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dc.contributor.authorCulpepper, W.J.
dc.contributor.authorMarrie, R.A.
dc.contributor.authorLanger-Gould, A.
dc.date.accessioned2019-09-13T17:02:33Z
dc.date.available2019-09-13T17:02:33Z
dc.date.issued2019
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85062393622&doi=10.1212%2fWNL.0000000000007043&partnerID=40&md5=93eaa969db43545ee5716b84181b28c4
dc.identifier.urihttp://hdl.handle.net/10713/10816
dc.description.abstractObjective: To develop a valid algorithm for identifying multiple sclerosis (MS) cases in administrative health claims (AHC) datasets. Methods: We used 4 AHC datasets from the Veterans Administration (VA), Kaiser Permanente Southern California (KPSC), Manitoba (Canada), and Saskatchewan (Canada). In the VA, KPSC, and Manitoba, we tested the performance of candidate algorithms based on inpatient, outpatient, and disease-modifying therapy (DMT) claims compared to medical records review using sensitivity, specificity, positive and negative predictive values, and interrater reliability (Youden J statistic) both overall and stratified by sex and age. In Saskatchewan, we tested the algorithms in a cohort randomly selected from the general population. Results: The preferred algorithm required ≥3 MS-related claims from any combination of inpatient, outpatient, or DMT claims within a 1-year time period; a 2-year time period provided little gain in performance. Algorithms including DMT claims performed better than those that did not. Sensitivity (86.6%–96.0%), specificity (66.7%–99.0%), positive predictive value (95.4%–99.0%), and interrater reliability (Youden J = 0.60–0.92) were generally stable across datasets and across strata. Some variation in performance in the stratified analyses was observed but largely reflected changes in the composition of the strata. In Saskatchewan, the preferred algorithm had a sensitivity of 96%, specificity of 99%, positive predictive value of 99%, and negative predictive value of 96%. Conclusions: The performance of each algorithm was remarkably consistent across datasets. The preferred algorithm required ≥3 MS-related claims from any combination of inpatient, outpatient, or DMT use within 1 year. We recommend this algorithm as the standard AHC case definition for MS.en_US
dc.description.urihttps://doi.org/10.1212/WNL.0000000000007043en_US
dc.language.isoen-USen_US
dc.publisherLippincott Williams and Wilkinsen_US
dc.relation.ispartofNeurology
dc.subject.meshAlgorithmsen_US
dc.subject.meshAdministrative Claims, Healthcareen_US
dc.subject.meshMultiple Sclerosisen_US
dc.titleValidation of an algorithm for identifying MS cases in administrative health claims datasetsen_US
dc.typeArticleen_US
dc.identifier.doi10.1212/WNL.0000000000007043
dc.identifier.pmid30770432


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