How have Economic Evaluations in Relapsing Multiple Sclerosis Evolved Over Time? A Systematic Literature Review.

Disease-modifying therapy Economic evaluation Health economics Multiple sclerosis Relapsing multiple sclerosis Systematic review

Journal

Neurology and therapy
ISSN: 2193-8253
Titre abrégé: Neurol Ther
Pays: New Zealand
ID NLM: 101637818

Informations de publication

Date de publication:
Dec 2021
Historique:
received: 02 06 2021
accepted: 06 07 2021
pubmed: 20 7 2021
medline: 20 7 2021
entrez: 19 7 2021
Statut: ppublish

Résumé

The introduction of disease-modifying therapies (DMTs) for relapsing multiple sclerosis (RMS) over the last two decades has prompted the economic assessments of these treatments by reimbursement authorities. The aim of this systematic literature review was to evaluate the modeling approach and data sources used in economic evaluations of DMTs for RMS, identify differences and similarities, and explore how economic evaluation models have evolved over time. MEDLINE®, Embase®, and EBM Reviews databases were searched using Ovid® Platform from database inception on 25 December 2019 and subsequently updated on 17 February 2021. In addition, health technology assessment agency websites, key conference proceedings, and gray literature from relevant websites were screened. The quality of included studies was assessed using the Drummond and Philips checklists. A total 155 publications and 30 Health Technology Assessment (HTA) reports were included. Most of these were cost-utility analysis (73 studies and 25 HTA reports) and funded by medicines manufacturers (n = 65). The top three countries where studies were conducted were the USA (n = 29), the UK (n = 16), and Spain (n = 10). Studies predominantly used Markov cohort models (94 studies; 25 HTAs) structured based on the Expanded Disability Status Scale (EDSS) with 21 health states (20 studies; 12 HTA reports). The London Ontario and British Columbia data sets were commonly used sources for natural history data (n = 33; n = 13). Twelve studies and ten HTAs from the UK assumed a waning of DMT effect over the long term, while this was uncommon in studies from other countries. Nineteen studies adjusted for multiple sclerosis (MS)-specific mortality estimates, while 18 studies used data from the national life table without adjustment. Studies prominently referred to mortality data that were about two decades old. The data on treatment effect was generally obtained from randomized controlled trials (43 studies; 7 HTAs) or from published evidence synthesis (23 studies; 24 HTAs). Utility estimates were derived from either published studies and/or supplemented with data from RCTs. Most of the models used the lifetime horizon (n = 37) with a 1-year cycle length (n = 63). As expected, similarities as well as differences were observed across the different economic models. Available evidence suggests models should continue using the Markov cohort model with 21 EDSS-based states, however, allowing the transition to a lower EDSS state and assuming a sustained treatment effect. With reference to the data sources, models should consider using a contemporary MS-specific mortality data, recent natural history data, and country-specific utility data if available. In case of data unavailability, a sensitivity analysis using multiple sources of data should be conducted. In addition, future models should incorporate other clinically relevant outcomes, such as the cognition, vision, and psychological aspects of RMS, to be able to present the comprehensive value of DMTs.

Identifiants

pubmed: 34279847
doi: 10.1007/s40120-021-00264-1
pii: 10.1007/s40120-021-00264-1
pmc: PMC8571458
doi:

Types de publication

Journal Article Review

Langues

eng

Pagination

557-583

Informations de copyright

© 2021. The Author(s).

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Auteurs

Anggie Wiyani (A)

Novartis Corporation (Malaysia) Sdn. Bhd., Petaling Jaya, Malaysia. anggie.wiyani@novartis.com.

Lohit Badgujar (L)

Novartis Healthcare Pvt. Ltd, Hyderabad, India.

Vivek Khurana (V)

Novartis Corporation (Malaysia) Sdn. Bhd., Petaling Jaya, Malaysia.

Nicholas Adlard (N)

Novartis Pharma AG, Basel, Switzerland.

Classifications MeSH