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Bayesian Partitioning for Modeling and Mapping Spatial Case–Control Data
Author(s) -
Costain Deborah A.
Publication year - 2009
Publication title -
biometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.298
H-Index - 130
eISSN - 1541-0420
pISSN - 0006-341X
DOI - 10.1111/j.1541-0420.2008.01193.x
Subject(s) - bayesian probability , computer science , variation (astronomy) , spatial variability , bayesian inference , spatial epidemiology , spatial analysis , data mining , econometrics , data science , statistics , artificial intelligence , mathematics , epidemiology , medicine , physics , astrophysics
Summary 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 etiology, 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.