Systematic Literature Review on Economic Evaluations of Disease-Modifying Treatments in Multiple Sclerosis: Current Insights and Future Directions

OBJECTIVES: Disease-modifying treatments (DMTs) for multiple sclerosis (MS) pose a challenge due to balancing high costs with clinical benefits and selecting the best option from available treatments. Economic evaluations are essential for assessing cost-effectiveness and guiding the equitable use of DMTs in healthcare systems. We conducted a systematic literature review (SLR) to assess economic evaluations of DMTs for MS by focusing on temporal trends, presenting the latest models, and evaluating their alignment with clinical practice.

METHODS: An SLR was performed following the methodology recommended by the NICE guidelines and reported in accordance with the PRISMA statement. We searched the following databases: PubMed, Embase, Cochrane CENTRAL, NHS Economic Evaluations Database, HTA Database, and EconLit, including all full-text English studies up to December 2022. Study selection was performed by two independent reviewers. Data extraction was conducted by one reviewer and verified by a second independent reviewer. Discrepancies were resolved through reviewer discussion. Extracted data included model characteristics (e.g., modelling approach, perspective, time horizon, and health states), clinical model inputs (e.g., disease natural history and quality of life), costs, and underlying model assumptions.

RESULTS: A total of 120 studies were included, primarily comprising cost-effectiveness and cost-utility analyses, with some budget impact and cost minimization analyses. Most models were based on the Expanded Disability Status Scale (EDSS).

CONCLUSIONS: The reviewed economic evaluations exhibit similarities and differences. Recent updates include incorporating disutilities for caregivers and treatment-related symptoms, while emphasizing the need for additional clinically relevant outcomes, such as cognitive impairment. EDSS-based models primarily focus on physical disability, missing the disease’s full impact. Studies also highlight gaps between existing economic models and clinical practice, often excluding treatment switching due to insufficient data. Addressing these gaps can enhance model robustness, better reflecting real-world complexities and the true impact on patients, which is crucial for optimizing healthcare decision-making.