MétaCan
Menu
Back to cohort
Record W4225016550 · doi:10.51731/cjht.2022.321

Harmonization of Public Coverage Policies for Biologic Drugs in the Treatment of Rheumatoid Arthritis

2022· article· en· W4225016550 on OpenAlexaboutno aff
Chris Vannabouathong, Peter Dyrda

Bibliographic record

VenueCanadian Journal of Health Technologies · 2022
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicPharmaceutical Economics and Policy
Canadian institutionsnot available
FundersU.S. Department of Veterans Affairs
KeywordsVeterans AffairsMedicinePublic administrationPolitical scienceInternal medicine

Abstract

fetched live from OpenAlex

For rheumatoid arthritis (RA), treatment guidelines and clinical evidence support the combination (either dual or triple) of conventional synthetic disease-modifying antirheumatic drugs (csDMARDs) before accessing a biologic DMARD (bDMARD). Federal, provincial, and territorial (FPT) drug plans currently have different coverage criteria for bDMARD eligibility in RA. To align their criteria, these plans should consider the inclusion of at least 1 line of combination csDMARDs before a bDMARD: For dual csDMARDs: Saskatchewan, Veterans Affairs Canada, and Canadian Armed Forces would need to change their current coverage criteria to include at least 1 line of dual csDMARDs before access to a bDMARD. For triple csDMARDs: Alberta, Saskatchewan, Manitoba, Veterans Affairs Canada, and Canadian Armed Forces would need to alter their current coverage criteria because each of these FPT drug plans only consider csDMARD monotherapy or dual csDMARDs in their current coverage criteria. Most FPT drug plans require a failure of at least 2 lines to 3 lines of csDMARD therapy before a bDMARD, except British Columbia, Ontario, Newfoundland and Labrador, Veterans Affairs Canada, and Canadian Armed Forces, which offer an option to access bDMARDs after 1 line of combination csDMARDs. British Columbia, Ontario, the Atlantic provinces, Yukon, Correctional Service of Canada, and Non-Insured Health Benefits include triple csDMARDs in their coverage criteria. Alberta, Manitoba, Veterans Affairs Canada, and Canadian Armed Forces include dual, but not triple, csDMARDs in their criteria; however, Veterans Affairs Canada and Canadian Armed Forces do not require a trial of dual csDMARDs if 2 lines of csDMARD monotherapy have been attempted. Saskatchewan is the only jurisdiction that only requires csDMARD monotherapy. Canadian private insurers have also reached a consensus to implement a trial requirement of dual csDMARDs before a bDMARD across their formularies. Evidence-based guidelines, including the 2012 Canadian Rheumatology Association guidelines, recommend csDMARD monotherapy (methotrexate [MTX] is preferred unless contraindicated) as first-line treatment for RA, although a guideline published in 2018 by the Brazilian Society of Rheumatology stated that combination therapy with 2 or more csDMARDs may also be used as a first-line treatment. These guidelines generally recommend combination csDMARDs after csDMARD monotherapy is deemed ineffective. A network meta-analysis found that triple csDMARDs is more efficacious than dual csDMARDs, etanercept monotherapy, and 4 mg/kg tocilizumab monotherapy and comparable to other bDMARDs (alone or in combination with MTX), targeted synthetic DMARDs in combination with MTX, and biosimilars in combination with MTX. Additionally, economic evidence demonstrated that triple csDMARDs is more cost-effective than etanercept plus MTX combination therapy. Time to first bDMARD was, on average, longer in Alberta, British Columbia, and Ontario than in Saskatchewan, Manitoba, and the Atlantic provinces by approximately 4 months, which may be partially explained by differences in coverage criteria for the number of prior lines of csDMARD therapy required. Increasing the time to initiating a bDMARD could lead to budget savings without impacting clinical outcomes.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.108
metaresearch head score (Gemma)0.117
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.976
Threshold uncertainty score0.572

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1080.117
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.003
Bibliometrics0.0050.004
Science and technology studies0.0030.003
Scholarly communication0.0100.005
Open science0.0070.008
Research integrity0.0100.008
Insufficient payload (model declined to judge)0.0120.003

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.094
GPT teacher head0.294
Teacher spread0.200 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueCanadian Journal of Health TechnologiesSame topicPharmaceutical Economics and PolicyFrench-language works237,207