Zoonotic <i>Escherichia coli</i> and urinary tract infections in Southern California
Bibliographic record
Abstract
ABSTRACT Extraintestinal pathogenic Escherichia coli (ExPEC) is the leading cause of urinary tract infections (UTIs) worldwide and may be transmitted from food animals to humans via contaminated meat. However, the contribution of zoonotic ExPEC strains to UTIs in metropolitan areas remains unclear. We estimated the proportion of UTIs attributable to zoonotic ExPEC across eight Southern California counties. Between 2017 and 2021, we collected 12,616 E. coli isolates from retail meat and 23,483 from UTI patients, sequencing a representative subset of 5,728 isolates. Using a Bayesian latent class model trained with 17 host-associated genetic markers, we inferred the host origin of each isolate. Demographic, clinical, and antimicrobial resistance profiles were compared between meat isolates and clinical isolates inferred to be of human or food-animal origin. Most UTI patients were female (88%), with a median age of 50 years; 37% were Hispanic and 31% non-Hispanic white. Zoonotic ExPEC strains accounted for 18% of UTIs overall, rising to 21.5% in high-poverty neighborhoods. Women had a higher zoonotic proportion than men (19.7% vs 8.5%, P < 0.001). Among men, those with zoonotic infections were older than those with non-zoonotic infections (median 73.0 vs 65.0 years, P = 0.028). These findings underscore the contribution of zoonotic ExPEC to the UTI burden in Southern California and the need for targeted interventions to reduce risk in vulnerable communities. IMPORTANCE Urinary tract infections (UTIs) are among the most common bacterial infections worldwide and are primarily caused by Escherichia coli . While E. coli is known to colonize both humans and food-producing animals, the extent to which zoonotic strains impact human disease remains poorly understood. Emerging evidence suggests that food animals may serve as an underrecognized reservoir for extraintestinal pathogenic E. coli (ExPEC). In this study, we used a genomic attribution model to quantify the contribution of zoonotic strains to UTIs in Southern California. We found that approximately 18% of E. coli UTIs were likely attributable to food animals. Individuals living in high-poverty neighborhoods had a 1.6-fold increased risk of zoonotic UTIs compared to those in low-poverty areas. These findings highlight zoonotic transmission as an important driver of UTIs and suggest that reducing ExPEC in food-animal reservoirs could help lower disease burden and address health disparities.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".