Reimbursement‐Based Economics – What Is It and How Can We Use It to Inform Drug Policy Reform?
Bibliographic record
Abstract
BACKGROUND: In Ontario, approximately $3.8 billion is spent annually on publicly funded drug programs. The annual growth in Ontario Public Drug Program (OPDP) expenditure has been limited to 1.2% over the course of 3 years. Concurrently, the Ontario Drug Policy Research Network (ODPRN) was appointed to conduct drug class review research relating to formulary modernization within the OPDP. Drug class reviews by ODPRN incorporate a novel methodological technique called reimbursement-based economics, which focuses on reimbursement strategies and may be particularly relevant for policy-makers. OBJECTIVES: To describe the reimbursement-based economics approach. METHODS: Reimbursement-based economics aims to identify the optimal reimbursement strategy for drug classes by incorporating a review of economic literature, comprehensive budget impact analyses, and consideration of cost-effectiveness. This 3-step approach is novel in its focus on the economic impact of alternate reimbursement strategies rather than individual therapies. RESULTS: The methods involved within the reimbursement-based approach are detailed. To facilitate the description, summary methods and findings from a recent application to formulary modernization with respect to the drug class tryptamine-based selective serotonin receptor agonists (triptans) used to treat migraine headaches are presented. CONCLUSIONS: The application of reimbursement-based economics in drug policy reforms allows policy-makers to consider the cost-effectiveness and budget impact of different reimbursement strategies allowing consideration of the trade-off between potential cost savings vs increased access to cost-effective treatments.
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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 | 0.061 | 0.138 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.004 | 0.021 |
| Scholarly communication | 0.020 | 0.029 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.009 | 0.010 |
| Insufficient payload (model declined to judge) | 0.008 | 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".