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

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  • Gautam Gowrisankaran

    (University of Minnesota)

  • Robert J. Town

    (University of California)

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    Abstract

    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.

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    Bibliographic Info

    Paper provided by Econometric Society in its series Econometric Society World Congress 2000 Contributed Papers with number 1773.

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    Date of creation: 01 Aug 2000
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    Handle: RePEc:ecm:wc2000:1773

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    1. John Geweke, . "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, MIT Press, vol. 115(2), pages 577-615, May.
    5. Cutler, D.M., 1992. "The Incidence of Adverse Medical Outcome Under Prospective Payment," Harvard Institute of Economic Research Working Papers 1603, Harvard - Institute of Economic Research.
    6. repec:cup:etheor:v:12:y:1996:i:3:p:409-31 is not listed on IDEAS
    7. 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.
    8. Mark McClellan & Douglas Staiger, 1999. "The Quality of Health Care Providers," NBER Working Papers 7327, National Bureau of Economic Research, Inc.
    9. 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.
    10. 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.
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