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Time series analysis in environmental epidemiology: challenges and considerations
Sandra Gudziunaite1, Zana Shabani2, Lisbeth Weitensfelder1
1Medical University of Vienna, Vienna, Austria (Department of Environmental Health, Center for Public Health).
Summary
This overview clarifies proper time series analysis methods for environmental epidemiology, focusing on acute health events from widespread exposures like air pollution. It highlights under-explored areas, including neurological and psychiatric endpoints, for this statistical tool.
Area of Science:
- Environmental Epidemiology
- Biostatistics
Background:
- Time series analyses are common in environmental epidemiology but often misunderstood.
- Confusion exists regarding appropriate link functions, seasonality control, and lag handling.
Purpose of the Study:
- To provide a clear overview of proper time series analysis execution in environmental epidemiology.
- To draw from other disciplines to address common challenges.
- To highlight under-explored health endpoints for this methodology.
Main Methods:
- Review of established time series analysis practices.
- Adaptation of methods from other disciplines for environmental epidemiology.
- Focus on acute events and widespread exposures.
Main Results:
- Discussion of key considerations for time series analysis in environmental epidemiology.
- Identification of specific statistical requirements (link function, seasonality, lags).
- Examples of typical exposures (air pollutants, meteorological factors).
Conclusions:
- Proper execution of time series analysis is crucial for accurate environmental epidemiology.
- Neurological and psychiatric endpoints represent under-explored research areas for time series analysis.
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