Estimation of Transition Probabilities from a Large Cohort (> 6000) of Australians Living with Multiple Sclerosis (MS) for Changing Disability Severity Classifications, MS Phenotype, and Disease-Modifying Therapy Classifications

Julie A Campbell; Glen J Henson; Valery Fuh Ngwa; Hasnat Ahmad; Bruce V Taylor; Ingrid van der Mei; MS Base Australian Researchers; Andrew J Palmer

doi:10.1007/s40273-024-01417-4

Estimation of Transition Probabilities from a Large Cohort (> 6000) of Australians Living with Multiple Sclerosis (MS) for Changing Disability Severity Classifications, MS Phenotype, and Disease-Modifying Therapy Classifications

Pharmacoeconomics. 2024 Aug 2. doi: 10.1007/s40273-024-01417-4. Online ahead of print.

Authors

Julie A Campbell^#¹, Glen J Henson^#², Valery Fuh Ngwa², Hasnat Ahmad², Bruce V Taylor², Ingrid van der Mei²; MS Base Australian Researchers; Andrew J Palmer³

Affiliations

¹ Medical Sciences Precinct, Menzies Institute for Medical Research, University of Tasmania, 17 Liverpool Street, Hobart, TAS, 7000, Australia. [email protected].
² Medical Sciences Precinct, Menzies Institute for Medical Research, University of Tasmania, 17 Liverpool Street, Hobart, TAS, 7000, Australia.
³ Medical Sciences Precinct, Menzies Institute for Medical Research, University of Tasmania, 17 Liverpool Street, Hobart, TAS, 7000, Australia. [email protected].

^# Contributed equally.

PMID: 39095665
DOI: 10.1007/s40273-024-01417-4

Abstract

Background: Multiple sclerosis (MS) is a chronic autoimmune/neurodegenerative disease associated with progressing disability affecting mostly women. We aim to estimate transition probabilities describing MS-related disability progression from no disability to severe disability. Transition probabilities are a vital input for health economics models. In MS, this is particularly relevant for pharmaceutical agency reimbursement decisions for disease-modifying therapies (DMTs).

Methods: Data were obtained from Australian participants of the MSBase registry. We used a four-state continuous-time Markov model to describe how people with MS transition between disability milestones defined by the Expanded Disability Status Scale (scale 0-10): no disability (EDSS of 0.0), mild (EDSS of 1.0-3.5), moderate (EDSS of 4.0-6.0), and severe (EDSS of 6.5-9.5). Model covariates included sex, DMT usage, MS-phenotype, and disease duration, and analysis of covariate groups were also conducted. All data were recorded by the treating neurologist.

Results: A total of N = 6369 participants (mean age 42.5 years, 75.00% female) with 38,837 person-years of follow-up and 54,570 clinical reviews were identified for the study. Annual transition probabilities included: remaining in the no, mild, moderate, and severe states (54.24%, 82.02%, 69.86%, 77.83% respectively) and transitioning from no to mild (42.31%), mild to moderate (11.38%), and moderate to severe (9.41%). Secondary-progressive MS was associated with a 150.9% increase in the hazard of disability progression versus relapsing-remitting MS.

Conclusions: People with MS have an approximately 45% probability of transitioning from the no disability state after one year, with people with progressive MS transitioning from this health state at a much higher rate. These transition probabilities will be applied in a publicly available health economics simulation model for Australia and similar populations, intended to support reimbursement of a plethora of existing and upcoming interventions including medications to reduce progression of MS.

Abstract

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