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Design Issues in Small-Area Studies of Environment and Health

Paul Elliott and David A. Savitz
Environmental Health Perspectives
Vol. 116, No. 8 (Aug., 2008), pp. 1098-1104
Stable URL: http://www.jstor.org/stable/25071149
Page Count: 7
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Abstract

Background: Small-area studies are part of the tradition of spatial epidemiology, which is concerned with the analysis of geographic patterns of disease with respect to environmental, demographic, socioeconomic, and other factors. We focus on etiologic research, where the aim is to make inferences about spatially varying environmental factors influencing the risk of disease. Methods and results: We illustrate the approach through three exemplars: a) magnetic fields from overhead electric power lines and the occurrence of childhood leukemia, which illustrates the use of geographic information systems to focus on areas with high exposure prevalence; b) drinking-water disinfection by-products and reproductive outcomes, taking advantage of large between- to within- area variability in exposures from the water supply; and c) chronic exposure to air pollutants and cardiorespiratory health, where issues of socioeconomic confounding are particularly important. Discussion: The small-area epidemiologic approach assigns exposure estimates to individuals based on location of residence or other geographic variables such as workplace or school. In this way, large populations can be studied, increasing the ability to investigate rare exposures or rare diseases. The approach is most effective when there is well-defined exposure variation across geographic units, limited within-area variation, and good control for potential confounding across areas. Conclusions: In conjunction with traditional individual-based approaches, small-area studies offer a valuable addition to the armamentarium of the environmental epidemiologist. Modeling of exposure patterns coupled with collection of individual-level data on subsamples of the population should lead to improved risk estimates (i.e., less potential for bias) and help strengthen etiologic inference.

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