Encouraging the Development of New Antibiotics: Are Financial Incentives the Right Way Forward? A Systematic Review and Case Study
Bibliographic record
Abstract
Ilinca A Dutescu, Sean A Hillier School of Health Policy & Management, Faculty of Health, York University, Toronto, ON, CanadaCorrespondence: Ilinca A DutescuYork University, 4700 Keele Street, Toronto, ON, M3J 1P3, CanadaTel +1 647-886-9829Email idutescu@gmail.comAbstract: Antibiotic resistance is an urgent public health threat that has received substantial attention from the world’s leading health agencies and national governmental bodies alike. However, despite increasing rates of antibiotic resistance, pharmaceutical companies are reluctant to develop new antibiotics due to scientific, regulatory, and financial barriers. Nonetheless, only a handful of countries have addressed this by implementing or proposing financial incentive models to promote antibiotic innovation. This study is comprised of a systematic review that aimed to understand which antibiotic incentive strategies are most recommended within the literature and subsequently analyzed these incentives to determine which are most likely to sustainably revitalize the antibiotic pipeline. Through a case study of Canada, we apply our incentive analysis to the Canadian landscape to provide decision-makers with a possible path forward. Based on our findings, we propose that Canada support the ongoing efforts of other countries by implementing a fully delinked subscription-based market entry reward. This paper seeks to spark action in Canada by shifting the national paradigm to one where antibiotic research and development is prioritized as a key element to addressing antibiotic resistance.Keywords: antimicrobial resistance, antibiotics, health policy, financial incentives, Canada
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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 | 0.081 | 0.236 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.016 | 0.029 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.002 |
| 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".