Canadian Trends and Projections in Prescription Drug Purchases: 2001–2023
Bibliographic record
Abstract

 Limited data are available to understand costs and trends over time in the Canadian pharmaceutical market across all sectors. To fill this gap, a retrospective time series analysis of annual prescription drug purchases in Canada between 2001 and 2020 was conducted using data from the IQVIA Canadian Drugstore and Hospital Purchases Audit.
 Spending has grown over the past 2 decades at a steady pace, with annual average growth of 5.3% and 7.1% in the retail and hospital sectors, respectively. Total prescription purchases in 2020 were approximately $32.7 billion, 4.3% higher than in 2019 (3.8% growth in retail, 6.9% in hospital).
 New approvals of specialty and oncology drugs and generic formulations of the top 25 drugs may influence drug purchases in 2021 to 2023.
 Overall drug purchases in Canada are projected to continue growing. The forecast for the outpatient sector is continued moderate levels of growth in drug spending (3% to 4%), with higher rates of growth (7% to 8%) in the hospital setting.
 Action should be taken to curb sustained growth in pharmaceutical spending in Canada. Otherwise, these costs may be shifted to other budgets, private industry, and/or patients.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.005 | 0.013 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".