Bayesian Markov Chain Monte Carlo Research Articles

HMO selection and Medicare costs: Bayesian MCMC estimation of a robust panel data tobit model with survival.

The fraction of US Medicare recipients enrolled in health maintenance organizations (HMOs) has increased substantially over the past 10 years. However, the impact of HMOs on health care costs is still hotly debated. In particular, it is argued that HMOs achieve cost reduction through 'cream-skimming' and enrolling relatively healthy patients. This paper develops a Bayesian panel data tobit model of HMO selection and Medicare expenditures for recent US retirees that accounts for mortality over the course of the panel. The model is estimated using Markov Chain Monte Carlo (MCMC) simulation methods, and is novel in that a multivariate t-link is used in place of normality to allow for the heavy-tailed distributions often found in health care expenditure data. The findings indicate that HMOs select individuals who are less likely to have positive health care expenditures prior to enrollment. However, there is no evidence that HMOs disenrol high cost patients. The results also indicate the importance of accounting for survival over the panel, since high mortality probabilities are associated with higher health care expenditures in the last year of life.