Do Reimbursement Recommendations by the Canadian Agency for Drugs and Technology in Health Translate Into Coverage Decisions for Orphan Drugs in the Canadian Province of Ontario?
Bibliographic record
Abstract
OBJECTIVES: Unlike other high-income countries, Canada has no national policy for drugs treating rare diseases (orphan drugs). Nevertheless, in 2022, the Canadian government committed to creating a national strategy to make access to these drugs more consistent. Our aim was to study whether recommendations made by the Canadian Agency for Drugs and Technology in Health (CADTH) translated into coverage decisions for orphan drugs in Ontario, the largest Canadian province. This study is the first to look at this question for orphan drugs, which are at the center of policy attention. METHODS: We included 155 orphan drug-indication pairs approved and marketed in Canada between October 2002 and April 2022. Cohen's kappa was used to test the agreement across health technology assessment (HTA) recommendations and coverage decisions in Ontario. Logistic regression was used to test which factors, relevant to decision-makers, might be associated with funding in Ontario. RESULTS: We found only fair agreement between CADTH's recommendations and coverage decisions in Ontario. Although a positive and statistically significant association between favorable HTA recommendations and coverage was found, more than half of the drugs with a negative HTA recommendation were available in Ontario, predominately through specialized funds. Successful pan-Canadian pricing negotiations were a strong predictor of coverage in Ontario. CONCLUSIONS: Despite efforts to harmonize access to drugs across Canada, considerable room for improvement remains. Introducing a national strategy for orphan drugs could help increase transparency, consistency, promote collaborations, and make access to orphan drugs a national priority.
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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 | 0.009 | 0.075 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".