A multidisciplinary review of the policy, intellectual property rights, and international trade environment for access and affordability to essential cancer medications
Bibliographic record
Abstract
In 2015, the World Health Organization (WHO) Expert Committee approved the addition of 16 cancer medicines to the WHO Model List of Essential Medicines (EML), bringing the total number of cancer medicines on the list to 46. This change represented the first major revision to the EML oncology section in recent history and reinforces international recognition of the need to ensure access and affordability for cancer treatments. Importantly, many low and middle-income countries rely on the EML, as well as the children's EML, as a guide to establish national formularies, and moreover use these lists as tools to negotiate medicine pricing. However, EML inclusion is only one component that impacts cancer treatment access. More specifically, factors such as intellectual property rights and international trade agreements can interact with EML inclusion, drug pricing, and accessibility. To better understand this dynamic, we conducted an interdisciplinary review of the patent status of EML cancer medicines compared to other EML noncommunicable disease medicines using the 17th, 18th, 19th, 20th, and 21st editions of the list. We also explored the interaction of intellectual property rights with the international trade regime and how trade agreements can and do impact cancer treatment access and affordability. Based on this analysis, we conclude that patent status is simply one factor in the complex international environment of health systems, IPR policies, and trade regimes and that aligning these oftentimes disparate interests will require shared global governance across the cancer care continuum.
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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 | 0.013 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.013 | 0.014 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".