Comparison of generic drug prices in Korea and eight high-income countries across four therapeutic classes
Bibliographic record
Abstract
BACKGROUND: The prices of generic drugs in general are known to be higher in Korea than in other countries. However, it remains unknown whether the price levels of generic drugs in Korea relative to other countries can differ by therapeutic class. Therefore, this study compared the prices of generic drugs in four commonly used drug classes in Korea with those in other high-income countries. METHODS: Using IQVIA's Pricing Insight data from 2018 to 2022, we calculated the Laspeyres price index for generic drugs in four therapeutic classes (antidiabetic drugs, lipid-modifying agents, antihypertensive drugs, and antibiotics). We selected eight high-income countries, such as Canada, France, Germany, Italy, Japan, Switzerland, the United Kingdom, and the United States, for comparison and Korea as the base country. Price to chemist was used and the currency conversion was based on the exchange rate and the purchasing power parity. RESULTS: Prices of generic drugs are lower in all of comparison countries combined than in Korea for lipid-modifying drugs and antihypertensive drugs. For these two drug classes, all countries but the U.S. have the index lower than one. The index for antidiabetic drugs was less than one in all countries except for Canada and the U.S. For antibiotics, all countries but France, Italy, and Japan have the index that is greater than one. Furthermore, the price index for generic antibiotics increased from 2018 to 2022 in all countries but Canada and Japan. CONCLUSION: The prices of generic drugs are higher in Korea than in other high-income countries for lipid-modifying agents and antihypertensive drugs. The prices of generic antibiotics are higher in many comparison countries and have further increased from 2018 to 2021.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".