Do Patents for Antiretroviral Drugs Constrain Access to AIDS Treatment in Africa?
Bibliographic record
Abstract
Public attention and debate recently have focused on access to treatment of acquired immunodeficiency syndrome (AIDS) in poor, severely affected countries, such as those in Africa. Whether patents on antiretroviral drugs in Africa are impeding access to lifesaving treatment for the 25 million Africans with human immunodeficiency virus infection is unknown. We studied the patent statuses of 15 antiretroviral drugs in 53 African countries. Using a survey method, we found that these antiretroviral drugs are patented in few African countries (median, 3; mode, 0) and that in countries where antiretroviral drug patents exist, generally only a small subset of antiretroviral drugs are patented (median and mode, 4). The observed scarcity of patents cannot be simply explained by a lack of patent laws because most African countries have offered patent protection for pharmaceuticals for many years. Furthermore, in this particular case, geographic patent coverage does not appear to correlate with antiretroviral treatment access in Africa, suggesting that patents and patent law are not a major barrier to treatment access in and of themselves. We conclude that a variety of de facto barriers are more responsible for impeding access to antiretroviral treatment, including but not limited to the poverty of African countries, the high cost of antiretroviral treatment, national regulatory requirements for medicines, tariffs and sales taxes, and, above all, a lack of sufficient international financial aid to fund antiretroviral treatment. We consider these findings in light of policies for enhancing antiretroviral treatment access in poor countries.
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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 | 0.003 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".