Canadian Trends in Estimated Pharmaceutical Drug Purchases and Projections: 2023
Bibliographic record
Abstract
In 2021, our group presented the first forecast of trends in the Canadian pharmaceutical market. The goal was to identify factors that may influence future spending to support public and private decision-makers in predicting the growth of national drug purchases. This report is the first annual update of estimated pharmaceutical drug purchases in Canada for 2022–2023. We conducted a time series analysis of annual estimated pharmaceutical drug purchases across Canada between 2001 and 2021 using IQVIA’s Canadian Drugstore and Hospital Purchases Audit, calculated total estimated purchases and relative percentage change annually for the retail and hospital sectors, and forecasted annual spending to 2023. Total drug purchases for 2021 were approximately $35.4 billion, 8.3% higher than in 2020 (7.3% growth in the retail sector; 12.4% growth in the hospital sector). Spending for the top 25 drugs was 31.2% and 52.3% of total spending in the retail and hospital sectors, respectively. The forecast for the retail sector is continued moderate levels of growth in drug spending (7% to 8% annually), with higher rates of growth (12% to 13% annually) in the hospital setting. New approvals of specialty and oncology drugs and generic formulations of the top 25 drugs are expected to influence drug purchases in 2022–2023. If no action is taken to curb sustained growth in pharmaceutical spending in Canada, costs may necessitate a shift in spending from other public budgets or to private industry and directly to 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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.004 | 0.009 |
| 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) | 0.009 | 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".