National early warning systems for emerging AMR in high-income countries: a systematic review
Bibliographic record
Abstract
Abstract Background An urgent need of implementing national surveillance systems for timely detection and reporting of emerging antimicrobial resistance (AMR) was recently advocated by the World Health Organization (WHO). However, public information on existing national early warning systems (EWSs) is often incomplete. Furthermore, when findings are available, understanding these systems is challenging due to different approaches used for data collection, reporting and definitions, with a comprehensive overview on this topic currently lacking. The aim of this study was to map existing EWSs for emerging AMR, focusing on high-income countries, and describe their main characteristics. Methods A systematic review was performed on bibliographic databases, and a targeted search was conducted on national websites. Any article, report or webpage describing national EWSs in high-income countries was eligible for inclusion. EWSs were identified considering the emerging AMR reporting WHO framework. Results We identified seven national EWSs in 72 high-income countries: two (Australia, Japan) in the East Asia and Pacific Region, three (France, Sweden, United Kingdom) in Europe and Central Asia, and two (United States, Canada) in North America. The systems were established quite recently; in most cases they covered both community and hospital settings, but their main characteristics varied widely across countries in terms of organization and microorganisms under surveillance, with also different definitions of emerging AMR and alert functioning. A formal system assessment was available only in Australia. Conclusions A broader implementation and investment of national surveillance systems that allow early detection of emerging AMR is still needed to establish EWSs in countries and regions lacking such capabilities. A more standardized data collection and reporting is also advisable to improve cooperation on a global scale. Key messages • This study provides a synthesis of publicly available information on national EWSs for emerging AMR in high-income countries, highlighting the urgent need for a broader implementation of such systems. • Main characteristics of EWSs have been outlined, varying widely across countries. Findings could help stakeholders in strengthening current standard national AMR surveillance systems.
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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 | 0.015 | 0.074 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.019 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".