A systematic review and meta-analysis on antibiotic resistance genes in Ghana
Bibliographic record
Abstract
Addressing antimicrobial resistance (AMR) poses a complex challenge, primarily because of the limited understanding of bacterial antibiotic resistance genes (ARGs) and the spread of these genes across different domains. To bridge this knowledge gap in Ghana, we undertook a comprehensive systematic review and meta-analysis to quantify and estimate the prevalence of circulating ARGs in bacteria isolated from human, animal, and environmental sources. A thorough literature search was conducted across three major databases—Web of Science, PubMed, and Scopus—to retrieve all relevant articles related to ARGs in Ghana from the inception of the databases to February 25, 2024. A risk-of-bias evaluation was performed using the Newcastle-Ottawa Scale (NOS), and the data analysis involved descriptive statistics and proportional meta-analysis. Of the 371 articles initially obtained, 38 met the inclusion criteria. These studies adequately covered Ghana geographically. The most prevalent ESBL gene identified was bla CTX-M , with a prevalence of 31.6% (95% CI: 17.6–45.7), followed by bla TEM (19.5% [95% CI: 9.7–29.3]), and bla SHV (3.5% [95% CI: 0.3–6.6]). The pooled prevalence of carbapenemase genes ranged from 17.2% (95% CI: 6.9–27.6) for bla NDM to 10.3% (95% CI: 1.9–18.7) for bla OXA . Additionally, other ARGs, including sul1 , qnrS , gyrA , erm(B) , and mecA , were detected, with prevalence ranging from 3.9% (95% CI: 0.0–8.5) to 16.4% (95% CI: 3.1–29.8). Several ARGs were shared across human, animal, and environmental sources. This review revealed that bacteria obtained from human, animal, and environmental samples in Ghana shared genes associated with AMR. This finding provides evidence on the interconnection of AMR across these three domains. Horizontal gene transfer, which enables the dissemination of ARGs between genetically diverse bacteria, can occur, necessitating a multidisciplinary approach to addressing antimicrobial resistance in Ghana.
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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 | 0.017 | 0.036 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.034 |
| Bibliometrics | 0.011 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 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".