Bayesian Inference for Hospital Quality in a Selection Model

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dc.contributor.author Geweke, John en_US
dc.contributor.author Gowrisankaran, G. en_US
dc.contributor.author Town, R.J. en_US
dc.contributor.editor en_US
dc.date.accessioned 2010-05-28T09:53:24Z
dc.date.available 2010-05-28T09:53:24Z
dc.date.issued 2003 en_US
dc.identifier 2008008253 en_US
dc.identifier.citation Geweke John, Gowrisankaran G., and Town R.J. 2003, 'Bayesian Inference for Hospital Quality in a Selection Model', Blackwell Publishing, vol. 71, pp. 1215-1238. en_US
dc.identifier.issn 0012-9682 en_US
dc.identifier.other C1UNSUBMIT en_US
dc.identifier.uri http://hdl.handle.net/10453/10004
dc.description.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. en_US
dc.language en_US
dc.publisher Blackwell Publishing en_US
dc.relation.isbasedon http://dx.doi.org/10.1111/1468-0262.00444 en_US
dc.rights The copyright to this article is held by the Econometric Society, http://www.econometricsociety.org/. It may be downloaded, printed and reproduced only for personal or classroom use. Absolutely no downloading or copying may be done for, or on behalf of, any for-profit commercial firm or for other commercial purpose without the explicit permission of the Econometric Society. For this purpose, contact the Editorial Office of the Econometric Society at econometrica@econometricsociety.org
dc.title Bayesian Inference for Hospital Quality in a Selection Model en_US
dc.parent Econometrica en_US
dc.journal.volume 71 en_US
dc.journal.number en_US
dc.publocation USA en_US
dc.identifier.startpage 1215 en_US
dc.identifier.endpage 1238 en_US
dc.cauo.name BUS.Faculty of Business en_US
dc.conference Verified OK en_US
dc.for 140302 en_US
dc.personcode 101228 en_US
dc.personcode 0000053748 en_US
dc.personcode 0000053749 en_US
dc.percentage 100 en_US
dc.classification.name Econometric and Statistical Methods en_US
dc.classification.type FOR-08 en_US
dc.edition en_US
dc.custom en_US
dc.date.activity en_US
dc.location.activity en_US
dc.description.keywords Bayesian inference, hospital quality, simultaneous equations, MCMC, Medicare, pneumonia, mortality. en_US


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