Outcomes-based reimbursement for gene therapies in practice: the experience of recently launched CAR-T cell therapies in major European countries
Bibliographic record
Abstract
Background: The experience of Kymriah® and Yescarta® provides real-world examples of how health-care systems approach and manage the reimbursement of one-off, high-cost, cell, and gene therapies, and the decision uncertainty and affordability challenges they present. Objective: To provide an overview of the reimbursement schemes used for Kymriah® and Yescarta® in France, Germany, Italy, Spain, and the UK (EU5) as per the final quarter of 2019; to identify challenges and derive learnings for future product launches. Methodology: Secondary research, complemented by primary research with key market access stakeholders. Findings: Kymriah® and Yescarta® have relatively uniform list prices across the EU5, and are reimbursed according to their marketing authorisations. In France and the UK, reimbursement is on the condition of collecting additional data (at the cohort level) and subject to future reassessments; elsewhere, rebates (Germany) or staged payments (Italy and Spain) are linked to individual patient outcomes. Conclusions: The experience of Kymriah® and Yescarta® shows an increased appetite for outcomes-based reimbursement (OBR) in the EU5, with notably novel approaches applied in Italy and Spain (outcomes-based staged payments). Thus, real-world evidence (RWE) has become an increasingly powerful lever for demonstrating the value of health benefits in the clinical setting.
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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 | 0.043 | 0.058 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".