Risk Factors for Community Colonization With Extended-Spectrum Cephalosporin-Resistant Enterobacterales (ESCrE) in Botswana: An Antibiotic Resistance in Communities and Hospitals (ARCH) Study
Bibliographic record
Abstract
BACKGROUND: The epidemiology of extended-spectrum cephalosporin-resistant Enterobacterales (ESCrE) in low- and middle-income countries (LMICs) is poorly described. Identifying risk factors for ESCrE colonization is critical to inform antibiotic resistance reduction strategies because colonization is typically a precursor to infection. METHODS: From 15 January 2020 to 4 September 2020, we surveyed a random sample of clinic patients at 6 sites in Botswana. We also invited each enrolled participant to refer up to 3 adults and children. All participants had rectal swabs collected that were inoculated onto chromogenic media followed by confirmatory testing. Data were collected on demographics, comorbidities, antibiotic use, healthcare exposures, travel, and farm and animal contact. Participants with ESCrE colonization (cases) were compared with noncolonized participants (controls) to identify risk factors for ESCrE colonization using bivariable, stratified, and multivariable analyses. RESULTS: A total of 2000 participants were enrolled. There were 959 (48.0%) clinic participants, 477 (23.9%) adult community participants, and 564 (28.2%) child community participants. The median (interquartile range) age was 30 (12-41) and 1463 (73%) were women. There were 555 cases and 1445 controls (ie, 27.8% of participants were ESCrE colonized). Independent risk factors (adjusted odds ratio [95% confidence interval]) for ESCrE included healthcare exposure (1.37 [1.08-1.73]), foreign travel [1.98 (1.04-3.77]), tending livestock (1.34 [1.03-1.73]), and presence of an ESCrE-colonized household member (1.57 [1.08-2.27]). CONCLUSIONS: Our results suggest healthcare exposure may be important in driving ESCrE. The strong links to livestock exposure and household member ESCrE colonization highlight the potential role of common exposure or household transmission. These findings are critical to inform strategies to curb further emergence of ESCrE in LMICs.
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".