A step forward in antibiotic use and resistance monitoring: a quarterly surveillance system pilot in 11 European Union/European Economic Area countries, September 2017 to May 2020
Bibliographic record
Abstract
Background Surveillance of antimicrobial resistance (AMR) and antimicrobial use (AMU) in Europe is currently annual. Aim To study the feasibility and scalability of a quarterly AMR/AMU surveillance system in the European Union/European Economic Area (EU/EEA). Methods We conducted a longitudinal study within the scope of the EU-JAMRAI project. Seventeen partners from 11 EU/EEA countries prospectively collected 41 AMU and AMR indicators quarterly from September 2017 to May 2020 for the hospital sector (HS) and primary care (PC). Descriptive statistics and coefficients of variation (CV) analysis were performed. Results Data from 8 million hospital stays and 45 million inhabitants per quarter were collected at national (n = 4), regional (n = 6) and local (n = 7) levels. Of all partners, five were able to provide data within 3 months after each preceding quarter, and eight within 3–6 months. A high variability in AMU was found between partners. Colistin was the antibiotic that showed the highest CV in HS (1.40; p < 0.0001). Extended-spectrum beta-lactamase-producing Escherichia coli presented the highest incidence in HS (0.568 ± 0.045 cases/1,000 bed-days per quarter), whereas ciprofloxacin-resistant E. coli showed the highest incidence in PC (0.448 ± 0.027 cases/1,000 inhabitants per quarter). Barriers and needs for implementation were identified. Conclusion This pilot study could be a first step towards the development of a quarterly surveillance system for AMU and AMR in both HS and PC in the EU/EEA. However, committed institutional support, dedicated human resources, coordination of data sources, homogeneous indicators and modern integrated IT systems are needed first to implement a sustainable quarterly surveillance system.
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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 | 0.024 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".