Antibiotic resistance genes circulating in Nigeria: a systematic review and meta-analysis from the One Health perspective
Bibliographic record
Abstract
BACKGROUND: The misuse of antibiotics in developing countries has created serious threats to public healthcare systems and reduced treatment options. Multidrug-resistant bacteria harbour antibiotic resistance genes that help them subdue the effectiveness of several available antibiotics. This review aimed to assess antimicrobial resistance genes circulating in Nigeria via a systematic review and meta-analysis. METHODS: A comprehensive literature search was performed using five electronic databases: PubMed, Web of Science, Scopus, Google Search, and African Journals Online (AJOL). Articles related to antibiotic resistance genes in Nigeria, published between January 1, 2015 and October 31, 2024, were included. The Newcastle-Ottawa scale (NOS) was used to assess the risk of bias. The meta-analysis for random effects was performed to determine the proportions and pooled prevalence of the resistance genes from the various One Health domains, as well as heterogeneity in the data, using R software (Version 4.3.3) and the metaprop package. RESULTS: Of the 762 articles retrieved, 56 (humans [n = 33], animals [n = 8], environment [n = 12], human/animal [n = 1], and human/animal/environment [n = 2]) from the six geopolitical zones in Nigeria met the inclusion criteria. The extended-spectrum beta-lactamase (ESBL) gene with the highest pooled prevalence was blaSHV (24.0% [95% CI: 12.0–44.0]), followed by blaCTX-M (23.0% [95% CI: 14.0–37.0]), and the least was blaTEM (18.0% [95% CI: 8.0–37.0]). Among the carbapenemase genes, blaKPC (33.0% [95% CI: 7.0–76.0]) was the most prevalent, followed by blaNDM (21.0% [95% CI: 9.0–41.0]), blaOXA (11.0% [95% CI: 2.0–46.0]) and the least was blaVIM (9.0% [95% CI: 3.0–26.0]). The mecA gene also had a high pooled prevalence (51.0% [95% CI: 14.0–86.0]). The pooled prevalence of the erm, sul, tet, and qnr genes ranged from 19.0% (95% CI: 8.0–38.0) to 27.0% (95% CI: 13.0–47.0). Some antibiotic resistance genes were shared among the three domains. CONCLUSION: This systematic review and meta-analysis has demonstrated the co-existence of antibiotic resistance genes among bacteria causing infection in Nigeria, via the One Health approach. There is a need for future research on the circulation of antibiotic resistance genes in developing countries using internationally approved approaches to track down this menace.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.010 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".