The surveillance of antimicrobial resistance in wastewater from a one health perspective: A global scoping and temporal review (2014–2024)
Bibliographic record
Abstract
Surveillance of antimicrobial resistance (AMR) via a One Health approach must consider the interconnectivity between humans, animals, and the environment. Traditionally, AMR surveillance has relied upon patient-based surveillance in healthcare settings. Wastewater surveillance (WWS) has recently been demonstrated for monitoring AMR to and/or from wastewater treatment plants (WWTPs) which represent a point of intersection between humans, animals and the environment. WWS can be associated with AMR presence and dissemination across entire communities or WWTP catchments, as well as the transfer of AMR to agricultural lands and receiving waters via genes and/or organisms. In this review, the various methodologies used for WWS of AMR and their interpretative significance are identified and discussed, in addition to the potential approaches and outcomes associated with AMR monitoring within WWTPs. A total of 177 reports were identified covering the period 2014 to October 2024, with 136 (76.8 %) appearing after 2019. These recent papers show a distinct emphasis on qPCR and sequencing-based approaches. Surveillance is now global in scope, albeit with a current emphasis on WWTPs in high-income countries. To achieve more effective, global WWS of AMR under a One Health lens, all relevant sectors must understand the principles and capabilities of available methodologies and technologies. Overall, this review seeks to illuminate the diverse interpretations that can be made from WWS of AMR in a One Health context and identify how best to inform future directions regarding AMR monitoring and prevention efforts.
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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 | 0.007 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.016 | 0.017 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".