Economic evaluation of antimicrobial use practices in animal agriculture: a case of poultry farming
Bibliographic record
Abstract
Background: The growing evidence of the contribution of antimicrobial use (AMU) in animal agriculture to the public health threat of antimicrobial resistance has highlighted to policymakers the importance of the need for prudent AMU in animal production. Livestock farming is an economic process, where farmers are using inputs such as antimicrobials to minimize their losses. Objectives: Using a large and unique dataset combining time-series data on economic performance and health records in conventional broiler production in France, we identify how improved healthcare management and disease prevention impact economic performance, AMU reduction and health outcomes. Methods: , by performing advanced regression models investigating the relative importance of medication and veterinary procedures. Results: In our study, 50% of the treatments (expressed as number of new treatments) are attributable to only 30% of all flocks. There is an inverted U-shaped relationship between AMU and economic performance. This finding implies that the marginal profit of antimicrobials is decreasing, meaning that using antimicrobials is only profitable up to a certain threshold. Results also show that the profit increases as the number of preventive treatments increase. Conclusions: Our findings suggest that policies encouraging farmers to work upstream from the occurrence of disease have the potential to perform better than regulations, as they would maintain a profitable activity while diminishing AMU. Encouraging adequate infection control practices by subsidizing or providing other incentives would benefit farmers and society.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".