Time Series Methods for Epidemiology

1. Time Series Methods for Epidemiology June 3, 2008 Patrick L. Kinney Associate Professor Mailman School of Public Health Columbia University

2. Overview Background on public health Issues in epidemiology Particular issues for time series Example analysis

3. Public Health vs. Clinical Medicine PH focuses on populations or communities rather than individuals PH tries to understand and promote behaviors and conditions that make for healthy communities Focus on primary prevention, rather than treatment of ailments

4. Three Tiers of Prevention Primary prevention avoids the development of a disease. Most population-based health promotion activities are primary preventative measures. Secondary prevention activities are aimed at early disease detection, thereby increasing opportunities for interventions to prevent progression of the disease and emergence of symptoms. Tertiary prevention reduces the negative impact of an already established disease by restoring function and reducing disease-related complications.

5. Public Health Success Stories Vaccination: smallpox, polio, etc. Motor vehicle safety measures: reduction of traffic deaths and injuries Clean water and sanitation: reduced typhoid and cholera etc. Smoking cessation: reduced cardiovascular disease and cancer Fluoridation of drinking water: healthy teeth Etc. etc�

6. Role of Epidemiology The central tool used by public health practitioners to understand causes of disease and to develop control measures Formally, epidemiology is �the study of the distribution and determinants of health-related states or events in specified populations, and the application of this study to the control of health problems�

7. Typical Use of Epi Do some descriptive studies (e.g., time series or cross-sectional) Do some etiologic studies (e.g., case-control or cohort) Draw conclusions or �inferences� about cause-effect relationships Carry out intervention studies Advise policy makers to reduce exposures to the risk factor Analyze whether the policy did any good

8. To design a study� Need to find and analyze exposure �contrasts� Can find exposure contrasts either across space (populations) or across time And hold everything else constant� that�s hard part

9. Data Needs for Epi Health outcome Examples in climate arena? Exposure of interest Examples in climate arena? Other variables. Also called �covariates� or �potential confounders�

12. Impacts of Climate Change on Health are Challenging to Characterize because: Health outcomes are not specific to climate change Climate changes occur on decadal or greater time scales Numerous other risk and protective factors (i.e., potential confounders) also change over time Exposure/response pathways are often complex Adequate population health statistics are often not available, especially in developing countries Unexposed �control� groups may be hard to find To date, funding and training levels for epidemiologic work have been inadequate

13. Two metholodogical approaches are being used: Establish baseline relationships between weather and health using historical data Develop scenario-based predictive models to assess potential future health risks

14. Time Series Epidemiology Addresses short-term exposure-response relationships Involves analysis of observations collected at equal time intervals, e.g., daily, monthly� Widely used Time series studies avoid many of the confounding factors that can affect spatial studies - e.g., � However, time-varying factors may confound time series associations - e.g., �

15. Methods Issues What health outcome data are available? Governmental data bases Research data What exposure data are available? Ground-based observations Remotely sensed data Modeled data Usually must adjust for time trends, seasonality, even day of week effects May want to examine time lags between exposure and effect Statistical methods

17. Ways to deal with cycles and trends: Subtract moving average Subtract sinusoidal function Subtract smooth function estimated using LOESS or SPLINE methods

20. Example - Effects of Weather and Air Pollution on Daily Deaths in NYC Metropolitan Area Objective was to characterize quantitatively the empirical relationships among these variables using recent observations We then planned to use the estimated �exposure-response� coefficients in future scenarios of climate change in the region (see Knowlton K et al, 2004; 2007)

21. Heat & Acute Deaths

22. Tropospheric Ozone & Acute Deaths Mortality effects of ozone have been demonstrated in time series studies, controlling for temperature and other pollutants E.g., Kinney and Ozkaynak, Environ Res 1991; Bell et al., JAMA 2004

23. Ozone Formation

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36. Summary Time series analysis is an important and useful tool for descriptive epidemiology Provides a quick and feasible tool for examining and quantifying exposure-response relationships Can be used as input to risk assessment models in policy context Must adjust for �temporal confounders� If relationships are detected using time series analysis, etiologic studies may then be designed and carried out

37. Questions?