The Impact of Prenatal Care on the Prevention of Neonatal Outcomes: A Systematic Review and Meta-Analysis of Global Health Interventions
Bibliographic record
Abstract
Background/Objectives: Neonatal outcomes, including low birth weight, preterm birth, and neonatal mortality, pose significant global health challenges, particularly in low- and middle-income countries. Prenatal care has emerged as a critical intervention in mitigating these risks through medical, nutritional, and psychosocial support. This study aimed to systematically assess the effectiveness of prenatal care interventions in preventing neonatal outcomes across diverse settings. Methods: A systematic review and meta-analysis were conducted according to PRISMA guidelines, with the protocol registered in PROSPERO (CRD42024601066). Fourteen peer-reviewed studies were included following a comprehensive search across five major databases. Eligible studies reported quantitative neonatal outcomes associated with prenatal care interventions, including nutritional supplementation, mental health services, telehealth, and routine antenatal care. Random-effects models were used for meta-analysis, and the risk of bias was assessed using RoB 2 and the Newcastle–Ottawa Scale. Results: Nutritional interventions, especially folic acid and iron supplementation, significantly reduced neonatal mortality by up to 40% (RR = 0.60, 95% CI: 0.54–0.68). High-quality prenatal care was associated with a 41% reduction in neonatal mortality. Psychosocial support reduced the risk of low birth weight and preterm birth, while telehealth interventions lowered NICU admissions in low-risk populations (RR = 0.88, 95% CI: 0.75–1.03). Heterogeneity was substantial (I2 = 70%), and publication bias was suggested. Conclusions: Comprehensive prenatal care, integrating medical, nutritional, and mental health interventions, significantly improves neonatal outcomes. The global implementation of accessible, high-quality prenatal services is essential, particularly in underserved populations, to reduce neonatal morbidity and mortality.
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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 | 0.022 | 0.050 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.025 | 0.053 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".