Replication of a Claims-based Algorithm to Estimate Multiple Sclerosis Disease Severity in a Commercially Insured Population

Joshua Toliver; Jamie C Barner; Kenneth Lawson; Kalyani Sonawane; Karen Rascati

doi:10.1016/j.msard.2020.102539

Replication of a Claims-based Algorithm to Estimate Multiple Sclerosis Disease Severity in a Commercially Insured Population

Mult Scler Relat Disord. 2020 Nov:46:102539. doi: 10.1016/j.msard.2020.102539. Epub 2020 Sep 28.

Authors

Joshua Toliver¹, Jamie C Barner², Kenneth Lawson³, Kalyani Sonawane⁴, Karen Rascati⁵

Affiliations

¹ The University of Texas at Austin, Austin, TX, USA. Electronic address: joshuatoliver@utexas.edu.
² The University of Texas at Austin, Austin, TX, USA. Electronic address: Jbarner@austin.utexas.edu.
³ The University of Texas at Austin, Austin, TX, USA. Electronic address: ken.lawson@austin.utexas.edu.
⁴ The University of Texas Health Science Center at Houston, Houston, TX, USA. Electronic address: Kalyani.B.Sonawane@uth.tmc.edu.
⁵ The University of Texas at Austin, Austin, TX, USA. Electronic address: krascati@mail.utexas.edu.

PMID: 33032054
DOI: 10.1016/j.msard.2020.102539

Abstract

Background/objective: Patients with multiple sclerosis (MS) tend to have significantly lower health-related quality of life, increased mortality and morbidity, and increased healthcare costs. The lack of a claims-based algorithm to correctly identify disease severity makes targeted selection of the MS patients for specific interventions an important limitation in real-world MS research.

Methods: Using the Optum claims dataset (2016 -2018), 11,429 persons with MS and >= 24 months of eligibility were identified. A previously developed claims-based algorithm was employed to categorize MS disease severity (low, moderate, high), using MS symptoms and healthcare utilization. Linear regression analysis was used to determine the relationship between disease severity and total cost, a proxy for disease severity. Flexible parametric models were used to determine the risk of 12-month follow-up MS-related relapses and MS-related hospitalizations among the MS disease severity groups.

Results: The risk of both MS-related relapses and MS-related hospitalizations increased as MS disease severity increased. The risk for MS-related relapses was significantly higher in the moderate (HR = 2.43, 95% CI 1.24 -2.64, P < 0.001) and high (HR = 5.97, 95% CI 5.19-6.83, P < 0.001) disease severity groups compared to the low disease severity group. The same trend is observed concerning MS-related hospitalization risk. Both the moderate (HR = 3.16, 95% CI 2.73-3.63, P> 0.001) and high disease severity (HR =12.70, 95% CI 10.57-15.09, P < 0.001) groups had significantly higher risk of MS-related hospitalization compared to the low disease severity group.

Conclusion: The claims-based disease severity algorithm performed well in explaining the total healthcare cost (excluding DMTs). The algorithm-determined disease severity categorization appears consistent with traditional measures of disease severity (MS relapse and hospitalizations). This claims-based algorithm may be a useful tool in determining MS disease severity in claims data.

Keywords: Algorithm; Costs; Disease severity; Multiple sclerosis; Retrospective database.

Publication types

Review

MeSH terms

Algorithms
Humans
Multiple Sclerosis* / diagnosis
Multiple Sclerosis* / epidemiology
Multiple Sclerosis* / therapy
Quality of Life
Retrospective Studies
Severity of Illness Index