Health technology assessment and price negotiation alignment for rare disorder drugs in Canada: Who benefits?
Bibliographic record
Abstract
BACKGROUND: Since 2014, the Canadian Agency for Drugs and Technologies in Health (CADTH), which performs health technology assessments for all federal, provincial and territorial government drug programs (except Quebec's) and the pan-Canadian Pharmaceutical Alliance (pCPA), which conducts price negotiations with manufacturers for all government drug programs, have been aligning their processes. OBJECTIVE: To examine trends in CADTH recommendations for non-oncology drugs for rare disorders (DRDs) released between 2014 and 2021, results of pCPA negotiations for the same drugs, and listings in government drug plans to assess who benefits from the alignment. RESULTS: Recommendations were positive in 87% of the reviews, although all included clinical criteria for use and/or economic conditions. Almost 90% of the DRDs with a positive recommendation had a successful price negotiation and 71% of those with a negative recommendation had no negotiation. Although no recommendation published before mid-2016 had a specified price reduction, almost 95% of those issued afterwards included the price reduction required to achieve a specific low cost-effectiveness threshold. The median time between the DRDs receiving marketing approval and a completed price negotiation was 663 days. Negotiations for DRDs completed after 2017 generally had fewer listings in government drug plans, but there was no distinct trend. The drug's price likely played a role in listing decisions. When DRDs were listed, drug plans had access criteria consistent with CADTH's or stronger for all the DRDs. CONCLUSIONS: The governments who own, fund and manage CADTH and the pCPA benefit from their alignment. The alignment is less beneficial for patients waiting for access to the DRDs. The time taken by CADTH and pCPA actions and individual government drug plans to make listing decisions delays access. CADTH's clinical criteria have become more extensive and are applied rigorously by drug plans which restricts patient access to DRDs. Canadians with rare disorders urgently need their governments to implement a long-overdue, comprehensive rare disease strategy to ensure DRDs are reviewed and reimbursed quickly and equitably to provide adequate health care to all who need them.
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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 | 0.059 | 0.218 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.008 | 0.015 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| 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".