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Case Studies in Bayesian Statistics pp 155–236Cite as

Modeling Mortality Rates for Elderly Heart Attack Patients: Profiling Hospitals in the Cooperative Cardiovascular Project

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Part of the book series: Lecture Notes in Statistics ((LNS,volume 121))

Abstract

Public debate on costs and effectiveness of health care in the United States has generated a growing emphasis on “profiling” of medical care providers. The process of profiling involves comparing resource use and quality of care among medical providers to community or to normative standards. Information from provider profiles may be used to determine whether providers deviate from acceptable standards in the type of care they deliver. In this paper we profile hospitals based on 30-day mortality rates for a cohort of 14,581 Medicare patients discharged with acute myocardial infarction (AMI) in 1993 from hospitals located in Alabama, Connecticut, Iowa, or Wisconsin. Clinical and socio-demographic information for the study cohort were collected retrospectively from individually reviewed medical charts and administrative files.

To account for within-hospital association among patient outcomes and between-hospital variability of practice patterns, we fit a Hierarchical Logistic Regression Model to the mortality data. We also employed multiple imputation methods to impute anomalous and missing values under the assumption that the complete multivariate data follow a General Location Model. Once predictor variables were selected and appropriately transformed, imputed datasets were then fitted to the Hierarchical Logistic Regression Model using Markov Chain Monte Carlo methods, and the results of each fit were combined across imputations to produce inferences for the model. We estimated several indices of excess mortality: (1) the posterior probability that hospital mortality was one and one half times the median mortality over all hospitals for patients of average admission severity, (2) the probability that the difference between adjusted and standardized hospital mortality was large, and (3) the probability that hospital mortality was greater than a benchmark value.

We found that the overall unadjusted 30-day mortality rate was 21%, and ranged from 18% in Connecticut to 23% in Alabama; observed hospital-specific mortality ranged from 0 to 67% across the 389 hospitals. After adjusting for patient age, mean arterial pressure, creatinine and respiration rate measured at admission in each hospital, hospital mortality ranged from 10% to 53%. The probability that hospital-specific mortality for the “average” patient was one and one half times the median mortality for similar patients was greater than 14% for one quarter of the sampled hospitals. The posterior probability of a large discrepancy between risk-adjusted and standardized hospital-specific mortality was less than 6% for three quarters of the hospitals.

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Authors
  1. Sharon-Lise T. Normand
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  2. Mark E. Glickman
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  3. Thomas J. Ryan
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Editor information

Editors and Affiliations

  1. Center for Statistical Sciences, Brown University, Box G-A416, Providence, RI, 02912, USA

    Constantine Gatsonis

  2. Division of Biostatistics, University of Minnesota-Twin Cities, School of Public Health, 2221 University Avenue SE, Minneapolis, MN, 55414-3075, USA

    James S. Hodges

  3. Department of Statistics, Carnegie-Mellon University, Pittsburgh, PA, 15213, USA

    Robert E. Kass

  4. Graduate School of Business, University of Chicago, Chicago, IL, 60637-1511, USA

    Robert McCulloch  & Peter Rossi  & 

  5. Department of Operations Research, The George Washington University, Washington, D.C., 20052, USA

    Nozer D. Singpurwalla

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Normand, SL.T., Glickman, M.E., Ryan, T.J. (1997). Modeling Mortality Rates for Elderly Heart Attack Patients: Profiling Hospitals in the Cooperative Cardiovascular Project. In: Gatsonis, C., Hodges, J.S., Kass, R.E., McCulloch, R., Rossi, P., Singpurwalla, N.D. (eds) Case Studies in Bayesian Statistics. Lecture Notes in Statistics, vol 121. Springer, New York, NY. https://doi.org/10.1007/978-1-4612-2290-3_4

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