Hierarchical Bayesian spatial modelling of small-area rates of non-rare disease.
We present Bayesian hierarchical spatial models for the analysis of the geographical distribution of a non-rare disease or event. The work is motivated by the need for ascertaining regional variations in health services outcomes and resource use and for assessing the potential sources of these variations. The models discussed herein readily accommodate random spatial effects and covariate effects. We discuss Bayesian inferential framework and implementation of a hybrid Markov chain Monte Carlo method for full Bayesian model inference. The methods are illustrated through an analysis of regional variation in chronic lung disease (CLD) rates among neonatal intensive care unit (NICU) patients across Canada. Specifically, we first present a random effects binomial model for spatially correlated CLD rates, with random spatial effects accounting for latent or covariate effects. These random spatial effects depict regional or spatial variation in chronic lung disease occurrence. We then extend this model to include covariates. With this extension, we assess residual spatial effects and the extent to which risk factors such as illness severity at NICU admission, low birth weight, and very low birth weight influence the CLD rate variation.
MacNab
Ying C
YC
Department of Health Care and Epidemiology, University of British Columbia, Canada. ymacnab@cw.bc.ca
D001499:Bayes Theorem; D002170:Canada / Q000453:epidemiology; D002908:Chronic Disease; D006801:Humans; D007231:Infant, Newborn; D007363:Intensive Care Units, Neonatal / Q000706:statistics & numerical data; D008171:Lung Diseases / Q000453:epidemiology*; D008390:Markov Chains; D009010:Monte Carlo Method; D012307:Risk Factors; D017062:Small-Area Analysis