Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
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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 -