RT Journal Article
SR Electronic
T1 Validation of an algorithm for identifying MS cases in administrative health claims datasets
JF Neurology
JO Neurology
FD Lippincott Williams & Wilkins
SP e1016
OP e1028
DO 10.1212/WNL.0000000000007043
VO 92
IS 10
A1 William J. Culpepper
A1 Ruth Ann Marrie
A1 Annette Langer-Gould
A1 Mitchell T. Wallin
A1 Jonathan D. Campbell
A1 Lorene M. Nelson
A1 Wendy E. Kaye
A1 Laurie Wagner
A1 Helen Tremlett
A1 Lie H. Chen
A1 Stella Leung
A1 Charity Evans
A1 Shenzhen Yao
A1 Nicholas G. LaRocca
A1 on behalf of the United States Multiple Sclerosis Prevalence Workgroup (MSPWG)
YR 2019
UL http://n.neurology.org/content/92/10/e1016.abstract
AB 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.AHC=administrative health claims; CI=confidence interval; DMT=disease-modifying therapy; ICD-9=International Classification of Disease, 9th revision; ICD-9-CM=International Classification of Disease, 9th revision, clinical modification; ICD-10-CA=International Classification of Disease, 10th revision, Canadian version; ICD-10-CM=International Classification of Disease, 10th revision, clinical modification; IMS=Intercontinental Marketing Services; IP=inpatient; KPSC=Kaiser Permanente Southern California; MS=multiple sclerosis; NPV=negative predictive value; OP=outpatient; PPV=positive predictive value; VA=Department of Veterans Affairs