Use of administrative data to identify health plan members with unrecognized bipolar disorder: A retrospective cohort study

Iver A. Juster, Michael Stensland, Lisa Brauer, Paul Thuras

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

Objective: This retrospective cohort study used an algorithmic case-finding system on claims data from nationwide commercial health plans to validate previously identified predictors of unrecognized bipolar disorder among adults. Study Design: Retrospective cohort design. Methods: Using logistic regression, 2 claims data sets were evaluated to explore potential predictors; the first included claims for all healthcare encounters (all-encounters data set); the second excluded mental health provider claims (carve-out data set). A total of 280 244 members aged 18 to 64 years were included from 2 commercial health plans. Results: Claims related to attention deficit-hyperactivity disorder, depression, depression treated with antipsychotics, use of 3 (of 5) classes of psychotherapeutic drugs, younger age, and sex were all significant predictors of a subsequent diagnosis of bipolar disorder. In the all-encounters data set, a predicted value of 5% or greater yielded a sensitivity of 9.8% and a specificity of 99.9%; a predicted threshold of 3% increased sensitivity, to 20.7%; area under the receiver operating characteristic curve (AUC) was 0.82. Performance of the model was acceptable in the carve-out data set, with AUC 0.69. Conclusions: The case-finding system described here, which compares favorably with other screening tests used in primary care, may have significant value in helping physicians to identify patients with unrecognized bipolar disorder.

Original languageEnglish (US)
Pages (from-to)578-584
Number of pages7
JournalAmerican Journal of Managed Care
Volume11
Issue number9
StatePublished - Sep 2005
Externally publishedYes

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