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Bayesian Inference for Hospital Quality in a Selection Model

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  • John Geweke
  • Gautam Gowrisankaran
  • Robert J. Town

Abstract

This paper develops new econometric methods to infer hospital quality in a model with discrete dependent variables and nonrandom 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 74,848 Medicare patients admitted to 114 hospitals in Los Angeles County from 1989 through 1992 with a diagnosis of pneumonia. It finds the smallest and largest hospitals to be of the highest quality. There is strong evidence of dependence between the unobserved severity of illness and the assignment of patients to hospitals, whereby patients with a high unobserved severity of illness are disproportionately admitted to high quality hospitals. Consequently a conventional probit model leads to inferences about quality that are markedly different from those in this study's selection model. Copyright The Econometric Society 2003.

Suggested Citation

  • John Geweke & Gautam Gowrisankaran & Robert J. Town, 2003. "Bayesian Inference for Hospital Quality in a Selection Model," Econometrica, Econometric Society, vol. 71(4), pages 1215-1238, July.
  • Handle: RePEc:ecm:emetrp:v:71:y:2003:i:4:p:1215-1238
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    References listed on IDEAS

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    16. John Geweke & Gautam Gowrisankaran & Robert J. Town, 2003. "Bayesian Inference for Hospital Quality in a Selection Model," Econometrica, Econometric Society, vol. 71(4), pages 1215-1238, July.
    17. Kreps,David M. & Wallis,Kenneth F. (ed.), 1997. "Advances in Economics and Econometrics: Theory and Applications," Cambridge Books, Cambridge University Press, number 9780521589826, March.
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    More about this item

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C34 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Truncated and Censored Models; Switching Regression Models

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