A longitudinal cohort study on the effects of reducing farm-level antimicrobial use on Escherichia coli antimicrobial resistance on dairy farms in Québec, Canada
Bibliographic record
Abstract
The effects of interventions that restrict antimicrobial use (AMU) in food production animals on antimicrobial resistance (AMR) remain unclear. We have already established a portrait of AMU and AMR on dairy farms in Québec before a regulation, implemented in 2019, that restricts the use of category I antimicrobials in food production animals. In the current study, involving 84 dairy farms in Québec, we assessed the effects of farm-level changes in AMU on farm-level AMR. Using an observational cohort study design, we gathered AMU data through veterinary invoicing software 2 years before and 2 years after the regulation was implemented. We sampled calves' and cows' feces and the manure pit on each farm during the 4 years. We tested the indicator bacterium Escherichia coli for susceptibility to 20 antimicrobials and evaluated putative extended spectrum β-lactamase/AmpC E. coli growth. Overall use of antimicrobials (all antimicrobials and all administration routes combined) significantly decreased after regulation implementation. Specifically, the 2 types of use that were implicated in the decrease were injectable third-generation cephalosporins and intramammary polymyxin B. A farm's probability of experiencing AMR appeared to increase with higher AMU during the same period, specifically when the use of all antimicrobials combined was considered. A farm's pre- versus post-regulation AMU variation (i.e., reduction, increase, stable) had no apparent effect on its post-regulation AMR. In conclusion, regulation and possibly other concomitant factors had a positive influence on the judicious use of critical antimicrobials and AMR. Moreover, a farm's AMR profile was better explained by its current than by its historical AMU management.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".