Peer-reviewed article
Child Malnutrition and Morbidity in Uttar-Pradesh: An Application of Structural Equation Modeling and Geo-Spatial Analysis.
Anuj Singh, Abhay Kumar Tiwari, Mayank Singh ·
Summary
Written by AI, not yet AICS's view · claude-haiku-4-5-20251001This study examined the causes of child malnutrition and its link to illness in Uttar Pradesh, India, using data from 59,232 children under five. The researchers found that maternal height, education, wealth, caste, and access to healthcare were key factors affecting whether children were stunted, wasted, or underweight. Malnourished children were significantly more likely to suffer from illness. Geographic analysis showed malnutrition clustered in specific districts, suggesting targeted local interventions could help.
Key points
- Maternal height, education, and wealth were significantly associated with all three forms of malnutrition (stunting, wasting, underweight)
- Malnourished children had significantly higher rates of morbidity (illness), with a positive association of β = 0.032
- Geographic clustering identified high-burden hotspots for malnutrition across districts in Uttar Pradesh
- Place of delivery and child age were significantly associated with stunting
- Socio-economic inequalities and healthcare access were identified as major factors influencing child nutrition outcomes
Limitations
This is an observational study using cross-sectional survey data, so it cannot establish causation, only associations. The study does not evaluate any specific intervention to improve nutrition or reduce morbidity.
Abstract
BACKGROUND: Child malnutrition remains a major public health concern in low and middle-income countries, and Uttar Pradesh (UP), India, continues to experience some of the highest levels of stunting, wasting, and underweight among children under five. OBJECTIVES: This study examines the determinants of child malnutrition and its association with morbidity using data from the National Family Health Survey (NFHS-5). METHODS: Univariate and bivariate analyses, generalized Structural Equation Modeling (GSEM), and spatial analysis (Moran's I statistics and LISA cluster maps) were applied to assess both individual-level and district-level patterns. The NFHS-5 sample for Uttar Pradesh included 59,232 children under five, providing statistically robust estimates at both state and district levels. RESULTS: GSEM results indicated that maternal height, place of delivery, child age, caste, wealth index, and maternal education were significantly associated with stunting. Wasting was influenced by maternal height, birth order, child age, caste, wealth index, place of residence, and maternal education. Underweight was associated with maternal height, work status, child age, caste, wealth index, and maternal education. Malnutrition had a significant positive association with childhood morbidity (β = 0.032), indicating higher morbidity levels among malnourished children. Spatial analysis revealed clear geographic clustering of stunting, wasting, and underweight across districts, identifying several high-burden hotspots. CONCLUSION: These findings highlight the need for integrated, geographically targeted interventions addressing socio-economic inequalities, healthcare access, maternal factors, and environmental conditions to improve child nutrition and health outcomes in Uttar Pradesh.