Regional disparities in obesity prevalence in the United States: A spatial regime analysis (original) (raw)
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Obesity, 2014
Objective: This study used spatial statistical methods to test the hypotheses that county-level adult obesity prevalence in the United States is (1) regionally concentrated at significant levels, and (2) linked to local-level factors, after controlling for state-level effects. Methods: Data were obtained from the Centers for Disease Control and Prevention and other secondary sources. The units of analysis were counties. The dependent variable was the age-adjusted percentage of adults who were obese in 2009 (body mass index >30 kg/m 2 ). Results: The prevalence of county-level obesity varied from 13.5% to 47.9% with a mean of 30.3%. Obesity prevalence across counties was not spatially random: 15.8% belonged to high-obesity regions and 13.5% belonged to low-obesity regions. Obesity was positively associated with unemployment, outpatient healthcare visits, physical inactivity, female-headed families, black populations, and less education. Obesity was negatively correlated with physician numbers, natural amenities, percent 65 years, Hispanic populations, and larger population size. A number of variables were notable for not reaching significance after controlling for other factors, including poverty and food environment measures. Conclusions: The findings demonstrate the importance of local-level factors in explaining geographic variation in obesity prevalence, and thus hold implications for geographically targeted interventions to combat the obesity epidemic.
Obesity
Objective-State-level estimates of obesity based on self-reported height and weight suggest a geographic pattern of greater obesity in the Southeastern US; however, the reliability of the ranking among these estimates assumes errors in self-reporting of height and weight are unrelated to geographic region. Design and Methods-We estimated regional and state-level prevalence of obesity (body mass index ≥ 30 kg/m 2) for non-Hispanic black and white participants aged 45 and over were made from multiple sources: 1) self-reported from the Behavioral Risk Factor Surveillance System (BRFSS 2003-2006) (n = 677,425), 2) self-reported and direct measures from the National Health and Nutrition Examination Study (NHANES 2003-2008) (n = 6,615 and 6,138 respectively), and 3) direct measures from the REasons for Geographic and Racial Differences in Stroke (REGARDS 2003-2007) study (n = 30,239). Results-Data from BRFSS suggest that the highest prevalence of obesity is in the East South Central Census division; however, direct measures suggest higher prevalence in the West North Central and East North Central Census divisions. The regions relative ranking of obesity prevalence differs substantially between self-reported and directly measured height and weight. Conclusions-Geographic patterns in the prevalence of obesity based on self-reported height and weight may be misleading, and have implications for current policy proposals.
Disparities in obesity rates: Analysis by ZIP code area
Social Science & Medicine, 2007
Obesity in the USA has been linked to individual income and education. Less is known about its geographic distribution. The goal of this study was to determine whether obesity rates in King County, Seattle, Washington state, at the ZIP code scale were associated with area-based measures of socioeconomic status and wealth. Data from the Behavioral Risk Factor Surveillance System were analyzed. At the ZIP code scale, crude obesity rates varied six-fold. In a model adjusting for covariates and spatial dependence, property values were the strongest predictor of the area-based smoothed obesity prevalence. Geocoding of health data provides new insights into the nature of social determinants of health. Disparities in obesity rates by ZIP code area were greater than disparities associated with individual income or race/ethnicity.
International Journal of Environmental Research and Public Health
There is much ongoing research about the effect of the urban environment as compared with individual behaviour on growing obesity levels, including food environment, settlement patterns (e.g., sprawl, walkability, commuting patterns), and activity access. This paper considers obesity variations between US counties, and delineates the main dimensions of geographic variation in obesity between counties: by urban-rural status, by region, by area poverty status, and by majority ethnic group. Available measures of activity access, food environment, and settlement patterns are then assessed in terms of how far they can account for geographic variation. A county level regression analysis uses a Bayesian methodology that controls for spatial correlation in unmeasured area risk factors. It is found that environmental measures do play a significant role in explaining geographic contrasts in obesity.
Geospatial health, 2016
Clinical and epidemiological research has reported a strong association between diabetes and obesity. However, whether increased diabetes prevalence is more likely to appear in areas with increased obesity prevalence has not been thoroughly investigated in the United States (US). The Bayesian structured additive regression model was applied to identify whether counties with higher obesity prevalence are more likely clustered in specific regions in 48 contiguous US states. Prevalence data adopted the small area estimate from the Behavioral Risk Factor Surveillance System. Confounding variables like socioeconomic status adopted data were from the American Community Survey. This study reveals that an increased percentage of relative risk of diabetes was more likely to appear in Southeast, Northeast, Central and South regions. Of counties vulnerable to diabetes, 36.8% had low obesity prevalence, and most of them were located in the Southeast, Central, and South regions. The geographic d...
Effects of socioeconomic factors on obesity rates in four southern states and Colorado
Ethnicity & disease, 2011
To examine the association between the increase in body mass index (BMI) and socioeconomic factors (eg, income level, % below poverty line, unemployment rates and persons receiving food stamps) in Mississippi, Alabama, Louisiana, Tennessee and Colorado. Data from Behavioral Risk Factor Surveillance System, United States Department of Agriculture and the United States Department of Labor/Bureau of Labor were obtained and analyzed for the years 1995-2008. Results from this study showed a strong association between obesity and the tested variables (R2 = .767). Factors more closely related with obesity were: income below poverty level; receipt of food stamps; unemployment; and general income level. The coefficient of determination for these variables were 0.438, 0.427, 0.103 and 0.018, respectively. The highest rate of obesity was found in Mississippi (26.5% +/- 4.13%) followed by Alabama (25.18% +/- 4.41%), while Colorado had the lowest rate of obesity (15.4% +/- 2.63%). By ethnicity, ...
The Impact of Socioeconomic and Spatial Differences on Obesity in West Virginia
Annual Meeting, …, 2006
Obesity constitutes an important public policy issue since it causes external costs to society through increased healthcare costs borne by taxpayers. This study employed random and fixed effects estimations and spatial autoregressive approaches under a panel data structure to unravel possible socioeconomic and built environment factors contributing to obesity. Though there is no statistical evidence for time invariant fixed effects, empirical evidence shows that obesity is a spatially non-random event. Educational attainment that raises both human and social capital as well as changes in the built environment could play a vital role in controlling obesity.
A spatial analysis of community level overweight and obesity
Journal of Human Nutrition and Dietetics, 2014
Background: Rates of overweight and obesity are now considered to be epidemic. Few studies have examined the spatial distribution of overweight and obesity at the community level, an area of geography recommended for prevention and intervention. Therefore, the present study aimed to examine the spatial variation of overweight and obesity using community geographic boundaries. Methods: A cross-sectional secondary spatial data analysis was conducted using three combined cycles of Canadian Community Health Survey data for the province of Nova Scotia with community level boundaries. Descriptive rates were calculated using standardised incidence ratio values and spatial analysis was carried out using Global and Local Moran's I and the GetisOrdGi* statistic for cluster identification. Results: Maps illustrating local cluster analysis showed a significant degree of similarity between neighbouring communities in urban areas more so than rural communities. Hot spot analysis maps showed communities clustering together in the urban centre tended to have lower incidence of overweight and obesity ('cool spots'), whereas clustered communities in a more rural area had a higher incidence of overweight and obesity ('hot spots). Conclusions: The present study showed that there was geographical variation in overweight and obesity between urban and rural communities, and also there was a tendency for communities to cluster based on the incidence of overweight and obesity. This highlights the importance of understanding community level obesity rates and associated behavioural determinants, such as diet and physical activity, as well as the role that urbanisation or rurality may play in intervention initiatives for these behavioural determinants. Specifically, public health nutrition efforts for community level food environments in rural areas should ensure an individualised approach is used, whereas urban areas may be amenable to more general approaches aiming to support healthy weight status among the broader population.