Digital Participatory Surveillance for Identifying Geographic Disparities in Childhood Malnutrition Risk: A Multi-Scale Spatial Analysis
DOI:
https://doi.org/10.64504/big.d.v3i4.1093Keywords:
Childhood Malnutrition, Digital Surveillance, Spatial Analysis, Health Disparities, Small Area EstimationAbstract
Childhood malnutrition remains a critical global health challenge, with profound and lasting impacts on individual development and societal well-being. Traditional surveillance methods, often reliant on infrequent national surveys, lack the granularity and timeliness needed to guide targeted public health interventions. This study addresses the urgent need for high-resolution, dynamic data on childhood nutritional status. We leveraged a digital participatory surveillance platform, NutriTrack, to collect real-time nutritional data from parents and guardians of children under five years of age across the United States between July 2024 and June 2025. Applying advanced Small Area Estimation (SAE) models, we generated granular, county-level estimates of malnutrition risk (stunting and wasting). Spatial clustering analysis was then employed to identify statistically significant hotspots of high malnutrition risk. Based on a nationally representative sample of 55,820 children, the survey-weighted estimate for malnutrition risk was 15.2% (95% confidence interval [CI]: 14.5%–15.9%). We uncovered substantial geographic heterogeneity, with county-level rates varying from 4.0% to 36.0%. Spatial analysis revealed significant clustering of high-risk counties, particularly in the rural Southeast and parts of the Southwest, forming distinct malnutrition hotspots that are largely invisible in national-level data. Our findings demonstrate that digital participatory surveillance, combined with advanced spatial analytics, can effectively identify and map childhood malnutrition disparities at a fine geographic scale, providing a powerful tool for public health authorities to move towards precise, data-driven interventions.
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