Hierarchical polytomous regression models with applications to health services research

The analysis of variations is an important area of interest in health services and outcomes research and has two main goals: to identify and quantify variability across units, such as geographic regions or health care providers, in terms of procedure utilization and outcomes, and to explore the links between process, such as regional or hospital practice patterns, and outcomes, such as patient mortality and functional status. Hierarchical regression models are well suited for this type of analysis. In this paper we formulate a hierarchical polytomous regression model and apply it to the analysis of variations in the utilization of alternative cardiac procedures in a national cohort of elderly Medicare patients who had an acute myocardial infarction during 1987. The model is designed to accommodate clustered multinomial data with covariate vectors available on individual cases and on clusters. We present a Bayesian approach to fitting and checking the model using simulated values from the posterior distribution of the parameters. The simulation algorithms are based on Gibbs sampling in combination with Metropolis steps. Using the hierarchical polytomous regression model, we examine how the rates of cardiac procedures depend on patient-level characteristics, including age, gender and race, and whether there exist interstate differences and regional patterns in the use of these procedures.