Quantifying antimicrobial use on Canadian dairy farms using garbage can audits
Bibliographic record
Abstract
Antimicrobial resistance in pathogenic bacteria is one of the preeminent concerns for the future of global health. There is a dose-dependent relationship between antimicrobial use (AMU) and the prevalence of antimicrobial-resistant pathogens. As most AMU in Canada is related to animal agriculture, there is a need to reduce overall AMU, which could be accomplished through surveillance of AMU in animal agriculture, including the dairy industry. The objective of this study was to quantify AMU on dairy farms across Canada. This study had two parts: a description of data collected in 2019-2020, and a meta-analysis comparing this data to previous estimates of AMU in the Canadian dairy industry. The first included a garbage can audit (GCA) on 107 farms in four Canadian provinces (British Columbia, Alberta, Ontario, and Nova Scotia) in 2020; AMU data were converted to the dose-based metrics of defined course doses (DCD) and defined daily doses (DDD). Mixed-effect linear models were fit to determine the relationship between province and use of different classes of antimicrobials. On average, for every 100 animals on the farm, 117 DCD of antimicrobials were administered per year (IQR: 55, 158). These treatments amounted to 623 DDD / 100 animal-yr (IQR: 302, 677 DDD/100 animal-years). Penicillins were the most used class of antimicrobials, followed by first-and third-generation cephalosporins. Farms in Ontario used more third-generation cephalosporins than other provinces. The second part of this study compared AMU in 2020 to previously reported Canadian studies through a meta-analysis. A GCA was conducted in 2007-2008 in Alberta, Ontario, Québec, and the Maritime provinces (Prince Edward Island, New Brunswick and Nova Scotia); another GCA was conducted in Québec in 2018. Overall, AMU was lower in 2018-2020 than in 2007-2008, with the exception of third-generation cephalosporin use, which increased.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".