Malnutrition is a significant public health concern, particularly in low- and middle-income countries (LMICs), including India. Despite various programs, India's ranking in the Global Hunger Index remains alarming, highlighting the need for targeted interventions. The first 1000 days of a child's life (from conception to age two) are considered a critical window of opportunity for nutritional development. During this period, children require energy and nutrient-dense foods for optimal physical and cognitive growth. Poor nutrition impairs future economic growth by reducing human potential. Dietary diversity is crucial, particularly in impoverished populations. The nutritional status of the mother significantly impacts the child's nutritional status, emphasizing the need for maternal health interventions. Birth weight is a strong predictor of subsequent weight gain, highlighting the importance of antenatal care. Wealthier households tend to have more diverse diets and better access to healthcare, leading to improved child development. In light of this, this study aims to explore the direct, indirect, and total effects of various socio-demographic, household, and environmental factors on stunting, wasting, and underweight among children aged 0–2 years in India. Furthermore, the study uses spatial analysis to identify geographical areas with a high prevalence of factors that contribute to malnutrition, to facilitate the development of geographically targeted interventions.

The existing literature consistently emphasizes the devastating impact of malnutrition on child health and mortality in LMICs. Studies highlight the strong association between malnutrition and increased morbidity and mortality among under-five children. Research has shown a strong link between dietary diversity and child nutritional status. Maternal nutritional status is frequently identified as a critical determinant of a child's nutritional well-being. Several studies have demonstrated the adverse effects of low birth weight on subsequent growth and development, underscoring the importance of antenatal care. Research also links household wealth and access to resources with improved child nutritional outcomes. Studies from various LMICs, including India, have shown that socioeconomic factors, such as maternal education and household wealth, strongly influence child malnutrition. The literature indicates a significant role of environmental factors, particularly access to safe water and sanitation, in child nutritional outcomes. Existing studies underscore the importance of focusing interventions on the first 1000 days of life, recognizing this period as a critical window of opportunity for growth and development. However, research on the spatial distribution of malnutrition determinants and their impact in the Indian context remains limited, creating a knowledge gap that this study aims to address.

This study utilized data from the National Family Health Survey-4 (NFHS-4), a nationally representative survey conducted in India during 2015–16. The NFHS-4 employed a two-stage stratified sampling frame based on the 2011 census of India, with primary sampling units as villages and census enumeration blocks in rural and urban areas, respectively. The final sample included 90,842 children aged 0–24 months. Outcome variables were stunting, wasting, and underweight. Exogenous variables included birth weight, exclusive breastfeeding, number of children ever born, and prenatal visits. Endogenous variables included diarrhea, maternal BMI, place of residence, household wealth index, maternal education, and desired pregnancy. Path analysis, using a structural equation model, was conducted to quantify the direct, indirect, and total effects of these variables on the outcome variables. The weighted least squares method, adjusted for mean and variance, was used for parameter estimation. Model fit indices such as Chi-square test, Comparative Fit Index (CFI), Tucker-Lewis Index (TLI), and Root Mean Square Error of Approximation (RMSEA) were used to assess the model fit. For spatial analysis, Moran's I was calculated to assess global spatial autocorrelation. Local Indicators of Spatial Association (LISA) statistics were used to identify hotspots and coldspots of key variables at the district level. Queen's first-order contiguity matrix was used to define spatial weights. Statistical significance was set at p < 0.05. Analyses were performed using STATA (version 15.1MP) and GeoDa software (version 1.14).

Path analysis revealed significant direct effects of diarrhea, exclusive breastfeeding, and birth weight on stunting, wasting, and underweight. Children with diarrhea were more likely to be malnourished, while exclusive breastfeeding had a protective effect. Lower birth weight was associated with an increased risk of malnutrition. Indirect effects were observed for maternal education, place of residence, and desired pregnancy. Mothers with higher education levels, residing in urban areas, and those who desired their pregnancy were less likely to have malnourished children. Global spatial autocorrelation analysis showed significant clustering for several key variables (diarrhea: Moran's I = 0.446; poor drinking water source: Moran's I = 0.638; exclusive breastfeeding: Moran's I = 0.345; low birth weight: Moran's I = 0.439; no prenatal visits: Moran's I = 0.620; poor toilet facility: Moran's I = 0.727). Spatial analysis identified hotspots and coldspots across India for each of these variables, indicating geographical variations in malnutrition risk. High concentrations of diarrhea were prevalent in Uttar Pradesh, while areas with poor access to safe water were common in Maharashtra. Low rates of exclusive breastfeeding were found in many districts in Uttar Pradesh, and poor sanitation was widespread in several states such as Bihar, Jharkhand, Odisha, Chhattisgarh, and Madhya Pradesh. Low rates of prenatal visits were concentrated in states such as Arunachal Pradesh, Nagaland, and Uttar Pradesh. The path analysis model demonstrated a good fit (CFI = 0.980, TLI = 0.927, RMSEA = 0.036).

The findings highlight the complex interplay of individual, household, and environmental factors contributing to child malnutrition in India. The strong direct effect of diarrhea underscores the importance of improving sanitation and access to clean water. The protective effect of exclusive breastfeeding emphasizes the need to promote and support breastfeeding practices. Low birth weight as a significant risk factor highlights the importance of antenatal care and maternal health interventions. The indirect effects of maternal education, residence, and desired pregnancy indicate the importance of addressing social determinants of health. The identified spatial clusters provide valuable insights for geographically targeted interventions. These findings emphasize the importance of a multisectoral approach, focusing on health, nutrition, sanitation, and education, to combat child malnutrition effectively. The concentration of risk factors in specific geographical areas implies that tailoring interventions to local needs is crucial. Interventions focusing on improving access to healthcare, promoting hygiene practices, and empowering women through education could help mitigate the risk of malnutrition.

This study provides valuable insights into the spatial and causal factors associated with child malnutrition in India. The findings emphasize the importance of integrated interventions targeting multiple risk factors, including improving sanitation, promoting exclusive breastfeeding, enhancing maternal health, and improving access to healthcare, especially in identified hotspots. Future research could explore the effectiveness of specific interventions in these hotspots, and analyze the long-term impacts of these interventions on child health and development. Investigating the interactions between various risk factors at a finer geographical scale could further refine targeted interventions.

This study uses a cross-sectional design, limiting the ability to establish causal relationships. The analysis is conducted at the district level, potentially leading to ecological fallacy. The data used from NFHS-4 might have limitations due to potential recall bias and the length of the survey. The study did not account for all potential confounders that may influence malnutrition.