title: Mixture models and disease mapping.
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.
authors.affiliation: Department of Epidemiology, Institute for Social Medicine, Free University Berlin, Germany.
authors.forename: P
authors.initials: P
authors.lastname: Schlattmann
authors.forename: D
authors.initials: D
authors.lastname: Böhning
mesh_terms: D001499:Bayes Theorem; D001604:Berlin / Q000453:epidemiology; D016000:Cluster Analysis; D003710:Demography; D004196:Disease Outbreaks / Q000706:statistics & numerical data*; D004812:Epidemiologic Methods; D006509:Hepatitis B / Q000453:epidemiology; D006801:Humans; D015995:Prevalence