Aetiology of invasive bacterial infection and antimicrobial resistance in neonates in sub-Saharan Africa: a systematic review and meta-analysis in line with the STROBE-NI reporting guidelines
Bibliographic record
Abstract
BACKGROUND: Aetiological data for neonatal infections are essential to inform policies and programme strategies, but such data are scarce from sub-Saharan Africa. We therefore completed a systematic review and meta-analysis of available data from the African continent since 1980, with a focus on regional differences in aetiology and antimicrobial resistance (AMR) in the past decade (2008-18). METHODS: We included data for microbiologically confirmed invasive bacterial infection including meningitis and AMR among neonates in sub-Saharan Africa and assessed the quality of scientific reporting according to Strengthening the Reporting of Observational Studies in Epidemiology for Newborn Infection (STROBE-NI) checklist. We calculated pooled proportions for reported bacterial isolates and AMR. FINDINGS: We included 151 studies comprising data from 84 534 neonates from 26 countries, almost all of which were hospital-based. Of the 82 studies published between 2008 and 2018, insufficient details were reported regarding most STROBE-NI items. Regarding culture positive bacteraemia or sepsis, Staphylococcus aureus, Klebsiella spp, and Escherichia coli accounted for 25% (95% CI 21-29), 21% (16-27), and 10% (8-10) respectively. For meningitis, the predominant identified causes were group B streptococcus 25% (16-33), Streptococcus pneumoniae 17% (9-6), and S aureus 12% (3-25). Resistance to WHO recommended β-lactams was reported in 614 (68%) of 904 cases and resistance to aminoglycosides in 317 (27%) of 1176 cases. INTERPRETATION: Hospital-acquired neonatal infections and AMR are a major burden in Africa. More population-based neonatal infection studies and improved routine surveillance are needed to improve clinical care, plan health systems approaches, and address AMR. Future studies should be reported according to standardised reporting guidelines, such as STROBE-NI, to aid comparability and reduce research waste. FUNDING: Uduak Okomo was supported by a Medical Research Council PhD Studentship.
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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 | 0.023 | 0.057 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.038 |
| Bibliometrics | 0.011 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 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".