Antibiotic resistance in hospital wastewater in West Africa: a systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: The occurrence of antibiotic-resistant bacteria (ARB) has become a global menace and therefore increases morbidity, mortality and healthcare costs. Globally, hospital wastewater (HWW) has been identified as a significant source of antibiotic-resistant elements. OBJECTIVES: This review aims to systematically review and to perform meta-analyses from evidence on antibiotic resistance studies in HWW in West Africa. METHODS: The review was conducted in compliance with PRISMA and included studies published between 1990 and 2024 in West Africa from the Scopus, PubMed, and Web of Science databases. Eligible studies that characterized resistant bacteria, genes, or antibiotic residues in HWW were included. Meta-analyses for resistant bacteria and genes as well risk of bias using the Newcastle-Ottawa scale were conducted. RESULTS: Out of 23 studies reviewed, resistant bacteria were reported in 39% (E. coli), 26% (K. pneumoniae), and 17% (P. aeruginosa), while 17 studies reported ARGs, with blaTEM (29%), blaOXA- 48 (18%), blaSHV (18%), and mecA (18%) being the most common. Only 4% and 9% of studies focused on toxin genes and antibiotic residues, respectively. Meta-analysis showed pooled prevalence rates for resistant bacteria: E. coli 42.6% (95% CI: 26.7%-60.3%) and K. pneumoniae 32.1% (95% Cl: 28.8%- 36.5%), and ARGs: blaTEM 76.0% (95% CI = 64.6%-84.6%) and blaSHV 59.3% (95% CI = 19.5%-89.8%). CONCLUSION: This systematic review highlights significant findings of high levels of ARGs and ARBs of public health concern in HWW in West Africa. This highlights the need to improve upon the monitoring of antibiotic resistance and treatment of HWW in West Africa.
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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 | 0.015 | 0.032 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.019 | 0.040 |
| Bibliometrics | 0.010 | 0.011 |
| 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".