Journal
HEALTH & PLACE
Volume 35, Issue -, Pages 136-146Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.healthplace.2015.08.002
Keywords
Obesogenic environments; Childhood obesity; Conditional random forest; Physical activity features; Food features; Social features
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Funding
- Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD) [U54HD070725]
- NICHD
- Office of Behavioral and Social Sciences Research (OBSSR)
- Johns Hopkins Global Obesity Prevention Center
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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. (C) 2015 Elsevier Ltd. All rights reserved.
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