Analysis of antibiotic use, biosecurity and mortality in semi-intensive broiler farms in Kenya
Bibliographic record
Abstract
The indiscriminate use of antibiotics in food-producing animals contributes to antimicrobial resistance (AMR), posing a global threat. Understanding the factors associated with antibiotic use is critical to combat resistance while maintaining animal health. This study examined antibiotic use practices, mortality rates, biosecurity levels, as well as the associations between biosecurity and antibiotic use, and between biosecurity and mortality, in semi-intensive broiler farms in Kenya.The study was conducted in 129 semi-intensive farms with total flock sizes between 200 and 2000 birds across three peri-urban counties in Kenya. Data were collected prospectively over one production cycle, with farms visited biweekly using questionnaires and a drug bin approach. Biosecurity levels were assessed by a panel of experts who weighted scores for various external and internal biosecurity subcategories. Directed acyclic graphs (DAG) described potential relationships between explanatory variables, confounders and outcome. Logistic regression analysis was conducted with antibiotic use as the outcome variable. Explanatory variables with P < 0.25 in the univariable logistic regression were included in the multivariable regression. Similarly, linear regression was conducted using mortality as the outcome.Overall, 72% of farms used antibiotics, primarily for prophylaxis (66%), with erythromycin and oxytetracycline being the most commonly used antibiotics. The median mortality rate across the production cycle was 6%. There was no significant difference in mortality between farms using antibiotics and those not using antibiotics. Biosecurity practices were low, with a median biosecurity score of 14.3/67.9. Univariable screening suggested potential associations between antibiotic use and vaccination of day-old chicks, flock size, cleaning protocol for chicken drinkers, resting period between batches, feed store cleaning, water source, distance from neighbouring farms, and age. However, these were not significant in multivariable logistic regression. Linear regression showed an association between mortality and biosecurity measures, specifically disease management and visitor entry regulation.This study highlights widespread antibiotic use, low biosecurity implementation, and variability in mortality rates in the farms surveyed. There is a gap in farmers' implementation of effective biosecurity measures and understanding of prudent antibiotic use. An urgent need exists to develop comprehensive data collection methodologies, education, and interventions to promote responsible antibiotic stewardship and cost-effective biosecurity practices among poultry farmers in Kenya.
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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) | 0.001 | 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".