Linking Childhood Obesity to the Built Environment: A Multi-level Analysis of Home and School Neighbourhood Factors Associated With Body Mass Index

Abstract

hildhood obesity has become a critical public health issue in Canada, as rates have tripled over the past three decades. 1 Over one in four Canadian children are either overweight or obese (17% and 9% respectively). 2 The increased prevalence of childhood obesity has been linked to the concurrent rise of physical health problems normally associated with adults, including Type 2 diabetes, hypertension, heart disease and pulmonary diseases, as well as socio-psychological afflictions such as discrimination, behavioural problems, negative self-esteem, anxiety and depression. [3][4][5][6] A rapidly expanding avenue of research suggests that rising rates of obesity are due not only to individual-level factors (i.e., genetics), but also to characteristics of our local built environments that may be encouraging or discouraging the healthy diets or active lifestyles associated with healthy body weights. [7][8][9][10] Previous research has confirmed that obesity is linked to the consumption of energy-rich, fast foods. 11 Large-scale US studies have found that adult obesity rates are positively associated with the density of neighbourhood fast-food outlets 12 and convenience stores. 13 Much of the emphasis on the link between food and children's health focuses on advertising 14 or food policies within schools; 15-17 however, some policy-makers and public health professionals are shifting their focus to the food environments surrounding schools, as new research indicates that many children visit food retailers on their way to and from school, mostly filling up on high-sugar or high-fat, energy-dense foods. 18 Several studies have shown that fast-food outlets are more prevalent near schools 19,20 and in low-income neighbourhoods, 21,22 suggesting that these vulnerable populations may be at heightened risk of developing poor eating habits as a result of increased exposure to unhealthy foods. Furthermore, it has been shown in London, ON,

A systematic review of the influence of the retail food environment around schools on obesity‐related outcomes

“…Gilliland et al . used multilevel structural equation modelling techniques to simultaneously test the effects of the school‐environment and home‐environment predictors on body mass index (BMI) scores and He et al . calculated individual participants' ‘junk food density’ based on the density of stores around both students' home and school address.…”

Natural Environments Near Schools: Potential Benefits for Socio‐Emotional and Behavioral Development in Early Childhood

“…For instance, another study limited by complicated clustering patterns that could not be resolved with a cross‐classified model accounted for the variance from both home and school neighborhoods by considering children from multiple neighborhoods within a school, as well as children within a neighborhood who attended different schools (Zuo, ). Other efforts that have examined both home neighborhood and school settings have treated neighborhood‐level conditions as individual attributes (e.g., Gilliland et al., ; Oberle, Schonert‐Reichl, & Zumbo, ), a strategy that simplifies the complexity of the data but eliminates the ability to examine individual variation within a neighborhood context. In general, analytic approaches vary widely in investigations that seek to address spatial interdependence across multiple contexts, depending upon the data structure and the study's foci (e.g., Delmelle, ).…”

A comparative analysis of the restaurant consumer food environment in Rochester (NY, USA) and London (ON, Canada): assessing children’s menus by neighbourhood socio-economic characteristics

“…Without this network buffer step, a restaurant just outside the block may be missed, resulting in an edge effect and inaccurate results (35) . The buffer distance of 800 m was chosen as it is commonly used among food access studies (36,37) and among children's FE studies (12) , and is a distance often recognized as walkable in 10 to 15 min (6) . After calculating the network buffers, the spatial join function was employed to determine the total number of restaurants and the sum of the children's menu scores within each buffer.…”