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

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.