Prescribed Drug Spending in Canada in 2019: A Focus on Public Drug Program
Bibliographic record
Abstract
Public drug program spending accounts for 43.1% of prescribed drug spending in Canada.This report provides an in-depth look at public drug program spending in Canada, using the Canadian Institute for Health Information's (CIHI) National Prescription Drug Utilization Information System.Public drug program spending does not include spending on drugs dispensed in hospitals or on those funded through cancer agencies and other special programs.Public drug program spending increased by 6.8% in 2018, compared to an increase of 5.3% in 2017.The growth in 2018 was largely because of the introduction of Ontario Health Insurance Plan+ (OHIP+), which extended drug coverage to all Ontario residents age 24 years or younger.Three of the top five classes in spending were biologic drugs, with anti-tumour necrosis factor drugs, used to treat conditions such as rheumatoid arthritis and Crohn's disease, accounting for the highest proportion of drug spending for the seventh consecutive year.The proportion of public drug program spending on highcost individuals continued to rise.In 2018, the 2.1% of individuals for whom a drug program paid $10,000 or more accounted for more than one-third of spending (38.8%, up from 36.6% in 2017).
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.012 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".