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

  • Arnab Mukherji

    ()

    (Centre for Public Policy, Indian Institute of Management Bangalore)

  • Satrajit Roychowdhury

    (Expert Statistical Methodologist, Novartis Pharmaceutical Company)

  • Pulak Ghosh

    (Department of QM & IS, Indian Institute of Management Bangalore)

  • Sarah Brown

    (Department of Economics, The University of Sheffield)

In this paper, 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 modelled 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.

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File URL: http://www.shef.ac.uk/economics/research/serps/articles/2012_027.html
File Function: First version, 2012
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Paper provided by The University of Sheffield, Department of Economics in its series Working Papers with number 2012027.

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Length: 37 pages
Date of creation: 2012
Date of revision:
Handle: RePEc:shf:wpaper:2012027
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  1. Winkelmann, Rainer, 2001. "Health Care Reform and the Number of Doctor Visits - An Econometric Analysis," CEPR Discussion Papers 3021, C.E.P.R. Discussion Papers.
  2. Smith, M. & Kohn, R., . "Nonparametric Regression using Bayesian Variable Selection," Statistics Working Paper _009, Australian Graduate School of Management.
  3. Michael D. Hurd & Kathleen McGarry, 2002. "The Predictive Validity of Subjective Probabilities of Survival," Economic Journal, Royal Economic Society, vol. 112(482), pages 966-985, October.
  4. Malay Naskar & Kalyan Das, 2006. "Semiparametric Analysis of Two-Level Bivariate Binary Data," Biometrics, The International Biometric Society, vol. 62(4), pages 1004-1013, December.
  5. Atella, Vincenzo & Deb, Partha, 2008. "Are primary care physicians, public and private sector specialists substitutes or complements? Evidence from a simultaneous equations model for count data," Journal of Health Economics, Elsevier, vol. 27(3), pages 770-785, May.
  6. Deb, Partha & Trivedi, Pravin K, 1997. "Demand for Medical Care by the Elderly: A Finite Mixture Approach," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 12(3), pages 313-36, May-June.
  7. Markus Jochmann & Roberto Leon-Gonzalez, 2003. "Estimating the Demand for Health Care with Panel Data: A Semiparametric Bayesian Approach," Working Papers 2003005, The University of Sheffield, Department of Economics, revised Oct 2003.
  8. Brian Neelon & A. James O'Malley & Sharon-Lise T. Normand, 2011. "A Bayesian Two-Part Latent Class Model for Longitudinal Medical Expenditure Data: Assessing the Impact of Mental Health and Substance Abuse Parity," Biometrics, The International Biometric Society, vol. 67(1), pages 280-289, 03.
  9. Roula Tsonaka & Geert Verbeke & Emmanuel Lesaffre, 2009. "A Semi-Parametric Shared Parameter Model to Handle Nonmonotone Nonignorable Missingness," Biometrics, The International Biometric Society, vol. 65(1), pages 81-87, 03.
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