HTA decision-making for drugs for rare diseases: comparison of processes across countries
Bibliographic record
Abstract
Abstract Introduction Drugs for rare diseases (DRDs) offer important health benefits, but challenge traditional health technology assessment, reimbursement, and pricing processes due to limited effectiveness evidence. Recently, modified processes to address these challenges while improving patient access have been proposed in Canada. This review examined processes in 12 jurisdictions to develop recommendations for consideration during formal government-led multi-sectoral discussions currently taking place in Canada. Methods (i) A scoping review of DRD reimbursement processes, (ii) key informant interviews, (iii) a case study of evaluations for and the reimbursement status of a set of 7 DRDs, and (iv) a virtual, multi-stakeholder consultation retreat were conducted. Results Only NHS England has a process specifically for DRDs, while Italy, Scotland, and Australia have modified processes for eligible DRDs. Almost all consider economic evaluations, budget impact analyses, and patient-reported outcomes; but less than half accept surrogate measures. Disease severity, lack of alternatives, therapeutic value, quality of evidence, and value for money are factors used in all decision-making process; only NICE England uses a cost-effectiveness threshold. Budget impact is considered in all jurisdictions except Sweden. In Italy, France, Germany, Spain, and the United Kingdom, specific factors are considered for DRDs. However, in all jurisdictions opportunities for clinician/patient input are the same as those for other drugs. Of the 7 DRDs included in the case study, the number that received a positive reimbursement recommendation was highest in Germany and France, followed by Spain and Italy. No relationship between recommendation type and specific elements of the pricing and reimbursement process was found. Conclusions Based on the collective findings from all components of the project, seven recommendations for possible action in Canada are proposed. These focus on defining “appropriate access”, determining when a “full” HTA may not be needed, improving coordination among stakeholder groups, developing a Canadian framework for Managed Access Plans, creating a pan-Canadian DRD/rare disease data infrastructure, genuine and continued engagement of patient groups and clinicians, and further research on different decision and financing options, including MAPs.
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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 | 0.113 | 0.263 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.011 | 0.005 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".