Profiling Nursing Homes Using Bayesian Hierarchical Modeling

Authors

  • Dan R. Berlowitz MD, MPH,

    1. From the Center for Health Quality, Outcomes, and Economic Research, Edith Nourse Rogers Memorial Veterans Hospital, Bedford, Massachusetts;
    2. Sections of General Internal Medicine and Geriatrics, Boston Medical Center, Boston, Massachusetts;
    3. Boston University Schools of Medicine and Public Health, Boston, Massachusetts; and
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  • Cindy L. Christiansen PhD,

    1. From the Center for Health Quality, Outcomes, and Economic Research, Edith Nourse Rogers Memorial Veterans Hospital, Bedford, Massachusetts;
    2. Boston University Schools of Medicine and Public Health, Boston, Massachusetts; and
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  • Gary H. Brandeis MD,

    1. Sections of General Internal Medicine and Geriatrics, Boston Medical Center, Boston, Massachusetts;
    2. Boston University Schools of Medicine and Public Health, Boston, Massachusetts; and
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  • Arlene S. Ash PhD,

    1. Sections of General Internal Medicine and Geriatrics, Boston Medical Center, Boston, Massachusetts;
    2. Boston University Schools of Medicine and Public Health, Boston, Massachusetts; and
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  • Boris Kader PhD,

    1. From the Center for Health Quality, Outcomes, and Economic Research, Edith Nourse Rogers Memorial Veterans Hospital, Bedford, Massachusetts;
    2. Sections of General Internal Medicine and Geriatrics, Boston Medical Center, Boston, Massachusetts;
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  • John N. Morris PhD,

    1. Research and Training Institute, Hebrew Rehabilitation Center for Aged, Boston, Massachusetts.
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  • Mark A. Moskowitz MD

    1. Sections of General Internal Medicine and Geriatrics, Boston Medical Center, Boston, Massachusetts;
    2. Boston University Schools of Medicine and Public Health, Boston, Massachusetts; and
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Address correspondence to Dan R. Berlowitz, MD, MPH, CHQOER, Bedford VA Hospital, 200 Springs Road, Bedford, MA 01730. E-mail dberlow@bu.edu

Abstract

OBJECTIVES: New methods developed to improve the statistical basis of provider profiling may be particularly applicable to nursing homes. We examine the use of Bayesian hierarchical modeling in profiling nursing homes on their rate of pressure ulcer development.

DESIGN: Observational study using Minimum Data Set data from 1997 and 1998.

SETTING: A for-profit nursing home chain.

PARTICIPANTS: Residents of 108 nursing homes who were without a pressure ulcer on an index assessment.

MEASUREMENTS: Nursing homes were compared on their performance on risk-adjusted rates of pressure ulcer development calculated using standard statistical techniques and Bayesian hierarchical modeling.

RESULTS: Bayesian estimates of nursing home performance differed considerably from rates calculated using standard statistical techniques. The range of risk-adjusted rates among nursing homes was 0% to 14.3% using standard methods and 1.0% to 4.8% using Bayesian analysis. Fifteen nursing homes were designated as outliers based on their z scores, and two were outliers using Bayesian modeling. Only one nursing home had greater than a 50% probability of having a true rate of ulcer development exceeding 4%.

CONCLUSIONS: Bayesian hierarchical modeling can be successfully applied to the problem of profiling nursing homes. Results obtained from Bayesian modeling are different from those obtained using standard statistical techniques. The continued evaluation and application of this new methodology in nursing homes may ensure that consumers and providers have the most accurate information regarding performance.

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