Regional Determinants of Child Mortality in Nigeria: Evidence from Survival Analysis
Bibliographic record
Abstract
Objective: This study investigated regional determinants of child mortality in northern Nigeria, focusing on socio-economic, demographic, and environmental factors. Methods: Data from the 2018 Nigerian Demographic and Health Survey (NDHS) on 10,400 under-five children were analyzed. Kaplan-Meier survival curves and the Cox Proportional Hazards model assessed survival probabilities and risk factors. However, the proportional hazards (PH) assumption was violated (χ² = 1190.00, df = 13, p < 2e-16), indicating time-varying effects. Consequently, the Weibull model was used for a more precise estimation. Results: Kaplan-Meier estimates revealed significant regional disparities in child survival (p < 0.0001), with North Central having the highest survival, North East intermediate, and North West the lowest. The log-rank test (χ² = 176.214, p < 0.001) confirmed these differences. The Weibull model identified male children as having a higher mortality riskcompared to female children, likely due to biological vulnerabilities and variations in healthcare. Larger households and shorter birth intervals increased mortality risk due to resource constraints. In contrast, improved sanitation and clean water access significantly reduced mortality. Higher maternal education, household wealth, and breastfeeding were strongly associated with better survival. Notably, not breastfed children had a 45%, 46%, and 49% higher mortality risk across regions. Birth intervals exceeding 35 months and maternal age at first birth between 29 and 36 years improved survival. Conclusion: These findings emphasized the need for policy interventions, including family planning, improved sanitation, maternal education, and breastfeeding promotion, to reduce child mortality and regional disparities in Nigeria.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".