The burden and predictors of hospital-acquired infection in intensive care units across Sub-Sahara Africa: systematic review and metanalysis
Bibliographic record
Abstract
BACKGROUND: Hospital-acquired infection (HAI) refers to an infection that occurs during hospitalization and typically manifests 48 h after admission. Evidence suggests that the prevalence of HAIs in Sub-Saharan Africa (SSA) is significantly higher compared to other regions. These infections remain a major concern in low-income countries, contributing to elevated morbidity and mortality rates. This study aimed to assess the burden and identify predictors of HAIs in intensive care units (ICUs) across SSA. METHODS: This review was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. We searched PubMed, Scopus, Embase, Web of Science, Africa Index Medicus, ScienceDirect, HINARI, and Google Scholar to identify relevant studies published in English. This systematic review encompasses 44 articles published between 2003 and 2024, with the majority (22 articles) published recently between 2020 and 2024. The actual database search was conducted between January 1, 2025, and February 1, 2025. Articles irrelevant to this study's objectives, those without abstracts or full texts, unpublished reports, editorials, studies that did not clearly define outcomes, and studies written in languages other than English were excluded. The analysis was conducted using Stata version 17. The protocol was registered with PROSPERO under the registration number CRD 63,194,923,892. Quality assessment was performed using the Newcastle-Ottawa Scale, and data extraction followed the Joanna Briggs Institute methodology. RESULTS: A total of 44 primary samples were included in this meta-analysis. Using the random effect DerSimonian model, we showed that the pooled prevalence of hospital-acquired infections (HAIs) in intensive care units was 28.22% (95% CI: 23.61-32.81). Determinants of HAIs in the intensive care units included neonatal or advanced age (> 50 years), intubation, trauma, surgery, presence of comorbidities, catheterization, prolonged hospital stay, and HIV-positive status. CONCLUSION AND RECOMMENDATIONS: Individuals in extreme age groups, those with chronic diseases or immunocompromised conditions, and patients with specific risk factors (e.g., catheterization, prolonged hospitalization) were more prone to HAIs. Strengthening the quality of care and implementing effective infection control measures are recommended to reduce the burden of healthcare-associated infections (HAIs) in sub-Saharan Africa.
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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 | 0.021 | 0.052 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.042 |
| Bibliometrics | 0.014 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".