title: Disease models implicit in statistical tests of disease clustering.
abstract: State and local health departments investigate an increasing number of cluster allegations, for which the selection of appropriate statistical methods is an important problem. Many of the methods for the spatial analysis of health data assume, either implicitly or explicitly, some model of disease occurrence, and comparisons of methods can be difficult when their underlying disease models differ. We review some of the issues involved in the statistical analysis of spatial disease patterns and describe several methods recently proposed to detect areas of increased disease rates. The disease models upon which the methods are based are explicitly described, and they provide a useful basis for comparing alternative clustering methods.
authors.affiliation: Division of Biostatistics, University of Minnesota, Minneapolis 55455-0392, USA.
authors.forename: L A
authors.initials: LA
authors.lastname: Waller
authors.forename: G M
authors.initials: GM
authors.lastname: Jacquez
mesh_terms: D016000:Cluster Analysis; D003627:Data Interpretation, Statistical; D015233:Models, Statistical; D016012:Poisson Distribution