Bayesian Spatio-Temporal Modeling for the Inpatient Hospital Costs of Alcohol-Related Disorders

Abstract

Understanding how health care costs vary across different demographics and health conditions is essential to developing policies for health care cost reduction. It may not be optimal to apply the conventional mean regression due to its sensitivity to the high level of skewness and spatio-temporal heterogeneity presented in the cost data. To find an alternative method for spatio-temporal analysis with robustness and high estimation efficiency, we combine information across multiple quantiles and propose a Bayesian spatio-temporal weighted composite quantile regression (ST-WCQR) model. An easy-to-implement Gibbs sampling algorithm is provided based on the asymmetric Laplace mixture representation of the error term. Extensive simulation studies show that ST-WCQR outperforms existing methods for skewed error distributions. We apply ST-WCQR to investigate how patients’ characteristics affected the inpatient hospital costs for alcohol-related disorders and identify areas that could be targeted for cost reduction in New York State from 2015 to 2017.