Factors associated with inequalities in malnutrition among children in Ghana using the 2022 GDHS and WHO HEAT framework
Bibliographic record
Abstract
BACKGROUND: Childhood malnutrition can impair physical and cognitive development, thereby diminishing productivity at adulthood. Current research recognizes the crucial impact of early childhood nutrition on the long-term health and well-being of children. Ghana has achieved notable progress in lowering childhood malnutrition; nevertheless, it remains a major concern. This study advances current understanding on the socioeconomic drivers and magnitude of child malnutrition in the Ghanaian context. METHODS: We used data from 2022 Ghana Demographic and Health Survey to analyze inequalities in malnutrition among children This study was an analytical cross-sectional survey that covered a total weighted sample of 4,935 women aged 15-49. Simple measures of inequality [difference (D), and Ratio (R)], and complex measures of inequality [Population Attributable risk (PAR), and Population Attributable Fraction (PAF)] were performed using the World Health Organization's Health Equity Assessment Toolkit (WHO's HEAT) software. Six equity stratifiers examined malnutrition among children in Ghana: child's age, economic status, educational status, place of residence, sex of child, and subnational region. RESULTS: Overall, out of the four child malnutrition indicators used, stunting was the highest with 17.4%. We observed age (D = 7.2, UI = 4.0- 10.4, PAF=-67.7, UI=-67.9- -67.4), maternal education (R = 1.8, UI = 1.5-2.1; PAF=-18.8, UI=-18.8--18.7), place of residence (R = 1.3, UI = 1.1-1.6; PAF=-13.4, UI=-13.4--13.3), and subnational region (D = 19.1, UI = 13.5-24.8; PAR=-6.9, UI=-10.1--3.8). CONCLUSION: This study revealed the existence of inequalities in malnutrition among children in Ghana, which is influenced by child's age, maternal economic status, maternal education, place of residence, and subnational region. To tackle the issues of child malnutrition in Ghana, it is vital to implement targeted interventions that address age-related disparities and implement social protection programs. Subnational region disparities must be addressed by directing resources to areas vulnerable to malnutrition, such as Northern Ghana, adapting interventions to local needs, and improving infrastructure to facilitate better healthcare and nutrition access in rural communities.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| 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) | 0.002 | 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".