List prices and clinical value of anticancer drugs in China, Japan, and South Korea: a retrospective comparative study
Bibliographic record
Abstract
Background High prices of anticancer drugs have raised concerns due to their financial impact on patients and healthcare systems. This study aimed to assess the initial and latest list prices and clinical value of reimbursed anticancer drugs in China, Japan, and South Korea. Methods We identified anticancer drugs newly approved by the National Medical Products Administration of China from January 2012 to June 2022, and by the Pharmaceuticals and Medical Devices Agency of Japan and the Ministry of Food and Drug Safety of South Korea up until June 2022. We compared initial and latest treatment prices between countries and assessed clinical value using patients' survival, quality of life (QoL), and European Society for Medical Oncology Magnitude of Clinical Benefit Scale (ESMO-MCBS). We calculated Spearman rank correlation coefficients of treatment prices with clinical value for individual countries and employed regression analyses to investigate whether the relationship between prices and clinical value was modified by the country setting. Findings Our cohort included 91 anticancer drug indications, with 60 listed for reimbursement in China, 91 in Japan, and 87 in South Korea. Median treatment prices were highest in Japan, followed by South Korea, and lowest in China, both for initial prices (US$64082 vs. US$45529 vs. US$19144, p < 0.0001) and latest prices (US$50859 vs. US$31611 vs. US$18666, p < 0.0001). Over time, China ( β = −0.047, p < 0.0001) and South Korea ( β = −0.049, p < 0.0001) witnessed more significant price reductions compared to Japan ( β = −0.013, p = 0.011). The correlations between both initial and latest treatment prices and clinical value (QoL and ESMO-MCBS) were more significant and stronger in China and South Korea than in Japan, although Japan exhibited slightly stronger correlations in terms of survival compared to China and South Korea. The relationship between clinical value and treatment prices may not be modified by the country setting. Interpretation In comparison, South Korea's list prices and their correlations with clinical value appear reasonable. Policymakers in Japan could enhance efficiency by controlling prices and aligning them with clinical value, while China would need to take substantial steps to expand anticancer drug coverage. Funding National Natural Science Foundation of China (72374149 and 72074163), and China Center for South Asian Studies, Sichuan University.
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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) | 0.001 | 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".