Neonatal sepsis management in Africa: A rapid systematic review and meta-analysis
Bibliographic record
Abstract
Neonatal sepsis is a leading cause of morbidity and mortality in Africa. This study aimed to examine neonatal sepsis treatment guidelines in Africa, compare them with WHO recommendations, identify similarities and deviations, and explore the impact of antimicrobial resistance and implementation challenges. A rapid systematic review was conducted following PRISMA-ScR guidelines. Five databases (Science Direct, PubMed, CINAHL, MEDLINE via Ovid , and Scopus) were systematically searched for studies published between 2014 and 2024 that reported national or regional guidelines on neonatal sepsis treatment. Data were extracted on first-line antibiotic selection, route of administration, treatment duration, supportive care measures, multidrug-resistant organisms and alignment with the WHO guidelines. The Newcastle-Ottawa Scale was used to assess the methodological quality of the included studies. Overall, 29 studies were included in the review. Key findings revealed that while ampicillin/gentamicin, a WHO-recommended first-line regimen, was widely adopted, high microbial resistance rates necessitated alternatives such as carbapenems. Gram-negative pathogens, particularly Klebsiella pneumoniae (up to 92% prevalence) dominated, with multidrug-resistant organisms (MDRO) showing a pooled prevalence of 59% (95% CI: 44.4–73.6%). Regional disparities were evident: Eastern Africa reported 51% MDRO, while Southern Africa reported 20.3% MDRO. The high statistical heterogeneity (I 2 = 99.4%) in the meta-analysis indicates variation in MDRO prevalence across studies, and the pooled estimate should therefore be interpreted with caution. Common implementation challenges included limited access to second-line antibiotics, inadequate training of healthcare workers and infrastructural constraints. Significant variations existed between neonatal sepsis treatment guidelines in a number of African countries and the WHO recommendations. These were driven by MDRO and healthcare resource limitations. While the WHO guidelines provide a global framework, country-specific adaptations are also necessary. There is a need to strengthen antimicrobial stewardship programs, improve diagnostic capacity, and enhance the training of healthcare workers.
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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 | 0.019 | 0.042 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.020 | 0.039 |
| Bibliometrics | 0.014 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".