The cost of entry: An analysis of pharmaceutical registration fees in low-, middle-, and high-income countries
Bibliographic record
Abstract
BACKGROUND: Advances in pharmaceuticals offer improved health outcomes for a wide range of illnesses, yet medicines are often inaccessible for many patients worldwide. One potential barrier to making medicines available to all is the cost of product registration, the fees for regulatory review and licensing for the sale of medicines beyond the cost of clinical trials, if needed. METHODS AND FINDINGS: We performed a cross-sectional analysis of pharmaceutical registration fees in low-, middle-, and high-income countries. We collected data on market authorization fees for new chemical entities and for generic drugs in 95 countries. We calculated measures of registration fee size relative to population, gross domestic product (GDP), and total health spending in each country. Each of the 95 countries had a fee for registering new chemical entities. On average, the ratio of registration fees to GDP was highest in Europe and North America and lowest in South and Central America. Across individual countries, the level of registration fees was positively correlated with GDP and total health spending, with relatively few outliers. DISCUSSION: We find that, generally speaking, the regulatory fees charged by medicines regulatory authorities are roughly proportional to the market size in their jurisdictions. The data therefore do not support the hypothesis that regulatory fees are a barrier to market entry in most 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 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".