Drug funding price negotiations: Towards achieving a balance between individual and population gains in health benefits.
Bibliographic record
Abstract
6641 Background: Drug price negotiation to lower cost to a cost-effectiveness threshold (λ) is a recognized approach to improve health care opportunities for the greater benefit of the population. Critics have raised concerns for this approach given the additional time required and speculated loss of quality-adjusted life-years (QALY) for patients. The current study aimed to quantify the incremental net health benefit (INHB) of publicly funded oncology drugs, if funding occurred at list prices without (w/o) negotiations. Methods: Pan-Canadian Oncology Drug Review submissions were reviewed to identify funded drugs with unique indications. For included drug indications economic guidance panel (EGP) reports were reviewed for incremental costs (ΔC) and ΔQALY from manufacturer’s base case cost-effectiveness analyses, EGP lower (LL) and upper limit (UL) re-analyzed estimates (based on list prices). Number of new cases in Ontario (most populous province in Canada) per indication (2017-18) was obtained from provincial databases. Annual QALY gain for each indication was determined by: (ΔQALY×cases). Provincial QALY gain/loss w/o price negotiations to reference λ was estimated by: (INHB= [ΔQALY− (ΔC/λ)] ×cases). Incremental net monetary benefit demonstrated annual monetary gain/loss w/o price negotiations to reference λ: (INMB= [(ΔQALY×λ) −ΔC] ×cases). Results: 34 drug indications including 4,629 new cases were identified. Annual QALY gain for funded indications using manufacturer, LL and UL estimates was 1,851, 1,617 and 1,301, respectively. At reference λ CAD$100,000/QALY, funding w/o negotiations resulted in loss of 2,176, 2,368, 2,451 QALY, representing budgetary diversions away from other health care interventions. This would result in a provincial net annual loss of 325, 751 and 1,150 QALY, respectively. INMB demonstrated provincial net monetary loss of CAD$32,472,389, $75,113,684 and $115,022,331, respectively. Conclusions: Despite an annual gain in QALY for funded drug indications, a net provincial loss in QALY w/o price negotiations was demonstrated. Thus, supportive evidence exists for drug price negotiations towards the promotion of health benefits for the population.
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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 | 0.054 | 0.116 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".