Geospatial, Multilevel, and Contextual Approaches in Cancer Control and Population Sciences

This funding expands on neighborhood resources developed under the U01 grant (geocoded residential histories, linkage to census and area databases, virtual neighborhood audits). Aim 1: Develop methods to use remote sensing data and machine learning techniques to characterize neighborhood attributes (e.g., green space, proximity to blue space, night-time light) and enhance the neighborhood infrastructure of the Pathways Study cohort. Aim 2: Demonstrate their applicability by examining the impact of social and built environment attributes on allostatic load among the Pathways cohort of breast cancer survivors. This work provides an opportunity to apply a comprehensive suite of neighborhood attributes to better understand multilevel factors and outcomes in breast cancer survivors.

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