TY - JOUR

T1 - Bayesian Partitioning for Modeling and Mapping Spatial Case-Control Data

AU - Costain, Deborah

PY - 2009/12

Y1 - 2009/12

N2 - Methods for modeling and mapping spatial variation in diseaserisk continue to motivate much research. In particular, spatialanalyses 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.

AB - Methods for modeling and mapping spatial variation in diseaserisk continue to motivate much research. In particular, spatialanalyses 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.

KW - Bayesian partitioning

KW - Geo-referenced case-control data

KW - Reversible jump MCMC

KW - Spatial variation in infant mortality

U2 - 10.1111/j.1541-0420.2008.01193.x

DO - 10.1111/j.1541-0420.2008.01193.x

M3 - Journal article

VL - 65

SP - 1123

EP - 1132

JO - Biometrics

JF - Biometrics

SN - 0006-341X

IS - 4

ER -