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MONITORING FOR CLUSTERS OF DISEASE: APPLICATION TO LEUKEMIA INCIDENCE IN UPSTATE NEW YORK
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1990
Year
The study proposes a procedure for detecting significant clusters of chronic diseases, particularly cancer, and compares it with two existing clustering methods. The authors implement a computer‑intensive duster evaluation permutation procedure, applied to leukemia incidence data from the New York State Cancer Registry and census files, to detect disease clusters. The procedure accommodates population‑density variations, eliminates post‑hoc hypothesis issues, addresses key cluster‑evaluation problems, and enables state health officials to prioritize investigations and respond efficiently to reported clusters.
Abstract The authors propose a procedure for the detection of significant clusters of chronic diseases, with particular reference to cancer. The procedure allows for variations in population density and avoids the problem of “post hoc” formation of hypotheses or self-defined populations. This accounts for several of the principal problems of cluster evaluations. The techniques are practical but “computer-intensive.” The procedure, termed the “duster evaluation permutation procedure”, is applied to leukemia incidence data for an Upstate New York region obtained from the New York State Cancer Registry and census files. Comparisons are made with two other recently proposed clustering methods, namely the U-statistic method of WhKtemore et al. (Biometrika 1987;74:631–7) and the “geographical analysis machine” of Openshaw et al. (Lancet 1988; 1:272–3). Routine examination of disease occurrence with the cluster evaluation permutation procedure would allow state health officials to prioritize case investigations and to respond in a timely and efficient manner to inquiries of reported clusters.