (0.923, 1.031) No PH for angina 1.002 (0.991, 1.013) 1.038 (1.014, 1.062) 0.997 (0.976, 1.019) PH for angina 0.976 (0.939, 1.014) 0.941 (0.862, 1.027) 0.964 (0.892, 1.042) No PH congestive heart failure 1.002 (0.991, 1.014) 1.033 (1.008, 1.059) 1.001 (0.978, 1.024) PH congestive heart failure 0.991 (0.967, 1.016) 1.029 (0.966, 1.096) 0.968 (0.921, 1.017) No PH for hypertension 1.000 (0.990, 1.010) 1.031 (1.008, 1.054) 0.996 (0.975, 1.017) PH for hypertension 0.997 (0.906, 1.098) 0.957 (0.741, 1.236) 0.900 (0.740, 1.094) No PH for ischemic heart disease 1.003 (0.992, 1.014) 1.038 (1.014, 1.062) 0.999 (0.977, 1.020) PH for ischemic heart disease 0.972 (0.939, 1.007) 0.956 (0.877, 1.042) 0.967 (0.902, 1.037) No PH for diabetes 0.999 (0.989, 1.010) 1.034 (1.011, 1.058) 0.993 (0.973, 1.015) PH for diabetes 1.021 (0.969, 1.076) 0.964 (0.841, 1.106) 1.019 (0.889, 1.168) No PH for stroke 1.000 (0.989, 1.011) 1.026 (1.003, 1.050) 0.997 (0.975, 1.018) PH for stroke 1.002 (0.966, 1.039) 1.088 (1.001, 1.181) 0.983 (0.913, 1.058) No PH for cancer (non-lung) 0.998 (0.988, 1.009) 1.025 (1.001, 1.049) 0.993 (0.971, 1.015) PH for cancer (non-lung) 1.016 (0.984, 1.049) 1.071 (1.005, 1.141) 1.012 (0.954, 1.074)

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Full-Year Warm Season Cold Season OR 95% CI OR 95% CI OR 95% CI Cardiovascular Disease Age ≥65 years 1.017 (1.004, 1.030) 1.028 (0.982, 1.076) 1.029 (1.002, 1.057) Age <65 years 1.006 (0.980, 1.033) 0.972 (0.922, 1.025) 1.049 (0.991, 1.111) Did not die during study window 1.021 (1.000, 1.042) 1.000 (0.960, 1.043) 1.007 (0.964, 1.053) Died during study window 1.011 (0.998, 1.026) 1.032 (0.991, 1.075) 1.044 (1.013, 1.075) Female 1.010 (0.992, 1.027) 0.998 (0.963, 1.034) 1.040 (1.002, 1.078) Male 1.020 (1.003, 1.037) 1.037 (0.984, 1.093) 1.026 (0.993, 1.061) Urban Area 1.014 (1.002, 1.026) 1.020 (0.980, 1.062) 1.028 (1.003, 1.054) Rural Area 1.023 (0.967, 1.083) 1.018 (0.888, 1.166) 1.115 (0.991, 1.253) Percent < high school education - Q1 1.028 (0.993, 1.063) 1.064 (0.967, 1.170) 1.038 (0.984, 1.095) Percent < high school education - Q2 1.006 (0.983, 1.030) 0.989 (0.942, 1.038) 1.030 (0.980, 1.082) Percent < high school education - Q3 1.030 (1.006, 1.054) 1.020 (0.949, 1.096) 1.065 (1.013, 1.120) Percent < high school education - Q4 1.000 (0.979, 1.023) 1.022 (0.976, 1.070) 1.005 (0.959, 1.053) Median family income - Q1 1.014 (0.994, 1.034) 1.015 (0.975, 1.058) 1.060 (1.017, 1.105) Median family income - Q2 1.003 (0.980, 1.027) 0.960 (0.914, 1.009) 1.019 (0.970, 1.070) Median family income - Q3 1.021 (0.996, 1.046) 1.029 (0.979, 1.082) 1.031 (0.978, 1.086) Median family income - Q4 1.025 (0.985, 1.066) 1.079 (1.021, 1.141) 1.002 (0.944, 1.064) No PH for COPD 1.009 (0.995, 1.025) 1.007 (0.977, 1.038) 1.024 (0.992, 1.057) PH for COPD 1.022 (1.004, 1.041) 1.028 (0.990, 1.068) 1.044 (1.004, 1.086) No PH for a COPD related condition 1.015 (1.001, 1.030) 1.013 (0.972, 1.055) 1.037 (1.006, 1.069) PH for COPD related condition 1.012 (0.992, 1.031) 1.033 (0.993, 1.075) 1.025 (0.981, 1.071) No PH for asthma 1.014 (1.002, 1.026) 1.016 (0.984, 1.050) 1.033 (1.007, 1.060) PH for asthma 1.013 (0.967, 1.061) 1.029 (0.937, 1.129) 1.001 (0.907, 1.106) No PH for a lower respiratory tract infection 1.012 (0.997, 1.028) 1.002 (0.956, 1.051) 1.035 (1.002, 1.069) PH for a lower respiratory tract infection 1.019 (0.999, 1.039) 1.043 (1.005, 1.082) 1.041 (0.993, 1.090) No PH for lung-cancer 1.014 (1.003, 1.026) 1.019 (0.981, 1.057) 1.029 (1.004, 1.055) PH for lung-cancer 1.005 (0.941, 1.073) 0.970 (0.851, 1.106) 1.105 (0.955, 1.278) No PH for a cardiovascular condition 1.014 (0.984, 1.044) 1.014 (0.954, 1.077) 0.966 (0.872, 1.071) PH for a cardiovascular condition 1.014 (1.002, 1.027) 1.015 (0.989, 1.042) 1.041 (1.014, 1.069) No PH for acute myocardial infarction 1.012 (0.995, 1.029) 1.010 (0.975, 1.047) 1.028 (0.981, 1.078) PH for acute myocardial infarction 1.021 (1.002, 1.041) 1.034 (0.994, 1.076) 1.063 (1.022, 1.106) No PH for angina 1.015 (1.002, 1.028) 1.011 (0.984, 1.038) 1.043 (1.014, 1.072) PH for angina 1.011 (0.986, 1.037) 1.032 (0.980, 1.087) 0.991 (0.940, 1.045) No PH congestive heart failure 1.009 (0.992, 1.027) 1.009 (0.974, 1.045) 1.024 (0.987, 1.061) PH congestive heart failure 1.019 (1.003, 1.035) 1.021 (0.977, 1.068) 1.047 (1.004, 1.092) No PH for hypertension 1.014 (1.002, 1.026) 1.018 (0.982, 1.055) 1.031 (1.006, 1.057) PH for hypertension 1.020 (0.960, 1.084) 1.055 (0.912, 1.220) 1.046 (0.919, 1.190) No PH for ischemic heart disease 1.017 (1.004, 1.031) 1.017 (0.990, 1.045) 1.028 (1.000, 1.057) PH for ischemic heart disease 1.003 (0.979, 1.028) 1.011 (0.962, 1.062) 1.045 (0.992, 1.101) No PH for diabetes 1.013 (1.001, 1.025) 1.018 (0.993, 1.044) 1.036 (1.010, 1.063) PH for diabetes 1.026 (0.989, 1.065) 0.980 (0.906, 1.059) 0.998 (0.920, 1.083) No PH for stroke 1.014 (1.001, 1.026) 1.020 (0.979, 1.062) 1.032 (1.005, 1.060) PH for stroke 1.015 (0.985, 1.047) 1.016 (0.954, 1.083) 1.031 (0.967, 1.098) No PH for cancer (non-lung) 1.015 (1.003, 1.028) 1.019 (0.993, 1.045) 1.029 (1.002, 1.057) PH for cancer (non-lung) 1.006 (0.975, 1.038) 0.988 (0.926, 1.055) 1.047 (0.979, 1.118) OR: Odds Ratio; 95% CI: 95% Confidence Interval; NA: Did not converge; PH: Previous Hospitalization; Italics indicate statistically significant difference in risk between strata; *Statistically significant seasonal effect within strata; All models adjusted for ambient temperature (Full-year quadratic and linear term at lag0; warm season linear term at lag0; cold season linear term at lag06), relative humidity (full-year at lag0; warm season at lag0; cold season at lag06), influenza events and holidays.

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4.5.8 Seasonal Effect

To determine the presence of a seasonal effect, I compared associations in the main and stratified

analyses across each of the warm and cold seasons. This effect was most pronounced amongst

COPD hospitalizations. The overall risk of COPD hospitalization per unit increase in AQHI was

higher in the warm season (OR 1.042; 95% CI 1.022, 1.063) compared to the cold (OR 0.991;

95% CI 0.969, 1.012 – Table 4-6). This effect was seen specifically in females, individuals aged

<65, those who lived in urban areas, and those with a history of hospitalization for COPD, or

lower respiratory tract infections (Table 4-7a). Individuals with COPD, but without a history of

hospitalization for: AMI, angina, asthma, heart failure, CVD, diabetes, hypertension, ischemic

heart disease, lung cancer and non-lung cancers also experienced greater health risks with

increasing AQHI in the warm season compared to the cold. Although the effect did not reach

statistical significance, higher risks for CVD hospitalizations were seen in the cold season (OR

1.043; 95% CI 1.017, 1.070) compared to the warm (OR 1.026; 95% CI 0.992, 1.061 – Table 4-

6). Overall, no seasonal effects were seen for non-accidental hospitalizations, or non-accidental

or disease-specific ED visits.

4.6 Discussion

This study is the largest case-crossover analyses to examine the impact of air quality on acute

HSU amongst individuals suffering from COPD. I used the AQHI as my primary exposure

measure, which is an aggregate measure that represents overall ambient air pollution levels, and

was designed to communicate complex health messages to the public in an understandable way.

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My study provides further evidence of the association between the AQHI and acute HSU amongst individuals with COPD, and identifies individuals with a history of hospitalization for

COPD and lower respiratory tract infection are more vulnerable to the effects of air pollution.

My study also shows a clear seasonal effect of air pollution with higher risks of COPD hospitalizations during the warm months of the year as compared to the cold. Although not statistically significant, my study also points towards a seasonal effect showing higher risks for

CVD outcomes during the cold months.

CVD is the most frequent and important comorbid condition amongst individuals with

COPD.(Soriano, Visick et al. 2005) A 2006 study concluded that cardiovascular diseases are among the main causes of death in mild/moderate COPD, while respiratory failure is the predominant cause in more advanced COPD.(Sin, Anthonisen et al. 2006) A number of potential mechanisms linking COPD to CVD have been proposed including systemic and pulmonary inflammation,(Agustı, Noguera et al. 2003, Gan, Man et al. 2004) as well as oxidative stress.(Ceriello and Motz 2004, MacNee 2005) Increased arterial stiffness,(Sabit, Bolton et al.

2007, Patel, Kowlessar et al. 2013) metabolic rate,(Schols, Fredrix et al. 1991) sedentary lifestyle,(Garcia-Aymerich, Lange et al. 2007) and smoking(Müllerova, Agusti et al. 2013) are also common amongst individuals with COPD, and may increase the risk of CVD.

Although previous research exists to support the seasonal effect observed in my study, the literature is not consistent. A case-crossover study published in 2006 evaluated the effect of O3 and particulate matter with an aerodynamic diameter of 10µm (PM10) on COPD hospitalizations in 36 US cities.(Medina-Ramon, Zanobetti et al. 2006) The authors showed a seasonal effect with higher percent increases in the risk of COPD hospitalization in the warm season as

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compared to the cold associated with increases in O3 (warm season: 0.27%; 95% CI 0.08, 0.47; cold season: -0.31%; 95% CI -0.61, -0.01) and PM10 (warm season: 1.47%; 95% CI 0.93, 2.01; cold season: 0.1%; 95% CI -0.30, 0.49). A similar seasonal effect was reported in a multi-city

Canadian study published in 2009(Stieb, Szyszkowicz et al. 2009) in which the association between O3 and ED visits for COPD was found to be nearly twice as high in the warm season compared to the whole year. Conversely, a study in Taiwan showed the effects of PM2.5 on respiratory disease hospitalization was statistically higher at lower temperatures (OR 1.42; 95%

CI 1.334-1.561 and OR 1.003; 95% CI 0.951-1.022 for temperatures < 25°C compared to ³ 25°C respectively). (Tsai, Chiu et al. 2014) To the best of my knowledge, studies that have examined the effect of season on the association between the AQHI and acute HSU have not included individuals with COPD. There is however evidence that season effects the association between the AQHI and ED visits for asthma(To, Shen et al. 2013, Szyszkowicz and Kousha 2014) and ischemic stroke.(Chen, Villeneuve et al. 2014)

Although not statistically significant, results of my study also point to an increase in acute HSU for CVD during the cold season. Similar results were found by a recent study in New York state.(Hsu, Hwang et al. 2017) Another study across 112 US cities(Zanobetti and Schwartz 2009) found the association between PM2.5 and CVD mortality was higher during the winter 0.70%

(95% CI 0.04% to 1.36%) as compared to the summer 0.03% (95% CI –0.75% to 0.69%) season.

In China, a time-series analysis(Cheng and Kan 2012) found a statistically significant difference in CVD mortality associated with O3 exposure on days with the lowest 15 percentile of temperature 2.57% (1.53%, 3.62%) compared to days with normal temperatures 0.88% (0.37%,

1.40%).

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Several factors may contribute to this observed seasonal effect. My previous work in Chapter 3 suggests that the level knowledge amongst persons with COPD regarding the assessment of air quality is lacking and does not translate to meaningful behavioural modification and exposure reduction.(Foty, Dell et al. 2015) Other studies have also shown that during the summer months,

Canadians spend more time outdoors than in the winter.(Leech, Nelson et al. 2002) I may then hypothesize that this additional time spent outdoors without proper exposure reduction strategies may increase exposure to air pollution and contribute to the seasonal effect. Differences in composition of air pollutants across seasons may also contribute to my observation that the

AQHI is associated with COPD hospitalization during the warm season, and with CVD hospitalization primarily during the cold season. The prevailing winds during Ontario winters bring cold and dry polar air masses from the northwest.(NAV Canada 2002) In summer however the winds shift and tropical southwest winds bring with them increased temperature, humidity and transboundary air pollution originating in the United States.(Ministry of the Environment and Climate Change 2005) Sulphate concentrations in particulate pollution have also been shown to increase at temperatures above 14°C which may also affect health risk.(Kavouras and Chalbot

2017) Additional work is necessary to better understand these relationships and the extent to which temperature affects the association between air pollution and health.

Also, interesting to note in my current study is that with the exception of COPD and lower respiratory tract infections, those with comorbidity did not experience a seasonal effect for

COPD hospitalization. Rather the seasonal effect was primarily seen amongst those with no comorbidity. One might hypothesize several potential explanations for this observation. It may be that the coexistence of COPD with conditions such as lower respiratory tract infections has important clinical implications, leading to more rigorous monitoring of these individuals and

144 resulting in fewer occurrences of acute HSU. The independent effect of comorbidity may be more strongly associated with acute HSU than is the seasonal effect. Thus, those with comorbid conditions are at equivalent risk of acute HSU regardless of the season. It may also be that those with comorbidities may be less likely to go outside and be exposed to air pollution, regardless of the season.

The presence of this seasonal effect may be suggestive of a more complex interaction between air pollution and temperature. For instance, the body’s thermoregulatory response to temperature may cause increased sensitivity to toxicants(Doull 1972, Gordon, Mohler et al. 1988, Gordon

2003) and vulnerability to air pollution.(Blatteis 1998) The independent health effects of short- term exposure to air pollution and temperature have been well established, and while some evidence exists of the combined effects of air pollution and temperature on mortality,(Ren,

Williams et al. 2008, Breitner, Wolf et al. 2014) further studies are necessary to elucidate if temperature modifies the association between air pollution and morbidity, independent of season.

This study is the largest case-crossover study to examine the effect of air pollution on acute HSU amongst persons suffering from COPD. My study population was drawn from 25 census divisions which at baseline represented approximately 86% (n = 10,528,185) of the population of

Ontario and 34% of the total Canadian population.(Statistics Canada 2016) While other studies examine COPD outcomes amongst a general population, this study specifically examines these outcomes amongst a separate and susceptible risk population. I used a validated algorithm and comprehensive administrative datasets to identify individuals with COPD and followed them over 10 years. The use of individual level data of such an extensive and diverse sample allowed us to identify even more vulnerable subpopulations while also testing for seasonal effects.

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Exposure was assigned to individuals based on residential postal code at the time of each acute

HSU event. In this way, I was able to account for changes in area of residence over time.

Furthermore, the use of a case-crossover approach controlled for constant, or slow-varying personal characteristics I would otherwise be unable to account for,(Maclure 1991) and the time- stratified design further reduced time trend biases. This comprehensive approach can be used as a framework in future studies to examine the association between the AQHI and acute HSU for other conditions vulnerable to the effects of air pollution.

There are however, some limitations worth noting. First, while my study sample is largely representative of the Ontario population, coverage is limited to areas for which AQHI values are recorded, and thus excludes much of rural northern Ontario. Therefore, my result suggesting those who live in rural areas are less likely to visit the ED due air pollution exposure should be interpreted with caution. As legislation over time has decreased the levels of air pollutants from stationary sources, the major exposures are now due to vehicular traffic.(Liu, Yan et al. 2016) As a result, I may hypothesize that as rural areas have less traffic, they may also experience less morbidity. Second, administrative data may be subject to inaccuracies in coding leading to some misclassification of disease status and outcomes.(Peabody, Luck et al. 2004) However, as this misclassification is likely non-differential and its presence would result in more conservative effect estimates. For instance, anyone misclassified into the COPD cohort (do not actually have

COPD) Third, while exposure data was reviewed and made publicly available by the Ontario

Ministry of the Environment and Climate Change and Environment Canada, there is no other way verify the accuracy of all measures. The assignment of exposure based on the daily pollution levels from fixed exposure monitoring sites averaged across relatively large geographic areas may have led to potential misclassification of exposure. Other methods such as inverse distance

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weighting or kriging can also be used to assign exposure. However, studies have shown that

depending on the air pollutant being modeled, these more complex methods may not be more

accurate.(Lee and Shaddick 2010) Nonetheless, any misclassification that does exist is likely

non-differential and would lead to more conservative effect sizes. Fourth, as my analysis was

focused on the AQHI, I did not consider other individual pollutants in my sensitivity analyses

such as carbon monoxide and sulfur dioxide which may contribute to acute HSU. Fifth, while the

case-crossover analysis controls for individual level confounders such as occupational and

household exposures by design, a certain level of daily variation in these exposures may exist

that I was unable to account for. Finally, my study may suffer from the multiple comparison

problem. This problem states the more hypotheses are tested, the more likely it is that

significance may be observed simply by chance. Sufficient evidence already exists in the

literature to support my study results and suggest my findings are not simply due to chance

alone. However, further studies should be conducted to confirm my findings.

4.7 Conclusions

My study suggests the AQHI can be used to communicate risk of non-accidental, COPD and

CVD hospitalization and ED visits amongst persons suffering from COPD. The AQHI was

designed to represent the overall mix of ambient air pollution and be easily interpretable by the

general population. While a single number index may lack detail, it has clear benefits for the

purposes of clinical utility, research, policy and ease of use.(Bowling 2005) My findings also

suggest the effects of air pollution on acute HSU may be modified by season, and a history of

hospitalization for lower respiratory tract infections. A deeper understanding of the effects of air

147 pollution on our health is critical, especially in vulnerable populations with chronic disease.

These results can serve as important insights to help inform public health and environmental policy. The timely measurement and communication of the health risks of air pollution exposure plays a crucial role in reducing the burden on health and improving quality of life.

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Chapter 5:

The Combined Effects Of Air Pollution And Temperature On The Health Of Persons Living With COPD: A Population-Based Study

Preface

Results of my second original research study show a significant association between the AQHI and acute HSU amongst individuals with COPD. Findings also suggest a significant seasonal effect, such that with risks of COPD hospitalization were observed to be higher during the warm season. As the global climate continues to change, unseasonal variations of temperature may become more common. It is therefore important to understand if the health risks of air pollution exposure vary across temperatures rather than simply across seasons, or if temperature modifies the association between air pollution and health.

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5.1 Abstract

Background: Considerable evidence exists that both ambient air pollution and temperature

extremes can cause severe acute exacerbation of chronic obstructive pulmonary disease (COPD)

symptoms. While the health effects of air pollution amongst individuals with COPD has been

shown to be higher in warmer seasons, little is known about the combined effects of air pollution

and temperature on morbidity in this population.

Objectives: The objectives of this study were to determine if the risk of hospitalization due to

ambient air pollution exposure (as measured by the Air Quality Health Index – AQHI) is

modified by temperature, and if there exists a temperature threshold above which this risk

becomes more pronounced.

Methods: Individuals with COPD who were hospitalized between 2003 and 2013 were

identified from administrative datasets in Ontario, Canada using a validated algorithm. A time-

stratified case-crossover design was used to compare estimates of the association between the

AQHI and hospitalization for non-accidental, COPD and CVD causes on days with temperatures

above the 75th percentile to days with temperatures below the 25th percentile. To examine

potential thresholds, additional stratified analyses examined the association by 1°C increases in

temperature. Age and sex were examined as possible susceptibility factors. Risk estimates were

obtained for each census division, and pooled using random-effects meta-analysis.

Results: Effect modification by temperature was observed only for the association between air

pollution and COPD hospitalization, with higher estimates seen at the warmest temperatures (OR

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1.041; 95% CI 1.015, 1.067), compared to the coldest (OR 0.976; 0.951, 1.001). Results also suggested risk of COPD hospitalization begins to increase at temperatures above 7˚C. A statistically significant association between the AQHI and CVD hospitalization was observed at all temperatures. Males were observed to be at higher risk of non-accidental hospitalization due to air pollution exposure at the warmest (OR 1.027; 95% CI 1.013, 1.042) compared to the coldest temperatures (OR 0.996; 95% CI 0.986, 1.006).

Conclusions: My results suggest that amongst individuals with COPD, temperature modifies the association between the AQHI and COPD hospitalization, such that risks were only significant at higher temperatures. However, associations with CVD hospitalizations were statistically significant at all temperatures. Greater efforts should be made in educating individuals with

COPD to be aware of air quality through at all temperatures and not just during the warmer months.

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5.2 Background

Chronic Obstructive Pulmonary Disease (COPD) is a progressive life-threatening lung disease

and one of the leading cause of morbidity and mortality in Canada(Chapman, Bourbeau et al.

2003, Public Health Agency of Canada 2007). Persons with COPD suffer from airway

obstruction, shortness of breath, cough and sputum production. The day to day burden of these

symptoms can substantially decrease quality of life and cause difficulties running errands, doing

chores, working, participating in sports, or leisure/social activities and getting a good night’s

sleep(Hernandez, Balter et al. 2009).

Canadian and international studies have shown both temperature extremes,(Medina-Ramon and

Schwartz 2007, Ostro, Rauch et al. 2010, Lavigne, Gasparrini et al. 2014, Gasparrini, Guo et al.

2015, Chen, Wang et al. 2016) and air pollution exposure can cause severe acute exacerbation of

COPD (AECOPD) symptoms. As observed in chapter 4, even at levels below national standards,

air pollution exposure can increase the risk of emergency department visits and

hospitalization,(Anderson, Spix et al. 1997, Medina-Ramon, Zanobetti et al. 2006, Stieb,

Szyszkowicz et al. 2009). Individuals who experience frequent AECOPD have been shown to

have a greater decline in lung function and are more often admitted to hospital with longer length

of stay compared to those who experience AECOPD infrequently.(Donaldson, Seemungal et al.

2002)

As it is recognized that both air pollution and temperature are correlated, studies that examine the

risk of one of these exposures, also generally control for the other in order to adequately quantify

their individual and independent effects. To gain further insight into this relationship, some

152 studies have conducted season specific analyses.(Medina-Ramon, Zanobetti et al. 2006, Stieb,

Szyszkowicz et al. 2009) My previous research in Chapter 4 for instance, showed that people with COPD living in Ontario, Canada had an increased risk of COPD hospitalization during the warmest months of the year (June-August), compared to the coldest (December-February). My results also suggested individuals with COPD may be at greater risk of cardiovascular disease

(CVD) hospitalization during the coldest months of the year compared to the warmest.

A limited number of studies have attempted to examine potential interactions between air pollution and temperature.(Ren, Williams et al. 2006, Basu, Feng et al. 2008, Ren, Williams et al. 2008, Stafoggia, Schwartz et al. 2008, Zanobetti and Schwartz 2008, Pattenden, Armstrong et al. 2010, Analitis, Michelozzi et al. 2014) These few focus primarily on mortality. While some of these studies suggest an enhanced effect of air pollution at higher temperatures, there exists no clear consensus. Furthermore, little is known about the combined effects of temperature and air pollution on morbidity. As the global climate continues to change, unseasonal variations of temperature may become more common. It is therefore important to understand if the health risks of air pollution exposure vary across temperatures rather than simply across seasons, or if temperature modifies the association between air pollution and health. Evidence of a combined effect between air pollution and temperature may also reveal a temperature threshold above which the health effects of air pollution become more pronounced. Such knowledge would have important public health implications to exposure reduction strategies and minimizing the health consequences of air pollution in this vulnerable population.

Understanding the effects of air pollution on health, and how to properly reduce exposures is a critical part of maintaining and improving quality of life. To this end, Health Canada and

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Environment Canada created the Air Quality Health Index (AQHI). The AQHI is a single

aggregate measure of air pollution that ranges from 1 (low health risk) to 10+ (very high health

risk). While its calculated based on the relative mortality risk of the combined impacts of

nitrogen dioxide (NO2), ozone (O3), and fine particulate matter less than 2.5 microns (PM2.5), the

AQHI has been shown to best represent the level of overall ambient air pollution.(Stieb, Burnett

et al. 2008) It was designed specifically as a means to communicate health risk and exposure

reduction strategies to the public in an understandable way.(Environment and Climate Change

Canada 2015) However, as the AQHI is based strictly on air pollution exposure, it does not

consider the potential combined effects of temperature. Furthermore, despite evidence of health

effects also existing in colder weather,(Analitis, Katsouyanni et al. 2008) air quality information

in Ontario must be actively sought during colder months, compared to summer months when air

pollution is typically higher, and air quality levels and advisories are disseminated regularly by

popular media outlets.

5.3 Objectives & hypotheses

The objectives of the current study were two-fold: 1) to determine if ambient air temperature

modifies the association between air pollution and risk of hospitalization amongst persons with

COPD, and 2) to determine if there is a threshold temperature above which the risk of

hospitalization due to air pollution exposure becomes more pronounced.

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5.4 Methods

5.4.1 Study Design

A 2-stage time-stratified case-crossover design(Maclure 1991) was used to determine if

temperature modified the association between air pollution and hospitalization. In a case-

crossover study, each individual serves as their own control, effectively adjusting for slow-

varying personal characteristics by design. The time-stratified design reduces the possibility of

introducing time trend biases (e.g., autocorrelation of the exposure measure), by matching case

and control days on year, month and day of the week.(Bateson and Schwartz 1999, Bateson and

Schwartz 2001) For each hospitalization in the current study, I compared a person’s AQHI

exposure on the day of a hospitalization (case day) to their exposure on a set of days when they

were not hospitalized (control days).

5.4.2 Data Source

I used health administrative data from a culturally diverse population of over 13 million people

living in Ontario, Canada. The Ontario Health Insurance Plan (OHIP) entitles all residents with a

valid health card to access provincial health-care services including emergency and preventive

care at no personal cost.(Ontario Ministry of Health and Long Term Care 2016) Details of these

services are captured in administrative databases housed at the Institute for Clinical Evaluative

Sciences (ICES) and can be linked at the individual level using a unique encrypted health card

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number assigned to each resident. Primary diagnosis for all hospitalizations was obtained from

the Canadian Institute for Health Information Discharge Abstract Database (CIHI-DAD) and

demographic data including date of birth, sex, and residential postal code from the Ontario

Registered Persons Database (RPDB).

5.4.3 Study Population

I identified individuals aged 35-99 years from the ICES Ontario Chronic Obstructive Pulmonary

Disease database. This database contains information on all Ontarians with physician-diagnosed

COPD, defined as having had 3 or more physician billing claims within 2 consecutive years,

and/or one or more hospital discharges with COPD diagnostic codes (ICD-9: 49, 492, 496; ICD-

10: J41, J42, J43, J44). This previously validated algorithm was found to be 96.9% (95% CI

94.4, 98.5) specific and 57.3% (47.8, 66.4) sensitive to identifying persons who have COPD, as

compared with real-world clinical evaluation by a physician.(Gershon, Wang et al. 2009)

Inclusion into the study was restricted to those individuals within the ICES COPD database for

whom a hospitalization was recorded between January 1, 2003 and March 31, 2013.

5.4.4 Outcome Measures

I used primary diagnosis codes recorded in the CIHI-DAD to identify hospitalizations that

occurred between Jan 1, 2003 and March 31, 2013. Based on my previous research in Chapter 4 I

chose to independently examine any non-accidental hospitalizations (ICD-9: <800; ICD-10:

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A00-R99), as well as hospitalizations due to COPD (ICD-9: 491, 492, 496; ICD-10: J41, J43,

J44), or CVD causes (ICD-9: 410, 412, 413, 427, 428, 432, 435, 436, 437; ICD-10: G45, G46,

H34.0, I06, I20-I25, I47.1, I47.2, I48-I50). To avoid dependencies amongst observations I

excluded events with identical classification codes that recurred within 30 days of the initial

claim.(Chen, Yang et al. 2005) I also excluded events for which an exposure measure could not

be assigned due to missing exposure or residential postal code data at the time of the

hospitalization.

5.4.5 Exposure Measure

In the current study, I chose the AQHI as an indicator of the overall mix of air pollution. As

individuals have no control over the composition of the ambient air pollution to which they are

exposed, health effects associated with exposure to the overall mix of air pollution may be more

immediately relevant and applicable to a person suffering from COPD. The AQHI was intended

to be used as a relative scale to help people reduce their exposure to the overall air pollution

mix,(Stieb, Burnett et al. 2008) and is therefore ideally suited for studies of this nature. In

addition, the AQHI is a single number index and as such has clear benefits for the purposes of

clinical utility, research, policy and ease of use.(Bowling 2005) My previous work has shown

ability of the AQHI to measure the association between air pollution and hospitalization amongst

Ontarians with COPD (Chapter 4), and that the goodness of fit (based on Akaike information

criterion) of models which used the AQHI was equivalent to that of those composed of multiple

individual pollutants.(Burnham and Anderson 2004)

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I obtained hourly AQHI data from the Ontario Ministry of the Environment and Climate Change.

Data were collected by 42 fixed-site air monitoring stations that are strategically positioned in the more densely populated areas in Ontario. These monitoring stations provide data for approximately 86% of the Ontario population across 25 of the 49 CDs. The AQHI is calculated as follows:

10 AQHI = × (100 × exp (0.000871 × NO − 1 + exp 0.000537 × 9 − 1 10.4 4 :

+ exp 0.000487 × ;<4.= − 1))

where NO2 and O3 are measured in parts per billion (ppb) and PM2.5 in micrograms per cubic meter (µg/m3).(Stieb, Burnett et al. 2008). I calculated daily maximum AQHI values for each monitoring station and averaged these data across all monitoring stations within each CD. If more than 25% of the daily hourly data was unavailable from a given station, the decision was made to consider the daily AQHI value as unreliable and was set as missing.(Stieb, Burnett et al.

2008) Missing daily values were imputed by taking the mean value from the year before, and the year after the station specific date in question. Individuals included in the study were assigned daily AQHI exposures based on their CD of residence at the time of hospitalization using their residential postal code.

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5.4.6 Covariates and Effect Modifiers

I obtained daily mean ambient temperature and relative humidity data from Environment Canada

for weather stations within Ontario CDs that also had available AQHI data. Exposure on days in

which less than 75% of the hourly data was available were set as missing. I obtained daily rate of

outpatient claims for influenza per 100,000 people in each CD, to account for any potential

confounding by influenza.(Wong, Yang et al. 2009) These covariates were assigned according to

each individual’s CD of residence at the time of hospitalization, determined using their

residential postal code. I also created an indicator variable to account for statutory holidays, as

these may affect the timing of seeking health care. Finally, I examined sex and age at baseline

(date of entry into the ICES COPD cohort, or January 1st, 2003 for those who entered the cohort

prior to the start of the study) as sensitivity factors that may potentially modify the association

between air pollution and hospitalization.

5.4.7 Statistical Analysis

Annual, and seasonal distributions of each exposure and outcome were calculated across all CDs

with available AQHI data. In the first stage of the case-crossover analysis, I used conditional

logistic regression to estimate the association between the AQHI and ambient temperature on all

non-accidental and disease-specific hospitalizations. To evaluate the presence of potential non-

linear associations between temperature and hospitalization, I conducted sensitivity analyses by

modeling the risk of hospitalization as a function of temperature using either quadratic, or natural

cubic spline functions with 3, 4, or 5 equal knots. I compared the Akaike Information Criterion

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(AIC) of the models to determine which model best fit the data. All models were adjusted for mean daily ambient temperature and relative humidity, daily influenza rate per 100,000 people, and statutory holidays.

To determine if temperature modified the association between air pollution and hospitalization, I estimated the association between the AQHI and hospitalizations on days with the highest (≥75th percentile – Q4) and lowest (≤25th percentile – Q1) mean daily temperatures. Models included in this stratified analysis were adjusted for relative humidity, daily influenza rate, and statutory holidays. Analyses were also conducted to examine if there exists a temperature threshold above which the risk of hospitalization due to exposure to air pollution are observed to be more pronounced. To do so, I systematically imposed a criterion for inclusion of hospitalizations in the analysis based on mean daily ambient temperature on the day of the hospitalization. I raised this criterion by increments of 1˚C for each analysis. In this way, the first of these “by ˚C” analyses included hospitalizations that occurred on days with temperatures greater than or equal to the lowest observed temperature of the entire study period (Tmin). The subsequent analysis included hospitalizations that occurred at temperatures greater than or equal to Tmin + 1˚C, followed by those greater than or equal to the Tmin + 2˚C, until the highest observed temperature was reached.

Risks of hospitalization associated with increasing AQHI values determined by this method are not mutually exclusive, but instead represent risk at or above a particular temperature. For this reason, the interpretation of these results cannot be used to compare risks at warm temperatures to those at cold temperatures. Instead, this approach is used to visually explore trends in risk as temperature increases.

My previous research finding (Chapter 4) suggested the strongest association between the AQHI

160 and hospitalization occurred at lag03 (average exposure on the day of the event (lag0) and the 3 preceding days). Therefore, a cumulative AQHI exposure at lag03 was used in the current study.

In addition, current literature suggests the maximum effect of temperature is linear and immediate (lag0 – day of hospitalization) during warmer seasons, and more prolonged (lag06) during colder seasons.(Braga, Zanobetti et al. 2002, Chen, Wang et al. 2016) Therefore, analyses conducted at temperatures greater than or equal to Q4 were stratified at temperature lag0, and those at temperatures less than or equal to Q1 at temperature lag06. My “by ˚C” analytic approach took into account both the immediate and prolonged effects of temperature by stratifying by temperature lag0 and adjusting for temperature at lag06 in each model.

Provincial average effects of the AQHI on hospitalizations at different temperature levels were estimated in the second stage of my study by summarizing CD level estimates using random- effect meta-analysis techniques.(DerSimonian and Laird 1986, Riley, Higgins et al. 2011) I assessed between census-division heterogeneity using the Cochran Q statistic.(Petitti 1999) In addition, I examined potential susceptibility factors by stratifying analyses by age (<65, 65+) and sex. All CD level analyses were conducted with SAS v9.4,(SAS Institute Inc. 2013) and meta- analyses using R statistical software(R Studio Team 2015) and the METAFOR(Viechtbauer

2010) package. All risks were reported as odds ratios (OR) per unit increase in the AQHI, with associated 95% confidence intervals (CI). I considered risk estimates across strata to be statistically significant if there existed no overlap between their associated 95% confidence intervals.

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5.4.8 Research Ethics Approval

Ethics approval was obtained from the Hospital for Sick Children (REB#:1000051010) and the

University of Toronto.

5.5 Results

5.5.1 Baseline Population

My study included 420,211 individuals with COPD who were hospitalized during the study

window, and who resided across the 25 Ontario CDs with available air pollution and temperature

exposure data. At baseline, 64.8% of this population were aged over 65 years and 51.8% were

male.

5.5.2 Distribution of Exposures and Outcomes Hospitalizations

The overall and temperature-level distribution of non-accidental, COPD and CVD

hospitalization by CD is described in Table 5-1. Between 2003-2013, the average annual non-

accidental hospitalization rate was 155.1 per 1000 persons with COPD (38.4 per 1000 within Q1

of temperature and 35.5 per 1000 within Q4). COPD hospitalizations occurred at a rate of 32 per

1000 persons (8.6 per 1000 within Q1 and 6.4 per 1000 within Q4 of temperature), while CVD

162 hospitalizations occurred at a rate of 24.3 per 1000 persons (5.9 per 1000 within Q1 and 5.6 per

1000 within Q4 of temperature).

The provincial annual average AQHI values decreased from 3.8 in 2003 to 3.0 in 2012 (last full year of data), and ranged from 2.6 to 4.3. The AQHI was highest each year from May to July, and lowest from October to December (Figure 5-1a). Over the study period, average annual overall temperatures in Ontario increased from 6.5˚C in 2003 to 8.7˚C in 2012 (Figure 5-1b).

Mean temperature across CDs ranged from 2.3˚C to 9.3˚C, with the 25th percentile ranging from

-7.1˚C to 1.5˚C, and the 75th from 13.3˚C to 18.6˚C.

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Table 5-1. Distribution of environmental exposures and hospitalizations by Ontario Census Divisions between 2003 and 2013 Annual Hospitalization Rate by Temperature Quartile** Ambient Non- AQHI Temperature (˚C) Accidental COPD CVD Average Daily 25th 75th Population* Maximum Mean percentile percentile Q1 Q4 Q1 Q4 Q1 Q4 Algoma 9,113 2.8 4.5 -4.1 14.8 42.0 47.9 9.4 8.7 7.3 8.4 Bruce 4,138 2.6 6.6 -1.0 15.8 19.1 17.0 4.0 2.5 3.4 2.9 Chatham-Kent 8,042 3.3 8.9 0.8 18.0 29.4 26.0 6.9 3.7 5.0 4.4 Durham 22,524 3.0 8.1 0.1 17.7 12.0 10.9 2.7 1.9 1.7 1.5 Essex 22,924 4.1 9.3 1.0 18.6 35.4 32.1 6.4 4.7 6.1 5.5 Frontenac 6,176 3.0 7.9 -0.1 17.7 27.3 23.2 8.0 5.1 3.9 3.7 Halton 13,977 3.7 9.0 1.5 18.1 48.5 44.4 11.2 7.7 7.2 6.6 Hamilton 22,293 3.8 8.4 0.6 17.6 52.7 47.8 11.8 8.7 8.7 8.1 Hastings 8,875 3.0 6.6 -1.3 16.2 42.4 38.8 11.5 8.9 6.9 6.5 Huron 4,018 3.1 7.9 -0.2 16.5 1.6 4.8 0.2 1.0 0.2 0.8 Lambton 8,699 3.7 8.9 0.7 18.0 33.4 33.9 6.0 5.4 6.3 6.2 Middlesex 16,562 3.5 8.1 -0.3 17.4 33.3 31.8 7.4 5.5 4.6 4.6 Niagara 23,751 3.3 9.1 1.2 18.4 33.7 32.5 8.7 6.4 5.2 5.5 Nipissing 6,608 2.9 4.2 -4.7 14.9 58.7 53.8 13.4 9.0 9.4 9.4 Ottawa 27,899 3.1 6.6 -2.4 17.1 42.3 37.1 11.2 7.7 6.2 5.7 Parry Sound 3,034 2.6 6.0 -2.5 16.3 18.3 14.0 3.3 2.4 2.4 2.2 Peel 25,004 3.7 8.7 0.5 18.2 45.5 41.4 10.8 7.5 6.9 6.1 Peterborough 9,095 2.8 6.9 -1.0 16.5 37.1 35.2 10.8 7.5 4.9 4.9 Simcoe 21,396 3.3 6.9 -1.1 16.4 50.3 46.3 12.9 9.6 8.0 7.4 Stormont/Dundas and Glengarry 8,497 2.9 6.3 -2.4 16.9 18.8 17.2 4.8 3.7 3.3 2.9 Thunder Bay 9,283 2.9 2.3 -7.1 13.3 48.9 50.7 10.4 8.9 8.6 9.2 Toronto 95,940 4.3 8.9 1.3 18.1 42.9 38.8 8.4 6.1 6.4 5.9 Waterloo 15,347 3.4 7.2 -0.9 16.4 31.7 27.5 8.0 5.8 4.9 3.9 Wellington 7,425 3.0 6.7 -1.4 16.0 17.0 15.9 4.9 3.3 2.5 2.5 York 19,591 3 8.1 -0.1 17.7 40.6 40.2 7.9 6.7 6.4 6.0 Total 420211 3.2 7.3 -0.9 16.9 38.4 35.5 8.6 6.4 5.9 5.6 Q1: Hospitalization rate per 1000 individuals occurring at temperatures ≤ 25th percentile; Q4: Hospitalization rate per 1000 individuals occurring at temperatures ≥ 75th percentile; *Number of individuals with COPD who were hospitalized between January 1, 2003 and March 31, 2013; **Based on the events with available exposure data included in this study.

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3 AQHI

0 Jul Jul Jul Jul Jul Jul Jul Jul Jul Jul Jan Jan Jan Jan Jan Jan Jan Jan Jan Jan Jan Oct Oct Oct Oct Oct Oct Oct Oct Oct Oct Apr Apr Apr Apr Apr Apr Apr Apr Apr Apr 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013

Figure 5-1a. Average monthly AQHI values in Ontario between 2003 and 2013

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100 100

90 80 80 ˚C) 70 60 60

40 50

40 20 30 Relative Humidity (%)

Ambient Temperature ( 20 0 10

-20 0 Jul Jul Jul Jul Jul Jul Jul Jul Jul Jul Jan Jan Jan Jan Jan Jan Jan Jan Jan Jan Jan Oct Oct Oct Oct Oct Oct Oct Oct Oct Oct Apr Apr Apr Apr Apr Apr Apr Apr Apr Apr 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013

Ambient Temperature (˚C) Relative Humidity (%)

Figure 5-1b. Time series of average monthly ambient temperature and relative humidity values in Ontario between 2003 and 2013

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5.5.3 Analysis by temperature quartile

The pooled province-wide estimates of the association between the AQHI and each of non-

accidental, COPD, and CVD hospitalization by temperature quartile are reported in Table 5-2.

When conducting the analyses on the complete sample, effect modification by temperature was

only observed for the association between air pollution and COPD hospitalization, where a

statistically stronger association was seen at the warmest temperatures (OR 1.041; 95% CI 1.015,

1.067), compared to the coldest (OR 0.976; 0.951, 1.001). While the association between air

pollution and non-accidental hospitalization was higher at the warmest temperatures (OR 1.025;

95% CI 1.008, 1.042), it was not observed to be statistically different than the association at the

coldest temperatures (OR 1.005; 95% CI 0.992, 1.018). Similarly, temperature did not modify

the association between air pollution and CVD hospitalization.

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Table 5-2. Pooled Results of the association between the AQHI and hospitalization amongst persons in Ontario living with COPD between 2003 and 2013, by ambient temperature level, age and sex.

Ambient Total Males Females Ages <65 Ages 65+ Temperature Level OR 95% CI OR 95% CI OR 95% CI OR 95% CI OR 95% CI Non-Accidental Hospitalization Full Range 1.006 (1.001, 1.011) 1.007 (1.000, 1.013)† 1.006 (0.997, 1.014) 1.007 (0.997, 1.017) 1.005 (1.000, 1.011) Q1* 1.005 (0.992, 1.018) 0.996 (0.986, 1.006) 1.015 (1.006, 1.025) 1.008 (0.996, 1.020) 1.007 (1.000, 1.015) Q4** 1.025 (1.008, 1.042) 1.027 (1.013, 1.042) 1.018 (0.998, 1.038) 1.018 (0.998, 1.039) 1.025 (1.007, 1.043) COPD Hospitalization Full Range 1.005 (0.994, 1.016) 1.005 (0.989, 1.021) 1.007 (0.991, 1.023) 1.014 (0.992, 1.037) 1.003 (0.990, 1.016) Q1* 0.976 (0.951, 1.001) 0.966 (0.947, 0.985) 0.996 (0.978, 1.014) 0.993 (0.966, 1.021) 0.979 (0.964, 0.993) Q4** 1.041 (1.015, 1.067) 1.044 (1.008, 1.080) 1.043 (1.001, 1.088) 1.078 (1.025, 1.133) 1.028 (0.998, 1.058) CVD Hospitalization Full Range 1.018 (1.006, 1.031) 1.021 (1.004, 1.038) 1.020 (1.001, 1.040) 1.006 (0.978, 1.035) 1.024 (1.010, 1.038) Q1* 1.040 (1.012, 1.070) 1.033 (1.012, 1.055) 1.060 (1.036, 1.086) 1.046 (1.009, 1.084) 1.045 (1.027, 1.063) Q4** 1.043 (1.000, 1.088)† 1.036 (1.000, 1.072) 1.018 (0.978, 1.060) 0.986 (0.931, 1.044) 1.057 (1.006, 1.111) Bolded values indicate statistical significance. Italicized values indicate effect modification by temperature. *At temperatures ≤ 25th percentile (lag0); **At temperature ≥ 75th percentile (lag06); † p-value for heterogeneity < 0.05

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To identify groups that were potentially more susceptible to the combined effects of air pollution

and temperature, I conducted stratified analyses by age and sex. Statistically significant effect

modification by temperature was observed in each stratum with the association between the

AQHI and COPD hospitalization consistently stronger at the warmest temperatures compared to

the coldest. The only exception was amongst females where this association followed the same

trend, but did not attain statistical significance. Results also suggest that amongst males, the

association between air pollution and non-accidental hospitalization is significantly higher at the

warmest temperatures (OR 1.027; 95% CI 1.013, 1.042) compared to the coldest (OR 0.996;

95% CI 0.986, 1.006). No such effect modification was observed amongst females, or across age

groups. Although the association between the AQHI and CVD hospitalization was higher at the

coldest temperatures amongst females and those aged less than 65 years, no statistically

significant effect modification was detected.

5.5.4 Analysis by °C

Temperature-specific analyses were conducted to determine if there is a specific temperature

above which risk of hospitalization due to air pollution exposure becomes more pronounced

(Figure 5-2a-c). Results of these analyses showed a consistent risk of COPD hospitalization up to

7˚C, at which point the risk was observed to increase. The highest statistically significant risks

occurred at temperatures at or above 19˚C (OR 1.049; 95% CI 1.014, 1.086). Results of the non-

accidental and CVD analyses show consistent risk of hospitalization across all temperatures.

When these analyses were stratified, I found that amongst those aged under 65 years, there was a

consistent risk of COPD hospitalization up until 7˚C at which point it began to increase (Figure

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5-3a), whereas the risk for those aged 65+ was observed to be more consistent at higher

temperatures (Figure 5-3b). Figures describing by °C analyses stratified by sex for COPD

hospitalization, as well as those stratified by age and sex non-accidental and CVD

hospitalizations can be found in the appendix (Supplemental Figures S.5-3c-l).

5.5.5 Sensitivity analyses

Sensitivity analyses were conducted using cubic spline functions for temperature with 3, 4, and 5

equal knots. Effect sizes were comparable to those using a quadratic temperature term, however

AIC values were slightly higher, indicating the model using the quadratic temperature term is a

better fit for the data.

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(a) (b) (c)

24 24 24 23 23 23 22 22 22 21 21 21 20 20 20 19 19 19 18 18 18 17 17 17 16 16 16 15 15 15 14 14 14 13 13 13 12 12 12 11 11 11 10 10 10 9 9 9 8 8 8 7 7 7 6 6 6

5 5 5 4 4 4 C)

° 3 3 3 2 2 2 1 1 1 0 0 0 -1 -1 -1 -2 -2 -2 -3 -3 -3 -4 -4 -4 -5 -5 -5 -6 -6 -6 -7 -7 -7 -8 -8 -8 -9 -9 -9 -10 -10 -10 -11 -11 -11 -12 -12 -12

Temperature Strata ( -13 -13 -13 -14 -14 -14 -15 -15 -15 -16 -16 -16 -17 -17 -17 -18 -18 -18 -19 -19 -19 -20 -20 -20 -21 -21 -21 -22 -22 -22 -23 -23 -23 -24 -24 -24 -25 -25 -25 -26 -26 -26 -27 -27 -27 -28 -28 -28 -29 -29 -29 0.85 0.9 0.95 1 1.05 1.1 1.15 0.85 0.9 0.95 1 1.05 1.1 1.15 0.85 0.9 0.95 1 1.05 1.1 1.15 Odds Ratio (95% Confidence Interval) Odds Ratio (95% Confidence Interval) Odds Ratio (95% Confidence Interval) Figures 5-2a-c. Pooled estimates of the risk of (a) Non-accidental; (b) COPD; (c) CVD hospitalization amongst individuals with COPD associated with each 1 unit increase in daily maximum AQHI (lag03) and increases in temperature (lag0), across Ontario census divisions. Risks are presented for each specified temperature range and adjusted for temperature (lag06), relative humidity (lag06), influenza events and statutory holidays.

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(a) (b)

24 24 23 23 22 22 21 21 20 20 19 19 18 18 17 17 16 16 15 15 14 14 13 13 12 12 11 11 10 10 9 9 8 8 7 7 6 6 5 5 4

4 3 3 C) 2 2 ° 1 1 0 0 -1 -1 -2 -2 -3 -3 -4 -4 -5 -5 -6 -6 -7 -7 -8 -8 -9 -9 -10 -10 -11 -11 -12 -12 Temperature Strata ( -13 -13 -14 -14 -15 -15 -16 -16 -17 -17 -18 -18 -19 -19 -20 -20 -21 -21 -22 -22 -23 -23 -24 -24 -25 -25 -26 -26 -27 -27 -28 -28 -29 -29 0.85 0.9 0.95 1 1.05 1.1 1.15 0.85 0.9 0.95 1 1.05 1.1 1.15 Odds Ratio (95% Confidence Interval) Odds Ratio (95% Confidence Interval)

Figures 5-3a-b. Pooled estimates of the risk of COPD hospitalization amongst individuals with COPD aged < 65 years (a) and 65+ years (b) associated with each 1 unit increase in daily maximum AQHI (lag03) and increases in temperature (lag0), across Ontario census divisions. Risks are presented for each specified temperature range and adjusted for temperature (lag06), relative humidity (lag06), influenza events and statutory holidays.

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5.6 Discussion

This is the largest population-based study to examine the combined effects of air pollution and

temperature on hospitalization amongst individuals with COPD. Results of this study showed

that amongst this population, temperature modifies the association between air quality and

COPD hospitalization, such that the risk due to poor air quality is only significant at warmer

temperatures. Results suggest the risk of COPD hospitalization becomes more pronounced at

temperatures above 7˚C, and more so amongst those aged under 65 years. I did not find

combined effects of temperature and air quality on risk of non-accidental, or CVD

hospitalizations in the full study population. However, my stratified analyses showed the risk of

hospitalization for non-accidental causes amongst males with COPD is higher at warmer

temperatures compared to colder, and the risk of CVD hospitalization amongst females with

COPD was higher at colder temperatures compared to warmer. These results have important

public health implications as they may suggest the combined effects of temperature and air

pollution should be considered when forming setting policy and public health initiatives such as

the calculation of the AQHI and the creation of its health messaging.

My findings are consistent with those of our previous research that showed a seasonal effect of

air pollution with higher risks of COPD hospitalizations associated with increases in the AQHI

during the warm months of the year (OR 1.042; 95% CI 1.022, 1.063) as compared to the cold

(OR 0.991; 95% CI 0.969, 1.012 - see Chapter 4). A limited number of studies have examined

the combined effects of air pollution and temperature on respiratory morbidity. However, none

have been conducted on such a large sample in a climate that experiences such extreme cold and

hot temperatures as Ontario, Canada. A 2006 study in Brisbane, Australia found a statistically

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significant interaction between temperature and PM10 (p < 0.01). At low levels of PM10 the risk of respiratory hospitalizations decreased as temperature increased. However at high levels of PM10 this inverse association was less extreme and no longer statistically significant.(Ren, Williams et al. 2006) A 2016 longitudinal study that followed 69 participants with COPD in Baltimore found statistically significant interaction terms between PM2.5 and temperature, and NO2 and temperature (p < 0.05 for all interaction terms) resulting in worsening (higher values) of daily

Breathlessness, Cough, and Sputum Scores (BCSS) and increases in rescue inhaler use.(McCormack, Belli et al. 2016)

Studies examining the combined effects of air pollution and temperature on non-accidental and disease-specific mortality show report results that are not consistent. In Europe, an Italian study found that season and temperature levels strongly modify the association between PM10 and non-

3 accidental mortality such that with every 10 µg/m increase in PM10, there was a 2.55% (95% CI

1.58, 3.52) increase in risk of mortality at temperatures above the 75th percentile compared to a

0.21% (95% CI -0.06, 0.47) at temperature below the 50th percentile.(Stafoggia, Forastiere et al.

2008) The same study found similar increases in risk respiratory mortality due PM10 exposure at temperatures above the 75th percentile, but these were not statistically higher than those at the lowest temperatures. Another study that included participants from across 9 European cities found the effect of heat waves on each of non-accidental, cardiovascular and respiratory mortality was larger during high O3 or high PM10 days.(Analitis, Michelozzi et al. 2014)

However statistically significant effect modification was only observed in the case of non- accidental mortality. In the United States, a study using data from the US National Morbidity,

Mortality and Air Pollution Study (NMMAPS)(Ren, Williams et al. 2008) found that O3 modified the association between temperature and cardiovascular mortality resulting in increased

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risk of death at higher compared to lower levels of O3, during the summer months. However, 2 other studies in the United States(Basu, Feng et al. 2008, Zanobetti and Schwartz 2008) found no effect modification between air pollution and temperature on the risk of non-accidental death. A

2014 study used data from 1981-1999 across 10 Canadian cities examined if synoptic weather types modified the association between air pollution (NO2, O3, PM2.5, SO2) non-accidental, respiratory and CVD mortality. Results showed the combined effects of weather and air pollution on respiratory mortality is greatest when moist tropical-type weather is present in the summer

(Relative Risk [RR] 1.263; 95% CI 1.067, 1.495). A follow-up study in 2015 in which the analysis was expanded to include 2 additional cities and data from 1981-2008, provided further evidence that weather type has a combined effect with air pollution on the non-accidental mortality.

Several mechanisms have been proposed to explain why temperature may modify the effect of air pollution on health outcomes. Biologically, the synergy between temperature and air pollution may be explained in part by the body’s thermoregulatory response to heat. When internal temperature increases, the body activates systems to dissipate excess heat. These systems in turn can have direct effects on the introduction of toxicants to the body. The primary thermoregulatory response to heat stress is a combination of an increase in skin temperature due to increased skin blood flow through peripheral vasodilation, and the body beginning to sweat.(Blatteis 1998) While efficient in dissipating internal heat, these systems can augment the absorption of toxicants through the skin. The body’s overall sensitivity to toxicants has also been shown to increase due to higher ambient temperature.(Doull 1972, Gordon, Mohler et al. 1988,

Gordon 2003)

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The combined effect observed between air pollution and temperature may also be influenced by differences in composition of air pollution across seasons. Transboundary air pollution originating from neighbouring cities in the United States, can affect air quality in Ontario.

During summer months, tropical air masses from the southwest bring high temperatures, humidity as well as additional pollutants from the United States, resulting in increases in ground- level ozone and particulate matter.(Ministry of the Environment and Climate Change 2005)

Conversely during the winter months, a shift occurs bringing cleaner, cold and dry polar air masses from the northwest.(NAV Canada 2002) In addition, evidence has shown that nitrate concentrations in particulate pollution increase as temperature deceases, while sulfate levels increase when temperatures increase beyond 14°C.(Kavouras and Chalbot 2017) This phenomenon may also contribute to my observation that the AQHI is associated with COPD hospitalization during the warm season, and with CVD hospitalization primarily during the cold season. Further study is required to better understand the extent to which these phenomena may contribute to the results observed in my study.

It is also possible that the stronger effects of air pollution seen at warmer temperatures may simply be the result of increased exposure to outdoor air pollution due to population behaviors in warmer weather. For instance, studies suggest increased exposure in warmer weather may be due to the amount of time windows are left open in the house,(Hänninen and Jantunen 2007) and the amount of time spent outdoors. A 2002 study showed that on average Canadians aged 18+ spend more time outdoors in the summer (148 minutes per day), compared to the winter (33 minutes per day).(Leech, Nelson et al. 2002). It has also been shown that when the body is under heat stress, respiration rate increases to reduce heat through evaporation. Which in turn, may also increase the total intake of airborne pollutants which can directly affect the airways.(Gordon

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2003, Mautz 2003) This may explain the increases in risk of COPD hospitalization as temperature gets warmer (beyond 7˚C) observed in the current study. A Statistics Canada report(Statistics Canada 2013) also showed increased physical activity in warmer weather amongst those under 65 years of age, which may contribute to the more pronounced effect observed amongst this age group in the current study, compared to their older counterparts.

This study also observed higher risks for CVD hospitalization at the coldest temperatures. This finding is supported by the results of our previous research (Chapter 4) which found a significant association between the AQHI and CVD hospitalization during cold seasons (Dec-Feb), as well as other studies that have shown an increased risk of CVD mortality associated with colder temperatures.(Analitis, Katsouyanni et al. 2008, Chen, Wang et al. 2016)

My results also have important implications for public health messaging. My previous work in

Chapter 3 suggests that although Canadians with COPD believe that air pollution affects health, their knowledge regarding the assessment of air quality is lacking and does not translate to meaningful behavioural modification and exposure reduction. Education of both patients and health professionals is key to improving this knowledge, however the availability and access to real-time AQHI values is also critical to health protection. Currently, air quality information in

Ontario must be actively sought during colder months, compared to warmer months when air pollution is typically higher, and air quality levels and advisories are disseminated regularly by popular media outlets. Evidence suggests the public relies on media advisories as cues for when to modify behavior to avoid air pollution exposure.(Radisic, Newbold et al. 2016) While my results suggest the effects of air pollution amongst individuals with COPD is higher at the warmest temperatures for COPD hospitalizations, statistically significant risks of hospitalization for CVD and non-accidental causes, are nevertheless present at colder temperatures. Therefore,

177 public health strategies should ensure air quality levels are actively disseminated to the public throughout the entire year regardless of temperature. In addition to air quality levels and associated health risks, public health messaging should also communicate simple strategies that result in meaningful exposure reduction. For instance, avoiding strenuous activities on high air pollution days, or rescheduling physical/outdoor activities to times of day when air pollution is not at its peak would reduce exposure while maintaining physically active and minimizing disruption to daily living.

My study provides important insights into the modifying effect of temperature on the association between air pollution and hospitalization amongst persons suffering from COPD. My study cohort was drawn from a population that represents approximately 86% (n = 10,528,185) of

Ontario residents and 34% of the total Canadian population.(Statistics Canada 2016) While other studies examine outcomes amongst a general population, this study specifically examines outcomes amongst a separate and susceptible risk population. I used a validated algorithm and comprehensive administrative datasets to identify my study cohort of individuals with COPD and follow them over 10 years. Use of a single aggregate exposure measure such as the AQHI provides a means of examining how temperature modifies the association between hospitalization and the overall mix of ambient air pollution. While other studies have used individual pollutant exposures, the AQHI may be a better representation of true air pollution exposure as people are exposed to a mixture of hundreds of substances present in ambient air pollution, which may act in combination or in combination.(Katsouyanni 2003, Chen and Copes

2013) Exposure was assigned to individuals based on residential postal code at the time of each acute HSU event. I was therefore able to account for changes in area of residence over time. Use

178 of the time-stratified case-crossover analysis reduced any potential time trend biases, and allowed us to control for constant, or slow-varying personal characteristics for which I would otherwise be unable to account.(Maclure 1991) Previous studies have been conducted in geographic areas with limited temperature range. However, the variable climate experienced in

Ontario over the course of the year allowed us to examine the association between air quality and hospitalization across a wide range of temperatures. The use of such a large sample also allowed us to explore at which potential temperatures health effects of air pollution become more pronounced, which has not previously been examined.

My study has some limitations worth noting. While my study cohort is largely representative of the province, AQHI monitoring is limited to areas that are more densely populated. As a result, my study includes 25 of the most populated CDs, but excludes much of northern rural Ontario.

Although the air quality in Ontario has improved over time, AQHI values remain relatively consistent throughout the study period. In spite of this lack of variation, I was able to detect health effects associated with air quality at a variety of temperature levels. While the AQHI provides a measure of overall air pollution, by using this single aggregate measure of exposure, I was unable to determine the independent effects of individual air pollutants. Although the case- crossover analysis has been shown to be mathematically equivalent to other approaches,(Lu and

Zeger 2007) I was unable to determine risk at individual degrees of temperature using this methodology. Future studies may be able to accomplish this and better identify potential temperature thresholds beyond which risk of hospitalization significantly increases. To account for both the immediate and long term effects of temperature, my “by ˚C” analyses were stratified by temperature at lag0 and included linear and quadratic terms for temperature at lag06 in each

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model. While these models may suffer from some degree of over-adjustment, the net result

would yield more conservative risk estimates.(Schisterman, Cole et al. 2009) I did not examine

risks associated with extreme temperature or pollution events. Future studies may consider

looking more closely at individual and prolonged extreme events to determine and their effects

on the health of individuals with COPD. Given the use of average exposure from fixed

monitoring sites across relatively large geographic areas, my study may also include some level

of misclassification bias from inaccuracies in administrative coding,(Peabody, Luck et al. 2004)

and exposure assignment. However, any misclassification that is present is likely non-differential

and would lead to more conservative effect estimates. Finally, while the case-crossover analysis

controls for individual level confounders such as occupational and household exposures by

design, there may be a level of daily variation in these exposures that I was unable to account for.

5.7 Conclusions

My study detected that amongst a susceptible population with COPD, there is a modifying effect

of temperature on the association between air quality and COPD hospitalization, such that risks

were only significant at higher temperatures. I did not find similar effect modification for non-

accidental and CVD hospitalizations. Rather these risks were statistically significant both cold

and warm temperatures. It is therefore important to ensure both physicians and the public

understand that health risks due to air pollution exposure are a concern at all temperatures, and

not just during the summer months.

Greater efforts should be made in educating individuals with COPD to be aware of air quality

180 through all seasons, and on strategies to avoid air pollution. Canadians with COPD have reported the belief that staying indoors, keeping windows shut, and moving to rural areas are useful approaches to reducing exposure.(Foty, Dell et al. 2015) Although effective, these strategies are also impractical. While it is paramount to continue to devise strategies to minimize air pollution at all levels (government, industries, community and individuals), air pollution is projected to pose serious health risk for the foreseeable future.(OECD 2012) Understanding when we are at most risk is essential to making better choices to minimize exposures and health consequences, while at the same time continuing to perform daily activities, and maximizing quality of life.

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Chapter 6:

Summary of Main Findings & General Discussion

Preface

There is convincing evidence to suggest the existence of small, but consistent associations

between short-term exposure to ambient air pollution and increases in morbidity. To effectively

reduce exposure to ambient air pollution and the associated health impact, it is important to have

a deep understanding of which populations are most vulnerable and when risk is at its peak. In

this dissertation, I have focused on: 1) understanding the current level of knowledge amongst

Canadians (including those with COPD), with regard to the health effects of air pollution, its

assessment, and exposure reduction strategies; 2) quantifying the association between air

pollution and acute HSU; 3) identifying potentially vulnerable subgroups of the population and;

4) examining potential seasonal effects and the role of temperature in modifying the association

between air pollution and acute HSU.

In the following sections, I will summarize the main results of my original research, and discuss

the overall findings and their implications.

6.1 Summary of findings

My first study examined the current level of knowledge of Canadians with regard to the health

effects of air pollution, its assessment, and exposure reduction strategies. Data were obtained

182 from a 2010 cross-sectional survey commissioned by Health Canada. The study population was drawn from a random sample of Canadians with, and without chronic diseases, including COPD.

As hypothesized, my results suggest that although Canadians with COPD believe strongly that air pollution affects health, their current level of knowledge regarding the assessment of air quality, and risks of exposure is lacking. Less than half of respondents with COPD reported using air quality forecasts such as the AQHI to assess air quality, yet over 75% believed they would be able to detect poor air quality using only their senses, by simply stepping outside.

Furthermore, their knowledge was no better than those who are free of chronic disease, and did not translate to meaningful behavioural modification. Only one third of my survey respondents with respiratory disease who were aware of local forecasts and took actions to reduce exposure to air pollution. In addition, while the protective actions reported may be effective (e.g. staying indoors, keeping windows shut, and moving to rural areas), they are rather impractical.

The knowledge that few individuals with COPD regularly use forecasts to assess air pollution and that behaviours are not effectively modified to reduce exposure, suggests it is unlikely that the results of my subsequent study would be affected by self-correcting behaviours that reduce exposure on high pollution days. These results provided a contextual framework with which I conducted my second study to quantify the association between air pollution and acute HSU

(hospitalization and ED visits) amongst those with COPD.

In my second study, I quantified the association between the air pollution (as measured by AQHI and multipollutant models), and acute HSU amongst individuals living with COPD in Ontario,

Canada between 2003 and 2013. I also examined potential seasonal effects, and identified subgroups of the population that are potentially more susceptible to the effects of air pollution.

This study used exposure data from the Ontario MOECC and hospitalization and ED visits from

183 administrative databases housed at ICES. As hypothesized, this study provides further evidence that the AQHI can be used to communicate risk of morbidity amongst populations living with

COPD. As expected, the risk of acute HSU increased with increasing concentration of overall ambient air pollution. However, when the analysis was based on full-year exposures, statistically significant increased risks were only seen for non-accidental and CVD hospitalizations, as well as CVD ED visits. Again, as I hypothesized, when analyses were restricted to the warm season

(June to August), statistically significant increased risks were observed for all non-accidental,

COPD, and CVD acute HSU. During the cold season (December to February), the association between air pollution and acute HSU was statistically significant for non-accidental causes and unexpectedly high for CVD, but not significant for COPD. When examining effect modification, a statistically significant seasonal effect was only observed in the case of COPD hospitalization, with higher risks observed in the warm season compared to the cold. Results also suggested that amongst individuals diagnosed with COPD, those who had a prior history of hospitalization for

COPD, or lower respiratory tract infections were more susceptible to the effects of air pollution.

The seasonal effect observed in this study may be suggestive of a more complex relationship between air pollution and temperature. While there exists some evidence to suggest an enhanced effect of air pollution on mortality at higher temperatures, there is no clear consensus.

Furthermore, little is known about the combined effects of air pollution and temperature on morbidity.

Building on the results of study 2, my third and final study focussed on the role of ambient temperature as an effect modifier in the association between air pollution and hospitalization amongst individuals with COPD. Using the same data sources and similar methodologies as

184 study 2, my results suggest that air temperature modifies the association between the AQHI and

COPD hospitalization amongst individuals living with COPD. As hypothesized, this risk was statistically higher at warmer temperatures. This risk was also observed to become more pronounced at temperatures above 7˚C, and results showed this to be more evident amongst those aged under 65 years. I found no combined effects of temperature and air quality on risk of non-accidental, or CVD hospitalizations in the entire study population, but rather positive associations between air pollution and these health outcomes were statistically significant and consistent across the full range of temperature. However, stratified analyses showed the risk of non-accidental hospitalization amongst males to be statistically higher at warmer compared to colder temperatures.

In sum, Canadians with COPD believed that air pollution affected health. However, their level of knowledge regarding assessment of air quality, and risks of exposure was lacking, and they did not effectively modify their behaviours to reduce exposure. The AQHI was shown to be effective research tool to measure the association between the overall mix of air pollution and acute HSU amongst individuals with COPD. Exposure to air pollution was associated with increased risk of acute HSU amongst individuals with COPD. This was especially true for non-accidental, COPD acute HSU at higher temperatures, and CVD acute HSU at colder temperatures. There was also evidence that amongst individuals with COPD, the association between COPD hospitalization and air pollution exposure is modified by temperature, and that those with a prior history of hospitalization for COPD, lower respiratory tract infection, or AMI may be more vulnerable to the health effects of air pollution.

Results of my work provide important insights that can be used to inform policy and public

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health strategy. These will be discussed in detail in the following section. Furthermore, the

methodologies used in this dissertation constitute a comprehensive framework that can be used

to examine the association between ambient air pollution and acute HSU for other conditions

potentially susceptible to the effects of poor air quality.

6.2 Health Effects of Air Pollution Amongst Individuals with COPD

Results from the original research conducted as part of this dissertation are consistent with

results from the literature suggesting small, but consistent adverse effects of air pollution on

health outcomes. The associations between air pollution and hospitalizations were observed to be

generally higher than associations with ED visits. My results also demonstrate the importance of

understanding the potential heterogeneity in the health effects of air pollution amongst

subpopulations. By restricting analyses to particular seasons, temperature ranges, and personal

characteristics (e.g. age, sex, comorbidity), as well as focusing on cause specific outcomes that

are known to be affected by air pollution, I was able to identify statistically significant

associations that may have otherwise been overlooked. For instance, when examining the

association between the AQHI and all non-accidental causes of hospitalization, I saw a 0.6%

increase in risk per unit increase in the AQHI (percent increase = OR– 1). Results are similar for

COPD hospitalization (0.5%), and higher for CVD hospitalization (1.8%). However, when I

stratify by season I find the risk of non-accidental and COPD hospitalization to be more

substantial, increasing to 1.8% and 4.2% in the warm season respectively, and CVD

hospitalization increasing to 4.3% in the cold season. The analysis stratified by quartiles of

temperature in study 3 revealed similar results, with risk of COPD hospitalization increasing as

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high as 7.4% for temperatures greater than or equal to the 75th percentile, amongst persons less

than 65 years of age.

While the risks estimated as part of this dissertation may be small, it is important to remember

that unlike mortality, health service use involves choice. A decision must be made to seek care,

and when to seek care. Although AECOPD are associated with morbidity, decreased quality of

life and mortality, studies have shown that nearly half of all exacerbations are unreported

(Langsetmo, Platt et al. 2008, Xu, Collet et al. 2010) and as such go untreated. Although these

unreported exacerbations are likely mild, they may have long-term consequences on the

individual’s quality of life.(Langsetmo, Platt et al. 2008, Xu, Collet et al. 2010) Therefore, we

may consider the effects observed in my original research studies to be conservative estimates of

the true effect of air pollution on morbidity.

6.3 Utility of the AQHI in Air Pollution Research

The majority of published research studies examine health risks associated with select air

pollutants of interest. At times, multipollutant models are used to quantify the independent health

effects of single pollutants in the presence of the others. In everyday life, the ambient air

pollution we are exposed to is composed of a mixture of various gases, particulates and VOCs.

The complexity of the associations, as well as synergies between these pollutants and with health

outcomes, are extremely difficult to represent in a single statistical model. Consequently, the

published literature does not consistently follow a common methodology to examine the health

effects of air pollution. The meta-analyses discussed in section 1.5.2.2 of this dissertation

187 generate summary estimates based on the literature. However, these estimates are produced from studies across various regions of the world, with different climates, and populations. Statistical analyses also differ in their methods of accounting for potential exposure lags effects, confounding variables and temporal trends. Although meta-analyses group similar studies and consider heterogeneity in their inclusion criteria, the summary estimates ultimately represent the health effects attributable to individual air pollutants. While these summary estimates are useful, they are not fully able consider all associations, or potential synergies effects between pollutants.

Determining which components of ambient air pollution pose the greatest risk to health can inform regulatory bodies and industries in their efforts to reduce toxic emissions. The purpose of my research however, is to produce knowledge to inform the at-risk population. As individuals have no control over the composition of the ambient air pollution to which they are exposed, health effects associated with exposure to the overall mix of air pollution may be more immediately relevant and applicable to a person suffering from COPD. The AQHI was intended to be used as a relative scale to help people reduce their exposure to the overall air pollution mix,(Stieb, Burnett et al. 2008) and is therefore ideally suited for studies of this nature.

Burnham and Anderson(Burnham and Anderson 2004) suggest our ability to make inferences in the sciences are guided by 3 principles: 1) simplicity and parsimony, 2) multiple working hypotheses, and; 3) strength of evidence. The first of these principles contributes to my rationale for using the AQHI as my primary exposure measure, while I use the second and third to support this decision. The principle of simplicity and parsimony is based on Occam’s razor which suggests that among competing hypotheses, the simplest solution should be selected. George Box stated “All models are wrong, but some are useful”.(Box and Draper 1987) The concept being

188 that there are no “true” models. Instead, we build models that can best approximate reality, based on the available information. A model with too few variables may be biased, but one with too many variables may have poor precision. This trade-off is the principle of parsimony.(Burnham and Anderson 2004) While the single number AQHI may lack detail, it has clear benefits over multipollutant models with regard to interpretation, clinical utility, research, policy and ease of use.(Bowling 2005)

The second principal is that of multiple working hypotheses. This principle advances science by testing a series of a priori hypotheses against each other. The expectation is that the results of this testing will support one of the hypotheses over the other. New hypotheses are then generated by these results and the process is repeated. The AQHI was designed using such an approach.

Similarly, in my original research I am comparing the utility of models using the AQHI to multipollutant models in their ability to quantify the association between air pollution and acute

HSU amongst individuals with COPD. The third principal judges the strength of the evidence, and requires some measure to determine which of my models best approximates the data. In section 4.6.6, I compared the AIC values of the AQHI models to that of the multipollutant models. The resulting AIC values are similar, suggesting that models using the AQHI and multipollutant models are equivalent. Since the purpose of my research is to quantify the health risks associated with exposure to the overall mix of air pollution, I chose to use the AQHI as my primary exposure measure.

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6.4 Use of the AQHI in the Population

The knowledge generated by my original research regarding seasonal and the combined effects

of air pollution with temperature must be properly translated in order to effectively reduce

exposure to air pollution and prevent acute HSU amongst the at-risk population. There are

however, several obstacles that must be overcome.

6.4.1 AQHI Awareness and Adoption

First is the issue of AQHI awareness and adoption. As was observed in Chapter 3 of this

dissertation, the majority of Canadians with COPD rely on their senses to assess daily air quality,

rather than using forecasting tools such as the AQHI. A 2016 study by Radii et al.(Radisic,

Newbold et al. 2016) found similar results in a convenience sample of 707 (25% had a pre-

existing respiratory condition) individuals 18 years and older living in Hamilton, Ontario,

Canada. Using the combination of a quantitative cross-sectional survey and qualitative

interviews, their results indicated that only ~35% of respondents understood the meaning of a

“high” AQHI level, and where to check for daily values. Only 21% of the at-risk population

(aged over 65 years and those with pre-existing cardiovascular, or respiratory conditions) had

fully adopted the AQHI (had heard of the AQHI, knew where to find daily values, and followed

its health messages). Significant predictors of AQHI adoption included being female, being 45-

55 years of age, knowledge/understanding of the AQHI, and where to check for daily values.

Participants indicated that they did not need to check the AQHI because they are able to assess

air quality using “self-smarts” or by “the way air looks”.

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6.4.2 Interpretation of the AQHI

Second, once the AQHI is adopted, it must be properly interpreted. Results of my study in

Chapter 3 observed that only one third of survey respondents with respiratory disease who were

aware of local forecasts, took actions to reduce exposure to air pollution. The protective actions

reported included staying indoors, keeping the windows shut, and moving to rural areas. As

effective as these actions may be at reducing exposure to air pollution, they are also impractical.

This may indicate issues related to AQHI health messaging. The AQHI gives very general advice

regarding when to modify behaviour. For instance, an AQHI of 7 is considered high risk and the

health messaging for the general population states "Consider reducing or rescheduling strenuous

activities outdoors if you experience symptoms such as coughing and throat irritation”(Table 1-

1). However, the index does not provide any specific suggestions, and interpretation of the terms

“rescheduling” and “strenuous” is left to the user. Staying indoors, or moving to rural areas

would certainly satisfy this recommendation, however the perceived cost of these behaviours

would likely outweigh the perceived benefits, and users may be less likely to consider any

changes to their daily routine.

6.4.3 Active Dissemination of Real-Time AQHI data

An understanding of the utility of the AQHI and an ability to effectively use it is key to reducing

air pollution exposure. However, the real-time AQHI data must also be accessible and readily

available. Although the results of my research suggest risk of COPD hospitalization to air

pollution exposure is higher in warmer weather, there nonetheless exists a statistically significant

increased risk of non-accidental and CVD acute HSU at the coldest temperatures. There is

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evidence that suggests a reliance on media advisories as cues for when to modify behavior to

avoid exposure to air pollution.(Radisic, Newbold et al. 2016) However, air quality information

in Ontario must be actively sought during colder months, compared to the warmer season when

air pollution is typically higher, and air quality levels and advisories are disseminated regularly

by popular media outlets.

Strategies to overcome these obstacles should be devised and tested by multi-disciplinary

collaborative groups that include patients, as well as representatives from governmental and non-

governmental organizations such as Environment and Climate Change Canada, and Health

Canada, the Canadian Lung Association, the Canadian Respiratory Research Network (CRRN)

and other patient groups.

6.5 Taking Action

The Health Belief Model (HBM) is one of the original and most extensively used theories used

to explain health behavior.(Glanz, Rimer et al. 2008) The theory was developed in the

1950s(Hochbaum 1958) and identifies 6 constructs that explain why people will choose to

engage in behaviours to prevent negative health effects. These include cues to action, self-

efficacy, as well as perceived susceptibility, severity, benefits and barriers. Put simply, a person

will engage in a preventative action if they believe they are susceptible to an exposure; that

following some course of action would be beneficial, and; that the benefits outweigh the costs.

The reported perceived benefits of adopting the AQHI in the study by Radisic et al. included

protection of health for self and those they care about, while perceived barriers included self-

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efficacy (e.g., are unsure how to use the index); lack of awareness about the AQHI; where to

check daily values, and; concern about the amount of time necessary to check and follow the

health messaging.

It is projected that air pollution will continue to pose a serious risk to human health for the

foreseeable future.(OECD 2012) In order to inform future strategies aimed at reducing air

pollution exposure and improving health outcomes, I propose a multi-pronged approach

involving education, behavioral change from individuals, as well as emission reduction

strategies.

6.5.1 Education

Education of both patients and health care providers is crucial to overcoming the barriers to

AQHI adoption listed in section 6.4, and reducing exposure to air pollution. In February of 2015,

the Ontario Ministry of Health and Long Term Care put forth its Patient’s First Action Plan for

Health Care, which includes providing education, information, and transparency to patients,

enabling them to make informed decisions about their health.(Ontario Ministry of Health and

Long Term Care 2015) However, only ~1% of Canada’s COPD population have access to

pulmonary rehabilitation programs,(Brooks, Sottana et al. 2007) and there is evidence that few

patients with COPD ever see a lung health educator, or receive an action plan.(Hernandez, Balter

et al. 2009)

Health care professionals are key players in patient education. Patients pay closer attention to air

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quality alerts and make better decisions to reduce risk when education is provided by a health

care professional.(Wen, Balluz et al. 2009) There may however, also be room to improve the

knowledge of these professionals regarding the health effects of air pollution. A 2014 American

Thoracic Society survey showed a clear consensus amongst its membership that climate change

is occurring, and is having a direct impact on patient health. Roughly 60% of respondents

believed physicians should be involved in advocacy pertaining to the health effects of climate

change, including patient education. However, only 7% reported being very knowledgeable and

31% moderately knowledgeable about the association between climate change and health.

6.5.2 Government Strategies

While associations are typically small, the substantial number of international studies observing

the negative effects of air pollution on health outcomes at levels below recommended guidelines,

as well as emerging evidence of plausible biological mechanisms for these effects, has made a

strong case for health authorities to establish air pollution abatement strategies.(Anderson,

Atkinson et al. 2005) All Canadian levels of government, along with non-governmental

organizations, industries, and individuals are taking action to reduce emissions of harmful

pollutants. The challenge is to be able to balance environmental protection goals with the need

for transport, energy, and consumer goods.(Environment and Climate Change Canada 2016)

Implementation of Ontario’s Drive Clean program in 1999 reduced NOx emissions by requiring

stricter standards for motor vehicles and federal restrictions on sulphur content in transportation

fuel.(Ministry of the Environment and Climate Change 2015) In 2003, Ontario committed to the

phasing-out of coal-fired electricity. At that time, one quarter of Ontario’s energy was generated

194 by coal power plants.(Ontario Ministry of Energy 2015) Legislation enacted by both the provincial and federal levels of government in 2007,(Government of Ontario 2007)

2012,(Environment and Climate Change Canada) and 2013(Legislative Assembly of Ontario

2013) On April 8, 2014, the last coal fire plant in Ontario was shut down, making Ontario the first jurisdiction in North America to cease the production of coal-fired energy to achieve public health and environmental goals. (Ontario Ministry of Energy 2015) The phasing out of coal-fired generating stations further reduced ambient NOx, SOx, other greenhouse gases, and airborne particulate matter.(Ministry of the Environment and Climate Change 2015) Coal has been replaced with a combination of natural gas, nuclear, and renewable energy sources (hydro- electric, wind, and solar power).(Environment and Climate Change Canada 2013) According to the Ontario ministry of Energy, the reduction in emissions attributable to this action was equivalent to taking 7 million cars off the road, and was the single largest greenhouse gas emissions reduction initiative in North America.(Ontario Ministry of Energy 2015)

Anti-smoking legislation has also reduced tobacco use and health risks caused by environmental smoke exposure. The city of Toronto, Ontario for instance, implemented its legislation in several phases.(Naiman, Glazier et al. 2010) In 1999, the legislation required all public spaces and workplaces to be smoke-free. In 2001, the second phase banned smoking in all restaurants, dinner theatres and bowling centres, with the exception of designated smoking rooms within these institutions. In 2004 the legislation required all bars, bingo halls, billiard halls, racetracks and casinos to be smoke free, except for their designated smoking rooms.(Naiman, Glazier et al.

2010) The Smoke-Free Ontario act(Environment and Climate Change Canada 2013) has now made it illegal to smoke: in any childcare facilities; on and around children’s play areas and public sports fields and surfaces; on all covered, or uncovered bar and restaurant patios; in motor

195 vehicles with anyone inside less than 16 years of age; within 9 meters of any entrance or exit of public, or private schools, hospitals or psychiatric facilities; in any residential care facility, and; common areas of hotels or multi-unit residences. It is also illegal to sell tobacco on any university or college campuses.

A study by Naiman et al. in 2010(Naiman, Glazier et al. 2010) examined the rates of hospital admission attributable to cardiovascular and respiratory conditions (including COPD) 3 years before (1996) and 2 years (2006) after the implementation of the initial stages of the smoking ban in the city of Toronto. Results were also compared to 2 other control municipalities in

Ontario that did not have smoking bans during the study period, and 3 control conditions that were considered to be unrelated to second-hand smoke exposure (acute cholecystitis, bowel obstruction and appendicitis). There were no significant reductions in hospital admissions for any of the control conditions in any region during the study period. Phase 1 of the legislation

(ban on smoking in public places and workspaces) resulted in no decline in annual hospitalization rates for COPD (0.433; 95% CI -0.33, 1.19). However, the largest statistically significant reduction was observed when comparing hospitalization for COPD rates after phase 2 of the implementation (ban on smoking in restaurants) to rates after phase 1 (-1.040; 95% CI -

1.81, -0.27; p = 0.008). A further reduction in rates was observed after the implementation of phase 3 (smoking in bars), compared to phase 2 (-0.748; 95% CI -1.56, 0.07). However, this last reduction was not statistically significant (p = 0.070). Significant reduction in hospitalization rates were also observed for AMI, angina, ischemic stroke, and asthma.

Provincial legislation has taken steps to improve overall air quality. While population studies have shown a reduction in hospital admissions following the introduction of anti-smoking

196

legislation, further investigation is required to determine if existing and future emission

reduction initiatives improve health outcomes.

6.6 Strengths

Taken together, the studies included in this dissertation have many strengths that provide

valuable insights into the association between air pollution and acute HSU amongst individuals

with COPD. Studies have evaluated the association between the AQHI to and other morbidity

outcomes.(To, Shen et al. 2013, Cakmak, Kauri et al. 2014, Chen, Villeneuve et al. 2014,

Szyszkowicz and Kousha 2014, Kousha and Valacchi 2015, To, Feldman et al. 2015, To, Zhu et

al. 2016) However, when interpreting results of these studies it is important to consider the

potential impact of the adoption and use of the AQHI within the population. A highly informed

population may be more likely to engage in self-correcting behaviours that reduce exposure on

high pollution days. This in turn would make it more difficult to detect associations between

short-term air pollution exposure and acute HSU. My study reported in Chapter 3 provides novel

insights into the perceived knowledge and behaviours of individuals with COPD towards air

pollution. Given the results that suggest knowledge of air pollution assessment and exposure

reduction strategies is lacking amongst Canadians including those with COPD, it is unlikely that

the results of my subsequent studies reported in Chapters 5 and 6 are affected by these kinds of

self-correcting behaviours. To the best of my knowledge, this is the first study to examine the

knowledge and attitudes of Canadians across the country with regard to air pollution health

effects and strategies to reduce exposure using a national sample of those with and without

chronic disease.

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The study population used in the studies presented in Chapters 4 and 5 was drawn from 25 census divisions which at baseline represented approximately 86% (n = 10,528,185) of the population of Ontario and 34% of the total Canadian population.(Statistics Canada 2016) While other studies examine outcomes amongst an general population, my studies area among very few that specifically examine these outcomes amongst a separate and susceptible risk population.

Furthermore, individuals with COPD were identified using a validated algorithm and comprehensive administrative datasets over a 10 year period. While COPD has been shown to be underdiagnosed in Canada(Evans, Chen et al. 2014), use of the administrative datasets from a large single-payer universal health care program ensures that all acute HSU events occurring in the province of Ontario between 2003 and 2013 are eligible for inclusion in analysis. The use of individual level data of such an extensive and diverse sample allowed the identification of even more vulnerable subpopulations and simultaneously testing seasonal effects. Exposure was assigned to individuals based on residential postal code at the time of each acute HSU event. In this way, I was able to account for changes in area of residence over time. Use of a case- crossover approach controlled for constant, or slow-varying personal characteristics for which I would otherwise be unable to account,(Maclure 1991) and the time-stratified design also controlled for temporal-trends by design.

While other studies have been conducted in geographic areas with limited temperature range, the variable climate experienced in Ontario allows us to examine the association between air quality and hospitalization across a wide range of temperatures. The increased association between the

AQHI and acute HSU amongst individuals with COPD seen in Chapter 5 supports our previous work(To, Feldman et al. 2015). The results suggesting that individuals with a history of

198

hospitalization for lower respiratory tract infections, or AMI may be more vulnerable, and the

potential existence of a seasonal effect and are novel insights.

The large study population also presented the unique opportunity to examine the specific role of

temperature rather than season in the association between the AQHI and hospitalization.

Understanding how the health risks of air pollution exposure vary across temperatures rather

than simply across seasons will become increasingly important as the global climate continues to

change and unseasonal variations of temperature become more common. Results reported in

Chapter 6 suggesting the effect of the AQHI on COPD hospitalization is modified by ambient

temperature adds support to the previously observed seasonal effect. The “by °C” analysis also

provided a novel visualization of this modifying effect and suggested an increase in risk beyond

7°C. Further research is required to verify these results and further elucidate the potential

combined effects of ambient air pollution and temperature on acute HSU.

6.7 Limitations

There are also several limitations to my studies that should be considered when interpreting the

results. The low response rate of the original questionnaire used in my first study challenges the

generalizability of my results. I therefore considered my study sample one of convenience and

recognize these results should be interpreted with caution. Nevertheless, as survey respondents

are likely individuals who are more engaged in self-management strategies, any non-response

bias present would have potentially generated more conservative results showing a higher level

of overall knowledge. Furthermore, similar results were recently published in a study examining

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AQHI adoption and use in a convenience sample of individuals living in Hamilton,

Ontario.(Radisic, Newbold et al. 2016)

Self-report of COPD diagnosis in study 1 may have led to misclassification bias. However self- report of COPD diagnosis has been shown to be highly specific. Therefore, any misclassification in this study would place persons with COPD in the reference population, attenuating the statistical differences between the groups and leading to more conservative results. The survey definition of COPD included emphysema, chronic bronchitis, and pneumonia. My COPD population may therefore include a fraction of respondents who had suffered from acute pneumonia, but were otherwise free of chronic respiratory disease. Questions regarding health problems and actions taken to reduce exposure are subject to potential recall-bias. These questions also included other members of the household that were affected, and were not restricted to the individual respondent. Finally, the survey collected only limited demographic information, and did not ask questions regarding socioeconomic status or access to health services that may have assisted in further explaining the differences in knowledge and behaviour between my COPD and the reference populations.

While the study population in studies 2 and 3 is largely representative of the Ontario population, inclusion in the study was restricted to those living in areas for which AQHI values are recorded, and excludes much of rural northern Ontario. Use of administrative data may be subject to inaccuracies in coding, leading to some degree of misclassification of disease status and outcomes.(Peabody, Luck et al. 2004) Identification of individuals with COPD is also less accurate than the use of objective measures of lung function including FEV1 and

FEV1/FVC.(Health Effects Institute. Panel on the Health Effects of Traffic-Related Air Pollution

200

2010) However, as this misclassification is likely non-differential, its presence would result in more conservative effect estimates. In the absence of a clinical disease registry of all individuals in Ontario with COPD, use of administrative data provides an unbiased and practical way to identify individuals with COPD, as well as measure acute HSU, and collect information on patient characteristic. This is especially true a large universal, single-payer health care system like that of Ontario, where all healthcare claims are compiled into a series of easily linkable databases.(Gershon, Wang et al. 2009)

Exposure data was reviewed and made publicly available by the Ontario Ministry of the

Environment and Climate Change and Environment Canada. However, there is no other way verify the accuracy of all measures. Throughout the study period of studies 2 and 3, AQHI levels in Ontario remained relatively low and consistent. Despite this lack of variation, my studies were able to detect an association between the AQHI and acute HSU indicating that even at these low levels, individuals with COPD have an increased risk of morbidity due to air pollution exposure.

Misclassification error may have resulted from the assignment of exposure based on the daily pollution levels from fixed exposure monitoring sites averaged across relatively large geographic areas. Other methods of exposure assignment have been suggested including inverse distance weighting and kriging. However, depending on the primary air pollutant being modelled, these more complex methods may not be any more accurate.(Lee and Shaddick 2010) Nonetheless, any misclassification that does exist is likely non-differential, leading to more conservative effect sizes. As my analyses was focused on the AQHI, I did not consider other pollutants such as carbon monoxide and sulfur dioxide which may contribute to acute HSU. However, as the objective of my dissertation was to quantify the association between overall ambient air pollution

201 and acute HSU, and not to determine which pollutant is most responsible, the AQHI is best suited for this analysis as it represents the overall mix of ambient air pollution.

Differences in composition of air pollutants across seasons and temperatures may have also contributed to my observation that the AQHI is associated with COPD hospitalization during the warm season, and with CVD hospitalization primarily during the cold season. Additional work is necessary to better understand these relationships and the extent to which temperature affects the association between air pollution and health.

Although the case-crossover analysis has been shown to be mathematically equivalent to other approaches,(Lu and Zeger 2007) I was unable to determine risk at individual degrees of temperature using this methodology. Future studies may be able to accomplish this and better identify potential temperature thresholds beyond which risk of hospitalization significantly increases. As each individual included in a case-crossover analysis acts as their own control, personal time-invariant characteristics are controlled for by design. While this also accounts for smoking status, it would have been interesting to examine whether current smoking modifies the relationship between air pollution and acute HSU amongst persons with COPD.

Finally, my studies may suffer from the multiple comparison problem. This problem states the more hypotheses are tested, the more likely it is that significance may be observed simply by chance. Sufficient evidence already exists in the literature to support my study results and suggest my findings are not simply due to chance alone. However, further studies should be conducted to confirm the observed seasonal effects and interaction between air pollution and temperature.

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6.8 Conclusions

In conclusion, the implications of my original research suggest a multi-pronged approach to

reducing the negative health effects of air pollution amongst individuals living with COPD. This

approach should involve education, behavioral change, and emission reduction strategies.

The majority of individuals with COPD in Ontario believe that air pollution negatively affects

health. However, nearly three-quarters of this population also believed they could assess air

pollution using their senses. Only a small percentage were aware of air quality forecasting tools

including the AQHI, and used it on a regular basis. General knowledge with regards to air

pollution health effects and assessment is lacking. Moreover, while reported strategies

implemented to reduce exposure are effective, they are impractical. Education is a critical

component in limiting exposure to air pollution and improving respiratory health. Health care

professionals should be directly involved in education and the development of action plans to

ensure patients perceive these strategies as being valuable, and in turn enable patients to make

informed changes, leading to meaningful improvements in their health.

I have also shown the AQHI to be a useful measure to quantify the association between air

pollution exposure and acute HSU amongst individuals with COPD. Results suggest models

using the AQHI are equivalent to multipollutant models composed of individual pollutants.

Therefore, the decision of which exposure measure to use should be based on the objectives of

the purpose of the research question. My research presented in this dissertation was intended to

produce knowledge to be applied to inform the at-risk population. For this purpose, the single

composite value of the AQHI is easily interpretable and ideally suited to represent the overall

mix of ambient air pollution.

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Individuals with COPD were observed to have increased risk of non-accidental, COPD, and

CVD outcomes due to exposure to air pollution. Furthermore, while temperature was only shown to modify the association between air pollution and COPD hospitalization such the association was stronger at higher temperatures, nonetheless statistically significant risks for non-accidental and CVD hospitalization also exist at colder temperatures. The implications of these findings again point to the importance of improving education, especially amongst vulnerable groups such as individuals living with COPD, and even more so for those with additional comorbidities.

Awareness and adoption of the AQHI must be improved. Public health strategies should ensure real-time AQHI values are actively disseminated through popular media sources and/or mobile technologies, regardless of ambient temperature. AQHI health messaging should also go beyond communicating when to be concerned about air quality, and include suggestions on how to reduce exposure using simple and effective strategies.

Municipal, provincial, and federal levels of government should also continue to introduce initiatives such as the existing Drive Clean program and anti-smoking legislation to further limit emissions and exposure to harmful air pollutants. Despite efforts however, exposure to ambient air pollution is likely to continue and pose serious health risks to individuals for the foreseeable future.(Canadian Medical Association 2008, OECD 2012) Understanding when risk is at its highest and which subpopulations are the most susceptible to the effects of air pollution is of utmost importance in the battle to reduce exposure and prevent negative health outcomes.

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Chapter 7:

Future Directions

Research and discovery are central to the scientific goal of understanding the world around us and explaining my observations. However, without the application of what we have learned, scientific research is simply an academic exercise. The knowledge generated by my original research has very practical applications that may be able to make a tangible difference in the way individuals with COPD perceive health risks associated with air pollution and approach the daily challenge of making effective choices to minimize exposures while maximizing quality of life.

My research has also created the opportunity to examine additional questions which remain unanswered.

Questions remain surrounding the rationale behind the high health effects of air pollution at warmer temperatures. Chapter 5.7 describes potential biological and chemical mechanisms that may explain these observations. However, it may simply be that health risks are higher at warmer temperatures as a result of increased time spent outdoors, or indoors with open windows.

The seasonal shift in composition due to imported transboundary air pollution may further increase the exposure risk. Studies have shown that Canadians spend more time outdoors in the summer compared to the winter months,(Leech, Nelson et al. 2002) however this was not specific to individuals with COPD. My first original research study described in Chapter 3 asked respondents with COPD about the amount of time they spend outdoors on a typical weekday, however this is restricted to the summer months. Future studies should consider seasonal

205 differences in time spent outdoors when assigning exposure to further elucidate the combined effects of air pollution and temperature are influenced primarily by biology, or behaviour.

There also remains some question as to whether individuals with COPD who are current smokers are more susceptible to the effects of air pollution. Use of the case-crossover methodology controls for smoking by design. Therefore, the resulting estimates of my analyses indicate the increased risk of acute HSU due to air pollution, independent of smoking status. However, in the absence of individual level smoke exposure data, I was unable to examine any potential effect modification by smoking. Smoking has been identified as the primary risk factor for the onset of

COPD(Kohansal, Martinez-Camblor et al. 2009) and is a key trigger AECOPD. (Global

Initiative for Chronic Obstructive Lung Disease 2017) In spite of this, many individuals diagnosed with COPD continue to smoke. According to the Public Health Agency of Canada’s

2011 Survey on Living with Chronic Diseases in Canada (SLCDC), 57% of Canadians with

COPD report having smoked since their diagnosis, and 36% reported being current smokers.

Smoking status data from national surveys such as the CCHS could be linked to my existing dataset using ICES unique identifiers. It would then be possible to identify and stratify my study population according to current-, former-, and non-smoker status and repeat the analyses from study 2. If results of this potential study indicate current smokers are much more susceptible to the negative health effects of air pollution, this knowledge could serve as even more incentive to actively monitor air quality, and engage in smoking cessation programs.

In individuals with COPD, smoking cessation is the intervention most capable of reducing negative health outcomes.(Global Initiative for Chronic Obstructive Lung Disease 2017)

However, implementation and long-term maintenance of behavioral change can be quite challenging,(Mohiuddin, Mooss et al. 2007) as tobacco dependence is itself is a chronic

206 condition. A variety of pharmacologic and non-pharmacologic therapies exist, but no one solution is effective for everyone. Studies indicate that even with dedication of resources and time, long-term quit rates are only in the neighbourhood of 25%-35%.(Anthonisen, Connett et al.

1994, Tønnesen 2013) The implementation of behavior modification to reduce short-term exposure to ambient air pollution is very different. Unlike tobacco smoke, air pollution has no addictive component. Although there is always some concentration of pollution in the air, simple changes in daily routines could make substantial differences in overall exposure. Even if behavior changes are not consistent, avoidance of exposure on any single day may avoid an ED visit, hospitalization, or death.

To make the most tangible difference in the lives of people with COPD, translation of the knowledge generated by my research would involves 3 initiatives: 1) education; 2) active dissemination of real-time AQHI levels; and 3) modification of the current AQHI algorithm and messaging.

First, as mentioned in section 3.7, education of both COPD patients and their health care providers is necessary to ensure the proper knowledge of air pollution health effects, its assessment, and strategies to reduce exposure are properly communicated. A Canadian study found that short educational sessions on the use and application of the AQHI can result in significant improvements in patient knowledge.(Abelsohn and Stieb 2011) These sorts of educational sessions could be integrated into respiratory disease education plans. However, as existing pulmonary rehabilitation programs in Canada lack the capacity to serve the entire COPD population,(Brooks, Sottana et al. 2007) other educational platforms could be considered. For example, patient and provider education could be accomplished through a series of short video

207 clips available on the internet, or a mobile platform. The necessary information could easily be accessed in this way from a patient, or provider’s own home.

Second, as discussed in section 5.7, AQHI information should be disseminated throughout the entire year. To monitor real-time AQHI levels throughout the year requires individuals to actively search for the information from official sources. Disseminating this information through local media outlets including radio and television, or through wearable, or mobile technologies has been suggested by individuals with COPD, and may improve AQHI awareness and adoption.(Chen and Copes 2013, Radisic and Newbold 2016, Radisic, Newbold et al. 2016) Very recently the government of Alberta released an AQHI Canada application for mobile devices which allows users to visualize real-time spatial distribution of the AQHI in a map screen, access local AQHI values based on GPS location, and receive push notifications when the AQHI reaches a certain level. A study examining the use and utility of this application amongst the

COPD population has yet to be published. Focus groups to obtain feedback regarding the application for target populations would also provide a better understanding of how potential users want to receive AQHI information, and interact with the interface.

Third, while I have demonstrated that the current AQHI can be used to communicate risk of morbidity amongst individuals with COPD, there are some modifications to its algorithm and messaging that may improve its utility amongst this vulnerable population. The differences in the

AQHI’s current health messaging has no for the “at risk” compared to the general population, is not based on empirical evidence. Future iterations of the AQHI should take into account the potential combined effects of air pollution and temperature in order to more accurately communicate health risk, especially to those who are at greater risk such as individuals with

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COPD. The AQHI was originally created using non-accidental mortality data. However, creating an AQHI based on morbidity may be more useful as prevention morbidity should also correspond to a decrease in mortality. There is also a lack of evidence to support the current

AQHI cut-offs for “low”, “moderate”, “high” and “very high” health risk for both the general population and at-risk groups. Additional research in this area would help improve the accuracy and utility of the AQHI health messaging. In addition, the current AQHI health messaging does not provide sufficient detail, or examples of effective behavior modifications. Interpretation left to the individual user may result in impractical changes as observed in study 1 of my original research. If instead the health messaging encouraged users move outdoor activities to indoor facilities for example, or specify the delay activities by even a few hours until the air quality improves, the AQHI could advise how to modify activities instead of simply when. Providing examples of these simple changes could assist in promoting behavior modifications result in meaningful reductions in exposure while maintaining quality of life.

In order to effectively apply the knowledge generated by my original research involves involvement from key stakeholders from the outset. New communication strategies should be designed by multi-disciplinary groups, and it is imperative that these new strategies be tested and retested within the target population to maximize the potential of adoption. Generating knowledge is only the first step. The health of the population can only be improved when that knowledge is put into action.

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Appendix:

Supplemental Tables

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Table S.3-1: Study population characteristics % (95% Confidence Interval) Heart Disease Diabetes without respiratory without respiratory disease (n = 75) disease (n = 99) Age Group 35 - 44 1.4 (0, 4.2)‡ 7.3 (2.1, 12.5)‡ 45 - 54 14.1 (6, 22.2) 17.7 (10.1, 25.4) 55 - 64 23.9 (14, 33.9) 33.3 (23.9, 42.8) 65 - 74 23.9 (14, 33.9) 26 (17.2, 34.8) 75 - 84 36.6 (25.4, 47.8) 15.6 (8.3, 22.9) Sex Female 50.7 (39.3, 62) 57.6 (47.8, 67.3) Male 49.3 (38, 60.7) 42.4 (32.7, 52.2) What is the highest level of education you have completed? Without high school diploma 11.1 (3.8, 18.4)‡ 14.4 (7.4, 21.4)‡ High School and some post-secondary 44.4 (32.9, 55.9)‡ 35.1 (25.5, 44.6)‡ Post-secondary certification (College/trade certificate, or University degree) 44.4 (32.9, 55.9)‡ 50.5 (40.5, 60.5)‡ How many hours would you likely spend outdoors on a typical weekday during the summer months Less than one hour 10.7 (3.7, 17.7)* 11.1 (4.9, 17.3)* 1 to 3 hours 42.7 (31.5, 53.9) 48.5 (38.6, 58.3) 3 to 6 hours 33.3 (22.6, 44) 28.3 (19.4, 37.2) More than 6 hours 9.3 (2.7, 15.9) 12.1 (5.7, 18.6) DEPENDS 2.7 (0, 6.3) 0 (0, 0) DK/NA 1.3 (0, 3.9) 0 (0, 0) Compared to other people your age, would you say your health is generally: Excellent/Very good 9.6 (1.6, 17.7)‡ 10.7 (2.6, 18.8)‡ Good 51.9 (38.3, 65.5) 58.9 (46, 71.9) Only fair/Poor 38.5 (25.2, 51.7) 30.4 (18.3, 42.4) *p<0.05 compared to the reference population; †p<0.01 compared to the reference population; ‡p<0.0001 compared to the reference population

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Table S.3-2: Reported knowledge of air pollution, method of air quality assessment, health outcomes, and behaviour modifications by reported chronic disease status. % (95% Confidence Interval) Heart Disease without Diabetes without respiratory disease respiratory disease (n = 75) (n = 99) Knowledge of the health effects of air pollution

In your view, to what extent does air pollution affect the health of Canadians? At Least Somewhat 90.7 (84.1, 97.3) 88.9 (82.7, 95.1) Not very much 4 (0, 8.4) 4 (0.2, 7.9) Not at all 2.7 (0, 6.3) 0 (0, 0) DK/NA 2.7 (0, 6.3) 7.1 (2, 12.1)

Do you think air pollution contributes to respiratory illnesses, such as bronchiolitis? Definitely contributes 70.4 (59.8, 81.1) 67.4 (57.8, 77) Likely contributes 26.8 (16.4, 37.1) 27.2 (18.1, 36.3) Likely does not contribute 2.8 (0, 6.7) 1.1 (0, 3.2) Definitely does not contribute 0 (0, 0) 1.1 (0, 3.2) Depends (e.g. on type of individual) 0 (0, 0) 0 (0, 0) DK/NA 0 (0, 0) 3.3 (0, 6.9)

Do you think that air pollution affects people's health at any level? Even at low levels 47.9 (36.2, 59.5) 43.5 (33.3, 53.6) Only when it reaches a certain level 46.5 (34.9, 58.1) 51.1 (40.9, 61.3) Depends (e.g. on type of person) 4.2 (0, 8.9) 2.2 (0, 5.2) DK/NA 1.4 (0, 4.2) 3.3 (0, 6.9)

Do you think the health effects of air pollution tend to be more immediate ones that people notice right away, or more longer-term problems that won't be evident for some time? More Immediate 15.5 (7.1, 23.9) 14.1 (7, 21.3) More Long Term 76.1 (66.1, 86) 81.5 (73.6, 89.5) Both Equally 7 (1.1, 13) 4.3 (0.2, 8.5) DK/NA 1.4 (0, 4.2) 0 (0, 0)

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Do you think there are any immediate health effects that people in Canada might experience as a result of air pollution?§ Yes 69.1 (56.8, 81.3) 80 (70.9, 89.1) No 23.6 (12.4, 34.9) 18.7 (9.8, 27.5) DK/NA 7.3 (0.4, 14.2) 1.3 (0, 3.9)

Are you familiar with air quality information available in your area? At Least Somewhat Familiar 69.3 (58.9, 79.8)* 44.4 (34.6, 54.3) Not very familiar 9.3 (2.7, 15.9) 22.2 (14, 30.4) Not at all familiar 20 (10.9, 29.1) 30.3 (21.2, 39.4) DK/NA 1.3 (0, 3.9) 3 (0, 6.4)

Are you aware of / heard of the Air Quality Health Index (AQHI) Aware of the AQHI 27.1 (14.5, 39.7)* 29.8 (16.7, 42.9)

Individual assessment of air quality

How do you know when the air quality in your area is poor?|| Using a combination of senses and forecasts 18 (7.3, 28.7) 17.8 (9, 26.6) Only using senses 60 (46.4, 73.6) 56.2 (44.8, 67.6) Only by forecast 22 (10.5, 33.5) 26 (15.9, 36.1)

Without the benefit of a local air quality or weather forecast, would you be able to tell on your own that the air quality is poor as soon as you step out of doors? Yes 58.3 (38.5, 78.1) 60.5 (45.8, 75.1)

How often do you personally use this type of air quality information? Frequently 22 (11.4, 32.6) 19.7 (10.1, 29.3) Occasionally 57.6 (45, 70.3) 48.5 (36.4, 60.6) Never 18.6 (8.7, 28.6) 28.8 (17.8, 39.7) DK/NA 1.7 (0, 5) 3 (0, 7.2)

Health Effects of Air pollution and Behaviour modification to reduce exposure

Have you or someone else in your household experienced any type of physical or health problems over the past two years that might have been attributed to air pollution at the time?

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Yes 20 (10.9, 29.1) 13.1 (6.5, 19.8)

Have you or others in your household taken specific actions to reduce your exposure to air pollution because of the impact it has had on your health?** Yes 57.1 (30.9, 83.3) 50 (21.4, 78.6)

What, if anything, do you believe people can do to reduce their exposure to air pollution and its harmful health effects? § Stay Indoors/Keep Windows Closed 24 (14.3, 33.7) 28.3 (19.4, 37.2) Avoid High Traffic Areas/Exposure at Certain Times of Day 12 (4.6, 19.4) 12.1 (5.7, 18.6) Wear A Mask 12 (4.6, 19.4) 13.1 (6.5, 19.8) Buy/Use Air Filters 8 (1.8, 14.2) 10.1 (4.2, 16) Move to Country/Rural Area 2.7 (0, 6.3) 6.1 (1.4, 10.8) None/No Way to Limit Exposure 6.7 (1, 12.3) 7.1 (2, 12.1) Quit Smoking/Avoid Second-hand Smoke 4 (0, 8.4) 3 (0, 6.4) Avoid/Reduce Strenuous Exercise/Physical Exertion 1.3 (0, 3.9) 2 (0, 4.8) Take Medication/Oxygen 1.3 (0, 3.9) 0 (0, 0) Reschedule Strenuous Exercise Until Air Quality Improves 1.3 (0, 3.9) 2 (0, 4.8) Protect Self from The Sun 0 (0, 0) 0 (0, 0)

If Yes to having had a health event attributable to air pollution, what steps have you taken to reduce your exposure? § No, Did Nothing 62.5 (28.3, 96.7) 66.7 (28.2, 100) Reduced Time Spent Outdoors 50 (14.7, 85.3) 83.3 (53, 100) Purchased/Installed/Used Air Purifier/Hepafilter/Humidifier 25 (0, 55.6) 0 (0, 0) Take Medication/Oxygen 12.5 (0, 35.8) 0 (0, 0) Get Out of The City/Away from Polluted Area 12.5 (0, 35.8) 0 (0, 0) Close/Open Windows at Certain Times 0 (0, 0) 0 (0, 0) Use Air Conditioner 25 (0, 55.6)* 33.3 (0, 71.8)

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Wear A Mask 0 (0, 0) 0 (0, 0) Cut Down on Strenuous Activity/Aerobic Exercise 0 (0, 0) 0 (0, 0) Saw Doctor/Health Professional 0 (0, 0) 0 (0, 0) Sought Out More Information on Advisory/Air Quality 0 (0, 0) 0 (0, 0) Avoid Second-Hand Smoke 0 (0, 0) 0 (0, 0) Protect Self from The Sun 0 (0, 0) 0 (0, 0) Drive Car Less/Take Transit/Do Not Idle in Car/Bike 0 (0, 0) 0 (0, 0) *p<0.05; †p<0.01; ‡p<0.0001; §If only believe air pollution effects are only long term, or don't know; ||Unprompted, multiple responses; ¶If familiar with air quality information available in their area; **If reported ‘yes’ to “Have you, or someone else in your household experienced any type of physical or health problems over the past two years that might have been attributed to air pollution at the time?”

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Table S.3-3: Associations between health outcomes, behaviour modifications and reported chronic disease status. Odds ratio (95% Confidence Interval) Heart Disease, or Diabetes without respiratory disease (n = 156)

Outcome Unadjusted Adjusted*

Health problem attributed to air pollution in the last 2 years: Yes, self, or someone else in the home. 0.8 (0.5, 1.3) 0.6 (0.3, 1.2)

Have you or others in your household taken specific actions to reduce your exposure to air pollution because of the impact it has had on your health?* 0.8 (0.3, 1.9) 0.9 (0.3, 3.4)

At any point in the past year have you, or another member of your household, taken any steps or made changes in your daily routine as a result of air quality forecast information you noticed or found? 0.1 (0.0, 1.1) - *Adjusted for age, sex, education and level of self-perceived health

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Table S.3-4: Association between method of air quality assessment and health outcomes amongst the Heart Disease, or Diabetes without respiratory disease group. Odds ratio (95% Confidence Interval) Heart Disease, or Diabetes without respiratory disease (n = 156)

Method of Air Quality Analysis Unadjusted Adjusted*

Exclusive use of senses† 0.8 (0.2, 3.9) 0.7 (0.1, 4.2)

Use of forecasts and senses† 0.3 (0.0, 3.9) 0.3 (0.0, 5.1) *Adjusted for age, sex, and education; † Compared to exclusive use forecasts

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NO2* O3** PM2.5† NO2* O3** PM2.5† OR 95% CI OR 95% CI OR 95% CI OR 95% CI OR 95% CI OR 95% CI Non-Accidental Lag0 1.000 (0.996, 1.004) 1.002 (0.997, 1.007) 1.001 (0.996, 1.006) 1.000 (0.996, 1.004) 0.999 (0.996, 1.002) 1.003 (1.000, 1.007) Lag1 1.000 (0.994, 1.005) 1.001 (0.997, 1.005) 1.004 (0.999, 1.009) 0.993 (0.991, 0.996) 1.001 (0.998, 1.003) 1.008 (1.005, 1.011) Lag2 1.001 (0.998, 1.005) 1.001 (0.998, 1.005) 1.000 (0.996, 1.005) 0.995 (0.992, 0.998) 1.001 (0.998, 1.003) 1.005 (1.002, 1.008) Lag3 1.002 (0.998, 1.006) 1.001 (0.997, 1.005) 0.999 (0.995, 1.003) 0.994 (0.990, 0.999) 1.003 (1.000, 1.005) 1.003 (0.999, 1.007) Lag01 1.002 (0.998, 1.006) 1.003 (0.997, 1.009) 1.003 (0.999, 1.008) 0.997 (0.995, 1.000) 1.001 (0.998, 1.004) 1.006 (1.003, 1.009) Lag02 1.002 (0.998, 1.006) 1.002 (0.994, 1.010) 1.003 (0.998, 1.007) 0.996 (0.993, 0.999) 1.003 (0.998, 1.007) 1.006 (1.003, 1.009) Lag03 1.003 (0.999, 1.007) 1.001 (0.992, 1.010) 1.002 (0.997, 1.008) 0.996 (0.993, 0.999) 1.003 (1.000, 1.006) 1.007 (1.004, 1.010) Chronic Obstructive Pulmonary Disease Lag0 0.991 (0.983, 1.000) 1.000 (0.991, 1.010) 1.003 (0.991, 1.014) 0.991 (0.983, 1.000) 1.006 (0.997, 1.015) 1.005 (0.995, 1.015) Lag1 0.992 (0.982, 1.003) 1.013 (1.004, 1.022) 1.008 (0.997, 1.018) 0.983 (0.975, 0.990) 1.010 (0.999, 1.021) 1.017 (1.007, 1.027) Lag2 0.993 (0.986, 1.001) 1.008 (0.998, 1.019) 1.011 (1.000, 1.021) 0.986 (0.976, 0.997) 1.003 (0.995, 1.011) 1.016 (1.006, 1.027) Lag3 1.000 (0.992, 1.008) 1.008 (1.000, 1.016) 0.997 (0.987, 1.007) 0.995 (0.985, 1.005) 1.001 (0.993, 1.008) 0.999 (0.988, 1.011) Lag01 0.992 (0.981, 1.002) 1.010 (1.000, 1.020) 1.005 (0.991, 1.019) 0.984 (0.975, 0.992) 1.012 (1.001, 1.024) 1.016 (1.004, 1.029) Lag02 0.991 (0.981, 1.000) 1.012 (0.996, 1.027) 1.007 (0.997, 1.016) 0.986 (0.976, 0.996) 1.013 (1.004, 1.022) 1.009 (1.000, 1.018) Lag03 0.991 (0.982, 1.000) 1.014 (0.997, 1.031) 1.001 (0.988, 1.015) 0.990 (0.980, 1.001) 1.008 (0.999, 1.017) 1.007 (0.996, 1.019) Cardiovascular Disease Lag0 1.003 (0.993, 1.012) 1.001 (0.985, 1.018) 1.011 (0.998, 1.023) 1.004 (0.993, 1.014) 1.010 (1.000, 1.020) 1.004 (0.990, 1.019) Lag1 1.003 (0.994, 1.013) 1.004 (0.991, 1.017) 1.007 (0.995, 1.018) 1.005 (0.996, 1.014) 1.000 (0.991, 1.009) 1.007 (0.994, 1.020) Lag2 1.004 (0.993, 1.015) 1.000 (0.991, 1.009) 1.000 (0.988, 1.011) 1.002 (0.992, 1.013) 0.996 (0.986, 1.006) 1.002 (0.991, 1.013) Lag3 1.004 (0.995, 1.014) 1.004 (0.995, 1.013) 0.997 (0.986, 1.008) 0.997 (0.989, 1.006) 1.008 (0.999, 1.016) 1.003 (0.993, 1.014) Lag01 1.003 (0.993, 1.013) 1.005 (0.990, 1.021) 1.011 (0.999, 1.022) 1.007 (0.998, 1.017) 1.005 (0.994, 1.015) 1.009 (0.995, 1.023) Lag02 1.007 (0.997, 1.017) 1.006 (0.994, 1.018) 1.008 (0.997, 1.018) 1.008 (0.998, 1.017) 1.000 (0.989, 1.011) 1.007 (0.997, 1.017) Lag03 1.011 (1.000, 1.021) 1.003 (0.991, 1.014) 1.007 (0.997, 1.017) 1.006 (0.996, 1.016) 1.004 (0.994, 1.015) 1.010 (1.000, 1.019)

OR: Odds Ratio; 95% CI: 95% Confidence Interval; *Adjusted for O3 and PM2.5; ** Adjusted for NO2 and PM2.5; † Adjusted for O3 and PM2.; Bolded values indicate statistical significance; All models adjusted for ambient temperature (quadratic and linear term at lag0), relative humidity (lag0), influenza events and holidays.

244

NO2* O3** PM2.5† NO2* O3** PM2.5† OR 95% CI OR 95% CI OR 95% CI OR 95% CI OR 95% CI OR 95% CI Non-Accidental Lag0 0.998 (0.989, 1.007) 1.007 (1.000, 1.014) 1.006 (0.997, 1.014) 0.992 (0.986, 0.999) 1.006 (1.002, 1.011) 1.004 (0.995, 1.012) Lag1 1.001 (0.988, 1.014) 1.002 (0.996, 1.009) 1.007 (0.998, 1.015) 0.997 (0.991, 1.004) 1.004 (0.999, 1.009) 1.004 (0.998, 1.010) Lag2 1.002 (0.996, 1.009) 1.005 (0.994, 1.015) 1.003 (0.995, 1.012) 1.004 (0.999, 1.009) 1.004 (0.999, 1.008) 1.002 (0.996, 1.008) Lag3 1.007 (0.998, 1.015) 1.003 (0.996, 1.010) 1.004 (0.995, 1.012) 1.004 (0.998, 1.010) 1.006 (1.000, 1.011) 1.000 (0.994, 1.006) Lag01 1.002 (0.981, 1.023) 1.004 (0.997, 1.011) 1.007 (0.999, 1.015) 1.014 (0.987, 1.041) 1.007 (1.002, 1.012) 1.002 (0.994, 1.009) Lag02 1.010 (0.986, 1.034) 1.002 (0.994, 1.011) 1.010 (1.002, 1.018) 0.995 (0.973, 1.017) 1.006 (0.999, 1.014) 1.004 (0.997, 1.012) Lag03 1.002 (0.985, 1.020) 1.001 (0.992, 1.010) 1.011 (1.002, 1.019) 1.004 (0.983, 1.025) 1.006 (0.999, 1.014) 1.006 (0.999, 1.013) Chronic Obstructive Pulmonary Disease Lag0 1.002 (0.980, 1.025) 1.004 (0.987, 1.022) 1.024 (0.994, 1.055) 1.002 (0.981, 1.023) 1.013 (0.998, 1.028) 1.008 (0.988, 1.028) Lag1 1.004 (0.987, 1.022) 1.019 (1.002, 1.035) 0.997 (0.976, 1.017) 1.013 (0.998, 1.028) 1.006 (0.991, 1.021) 1.015 (0.989, 1.042) Lag2 1.024 (0.994, 1.055) 1.018 (0.999, 1.037) 1.002 (0.981, 1.023) 1.008 (0.988, 1.028) 1.009 (0.994, 1.025) 1.014 (0.987, 1.041) Lag3 0.997 (0.976, 1.017) 1.017 (1.000, 1.034) 0.997 (0.976, 1.019) 1.015 (0.989, 1.042) 1.014 (0.997, 1.032) 0.987 (0.965, 1.010) Lag01 1.017 (1.000, 1.034) 1.011 (0.994, 1.028) 1.006 (0.985, 1.027) 1.014 (0.997, 1.032) 1.011 (0.995, 1.026) 1.012 (0.991, 1.034) Lag02 0.997 (0.976, 1.019) 1.015 (0.998, 1.033) 1.006 (0.986, 1.026) 0.987 (0.965, 1.010) 1.018 (1.002, 1.034) 1.006 (0.985, 1.026) Lag03 1.006 (0.985, 1.027) 1.022 (1.002, 1.042) 0.998 (0.976, 1.020) 1.012 (0.991, 1.034) 1.017 (1.000, 1.035) 1.002 (0.978, 1.026) Cardiovascular Disease Lag0 0.991 (0.965, 1.017) 1.009 (0.992, 1.026) 1.016 (0.993, 1.039) 1.004 (0.982, 1.026) 1.004 (0.988, 1.020) 1.013 (0.992, 1.035) Lag1 0.990 (0.967, 1.013) 1.018 (0.999, 1.038) 0.997 (0.976, 1.019) 0.993 (0.972, 1.015) 1.009 (0.993, 1.026) 0.995 (0.974, 1.017) Lag2 1.010 (0.986, 1.034) 1.002 (0.985, 1.020) 1.002 (0.981, 1.024) 0.995 (0.973, 1.017) 1.004 (0.983, 1.025) 0.998 (0.977, 1.018) Lag3 1.014 (0.990, 1.038) 1.009 (0.992, 1.026) 0.995 (0.974, 1.017) 0.993 (0.971, 1.015) 1.003 (0.987, 1.019) 1.021 (0.990, 1.053) Lag01 0.994 (0.970, 1.018) 1.017 (0.991, 1.044) 1.002 (0.982, 1.024) 0.993 (0.967, 1.020) 1.004 (0.987, 1.021) 1.006 (0.987, 1.026) Lag02 1.014 (0.989, 1.040) 1.005 (0.977, 1.034) 1.011 (0.991, 1.032) 0.999 (0.968, 1.031) 0.998 (0.974, 1.023) 1.008 (0.989, 1.028) Lag03 1.023 (0.994, 1.052) 0.997 (0.970, 1.026) 1.013 (0.993, 1.033) 1.001 (0.973, 1.029) 1.000 (0.975, 1.025) 1.013 (0.994, 1.032)

OR: Odds Ratio; 95% CI: 95% Confidence Interval; *Adjusted for O3 and PM2.5; ** Adjusted for NO2 and PM2.5; † Adjusted for O3 and PM2.; Bolded values indicate statistical significance; All models adjusted for ambient temperature (linear term at lag0), relative humidity (lag0), influenza events and holidays.

245

NO2* O3** PM2.5† NO2* O3** PM2.5† OR 95% CI OR 95% CI OR 95% CI OR 95% CI OR 95% CI OR 95% CI Non-Accidental Lag0 0.995 (0.988, 1.003) 0.997 (0.986, 1.008) 1.012 (1.001, 1.023) 0.992 (0.987, 0.997) 0.992 (0.984, 1.000) 1.024 (1.016, 1.032) Lag1 1.004 (0.997, 1.011) 0.992 (0.980, 1.003) 1.011 (1.000, 1.021) 0.987 (0.981, 0.993) 1.001 (0.993, 1.010) 1.031 (1.022, 1.040) Lag2 0.996 (0.989, 1.003) 1.004 (0.992, 1.015) 1.008 (0.998, 1.019) 0.988 (0.983, 0.994) 0.996 (0.988, 1.004) 1.012 (1.004, 1.019) Lag3 1.002 (0.995, 1.009) 1.006 (0.995, 1.018) 0.994 (0.984, 1.005) 0.992 (0.986, 0.999) 1.012 (1.004, 1.020) 1.004 (0.996, 1.013) Lag01 1.001 (0.993, 1.010) 0.998 (0.985, 1.012) 1.013 (1.004, 1.023) 0.991 (0.986, 0.996) 0.998 (0.986, 1.010) 1.028 (1.020, 1.037) Lag02 1.000 (0.989, 1.011) 1.001 (0.983, 1.020) 1.013 (1.001, 1.025) 0.989 (0.983, 0.995) 1.002 (0.991, 1.012) 1.024 (1.016, 1.032) Lag03 1.002 (0.990, 1.015) 1.014 (0.992, 1.036) 1.008 (0.997, 1.019) 0.989 (0.983, 0.995) 1.016 (1.002, 1.030) 1.023 (1.015, 1.032) Chronic Obstructive Pulmonary Disease Lag0 0.974 (0.958, 0.991) 0.992 (0.964, 1.022) 1.040 (1.017, 1.063) 0.985 (0.970, 1.000) 0.989 (0.967, 1.012) 1.039 (1.014, 1.065) Lag1 0.981 (0.966, 0.996) 1.010 (0.986, 1.035) 1.037 (1.014, 1.061) 0.965 (0.951, 0.979) 1.007 (0.984, 1.030) 1.052 (1.030, 1.074) Lag2 0.970 (0.955, 0.985) 1.024 (0.990, 1.059) 1.038 (1.010, 1.068) 0.970 (0.956, 0.984) 1.002 (0.980, 1.026) 1.025 (1.000, 1.050) Lag3 0.984 (0.968, 1.001) 1.020 (0.996, 1.045) 0.997 (0.970, 1.024) 0.982 (0.966, 0.997) 1.014 (0.991, 1.038) 1.003 (0.973, 1.034) Lag01 0.979 (0.963, 0.995) 1.007 (0.973, 1.041) 1.039 (1.018, 1.060) 0.974 (0.959, 0.989) 1.004 (0.977, 1.032) 1.046 (1.027, 1.066) Lag02 0.966 (0.950, 0.982) 1.027 (0.978, 1.079) 1.042 (1.022, 1.062) 0.971 (0.956, 0.987) 1.017 (0.987, 1.049) 1.038 (1.020, 1.057) Lag03 0.965 (0.948, 0.982) 1.061 (1.007, 1.118) 1.039 (1.014, 1.064) 0.973 (0.957, 0.989) 1.033 (1.000, 1.067) 1.033 (1.013, 1.053) Cardiovascular Disease Lag0 0.999 (0.981, 1.018) 1.001 (0.973, 1.030) 1.037 (1.009, 1.066) 0.988 (0.971, 1.005) 0.990 (0.956, 1.026) 1.034 (1.008, 1.062) Lag1 1.008 (0.990, 1.027) 1.003 (0.974, 1.033) 1.030 (1.002, 1.058) 0.990 (0.973, 1.007) 0.991 (0.964, 1.019) 1.051 (1.025, 1.078) Lag2 1.001 (0.983, 1.019) 0.998 (0.969, 1.027) 1.023 (0.995, 1.051) 1.006 (0.989, 1.024) 0.973 (0.938, 1.010) 1.008 (0.982, 1.034) Lag3 1.009 (0.991, 1.028) 1.008 (0.975, 1.043) 0.993 (0.966, 1.021) 1.004 (0.987, 1.022) 1.014 (0.987, 1.043) 1.008 (0.982, 1.035) Lag01 1.007 (0.988, 1.027) 1.002 (0.968, 1.037) 1.037 (1.012, 1.062) 0.991 (0.973, 1.010) 0.986 (0.954, 1.018) 1.043 (1.020, 1.067) Lag02 1.010 (0.990, 1.030) 0.993 (0.956, 1.031) 1.028 (1.005, 1.051) 0.990 (0.971, 1.009) 0.985 (0.950, 1.021) 1.039 (1.017, 1.061) Lag03 1.018 (0.997, 1.039) 1.010 (0.969, 1.051) 1.019 (0.997, 1.040) 0.997 (0.978, 1.017) 1.004 (0.966, 1.044) 1.037 (1.016, 1.058)

OR: Odds Ratio; 95% CI: 95% Confidence Interval; *Adjusted for O3 and PM2.5; ** Adjusted for NO2 and PM2.5; † Adjusted for O3 and PM2.; Bolded values indicate statistical significance; All models adjusted for ambient temperature (linear term at lag06), relative humidity (lag06), influenza events and holidays.

246

24 24 23 23 22 22 21 21 20 20 19 19 18 18 17 17 16 16 15 15 14 14 13 13 12 12 11 11 10 10 9 9 8 8 7 7 6 6 5 5 4 4 3 3 2 C) 2 ° 1 1 0 0 -1 -1 -2 -2 -3 -3 -4 -4 -5 -5 -6 -6 -7 -7 -8 -8 -9 -9 -10 -10 -11 -11 -12

Temperature Strata ( -12 -13 -13 -14 -14 -15 -15 -16 -16 -17 -17 -18 -18 -19 -19 -20 -20 -21 -21 -22 -22 -23 -23 -24 -24 -25 -25 -26 -26 -27 -27 -28 -28 -29 -29 0.85 0.9 0.95 1 1.05 1.1 1.15 0.85 0.9 0.95 1 1.05 1.1 1.15 Odds Ratio (95% Confidence Interval) Odds Ratio (95% Confidence Interval)

Figures S.5-3c and S.5-3d. Pooled estimates of the risk of non-accidental hospitalization amongst individuals with COPD aged < 65 years (c) and 65+ years (d) associated with each 1 unit increase in daily maximum AQHI (lag03) and increases in temperature (lag0), across Ontario census divisions. Risks are presented for each specified temperature range and adjusted for temperature (lag06), relative humidity (lag06), influenza events and statutory holidays.

247

(e) (f)

Figures S.5-3e and S.5-3f. Pooled estimates of the risk of CVD hospitalization amongst individuals with COPD aged < 65 years (e) and 65+ years (f) associated with each 1 unit increase in daily maximum AQHI (lag03) and increases in temperature (lag0), across Ontario census divisions. Risks are presented for each specified temperature range and adjusted for temperature (lag06), relative humidity (lag06), influenza events and statutory holidays.

248

(g) (h)

24 24 23 23 22 22 21 21 20 20 19 19 18 18 17 17 16 16 15 15 14 14 13 13 12 12 11 11 10 10 9 9 8 8 7 7 6 6 5 5 4 4

C) 3 3 ° 2 2 1 1 0 0 -1 -1 -2 -2 -3 -3 -4 -4 -5 -5 -6 -6 -7 -7 -8 -8 -9 -9 -10 -10 -11 -11 Temperature Strata ( -12 -12 -13 -13 -14 -14 -15 -15 -16 -16 -17 -17 -18 -18 -19 -19 -20 -20 -21 -21 -22 -22 -23 -23 -24 -24 -25 -25 -26 -26 -27 -27 -28 -28 -29 -29 0.85 0.9 0.95 1 1.05 1.1 1.15 0.85 0.9 0.95 1 1.05 1.1 1.15 Odds Ratio (95% Confidence Interval) Odds Ratio (95% Confidence Interval)

Figures S.5-3g and S.5-3h. Pooled estimates of the risk of COPD hospitalization amongst males (g) and females (h) with COPD associated with each 1 unit increase in daily maximum AQHI (lag03) and increases in temperature (lag0), across Ontario census divisions. Risks are presented for each specified temperature range and adjusted for temperature (lag06), relative humidity (lag06), influenza events and statutory holidays.

249

(i) (j)

Figures S.5-3i and S.5-3j. Pooled estimates of the risk of non-accidental hospitalization amongst males (i) and females (j) with COPD associated with each 1 unit increase in daily maximum AQHI (lag03) and increases in temperature (lag0), across Ontario census divisions. Risks are presented for each specified temperature range and adjusted for temperature (lag06), relative humidity (lag06), influenza events and statutory holidays.

250

(k) (l)

24 24 23 23 22 22 21 21 20 20 19 19 18 18 17 17 16 16 15 15 14 14 13 13 12 12 11 11 10 10 9 9 8 8 7 7 6 6 5 5 4 4 3 C) 3 ° 2 2 1 1 0 0 -1 -1 -2 -2 -3 -3 -4 -4 -5 -5 -6 -6 -7 -7 -8 -8 -9 -9 -10 -10 -11 -11 Temperature Strata ( -12 -12 -13 -13 -14 -14 -15 -15 -16 -16 -17 -17 -18 -18 -19 -19 -20 -20 -21 -21 -22 -22 -23 -23 -24 -24 -25 -25 -26 -26 -27 -27 -28 -28 -29 -29 0.85 0.9 0.95 1 1.05 1.1 1.15 0.85 0.9 0.95 1 1.05 1.1 1.15 Odds Ratio (95% Confidence Interval) Odds Ratio (95% Confidence Interval)

Figures S.5-3k and S.5-3l. Pooled estimates of the risk of CVD hospitalization amongst males (k) and females (l) with COPD associated with each 1 unit increase in daily maximum AQHI (lag03) and increases in temperature (lag0), across Ontario census divisions. Risks are presented for each specified temperature range and adjusted for temperature (lag06), relative humidity (lag06), influenza events and statutory holidays.

251

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JOURNAL OF CLINICAL INVESTIGATION. ONLINE

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