Abstract
Childhood obesity is a major risk factor for adult cardiovascular disease. Current obesity-prediction models were not developed in diverse populations and do not include heterogeneous social, environmental, and climate factors that may impact body mass index across the full pediatric spectrum. Additionally, they consider only the immediate neighborhood within which a child lives, ignoring contextual factors from expanded (i.e., distal) neighborhoods. This study uses expanded neighborhoods' social, environmental, and climate data to improve individual-level body mass index prediction-from underweight through obesity-using a novel machine learning approach. We obtained demographic and clinical data from the electronic health records of the Duke University Health System, identifying 12,226 children aged 6-18 years in Durham County, North Carolina, with body mass index data from 2014 to 2022. Participants' data were linked to socioeconomic and environmental information at the census block group level. We captured expanded neighborhood effects with a graph neural network and combined this information with individual-level factors to predict body mass index. Our model predicted body mass index more accurately than simpler models for children aged 6-11 (R(2) = 0.234, mean absolute error = 3.352, root mean square error = 4.370) and 12-18 (R(2) = 0.147, mean absolute error = 4.980, root mean square error = 6.343) using all features. Key predictive factors identified included rent burden, poverty rate, and tree coverage. This research highlights the value of including broader socioeconomic and environmental factors in body mass index prediction, offering insights that could guide targeted, neighborhood-level interventions.