OBJECTIVES: Prior research has employed claims-based algorithms to measure multiple sclerosis (MS) disease severity. This study estimates the risk of traditional claims-based disease severity measures (relapses and hospitalizations) across algorithm determined disease severity levels.

METHODS: A sample of 6,559 patients was identified from Humana Medicare claims data from 1/1/2013-12/31/2015. Patients with MS, over the age of 18 with ≥2 years of continuous enrollment were included. A previously developed algorithm was used to categorize MS disease severity (low, moderate, high). Patients were divided into two groups based on Medicare eligibility, the age-eligible (over 65) and the disability-eligible (under 65) groups. Flexible parametric and cox proportional models were used to estimate the hazard ratios (HR) for relapse and MS-related hospitalization associated with disease severity and Medicare eligibility.

RESULTS: The disability-eligible group had higher risk for MS relapses (HR = 1.76, 95% CI 1.53-2.02) and hospitalizations (HR = 1.38, 95% CI 1.13-1.69) compared to those in the age-eligible group. The risk for MS relapses increased in the moderate (HR = 1.89, 95% CI 1.68-2.14) and high (HR = 3.69, 95% CI 3.09-4.41) disease severity groups compared to the low disease severity group. The same pattern was observed in MS-related hospitalizations, both the moderate (HR = 1.83, 95% CI 1.49-2.25) and high disease severity (HR = 6.42, 95% CI 5.07-8.13) groups had increased risk compared to the low disease severity reference group. When both eligibility and disease severity were considered simultaneously, the risk increased for both relapses and hospitalizations as disease severity increased. The eligibility types, however, did not appear to have a clear pattern across MS risk groups for either MS relapse or hospitalizations.

CONCLUSIONS: Among MS patients, an algorithm-determined disease severity categorization appears consistent with relapses and hospitalizations, traditional claims-based measures of disease severity.