Climate change and health epidemiologic methods - Dr Dung Phung

Climate Change and Health Epidemiologic Methods

Dr. Dung PhungCentre for Environment and Population Health, Griffith University

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

Climate Change and Health Risk





Epidemiologic Methods

• No limited

• The most common quantitative methods:• Time-series• Case-Crossover

• Measuring health risk associated with variation in climate change related factors


TIME SERIES


TIME-SERIES

Special type of longitudinal study

Examine short-term relationship

Develop prediction model

Projecting future health risk

ExposureOutcomes


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?


TIME-SERIES

Temperature

Hospitalization


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)


TIME-SERIES


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



• Plot of exposure variable(s) against time• Plot of outcome against time



• Correlation matrix for exposure and outcome variables


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


Health outcome TemperatureHumidity

Rainfall

Function of time Day of a Week


• Time-series regression model

Method & Result

Time-series regression



• Model checking• Diagnostic plots based on deviance residuals


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

High Temperature & Risk of Hospitalization in The Mekong Delta

Multi-City

Dr. Dung Phung et al, 2016Centre for Environment and Population Health, Griffith University

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

Method & Result

Time-series for city-specific effect

Method & Result

Meta-analysis for Regional effect by Gender and Age

Method & Result

Meta-analysis for Regional effect- delayed effect

Method & Result

Meta-regression for socio-economic effects

on temperature-hospitalization

relationship

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


CASE-CROSSOVER


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


CASE-CROSSOVER

Analysis likewise a matched case-control

a bc d

RiskPeriod

Exp.

No

Control

Exp. No

OR = b/c

Multi-variable Logistic regression to control for other factors


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

The relationship between particulate air pollution and emergency hospital visits for

hypertension in Beijing, China

Yuming Guo et al, 2010School of Public Health, University of Queensland

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.

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

ResultsRelationship between PM and emergency hospital admissions due to hypertension

Results

Delayed effects of PM on emergency hypertension

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

Thank You for Your Attention