In which developing countries are patents on essential medicines being filed?
Bibliographic record
Abstract
BACKGROUND: This article is based upon data gathered during a study conducted in partnership with the World Intellectual Property Organization on the patent status of products appearing on the World Health Organization's 2013 Model List of Essential Medicines (MLEM). It is a statistical analysis aimed at answering: in which developing countries are patents on essential medicines being filed? METHODS: Patent data were collected by linking those listed in the United States and Canada's medicine patent registers to corresponding patents in developing countries using two international patent databases (INPADOC and Derwent) via a commerical-grade patent search platform (Thomson Innovation). The respective supplier companies were then contacted to correct and verify our data. We next tallied the number of MLEM patents per developing country. Spearman correlations were done to assess bivariate relationships between variables, and a multivariate regression model was developed to explain the number of MLEM patents in each country using SPSS 23.0. RESULTS: A subset of 20 of the 375 (5%) products on the 2013 MLEM fit our inclusion criteria. The patent estate reports (i.e., the global list of patents for a given drug) varied greatly in their number with a median of 48 patents (interquartile range [IQR]: 26-76). Their geographic reach had a median of 15% of the developing countries sampled (IQR: 8-28%). The number of developing countries covered appeared to increase with the age of the patent estate (r = .433, p = 0.028). The number of MLEM patents per country was significantly positively associated with human development index (HDI), gross domestic income (GDI) per capita, total healthcare expenditure per capita, population size, the Rule of Law Index, and average education level. Population size, GDI per capita, and healthcare expenditure (in % of national expenditure) were predictors of the number of MLEM patents in countries (p = 0.001, p = 0.001, p = 0.009, respectively). Population size was the most important predictor (β = 0.59), followed by income (GDI per capita) (β = 0.32), and healthcare expenditure (β = 0.15). Holding the other factors constant, (i) 14.3 million more people, (ii) $833.33 more per capita (GDI), or (iii) 0.88% more of national spending on healthcare resulted in 1 additional essential medicine patent. CONCLUSION: Population was a powerful predictor of the number of patent filings in developing countries along with GDI and healthcare expenditure. The age and historical context of the patent estate may make a difference in the number of patents and countries covered. Broad surveillance and benchmarking of the global medicine patent landscape is valuable for detecting significant shifts that may occur over time. With improved international medicine patent transparency by companies and data available through third parties, such studies will be increasingly feasible.
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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 | 0.002 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".