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Previous literature has shown that the availability of healthy food in neighborhoods is associated with area characteristics and dietary quality. This study investigated the association between the availability of healthy foods and body mass index (BMI) among 2,616 participants living in Baltimore City.
Trained staff collected demographic information, height, weight and 24-hr dietary recalls between 2004 and 2008. Healthy food availability was determined in 34 census tracts of varying racial and SES composition using the Nutrition Environment Measures Survey- Stores in 2007. Multilevel linear regression was used to estimate associations between healthy food availability and BMI.
Among individuals living in predominately white neighborhoods, high availability of healthy foods was associated with significantly higher BMI compared to individuals living in low healthy food availability neighborhoods after adjustment for demographic variables (β=3.22, p=0.001). Associations were attenuated but remained significant after controlling for dietary intake (β=2.81, p=0.012).
Contrary to expectations, there was a positive association between the availability of healthy food and higher BMI among individuals living in predominately white neighborhoods. This result could be due to individuals in low healthy food availability areas traveling outside their neighborhood to obtain healthy food.
The prevalence of obesity in U.S. adults has significantly increased over the past several decades (Mokdad, Bowman et al. 2001) and this condition is known to increase the risk for many chronic conditions including cardiovascular disease (Yanovski 2000; Mokdad, Bowman et al. 2001; Kenchaiah, Evans et al. 2002; Calle, Rodriguez et al. 2003). Given the high prevalence of obesity, recent research has focused on the local food environment, including the types of food stores and the quality and availability of foods in a neighborhood, and their influence on health outcomes and behaviors. There is evidence that dietary patterns differ across neighborhoods and that these differences are not fully explained by individual-level socioeconomic characteristics (Ellaway and Macintyre 1996; Diez-Roux, Nieto et al. 1999; Dubowitz, Heron et al. 2008). Data has shown that supermarkets are more likely located in wealthier neighborhoods compared to poorer neighborhoods (Morland, Wing et al. 2002; Zenk, Schulz et al. 2005; Block and Kouba 2006; Moore and Diez Roux 2006; Powell, Slater et al. 2007). Furthermore, the presence of supermarkets and fewer fast food restaurants has been associated with less obesity and better dietary intake (Green, Hoppa et al. 2003; Morland, Diez Roux et al. 2006; Larson, Story et al. 2008; Li, Harmer et al. 2008; Morland and Evenson 2008).
Despite demonstrated racial and socioeconomic disparities, few studies have assessed the association between the availability of healthy food in neighborhoods and dietary intake or body mass index (BMI) by neighborhood characteristics (Cheadle, Psaty et al. 1991; Franco, Diez Roux et al. 2008; Franco, Diez-Roux et al. 2009). Therefore, this study investigated the association between the availability of healthy foods and BMI. It was hypothesized that lower healthy food availability would be associated with higher BMI. Moreover, since healthy food availability has been shown to be associated with neighborhood characteristics, a secondary hypothesis was that the association between neighborhood healthy food availability and BMI would differ by neighborhood race and socioeconomic status (SES).
The HANDLS study is a multidisciplinary, prospective epidemiologic study set in Baltimore City and examines the influence and interaction of race and SES on the development of health disparities among minority and lower SES groups (National Institute on Aging 2004). The study design was stratified across four factors: age, sex, race, and SES. Baseline recruitment included 2,616 black and white adults aged 30–64 of high- and low-SES living in 34 census tracts across Baltimore City (average of 77 participants per census tract). Data collection was implemented in two stages by trained staff and physicians: (1) an in-home household survey, and (2) a physical examination and medical history in mobile research vehicles (MRV). Baseline data collection occurred from 2004–2008. Inclusion criteria for participants included age 30–64 and the ability to give informed consent, perform at least five measures, present a valid picture identification. Exclusion criteria included pregnancy, being within six months of active cancer treatment, and multi-ethnic individuals who did not identify strongly with either the black or white race. Survey and medical information is confidential and approved by the National Institutes of Health Institutional Review Board.
Demographic measures from the HANDLS in-home questionnaire included self-reported age, sex, race, education, income and general health status (Jenkinson, Layte et al. 1997). Low-SES status was defined as having a household income below 125% of the poverty threshold. Participants reported on neighborhood crime and on the main mode of transportation used for traveling outside their neighborhood (e.g., car, walking).
Dietary intake was reported as an average of two 24-hour dietary recalls taken during the in-home and MRV visits to increase the accuracy of dietary intake and collected using the Automated Multiple Pass Method (Blanton, Moshfegh et al. 2006) by trained interviewers. Participants were asked to report all types and amounts of foods and beverages consumed in the past 24 hours. Dietary intake was quantified using the Healthy Eating Index-2005 (HEI) and selected HEI components; higher HEI scores indicate a higher quality diet (total HEI range 0–100) (Guenther, Reedy et al. 2008). Medical staff measured height and weight using standard measurement tools to determine BMI (kg/m2).
Neighborhood race and SES were determined using data from the 2000 U.S. Census. Neighborhoods were classified as predominately black or white if ≥60% of the residents were black or white, respectively (Morland, Wing et al. 2002). Since only 3 tracts failed to meet these criteria and the racial composition included few (<2%) non-blacks or non-whites, these racially mixed tracts were classified by the racial majority. Census tracts with ≥25% of residents below the poverty threshold were categorized as low-SES and <25% as high-SES. These cut-points were determined based on median values for census tract percent poverty in the HANDLS study.
NEMS-S data collected in 2006 as part of a previous study (Franco, Diez Roux et al. 2008) was used to determine healthy food availability in HANDLS census tracts. A total of 226 Baltimore stores were assessed for the availability of eight food groups and a healthy food availability index (HFAI) was calculated for each store based on the items available (range 0–27) (Franco, Diez Roux et al. 2008). Stores were categorized based on the Standard Industrial Classification Codes (SIC) (The NAICS Association): supermarkets (a chain store or employs >50 personnel), grocery stores (stores with < 50 employees), convenience stores (food marts attached to gas stations or 7-Eleven-type stores), and behind-glass stores (food items displayed behind bullet-proof glass).
Results from the Baltimore MESA study indicated that a higher percentage of predominately black and lower-income neighborhoods were categorized in the lowest HFAI tertile. Furthermore, supermarkets in predominately black and lower-income neighborhoods had significantly lower HFAI scores; findings were similar for grocery stores (Franco, Diez Roux et al. 2008). Given the policy implications of these main results and the known inaccuracies of national business data (Cates S. 2000), all food stores in Baltimore City were characterized by type (e.g., supermarket) in 2007 since the Baltimore MESA study only assessed stores located in select Baltimore City census tracts. To characterize food stores, data collectors compared Baltimore City information on food stores in the area obtained from (1) InfoUSA, (2) Baltimore area phone books, and (3) Baltimore City Health Department food license records. Data collectors visited each store, verified the correct categorization and revised the list of stores by adding stores omitted from the records and removing stores that were closed upon visit.
Healthy food availability index scores were imputed for all stores in HANDLS neighborhoods using values from the Baltimore MESA study; the imputation was based on the racial composition of the census tract and the store type for each food store located in a HANDLS census tract. Thus, a supermarket located in a predominately black neighborhood was assigned a lower score than a supermarket located in a predominately white neighborhood; supermarkets were assigned higher scores than grocery and convenience stores. Racial composition, rather than income, was chosen for imputation based on the stronger trend in HFAI scores in supermarkets and grocery stores.
Participant characteristics were presented stratified by tertiles of neighborhood healthy food availability. Mean BMI for each healthy food availability tertile was calculated using one-way analysis of variance.
Linear regression coefficients (ϐ) were estimated using multilevel (random-effects) linear models with a random intercept for each census tract. The main exposure variable was the average HFAI in a census tract. The main dependent variable was BMI. Dietary intake, main mode of transportation, and perceived crime were investigated as potential mediators in independent regression models; adjustment for all three potential mediators in the same model was also assessed.
All regression models were adjusted for potential confounders including age, sex, race, education, poverty status and self-reported health. Each analysis was stratified by neighborhood race and SES. All regression analyses were conducted using STATA (Version 10.0) and the xtreg procedure (2007). Participants with missing data for the primary outcome (BMI) were excluded from analyses and evaluated for exclusion bias (n=874). There were no differences between participants that were included and excluded from analysis by race, poverty status, age, gender or education.
The average age of participants was 48 years and 55% were female (Table 1). Significantly more individuals without a high school diploma resided in neighborhoods with higher healthy food availability (p<0.001) although more individuals above the poverty threshold resided in neighborhoods with high healthy food availability (p=0.055). Overall, the mean BMI of participants reflected unhealthy body weights (BMI=30). Participant HEI scores were low compared to national estimates (Guenther, Juan W.Y. et al. 2008).
Body mass index was higher in neighborhoods with high healthy food availability among individuals residing in predominately white neighborhoods (p<0.001) (Table 2). Conversely, mean BMI was lower in high healthy food availability neighborhoods among individuals residing in predominately black (p=0.017) and low-SES neighborhoods (p=0.001).
Overall, there was no association between food availability in neighborhoods and BMI (Table 3). Among individuals living in predominately white neighborhoods, residing in neighborhoods with medium or high food availability was associated with significantly higher BMI compared to individuals residing in neighborhoods with low availability (β=3.90, p<0.001; β=3.22, p=0.001, respectively). After adjusting for dietary intake, associations were attenuated but remained significant (β=3.49, p=0.003; β=2.81, p=0.012, respectively) (data not shown). Additional adjustment for perceived crime and main mode of transportation did not further attenuate or alter significance findings.
Earlier research indicates that the types of food stores and food availability in neighborhoods are associated with neighborhood characteristics (Morland, Wing et al. 2002; Zenk, Schulz et al. 2005; Block and Kouba 2006; Moore and Diez Roux 2006; Powell, Slater et al. 2007), dietary intake (Morland, Wing et al. 2002; Franco, Diez-Roux et al. 2009), and obesity (Green, Hoppa et al. 2003; Larson, Story et al. 2008; Li, Harmer et al. 2008; Morland and Evenson 2008). Few studies have examined these associations stratified by neighborhood race and SES.
Contrary to the study hypothesis, greater healthy food availability was associated with higher BMI among individuals living in predominately white neighborhoods after adjustment for demographic variables and dietary intake. One explanation for this unexpected finding is that individuals living in neighborhoods with low healthy food availability choose to travel outside their neighborhood to obtain healthy food. Indeed, individuals residing in neighborhoods with low healthy food availability reported more often using a car as the main mode of transportation (83%) and reported virtually no walking (1%) compared to individuals in this subgroup residing in medium and high healthy food availability neighborhoods (55%, 60% for car use and 7%, 8% for walking, respectively; p<0.001). Furthermore, individuals in low healthy food availability neighborhoods had better dietary intake (mean HEI=50) compared to their counterparts residing in medium and high healthy food availability areas (mean HEI=47, HEI=48 respectively; p<0.001). Thus, in this urban, predominately white population, higher neighborhood healthy food availability was not a marker for either healthier dietary intake or body weight.
Few studies have empirically assessed healthy food availability and the association with health outcomes. A cross-sectional study in 12 suburban/urban communities measured the availability of low-fat and high-fiber products and found positive, significant correlations between neighborhood availability of these products and self-reported healthfulness of individual diet (Cheadle, Psaty et al. 1991). In another cross-sectional study, lower healthy food availability, measured by the NEMS-S, was significantly associated with poorer dietary patterns (fat and processed meats pattern) in urban and suburban Baltimore (Franco, Diez-Roux et al. 2009). The association became insignificant when adjusted for race; higher neighborhood healthy food availability was not significantly associated with better dietary patterns (whole grains and fruit pattern). The authors noted that healthy food availability might be a proxy for neighborhood racial composition, given the strong correlation that was documented between the two factors (Franco, Diez Roux et al. 2008). Thus, the association between healthy food availability and diet quality would be masked after controlling for race. With the exception of individuals in predominately white HANDLS neighborhoods, unadjusted results were insignificant for BMI. This suggests that neighborhood healthy food availability, as assessed in the current study, may not be an accurate measure to capture food consumption patterns in this population. Information on the use of restaurants and the location of where participants most frequently shop for food may begin to clarify the influence the neighborhood food environment has on health.
There may be several explanations for the lack of significant results among individuals living in predominately black or low-SES neighborhoods. Recent literature has documented important implications and considerations for measuring food availability in minority and low-income neighborhoods (Gittelsohn and Sharma 2009; Odoms-Young, Zenk et al. 2009). Social constructs likely play an important role for understanding neighborhood disorder and safety concerns that may impede the use of local food stores, regardless of availability (Odoms-Young, Zenk et al. 2009). Thus, the availability of healthy foods would have little impact on health outcomes in low-income, minority neighborhoods. In predominately black and low-SES HANDLS neighborhoods, individuals residing in medium or high healthy food availability neighborhoods more often reported seeing serious crime as a common occurrence compared to their counterparts residing in low healthy food availability neighborhoods (p<0.001, data not shown). Second, immigrant groups, particularly Asian Americans in Baltimore City, have operated businesses in low-income, black neighborhoods for a number of years (Gittelsohn and Sharma 2009; Odoms-Young, Zenk et al. 2009). There may be language and cultural barriers and feelings of discrimination by local food store owners that reduce the use these neighborhood establishments. Third, consumer interests need consideration when assessing the effects of neighborhood food availability. Although foods of cultural preference would be expected to be available in a neighborhood, these foods may be inadequately captured on standard surveys (e.g., NEM-S). Thus, if measures of the food availability do not capture food relevant for the population, the power to detect neighborhood effects is reduced. Finally, consumers residing in low-income, minority neighborhoods, may often have concerns that food quality, fresh or otherwise, is poor and choose to purchase foods outside their neighborhood (Gittelsohn and Sharma 2009).
This study has several strengths. First, BMI was objectively measured; this method, rather than self-report, are preferred for large epidemiologic studies. Second, a systematic assessment of food stores was conducted in Baltimore City. Since national business data may inaccurately classify food stores (Cates S. 2000), this method was a significant improvement from previous studies. Finally, the stratified sampling design allowed for associations to be compared by neighborhood characteristics.
Nevertheless, this study has some limitations. First, the study was cross-sectional which limited the ability to make causal statements about observed associations. Second, census tract boundaries were used to approximate neighborhoods, which created the potential for measurement error when determining neighborhood food availability. If measurement error were present, it would be expected to be non-differential; thus, results would be biased towards the null. Third, no information was available on where participants shopped. It was assumed that the neighborhood environment was most influential on food procurement behaviors. Fourth, food store data was collected in 2006–2007 while individual baseline data was collected from 2004–2008. The current analysis assumes that neighborhood characteristics and individual behaviors and health outcomes were relatively stable during this time period. The time-point in this study represents the mid-point of the baseline data collection years, which minimizes the magnitude of this potential bias. Finally, healthy food availability scores were imputed based on a previous study implemented in Baltimore. Given that the characterization of food stores was completed using the same procedures and in the same geographic location as the current study, it is assumed that these imputed values are solid estimates of the true HFAI. Furthermore, a prior study suggests that healthy food availability may be a proxy for neighborhood racial composition (Franco, Diez Roux et al. 2008); stratification by neighborhood characteristics was a strategy used to circumvent this issue and attempt to observe the independent effect of healthy food availability.
Neighborhood food availability is only one part of the built environment that may facilitate or provide the opportunity for individuals to make healthier choices and ultimately reduce BMI. Taken together with previous work, it is likely that the influence of the food environment operates differently across neighborhoods of varying characteristics. The mechanisms for these associations deserve future investigation since neighborhood food availability may partially account for racial and SES disparities in obesity and dietary intake. The potentially large public health impact that could be gained from further investigation warrants continued exploration.
This research was supported in part by the Intramural Research Program of the NIH, National Institute on Aging. Data on healthy food availability was supported by the Center for a Livable Future at the Johns Hopkins Bloomberg School of Public Health.
The authors declare that there are no conflicts of interest.