Built environment and obesity by urbanicity in the U.S.

Xu, Yanqing; Wang, Fahui

doi:10.1016/j.healthplace.2015.03.010

Cited by 70 publications

(60 citation statements)

References 45 publications

(48 reference statements)

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“…However, public health researchers have begun to challenge the stationarity assumption. Using spatial regression modeling, such as geographically weighted regression (GWR), a technique that allows for spatial variations in parameter estimates (19,20), investigators have highlighted variations in associations across space between a range of environmental exposures and outcomes such as diet (21), obesity (22)(23)(24)(25)(26)(27), active transportation (28), and birth weight (29). Fraser et al (21) have observed marked spatial variations, in both magnitude and nature, in the relationship between residential exposure to fast-food outlets and fast-food consumption among adolescents living in Bristol, United Kingdom.…”

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confidence: 99%

“…Higher exposure was significantly associated with increased consumption in some areas but decreased consumption in others, even after adjustment for deprivation, sex, and physical activity levels. Overall, local modeling, when compared with global modeling, has shown better performance in terms of improved goodness of fit (22,25,27,28), increased R² (22,23,25,27,28), and decreased spatial autocorrelation in regression residuals (22,23,25,27). Where nonstationarity has been pointed out, authors have therefore raised a caveat associated with the use of global models (22,30).…”

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confidence: 99%

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The Local Food Environment and Fruit and Vegetable Intake: A Geographically Weighted Regression Approach in the ORiEL Study

Clary¹,

Lewis²,

Flint³

et al. 2016

Am. J. Epidemiol.

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Studies that explore associations between the local food environment and diet routinely use global regression models, which assume that relationships are invariant across space, yet such stationarity assumptions have been little tested. We used global and geographically weighted regression models to explore associations between the residential food environment and fruit and vegetable intake. Analyses were performed in 4 boroughs of London, United Kingdom, using data collected between April 2012 and July 2012 from 969 adults in the Olympic Regeneration in East London Study. Exposures were assessed both as absolute densities of healthy and unhealthy outlets, taken separately, and as a relative measure (proportion of total outlets classified as healthy). Overall, local models performed better than global models (lower Akaike information criterion). Locally estimated coefficients varied across space, regardless of the type of exposure measure, although changes of sign were observed only when absolute measures were used. Despite findings from global models showing significant associations between the relative measure and fruit and vegetable intake (β = 0.022; P < 0.01) only, geographically weighted regression models using absolute measures outperformed models using relative measures. This study suggests that greater attention should be given to nonstationary relationships between the food environment and diet. It further challenges the idea that a single measure of exposure, whether relative or absolute, can reflect the many ways the food environment may shape health behaviors. absolute exposure; fruit and vegetable intake; geographically weighted regression; local food environment; nonstationarity; relative exposure Abbreviations: AICc, corrected Akaike information criterion; GWR, geographically weighted regression; ORiEL, Olympic Regeneration in East London.Over the last decade, an extensive body of research has investigated how the local food environment may be related to dietary behaviors (1). A large majority of studies have used global regression to model the association between exposure to either healthy or unhealthy food environments and diet-related outcomes (2, 3). However, reviews have highlighted the lack of consistency in findings (2, 3), with associations for the same type of relationship being positive (4-10), negative (11), or nonexistent (12-18).By using global regression models, researchers have implicitly relied on the assumption of a stationary relationship; that is, parameter estimates describe what is assumed to be an invariant relationship across space. However, public health researchers have begun to challenge the stationarity assumption. Using spatial regression modeling, such as geographically weighted regression (GWR), a technique that allows for spatial variations in parameter estimates (19,20), investigators have highlighted variations in associations across space between a range of environmental exposures and outcomes such as diet (21), obesity (22-27), active transportation (28), and bi...

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The Local Food Environment and Fruit and Vegetable Intake: A Geographically Weighted Regression Approach in the ORiEL Study

Clary¹,

Lewis²,

Flint³

et al. 2016

Am. J. Epidemiol.

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“…National-level studies examining the built environment (using measures such as street connectivity or parks) have consistently found an association with obesity – these studies have adjusted for income rather than stratifying by income level. 28,29 A study from Massachusetts showed a much stronger reduction in the association with childhood obesity when adjusting for socioeconomic status compared with adjusting for the built environment characteristics. 30 While income plays a role in the development of obesity, our data suggest that community resources may not be the primary driver.…”

Section: Discussionmentioning

confidence: 99%

Moderating Effects of Components of Resilience on Obesity Across Income Strata in the National Survey of Children's Health

Foster

Weinstein

2019

Academic Pediatrics

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“…Built environment refers to all human-made places that constitute people's living environment including parks and recreation centers, play grounds, community layout, roads, and transportation system [7]. Built environment factors such as land use, population density, road connectivity, transportation patterns, and food environment have been investigated in relation to physical activity and obesity risk [3] [8] [9].…”

Section: Introductionmentioning

confidence: 99%

A Longitudinal Analysis of Environment and Risk of Obesity in the US

Xu¹,

Liu²,

Chen³

et al. 2017

GEP

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Obesity is a fast-growing global health crisis and the epidemic is about to get worse. Environment has been shown to influence physical activity and people's body weight. Utilizing Centers for Disease Control and Prevention 2004-2010 waves of the continuous obesity data, this study conducted longitudinal analyses to examine neighborhood built environment and obesity risk controlling for the effects of socio-demographic characteristics. This study presents a comprehensive effort to understand the relationship between the environment and physical inactivity and obesity across the entire contiguous US. When constructing measures of the built environment, Geographic Information Systems (GIS) were used to calculate street connectivity, walk score and food environment. In addition to the built environment, the natural environment, which was represented by the annual number of extreme weather events from 2004-2010, was taken into account in explaining variation of physical inactivity and obesity across the contiguous US. Results show that higher street connectivity and walk score are related to lower physical inactivity and obesity rates, while the ratio of fast-food restaurants and number of extreme weather events are positively related to physical inactivity and obesity. The results are believed to provide policy-makers and planners with useful insights into the dynamics between the environment and obesity epidemic. Further, the significant effects of extreme weather invite more studies to investigate the relationship between the natural environment and obesity.

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Built environment and obesity by urbanicity in the U.S.

Cited by 70 publications

References 45 publications

The Local Food Environment and Fruit and Vegetable Intake: A Geographically Weighted Regression Approach in the ORiEL Study

The Local Food Environment and Fruit and Vegetable Intake: A Geographically Weighted Regression Approach in the ORiEL Study

Moderating Effects of Components of Resilience on Obesity Across Income Strata in the National Survey of Children's Health

A Longitudinal Analysis of Environment and Risk of Obesity in the US

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