Carbapenem-resistant Enterobacteriaceae in the livestock, humans and environmental samples around the globe: a systematic review and meta-analysis
Bibliographic record
Abstract
Carbapenem-resistant Enterobacteriaceae (CRE) have diminished treatment options causing serious morbidities and mortalities. This systematic review and meta-analysis assessed the prevalence and associated factors of Enterobacteriaceae infections in clinical, livestock and environmental settings globally. The population intervention comparison and outcome strategy was used to enroll studies using the preferred reporting system for systematic review and meta-analysis to include only cross-sectional studies. Search engines used to retrieve articles included journal author name estimator, PubMed, Google Scholar and African Journals Online (AJOL). The Newcastle-Ottawa scale was used to assess the quality of studies. Sixteen articles from 2013 to 2023 in Africa, Asia, Europe and South America were studied. The pooled prevalence of CRE was 43.06% (95% CI 21.57-66.03). Klebsiella pneumoniae (49.40%), Escherichia coli (26.42%), and Enterobacter cloacae (14.24%) were predominant. Klebsiella pneumoniae had the highest resistance with the blaKPC-2 in addition to blaNDM, blaOXA-48, blaIMP and blaVIM. The blaKPC-2 genes occurrence was associated with environmental (P-value < 0.0001) and South American studies (P-value < 0.0001), but there was no difference in the trends over time (P-value = 0.745). This study highlights the high rates of CRE infections, particularly within blaKPC production. Monitoring and surveillance programs, research and infection control measures should be strengthened. Additionally, further studies are needed to explore the mechanisms driving the predominance of specific bacterial species and the distribution of resistance genes within this bacterial family.
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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 | 0.013 | 0.025 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.022 | 0.048 |
| Bibliometrics | 0.012 | 0.012 |
| 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) | 0.003 | 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".