Reimbursement of Drugs for Rare Diseases through the Public Healthcare System in Canada: Where Are We Now?
Bibliographic record
Abstract
INTRODUCTION: Over the past 20 years, the number of therapies developed for rare diseases has rapidly increased. Often, these therapies represent the only active treatment for debilitating and/or life-threatening conditions. However, they create significant challenges for public and private payers. Because they target small patient populations, clinical evidence of efficacy/effectiveness is typically limited, while the cost per patient is high. In Canada, each province/territory establishes its own mechanisms for determining which drugs for rare diseases (DRDs) to provide. OBJECTIVES: To compare current mechanisms across provinces and territories, and explore their impact on access. METHODS: A systematic review of relevant published and unpublished documents was performed. Electronic bibliographic databases, the internet, and government websites were scanned using structured search strategies. Information was extracted independently by two researchers, and included aspects such as program type, condition/patient/therapy eligibility criteria, role of health technology assessment (HTA), decision options, ethical assumptions, and stakeholder input. It was validated through member-checking with provincial/territorial policy experts and tabulated to facilitate qualitative analyses. Impact on access was assessed through a cross-province/territory comparison of the coverage status of all non-cancer therapies reviewed by the Common Drug Review for indications affecting <1/2,000 Canadians using the Kappa statistic. Reasons for variations were explored using qualitative techniques. RESULTS: Each province/territory has formal and informal mechanisms through which such therapies may be accessed. In most cases, formal mechanisms constitute the centralized HTA processes that also apply to common therapies. While several provinces have established dedicated processes/programs, whether they have affected access is not clear. Despite broadly comparable approaches, there is less than perfect agreement on publicly funded DRDs across jurisdictions. CONCLUSIONS: Individual jurisdictions have developed different approaches to providing access to these therapies. However, as the number increases, a more systematic approach to decision-making may be needed.
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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 | 0.016 | 0.070 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.028 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".