Space-time mixture modelling of public health data

Space-time mixture modelling of public health data

This paper aims to enlarge the usual scope of disease mapping by means of dynamic mixtures (DMDM) in case a time component is involved in the data. A special mixture model is suggested which looks for space‐time components (clusters) simultaneously. The idea is illustrated using data on female lung...

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Veröffentlicht in:Statistics in medicine 2000-09, Vol.19 (17-18), p.2333-2344
Hauptverfasser: Böhning, Dankmar, Dietz, Ekkehart, Schlattmann, Peter
Format: Artikel
Sprache:eng
Schlagworte:
Algorithms
Female
Germany, East - epidemiology
Humans
Incidence
Lung Neoplasms - epidemiology
Maps as Topic
Poisson Distribution
Registries
Risk Factors
Space-Time Clustering
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container_title Statistics in medicine
container_volume 19
creator Böhning, Dankmar
Dietz, Ekkehart
Schlattmann, Peter
description This paper aims to enlarge the usual scope of disease mapping by means of dynamic mixtures (DMDM) in case a time component is involved in the data. A special mixture model is suggested which looks for space‐time components (clusters) simultaneously. The idea is illustrated using data on female lung cancer from the East German cancer registry for 1960–1989. The conventional mixed Poisson regression model is used as a third model for comparison. The models are discussed in terms of their benefits, difficulties and ease in interpretation, as well as their statistical meaning. Some ideas on evaluation of these models are also included. Copyright © 2000 John Wiley & Sons, Ltd.
doi_str_mv 10.1002/1097-0258(20000915/30)19:17/18<2333::AID-SIM573>3.0.CO;2-Q
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subjects Algorithms
Female
Germany, East - epidemiology
Humans
Incidence
Lung Neoplasms - epidemiology
Maps as Topic
Poisson Distribution
Registries
Risk Factors
Space-Time Clustering
title Space-time mixture modelling of public health data
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