IDEAS home Printed from https://ideas.repec.org/p/shf/wpaper/2012027.html
   My bibliography  Save this paper

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

Author

Listed:
  • 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)

Abstract

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.

Suggested Citation

  • Arnab Mukherji & Satrajit Roychowdhury & Pulak Ghosh & Sarah Brown, 2012. "Estimating Healthcare Demand for an Aging Population: A Flexible and Robust Bayesian Joint Model," Working Papers 2012027, The University of Sheffield, Department of Economics.
  • Handle: RePEc:shf:wpaper:2012027
    as

    Download full text from publisher

    File URL: http://www.shef.ac.uk/economics/research/serps/articles/2012_027.html
    File Function: First version, 2012
    Download Restriction: no

    References listed on IDEAS

    as
    1. Markus Jochmann & Roberto León-González, 2004. "Estimating the demand for health care with panel data: a semiparametric Bayesian approach," Health Economics, John Wiley & Sons, Ltd., vol. 13(10), pages 1003-1014.
    2. 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, March.
    3. 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, March.
    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. Smith, Michael & Kohn, Robert, 1996. "Nonparametric regression using Bayesian variable selection," Journal of Econometrics, Elsevier, vol. 75(2), pages 317-343, December.
    6. Rainer Winkelmann, 2004. "Health care reform and the number of doctor visits-an econometric analysis," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 19(4), pages 455-472.
    7. 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.
    8. 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.
    9. 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 313-336, May-June.
    Full references (including those not matched with items on IDEAS)

    More about this item

    Keywords

    aging; Bayesian methods; healthcare demand; joint model; splines;

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • I10 - Health, Education, and Welfare - - Health - - - General

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:shf:wpaper:2012027. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Jacob Holmes). General contact details of provider: http://edirc.repec.org/data/desheuk.html .

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.