Should Canada adopt managed access agreements in Canada for expensive drugs?
Bibliographic record
Abstract
Drugs are increasingly authorized based on less mature evidence, leaving payors faced with significant clinical and cost-effectiveness uncertainties. As a result, payors must often choose between reimbursing a drug that may not turn out to be cost-effective (or may even be unsafe) or delaying the reimbursement of a drug that is cost-effective and offers clinical benefit to patients. Novel reimbursement decision models and frameworks, such as managed access agreements (MAAs), may address this decision challenge. Here, we provide a comprehensive overview of the legal limitations, considerations, and implications for adopting MAAs in Canadian jurisdictions. We begin with an overview of current drug reimbursement processes in Canada, terminology and definitions of the different types of MAAs, and select international experiences with MAAs. We discuss the legal barriers to MAA governance frameworks, design and implementation considerations, and legal and policy implications of MAAs. Finally, we provide recommendations to guide policy development for implementing MAAs in Canada, based on existing literature, international experience, and our legal analysis. We conclude that legal and policy barriers likely prevent the adoption of a pan-Canadian MAA governance framework. More feasible is a quasi-federal or provincial approach, building on existing infrastructure.
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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 | 0.022 | 0.060 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.012 | 0.008 |
| Scholarly communication | 0.012 | 0.007 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.009 | 0.010 |
| Insufficient payload (model declined to judge) | 0.006 | 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".