Graph Attention Networks Unveil Determinants of Intra- and Inter-city Health Disparity

• URL: http://arxiv.org/abs/2210.10142v2
• Date: Thu, 20 Oct 2022 20:23:55 GMT
• Title: Graph Attention Networks Unveil Determinants of Intra- and Inter-city Health Disparity
• Authors: Chenyue Liu (1), Chao Fan (2), Ali Mostafavi (1) ((1) Urban Resilience.AI Lab, Zachry Department of Civil and Environmental Engineering, Texas A&M University, College Station, United States, (2) Glenn Department of Civil Engineering, Clemson University, Clemson, South Carolina, United States)
• Abstract summary: Multiple heterogeneous urban features could modulate the prevalence of diseases across different neighborhoods in cities and across different cities. This study examines features related to socio-demographics, population activity, mobility, and the built environment to examine intra- and inter-city disparity in prevalence of four disease types: obesity, diabetes, cancer, and heart disease.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Understanding the determinants underlying variations in urban health status is important for informing urban design and planning, as well as public health policies. Multiple heterogeneous urban features could modulate the prevalence of diseases across different neighborhoods in cities and across different cities. This study examines heterogeneous features related to socio-demographics, population activity, mobility, and the built environment and their non-linear interactions to examine intra- and inter-city disparity in prevalence of four disease types: obesity, diabetes, cancer, and heart disease. Features related to population activity, mobility, and facility density are obtained from large-scale anonymized mobility data. These features are used in training and testing graph attention network (GAT) models to capture non-linear feature interactions as well as spatial interdependence among neighborhoods. We tested the models in five U.S. cities across the four disease types. The results show that the GAT model can predict the health status of people in neighborhoods based on the top five determinant features. The findings unveil that population activity and built-environment features along with socio-demographic features differentiate the health status of neighborhoods to such a great extent that a GAT model could predict the health status using these features with high accuracy. The results also show that the model trained on one city can predict health status in another city with high accuracy, allowing us to quantify the inter-city similarity and discrepancy in health status. The model and findings provide novel approaches and insights for urban designers, planners, and public health officials to better understand and improve health disparities in cities by considering the significant determinant features and their interactions.

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