Exploring the forest instead of the trees: An innovative method for defining obesogenic and obesoprotective environments. (September 2015) - Nau, Claudia; Ellis, Hugh; Huang, Hongtai; Schwartz, Brian S.; Hirsch, Annemarie; Bailey-Davis, Lisa; Kress, Amii M.; Pollak, Jonathan; Glass, Thomas A.
Abstract: Past research has assessed the association of single community characteristics with obesity, ignoring the spatial co-occurrence of multiple community-level risk factors. We used conditional random forests (CRF), a non-parametric machine learning approach to identify the combination of community features that are most important for the prediction of obesegenic and obesoprotective environments for children. After examining 44 community characteristics, we identified 13 features of the social, food, and physical activity environment that in combination correctly classified 67% of communities as obesoprotective or obesogenic using mean BMI- z as a surrogate. Social environment characteristics emerged as most important classifiers and might provide leverage for intervention. CRF allows consideration of the neighborhood as a system of risk factors. Highlights: Obesogenic environments (OGE) consist of a diverse cluster of spatially co-occurring risk factors. We used a machine learning algorithm to identify the risk factors that matter most for childhood obesity. OGEs are best described by a diverse set of social, physical activity, and food features. Social features are of particular importance. Obesogenic and obesoprotective environments are best defined by different sets of risk factors.