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

Claudia Nau, Hugh Ellis, Hongtai Huang, Brian S. Schwartz, Annemarie Hirsch, Lisa Bailey-Davis, Amii M. Kress, Jonathan Pollak, Thomas A. Glass

Research output: Contribution to journalArticle

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 languageEnglish (US)
Pages (from-to)136-146
Number of pages11
JournalHealth and Place
Volume35
DOIs
StatePublished - 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

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