Estimated cost of universal public coverage of prescription drugs in Canada
Bibliographic record
Abstract
BACKGROUND: With the exception of Canada, all countries with universal health insurance systems provide universal coverage of prescription drugs. Progress toward universal public drug coverage in Canada has been slow, in part because of concerns about the potential costs. We sought to estimate the cost of implementing universal public coverage of prescription drugs in Canada. METHODS: We used published data on prescribing patterns and costs by drug type, as well as source of funding (i.e., private drug plans, public drug plans and out-of-pocket expenses), in each province to estimate the cost of universal public coverage of prescription drugs from the perspectives of government, private payers and society as a whole. We estimated the cost of universal public drug coverage based on its anticipated effects on the volume of prescriptions filled, products selected and prices paid. We selected these parameters based on current policies and practices seen either in a Canadian province or in an international comparator. RESULTS: Universal public drug coverage would reduce total spending on prescription drugs in Canada by $7.3 billion (worst-case scenario $4.2 billion, best-case scenario $9.4 billion). The private sector would save $8.2 billion (worst-case scenario $6.6 billion, best-case scenario $9.6 billion), whereas costs to government would increase by about $1.0 billion (worst-case scenario $5.4 billion net increase, best-case scenario $2.9 billion net savings). Most of the projected increase in government costs would arise from a small number of drug classes. INTERPRETATION: The long-term barrier to the implementation of universal pharmacare owing to its perceived costs appears to be unjustified. Universal public drug coverage would likely yield substantial savings to the private sector with comparatively little increase in costs to government.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| 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 | 0.000 |
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".