An international comparative analysis of public reimbursement of orphan drugs in Canadian provinces compared to European countries
Bibliographic record
Abstract
BACKGROUND: The Canadian government has committed to developing a national strategy for drugs for rare diseases starting in 2022. Considering this announcement, we conducted a comparative analysis to examine patient access to therapies for rare disease in Canada relative to Europe and the U.S. METHODS: Given its similarity to the Canadian health care system, we used Europe as the reference point to analyze all of the therapies with an orphan drug designation approved by the European Medicine Agency (EMA) from 1 January 2015 to 31 March 2020. We then contrasted access to these drugs in Canada (Health Canada) and the U.S. (Food and Drug Administration, FDA). We focused on: (1) the number of therapies for rare diseases entering the Canadian market; (2) the percentage of these therapies that are publicly available to Canadians; and (3) the timelines for patients to access these therapies in Canada. RESULTS: Sixty-three approved therapies with an orphan drug designation from the EMA were identified. Fifty-three (84%) of these drugs had also been submitted to the FDA for approval, and 41 (65%) were submitted to Health Canada for approval. In Europe, Germany, Denmark, and the U.K. had the highest percentage of publicly reimbursed orphan drugs (84%, 70%, 68%, respectively). In comparison, Ontario (32%), Quebec (25%), and Alberta (25%) had the highest percentage of drugs reimbursed among the Canadian provinces. The shortest median duration (in months) from EMA approval to jurisdictional decision on reimbursement was in Austria (3.2), followed by Germany (4.1), and Finland (6.0). In Canada, the shortest median duration (in months) from regulatory approval to reimbursement was in British Columbia (17.3), Quebec (19.6) and Manitoba (19.6), while the longest duration was in P.E.I (38.5), followed by Nova Scotia (25.9), and Newfoundland (25.1). CONCLUSIONS: Our comparative analysis found that relative to the EU Canadians had less frequent and timely access to therapies for rare diseases. This highlights the need for a rare disease strategy in Canada that allows for clear identification and transparent tracking of the pathway for rare disease drugs, and ultimately optimizes the number of patients with access to these therapies.
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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 | 0.005 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.022 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".