MétaCan
Menu
Back to cohort

Variation in market access decisions for cell and gene therapies across the United States, Canada, and Europe

2021· article· en· W3207081274 on OpenAlexaboutno aff
Sean Tunis, Eve Hanna, Peter J. Neumann, Mondher Toumi, Omar Dabbous, Michael Drummond, Frank-Ulrich Fricke, Sean D. Sullivan, Daniel C. Malone, Ulf Persson, James D. Chambers

Bibliographic record

VenueHealth Policy · 2021
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHealth Systems, Economic Evaluations, Quality of Life
Canadian institutionsnot available
FundersTufts Medical Center
KeywordsHealth technologyContext (archaeology)Market accessBusinessTransformative learningHealth carePublic economicsMedicineEconomic growthEconomicsPsychology

Abstract

fetched live from OpenAlex

Transformative cell and gene therapies have now launched worldwide, and many potentially curative cell and gene therapies are in development, offering the prospect of significant health gains for patients. Access to these therapies depend on decisions made by health technology assessment (HTA) and payer organizations. We sought to describe the emerging cell and gene therapies market access landscape by analyzing 17 US commercial payer medical policies, and HTA reports from five European countries and Canada. We found that some US health plans applied coverage restrictions more often than others (four plans applied restrictions in all decisions, while four plans applied restrictions in <30% of decisions). The European and Canadian HTA bodies recommend access to fewer therapies than US health plans, reflecting a more stringent approach in the context of limited evidence and high scientific uncertainty that is commonly associated with these treatments. Our findings suggest that patient access to approved cell and gene therapies is restricted in all regions studied, though the nature of these restrictions differs between US health plans and the European/Canada HTA recommendations. Payers, HTA groups, pharmaceutical companies, and other stakeholders should collaborate to more clearly define the "uncertainties" and develop market access policies that balance benefits of early access with ongoing data collection to close evidence gaps over time.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.008
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.293
Threshold uncertainty score0.478

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.319
GPT teacher head0.478
Teacher spread0.159 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations26
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueHealth PolicySame topicHealth Systems, Economic Evaluations, Quality of LifeFrench-language works237,207