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EVALUATION OF INTERACTIVE EFFECTS

BETWEEN TEMPERATURE AND AIR POLLUTION

ON HEALTH OUTCOMES

BY

CIZAO REN

Bachelor of Medicine, Master of Medicine, Master of Science (Epidemiology)

A thesis submitted for the Degree of Doctor of Philosophy in the School

of Public Health, Queensland University of Technology

April 2007

I

SUMMARY

A large number of studies have shown that both temperature and air pollution (eg, particulate

matter and ozone) are associated with health outcomes. So far, it has received limited

attention whether air pollution and temperature interact to affect health outcomes. A few

studies have examined interactive effects between temperature and air pollution, but produced

conflicting results. This thesis aimed to examine whether air pollution (including ozone and

particulate matter) and temperature interacted to affect health outcomes in Brisbane, Australia

and 95 large US communities.

In order to examine the consistency across different cities and different countries, we used

two datasets to examine interactive effects of temperature and air pollution. One dataset was

collected in Brisbane City, Australia, during 1996-2000. The dataset included air pollution

(PM10, ozone and nitrogen dioxide), weather conditions (minimum temperature, maximum

temperature, relative humidity and rainfall) and different health outcomes. Another dataset

was collected from the 95 large US communities, which included air pollution (ozone was

used in the thesis), weather conditions (maximum temperature and dew point temperature)

and mortality (all non-external cause mortality and cardiorespiratory mortality).

Firstly, we used three parallel time-series models to examine whether maximum temperature

modified PM10 effects on cardiovascular hospital admissions (CHA), respiratory hospital

admissions (RHA), cardiovascular emergency visits (CEV), respiratory emergency visits

(REV), cardiovascular mortality (CM) and non-external cause mortality (NECM), at lags of

0-2 days in Brisbane. We used a Poisson generalized additive model (GAM) to fit a bivariate

model to explore joint response surfaces of both maximum temperature and particulate matter

II

less than 10 m in diameter (PM10) on individual health outcomes at each lag. Results show

that temperature and PM10 interacted to affect different health outcomes at various lags. Then,

we separately fitted non-stratification and stratification GAM models to quantify the

interactive effects. In the non-stratification model, we examined the interactive effects by

including a pointwise product for both temperature and the pollutant. In the stratification

model, we categorized temperature into two levels using different cut-offs and then included

an interactive term for both pollutant and temperature. Results show that maximum

temperature significantly and positively modified the associations of PM10 with RHA, CEV,

REV, CM and NECM at various lags, but not for CHA.

Then, we used the above Poisson regression models to examine whether PM10 modified the

associations of minimum temperature with CHA, RHA, CEV, REV, CM and NECM at lags

of 0-2 days. In this part, we categorized PM10 into two levels using the mean as cut-off to fit

the stratification model. The results show that PM10 significantly modified the effects of

temperature on CHA, RHA, CM and NECM at various lags. The enhanced adverse

temperature effects were found at higher levels of PM10, but there was no clear evidence for

synergistic effects on CEV and REV at various lags. Three parallel models produced similar

results, which strengthened the validity of these findings.

Thirdly, we examined whether there were the interactive effects between maximum

temperature and ozone on NECM in individual communities between April and October,

1987-2000, using the data of 60 eastern US communities from the National Morbidity,

Mortality, and Air Pollution Study (NMMAPS). We divided these communities into two

regions (northeast and southeast) according to the NMMAPS study. We first used the

bivariate model to examine the joint effects between temperature and ozone on NECM in

III

each community, and then fit a stratification model in each community by categorizing

temperature into three levels. After that, we used Bayesian meta-analysis to estimate overall

effects across regions and temperature levels from the stratification model. The bivariate

model shows that temperature obviously modified ozone effects in most of the northeast

communities, but the trend was not obviously in the southeast region. Bayesian meta-analysis

shows that in the northeast region, a 10-ppb increment in ozone was associated with 2.2%

(95% posterior interval [PI]: 1.2%, 3.1 %), 3.1% (95% PI: 2.2%, 3.8 %) and 6.2 % (95% PI:

4.8%, 7.6 %) increase in mortality for low, moderate and high temperature levels, respectively,

while in the southeast region, a 10-ppb increment in ozone was associated with 1.1% (95% PI:

-1.1%, 3.2 %), 1.5% (95% PI: 0.2%, 2.8%) and 1.3% (95% PI: -0.3%, 3.0 %) increase in

mortality.

In addition, we examined whether temperature modified ozone effects on cardiovascular

mortality in 95 large US communities between May and October, 1987-2000 using the same

models as the above. We divided the communities into 7 regions according to the NMMAPS

study (Northeast, Industrial Midwest, Upper Midwest, Northwest, Southeast, Southwest and

Southern California). The bivariate model shows that temperature modified ozone effects in

most of the communities in the northern regions (Northeast, Industrial Midwest, Upper

Midwest, Northwest), but such modification was not obvious in the southern regions

(Southeast, Southwest and Southern California). Bayesian meta-analysis shows that

temperature significantly modified ozone effects in the Northeast, Industrial Midwest and

Northwest regions, but not significant in Upper Midwest, Southeast, Southwest and Southern

California. Nationally, temperature marginally positively modified ozone effects on

cardiovascular mortality. A 10-ppb increment in ozone was associated with 0.4% (95%

posterior interval [PI]: -0.2, 0.9 %), 0.3% (95% PI: -0.3%, 1.0%) and 1.6% (95% PI: 4.8%,

IV

7.6%) increase in mortality for low, moderate and high temperature levels, respectively. The

difference of overall effects between high and low temperature levels was 1.3% (95% PI: -

0.4%, 2.9%) in the 95 communities.

Finally, we examined whether ozone modified the association between maximum temperature

and cardiovascular mortality in 60 large eastern US communities during the warmer days,

1987-2000. The communities were divided into the northeast and southeast regions. We

restricted the analyses to the warmer days when temperature was equal to or higher than the

median in each community throughout the study period. We fitted a bivariate model to

explore the joint effects between temperature and ozone on cardiovascular mortality in

individual communities and results show that in general, ozone positively modified the

association between temperature and mortality in the northeast region, but such modification

was not obvious in the southeast region. Because temperature effects on mortality might

partly intermediate by ozone, we divided the dataset into four equal subsets using quartiles as

cut-offs. Then, we fitted a parametric model to examine the associations between temperature

and mortality across different levels of ozone using the subsets. Results show that the higher

the ozone concentrations, the stronger the temperature-mortality associations in the northeast

region. However, such a trend was not obvious in the southeast region.

Overall, this study found strong evidence that temperature and air pollution interacted to

affect health outcomes. PM10 and temperature interacted to affect different health outcomes at

various lags in Brisbane, Australia. Temperature and ozone also interacted to affect NECM

and CM in US communities and such modification varied considerably across different

regions. The symmetric modification between temperature and air pollution was observed in

the study. This implies that it is considerably important to evaluate the interactive effect while

V

estimating temperature or air pollution effects and further investigate reasons behind the

regional variability.

VI

Publications by the candidate on matters relevant to the thesis

Journal Articles

Ren C and Tong S. (2006) Temperature modifies the health effects of particulate matter in

Brisbane, Australia. Int J Biometerol. 51:87-96.

Ren C, Williams GM, Tong S. (2006) Does Particulate Matter Modify the Association

between Temperature and Cardiorespiratory Diseases? Environ Health Perspect 2006;

114:1690-1696.

Ren C, Tong S, Williams GM, Mengersen K. (2006) Does Temperature Modify Short-Term

Effects of Ozone on Total Mortality in 60 large Eastern US Communities? ( To be submitted)

Ren C, Williams GM, Tong S. (2006) Ozone, Temperature, and Cardiovascular Mortality in

95 Large US Communities, 1987-2000 -- Assessment Using the NMMAPS data. (To be

submitted)

Ren C, Williams GM, Morawska L, Mengersen K, Tong S. (2006). Ozone Modifies the

Associations between Temperature and Cardiorespiratory Mortality in 60 US Eastern Cities.

(To be submitted).

VII

Conference Presentation

Ren C, Williams GM, Tong S. Ozone modifies the association between temperature and

mortality in 12 US cities, 1987-2000.

Oral Presentation. The International Conference on Environmental Epidemiology and

Exposure. Paris. 2-6 September 2006

Ren C, Tong S. Temperature modifies the short-term effects of particulate matter on

cardiorespiratory diseases in Brisbane, Australia.

Poster Discussion. The International Conference on Environmental Epidemiology and

Exposure. Paris. 2-6 September 2006

Ren C. Williams GM, Tong S. The role of temperature in estimating the acute ozone effect

on cariorespiratory mortality in 95 large US communities during 1987-2000.

Oral Presentation. The 15th annual scientific meeting of the Australiasian

Epidemiological Association. Melbourne, Australia, 18-19 September 2006-10-06

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STATEMENT OF AUTHORSHIP

The work contained in this thesis has not been previously submitted for a degree or diploma at

any other higher education institute. To the best of my knowledge and belief, the thesis

contains no materials previously published or written by another person except where

reference is made.

__________________

__________________

IX

Acknowledgements

I would like to thank the following people who made this thesis possible.

Thanks to my supervisors, A/Prof. Shilu Tong, Prof. Gail M Williams and Prof. Lidia

Morawska for their capable and experienced professional guidance. As a student from non-

English speaking background, I have been very lucky to be generously instructed by my

supervisor team, not only academically, but also linguistically and culturally. I would like to

thank A/Prof. Shilu Tong, the principal supervisor for his significant amount of time spent on

my professional guidance throughout my PhD study. I would also like to thank Prof. Gail M

Williams, industrial supervisor for her insightful statistical advice and guidance. I am grateful

to Prof. Lidia Morawska for her thoughtful instructions to my thesis as well.

In addition to my supervisors, I would like to thank Prof. Beth Newman and Prof. Kerrie

Mengersen for their time and energy to help and guide me for this thesis. Their contributions

made the thesis go more smoothly. Id like to extend my appreciation to Genny Carter for

generous assistance during the last three years.

I am also grateful to Profs. Jonathan M Samet, Johns Hopkins University, H. Ross Anderson,

University of London, for their insightful comments on some of our manuscripts, and to Dr.

Roger Peng and his colleagues, Johns Hopkins University for their time and efforts in making

the NMMAPS data publicly available.

Besides the professional helpers, I am extremely grateful to my wife, parents and children, for

their patience, encouragement and emotional support throughout my PhD study.

X

I would also like to acknowledge all my colleagues and folks in the school, faculty and IHBI

for their advice and assistance with my research and personal friendship. I remember all of

you in my deep heart.

Finally, I would like to especially acknowledge the examination panel of my PhD study for

their contributions to improving this thesis.

XI

CONTENTS

CHAPTER 1: INTRODUCTION .............................. 1

1.1 BACKGROUND .........1

1.2 AIMS AND HYPOTHESIS .. 4

1.3 SIGNIFICANCE OF THE STUDY .. 5

1.4 CONTENTS AND STRUCTURE OF THIS THESIS . 6

CHAPTER 2: HEALTH EFFECTS OF AIR POLLUTION:

A LITERATURE VIEW .. 8

2.1 INTRODUCTION .............................................................................................................. 8

2.2 MAIN AIR POLLUTANTS AND BIOLOGICAL MECHEMISMS ................................ 9

2.3 MAIN RESEARCH DESIGNS IN AIR POLLUTION EPIDEMIOLOGICAL STDUIES

............................................................................................................................................ 12

2.4 HEALTH EFFECTS OF AIR POLLUTION .................................................................... 25

2.5 CURRENT ISSUES .......................................................................................................... 36

2.6 SUMMARY ......................................................................................................................40

CHPATER 3: STUDY DESIGN AND METHODOLOGY ........................ 41

3.1 STUDY POPULATION ................................................................................................... 41

3.2 STUDY DESIGN ............................................................................................................ 42

3.3 DATA COLLECTION ................................................................................................. 42

3.4 DATA MANAGEMENT ................................................................................................. 45

3.5 ANALYTICAL PROTOCOL .......................................................................................... 45

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CHAPTER 4: TEMPERATURE MODIFIES THE HEALTH EFFECTS

OF PARTICULATE MATTER IN BRISBANE, AUSTRALIA ............. 53

ABSTRACT ...................................................................................................................... 54

4.1 INTRODUCTION ...................................................................................................... 55

4.2 MATERIALS AND METHODS ................................................................................. 57

4.3 RESULTS ................................................................................................................... 62

4.4 DISCUSSION ............................................................................................................. 73

ACKNOWLEDGEMENTS ................................................................................................ 77

REFERENCES ..................................................................................................................... 78

CHAPTER 5: DOES PARTICULATE MATTER MODIFY THE

ASSOCIATION BETWEEN TEMPERATURE AND CARDIO-

RESPIRATORY DISEASES? ....................................................................... 83

ABSTRACT ...................................................................................................................... 84

5.1 INTRODUCTION ...................................................................................................... 85


5.3 RESULTS ................................................................................................................... 92

5.4 DISCUSSION ............................................................................................................. 103


REFERENCES ..................................................................................................................... 109

CHAPTER 6: DOES TEMPERATURE MODIFY SHORT-TERM

EFFECTS OF OZONE ON TOTAL MORTALITY IN 60 LARGE US

COMMUNITIES? AN ASSESSMENT USING THE NMMAPS DATA

................................................................................................................. 114

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ABSTRACT ...................................................................................................................... 115

6.1 INTRODUCTION ...................................................................................................... 116


6.3 RESULTS ................................................................................................................... 123

6.4 DISCUSSION ............................................................................................................. 128


REFERENCES ..................................................................................................................... 135

CHAPTER 7: OZONE, TEMPERATURE AND CARDIOVASCULAR

MORTALITY IN 95 US COMMUNITIES, 1987-2000 ASSESSMENT

USING NMMAPS DATA ............................................................................. 139

ABSTRACT ...................................................................................................................... 140

7.1 INTRODUCTION ...................................................................................................... 142

7.2 MATERIALS AND METHODS .............................................................................. 143

7.3 RESULTS .................................................................................................................. 147

7.4 DISCUSSION ............................................................................................................ 152


REFERENCES ..................................................................................................................... 158

CHAPTER 8: OZONE MODIFIED ASSOCIATION BETWEEN

TEMPERATURE AND CARDIOVASCULAR MORTALITY IN 60

EASTERN US COMMUNITIES IN WARM DAYS, 1978-2000

......................................................................................................................... 162

ABSTRACT ...................................................................................................................... 163

8.1 INTRODUCTION ...................................................................................................... 165

XIV


8.3 RESULTS ................................................................................................................... 170

8.4 DISCUSSION ............................................................................................................. 175


REFERENCES ..................................................................................................................... 180

CHAPTER 9: GENERAL DISCUSSION ................................................... 184

9.1 OVERVIEW ................................................................................................................... 184

9.2 RESEARCH HYPOTHESIS .......................................................................................... 184

9.3 KEY FINDINGS ............................................................................................................ 185

9.4 METHODOLOGICAL DEVELOPMENT ................................................................... 187

9.5 INTERPRETATION OF FINDINGS ............................................................................. 189

9.6 STRENTHS AND LIMITATIONS................................................................................. 198

9.7 OPPORTUNITIES FOR FUTURE RESEARCH............................................................ 199

9.8 PUBLIC HEALTH IMPLICATIONS ............................................................................ 202

9.9 CONCLUSIONS ............................................................................................................. 204

REFERENCES .............................................................................................. 205

XV

LIST OF TABLES

TABLE 2.1: TIME-SERIES STUDIES OF SHORT-TERM HEALTH EFFECTS OF AIR

POLLUTION AFTER 2000 ........................................................................... 26

TABLE 4.1: SUMMARY STATISTICS OF HEALTH OUTCOMES, AIR POLLUTION

AND METEOROLOGICAL CONDITIONS IN BRISBANE, AUSTRALIA,

1996-2001 ..........................................................................................................62

TABLE 4.2: COEFFICIENTS OF MAIN AND INTERACTIVE EFFECTS OF MAXIMUM

TEMPERATURE AND PM10 ON MORBIDITY/MORTALITY .................. 69

TABLE 4.3: PERCENT INCREMENT OF MORBIDITY/MORTALITY PER 10g/m

INCREASE IN PM10 ACROSS TEMPERATURE LEVELS .......................... 72

TABLE 4.4: PERCENT INCREMENT OF MORBIDITY/MORTALITY PER 10g/m

INCREASE IN PM10 ACROSS TEMPERATURE LEVELS USING

DIFFERENT CUT-OFFS ON CURRENT DAY ............................................. 72

TABLE 5.1: SUMMARY STATISTICS FOR HEALTH OUTCOMES, AIR POLLUTANTS

AND METEOROLOGICAL CONDISITONS ................................................ 92

TABLE 5.2 COEFFICIENTS FOR MAIN AND INTERACTIVE EFFECTS OF

MINIMUM TEMPERATURE (C) AND PM10 (g/m)ON MORBIDITY/

MORTALITY .................................................................................................. 99

TABLE 5.3 PERCENT CHANGE IN CARDIO-RESPISTORY MORBIDITY/

MORTALITY PER 10C INCREASE IN TEMPERATURE ACROSS THE

LEVELS OF PM10 .......................................................................................... 102

TABLE 6.1 MEANS OF DAILY OZONE, MAXIMUM TEMPERAUTRE AND THEIR

PEARSON CORRELATION COEFFICIENTS OF 60 EASTERN US

COMMUNITIES BETWEEN APRIL AND OCTOBER, 1987-

2000 ................................................................................................................ 123

XVI

TABLE 7.1 PEARSON CORRELATION COEFFICIENTS BETWEEN DAILY

MAXIMUM TEMPERATURE AND OZONE ACROSS DIFFERENT

REGIONS DURING THE SUMMER (MAY TO OCTOBER) ....................148

TABLE 7.2 PERCENTAGE CHANGES IN DAILY CARDIOVASCULAR MORTALITY

PER 10 PPB INCREASE IN OZONE ACROSS REGIONS AND

TEMPERATURE LEVELS DURING THE SUMMER USING BAYESIAN

META-ANALYSIS (LOG RELATIVE RATE) ........................................... 152

TABLE 8.1 DESCRIPTION SUMMARIES FOR MEANS OF DAILY OZONE,

MAXIMUM TEMPERATURE AND THEIR PEARSON CORRELATION

COEFFICIENTS OF 60 EASTERN US COMMUNITIES DURING WARM

DAYS, 1987-2000 .......................................................................................... 171

TABLE 8.2 PERCENT CHANGE IN DAILY CARDIORESPIRATORY MORTALITY

PER 10 C INCREASE IN MAXIMUM TEMPERATURE (THREE

PREVIOUS DAY AVERAGE) ACROSS REGIONS AND LEVELS OF

OZONE (THREE PREVIOUS DAY AVERAGE) DURING THE WARMER

DAY IN 60 EASTERN US COMMUNITIES (LOG RELATIVE RATE) .... 174

TABLE 9.1 SUMMARY OF INTERACTIVE EFFECTS BETWEEN PM10 AND

TEMPERATURE ON DIFFERENT HEALTH OUTCOMES IN BRISBANE,

AUSTRALIA, 1996-2001 ............................................................................... 186

TABLE 9.2 SUMMARY OF INTERACTIVE EFFECTS BETWEEN TEMPERATURE

AND OZONE ON DIFFERNET HEALTH OUTCOMES IN THE US

COMMUNITIES, 1996-2000 ........................................................................ 187

XVII

LIST OF FIGURES

FIGURE 2.1 HARVESTING PHENOMENON .................................................................. 32

FIGURE 3.1 REGIONAL GROUPS OF US OMMUNITIES ........................................... 44

FIGURE 4.1 JOINT PM10-TEMPERATURE RESPONSE SURFACES ON HEALTH

OUTCOMES AT LAG 2 ................................................................................ 64

FIGURE 4.2 DOSE-RESPONSE ASSOCIATION BETWEEN MZXIMUM

TEMPERATURE AND HEALTH OUTCOMES IN BRISBANE,

AUSTRALIA, 1996-2001 ................................................................................. 66

FIGURE 4.3 THE RELATIONSHIPS BETWEEN RESIDUALS AND DAYS OF YEAR

FOR RHA, CHA, REV, CEV, NECM AND CM AT ALG 2 ...........................67

FIGURE 4.4 THE SYNERGISTIC EFFECT BETWEEN MAXIMUM TEMPERATURE (C)

AND PM10 ON RHA (TOP PANEL ) AND NECM (BOTTOM PANEL) ON

THE CURRENT DAY........................................................................................70

FIGURE 5.1 TIME SERIES DISTRIBUTION OF PM10, MINIMUM TEMPERATURE

AND HEALTH OUTCOMES DURING 1996-2001 IN BRISBANE ............. 93

FIGURE 5.2 TEMPERATURE-MORBIDITY/MORTALITY RELATIONSHIPS ............. 94

FIGURE 5.3 RELATIONSHIPS BETWEEN RESIDUALS AND DAYS OF YEAR FOR

DIFFERETN HEALTH OUTCOMES...............................................................96

FIGURE 5.4 BIVARIATE RESPONSE SURFACES OF MINIMUM TEMPERATURE

AND PM10 ON HEALTH OUTCOMES ON CURRENT DAY ..................... 98

FIGURE 5.5 THE SYNERGISTIC EFFECTS BETWEEN MINIMUM TEMPERATURE (C)

AND PM10 ON RHA (TOP PANEL) AND NECM (BOTTOM PANEL) ON

THE CURRENT DAY......................................................................................100

XVIII

FIGURE 6.1 PEARSONS CORRELATION COEFFICIENTS BETWEEN DAILY OZONE

AND MAXIMUM TEMPERATURE WITH LATITUDE AND MEANS OF

MAXIMUM TEMPERATURE BETWEEN APRIL AND OCTOBER, 1987-

2000 IN 60 EASTERN US COMMUNITIES ................................................ 124

FIGURE 6.2 BIVARIATE RESPONSE SURFACES OF THREE-DAY MOVING

AVERAGES OF MAXIMUM TEMPERATURE AND OZONE ON TOTAL

NON-EXTERNAL DEATHS BETWEEN APRIL AND OCTOBER, 1987-

2000 ................................................................................................................. 125

FIGURE 6.3 PERCENT INCREASE IN DAILY MORTALITY PER 10 PPB INCREASE IN

THREE DAY AVERAGES OF OZONE ...................................................... 127

FIGURE 7.1 BIVARIATE RESPONSE SURFACES OF OZONE AND TEMPERATURE

ON CARDIOVASCULAR MORTALITY BETWEEN MAY AND OCTOBER,

1987-2000 ....................................................................................................... 149

FIGURE 7.2 COMMUNITY-SPECIFIC, REGIONAL AND NATIONAL BAYESIAN

ESTIMATES ACROSS TEMPERATURE LEVELS .................................... 151

FIGURE 8.1 BIVARIATE RESPONSE SURFACES OF THREE-DAY MOVING

AVERAGES OF MAXIMUM TEMPERATURE AND OZONE ON TOTAL

NON-EXTERNAL DEATHS DURING THE WARMER DAYS, 1987-

2000 ................................................................................................................. 172

XIX

LIST OF ABBREVIATION

CEV: Cardiovascular emergency visits

CHA: Cardiovascular hospital admissions

CI: Confidential interval

CM: Cardiovascular mortality

CVD: Cardiovascular deaths

EPA: Environmental protection agency

GAM: Generalized additive models

GLM: Generalized linear models

ICD: International Classification of Diseases

iHAPSS: Internet-based Health and Air Pollution Surveillance System

NECM: Non-external cause mortality

NMMAPS: The National Morbidity, Mortality, and Air Pollution Study

NO2: Nitrogen Dioxide

O3: Ozone

PI: Posterior interval

PM: Particulate matter

PM10: Particulate matter less than 10m in aerodynamic diameter

PM2.5: Particulate matter less than 2.5m in aerodynamic diameter

REV: Respiratory emergency visits

RHA: Respiratory hospital admissions

SO2: Sulphur dioxide

TSP: Total suspended particles

1

Chapter 1: Introduction

1.1 Background

In the past decades, the public awareness of possible health impacts of air pollution has risen

considerably. Many epidemiological studies have shown that ambient particulate matter (PM),

sulphur dioxide (SO2), nitrogen dioxide (NO2) and ozone (O3) were associated with the

increased morbidity and mortality from respiratory and cardiovascular diseases (Dominici et

al. 2003a; Katsouyanni et al. 1997; Pope et al. 1995). Recently, several large multi-site time-

series studies have shown that PM and ozone are consistently associated with various health

outcomes (Bell et al. 2004; Dominici et al. 2006; Gryparis et al. 2004; Ito et al. 2005; Levy et

al. 2005; Samet et al. 2000a, b, c).

Similarly, many studies have shown that episodes of heat waves have caused excess death in

the world (Basu & Samet 2002; Jones et al. 1982; Martens 1998; Patz et al. 2000; Tertre et al.

2006; Vanhemes & Gambotti 2003). For example, during a heat wave in St. Louis in 1980,

the maximum temperature was over 37.8C for 16 days in July and resulted in a 56.7%

increment in mortality (ie, 850 excess deaths) (Jones et al. 1982). A large number of

epidemiological time-series studies have also shown that ambient temperature is associated

with health outcomes, and J-, U- or V-shaped patterns are usually observed between

temperature and mortality (Basu & Samet 2002; Patz et al. 2000). For example, Currie et al

(2002) examined the relationships between temperature and total mortality in 11 US cities and

found that the relationships between temperature and mortality showed J- or U-shaped

2

patterns, especially in northern areas, such as Boston, Chicago, New York, Philadelphia and

Baltimore, but such U- or J-shaped were much less obvious in southern areas.

Over the last two decades, air pollutants have declined and their health effects of short

episodes became more difficult to ascertain in the developed countries (Nyber & Pershgen

2000). In recent air pollution and temperature epidemiological studies, ecological time-series

analysis designs have become dominant due to its advantages (Dominici et al. 2003a). The

major advantages of time-series study designs include: weather-driven variations in air

pollution concentrations produce large contrasts in exposure over time; population act as their

own controls; and studies can often use routinely collected monitoring data including health

outcomes, air pollution and weather conditions. As a result, the number of deaths or hospital

admissions in studies can easily be done in the hundreds of thousands, leading to sufficient

statistical power to detect small adverse health effects of air pollution and temperature

(Brunekreef & Holgate 2002).

In time-series studies, the associations of air pollution and temperature with health outcomes

can be confounded to some extent by many covariates, such as weather variables, seasonality,

short-term variation, long-term trend, and other co-existing factors (Samet et al. 2000c;

Zanobetti et al. 2000a, b; Zanobetti et al. 2002). In order to control for potential confounders,

generalized linear models (GLM) and generalized additive models (GAM) have been widely

adopted in the assessment of the relationship between exposure to air pollutants or ambient

temperature and health outcomes (Dominici et al. 2003a). As the GAM can include

nonparametric smooth functions to account for the potential nonlinear effects of confounding

factors on health outcomes, such as weather conditions and seasonal variables (Hastie &

Tibshirani 1990; Katsouyanni et al. 1997; Samet et al. 1998; Schwartz 2000a, b, c), it has

3

been widely used in the evaluation of the impacts of air pollution and temperature on health

outcomes. GLM with parametric smoothing splines (B-spline or natural cubic spline) is also

often adopted in the assessment of air pollution or temperature effects in time-series studies

(McCullagh & Nelder, 1989).

Although a great number of time-series studies have reported the association of air pollution

and temperature with health outcomes, it remains largely unclear whether there is a

synergistic effect of air pollution and temperature on human health. Some studies have

investigated the interaction between season and air pollutants (Katsouyanni et al. 1997; Levy

et al. 2005; Smith et al. 2000; Wong et al. 2001). For example, Levy et al. (2005) conducted a

meta-analysis for the health effects of ozone and found that the magnitude of the ozone-

mortality relationship differs substantially across seasons (higher in summer than in winter).

Several studies have examined whether temperature modifies the association of air pollution

with morbidity or mortality, but they produced contradictory results. Some authors found the

synergistic effects between weather conditions and air pollution on health outcomes (Choi et

al. 1997; Katsouyanni et al. 1993; Roberts 2004), but others did not (Samet et al. 1998). So far,

few studies have explored whether air pollution modifies the association of temperature with

health outcomes although some recent studies have adjusted for air pollution in the

assessment of temperature effects (ONell et al, 2003; Raimham & Smoyer-Tomic 2003).

Therefore, the primary aim of this thesis was to investigate whether there were interactive

effects between temperature and air pollution on morbidity/mortality.

4

1.2 Aims and Hypothesis

This study aimed to examine interactive effects between temperature and air pollution on

health outcomes in Brisbane, Australia, and in ninety-five large communities, in the United

States. It is biologically plausible that temperature and air pollution symmetrically modify

each others effects because a lot of studies show that air pollutants have adverse impacts on

human health (Holgate et al. 1999), and that marked changes in ambient temperature can

cause physiological stress and alter a persons physiological response to toxic agents (Gordon

2003). This study used two datasets mainly because of two reasons: firstly, it is necessary to

examine the consistency of research findings across different cities and countries; secondly,

these datasets were readily available for this study. At the early stage of the thesis, we just

used the single-site dataset collected from Brisbane to examine whether particulate matter and

temperature interacted to affect various health outcomes. At the late stage, we used the multi-

site dataset from 95 large US communities to examine interactive effects of ozone and

temperature on mortality (Bell et al. 2004) because most of the US communities just

monitored particulate matter once six days but they recorded daily monitored data on ozone

during the peak season. Therefore, we used the US dataset to examine interactive effects of

ozone and temperature on mortality using the multi-site design.

1.2.1 Specific Objectives

To visualize the associations of temperature and particulate matter less than 10m in

aerodynamic diameter (PM10) with morbidity and mortality in Brisbane, Australia.

To visualize the associations of temperature and ozone with morbidity and mortality in

95 large US communities.

5

To visualize the joint effects of both temperature and PM10 on morbidity and mortality

in Brisbane, Australia.

To visualize the joint effects of both temperature and ozone on mortality in 95 large

US communities.

To quantitatively estimate the interactive effects between temperature and PM10 on

morbidity and mortality in Brisbane, Australia.

To quantitatively estimate the interactive effects between temperature and ozone on

mortality in 95 large US communities.

1.2.2 Hypothesis to Be Tested

Joint effects exist between temperature and ozone or PM10 on morbidity and mortality in

Brisbane, Australia and 95 large US communities.

1.3 Significance of the Study

This study systematically explored interactive effects between temperature and air pollutants

on morbidity and mortality in different countries and multi-cities. It is the first investigation

of the symmetrical effect modification of air pollution and temperature across geographic

regions. This study used three parallel time-series models to investigate the interactive effects

between temperature and air pollution on health outcomes, and this methodological approach

is useful in exploring the effect modification of temperature and air pollution. This study also

developed multi-stage Bayesian meta-analyses to estimate overall effects of temperature and

air pollution (eg, ozone). Therefore, the findings of this study contribute to both the

6

estimation of the health effects of air pollution and temperature, and to methodological

advance in epidemiological research.

1.4 Contents and Structure of this Thesis

This thesis is presented in the publication style. As such, it consists of five manuscripts, each

designed to stand on its own. Chapter 2 provides a critical literature review to cover both

previous research findings and current knowledge gaps in this area. Chapter 3 describes the

study design, materials and data analytical protocol.

The five manuscripts are presented in Chapters 4-8. Each manuscript is written in the

conventional publication style according to a particular journal, including the reference styles.

Because each manuscript was designed to stand alone, there was an inevitable degree of

repetitiveness in their introduction, methods and discussion.

The first manuscript examined whether temperature modified the effects of particulate matter

on cardiorespiratory hospital admissions, emergency visits and mortality at each of 0-2 lags in

Brisbane, Australia, 1996-2000. The second manuscript examined whether particulate matter

modified the temperature effects on cardiorespiratory hospital admissions, emergency visits

and mortality at each of 0-2 lags in Brisbane, 1996-2000. The third manuscript investigated

whether temperature modified ozone effects on non-external mortality and whether the

modification was heterogeneous across different regions in 60 large US communities, 1987-

2000. The fourth manuscript examined whether temperature modified ozone effects on

cardiorespiratory mortality in 95 large US communities in summer season (April to

September) through 1987-2000. It also examined whether the modification was

7

heterogeneous across geographic regions. The last manuscript investigated whether ozone

modified the association between temperature and non-external mortality in 60 large US

communities.

Chapter 9 summarizes the key findings of Chapters 4-8 and then discusses issues raised in the

previous chapters. This chapter also discusses biological plausibility, strengths, limitations,

opportunities for future research, and public health implications. Finally, the conclusions are

made based on the findings observed in the five manuscripts.

Tables and figures are provided in the text to facilitate reading. The references for each of the

manuscripts are presented at the end of their corresponding chapters. A complete reference

list (including references cited in the Chapters 1, 2, 3 and 9) is provided at the end of the

thesis.

8

Chapter 2: Health Effects of Air Pollution

- A Literature Review

2.1 Introduction

It is well known that exposure to high levels of air pollution can adversely affect human

health. A number of air pollution catastrophes occurred in industrial countries between 1950s

and 1970s, such as the London smog of 1952 (Bell et al. 2001; Minister of Health 1954).

Subsequent legislation has effectively controlled the emission of air pollutants, and air quality

in Western countries has significantly improved since the 1970s. However, adverse health

effects of exposure to low level of air pollution remain a regulatory and public concern,

motivated largely by a number of the recent epidemiological studies which have shown

positive associations between low levels of air pollution and health outcomes using time-

series designs (Samet et al. 2000a, b, c; Schwartz 2000a, b, c). Health effects of temperature

changes have also been recognised for a long time. Several international and regional

assessments have been conducted to address this issue (Le Tertre et al. 2006; Vandentrren et

al. 2004; Watson et al. 1998; WHO 1996). The projections of future climate change have

compelled health scientists to re-examine weather/disease relationships: including the health

impacts of temperature rise, sea level rise, and extremes in the hydrologic cycle (McMichael

et al. 2006; WHO 1996).

9

Air pollution and temperature epidemiological studies are broad topics and similar

methodologies are used in both types of research. To provide a literature review and

background in this area, this chapter highlights the major epidemiological studies of ambient

air pollution on morbidity and mortality over the last two decades because the methodologies

for temperature epidemiological studies are similar. Firstly, we briefly discuss main air

pollutants and their biological mechanisms. Secondly, we focus on main epidemiological

research designs in current air pollution studies. Then, we review the main findings in the

epidemiological studies of air pollution. Finally, we attempt to identify research opportunities

for future air pollution epidemiological studies.

2.2 Main Air Pollutants and Biological Mechanisms

Air pollution is a complex mixture of chemicals. It has different impacts on the atmosphere

and human health and well-being depending on the source of the pollutants and levels of

human exposure. The following section provides an overview of the diverse array of air

pollutants and the relevant mechanisms of their health effects.

2.2.1 Carbon Monoxide (CO)

Carbon monoxide is a colourless and odourless gas with approximately the same density as

air. It is produced when substances containing carbon are combusted with an insufficient

supply of air. CO can have serious health impacts on humans and animals. When inhaled, CO

binds with haemoglobin in the blood and forms a very stable carboxyhaemoglobin complex.

This reduces the capacity of the haemoglobin to transport oxygen in the blood stream and

10

decreases the supply of oxygen to tissues and organs, especially the heart and brain (Maynard

& Waller 1999).

2.2.2 Ozone (O3)

Ozone is an indicator of photochemical smog and is a strong oxidising agent produced by

photochemical reactions involving other air pollutants, such as NOx (WHO 2000).

Concentrations in city centres tend to be lower than those in suburbs, mainly as a result of

scavenging of ozone by nitric oxide originating from traffic. It has been shown experimentally

that exposure to O3 can affect the human cardiac and respiratory systems, and irritate the eyes,

nose, throat, and lungs (Chan-Yeunf 2000; Schwela 2002; Thurston & Ito 1999).

2.2.3 Nitrogen Oxide (NOx)

Nitrogen oxide (NOx), a mixture of nitric oxide (NO) and nitrogen dioxide (NO2), is formed

from natural sources, motor vehicle emissions, and other fuel combustion processes (WHO

2000). Nitric oxide is colourless and odourless and is rapidly transformed into nitrogen

dioxide in the atmosphere in reaction with atmospheric oxidants such as ozone (Brunekreef &

Holgate 2002). Nitrogen dioxide is an odorous, brown, acidic, highly-corrosive gas and can

affect human health and the environment. Elevated levels of nitrogen dioxide cause damage to

the mechanisms of the human respiratory tract and can increase ones susceptibility to

respiratory infection and asthma (Brunekreef & Holgate 2002). Long-term exposure to high

levels of NO2 can result in chronic lung diseases. It may also affect sensory perception by

11

reducing a persons ability to smell an odour (Ackermann-Liebrich & Rapp 1999; CDHAC

2000; Queensland EPA 2007).

2.2.4 Particulate Matter (PM)

Ambient particulate matter is the term used to describe particles that suspends in the air.

Particulate matter is a mixture of solid, liquid, or solid and liquid particles. The main sources

of ambient particles are fossil fuel combustion, biomass burning, and the processing of metals.

Road transportation is the major source in urban areas (Holmam 1999; Pooley & Mille 1999).

Size is the main determinant of the behaviour of ambient particles. In practical terms, a

distinction is made in terms of the aerodynamic diameter which refers to unit density of

spherical particles with the same aerodynamic properties, such as falling speed. Recent

attention has focused on PM10 (thoracic particles smaller than 10 m in aerodynamic

diameter), PM2.5 (respiratory particles smaller than 2.5m in aerodynamic diameter) (WHO

2000). Their biological effects are related to the sizes of the particles. Particles with an

aerodynamic diameter of more than 5 m are often deposited in the upper or larger airways

and smaller particles or PM2.5 are often deposited in the small airways (bronchioli) and

alveoli. Fine particles have the potential to penetrate the airway epithelium and vascular walls

(Jeffrey 1999). Therefore, small particles have stronger effect (Rom and Samet 2006).

12

2.2.5 Sulphur Dioxide (SO2)

Sulphur dioxide is a colourless gas with a sharp, irritating odour. It is produced from the

burning of fossil fuels (coal or oil) and the smelting of mineral ores that contain sulphur.

When SO2 combines with water, it forms sulphuric acid, which is the main component of acid

rain. Sulphur dioxide can affect the respiratory system, damaging the function of the lungs

and irritate eyes. When sulphur dioxide irritates the respiratory tract, it causes coughing and

mucus secretion, aggravates conditions such as asthma and chronic bronchitis, and makes

people more prone to respiratory tract infections. Sulphur dioxide can also attach itself to

particles which, if particles are inhaled, can cause serious damage (Schlesinger 1999).

2.3 Main Research Designs in Air Pollution Epidemiological

Studies

Observational studies are dominant in air pollution epidemiological studies, most of which are

opportunistic, combining data from different sources which have been collected for other

purposes (Dominici et al. 2003a). One of the main reasons to facilitate this type of research is

the availability of ambient monitoring data, meteorological records and regular health event

registrations in many parts of the world, particularly in developed countries. Monitors are

usually located at fixed sites and record the concentrations of local ambient pollutants and

meteorological conditions on a regular basis. These time-series data are widely used in

epidemiologic studies of air pollution (Bell et al. 2004; Brunekreef & Holgate 2002;

Dominici et al. 2006).

13

Health effects of air pollution can be either acute or chronic (American Thoracic Society

2000). Acute effects are due to short-term and transient exposure. Chronic effects are more

likely due to the cumulative effects of exposure, but could be associated with more complex

functions of lifetime exposure. Health outcomes can include major or minor life events (e.g.,

death or onset of symptoms), changes in function (e.g., vital capacity, lung growth and

symptom severity) or biomarkers (American Thoracic Society 2000). The nature of the

outcomes (e.g., binary or continuous) and the structure of the data decide the selection of an

appropriate model and the types of effects to be estimated. Regression models are generally

the methods of choice.

Most recent air pollution epidemiological studies belong to the following types: ecological

time-series, case-crossover or case-control, panel, and cohort study designs. Time series, case-

crossover, and panel designs are appropriate for estimating the acute effects of air pollution,

while the cohort study design is suited to estimate both acute and chronic effects (Dominici et

al. 2003a). A case-crossover study is a specialised case-control design, suited to the

examination of a transient effect of an intermittent exposure. Therefore, case-control and

case-crossover designs are discussed in the same section. Panel studies collect individual

temporally and spatially-varying exposures, confounders and outcomes, which are based on

spatially and /or temporally aggregated data (Dominici et al. 2003a; WHO 2004). In practice,

panel studies also depend on group-level data, and therefore, panel studies combine different

basic epidemiological study designs. Cross-sectional studies compare the prevalence of health

outcomes in different populations at the time of ascertainment while the exposure to air

pollutants is simultaneously measured. This study is generally the most economic and feasible

(Rothman and Greenland 1998). However, a major weakness is that the data are collected at

one point in time and therefore it fails to consider temporality, which is an important criterion

14

for causality. Because of its disadvantages it is infrequently employed in air pollution studies

(Samet and Jaakkola 1999), and this review therefore does not consider the cross-sectional

design. The following sections will discuss the different types of epidemiological designs

used in air pollution epidemiological studies.

2.3.1 Time-series Study Design

In western countries, concentrations of air pollutants are now generally much lower than 30

years ago and their short-term health effects have become quite small. Thus, traditional

methods may fail to detect their effects (Brunekreef & Holgate 2002). Time-series study

designs have dominated in air pollution research for the last two decades. Time-series studies

associate time-varying exposure with time-varying event counts. These are ecologic study

designs because they analyse daily population-averaged outcomes and exposure levels

without individual information. In time-series studies, the generalised linear model (GLM)

with parametric splines (e.g. B-spline or natural cubic spline) (McCullagh & Nelder 1989)

and generalised additive models (GAM) with non parametric splines (e.g. cubic smoothing

spline or LOESS smoother) (Hastie & Tibshirani 1990) are usually adopted to estimate effects

of exposure to air pollution on human health (morbidity or mortality). Recently, the GAM has

been widely applied in the assessment of air pollution effects because it allows for non-

parametric adjustment for non-linear confounding effects such as seasonality and weather

conditions, and is a more flexible approach than fully-parametric alternatives like the GLM

with natural cubic spline (Dominici et al 2003a). The following section will focus on the

application of a GAM approach. The formula for GLM is similar to that of GAM.

15

GAM assumes that the daily number of cases tY has an overdispersed distribution

( ttYE ( ), ttY ]var[ ) (Dominici et al. 2004). GAM model is presented as follows:

t

l

iltit DOWtimesconfsXYELog

),(),())((

0

(1)

where tX means daily levels of exposure at time t. DOW is the indicator variable for day of

the week, which is used to adjust for short-term fluctuation because the patients are reluctant

to attend hospital on weekends and air pollution also is weaker due to fewer cars on roads on

weekends (Barnett and Dobson, 2005). is a vector of coefficients. time is referred to as

calendar time or some function transfer to adjust for the seasonal fluctuation. Conf means

other potential confounders in the models, such as other air pollutants and meteorological

variations. l means the lags of the exposure. The smooth function )(.,s denotes a smooth

function of a covariate often constructed using smoothing splines, LOESS smoothers, or

natural cubic splines with a smoothing parameter . The smooth parameter represents the

number of degrees of freedom in the smooth spline, the span in loess smoother, -2 interior

knots in the natural cubic splines. The parameter of interest represents the changes in the

logarithm of the population average mortality count per unit change in ltX .

It is unnecessary to include an over-dispersion parameter in GAMs. Over-dispersion is a

separate statistical matter to GAMs. Using over-dispersion makes GAMs more flexible than

the standard Poission model by breaking the assumption of the mean equalling the variance.

Also, it would have been interesting to see the actual over-dispersion parameter in the results

(Hastie and Tibshirani 1990).

16

Since the mid 1990s, a growing number of studies have used distributed lag models and time-

scale models to estimate the cumulative and longer time-scale health effects of air pollution.

Distributed lag models (Almon 1965; Zanobetti et al. 2000b, 2002) are used to estimate

associations between health outcomes on a given day t, and air pollution several days prior by

replacing

l

ilti X

1

in (1) with ltl

ll X

1

, ltl

ll X

1

=1 where measures the cumulative

effect, and l measures the contribution of the lagged exposure ltX to the estimation of .

Time-scale models (Dominici et al. 2003b; Schwartz 2001; Zeger et al 1999) are used to

estimate the relationship between smooth variations of air pollution and daily health outcomes

by replacing

l

ilti X

1

in (1) with tkk

kkW

1

, ttkk

kk XW

1

, where ,...,,...,1 ktt WW KtW is a set of

orthogonal predictors obtained by applying a Fourier decomposition to tX . The

parameter k represents the log relative rate of the health outcome for increment of air

pollution at time scale k. Time scales of interest may be short- (1 to 4 days) and longer-term

variations (1 to 2 months) of air pollution. Beyond two months, any effects are possibly

dominated by seasonal confounding (Zeger et al. 1999).

Some time-series studies have examined synergistic effects between air pollution and

temperature by including an interactive term in GAM models (Morris and Naumova 1998;

Roberts 2004). For example, Roberts (2004) used bivariate models to examine the joint effect

of PM10 and temperature on mortality in two US cities and found that temperature might

synergistically modify the effect of particulate matter. Morris and Naumova (1998) reported

that the association between carbon oxide and hospital admissions varied with temperature.

When temperature was high, the association was strong.

17

Several studies have discussed how to choose the parameters of smoothing (Cakmak et al.

1998; Kelshal et al. 1997). For seasonality, the parameter should be big enough to eliminate

fluctuation of seasonal changes so that a shorter-term variation in health outcomes and

exposure is less than 2 months (Kelshal et al. 1997; Samet at al. 1995). To control weather

variation, a suitable parameter of smoothing is also necessary. For example, in many US cities

mortality decreases smoothly with increased temperature before a certain temperature point

and then increases quite sharply with temperature above a certain temperature point. Here a

smoothness parameter greater than three is necessary to capture the highly non-linear bend in

mortality as a function of temperature (Dominici et al. 2003a).

There are several potential biases with gam function in S-Plus software. Two main sources of

bias have been identified when gam function in S-Plus software is used to fit a Poisson

regression model in air pollution time-series studies (Dominici et al. 2002; Rasmay et al.

2003). One arises from the default criteria of convergence and could be reduced by the use of

the stringent criteria, say 1.010-10 (Dominici et al. 2002). Another source of bias in the gam

function is due to concurvity, which has the potential to reduce underestimation of standard

errors of coefficients for parametric terms when a semi-parametric model is fitted (Ramsay et

al. 2003). The gam.exact function was developed to correct the concurvity problem in gam

function (Dominici et al. 2004). However, when we conducted a Poisson regression with the

stringent criteria for convergence using gam.exact function to calculate asymptotically exact

standard errors, the convergence criteria could not be obtained with some datasets even

though we used a large number of iterations, say 5000. In this situation, GLM is a good option

because the maximum-likelihood estimates of the parameter could be obtained by

iteratively-reweighted least squares (IRLS) in GLM (McCullagh and Nelder 1989).

18

There are several advantages and disadvantages with time-series study designs. The first

advantage is the ability to use a large amount of available monitoring data to control for long-

or short-term variance with smoothing functions without rigid linear assumption between

outcome and predictors. The second advantage is that it is suited to estimate the short-term

effect of exposures. The third advantage is that the design is economic and statistically

powerful enough to estimate the weak effect of air pollution or weather conditions due to a

large amount of available monitoring data on air pollution, weather conditions, and regulatory

health event registrations. However, this study design has several disadvantages as well. First,

it is difficult to determine the best model, such as how to select the degrees of freedom, how

to determine lag effects, and how to control for other covariates with lag effects. Secondly, it

would be likely to produce biased estimates due to lack of individual-level exposures. In time-

series studies, air pollution and meteorological measures used are generally based on

monitoring records which may be poorly represent of individual exposures. Thirdly, it is

difficult to control for some potential confounders resulting from individual-level covariates

and human behaviour changes. Finally, it is unable to be used to estimate long-term or

cumulative effects of exposure.

2.3.2 Case-Crossover Study (or Case-Control Study) Design

In a case-control study, cases that develop a certain event are identified and their past

exposure to suspected aetiological factors is compared with that of controls or referents that

do not experience the event (Rotheman and Greenland, 1998). The case-crossover design is a

special case of a case-control design, suited to the study of a transient effect of an intermittent

exposure on the subsequent risk of a rare acute-onset disease hypothesised to occur a short

time after exposure (Maclure 1991). The principle of the case-crossover study design is that

19

exposures of cases just prior to the event are compared with the distribution of exposures of

the same cases from separate time periods. Therefore, it can be considered as a modification

of the matched case-control design (Schlesselman 1994). More specifically in a time-series

study, the exposure at the time just prior to the event (the case or index time) is compared to a

set of controls or referents. In this way, some measured and unmeasured time-invariant

characteristics of the subject (such as gender, age and smoking status) are matched, and

therefore the potential confounding originating from those unmeasured characteristics is

minimised (Maclure 1991).

The first decade of practice with case-crossover study design has shown that this design

applies best if the exposure is intermittent and transient, and the effect is immediate and

abrupt (Janes et al. 2005; Maclure & Mittleman 2000). This design has been found to be

effective for estimating the risk of a rare event associated with a short-term exposure because

the widespread availability of ambient monitoring data presents opportunities to further

analyse existing case series from case-control studies (Levy et al. 2001b).

Here a fixed number of referent days before and possibly after the case in a short time frame

are used, in order to restrict the referents in time to reduce seasonal confounding (Lumley &

Levy 2000). The data in the case-crossover design consist of exposure measures itZ for

subject i = 1,, n at time t = 1,,T. The referent sampling scheme determines the referent

sets iW of exposure that are used in the analysis. Estimates of the relative risks are obtained

by the following equation:

ii

is

it

iWt Ws

X

Xit

itie

eZZU

)( (2)

20

This is the conditional likelihood estimating function typically used in environmental and

other case-crossover studies (Lee & Schwartz 1999; Mittelman et al. 1995; Neas et al. 1999).

Standard computer softwares such as SAS PHREG (SAS Institute Inc 2004) can be used for

this analysis.

A key difficulty in case-crossover studies is how to properly define the referent sets iW .

Control for bias in the estimation of the relative risk is the dominant concern in the choice

of the referent sampling strategy, although the size of the referent set also affects efficiency.

Two main sources of bias in case-crossover studies have been identified (Janes et al 2005;

Lumley & Levy 2000). The first source of bias arises from the trend and seasonality in the

time series analyses of air pollution health effects. Since case-crossover comparisons are

made within subjects at different points in time, the case-crossover analysis implicitly

depends on the assumption that the exposure distribution is stationary. The long-term time

trends and seasonal variation inherent in the time series violate this assumption (Baseson &

Schwartz 1999, 2001; Levy et al. 2001a; Navidi 1998). The second source of bias is called

overlap bias. If the referent windows iW , i = 1, , n, are exactly determined by the case

period and are not disjoint, then the independent sampling inherent in the conditional

likelihood approach is invalidated (Austin 1989; Lumley & Levy 2000). Lumley and Levy

(2000) quantified the overlap bias analytically and simulations studies showed that the

direction is unpredictable and that the magnitude is a function of the size of the coefficient

(Lumley & Levy, 2000). However, for the small effects seen in exposure to air pollution,

current experience suggests that overlap bias is similar to the small-sample bias, e.g. the bias

obtained by estimating in (2) with a small number of referents (Dominici et al. 2003a).

21

In order to control for bias in case-crossover studies, many authors have proposed several

approaches to select referents, including ambidirectional, symmetric bidirectional, semi-

symmetric directional and time-stratified sampling (Janes et al. 2005; Navidi 1998; Navidi &

Winhandle 2002; Schwartz and Lee 1999).

There are several major advantages to use a case-crossover study design in air pollution

epidemiology. Firstly, because the cases and controls in the design are the same subjects, it

can control for some unmeasured confounders of individual characteristics, which might

confound the estimates. Secondly, it is suited to estimate the short-term transient effects

(Levy et al. 2001). Thirdly, this design can control for the second variable although the

matching will consume a lot of time (Barnett et al. 2006; Schwartz 2005). For example, the

study design can match temperature highly correlated with ozone when estimating ozone

effects (Schwartz 2005). Additionally, bi-directional selection of control periods allows

individual adjustment for seasonal and secular trends (Jaakkola 2003). Finally, it can use a

large amount of available monitoring data to estimate effects of exposure. However, there are

also several disadvantages with a case-crossover design. Firstly, it is difficult to select

referents because an improper referent will result in a biased estimation (Levy et al. 2001a)

and matching will take a lot of time. Secondly, it usually makes a linear assumption between

the health outcome and the predictors. If the assumption is substantially violated, bias may be

induced. For example, some studies have shown that the relationship between temperature

and health outcomes is nonlinear (V or U pattern). Stratification for some variables could

solve the nonlinear problem (Barnett 2007), but it is difficult to determine cut-offs.

Additionally, compared with Poisson regression time-series analysis, the statistical power will

reduce approximately 50% (Bateson & Schwartz 1999; Jaakkola 2003). Finally, it is not

suitable for estimation of long-term or cumulative effects of exposure.

22

2.3.3 Panel Study

A panel study enrols a cohort of individuals at the commencement of the study and then

follows them over time to repeatedly measure changes in health outcomes (WHO 2004). The

exposure measurement could be from a fixed-site ambient monitor or personal monitors. The

panel study design is effective for assessing short-term health effects of air pollutants.

A variety of models have been used to estimate the effects of air pollutants on health in a

panel study setting depending on how to deal with the longitudinal data. Repeated

measurements of health outcomes and exposure in individuals results in the variation within

the individuals (Armitage et al. 2002). Modern approaches to accommodate the complex

variance induced by such longitudinal data include mixed, marginal, transition models and

Bayesian hierarchical models (Diggle et al. 1994) and the analysis can be accomplished by

several software packages such as SAS GENMOD, MIXED and GLIMMIX (SAS Institute

Int. 2006) and Bayesian software such as WinBugs package (Spiegelhalter et al. 2000). A

study that follows a panel of individuals over a longer time period, say multiple years, is

generally known as a cohort or longitudinal study rather than a panel study (Dominici et al.

2003a).

A panel study is often applied to estimate the short-term health effects of air pollution, but the

interpretation of these results is often unclear because it is difficult to observe all individuals

on the same days in a panel study. If all individuals are not observed at the same period, the

estimated effects of the exposure should be considered carefully (Dominici et al. 2003a).

Another concern in the interpretation of a panel study is the within-person variation which

23

may be induced by time-varying confounders such as indicators for day or week, function of

seasonality and weather, and time-varying personal behaviours (Dominici et al. 2003a).

2.3.4 Cohort Study

Cohort studies have been increasingly conducted to estimate long-term health effects of

exposure to air pollution over the last decade (Abbey et al. 1999; Dockery et al. 1993; Hoek et

al. 2002; Jalaludin et al. 2004; Klot et al. 2005; Pope et al. 2002). Both prospective (Filleul et

al. 2005) and retrospective (Hansen et al. 2006) cohort study designs have been used. In a

prospective cohort study design, participants are enrolled at the beginning of study to collect

information about age, sex, education and other subject-specific characteristics (Rothman and

Greenland 1998). They are followed up over time to collect information about health events,

exposure and confounders. A cumulative exposure is often used as the exposure variable

(Dockery et al. 1993). A key design consideration for air pollution cohort studies is to identify

a cohort with sufficient exposure variation in cumulative exposure, particularly when ambient

air pollution measurements are used (Pope et al. 2002). However, in maximizing the

geographical variability of exposure the relative risk estimates from cohort studies are likely

to be confounded by area-specific characteristics (Dominici et al. 2003a).

Survival analysis tools can be used to evaluate the association between air pollution and

mortality. Typically the Cox proportional-hazards model is used to estimate mortality rate

ratios while adjusting for potential confounding variables (McMichael et al. 1996; Watson et

al. 1998). Relative risk is estimated as the ratio of hazards for an exposed group relative to an

unexposed or reference group. The hazard for individual i, )(ti , is modelled as

24

)exp()()( 0 sConfounderZtt ijiij (3)

where )(0 ti represents the baseline hazard for the ith stratum at time t, ijZ is the long term

exposure for the individual. is the coefficient or relative risk parameter, which is assumed to

be the same for all individuals across all strata. Time may be defined as calendar time or age.

Stratification of the baseline hazard function into disjoint groups makes the proportional

hazards assumption flexible and allows separate and not necessarily proportional hazards

across strata (Dominici et al. 2003a).

The cohort study design has some important advantages. Firstly, it can be used to estimate a

long-term or cumulative effect. Secondly, it is relatively easier to control confounders of

individuals characteristics such as gender, age and occupational exposure. Additionally, it

provides the strongest evidence on causality in observational studies (Rothman and Greenland

1998). However, the disadvantages are also obvious. Firstly, it is sometimes unaffordably

expensive in air pollution studies. The reasons are that (1) the health effect of air pollution is

usually small and a large number of participants need to be followed up; (2) all the

participants need to be followed up for a relatively long time so that the long-term or

cumulative effects of exposure can be estimated. Secondly, it is still difficult to avoid

exposure misclassification because exposure to air pollution is still measured at fixed central

sites and air pollution levels vary across the geographic areas during the follow-up period.

Such the misclassification could be reduced to some degree by setting more monitoring

stations close to subjects or using individual-level monitors.

25

2.4 Health effects of Air Pollution

2.4.1 Time-series Studies

There have been numerous studies on the short-term health effects of air pollution, with

emphasis on mortality and hospital admissions. Time-series design is a major approach to

estimating short-term health effects of air pollution in epidemiological studies. Many studies

have found associations between daily changes in ambient air pollution and increased

cardiorespiratory hospital admissions (Anderson et al. 1996; Burnett et al. 1997; Linn et al.

2000; Moolgavkar et al.1997; Moolgavkar 2000; Morris et al. 1995; Schwartz 1999), along

with cardiorespiratory mortality (Hoek et al. 2001; Mar et al. 2000; Rossi et al. 1999) and all

cause mortality (Roemer and van Wijnen 2001). Table 2.1 summarises some recent time-

series studies on short-term health effects of PM10 and ozone around the world, omitting

earlier studies because early findings have already been reviewed (Brunekreef & Holgate

2002; Nyberg & Peshagen 2000).

Single-site time-series studies have been criticized because of substantial variation of the air

pollution effects and the heterogeneity of the statistical approaches used in different studies

(Dominici et al. 2006; Li and Roth 1995). Recently, several multi-site time-series studies have

been conducted in Europe and the United States. Two large collaborative air pollution

projects in Europe and U.S. are summarised below.

26

Table 2.1 Time-series studies of short-term health effects of air pollution after 2000

Study Pollutant Population Methodology Main findings

Czech Republic and rural

region in Germany (Peter et al.

2000)

TSPMortality 1982-

1994

Poisson regression

(GAM)

Czech Republic: 3.8% increase (95% CI: 0.8%, 9.6%) per 100

g/m; No evidence for association in the rural area in German at

the Czech border.

10 US cities (Schwartz 2000b) PM10Mortality 1986-

1993

Poisson regression

(GAM)

0.67% increase for a 10g/m (95% CI: 0.52%, 0.81%). No

difference between summer and winter.

New Zealand (Hales et al.

2000)PM10

Mortality Jun

1988-Dec 1993

Poisson regression

(GAM)

1% increase for all-cause mortality (95% CI: 0.5%, 2.2%); 4%

increase for respiratory diseases (95% CI: 1.5%, 5.9%)

10 US cities (Schwartz 2000c) PM10Mortality 1986-

1993

Distributed lag

model (GAM)

1.4% (95% CI: 1.15%, 1.68%) increase for 10g/m on a single

day using a quadratic distributed lag model; 1.3% increase (95%

CI: 1.04%, 1.56%) using an unstrained lag model

20 US cities (Samet et al.

2000c)

PM10, O3,

SO2, CO,

NO2

Mortality 1987-

1994

Poisson regression

(GAM)

PM10: 0.51% increase (95% CI: 0.07%, 0.93%) per 10 g/m for

all causes; 0.68% increase per 10 g/m for cardiovascular and

respiratory diseases (95% CI: 0.20%, 1.16%)

O3: weaker evidence during the summer;

Other pollutants: no evidence

27

Table 2.1 Time-series studies of short-term health effects of air pollution after 2000 (continued)


Hong Kong (Wong et al. 2002) PM10, SO2Morality 1995-

1998

Poisson regression

(GAM)

Significant associations were found between mortalities for all

respiratory diseases and ischaemic heart diseases (IDH). The

increases for all respiratory mortalities (for a 10 g/m increase

in the concentration) are 0.8% (95% CI: 0.1%, 1.4%) for

PM10and 1.5% (95% CI: 0.1%, 2.9%) for SO2 ; the increases for

IDH are 0.9 % (95% CI: 0.0%, 1.8%) for O3 and 2.8% (95% CI:

1.2%, 4.4%) for SO2.

Seoul Korea (Kim et al. 2003) PM10Mortality 1995-

1999

Poisson regression

(GAM)

3.7% increase (95% CI: 2.1%, 5.4%) for non-accident causes,

13.9% increase (95% CI: 6.8%, 21.5%) for respiratory disease,

4.4 % increase (95% CI: -1.0%, 9.0%) for cardiovascular disease

and 6.3% increase (95% CI: 2.3%, 10.5%) for cerebrovascular

disease per interquartile increase of PM10 (43.12g/m)

Shanghai, China (Kan & Chen

2003)

PM10,

SO2, NO2

Mortality Jun 2000

to Dec 2001

Poisson regression

(GAM)

0.3% increase (95% CI: 0.1%, 0.5%) for PM10, 1.4% increase

(95% CI: 0.8%, 2.0%) for SO2 and 1.5% increase (95% CI:

0.8%, 2.2%) for NO2 per 10g/m

28



Brisbane, Australia

(Petroeschevsky et al. 2001)

BSP, O3,

SO2, NO2

Hospital admission

1987-1994

Poisson regression

(GLM)

BSP: 1.5% increase (95% CI: 0.6%, 2.3%) for respiratory

diseases per 24-hr 10 5 /m increase.

O3: 2.3% increase (95% CI: 0.6%, 2.3%) for respiratory disease

per 8-hr unit increase (pphm).

SO2: 8.0% increase (95% CI: 3.0%, 13.1%) for respiratory

disease per 24-hr unit increase (pphm).

NO2: -0.1% increase (95% CI: -0.3%, 0.2%) for respiratory

disease per 1-hr-max unit increase (pphm).

29



Brazil (Braga et al. 2001)

PM10, O3,

SO2, CO,

NO2

Respira-tory

disease Hospital

admission 1993-

1997

Distributed lag

model

9.4% increase (95% CI: 7.9%, 10.9%) for 2 or less years old

group and 7.0% (95% CI: 5.7%, 8.2%) for all age group per

interquartile PM10 increase (35g/m);


group and 0.8% (95% CI: -7.5%, 9.2%) for all age group per

interquartile O3 increase (46g/m);



interquartile SO2 increase (14g/m);



interquartile CO increase (3ppm);



interquartile NO2 increase (80g/m);

30

In Europe, the APHEA (Air Pollution and Health: a European Approach) studies have

provided many new insights. Initial studies were based on older data (APHEA-1)

(Katsouyanni et al. 1996, 1997; Touloumi et al. 1996), and a new series of studies (APHEA-2)

used data of the PM10 fraction since the late 1990s (Atkinson et al. 2001; Katsouyanni et al.

2001; Tertre et al. 2002). The APHEA-2 mortality studies covered over 43 million people and

29 European cities, which were all studied for more than 5 years in the 1990s. The combined

effect estimate showed that all-cause daily mortality increased by 0.6% (95% CI 0.4%-0.8%)

for each 10 g/m3 increase in PM10 from data involving 21 cities. It was found that there was

heterogeneity between cities with different levels of NO2. The estimated increase in daily

mortality for an increase of 10 g/m in PM10 were 0.2% (95% CI: 0.0%, 0.4%), and 0.8%

(95% CI: 0.7%, 0.9%) in cities with low and high average NO2, respectively (Katsouyanni et

al. 2001). The APHEA-2 hospital admission study involved 38 million people living in eight

European cities. Hospital admissions for asthma and chronic obstructive pulmonary disease

(COPD) increased by 1.0% (95% CI: 0.4%, 1.5%) per 10 g/m3 PM10 increment among

people older than 65 years (Atkinson et al. 2001). Tertre et al. (2002) reported that in 8

European cities, the pooled increase in cardiovascular admissions associated with a 10 g/m

increase in PM10 and black smoke was 0.7% (95% CI: 0.2%, 0.8%) and 1.1% (95% CI: 0.4%,

2.2%), respectively.

In the United States, the National Morbidity, Mortality and Air Pollution Studies (NMMAPS)

focused on the 20 largest metropolitan areas in the USA, involving 50 million inhabitants,

during 1987-94 (Samet et al. 2000a; 2000b; 2000c). All-cause mortality increased by 0.5 %

(95% CI: 0.1%, 0.9%) for each increase of 10 g/m3 in PM10. The estimated increase in the

relative rate of death from cardiovascular and respiratory disease was 0.7 % (95% CI: 0.2%,

1.2%) (Samet et al. 2000c). Effects on hospital admissions were studied in ten cities with a

31

combined population of 1 843 000 individuals older than 65 years (Zanobetti et al. 2000a).

The model used considered simultaneously the effects of PM10 up to the lag of 5 days and

effects of PM10 on chronic obstructive pulmonary disease admissions to be 2.5% (95% CI:

1.8%, 3.3%) and on cardiovascular disease admissions to be 1.3% (95% CI: 1.0%, 1.5%) for

an increase of 10 g/m3 in PM10. Bell et al. (2004) analysed the 95 NMMAPS community

data to examine the association between ozone concentrations and mortality, showing that a

10-ppb increase in the previous weeks ozone was associated with a 0.5% (95% posterior

interval [PI], 0.3% - 0.8%) increase in daily mortality and a 0.64 %(95% PI, 0.31% -0.98%)

increase in cardiovascular and respiratory mortality. The effect estimates of the exposure over

the previous week were larger than those considering only a single days exposure (Bell at el.

2004). Recently, Dominici et al. (2006) examined the short-term association between fine

particulate air pollution and hospital admissions and found that exposure to PM2.5 was

associated with different health outcomes. The largest association was observed for heart

failure, viz., and a 10 g/m increase in PM2.5 was found to be associated with a 1.3% (95%

PI: 0.8%, 1.8%) increase in hospital admissions from heart failure on the same day.

Although time-series studies have shown that day-to-day variations in air pollutant

concentrations are associated with daily deaths and hospital admissions, it is still unclear how

many days, weeks or months air pollution has brought such events forward. Harvesting or

mortality/morbidity displacement means that some cases are occurring only in those to whom

it would have happened in a few days anyway (Schwartz 2000a; Zanobetti et al. 2002). If so,

the increase in cases during and immediately after exposure would be offset by a deficit in

daily deaths a few days later (Schwartz 2001; Zanobetti et al. 2002; Zeger et al. 1999) (Figure

2.1). If air pollution has harvesting or long term effects, normal time-series models are unable

to estimate the effects due to the issues of collinearity and statistical power (Schwartz 2000a,

32

c). The distributed polynomial model (Schwartz 2000c) and the time-scale model (Zanobetti

et al. 2002) have been adopted to explore whether air pollution has harvesting or displacement

effects on daily deaths or hospital admissions. While recent studies have not found obvious

evidence of harvesting, they have found that estimated effects increase when longer lags of air

pollution are included (Schwartz, 2001; Zanobetti et al. 2002).

Figure 2.1 Harvesting phenomenon (Schwartz, Epidemiology, 2001; 12: 56-61)

2.4.2 Case-crossover Studies

Case-crossover study design is another way to estimate short-term health effects of air

pollution in epidemiological studies. In the last decade, the case-crossover design has been

applied in many studies of air pollution and health (Barnett et al. 2005; 2006; Levy et al.

2001b; Neas et al.1999; Schwartz & Lee 1999; Schwartz 2005). For example, Neas et al.

(1999) used a case-crossover study design to estimate the association between air pollution

and mortality in Philadelphia and found a 100 g/m increment in the 48 hours mean level of

TSP was associated with increased all-cause mortality [odd ratio (OR) = 1.06 (95% CI: 1.03,

1.09)]. A similar association was observed for deaths in individuals over 65 years of age (OR:

hallaThis figure is not available online. Please consult the hardcopy thesis available from the QUT Library

33

1.07 (95% CI: 1.04, 1.11)). Levy et al. (2001) estimated the effect of short-term changes in

exposure to particulate matter on the rate of sudden cardiac arrest. The cases were obtained

from a previously conducted population-based case-control study and were combined with

ambient air monitoring data. The results did not show any evidence of a short-term effect of

particulate air pollution on the risk of sudden cardiac arrest in people without previously

recognised heart disease. Schwartz (2005) conducted a case-crossover study to examine the

sensitivity of the association between ozone and mortality when adjusted for temperature and

found that ozone was significantly associated with daily deaths after adjusting for temperature

in 14 US cities. Barnett et al. (2005; 2006) examined the association between air pollution and

cardio-respiratory hospital admissions in Australia and New Zealand cities. The results show

that air pollution arising from common emission sources (eg, CO, NO2 and PM) was

significantly associated with cardiovascular health outcomes in the elderly and respiratory

health outcomes in children.

2.4.3 Panel studies

Many air pollution panel

evaluation of interactive effects …eprints.qut.edu.au/16384/1/cizao_ren_thesis.pdfevaluation of...

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