Epilepsy Among Elderly Medicare Beneficiaries: A Validated Approach to Identify Prevalent and Incident Epilepsy. Issue 4 (April 2019)

• Record Type: Journal Article
• Authors: Moura, Lidia M.V.R.; Smith, Jason R.; Blacker, Deborah; Vogeli, Christine; Schwamm, Lee H.; Cole, Andrew J.; Hernandez-Diaz, Sonia; Hsu, John
• Is Part Of: Medical care. Volume 57:Issue 4(2019)
• Publication Date: 2019-04
• Subjects: epilepsy -- epidemiology -- elderly -- claims data -- algorithms; Economics, Medical -- Periodicals; Insurance, Health -- Periodicals; Santé, Services de -- Administration -- Périodiques; Soins médicaux -- Périodiques; Medical economics -- Periodicals; Health insurance -- Periodicals; Medical economics -- United States -- Periodicals; Health insurance -- United States -- Periodicals; Comprehensive Health Care -- Periodicals; Personal Health Services -- Periodicals; Gezondheidszorg; Économie de la santé -- Périodiques; Santé, Services de -- Périodiques; Health insurance; Medical economics; United States; Periodicals; 362.10973
• DOI: 10.1097/MLR.0000000000001072
• Languages: English
• ISSNs: 0025-7079

Abstract

Abstract : Background: Uncertain validity of epilepsy diagnoses within health insurance claims and other large datasets have hindered efforts to study and monitor care at the population level. Objectives: To develop and validate prediction models using longitudinal Medicare administrative data to identify patients with actual epilepsy among those with the diagnosis. Research Design, Subjects, Measures: We used linked electronic health records and Medicare administrative data including claims to predict epilepsy status. A neurologist reviewed electronic health record data to assess epilepsy status in a stratified random sample of Medicare beneficiaries aged 65+ years between January 2012 and December 2014. We then reconstructed the full sample using inverse probability sampling weights. We developed prediction models using longitudinal Medicare data, then in a separate sample evaluated the predictive performance of each model, for example, area under the receiver operating characteristic curve (AUROC), sensitivity, and specificity. Results: Of 20, 945 patients in the reconstructed sample, 2.1% had confirmed epilepsy. The best-performing prediction model to identify prevalent epilepsy required epilepsy diagnoses with multiple claims at least 60 days apart, and epilepsy-specific drug claims: AUROC=0.93 [95% confidence interval (CI), 0.90–0.96], and with an 80% diagnostic threshold, sensitivity=87.8% (95% CI, 80.4%–93.2%), specificity=98.4% (95% CI, 98.2%–98.5%). A similar model also performed well in predicting incident epilepsy ( k =0.79; 95% CI, 0.66–0.92). Conclusions: Prediction models using longitudinal Medicare data perform well in predicting incident and prevalent epilepsy status accurately. Abstract : Supplemental Digital Content is available in the text.