Geographic inequities in neonatal survival in Nigeria: a cross-sectional evidence from spatial and artificial neural network analyses
Bibliographic record
Abstract
This study was conducted to provide empirical evidence of geographical variations of neonatal mortality and its associated social determinants with a view to improving neonatal survival at the subnational level in Nigeria. With a combination of spatial analysis and artificial intelligence techniques, this study analysed data from the 2016/2017 Nigeria Multiple Indicator Cluster Survey. The analysis focused on the neonatal period of a weighted national representative population of 30,924 live births delivered five years before the survey commencement. Global Moran's I index and local indicator of spatial autocorrelation cluster maps were used to determine hot and cold spots. A multilayer perceptron neural network was used to identify the key determinants of neonatal mortality across the states and geopolitical zones in Nigeria. The overall neonatal mortality rate was 38 deaths per 1000 live births. There is evidence of geographic clustering of neonatal mortality across Nigeria (worse in the North-Central and North-West zones), majorly driven by poor maternal access to mass media (which plays a critical role in promoting positive health behaviours), short birth interval, a higher position in a family birth order, and young maternal age at child's birth. This study highlights the need for a policy shift towards implementing state and region-specific strategies in Nigeria. Gender-responsive, culturally, and regionally appropriate reproductive, maternal, and child health-targeted interventions may address geographical inequity in neonatal survival.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".