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Open Access Highly Accessed Methodology

An exact test to detect geographic aggregations of events

Rhonda J Rosychuk12* and Jason L Stuber3

Author Affiliations

1 Department of Pediatrics, 11402 University Avenue NW, Edmonton, Alberta, Canada

2 Women and Children's Health Research Institute, Edmonton, Alberta, Canada

3 Department of Chemistry, 200 University Avenue West, Waterloo, Ontario, Canada

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International Journal of Health Geographics 2010, 9:28  doi:10.1186/1476-072X-9-28

Published: 7 June 2010

Abstract

Background

Traditional approaches to statistical disease cluster detection focus on the identification of geographic areas with high numbers of incident or prevalent cases of disease. Events related to disease may be more appropriate for analysis than disease cases in some contexts. Multiple events related to disease may be possible for each disease case and the repeated nature of events needs to be incorporated in cluster detection tests.

Results

We provide a new approach for the detection of aggregations of events by testing individual administrative areas that may be combined with their nearest neighbours. This approach is based on the exact probabilities for the numbers of events in a tested geographic area. The test is analogous to the cluster detection test given by Besag and Newell and does not require the distributional assumptions of a similar test proposed by Rosychuk et al. Our method incorporates diverse population sizes and population distributions that can differ by important strata. Monte Carlo simulations help assess the overall number of clusters identified. The population and events for each area as well as a nearest neighbour spatial relationship are required. We also provide an alternative test applicable to situations when only the aggregate number of events, and not the number of events per individual, are known. The methodology is illustrated on administrative data of presentations to emergency departments.

Conclusions

We provide a new method for the detection of aggregations of events that does not rely on distributional assumptions and performs well.