Incongruency of youth food and physical activity environments in the United States: Variations by region, rurality, and income

The neighourhood obesogenic built environment characteristics (OBCT) index: Practice versus theory

BACKGROUND

Obesity is a key risk factor for major chronic diseases such as type 2 diabetes and cardiovascular diseases. To extensively characterise the obesogenic built environment, we recently developed a novel Obesogenic Built environment CharacterisTics (OBCT) index, consisting of 17 components that capture both food and physical activity (PA) environments.

OBJECTIVES

We aimed to assess the association between the OBCT index and body mass index (BMI) in a nationwide health monitor. Furthermore, we explored possible ways to improve the index using unsupervised and supervised methods.

METHODS

The OBCT index was constructed for 12,821 Dutch administrative neighbourhoods and linked to residential addresses of eligible adult participants in the 2016 Public Health Monitor. We split the data randomly into a training (two-thirds; n = 255,187) and a testing subset (one-third; n = 127,428). In the training set, we used non-parametric restricted cubic regression spline to assess index's association with BMI, adjusted for individual demographic characteristics. Effect modification by age, sex, socioeconomic status (SES) and urbanicity was examined. As improvement, we (1) adjusted the food environment for address density, (2) added housing price to the index and (3) adopted three weighting strategies, two methods were supervised by BMI (variable selection and random forest) in the training set. We compared these methods in the testing set by examining their model fit with BMI as outcome.

RESULTS

The OBCT index had a significant non-linear association with BMI in a fully-adjusted model (p<0.05), which was modified by age, sex, SES and urbanicity. However, variance in BMI explained by the index was low (<0.05%). Supervised methods increased this explained variance more than non-supervised methods, though overall improvements were limited as highest explained variance remained <0.5%.

DISCUSSION

The index, despite its potential to highlight disparity in obesogenic environments, had limited association with BMI. Complex improvements are not necessarily beneficial, and the components should be re-operationalised.

Food deserts exposure, density of fast-food restaurants, and park access: Exploring the association of food and recreation environments with obesity and diabetes using global and local regression models

To prevent obesity and diabetes environmental interventions such as eliminating food deserts, restricting proliferation of food swamps, and improving park access are essential. In the United States, however, studies that examine the food and park access relationship with obesity and diabetes using both global and local regression are lacking. To guide county, state, and federal policy in combating obesity and diabetes, there is a need for cross-scale analyses to identify that relationship at national and local levels. This study applied spatial regression and geographically weighted regression to the 3,108 counties in the contiguous United States. Global regression show food deserts exposure and density of fast-food restaurants have non-significant association with obesity and diabetes while park access has a significant inverse association with both diseases. Geographically weighted regression that takes into account spatial heterogeneity shows that, among southern states that show high prevalence of obesity and diabetes, Alabama and Mississippi stand out as having opportunity to improve park access. Results suggest food deserts exposure are positively associated with obesity and diabetes in counties close to Alabama, Georgia, and Tennessee while density of fast-food restaurants show positive association with two diseases in counties of western New York and northwestern Pennsylvania. These findings will help policymakers and public health agencies in determining which geographic areas need to be prioritized when implementing public interventions such as promoting healthy food access, limiting unhealthy food options, and increasing park access.

Urban-rural disparities in childhood obesogenic environments in the United States: Application of differing rural definitions

BACKGROUND

Research is needed that identifies environmental resource disparities and applies multiple rural definitions. Therefore, this study aims to examine urban-rural differences in food and physical activity (PA) environment resource availability by applying several commonly used rural definitions. We also examine differences in resource availability within urban-rural categories that are typically aggregated.

METHODS

Six food environment variables (access to grocery/superstores, farmers' markets, fast food, full-service restaurants, convenience stores, and breastfeeding-friendly facilities) and 4 PA environment variables (access to exercise opportunities and schools, walkability, and violent crimes) were included in the childhood obesogenic environment index (COEI). Total COEI, PA environment, and food environment index scores were generated by calculating the average percentile for related variables. US Department of Agriculture Urban Influence Codes, Office of Management and Budget codes, Rural-Urban Continuum Codes, Census Bureau Population Estimates for percent rural, and Rural Urban Commuting Area Codes were used. One-way ANOVA was used to detect urban-rural differences.

RESULTS

The greatest urban-rural disparities in COEI (F=310.2, P<.0001) and PA environment (F=562.5, P<.0001) were seen using RUCC codes. For food environments, the greatest urban-rural disparities were seen using Census Bureau percent rural categories (food: F=24.9, P<.0001). Comparing remote rural categories, differences were seen for food environments (F=3.1, P=.0270) and PA environments (F=10.2, P<.0001). Comparing metro-adjacent rural categories, differences were seen for PA environment (F=4.7, P=.0090).

CONCLUSION

Findings inform future research on urban and rural environments by outlining major differences between urban-rural classifications in identifying disparities in access to health-promoting resources.