Antimicrobial resistance: Prevalence, genetics and associations with antimicrobial use in food-producing animals
Bibliographic record
Abstract
Antimicrobial use (AMU) in livestock has come under growing criticism. There is increasing pressure to optimize AMU in food-producing animals, which will likely entail restrictions and voluntary reductions of their use, as well as implementation of protocols promoting antimicrobial stewardship. In this thesis, 1) methods were compared for obtaining AMU data on dairy farms, 2) factors associated with the prevalence of antimicrobial resistance (AMR) in non-aureus staphylococci (NAS) isolated from intramammary infections were studied, 3) treatment strategies for non-severe clinical mastitis (CM) in dairy cattle were contrasted, and 4) effects of restricted antimicrobial use in food-producing animals towards the prevalence of AMR genes (ARGs) were evaluated. Chapter 2 confirmed that treatment records accurately quantified AMU in well-managed dairy herds. Yet, their widespread adoption into AMU surveillance cannot be recommended, due to an underestimation of AMU in herds with elevated bulk tank somatic cell count. In regard to AMR, Chapter 3 demonstrated that resistance against tetracycline, penicillin and erythromycin in NAS was common in Canadian dairy herds. In Chapter 4, factors associated with AMR were further explored. An association between AMR in NAS and AMU was present when penicillins, 3rd-generation cephalosporins or macrolides were administered systemically, whereas intramammary use of antimicrobials were not associated with AMR. As antimicrobials classified as critically important antimicrobials (CIAs) for humans were associated with AMR, in Chapter 5 a systematic review was done to assess whether CIAs and non-CIAs had comparable efficacy to treat non-severe bovine CM caused by the most prevalent bacteria causing mastitis worldwide. No protocol including the use of CIAs had superior bacteriological cure rates of non-severe CM than protocols relying on non-CIAs. Therefore, no adverse effects in terms of animal health should be expected by ceasing use of CIAs for treating non-severe CM in dairy herds. A second systematic review showed that restricted AMU in food animals was associated with a lower presence of ARGs in bacteria isolated from animals and humans. Reducing use of CIAs to treat non-severe CM in typical dairy herds may reduce load of ARGs without significant impacts on animal health and welfare.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".