Dataset Information

Epilepsy Among Elderly Medicare Beneficiaries: A Validated Approach to Identify Prevalent and Incident Epilepsy.

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.

SUBMITTER: Moura LMVR

PROVIDER: S-EPMC6417929 | biostudies-literature | 2019 Apr

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Epilepsy Among Elderly Medicare Beneficiaries: A Validated Approach to Identify Prevalent and Incident Epilepsy.

Moura Lidia M V R LMVR Smith Jason R JR Blacker Deborah D Vogeli Christine C Schwamm Lee H LH Cole Andrew J AJ Hernandez-Diaz Sonia S Hsu John J

Medical care 20190401 4

<h4>Background</h4>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.<h4>Objectives</h4>To develop and validate prediction models using longitudinal Medicare administrative data to identify patients with actual epilepsy among those with the diagnosis.<h4>Research design, subjects, measures</h4>We used linked electronic health records and Medicare administrative data including cl ...[more]

PMID: 30762723

Similar Datasets

Project description:Background: Incident cancer diagnosis may increase the risk of coronary artery disease (CAD)-related hospitalizations, especially in older individuals. Adherence to statins and/or angiotensin-converting enzyme inhibitors (ACEIs)/angiotensin II receptor blockers (ARBs)/β-blockers reduces CAD-related hospitalizations. This study examined the relationship between medication adherence and CAD-related hospitalizations immediately following cancer diagnosis. Patients and Methods: A retrospective observational longitudinal study was conducted using SEER-Medicare data. Elderly Medicare fee-for-service beneficiaries with preexisting CAD and incident breast, colorectal, or prostate cancer (N=12,096) were observed for 12 months before and after cancer diagnosis. Hospitalizations measured every 120 days were categorized into CAD-related hospitalization, other hospitalization, and no hospitalization. Medication adherence was categorized into 5 mutually exclusive groups: adherent to both statins and ACEIs/ARBs/β-blockers (reference group), not adherent to both statins and ACEIs/ARBs/β-blockers, adherent to either statins or ACEIs/ARBs/β-blockers, use of one medication class and adherent to that class, and use of one medication class and not adherent to that class. The relationship between medication adherence and hospitalization was analyzed using repeated measures multinomial logistic regressions. Inverse probability treatment weights were used to control for observed group differences among medication adherence categories. Results: Adherence to both statins and ACEIs/ARBs/β-blockers was estimated at 31.2% during the 120-day period immediately following cancer diagnosis; 13.7% were not adherent to both medication classes during the same period, and 27.4% had CAD-related hospitalizations immediately after cancer diagnosis, which declined to 10.6% during the last 4 months of the postdiagnosis period. In the adjusted analyses, those not adherent to both statins and ACEIs/ARBs/β-blockers were more likely to have CAD-related hospitalization compared with those adherent to both medication classes (adjusted odds ratio, 1.82; 95% CI, 1.72-1.92; P<.0001). Conclusions: Given the complexity of interaction between CAD and cancer, it is important to routinely monitor medication adherence in general clinical practice and to provide linkages to support services that can increase medication adherence.

Project description:ObjectiveThere have been no validated Medicare claims-based algorithms available to identify epilepsy by discrete etiology of stroke (e.g., post-stroke epilepsy, PSE) in community-dwelling elderly individuals, despite the increasing availability of large datasets. Our objective was to validate algorithms that detect which patients have true PSE.MethodsWe linked electronic health records (EHR) to Medicare claims from a Medicare Pioneer Accountable Care Organization (ACO) to identify PSE. A neurologist reviewed 01/2012-12/2014 EHR data from a stratified sample of Medicare patients aged 65+ years to adjudicate a reference-standard to develop an algorithm for identifying patients with PSE. Patient sampling strata included those with: A) epilepsy-related claims diagnosis (n = 534 [all]); B) no diagnosis but neurologist visit (n = 500 [randomly sampled from 4346]); C) all others (n = 500 [randomly sampled from 16,065]). We reconstructed the full sample using inverse probability sampling weights; then used half to derive algorithms and assess performance, and the remainder to confirm performance. We evaluated predictive performance across several measures, e.g., specificity, sensitivity, negative and positive predictive values (NPV, PPV). We selected our best performing algorithms based on the greatest specificity and sensitivity.ResultsOf 20,943 patients in the reconstructed sample, 13.6% of patients with epilepsy had reference-standard PSE diagnosis, which represents a 3-year overall prevalence of 0.28% or 28/10,000, and a prevalence within the subpopulation with stroke of 3%. The best algorithm included three conditions: (a) at least one cerebrovascular claim AND one epilepsy-specific anticonvulsant OR (b) at least one cerebrovascular claim AND one electroencephalography claim (specificity 100.0% [95% CI 99.9%-100.0%], NPV 98.8% [98.6%-99.0%], sensitivity 20.6% [95% CI 14.6%-27.9%], PPV 86.5% [95% CI 71.2%-95.5%]).ConclusionMedicare claims can identify elderly Medicare beneficiaries with PSE with high accuracy. Future epidemiological surveillance of epilepsy could incorporate similar algorithms to accurately identify epilepsy by varying etiologies.

Dataset Information

Epilepsy Among Elderly Medicare Beneficiaries: A Validated Approach to Identify Prevalent and Incident Epilepsy.

Publications

Epilepsy Among Elderly Medicare Beneficiaries: A Validated Approach to Identify Prevalent and Incident Epilepsy.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets