Using a Hierarchical Model to Estimate Risk-Adjusted Mortality for Hospitals Not Included in the Reference Sample

Authors

  • David E. Clark,

    1. Department of Surgery, Maine Medical Center, 887 Congress Street, Suite 210, Portland, ME 04102
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    • Address correspondence to David E. Clark, M.D., Department of Surgery, Maine Medical Center, 887 Congress Street, Suite 210, Portland, ME 04102; e-mail clarkd@mmc.org. Edward L. Hannan, Ph.D., is with the Department of Health Policy, School of Public Health, State University of New York, University at Albany, One University Place, Rensselaer, NY. Stephen W. Raudenbush, Ph.D., is with the Department of Sociology, University of Chicago, Chicago, IL.

  • Edward L. Hannan,

    1. Department of Health Policy, School of Public Health, State University of New York, University at Albany, One University Place, Rensselaer, NY
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  • Stephen W. Raudenbush

    1. Department of Sociology, University of Chicago, Chicago, IL
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Abstract

Objective. To provide a method for any hospital to evaluate patient mortality using a hierarchical risk-adjustment equation derived from a reference sample.

Data Source. American College of Surgeons National Trauma Data Bank (NTDB).

Study Design. Hierarchical logistic regression models predicting mortality were estimated from NTDB data. Risk-adjusted hospital effects obtained directly from models using standard software were compared with approximations derived from a summary equation and data from each individual hospital.

Principal Findings. Theoretical approximations were similar to results using standard software.

Conclusions. To allow independent verification, agencies using reference databases for hospital mortality “report cards” should publish their risk-adjustment equations. Similar hospitals not in the reference database may also use the published equations along with the approximations described to evaluate their own outcomes using their own data.

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