Assessing the Value of Nusinersen for Spinal Muscular Atrophy: A Comparative Analysis of Reimbursement Submission and Appraisal in European Countries
Bibliographic record
Abstract
Background: Nusinersen is an orphan drug intended for the treatment of spinal muscular atrophy (SMA), a severe genetic neuromuscular disorder. Considering the very high costs of orphan drugs and the expected market entry of cell and gene therapies, there is increased interest in the use of health technology assessment (HTA) for orphan drugs. This study explores the role of the economic evaluation and budget impact analysis on the reimbursement of nusinersen. Methods: Appraisal reports for nusinersen were retrieved from reimbursement and HTA agencies in Belgium, Canada, France, England and Wales, Germany, Italy, Ireland, Scotland, Sweden, the Netherlands, and the United States. Detailed information was extracted on the economic evaluation, the budget impact, the overall reimbursement decision, and the managed entry agreement (MEA). Costs were adjusted for inflation and currency. Results: Overall, the reports included limited data on budget impact, excluding information on the sources of data for cost and patient estimates. Only three jurisdictions reported on total budget impact, estimated between 30 and 40 million euros per year. For early-onset SMA, the incremental cost-effectiveness threshold (ICER) ranged from €464,891 to €6,399,097 per quality-adjusted life year (QALY) gained for nusinersen versus standard of care. For later-onset SMA, the ICER varied from €493,756 to €10,611,936 per QALY. Although none of the jurisdictions found nusinersen to be cost-effective, reimbursement was granted in each jurisdiction. Remarkably, only four reports included arguments in favor of reimbursement. However, the majority of the jurisdictions set up an MEA, which may have promoted a positive reimbursement decision. Conclusion: There is a need for more transparency on the appraisal process and conditions included in the MEA. Additionally, by considering all relevant criteria explicitly during the appraisal process, decision-makers are in a better position to justify their allocation of funds among the rising number of orphan drugs that are coming to the market in the near future.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".