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

Abstract

SUMMARYWe analyse two frequently used measures of the demand for health-hospital visits and out-of-pocket health care expenditure-which have been analysed separately in the existing literature. Given that these two measures of health demand are highly likely to be closely correlated, we propose a framework to jointly model hospital visits and out-of-pocket medical expenditure, which allows for the presence of nonlinear effects of covariates using splines to capture the effects of aging on health demand. The findings from our empirical analysis of the US Health and Retirement Survey indicate that the demand for health varies with age.

A Bayesian approach of analysing semi-continuous longitudinal data with monotone missingness

“…), but excludes the costs reimbursed through health insurance. In health economics, OOPME is treated as an important measure of financial risk (Mukherji et al, 2016). In our data, we have three different health insurances: (a) employment insurance, (b) government insurance and (c) other (private) insurance.…”

“…Here, b i is the subject-specific random effects, and the residuals e it are assumed to follow normal distribution with mean = 0 and variance = 1. For non-zero response, however, we consider log transformed data following Mukherji et al (2016). Typically, medical expenditure data are highly positively skewed and hence a log transformation is useful from the modelling perspective.…”

“…Ghosh and Tu (2008), Boone et al (2012), Buu et al (2012), Alfo and Maruotti (2010) and Yang et al (2016) developed models for zero-inflated longitudinal count data. Similar models have also been proposed for continuous longitudinal measurements with excess zeros (Olsen and Schafer, 2001;Mukherji et al, 2016, Farewell et al, 2017.…”

“…Note that at each wave, OOPME is the total cost of all medical services excluding the cost that is reimbursed or paid through health insurance. The OOPME is an accurate measure of the actual financial burden of medical expenditure incurred by individuals (Mukherji et al, 2016), and contains excess zeros. The objective of our study is to model and predict OOPME in terms of the covariates measuring physical and financial health.…”

A Bayesian two-stage regression approach of analysing longitudinal outcomes with endogeneity and incompleteness

“…The goal of the study was to understand the challenges in aging, and the effects of the disease status and financial health on the OOPME. In health economics, OOPME is considered as an important measure of financial risk (Mukherji et al, 2016), as well as the physical health condition.…”