Mixture models and disease mapping

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Abstract
The analysis and recognition of disease clustering in space and its representation on a map is one of the oldest problems in epidemiology. Some traditional methods of constructing such a map are presented. An alternative approach using mixture models to identify population heterogeneity and map construction within an empirical Bayes framework is described. For hepatitis B data from Berlin in 1989, a map is presented and the different methods are evaluated using a parametric bootstrap approach.