ACTIVITIES OF THE PAN-CANADIAN PHARMACEUTICAL ALLIANCE: AN OBSERVATIONAL ANALYSIS
Bibliographic record
Abstract
BACKGROUND: The pan-Canadian Pharmaceutical Alliance (pCPA) was established in 2010 to negotiate confidential prices for drugs coming forward from Canada's centralized health technology assessment (HTA) agency reviews, on behalf of the participating public drug plans. OBJECTIVE: To analyze the activities of the pCPA, to determine: alignment of HTA agency recommendations and pCPA negotiation decisions; the role of health economics in pCPA activities; and patterns of implicit prioritization. METHODS: The analysis was based on the archive of drugs handled through the pCPA, as posted on its website. The period of observation was from inception to August 31, 2017. HTA recommendations were sourced from the websites of the Common Drug Review (CDR) and the pan-Canadian Oncology Drug Review. Descriptive and statistical analyses were conducted. RESULTS: The dataset contained 206 drug-indication pairings. There was close but imperfect alignment between HTA agency recommendations and the pCPA's decisions to negotiate; deviations occurred only with CDR-reviewed drugs. The median incremental cost-effectiveness ratio of negotiated drugs was $168K/QALY for oncology drugs, but $70K/QALY for non-oncology drugs. The time to initiate negotiations was dramatically shorter for oncology versus non-oncology drugs (mean 54 versus 263 days), and also differed between therapeutic areas at CDR. The time required for PCPA activity was surprisingly similar for drugs recommended without a price condition and for those conditional on a price reduction. CONCLUSION: These findings revealed a strong alignment between HTA recommendations and pCPA negotiations, an implicit prioritization favouring oncology drug negotiations, and an evolving role for health economics in Canada's reimbursement process.
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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 | 0.008 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.014 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".