Title: Comparative Drug Pricing Reforms in the U.S., Canada, and the UK: Toward a Hybrid MFN–VBC Framework for Universal Health Coverage
Bibliographic record
Abstract
This study examines how different national systems approach the persistent challenge of prescription drug pricing, with a comparative focus on the United States, Canada, and the United Kingdom. The purpose is to assess the trade-offs across four key policy dimensions: cost containment, innovation incentives, equity in access, and implementation feasibility, using a structured trade-off matrix informed by Bardach and Patashnik’s eightfold path to policy analysis. Data sources include peer-reviewed literature, policy documents, and regulatory reports published between 2007 and 2025. Documents were coded into the four trade-off dimensions (Appendix C) to provide a transparent basis for comparison. A PRISMA-style flow diagram (Appendix A) and extended matrices (Appendices B–E) ensure methodological clarity and reproducibility. The findings show that the U.S. MFN rule offered potential savings but was legally and politically vulnerable. At the same time, Value-Based Care (VBC) pilots align with long-term value but face infrastructural and equity challenges. Canada achieves strong cost containment and equity through the Patented Medicine Prices Review Board (PMPRB), but innovation incentives are weaker. The UK balances all four dimensions most effectively through the NICE and VPAG frameworks. The expected outcome is the articulation of a hybrid U.S. framework that combines international reference pricing as a negotiation baseline with outcome-based reimbursement contracts tied to value. This framework is designed to advance Universal Health Coverage (UHC) by integrating affordability,
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 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 | 0.019 | 0.060 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.011 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.013 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".