Canadian Trends in Estimated Drug Purchases and Projections: 2024 and 2025
Bibliographic record
Abstract
What Is the issue? Data on trends in the pharmaceutical market remain limited. We offer an annual update on estimated drug purchases in Canada and highlight key factors that could impact future spending. What Did We do? We conducted a retrospective time-series analysis of annual estimated drug purchases across Canada between 2001 and 2023 using IQVIA’s Canadian Drugstore and Hospital Purchases Audit. We calculated total estimated drug purchases and relative percentage change annually, stratified by sector (retail and hospital), and forecast annual spending to 2025. What Did We Find? Total estimated drug purchases for 2023 were approximately $43.5 billion, 13.7% higher than in 2022. Overall, expenditure on the top 25 high-cost drugs accounted for 32.5% and 53.3% of total spending in the retail and hospital sectors, respectively. Pharmaceutical spending has grown over the past 2 decades, with an annual average growth of 5.8% and 8.2% in the retail and hospital sectors, respectively. Drug expenditure in the retail sector is expected to increase annually by 10.9% to 10.1% and spending within the hospital sector is expected to increase by 14.6% to 12.9% for 2024 and 2025, respectively. What Does This Mean? The accelerated growth in overall estimated drug purchases is likely driven by the increasing use of new diabetes and obesity treatments. Continued growth in drug purchases is projected across the Canadian market, which will be influenced by new approvals of specialty and oncology drugs, as well as generic and biosimilar versions of the top 25 drugs. Without measures to address this ongoing increase in pharmaceutical spending, there may be a need to reallocate funds from other public sectors or shift costs to private industry and 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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.010 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 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".