Incidence and predictors of mortality among low birth weight neonates in Africa: a systematic review and meta-analysis
Bibliographic record
Abstract
Background In Africa, the burden of low birth weight (LBW) neonatal mortality remains substantial, yet comprehensive evidence is lacking, with varied and inconclusive findings from primary studies. This systematic review and meta-analysis aimed to assess the pooled incidence and predictors of LBW neonatal mortality in Africa. Methods In accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, we systematically searched PubMed, EMBASE, Cochrane Library, African Journals Online, Web of Science, Google Scholar, and Google for relevant studies. The Newcastle Ottawa Scale was used to assess study quality. Heterogeneity and publication bias were evaluated using the I2 statistic and Egger's test. A random-effects model was applied due to significant heterogeneity, with pooled incidence and 95% confidence intervals (CIs) calculated. Subgroup and sensitivity analyses explored sources of heterogeneity. Results This meta-analysis included 28 studies involving 56,775 LBW neonates. The pooled incidence of LBW neonatal mortality in Africa was 33.1% per 100 person-years (95% CI: 19.54–46.65), with substantial heterogeneity (I2 = 99.9%, P < 0.001). Predictors associated with LBW neonatal mortality included extremely low birth weight (PHR = 4.37, 95% CI: 2.62–7.29), lack of antenatal care follow-up (PHR = 2.84, 95% CI: 1.21–6.67), perinatal asphyxia (PHR = 1.73, 95% CI: 1.38–2.16), necrotizing enterocolitis (PHR = 2.80, 95% CI: 2.03–3.86), preterm birth (PHR = 3.17, 95% CI: 1.88–5.35), respiratory distress syndrome (PHR = 1.87, 95% CI: 1.57–2.23), sepsis (PHR = 2.04, 95% CI: 1.59–2.63), lack of kangaroo mother care (PHR = 5.29, 95% CI: 2.76–10.16), maternal diabetes mellitus (PHR = 2.74, 95% CI: 1.87–4.01), and maternal HIV infection (PHR = 4.47, 95% CI: 2.06–9.67). Conclusions This study highlights a concerning high incidence of LBW neonatal mortality in Africa. Strategies targeting these predictors, such as improving antenatal care, promoting kangaroo mother care, and managing maternal health conditions like diabetes and HIV, could substantially reduce LBW neonatal mortality in the region. Policymakers and healthcare providers should prioritize these interventions to mitigate the high burden of LBW neonatal mortality and improve neonatal health outcomes across Africa. Systematic Review Registration identifier, CRD42024560375.
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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 | 0.018 | 0.039 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.023 | 0.054 |
| Bibliometrics | 0.011 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| 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".