Estimated effects of adding universal public coverage of an essential medicines list to existing public drug plans in Canada
Bibliographic record
Abstract
BACKGROUND: Canada's universal health care system does not include universal coverage of prescription drugs. We sought to estimate the effects of adding universal public coverage of an essential medicines list to existing public drug plans in Canada. METHODS: We used administrative and market research data to estimate the 2015 shares of the volume and cost of prescriptions filled in the community setting that were for 117 drugs on a model list of essential medicines for Canada. We compared prices of these essential medicines in Canada with prices in the United States, Sweden and New Zealand. We estimated the cost of adding universal public drug coverage of these essential medicines based on anticipated effects on medication use and pricing. RESULTS: The 117 essential medicines on the model list accounted for 44% of all prescriptions and 30% of total prescription drug expenditures in 2015. Average prices of generic essential medicines were 47% lower in the US, 60% lower in Sweden and 84% lower in New Zealand; brand-name drugs were priced 43% lower in the US. Estimated savings from universal public coverage of these essential medicines was $4.27 billion per year (range $2.72 billion to $5.83 billion; 28% reduction) for patients and private drug plan sponsors, at an incremental government cost of $1.23 billion per year (range $373 million to $1.98 billion; 11% reduction). INTERPRETATION: Our analysis showed that adding universal public coverage of essential medicines to the existing public drug plans in Canada could address most of Canadians' pharmaceutical needs and save billions of dollars annually. Doing so may be a pragmatic step forward while more comprehensive pharmacare reforms are planned.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".