Patterns of Obesogenic Neighborhood Features and Adolescent Weight: A Comparison of Statistical Approaches

American Journal of Preventive MedicineAmerican Journal of Preventive Medicine, May 2012, Vol. 42, No. 5

“Background: Few studies have addressed the potential influence of neighborhood characteristics on adolescent obesity risk, and fındings have been inconsistent.

“Purpose: Identify patterns among neighborhood food, physical activity, street/transportation, and socioeconomic characteristics and examine their associations with adolescent weight status using three statistical approaches.

“Methods: Anthropometric measures were taken on 2682 adolescents (53% female, mean age 14.5 years) from 20 Minneapolis/St. Paul MN schools in 2009–2010. Neighborhood environmental variables were measured using GIS data and by survey. Gender-stratifıed regressions related to BMI z-scores and obesity to (1) separate neighborhood variables; (2) composites formed using factor analysis; and (3) clusters identifıed using spatial latent class analysis in 2012.

“Results: Regressions on separate neighborhood variables found a low percentage of parks/recreation, and low perceived safety were associated with higher BMI z-scores in boys and girls. Factor analysis found fıve factors: away-from-home food and recreation accessibility, community disadvantage, green space, retail/transit density, and supermarket accessibility. The fırst two factors were associated with BMI z-score in girls but not in boys. Spatial latent class analysis identified six clusters with complex combinations of both positive and negative environmental influences. In boys, the cluster with highest obesity (29.8%) included low SES, parks/recreation, and safety; high restaurant and convenience store density; and nearby access to gyms, supermarkets, and many transit stops.

“Conclusions: The mix of neighborhood-level barriers and facilitators of weight-related health behaviors leads to diffıculties disentangling their associations with adolescent obesity; however, statistical approaches including factor and latent class analysis may provide useful means for addressing this complexity.”