IDEAS home Printed from
   My bibliography  Save this paper

Inferring Hospital Quality from Patient Discharge Records Using a Bayesian Selection Model


  • Gautam Gowrisankaran

    (University of Minnesota)

  • Robert J. Town

    (University of California)


This paper develops new econometric methods to estimate hospital quality and other models with discrete dependent variables and non-random selection. Mortality rates in patient discharge records are widely used to infer hospital quality. However, hospital admission is not random and some hospitals may attract patients with greater unobserved severity of illness than others. In this situation the assumption of random admission leads to spurious inference about hospital quality. This study controls for hospital selection using a model in which distance between the patient's residence and alternative hospitals are key exogenous variables. Bayesian inference in this model is feasible using a Markov chain Monte Carlo posterior simulator, and attaches posterior probabilities to quality comparisons between individual hospitals and groups of hospitals. The study uses data on 77.937 Medicare patients admitted to 117 hospitals in Los Angeles County from 1989 through 1992 with a diagnosis of pneumonia. It finds higher quality in smaller hospitals than larger, and in private for-profit hospitals than in hospitals in other ownership categories. Variations in unobserved severity of illness across hospitals is at least a great as variation in hospital quality. Consequently a conventional probit model leads to inferences about quality markedly different than those in this study's selection model.

Suggested Citation

  • Gautam Gowrisankaran & Robert J. Town, 2000. "Inferring Hospital Quality from Patient Discharge Records Using a Bayesian Selection Model," Econometric Society World Congress 2000 Contributed Papers 1773, Econometric Society.
  • Handle: RePEc:ecm:wc2000:1773

    Download full text from publisher

    File URL:
    File Function: main text
    Download Restriction: no

    References listed on IDEAS

    1. John Geweke, "undated". "Posterior Simulators in Econometrics," Computing in Economics and Finance 1996 _019, Society for Computational Economics.
    2. Chib, Siddhartha & Greenberg, Edward, 1996. "Markov Chain Monte Carlo Simulation Methods in Econometrics," Econometric Theory, Cambridge University Press, vol. 12(03), pages 409-431, August.
    3. McClellan, Mark & Noguchi, Haruko, 1998. "Technological Change in Heart-Disease Treatment: Does High Tech Mean Low Value?," American Economic Review, American Economic Association, vol. 88(2), pages 90-96, May.
    4. Daniel P. Kessler & Mark B. McClellan, 2000. "Is Hospital Competition Socially Wasteful?," The Quarterly Journal of Economics, Oxford University Press, vol. 115(2), pages 577-615.
    5. Mark B. McClellan & Douglas O. Staiger, 2000. "Comparing Hospital Quality at For-Profit and Not- for-Profit Hospitals," NBER Chapters,in: The Changing Hospital Industry: Comparing For-Profit and Not-for-Profit Institutions, pages 93-112 National Bureau of Economic Research, Inc.
    6. Mark McClellan & Douglas Staiger, 1999. "The Quality of Health Care Providers," NBER Working Papers 7327, National Bureau of Economic Research, Inc.
    7. Cutler, David M, 1995. "The Incidence of Adverse Medical Outcomes under Prospective Payment," Econometrica, Econometric Society, vol. 63(1), pages 29-50, January.
    8. Edward C. Norton & Douglas O. Staiger, 1994. "How Hospital Ownership Affects Access to Care for the Uninsured," RAND Journal of Economics, The RAND Corporation, vol. 25(1), pages 171-185, Spring.
    9. repec:cup:etheor:v:12:y:1996:i:3:p:409-31 is not listed on IDEAS
    10. Gowrisankaran, Gautam & Town, Robert J., 1999. "Estimating the quality of care in hospitals using instrumental variables," Journal of Health Economics, Elsevier, vol. 18(6), pages 747-767, December.
    11. Roberts, G. O. & Smith, A. F. M., 1994. "Simple conditions for the convergence of the Gibbs sampler and Metropolis-Hastings algorithms," Stochastic Processes and their Applications, Elsevier, vol. 49(2), pages 207-216, February.
    Full references (including those not matched with items on IDEAS)

    More about this item


    Access and download statistics


    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:ecm:wc2000:1773. 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: (Christopher F. Baum) or (Christopher F. Baum). General contact details of provider: .

    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.