Antimicrobial resistance in Ethiopia: current landscape, challenges, and strategic interventions
Bibliographic record
Abstract
Antimicrobial resistance (AMR) is a critical global health concern, characterized by microorganisms' ability to resist the effects of antimicrobial drugs. This phenomenon, driven by misuse of antibiotics, poses significant challenges to healthcare systems. Ethiopia faces a rising burden of AMR, exacerbated by poor infection control practices and inadequate surveillance, with high rates of multidrug-resistant bacteria documented across the country. This study aims to assess the current AMR landscape in Ethiopia, evaluate policy effectiveness, understand contributing factors, and propose strategic interventions to mitigate the issue. A comprehensive literature review was conducted using electronic databases such as PubMed, MEDLINE, Scopus, and Google Scholar. Studies published between 2014 and 2024 that focused on Ethiopia's healthcare-related AMR were selected. Quantitative and qualitative studies, including observational and case–control studies, were analyzed. Data extraction and synthesis focused on AMR patterns, contributing factors, and challenges. The quality of studies was assessed using the Newcastle–Ottawa Scale and the CASP checklist. The review revealed alarming AMR rates in Ethiopia, particularly among extended-spectrum beta-lactamase (ESBL)-producing and multidrug-resistant bacteria. The prevalence of ESBL-producing bacteria ranged from 18% to 55.5%, while multidrug-resistant bacteria were identified in 62.9% to 87.4% of cases. Methicillin-resistant Staphylococcus aureus (MRSA) and vancomycin-resistant enterococci (VRE) were also common, with significant resistance to multiple antibiotics. Contributing factors included inappropriate antibiotic use, inadequate infection control, and a lack of surveillance. The study underscores the urgent need for enhanced AMR surveillance, improved infection control measures, and antimicrobial stewardship programmes in Ethiopia. Comprehensive strategies involving public awareness, policy enforcement, and healthcare infrastructure development are essential to curbing the rising AMR burden.
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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 | 0.000 | 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 | 0.000 |
| 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".