Hierarchical Bayesian modeling of spatially correlated health service outcome and utilization rates.
We present Bayesian hierarchical spatial models for spatially correlated small-area health service outcome and utilization rates, with a particular emphasis on the estimation of both measured and unmeasured or unknown covariate effects. This Bayesian hierarchical model framework enables simultaneous modeling of fixed covariate effects and random residual effects. The random effects are modeled via Bayesian prior specifications reflecting spatial heterogeneity globally and relative homogeneity among neighboring areas. The model inference is implemented using Markov chain Monte Carlo methods. Specifically, a hybrid Markov chain Monte Carlo algorithm (Neal, 1995, Bayesian Learning for Neural Networks; Gustafson, MacNab, and Wen, 2003, Statistics and Computing, to appear) is used for posterior sampling of the random effects. To illustrate relevant problems, methods, and techniques, we present an analysis of regional variation in intraventricular hemorrhage incidence rates among neonatal intensive care unit patients across Canada.
MacNab
Ying C
YC
Division of Biostatistics and Epidemiology, Department of Health Care and Epidemiology, University of British Columbia, Vancouver, B.C., Canada V6H 3V4. ymacnab@cw.bc.ca
D001499:Bayes Theorem; D002170:Canada / Q000453:epidemiology; D002391:Catchment Area, Health; D003695:Delivery of Health Care / Q000706:statistics & numerical data*; D006470:Hemorrhage / Q000453:epidemiology; D006801:Humans; D007231:Infant, Newborn; D019102:Infant, Very Low Birth Weight; D007363:Intensive Care Units, Neonatal; D008390:Markov Chains; D015233:Models, Statistical; D009010:Monte Carlo Method; D015995:Prevalence; D011446:Prospective Studies; D012307:Risk Factors; D017062:Small-Area Analysis