Epidemiology of surgical site infections post-cesarean section in Africa: a comprehensive systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Surgical site infections (SSIs) are among the most common postoperative complications following cesarean section, particularly in Africa. These infections pose maternal health risks, including prolonged hospitalization, increased healthcare costs, and mortality. This systematic review and meta-analysis aimed to evaluate the epidemiology, pooled prevalence, and risk factors for SSIs after cesarean section in Africa. METHODS: A systematic search of PubMed/MEDLINE, Scopus, and Web of Science databases was conducted to identify studies published between January 2000 and December 2023. The review followed PRISMA 2020 guidelines, and 41 studies spanning 18 African countries met the inclusion criteria. Data on SSI prevalence and risk factors were extracted, and the quality of studies was assessed using the Newcastle-Ottawa Scale. A random-effects model was used to estimate pooled prevalence, with subgroup analysis, sensitivity analyses, and meta-regression exploring variations across study characteristics. Publication bias was assessed using funnel plots. RESULTS: = 97%, < 0.001). Regional variations were observed, with the highest prevalence in Tanzania (34.1%) and Uganda (15%), and the lowest in Tunisia (5%) and Egypt (5.3%). Temporal trends revealed a peak in prevalence (16%) during 2011-2015, declining to 9.8% by 2016-2020. Prolonged rupture of membranes (PROM) was the most frequently reported risk factor (OR: 4.45-13.9), followed by prolonged labor (> 24 h) (OR: 3.48-16.17) and chorioamnionitis (OR: 4.37-9.74). Potential publication bias indicated by asymmetrical funnel plots. CONCLUSION: SSIs following cesarean section remain a burden in Africa, with wide regional variations and multiple preventable risk factors. The findings highlight the need for targeted interventions, including improved infection control practices, antenatal care, and timely management of obstetric complications.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.011 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".