Environmental contamination in a high-income country (France) by antibiotics, antibiotic-resistant bacteria, and antibiotic resistance genes: Status and possible causes
Bibliographic record
Abstract
Antimicrobial resistance (AMR) is a major global public health concern, shared by a large number of human and animal health actors. Within the framework of a One Health approach, actions should be implemented in the environmental realm, as well as the human and animal realms. The Government of France commissioned a report to provide policy and decision makers with an evidential basis for recommending or taking future actions to mitigate AMR in the environment. We first examined the mechanisms that underlie the emergence and persistence of antimicrobial resistance in the environment. This report drew up an inventory of the contamination of aquatic and terrestrial environments by AMR and antibiotics, anticipating that the findings will be representative of some other high-income countries. Effluents of wastewater treatment plants were identified as the major source of contamination on French territory, with spreading of organic waste products as a more diffuse and incidental contamination of aquatic environments. A limitation of this review is the heterogeneity of available data in space and time, as well as the lack of data for certain sources. Comparing the French Measured Environmental Concentrations (MECs) with predicted no effect concentrations (PNECs), fluoroquinolones and trimethoprim were identified as representing high and medium risk of favoring the selection of resistant bacteria in treated wastewater and in the most contaminated rivers. All other antibiotic molecules analyzed (erythromycin, clarithromycin, azithromycin, tetracycline) were at low risk of resistance selection in those environments. However, the heterogeneity of the data available impairs their full exploitation. Consequently, we listed indicators to survey AMR and antibiotics in the environment and recommended the harmonization of sampling strategies and endpoints for analyses. Finally, the objectives and methods used for the present work could comprise a useful example for how national authorities of countries sharing common socio-geographic characteristics with France could seek to better understand and define the environmental dimension of AMR in their particular settings.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".