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Improved Comorbidity Adjustment for Predicting Mortality in Medicare Populations

Authors

  • Sebastian Schneeweiss,

    1. Address correspondence to Sebastian Schneeweiss, M.D., Sc.D., Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, 221 Longwood Ave (BLI-341), Boston, MA 02115.
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  • Philip S. Wang,

    1. Philip S. Wang is also with the Division of Pharmacoepidemiology and Pharmacoeconomics at Brigham and Women's Hospital as well as the Department of Epidemiology, Harvard School of Public Health, Boston.
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  • Jerry Avorn,

    1. Jerry Avorn and Robert J. Glynn are with the Division of Pharmacoepidemiology and Pharmacoeconomics at Brigham and Women's. Additionally, Dr. Glynn is with the Department of Preventive Medicine at Brigham and Women's.
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  • Robert J. Glynn

    1. Jerry Avorn and Robert J. Glynn are with the Division of Pharmacoepidemiology and Pharmacoeconomics at Brigham and Women's. Additionally, Dr. Glynn is with the Department of Preventive Medicine at Brigham and Women's.
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  • Supported by research grant no. R01-HS10881 from the U.S. Agency for Healthcare Research and Quality and no. R03-AG18395 from the National Institute on Aging to Dr. Schneeweiss, and no. RO1-AG18833 from the National Institute on Aging to Dr. Glynn.

Abstract

Objective. To define and improve the performance of existing comorbidity scores in predicting mortality in Medicare enrollees.

Data Sources. Study participants were two Medicare populations who had complete drug coverage either through Medicaid or a statewide pharmacy assistance program: New Jersey Medicare enrollees (NNJ=235,881) and Pennsylvania Medicare enrollees (NPA=230,913).

Study Design. Frequently used comorbidity scores were computed for all subjects during the baseline year (January 1, 1994, to December 31, 1994, and one year later in Pennsylvania). The study outcome was one-year mortality during the following year. Performance of scores was measured with the c-statistic derived from multivariate logistic regression models. Empirical weights were derived in the New Jersey population and the performance of scores with new weights was validated in the Pennsylvania population.

Principal Findings. A score based on ICD-9-diagnoses (Romano) performed 60 percent better than one based on patterns of medication use (Chronic Disease Score, or CDS-1) (c=0.771 vs. c=0.703). The performance of the Romano score was further improved slightly by inclusion of the number of different prescription drugs used during the past year. Modeling the 17 conditions included in the Romano score as separate binary indicators increased its performance by 8 percent (c=0.781). We derived elderly-specific weights for these scores in the New Jersey sample, including negative weights for the use of some drugs, for example, lipid lowering drugs. Applying these weights, the performance of Romano and CDS-1 scores improved in an independent validation sample of Pennsylvania Medicare enrollees by 8.3 percent and 43 percent compared to the scores with the original weights. When we added an indicator of nursing home residency, age, and gender, the Romano score reached a performance of c=0.80.

Conclusions. We conclude that in epidemiologic studies of the elderly, a modified diagnosis-based score using empirically derived weights provides improved adjustment for comorbidity and enhances the validity of findings.

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