An innovative application of Bayesian disease mapping methods to patient safety research: a Canadian adverse medical event study

Abstract

Recently developed disease mapping and ecological regression methods have become important techniques in studies of disease epidemiology and in health services research. This increase in importance is partially a result of the development of Bayesian statistical methodologies that make it possible to study associations between health problems and risk factors at an aggregate (i.e. areal) level while taking into account such matters as unmeasured confounding and spatial relationships. In this paper we present a demonstration of the joint use of empirical Bayes (EB) and full Bayesian inferential techniques in a small area study of adverse medical events (also known as 'iatrogenic injury') in British Columbia, Canada. In particular, we illustrate a unified Bayesian hierarchical spatial modelling framework that enables simultaneous examinations of potential associations between adverse medical event occurrence and regional characteristics, age effects, residual variation and spatial autocorrelation. We propose an analytic strategy for complementary use of EB and FB inferential techniques for risk assessment and model selection, presenting an EB-FB combined approach that draws on the strengths of each method while minimizing inherent weaknesses. The work was motivated by the need to explore relatively efficient ways to analyse regional variations of health services outcomes and resource utilization when a considerable amount of statistical modelling and inference are required.