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American Journal of Clinical Nutrition, Vol. 87, No. 6, 1883-1891, June 2008
© 2008 American Society for Nutrition

ORIGINAL RESEARCH COMMUNICATION

Neighborhood socioeconomic status and fruit and vegetable intake among whites, blacks, and Mexican Americans in the United States1,2,3,4

Tamara Dubowitz, Melonie Heron, Chloe E Bird, Nicole Lurie, Brian K Finch, Ricardo Basurto-Dávila, Lauren Hale and José J Escarce

1 From the RAND Corporation, Pittsburgh, PA (TD), Santa Monica, CA (CEB, JJE, and RBD), and Washington, DC (NL); the Centers for Disease Control and Prevention/National Center for Health Statistics, Hyattsville, MD (MH); San Diego State University, San Diego, CA (BKF); the State University of New York Stony Brook, Stony Brook, NY (LH); and the University of California Los Angeles/RAND Corporation, Los Angeles, CA (JJE)

2 The findings and conclusions in this manuscript are those of the authors and do not necessarily represent the views of the RAND Corporation or the Centers for Disease Control and Prevention.

3 Supported by grant no. 1P50ES012383-01 from the National Institute of Environmental Health Sciences.

4 Reprints not available. Address correspondence to T Dubowitz, RAND Corporation, 4570 Fifth Avenue, Suite 600, Pittsburgh, PA 15213. E-mail: dubowitz{at}rand.org.


   ABSTRACT
TOP
 ABSTRACT
INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 
Background: Socioeconomic and racial-ethnic disparities in health status across the United States are large and persistent. Obesity rates are rising faster in black and Hispanic populations than in white populations, and they foreshadow even greater disparities in chronic illnesses such as diabetes and cardiovascular disease in years to come. Factors that influence dietary intake of fruit and vegetables in these populations are only partly understood.

Objectives: We examined associations between fruit and vegetable intake and neighborhood socioeconomic status (SES), analyzed whether neighborhood SES explains racial differences in intake, and explored the extent to which neighborhood SES has differential effects by race-ethnicity of US adults.

Design: Using geocoded residential addresses from the Third National Health and Nutrition Examination Survey, we merged individual-level data with county and census tract–level US Census data. We estimated 3-level hierarchical models predicting fruit and vegetable intake with individual characteristics and an index of neighborhood SES as explanatory variables.

Results: Neighborhood SES was positively associated with fruit and vegetable intake: a 1-SD increase in the neighborhood SES index was associated with consumption of nearly 2 additional servings of fruit and vegetables per week. Neighborhood SES explained some of the black-white disparity in fruit and vegetable intake and was differentially associated with fruit and vegetable intake among whites, blacks, and Mexican Americans.

Conclusions: The positive association of neighborhood SES with fruit and vegetable intake is one important pathway through which the social environment of neighborhoods affects population health and nutrition for whites, blacks, and Hispanics in the United States.


   INTRODUCTION
TOP
 ABSTRACT
 INTRODUCTION
MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 
Socioeconomic and racial-ethnic disparities in health status across the United States are large and persistent, and they show few signs of decreasing. Multiple studies have linked both individual and neighborhood socioeconomic status (SES) to health, but few studies have assessed the pathways through which neighborhood SES may do this.

Diet is an important determinant of obesity and chronic disease. Adequate fruit and vegetable consumption is associated with a lower risk of some of the main causes of mortality in the United States, including type 2 diabetes, heart disease, stroke, and obesity (1-7). Diets rich in fruit and vegetables are also associated with a lower incidence of several common neoplasms, especially those of the respiratory and digestive tract (8). Researchers have found differences in fruit and vegetable intake by race and ethnicity, SES, and sex (9-12). Other studies have assessed racial and ethnic differences in additional dimensions of diet, including intake of fat, cholesterol, and fiber (13, 14), and in adherence to healthy diets (15-19). Understanding the sources of racial and ethnic differences in diet is important in view of the possible contribution of diet to disparities in health outcomes.

The growing literature on the relation of the social and built environment to obesity has introduced a strong theoretical foundation for how the neighborhood environment may influence diet. In particular, the socioeconomic characteristics of a neighborhood, also referred to as neighborhood SES, could influence diet through the quantity and quality of food stores and restaurants in the area, which, in turn, may determine access to nutritious foods; the availability and affordability of fresh produce; and the ease of transportation to grocery stores or healthy food options (20-33). The documented association of neighborhood SES and health outcomes such as cardiovascular disease prevalence (34) and mortality (35) lends additional support to the notion that neighborhood SES may be associated with diet.

An association of neighborhood SES and diet raises 2 important questions regarding racial and ethnic differences in diet. The first question is whether neighborhood SES may help to explain these differences, because blacks and Hispanics generally live in more disadvantaged neighborhoods than do whites. The second question is whether neighborhood SES may operate differently with respect to different racial and ethnic groups. Numerous scholars have posited that the influence of context on individual persons is patterned by race and ethnicity (36, 37). Thus, for example, blacks who live in affluent neighborhoods may benefit less than do similarly placed whites from the opportunities in those neighborhoods for maintaining healthy diets (38).

To date, however, limited research has examined the relation between neighborhood characteristics, including neighborhood SES, and diet, and no study has assessed whether neighborhood characteristics explain racial and ethnic differences in diet or whether those characteristics affect different racial and ethnic groups differently. To fill these gaps, we used a geocoded version of the Third National Health and Nutrition Examination Survey (NHANES III) to examine, first, the independent associations between fruit and vegetable intake and neighborhood SES, after control for individual attributes that may influence diet; second, whether neighborhood SES explains racial differences in fruit and vegetable intake; and, third, the extent to which the associations between fruit and vegetable intake and neighborhood SES differ by race and ethnicity. This research builds on a neighborhood deprivation framework that was developed with an understanding that socioeconomic characteristics of neighborhoods or residential areas also share physical (eg, pollution and availability of nutritious food), social (eg, crime and behavioral norms), and service (eg, transportation, health care, and police protection) environments. These shared environments could influence health and health behaviors (eg, diet) above and beyond the health effects of the socioeconomic characteristics of residents living within them (39, 40). The findings of the present study provide additional information that can support the development of policies to improve diet for low-income and minority Americans by addressing factors related to dietary intake.


   MATERIALS AND METHODS
TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
RESULTS
 DISCUSSION
 REFERENCES
 
Data source and study samples
NHANES III, conducted from 1988 through 1994, is a nationally representative, cross-sectional study of the noninstitutionalized civilian population of the United States. The sampling design oversampled blacks and Mexican Americans, and data collection included survey, medical examination, and laboratory components. Overall, 86% of persons recruited for the study were interviewed in their homes, and 77% underwent standardized clinical examinations and additional interviews in a mobile examination center. The dietary recall segment was conducted by trained interviewers who collected information on all dietary intake within the previous 24-h period (midnight to midnight) by using a computerized interview and coding system. Of the samples examined at the mobile examination center, 94% had a complete 24-h dietary recall.

As was done in earlier studies of neighborhood effects (41, 42), we used census tracts as proxies for neighborhoods and merged the NHANES III with census tract–level data from the US Census Bureau by using geocoded residential addresses. Approximately 86% of the sample was geocoded to a census tract via a match to an exact address or to a street intersection. The remaining 14% of the sample could be matched only to a ZIP code or county centroid; therefore, we excluded these subjects from our analyses because of concerns about the validity of merging tract-level data based on such matches. These subjects were overwhelmingly situated in low-density areas; consequently, our results may not be representative of rural residents.

We further restricted the study samples to adults aged ≥20 y; we excluded pregnant women, whose diets are likely to be atypical, and subjects who had questionable values for dietary intake (eg, >18 servings fruit or vegetables/d) or who were missing key variables for the analyses. Thus, the final study samples consisted of nonpregnant adults who were geocoded to census tracts and who had complete fruit or vegetable intake information and complete data on other key analytic variables. The sample sizes were 13 310 for the analyses of fruit intake, 13 296 for the analyses of vegetable intake, and 13 281 for the analyses of combined fruit and vegetable intake.

The final study samples comprised {approx}75.6% of the geocoded NHANES adult sample. Excluded subjects were significantly more likely to be younger, to be US-born, to be non-Hispanic white, to have lower educational attainment and family income, to reside in the South or Midwest, and to live in poorer neighborhoods with fewer minorities than did included subjects. However, there were no significant differences between the 2 groups of subjects in terms of fruit and vegetable intake or sex.

The work described in this report was carried out at the RAND Center for Population Health and Health Disparities and at the National Center for Health Statistics Data Center.

Measures
The dependent variables in our analyses were number of servings per day of fruit, number of servings per day of vegetables, and combined servings per day of fruit and vegetables, derived from the 24-h dietary recall. The individual characteristics used as independent variables included age; sex; race-ethnicity, categorized as non-Hispanic white (hereafter referred to as "white"), non-Hispanic black (hereafter referred to as "black"), Mexican American, or other; nativity, categorized as US-born or foreign-born; educational attainment, categorized as grade school only, some high school, high school graduate, or post-high school; family income relative to the federal poverty level (FPL), categorized as poor (<1x FPL), low income (1–2x FPL), middle income (2–4x FPL), or high income (>4x FPL); employment status, categorized as not in the labor force, employed, or unemployed; and region of the United States, categorized as Northeast, Midwest, South, or West.

We constructed a neighborhood SES index at the level of census tracts by using 6 variables obtained from the census. To do this, we first identified 12 theoretically relevant census tract–level variables and conducted an exploratory factor analysis. Six variables loaded highly on the factor we interpreted as an indicator of SES (the loading factor alpha was between 0.80 and 0.93), and thus we selected them to be included in the neighborhood SES index. These variables were 1) the percentage of adults >25 y old with less than a high school education; 2) the percentage of unemployed males; 3) the percentage of households with income below the poverty line; 4) the percentage of households receiving public assistance; 5) the percentage of households with children that are headed by a female; and 6) median household income. We then transformed the variables so that higher values corresponded to higher neighborhood SES. Second, the 6 individual items were summed and standardized to create a neighborhood SES scale with a mean of 0 and an SD of 1. Thus, an index score >0 denotes a tract with an SES above the sample average.

Statistical analysis
We estimated 3-level hierarchical linear models to adjust for the clustering of observations at the tract and county levels, and we partitioned the variance of the dependent variables into individual, tract, and county components. For each of the 3 dependent variables, we estimated 1) a model that included only the individual characteristics, to investigate the associations between these factors and the intake of fruit and vegetables; 2) a model that also included the neighborhood SES index, to assess the association between neighborhood SES and fruit and vegetable intake and the extent to which individual-level associations changed after the inclusion of neighborhood SES; and 3) a model that included the individual characteristics, neighborhood SES, and the interaction of neighborhood SES and race-ethnicity, to test whether the associations between neighborhood SES and dietary intake varied by racial-ethnic group. All analyses were weighted by using weights that account for the complex sampling design of NHANES III and for survey nonresponse, and P ≤ 0.05 was chosen as the criterion for statistical significance in all analyses.

We conducted the analyses at the secure Research Data Center of the National Center for Health Statistics (NCHS, Hyattsville, MD) by using SAS software (version 9.1; SAS Institute, Cary, NC). Approval for this study was obtained from the institutional review boards of NCHS and the RAND Corporation.


   RESULTS
TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
DISCUSSION
 REFERENCES
 
Descriptive data
Our study sample averaged 1.53 ± 2.05 servings fruit/d, 3.24 ± 2.62 servings vegetables/d, and 4.76 ± 3.52 servings fruit and vegetables combined/d (Table 1Go). Whites consumed significantly more combined servings of fruit and vegetables than did either blacks or Mexican Americans. Specifically, whites averaged 4.90 ± 3.53 servings fruit and vegetables/d, compared with 4.57 ± 3.40 servings/d for Mexican Americans and 3.99 ± 3.38 servings/d for blacks.


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  		TABLE 1. Weighted descriptive analysis of the dependent variables1

 
Table 1Go also reports unadjusted intraclass correlation coefficients (ICCs) (43), which reflect the proportion of total variance in dietary intake that occurs between tracts within counties (level 2) and between counties (level 3). We found substantial between-tract variation in combined fruit and vegetable intake, fruit intake, and vegetable intake, with unadjusted ICCs of 18.7%, 15.6%, and 16.9%, respectively. Between-county variation in combined fruit and vegetable intake (1.5%) and fruit intake (2.7%) was smaller, and there was no between-county variation in vegetable intake.

Approximately 74.0% of the sample was white, 11.7% was black, and 5.5% was Mexican American; the remainder made up the category of "Other" (Table 2Go). The study sample had a mean age of 44.57 y; in addition, just over one-half of the participants were women, 12.7% were poor, 23.7% did not graduate from high school, and 15.4% were foreign-born. On average, whites were older, had higher income, and had higher educational attainment than did blacks and Mexican Americans. However, Mexican Americans were much more likely than whites or blacks to be foreign-born. Approximately one-half of all blacks lived in the South, and more than three-fifths of Mexican Americans lived in the West.


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  		TABLE 2. Weighted characteristics of the study sample1

 
The neighborhood SES index ranged from –7.72 to 1.99, with a mean of 0 and an SD of 1. As expected, whites lived in higher-SES neighborhoods than did blacks or Mexican Americans. The neighborhood SES index ranged from –3.81 to 1.99 for whites (mean: 0.24; median: 0.34), from –7.72 to 1.74 for blacks (mean: –1.04; median: –0.88), and from –6.06 to 1.64 for Mexican Americans (mean: –0.65; median: –0.57).

Multivariate results
In the models that included only individual characteristics as explanatory variables, we found that blacks consumed 0.42 fewer combined daily servings of fruit and vegetables than did whites (P = 0.0003), after adjustment for other characteristics, whereas the intake of fruit and vegetables was similar for whites and Mexican Americans (Table 3Go). Older age, male sex, foreign birth, higher educational attainment, and higher family income were associated with higher intake of fruit and vegetables combined. Specifically, men consumed 0.71 more daily servings of fruit and vegetables than did women (P < 0.0001); US-born subjects consumed 0.85 fewer daily servings than did foreign-born subjects (P < 0.0001); persons who completed only grade school consumed 1.19 daily fewer servings than did those who received education beyond high school (P < 0.0001); and persons in poor families consumed 0.62 fewer daily servings than did those in high-income families (P = 0.0009).


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  		TABLE 3. Regression coefficients (fixed effects), random effects, and significance of multivariate hierarchical linear models that include only individual-level characteristics as explanatory variables for fruit and vegetable intake, using the Third National Health and Nutrition Examination Survey, 1988-19941

 
Separate analyses of fruit and vegetable intake yielded additional noteworthy findings (Table 3Go). Blacks consumed 0.50 fewer daily servings of vegetables than did whites (P < 0.0001), but there were no differences across racial-ethnic groups in fruit intake. Men consumed more servings of vegetables than did women, but men and women did not significantly differ in fruit intake. In addition, US-born persons consumed fewer daily servings of fruit than did their foreign-born counterparts, but there was no difference in vegetable consumption by nativity. Otherwise, associations were similar to those in the analyses of fruit and vegetables combined.

Adjustment for individual-level factors reduced the tract ICCs, but only slightly (Table 3Go). Specifically, the tract ICC for combined fruit and vegetable intake was reduced to 17.0%, that for fruit intake was reduced to 14.1%, and that for vegetable intake was reduced to 16.3%. The county ICCs were also reduced, but these ICCs were small to begin with. Thus, geographic variation in dietary intake persisted after adjustment for individual-level factors, especially at the tract level.

When we included the neighborhood SES index in the models, we found positive associations between neighborhood SES and fruit and vegetable intake (Table 4Go). A 1-SD increase in the neighborhood SES index was associated with consumption of an additional 0.24 daily servings of fruit and vegetables combined (P < 0.0001). Moreover, the effect sizes for neighborhood SES were similar for fruit and for vegetables when we examined them separately. Thus a 1-SD increase in the neighborhood SES index was associated with consumption of an additional 0.13 servings fruit/d (P < 0.0001) and an additional 0.11 servings vegetables/d (P = 0.0006).


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  		TABLE 4. Regression coefficients (fixed effects), random effects, and significance of multivariate hierarchical linear models that include neighborhood socioeconomic status (SES) in addition to individual characteristics as explanatory variables for fruit and vegetable intake, using the Third National Health and Nutrition Examination Survey, 1988-19941

 
Including neighborhood SES in the models reduced the black-white disparity in combined intake of fruit and vegetables that we found in the earlier models that included only individual characteristics as explanatory variables. Specifically, in the model that included neighborhood SES, blacks consumed 0.24 fewer daily combined servings of fruit and vegetables than did whites (P = 0.051) (Table 4Go), which corresponds to approximately one-half of the black-white gap of 0.42 daily servings found in the earlier analyses (Table 3Go). Moreover, in the model that included neighborhood SES, blacks consumed 0.16 more daily servings of fruit than did whites (P = 0.025) (Table 4Go), whereas there was no difference between blacks and whites in fruit intake in the model that included only individual characteristics (Table 3Go). Including neighborhood SES in the models did not substantially alter the black-white gap in daily servings of vegetables, nor did it have an appreciable effect on the magnitude and statistical significance of the associations of combined fruit and vegetable intake with Mexican American ethnicity, age, sex, nativity, educational attainment, family income, employment status, or region of residence (compare Tables 3Go and 4Go).

Notably, although neighborhood SES was strongly associated with fruit and vegetable intake, the inclusion of neighborhood SES in the models had only a small effect on the tract and county ICCs (compare Tables 3Go and 4Go). Thus, neighborhood SES did not explain the remaining variance in dietary intake across census tracts.

To assess whether the effects of neighborhood SES on fruit and vegetable intake differed across racial-ethnic groups, we estimated models with individual characteristics, neighborhood SES, and the interaction of neighborhood SES with race-ethnicity. Neighborhood SES was positively associated with combined fruit and vegetable intake for whites, blacks, and Mexican Americans (Table 5Go). However, the neighborhood SES effect size for whites (0.35 servings/d for each 1-SD increase in neighborhood SES, P < 0.0001) was roughly twice that for blacks (0.13; P = 0.0283) and Mexican Americans (0.18; P = 0.0040). Blacks and Mexican Americans did not have significantly different neighborhood SES effect sizes, but the effect sizes of both of those groups differed significantly from the effect size of whites (P < 0.05). Separate analyses of fruit and vegetable intake showed similar patterns in the point estimates—ie, neighborhood SES effect sizes were consistently larger for whites than for blacks or Mexican Americans. However, several of the associations failed to reach statistical significance (Table 5Go).


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  		TABLE 5. Regression coefficients (fixed effects), random effects, and significance of multivariate hierarchical linear models that include individual-level characteristics and neighborhood socioeconomic status (NSES) as explanatory variables for fruit and vegetable intake and that test the interaction of race-ethnicity with neighborhood SES, using the Third National Health and Nutrition Examination Survey, 1988-19941

 

   DISCUSSION
TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
REFERENCES
 
Dietary intake, particularly fruit and vegetable consumption, is important to health. The findings of the present study suggest that the socioeconomic characteristics of neighborhoods influence the intake of fruit and vegetables. Thus effects on diet may be one of the mechanisms through which neighborhood SES affects health.

As was done in earlier studies (44-46), we found that individual characteristics, including sex, nativity, educational attainment, and family income, were related to fruit and vegetable intake. However, our key finding—the observation that merits emphasis—was that neighborhood SES exhibited a positive and statistically significant association with fruit and vegetable intake even after control for individual characteristics. Furthermore, the coefficients for most individual characteristics did not change appreciably after neighborhood SES was included in the models, which suggests that our analyses were able to separate the effects of neighborhood SES from those of individual attributes.

Our findings regarding racial and ethnic differences in fruit and vegetable intake and the extent to which these differences are explained by neighborhood SES are noteworthy. In the analyses that accounted for individual characteristics but not for neighborhood SES, we found that blacks consumed 0.42 fewer daily servings of fruit and vegetables combined and 0.50 fewer daily servings of vegetables than did whites. When we accounted for neighborhood SES, however, we found that nearly one-half of the black-white gap in combined intake of fruits and vegetables was explained by neighborhood SES. The difference between blacks and whites in vegetable intake remained sizable. The analyses also showed that blacks consumed more daily servings of fruits than whites when neighborhood SES was taken into account. Thus, neighborhood SES explained a substantial portion of the black-white disparity in fruit and vegetable intake. By contrast, fruit and vegetable intake did not differ significantly between Mexican Americans and whites, irrespective of whether the analyses accounted for neighborhood SES.

Our findings regarding the interaction between race-ethnicity and neighborhood SES suggest that neighborhood SES may influence dietary intake differently for different racial-ethnic groups. We found a positive and significant association between neighborhood SES and combined fruit and vegetable intake for whites, blacks, and Mexican Americans. However, the effect size was much larger for whites, which suggests that whites may be better able than blacks or Mexican Americans to take advantage of the enhanced opportunities for maintaining a healthy diet in more-affluent neighborhoods. Another possibility is that strong cultural influences on the diets of blacks and Mexican Americans make them less susceptible than whites to the effects of environmental factors (47, 48).

Because the racial composition of neighborhoods is highly correlated with neighborhood SES, we were concerned that our findings for neighborhood SES may in part reflect associations of fruit and vegetable intake with neighborhood racial composition. (In our data, r = 0.75 for the correlation between neighborhood SES and the percentage of the minority population across census tracts.) To address this concern, we estimated additional models that included both neighborhood SES and the percentage of the minority population in the census tract. We found that only neighborhood SES was significant in the models for intake of fruit and vegetables combined and for fruit intake (P < 0.0001 for both) and that the neighborhood SES effect sizes were virtually the same in the models that did and the models that did not include neighborhood racial composition. The neighborhood SES effect size was slightly reduced in the model for vegetable intake when we included the percentage of minority population, but neighborhood SES remained nearly significant (P = 0.0592) in the expanded model, whereas the percentage of minority population was not significant. These findings suggest that neighborhood racial composition was not an important omitted confounder in our analyses.

Several limitations of our study deserve mention. First, our findings regarding the extent to which neighborhood SES explains racial and ethnic disparities in fruit and vegetable consumption should be interpreted with caution, owing to the degree of racial and economic segregation in the data. Whites tend to live in higher-SES neighborhoods than do blacks and Mexican Americans; consequently, the overlap in the distributions of neighborhood SES across racial and ethnic groups was limited, especially at the lower end of the distributions.

Second, potential selection processes that sort persons into neighborhoods are a challenge for observational neighborhood studies, and they may limit our ability to draw causal inferences. In particular, there may be unmeasured factors that are correlated with both people's concern about their diets or about their health and their choice of neighborhood.

Third, research on neighborhoods is limited by the need to operationalize such a complex conceptual construct regarding geographic spaces. At the national level, in particular, it is difficult to define neighborhoods that are meaningful from place to place by using previously established geographic boundaries. Nonetheless, census tracts, although imperfect proxies for neighborhoods, have been used in the vast majority of neighborhood studies (47, 48). Useful properties of census tracts include the relative consistency of population size across tracts and the relative homogeneity of the population within tracts.

Finally, the dietary data in NHANES are based on only one recall over a 24-h period. A single 24-h dietary recall is unlikely to be representative of usual individual intake, because day-to-day intake is highly variable for many persons (49). However, a single 24-h recall is adequate for estimates of group means (50). Moreover, measurement error introduced by the use of a single 24-h recall is likely to reduce the precision of estimated effect sizes in multivariate analyses—and hence the ability to detect significant associations—but is not expected to bias the point estimates.

From a policy perspective, understanding the associations between neighborhood SES and health behaviors, such as diet, is one important step toward improving overall health status. Our finding that neighborhood SES was positively associated with fruit and vegetable intake suggests that special efforts—whether by community groups, business, or government—to increase the availability of fresh produce and other healthy foods in disadvantaged neighborhoods may help local residents improve their diets and are likely to be warranted. In addition, our finding that neighborhood SES mattered less for blacks and Mexican Americans than for whites suggests that these groups may respond differently to the availability of healthy food options. Additional research is needed to investigate the mechanisms underlying this finding and their implications for policy.


   ACKNOWLEDGMENTS
 
We thank D Phuong Do for her work in setting up the data, Adria Dobkin for helping to prepare the data files, and Denise Miller and Jenny Gelman for their administrative and reference support. We also thank Robert Anderson, Julia Holmes, Susan Schober, and Jennifer Madans of the National Center for Health Statistics for their comments on a draft of this manuscript.

The authors’ responsibilities were as follows—TD, MH, and JJE: contributed substantially to the study conception and design, data analysis and interpretation, and writing; CEB and BKF: oversaw the "host" study under which this research was pursued and reviewed and edited drafts of the manuscript; RDB: assisted with analysis and interpretation of results; LH: assisted with interpretation of data and reviewed manuscript drafts; NL: assisted with the conceptual grounding of the paper and with analysis interpretation and writing. None of the authors ahd a personal or financial conflict of interest.


   REFERENCES
TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 

  1. Fung TT, Stampfer MJ, Manson JE, Rexrode KM, Willett WC, Hu FB. Prospective study of major dietary patterns and stroke risk in women. Stroke 2004;35(9):2014–9.[Abstract/Free Full Text]
  2. Hu FB, Manson JAE, Stampfer MJ, et al. Diet, lifestyle, and the risk of type 2 diabetes mellitus in women. N Engl J Med 2001;345(11):790–7.[Abstract/Free Full Text]
  3. Hung HC, Joshipura KJ, Jiang R, et al. Fruit and vegetable intake and risk of major chronic disease. JNCI 2004;96(21):1577–84.[Abstract/Free Full Text]
  4. Joshipura KJ, Hu FB, Manson JAE, et al. The effect of fruit and vegetable Iitake on risk for coronary heart disease. Ann Intern Med 2001;134(12):1106–14.[Abstract/Free Full Text]
  5. Serdula MK, Byers T, Mokdad AH, Simoes E, Mendlein JM, Coates RJ. The association between fruit and vegetable intake and chronic disease risk factors. Epidemiology 1996;7(2):161–5.[Medline]
  6. Terry P, Suzuki R, Hu FB, Wolk A. A prospective study of major dietary patterns and the risk of breast cancer. Cancer Epidemiol Biomarkers Prev 2001;10(12):1281–5.[Abstract/Free Full Text]
  7. Winkleby MA, Cubbin C. Changing patterns in health behaviors and risk factors related to chronic diseases, 1990–2000. Am J Health Promot 2004;19(1):19–27.[Medline]
  8. La Vecchia C, Altieri A, Tavani A. Vegetables, fruit, antioxidants and cancer: a review of Italian studies. Eur J Nutr 2001;40(6):261–7.[Medline]
  9. Block G, Rosenberger WF, Patterson BH. Calories, fat and cholesterol: intake patterns in the US population by race, sex and age. Am J Public Health 1988;78(9):1150–5.[Abstract/Free Full Text]
  10. Kumanyika S, Krebs-Smith S. Preventive nutrition issues in ethnic and socioeconomic groups in the United States. In: Bendich A, Deckelbaum RJ, eds. Primary and secondary preventive nutrition. Totowa, NJ: Humana Press Inc, 2001:325–55.
  11. Forshee RA, Storey ML. Demographics, not beverage consumption, is associated with diet quality. Int J Food Sci Nutr 2006;57(7–8):494–511.[Medline]
  12. Krebs-Smith SM, Heimendinger J, Patterson BH, Subar AF, Kessler R, Pivonka E. Psychosocial factors associated with fruit and vegetable consumption. Am J Health Promot 1995;10(2):98–104.[Medline]
  13. Hu FB, Willett WC. Optimal diets for prevention of coronary heart disease. JAMA 2002;288:2569–78.[Abstract/Free Full Text]
  14. McCullough ML, Feskanich D, Stampfer MJ, et al. Diet quality and major chronic disease risk in men and women: moving toward improved dietary guidance. Am J Clin Nutr 2002;76(6):1261–71.[Abstract/Free Full Text]
  15. Ford ES, Mokdad AH. Dietary magnesium intake in a national sample of US adults. J Nutr 2003;133:2879–82.[Abstract/Free Full Text]
  16. Huang MH, Schocken M, Block G, et al. Variation in nutrient intakes by ethnicity: results from the Study of Women's Health Across the Nation (SWAN). Menopause 2002;9(5):309–19.[Medline]
  17. Kronsberg SS, Obarzanek E, Affenito SG, et al. Macronutrient intake of black and white adolescent girls over 10 years: the NHLBI Growth and Health Study. J Am Dietet Assoc 2003;103(7):852–60.[Medline]
  18. Lovejoy JC, Champagne CM, Smith SR, de Jonge L, Xie H. Ethnic differences in dietary intakes, physical activity, and energy expenditure in middle-aged, premenopausal women: the Healthy Transitions Study. Am J Clin Nutr 2001;74(1):90–5.[Abstract/Free Full Text]
  19. Robinson ME, Hunter PH. Nutritional assessment of a predominately African-American inner-city clinic population. WMJ 2001;100(9):32–8.[Medline]
  20. Wandel M. Dietary intake of fruits and vegetables in Norway: influence of life phase and socio-economic factors. Int J Food Sci Nutr 1995;46(3):291–301.[Medline]
  21. Devine CM, Wolfe WS, Frongillo EA Jr, Bisogni CA. Life-course events and experiences: association with fruit and vegetable consumption in 3 ethnic groups. J Am Diet Assoc 1999;99(3):309–14.[Medline]
  22. Dibsdall LA, Lambert N, Bobbin RF, Frewer LJ. Low-income consumers’ attitudes and behaviour towards access, availability and motivation to eat fruit and vegetables. Public Health Nutr 2003;6(2):159–68.[Medline]
  23. Shohaimi S, Welch A, Bingham S, et al. Residential area deprivation predicts fruit and vegetable consumption independently of individual educational level and occupational social class: a cross-sectional population study in the Norfolk cohort of the European Prospective Investigation into Cancer (EPIC-Norfolk). J Epidemiol Community Health 2004;58(8):686–91.[Abstract/Free Full Text]
  24. Morland K, Wing S, Diez Roux A. The contextual effect of the local food environment on residents’ diets: the atherosclerosis risk in communities study. Am J Public Health 2002;92(11):1761–7.[Abstract/Free Full Text]
  25. Diez-Roux AV, Nieto FJ, Caulfield L, Tyroler HA, Watson RL, Szklo M. Neighbourhood differences in diet: the Atherosclerosis Risk in Communities (ARIC) Study. J Epidemiol Community Health 1999;53(1):55–63.[Abstract]
  26. Moore LV, Diez Roux AV. Associations of neighborhood characteristics with the location and type of food stores. Am J Public Health 2006;96(2):325–31.[Abstract/Free Full Text]
  27. Block JP, Scribner RA, DeSalvo KB. Fast food, race/ethnicity, and income: a geographic analysis. Am J Prev Med 2004;27(3):211–7.[Medline]
  28. Egger G, Swinburn B. An "ecological" approach to the obesity pandemic. BMJ 1997;315(7106):477–80.[Free Full Text]
  29. French SA, Story M, Jeffery RW. Environmental influences on eating and physical activity. Annu Rev Public Health 2001;22:309–35.[Medline]
  30. Lake A, Townshend T. Obesogenic environments: exploring the built and food environments. J R Soc Health 2006;126(6):262–7.[Medline]
  31. Macintyre S. Deprivation amplification revisited; or, is it always true that poorer places have poorer access to resources for healthy diets and physical activity? Int J Behav Nutr Phys Act 2007;4:32.[Medline]
  32. Swinburn BA, Caterson I, Seidell JC, James WP. Diet, nutrition and the prevention of excess weight gain and obesity. Public Health Nutr 2004;7(1A):123–46.[Medline]
  33. White M. Food access and obesity. Obes Rev 2007;8(suppl):99–107.
  34. Diez-Roux AV, Nieto FJ, Muntaner C, et al. Neighborhood environments and coronary heart disease: a multilevel analysis. Am J Epidemiol 1997;146(1):48–63.[Abstract/Free Full Text]
  35. Nordstrom CK, Diez Roux AV, Jackson SA, Gardin JM. The association of personal and neighborhood socioeconomic indicators with subclinical cardiovascular disease in an elderly cohort. The cardiovascular health study. Soc Sci Med 2004;59(10):2139–47.[Medline]
  36. Krieger N. Discrimination and health. Soc Epidemiol 2000;1:36–75.
  37. Williams DR. Patterns and causes of disparities in health. In: Mechanic D, Rogut LB, Colby DC, eds. Policy challenges in modern health care. Piscataway, NJ: Rutgers University Press, 2005:115–35.
  38. Acevedo-Garcia D, Osypuk TL, McArdle N, Williams D. Toward a policy-relevant analysis of geographic and racial/ethnic disparities in child health. Health Affairs 2008;27(2):1–13.
  39. Ellen IG, Mijanovich T, Dillman KN. Neighborhood effects on health: exploring the links and assessing the evidence. J Urban Affairs 2001;23:391–408.
  40. Macintyre S, Ellaway A, Cummins S. Place effects on health: how can we conceptualise, operationalise and measure them? Soc Sci Med 2002;55(1):125–39.[Medline]
  41. Krieger N, Chen JT, Waterman PD, Rehkopf DH, Subramanian SV. Race/ethnicity, gender, and monitoring socioeconomic gradients in health: a comparison of area-based socioeconomic measures—the Public Health Disparities Geocoding Project (US). Am Public Health Assoc 2003;93:1655–71.
  42. Pickett KE, Pearl M. Multilevel analyses of neighbourhood socioeconomic context and health outcomes: a critical review. J Epidemiol Community Health 2001;55(2):111–22.[Abstract/Free Full Text]
  43. Bryk AS, Raudenbush SW. Hierarchical linear models: applications and data analysis methods. Thousand Oaks, CA: Sage Publications, 1992.
  44. Blanchette L, Brug J. Determinants of fruit and vegetable consumption among 6–12-year-old children and effective interventions to increase consumption. J Hum Nutr Diet 2005;18(6):431–43.[Medline]
  45. Kamphuis CB, Giskes K, de Bruijn GJ, Wendel-Vos W, Brug J, van Lenthe FJ. Environmental determinants of fruit and vegetable consumption among adults: a systematic review. Br J Nutr 2006;96(4):620–35.[Medline]
  46. Rasmussen M, Krølner R, Klepp KI, et al. Determinants of fruit and vegetable consumption among children and adolescents: a review of the literature. Part I: quantitative studies. Int J Behav Nutr Phys Activ 2006;3:22.
  47. Akresh IR. Dietary assimilation and health among Hispanic immigrants to the United States. J Health Soc Behav 2007;48(4):404–17.[Medline]
  48. Lawrence JM, Devlin E, Macaskill S, et al. Factors that affect the food choices made by girls and young women, from minority ethnic groups, living in the UK. J Hum Nutr Dietet 2007;20(4):311–9.[Medline]
  49. Buzzard M. 24-hour dietary recall and food record methods. In: Willett W, ed. Nutritional epidemiology. 2nd ed. New York, NY: Oxford University Press, 1998:50–73.
  50. National Research Council, National Academy of Sciences, Subcommittee on Criteria for Dietary Evaluation, Coordinating Committee on Evaluation of Food Consumption Surveys. Nutrient adequacy: assessment using food consumption surveys. Washington, DC: National Academy Press, 1986.
Received for publication September 17, 2007. Accepted for publication February 28, 2008.




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