Prevalence of antimicrobial resistance genes and its association with restricted antimicrobial use in food-producing animals: a systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: There is ongoing debate regarding potential associations between restrictions of antimicrobial use and prevalence of antimicrobial resistance (AMR) in bacteria. OBJECTIVES: To summarize the effects of interventions reducing antimicrobial use in food-producing animals on the prevalence of AMR genes (ARGs) in bacteria from animals and humans. METHODS: We published a full systematic review of restrictions of antimicrobials in food-producing animals and their associations with AMR in bacteria. Herein, we focus on studies reporting on the association between restricted antimicrobial use and prevalence of ARGs. We used multilevel mixed-effects models and a semi-quantitative approach based on forest plots to summarize findings from studies. RESULTS: A positive effect of intervention [reduction in prevalence or number of ARGs in group(s) with restricted antimicrobial use] was reported from 29 studies for at least one ARG. We detected significant associations between a ban on avoparcin and diminished presence of the vanA gene in samples from animals and humans, whereas for the mecA gene, studies agreed on a positive effect of intervention in samples only from animals. Comparisons involving mcr-1, blaCTX-M, aadA2, vat(E), sul2, dfrA5, dfrA13, tet(E) and tet(P) indicated a reduced prevalence of genes in intervention groups. Conversely, no effects were detected for β-lactamases other than blaCTX-M and the remaining tet genes. CONCLUSIONS: The available body of scientific evidence supported that restricted use of antimicrobials in food animals was associated with an either lower or equal presence of ARGs in bacteria, with effects dependent on ARG, host species and restricted drug.
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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 | 0.012 | 0.034 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.029 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".