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The effects of scale on tests for disease clustering
Author(s) -
Waller Lance A.,
Turnbull Bruce W.
Publication year - 1993
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.4780121913
Subject(s) - cluster analysis , incidence (geometry) , computer science , confidentiality , population , data mining , scale (ratio) , partition (number theory) , statistics , geography , environmental health , medicine , cartography , mathematics , machine learning , computer security , geometry , combinatorics
Abstract Surveillance of a large geographic region for ‘clusters’ of adverse health events, particularly cancers, often involves searching for raised incidence in the vicinity of prespecified putative sources of hazard. For reasons of practicality or of confidentiality, incidence and population data are usually only available aggregated over subregions or ‘cells’. The performance of statistical procedures designed to detect the presence of clusters can be highly sensitive to the level of aggregation, that is to the choice of partition of the region into the cells. We investigate this sensitivity in the cases of three recently proposed procedures, namely those of Besag and Newell, Stone, and Waller et al . For illustration, we use leukaemia incidence data for 1978–82 in a region of upstate New York, with inactive hazardous waste sites containing trichloroethylene acting as suspected sources.