Global antibiotic hotspots and risks: A One Health assessment
Bibliographic record
Abstract
Antibiotics are increasingly prevalent in global environments, driving antimicrobial resistance and disrupting microbial cycling. These impacts pose threats to human, animal, and environmental health. Therefore, addressing this emergent issue necessitates a One Health framework that integrates these interconnected dimensions. Here we systematically review 137 antibiotics across diverse global environmental compartments. We find that sulfonamides, macrolides, quinolones, and tetracyclines are globally ubiquitous, particularly prevalent in Asia and Africa, whereas β-lactams dominates in Europe. Hierarchical clustering revealed ten priority antibiotics in liquid phases and eight in solid phases requiring urgent attention. Regional analysis indicated the highest antibiotic concentrations within wastewater treatment plant liquids in the Americas and surface waters in Africa, with generally lower levels detected in Asia and Europe. Utilizing a One Health assessment framework, we integrated Predicted No-Effect Concentrations for antibiotic resistance selection (PNEC RS ) relevant to human and animal health with Minimum Inhibitory Concentrations (MICs) affecting microbial nitrogen cycling processes. Risk assessment highlighted wastewater treatment plant liquids (20% average exceedance) and animal manure (44% average exceedance) as the most critical compartments. Africa exhibited the highest overall risk, averaging a 53% exceedance rate. Notably, ciprofloxacin and ofloxacin in liquid phases, as well as enrofloxacin and norfloxacin in solid phases, emerged as antibiotics posing significant One Health risks. This study advances our understanding of antibiotic distribution globally, offering a foundation for targeted interventions to mitigate antibiotic-related risks across human, animal, and environmental health sectors. • Global dataset compiles 137 antibiotics across eight environmental media in 46 countries. • It combines Predicted No-Effect Concentrations for Resistance Selection with Minimum Inhibitory Concentrations. • Wastewater treatment plant liquids (20%) and animal manure (44%) are the highest-risk media for antibiotic exceedance. • Africa emerges as the continent most severely impacted by antibiotic contamination risks (53% average exceedance). • Ciprofloxacin, ofloxacin, enrofloxacin, and norfloxacin are critical antibiotics posing global environmental threats.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".