The Detection of Disease Clustering in Time

Abstract

In epidemiological studies where the etiology of a disease is not yet well-established, it is sometimes required to examine data for evidence of temporal clustering, or of cyclical clustering, as in seasonal variation. A new index is for the level of disease clustering in time, which is devised to the case where the data are grouped into several equally spaced intervals. This index is applicable to both temporal and cyclical clustering. The asymptotic distribution of this index is derived under the null hypothesis of no clustering in time. Monte Carlo simulation studies show that the asymptotic results are good approximations when the sample size is as small as the number of intervals, an average of one per interval. The powers of the test based on this index for both types of clustering are compared with those of several existing procedures. Tables of upper percentage points of this index are given.