Availability and Affordability of Drugs With a Conditional Approval by the European Medicines Agency; Comparison of Korea With Other Countries and the Implications
Bibliographic record
Abstract
Introduction: There have been concerns with the availability and affordability of EMA’s recently approved medicines with a conditional approval in Korea. This needs to be addressed to provide future guidance to the authorities in Korea. Objective: Compare the availability and affordability of medicines with a conditional approval by the European Medicine Agency (EMA) among 12 countries (US, UK, France, Germany, Swiss, Italy, Japan, Canada, Taiwan, Australia, New Zealand and Korea) in light of access to medicine concerns in Korea. Methods: 13 medicines were selected and compared in terms of their availability and affordability across 12 countries. Approval rate for the selected medicines and time lag to approval on the basis of EMA’s approval dates were calculated. Reimbursement status and prices were compared as proxies of affordability. Results: The average approval rate was 35.2% for the selected medicines for all countries outside the EU countries. The highest rate was in US (69.2%) followed by Korea (46.5%). An average of 182 days was taken among the countries for approval. The US (Median -355 days) was the country where the medicines were most rapidly approved. Korea (152 days) ranked the fifth most rapidly approving country. An average listing or reimbursement rate for all countries was 51.1%. The US ranked 100% for the listing of their approved medicines followed by Germany (92.3%). Korea (66.7%) ranked seventh. Price dispersion ranged from 1.1 to 2.8. When the prices of 4 drugs listed in Korea were compared, Korean prices were relatively high. Conclusions: Korea was found to be a county where market authorization tended to be made earlier than others and subsequent reimbursement and pricing were not rigid even generous compared to other Asian-pacific countries. Korean drug benefit policies for listing and pricing did not appear to hinder access to medicines even with a conditional approval in comparison with others.
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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 | 0.001 | 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 | 0.004 |
| 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".