Changing patterns of gender inequities in childhood mortalities during the Sustainable Development Goals era in Nigeria: findings from an artificial neural network analysis
Bibliographic record
Abstract
OBJECTIVES: In line with the child survival and gender equality targets of Sustainable Development Goals (SDG) 3 and 5, we aimed to: (1) estimate the age and sex-specific mortality trends in child-related SDG indicators (ie, neonatal mortality rate (NMR) and under-five mortality rate (U5MR)) over the 1960s-2017 period, and (2) estimate the expected annual reduction rates needed to achieve the SDG-3 targets by projecting rates from 2018 to 2030. DESIGN: Group method of data handling-type artificial neural network (GMDH-type ANN) time series. METHODS: This study used an artificial intelligence time series (GMDH-type ANN) to forecast age-specific childhood mortality rates (neonatal and under-five) and sex-specific U5MR from 2018 to 2030. The data sets were the yearly historical mortality rates between 1960s and 2017, obtained from the World Bank website. Two scenarios of mortality trajectories were simulated: (1) status quo scenarios-assuming the current trend continues; and (2) acceleration scenarios-consistent with the SDG targets. RESULTS: At the projected rates of decline of 2.0% for NMR and 1.2% for U5MR, Nigeria will not achieve the child survival SDG targets by 2030. Unexpectedly, U5MR will begin to increase by 2028. To put Nigeria back on track, annual reduction rates of 7.8% for NMR and 10.7% for U5MR are required. Also, female U5MR is decreasing more slowly than male U5MR. At the end of SDG era, female deaths will be higher than male deaths (80.9 vs 62.6 deaths per 1000 live births). CONCLUSION: Nigeria is not likely to achieve SDG targets for child survival and gender equities because female disadvantages will worsen. A plausible reason for the projected increase in female mortality is societal discrimination and victimisation faced by female child. Stakeholders in Nigeria need to adequately plan for child health to achieve SDG targets by 2030. Addressing gender inequities in childhood mortality in Nigeria would require gender-sensitive policies and community mobilisation against gender-based discrimination towards female child.
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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 | 0.001 | 0.003 |
| 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 | 0.001 | 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".