A mixed-methods study of the drivers of stunting reduction among children under-5 in Ghana, 2003–2017
Bibliographic record
Abstract
BACKGROUND: Childhood stunting prevalence declined rapidly in Ghana in recent decades. Substantial economic growth over the same period will have contributed directly and indirectly to improved population health and nutrition in the country, but Ghana's progress in reducing stunting has outpaced that of multiple other countries with comparable or higher economic growth rates. OBJECTIVES: We aimed to better understand Ghana's exemplary progress in stunting reduction by examining the national-, community-, household-, and individual-level factors associated with the steep decline in stunting prevalence in recent decades. METHODS: This mixed-methods study included literature review; secondary analysis of national household survey data, including Oaxaca-Blinder decomposition analysis; primary qualitative data collection and analysis; and a policy and program review. RESULTS: Estimated from household surveys, under-5 stunting prevalence in Ghana declined from 35.1% in 2003 to 17.5% in 2017 and mean height-for-age z-score increased by 0.50, with the country's high-burden northernmost regions achieving the most rapid progress. Our modeling predicted 64% of the observed 0.43 height-for-age z-score increase among survey index children, with increases over time in mosquito net ownership, skilled birth attendance and antenatal care coverage, mean maternal age, urban residency, and household wealth accounting for most of the improvement in child growth over time. Qualitative findings highlighted similar and additional distal (e.g., income, maternal education, employment, women's empowerment, and political stability); intermediate (e.g., water and sanitation, infrastructure); and proximal (e.g., disease prevention and control programs, maternal care, and diet improvements) factors associated with stunting reduction. Of 134 nutrition-related policies and programs identified in our review, 23 national initiatives were assessed as having contributed importantly to reducing stunting in Ghana, reflecting the effectiveness of multisectoral action. CONCLUSIONS: Stunting reduction can be accelerated even further in Ghana through increased coverage of high-quality nutrition-specific interventions and greater health and nonhealth sector investments.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".