Long-term trends in use of and expenditures for cardiovascular medications in Canada
Bibliographic record
Abstract
BACKGROUND: Medication expenditures have become the fastest growing sector of costs within the Canadian health care system. Evaluation of the use of cardiovascular medications is important to determine the magnitude of the growth, to identify which medications dominate the landscape and to detect interprovincial differences in utilization. We describe long-term trends in the use of and expenditures for cardiovascular medications in Canada, by drug class and by province. METHODS: For these analyses, we used volume and expenditure data related to prescriptions for cardiovascular medications obtained from IMS Health Canada's CompuScript Audit database for the period 1996-2006. Here, we describe national and provincial patterns of utilization and expenditures for specified classes of cardiovascular medications. RESULTS: The use of cardiovascular medications increased sharply in Canada during the study period, with related costs rising by over 200% during this period to surpass $5 billion in 2006. Changes in population demographics, risk factors and inflation appeared to account for about two-thirds of the observed growth in expenditures. Use of newer medication classes (statins, angiotensin-receptor blockers, angiotensin-converting-enzyme inhibitors), for which patented brand name medications predominate, accounted for almost one-third of the cost increases. Interprovincial differences in total expenditures for cardiovascular drugs portrayed a descending gradient from east to west, with greatest variability for the newer drug classes. INTERPRETATION: Prescriptions and expenditures for cardiovascular medications in Canada escalated over the study period. Projected increases may reach potentially unsustainable levels. Greater emphasis on the use of cost-effective medications is required to limit further increases. Factors influencing interprovincial differences warrant further study.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".