Historical and projected public spending on drugs for rare diseases in Canada between 2010 and 2025
Bibliographic record
Abstract
OBJECTIVE: Rare diseases are life-threatening, debilitating, or serious chronic conditions that affect < 50/100,000 people. Canadians can only access approximately 60% of drugs for rare diseases (DRDs), which is partially related to high per-patient costs and payers' affordability concerns. However, limiting access to DRDs can reduce survival and quality of life among patients and caregivers. Therefore, we projected Canadian non-oncology DRD spending relative to total public drug spending to provide perspective for decision makers. METHODS: Candidate historical (2010-2020) and pipeline (2021-2025) Canadian-marketed non-oncology DRDs were identified using definitions from the European Medicines Agency and the US Food and Drug Administration databases. Inclusion and exclusion criteria were applied to identify eligible DRDs. Public payer claims data, prevalence rates, regulatory, and health technology assessment factors were used to project DRD spending in relation to total Canadian public drug spending. RESULTS: We included 42 historical DRDs and 122 pipeline DRDs. Public spending on DRDs grew from $14.8 million in 2010 (11 DRDs) to $380.9 million in 2020, then a projected $527.6 million in 2021 (59 potential DRDs) and $1.6 billion in 2025 (164 potential DRDs). Projected DRD spending increased from 3.2% of $16.5 billion public drug spending in 2021 to 8.3% of $19.4 billion in 2025. These projections do not include confidential manufacturer discounts, health outcome-related offsets, or additional safety-related costs. CONCLUSIONS: Projected DRD spending shows robust growth but remains a fraction of total public drug spending. Limiting DRD access because of this growth is not aligned with Canadian patient or societal values. Given the renewed interest in a Canadian DRD framework, our results may help guide discussions that aim to balance control of public drug spending with the well-being of patients with rare diseases.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".