Our findings suggest that the characteristics of local neighborhood environments are linked to BMI and obesity risk. We find support for the traditional measures of the 3D’s with population density, neighborhood age, and walk-to-work measures being inversely related to BMI and obesity risk consistent with some past studies (Lopez-Zetina et al., 2006
; Lopez, 2004
; Rundle et al., 2007
; Smith et al., 2008
; Stafford et al., 2007
; Vandegrift & Yoked, 2004
). But, we also find that the magnitude of all of these effects differs significantly by neighborhood income, suggesting that neighborhood design interventions targeted at reducing obesity risk might be more effective if they are tailored to the residents’ socioeconomic circumstances.
The estimated relationship between intersection density and BMI/obesity risk, although significant, is counter to the hypothesis that greater street connectivity should be associated with lower BMI. It may be that greater street connectivity reflects greater car traffic, all other things held constant, that would discourage individuals from using active forms of transportation such as walking or biking. In addition, Salt Lake County has unusually wide streets and large city blocks. This feature might also discourage walking in block groups with more intersections as previous research has found an inverse relationship between city block size and pedestrian volume holding other factors constant (Hess, Moudon, Snyder, & Stanilov, 1999
). Unfortunately, testing these propositions is beyond the scope of our investigation.
The presence of healthy grocery options in the immediate neighborhood may reduce the time costs of making healthy food purchases for low income individuals who are more likely to rely on public transportation and/or walking/biking for groceries, or they may reduce the time costs of making multiple supermarket trips for healthy but perishable foods. This finding represents a case where access to a healthy food option within one’s neighborhood may provide the greatest benefits to the most vulnerable.
For individuals living in non-low income neighborhoods, the presence of healthy grocery options is not associated with lower BMI or a reduced risk of being obese, compared with no food options. Individuals living in these neighborhoods may be more likely to rely on automobile transportation to do their major grocery shopping given that one often has numerous bags to carry. If so, these individuals may be less constrained by neighborhood options and thus the presence of healthy grocery options would have little impact on their BMI or risk of obesity.
The differential importance of the local food environment for individuals living in low income neighborhoods is also revealed by shifts in the relationships between BMI/obesity risk and the presence of full-service restaurants in the block group. While full-service restaurants are linked to lower BMI for individuals in non-low income neighborhoods, the relationship disappears for individuals in low income neighborhoods. This difference may be attributable to the fact that individuals living in low income neighborhoods are less likely to have the financial resources to eat at full-service restaurants.
We hypothesize that multiple food options in a neighborhood increases the diversity of walkable destinations within a reasonable time frame and thus, residents living in such neighborhoods would have lower BMIs relative to those living in neighborhoods with no retail food options. This hypothesis is confirmed although the magnitudes of the multiple food option coefficients are generally smaller than those associated with other neighborhood retail food configurations. “Multiple retail food options,” however, is the one independent variable that has statistically significant coefficients for both the low income neighborhood and non-low income neighborhood equations that are not significantly different from one another. Thus, it appears that individuals living in both low income and non-low income neighborhoods benefit equally from having diverse retail food options in their block groups.
The question of why food environment effects vary by neighborhood income level merits further investigation. It may be that differential access to transportation heightens the importance of the immediate food environment for individuals living in low-income neighborhoods. It is also plausible that our measures of the food environment are serving as proxies for neighborhood disorder.
Prior research (Burdette & Hill, 2008
; Ross & Mirowsky, 2001
) has found that individual health is inversely related to the degree of neighborhood disorder. If the presence of multiple food options represents lower levels of disorder, then we may be detecting disorder effects rather than walkability effects in the current analyses. This link is questionable however as other studies have found that nonresidential land uses of many sorts actually invite more incivilities and neighborhood disorder (McCord, Ratcliffe, Garcia, & Taylor, 2007
; Taylor, Koons, Kurtz, Greene, & Perkins, 1995
). Unfortunately, these alternative hypotheses cannot be tested with our data.
Our findings are circumscribed by several caveats. Specifically, self-reported BMI can systematically underestimate true BMI. Likewise, while the use of census block groups as the geographic unit improves upon the larger geographic units used in many previous studies, there is still the possibility of measurement error in classifying residents’ proximity to retail food establishments, particularly if the coverage of the Dun & Bradstreet data is incomplete in low-income neighborhoods. In addition, neighborhood environment measures available in the census are only proxies of local neighborhood density, diversity, and design. Finally, our data include only the 89.5% of Salt Lake County residents between the ages of 25 and 64 who have a driver license or driver privilege card. Those excluded from our sample may be the most economically disadvantaged who may be at a higher risk of being obese. All of these study limitations make our findings conservative.
Other research constraints in the current study have potentially ambiguous effects on our findings. First, few individual measures are available in the UPDB. Thus, other potential controls (e.g., number of years in the neighborhood, individual race/ethnicity, individual income, individual education) could not be included in the analyses. As such, our analysis reflects associations between neighborhood characteristics and BMI/obesity risk but they do not imply causality. Second, our study is based on one (albeit large) county. It will be important to replicate these findings in other locales. Because the analysis is cross-sectional, it is possible that those who value healthy weight may move to walkable neighborhoods. Future work should address all of the above limitations.
A final caveat relates to our use of a single measure of SES to capture neighborhood economic disadvantage. It is possible, although not probable, that the use of a multidimensional index to differentiate disadvantaged from advantaged neighborhoods would have yielded different results. Nevertheless, the use of this single indicator has the distinct advantage of using a commonly available measure to identify at-risk neighborhoods and it builds on a number of previous studies that have also found robust relationships between neighborhood income and health outcomes.
Our paper provides some “food-for-thought” for policymakers and urban planners interested in reducing obesity risk. By 2030 almost half the buildings in the U.S. will have been built since 2000 (Nelson, 2004
), creating opportunities for evidence-based health data to inform community design. Planners who have embraced new urbanism models advocate community designs that emphasize mixed land use, increased density, and mixed housing. The current analyses suggest that these new urbanist designs may well serve to reduce obesity risk.
In existing neighborhoods, policymakers concerned with reducing obesity risk have recently begun to argue for novel policies such as imposing a moratorium on the building of fast food restaurants (Hennessy-Fisk, 2008
) or directing public funds to grocers in low-income areas so that they might expand their offerings of fresh produce (California Food Policy Advocates, 2006
). While our results suggest that fast food outlet restriction policies may not be effective, initiatives that increase neighborhood food options may be effective in reducing individuals’ obesity risks, especially if these efforts are focused on low-income neighborhoods.