Health system interventions and responses to anti-microbial resistance: A scoping review of evidence from 15 African countries
Bibliographic record
Abstract
The global rise in antimicrobial resistance (AMR) is claiming the lives of more than 1.2 million people each year. According to the World Health Organization (WHO) this global health crisis is particularly acute in Africa, largely due to fragile and underfunded health systems. Efforts to combat this public health threat have led to the implementation of health system interventions worldwide aimed at managing and containing the spread of AMR. However, the literature on the real time impacts and the barriers that hinder the implementation of these interventions in the African context is limited. The objective of this scoping review was to identify AMR interventions in African health systems, their impact, and the challenges of the implementation. Drawing on Muka and colleague's 24 step approach for scoping reviews, two major public health databases (PubMed and Global Health) were searched for articles in accordance with the PRISMA guidelines resulting in 4,783 records. Screening and retrieval of articles was done using Rayyan software based on specified inclusion criteria and 36 articles included in the final list. These articles were synthesized after extracting specific data on AMR interventions and their impact on African health systems. The review identified four broad impacts of AMR interventions including 1. Reduction in antibiotics use, 2. Increased adherence to guidelines and protocols, 3. Enhanced laboratory-based AMR surveillance, 4. Development of antimicrobial stewardship (AMS) Action Plans and Teams. However, challenges such as poor laboratory infrastructure, logistical challenges, poor financial commitment and inadequate education and training were identified as challenges impeding the successful implementation of AMR interventions in Africa. Our findings reveal a range of successful AMR interventions in African health systems although infrastructural and financial challenges remain. Better standardization and reporting of AMR diagnosis while leveraging the available information is needed to improve the optimization of treatment guidelines across Africa.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".