Health-exposure modeling and the ecological fallacy.
Recently, there has been an increased interest in modeling the association between aggregate disease counts and environmental exposures measured, for example via air pollution monitors, at point locations. This paper has two aims: first, we develop a model for such data in order to avoid ecological bias; second, we illustrate that modeling the exposure surface and estimating exposures may lead to bias in estimation of health effects. Design issues are also briefly considered, in particular the loss of information in moving from individual to ecological data, and the at-risk populations to consider in relation to the pollution monitor locations. The approach is investigated initially through simulations, and is then applied to a study of the association between mortality in those over 65 in the year 2000 and the previous year's SO2, in London. We conclude that the use of the proposed model can provide valid inference, but the use of estimated exposures should be carried out with great caution.
Wakefield
Jon
J
Department of Statistics, University of Washington, Seattle, WA, USA. jonno@u.washington.edu
Shaddick
Gavin
G
D000368:Aged; D000397:Air Pollution / Q000009:adverse effects* / Q000032:analysis; D015982:Bias; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D004781:Environmental Exposure / Q000009:adverse effects / Q000032:analysis; D005843:Geography; D006801:Humans; D008131:London / Q000453:epidemiology; D008171:Lung Diseases / Q000401:mortality*; D015233:Models, Statistical; D013458:Sulfur Dioxide / Q000009:adverse effects*
D013458:Sulfur Dioxide