ESTIMATING THE EFFECTS OF AMBIENT TEMPERATURE ON … · Guo Y, Barnett AG, Zhang Y, Tong S, Yu W, Pan X. (2010) The short-term effect of air pollution on cardiovascular mortality

ESTIMATING THE EFFECTS OF AMBIENT TEMPERATURE ON MORTALITY:

METHODOLOGICAL CHALLENGES AND PROPOSED SOLUTIONS

BY

YUMING GUO

Bachelor of Medicine, Master of Medicine

A thesis submitted for the Degree of Doctor of Philosophy

School of Public Health and Social Work

Faculty of Health

Queensland University of Technology

May 2012

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ii

ABSTRACT

The health impacts of exposure to ambient temperature have been drawing increasing

attention from the environmental health research community, government, society, industries,

and the public. Case−crossover and time series models are most commonly used to examine

the effects of ambient temperature on mortality. However, some key methodological issues

remain to be addressed. For example, few studies have used spatiotemporal models to assess

the effects of spatial temperatures on mortality. Few studies have used a case−crossover

design to examine the delayed (distributed lag) and non-linear relationship between

temperature and mortality. Also, little evidence is available on the effects of temperature

changes on mortality, and on differences in heat-related mortality over time.

This thesis aimed to address the following research questions:

1. How to combine case−crossover design and distributed lag non-linear models?

2. Is there any significant difference in effect estimates between time series and

spatiotemporal models?

3. How to assess the effects of temperature changes between neighbouring days on

mortality?

4. Is there any change in temperature effects on mortality over time?

To combine the case-crossover design and distributed lag non-linear model, datasets

including deaths, and weather conditions (minimum temperature, mean temperature,

maximum temperature, and relative humidity), and air pollution were acquired from Tianjin

China, for the years 2005 to 2007. I demonstrated how to combine the case−crossover design

with a distributed lag non-linear model. This allows the case−crossover design to estimate the

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non-linear and delayed effects of temperature whilst controlling for seasonality. There was

consistent U-shaped relationship between temperature and mortality. Cold effects were

delayed by 3 days, and persisted for 10 days. Hot effects were acute and lasted for three days,

and were followed by mortality displacement for non-accidental, cardiopulmonary, and

cardiovascular deaths. Mean temperature was a better predictor of mortality (based on model

fit) than maximum or minimum temperature.

It is still unclear whether spatiotemporal models using spatial temperature exposure produce

better estimates of mortality risk compared with time series models that use a single site’s

temperature or averaged temperature from a network of sites. Daily mortality data were

obtained from 163 locations across Brisbane city, Australia from 2000 to 2004. Ordinary

kriging was used to interpolate spatial temperatures across the city based on 19 monitoring

sites. A spatiotemporal model was used to examine the impact of spatial temperature on

mortality. A time series model was used to assess the effects of single site’s temperature, and

averaged temperature from 3 monitoring sites on mortality. Squared Pearson scaled residuals

were used to check the model fit. The results of this study show that even though

spatiotemporal models gave a better model fit than time series models, spatiotemporal and

time series models gave similar effect estimates. Time series analyses using temperature

recorded from a single monitoring site or average temperature of multiple sites were equally

good at estimating the association between temperature and mortality as compared with a

spatiotemporal model.

A time series Poisson regression model was used to estimate the association between

temperature change and mortality in summer in Brisbane, Australia during 1996–2004 and

Los Angeles, United States during 1987–2000. Temperature change was calculated by the

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current day’s mean temperature minus the previous day’s mean. In Brisbane, a drop of more

than 3 °C in temperature between days was associated with relative risks (RRs) of 1.16 (95%

confidence interval (CI): 1.02, 1.31) for non-external mortality (NEM), 1.19 (95% CI: 1.00,

1.41) for NEM in females, and 1.44 (95% CI: 1.10, 1.89) for NEM aged 65–74 years. An

increase of more than 3 °C was associated with RRs of 1.35 (95% CI: 1.03, 1.77) for

cardiovascular mortality and 1.67 (95% CI: 1.15, 2.43) for people aged < 65 years. In Los

Angeles, only a drop of more than 3 °C was significantly associated with RRs of 1.13

(95% CI: 1.05, 1.22) for total NEM, 1.25 (95% CI: 1.13, 1.39) for cardiovascular mortality,

and 1.25 (95% CI: 1.14, 1.39) for people aged ≥ 75 years. In both cities, there were joint

effects of temperature change and mean temperature on NEM. A change in temperature of

more than 3 °C, whether positive or negative, has an adverse impact on mortality even after

controlling for mean temperature.

I examined the variation in the effects of high temperatures on elderly mortality (age ≥ 75

years) by year, city and region for 83 large US cities between 1987 and 2000. High

temperature days were defined as two or more consecutive days with temperatures above the

90th

percentile for each city during each warm season (May 1 to September 30). The

mortality risk for high temperatures was decomposed into: a “main effect” due to high

temperatures using a distributed lag non-linear function, and an “added effect” due to

consecutive high temperature days. I pooled yearly effects across regions and overall effects

at both regional and national levels. The effects of high temperature (both main and added

effects) on elderly mortality varied greatly by year, city and region. The years with higher

heat-related mortality were often followed by those with relatively lower mortality.

Understanding this variability in the effects of high temperatures is important for the

development of heat-warning systems.

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In conclusion, this thesis makes contribution in several aspects. Case−crossover design was

combined with distribute lag non-linear model to assess the effects of temperature on

mortality in Tianjin. This makes the case−crossover design flexibly estimate the non-linear

and delayed effects of temperature. Both extreme cold and high temperatures increased the

risk of mortality in Tianjin. Time series model using single site’s temperature or averaged

temperature from some sites can be used to examine the effects of temperature on mortality.

Temperature change (no matter significant temperature drop or great temperature increase)

increases the risk of mortality. The high temperature effect on mortality is highly variable

from year to year.

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KEY WORDS

CLIMATE CHANGE

TEMPERATURE

TEMPERATURE CHANGE

UNSTABLE WEATHER

HEAT EFFECT

MORTALITY

TIME SERIES

CASE–CROSSOVER

SPATIOTEMPORAL MODEL

DISTRIBUTED LAG NON-LINEAR MODEL

HEATWAVES WARNING SYSTEM

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PUBLICATIONS OR MANUSCRIPTS BY THE CANDIDATE ON MATTERS

RELEVENT TO THE THESIS

Guo Y, Barnett AG, Pan X, Yu W, Tong S. (2011) The impact of temperature on mortality in

Tianjin, China: a case−crossover design with a distributed lag non-linear model.

Environmental Health Perspectives 119:1719–1725.

Guo Y, Barnett AG, Tong S. Spatiotemporal model or time series model for assessing city-

wide temperature effects on mortality? Environmental Research (in press), doi:

10.1016/j.envres.2012.09.001.

Guo Y, Barnett AG, Yu W, Pan X, Ye X, Huang C, Tong S. (2011) A large change in

temperature between neighbouring days increases the risk of mortality. PLoS ONE, 6(2),

e16511.

Guo Y, Barnett AG, Tong S. Associations between high temperatures and elderly mortality

differed by year, city and region in the United States. Scientific Reports (In revision).

viii

PUBLICATIONS OR MANUSCRIPTS BY THE CANDIDATE DURING PHD STUDY

Guo Y, Punnasiri K, Tong S (2012). Effects of Temperature on Mortality in Chiang Mai,

Thailand: a time series study. Environmental Health, 11(36), doi:10.1186/1476-069X-11-36).

Guo Y, Jiang F, Peng L, Zhang J, Geng F, Xu J, Zhen C, Shen X, Tong S (2012). The

association between cold spells and pediatric outpatient visits for asthma in Shanghai, China.

PLoS ONE, (7): e42232.



Environmental Health Perspectives 119:1719–1725.



e16511.

Guo Y, Barnett AG, Zhang Y, Tong S, Yu W, Pan X. (2010) The short-term effect of air

pollution on cardiovascular mortality in Tianjin, China: comparison of time series and case–

crossover analyses. Science of the Total Environment, 409(2), pp. 300–306.

Guo Y, Tong S, Zhang Y, Barnett AG, Jia Y, Pan X. (2010) The relationship between

particulate air pollution and emergency hospital visits for hypertension in Beijing, China.

Science of the Total Environment, 408(20), pp. 4446–4450.

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Guo Y, Tong S, Li S, Barnett AG, Yu W, Zhang Y, Pan X. (2010) Gaseous air pollution and

emergency hospital visits for hypertension in Beijing, China: a time-stratified case-crossover

study. Environmental Health, 9(1), pp. 57–63.

Guo Y, Barnett AG, Tong S. Spatiotemporal model or time series model for assessing city-

wide temperature effects on mortality? Environmental Research (in press), doi:

10.1016/j.envres.2012.09.001.

Guo Y, Barnett AG, Tong S. Associations between high temperatures and elderly mortality

differed by year, city and region in the United States. Scientific Reports (In revision).

Guo Y, Li S, Barnett AG, Jaakkola J, Tong S, Zhang Y, Gasparrini A, Pan X.The effects of

ambient temperature on cerebrovascular mortality: an epidemiologic study in four climatic

zones in China. American Journal of Epidemiology (In revision).

Kimlin M, Guo Y (2012). Assessing the impacts of lifetime sun exposure on skin damage

and skin aging using a non-invasive method. Science of the Total Environment 425: 35-41.

Zhang Y, Guo Y, Li G, Zhou J, Jin X, Wang W, Pan X (2012). The Spatial Characteristics

for Ambient Particulate Matter and Mortality in Urban Area of Beijing, China. Science of

The Total Environment, 7 (28);435-436C:14-20.

Tong S, Wang X, Guo Y (2012). Assessing the short-term effects of heatwaves on mortality

and morbidity in Brisbane, Australia: Comparison of case-crossover and time series analyses.

PLoS ONE, 7(5): e37500, doi:10.1371/journal.pone.0037500.

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Banu S, Hu W, Guo Y, Zahirul Islam M, Tong S (2012). Space-time clusters of dengue fever

in Bangladesh. Tropical Medicine & International Health, 7 (19), doi: 10.1111/j.1365-

3156.2012.03038.x.

Xu Z, Eetzel R, Su H, Huang C, Guo Y, Tong S (2012). Impact of ambient temperature on

children's health: A systematic review, Environmental Research 2012, 8 (117):120-31.

Bi Y, Hu W, Liu H, Xiao Y, Guo Y, Chen S, Zhao L, Tong S. Can slide positivity rates

predict malaria transmission? Malaria Journal, 11(117), doi: 10.1186/1475-2875-11-117.

Madaniyzi L, Guo Y, Ye X, KIMDS, Zhang Y, Pan X (2012). The Effects of Metal

Components of Ambient Particulate Matter on Schoolchildren Lung Function in Inner

Mongolia of China, Journal of Occupational and Environmental Medicine (In press).

Yu W, Guo Y, Hu W, Mengersen K, Tong S (2011). The effect of various temperature

indicators on different mortality categories in a subtropical city of Brisbane, Australia.

Science of the Total Environment 409 (18): 3431-3437.

Yu W, Hu W, Mengersen K, Guo Y, Tong S (2011). Assessing the relationship between

global warming and mortality: Lag effects of temperature fluctuations by age and mortality

categories. Environmental Pollution 159 (2011): 1789-1793.

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Yu W, Mengersen K, Hu W, Guo Y, Tong S (2011). Time course of temperature effects on

cardiovascular mortality in Brisbane, Australia. Heart 97(13); 1089-93.

Huang C, Vaneckova P, Wang X, Guo Y, Shilu Tong. Constraints and barriers to public

health adaptation to climate change. American Journal of Preventive Medicine 402: 183–109.

Yu W, Mengersen K, Ye X, Guo Y, Pan X, Huang C, Wang X, Tong S. Daily average

temperature and mortality among the elderly: A meta-analysis and systematic review of

epidemiological literature. International Journal of Biometeorology: 1-13.

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CONFERENCE PRESENTATIONS

GuoY, Li S, Zhang Y, Pan X, Barnett A, Tong S. The effects of ambient temperature on

cerebrovascular deaths in five cities, China.

Oral presentation. The 2012 International Conference of International Society for

Environmental Epidemiology. Columbia, United States. 26–30, August 2011

Madaniyzi L, Guo Y, Ye X, KIMDS, Zhang Y, Pan X. The effects of metal components of

ambient particulate matter on schoolchildren lung function in Inner Mongolia of China.

Poster presentation. The 2012 International Conference of International Society for


Zhang Y, Guo Y, Li G, Zhou J, Jin X, Wang W, Pan X. The Spatial Characteristics for

Ambient Particulate Matter and Mortality in Urban Area of Beijing, China



Yu W, Megersen K, Ye X, Turner L, Hu W, Guo Y, Wang X, Tong S. Projecting Future

Transmission of Malaria under Climate Change Scenarios: Challenges and Opportunities



Guo Y, Barnett AG, Tong S, Yu W, Pan X. The impacts of extreme cold and hot

temperatures on mortality in Tianjin, china: towards response for climate change.

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Environmental Epidemiology. Barcelona, Spain. 13–16, September 2011

Guo Y, Barnett AG, Tong S. The effects of high temperatures on elderly mortality differed

by year, city and region in the United States.



Zhang Y, Guo Y, Tao H, Wang L, Pan, X. The study on the relationship between personal

hygiene and intestinal infectious diseases of rural residents.



Banu S, Tong S, Hu W, Hurst C, Guo Y, Islam MZ. Spatiotemporal clustering analysis of

dengue incidence in Bangladesh.



Ye X, Tong S, Wolff R, Pan X, Guo Y, Vaneckova P. The effect of hot and cold

temperatures on emergency hospital admissions for respiratory and cardiovascular diseases in

Brisbane, Australia.


Environmental Epidemiology. Seoul, Korea August 28–September 1, 2010

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Tong J, Su C, Guo Y, Wang J, Zhang M, Pan X. Study on the status and distribution of ultra-

fine particles during Beijing Olympics in 2008.


Environmental Epidemiology. Seoul, Korea August 28–September 1, 2010

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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 my best knowledge and belief, the thesis contains

no materials previously published or written by another person except where reference is

made.

Signature: ........................................................

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ACKNOWLEDGEMENTS

I would like to thank the following people who have helped me make this thesis possible.

Without their help, I could not finish my PhD study.

Thanks to my supervisors, Professor Shilu Tong, Associate Professor Adrian Barnett, and

Professor Xiaochuan Pan for their experienced professional guidance. Professor Shilu Tong,

my principal supervisor, provided me with an opportunity to conduct my PhD study at QUT.

He gave me full freedom to realise my ideas for the PhD research and provided his best

support. He responded and revised my manuscripts and documents very promptly and

provided detailed feedback, even though he was very busy. Associate Professor Adrian

Barnett, my associate supervisor, spent a lot of time to teach me statistics. I learned greatly

from him, especially concerning the R language. I cannot forget how hard he helped me

check models at the regular meeting every week. I thank him for his flexibility and patience

for my PhD study. Professor Xiaochuan Pan, my external associated supervisor, was very

supportive and helped me greatly in data collection and data management.

I also want to thank Miss Xiaoyu Wang, Dr. Weiwei Yu and my other colleagues for their

help during my PhD study.

I would like to thank Queensland University of Technology for providing me with

scholarships to conduct my PhD study.

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I want to thank School of Public Health and Institute of Health and Biomedical Innovation,

language advisors in learning and training department, High Performance Computer and

Research Support Unit, and IT help desk who have helped my research proceed smoothly.

I would like to thank my family and friends for their encouragement and care. They gave me

unconditional love and support.

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LIST OF CONTENTS

ABSTRACT ............................................................................................................................... ii

KEY WORDS ........................................................................................................................... vi

PUBLICATIONS OR MANUSCRIPTS BY THE CANDIDATE ON MATTERS

RELEVENT TO THE THESIS ............................................................................................... vii

PUBLICATIONS OR MANUSCRIPTS BY THE CANDIDATE DURING PHD STUDY viii

CONFERENCE PRESENTATIONS ...................................................................................... xii

STATEMENT OF AUTHORSHIP ......................................................................................... xv

ACKNOWLEDGEMENTS .................................................................................................... xvi

LIST OF TABLES ................................................................................................................. xxii

LIST OF FIGURES .............................................................................................................. xxiv

LIST OF ABBREVIATION .............................................................................................. xxviii

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

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

1.2 AIM AND OBJECTIVES ................................................................................................ 5

1.3 SIGNIFICANCE OF THE STUDY ................................................................................. 6

1.4 CONTENTS AND STRUCTURE OF THIS THESIS .................................................... 7

CHAPTER 2: THE EFFECTS OF AMBIENT TEMPERATURE ON MORTALITY: A

LITERATURE REVIEW .......................................................................................................... 8

2.1 CLIMATE CHANGE ...................................................................................................... 8

CLIMATE CHANGE, THE INDOOR ENVIRONMENT, AND HEALTH .......................... 10

2.2 THE RELATIONSHIP BETWEEN TEMPERATURE AND MORTALITY .............. 11

2.3 THE EFFECTS OF TEMPERATURE ON THE HUMAN BODY .............................. 15

2.4 MODELS FOR ASSESSING THE RELATIONSHIP BETWEEN TEMPERATURE

AND MORTALITY............................................................................................................. 20

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2.5 MODELS ASSESSING THE LAG EFFECTS OF TEMPERATURE IN MORTALITY

.............................................................................................................................................. 27

2.6 TEMPERATURE MEASURES AND MORTALITY .................................................. 31

2.7 INTERACTIVE EFFECTS BETWEEN TEMPERATURE AND AIR POLLUTION

ON MORTALITY ............................................................................................................... 33

2.8 GROUPS VULNERABLE TO TEMPERATURE EFFECTS ...................................... 34

2.9 SUMMARY ................................................................................................................... 37

2.10 REFERENCES ............................................................................................................. 38

CHAPTER 3: STUDY DESIGN AND METHODOLOGY ................................................... 57

3.1 STUDY POPULATION ................................................................................................ 57

3.3 DATA COLLECTION AND MANAGEMENT ........................................................... 61

3.4 DATA ANALYSIS ........................................................................................................ 64

3.4 RATIONALE FOR CHOOSING STUDY SITES OR TEMPERATURE MEASURES

.............................................................................................................................................. 67

3.5 REFERENCES ............................................................................................................... 69

CHAPTER 4: THE IMPACT OF TEMPERATURE ON MORTALITY IN TIANJIN,

CHINA: A CASE−CROSSOVER DESIGN WITH A DISTRIBUTED LAG NON-LINEAR

MODEL ................................................................................................................................... 71

4.2 INTRODUCTION.......................................................................................................... 74

4.3 MATERIALS AND METHODS ................................................................................... 76

4.4 RESULTS ...................................................................................................................... 80

4.5 DISCUSSION ................................................................................................................ 88

4.6 CONCLUSIONS ............................................................................................................ 94

4.7 REFERENCES ............................................................................................................... 95

4.8 SUPPLEMENTAL MATERIAL CHAPTER 4 ........................................................... 100

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CHAPTER 5: SPATIOTEMPORAL MODEL OR TIME SERIES MODEL FOR

ASSESSING CITY-WIDE TEMPERATURE EFFECTS ON MORTALITY? ................... 110

5.1 ABSTRACT ................................................................................................................. 111

5.2 INTRODUCTION........................................................................................................ 112

5.3 MATERIALS AND METHODS ................................................................................. 113

5.4 RESULTS .................................................................................................................... 120

5.5 DISCUSSION .............................................................................................................. 125

5.6 CONCLUSION ............................................................................................................ 130

5.7 REFERENCES ............................................................................................................. 131

5.8 SUPPLEMENTAL MATERIALS CHAPTER 5......................................................... 135

CHAPTER 6: A LARGE CHANGE IN TEMPERATURE BETWEEN NEIGHBOURING

DAYS INCREASES THE RISK OF MORTALITY ............................................................ 142

6.1 ABSTRACT ................................................................................................................. 143

6.2 INTRODUCTION........................................................................................................ 144

6.3 MATERIAL AND METHODS ................................................................................... 145

6.4 RESULTS .................................................................................................................... 148

6.5 DISCUSSION .............................................................................................................. 157

6.6 CONCLUSION ............................................................................................................ 161

6.7 REFERENCES ............................................................................................................. 162


CHAPTER 7: ASSOCIATIONS BETWEEN HIGH TEMPERATURES AND ELDERLY

MORTALITY DIFFERED BY YEAR, CITY AND REGION IN THE UNITED STATES

................................................................................................................................................ 171

7.1 ABSTRACT ................................................................................................................. 172

7.2 INTRODUCTION........................................................................................................ 173

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7.3 MATERIAL AND METHODS ................................................................................... 174

7.4 RESULTS .................................................................................................................... 178

7.5 DISCUSSION .............................................................................................................. 184

7.6 CONCLUSION ............................................................................................................ 188

7.7 REFERENCES ............................................................................................................. 189


CHAPTER 8: GENERAL DISCUSSION ............................................................................. 198

8.1 METHODOLOGICAL DEVELOPMENT .................................................................. 198

8.2 IMPLICATION OF THE RESEARCH ....................................................................... 200

8.3 STRENGTHS OF THIS THESIS ................................................................................ 203

8.4 LIMITATIONS OF THIS THESIS ............................................................................. 205

8.5 RECOMMENDATIONS FOR FUTURE RESEARCH DIRECTIONS ..................... 206

8.6 CONCLUSIONS .......................................................................................................... 208

REFERENCES ...................................................................................................................... 210

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LIST OF TABLES

TABLE 2.1: THE REPORTS FOR CLIMATE CHANGE AND HEALTH FROM LOCAL

GOVERNMENTS TO INTERNAL ORGANIZATIONS. ............................................. 10

TABLE 4.1: SUMMARY STATISTICS OF DAILY WEATHER CONDITIONS AND

MORTALITY IN TIANJIN, CHINA, 2005–2007 ................................................. 81

TABLE 4.2: SPEARMAN’S CORRELATION COEFFICIENTS BETWEEN WEATHER

CONDITIONS IN TIANJIN, CHINA, 2005–2007 ................................................. 81

TABLE 4.3: THE CUMULATIVE COLD AND HOT EFFECTS OF MEAN TEMPERATURE ON

MORTALITY CATEGORIES ALONG THE LAG DAYS, USING A “DOUBLE

THRESHOLD-NATURAL CUBIC SPLINE” DLNM WITH 4 DEGREES OF FREEDOM

NATURAL CUBIC SPLINE FOR LAG. .................................................................. 87

TABLE 5.1: SUMMARY STATISTICS FOR KRIGED TEMPERATURE, AVERAGED

TEMPERATURE, BRISBANE CENTRE’S TEMPERATURE, PM10, O3, RELATIVE

HUMIDITY, ELEVATION, AND MORTALITY IN BRISBANE CITY BETWEEN 2000

AND 2004 .................................................................................................... 124

TABLE 5.2: SPEARMAN CORRELATIONS BETWEEN KRIGED TEMPERATURE,

AVERAGED TEMPERATURE, BRISBANE CENTRE’S TEMPERATURE, PM10, O3,

AND RELATIVE HUMIDITY IN BRISBANE CITY BETWEEN 2000 AND 2004 ...... 126

TABLE 5.3: RELATIVE RISKS OF MORTALITY ASSOCIATED WITH HOT AND COLD

TEMPERATURES USING FOUR DIFFERENT MODELS ASSUMING A V-SHAPED

TEMPERATURE RISK WITH A THRESHOLD AT 28 °C ...................................... 127

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TABLE 6.1: SUMMARY STATISTICS FOR DAILY WEATHER CONDITIONS, AIR

POLLUTANTS, AND MORTALITY IN BRISBANE, AUSTRALIA AND LOS ANGELES,

UNITED STATES ........................................................................................... 149

TABLE 6.2: SPEARMAN’S CORRELATION BETWEEN DAILY WEATHER CONDITIONS

AND AIR POLLUTANTS IN BRISBANE, AUSTRALIA AND LOS ANGELES, UNITED

STATES ........................................................................................................ 150

TABLE 6.3: THE ASSOCIATIONS BETWEEN TEMPERATURE CHANGE AND MORTALITY

IN BRISBANE, AUSTRALIA AND LOS ANGELES, UNITED STATES.................. 151

TABLE 7.1: THE DISTRIBUTION OF YEARLY HIGH TEMPERATURE EFFECTS ON

ELDERLY MORTALITY BY REGION BETWEEN 1987 AND 2000 ....................... 179

TABLE 7.2: POOLED HIGH TEMPERATURE EFFECTS ON ELDERLY MORTALITY BY

REGION BETWEEN 1987 AND 2000 ............................................................... 180

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LIST OF FIGURES

FIGURE 2.1: COMMON SHAPES DESCRIBING THE RELATIONSHIP BETWEEN

TEMPERATURE AND MORTALITY. ................................................................... 12

FIGURE 2.2: FITTED DEATHS (SCALED TO BE A PERCENTAGE OF MEAN DAILY

DEATHS) IN SOFIA (LEFT) AND LONDON. PLOTTED AGAINST TEMPERATURE,

TWO DAY MEAN (TOP) AND TWO WEEK MEAN (PATTENDEN, NIKIFOROV, &

ARMSTRONG, 2003). ..................................................................................... 13

FIGURE 2.3: MODES OF HEAT TRANSFER BETWEEN HUMAN BODY AND

ENVIRONMENT. SOURCE: HTTP://WWW.THERMOANALYTICS.COM/HUMAN-

SIMULATION/THERMAL-MANIKIN. ................................................................. 16

FIGURE 2.4: HUMAN THERMOREGULATION WHEN PEOPLE EXPOSE TO HOT AND

COLD ENVIRONMENTAL TEMPERATURES. SOURCE:

HTTP://EXERCISEPHYSIOLOGIST.WORDPRESS.COM/2012/02/15/THE-HUMAN-

HOMOEOTHERMY/. ......................................................................................... 19

FIGURE 2.5: 3-D PLOT OF RR ALONG TEMPERATURE AND LAGS, USING DATA FROM

NMMAPS FOR CHICAGO DURING THE PERIOD 1987–2000. .......................... 28

FIGURE 2.6: PLOT OF OVERALL RR, USING DATA FROM THE NMMAPS FOR

CHICAGO DURING THE PERIOD 1987–2000. ................................................... 29

FIGURE 3.1: THE LOCATION OF TIANJIN, CHINA. .................................................. 58

FIGURE 3.2: THE LOCATION OF BRISBANE, AUSTRALIA. ...................................... 59

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FIGURE 3.3: THE 83 LARGE CITIES AND 7 REGIONAL GROUPS IN UNITED STATES

FROM NMMAPS STUDY. .............................................................................. 60

FIGURE 4.1: RELATIVE RISKS OF MORTALITY TYPES BY MEAN TEMPERATURE (°C),

USING A NATURAL CUBIC SPLINE–NATURAL CUBIC SPLINE DLNM WITH 5 DF

NATURAL CUBIC SPLINE FOR TEMPERATURE AND 4 DF FOR LAG. (A)

NONACCIDENTAL, (B) CARDIOPULMONARY, (C) CARDIOVASCULAR, AND (D)

RESPIRATORY MORTALITY. ............................................................................ 83

FIGURE 4.2: THE ESTIMATED OVERALL EFFECTS OF MEAN TEMPERATURE (°C)

OVER 28 DAYS ON MORTALITY TYPES, USING A NATURAL CUBIC SPLINE–

NATURAL CUBIC SPLINE DLNM WITH 5 DF NATURAL CUBIC SPLINE FOR

TEMPERATURE AND 4 DF FOR LAG. (A) NONACCIDENTAL, (B)

CARDIOPULMONARY, (C) CARDIOVASCULAR, AND (D) RESPIRATORY

MORTALITY. THE BLACK LINES ARE THE MEAN RELATIVE RISKS, AND THE

BLUE REGIONS ARE 95% CIS. ........................................................................ 84

FIGURE 4.3: THE ESTIMATED EFFECTS OF A 1°C DECREASE IN MEAN TEMPERATURE

BELOW THE COLD THRESHOLD (LEFT) AND OF A 1°C INCREASE IN MEAN

TEMPERATURE ABOVE THE HOT THRESHOLD (RIGHT) ON MORTALITY TYPES

OVER 27 DAYS OF LAG, USING A DOUBLE THRESHOLD–NATURAL CUBIC SPLINE

DLNM WITH 4 DF NATURAL CUBIC SPLINE FOR LAG. (A) NONACCIDENTAL, (B)

CARDIOPULMONARY, (C) CARDIOVASCULAR, AND (D) RESPIRATORY

MORTALITY. THE BLACK LINES ARE MEAN RELATIVE RISKS, AND BLUE

xxvi

REGIONS ARE 95% CIS. THE COLD AND HOT THRESHOLDS WERE 0.8°C AND

24.9°C FOR NONACCIDENTAL MORTALITY (A), 0.1°C AND 25.3°C FOR

CARDIOPULMONARY MORTALITY (B), 0.6°C AND 25.1°C FOR

CARDIOVASCULAR MORTALITY (C), 0.7°C AND 24.8°C FOR RESPIRATORY

MORTALITY (D). ............................................................................................ 85

FIGURE 4.4: COMPARISON OF THE IMPACTS OF TEMPERATURE ON NONACCIDENTAL

MORTALITY IN DIFFERENT POPULATIONS ORDERED BY LATITUDE. ................. 89

FIGURE 5.1: THE 19 MONITORING SITES FOR TEMPERATURE IN OR AROUND

BRISBANE CITY, THE GREY REGIONS ARE STATISTICAL LOCAL AREAS OF

BRISBANE CITY, THE BLUE AREAS ARE WATER. ........................................... 116

FIGURE 5.2: MEAN DAILY MAXIMUM TEMPERATURES FOR THE 163 STATISTIC

LOCAL AREAS OF BRISBANE CITY BETWEEN JANUARY 2000 AND DECEMBER

2004. THE BLUE AREAS ARE WATER. ........................................................... 120

FIGURE 5.3: THE RELATIONSHIP BETWEEN TEMPERATURE AND MORTALITY IN

BRISBANE BETWEEN 2000 AND 2004, USING DIFFERENT MODELS WITH THREE

DEGREES OF FREEDOM FOR TEMPERATURE. ................................................. 121

FIGURE 5.4: THE RELATIONSHIP BETWEEN TEMPERATURE AND MORTALITY IN

BRISBANE BETWEEN 2000 AND 2004, USING DIFFERENT MODELS WITH FOUR

DEGREES OF FREEDOM FOR TEMPERATURE. ................................................. 122

FIGURE 6.1: THE ASSOCIATIONS BETWEEN TEMPERATURE CHANGE AND NON-

EXTERNAL MORTALITY, CARDIOVASCULAR MORTALITY, AND RESPIRATORY

xxvii

MORTALITY USING MODEL (6.1) IN BRISBANE, AUSTRALIA (LEFT SIDE) AND

LOS ANGELES, UNITED STATES (RIGHT SIDE). ............................................. 153

FIGURE 6.2: THE ASSOCIATIONS BETWEEN TEMPERATURE CHANGE AND NON-

EXTERNAL MORTALITY BY AGE GROUP USING MODEL (6.1) IN BRISBANE,

AUSTRALIA (LEFT SIDE) AND LOS ANGELES (RIGHT SIDE), UNITED STATES.

.................................................................................................................... 154

FIGURE 6.3: BIVARIATE RESPONSE SURFACES OF THE TEMPERATURE CHANGE AND

MEAN TEMPERATURE FOR NON-EXTERNAL MORTALITY, SUBGROUPS OF

MORTALITY USING MODEL (6.3) IN BRISBANE, AUSTRALIA. ........................ 155

FIGURE 6.4: BIVARIATE RESPONSE SURFACES OF THE TEMPERATURE CHANGE AND

MEAN TEMPERATURE FOR NON-EXTERNAL MORTALITY, SUBGROUPS OF

MORTALITY USING MODEL (6.3) IN LOS ANGELES, UNITED STATES. ........... 156

FIGURE 7.1: BOXPLOTS OF THE YEARLY HIGH TEMPERATURE EFFECTS ON ELDERLY

MORTALITY BY CITIES BETWEEN 1987 AND 2000. CITY ABBREVIATIONS ARE

EXPLAINED IN SUPPLEMENTAL MATERIAL CHAPTER 7, TABLE S7.1. .......... 181

FIGURE 7.2: MEAN HIGH TEMPERATURE EFFECTS ON ELDERLY MORTALITY BY

REGION BETWEEN 1987 AND 2000 USING A UNIVARIATE META-ANALYSIS. . 182

FIGURE 7.3: TREND IN THE EFFECTS OF HIGH TEMPERATURES ON THE ELDERLY

MORTALITY BY REGION BETWEEN 1987 AND 2000 USING A BAYESIAN

HIERARCHICAL MODEL. ............................................................................... 183

xxviii

LIST OF ABBREVIATION

ACF

Auto-correlation function

AIC

Akaike’s information criterion

CI

Confidence interval

CVM

Cardiovascular mortality

DF

Degree of freedom

DLNM

Distributed lag non-linear model

GAM

Generalized additive model

GAMM

Generalized additive mixed model

ICD

International Classification of Diseases

IPCC

Intergovernmental Panel on Climate Change

NEM

Non-external mortality

NMMAPS The National Morbidity Mortality Air Pollution Study

NO2

Nitrogen dioxide

O3

Ozone

PACF

Partial auto-correlation function

PM10

Particulate matter less than 10 μm in aerodynamic diameter

RM

Respiratory mortality

RR

Relative risk

SO2

Sulphur dioxide

UNEP

United Nations Environment Programme

WHO

World Health Organization

WMO

World Meteorological Organization

1

CHAPTER 1: INTRODUCTION

1.1 Background

Climate change is one of the most serious challenges for human health in the 21st century, as

it will directly or indirectly affect most populations (Costello et al., 2009). Future climate

change will increase the frequency, intensity and duration of heat waves (IPCC, 2007a).

Heat-related mortality has become a matter of increasing public health significance, as

climate change continues.

Studies have examined hot and cold temperatures in relation to total non-accidental deaths

and cause-specific deaths (Stafoggia, Forastiere, Agostini, Biggeri, Bisanti, Cadum, Caranci,

de' Donato, et al., 2006). Exposure to both low and high ambient temperatures increases the

risk of death and therefore the temperature-mortality relations appear J-, V- or U-shaped,

with thresholds corresponding to the lowest mortality. Temperature thresholds for an

increased mortality are generally higher in warmer climates (Patz, Campbell-Lendrum,

Holloway, & Foley, 2005; Yu, Mengersen, Wang, et al., 2011), as people adapt to their local

climates, through physiological, behavioural and cultural adaptation.

Many personal and environmental factors may modify the effects of temperature on human

health, including age, gender, chronic disease, economic disadvantage, demographic factors,

intensity of urban heat islands, housing characteristics, access to air conditioning and

availability of health care services (Kovats & Hajat, 2008). Populations in developing

countries are anticipated to be especially sensitive to impacts of climate change, as they have

limited adaptive capacity and more vulnerable people (Costello, et al., 2009).

2

Previous studies have identified that extreme temperatures have impacts on vulnerable people

(Basu, 2009b; Basu & Ostro, 2008a; Kovats & Hajat, 2008). The elderly and women are

particularly vulnerable to extreme temperatures (Hajat, Kovats, & Lachowycz, 2007b; P.

Vaneckova, Beggs, de Dear, & McCracken, 2008). People with particular diseases such as

cardiovascular, respiratory problems, diabetes, mental disorders are more sensitive to

temperature than healthy people (Basu, Dominici, & Samet, 2005; McMichael et al., 2008;

Stafoggia et al., 2006).

There are a few challenges in the assessment of temperature-mortality relationship. The

frequency, intensity and duration of weather extremes (e.g. heat waves, floods and cyclones)

are projected to increase as climate change continues (WHO/WMO/UNEP, 1996), and

unstable weather patterns (e.g. a significant drop/increase in temperature) are also more likely

to occur in the coming decades (Faergeman, 2008). However, less evidence is available on

the possible mortality effects due to temperature change between neighbouring days.

Epidemiological studies on heat-related mortality could be used by decision makers to

establish a warning system for high temperatures, by giving information on the heat threshold

and the expected increase in deaths above the threshold. Such studies are also useful for

estimating the potential health effects of climate change. However, most previous studies

only considered high temperature effects by averaging over the whole study period, and

ignored the variability in effects from year to year. Effects may vary from year to year

because of differences in the at-risk population (e.g., more elderly people), or because of

increased adaptation over time (Sheridan & Kalkstein; Stafoggia, Forastiere, Michelozzi, &

Perucci, 2009a).

3

Time series and case–crossover analyses are the most common methods used to estimate the

short-term effects of temperature (or air pollution) on health (Fung, Krewski, Chen, Burnett,

& Cakmak, 2003; J. Schwartz, 2004). Time series analysis allows for over dispersion

associated with the Poisson distribution and controls for long-term trend and seasonality

using nonparametric or parametric splines. The case−crossover design controls for seasonal

effects and secular trends by matching case and control days in relatively small time windows

(e.g., calendar month). This controls for season using a step-function rather than a smooth

spline function (Barnett & Dobson, 2010).

Most previous studies used the case–crossover design with relatively inflexible models to

investigate the effects of temperature on mortality, such as assuming a linear effect for

temperature in each season, with a single lag model, or moving average lag model (Basu,

Feng, & Ostro, 2008; Green et al., 2010). No study has examined non-linear and delayed

effects of temperature on mortality within a case–crossover design.

To estimate the impact of temperature on mortality, most studies used daily temperature data

from one monitoring site or daily mean values from a network of monitoring sites, which

may result in a measurement error for temperature exposure (Zhang et al., 2011). Studies

have shown that there is spatial variation in outdoor temperatures within cities and their

surroundings (Aniello, Morgan, Busbey, & Newland, 1995; Kestens et al., 2011; Lo,

Quattrochi, & Luvall, 1997). Urban areas usually have higher temperatures because of the

heat island effect (www.epa.gov/hiri/about/index.html). Smargiassi et al. (2009) found that

hotter areas within a city had a greater risk of heat-related deaths compared with cooler areas.

These results suggest that using temperature from one monitoring site or averaged values

http://www.epa.gov/hiri/about/index.html

4

from a network of sites may underestimate the risks of temperature on mortality. Geo-

statistical techniques have been used to model regional temperatures. Previously, these

models have been used to estimate the health effects of air pollution within cities (Lee &

Shaddick, 2010; Shaddick, Lee, Zidek, & Salway, 2008; Whitworth, Symanski, Lai, & Coker,

2011). However, few studies have used spatial methods to quantify the impact of temperature

on mortality (Kestens, et al., 2011; Smargiassi, et al., 2009).

5

1.2 Aim and objectives

Aim

This thesis aims to address methodological issues when examining the relationship between

temperature and mortality, and to assess the effects of temperature change between

neighbouring days on mortality, and to examine the variation in the effects of high

temperatures on mortality.

Specific objectives

1.2.1 To combine the case-crossover design and distributed lag non-linear model, I examined

the effects of temperature on cause-specific mortality in Tianjin, China.

1.2.2 To compare time series and spatiotemporal analyses, I examined the effects of

temperature on mortality in Brisbane, Australia.

1.2.3 To estimate the association between temperature change and mortality, I used time

series Poisson regression models for data from Brisbane, Australia and Los Angeles, United

States.

1.2.4 To examine the variation in the effects of high temperatures on elderly mortality

(age ≥ 75 years) by year, city, and region, I used data from 83 large US cities.

6

1.3 Significance of the study

This thesis will help gain a better understanding of some key methodological issues in the

assessment of the temperature effects on mortality. It will supply significant information for

future research on heat-related mortality concerning the model choice.

I also examined the effects of temperature change between neighbouring days on mortality,

which will provide additional information for the development of government policies and

public health strategies concerning temperature effects. In addition, I assessed the variation in

temperature effects on elderly mortality across years, which can be used to promote capacity

building for public health adaptation to cope with high temperature effects.

This thesis estimated the impacts of temperature on mortality in Tianjin. It provides

significant insights to assist policy makers in planning and communicating the health risks of

temperatures to the public in Tianjin. It may also promote considerations on capacity building

for adaptation in the face of extreme temperatures, by broadening and deepening the mindset

of stakeholders involved in the process of exploring possible futures.

Evidence-based assessment of temperature effects on mortality is a key challenge to both

society and government decision makers worldwide. In this thesis, I used most advanced

models to quantify both linear and non-linear, acute and delayed effects of temperature on

mortality. Additionally, I examined whether spatiotemporal model is better than time series

model in assessing temperature-mortality relationship. These findings may make a

contribution to this internationally important but challenging field.

7

1.4 Contents and structure of this thesis

I wrote this thesis using a publication style based on four manuscripts.

Chapter 2 provides a literature review for the temperature effects on mortality. This chapter

summarises previous research findings and current knowledge gaps when examining the

effects of temperature on mortality. Chapter 3 introduces the study design, materials and data

analysis.

The four manuscripts are presented in chapters 4–7. They are written in their conventional

publication style according to each particular journal. Chapter 4 combines a case–crossover

design with distributed lag non-linear model to examine the effects of temperature on cause-

specific mortality in Tianjin, China. Chapter 5 compares the time series and spatiotemporal

analyses for the effects of temperature on mortality in Brisbane, Australia. Chapter 6

examines the effects of temperature change between neighboring days on cause-specific

mortality in Brisbane, Australia, and Los Angeles, United States. Chapter 7 examines the

variation in high temperature effects on elderly mortality across years in 83 large cities in

United States. Chapter 8 discusses overall significance, limitations and probable implications.

The conclusions are made based on results of the four manuscripts. Some directions for

future research are also proposed.

8

CHAPTER 2: THE EFFECTS OF AMBIENT TEMPERATURE ON

MORTALITY: A LITERATURE REVIEW

2.1 Climate change

Global climate is rapidly changing within the last century, due to greenhouse gas emissions

largely driven by human activity. The Intergovernmental Panel on Climate Change has

concluded that warming of the climate system is unequivocal (IPCC, 2007b). From 1906 to

2005 the planet’s average temperature has increased by 0.74 °C, and the temperature has

increased by 0.55 °C from 1982 to 2007. Climate change is projected not only to increase the

global average temperature by between 1.1 °C and 6.4 °C by 2100, but also to increase the

frequency of extreme weather events (e.g., heat waves, cyclones and storms) (Medina-Ramón

& Schwartz, 2007).

Observational evidence from around the world shows that many systems are already being

affected by climate change, particularly temperature increases (IPCC, 2007a). Not only is

climate change an environmental issue, but it also affects human health directly and indirectly

through various pathways (e.g., extreme temperature, floods, droughts and infectious

diseases).

Climate change potentially affects every person, but there will likely be a greater impact on

vulnerable people (e.g., elderly, children, and people with chronic diseases) by climate

change (WHO, 2008). Vulnerability not only depends on the level of climate change, but also

population characteristics (e.g., age, gender and adaptation ability). The elderly and children

are more sensitive and have less adaptive capacity to deal with climate change (e.g., heat

9

waves, cold spells) (Adger, 2006; IPCC, 2007a). The health impacts will depend on the rate

and magnitude of changes in climate, and will be modified by social, economic, demographic

and infrastructure factors. All these factors can influence the sensitivity of populations to

climate change, and their adaptive capacity to manage the health effects of climate change

(Ebi, Kovats, & Menne, 2006a; Haines, Kovats, Campbell-Lendrum, & Corvalan, 2006;

Kovats & Hajat, 2008; Reid et al., 2009; Rey et al., 2009).

Policy makers from local and national governments to international organizations have been

increasing awareness of the influence of climate change on human health, and developed a

number of programs towards climate change mitigation and adaptation, and projected the

impacts of future climate change (Table 2.1).

There has been a growing interest in assessing how climate change influences health,

especially for relationships between ambient temperature and mortality and morbidity

(Barnett, Tong, & Clements, 2010; Michelozzi et al., 2006; Stafoggia, et al., 2009a). This

literature review assesses the effects of ambient temperature on mortality, focusing on the

methodological challenges and research opportunities in examining the relationship between

temperature and health.

10

Table 2.1: The reports for climate change and health from local governments to internal

organizations.

Organisation Year Title

Queensland,

Australia

2011

Climate change: Adaptation for Queensland

PwC Australia 2011

Protecting human health and safety during severe and

extreme heat events: A national framework

Marmot Review

Team, UK

2011

The health impacts of cold homes and fuel poverty

Committee on the

Effect of Climate

Change on Indoor

Air Quality and

Public Health, USA

2011

Climate change, the indoor environment, and health

WHO 2009 Protecting health from climate change: Connecting science,

policy and people

IPCC 2012 Managing the risks of extreme events and disasters to

advance climate change adaptation

11

2.2 The relationship between temperature and mortality

Evidence shows that temperature can directly or indirectly impact human health (Alderson,

1985; Baker-Blocker, 1982; Rogot & Blackwelder, 1970). Exposure to extreme temperatures

(heat waves or cold spells) is related to both mortality and morbidity (Luber & McGeehin,

2008). A number of epidemiological studies have reported both cold and high temperatures

were associated with non-accidental deaths (Baccini et al., 2008; Curriero et al., 2002a;

McMichael, et al., 2008; Stafoggia, et al., 2006), cause-specific deaths (Barnett, 2007; Pan,

Li, & Tsai, 1995; Rey et al., 2007), and other health outcomes such as emergency hospital

visits and hospital admissions (Hansen et al., 2008; Knowlton et al., 2009; Smith, Coyne,

Smith, & Mercier, 2003; Wang, Barnett, Hu, & Tong, 2009).

Extreme temperatures have significant impacts on health (Kovats & Hajat, 2008). For

example, over 700 excess people died during the 1995 Chicago heatwave (Semenza et al.,

1996a). Heatwaves in 2003 caused 15,000 excess deaths in France alone (Fouillet et al., 2007;

Tertre et al., 2006), and over 70,000 deaths across Europe countries (Conti et al., 2005;

Johnson et al., 2005). There were 274 excess cardiovascular deaths during the 1987 Czech

Republic cold spells (Kysely, Pokorna, Kyncl, & Kriz, 2009), and 370 excess deaths occurred

during the 2006 Moscow cold spells (Revich & Shaposhnikov, 2008a).

The relationship between temperature and mortality tend to be V-, U- or J-shaped in most

studies (Figures 2.1 and 2.2), with thresholds corresponding to the lowest mortality or

morbidity (Curriero et al., 2002; Kalkstein & Davis, 1989). The base of the V-, U- or J-shape

is the temperature (or temperature range) at which mortality rates are smallest, and from

12

which mortality levels will increase if the temperature increases or decreases (Kalkstein &

Davis, 1989).

Figure 2.1: Common shapes describing the relationship between temperature and mortality.

Cold and hot thresholds are generally higher in cities closer to the equator (Patz, et al., 2005),

as people have acclimatised to their local climates, through physiological, behavioural and

cultural adaptation. The optimum temperature varies according to the population, region and

climate type. For example, in the Netherlands during 1979–1997, the optimum temperature

13

was 16.5 °C for minimising non-accidental, cardiovascular and respiratory mortality, while

the optimum temperature in the younger age group was 15.5 °C for mortality due to

malignant neoplasm and 14.5 °C for non-accidental mortality (Huynen, 2001).

Figure 2.2: Fitted deaths (scaled to be a percentage of mean daily deaths) in Sofia (left) and

London. Plotted against temperature, two day mean (top) and two week mean (Pattenden,

Nikiforov, & Armstrong, 2003).

Several methods have been used to find cold and hot thresholds. One simple way is to plot

the association between temperature and mean mortality at each temperature. Visual

14

inspection can then be used to find the temperature threshold (Donaldson, Keatinge, &

Nayha, 2003). Many recent studies have used splines (such as natural cubic spline,

polynomial, B-spline, penalised spline) to examine the effects of temperature on mortality

and morbidity. Smoothed curves for the temperature-mortality relationship are estimated and

plotted using spline functions. Temperature points that correspond to the lowest mortality risk

were usually chosen as the temperature thresholds (El-Zein, Tewtel-Salem, & Nehme, 2004).

In recent years, advanced statistical methods have been developed to find temperature

thresholds. These models also considered the lag effects of temperature. A segmented method

was developed to find temperature thresholds (Michelozzi et al., 2006; Muggeo, 2003).

Another way is assuming the temperature-mortality relationship is linear in different seasons.

So the temperature thresholds were not tested, but instead the impact of temperature was

examined separately in Spring, Summer, Autumn, and Winter (Basu & Samet, 2002; Carson,

Hajat, Armstrong, & Wilkinson, 2006). Recently, studies used low and high percentiles of

temperature (e.g., 99th

against 90th

, 1st against 10

th) to examine cold and hot effects on

mortality (Anderson & Bell, 2009), as sometimes the non-linear function for temperature

might adequately capture the effect of temperature on mortality.

15

2.3 The effects of temperature on the human body

The human adjusts the core body temperature within a narrow range around 37 °C, and this

system is independent to the fluctuations in the ambient temperature (Sessler, 2009). The

human body constantly exchanges heat with its surrounding environments to keep the core

body temperature constant (Figure 2.3). Evaporation, radiation, conduction or convection are

the four main modes of heat transfer ("Metabolism, Energy Balance, and Temperature

Regulation," 2008). In a normal environment, about 30% of the total heat exchange of the

human body is by convection, and each person evaporates about 1 litre per day and dissipates

about one-quarter of the total daily loss of heat. Heat exchange through conduction and

radiation depends on the conductivity of objects and materials in contact with the skin, or

temperature difference between the skin and adjacent surfaces (Kroemer & Grandjean, 1997).

16

Figure 2.3: Modes of heat transfer between human body and environment. Source:

http://www.thermoanalytics.com/human-simulation/thermal-manikin.

http://www.thermoanalytics.com/human-simulation/thermal-manikin

17

Thermoregulation is a very complex process (Figure 2.4). When people are exposed to

extreme high temperatures above the body’s core temperature (i.e., 37 °C), blood may flow

increase to the skin’s vessels to increase heat loss which is simulated by homeostatic control.

When people are exposed to high temperatures for long periods the sweat glands may not

work well, and metabolic reaction may slow down (Jiang, Qu, Shang, & Zhang, 2004).

During continuous exposure to heat, the central nervous blood volume decreases as the

coetaneous vessels dilate. The stroke volume falls, while the heart rate increases to maintain

cardiac output. The effective circulatory volume also decreases as water is lost through

sweating (Parsons, 2003). The decrease in sweating promotes a further increase in core

temperature to beyond 38–39 °C where collapse of homeostatic control may occur.

Hyperthermia happens when the body temperature reaches about 40 °C (Axelrod & Diringer,

2006; http://en.wikipedia.org/wiki/Hyperthermia"), and heat stroke may occur when the

temperature is above 41 °C (Parsons, 2003).

Extreme high temperatures induced an acute event in people with previous myocardial

infarction or stroke (Muggeo & Hajat, 2009). The extra heat load can be fatal for people with

congestive heart failure (Näyhä, 2005). Exposure to high temperatures might cause

dehydration, salt depletion and increased surface blood circulation, which can lead to a

failure of thermoregulation (Bouchama & Knochel, 2002). High temperatures may also be

associated with elevated blood viscosity, cholesterol levels and sweating thresholds

(McGeehin & Mirabelli, 2001).

When people are exposed to cold temperatures, skin vessels contract and muscles shiver to

generate energy to maintain core body temperature (Figure 2.4). Shivering is an effective way

to increase the body’s heat production (Clark & Edholm, 1985). During prolonged cold

18

exposure, the body can activate another slower mechanism, by increasing the thyroid

hormone in the blood stream from the thyroid gland. The thyroid hormone reaches all the

cells of the body and increases their metabolic activity which increases heat production

(Wyndham, 1969). When the air temperature drops extensively in a short time the body finds

it difficult to cope. Hypothermia can occur when the air temperature drops the body

temperature below 35 °C (Parsons, 2003). The risk of death would be increased if the core

body temperature drops below 32 °C. When the core body temperature is less than 28 °C, the

life is threatened immediately if there is not any medical attention (Parsons, 2003).

Cold temperatures are also significantly associated with human health (Huynen, Martens,

Schram, Weijenberg, & Kunst, 2001; Kysely, et al., 2009). Cold temperatures increase the

rates of myocardial ischemia, myocardial infarction and sudden deaths (Hong et al., 2003;

Stewart, McIntyre, Capewell, & McMurray, 2002). Exposure to cold temperatures is

associated with an increase in blood pressure, blood cholesterol, heart rate, plasma fibrinogen,

platelet viscosity and peripheral vasoconstriction, (Ballester, Corella, Perez-Hoyos, Saez, &

Hervas, 1997a; Carder et al., 2005b). Skin cooling increases systematic vascular resistance,

heart rate and blood pressure.

19

Figure 2.4: Human thermoregulation when people expose to hot and cold environmental

temperatures. Source: http://exercisephysiologist.wordpress.com/2012/02/15/the-human-

homoeothermy/.

http://exercisephysiologist.wordpress.com/2012/02/15/the-human-homoeothermy/

http://exercisephysiologist.wordpress.com/2012/02/15/the-human-homoeothermy/

20

2.4 Models for assessing the relationship between temperature and mortality

A variety of models have been used to assess the impacts of temperature on mortality and

morbidity, such as descriptive models (Reid, et al., 2009), case-only models (Schwartz, 2005),

case-crossover models (Stafoggia, et al., 2006), time-series models (Hajat, Kovats, Atkinson,

& Haines, 2002) and spatial models (Vaneckova, Beggs, & Jacobson, 2010). In general, time-

series and case-crossover analyses are the most commonly used in a single or in multiple

locations over a time period from years to decades (Basu, et al., 2005). The main aim of these

analyses is to examine associations between health and the exposure (e.g. daily counts of

death and daily temperatures), after controlling for potential confounders such as temporal

trends and seasonality (Kinney, O'Neill, Bell, & Schwartz, 2008; Kovats & Hajat, 2008).

Season and long-term trends are considered as confounders in examining short-term effects

of temperature on deaths. Most previous studies have tried to control for both seasonality and

long-term trend (Rose, 1966; Anderson & Rochard, 1979). Some studies separated the data

into four seasons (spring, summer, autumn, and winter) to control for season. Time series

methods with a smooth function for calendar time are now commonly used to control for

season and long-term trend (Dominici, McDermott, Zeger, & Samet, 2002; El-Zein, et al.,

2004; Hales, Salmond, Town, Kjellstrom, & Woodward, 2000b; Kim & Jang, 2005; Revich

& Shaposhnikov, 2008). The other design is the case-crossover which controls for seasonal

effects and secular trends by matching case and control days in relatively small time windows

(e.g., calendar month). This controls for season using a step-function rather than the smooth

function used by time series (Barnett & Dobson, 2010).

2.4.1 Time series design

21

In the following sections I review the main statistical methods used to estimate the health

effects of temperature. Most studies in this area use daily data on deaths from a city or region

with a similar climate. These data sets are usually between 2 and 20 years long. The data are

time series of daily deaths and temperature, hence time series methods are usually applied.

The generalised linear time series model and generalised additive time series model are most

commonly used to examine the effects of temperature on mortality. Both models used

smoothing for calendar time to control for season and long-term trend.

The generalised linear model was first proposed by Nelder and Wedderburn in 1972 (Nelder

& Wedderburn, 1972). Generalised linear models can be used to fit regression models to non-

normal data with a minimum of extra complication compared with normal linear regression.

Generalised linear models are an extension of multiple linear models and are flexible enough

to include a wide range of common situations, including normal linear regression. This model

generalised the classic linear model to four distributions: normal, binomial (probit analysis,

etc.), Poison and Gamma, which can be transformed from an exponential family and link

functions to a linear basis (Nelder & Wedderburn, 1972). Zeger first used generalised linear

models to examine the effects of weather on human health in 1988 (Zeger, 1988).

The generalised additive model was developed by Hastie and Tibshirani to combine the

generalised linear model and additive model (Hastie & Tibshirani, 1990). The purpose was to

maximise the quality of prediction for various distributions, by using non-parametric

smoothing functions of the independent variables which are "connected" to the dependent

variable via a link function (Dominici, et al., 2002). Most smoothers attempt to mimic

category averaging through local averaging, that is, averaging the response-values of

22

observations having predictor values close to a target value. The averaging is done in

neighbourhoods around the target value (Hastie & Tibshirani, 1990).

Two main decisions need to be made when using a smoothing function: how to average the

response values in each neighborhood, and how big to make the neighborhoods. The former

is the question of which type of smoothing to use, and the latter concerns the degrees of

freedom expressed in terms of an adjustable smoothing parameter (Hastie, Tibshirani, &

Friedman, 2004). Methods for smoothing include nonparametric splines such as smoothing

splines, locally-weighted running-line smoothers (loess) and kernel splines; and parametric

splines such as natural cubic regression and B-splines (Hastie & Tibshirani, 1990).

Selecting an appropriate model is very important when examining the effects of temperature

on mortality, as the choice of model impacts on the prediction ability. Model choice can be

informed by model fit criteria such as residual testing, deviance statistics, Akaike’s

information criterion (AIC), or the partial auto-correlation function (PACF) using the

residuals to determine the degree of remaining autocorrelation (Gouveia, Hajat, &

Armstrong, 2003). Residual plots are also useful for finding outliers and non-random

variations.

The model selection sometimes depends on the study design. In general, simple models are

easy to interpret. The simple models are particularly attractive in multicity to compare

associations across cities. Complex models are sometimes required to better fit the data. The

flexible models are useful for single-city studies and can indicate to what extent there are

systematic effects of temperature beyond that captured in the simple model.

23

2.4.2 Case-crossover design

The case–crossover design has been used widely to examine the effects of temperature on

mortality and morbidity in the past decade (Basu, 2009b). The case–crossover design is a

special case of matched case-control study; each case in the case-crossover study is used as

their own control. Therefore, confounders related to individual characteristics that remain

relatively constant (e.g., age, sex and smoking) are controlled for by design.

For the time series data on deaths and temperature, the case–crossover design compares

temperatures on a case day when events occurred (e.g., deaths) with temperatures on nearby

control days to examine whether the events are associated with temperature. Because control

days are selected close to the case days, seasonality is controlled by design (which makes it

useful for studying the effects of short-term changes in temperature). There are many

different designs for choosing control days relative to a case day.

A unidirectional design selects fixed control day(s) per case day only before or after the case

day (with all controls either being selected before the case, or all controls selected after the

case). This design may not control for trends over time in temperature or health outcomes,

and so is subject to bias (Greenland, 1996).

Bidirectional designs include the full-stratum bidirectional (Navidi, 1998), symmetric

bidirectional (Bateson & Schwartz, 1999), and semi-symmetric case–crossover (Navidi &

Weinhandl, 2002). The full-stratum bidirectional case–crossover includes control days as all

days in the time series before and after the case days. This design controls for time trends in

24

exposure, but does not control for seasonal patterns in exposure or health outcomes (Bateson

& Schwartz, 1999).

The symmetric bidirectional design uses control days both before and after the case day. This

method can successfully control for seasonality in exposures and outcomes. However, there is

the potential for selection bias, because the case days at the beginning or end of the data

series have fewer control days for matching. Navidi and Weinhandl (2002) noted that the

symmetric case–crossover design might still be biased by time trends in the exposure.

The semi-symmetric design randomly selects a control day before or after the case day. This

design can also control for long-term trends and seasonality. However, because only one

control day is selected at a fixed interval, the estimates may still be biased (Levy et al., 2001).

Lumley and Levy (2000) illustrated how selection biases do not appear when cases may

occur at any time in the strata from which the controls are selected (time-stratified case–

crossover). Lumley and Levy also demonstrated that most of the other designs are biased

because the controls are not chosen independently of the case day. This bias is called the

‘overlap bias’ and occurs in case–crossover designs with non-disjointed strata (Lumley &

Levy, 2000).

The time-stratified case–crossover uses fixed and disjointed time strata (e.g., calendar month),

so the overlap bias is avoided. Janes et al. (Janes, Sheppard, & Lumley, 2005) demonstrated

that the overlap bias is not an issue for the time-stratified design. Time-stratified case–

crossover analyses are equivalent to time series analyses (Basu, et al., 2005; Fung, et al.,

2003).

25

Conditional logistic regression is usually used to estimate the model parameters for a case-

crossover design. The conditional logistic regression used in case–crossover analysis is a

special case of time series log-linear model (Lu, Symons, Geyh, & Zeger, 2008; Lu & Zeger,

2007). Hence log-linear models can also be used to estimate the parameters for a case–

crossover design. The advantage of log-linear model is that we can obtain the model residuals,

which can be examined to evaluate the adequacy of the model.



temperature in each season, with a single lag model, or a moving average lag model (Basu, et

al., 2008; Green, et al., 2010). Few studies have demonstrated how to fit non-linear and

delayed effects of temperature on mortality within a case–crossover design.

2.4.3 Spatiotemporal analysis


from one monitoring site or daily mean values from a network of sites, which may result in a

measurement error for temperature exposure (Zhang, et al., 2011). Random measurement

error in temperature will bias the effect estimates towards the null (Hutcheon, Chiolero, &

Hanley, 2010). Studies have shown that there is spatial variation in ambient temperatures

within cities and their surroundings (Aniello, et al., 1995; Kestens, et al., 2011; Lo, et al.,

1997). Urban areas usually have higher temperatures because of the heat island effect

(www.epa.gov/hiri/about/index.html). Smargiassi et al. (2009) found that hotter areas within

a city had a greater risk of heat-related death compared with cooler areas (Smargiassi, et al.,

2009). These results suggest that using temperature from one monitoring site or averaged


26

values from a network of sites may underestimate the risks of temperature on mortality.

However, few studies have used spatial methods to quantify the impact of temperature on

mortality (Kestens, et al., 2011; Smargiassi, et al., 2009). If spatial exposures of temperature

are significantly more accurate than standard methods then they may improve our

understanding of the association between temperature and mortality.

Geo-statistical techniques have been used to model regional temperatures (Benavides,

Montes, Rubio, & Osoro, 2007; Zhang, et al., 2011). Recent studies have used spatial models

to examine climate variables like ambient temperature in the field of agriculture and forestry

science (Benavides, et al., 2007; Chuanyan, Zhongren, & Guodong, 2005). Different

techniques (inverse distance interpolation weighting, voronoi tessellation, regression analysis,

and geo-statistical methods) have been developed to predict regional temperature from station

data (Bhowmik & Cabral, 2011). These models have also been used to estimate the health

effects of air pollution within cities (Lee & Shaddick, 2010; Shaddick, et al., 2008;

Whitworth, et al., 2011). However, few studies have used spatiotemporal models to examine

the association between temperature and mortality.

27

2.5 Models assessing the lag effects of temperature in mortality

Mortality risk depends not only on exposure to the current day’s temperature, but also on

several previous days’ exposure (Anderson & Bell, 2009). The distributed lag model has been

applied to explore the delayed effect of temperature on mortality (Analitis et al., 2008;

Baccini, et al., 2008; Hajat, Armstrong, Gouveia, & Wilkinson, 2005). To overcome the

strong correlation between temperatures in the close days, constrained distributed lag

structures are used (Armstrong, 2006). The estimates are constrained by smoothing using

methods such as natural cubic splines, polynomials or stratified lags. Both unconstrained and

constrained distributed lag models assume a linear relationship between temperature below

(above) the cold (hot) threshold and mortality, so these models may not be sufficiently

flexible to capture the non-linear effects of temperature on mortality.

Recently, a distributed lag non-linear model (DLNM) was developed to simultaneously

estimate the non-linear and delayed effects of temperature on mortality (or morbidity)

(Armstrong, 2006; Gasparrini, Armstrong, & Kenward, 2010). DLNMs use a “cross-basis”

function that describes a two-dimensional temperature-response relationship along the

dimensions of temperature and lag. The choice of “cross-basis” functions for the temperature

and lag are independent, so spline or linear functions can be used for temperature, while the

polynomial functions can be used for the lag. The estimates can be plotted using a 3-

dimensional graph to show the relative risks along both temperature and lags (Figure 2.5).

We can estimate the relative risks for a certain temperature or lag, by extracting a “slice”

from the 3-dimensional graph. We can compute the overall effect by summing the log

relative risks of all lags (Figure 2.6).

28

Figure 2.5: 3-D plot of RR along temperature and lags, using data from NMMAPS for

Chicago during the period 1987–2000.

29

Figure 2.6: Plot of overall RR, using data from the NMMAPS for Chicago during the period

1987–2000.

30

One of the main advantages of DLNM is that it allows the model to contain detailed lag

effects of exposure on response, and provides the estimate of the overall effect that is

adjusted for harvesting (for example, a heat wave was followed by a decrease in mortality

during the subsequent days or weeks) (Gasparrini, et al., 2010). The DLNM can flexibly

show different temperature-mortality relationships for lags using smoothing functions. The

DLNM can adequately model the main effects of temperature (Armstrong, 2006).

There are also some issues in the selection of the DLNM, such as cross-basis type, maximum

lag, and degrees of freedom (knots and placement) for exposure and lag (Armstrong, 2006;

Gasparrini, et al., 2010). These values are generally pre-defined by the researcher based on

previous papers and biological plausibility. Because the DLNM is combined with a

regression model (e.g., Poisson regression), the residual deviance and autocorrelation plot,

maximum likelihood, Akaike’s information criteria or Bayesian information criteria can be

used to check the model, and the appropriateness of the selected lag and degrees of freedom.

The lag and degrees of freedom can be chosen according to the best model fit (e.g., AIC).

Previous studies recommend choosing a DLNM that is easy to interpret from an

epidemiological perspective (Armstrong, 2006; Gasparrini, et al., 2010). However, it is

sensible to conduct sensitivity analyses to assess the key conclusions for different lags and

degrees of freedom.

31

2.6 Temperature measures and mortality

Previous studies examining the relationship between temperature and mortality have used

maximum, mean, or minimum temperatures as an indicator of exposure, while controlling for

relative humidity (Anderson & Bell, 2009). Other studies have used apparent temperature, the

Humidex and temporal synoptic index as exposure measures (Zanobetti & Schwartz, 2008).

A large study of mortality in the US found that the different measures of temperature had a

similar ability to predict the impacts of temperature on mortality (Barnett, Tong, & Clements,

2010). The authors suggested that the best measure of temperature should be that with the

least amount of missing data and best spatial coverage of the study area. Other US studies

considered the best predictor of mortality in heat waves and found a great variability in the

best temperature model between cities (Basu, et al., 2008; Bobb, Dominici, & Peng, 2011;

Hajat & Kosatky, 2010).

Mean temperature is the average of maximum and minimum temperature or 24-hour

monitoring averaged mean temperature. Many studies have showed that mean temperature is

a better predictor of mortality and morbidity than minimum and maximum temperatures

(Gouveia, et al., 2003). This might be because mean temperature covers the whole day and

night, so it can represent the whole day’s exposure compared with minimum or maximum

temperature which only occur for a relatively short time. However, some studies suggest that

minimum temperature should be better at predicting heat-related health effects, while

maximum temperature should be used to examine the cold effects (Kinney, et al., 2008).

Studies examining heat wave effects used both minimum and maximum temperature at the

same model, as the small differences between minimum and maximum temperatures are

related to mortality (Filleul et al., 2006). A study found that the maximum temperature was

32

more closely correlated with mortality than minimum temperature in extreme cold events in

Madrid (DÃaz et al., 2004).

The diurnal temperature difference is the difference between a day’s maximum and minimum

temperatures, and so represents the variation in temperature for each day. Several studies

have shown that diurnal temperature had negative effects on mortality (Kan et al., 2007;

Revich & Shaposhnikov, 2008). Kan et al. (2007) found that a large diurnal temperature

change significantly increased the risk of non-accidental mortality. The estimated effect of

diurnal temperature was not changed when adjusted for ambient temperature days (Haidong

Kan, et al., 2007).

Recently, some studies have used other temperature measures that combined air temperature

and other weather metrics together (Kalkstein & Valimont, 1986; Stafoggia, Forastiere,

Michelozzi, & Perucci, 2009b), for example, apparent temperature and Steadman index.

Some studies have used sensitivity analysis to selected a better temperature measure for a

specific climate (Anderson & Bell, 2009). And some studies used multiple temperature

measures at the same time (Medina-Ramón & Schwartz, 2007; Michelozzi et al., 2000).

Overall there is no one temperature measure that works best in all climates. This is probably

because of the strong correlation between daily estimates such as mean and maximum

temperature (Barnett, Tong, et al., 2010).

33

2.7 Interactive effects between temperature and air pollution on mortality

There is a consistent association between air pollution and increased mortality across the

world (Bell, McDermott, Zeger, Samet, & Dominici, 2004; Dominici, McDermott, Daniels,

Zeger, & Samet, 2005; Hales, Salmond, Town, Kjellstrom, & Woodward, 2000a; Hong et al.,

2002; Keatinge & Donaldson, 2001; Leah & Scott, 2005; Neas, Schwartz, & Dockery, 1999;

Rossi et al., 1999; Saldiva & Pope, 1995; Samoli et al., 2001; Schwartz, 2000; Schwartz et

al., 2001; Tsai, Chen, Hsieh, Chang, & Yang, 2006; Yang et al., 2004; Zanobetti et al., 2003).

Air pollution includes particular matter with aerodynamic diameters less than 10 μg/m3

(PM10), sulphur dioxide (SO2), nitrogen dioxide (NO2) and ozone (O3). Mortality includes

mortality from all-causes, respiratory and cardiovascular diseases.

The interactive effects between air pollution and temperature on mortality have been known

for many years since the disaster of fog in London in 1952. Researchers found that the low

temperature together with air pollution killed thousands of people at that time (Wilson, 2003).

Some air pollutants, like ozone for example, are dependent on the temperature (Sartor,

Snacken, Demuth, & Walckiers, 1995). There are interactive effects between O3, PM10 and

temperature (Ren, Williams, Morawska, Mengersen, & Tong, 2008b; Ren, Williams, &

Tong, 2006; J. Samet, Zeger, Kelsall, Xu, & Kalkstein, 1998) on mortality, however, little

literature has adjusted for interactive effects when examine the relationship between

temperature and mortality.

34

2.8 Groups vulnerable to temperature effects

Previous studies have identified particular health outcomes and vulnerable subgroups to heat

(Basu, 2009b; Basu & Ostro, 2008a; Kovats & Hajat, 2008). People with cardiovascular,

respiratory problems, diabetes, chronic mental disorders or other pre-existing medical

conditions are at greater risk from heat exposure (Basu, et al., 2005; McMichael, et al., 2008;

Stafoggia, et al., 2006). Studies have also shown that the effects of thermal stress were

highest in the elderly following heat waves (Hajat, et al., 2007b; Vaneckova, et al., 2008).

Other factors can also affect the risks of heat-related mortality, such as: socio-economic

status, income, education, social isolation, intensity of urban heat islands, housing

characteristics, access to air conditioning, and availability of health care services (Ebi, 2007;

Ebi & Schmier, 2005; Kovats & Hajat, 2008; Luber & McGeehin, 2008; Vandentorren et al.,

2006; WHO, 2009).

2.8.1 Age

The elderly are susceptible to the impact of ambient temperature (Bull & Morton, 1975).

Almost all the literature has confirmed this result regardless of time periods, regions and

methods used (Guy, 1858; Kovats & Hajat, 2008), although some articles showed a slightly

weaker effect in the oldest age group compare with all ages (Hales, et al., 2000b; Vaneckova,

Beggs, Dear, & McCracken, 2007).

There has been heterogeneity of the vulnerability to temperature in the elderly. People older

than 85 years old were more affected by temperature than other elderly groups (Hajat,

Kovats, & Lachowycz, 2007a; Stafoggia et al., 2008). A US study found that elderly females

35

were more sensitive to heat exposure while elderly males were more vulnerable in cold

climates (Macey & Schneider, 1993). Compared to white elderly, non-white older people had

an increased risk of temperature-related mortality (Martinez et al., 1989).

The elderly are more sensitive than younger people to the climate both in terms of physiology

and behaviour (Macey & Schneider, 1993). Their body temperature lowers with age and their

comfort zone becomes narrower (Watts, 1972). The thermal regulation system weakens with

age, for example, skin sensory perception may diminish and thermal homeostasis may decline

(Collins, 1987). The blood vessel muscles lack vigor with increasing age (Collins, 1987).

This means the elderly cannot easily get relief from heat or cold stress. A higher prevalence

of cardiovascular and respiratory morbidity and mortality in older people is another reason

why the elderly are vulnerable to temperature (Wyndham, 1978).

2.8.2 Gender

There have been no uniform results in difference in temperature-risk by gender. Some studies

found that females were sensitive to heat while males were more sensitive to cold (Hajat, et

al., 2007a; Ishigami et al., 2008; Koutsavlis & Kosatsky, 2003; Macey & Schneider, 1993;

Mannino & Washburn, 1989; Stafoggia, Forastiere, Agostini, Biggeri, Bisanti, Cadum,

Caranci, Lisio, et al., 2006; Vaneckova, Beggs, Dear, & McCracken, 2008). Some

researchers found that men were more at a risk of coronary mortality than women in cold

climates (Gorjanc, Flanders, VanDerslice, Hersh, & Malilay, 1999; Gyllerup, Lanke,

Lindholm, & Schersten, 1991), while others found there is no difference between the

sensitivity of men and women to the cold or heat (Basu & Ostro, 2008b; Kan, et al., 2007;

O'Neill, Zanobetti, & Schwartz, 2003; Stafoggia, Forastiere, et al., 2008). One reason that

36

women may be found to be more sensitive to temperature is because of their generally older

age. So observed differences due to gender may be caused by confounding with age.

2.8.3 Socio-economic status

Economic disadvantage was proposed as an explanation of temperature-related mortality

(Healy, 2003), but its real role remains to be determined. Low education attainment

(Curriero, et al., 2002; Ramón, Zanobetti, Cavanagh, & Schwartz, 2006; O'Neill, Zanobetti,

& Schwartz, 2003), low income (Stafoggia, Forastiere, et al., 2008), living in poverty

(Curriero, et al., 2002) and black ethnicity (Basu & Ostro, 2008b; O'Neill, et al., 2003)

significantly increased susceptibility to the effects of temperature. But other researchers

found no evidence that there was a relation between socioeconomic deprivation and mortality

(Gemmell, McLoone, Boddy, Dickinson, & Watt, 2000; Hajat, et al., 2007a; Healy, 2003;

Lawlor, Maxwell, & Wheeler, 2002; Shah & Peacock, 1999; Stafoggia, Forastiere, et al.,

2008).

2.8.4 Physiologic acclimatisation

There are several ways to protect people from death in the uncomfortable temperatures. The

physical way is to have the appropriate skin colour and body shape for the local climate

(Voelker, 1995). Clothing, shelter and fire are the natural and effective human adaptations to

the cold (Donaldson, Tchernjavskii, Ermakov, Bucher, & Keatinge, 1998; Wyndham, 1969).

The thermoregulation reaction can become adapted when someone has lived in the same

place for a long time, as the core body temperature can change according to the environment

(Wyndham, 1969).

37

2.9 Summary

As the global average temperature continues to increase over the coming decades, there has

been increasing interest in assessing the relationship between temperature and mortality.

Generally, the temperature-mortality relationships were U-, V-, and J-shaped, with thresholds

corresponding to lowest mortality. The elderly are more vulnerable to temperature than the

young. Temperature-mortality relationships vary across different cities. Therefore, we need to

consider the climate adaptation to examine city-specific temperature effects on mortality.

Statistical models for examining temperature-related morality are well established. However,

it remains unknown whether time series and spatiotemporal models produce similar effect

estimates. No study has used case-crossover design to examine non-linear temperature effects

on mortality. No study has examined the effects of temperature change between neighbouring

days on mortality. No study has considered the variation in temperature effects on mortality

by years. There is a need to address these issues in further research.

38

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57

CHAPTER 3: STUDY DESIGN AND METHODOLOGY

This chapter introduces the study populations, data collection, data management and analysis.

The detailed and specific materials and methodologies are described in each corresponding

chapter.

3.1 Study population

This thesis was conducted in three settings: Tianjin, China; Brisbane, Australia; and 83 cities

in the United States.

Tianjin is a city in northeastern China (Figure 3.1), and is adjacent to Beijing and Hebei

Province, along the coast of Bohai Gulf (39° 07' North, 117° 12' East). Tianjin has four

distinct seasons, with cold, windy, dry winters influenced by the vast Siberian anticyclone,

and hot, humid summers due to the monsoon. It is the fifth largest Chinese city in terms of

urban land area. The population in the urban area was 4.2 million in 2005.

Brisbane is the capital city of the state of Queensland in Australia, and is located on the east

coast of the country (27° 30' south, 153° 00' east) (Figure 3.2). It has a humid subtropical

climate, with the average temperature of 25 °C in summer (Dec–Feb).

The third data set was obtained from the publicly available National Morbidity and Mortality

Air Pollution Study (NMMAPS) study (Samet, Dominici, Zeger, Schwartz, & Dockery, 2000;

Samet et al., 2000) (Figure 3.3). This study included daily climatic conditions, air pollution

levels, and mortality in 108 cities in the United States We excluded data for small cities

58

(population under 200,000) and cities with more than 0.5% missing data for air or dew point

temperature, which left 83 cities. We stratified the cities into seven regions (Industrial

Midwest, North East, North West, South East, South West, Southern California, and Upper

Midwest).

Figure 3.1: The location of Tianjin, China.

59

Figure 3.2: The location of Brisbane, Australia.

60

Figure 3.3: The 83 large cities and 7 regional groups in United States from NMMAPS study.

61

3.3 Data collection and management

3.3.1 Tianjin data for chapter 4

Mortality data was obtained from the China Information System for Death Register and

Report of Chinese Centre for Disease Control and Prevention from January 1, 2005 to

December 31, 2007. The mortality data were from six urban districts of Tianjin (Heping,

Hedong, Hexi, Nankai, Hebei and Hongqiao). Non-accidental mortality was classified

according to the International Classification of Diseases, 10th revision (ICD-10: A00–R99)

(World Health Organization, 2007). Cardiopulmonary (ICD-10:I00–I99 and ICD-10:J00–

J99), cardiovascular mortality (ICD-10:I00–I99) and respiratory mortality (ICD-10:J00–J99)

were examined separately.

Daily meteorological data on maximum, mean and minimum temperature, and relative

humidity, were obtained from the China Meteorological Data Sharing Service System

(http://cdc.cma.gov.cn). Daily air pollution data on particulate matter less than 10 μm in

aerodynamic diameter (PM10), sulphur dioxide (SO2) and nitrogen dioxide (NO2) were

obtained from the Tianjin Environmental Monitoring Centre.

3.3.2 Brisbane data for chapters 5 and 6

For chapter 5, daily morality data were obtained for non-accidental causes between January

2000 and December 2004 at the level of statistical local areas in Brisbane city from the Office

of Economic and Statistical Research of the Queensland Treasury. There are 163 statistical

local areas in Brisbane city. The causes of non-accidental mortality were coded according to

62

the International Classification of Diseases, ninth version tenth version (ICD-10) (ICD-10:

A00–R99). All deaths were residents of Brisbane city. Population data were obtained from

the Australia Bureau of Statistics for each statistical local area. The census of population and

housing for each statistical local area is conducted once every five years. I used the 1996

census for the year 2000, and the 2001 census for years 2001−2004.

Daily data on maximum temperature were obtained from the Australian Bureau of

Meteorology at 19 sites in or around Brisbane city. Daily data on relative humidity were

obtained from one monitoring site (Brisbane airport). Daily data on air pollution were

obtained from the Queensland Environmental Protection Agency. The daily PM10

concentrations were averaged from 13 monitoring sites. The daily O3 concentrations were

averaged from 10 monitoring sites.

For chapter 6, the Brisbane data on daily deaths of non-external causes between Jan, 1996

and Dec, 2004 were gathered from the Office of Economic and Statistical Research of the

Queensland Treasury. The causes of non-external mortality were coded according to the

International Classification of Diseases, ninth version (ICD-9) (ICD-9: 001–799) before

December 1996 and tenth version (ICD-10) (ICD-10: A00–R99) between December 1996

and December 2004. Cardiovascular mortality (CVM, ICD-9:390–459, ICD-10:I00–I79) and

respiratory mortality (RM: ICD-9: 460–519, ICD-10:J00–J99) were extracted from the

mortality database. Influenza deaths (ICD-9: 487.0–487.8 or ICD-10: J10–J11) were

excluded from respiratory mortality. All deaths were for residents of Brisbane city. Non-

external mortality were stratified by gender and age (3 groups: 0–64, 65–74, and ≥ 75 years).

63

Values of temperature change were calculated using the current day’s mean temperature

minus the previous day’s mean temperature. Temperature change between the neighbouring

days is a measure of temperature stability, with large positive and negative values indicating

an unstable temperature. The air pollutants including daily mean ozone (O3) and PM10 were

monitored at a central site in Brisbane. We collected these data from the Queensland

Environmental Protection Agency.

3.3.3 NMMAPS data for chapters 6 and 7

The NMMAPS (The National Morbidity Mortality Air Pollution Study) included daily

climatic conditions, air pollution levels, and mortality in 108 cities in the United States from

1987 to 2000. Data on maximum and minimum temperatures came from the National

Climatic Data Center, and daily mortality counts came from the National Center for Health

Statistics. Daily non-external deaths consisted of death counts among residents, excluding

injuries and external causes. More information is available from the NMMAPS web site

(http://www.ihapss.jhsph.edu).

For chapter 6, Los Angeles’ data were obtained from NMMAPS database. Mean temperature,

relative humidity, O3, NEM, CVM, RM, and NEM in age groups (0–64, 65–74, and ≥ 75

years) were used here. Mortality counts were not split by gender in the NMMAPS, so the

impact of temperature change on mortality by gender could not be analysed in Los Angeles.

For chapter 7, I limited analyses to elderly morality (age ≥ 75 years) in the warm season

(1 May–30 September) as I was interested in the effects of heat on a susceptible population.

http://www.ihapss.jhsph.edu/

64

Mean temperature (i.e., average of maximum and minimum temperatures) was used as the

main exposure variable.

3.4 Data analysis

3.4.1 Case−crossover design and distributed lag non-linear model

Chapter 4 introduces how to combine the time-stratified case–crossover and a distributed lag

non-linear model. These models were demonstrated using a motivating example of the

temperature-mortality relationship in Tianjin, China. A Poisson regression model that allows

for over-dispersion was used to combine the case–crossover design with a distributed lag

non-linear model, as the case–crossover using conditional logistic regression is a special case

of time series analysis (Lu & Zeger, 2007).

The distributed lag non-linear model was used to get the predicted effects and standard errors

for combinations of temperature and lags. Graphs, summaries, and statistical inference were

obtained from the estimates and standard errors of distributed lag non-linear model

(Armstrong, 2006).

3.4.2 Comparing time series and spatiotemporal analyses

Chapter 5 demonstrates the comparison of time series and spatiotemporal analyses, using the

effects of temperature on mortality in Brisbane as an example.

65

Ordinary kriging was used to interpolate the daily values of temperature to the centroids of

163 locations across Brisbane city from the values of 19 monitoring sites.

A generalized additive mixed model (GAMM) with Poisson regression was used to examine

the association between the spatial temperature and mortality (Augustin et al., 2009), to

perform the spatiotemporal model,

To examine association between non-spatial temperature exposure and mortality, I used

temperature data from single monitoring site (Brisbane centre) using a generalized additive

model (GAM).

To examine the effects of non-spatial temperature exposure using multiple monitoring sites

on morality, I used daily temperature averaged from 3 sites (Brisbane centre, Brisbane airport,

Archerfield Airport)

Squared Pearson scaled residuals were used to compare the fit of the spatiotemporal and time

series models.

3.4.3 The effect of temperature change on mortality

Chapter 6 describes the effect of temperature change between neighbouring days on mortality.

Values of temperature change were calculated using the current day’s mean temperature

minus the previous day’s mean temperature. Temperature change between the neighbouring

66

days is a measure of temperature stability, with large positive and negative values indicating

unstable temperatures.

Firstly, a Poisson generalized additive model (GAM) was used to examine the effects of

short-term changes in temperature between neighbouring days on mortality. Regression

spline was used for temperature change.

Secondly, as an alternative model temperature change was categorised into 3 groups: a drop

of more than 3 °C; a rise of more than 3 °C; a change in either direction of less than 3 °C.

3.4.4 The variation in the effects of temperature on mortality

Chapter 7 describes the variation in the effects of temperature on elderly mortality in 83 US

cities.

A city-specific Poisson regression model was used to examine each year’s high temperature

effect on elderly mortality. In the city-specific model, the heat effects were divided into a

“main effect” and an “added effect” according to a previous study (Gasparrini & Armstrong,

2011b). These estimates (main effect and added effect separately) were then combined using

a univariate meta-analysis to create yearly estimates for each region and for the entire US.

The pooled yearly national and regional main effects and added effects were plotted from

1987 to 2000.

A Bayesian hierarchical model was used to estimate an overall high temperature effect (main

effect and added effect separately) for each region and the nation by combining the yearly

67

estimated effects of high temperatures within each city and incorporating the estimates’

variance (Everson & Morris, 2000). Each city was given a random intercept to model its

mean heat effect, and a random linear effect of time to model linear trends over time. The

model fit was assessed using the Deviance Information Criteria (Spiegelhalter, Best, Carlin,

& Van Der Linde, 2002).

3.4 Rationale for choosing study sites or temperature measures

In chapter 4, we used Tianjin data as attractive example to illustrate how to combine case–

crossover and a distributed lag non-linear model, because no study has been conducted in this

city. We also examined which temperature measure is the best to predict mortality. On one

hand, we showed the process for combining case–crossover design and distributed lag non-

linear models. On the other hand, we provided useful information on the association between

temperature and mortality in Tianjin city.

In chapter 5, we used Brisbane data to compare spatiotemporal and time series models, as we

collected high quality data on spatial temperature and mortality in this city. We used

maximum temperature as exposure, because previous studies shown that mean, minimum and

maximum temperature had similar ability to predictive mortality. Also, the maximum

temperature can be freely downloaded from the website of Australia Bureau of Meteorology

(http://www.bom.gov.au/climate/data/).

In chapter 6, we used Brisbane and Los Angles data to examine the effects of temperature

change on mortality, because the two cities have different subtropical weather. We used two

cities in different countries with different subtropical climates to confirm the findings.

http://www.bom.gov.au/climate/data/

68

In chapter 7, we used 83 cities’ data from The National Morbidity Mortality Air Pollution

Study to examine the variability in the temperature effects on mortality across years, cities,

and regions, because this dataset is free and provides high quality data on temperature and

mortality. Results can be compared within cities and regions. Evidence from large dataset is

should be reliable.

69

3.5 References



Augustin, N. H., Musio, M., von Wilpert, K., Kublin, E., Wood, S. N., & Schumacher, M.

(2009). Modeling spatiotemporal forest health monitoring data. Journal of the

American Statistical Association, 104(487), 899-911.

Everson, P. J., & Morris, C. N. (2000). Inference for multivariate normal hierarchical models.

Journal of the Royal Statistical Society: Series B (Statistical Methodology), 62(2),

399-412.

Gasparrini, A., & Armstrong, B. (2011). The impact of heat waves on mortality.




19(2), 169-175.



Peng, R. D., & Dominici, F. (2008). Statistical Methods for Environmental Epidemiology in

R: A Case Study in Air Pollution and Health: New York, NY: Springer Publishing

Company.

Rothpearl, A. (1989). The jackknife technique in statistical analysis. Chest, 95(4), 940.

Samet, J. M., Dominici, F., Zeger, S. L., Schwartz, J., & Dockery, D. W. (2000). The

National Morbidity, Mortality, and Air Pollution Study. Part I: Methods and

methodologic issues. Res Rep Health Eff Inst(94 Pt 1), 5-14; discussion 75-84.

70

Samet, J. M., Zeger, S. L., Dominici, F., Curriero, F., Coursac, I., Dockery, D. W., et al.

(2000). The National Morbidity, Mortality, and Air Pollution Study. Part II:

Morbidity and mortality from air pollution in the United States. Res Rep Health Eff

Inst, 94(Pt 2), 5-70; discussion 71-79.

Spiegelhalter, D. J., Best, N. G., Carlin, B. P., & Van Der Linde, A. (2002). Bayesian

measures of model complexity and fit. Journal of the Royal Statistical Society: Series

B (Statistical Methodology), 64(4), 583-639.

World Health Organization. (2007). International Statistical Classification of Diseases and

Related Health Problems, 10th Revision, Version for 2007.

Http://apps.who.int/classifications/apps/icd/icd10online/.

http://apps.who.int/classifications/apps/icd/icd10online/

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CHAPTER 4: THE IMPACT OF TEMPERATURE ON MORTALITY IN TIANJIN,

CHINA: A CASE−CROSSOVER DESIGN WITH A DISTRIBUTED LAG NON-

LINEAR MODEL

Citation:



Environmental Health Perspectives 119:1719-1725.

Statement of the joint authorship:

Yuming Guo (Candidate): Conceived and conducted the study design, performed data

analysis and wrote the manuscript;

Adrian Barnett: Performed data analysis, reviewed, edited, and revised the manuscript;

Xiaochuan Pan: Reviewed, edited, and revised the manuscript;

Weiwei Yu: Reviewed, edited, and revised the manuscript;

Shilu Tong: Reviewed, edited, and revised the manuscript.

72

4.1 Abstract

Background: There has been increasing interest in assessing the impacts of temperature on

mortality. However, few studies have used a case–crossover design to examine non-linear

and distributed lag effects of temperature on mortality. Additionally, little evidence is

available on the temperature-mortality relationship in China, or what temperature measure is

the best predictor of mortality.

Objectives: To use a distributed lag non-linear model (DLNM) as a part of case–crossover

design. To examine the non-linear and distributed lag effects of temperature on mortality in

Tianjin, China. To explore which temperature measure is the best predictor of mortality;

Methods: The DLNM was applied to a case−crossover design to assess the non-linear and

delayed effects of temperatures (maximum, mean and minimum) on deaths (non-accidental,

cardiopulmonary, cardiovascular and respiratory).

Results: A U-shaped relationship was consistently found between temperature and mortality.

Cold effects (significantly increased mortality associated with low temperatures) were

delayed by 3 days, and persisted for 10 days. Hot effects (significantly increased mortality

associated with high temperatures) were acute and lasted for three days, and were followed

by mortality displacement for non-accidental, cardiopulmonary, and cardiovascular deaths.

Mean temperature was a better predictor of mortality (based on model fit) than maximum or

minimum temperature.

Conclusions: In Tianjin, extreme cold and hot temperatures increased the risk of mortality.

Results suggest that the effects of cold last longer than the effects of heat. It is possible to

combine the case−crossover design with DLNMs. This allows the case−crossover design to

flexibly estimate the non-linear and delayed effects of temperature (or air pollution) whilst

controlling for season.

73

Key words: Cardiovascular mortality; Case−crossover; Distributed lag non-linear model; Mortality;

Respiratory mortality; Temperature

74

4.2 Introduction

Heat-related mortality has become a matter of increasing public health significance,

especially in the light of climate change. Studies have examined hot and cold temperatures in

relation to total non-accidental deaths and cause-specific deaths (Stafoggia, Forastiere,

Agostini, Biggeri, Bisanti, Cadum, Caranci, Donato, et al., 2006). The city- or region-specific

temperature-mortality relationship is often V-, U- or J-shaped, with increases in mortality at

temperatures below (above) the cold (hot) threshold (Hajat & Kosatky, 2010). The

temperature-mortality relationship varies greatly by geographic, climate and population

characteristics (The Eurowinter Group, 1997). Social, economic, demographic and

infrastructure factors can influence the sensitivity of populations to temperature (Ebi, Kovats,

& Menne, 2006b). In China, only a few studies on temperature-mortality relationship have

been conducted in Shanghai (Kan, Jia, & Chen, 2003), Hong Kong (Chan, Goggins, Kim, &

Griffiths, 2010) and Beijing (Liu et al., 2011). No research has been undertaken in Tianjin,

one of the largest cities in northeastern China.

A previous study found that no temperature measure (maximum, mean or minimum

temperature) was consistently better at predicting mortality in the US. The best temperature

measure differed by age group, season and region (Barnett, Tong, et al., 2010). It is unknown

which temperature measure is the best predictor of mortality in Tianjin.

Mortality risk depends not only on exposure to the current day’s temperature, but also on

several previous days’ exposure (Anderson & Bell, 2009). The distributed lag model has been

applied to explore the delayed effect of temperature on mortality (Analitis, et al., 2008;

Baccini, et al., 2008; Hajat, et al., 2005). To overcome the strong correlation between daily

75

temperatures over short time periods, constrained distributed lag structures are used in time

series regressions (Armstrong, 2006). The estimates are constrained by smoothing using

methods such as natural cubic splines, polynomials, or stratified lag. Both unconstrained and

constrained distributed lag models assume a linear relationship between temperature below

(above) the cold (hot) threshold and mortality, so these models may not be sufficiently

flexible to capture the effects of temperature on mortality.

Recently, a distributed lag non-linear model (DLNM) was developed to simultaneously

estimate the non-linear and delayed effects of temperature (or air pollution) on mortality (or

morbidity) (Armstrong, 2006; Gasparrini, et al., 2010). DLNMs use a “cross-basis” function

that describes a two-dimensional temperature-response relationship along the dimensions of

temperature and lag. The choice of “cross-basis” functions for the temperature and lag are

independent, so the spline or linear functions can be used for temperature, while the

polynomial functions can be used for the lag. The estimates can be plotted using a 3-

dimensional graph to show the relative risks along both temperature and lags. We can predict

the relative risks for a certain temperature or lag, by extracting a “slice” from the 3-

dimensional graph. We can compute the overall effect by summing the log relative risks of

each lag. Separate smoothing functions are applied to time in order to control for season and

secular trends.

The case−crossover design controls for seasonal effects and secular trends by matching case

and control days in relatively small time windows (e.g., calendar month). This controls for

season using a step-function rather than a smooth spline function (Barnett & Dobson, 2010).



76

temperature in each season, with a single lag model, or moving average lag model (Basu, et

al., 2008; Green, et al., 2010). Few studies have demonstrated how to fit non-linear and

delayed effects of temperature on mortality within a case–crossover design.

We used DLNMs combined with the case–crossover design, making it possible to fit more

sophisticated estimates of the effects of temperature (or air pollution) using a case–crossover

design. We demonstrated these models here using a motivating example of the temperature-

mortality relationship in Tianjin, China, and also investigated which temperature measure had

the best predictive ability for mortality.

4.3 Materials and methods

4.3.1 Data collection

Tianjin is a city in northeastern China, and is adjacent to Beijing and Hebei Province, along

the coast of Bohai Gulf (39° 07' North, 117° 12' East). Tianjin has four distinct seasons, with

cold, windy, dry winters influenced by the vast Siberian anticyclone, and hot, humid

summers due to the monsoon. It is the fifth largest Chinese city in terms of urban land area.

The population in the urban area was 4.2 million in 2005.

Mortality data was obtained from the China Information System for Death Register and

Report of Chinese Centre for Disease Control and Prevention from January 1, 2005 to

December 31, 2007. The mortality data were from six urban districts of Tianjin (Heping,

Hedong, Hexi, Nankai, Hebei and Hongqiao). Non-accidental mortality was classified

according to the International Classification of Diseases, 10th revision (ICD-10: A00–R99)

(World Health Organization, 2007). Cardiopulmonary (ICD-10:I00–I99 and ICD-10:J00–

77

J99), cardiovascular mortality (ICD-10:I00–I99) and respiratory mortality (ICD-10:J00–J99)

were examined separately.

Daily meteorological data on maximum, mean and minimum temperature, and relative

humidity, were obtained from the China Meteorological Data Sharing Service System

(http://cdc.cma.gov.cn). Daily air pollution data on particulate matter less than 10 μm in

aerodynamic diameter (PM10), sulphur dioxide (SO2) and nitrogen dioxide (NO2) were

obtained from the Tianjin Environmental Monitoring Centre.

4.3.2 Data analysis

The time-stratified case–crossover using a fixed and disjointed window (e.g., calendar month)

avoids the “overlap bias” (Janes, et al., 2005). The case–crossover using conditional logistic

regression is a special case of time series analysis (Lu & Zeger, 2007). This equivalence

provides computational convenience and permits model checking for the case–crossover

design using standard log-linear model diagnostics (Lu, et al., 2008). We used a Poisson

regression model that allows for over-dispersion to combine the case–crossover design with a

DLNM:

Yt ~ Poisson(μt)

Log (μt) = α + βTt,l + S(RHt, 3) + S(PM10t, 3) + S(SO2t, 3) + S(NO2t, 3)

+ λStratat + ηDOWt + υHolidayt + δInfluenzat

= α + βT t,l + COVs , [4.1]

where t is the day of the observation; Yt is the observed daily death counts on day t; α is the

intercept; Tt,l is a matrix obtained by applying the DLNM to temperature, β is vector of

coefficients for Tt,l, and l is the lag days. S(.) is a natural cubic spline. Three degrees of

78

freedom were used to smooth relative humidity, PM10, NO2, and SO2 according to previous

studies (Anderson & Bell, 2009; Stafoggia, Schwartz, Forastiere, & Perucci, 2008). Stratat is

a categorical variable of the year and calendar month used to control for season and trends,

and λ is vector of coefficients. DOWt is day of the week on day t, and η is vector of

coefficients. Holidayt is a binary variable that is “1” if day t was a holiday. Influenzat is a

binary variable that is “1” if there were any influenza deaths on day t.

Based on the vector of estimated coefficients β in model [4.1], the DLNM was used to get the

predicted effects and standard errors for combinations of temperature and lags. Graphs,

summaries, and statistical inference can be obtained from the DLNM estimates and standard

errors (Armstrong, 2006).

We used a “natural cubic spline-natural cubic spline” DLNM that modelled both the non-

linear temperature effect and the lagged effect using a natural cubic spline. We placed spline

knots at equal spaces in the temperature range to allow enough flexibility in the two ends of

temperature distribution. We placed spline knots at equal intervals in the log scale of lags to

allow more flexible lag effects at shorter delays. To completely capture the overall

temperature effect and adjust for any potential harvesting (heat-related excesses of mortality

were followed by deficits), we used lags up to 27 days according to a previous study

(Armstrong, 2006). The median value of temperature was defined as the baseline temperature

(“centering value”) for calculating the relative risks. To choose the degree of freedom (knots)

for temperature and lag, we used Akaike information criterion (AIC) for quasi-Poisson

models (Gasparrini, et al., 2010; Peng, Dominici, & Louis, 2006). We found that 5 degrees of

freedom for temperature and 4 degrees of freedom for lag produced the best model fitting.

We plotted the relative risks against temperature and lags to show the entire relationship

79

between temperature and mortality. We also plotted the overall effect of temperature on

morality summed over lag days.

Our initial analysis found that the temperature-mortality relationships were U-shaped, with

potential cold and hot thresholds. Thus we also used a “double threshold-natural cubic spline”

DLNM that assumes the effect of cold temperature is linear below the cold threshold while

the effect of high temperature is linear above the hot threshold, and models the lag effects

using a natural cubic spline with 4 degrees of freedom. Formula [4.1] was altered by

modifying the βTi,l term into two linear threshold terms:

Log (μt) = α + βcTCt,l + βHTHt,l + COVs , [4.2]

where TCt,l (THt,l) is a matrix obtained by applying the “double threshold-natural cubic

spline” DLNM to temperatures below the cold threshold and above the hot threshold.

Temperature thresholds used in the model [4.2] were determined by testing multiple

thresholds. For example, for mean temperature, our initial analysis indicated that the potential

cold threshold was within −5 to 5 °C, and the potential hot threshold was within 19 to 29 °C.

Hence we examined combinations of cold thresholds from −5.0 to 5.0 °C (in 0.1 °C gaps) and

hot thresholds from 19.0 to 29.0 °C (in 0.1 °C gaps) to identify the combination that

minimised the residual deviance. We then estimated the relative risks of mortality for a 1 °C

decrease in temperature below the cold threshold and a 1 °C increase above the hot threshold.

The temperature-mortality relationships for combinations of temperature measures

(maximum, mean, and minimum temperatures) and mortality categories (Non-accidental,

cardiopulmonary, cardiovascular, and respiratory deaths) were each examined using the

80

above steps. The AIC was used to choose the temperature measure that best predicted

mortality.

Sensitivity analyses were performed by changing the window length in the case–crossover

from calendar month to 30, 28 and 21 days to control for season, and varying the maximum

lags to 20 and 30 days for the DLNM.

All statistical tests were two-sided and values of P<0.05 were considered statistically

significant. Spearman’s correlation coefficients were used to summarize the similarities in

daily weather conditions. The R software (version 2.12.1, R Development Core Team 2009)

was used to fit all models, with the “dlnm” package to create the DLNM (Gasparrini &

Armstrong, 2011a).

A detailed explanation of how to combine the case–crossover with DLNM is provided in the

supplemental material (see Supplemental Material Chapter 4, R code).

4.4 Results

The average daily maximum temperature was 19 °C, mean temperature 13 °C, minimum

temperature 8 °C, and relative humidity 60%. On average there were 56 daily non-accidental

deaths, 34 cardiopulmonary deaths, 30 cardiovascular deaths, and 4 respiratory deaths (Table

4.1). The three temperature measures were strongly correlated (Table 4.2).

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Table 4.1: Summary statistics of daily weather conditions and mortality in Tianjin, China,

2005–2007

Variables Minimum 25% Median 75% Maximum Mean SD

Maximum temperature (°C) –6 8 21 30 40 19 12

Mean temperature (°C) –11 3 14 24 31 13 11

Minimum temperature (°C) –14 –2 10 19 29 8 11

Humidity (%) 13 46 61 74 97 60 19

Non-accidental death 26 46 55 66 106 56 14

Cardiopulmonary death 13 27 33 40 77 34 9

Cardiovascular death 9 24 29 35 67 30 8

Respiratory death 0 3 4 6 15 4 2

Influenza death 0 0 0 0 2 0 0.1

SD = standard deviation

Table 4.2: Spearman’s correlation coefficients between weather conditions in Tianjin, China,

2005–2007

Temperature measures Mean

temperature

Minimum

temperature

Humidity

Maximum temperature 0.98** 0.94** 0.16*

Mean temperature 0.98** 0.24*

Minimum temperature 0.32*

*P<0.05

**P<0.01

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Mean temperature generally gave the lowest AIC values (i.e., had the best predictive ability

for mortality) in Tianjin (see Supplemental Material Chapter 4, Table S4.1). The “double

threshold-natural cubic spline” DLNM generally fit the data better than the “natural cubic

spline-natural cubic spline” DLNM (see Supplemental Material Chapter 4, Table S4.1).

Therefore we report results for associations with mean temperature only.

The 3-dimensional plots show the entire surface between mean temperature and mortality

categories at all lag days (Figure 4.1). The estimated effects of temperature were non-linear

for all mortality types, with higher relative risks at hot and cold temperatures. For example,

extreme hot temperature (30 °C) was positively associated with non-accidental mortality on

current day, whilst extreme cold temperature (–6 °C) significantly increased non-accidental

mortality after 3-days lag. Neither hot effects (i.e., significant increases in mortality

associated with hot temperatures) nor cold effects (i.e., significant increases in mortality

associated with cold temperatures) were apparent after a 20-day lag, with relative risks close

to one across the entire range of temperatures (see Supplemental Material Chapter 4, Figure

S4.1).

83

Figure 4.1: Relative risks of mortality types by mean temperature (°C), using a natural cubic

spline–natural cubic spline DLNM with 5 df natural cubic spline for temperature and 4 df for

lag. (A) Nonaccidental, (B) cardiopulmonary, (C) cardiovascular, and (D) respiratory

mortality.

84

Figure 4.2: The estimated overall effects of mean temperature (°C) over 28 days on mortality

types, using a natural cubic spline–natural cubic spline DLNM with 5 df natural cubic spline

for temperature and 4 df for lag. (A) Nonaccidental, (B) cardiopulmonary, (C) cardiovascular,

and (D) respiratory mortality. The black lines are the mean relative risks, and the blue regions

are 95% CIs.

85

Figure 4.3: The estimated effects of a 1°C decrease in mean temperature below the cold

threshold (left) and of a 1°C increase in mean temperature above the hot threshold (right) on

mortality types over 27 days of lag, using a double threshold–natural cubic spline DLNM

with 4 df natural cubic spline for lag. (A) Nonaccidental, (B) cardiopulmonary, (C)

cardiovascular, and (D) respiratory mortality. The black lines are mean relative risks, and

blue regions are 95% CIs. The cold and hot thresholds were 0.8°C and 24.9°C for

nonaccidental mortality (A), 0.1°C and 25.3°C for cardiopulmonary mortality (B), 0.6°C and

25.1°C for cardiovascular mortality (C), 0.7°C and 24.8°C for respiratory mortality (D).

86

Figure 4.2 shows the estimated effect of mean temperature over 28 days on mortality. There

were U-shaped relationships between mean temperature and all mortality types, with large

“comfortable” temperature ranges where the relative risks of mortality were close to one. The

cold and hot thresholds (i.e., the temperatures below and above which estimates were

constrained to be linear by the model, which do not necessarily coincide with temperatures

associated with increased mortality by model [4.1]) were 0.8 °C and 24.9 °C for non-

accidental mortality, 0.1°C and 25.3 °C for cardiopulmonary mortality, 0.6 °C and 25.1 °C

for cardiovascular mortality, 0.7 °C and 24.8 °C for respiratory mortality.

Significant cold effects appeared after after a 3-day lag, while significant hot effects occurred

within 0 to 3 days (Figure 4.3). Associations between cold and mortality lasted longer than

associations with heat. Heat-related excesses of non-accidental, cardiopulmonary, and

cardiovascular mortality were followed by deficits in mortality, consistent with some

mortality displacement caused by hot temperatures.

We calculated the overall effects of mean temperature on non-accidental, cardiopulmonary,

cardiovascular and respiratory mortality along the lags (Table 4.3). For cold effects over lag

0–18 days, a 1 °C decrease in mean temperature below the cold thresholds was associated

with a 2.99% (95% confidence interval (CI): 0.85–5.17%) increase in non-accidental deaths,

5.49% (95% CI: 2.29–8.79%) increase in cardiopulmonary deaths, 4.05% (95% CI: 1.14–

7.06%) increase in cardiovascular deaths, and 9.25% (95% CI: 1.70–17.37%) increase in

respiratory deaths. For hot effects over lag 0–2 days, a 1 °C increase in mean temperature

above the hot thresholds was associated with a 2.03% (95% CI: 0.70–3.38%) increase in non-

accidental deaths, 3.04% (95% CI: 1.24–4.87%) increase in cardiopulmonary deaths, 2.80%

(95% CI: 0.95–4.68%) in cardivascular deaths, and 3.36% (95% CI: –0.77 to 7.67%) increase

87

in respiratory deaths. In general, cold effects of lag 0–27 days were greater than hot effects of

lag 0-27 days except for respiratory mortality.

Table 4.3: The cumulative cold and hot effects of mean temperature on mortality categories

along the lag days, using a “double threshold-natural cubic spline” DLNM with 4 degrees of

freedom natural cubic spline for lag.

Effects

Lag

(days)

% increase in mortality (95% CI)

Non-accidental Cardiopulmonary Cardiovascular Respiratory

Cold effect a 0–2 –0.27 (–1.25, 0.72) –0.19 (–1.49, 1.12) –0.14 (–1.43, 1.17) –1.65 (–4.75, 1.55)

0–18 2.99 (0.85, 5.17)* 5.49 (2.29, 8.79)* 4.05 (1.14, 7.06)* 9.25 (1.70, 17.37)*

0–27 2.13 (–0.44, 4.78) 4.16 (0.27, 8.21)* 2.66 (–0.86, 6.30) 7.99 (–1.08, 17.9)

Hot effect b 0–2 2.03 (0.70, 3.38)* 3.04 (1.24, 4.87)* 2.80 (0.95, 4.68)* 3.36 (–0.77, 7.67)

0–18 –0.78 (–4.20, 2.77) 2.32 (–2.59, 7.49) 0.86 (–4.02, 5.98) 8.60 (–2.78, 21.31)

0–27 0.31 (–3.48, 4.24) 3.83 (–1.75, 9.72) 2.47 (–2.99, 8.24) 8.79 (–3.62, 22.80)

*P<0.05

a The percent increase in mortality for a 1 °C of temperature decrease below the cold thresholds

(0.8 °C for non-accidental, 0.1 °C for cardiopulmonary 0.6 °C for cardiovascular, and 0.7 °C

respiratory mortality).

b The percent increase in mortality for a 1 °C of temperature increase above the hot thresholds

(24.9 °C for non-accidental, 25.3 °C for cardiopulmonary 25.1 °C for cardiovascular, and 24.8 °C for

respiratory mortality).

88

Sensitivity analysis

We changed the window length of calendar month in the case–crossover to 30, 28, and 21

days, which gave similar results (data not shown). In addition, we changed the maximum lag

to 20 and 30 days, which gave similar results (data not shown). Consequently, we believe that

the models used in this study adequately captured the main effects of temperature on

mortality.

4.5 Discussion

4.5.1 Temperature-mortality relationship

The temperature-mortality relationship in Tianjin was U-shaped, with a large range of

temperatures that were not associated with excess mortality. Significant associations between

cold temperatures and mortality (cold effects) appeared after 3 days and lasted longer than the

associations between high temperatures and mortality (hot effects), which were acute and of

short duration. There was evidence of some mortality displacement due to effects of high

temperatures on non-accidental, cardiopulmonary, and cardiovascular deaths.

Many studies have examined the temperature-mortality relationship worldwide, but few are

from China (Hajat & Kosatky, 2010). We compared our results with studies that examined

both cold and hot effects using mean temperature for non-accidental mortality (Curriero et al.,

2002b; El-Zein, Tewtel-Salem, & Nehme, 2004; Revich & Shaposhnikov, 2008b; Rocklov &

Forsberg, 2008; Yu, Mengersen, Hu, et al., 2011) (Figure 4.4). Results show that estimated

temperature effects varied by region and population. Compared with populations living at

similar latitudes, our results suggest a stronger cold effect and smaller hot effect. The reason

89

might be that short lags were used in other studies, while we examined overall cold and hot

effects of lag 0–27 days. Studies using short lags may have underestimated the cold effect, as

in our results the estimated cold effect was delayed by three days and lasted for 10 days.

Studies using short lags may overestimate the hot effect, as in our results there was evidence

of some mortality displacement which can only be captured by using longer lags (Anderson

& Bell, 2009). Compared with other median or lower income populations (e.g., Bangkok,

Mexico City, São Paulo, Delhi, Santiago, and Cape Town), Tianjin had lower cold and hot

effects. The reason might be that people in Tianjin used protection measures in summer and

winter (e.g., air conditioning and heating system) (McMichael, et al., 2008).

Figure 4.4: Comparison of the impacts of temperature on nonaccidental mortality in different

populations ordered by latitude.

90

We can compare our results with those from similar cities in China. Kan et al. (2003) found a

V-shaped relationship between lag 0–2 days’ temperature and non-accidental mortality in

Shanghai, with an optimum temperature of 26.7 °C. A 1 °C decrease (increase) in

temperature below (above) 26.7 °C increased non-accidental mortality by 1.21% (0.73%).

Liu et al. (2011) found both cold and hot temperatures were associated with increased

cardiopulmonary mortality in Beijing, which has a climate that is similar to Tianjin’s. They

also found an acute and short-term hot effect followed by some mortality displacement for

cardiovascular mortality, consistent with our results.

An interesting finding is that the range of temperatures that are not associated with increased

mortality is quite large in Tianjin, but extreme temperatures still had adverse effects on

mortality. The exchange of heat between the body and surrounding temperature is regulated

constantly by physiological control. Extreme high temperatures may cause a failure of

thermoregulation, which may be impaired by dehydration, salt depletion and increased

surface blood circulation (Bouchama & Knochel, 2002). Elevated blood viscosity, cholesterol

levels and sweating thresholds may also be the cause of heat-related mortality (McGeehin &

Mirabelli, 2001). Cold temperatures increase the heart rate, peripheral vasoconstriction, blood

pressure, blood cholesterol levels, plasma fibrinogen concentrations, and platelet viscosity

(Ballester, et al., 1997a; Carder, et al., 2005b). In Tianjin urban city, eighty-three percent of

houses had central heating in winter (Tianjin Statistic Bureau, 2005) and ninety percent of

homes had air conditioners (Tianjin Statistic Bureau, 2004). However, although the majority

of the urban population were potentially protected from the weather, there were still some

increased risks during extreme cold and hot days.

91

We investigated lag effects over 28 days on mortality for both hot and cold days. In general,

cold effects lasted about 10 days after the extreme cold days. Previous studies also reported

similarly delayed cold effects on mortality (Anderson & Bell, 2009; Goodman, Dockery, &

Clancy, 2004). The findings indicate that using short lags cannot completely capture the cold

effect, and so longer lags are required to examine the cold impact.

The hot effects were more acute and short-term. Studies have shown that hot temperatures

induce an acute event in people with pre-existing diseases (e.g., a previous myocardial

infarction or stroke) and in those who may find it difficult to deal with heat (e.g., the elderly)

(Muggeo & Hajat, 2009). In people with congestive heart failure, the extra heat load may lead

to fatal consequences (Näyhä, 2005). The hot effect also led mortality displacement for non-

accidental, cardiopulmonary, and cardiovascular deaths, which is in agreement with studies

conducted in Europe (Hajat, et al., 2005; Pattenden, Nikiforov, & Armstrong, 2003) and US

(Braga, Zanobetti, & Schwartz, 2001). Therefore, using short lags cannot adequately assess

the hot effects, as the harvesting effects were ignored.

Studies of heat-related mortality have examined maximum, mean, or minimum temperatures,

controlling for relative humidity (Anderson & Bell, 2009). Other studies have used apparent

temperature, the humidex and temporal synoptic index (Zanobetti & Schwartz, 2008). A large

study of mortality in the US found that the different measures of temperature had a similar

ability to predict the impacts of temperature on mortality (Barnett, Tong, et al., 2010). We

found that maximum, mean, and minimum temperatures had similar predictive ability,

probably because of their strong correlation. Overall, mean temperature performed best

according to the AIC.

92

4.5.2 Case−crossover design and DLNM

Many models have been used to assess the impacts of temperature and air pollution on

mortality and morbidity, such as descriptive (Reid, et al., 2009), case-only (Schwartz, 2005),

case–crossover (Stafoggia, Forastiere, Agostini, Biggeri, Bisanti, Cadum, Caranci, Donato, et

al., 2006), time-series (Hajat, et al., 2002) and spatial analysis (Vaneckova, et al., 2010).

Generally, time-series and case–crossover designs are the most commonly used in a single or

in multiple locations over a time period. The main aim of both analyses is to examine

associations between health and temperature, after controlling for potential confounding

factors such as secular trends and seasonal cycles (Basu, et al., 2005). Using the case–

crossover design each subject is their own control, and so any confounding by fixed

characteristics is removed. Another advantage of the case-crossover is that it controls for

long-term and seasonal trends by design through short-interval strata (e.g. calendar month).

We compared the case–crossover design and a time series design using a natural cubic spline

with 7 degrees of freedom for time per year. The case–crossover design performed better than

time series analysis for this particular data based on AIC and residuals. However, we cannot

conclude the case–crossover is better than time series for other data. We suggest checking the

model fit and residuals when using case–crossover or time series designs. In this study, we

illustrated how to combine the DLNM with a case−crossover design. This allows

sophisticated non-linear and delayed temperatures to be fitted using the case−crossover

design.

One of the main advantages of DLNM is that it allows the model to contain detailed lag

effects of exposure on response, and provides the estimate of the overall effect that is

93

adjusted for harvesting (Gasparrini, et al., 2010). The DLNM can flexibly show different

temperature-mortality relationships for lags using different smoothing functions. The DLNM

can adequately model the main effects of temperature (Armstrong, 2006).

There are also some issues in the selection of the DLNM, such as cross-basis type, maximum

lag day, and degrees of freedom (knots and placement) for exposure and lag (Armstrong,

2006; Gasparrini, et al., 2010). Because the DLNM is combined with a regression model (e.g.,

Poisson regression), the residual deviance and autocorrelation plot, maximum likelihood,

Akaike’s information criteria or Bayesian information criteria can be used to check the model.

The options for the DLNM can be chosen according to the best model fit. Previous studies

recommend choosing a DLNM that is easy to interpret from an epidemiological perspective

(Armstrong, 2006; Gasparrini, et al., 2010). However, it is necessary to conduct sensitivity

analyses to assess the key conclusions on model choice. In this study, we used AIC to select

the degrees of freedom, and used residual deviance to choose both cold and hot thresholds,

but used a priori arguments to choose cross-basis type and maximum lag day.

4.5.3 Strengths and limitations

We examined both cold and hot lag effects on four types of mortality, and explored which

temperature measure was the best predictor of mortality. Our findings can be used to promote

capacity building for local response for extreme temperatures.

A limitation is that the data are only from one city, so it is difficult to generalise our results to

other cities or to rural areas. We used the data on temperature and air pollution from fixed

sites rather than individual exposure, so there may be some inevitable measurement error.

94

The influence of ozone was not controlled for, because data on ozone were unavailable. In

previous research, hot effects were slightly reduced when ozone was controlled for, but cold

effects were not changed (Anderson & Bell, 2009). Some studies found a potential interaction

between temperature and ozone (Ren, Williams, Morawska, Mengersen, & Tong, 2008a).

Further study needs to be conducted for this issue.

4.6 Conclusions

DLNM can be applied in a case−crossover design, so that the case−crossover can be used to

examine sophisticated non-linear and delayed effects of exposure (e.g., temperature or air

pollution). Even though there was a relatively large temperature range that was not associated

with excess mortality, extreme cold and hot temperatures were associated with an increased

risk of mortality in Tianjin, China. Cold temperatures had longer lasting effects on mortality,

while hot temperatures had acute and short-term effects.

95

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100

4.8 Supplemental Material Chapter 4

Supplemental Material Chapter 4, Table S4.1: Akaike information criteria (AIC) values for

the relationship between temperature measures and mortality categories by DLNM type

DLNM type Temperature measure

AIC


Natural cubic

spline-natural

cubic spline a

Maximum temperature 7494 6860 6679 4562

Mean temperature 7472 6841 6658 4570

Minimum temperature 7472 6840 6660 4580

Double threshold-

natural cubic

spline b

Maximum temperature 7488 6849 6662 4568

Mean temperature 7473 6833 6653 4558

Minimum temperature 7481 6845 6666 4556

a Using “natural cubic spline-natural cubic spline” DLNM with smoothing of 5 degrees of

freedom for temperature and 4 degrees of freedom for lag;

b Using “double threshold-natural cubic spline” DLNM with smoothing of 4 degrees of

freedom for lag; the cold and hot thresholds are shown in Supplemental Material, Table 2.

101

Supplemental Material Chapter 4, Table S4.2: Cold and hot thresholds (°C) used by the

“double threshold-natural cubic spline”

Threshold type Temperature measure

Mortality type


Cold threshold

(°C)

Maximum temperature 4.8 4.3 4.5 4.7

Mean temperature 0.8 0.1 0.6 0.7

Minimum temperature –3.9 –4.1 –3.8 –3.5

Hot threshold

(°C)

Maximum temperature 31.2 31.3 31.1 31.5

Mean temperature 24.9 25.3 25.1 24.8

Minimum temperature 20.1 22.0 21.9 21.6

102

Supplemental Material Chapter 4, Figure S4.1: Relative risks by lag at specific mean

temperatures (left) and relative risks by mean temperature at specific lags (right) for non-

accidental mortality, using a “natural cubic spline-natural cubic spline” DLNM with 5

degrees of freedom for temperature and 4 degrees of freedom for lag. The reference

temperature is 14 °C.

103

Supplemental Material Chapter 4, R code

As our data from Tianjin is not publicly available, we used data from Jersey city as an

example. The data were from the National Morbidity, Mortality, and Air Pollution Study

(NMMAPS) (J. M. Samet, Dominici, et al., 2000; J. M. Samet, Zeger, et al., 2000).

Load packages and prepare dataset:

>library(dlnm); library (NMMAPSlite)

>initDB()

>cities <- listCities()

# Jersey City: jers (city number 43)

>data <- readCity(cities[43], collapseAge = TRUE)

>data <- data[,c("city","date","death","inf","tmpd","rhum","so2mean","pm10trend")]

>data$temp <- (data$tmpd-32)*5/9 # Transfer temperature to Celsius

>data$time<-1:length(data[,1]) # Create time

>data$dow<-as.numeric(format(data$date,"%w")) # Create day of the week

>data$year<-as.numeric(format(data$date,"%Y")) # Create year

>data$month<-as.numeric(format(data$date,"%m")) # Create month

>data$strata<-data$year*100+data$month # Case-Control strata

Create Cross-basis matrix using “natural cubic spline-natural cubic spline” DLNM

with 5 df for temperature and 4 df for lag

>range <- range(data$temp,na.rm=T)

>nknots<-4 # Number of knots for temperature

>nlagknots<-2 # Number of knots for lag

104

>ktemp <- range[1] + (range[2]-range[1])/(nknots+1)*1:nknots # Knots for temperature

>klag<-exp((log(27))/(nlagknots+2)*1:nlagknots) # Knots for lag

>basis.temp <- crossbasis(data$temp, vartype="ns", varknots=ktemp,

cenvalue=median(data$temp,na.rm=T), lagtype="ns", lagknots=klag,maxlag=27)

Combine the case-crossover design with DLNM

>model.month <- glm(death ~ basis.temp + ns(rhum,df=3) + ns(pm10trend,df=3) +

ns(so2mean,df=3) + as.factor(I(inf>0)) + as.factor(strata)+as.factor(dow),

family=quasipoisson(), data)

Derive the predicted effects and standard errors for temperature and lags using DLNM

>pred.month <- crosspred(basis.temp, model.month, at=-16:32)

Plot 3D and overall effect graphics

> plot (pred.month,"3d",zlab="Relative Risk", r=90, d=0.3, col="red", xlab="Temperature",

main="3D graphic for Jersey City", expand=0.6,lwd=0.5)

>plot(pred.month,"overall", xlab="Temperature (°C)", ylab=" Relative Risk ",

main="Overall effect of temperature on mortality\n between 1987-2000 for Jersey City")

Determine the cold and hot thresholds (in °C) using “double threshold-natural cubic

spline” DLNM

Based on the above 3D plot and overall effect plot, there are two potential thresholds for

temperature. The cold threshold is somewhere between 0 to 8 °C, and hot threshold is

somewhere between 19 to 26 °C. We used the following models to determine which

combination of cold and hot thresholds gave the lowest residual deviance.

105

>cold.thr<-0:8 # In 1°C increments (In our study, we used 0.1°C increments)

>hot.thr<-19:26 # In 1°C increments (In our study, we used 0.1°C increments)

>deviance.matrix<-matrix(data = NA, nrow = length(cold.thr), ncol = length(hot.thr), byrow

= FALSE, dimnames = list(paste("cold.thr", cold.thr,sep="."),

paste("hot.thr", hot.thr,sep=".")))

>for (i in 1:length(cold.thr)){

for (j in 1:length(hot.thr)){

basis.try <- crossbasis(data$temp, vartype="dthr",varknots=c(cold.thr[i],hot.thr[j]),

lagtype="ns", lagknots=klag, maxlag=27)

model <- glm(death ~ basis.try + ns(rhum,df=3) + ns(pm10trend,df=3) + ns(so2mean,df=3)

+ as.factor(I(inf>0)) + as.factor(strata)+as.factor(dow), family=quasipoisson(), data)

deviance.matrix[i,j]<-model$deviance

}

}

>row.col <- arrayInd(which.min(deviance.matrix), dim(deviance.matrix))

>rowname<-rownames(deviance.matrix)[row.col[,1]]

>colname<-colnames(deviance.matrix)[row.col[,2]]

>rowname;colname # Get the cold and hot thresholds

[1] "cold.thr.4" # The best cold threshold is 4°C

[1] "hot.thr.22" # The best hot threshold is 22 °C

Examine the cold (hot) effects below (above) the cold (hot)threshold using “Double

threshold-natural cubic spline” DLNM

106

The cold threshold 4 °C and hot threshold 22 °C are used for a “Double threshold-natural

cubic spline” DLNM.

>basis.cold.hot<- crossbasis(data$temp, vartype="dthr",varknots=c(4,22),

lagtype="ns", lagknots=klag, maxlag=27)

>model.cold.hot <- glm(death ~ basis.cold.hot + ns(rhum,df=3) + ns(pm10trend,df=3) +

ns(so2mean,df=3) + as.factor(I(inf>0)) + as.factor(strata)+as.factor(dow),


>cold.hot.pred <- crosspred(basis.cold.hot,model.cold.hot,at=-16:32)

> plot(cold.hot.pred,"3d",zlab="Relative Risk", r=90,d=0.3,col="red",xlab="Temperature",

main="\n3D graphic for Jersey City\nfor double threshold",expand=0.6,lwd=0.5) # 3D plot

>par(mfrow=c(2,1))

>plot(cold.hot.pred,"slices",var=c(3),main="Cold effect", xlab="", ylab=" Relative Risk ",

ylim=range(0.99,1.01))

>plot(cold.hot.pred,"slices",var=c(23),main="Hot effect",xlab="Lag (day)",

ylab=" Relative Risk", ylim=range(0.99,1.01))

Sensitivity analysis using 20 days as the maximum lag

> nlagknots<-2 # Number of knots for lag

> klag.20<-exp(log(20)/(nlagknots+2)*1:nlagknots) # Knots for lag

> basis.temp.20 <- crossbasis(data$temp, vartype="ns", varknots=ktemp,

cenvalue=median(data$temp,na.rm=T), lagtype="ns",lagknots=klag.20,maxlag=20)

> model.month.20 <- glm(death ~ basis.temp.20 + ns(rhum,df=3) + ns(pm10trend,df=3) +

ns(so2mean,df=3) + as.factor(I(inf>0)) +as.factor(strata)+as.factor(dow),


107

> pred.month.20 <- crosspred(basis.temp.20, model.month.20, at=-16:32)

> plot(pred.month.20,"overall", xlab="Temperature (°C)", ylab="Relative risk",

main="Overall effect of temperature on mortality\n between 1987-2000 for Jersey

City using maximum lag of 20 days")

Sensitivity analysis using 30 days as strata

>strata30<-floor((data$time-min(data$time))/30) # Create strata as 30 days

>model.strata30<- glm(death ~ basis.temp + ns(rhum,df=3) + ns(pm10trend,df=3) +

ns(so2mean,df=3) + as.factor(I(inf>0)) +as.factor(strata30)+as.factor(dow),


>pred.strata30<- crosspred(basis.temp, model.strata30, at=-16:32, cumul=T)

>plot(pred.strata30,"overall", xlab="Temperature (°C)", ylab=" Relative risk ",

main="Overall effect of temperature on mortality\n between 1987-2000 for Jersey

City using 30 days as strata")

Comparison of time series and case–crossover design

# ignore humidity & pollution to remove influence of missing values

# case-crossover using calendar month as strata

>model.month <- glm(death ~ basis.temp + as.factor(I(inf>0))

+as.factor(strata)+as.factor(dow), family=quasipoisson(), data)

# time series with 7 degrees of freedom for time per year

>model.ts <- glm(death ~ basis.temp + as.factor(I(inf>0)) +ns(time,98)+as.factor(dow),


108

Plot the residual distribution

>par(mfrow=c(2,1))

> hist(resid(model.month),main="Residual distribution for case-crossover design\nusing

calendar month as strata", xlim=range(-4,5),ylim=range(0,1100),xlab="Residuals",col="red",

font.lab=2,las=1)

>hist(resid(model.ts),main="Residual distribution for time series design\nusing 7 df for time

per year", xlim=range(-4,5),ylim=range(0,195),xlab="Residuals",col="red",font.lab=2,las=1)

>par(mfrow=c(1,1))

Calculate AIC value for case-crossover

>AIC.cc<- -2*sum( dpois( model.month$y, model.month$fitted.values, log=TRUE))+

2*summary(model.month)$df[3]*summary(model.month)$dispersion

AIC.cc="26364.29"

Calculate AIC value for time series

>AIC.ts <- -2*sum( dpois( model.ts $y, model.ts $fitted.values, log=TRUE))+

2*summary(model.ts )$df[3]*summary(model.ts )$dispersion

AIC.ts =" 26297.70"

For Jersey City, a time series design performs better than case-crossover as judged by the

AIC. However, both designs give similar residuals. (For Tianjin, a case–crossover performed

better than a time series according to both the AIC and residuals)

109

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Health Eff Inst(94 Pt 1): 5-14; discussion 75-84.

Samet JM, Zeger SL, Dominici F, Curriero F, Coursac I, Dockery DW, et al. 2000b. The

National Morbidity, Mortality, and Air Pollution Study. Part II: Morbidity and

mortality from air pollution in the United States. Res Rep Health Eff Inst 94(Pt 2): 5-70;

discussion 71-79.

110

CHAPTER 5: SPATIOTEMPORAL MODEL OR TIME SERIES MODEL FOR

ASSESSING CITY-WIDE TEMPERATURE EFFECTS ON MORTALITY?

Citation:

Guo Y, Barnett AG, Tong S. (2011) Spatiotemporal model or time series model for assessing

city-wide temperature effects on mortality? Environmental Research (in press), doi:

10.1016/j.envres.2012.09.001.






111

5.1 Abstract

Most studies examining the temperature-mortality association in a city used temperatures

from one site or the average from a network of sites. This may cause measurement error as

temperature varies across a city due to effects such as urban heat islands. We examined

whether spatiotemporal models using spatially resolved temperatures produced different

associations between temperature and mortality compared with time series models that used

non-spatial temperatures. We obtained daily mortality data in 163 areas across Brisbane city,

Australia from 2000 to 2004. We used ordinary kriging to interpolate spatial temperature

variation across the city based on 19 monitoring sites. We used a spatiotemporal model to

examine the impact of spatially resolved temperatures on mortality. Also, we used a time

series model to examine non-spatial temperatures using a single site and the average

temperature from three sites. We used squared Pearson scaled residuals to compare model fit.

We found that kriged temperatures were consistent with observed temperatures.

Spatiotemporal models using kriged temperature data yielded slightly better model fit than

time series models using a single site or the average of three sites’ data. Despite this better fit,

spatiotemporal and time series models produced similar associations between temperature

and mortality. In conclusion, time series models using non-spatial temperatures were equally

good at estimating the city-wide association between temperature and mortality as

spatiotemporal models.

Key words: Mortality; Spatiotemporal model; Temperature effect; Time series model

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5.2 Introduction

It is widely recognised that the Earth is becoming warmer in response to an accumulation of

greenhouse gas emissions (IPCC, 2007c). Climate change will have wide ranging impacts on

health, including increased heat-related mortality which has become a matter of increasing

public health concern. Studies have examined the association between temperature and

mortality, and both high temperatures and cold temperatures increase the risks of mortality

(Baccini, et al., 2008; Curriero, et al., 2002a; McMichael, et al., 2008; Stafoggia, et al., 2006).


from one monitoring site or daily mean values from a network of sites, which may result in a

measurement error for temperature exposure (Zhang, et al., 2011). Random measurement

error in temperature will bias the effect estimates towards the null (Hutcheon, et al., 2010).

Studies have shown that there is spatial variation in outdoor temperatures within cities and

their surroundings (Aniello, et al., 1995; Kestens, et al., 2011; Lo, et al., 1997). Urban areas

usually have higher temperatures because of the heat island effect

(www.epa.gov/hiri/about/index.html). Smargiassi et al. (2009) found that hotter areas within

a city had a greater risk of heat-related death compared with cooler areas (Smargiassi, et al.,

2009). These results suggest that using temperature from one monitoring site or averaged

values from a network of sites may underestimate the risks of temperature on mortality.

Geo-statistical techniques have been used to model regional temperatures (Benavides, et al.,

2007; Zhang, et al., 2011). Recent studies have used spatial models to examine climate

variables like ambient temperature in the field of agriculture and forestry science (Benavides,

et al., 2007; Chuanyan, et al., 2005). Different techniques (Inverse distance interpolation


113

weighting, Voronoi tessellation, regression analysis, and geo-statistical methods) have been

developed to predict regional temperature from station data (Bhowmik & Cabral, 2011).

Previously, these models have also been used to estimate the health effects of air pollution

within cities (Lee & Shaddick, 2010; Shaddick, et al., 2008; Whitworth, et al., 2011).

Few studies have used spatial methods to quantify the impact of temperature on mortality

(Kestens, et al., 2011; Smargiassi, et al., 2009). If spatial exposures of temperature are

significantly more accurate than standard methods then they may improve our understanding

of the association between temperature and mortality. In this study, we examined the

temperature effect on mortality in Brisbane using spatial temperatures, single site’s

temperatures and averaged temperatures, and compared the risk estimate using a

spatiotemporal model with those using time series models.

5.3 Materials and methods

5.3.1Data collection

Brisbane is the capital city of the state of Queensland in Australia, and is on the east coast of

the country (27° 30' south, 153° 00' east) (Figure 5.1). It has a humid subtropical climate.

We obtained daily morality data on non-accidental causes between January 2000 and

December 2004 at the level of statistical local areas in Brisbane city from the Office of

Economic and Statistical Research of the Queensland Treasury. There are 163 statistical local

areas in Brisbane city (Figure 5.1). The causes of non-accidental mortality were coded

according to the International Classification of Diseases, ninth version tenth version (ICD-10)

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(ICD-10: A00–R99). All deaths were residents of Brisbane city. We obtained population data

from the Australia Bureau of Statistics for each statistical local area. The census of

population and housing for each statistical local area is conducted once every five years. We

used the 1996 census for the year 2000, and the 2001 census for years 2001−2004.

We obtained daily data on maximum temperature from the Australian Bureau of Meteorology

at 19 sites in or around Brisbane city (Figure 5.1). We used maximum temperature in this

study, because a previous study found that all temperature measures (mean, minimum,

maximum, apparent) have a similar ability to predict mortality (A.G. Barnett, S. Tong, et al.,

2010). We obtained daily data on relative humidity from one monitoring site (Brisbane

airport). We obtained daily data on air pollution from the Queensland Environmental

Protection Agency. The daily PM10 concentrations were averaged from 13 monitoring sites.

The daily O3 concentrations were averaged from 10 monitoring sites.

5.3.2Data analysis

5.3.2.1Modelling spatio-temporal temperatures

Ordinary kriging is a geo-statistical technique that interpolates the value of a random field

with observed spatial information (e.g., elevation, longitude, latitude, and land use) into

nearby unobserved locations (Bhowmik & Cabral, 2011; Lefohn, Knudsen, & McEvoy,

1988). Ordinary kriging assumes that the spatial variation is statistically homogeneous

throughout the study area (Isaaks & Srivastava, 1989; Wartenberg, Uchrin, & Coogan, 1991).

We used ordinary kriging to interpolate the daily values of temperature to the centroids of

163 locations across Brisbane city from the values of 19 monitoring sites.

115

To verify the reliability of the kriged temperatures we compared the kriged and observed

temperatures at the 19 monitoring sites using Spearman correlations and the absolute

difference. We also compared ordinary kriging with universal kriging, inverse distance

weighting models, land-use regression models, and spatiotemporal models.

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Figure 5.1: The 19 monitoring sites for temperature in or around Brisbane city, the grey

regions are statistical local areas of Brisbane city, the blue areas are water.

The monitoring sites are Amberley Amo (AMA), Cape Moreton Lighthouse (CML),

University of Queensland Gatton (UQG), Point Lookout (PL), Archerfield Airport (ARA),

Redlands Hrs (RH), Beerburrum Forest Station (BFS), Gatton Qdpi Research Station

(GQRS), Hinze Dam (HD), Jimna Forestry (JF), Coolangatta (CA), Gold Coast Seaway

(GCS), Brisbane Airport (BA), Baroon Pocket Dam (BPD), Logan City Water Treatment

Plant (LCWTP), Sunshine Coast Airport (SCA), Brisbane Centre (BC), Toowoomba Airport

(TA), Murwillumbah (MWB).

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5.3.2.2Modelling the relationships between temperatures and mortality

To perform the spatiotemporal model, a generalized additive mixed model (GAMM) with

Poisson regression was used to examine the association between the spatial temperature and

mortality (Augustin, et al., 2009). To examine the non-linear temperature-mortality

relationship (Guo, Barnett, Pan, Yu, & Tong, 2011a), we used a natural cubic spline for

temperature (Gasparrini, et al., 2010). Hot effects are acute and short-term, whereas cold

effects occur late and last longer. Hence we used moving average lag 0–3 days and lag 0–10

days respectively to examine the temperature-mortality relationships. We used GAMMs to

model the random effects of locations using a random intercept, in order to model those areas

with higher death rates (Ruppert, Wand, & Carroll, 2003).

We assumed the daily number of deaths had an over-dispersed Poisson distribution. A natural

cubic spline for time was used to control for long-term trends and seasonal patterns in

mortality (Daniels, Dominici, Samet, & Zeger, 2000). We controlled for day of the week

(DOW), relative humidity, PM10 and O3. The population in each location was modelled using

an offset. The model was:

Log (E (Yi, t)) = α + S(Tempi,t,l, 3) + S(RHt, 3) + βPM10 t + λO3t + S(t, 7×Years)

+ µDOWt + log(popi, t) + πZi,

= α + S(Tempi,t,l, 3) + COVs + πZi , [5.1]

where i is the location (Statistical local area=1, …, 163); t is the day. Yi, t is the daily death

counts in location i on day t; α is the intercept; S(.) is a natural cubic spline; Tempi,t,l is the

maximum temperature in location i on day t, and l is the lag in days. Three degrees of

freedom (df) spline was used for temperature; RHt is relative humidity on day t, and three df

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spline was used for relative humidity; PM10 t is averaged PM10 from 13 monitoring sites on

day t; O3 t is averaged O3 from 10 monitoring sites on day t; 7 df spline per year for time was

used to control for season and long-term trend; DOWt is the categorical variable day of the

week on day t; popi, t is the population in location i on day t; and Zi is a random intercept for

locations.

To examine a non-spatial temperature exposure using the time series model, we used

temperature data from single monitoring site (Brisbane centre) using a generalized additive

model (GAM) as follows:

Log (E (Yi, t)) = α + S(Tempt,l, 3) + S(RHt, 3) + βPM10t + λO3t + S(t, 7×Years)

+ µDOWt + log(popi, t),

= α + S(Tempt,l, df) + COVs , [5.2]

Tempt,l is the maximum temperature at Brisbane centre on day t, and l is the lag in days.

To examine a non-spatial temperature exposure using multiple monitoring sites, we used

daily temperature averaged from 3 sites (Brisbane centre, Brisbane airport, Archerfield

Airport; Figure 5.1). The model is as equation [5.2] but Tempt,l is the maximum temperature

averaged over 3 sites.

Our initial results showed that the threshold for the temperature-mortality relationship was

28 °C. We used the above models to assess the linear hot (cold) temperature-mortality

relationship above (below) 28 °C. We examined the relative risk of mortality associated with

a 1 °C increase (decrease) above (below) the 28 °C threshold.

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Squared Pearson scaled residuals were used to compare the fit of the spatiotemporal and time

series models. Relative risks (RRs) of mortality and confidence intervals (CIs) were

calculated. All statistical tests were two-sided and values of P<0.05 were considered

statistically significant.

Sensitivity analyses

Sensitivity analyses were used to check our main findings. We varied the degrees of freedom

for temperature (2– 5 degrees of freedom) and time (4– 9 degrees of freedom per year). We

used natural cubic splines with 2 to 4 degrees of freedom for relative humidity, PM10 and O3,

respectively.

On the suggestion of a reviewer, we used an area and time stratified case–crossover model to

verify the results from the GAMM. Control days were matched to case days from the same

calendar month and day of the week. Temperatures on the case and control days were

compared within the statistical local areas.

Software

The R software (version 2.15.0, R Development Core Team 2009) was used to fit all models.

The “mgcv” package was used to fit the GAMM and GAM. The “maptools” package was

used to create the maps. A High Performance Computer was used to run the GAMMs, as this

particular model used a large internal memory (220 GigaBytes) and long running time (5

hours).

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Figure 5.2: Mean daily maximum temperatures for the 163 statistic local areas of Brisbane

city between January 2000 and December 2004. The blue areas are water.

5.4 Results

There were only small differences in temperatures among the 19 monitoring sites

(supplemental material chapter 5, Table S5.1), and the correlations in daily temperatures

between sites were strong (supplemental material chapter 5, Table S5.2).

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Figure 5.3: The relationship between temperature and mortality in Brisbane between 2000

and 2004, using different models with three degrees of freedom for temperature.

RR = relative risk. BC is Brisbane centre.

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Figure 5.4: The relationship between temperature and mortality in Brisbane between 2000

and 2004, using different models with four degrees of freedom for temperature.

RR = relative risk. BC is Brisbane centre.

123

Ordinary kriging gave better spatial predictions of temperature than universal kriging, inverse

distance weighting models, land-use regression models or spatiotemporal models according

to differences between predicted and observed temperatures (supplemental material chapter 5,

Table S5.3). Ordinary kriging using latitude, longitude and elevation (transferred into log

scale) as explanatory variables performed slightly better than including greening rate,

distance to water, and population density. The predicted temperatures from ordinary kriging

were strongly correlated with observed temperatures (generally r>0.95, P<0.01)

(supplemental material chapter 5, Table S5.4). The differences between predicted and kriged

temperatures were not statistically significant (supplemental material chapter 5, Table S5.4).

Predicted daily temperatures were similar to observed temperatures (supplemental material

chapter 5, Figure S5.1).

Generally, locations close to the bay had lower temperatures (Figure 5.2). The kriged

temperature had a wider range (10.9 to 44 °C) than single site’s temperature (12 to 41.7 °C)

and averaged temperature (12.4 to 41.2 °C) (Table 5.1). The kriged spatial temperature was

strongly correlated with single site’s temperature and averaged temperature (generally r>0.97,

P<0.01, Table 5.2).

Figure 5.3 shows the relationship between temperature and mortality using three models with

3 df for lag 0–3 and lag 0–10. Three models produced similar temperature-mortality

relationships for lag 0–3 and lag 0–10, respectively. Both lag 0–3 and lag 0–10 temperatures

had no cold effect. However, spatiotemporal models (GAMM) using spatial temperatures

gave the best model fit according to the Pearson scaled residuals (supplemental material

chapter 5, Table S5.5).

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Table 5.1: Summary statistics for kriged temperature, averaged temperature, Brisbane

centre’s temperature, PM10, O3, relative humidity, elevation, and mortality in Brisbane city

between 2000 and 2004a

Variables Distribution

Min 25% Median 75% max Mean SD

Kriged temperature (°C) 10.9 23.3 26.1 28.7 44.0 26.1 3.7

Averaged temperature (°C) 12.4 23.1 26.0 28.7 41.2 26.0 3.7

BC’s temperature (°C) 12.0 24.0 27.0 29.0 41.7 26.6 3.8

Relative humidity (%) 23.8 66.8 73.1 79.0 96.3 72.0 1.1

PM10 (µg/ml) 5.0 13.7 16.6 20.7 149.6 18.1 8.1

O3 (ppt) 4.8 12.0 14.9 18.4 36.8 15.4 4.5

Elevation (m) 4 15 26 44 177 33 25

Deaths (N) 1 13 15 18 42 15 4

a SD: Standard deviation; BC: Brisbane centre.

Table 5.3 shows the cold (hot) effects below (above) 28 °C on mortality using the three

models for lag 0–3 and lag 0–10. All models produced similar relative risks for hot and cold

effects for lag 0–3 and lag 0–10, respectively. The relative risks due to hot temperatures using

a shorter lag of 0–3 days were greater than those using a longer lag of 0–10 days.

We conducted sensitivity analyses to check our findings. We changed the degrees of freedom

for temperature (2, 4 and 5 df), spatiotemporal models still gave similar temperature-mortality

relationships as time series models (Figure 5.4). When we changed the degrees of freedom

for time (4, 5 and 6 degrees of freedom per year), the estimates of temperature effects were

similar to those using 7 degrees of freedom. Even we used distributed lag non-linear models

125

for temperature and a spline for relative humidity, PM10 and O3, spatiotemporal models still

produced similar temperature-mortality relationships as time series models. Spatial

temperatures modelled by universal kriging, inverse distance weighting models, land-use

regression models gave a similar temperature-mortality relationship as ordinary kriging.

We used an area and time stratified case-crossover to verify the results of the GAMM, and

the results were similar (supplemental material chapter 5, Figure S5.2).

5.5 Discussion

We used ordinary kriging to predict daily spatial temperatures in 163 locations across

Brisbane city. The predicted temperatures were consistent with observed temperatures at 19

monitoring sites. The kriged spatial temperatures across Brisbane city had a wider range than

single or average temperature, but were strongly correlated with single site’s and averaged

temperatures. We compared the association between temperature and mortality using

spatiotemporal with spatial temperature and time series models using non-spatial temperature.

The spatiotemporal models had the best model fit, but gave similar relative risks as the non-

spatial models.

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Table 5.2: Spearman correlations between kriged temperature, averaged temperature,

Brisbane centre’s temperature, PM10, O3, and relative humidity in Brisbane city between

2000 and 2004a

Averaged temperature BC’ temperature Relative humidity PM10 O3

Kriged temperature 0.97** 0.97** 0.06 0.21* 0.20*

Averaged temperature 0.99** 0.03 0.21* 0.18*

BC’ temperature 0.08 0.19* 0.20*

Relative humidity −0.23* −0.29*

PM10 0.28*

a BC: Brisbane centre;

*P<0.05;

**P<0.01;

Meteorological data are routinely monitored in or around urban areas throughout the world.

These data are often used in epidemiological studies to examine the impacts of temperature

on health outcomes (mortality or morbidity) using time series and case–crossover analyses

(Basu & Ostro, 2008a; Guo et al., 2011). Temperature records from a single site or averaged

from a network of monitor sites are required on each day. This means that these sites are

assumed to be representative of the study region. However, temperature can vary over space

due to the differences in altitude, and environment (e.g., living near a park or ocean). Studies

have demonstrated hotter areas within a city due to the urban heat island effect

(www.epa.gov/hiri/about/index.html). To account for the spatial variation of temperature,

geo-statistical models have been used to predict temperatures using observed temperatures

from a network of monitors in or around the study region (Benavides, et al., 2007; Chuanyan,

et al., 2005; Ustrnul & Czekierda, 2005).


127

Table 5.3: Relative risks of mortality associated with hot and cold temperatures using four

different models assuming a V-shaped temperature risk with a threshold at 28 °C a

Models Relative risk

Hot effect b Cold effect

c

Lag 0−3 Spatiotemporal model (kriged

temperature)

1.044 (1.029, 1.058)* 1.004 (0.993, 1.015)

Time series model (BC’s

temperature)

1.040 (1.026,1.054)* 1.004 (0.992, 1.015)

Times series model (averaged

temperature)

1.045 (1.030, 1.060)* 1.002 (0.990, 1.014)

Lag 0−10 Spatiotemporal model (kriged

temperature)

1.027 (1.006, 1.047)* 1.004 (0.988, 1.020)

Time series model (BC’s

temperature)

1.023 (1.002, 1.044)* 1.003 (0.986, 1.021)

Times series model (averaged

temperature)

1.028 (1.006, 1.051)* 1.002 (0.984, 1.020)

a BC: Brisbane centre;

b Relative risk associated with a 1 °C increase in temperature above 28 °C.

c Relative risk associated with a 1 °C decrease in temperature below 28 °C.

*P<0.05

We used ordinary kriging to predict temperature in each statistical local area of Brisbane city,

as it gave a better model fit than universal kriging, inverse distance weighting models, land-

use regression models, and spatiotemporal models. These results are supported by a previous

study in the Detroit, USA (Zhang, et al., 2011). However, in Brisbane, the ordinary kriging

using latitude, longitude and elevation (transferred into log scale) as explanatory variables

performed slightly better than including greening rate, distance to water, and population

128

density. The reason might be that green place and distance to water have small variability in

Brisbane.

The time series analysis using non-spatial single site’s temperature or averaged temperature

may not produce an accurate temperature effect across a city, especially given the potentially

variation in temperature at different places. For example, if a monitor is close to city centre

(which is generally warmer), then temperature from this monitoring site may overestimate the

population’s temperature exposure. Our results show there was spatial variation in ambient

temperature (Figure 5.2). Time series analyses using single site’s temperature, and averaged

temperature gave similar hot and cold effects. The lack of difference could be due to the fact

that daily temperatures across the city had a very strong correlation (supplemental material

chapter 5, Table S5.2), and so one site’s temperature and averaged temperature can be used to

substitute other places’ temperature.

Considering the random effect of location, spatiotemporal models using spatial temperature

gave similar hot and cold effects as time series models using single site’s temperature, and

averaged temperature. Lee and Shaddick (2010) compared a Bayesian spatiotemporal model

with the time series analysis for the effect of air pollution on mortality. They investigated the

impact of spatial variation, monitor placement, and measurement error in the pollution data,

and found that, for pollutants such as O3 that do not vary greatly by monitor sites,

spatiotemporal analysis for spatial-time data and time series analysis for averaged data

produced similar risk estimates. In this case, they suggested that time series analysis using the

non-spatial averaged pollution data is the easiest method, and should be used in future studies.

In our study, we also found spatiotemporal model using spatial temperature produced similar

temperature effects on mortality as time series models using averaged temperature and single

129

site’s temperature. That means time series analysis using non-spatial estimates of exposure

might be equally good at estimating the temperature effect on mortality as spatiotemporal

models.

The spatiotemporal and time series models gave similar estimates for the city-wide

temperature-mortality association in Brisbane. However, this does not mean that the

association is the same in all areas. There may be area-level heterogeneity according to local

characteristics such as building density (Smargiassi et al., 2009), socioeconomic status (Yu et

al., 2010), or the use of air condition. Also, there might be Berkson error for time series

models using one site’s temperature or averaged temperature from a network of sites.

Berkson error is a random error in measurement, which reduces the power of a study, but risk

estimates are not attenuated. Further studies are needed to compare temperature effects across

different areas, for example, examining urban heat islands.

Strengths and limitations

We examined whether time series models using single site’s temperature or averaged

temperature from a network of monitoring sites are appropriate to assess the temperature

effect on human health. Also, we compared spatiotemporal model using spatial temperature

with time series models that used single site’s temperature and averaged temperature. We

found that all the models had similar ability to predict the temperature effect on mortality,

even though the spatiotemporal model performed better than others in terms of model fit. We

conclude that previous studies using single site’s temperatures or averaged temperatures were

appropriate for examining temperature effects.

130

There are some limitations to this study. Although kriged spatial temperatures can be used to

assess the temperature-mortality relationship, they are still not the observed temperatures, and

hence there will still be some measurement error. The findings of this study might not be

generalisable to other cities, particularly places with high variability in green place, distance

to water, and topography.

5.6 Conclusion

Spatiotemporal analysis using spatial temperature produced similar temperature-mortality

relationships as time series analyses using single site temperature and averaged temperature.

Although the spatiotemporal model using spatial temperatures had better model fit, we still

recommend using time series analysis with averaged temperatures or single site temperatures

in future studies, as it is easy to fit and does not require a high performance computer as our

spatial model did.

131

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Detroit metropolitan region. Environmental Research 2011, 111: 1046-53.

135

5.8 Supplemental Materials Chapter 5

Supplemental material chapter 5, Table S5.1: Summary statistics for daily observed

maximum temperatures (°C) at 19 monitoring stations in or around Brisbane, Australia

between 2000 and 2004

Stations Distribution Mean SD

Min 25% Median 75% max

AMA 12.9 24.1 27.6 30.5 42.3 27.55 4.45

CML 15.2 21.4 24.0 26.6 35.4 23.96 3.31

UQG 14.7 23.9 27.8 31.2 42.6 27.78 4.91

PL 11.9 22.6 25.1 27.6 36.5 25.03 3.32

ARA 13.5 23.5 26.7 29.3 41.8 26.59 4.0

RH 11.9 23.0 25.7 28.0 37.0 25.58 3.54

BFS 12.0 23.9 26.8 29.0 42.0 26.57 4.06

GQRS 13.8 23.2 27.1 30.7 41.9 27.16 4.88

HD 13.2 22.4 25.4 28.2 40.9 25.64 4.12

JF 8.0 20.0 24.0 27.0 39.0 23.80 4.78

CA 15.7 22.2 24.6 26.9 40.0 24.58 3.21

GCS 16.2 23.1 25.7 28.0 40.5 25.72 3.57

BA 11.8 23.0 25.7 28.0 40.2 25.55 3.43

BPD 11.9 22.2 25.1 27.9 40.2 25.26 4.03

LCWTP 12.3 23.3 26.1 28.6 41.0 26.06 3.74

SCA 15.7 22.6 25.5 27.6 38.7 25.18 3.42

BC 12.0 24.0 27.0 29.0 41.7 26.58 3.78

TA 9.6 19.0 23.3 26.8 37.7 23.14 5.12

MWB 11.8 23.0 25.9 28.8 42.9 26.09 4.18

136

Supplemental material chapter 5, Table S5.2: Spearman correlations between daily maximum temperatures at 19 monitoring stations in or

around Brisbane city between 2000 and 2004a

Stations AMA CML UQG PL ARA RH BFS GQRS HD JF CA GCS BA BPD LCWTP SCA BC TA

CML 0.82

UQG 0.97 0.82

PL 0.81 0.94 0.79

ARA 0.97 0.87 0.94 0.85

RH 0.82 0.85 0.81 0.84 0.85

BFS 0.95 0.84 0.92 0.82 0.96 0.83

GQRS 0.98 0.84 0.98 0.81 0.95 0.82 0.93

HD 0.87 0.77 0.85 0.74 0.87 0.86 0.86 0.86

JF 0.94 0.84 0.93 0.80 0.93 0.82 0.94 0.94 0.85

CA 0.85 0.89 0.82 0.89 0.90 0.84 0.86 0.84 0.80 0.83

GCS 0.88 0.87 0.85 0.86 0.91 0.82 0.89 0.87 0.83 0.86 0.90

BA 0.91 0.90 0.87 0.90 0.95 0.88 0.93 0.89 0.83 0.88 0.93 0.90

BPD 0.90 0.82 0.88 0.79 0.91 0.86 0.92 0.89 0.87 0.91 0.82 0.84 0.88

LCWTP 0.95 0.86 0.92 0.85 0.97 0.86 0.95 0.93 0.88 0.92 0.90 0.92 0.95 0.90

SCA 0.88 0.89 0.85 0.87 0.91 0.85 0.92 0.87 0.82 0.88 0.90 0.87 0.94 0.88 0.91

BC 0.95 0.87 0.92 0.87 0.98 0.86 0.95 0.93 0.86 0.92 0.91 0.91 0.97 0.90 0.97 0.92

TA 0.96 0.83 0.97 0.80 0.93 0.82 0.90 0.98 0.85 0.94 0.82 0.85 0.86 0.88 0.91 0.85 0.91

MWB 0.94 0.82 0.91 0.81 0.94 0.81 0.93 0.92 0.88 0.90 0.87 0.91 0.88 0.88 0.94 0.86 0.92 0.90 a All the correlations were statistically significant (P<0.01); see figure 5.1 for site acronyms.

137

Supplemental material chapter 5, Table S5.3: Differences between predicted and observed

temperatures using five spatial models of temperature at 19 monitoring sites during 2000 to

2004.

Site Mean difference (standard deviation) (°C)

Ordinary

kriging

Universal

kriging

Inverse distance

weighting

Land use

model

Spatiotemporal

AMA −0.02

(0.26)

−0.41

(0.36)

−0.35 (0.31) −0.26 (0.29) −0.13 (0.32)

CML 0.44

(1.01)

0.50

(1.10)

0.52 (1.06) −0.48 (1.05) 0.46 (1.03)

UQG −0.20

(0.69)

−0.32

(0.072)

−0.26 (0.71) −0.18 (0.62) −0.30 (0.77)

PL −0.48

(0.84)

−0.53

(0.086)

0.52 (0.82) −0.46 (0.82) 0.58 (0.95)

ARA −0.08

(0.55)

−0.10

(0.60)

0.12 (0.57) −0.20 (0.63) −0.15 (0.63)

RH 0.27

(1.35)

0.31

(1.37)

0.36 (1.41) 0.42 (1.42) −0.36 (1.38)

BFS −0.24

(0.33)

−0.22

(0.35)

−0.26 (0.37) −0.29 (0.42) −0.33 (0.42)

GQRS 0.42

(0.67)

−0.46

(0.70)

0.38 (0.62) 0.50 (0.71) −0.42 (0.75)

HD −0.27

(0.78)

−0.38

(0.88)

0.32 (0.76) −0.41 (0.92) −0.29 (0.79)

JF −0.29

(0.85)

−0.53

(0.96)

−0.43 (0.87) −0.36 (0.89) −0.39 (0.94)

CA 0.50

(0.87)

−0.55

(0.89)

−0.52 (0.89) 0.61 (0.93) −0.48 (0.67)

GCS −0.37

(0.64)

−0.32

(0.61)

−0.43 (0.67) −0.38 (0.96) 0.47 (0.75)

BA 0.37

(0.63)

0.46

(0.67)

0.41 (0.62) 0.39 (0.98) 0.42 (0.69)

BPD −0.86

(1.13)

−0.92

(1.17)

0.88 (1.12) −0.81 (1.09) −0.79 (1.15)

LCWTP −0.04

(0.86)

−0.12

(0.95)

−0.08 (0.87) 0.12 (0.98) 0.20 (0.91)

SCA −0.01

(0.18)

−0.09

(0.23)

−0.01 (0.18) 0.08 (0.22) −0.06 (0.25)

BC −0.22

(0.39)

−0.31

(0.46)

−0.45 (0.48) −0.36 (0.56) −0.37 (0.49)

TA 0.71

(0.74)

0.73

(0.77)

0.86 (0.91) 0.82 (0.69) −0.79 (0.81)

MWB −0.24

(0.40)

−0.56

(0.53)

−0.33 (0.45) −0.35 (0.47) −0.52 (0.48)

138

Supplemental material chapter 5, Table S5.4: Spearman correlations and differences between

kriged and observed temperatures at 19 monitoring sites during 2000 to 2004 a

Sites Correlation Difference (°C)

Mean SD

AMA 1.00 −0.02 0.26

CML 0.95 0.44 1.01

UQG 0.99 −0.20 0.69

PL 0.97 −0.48 0.84

ARA 0.99 −0.08 0.55

RH 0.95 0.27 1.35

BFS 1.00 −0.24 0.33

GQRS 0.99 0.42 0.67

HD 0.98 −0.27 0.78

JF 0.99 −0.29 0.85

CA 0.97 0.50 0.87

GCS 0.99 −0.37 0.64

BA 0.98 0.37 0.63

BPD 0.96 −0.86 1.13

LCWTP 0.97 −0.04 0.86

SCA 1.00 −0.01 0.18

BC 0.99 −0.22 0.39

TA 0.99 0.71 0.74

MWB 1.00 −0.24 0.40

a All the correlations were statistically significant (P<0.01); see figure 5.1 for site acronyms;

SD: Standard deviation.

139

Supplemental material chapter 5, Table S5.5: Descriptive statistics for Pearson scaled

residuals using spatial and non-spatial models

Models Summary statistic χ2 a

Min 25% Median Mean 75% Max

Lag 0−3 GAMM (kriged temperature) −0.8 −0.3 −0.3 0.0 −0.1 23 290929

GAM (BC’s temperature) −2.0 −0.3 −0.2 0.1 −0.2 20 398215

GAM (averaged temperature) −2.0 −0.3 −0.2 0.1 −0.2 20 398713

Lag 0−10 GAMM (kriged temperature) −0.7 −0.3 −0.3 0.0 −0.1 22 289802

GAM (BC’s temperature) −2.0 −0.3 −0.2 0.1 −0.2 20 396230

GAM (averaged temperature) −2.0 −0.3 −0.2 0.1 −0.2 20 397538

a Squared Pearson scaled residuals.

140

Supplemental material chapter 5, Figure S5.1: Predicted daily temperatures using kriging

(blue circles) and observed (red lines) temperatures at 10 monitoring stations in or around

Brisbane city during 2000 and 2004.

141

Supplemental material chapter 5, Figure S5.2: The association between temperature and

mortality in Brisbane between 2000 and 2004, using an area and time-stratified case-

crossover model with three degrees of freedom for temperature and a maximum lag of 3 and

10 days.

142

CHAPTER 6: A LARGE CHANGE IN TEMPERATURE BETWEEN

NEIGHBOURING DAYS INCREASES THE RISK OF MORTALITY

Citation:



e16511.





Weiwei Yu: Reviewed, edited, and revised the manuscript;

Xiaochuan Pan: Reviewed, edited, and revised the manuscript;

Xiaofang Ye: Reviewed, edited, and revised the manuscript;

Cunrui Huang: Reviewed, edited, and revised the manuscript;


143

6.1 Abstract

Background: Previous studies have found high temperatures increase the risk of mortality in

summer. However, little is known about whether a sharp decrease or increase in temperature

between neighbouring days has any effect on mortality.

Method: Poisson regression models were used to estimate the association between

temperature change and mortality in summer in Brisbane, Australia during 1996–2004 and

Los Angeles, United States during 1987–2000. The temperature change was calculated as the

current day’s mean temperature minus the previous day’s mean.

Results: In Brisbane, a drop of more than 3 °C in temperature between days was associated

with relative risks (RRs) of 1.157 (95% confidence interval (CI): 1.024, 1.307) for total non-

external mortality (NEM), 1.186 (95%CI: 1.002, 1.405) for NEM in females, and 1.442

(95%CI: 1.099, 1.892) for people aged 65–74 years. An increase of more than 3 °C was

associated with RRs of 1.353 (95%CI: 1.033, 1.772) for cardiovascular mortality and 1.667

(95%CI: 1.146, 2.425) for people aged < 65 years. In Los Angeles, only a drop of more than

3 °C was significantly associated with RRs of 1.133 (95%CI: 1.053, 1.219) for total NEM,

1.252 (95%CI: 1.131, 1.386) for cardiovascular mortality, and 1.254 (95%CI: 1.135, 1.385)

for people aged ≥75 years. In both cities, there were joint effects of temperature change and

mean temperature on NEM.

Conclusion: A significant change in temperature of more than 3 °C, whether positive or

negative, has an adverse impact on mortality even after controlling for the current

temperature.

Key words: Temperature change; Summer; Climate change; Mortality; Cardiovascular;

Respiratory;

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6.2 Introduction

Climate change is unequivocal, with a general increase in both mean temperature and

temperature variability over the last half a century. These changes are primarily due to

emissions of greenhouse gases caused by human activity (McMichael, 1993; WHO, 2008).

The frequency, intensity and duration of weather extremes (e.g. heat waves, floods and

cyclones) are projected to increase as climate change continues (WHO/WMO/UNEP, 1996),

and unstable weather patterns (e.g. a significant drop/increase in temperature) are also more

likely to occur in the coming decades (Faergeman, 2008). As well as being an enormous

environmental issue, climate change affects human health via extreme weather events and

associated socio-ecological changes (Intergovernmental Panel on Climate Change (IPCC),

2001; WHO, 2008).

Much recent research has assessed the relationship between temperature and human health.

Morbidity and mortality are known to be seasonal, with excess morbidity and mortality

during cold winters and hot summers (Kalkstein & Greene, 1997; Kilbourne, 1999). The

effects of temperature on mortality and morbidity have been examined in various climates,

and J-, V-, or U-shaped associations have been observed (Baccini, et al., 2008; Braga,

Zanobetti, & Schwartz, 2002; Curriero, et al., 2002b; Huynen, Martens, Schram, Weijenberg,

& Kunst, 2001; Keatinge, Donaldson, Cordioli, et al., 2000; Nakaji et al., 2004). However,

less evidence is available on the possible effects on mortality due to temperature change

between neighbouring days.

In this study we hypothesized that if the temperature changed sharply between neighbouring

days, it would result in adverse impacts on human health. Because the effects of temperature

145

are strongly dependent on season, we only analysed the relationship between temperature

change and mortality in summer. Poisson regression models were used to examine the effects

on mortality due to short-term changes in temperature between neighbouring days in

Brisbane, Australia and Los Angeles, United States.

6.3 Material and methods


Brisbane is the capital city of the state of Queensland in Australia, and is on the east coast of

the country (27° 30' south, 153° 00' east). It has a humid subtropical climate, with the average

temperature of 25 °C in summer (Dec–Feb). Los Angeles is the largest city in the state of

California and the Western United States (34° 03' north, 118° 15' west). Los Angeles has a

dry-summer subtropical climate, with an average temperature of 21 °C in summer (Jun–Aug).

We chose these two cities, because they have a sub-tropical climate pattern and we aimed to

explore whether the temperature change between neighbouring days has health effects in both

the Northern and Southern Hemisphere.

We gathered the Brisbane data on daily deaths of non-external causes in summers between

Jan, 1996 and Dec, 2004 from the Office of Economic and Statistical Research of the

Queensland Treasury. The causes of non-external mortality (NEM) were coded according to

the International Classification of Diseases, ninth version (ICD-9) (ICD-9: 001–799) before

December 1996 and tenth version (ICD-10) (ICD-10: A00–R99) between December 1996

and December 2004. Cardiovascular mortality (CVM; ICD-9:390–459, ICD-10:I00–I79) and

respiratory mortality (RM; ICD-9: 460–519, ICD-10:J00–J99) were extracted from the

146

mortality database. Influenza deaths (ICD-9: 487.0–487.8 or ICD-10: J10–J11) were

excluded from respiratory mortality. All deaths were for residents of Brisbane city. We

stratified NEM by gender and age (3 groups: 0–64, 65–74, and ≥75 years).

We gathered daily meteorological data including mean temperature and mean relative

humidity (RH) from the Australian Bureau of Meteorology. Values of temperature change

were calculated using the current day’s mean temperature minus the previous day’s mean

temperature. Temperature change between the neighbouring days is a measure of temperature

stability, with large positive and negative values indicating an unstable temperature. The air

pollutants including daily mean ozone (O3) and particulate matter less than 10 μm in

aerodynamic diameter (PM10) were monitored at a central site in Brisbane. We collected

these data from the Queensland Environmental Protection Agency.

Los Angeles’ data were obtained from the National Morbidity and Mortality Air Pollution

Study (NMMAPS) which is publicly available and covers the years 1987 to 2000. Mean

temperature, relative humidity, O3, NEM, CVM, RM, and NEM in age groups (0–64, 65–74,

and ≥75 years) were used here. We excluded PM10, because there was a large number of

missing values, reflecting requirements for regulatory monitoring. Mortality counts were not

split by gender in the NMMAPS, so the impact of temperature change on mortality by gender

could not be analysed in Los Angeles.

6.3.2 Data analysis

Poisson generalized additive models (GAMs) were used to examine the effects of short-term

changes in temperature between neighbouring days on mortality. We used GAMs because the

147

associations between temperature change and mortality are non-linear, and the daily number

of deaths has an over-dispersed Poisson distribution. We adjusted for day of the week (DOW)

using a categorical variable. Regression splines for calendar time and year were used to

control for long-term trends and seasonal patterns (Ren, et al., 2006). We controlled for

relative humidity, PM10, and O3 using regression splines. To assess the effects of temperature

change on mortality, we used the following model:

[ ( | )] α ( ) ( ) ( ) ( )

( ) ( ) ( )

α ( ) ( ) [6.1]

where i is the day of the observation; j is the lag days; E(Yi|X) are the estimates of daily death

counts on day i; α is the intercept; s ( , £) is a regression spline with £ degrees of freedom.

TCi-j is temperature change, MEANTi-j is daily mean temperature, RHi-j is relative humidity,

PM10i-j, is particulate matter, and O3i-j is ozone; timei is days of calendar time on day i; yeari is

the year on day i; DOWi is the day of the week on day i, and κ is vector of coefficients for

DOW; COVs represents all other covariates in the model.

As an alternative model to compare the effects of large changes in temperature on mortality

with moderate changes, temperature change was categorised into 3 groups: a drop of more

than 3 °C; a rise of more than 3 °C; a change in either direction of less than 3 °C. We used

3 °C as cut-off value because values less than 3 °C produced non-significant effect estimates.

Model (6.1) was altered by modifying the single terms of TCi-j into a categorical variable as

follows:

[ ( | )] α ( ) [6.2]

where TCi-j is a categorical variable, λ is vector of coefficients for categories of temperature

change.

148

The joint effects of temperature change and mean temperature were estimated using GAMs.

We plotted these estimates to assess whether there was an interaction between temperature

change and mean temperature on mortality. Model (6.1) was modified by changing the single

terms of TCi-j and MEANTi-j into a bivariate term as follows:

[ ( | )] α ( ) [6.3]

where s (TCi-j, MEANTi-j,6) is the joint effect of temperature change and mean temperature on

mortality, which we modelled using a regression spline with 6 degrees of freedom.

The Akaike information criterion (AIC) was used to measure goodness of fit. Residuals were

examined to evaluate the adequacy of the models. Sensitivity analyses were performed

through changing degrees of freedom for time and removing PM10 from the Brisbane data.

Relative risks (RRs) and confidence intervals (CIs) were calculated. All statistical tests were

two-sided. Values of P<0.05 were considered statistically significant. Spearman correlation

coefficients were used to summarize the correlations between daily weather conditions and

air pollutants in each city. The R software (version 2.10.1, R Development Core Team 2009)

was used to fit all models.

6.4 Results

Table 6.1 summarises the daily weather conditions, air pollutants, and mortality in summers

in Brisbane from 1996 to 2004 and Los Angeles from 1987 to 2000. The temperature change

ranged from –6.5 °C to 5.0 °C in Brisbane, and from –5.3 °C to 5.8 °C in Los Angeles. The

mean temperature was higher in Brisbane (24.4 °C) than in Los Angeles (21.3 °C). There

149

were, on average, 16 daily deaths from non-external causes in Brisbane, and 138 in Los

Angeles.

Table 6.1: Summary statistics for daily weather conditions, air pollutants, and mortality in

Brisbane, Australia and Los Angeles, United States

City Variable Frequency distribution Mean SD Sum

Min 25% Median 75% Max

Brisbane TC (°C) -6.5 -0.6 0.1 0.8 5.0 0.01 1.2 —

MEANT (°C) 18.8 23.2 24.4 25.6 31.9 24.4 1.8 —

RH (%) 38.9 68.0 73.4 79.1 97.5 73.5 8.3 —

O3 (ppb) 0.0 8.0 10.8 14.0 45. 11.4 5.2 —

PM10 (μg/m3) 3.9 12.6 15.5 19.1 84.5 16.9 7.4 —

NEM 1 13 15 18 43 16 4.4 12,364

CVM 0 5 6 8 31 6 2.9 5,076

RM 0 0 1 2 6 1 1.1 916

Age <65 years 0 2 3 4 12 3 1.7 2,372

Age 65–74 years 0 1 2 4 11 3 1.8 2,133

Age ≥75 years 1 8 10 10 12 37 3.5 7,859

Male 1 5 8 9 20 8 2.9 6,093

Female 1 6 8 9 30 8 3.0 6,271

Los Angeles TC (°C) -5.3 -0.56 0 0.56 5.8 0 1.0 —

MEANT (°C) 14.3 20.0 21.1 22.4 29.4 21.3 2.1 —

RH (%) 34.2 71.9 76.3 79.6 89.6 75.2 6.8 —

O3 (ppb) -18.2 4.9 10.0 15.2 44.9 10.2 7.9 —

NEM 95 12.9 138 147 217 138 13.4 177,384

CVM 34 57 63 70 106 64 9.4 81,913

RM 3 9 11 14 22 12 3.6 14,917

Age <65 years 17 34 39 44 70 39 7.4 50,163

Age 65–74 years 14 25 28 32 50 29 5.8 37,021

Age ≥75 years 44 64 70 76 109 70 9.4 90,200

150

Table 6.2 shows the Spearman’s correlations between daily weather conditions and air

pollutants. Temperature change was positively correlated with mean temperature in both

cities. In Brisbane, there were no statistically significant correlations between temperature

change and humidity, while temperature change was negatively correlated with humidity but

positively with O3.

Table 6.2: Spearman’s correlation between daily weather conditions and air pollutants in

Brisbane, Australia and Los Angeles, United States

Brisbane Los Angeles

MEANT TC RH O3 MEANT TC RH

TC 0.30** 0.20**

RH 0.21** 0.05 -0.10** -0.28**

O3 0.19** 0.03 –0.15** 0.04 0.17** 0.21**

PM10 0.20** 0.07 –0.24** 0.40** —— —— ——

**P<0.01

In both cities, there was little effect of temperature change on mortality, when the

temperature change ranged from –3 °C to 3 °C (Figure 6.1). Therefore, we divided

temperature change into three categories: less than –3 °C, –3 °C to 3 °C, and more than 3 °C.

Figure 6.2 shows the association between temperature change and NEM by age group. In

Brisbane, people aged <65 years appeared to be vulnerable to a sharp increase in temperature,

while those aged 65–74 years were sensitive to a sudden drop in temperature. In Los Angeles,

151

both people aged 65–74 and ≥75 years were vulnerable to a sudden temperature drop, while

no significant effects of temperature change were found for those aged <65 years.

Table 6.3: The associations between temperature change and mortality in Brisbane, Australia

and Los Angeles, United States

RR (95% CI)

1°C increase in TC (°C)a TC < –3 °C

b TC > 3 °C

b

Brisbane NEM 0.993 (0.977, 1.008) 1.157 (1.024, 1.307)** 1.198 (0.997, 1.438)

CVM 0.986 (0.962, 1.011) 1.115 (0.923, 1.347) 1.353 (1.033, 1.772)**

RM 0.997 (0.941, 1.057) 1.202 (0.774, 1.867) 1.608 (0.925, 2.794)

Age<65 years 1.021 (0.985, 1.059) 1.135 (0.859, 1.501) 1.667 (1.146, 2.425)**

Age 65–74 years 0.971 (0.935, 1.009) 1.442 (1.099, 1.892)** 1.016 (0.631, 1.634)

Age ≥75 years 0.990 (0.971, 1.010) 1.088 (0.930, 1.273) 1.118 (0.885, 1.413)

Male 1.001 (0.978, 1.024) 1.131 (0.949, 1.348) 1.225 (0.941, 1.596)

Female 0.985 (0.963, 1.007) 1.186 (1.002, 1.405)* 1.174 (0.910, 1.513)

Los Angeles NEM 0.994 (0.989, 1.000) 1.133 (1.053, 1.219)** 1.039 (0.971, 1.112)

CVM 0.988 (0.979, 0.997)** 1.252 (1.131, 1.386)** 1.031 (0.933, 1.140)

RM 1.008 (0.988, 1.029) 1.006 (0.767, 1.321) 1.002 (0.792, 1.266)

Age<65 years 1.001 (0.990, 1.013) 0.957 (0.825, 1.108) 1.014 (0.893, 1.153)

Age 65–74 years 1.004 (0.991, 1.018) 1.092 (0.929, 1.284) 1.106 (0.955, 1.280)

Age ≥75 years 0.986 (0.978, 0.995)** 1.254 (1.135, 1.385)** 1.026 (0.933, 1.129)

*P<0.05; **P<0.01;

aTC as a continuous variable, using model (6.1);

bTC as a categorical variable, using model (6.2);

The delayed effect of temperature change on mortality was examined using model (6.1). The

change in temperature between current day and previous day (lag 0) had the highest impact

on current day’s mortality (results not shown). Therefore, we only show the associations

152

between temperature change and mortality on the current day (Table 6.3). As a continuous

variable, temperature change only had statistically significant effects on CVM and NEM in

those aged ≥75 years in Los Angeles but not on other groups. However, further analyses

using model (6.2) show that a temperature drop of more than 3 °C had statistically significant

adverse impacts on total NEM, NEM among those aged 65–74 years, and women in Brisbane;

on total NEM, CVM, and NEM among those aged ≥75 years in Los Angeles. A temperature

increased of more than 3 °C was significantly associated with CVM and NEM among those

aged < 65 years in Brisbane.

Figure 6.3 and Figure 6.4 illustrate the joint effects of temperature change and mean

temperature on NEM and subgroups of NEM using model (6.3). The adverse effects of mean

temperature on mortality occurred when the mean temperature was under 26 °C in Brisbane

and under 24 °C in Los Angeles. In contrast, when we used model (6.1), mean temperature

had no adverse effect on mortality in the temperature range under 26 °C in Brisbane and

24 °C in Los Angeles (Supplemental material chapter 6, Figure S6.1 and Figure S6.2). The J-

shaped relationships between mean temperature and mortality in model (6.1) become

approximately U-shaped relationships when the joint effect of temperature change and mean

temperature was modelled (except for RM and NEM among those aged ≥75 years in

Brisbane). These results suggest that there were joint effects of temperature change and mean

temperature on mortality.

In order to perform sensitivity analyses, we changed degrees of freedom for time and

removed PM10 from Brisbane data. The results showed that there were no substantial change

in effect estimates. Also, the residual analyses showed that models were a good fit to the data.

153

Figure 6.1: The associations between temperature change and non-external mortality,

cardiovascular mortality, and respiratory mortality using model (6.1) in Brisbane, Australia

(left side) and Los Angeles, United States (right side).

154

Figure 6.2: The associations between temperature change and non-external mortality by age

group using model (6.1) in Brisbane, Australia (left side) and Los Angeles (right side),

United States.

155

Figure 6.3: Bivariate response surfaces of the temperature change and mean temperature for

non-external mortality, subgroups of mortality using model (6.3) in Brisbane, Australia.

156

Figure 6.4: Bivariate response surfaces of the temperature change and mean temperature for

non-external mortality, subgroups of mortality using model (6.3) in Los Angeles, United

States.

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6.5 Discussion

This study examined the effect of temperature change on mortality, and explored the joint

effects of temperature change and mean temperature on mortality in Brisbane, Australia, and

Los Angeles, United States. In Brisbane, a relatively large decrease in temperature between

neighbouring days increased the risk of total NEM, and NEM among those aged 65–74 years

and in women overall. A sharp increase in temperature was significantly associated with

increased CVM and NEM among those aged < 65 years. A significant drop in temperature

increased the risks of total NEM, CVM, and NEM among those aged ≥75 in Los Angeles.

Also, joint effects of temperature change and mean temperature on mortality were found in

both locations.

These increased risks of death during periods of temperature fluctuations highlight the

importance of not only considering hot absolute temperatures in relation to human health, but

also sudden changes in temperature, particularly for a relatively large temperature changes

(more than 3 C).

We assessed whether temperature change had an adverse impact on mortality in different

subtropical climates and in different locations. Both Brisbane and Los Angeles have a

subtropical summer climate, but Brisbane is humid while Los Angeles is dry. The non-linear

pattern in the increased risk of mortality for a change in temperature was similar in the two

cities, although there were some differences. These differences might be caused by

population characteristics (e.g. racial composition), geographic location, and living

conditions including air conditioning and family income, as well as access to health care

(Stafoggia, Forastiere, et al., 2008).

158

Some previous studies have examined the effects of sudden changes in temperature on

cardiovascular disease, and found similar results. For example, Kyobutungi et al. (2005)

investigated the relationship between ischemic stroke occurrence and the temperature change

in 24 hours, but without controlling for season. The results showed that sudden temperature

changes of more than 5 °C, regardless of whether the change was negative or positive, were

associated with an increased risk of acute ischemic stroke. Schneider et al. (2008) carried out

a longitudinal study to examine the impact of weather parameters on cardiovascular patients.

Results showed that a rise or fall in air temperature was associated with an increase in heart

rate. Ebi et al. (2004) found that a 3 °C increase in minimum temperature or decrease in

maximum temperature caused a significant increase in hospital admissions for cardiovascular

diseases and stroke in three Californian regions, with a stronger association for the oldest age

group. However, Plavcova et al. (2010) only found a significant increase in mortality for

large increases in temperature.

A large change in temperature might impact on mortality, whether it is positive or negative,

because the automatic thermoregulation system cannot adapt to sudden temperature change,

particularly for people with certain medical conditions. Sudden changes in temperature have

been associated with risk factors for human health, such as increases in blood cholesterol

levels, blood pressure, plasma fibrinogen concentrations, peripheral vasoconstriction, heart

rate, platelet viscosity, and reducing the immune system’s resistance (Ballester, Corella,

Perez-Hoyos, Saez, & Hervas, 1997b; Carder et al., 2005a; Schneider, et al., 2008).

In this study, we found females were more sensitive to a drop in temperature than males in

Brisbane. Previous studies have found that gender can modify the association between

159

temperature and health (Goodman, et al., 2004; Ishigami, et al., 2008; Stafoggia, Forastiere,

Agostini, Biggeri, Bisanti, Cadum, Caranci, De Lisio, et al., 2006; Vaneckova, et al., 2008).

There is evidence that women are more vulnerable to heat-related mortality than men

(Goodman, et al., 2004; Ishigami, et al., 2008; Stafoggia, Forastiere, Agostini, Biggeri,

Bisanti, Cadum, Caranci, Lisio, et al., 2006; Vaneckova, et al., 2008). Studies have also

found that women have higher risks for ischemic, arrhythmic and blood pressure effects

associated with the weather (Diaz et al., 2002; Douglas, Dunnigan, Allan, & Rawles, 1995).

However, Basu (2009a) pointed out that the differences of the effect of temperature on

women and men was dependent on location and population. For example, the impact of hot

temperature on mortality was higher for men in São Paulo, but higher for women in Mexico

City (Bell et al., 2008).

The association between temperature change and mortality varied by age group, and the

effect of age differed between Brisbane and Los Angeles. This may be due to different life

styles, living conditions, family income, as well as access to health care. Many studies have

shown that age is a modifier of the association between temperature and health (Goodman, et

al., 2004; Stafoggia, Forastiere, Agostini, Biggeri, Bisanti, Cadum, Caranci, Lisio, et al., 2006;

Vaneckova, et al., 2008). Keatinge et al. (2000) found that people aged 65–74 years were the

most vulnerable subgroup to cold in seven European countries. Hajat et al. (2007b) found that

the elderly were the most vulnerable group to temperature in England and Wales, both for

cold and hot weather.

Our findings suggest that people with cardiovascular diseases are more vulnerable to short-

term changes in temperature than those with respiratory diseases (Figure 6.3 and Figure 6.4).

Many studies have shown that temperature is associated with physiological changes in the

160

circulatory system, including blood pressure, heart rate, blood cholesterol levels, plasma

fibrinogen concentrations, peripheral vasoconstriction, and platelet viscosity (Ballester, et al.,

1997b; Carder, et al., 2005a; Schneider, et al., 2008). These factors are directly associated

with cardiovascular function. Respiratory mortality is generally attributed to the immune

system’s resistance to respiratory infections caused by exposure to cold or hot temperatures

(Curriero, et al., 2002b). Therefore, people with some pre-existing cardiovascular disease

might be more sensitive than those with pre-existing respiratory disease to short-term changes

in temperature.

We controlled for mean temperature as many studies have illustrated a consistent relationship

between temperature and human health (Baccini, et al., 2008; Braga, et al., 2002; Curriero, et

al., 2002b; Huynen, et al., 2001; Keatinge, Donaldson, Cordioli, et al., 2000; Nakaji, et al.,

2004). Saez et al. (1995) found a 1 °C increase in temperature was associated with 1.7%,

4.2%, and 13.2% increase in NEM, CVM, and RM respectively. Schwartz (2005) found that

people with cardiovascular diseases, chronic obstructive pulmonary disease, or diabetes

appeared more vulnerable to the effects of hot weather. Mean temperature was also

associated with mortality in the present study (Supplemental material chapter 6, Table S6.1).

The mechanisms of heat-related deaths may result from failure in the thermoregulation which

may be impaired by dehydration, salt depletion and increased surface blood circulation

during hot period (Basu, 2009a; Bouchama & Knochel, 2002). Elevated blood viscosity,

cholesterol levels and sweating thresholds might also trigger heat-related mortality (Basu,

2009a; McGeehin & Mirabelli, 2001). The reduced sweat gland output and skin blood flow,

reduction in cardiac output and less redistribution of blood flow from renal and splanchnic

circulations will impair thermoregulation. We only examined the effect of mean temperature,

not maximum, minimum, and apparent temperature. A recent study has shown how these

161

different measures of temperature gave similar results for predicting mortality, so we would

anticipate similar results if we used alternative temperature measures (Barnett, Tong, &

Clements, 2010).

This study has some limitations. The findings of this study may not be generalisable to other

locations, particularly places with different climates. We used the data on temperature and air

pollution from fixed sites rather than individual exposure. Therefore, there might be the

potential for exposure measurement bias.

6.6 Conclusion

In conclusion, we found there were adverse effects due to relatively large changes in

temperature on NEM, particularly for females and people aged 65–74 years in summer in

Brisbane, as well as on NEM, CVM, and NEM among people aged ≥ 75 years in Los Angeles.

A significant increase in temperature was also associated with CVM and NEM in those < 65

years in Brisbane. In addition, there were joint effects of temperature change and mean

temperature on NEM and most subgroups in both cities. These findings suggest that people

should not only pay attention to the increases in absolute temperature in summer, but also to

temperature changes of 3 C of more. The findings might provide an important impetus for

evaluating population vulnerability, and improving the climate change adaption strategies.

162

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167


Supplemental material chapter 6, Table S6.1: The associations between a 1 °C increase in

mean temperature and mortality in Brisbane, Australia and Los Angeles, United States

RR (95% CI)

Brisbane Los Angeles

NEM 1.029 (1.017, 1.041)** 1.004 (1.001, 1.007 )**

CVM 1.026 (1.008, 1.044)** 1.004 (1.000, 1.008)

RM 1.066 (1.023, 1.110)** 1.006 (0.996, 1.016)

Age <65 years 1.019 (0.993, 1.045) 1.001 (0.996, 1.007)

Age 65–74 years 1.031 (1.003, 1.059)* 1.001 (0.995, 1.007)

Age ≥75 years 1.034 (1.019, 1.049)** 1.008 (1.003,1.012)**

Male 1.020 (1.004, 1.036)* ———————

Female 1.036 (1.019, 1.053)** ———————

**P<0.01; *P<0.05

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Supplemental material chapter 6, Figure S6.1: The associations between the mean

temperature and non-external mortality, cardiovascular mortality, and respiratory mortality

using model (6.1) in Brisbane, Australia (left side) and Los Angeles, United States (right

side).

169

Supplemental material chapter 6, Figure S6.2: The associations between the mean

temperature and age groups of non-external mortality using model (6.1) in Brisbane,

Australia (left side) and Los Angeles, United States (right side).

170

171

CHAPTER 7: ASSOCIATIONS BETWEEN HIGH TEMPERATURES AND

ELDERLY MORTALITY DIFFERED BY YEAR, CITY AND REGION IN THE

UNITED STATES

Citation:

Guo Y, Barnett AG, Tong S. (2011) Associations between high temperatures and elderly

mortality differed by year, city and region in the United States. Scientific Reports (In

revision).






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7.1 Abstract

Studies examining the impacts of high temperatures on mortality are useful for establishing

warning systems to prevent heat effects. However, most previous studies have rarely

considered the variability in effects across different years. In this study, We aimed to examine

the variation in the effects of high temperatures on elderly mortality (age ≥ 75 years) by year,

city and region for 83 large US cities between 1987 and 2000. We defined high temperature

days as two or more consecutive days with temperatures above the 90th

percentile for each

city during each warm season (May 1 to September 30). We used a Poisson regression model

and decomposed the mortality risk for high temperatures into: a “main effect” due to high

temperatures using a distributed lag non-linear function, and an “added effect” due to

consecutive high temperature days. We pooled yearly effects across regions and overall

effects at both regional and national levels. We found that the high temperature effects (both

main and added effects) on elderly mortality varied greatly by year, city and region. The

results also show that the years with higher heat-related mortality were often followed by

those with relatively lower mortality. In conclusion, it is important to take the variability in

high temperature effect into account in the development of heat-warning systems.

Key words: High temperature effect; elderly mortality; climate change;

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7.2 Introduction

There is increasing public health concern for heat-related mortality worldwide, as the climate

is rapidly changing (Gosling, Lowe, McGregor, Pelling, & Malamud, 2009; Kan, Chen, &

Tong, 2011). The Intergovernmental Panel on Climate Change has concluded that, without

increased investments in countermeasures, heat waves will cause increased adverse health

impacts, including heat-related mortality (IPCC, 2007a). Urban areas may be particularly

vulnerable to heat waves because of high concentrations of susceptible population groups and

the urban heat island effect (Kinney, et al., 2008; Luber & McGeehin, 2008).

Many studies have shown that high temperatures are related to non-accidental deaths (Baccini,

et al., 2008; Curriero, et al., 2002b; McMichael, et al., 2008; Stafoggia, et al., 2006), cause-

specific deaths (Barnett, 2007; Rey, et al., 2007), and morbidity such as emergency hospital

visits and hospital admissions (Hansen, et al., 2008; Knowlton, et al., 2009; Smith, et al.,

2003). Other studies have specifically examined the impacts of heat waves on mortality

(Kaiser et al., 2007; Vandentorren et al., 2004; Weisskopf et al., 2002). Gasparrini and

Armstrong (2011) decomposed the effects of heat waves into two parts: a “main effect”

related to the independent effects of high temperatures, and an “added effect” due to heat

waves. Results showed that the “main effect” contributed the most to the excess risk of

mortality, while a smaller added effect appeared during heat wave days. Joacim et al. (2010)

found an added effect of heat wave days due to two or more days above the 98th

percentile of

temperature. Anderson and Bell (2011) examined the modification of heat wave

characteristics (e.g., intensity, duration, and timing in summer) on the effects of heat waves

on mortality. They found higher mortality risks for heat waves that were more intense or

longer, or occurred in early summer

174

Epidemiological studies on heat-related mortality could be used by decision makers to

establish a warning system for high temperatures, by giving information on the heat threshold

and the expected increase in deaths above the threshold. Such studies are also useful for

estimating the potential health effects of climate change. However, most previous studies

only considered high temperature effects in the whole study period, and ignored the potential

variability of effects from year to year. Effects may vary from year to year because of

differences in the at-risk population (e.g., more elderly people), or because of increased

adaptation over time (Sheridan & Kalkstein; Stafoggia, et al., 2009a).

Temperature-related deaths are more pronounced in the elderly (Anderson & Bell, 2009), as

they are more sensitive to temperature (Macey & Schneider, 1993). The thermal regulation

system weakens with age; skin sensory perception may diminish and thermal homeostasis

may decline (Collins, 1987). This means that the elderly are not as well equipped to get relief

from heat or cold stress. A higher incidence of pre-existing cardiovascular and respiratory

disease in the elderly might be another reason why they are vulnerable to temperature

extremes (Wyndham CH, 1978). Studies have shown that the effects of thermal stress were

highest in the elderly following heat waves (Hajat, et al., 2007b; P. Vaneckova, et al., 2008).

Therefore, in this study, we examined the variation in high temperature effects on elderly

mortality by year, city and region in the United States.

7.3 Material and methods


175

We used the data from the publicly available National Morbidity and Mortality Air Pollution

Study (NMMAPS) study (Samet, Dominici, et al., 2000; Samet, Zeger, et al., 2000). This

study included daily climatic conditions, air pollution levels, and mortality in 108 cities in the

United States from 1987 to 2000. Data on maximum and minimum temperatures came from

the National Climatic Data Center, and daily mortality counts came from the National Center

for Health Statistics. Daily non-external deaths consisted of death counts among residents,

excluding injuries and external causes. More information is available from the NMMAPS

web site (http://www.ihapss.jhsph.edu).

We excluded data for small cities (population under 200,000) and cities with more than 0.5%

missing data for air or dew point temperature, which left 83 cities. We stratified the cities into

seven regions (Industrial Midwest, North East, North West, South East, South West,

Southern California, and Upper Midwest) (See Supplemental Material chapter 7, Table S7.1).

We limited analyses to elderly morality (age ≥ 75 years) in the warm season (1 May–30

September) as we were interested in the effects of heat on a susceptible population. Mean

temperature (i.e., average of maximum and minimum temperatures) was used as the main

exposure variable.

7.3.2 Data analysis

A city-specific Poisson regression model was used to examine each year’s high temperature

effect on elderly mortality. In the city-specific model, the heat effects were divided into a

“main effect” and an “added effect” according to a previous study (Gasparrini & Armstrong,

2011b). These estimates (main effect and added effect separately) were then combined using

a univariate meta-analysis to create yearly estimates for each region and for the entire US.

http://www.ihapss.jhsph.edu/

176

The estimates were then combined using a Bayesian meta-analysis to create overall estimates

for each region and the entire US.

To examine the city-specific “main effect” and “added effect” in each warm season, we used

the following Poisson regression model:

Yt ~ Poisson(μt), t =1,...,5114,

Log (μt) = α + βTt,l + υHTt + S(timet, 3) + λDOW + εt, [7.1]

where Yt is the observed daily death count on day t; α is the intercept; Tt,l is a vector obtained

by applying the distributed lag non-linear model (DLNM) to mean temperature, β is vector of

coefficients for Tt,l, and l is the lag days; S(timet, 3) is natural cubic spline of time, with 3

degrees of freedom (df) per warm season (1 May–30 September) used to control for trends

and seasonal patterns in mortality; HTt is a categorical variable for high temperature days on

day t, HT=0 if day t was a non-high temperature day, HT=1 if day t was a high temperature

day (defined below); DOW is a categorical variable for day of the week, and λ is vector of

coefficients. ε are the residuals.

We modelled the main effect of high temperatures on day t using the term βTt,l which is fitted

using a DLNM. A DLNM is a two-dimensional spline which models the main effect of heat

along both dimensions of temperature and lags. DLNMs overcome the fact that temperatures

within a couple of days are strongly correlated, and constrain the effect of temperature using

a spline (Armstrong, 2006). We used a cubic spline with 3 df to model the U-shaped

relationship between temperature and mortality. A cubic spline with 5 df was used to model

the lagged (delayed) effect of temperature on mortality up to 10 days (Gasparrini &

Armstrong, 2011b). The DLNM provides an estimate of the overall effect along lag days

(Gasparrini, et al., 2010). We modelled the main effect for each city’s warm season from the

177

term βTt,l. We estimated the percent change of mortality between the median temperature

among high temperature days against the 75th percentile of temperature.

The added effect of high temperatures was modelled by υHTt. We defined high temperatures

as two or more than two consecutive days with temperature above the 90th

percentile for each

city during each warm season (May 1 to September 30).

To examine the variation in the high temperature effects on mortality from year to year, we

pooled the yearly national and regional high temperature effects (main effect and added effect

separately) across cities using a univariate meta-analysis with a random effect for each city.

We plotted the pooled yearly national and regional main effects and added effects from 1987

to 2000.

We used a Bayesian hierarchical model to estimate an overall high temperature effect (main

effect and added effect separately) for each region and the nation by combining the yearly

estimated effects of high temperatures within each city and incorporating the estimates’

variance (Everson & Morris, 2000). Each city was given a random intercept to model its

mean heat effect, and a random linear effect of time to model linear trends over time. A

sensitivity analysis was conducted to examine random non-linear effects of time. The model

fit was assessed using the Deviance Information Criteria (Spiegelhalter, et al., 2002).

Bayesian hierarchical models have been increasingly used to combine estimated effects

across communities in air pollution and temperature studies (Anderson & Bell, 2011; Barnett,

2007).

7.3.2 Sensitivity Analyses

178

Sensitivity analyses were carried out on the parameters for the city-specific model to test the

robustness of the results to our assumptions concerning the temperature-mortality relationship.

We modified the df of smoothing for time (4 to 8), and varied the df for the splines for

temperature (4 to 8) and lag (3 to 8) in the DLNM.

To test the sensitivity of our results to the definition of high temperatures, three alternative

high temperature definitions were used: 95th

and 97th

percentile for at least 2 consecutive days.

The Akaike information criterion was used to measure goodness of fit for city-specific

models. Residuals were examined to evaluate the adequacy of the city-specific models. The R

software (version 2.11.0, R Development Core Team 2009) was used to fit city specific

models. The “dlnm” package of R software was used to create DLNM. The “metafor”

package of R software was used to fit univariate meta-analyses. WinBUGS software (version

1.4) was used to fit the Bayesian hierarchical model.

7.4 Results

Figure 7.1 shows the variability in the estimated percent increase in elderly mortality due to

the high temperature effects (including both main and added effects) between 1987 and 2000.

Each city’s yearly high temperature effects on elderly mortality varied greatly, and there were

large differences between cities (supplemental Material chapter 7, Figures S7.1 and S7.2).

For example, the main effects varied greatest in Lexington (the percentage change in elderly

mortality due to main effect ranged from −80% to 150%), while there was a smallest

179

variation in San Diego (the percentage change in elderly mortality due to added effect range

from −57% to 29%).

Table 7.1: The distribution of yearly high temperature effects on elderly mortality by region

between 1987 and 2000

Region Main effect (percent change %) Added effect (percent change %)

Min 25% Median 75% Max Min 25% Median 75% Max

Industrial Midwest –8.1 0.5 6.5 8.9 16.4 –6.9 –5.1 –2.2 –0.4 7.8

North East –16.4 –1.4 5.5 14.2 19.1 –9.8 –5.8 0.0 7.0 20.7

North West –8.5 –1.1 6.6 15.3 32.3 –16.1 –3.5 1.7 3.6 14.2

South East –10.9 –6.4 0.5 7.6 12.9 –11.3 –3.4 0.4 7.0 8.7

South West –12.9 –5.7 0.0 11.7 32.4 –16.6 –8.2 –2.6 5.5 7.9

Southern California –6.9 4.8 7.0 13.1 15.9 –9.8 –4.2 –2.0 0.6 11.1

Upper Midwest –30.2 –1.5 1.4 9.7 38.3 –14.7 –0.7 5.8 15.1 22.8

National –0.3 2.6 4.1 7.7 12.2 –5.3 –1.4 0.1 0.6 2.2

We found a similar variation in high temperature effects on elderly morality at the regional

level (Table 7.1). Figure 7.2 shows the trend of the estimated percent increase in elderly

mortality due to the high temperature effects by year and region. There was a great variation

from year to year in the mean effects of high temperatures. In every region the main and

added effects of high temperatures were negative in at least one year (meaning that heat was

associated with a decreased risk of death). Very high increases in mortality were often

followed by much lower increases, and vice versa.

According to the Deviance Information Criteria, the Bayesian hierarchical model fit was not

improved by using a random nonlinear term for trends over time, so the combined estimates

180

were based on modelling a linear trend over time. Figure 7.3 shows the linear trends over

time of high temperature effects by geographical region using the Bayesian hierarchical

model. The main effect declined over time in all of the regions.

Table 7.2: Pooled high temperature effects on elderly mortality by region between 1987 and

2000

Region % increase (95% CI)

Main effect Added effect

Industrial Midwest 6.8 (2.1, 11.5) –1.6 (–4.6, 1.4)

North East 3.3 (–1.9, 8.5) 3.7 (0.3, 7.2)

North West 9.0 (1.9, 16.0) 0.7 (–4.0, 5.4)

South East 3.6 (–0.6, 7.9) 0.3 (–3.0, 3.4)

South West 1.1 (–5.1, 7.3) –1.9 (–6.8, 2.9)

Southern California 8.5 (3.5, 13.6) –1.2 (–5.2, 2.9)

Upper Midwest 3.8 (–7.3, 15.0) 6.4 (–1.0, 13.7)

National 5.1 (2.9, 7.3) 0.4 (–1.1, 1.9)

CI = confidence interval

Table 7.2 shows the overall high temperature effects pooled from each city across different

geographical regions and the whole United States. The high temperature effects differed by

geographical regions. The highest main effects were in the North West with a 9.0% (95%

confidence interval (CI): 1.9%, 16.0%) increase in elderly mortality, while the highest added

effect occurred in the Upper Midwest with an average 6.4% (95% CI: –1.0%, 13.7%)

increase in elderly morality. For the whole USA, the main effect of high temperature was

greater than the added effect.

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Figure 7.1: Boxplots of the yearly high temperature effects on elderly mortality by cities

between 1987 and 2000. City abbreviations are explained in Supplemental Material Chapter 7,

Table S7.1.

182

Figure 7.2: Mean high temperature effects on elderly mortality by region between 1987 and

2000 using a univariate meta-analysis.

183

Figure 7.3: Trend in the effects of high temperatures on the elderly mortality by region between

1987 and 2000 using a Bayesian hierarchical model.

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7.5 Discussion

7.5.1 Variation in high temperature effects

This study characterized the excess risk for elderly mortality due to high temperature effects

on a yearly basis. We found that the both main and added effects varied by year, city and

geographic region (Figure 7.2).

Our results have potentially important consequences for heat warning systems. The great

variation of high temperature effects shown here means that heat warning systems, which

warn of approaching dangerous temperatures (Hajat, O'Connor, & Kosatsky, 2010; Hajat et

al., 2010; O'Neill et al., 2009), are likely to suffer false positives as in some years an

anticipated spike in deaths will not occur (whereas in other years the spike could be much

greater than expected). These false alarms have financial implications, as much of the

preventive actions planned under the current system (such as freeing-up hospital beds) are

wasted. Unnecessarily raising warnings may also undermine confidence in the system if the

warnings are perceived to be too frequent and without any real need (Kovats & Kristie, 2006).

There is a regular pattern of heat effects which often had “bad” years followed by relatively

“good” years (Figure 7.2). The great variations of heat effects found here might be caused by

the interaction between temperature-related mortality in summer and the previous summer or

winter (Ha, Kim, & Hajat, 2011; Rocklov, Forsberg, & Meister, 2009; Stafoggia, et al.,

2009a). A summer with a relatively high level of mortality leaves less people susceptible to

heat-related mortality in the following winter or summer. Studies have also shown that high

winter mortality leads to lower mortality in the following summer (Ha, et al., 2011; Rocklov,

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et al., 2009; Stafoggia, et al., 2009a), as well as high summer mortality following a lower

winter mortality (Valleron & Boumendil, 2004).

We found that the regional patterns of high temperature-mortality relationship (both main and

added effects) were different. The heterogeneity in high temperature effects between cities or

geographic regions might be due to differences in: physical acclimatization to high

temperatures (e.g., air conditioning use, housing structure, clothing type); exposure to high

temperatures; city-level responses to extreme high temperatures; sociodemographic

characteristics, or meteorological factors within cities or regions that might influence or

modify temperature–mortality relationships (Anderson & Bell, 2011).

7.5.2 Overall high temperature effects and trends over time

The main high temperature effect was greater than added effect. In general, we observed that

the main effect was higher in the Northwest and South California, while the added effect was

higher in Northeast and Upper Midwest. These findings are consistent with other studies of

heat waves or high temperatures (Anderson & Bell, 2009; Braga, et al., 2001). For example,

Anderson and Bell (2011) found that heat wave had smaller effects on mortality in the South

than elsewhere.

There was a decline in the main effect of high temperature-related death from 1987 to 2000 in

all regions (Figure 7.3). This is consistent with previous US studies (Barnett, 2007; Davis,

Knappenberger, Michaels, & Novicoff, 2003). An increased use of air conditioning may be

an underlying reason for this reduction in heat-related mortality (Barnett, 2007; Davis, et al.,

2003; O’Neill, 2003). US studies have shown that the effect of hot temperatures on mortality

186

was related to the level of air conditioning use (Braga, et al., 2001; Curriero, et al., 2002b;

Nunes, Paixao, Dias, Nogueira, & Marinho Falcao, 2011), and that the use of air conditioning

steadily increased in all areas of the United States during 1980 and 2000 (Barnett, 2007).

During the 1995 Chicago heat waves, moving from unventilated, indoor locations to places

with air conditioning decreased the mortality risk (Chan, Stacey, Smith, Ebi, & Wilson, 2001;

Semenza et al., 1996b).

We defined high temperature days as 2 or more consecutive days with temperature above the

90th

percentile during each warm season (May 1 to September 30), as there were only 153

days in each warm season. If we used a more strict definition (e.g., 2 or more consecutive

days with temperature above the 95th

percentile), there were often no high temperature days

in some warm seasons in some cities. However, the findings of our study are consistent with

previous study (Gasparrini & Armstrong, 2011b), which showed that the main effect of high

temperatures is greater than the added heat wave effect.

We decomposed the heat-mortality association into main and added effects, because main

effect itself cannot completely capture the effects of temperature on mortality. We did not

consider the impact of high temperature characteristics on mortality. Previous studies have

shown that individual days of extreme heat increased the risk of mortality (Baccini, et al.,

2008; Hajat et al., 2006), and that the added high temperature effect on mortality was greater

for consecutive high temperature days compared with non-consecutive high temperature days

(Anderson & Bell, 2011; Gasparrini & Armstrong, 2011b). Additionally, Anderson and Bell

(2011) illustrated that the added effect on mortality were associated with a heat wave’s

intensity, duration, and timing in the season (Anderson & Bell, 2011).

187

In this study, air pollution was not controlled for, as a previous study showed that heat effects

on mortality in the United States were robust to air pollution (Anderson & Bell, 2009). Other

related studies also did not control for air pollution when assessing the impact of temperature

on mortality (Anderson & Bell, 2011; Gasparrini & Armstrong, 2011b; Ha, et al., 2011).

7.5.3 Sensitivity analyses

Sensitivity analyses were carried out by modifying the df of smoothing for time, and varying

the df of the splines for temperature and lag in the DLNM. The results remained broadly

similar (results not shown). The model fit appeared better in some regions as judged by

akaike information criterion, but worse in other regions.

We examined the main and added effects using different high temperature definitions (95th

and 97th

percentile with 2 consecutive days of duration). The results showed that both the

main and added effects of high temperature increased if the definition of 97th

percentile with

2 days of duration was used compared with the definition of 95th

percentile with 2 days of

duration (results not shown).

7.5.4 Strengthens and limitations

This study has several strengths. The high temperature effect on elderly mortality is highly

variable from year to year. This finding is important from a public health perspective,

because this issue needs to be taken into account when establishing a warning system for high

temperatures and projecting the impact of future climate change. We used a flexible DLNM

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to capture the main effect of high temperature on elderly mortality and consider lagged

effects. We used a large database of 85 cities in the United States.

This study also has some limitations. We only used 14 years of data. A longer period may be

better to model the exact fluctuation in high temperature-related mortality over years. We did

not have individual exposure data, and so assumed a common daily exposure to temperature

for people in the same city. Additionally, the influence of air pollution and socioeconomic

factors was not controlled for. We did not consider the modification of high temperature

intensity, duration, and timing in the season, as a previous study has addressed this question

(Anderson & Bell, 2011).

7.6 Conclusion

Both the main and added effects of high temperatures on elderly mortality varied greatly from

year to year, making it difficult to predict the health effects of heat in any one year. There

was a gradual reduction in high temperature-related elderly mortality (particularly the main

effect) over the study period. Cities using heat warning systems need to be aware of both the

long-term changes in heat-related mortality and the year-to-year variations.

189

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Gosling, S. N., Lowe, J. A., McGregor, G. R., Pelling, M., & Malamud, B. D. (2009).

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218-223.

O'Neill, M. S., Carter, R., Kish, J. K., Gronlund, C. J., White-Newsome, J. L., Manarolla, X.,

et al. (2009). Preventing heat-related morbidity and mortality: new approaches in a

changing climate. Maturitas, 64(2), 98-103.

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episode in France. Comptes Rendus Biologies, 327(12), 1125-1141.

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(2002). Heat wave morbidity and mortality, Milwaukee, Wis, 1999 vs 1995: an

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Supplemental Material chapter 7, Table S7.1: List of the 83 US cities by region.

Region City Abbreviation State Region City Abbreviation State

Industrial Midwest Akron akr OH South East Atlanta atla GA

Buffalo buff NY Baton Rouge batr LA

Chicago chic IL Birmingham birm AL

Cincinnati cinc OH Cayce cayc SC

Cleveland clev OH Charlotte char NC

Columbus clmo OH Dallas/Fort Worth dlft TX

Dayton dayt OH Greensboro grnb NC

Detroit det MI Houston hous TX

Fort Wayne ftwa IN Huntsville hunt AL

Grand Rapids gdrp MI Jackson jcks MS

Indianapolis indi IN Jacksonville jckv FL

Lexington lex KY Knoxville knox TN

Louisville loui KY Memphis memp TN

Madison madi WI Miami miam FL

Milwaukee milw WI Mobile mobi AL

Pittsburgh pitt PA Nashville nash TN

St. Louis stlo MO New Orleans no LA

Toledo tole OH Orlando orla FL

Raleigh ral NC

North East Baltimore balt MD Shreveport shr LA

Boston bost MA St. Petersburg stpe FL

Jersey City jers NJ Tampa tamp FL

Norfolk nor VA Tulsa tuls OK

Newark nwk NJ

New York ny NY South West Albuquerque albu NM

Philadelphia phil PA Austin aust TX

Providence prov RI Corpus Christi corp TX

Rochester roch NY El Paso elpa TX

Syracuse syra NY Las Vegas lasv NV

Washington dc DC Lubbock lubb TX

Oklahoma City okla OK

North West Denver denv CO Phoenix phoe AZ

Oakland oakl CA San Antonio sana TX

Portland port OR Tucson tucs AZ

Sacramento sacr CA

Salt Lake City salt UT Southern California Fresno fres

San Francisco sanf CA Los Angeles la CA

San Jose sanj CA Riverside rive CA

Seattle seat WA San Bernardino sanb CA

Spokane spok WA San Diego sand CA

Tacoma taco WA Santa Ana/Anaheim staa CA

Upper Midwest Des Moines desm IA

Kansas City kan MO

Lincoln linc NE

Minneapolis/St. Paul minn MN

Wichita wich KS

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Supplemental Material chapter 7, Figure S7.1: Main effects of high temperature on elderly

mortality by city between 1987 and 2000. The white colour represents high risk of mortality

while the red colour represents low risk of mortality. City abbreviations are explained in

Supplemental Material Chapter 7, Table S7.1.

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Supplemental Material chapter 7, Figure S7.2: Added effects of high temperature on elderly

mortality by city between 1987 and 2000. The white colour represents high risk of mortality

while the red colour represents low risk of mortality. City abbreviations are explained in

Supplemental Material Chapter 7, Table S7.1.

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CHAPTER 8: GENERAL DISCUSSION

Each of the previous results chapter (Chapters 4–7) has its own discussion section in which

major findings were discussed in relation to the literature, interpretation of the results, and

public health implications. This chapter discusses the methodological development,

implications of the overall research, and the strengths and limitations of the thesis. It also

includes recommendations for future research directions.

8.1 Methodological development

Although a number of studies have examined the impact of temperature on human health,

such as mortality and morbidity, a few key methodological issues remain to be resolved.

The case–crossover design has its advantages. For example, case–crossover studies use each

subject as their own control, so any fixed confounding is removed and exposure

misclassification can be minimised; in the mean time, long-term and seasonal trends were

controlled by using short-interval strata. However, most previous case–crossover studies used

linear parametric models to examine the effects of temperature on mortality and used a single

lag model, or moving average lag model (Basu, et al., 2008; Green, et al., 2010). These

models are not adequate for exploring the complex relationship between temperature and

mortality, which is generally non-linear. Additionally, exposure to ambient temperature

usually has lagged effects which might also be non-linear. This thesis combined a distributed

lag non-linear model with a case–crossover design, making it possible to use such a design to

fit more sophisticated estimates of the effects of temperature. The distributed lag non-linear

model is developed on the basis of a ‘‘cross-basis’’ function, which allows simultaneously

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estimating the non-linear effects of temperature at each lag and also the non-linear effects

across lags (Gasparrini , Armstrong , & Kenward, 2010).

Another methodological issue is to examine whether there is a significant difference in

assessment of temperature effects using a spatiotemporal approach or a non-spatial approach.

There is variation in ambient temperature within most cities. Some time series studies

consider this spatial difference, but the standard approach is to average the observed data to

create a non-spatial daily exposure. Some time series studies only used one monitoring site’s

data, which similarly does not consider the spatial variance in temperature across the study

region. There have been some concerns that simple models using averaged temperature or a

single site’s temperature might not accurately estimate the effects of temperature on mortality

across the region, which may bias the effect estimates. In this thesis, I used an ordinary

kriging approach to model the spatial temperature across Brisbane city. Then, I compared

simple time series and spatiotemporal models, and found that the two models had a similar

ability to predict the effects of temperature on mortality. Therefore, I conclude that time

series studies using single site’s temperature or averaged temperature are generally

appropriate for examining temperature effects. This finding will be useful for future studies

aiming to estimate the effects of temperature on mortality, especially as time series models

are relatively easier to implement than spatiotemporal models. However, the lack of a

difference between the simple and spatial models here may be because of a lack of spatial

detail in both the temperature monitoring and mortality data. Other cities may still show a

spatial pattern in the effects of temperature if they use more detailed spatial data, or have a

strong spatial heterogeneity in housing characteristics (e.g., particular suburbs with low rates

of air conditioning), and city heat island.

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8.2 Implication of the research

8.2.1 Heat warning system

This thesis has some implications for the prevention and control of heat-related mortality.

Currently, health authorities issue public warnings of heat-related mortality risk based on the

association between temperature and mortality using relatively long time series (e.g., 10 years

data or 20 years data). This thesis found that the associations between temperature and

mortality were different by year within the same city. Previous studies similarly found that

the 2006 heat waves in western Europe had much less impact on mortality than the 2003 heat

waves (Empereur-Bissonnet, Salines, Berat, Caillere, & Josseran, 2006). In Chicago, there

were fewer heat-related deaths in 1999 heatwaves, compared with 1995 heatwaves. The

impacts of heat waves on mortality varied either across cities or over time in the same city

(Delaroziere & Sanmarco, 2004; Kovats & Kristie, 2006). This variation in heat-related

mortality might be attributed to the successful implementation of prevention measures, such

as the opening of cooling centres, and increase of air conditioning use over recent years.

Another explanation is a significant increase in public health messages and general level of

awareness of heat wave impacts (Palecki, Changnon, & Kunkel, 2001). This means that cities

need to constantly update their heat warnings (for example, reanalysing data at the end of

every summer) as heat-related health risks change over time.

Identification and prevention of the heat-related mortality is a very important public health

challenge, and general warnings should be made at the beginning of every summer.

Intervention policies should best geared towards the local climate and population

characteristics by the local health agencies and social services (Kovats & Hajat, 2008).

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Efficient warnings of heat-related mortality risk should be generated after taking into account

the variation in temperature effects on mortality over time. Ignoring this variation could lead

to false positives, as in some years an anticipated spike in deaths will not occur (whereas in

other years the spike could be much greater than expected) (Hajat, O'Connor, et al., 2010;

Hajat, Sheridan, et al., 2010; O'Neill, et al., 2009).

This thesis showed that the effects of high temperature on mortality were highly variable

from year to year. This finding suggests that future estimates of climate change may not have

correctly accounted for the uncertainty in the association between heatwaves and mortality,

and so their confidence intervals may be too narrow. Policy makers for public health need to

consider this issue when they establish a warning system for high temperatures and projecting

the impact of future climate change.

In this thesis, I found there is an apparent pattern of heat effects which often had “high risk”

years followed by relatively “low risk” years. This variation in heat effects may be related to

the differences in the at-risk population (e.g., more elderly people), or because of increased

adaptation over time (Sheridan & Kalkstein; Stafoggia, et al., 2009). To conform if this is a

regular pattern we would need a longer time series (possibly 50 years or more). However, this

finding is potentially important for heat warning systems. If policy makers consider this

variation in temperature effects, the heat warning systems might better predict heat-related

mortality.

8.2.2 Health effects of unstable weather

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Unstable weather is anticipated to increase, as climate change is accelerating. Understanding

whether unstable weather is associated with increased or decreased mortality can lead to

improved understanding of climate change effects on human health. Thus, this thesis

examined mortality risks when people exposed to an unstable temperatures between

neighbouring days. I found that unstable temperatures (relatively large increases or decreases

in temperature between neighbouring days) were associated with an increase in mortality.

That means unstable temperature is hazardous to human health. Public health policy makers

should consider the impacts of unstable temperatures on population health when they make

policy choices. For example, weather forecast announcers could remind residents to pay

attention to an unstable temperature before a sharp increase or decrease in temperature occurs.

People, especially the elderly, young children or those with chronic diseases, who are most

vulnerable to temperature should stay in a controlled temperature environment or take other

measures to maintain an appropriate temperature range in order to protect them from the

harm of unstable temperatures.

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8.3 Strengths of this thesis

This thesis has six major strengths:

I detailed how to apply a distributed lag non-linear model in the case−crossover design. This

allows the case−crossover design to flexibly estimate the non-linear and delayed effects of

temperature (or air pollution) on human health.

I compared time series and spatiotemporal models in assessing the association between

temperature and mortality. It provides important information for the future research on

temperature and mortality. For example, in general, time series models using single site’s

temperature or averaged temperature can be used to examine the temperature effects on

mortality.

I examined whether temperature change between neighbouring days increases the risk of

mortality in summer. We used two cities in different countries with different subtropical

climates to confirm the findings. The findings suggest not only absolute temperature affects

human health, but also temperature changes (both significant temperature drops and

temperature increases) had significant impacts on human health. This study provides an

important impetus for evaluating population vulnerability, and improving public health

adaptation strategies for reducing the burden of temperature.

I found that the high temperature effect on mortality is highly variable from year to year. This

finding is important for policy makers to develop public health strategies, as this issue needs

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to be taken into account when establishing a weather/health warning system and projecting

the health impact of future climate change.

I assessed the relationship between temperature and mortality in Tianjin, China using

advanced statistical models. The findings can be used to promote capacity building for local

response to extreme temperatures.

Finally, this thesis used comprehensive data including weather, air pollution, and mortality

from many cities (Tianjin, Brisbane, and 83 large US cities), and including whole population

in these cities as study samples, so the statistical power is high.

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8.4 Limitations of this thesis

This thesis also has some limitations:

Like other ecological studies, this thesis lacked information on individual data, such as

personal behaviours, indoor and outdoor activities, housing characteristics, and individual

medical history. The environmental data were also limited, as I used the data on ambient

temperature and air pollution from fixed sites rather than individual exposure, so there may

be some inevitable measurement error.

This thesis only used a single city’s data to compare time series and spatiotemporal models.

The finding might be difficult to be generalised to other cities that have great spatial variation

in temperature or mortality. But I provided detailed information on how to choose such

models for other studies.

This thesis did not control some potential confounders, such as fine particulate matter and

Ozone, because these data were not available in Tianjin, and had missing values in US cities.

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8.5 Recommendations for future research directions

As global climate continues to change, assessing the health effects of temperature is a very

broad and complex topic. Based on the findings from this thesis, I draw a number of

recommendations for future directions.

Many personal and environmental factors can modify the effects of temperature on human

health. Populations acclimatize to their local climates, so what is considered an extreme hot

day in some cities may be regarded as a normal summer day in others (Kovats & Hajat, 2008).

Therefore, it is necessary to examine city-specific temperature-mortality association

worldwide, paying particular attention to differences in local climate patterns and socio-

economic conditions.

Populations in developing countries are anticipated to be especially sensitive to impacts of

climate change, as they have limited adaptive capacity and more vulnerable people (Costello,

et al., 2009). To date there has been little research on temperature effects in developing

countries (McMichael, et al., 2008), including China (Guo, Barnett, Pan, Yu, & Tong, 2011b).

This thesis found both cold and high temperatures are related to mortality increase in Tianjin,

China. Future studies are needed to examine the associations between temperature and

mortality in other Chinese cities and other developing countries.

Studies on the associations between ambient temperature and mortality are mainly from

urban cities, either in developed counties or developing countries (Basu & Samet, 2002;

McMichael, et al., 2008). Few studies have examined the relationship between temperature

and mortality in rural areas, especially in developing countries (Hashizume et al., 2009). In

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fact, 57% of the population are living in rural area in developing countries (United Nations.,

2006). Populations living in rural areas of developing countries may be vulnerable to

temperature effects, because of the level of socioeconomic development and access to

medical care. I therefore suggest examining the effects of ambient temperature on human

health in rural areas, especially for developing countries. This will be more challenging than

working with data from cities because rural areas have a much wider spatial range, and are

unlikely to have the network of temperature monitoring stations that exist in most cities.

Given the large amount of extra computing time needed to fit spatiotemporal models, I

recommend that future studies use the simple time series model to examine the effects of

temperature on mortality. This may not be applicable to data with more spatially detailed

estimates of temperature and/or health. Therefore, comparison between time series and

spatiotemporal models still need to be confirmed in other cities, especially for those with

large variations in socio-economic status.

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8.6 Conclusions

There has been increasing concern for the health impacts of exposure to ambient temperature,

because climate change is continuing. Case−crossover and time series models are most

commonly used to examine the effects of temperatures on mortality. Previously, few studies

have used case−crossover to examine the delayed (distributed lag) and non-linear relationship

between temperature and mortality. This thesis made it possible to combine a case–crossover

design and distributed lag non-linear model, which may have significant benefits for future

research to assess temperature related health effects.

Most studies used daily temperature data from one monitoring site or daily mean values from

a network of monitoring sites to estimate the impact of temperature on mortality, which may

result in a measurement error for temperature exposure (Zhang, et al., 2011). In this thesis,

results show that that time series and spatiotemporal models produced similar effect estimates

of temperature exposure. The findings may also be important, because the time series models

are relatively easy to fit and take less time than spatial models.

Climate change may not only increase the frequency, intensity and duration of weather

extremes (e.g. heat waves, floods and cyclones) (WHO/WMO/UNEP, 1996), but also

increase unstable weather patterns (e.g. a significant drop/increase in temperature) in the

coming decades (Faergeman, 2008). However, less evidence is available on the possible

mortality effects due to temperature change between neighbouring days. In this thesis, results

show that a significant change in temperature between the neighbouring days might increase

the risk of mortality in Brisbane, Australia and Los Angeles, USA, which suggests that

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temperature change is a significant risk factor for mortality. People sensitive to unstable

weather should protect themselves from the temperature change.

Most previous studies only considered high temperature effects by averaging over the whole

study period, and ignored the variability in effects from year to year. In this thesis, results

show that high temperature effects on mortality varied greatly by year, city and region in US

cities, which demonstrates that it is important to take the variability in high temperature

effects into account in the development of weather/health warning systems. Policy makers for

public health need to consider this issue when they develop a warning system for high

temperatures and projecting the impact of future climate change.

210

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