Hierarchical Bayes Analysis of Multilevel Health Services Data: A Canadian Neonatal Mortality Study
This article presents a comprehensive analysis of multilevel health services data in order to provide detailed information about important sources of outcome variation. We present a study of neonatal mortality variation among 17 neonatal intensive care units across Canada and briefly introduce the Neonatal Health Services in Canada Project, under which this work was carried out. We illustrate the potential of hierarchical Bayes modeling as a general strategy for systematically evaluating response-covariate associations at each level of the hierarchy, examining cross-level interaction, and attributing unexplained variability. We engage in an extensive discussion on relevant issues that are important for institutional comparison, provider profiling, and quality improvement.