Access to drugs for rare diseases (DRD) in Canada: a comprehensive review of the provincial DRD-specific programs
Bibliographic record
Abstract
Access to drugs for rare diseases (DRDs) in Canada depends largely on the province of living and the specific disease. The federal government and each of the ten provincial governments have their own drug review processes to determine DRD coverage, resulting in a fragmented system. This fragmentation leads to disparities in coverage, delays in treatment access, and exacerbates the challenges faced by patients with rare diseases (RDs) seeking lifesaving treatments. Canada lags behind many developed countries in adopting a national strategy for DRDs. In 2019, the Canadian government announced a plan to develop such a strategy to improve access, affordability, and consistency. A plan was launched in March 2023, though details were sparse. Although a pilot strategy involving two DRDs began in July 2024 through a bilateral agreement between the federal government and British Columbia, there remains an opportunity for Canada to learn from both domestic and international approaches. By examining these strategies, Canada is well-positioned to develop a robust, evidence-based national policy. Employing a qualitative document review approach, this study examined and compared the specific programs that target DRDs, which have been implemented in five provinces-Ontario (ON), Alberta (AB), New Brunswick (NB), British Columbia (BC), and Saskatchewan (SK)-to facilitate access to DRDs. The study, conducted between February 2019 and April 2022, reviewed policy documents related to these programs (referred to as DRD-SPs), as these programs have not been formally evaluated. The review revealed that AB and NB have active DRD-SPs, while ON's DRD-SP framework has been terminated. Despite reliable sources indicating otherwise, SK does not have a DRD-SP. The status of BC's DRD-SP, if one exists, remained unclear. The investigation into DRD-SPs demonstrated the creation of an uncoordinated, inefficient, patchwork of programs that often neither covered all DRDs, nor provided clear guidelines for accessing them. These policies lacked defined objectives and performance measures for periodic review. A unified, evidence-based national DRD policy is needed to ensure consistent and timely access, incorporating insights from both domestic and international approaches.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".