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Diet, Diarrhoea and Maternal Factors Associated With Wasting in Ghanaian Children Aged 6-23 Months: A Machine
Luis Javier Sánchez-Martínez1, Mohammed Bukari2, Martin Nyaaba Adokiya3
1Unit of Physical Anthropology, Department of Biodiversity, Ecology and Evolution, Faculty of Biological Sciences, Complutense University of Madrid, Madrid, Spain.
Objectives:
This study estimates the prevalence of wasting and examines associated factors among children aged 6-23 months in Ghana from a machine-learning perspective, with a particular focus on predicting new cases to inform preventive efforts.
Methods:
This study is a secondary analysis of the 2022 Ghana Demographic and Health Survey, which includes 1506 children. Wasting status was assessed using the weight-for-height z-score. A broad set of 150 child-, maternal-, household- and environmental-level variables was considered. Factors associated with wasting were first identified using survey-weighted logistic regression, whereas Multiple Correspondence Analysis was applied to explore structural relationships among predictors. Subsequently, an ensemble machine learning model was fitted to evaluate the combined predictive capacity of these factors in classifying wasting status, also comparing algorithm performance.
Results:
Wasting prevalence in Ghana was 9.4%, with marked regional heterogeneity. Lower odds of wasting were associated with higher maternal body weight, maternal formal employment, female sex of child and consumption of animal-source foods, whereas recent diarrhoeal episodes were associated with higher odds. The exploratory analyses further showed that these factors clustered into coherent vulnerability profiles, distinguishing more clearly between wasting and non-wasting patterns. The ensemble model outperformed individual algorithms, achieving an AUC of 0.79, with high sensitivity (0.85) and moderate specificity (0.62), indicating moderate-to-good internal predictive performance.
Conclusions:
These findings suggest that wasting is associated with interacting biological vulnerabilities and ecological constraints during a critical developmental window. Integrating nutritional quality, infection control and maternal socioeconomic conditions is essential for addressing acute malnutrition in early childhood and for advancing a holistic understanding of child growth within human populations, guiding data-driven public policies.
