Assessment of seasonality in antimicrobial susceptibility testing and resistance of urinary Escherichia coli from dogs and cats in the United States (2019 – 2022)
Bibliographic record
Abstract
Antimicrobial resistance (AMR) poses a significant global health challenge, impacting humans, animals, and the environment. Dogs and cats are vulnerable to urinary tract infections (UTIs), mostly caused by antimicrobial-resistant Escherichia coli, necessitating antimicrobial susceptibility testing (AST) for optimal treatment. This study investigated and evaluated the seasonality of AST and AMR in urinary E. coli isolates from dogs and cats in the USA and evaluated the potential influence of climatic zones on these patterns. Retrospective data from IDEXX Laboratories, from January 2019 to December 2022, were analyzed. The dataset included 344,862 urinary E. coli isolates (74.2 % from dogs, 25.8 % from cats) tested against seven antimicrobials. Linear regression and negative binomial regression models assessed seasonality and trends, accounting for climatic zone variability. An increasing trend in AST was observed, with a seasonal peak in the summer for both species. Urinary E. coli isolates from dogs and cats had the highest resistance to amoxicillin at 27.9 % (95 % CI: 27.7-28.1) and 28.4 % (95 % CI: 28.1-28.7), respectively, among all antimicrobials tested. Resistance rates significantly declined (p < .01) for all drugs tested in dogs, while in cats, declines were significant only for cefovecin, marbofloxacin, and enrofloxacin. No seasonality in AMR was found at the national level or across climatic zones, though AMR rates varied significantly by climatic zone (p < .01). The hot-humid zone had the highest resistance rates but the lowest AST rates per one million dogs and cats. Seasonality in AST suggests a seasonal pattern for UTIs; however, no seasonal pattern in AMR could be observed nationally or regionally for urinary E. coli isolates.
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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 | 0.003 |
| 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 | 0.001 | 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".