Analysis of drug coverage before and after the implementation of Canada’s Common Drug Review
Bibliographic record
Abstract
BACKGROUND: Canada's Common Drug Review was implemented to provide publicly funded drug plans (provincial and federal) with a transparent, rigorous and consistent approach for assessing the clinical effectiveness and cost-effectiveness of new drugs. We compared uptake of drug coverage across jurisdictions before and after the implementation of the Common Drug Review. METHODS: Using the IMS Brogan formulary acceptance: monitoring and evaluation database, we identified new drug products in Canada five years before and five years after the first recommendation was made by the Common Drug Review. For each jurisdiction, we compared the proportion of drugs listed, the median time-to-listing and the agreement between the listing decisions of the drug plans and the recommendations of the Common Drug Review. RESULTS: We identified 198 new drugs approved for use in Canada between May 26, 1999, and May 27, 2009, of which 53 had a recommendation from the Common Drug Review. The proportion of drugs listed decreased after the introduction of the Common Drug Review for all participating drug plans (81.1% to 71.3% overall [p ≤ 0.01 for all plans, with the exceptions of Ontario and Quebec [p = 0.07]). The change in median time-to-listing between the periods before and after the Common Drug Review varied by jurisdiction, ranging from a decrease of 691 days to an increase of 250 days. The change in median time-to-listing was not statistically significant for most jurisdictions, with the exceptions of Saskatchewan (increased, Mann-Whitney U test p = 0.01) and New Brunswick, Prince Edward Island, and Newfoundland and Labrador (all decreased, Mann-Whitney U test p < 0.01). INTERPRETATION: There was a decline in the proportion of new drugs listed after the introduction of the Common Drug Review for both participating and nonparticipating jurisdictions. The introduction of the review was associated with a decreased time-to-listing for certain smaller provinces.
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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 | 0.004 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".