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来源类型Working Paper
规范类型报告
DOI10.3386/w8497
来源IDWorking Paper 8497
Bayesian Inference for Hospital Quality in a Selection Model
John Geweke; Gautam Gowrisankaran; Robert J. Town
发表日期2001-10-01
出版年2001
语种英语
摘要This paper develops new econometric methods to infer hospital quality in a model 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 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 high quality and public hospitals to be of low quality. There is strong evidence of dependence between the unobserved severity of illness and the assignment of patients to hospitals. Consequently a conventional probit model leads to inferences about quality markedly different than those in this study's selection model.
主题Econometrics ; Estimation Methods
URLhttps://www.nber.org/papers/w8497
来源智库National Bureau of Economic Research (United States)
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资源类型智库出版物
条目标识符http://119.78.100.153/handle/2XGU8XDN/566101
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GB/T 7714
John Geweke,Gautam Gowrisankaran,Robert J. Town. Bayesian Inference for Hospital Quality in a Selection Model. 2001.
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