Detecting and analyzing patterns in spatial data

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Perceived patterns in spatial distributions are among the most provocative clues to environmental etiologies. The easy availability of maps made from vital events data have heightened public interest and awareness of these differences in disease patterns. Epidemiologists, on the other hand, are uneasy about the methodological limitations of these kinds of data and many believe follow-up is unproductive. Previous methodological critiques of cluster and ecological (group-level) analyzes are largely theoretical, and it not known to what degree problems actually occur in practice or why. Answering these questions requires detailed individual- level spatial epidemiological data ("the gold standard") for comparison, but these data are rarely available. Geographically coded data from Cape Code on cancer (from the current SBRP center) and reproductive/developmental effects (Project 1, this application) provide such a resource. Two new methods, one for identifying disease "hotspots" (developed by our group), and one for ecological analysis (King's "EI method") show promise for use in environmental epidemiology. In this project we will continue development of the adaptive rate smooth method for showing local disease excesses, adding the ability to adjust for covariates; and develop methodological tools to analyze and visualize the potential for problems form ecologic data. We will apply these methods to the Cape data, examining the degree of clustering caused by spatial confounding and compare ecological inference with individual-level inference of the Cape data using both conventional and novel ecological methods.