Geographic and socioeconomic inequalities in the survival of children under-five in Nigeria
Bibliographic record
Abstract
Despite a substantial decline in child mortality globally, the high rate of under-five mortality in Nigeria is still one of the main public health concerns. This study investigates inequalities in geographic and socioeconomic factors influencing survival time of children under-five in Nigeria. This is a retrospective cross-sectional quantitative study design that used the latest Nigeria Demographic Health Survey (2018). Kaplan-Meier survival estimates, Log-rank test statistics, and the Cox proportional hazards were used to assess the geographic and socioeconomic differences in the survival of children under-five in Nigeria. The Kaplan-Meier survival estimates show most under-five mortality occur within 12 months after birth with the poorest families most at risk of under-five mortality while the richest families are the least affected across the geographic zones and household wealth index quintiles. The Cox proportional hazard regression model results indicate that children born to fathers with no formal education (HR: 1.360; 95% CI 1.133-1.631), primary education (HR: 1.279; 95% CI 1.056-1.550) and secondary education (HR: 1.204; 95% CI 1.020-1.421) had higher risk of under-five mortality compared to children born to fathers with tertiary education. Moreover, under-five mortality was higher in children born to mothers' age ≤ 19 at first birth (HR: 1.144; 95% CI 1.041-1.258). Of the six geopolitical zones, children born to mothers living in the North-West region of Nigeria had 63.4% (HR 1.634; 95% CI 1.238-2.156) higher risk of under-five mortality than children born to mothers in the South West region of Nigeria. There is a need to focus intervention on the critical survival time of 12 months after birth for the under-five mortality reduction. Increased formal education and target interventions in geopolitical zones especially the North West, North East and North Central are vital towards achieving reduction of under-five mortality 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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".