Trends in Canadian prescription drug purchasing: 2001–2020
Bibliographic record
Abstract
BACKGROUND: In 2019, more than $34.5 billion was spent on prescription drugs in Canada. However, little is known about the distribution of this spending across medications and settings (outpatient and inpatient) over time. The objective of this paper is to describe the largest expenditures by medication class over time in inpatient and outpatient settings. This information can help to guide policies to control prescription medication expenditures. METHODS: IQVIA's Canadian Drugstore and Hospital Purchases Audit data from January 1, 2001, to December 31, 2020, were used. In this dataset, purchasing was stratified by outpatient drugstore and inpatient hospital. Spending trajectories in both settings were compared to total expenditure over time. Total expenditure of the 25 medications with the largest expenditure were compared over time, stratified by setting. Nominal costs were used for all analysis. RESULTS: In 2001, spending in the outpatient and inpatient settings was greatest on atorvastatin ($467.0 million) and erythropoietin alpha ($91.2 million), respectively. In 2020, spending was greatest on infliximab at $1.2 billion (outpatient) and pembrolizumab at $361.6 million (inpatient). Annual outpatient spending, although increasing, has been growing at a slower rate (5.3%) than inpatient spending (7.0%). In both settings, spending for the top 25 medications has become increasingly concentrated on biologic agents, with a reduction in the diversity of therapeutic classes of agents over time. DISCUSSION: Identification of the concentration on spending on biologic agents is a key step in managing costs of prescription medications in Canada. Given the increases in spending on biologic agents over the last 20 years, current cost-control mechanisms may be insufficient. Future research efforts should focus on examining the effectiveness of current cost-control mechanisms and identifying new approaches to cost control for biologic agents.
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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 | 0.001 |
| Bibliometrics | 0.008 | 0.022 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".