Estimating Health Care Demand for an Aging Population: A Flexible and Robust Bayesian Joint Model

• Activity Number: 654
• Type: Contributed
• Date/Time: Thursday, August 8, 2013 : 8:30 AM to 10:20 AM
• Sponsor: Health Policy Statistics Section
• Author(s): Satrajit Roychoudhury*+ and Arnab Mukherjee and Pulak Ghosh and Sarah Brown
• Companies: Novartis Pharmaceuticals Corporation and Indian Institute of Management Bangalore and Indian Institute of Management Bangalore and University of Sheffield
• Keywords: Aging ; Bayesian Methods ; Healthcare Demand ; Joint Model ; Splines

Abstract

We analyse two frequently used measures of the demand for health care, namely hospital visits and out-of-pocket health care expenditure, which have been analysed separately in the existing literature. Given that these two measures of healthcare demand are highly likely to be closely correlated, we propose a framework to jointly model hospital visits and out-of-pocket medical expenditure. Furthermore, the joint framework allows for the presence of non-linear effects of covariates using splines to capture the effects of aging on healthcare demand. Sample heterogeneity is modeled robustly with the random effects following Dirichlet process priors with explicit cross-part correlation. The findings of our empirical analysis of the U.S. Health and Retirement Survey indicate that the demand for healthcare varies with age and gender and exhibits significant cross-part correlation that provides a rich understanding of how aging affects health care demand, which is of particular policy relevance in the context of an aging population.