Cost-effectiveness of the top 100 drugs by public spending in Canada, 2015–2021: a repeated cross-sectional study
Bibliographic record
Abstract
OBJECTIVES: To assess the distribution and spending by cost-effectiveness category among those drugs with the highest public spending levels in Canada. DESIGN: Repeated cross-sectional study. SETTING: The Canadian provinces of Manitoba, Ontario, New Brunswick, Nova Scotia, Prince Edward Island and Newfoundland. MAIN OUTCOMES AND MEASURES: Cost-effectiveness assessments by the Canadian Agency for Drugs and Technologies in Health (CADTH) for top-100 brand-name outpatient drugs by gross public plan spending in any year between 2015 and 2021 in Canada Institute for Health Information's National Prescription Drug Utilization Information System data. Gross public plan spending by cost-effectiveness category. RESULTS: From 2015 to 2021, 152 brand-name drugs occupied a top-100 rank and were included in the analysis. Of those, 117 had been assessed by CADTH. During the 7-year period, there was an increase in both top-100 drugs with cost-effective (from 18 to 24) and cost-ineffective (from 29 to 41) assessments, while drugs not assessed or with an unclear assessment declined (from 31 to 19 and from 22 to 16, respectively). As a share of spending on top-100 drugs with an assessment, spending on cost-effective drugs was mostly stable at 40%-46% from 2015 to 2021, while spending on cost-ineffective drugs increased from 30% to 45%. CONCLUSION: A large and growing share of public drug spending has been allocated to cost-ineffective drugs in Canada. Dedicating large budgets to such treatments prevents spending with greater health impact elsewhere in the healthcare system and could restrain the capacity to pay for groundbreaking pharmaceutical innovation in the future.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".