Alignment of health technology assessments and price negotiations for new drugs for rare disorders in Canada: Does it lead to improved patient access?
Bibliographic record
Abstract
A previous assessment of submissions for rare disorder drugs made to the Canadian Agency for Drugs and Technologies in Health (CADTH) found that, from 2012, all positive recommendations included criteria advocating a price reduction. Since 2016, CADTH and the pan-Canadian Pharmaceutical Alliance (pCPA), which conducts drug price negotiations with manufacturers for all public drug programs, have aligned their processes. This analysis examined drugs for rare and ultra-rare disorders (DRDs and DURDs)-prevalence of ≤20 to >2 and ≤2 per 100,000, respectively-with a completed pCPA negotiation or no negotiation between 2014 and 2018, together with their reimbursement recommendations and listings in public drug programs. A positive recommendation led to a successful price negotiation for 81.8% and 78.6% of the DRD and DURD submissions and a negative recommendation to no negotiation for 100.0% and 66.7%. Less than half the recommendations for DURDs reported before 2016 mentioned the need for a substantial price reduction, but this increased to 80% in those reported from 2016 onwards. A successful price negotiation led to listing in the majority of the public drug programs and a negative recommendation usually led to no listing. The CADTH-pCPA alignment is working for the governments who own and fund public drug programs but has yet to lead to coverage for all appropriate patients in all provinces. There is still a way to go to ensure that patients with unmet needs can access high-cost innovative medicines that alleviate suffering, prevent premature death, and/or significantly improve their quality of life.
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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 | 0.072 | 0.240 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.011 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.014 | 0.006 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 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".