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Detecting Clusters in Disease Incidence

Daniel Rabinowitz
Lecture Notes-Monograph Series
Vol. 23, Change-Point Problems (1994), pp. 255-275
Stable URL: http://www.jstor.org/stable/4355778
Page Count: 21
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Detecting Clusters in Disease Incidence
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Abstract

This paper is concerned with searching for localized environmental risk factors. The approach taken here uses case-control data to search for clusters of disease cases. In this context, case-control data means a sample of locations associated with diseased subjects (cases) and healthy subjects (controls). A cluster of cases is a region where the number of cases appears to be larger than what would have been expected had the cases occurred randomly in the underlying population. Clusters indicate areas where localized risk factors are likely. The methodology developed here produces a random field over the region where the cases and controls are located. The field is large where there are clusters of cases. Asymptotically, as the number of cases and controls becomes large, the field tends in distribution to a smooth Gaussian field. The operating characteristics of inferential procedures based on the random field may be approximated by considering the random field's limiting distribution.

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