Associations of the Swiss national reporting system's antimicrobial use data and management practices in dairy cows on tiestall farms
Bibliographic record
Abstract
Antimicrobial use (AMU) in Switzerland is above target and requires reduction, especially in dairy cattle. Measuring AMU is pivotal to identifying starting points for AMU reduction, and so are studies investigating its potential drivers in dairy farms worldwide. However, although AMU in dairy farms is high, studies estimating AMU specifically in tiestall farms are scarce. Tiestalls are a common housing system and their prevalence among dairy farms accounts to approximatively 73%, 41%, and 40% in Canada, the United States, and Switzerland, respectively. The objectives of this cross-sectional, retrospective observational study were to estimate AMU using the newly established Swiss national reporting system for AMU in livestock and to identify associated factors on Swiss tiestall dairy farms. We calculated the treatment incidence (TI) by using the European Medicines Agency methodology and their defined daily dose (DDD) and defined course dose (DCD) standards. Data on factors potentially associated with AMU were obtained through personal interviews with farm managers on 221 farms. Retrospectively, during a 1-year period, data on a total of 7,619 treatments were extracted from the national database. Associations between management factors and TI were analyzed using a generalized linear model with gamma distribution. The mean (± SD) overall TI was 5.46 DDD/cow-year (± 4.10 DDD/cow-year). Intramammary treatment during lactation accounted for highest TI (3.24 ± 3.16 DDD/cow-year), whereas dry-cow therapy accounted for lowest TI (0.44 ± 0.49 DCD/cow-year). We found that 5 of the investigated management factors were significantly associated with TI. Organic production (estimate = -2.16; 95% CI = -3.62 to -0.70) and herd size (estimate = -0.81; 95% CI = -1.23 to -0.39) were negatively associated with TI. Specific cow breeds (Brown Swiss estimate = 1.56; 95% CI = 0.45-2.68) and Holstein Friesian (estimate = 1.42; 95% CI = 0.03-2.82; reference: other breeds) and the use of hygienic powders on the lying area (estimate = 1.10; 95% CI = 0.04-2.17) were positively associated with TI. Overall, the Swiss national reporting system is a valuable tool for AMU estimation. Several herd characteristics and management factors were associated with AMU in tiestall farms. Further studies focusing on factors associated with AMU and which are amenable to intervention will help improve stewardship programs and subsequently reduce AMU in dairy cows.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".