Research articleObesity relationships with community design, physical activity, and time spent in cars
Introduction
I n the United States, obesity has steadily increased from the 1980s across all states, genders, age groups, ethnicities, and education levels.1 There are, however, significant disparities in the prevalence of obesity by ethnicity, with blacks—especially black women—more likely to be obese than their white counterparts.2, 3 Estimated at 31% of U.S. adults,2 obesity is commonly associated with poor health status.3 With an estimated 280,000 deaths of U.S. adults per year attributed to obesity,4 overweight and obesity have been found to be significantly associated with diabetes, high blood pressure, high cholesterol, asthma, arthritis and poor health status.5 Obesity-related morbidity was estimated to account for 9.1% of total annual U.S. medical expenditures in 1998.6 Modest but attainable increases in the level of physical activity, especially for those who are currently inactive or sedentary, could have important positive health effects. For instance, one estimate predicts that these diseases would be reduced by almost one third if the most inactive portions of the population increased their activity levels.7
Recent research has begun to focus on the link between public health and the built environment in an effort to combat increasing rates of overweight and obesity found in many Westernized nations.8 The urban planning and transportation literature has investigated the relationship between the built environment and transportation mode choice, including walking and bicycling,9, 10 and research in leading medical and public health journals advocates increased walking and bicycling as good forms of moderate-intensity physical activity to improve public health.11, 12, 13 Public health practitioners have also begun working with researchers in urban planning and related fields, and several resulting studies are beginning to bear fruit.
In a recent national study, Ewing et al.14 found that the probability of being overweight or obese, and to a lesser extent of being physically active, is significantly associated with the overall urban form of the county in which a person lives. This important study controlled for education, a strong covariate of income, but did not control for income due to missing data, which would have significantly reduced the power of the analysis. The aggregate data used in this study required that the built environment be measured at the county level. The walkability of the built environment varied considerably from one neighborhood street to the next, suggesting that whole counties may not capture the unique urban form stimuli experienced by each person at their place of residence.15 This study did not test the differential effects of these urban form features on population subgroups, such as by gender and ethnicity.
The present study provides a much more localized, observation-specific assessment of urban form relationships with transportation activity patterns and obesity, adding new information by looking at the effects by ethnicity and gender, while controlling for age, education attainment, and, for the first time, by income as well.
An increasing body of evidence shows that the physical design of the places where people live and work affects their overall travel choices and how much they walk or bike for utilitarian travel.10, 16, 17, 18, 19, 20, 21 Research thus far has had limited ability to show any causation between environmental correlates and transportation-related physical activity levels.19 To date, little research has been performed that uses individual-level data and objective measures of the built environment at a scale relevant to those individuals. One recent study22 used objective physical activity data and environmental characteristics based on perceptions of study participants and found significantly higher physical activity levels and lower obesity in a more walkable environment. Even though we address some of these limitations, the current cross-sectional study also cannot show causation.
Section snippets
Methods
This analysis used cross-sectional travel survey data from the Strategies for Metro Atlanta's Regional Transportation and Air Quality (SMARTRAQ) study and included 10,878 participants. Study participants were recruited from the 13-county Atlanta region, using a computer-aided telephone interview that screened and selected people based on household income, household size, and residential density (the number of households per square kilometer) in which the household was located. Recruitment based
Sample characteristics
Table 2 shows the descriptive statistics by gender/ethnicity classification for BMI, and the independent variables in the final estimated model. The mean BMI was highest among blacks, with the average for all groups being 25.6; 33% of those observed were classified as overweight (BMI of ≥25 and <30), and 17% of those observed were classified as obese (BMI≥30). As stated above, education and income were measured as categorical variables. The average black person had some technical/vocational
Discussion
Both logistical regression and linear Pearson correlation approaches to data analysis are presented. Logistical regression results, weighted to be generalizable to the Atlanta region's population, revealed that land use mix, car time, and distance walked were significantly associated with obesity when adjusting for age, income, and education attainment for all gender/ethnicity classifications. While no causality can be affirmed, these results lend considerable support to a very limited evidence
Acknowledgements
The Centers for Disease Control and Prevention, the Georgia Department of Transportation, and the Georgia Regional Transportation Authority provided financial support of this research. We are grateful to James Chapman, Peter Engelke, Ann Carpenter, and Steve French of Georgia Tech, where this study was initiated, for their work, and to Brian Saelens, James Sallis, and Marlon Boarnet for their insights and comments on an earlier version of the manuscript. We thank William Bachman with GeoStats
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