Variation in the prices of oncology medicines across Europe and the implications for the future
Bibliographic record
Abstract
Introduction/Objectives: Health authorities are facing increasing challenges to the sustainability of their healthcare systems because of the growing expenditures on medicines, including new, high-priced oncology medicines, and changes in disease prevalence in their ageing populations. Medicine prices in European countries are greatly affected by the ability to negotiate reasonable prices. Concerns have been expressed that prices of patented medicines do not fall sufficiently after the introduction of lower-cost generic oncology medicines. The objective of this study was to examine the associations over time in selected European countries between the prices of oral oncology medicines, population size, and gross domestic product (GDP) before and after the introduction of generic versions. Evidence of periodic reassessments of the price, value, and place in treatment of these medicines was also looked for. The goal of this review was to stimulate debate about possible improvements in approaches to reimbursement negotiations. Methodology: Analysis was performed of reimbursed prices of three oral oncology medicines (imatinib, erlotinib and fludarabine) between 2013 and 2017 across Europe. Correlations were explored between GDP, population size, and prices. Findings were compared with previous research regarding prices of generic oral oncology medicines. Results: The prices of imatinib, erlotinib and fludarabine varied among European countries, and there was limited price erosion over time in the absence of generics. There appeared to be no correlation between population size and price, but higher prices of on-patent oral cancer medicines were seen among countries with higher GDP per capita. Conclusion: Limited price erosion for patented medicines contributed to increases in oncology medicine budgets across the region. There was also a concerning lack of evidence re-assessments of the price, value, and place in treatment of patented oncology medicines following the loss of patent protection of standard medicines. The use of such proactive re-assessments in negotiating tactics might positively impact global expenditures for oncology medicines.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".