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.
Original language | English (US) |
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Pages (from-to) | 136-146 |
Number of pages | 11 |
Journal | Health and Place |
Volume | 35 |
DOIs | |
State | Published - Sep 1 2015 |
Keywords
- Childhood obesity
- Conditional random forest
- Food features
- Obesogenic environments
- Physical activity features
- Social features
ASJC Scopus subject areas
- Health(social science)
- Sociology and Political Science
- Life-span and Life-course Studies