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Bayesian Partitioning for Modeling and Mapping Spatial Case-Control Data

Costain, Deborah (2009) Bayesian Partitioning for Modeling and Mapping Spatial Case-Control Data. Biometrics, 65 (4). pp. 1123-1132. ISSN 0006-341X

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Methods for modeling and mapping spatial variation in disease risk continue to motivate much research. In particular, spatial analyses provide a useful tool for exploring geographical heterogeneity in health outcomes, and consequently can yield clues as to disease aetiology, direct public health management and generate research hypotheses. This article presents a Bayesian partitioning approach for the analysis of individual level geo-referenced health data. The model makes few assumptions about the underlying form of the risk surface, is data adaptive and allows for the inclusion of known determinants of disease. The methodology is used to model spatial variation in neonatal mortality in Porto Alegre, Brazil.

Item Type: Journal Article
Journal or Publication Title: Biometrics
Uncontrolled Keywords: Bayesian partitioning ; Geo-referenced case-control data ; Reversible jump MCMC ; Spatial variation in infant mortality
Subjects: ?? qa ??
Departments: Faculty of Science and Technology > Mathematics and Statistics
ID Code: 60080
Deposited By: ep_importer_pure
Deposited On: 19 Nov 2012 09:32
Refereed?: Yes
Published?: Published
Last Modified: 11 Apr 2018 00:47
Identification Number:

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