Vancomycin-resistant <i>Enterococcus</i> prevalence and its association along the food chain: a systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Vancomycin-resistant Enterococcus (VRE) are present across the One Health continuum and pose a considerable risk for transmission along the food chain. This systematic review and meta-analysis estimates the prevalence of VRE colonization in livestock, food of animal origin, and in human populations. METHODS: Embase, MEDLINE and CAB Abstracts were searched for eligible literature. A total of 54 manuscripts passed inclusion criteria by providing prevalence estimates of VRE in a human population and at least one of either livestock or food. Random effects meta-analysis was conducted to determine prevalence estimates, and risk of bias in pooled estimates was assessed using funnel plots and Egger regression. RESULTS: Global pooled prevalence of VRE colonization was highest in poultry and poultry meat at 16% (95% CI: 6%-28%) and 15% (95% CI: 1%-39%), respectively. Human-associated VRE colonization was highest in livestock workers, with a pooled prevalence of 11% (95% CI: 2%-25%), and lowest in the general public at 2% (95% CI: 0%-3%). Meta-regression demonstrated that human VRE prevalence increased at a rate of 0.75% (95% CI: 0.46%-1.04%; P < 0.001) per 1% increase in livestock VRE colonization. CONCLUSIONS: This meta-analysis established a clear link of VRE across One Health sectors. VRE colonization is likely elevated for those in contact with colonized animals or contaminated food products. Quality of evidence in pooled prevalence estimates was limited by publication bias and heterogeneity. The results of this study enhance calls for a One Health approach for mitigating the global burden of priority antimicrobial resistance pathogens.
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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 | 0.014 | 0.033 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.045 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".