Running to the Store? The relationship between neighborhood environments and the risk of obesity☆
Introduction
The growing obesity epidemic in the United States (Flegal, Carroll, Ogden, & Johnson, 2002) has served as the catalyst for a spate of studies examining possible linkages between modifiable neighborhood features and the risk of residents being overweight and/or obese. The authors of these studies typically hypothesize that neighborhood characteristics are associated with an individual's body mass index (BMI) either because they affect residents' access to food options (i.e., energy intake) or because they alter residents' propensity to be physically active (i.e., energy expenditure). In this paper, we evaluate both dimensions. We also allow for differing effects in low income and non-low income neighborhoods with the aim of developing a more complete picture of the linkages between neighborhood characteristics and residents' BMI/obesity risk.
Previous research on the associations between local food environments and BMI has generally focused on the proximity of fast food outlets and/or full-service grocery stores and convenience stores (Burdette and Whitaker, 2004, Jeffery et al., 2006, Lopez, 2007, Maddock, 2004, Mehta and Chang, 2008, Morland et al., 2006, Powell et al., 2007, Powell et al., 2007, Rose and Richards, 2004, Rundle et al., 2009, Simmons et al., 2005, Sturm and Datar, 2005, Wang et al., 2007a). Although most food outlets offer both healthy and unhealthy options, an observation that has guided this work is that full-service restaurants and grocery stores typically offer healthier foods than fast food outlets and convenience stores (Sallis, Nader, Rupp, Atkins, & Wilson, 1986). However, a number of studies have found no association between the proximity of fast food outlets and BMI (Jeffery et al., 2006, Lopez, 2007, Simmons et al., 2005, Sturm, 2005, Sturm and Datar, 2005) while others have found a positive association (Maddock, 2004, Mehta and Chang, 2008, Rundle et al., 2009). Likewise, the smaller literature on proximity to large supermarkets versus small convenience stores is mixed with several studies reporting that access to a supermarket is associated with a lower risk of obesity (Lopez, 2007, Morland et al., 2006) or obesity related behaviors (Rose & Richards, 2004) while others have found no such relationship (Wang, Kim, Gonzalez, MacLeod, & Winkleby, 2007b).
It is not surprising that a clear consensus regarding the relationship between local food environments and BMI has not emerged. Researchers are often limited in how they measure access to the local food retailers by the availability of data. Geographic scales for food environment measures vary widely, including an individual's state (Maddock, 2004), county (Mehta & Chang, 2008), ZIP code (Lopez, 2007, Powell et al., 2007, Sturm and Datar, 2005), census tract (Morland et al., 2006, Morland et al., 2002), census block group (Wang et al., 2006, Wang et al., 2007b), or half-mile radius from the individual's residence (Rundle et al., 2009). Conceptually, the geographic unit should approximate the individual's shopping neighborhood (i.e., those destinations that s/he can get to within a reasonable time frame). Restricting prior studies to those that use a smaller geographic unit still yields conflicting results, however. Wang et al. (2007b) find that closer proximity to a supermarket is linked to higher BMI in women, while Morland et al. (2006) report that the presence of a supermarket in a resident's census tract is associated with a lower risk of overweight/obesity for men and women.
Prior work has also typically focused on one or two dimensions of the local food environment (e.g., proximity to fast food restaurants), which may lead to spurious findings if healthy or unhealthy food options tend to be clustered in the same geographic areas. Only two previous studies have included both grocery shopping options and options for purchasing meals away from home (Lopez, 2007, Rundle et al., 2009).
A separate line of research has linked physical environments to health by examining the links between “walkable” neighborhoods and BMI. In these studies, design features of a neighborhood are measured in many different ways, with no consensus about a best measure in all circumstances. Studies at the neighborhood scale or larger typically include some combination of measures of the “3D's”: population density, pedestrian-friendly design, and a diversity of destinations (Cervero & Kockelman, 1997). As with the research on local food environments, the findings of these studies have produced mixed evidence on the relationship between neighborhoods that are expected to facilitate physical activity (e.g., walking, biking) and the risk of obesity.
Greater population density, which is hypothesized to be associated with the development of more walking destinations within a neighborhood, has been associated with fewer weight problems in many studies (Lopez, 2004, Lopez-Zetina et al., 2006, Rundle et al., 2007, Smith et al., 2008, Stafford et al., 2007, Vandegrift and Yoked, 2004) but not all studies (Frank et al., 2004, Pendola and Gen, 2007, Ross et al., 2007). Similarly, mixed results have been found in studies linking BMI to pedestrian-friendly neighborhood designs as measured by the density of intersections per area or the presence/quality of sidewalks (Boehmer et al., 2007, Doyle et al., 2006, Frank et al., 2004, Giles-Corti et al., 2003, Rundle et al., 2007, Smith et al., 2008). In addition, areas with broad mixes of land use are associated with lower weight in most (Frank et al., 2004, Mobley et al., 2006, Rundle et al., 2007, Smith et al., 2008, Stafford et al., 2007, Tilt et al., 2007) but not all studies (Boehmer et al., 2007, Rutt and Coleman, 2005). A recent review shows stronger, more consistent relationships between obesity and neighborhood walkability and physical activity supports than between obesity and neighborhood food environments (Black & Macinko, 2008).
Part of the inconsistency may be related to variations in data availability, definitions of walkability, and geographic levels of analysis. Variables that capture key features of the walking environment within a local neighborhood are likely to provide good measures of individuals' time-related travel choices (e.g., walking versus driving to the grocery store). Therefore, we examine walkability indicators at the level of the census block group or 1 km buffer when possible.
When measures of food environment are combined with walkability indicators, we argue that neighborhood food environment measures may also capture dimensions of land use diversity. Proximity to grocery stores, full-service restaurants, and even convenience stores and fast food outlets could increase an individual's energy output if residents walk to these facilities rather than drive to them. Proximity to food outlets might increase fruit and vegetable consumption if a nearby food outlet makes it convenient to purchase perishable but healthy foods more frequently. Alternatively, nearby unhealthy food options may make over-consumption more convenient as well. The presence of nearby grocery stores and full-service restaurants enhance healthy food options and reduce the time costs of using active modes of transportation (e.g., walking, biking) to purchase food. In contrast, the presence of local convenience stores and fast food outlets may not offer as many healthy food options but they do represent diverse walking/biking destinations within a neighborhood. Thus, the net impact of local food options on BMI may be unclear.
Past research also reveals that a neighborhood's more general socioeconomic status (SES) is often linked to various dimensions of residents' health (e.g., atherosclerosis risk, smoking risk, obesity risk, mortality risk). Some of these studies make use of multidimensional indices to measure neighborhood disadvantage (Cohen et al., 2006, Mujahid et al., 2005, Stimpson et al., 2007) while others control for neighborhood SES with separate variables (Krieger et al., 2005, Lopez, 2007, Rehkopf et al., 2006, Ross et al., 2007, Rundle et al., 2007, Waitzman and Smith, 1998a, Waitzman and Smith, 1998b).
We elect to follow the second strategy for three reasons. First, there is little agreement as to what should be included in such indices with the number of included elements proposed for the USA ranging from four (Cohen et al., 2006) to eleven (Stimpson et al., 2007) in recently published studies. Second, it is difficult to draw policy and/or educational implications from any observed association between a multidimensional index of neighborhood conditions and BMI/obesity risk. Finally, past studies that make use of an income based measure of SES have demonstrated strong relationships between neighborhood income and the health of individuals (Macintyre et al., 2002, Pickett and Pearl, 2001, Waitzman and Smith, 1998a, Waitzman and Smith, 1998b). Low income neighborhoods may offer difficult conditions for both physical activity and healthy food consumption. Some researchers have emphasized how these neighborhoods are more likely to be “food deserts” or provide only unhealthy local food choices (Regan, Lee, Booth, & Reese-Smith, 2006). Those who sell healthy food may choose not to locate in low income neighborhoods or they may choose to stock their shelves with less healthy or more expensive food. Individuals in low income neighborhoods may not have the time or money resources to obtain healthy foods (Inagami, Cohen, Finch, & Asch, 2006) or crime and other incivilities in their neighborhoods may promote greater distress on the part of residents (Burdette and Hill, 2008, Ross and Mirowsky, 2001) which in turn could negatively affect eating behaviors. Similar restrictions may exist for physical activity resources (Papas et al., 2007). Consequently, we explicitly test to see if there are differences in the association between neighborhood characteristics and BMI in low income versus non-low income neighborhoods.
Our analysis contributes to the literature in several ways. First, we make use of data on neighborhood characteristics measured at the census block group level in an attempt to capture potentially important elements of the local environment. While most other research has measured neighborhood characteristics within larger geographic units (e.g., census tracts, ZIP codes, counties), we are able to gauge a range of local neighborhood features including food destinations that an individual can walk/bike to within a reasonable time frame from his/her home. Block groups are not necessarily the perfect geographic unit as in high density areas individuals may be very close to food sources that are in a different block group. Nonetheless, relative to other spatial units, block groups more closely approximate the local environment for an individual while they also contain important socioeconomic information unavailable at the block level.
Second, we operationalize the food environment measures in ways that test for food environment diversity effects. Specifically, we are able to compare and contrast BMI measures for individuals living in census block groups that lack retail food options, that have only one type of option, and that have multiple options.
Third, building on the work of Rundle et al. (2009), our analysis is only the second one we know of to assess the relationship between the local food environment and BMI controlling for other walkable features in the neighborhood. Including both the food environment and neighborhood walkability measures increases our confidence that the relationships we observe are not simply spurious.
Finally, we allow for neighborhood effects to vary by the economic status of the neighborhood. This allows us to test the hypothesis that the relationship between BMI and environmental factors differs in low income and non-low income neighborhoods.
Section snippets
The data
This study utilizes data from the Utah Population Database (UPDB). The UPDB is one of the world's richest sources of linked population-based information that focus on demographic, genetic, epidemiological, and public health outcomes. The UPDB contains 2005 driver license data from the Driver License Division (DLD) of the Utah Department of Public Safety. To protect confidentiality of driver license holders, all personal information from the Driver License Division was removed before the data
Results
Table 1 displays descriptive statistics. In Salt Lake County, 25 percent of adults age 25–64 who have driver licenses are obese. When focusing on residents of non-low income neighborhoods, the figure is slightly lower at 24 percent while in low income neighborhoods 28 percent are obese. These rates are slightly higher than the adult obesity rate for 2005–2007 in Utah which is 21.8 percent (Trust for Healthy Americans, 2008). Our data are for only one county and we exclude the elderly, young
Discussion and conclusions
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, 2004, Lopez-Zetina et al., 2006, Rundle et al., 2007, Smith et al., 2008, Stafford et al., 2007, Vandegrift and Yoked, 2004). But, we also find that the magnitude of
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This research was supported in part by NIDDK Grant Number 1R21DK080406-01A1. We want to thank Karen Hamrick and Susan Chen for their comments on earlier drafts of this paper. Linda Keiter provided valuable assistance with the Dun & Bradstreet data.