Patient Access in 14 High-Income Countries to New Antibacterials Approved by the US Food and Drug Administration, European Medicines Agency, Japanese Pharmaceuticals and Medical Devices Agency, or Health Canada, 2010–2020
Bibliographic record
Abstract
BACKGROUND: Inaccessibility of medicines in low- and middle-income countries is a frequent challenge. Yet it is typically assumed that high-income countries have complete access to the full arsenal of medicines. This study tests this assumption for new antibacterials, which are saved as a last resort in order to prevent the development of resistance, resulting in insufficient revenues to offset costs. Prior studies report only regulatory approval, missing the important lag that occurs between approval and commercial launch, although some antibiotics never launch in some countries. METHODS: We identified all antibacterials approved and launched in the G7 and 7 other high-income countries in Europe for the decade beginning 1 January 2010, using quantitative methods to explore associations. RESULTS: Eighteen new antibacterials were identified. The majority were accessible in only 3 countries (United States, United Kingdom, and Sweden), with the remaining 11 high-income countries having access to less than half of them. European marketing authorization did not lead to automatic European access, as 14 of the antibacterials were approved by the European Medicines Agency but many fewer were commercially launched. There was no significant difference in access between "innovative" and "noninnovative" antibacterials. Median annual sales in the first launched market (generally the United States) for these 18 antibiotics were low, $16.2M. CONCLUSIONS: Patient access to new antibacterials is limited in some high-income countries including Canada, Japan, France, Germany, Italy, and Spain. With low expected sales, companies may have decided to delay or forego commercialization due to expectations of insufficient profitability.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".