A semi-parametric Bayesian dynamic hurdle model with an application to the health and retirement study

Article Type

Research Article

Publication Title

Computational Statistics

Abstract

All developed countries are facing the problem of providing affordable and high quality healthcare in recent years. This is due to the combination of an ageing population and the technological advancement in health science which leads to an increased life expectancy. This will affect the future use of hospital inpatient and outpatient services which in turn will place a significant stress on the economy since most medical services for the elderly are apportioned and funded under a national system. Thus, understanding the demand for healthcare and other key factors influencing the demand is crucial to better serve citizens. Hospital admission is considered to be a key proxy of the demand for healthcare, especially in the context of ageing populations as experienced globally. However, modeling hospital admissions, although very important, is often complicated by zero-inflation, by the covariates with time-varying effects, and by the necessity of borrowing information across individuals. Additionally, the rate of hospital admissions might differ between the group of individuals who have been hospitalized before and the group yet to be hospitalized. Also when individuals are clustered based on their baseline self-assessed health status, the distribution of hospital admissions and its relation to predictors may be quite different across and within different groups. In this paper we propose a unified Bayesian dynamic hurdle model which accommodates these features of the data in a semi-parametric approach. We analyze the data collected by the United States Health and Retirement Study in which the rate of hospital admissions varies across different self-assessed health groups. Simulation studies are performed for assessing the usefulness of the proposed model.

First Page

837

Last Page

863

DOI

10.1007/s00180-021-01143-x

Publication Date

4-1-2022

Comments

Open Access, Green

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