Prevalence of Neonatal Mortality and its Associated Factors: A Meta.analysis of Demographic and Health Survey Data from 21 Developing Countries
Bibliographic record
Abstract
Neonatal mortality is high in developing countries, and reducing neonatal mortality is an indispensable part of the third Sustainable Development Goal. This study estimated the prevalence of neonatal mortality and the impact of maternal education, economic status, and utilization of antenatal care (ANC) services on neonatal mortality in developing countries. We used a cross-sectional study design to integrate data from 21 developing countries to acquire a wider perspective on neonatal mortality. A meta-analysis was conducted using the latest Demographic and Health Survey data from 21 developing countries. In addition, sensitivity analysis was adopted to assess the stability of the meta-analysis. The random-effects model indicated that women with higher education were less likely to experience neonatal death than mothers with up to primary education (odds ratio [OR] 0.820, 95% confidence interval [CI] 0.740-0.910). Women with higher socioeconomic status were less likely to experience neonatal death than mothers with lower socioeconomic status (OR 0.823, 95% CI 0.747-0.908). Mothers with ANC were less likely to experience neonatal death than those with no ANC (OR 0.374, 95% CI 0.323-0.433). Subgroup analysis showed that maternal education and ANC were more effective in Asian countries. In this study, mothers' lower educational level, poor economic status, and lack of ANC were statistically significant factors associated with neonatal death in developing countries. The effect of these factors on neonatal death differed in different regions.
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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 | 0.029 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.013 | 0.064 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".