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Poverty and InequalitySexual and Reproductive HealthFamily, Maternal & Child HealthMethodology

Exploring the forest instead of the trees: An innovative method for defining obesogenic and obesoprotective environments

TitleExploring the forest instead of the trees: An innovative method for defining obesogenic and obesoprotective environments
Publication TypeJournal Article
Year of Publication2015
AuthorsNau, C, Ellis, H, Huang, H, Schwartz, BS, Hirsch, A, Bailey-Davis, L, Kress, AM, Pollak, J, Glass, TA
JournalHealth Place
Volume35
Pagination136-146
Date PublishedSep 18
ISBN Number1353-8292
Accession Number26398219
KeywordsChildhood obesity, Conditional random forest, Food features, Obesogenic environments, Physical activity features, Social features
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.