Healthcare Payer Perspectives on the Assessment and Pricing of Oncology Multi-Indication Products: Evidence from Nine OECD Countries
Bibliographic record
Abstract
BACKGROUND: New pharmaceuticals are increasingly being developed for use across multiple indications. Countries across Europe and North America have adopted a range of different approaches to capture differences in the value of individual indications. OBJECTIVE: The three aims of this study were (i) to review the price-setting practice over the past 5 years for multi-indication products across England, France, Italy, Spain, Belgium, Switzerland, Turkey, Canada and the USA; (ii) to assess the impact of current practices on launch strategy; and (iii) to identify issues in the implementation of indication-based pricing. METHODS: Ten current and former members of health insurance organisations, healthcare payer organisations or health technology assessment agencies with expertise on pharmaceutical purchasing were invited to participate in semi-structured interviews. Interview transcripts were imported into NVivo 12 for thematic analysis. RESULTS: The majority of countries studied require full assessments upon launch of a new indication. Five different approaches to pricing were identified: weighted pricing, differential discounting, mandatory discount, price anchoring and free pricing. Manufacturers show a tendency to launch first in niche indications with high unmet need to achieve a high price. Stakeholders from England, France, Italy, Belgium and Switzerland consider their current system fit for purpose, while other countries expressed concern over the administrative burden of monitoring products at indication level. CONCLUSIONS: Given the high administrative burden, it is questionable whether indication-based pricing would provide additional public benefit above and beyond current weighted dynamic single pricing and differential discounting practices for multi-indication products.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.016 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".