Compulsory Licensing Often Did Not Produce Lower Prices For Antiretrovirals Compared To International Procurement
Bibliographic record
Abstract
Compulsory licensing has been widely suggested as a legal mechanism for bypassing patents to introduce lower-cost generic antiretrovirals for HIV/AIDS in developing countries. Previous studies found that compulsory licensing can reduce procurement prices for drugs, but it is unknown how the resulting prices compare to procurements through the Global Fund to Fight AIDS, Tuberculosis, and Malaria; UNICEF; and other international channels. For this study we systematically constructed a case-study database of compulsory licensing activity for antiretrovirals and compared compulsory license prices to those in the World Health Organization's (WHO's) Global Price Reporting Mechanism and the Global Fund's Price and Quality Reporting Tool. Thirty compulsory license cases were analyzed with 673 comparable procurements from WHO and Global Fund data. Compulsory license prices exceeded the median international procurement prices in nineteen of the thirty case studies, often with a price gap of more than 25 percent. Compulsory licensing often delivered suboptimal value when compared to the alternative of international procurement, especially when used by low-income countries to manufacture medicines locally. There is an ongoing need for multilateral and charitable actors to work collectively with governments and medicine suppliers on policy options.
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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 | 0.007 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".