Validation of an algorithm for identifying MS cases in administrative health claims datasets

William J Culpepper; Ruth Ann Marrie; Annette Langer-Gould; Mitchell T Wallin; Jonathan D Campbell; Lorene M Nelson; Wendy E Kaye; Laurie Wagner; Helen Tremlett; Lie H Chen; Stella Leung; Charity Evans; Shenzhen Yao; Nicholas G LaRocca; United States Multiple Sclerosis Prevalence Workgroup (MSPWG)

doi:10.1212/WNL.0000000000007043

Validation of an algorithm for identifying MS cases in administrative health claims datasets

Neurology. 2019 Mar 5;92(10):e1016-e1028. doi: 10.1212/WNL.0000000000007043. Epub 2019 Feb 15.

Authors

Collaborators

United States Multiple Sclerosis Prevalence Workgroup (MSPWG):
Albert Lo, Robert McBurney, Oleg Muravov, Bari Talente, Leslie Ritter

Affiliations

¹ From the Department of Veterans Affairs Post Deployment Health Services (W.J.C., M.T.W.), Multiple Sclerosis Center of Excellence; University of Maryland (W.J.C.), Baltimore; Departments of Internal Medicine and Community Health Sciences (R.A.M., S.L.), Max Rady College of Medicine, Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, Canada; Neurology Department (A.L.-G., L.H.C.), Kaiser Permanente Southern California, Los Angeles; Georgetown University School of Medicine (M.T.W.), Washington, DC; University of Colorado (J.C.), Denver; Stanford University School of Medicine (L.M.N.), CA; McKing Consulting Corp (W.E.K., L.W.), Atlanta, GA; Faculty of Medicine (Neurology) and Centre for Brain Health (H.T.), University of British Columbia, Vancouver; College of Pharmacy and Nutrition (C.E.), University of Saskatchewan; Health Quality Council (Saskatchewan) (S.Y.), Saskatoon, Canada; and National Multiple Sclerosis Society (N.G.L.), New York, NY. William.Culpepper@va.gov.
² From the Department of Veterans Affairs Post Deployment Health Services (W.J.C., M.T.W.), Multiple Sclerosis Center of Excellence; University of Maryland (W.J.C.), Baltimore; Departments of Internal Medicine and Community Health Sciences (R.A.M., S.L.), Max Rady College of Medicine, Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, Canada; Neurology Department (A.L.-G., L.H.C.), Kaiser Permanente Southern California, Los Angeles; Georgetown University School of Medicine (M.T.W.), Washington, DC; University of Colorado (J.C.), Denver; Stanford University School of Medicine (L.M.N.), CA; McKing Consulting Corp (W.E.K., L.W.), Atlanta, GA; Faculty of Medicine (Neurology) and Centre for Brain Health (H.T.), University of British Columbia, Vancouver; College of Pharmacy and Nutrition (C.E.), University of Saskatchewan; Health Quality Council (Saskatchewan) (S.Y.), Saskatoon, Canada; and National Multiple Sclerosis Society (N.G.L.), New York, NY.

Abstract

Objective: 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.

Publication types

Multicenter Study
Research Support, Non-U.S. Gov't
Validation Study

MeSH terms

Adolescent
Adult
Aged
Algorithms*
Databases, Factual
Diagnosis, Computer-Assisted*
Female
Humans
Male
Medical Records*
Middle Aged
Multiple Sclerosis / diagnosis*
Multiple Sclerosis / epidemiology
Observer Variation
Prevalence
Retrospective Studies
Sensitivity and Specificity
Young Adult

Abstract

Publication types

MeSH terms

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