Socio-demographic and built environment influences on the odds of being overweight or obese: The Atlanta experience (original) (raw)
Related papers
Urban living and obesity: is it independent of its population and lifestyle characteristics?
Tropical Medicine & International Health, 2008
objectives Living in an urban area influences obesity. However, little is known about whether this relationship is truly independent of, or merely mediated through, the demographic, socio-economic and lifestyle characteristics of urban populations. We aimed to identify and quantify the magnitude of this relationship in a Sri Lankan population.
The built environment and risk of obesity in the United States: Racial–ethnic disparities
Health & Place, 2012
Using data from the 2003-2008 waves of the continuous National Health Nutrition Examination Survey merged with the 2000 census and GIS-based data, this study conducted genderspecific analyses to explore whether neighborhood built environment attributes are significant correlates of obesity risk and mediators of obesity disparities by race-ethnicity. Results indicate that the built environment is a significant correlate of obesity risk but is not much of a mediator of obesity disparities by race-ethnicity. Neighborhood walkability, density, and distance to parks are significant covariates of obesity risks net of individual and neighborhood controls. Gender differences are found for some of these associations.
Food environment and socioeconomic status influence obesity rates in Seattle and in Paris
International journal of obesity (2005), 2014
To compare the associations between food environment at the individual level, socioeconomic status (SES) and obesity rates in two cities: Seattle and Paris. Analyses of the SOS (Seattle Obesity Study) were based on a representative sample of 1340 adults in metropolitan Seattle and King County. The RECORD (Residential Environment and Coronary Heart Disease) cohort analyses were based on 7131 adults in central Paris and suburbs. Data on sociodemographics, health and weight were obtained from a telephone survey (SOS) and from in-person interviews (RECORD). Both studies collected data on and geocoded home addresses and food shopping locations. Both studies calculated GIS (Geographic Information System) network distances between home and the supermarket that study respondents listed as their primary food source. Supermarkets were further stratified into three categories by price. Modified Poisson regression models were used to test the associations among food environment variables, SES a...
Neighbourhood typologies and associations with body mass index and obesity: A cross-sectional study
Preventive Medicine, 2018
Little research has investigated associations between the combined food and physical activity (PA) environment, BMI (body-mass-index) and obesity. Cross-sectional data (n=22,889, age 18-86 years) from the Yorkshire Health Study were used [2010-2013]. BMI was calculated using self-reported height and weight; obesity=BMI≥30. Neighbourhood was defined as a 2km radial buffer; food outlets and PA facilities were sourced from Ordnance Survey Points of Interest (PoI) and categorised into 'fast-food', 'large supermarkets', 'convenience and other food retail outlets' and 'physical activity facilities'. Parks were sourced from Open Street Map. Availability was defined by quartiles of exposure and latent class analysis (LCA) was conducted on these five environmental variables. Linear and logistic regression were then conducted for BMI and obesity respectively for different neighbourhood types. Models adjusted for age, gender, ethnicity, area-level deprivation, and rural/urban classification. A five-class solution demonstrated best fit and was interpretable. Neighbourhood typologies were defined as; low availability, moderate availability, moderate PA, limited food, saturated and moderate PA, ample food. Compared to low availability, one typology demonstrated lower BMI (saturated, b=-0.50, [95% CI=-0.76,-0.23]), while three showed higher BMI (moderate availability, b= 0.49 [0.27,0.72]; moderate PA, limited food, b=0.30 [0.01,0.59]; moderate PA, ample food, b=0.32 [0.08,0.57]). Compared to the low availability, saturated neighbourhoods showed lower odds of obesity (OR=0.86 [0.75,0.99]) while moderate availability showed greater odds of obesity (OR=1.18 [1.05,1.32]). This study supports population-level approaches to tackling obesity however neighbourhoods contained features that were healthpromoting and-constraining. Embracing environmental complexity will be an important next step for researchers and policymakers in providing healthy places.
Preventive Medicine
The study aimed to examine associations of neighborhood built environments and proximity of food outlets (BE measures) with body weight status using pooled data from an international study (IPEN Adult). Objective BE measures were calculated using geographic information systems for 10,008 participants (4463 male, 45%) aged 16-66 years in 14 cities. Participants self-reported proximity to three types of food outlets. Outcomes were body mass index (BMI) and overweight/obesity status. Male and female weight status associations with BE measures were estimated by generalized additive mixed models. Proportion (95% CI) of overweight (BMI 25 to < 30) ranged from 16.6% (13.1, 19.8) to 41.1% (37.3, 44.7), and obesity (BMI ≥ 30) from 2.9% (1.3, 4.4) to 31.3% (27.7, 34.7), with Hong Kong being the lowest and Cuernavaca, Mexico highest for both proportions. Results differed by sex. Greater street intersection density, public transport density and perceived proximity to restaurants (males) were associated with lower odds of overweight/obesity (BMI ≥ 25). Proximity to public transport stops (females) was associated with higher odds of overweight/obesity. Composite BE measures were more strongly related to BMI and overweight/obesity status than single variables among men but not women.
Journal of Epidemiology & Community Health, 2007
To determine whether socioeconomic and food-related physical characteristics of the neighbourhood are associated with body mass index (BMI; kg/m 2) independently of individual-level sociodemographic and behavioural characteristics. Design and methods: Observational study using (1) individual-level data previously gathered in five crosssectional surveys conducted by the Stanford Heart Disease Prevention Program between 1979 and 1990 and (2) neighbourhood-level data from (a) the census to describe socioeconomic characteristics and (b) data obtained from government and commercial sources to describe exposure to different types of retail food stores as measured by store proximity, and count of stores per square mile. Data were analysed using multilevel modelling procedures. The setting was 82 neighbourhoods in agricultural regions of California. Participants: 7595 adults, aged 25-74 years. Results: After adjusting for age, gender, ethnicity, individual-level socioeconomic status, smoking, physical activity and nutrition knowledge, it was found that (1) adults who lived in low socioeconomic neighbourhoods had a higher mean BMI than adults who lived in high socioeconomic neighbourhoods; (2) higher neighbourhood density of small grocery stores was associated with higher BMI among women; and (3) closer proximity to chain supermarkets was associated with higher BMI among women. Conclusion: Living in low socioeconomic neighbourhoods, and in environments where healthy food is not readily available, is found to be associated with increased obesity risk. Unlike other studies which examined populations in other parts of the US, a positive association between living close to supermarkets and reduced obesity risk was not found in this study. A better understanding of the mechanisms by which neighbourhood physical characteristics influence obesity risk is needed.
A Longitudinal Analysis of Environment and Risk of Obesity in the US
Journal of Geoscience and Environment Protection
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.