The relative contribution of layers of the Social Ecological Model to childhood obesity

• Published in: Public health nutrition Vol. 18; no. 11; pp. 2055 - 2066
• Main Authors: Ohri-Vachaspati, Punam, DeLia, Derek, DeWeese, Robin S, Crespo, Noe C, Todd, Michael, Yedidia, Michael J
• Format: Journal Article
• Language: English
• Published: Cambridge, UK Cambridge University Press 01.08.2015
• Subjects: Adolescent; Adult; Age; Body Mass Index; Body Weight; Built environment; Child; Child, Preschool; childhood obesity; children; Children & youth; Cross-Sectional Studies; data collection; demographic statistics; Demographics; Diet; Ecological models; Economics and environment; Education; Environment; Ethnicity; etiology; Exercise; Female; Food; Gender; Hispanic Americans; Households; Humans; Logistic Models; Low income groups; Male; Obesity; Overweight; Parents; Pediatric Obesity - etiology; Pediatric Obesity - prevention & control; physical activity; prediction; Prevention; regression analysis; Research Papers; Residence Characteristics; Social Environment; sociodemographic characteristics; surveys; Surveys and Questionnaires; System theory; Variables; Young Adult; United States > US; New Jersey; Social Ecological Model; Childhood obesity
• DOI: 10.1017/S1368980014002365
• ISSN: 1368-9800; 1475-2727

The Social Ecological Model (SEM) has been used to describe the aetiology of childhood obesity and to develop a framework for prevention. The current paper applies the SEM to data collected at multiple levels, representing different layers of the SEM, and examines the unique and relative contribution of each layer to children's weight status. Cross-sectional survey of randomly selected households with children living in low-income diverse communities. A telephone survey conducted in 2009-2010 collected information on parental perceptions of their neighbourhoods, and household, parent and child demographic characteristics. Parents provided measured height and weight data for their children. Geocoded data were used to calculate proximity of a child's residence to food and physical activity outlets. Analysis based on 560 children whose parents participated in the survey and provided measured heights and weights. Multiple logistic regression models were estimated to determine the joint contribution of elements within each layer of the SEM as well as the relative contribution of each layer. Layers of the SEM representing parental perceptions of their neighbourhoods, parent demographics and neighbourhood characteristics made the strongest contributions to predicting whether a child was overweight or obese. Layers of the SEM representing food and physical activity environments made smaller, but still significant, contributions to predicting children's weight status. The approach used herein supports using the SEM for predicting child weight status and uncovers some of the most promising domains and strategies for childhood obesity prevention that can be used for designing interventions.