Data-Driven Analysis of Child Malnutrition Indicators in Sokoto State

Child malnutrition in Sokoto State represents one of the most severe nutritional crises in Nigeria, and data-driven analysis of its prevalence and determinants can prioritise life-saving interventions. This study analysed child malnutrition data from the 2021 Multiple Indicator Cluster Survey and community health worker records from 180 Primary Healthcare Centres across Sokoto, Bodinga, and Gwadabawa Local Government Areas, Sokoto State, North West Nigeria. Key indicators including stunting, wasting, underweight prevalence, exclusive breastfeeding rates, and dietary diversity scores were analysed for 2,400 children under five. Descriptive statistics, correlation analysis, and binary logistic regression were applied. Prevalence of stunting was 54.3 percent, wasting 16.7 percent, and underweight 38.9 percent, all significantly above national averages. Logistic regression identified maternal education below primary level (OR = 3.41), household food insecurity (OR = 4.72), and lack of exclusive breastfeeding (OR = 2.18) as the strongest risk factors for wasting. LGAs with lower coverage of Vitamin A supplementation showed 1.9 times higher severe wasting rates. Spatial mapping identified three LGA clusters with co-occurring severe stunting and wasting, suggesting acute combined nutritional crises. Recommendations include deploying targeted therapeutic feeding programmes in identified crisis clusters, strengthening community health worker reporting systems, integrating maternal nutrition education into antenatal care, and establishing a Sokoto State real-time child nutrition monitoring dashboard linked to PHC reporting systems.

Keywords: child malnutrition, Sokoto State, stunting, logistic regression, public health analytics