Climate change, health, and an introduction to epidemiologic methods. This lecture was held in the researcher training sessions which are part of the Adapting to Climate Change in China II project. http://www.ccadaptation.org.cn/

1. Climate Change and Health
Epidemiologic Methods
Dr. Dung Phung
Centre for Environment and Population Health, Griffith University

2. Climate Change & Health Risk
Global
warming
Rising
Temperature
More Extreme
Weather
Increasing Air
Pollution
Rising Sea Levels
• Mortality
• Morbidity
Long-term effect Short-term effect

3. Climate Change and Health Risk

4. Climate Change and Health Risk

5. Climate Change and Health Risk

6. Climate Change and Health Risk

7. Climate Change and Health Risk

8. Epidemiologic Methods
• No limited
• The most common quantitative methods:
• Time-series
• Case-Crossover
• Measuring health risk associated with variation in
climate change related factors

9. Epidemiologic Methods
TIME SERIES

10. Epidemiologic Methods
TIME-SERIES
 Special type of longitudinal study
 Examine short-term relationship
 Develop prediction model
 Projecting future health risk
Exposure
Outcomes

11. Epidemiologic Methods
TIME-SERIES
 Health outcomes (Yt) & Short-term variation in
Exposure factors (Xt)
Yt = f(Xt)
Example: Is there an association between day-to-day
variation in ambient temperature and daily risk of
hospitalization?

12. Epidemiologic Methods
TIME-SERIES
Temperature
Hospitalization

13. Epidemiologic Methods
TIME-SERIES
Data needs
 Health outcome (Yi): e.g. Daily mortality, hospital
admission, regular disease surveillance report
 Exposure variables (Xt): e.g. Temperature, humidity,
air pollutants
 Confounding variables: unusual events (influenza
outbreak, etc.), and others (seasonality, calendar
effects, long-term trends)

14. Epidemiologic Methods
TIME-SERIES

15. Epidemiologic Methods
TIME-SERIES
Key considerations and steps in time-series analysis
• Plot of exposure variable(s) against time
• Plot of outcome against time
• Correlation matrix for exposure and outcome variables
• Summary statistics for each variable
• Summary of missing data in each variable
• Regress time-series model
• Control for seasonality and long-term trend
• Individual lag models and distributed lag model
• Consider possible non-linear associations
• Model checking
• Diagnostic plots based on deviance residuals
• Multiple sensitivity analyses changing key modeling decisions

16. Epidemiologic Methods
TIME-SERIES
Key considerations and steps in time-series analysis
• Plot of exposure variable(s) against time
• Plot of outcome against time

17. Epidemiologic Methods
TIME-SERIES
Key considerations and steps in time-series analysis
• Correlation matrix for exposure and outcome variables

18. Epidemiologic Methods
TIME-SERIES
Key considerations and steps in time-series analysis
• Summary statistics for each variable

19. Epidemiologic Methods
Health outcome Temperature
Humidity
Rainfall
Function of time Day of a Week
TIME-SERIES
Key considerations and steps in time-series analysis
• Time-series regression model

20. Method & Result
Time-series regression

21. Epidemiologic Methods
TIME-SERIES
Key considerations and steps in time-series analysis
• Model checking
• Diagnostic plots based on deviance residuals

22. Epidemiologic Methods
TIME-SERIES
Advantages
• Quantify short-term association between environmental exposures
and health outcomes;
• Naturally avoid long-term change confounding factors
e.g. smoking habits, social class
• Be able to control for long-term fluctuation (season) and time-
varying factors (temperature, humidity, influenza, day of the week) by
regression analysis
Disadvantages
• Require long time-span of data
• Ecological fallacy
• Can not control for individual-level risk factors

23. High Temperature & Risk of Hospitalization in The Mekong Delta Multi-City
Dr. Dung Phung et al, 2016
Centre for Environment and Population Health, Griffith University

24. Background & Aim
• Highly vulnerable to climate change
• The air temperature increases up to
4°C from 2030-2100
• The sea level rises up to 1m by the
2100
• The rainfall increases from 0.3-8.8%
by the period of 2020-2100 with wide
variation through the region
• The floods are unusual patterns
• Increasing likelihood of extreme
floods.
Examine the relationship between ambient temperature and risk of
hospitalization in the multiple cities of the Mekong Delta Region

25. Method & Result
Time-series for city-specific effect

26. Method & Result
Meta-analysis for Regional effect by Gender and Age

27. Method & Result
Meta-analysis for Regional effect
- delayed effect

28. Method & Result
Meta-regression for
socio-economic effects
on temperature-
hospitalization
relationship

29. Conclusion
• Effects of high temperature
on hospitalization varied by
provinces
• Significant effects of high
temperature on all-cause,
infectious and respiratory
hospitalizations on Lag-0 day
• Females and elderly are likely
more sensitive
• High population density and
% of population with illiterate
increase the temperature-
hospitalization risk

30. Epidemiologic Methods
CASE-CROSSOVER

31. Epidemiologic Methods
CASE-CROSSOVER
 Alternative approach for time-series
 Special type of case-control study
 Examine transient effects on the risk of acute health events
Exposed? Exposed?
Control period Risk period Health outcome

32. Epidemiologic Methods
CASE-CROSSOVER
Analysis likewise a
matched case-control
a b
c d
Risk
Period
Exp.
No
Control
Exp. No
OR = b/c
Multi-variable Logistic regression to control for other factors

33. Epidemiologic Methods
CASE-CROSSOVER
Advantages
• Applicable for a short time-span of data
• Comparing exposure levels for a given day (t) when health event
occurs vs. level before (t-7) and after (t+7) the health event
• Allows to control for many individual factors (age, gender, smoking,
etc.) because both case and control are the same person
Disadvantages
• Seasonal and long-term trend is crudely controlled
• Can not control for over-dispersion effectively
• Not easy to determine number of strata for controls

34. The relationship between particulate air pollution
and emergency hospital visits for
hypertension in Beijing, China
Yuming Guo et al, 2010
School of Public Health, University of Queensland

35. Background & Aim
• Air pollution is a very serious issue for human health,
particularly in urban areas in developing countries
• Research conducted in Beijing, China shows that about 47%
of people have hypertension
• It is unknown whether particulate air pollution induces acute
hypertension events in persons with preexisting
hypertension
To analyze the relationship between ambient air pollution and EHVs for
hypertension, and to discover whether a short-term increase in ambient air
pollution is associated with the onset of hypertension.

36. Methods
• Case-crossover design
• Compare level of PM on exposed day with that of control days
• Data collection:
• Emergency hospital admission
• Weather factors: temperature, humidity
• Air pollutants: PM2.5, PM10, NO2, SO2
• Data analysis:
• Comparing PM level of case with 3 control within 28 days
• Examining delayed effect of PM in 4 days using distributed lag model
• Controlling for weather factors and other air pollutants

37. Results
Relationship between PM and emergency
hospital admissions due to hypertension

38. Results
Delayed effects of PM on emergency hypertension

39. Conclusions
 Elevated concentrations of
ambient particulate matter air
pollutants were associated with a
increase in the EHVs for
hypertension in Beijing during
2007
 The statistically significant two-day
lag effects were found for both
PM2.5 and PM10
 The findings provide additional
information about the health
effects of air pollution in Beijing,
China and may have implications
for local environmental and public
health

40. Thank You for Your Attention