Disentangling the link between social determinants of health and child survival in Nigeria during the Sustainable Development Goals era: a hierarchical path analysis of time-to-event outcome
Bibliographic record
Abstract
While social determinants of health have been perennially linked to child survival in resource-limited countries, the precise and tested pathways to effect are not clearly understood. The objective of this study was therefore to identify the critical pathways as posited a priori in a model through which social factors (at maternal, household, and community levels) determine neonatal, infant, and under-five mortalities in Nigeria. Using a novel analytic approach (hierarchical path modelling for predicting accelerated failure time) to estimate (in)direct and total effects of social determinants of child survival, we analysed 30,960 live births (weighted data for representativeness), obtained from the 2016/2017 Nigeria Multiple Indicator Cluster Survey. There were three outcome variables: time until occurrence of neonatal, infant, and under-five mortalities. The independent variables were layered factors related to child, maternal, household and community. Geographical region, rurality of residence, infrastructural development, maternal education, contraceptive use, marital status, and maternal age at birth were found to operate more indirectly on neonatal, infant, and under-five survival. Child survival is due to direct effects of child's sex (female), gestational type (singleton), birth spacing (children whose mothers delivered at least two years apart), and maternal age at delivery (20-34 years). According to the path coefficients, the indirect effects of geographical regions are the most influential determinants of child survival, accounting for 30% (neonatal), 37.1% (infant) and 39.9% (under-five) of the total effects. This study offers comprehensive set of factors, and linked pathways, at the maternal, household, and community levels that are associated with child survival in Nigeria. To accelerate progress towards Sustainable Development Goal targets for child survival and reduce geographical inequities, stakeholders should implement more impactful policies that promote maternal education, contraceptive use and improve living conditions of women (especially in rural areas of northern Nigeria). Future research should focus on identifying the most effective interventions for addressing these social determinants of child survival in Nigeria.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".