Pharmaceutical expenditure on drugs for rare diseases in Canada: a historical (2007–13) and prospective (2014–18) MIDAS sales data analysis
Bibliographic record
Abstract
BACKGROUND: Health Canada has defined rare diseases as life-threatening, seriously debilitating, or serious chronic conditions affecting a very small number of patients (~1 in 2,000 persons). An estimated 9 % of Canadians suffer from a rare disease. Drugs treating rare diseases (DRDs) are also known as orphan drugs. While Canada is currently developing an orphan drug framework, in the United States (US), the Orphan Drug Act (ODA) of 1983 established incentives for the development of orphan drugs. This study measured total annual expenditure of orphan drugs in Canada (2007-13) and estimated future (2014-18) orphan drug expenditure. METHODS: Orphan drugs approved by the US Food and Drug Administration (FDA) in the US were used as a proxy for the orphan drug landscape in Canada. Branded, orphan drugs approved by the FDA between 1983 through 2013 were identified (N = 356 unique products). Only US orphan drugs with the same orphan indication(s) approved in Canada were included in the analysis. Adjustment via an indication factoring was applied to products with both orphan and non-orphan indications using available data sources to isolate orphan-indication sales. The IMS Health MIDAS database of audited biopharmaceutical sales was utilized to measure total orphan drug expenditure, calculated annually from 2007-2013 and evaluated as a proportion of total annual pharmaceutical drug expenditure (adjusted to 2014 CAD). RESULTS: Between 2007 and 2013, expenditure was measured for a final N = 147 orphan drugs. Orphan drug expenditure totaled $610.2 million (M) in 2007 and $1,100.0 M in 2013, representing 3.3- 5.6 % of total Canadian pharmaceutical drug expenditure in 2007-2013, respectively. Future trend analysis suggests orphan drug expenditure will remain under 6 % of total expenditure in 2014-18. CONCLUSIONS: While the number of available orphan drugs and associated expenditure increased over time, access remains an issue, and from the perspectives of society and equity, overall spending on orphan drugs is lower relative to the number of patients affected with an orphan disease in Canada. The overall budget impact of orphan drugs is small and fairly stable relative to total pharmaceutical expenditure. Concerns that growth in orphan drug expenditure may lead to unsustainable drug expenditure do not appear to be justified.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.022 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".