Antibiotic stewardship and antimicrobial resistance in conflict-affected Sudan: a situational analysis
Bibliographic record
Abstract
Introduction: Antimicrobial resistance (AMR) is a global concern, the drivers and consequences of which are exacerbated by poverty and inequality, with Low- and Middle-Income Countries (LMICs) bearing the highest rates, particularly Sub-Saharan Africa. Armed conflict further exasperates problem, by damaging healthcare systems, limiting access to essential medicines, and increasing the use of antibiotics in uncertain environments. This study sought to explore healthcare professionals' views on AMR and antimicrobial stewardship challenges before and during the conflict to investigate the impact of armed conflict on antimicrobial stewardship (ASP) in Sudan, a low-income country facing a humanitarian crisis exacerbated by ongoing violence. Methods: Data was collected via an anonymous qualitative online survey completed by healthcare workers with experience before and during the conflict and was analysed via thematic analysis. Results: Sudan is experiencing significant challenges due to its fragile healthcare system, poverty, and inequality. The results identify barriers to ASPs before and during the conflict, including lack of laboratory facilities, inadequate training, and the indiscriminate use of antibiotics. The findings reveal that the conflict has intensified the risk of AMR, with disrupted healthcare services leading to increased self-medication and reliance on broad-spectrum antibiotics. Discussion: The study underscores the urgent need for multi-faceted interventions that address immediate healthcare needs while implementing long-term strategies to control AMR in conflict-affected regions. Recommendations include enhancing healthcare infrastructure, improving diagnostic capabilities, and fostering collaborations among various stakeholders to ensure sustainable AMR prevention strategies.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| 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".