A mixed-methods study of the drivers of stunting reduction among children under five in Nigeria, 2008–2018
Bibliographic record
Abstract
BACKGROUND: Although stunting reduction at the national level in Nigeria has been modest in recent decades, especially considering the country's rapid economic growth, there is much subnational variation. OBJECTIVES: The objective of this study was to identify the factors associated with declining stunting prevalence in those states in Nigeria where the most progress was made between 2008 and 2018. METHODS: This mixed-methods study included quantitative analysis of household survey data using regression-based Oaxaca-Blinder decomposition analysis to identify factors associated with a change in mean height-for-age z-score (HAZ) over time; deductive thematic analysis of qualitative data collected through key informant interviews and focus group discussions; and policy and program review. RESULTS: Improvement in child linear growth over the past decade is evident in exemplar states in both the north and south of Nigeria, driven largely by the same factors. Our modeling predicted 66% of the observed +0.25 increase in mean HAZ over time in exemplar states, with nearly 60% of the predicted increase associated with improvements in non-health sector factors: parental education (43%), household wealth (8%), and household sanitation (3%). Malaria prevention was associated with an additional 29% of the predicted HAZ change. Qualitative participants highlighted insecurity, poverty, and lower education levels in some parts of the country as barriers to improving child health and nutritional status, along with insufficient human resources for health despite an increase in the number of healthcare facilities in the country. Participants identified a range of policies and programs across multiple sectors as having likely contributed to the decline in stunting prevalence. CONCLUSIONS: A multisectoral approach to stunting reduction in Nigeria appears to have been key, with progress having been driven by both the health sector and, especially, non-health sector action.
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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 | 0.009 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".