title: Exploring the forest instead of the trees: An innovative method for defining obesogenic and obesoprotective environments.
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 obesogenic 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.
authors.affiliation: Johns Hopkins Bloomberg School of Public Health Global Obesity Prevention Center, 615 N Wolfe Street, Baltimore, MD 21205, USA. Electronic address: cnau1@jhu.edu.
authors.forename: Claudia
authors.initials: C
authors.lastname: Nau
authors.affiliation: Johns Hopkins Bloomberg School of Public Health Global Obesity Prevention Center, 615 N Wolfe Street, Baltimore, MD 21205, USA; Johns Hopkins Whiting School of Engineering, 3400 North Charles Street, Baltimore, MD 21218, USA.
authors.forename: Hugh
authors.initials: H
authors.lastname: Ellis
authors.affiliation: Johns Hopkins Bloomberg School of Public Health Global Obesity Prevention Center, 615 N Wolfe Street, Baltimore, MD 21205, USA.
authors.forename: Hongtai
authors.initials: H
authors.lastname: Huang
authors.affiliation: Johns Hopkins Bloomberg School of Public Health Global Obesity Prevention Center, 615 N Wolfe Street, Baltimore, MD 21205, USA; Geisinger Center for Health Research, 100 North Academy Avenue, Danville, PA 1728, USA.
authors.forename: Brian S
authors.initials: BS
authors.lastname: Schwartz
authors.affiliation: Geisinger Center for Health Research, 100 North Academy Avenue, Danville, PA 1728, USA.
authors.forename: Annemarie
authors.initials: A
authors.lastname: Hirsch
authors.affiliation: Geisinger Center for Health Research, 100 North Academy Avenue, Danville, PA 1728, USA.
authors.forename: Lisa
authors.initials: L
authors.lastname: Bailey-Davis
authors.affiliation: Johns Hopkins Bloomberg School of Public Health Global Obesity Prevention Center, 615 N Wolfe Street, Baltimore, MD 21205, USA.
authors.forename: Amii M
authors.initials: AM
authors.lastname: Kress
authors.affiliation: Johns Hopkins Bloomberg School of Public Health Global Obesity Prevention Center, 615 N Wolfe Street, Baltimore, MD 21205, USA.
authors.forename: Jonathan
authors.initials: J
authors.lastname: Pollak
authors.affiliation: Johns Hopkins Bloomberg School of Public Health Global Obesity Prevention Center, 615 N Wolfe Street, Baltimore, MD 21205, USA.
authors.forename: Thomas A
authors.initials: TA
authors.lastname: Glass
mesh_terms: D000293:Adolescent; D000465:Algorithms; D002648:Child; D005260:Female; D006801:Humans; D000069550:Machine Learning; D008297:Male; D063766:Pediatric Obesity; D010414:Pennsylvania; D012111:Residence Characteristics / Q000145:classification*; D012307:Risk Factors
keywords: Childhood obesity; Conditional random forest; Food features; Obesogenic environments; Physical activity features; Social features