Evidence map›Paper›PMID 36341111›Full record

ReviewFrontiers in neurology2022

Societal economic burden of multiple sclerosis and cost-effectiveness of disease-modifying therapies.

Steven Simoens

Abstract readReview
In one paragraph

Review in Frontiers in neurology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.

0numbers the graph read from it
0cells of the map it votes in
16citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

16 citing papers in PubMed.

  1. Trial
  2. Article
  3. Article
  4. Review
  5. Article
  6. Article
  7. Article
  8. Review
  9. Article
  10. Review
  11. Article
  12. Article
  13. Observational
  14. Article
  15. Article
  16. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

1 author.

Steven SimoensDepartment of Pharmaceutical and Pharmacological Sciences, KU Leuven, Leuven, Belgium.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: In an era of scarce resources, policy makers, neurologists and other stakeholders need to be aware of the economic burden of multiple sclerosis and the cost-effectiveness of disease-modifying therapies. The aim of this article is to provide a mini-review of these health economic facets of multiple sclerosis. Methods: An umbrella review was conducted by searching PubMed and Google Scholar from 2002 until June 2022 for peer-reviewed systematic and narrative literature reviews. Results: An extensive body of evidence corroborates that multiple sclerosis is associated with a substantial economic burden within and outside the health care sector, that costs of secondary progressive multiple sclerosis exceed those of relapsing-remitting multiple sclerosis, that costs increase with disease severity and are influenced by the occurrence of relapses and therapy adherence. However, cost estimates and their breakdown into various components vary between countries. Economic evaluations show that disease-modifying therapies for relapsing-remitting multiple sclerosis are generally not cost-effective, but these results depend on the local setting. Cost-effectiveness of disease-modifying therapies improves when a societal perspective is taken and efficacy does not wane over a lifetime horizon, when oral administration forms or dosing strategies requiring less maintenance are introduced, and when generic versions enter the market. Reimbursement recommendations related to disease-modifying therapies also differ between countries. Conclusion: The local context matters when calculating the societal economic burden of multiple sclerosis and the cost-effectiveness of disease-modifying therapies.

Indexed as

cost-effectivenesscostsdisease-modifying therapieseconomic burdenmultiple sclerosis

Identifiers

PMID36341111
PMCPMC9631775

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.