Risk factors for household colonization by extended-spectrum cephalosporin-resistant enterobacterales (ESCrE) in Botswana
Bibliographic record
Abstract
BACKGROUND: The epidemiology of community colonization with extended-spectrum cephalosporin-resistant Enterobacterales (ESCrE) in low- and middle-income countries (LMICs) is largely uncharacterized. In the community, the household is of particular importance. Identifying risk factors for household ESCrE colonization is critical to inform antibiotic resistance reduction strategies. METHODS: Participants were enrolled at 6 clinics in Botswana. All participants had rectal swabs collected for selective plating and confirmation of ESCrE. Data were collected on demographics, comorbidities, antibiotic use, healthcare exposures, travel, and farm/animal contact. Households were considered exposed if any member had the exposure of interest. Households with ESCrE colonization (cases) were compared to non-colonized households (controls) to identify risk factors for household ESCrE colonization. RESULTS: From 1/1/20 - 9/4/20, 327 households were enrolled. The median (IQR) number of people enrolled per household was 3 (2-4) ranging from 2 to 10. The median (IQR) age of subjects was 18 years (5-34) and 304 (93%) households included at least one child. Of 327 households, 176 (54%) had at least one household member colonized with ESCrE. Independent risk factors [adj OR (95%CI)] for household colonization were: (1) horse/donkey exposure [2.32 (1.05, 5.10)]; (2) yogurt consumption [1.73 (1.04, 2.88)]; (3) region [2.83 (1.48,5.43)]; and (4) enrollment during pre-COVID lockdown [2.90 (1.66, 5.05)]. CONCLUSIONS: ESCrE household colonization was common with evidence of geographic variability as well as a possible role of animal exposure. The role of yogurt exposure requires further study with consideration of source (commercial, homemade). Further prospective studies of household ESCrE colonization with longitudinal assessments of exposures are required to identify effective prevention strategies.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".