Antibiotic resistance in Nigeria’s water system: an urgent public health challenge impeding the achievement of Sustainable Development Goals (SDGs)
Bibliographic record
Abstract
This review addresses the critical issue of antibiotics and antibiotic resistance genes (ARGs) in Nigerian water systems, which pose significant public health and environmental challenges. Reported antibiotic concentrations in Nigerian surface waters was a maximum of 272.15 μg/L, frequently exceeding the World Health Organization (WHO) recommended safety limit of 72 out of 75 antibiotics. Sulfonamides, beta-lactams, tetracyclines, fluoroquinolones, carbapenems, and cephalosporins are the most common classes of antibiotics detected, with residues reported in >80% of surface water studies. Many ARGs such as beta-lactamase (bla), tetracycline (tet), and sulfonamide (sul) genes have been found in 70%-90% of water sources in Nigeria, and they are also common in Ghana. The prevalence of these antibiotics and ARGs in Nigerian water sources are linked to human and animal activities, including medical facilities, pharmaceutical industries, and agricultural practices. These sources contribute to the widespread distribution of ARGs, which are exacerbated by inadequate wastewater treatment and regulatory policies. The persistence of these contaminants threatens aquatic ecosystems and human health by promoting the spread of resistant infections. This article examines the sources, occurrence, and spatial distribution of antibiotics and ARGs in Nigeria, highlighting the mechanisms of ARG transmission and the factors influencing their spread. Additionally, it discusses the human health risks and ecological impacts associated with antibiotic contamination, underscoring the urgent need for effective intervention strategies. Addressing these issues is also vital for achieving sustainable developments, particularly those related to health, clean water, and life below water. The review calls for advanced wastewater treatment, regulatory improvements, and increased public awareness to mitigate the impact of antibiotics and ARGs on Nigerian water systems.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".