Variation in market access decisions for cell and gene therapies across the United States, Canada, and Europe
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.013 | 0.045 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".