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Identifying Physician-Recognized Depression from Administrative Data: Consequences for Quality Measurement

Authors

  • Claire M. Spettell,

    1. Address correspondence to Claire M. Spettell, Ph.D., Research Director, Health Informatics/USQA, 980 Jolly Road, Mailstop U38A, Blue Bell, PA 19422.
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  • Terry C. Wall,

    1. Terri C. Wall, M.D., M.P.H., is Assistant Professor of General Pediatrics, University of Alabama at Birmingham, and is with the UAB Center for Outcomes and Effectiveness Research and Education.
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  • Jeroan Allison,

    1. Jeroan J. Allison, M.D., M.S., Epi., is Associate Professor of Medicine, University of Alabama at Birmingham, and is with the UAB Center for Outcomes and Effectiveness Research and Education, the UAB Division of General Internal Medicine, and the UAB Division of Preventive Medicine.
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  • Jaimee Calhoun,

    1. Jaimee Calhoun, M.A., Ed., is Program Coordinator, University of Alabama at Birmingham, UAB Center for Outcomes and Effectiveness Research and Education.
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  • Richard Kobylinski,

    1. Richard Kobylinski is Senior Research Analyst, USQA/Aetna, Inc., Blue Bell, PA.
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  • Rachel Fargason,

    1. Rachel Fargason, M.D., is with the Department of Psychiatry, University of Alabama at Birmingham.
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  • Catarina I. Kiefe

    1. Catarina Kiefe, Ph.D., M.D., is Professor of Medicine and Biostatistics, University of Alabama at Birmingham, and is with the UAB Center for Outcomes and Effectiveness Research and Education, the UAB Division of Preventive Medicine, and the Birmingham Veterans Affairs Medical Center.
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  • Supported in part by the Academic Medicine and Managed Care Forum, grant nos. HS09446 and HS1112403

Abstract

Background. Multiple factors limit identification of patients with depression from administrative data. However, administrative data drives many quality measurement systems, including the Health Plan Employer Data and Information Set (HEDIS®).

Methods. We investigated two algorithms for identification of physician-recognized depression. The study sample was drawn from primary care physician member panels of a large managed care organization. All members were continuously enrolled between January 1 and December 31, 1997. Algorithm 1 required at least two criteria in any combination: (1) an outpatient diagnosis of depression or (2) a pharmacy claim for an antidepressant. Algorithm 2 included the same criteria as algorithm 1, but required a diagnosis of depression for all patients. With algorithm 1, we identified the medical records of a stratified, random subset of patients with and without depression (n=465). We also identified patients of primary care physicians with a minimum of 10 depressed members by algorithm 1 (n=32,819) and algorithm 2 (n=6,837).

Results. The sensitivity, specificity, and positive predictive values were: Algorithm 1: 95 percent, 65 percent, 49 percent; Algorithm 2: 52 percent, 88 percent, 60 percent. Compared to algorithm 1, profiles from algorithm 2 revealed higher rates of follow-up visits (43 percent, 55 percent) and appropriate antidepressant dosage acutely (82 percent, 90 percent) and chronically (83 percent, 91 percent) (p<0.05 for all).

Conclusions. Both algorithms had high false positive rates. Denominator construction (algorithm 1 versus 2) contributed significantly to variability in measured quality. Our findings raise concern about interpreting depression quality reports based upon administrative data.

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