Race, Place, and Obesity: The Complex Relationships Among Community Racial/Ethnic Composition, Individual Race/Ethnicity, and Obesity in the United States

Objectives. We explored the association between community racial/ethnic composition and obesity risk.

Methods. In this cross-sectional study, we used nationally representative data from the Medical Expenditure Panel Survey linked to geographic data from the US Decennial Census and Census Business Pattern data.

Results. Living in communities with a high Hispanic concentration (= 25%) was associated with a 0.55 and 0.42 increase in body mass index (BMI; defined as weight in kilograms divided by the square of height in meters) and 21% and 23% higher odds for obesity for Hispanics and non-Hispanic Whites, respectively. Living in a community with a high non-Hispanic Asian concentration (= 25%) was associated with a 0.68 decrease in BMI and 28% lower odds for obesity for non-Hispanic Whites. We controlled for individual- and community-level social, economic, and demographic variables.

Conclusions. Community racial/ethnic composition is an important correlate of obesity risk, but the relationship differs greatly by individual race/ethnicity. To better understand the obesity epidemic and related racial/ethnic disparities, more must be learned about community-level risk factors, especially how built environment and social norms operate within communities and across racial/ethnic groups. (Am J Public Health. 2012;102:1572-1578.)

The prevalence of obesity has risen significantly over the past few decades and is now considered one of the most pressing public health problems in the United States.[ 1-5] Currently, more than one third of adults older than 20 years are obese, defined as having a body mass index (BMI; defined as weight in kilograms divided by the square of height in meters) of 30 or more.[ 2] Obesity and sedentary lifestyle are risk factors for a variety of chronic conditions and are leading causes of premature mortality and years of life lost.[ 6-9] Various estimates put the number of deaths attributable to obesity in the United States between 112 000 and 300 000, second only to cigarette smoking.[ 10-12] Members of the current US generation may have a shorter life expectancy than their parents if the obesity epidemic continues. [ 13-16] There are large financial costs associated with obesity, too.[ 17] For example, a recent study found that the annual medical burden of obesity and overweight is nearly 10% of total medical spending, or $147 billion in 2008.[ 18]

The US obesity epidemic has disproportionately affected certain racial and ethnic minority groups.[ 2][ 5][ 19] For example, approximately 50% of African American women are obese compared with only 33% of Whitewomen.[ 2] Individual factors such as income and education explain some of the observed racial/ ethnic differences in obesity, but wide differences persist even after a large number of individual characteristics are held constant.[ 20] Recent research suggests that community-level characteristics such as the availability and accessibility of sidewalks, parks, and recreational facilities[ 21-26] and food selection and cost[ 27-30] are related to obesity independent of individual characteristics. Thus, one line of research documents wide racial/ethnic disparities in the risk of obesity at the individual level, whereas another identifies several community characteristics that are associated with both the risk of obesity and related racial/ethnic disparities.

There is a gap between these 2 lines of research; little is known about how the racial and ethnic composition of residential communities is associated with individual-level obesity risk and how this association might differ by individual-level race/ethnicity. Investigating community-level racial/ethnic composition as a risk factor for obesity, above and beyond individuals' own characteristics, will help improve understanding of racial/ethnic disparities in health, and guide future interventions to eliminate health disparities, which is a national priority.[ 31]

Although little empirical evidence exists on the possible relationship between community racial/ethnic composition and obesity, there is reason to hypothesize that a relationship might exist. Social norms regarding body weight may differ significantly across ethnic groups. For example, perceived ideal body size for non-Hispanic Black women is larger than for non-Hispanic White women, and non- Hispanic White women report dissatisfaction with their bodies at significantly lower weights than do non-Hispanic Black women.[ 32] Furthermore, non-Hispanic Black men report a preference for larger body sizes in female partners than do non-Hispanic White men.[ 33] Given that obesity rates and norms regarding body weight differ across racial and ethnic groups and, at the same time, the United States is highly segregated along racial and ethnic lines, it is plausible that norms regarding body weight could develop at the community level. This, in turn, may give rise to an association between community-level racial/ethnic composition and the risk of obesity independent of the characteristics of individuals.

This study addresses 2 main questions. First, is the racial/ethnic composition of communities associated with obesity beyond what would be expected given the characteristics of the individuals? Second, if an association exists, does it differ on the basis of an individual's own racial/ethnic identity? Our findings will enhance understanding of disparities in the US obesity epidemic, and the mechanisms by which certain racial/ethnic minorities are disproportionately affected.

The main data source for this study was the Medical Expenditure Panel Survey (MEPS), sponsored by the Agency for Healthcare Research and Quality. The MEPS is a series of nationally representative surveys of the US civilian noninstitutionalized population conducted annually since 1996 using an overlapping panel design.[ 34][ 35] The MEPS collects information on medical care use and expenditures, as well as additional data on health status, medical conditions, and sociodemographic variables. To achieve adequate sample sizes to conduct statistical tests by individual race/ ethnicity and community racial/ethnic composition, we pooled MEPS data from 2002 to 2007.

We obtained community information on poverty and racial/ethnic composition from the 2000 US Decennial Census. We merged this information onto the MEPS at the census block group level. Block groups are the smallest geographic areas for which community-level statistics are reported, containing between 600 and 3000 people.[ 36] To control for other environmental contributors to obesity such as food availability and opportunities for physical activity, we also merged in information on community services and establishments from Census Business Pattern data at the zip code level.[ 37] After we excluded pregnant women; people who were not non-Hispanic White, non-Hispanic Black, Hispanic, or non-Hispanic Asian; and those with extreme BMI (< 14 kg/m[ 2] or > 50 kg/m[ 2]) or missing BMI, the final sample consisted of 123 192 individuals aged 18 years and older.

TABLE: TABLE 1-Descriptive Statistics for Body Mass Index, Obesity, and Explanatory Variables by Individual Race/Ethnicity: 2002-2007 Medical Expenditure Panel Survey, Agency for Healthcare Research and Quality

Variables White (n = 71 774), Mean (SE) Black (n = 18 883), Mean (SE) Hispanic (n = 28 500), Mean (SE) Asian (n = 5475), Mean (SE)

Individual variables BMI 26.91 (0.04) 28.61[* * *] (0.08) 27.68[* * *] (0.08) 23.99[* * *] (0.09) Obese, % 24.49 (0.30) 36.13[* * *] (0.50) 28.65[* * *] (0.60) 7.13[* * *] (0.60) Age, y 48.05 (0.18) 43.66[* * *] (0.25) 39.98[* * *] (0.26) 43.5[* * *]3 (0.41) Male, % 50.26 (0.20) 47.26[* * *] (0.60) 54.01[* * *] (0.50) 49.13 (0.80) Below poverty, % 7.47 (0.20) 19.61[* * *] (0.60) 16.85[* * *] (0.60) 8.30 (0.70) Education, % < 12 y 14.60 (0.30) 24.31[* * *] (0.70) 44.22[* * *] (0.80) 14.24 (0.90) 12 y 32.50 (0.40) 36.69[* * *] (0.60) 28.01[* * *] (0.60) 19.41[* * *] (1.00) 13-15 y 24.23 (0.30) 23.13 (0.60) 16.37[* * *] (0.50) 19.78[* * *] (0.80) ≥ 16 y 28.68 (0.60) 15.86[* * *] (0.80) 11.40[* * *] (0.40) 46.57[* * *] (1.40) Census region, % Northeast 20.05 (1.00) 17.40 (1.30) 14.74[* * *] (1.20) 21.89 (2.20) Midwest 26.44 (1.00) 17.82[* * *] (1.40) 8.08[* * *] (0.80) 11.14[* * *] (1.40) South 33.86 (1.20) 55.90[* * *] (1.90) 36.65 (2.50) 19.96[* * *] (1.90) West 19.65 (1.40) 8.86[* * *] (0.90) 40.53[* * *] (2.10) 47.00[* * *] (3.00) Residence in metropolitan statistical area, % 79.08 (0.90) 88.3[* * *]2 (1.50) 92.61[* * *] (1.20) 96.45[* * *] (1.20) Community variables Racial/ethnic composition, % ≥25 Black 5.32 (0.40) 63.51[* * *] (1.30) 11.19[* * *] (0.90) 7.16 (0.90) ≥25 Hispanic 5.90 (0.40) 12.61[* * *] (1.00) 60.06[* * *] (1.60) 18.72[* * *] (1.70) ≥25 Asian 0.72 (0.10) 1.18[* * *] (0.30) 2.87 (0.50) 16.99[* * *] (2.30) ≥25 White 98.24 (0.10) 54.13[* * *] (1.30) 63.19[* * *] (1.50) 81.87[* * *] (2.10) Residents in poverty, % 9.42 (0.15) 18.92[* * *] (0.47) 17.17[* * *] (0.43) 10.16 (0.49) Presence of a supermarket, % 73.27 (0.90) 71.51 (1.50) 79.81[* * *] (1.10) 82.06[* * *] (1.30) Grocery stores/1000 0.09 (0.00) 0.16[* * *] (0.01) 0.14[* * *] (0.01) 0.13[* * *] (0.01) Convenience stores/1000 0.11 (0.01) 0.13 (0.00) 0.10[**] (0.00) 0.11 (0.01) Specialty stores/1000 0.06 (0.00) 0.06[* * *] (0.00) 0.06[* * *] (0.00) 0.07[**] (0.01) Gyms, fitness centers/1000 0.12 (0.00) 0.07[* * *] (0.00) 0.08[* * *] (0.00) 0.14* (0.01) Restaurants/1000 0.81 (0.02) 0.53[* * *] (0.02) 0.64[* * *] (0.01) 0.91[*] (0.05) Takeout food services/1000 0.92 (0.02) 0.77[* * *] (0.02) 0.83[* * *] (0.01) 1.13[* * *] (0.05) All business establishments/1000 28.39 (0.71) 21.23[* * *] (0.60) 23.08[* * *] (0.48) 33.03* (1.96)

Note. BMI = body mass index (defined as weight in kilograms divided by the square of height in meters).

[*]P < .05; [**]P < .01; [* * *]P < .001 vs non-Hispanic Whites.

There were 2 main outcome variables in this study: BMI and obesity. We calculated BMI on the basis of self-reported weight and height. Individuals were identified as obese if BMI exceeded 30. Research has shown a strong correlation between BMI and obesity obtained from self-reports and that obtained from actual measurement.[ 38][ 39]

The main explanatory variables in this study were race/ethnicity at the individual level and racial/ethnic composition at the block group level. We measured individual-level race/ ethnicity using responses from 2 questions; the first ascertained whether someone is of Hispanic background, and the second requested individuals to select the racial category or categories that best describes them. From the responses, we constructed 4 mutually exclusive dichotomous variables identifying individuals as Hispanic (of any race), non-Hispanic White, non-Hispanic Black, or non-Hispanic Asian. We excluded individuals in other racial/ethnic categories, including multiple race categories,

because the constructed categories were either too heterogeneous or too small. We measured community-level racial/ethnic composition by using 4 dichotomous variables that identified block groups as having 25% or more of each racial/ethnic group. Our analysis also included variables that defined the interactions between community-level racial/ethnic composition and individual-level race/ethnicity.

Note that the community-level dichotomous variables were not mutually exclusive. For example, many block groups that contained more than 25% non-Hispanic Asians also contained more than 25% non-Hispanic Whites. Although our use of 25% as a cutoff to measure the racial/ethnic composition of a block group was somewhat arbitrary, our results were robust to different operationalizations; we estimated all models with various cutoffs ranging from 10% to 35% and the results were similar.

The differences observed in obesity by community racial/ethnic composition might simply be a reflection of the characteristics of the individuals that live in those communities. Our multivariate analysis therefore controlled for a variety of individual characteristics. These variables were age, gender, poverty status, education, census region, and residence in a metropolitan statistical area. All of these variables are associated both with body weight and racial/ethnic composition. Observed differences by community racial/ethnic composition might also be a reflection of other community characteristics. Minority communities, for example, might have poverty rates and food choices that differ from those of predominantly non-Hispanic White communities. Our analysis, therefore, included variables that recorded the poverty rate, and the number of grocery stores, convenience stores, specialty meat or vegetable stores, gym or fitness facilities, full service restaurants, and fast-food restaurants per capita. Also included was a dichotomous variable that recorded whether a zip code had at least 1 supermarket.

We first provided descriptive statistics on all characteristics including BMI and obesity by race/ethnicity. Next we fitted a series of linear regression models on BMI and logistic regression models on obesity with the individual and community characteristics as explanatory variables. We focused on the coefficient estimates and odds ratios of race/ethnicity, community racial/ethnic composition, and the interaction between the 2. These models enabled us to estimate the extent to which living in communities with different racial/ethnic mixes is associated with BMI and obesity net of a variety of individual and community characteristics. We adjusted all estimates and standard errors for the complex sample design of MEPS by using a first order Taylor-series linearization approach available in the survey commands in Stata version 11 (StataCorp LP, College Station, TX).[ 40] This approach also provides correct standard errors in the presence of clustering within block groups, zip codes, and other geographic units that are not explicitly related to the sample design.41 To investigate whether the relationship between obesity and community racial/ethnic composition differed between men and women, we estimated all models separately by gender. Few gender differences emerged, so we report results from the models with pooled data only, noting in the text when the relationship between community racial/ethnic composition and obesity differed significantly between men and women (results available upon request).

TABLE: TABLE 2-Mean Body Mass Index and Percentage Obese by Individual Race/Ethnicity and Community Racial/Ethnic Composition: 2002-2007 Medical Expenditure Panel Survey, Agency for Healthcare Research and Quality

Characteristic Community Racial/Ethnic Composition: ≥25% Non-Hispanic Black Community Racial/Ethnic Composition: < 25% Non-Hispanic Black Community Racial/Ethnic Composition: ≥ 25% Hispanic Community Racial/Ethnic Composition: < 25% Hispanic Community Racial/Ethnic Composition: ≥ 25% Non-Hispanic Asian Community Racial/Ethnic Composition: < 25% Non-Hispanic Asian Individual race/ethnicity, mean BMI (SE) All races/ethnicities 28.02 (0.09) 26.93[*] (0.04) 27.54 (0.09) 27.00[*] (0.04) 25.56 (0.25) 27.10[*] (0.04) Non-Hispanic White 27.20 (0.18) 26.90 (0.04) 27.22 (0.13) 26.90[*] (0.04) 25.76 (0.31) 26.92[*] (0.04) Non-Hispanic Black 28.68 (0.09) 28.50 (0.12) 28.34 (0.20) 28.65 (0.08) 28.53 (0.44) 28.61 (0.08) Hispanic 27.67 (0.16) 27.68 (0.08) 27.94 (0.10) 27.28[*] (0.10) 27.48 (0.37) 27.68 (0.08) Non-Hispanic Asian 24.55 (0.36) 23.94 (0.09) 24.07 (0.20) 23.97 (0.09) 24.01 (0.21) 23.98 (0.09) Individual race/ethnicity, % obese (SE) All races/ethnicities 31.94 (0.62) 24.63[*] (0.27) 28.76 (0.66) 25.03[*] (0.27) 15.82 (1.58) 25.73[*] (0.26) Non-Hispanic White 26.31 (1.25) 24.38 (0.30) 27.43 (1.00) 24.30[*] (0.30) 16.38 (1.94) 24.54[*] (0.30) Non-Hispanic Black 36.62 (0.62) 35.29 (0.89) 34.43 (1.59) 36.38 (0.56) 38.30 (3.63) 36.11 (0.54) Hispanic 28.00 (1.40) 28.75 (0.63) 30.53 (0.78) 25.86[*] (0.79) 26.90 (2.61) 28.72 (0.59) Non-Hispanic Asian 11.00 (2.13) 6.83 (0.57) 9.71 (1.19) 6.54 (0.60) 6.22 (1.07) 7.32 (0.63)

Note. BMI = body mass index, defined as weight in kilograms divided by the square of height in meters.

[*]P < .05 between individuals living in communities with ≥ 25% and < 25% of the specified minority group.

Table 1 shows that, overall, non-Hispanic Blacks were most likely to be obese (prevalence was 36.1%), followed by Hispanics (28.7%), non-Hispanic Whites (24.5%), and non-Hispanic Asians (7.1%). Mean BMI values for the groups were 28.6, 27.7, 26.9, and 24.0, respectively.These results are consistent with previous research on racial/ethnic differences in BMI and obesity.[ 4][ 42]

Other variables at both the individual and community levels differed substantially by race/ethnicity, too. Racial/ethnic minorities were, on average, younger, more frequently impoverished, and more likely to reside in a metropolitan statistical area compared with non-Hispanic Whites. Non-Hispanic Blacks and Hispanics generally had lower education. Non-Hispanic Blacks were more likely to live in the South whereas Hispanics and non-Hispanic Asians were more likely to reside in the West. Compared with non-Hispanic Whites, proportionately more non-Hispanic Blacks and Hispanics lived in impoverished communities. The community-level variables capturing food and service availability showed a more complex pattern. Compared with non-Hispanic Whites, racial/ethnic minorities tended to live in communities with more grocery stores and specialty stores per capita. Compared with non- Hispanic Whites and non-Hispanic Asians, Hispanics and non-Hispanic Blacks lived in communities with fewer gyms and fitness centers, restaurants, and take-out food services.

Table 1 shows a strong pattern of residential segregation by race/ethnicity; individuals tended to live in block groups where at least 25% of residents shared their race/ethnicity. For example, 63.5% of non-Hispanic Blacks lived in block groups where at least 25% of residents were also non-Hispanic Blacks. In comparison, the percentages of non-Hispanic White, Hispanic, and non-Hispanic Asian individuals living in block groups with at least 25% non-Hispanic Black residents were only 5.3%, 11.2%, and 7.2%, respectively. Similarly, 60.1% of Hispanic individuals lived in block groups where at least 25% of the residents were Hispanic, and the corresponding percentages for non-Hispanic Whites, non- Hispanic Blacks, and non-Hispanic Asians were only 5.9%, 12.6%, and 18.7%, respectively. These findings are consistent with research on patterns of racial/ethnic residential segregation in the United States.[ 43-46]

Table 2 reveals an association between community racial/ethnic composition and obesity or BMI. Overall, individuals living in communities with a high proportion (> 25%) of non-Hispanic Blacks or Hispanics had significantly higher BMI and were significantly more likely to be obese than those living in other block groups. Individuals living in communities with a high proportion of non- Hispanic Asians had lower average BMI and were less likely to be obese those living in other communities. When we examined differences by individual race/ethnicity, however, the association between community racial/ethnic composition and BMI or obesity was significant in only 3 situations. First, Hispanics who lived in communities where more than 25% of the residents were also Hispanic had higher BMI values, on average, than their counterparts in other communities (27.9 kg/m[ 2] vs 27.3 kg/m[ 2]) and were more likely to be obese (30.5% vs 25.9%). Second, non-Hispanic Whites who lived in communities where more than 25% of the residents were Hispanic had a higher BMI than their counterparts in other communities (27.2 kg/m[ 2] vs 26.9 kg/m[ 2]) and were more likely to be obese (27.4% vs 24.3%). Finally, non-Hispanic Whites who lived in Asian communities scored lower on BMI than those in other communities (25.7 kg/m[ 2] vs 26.9 kg/m[ 2]) and were less likely to be obese (16.4% vs 24.5%). These results clearly show the importance of interacting individual-level race/ethnicity with community-level racial/ethnic composition when one examines BMI and obesity.

Note. BMI = body mass index (defined as weight in kilograms divided by the square of height in meters). These estimates represent expected differences in BMI between individuals living in communities where 25% or more of the residents are of the specified race/ethnicity and those living in communities where no minority group makes up more than 25% of the population, adjusted for all variables in shown in Table 1. Lines represent 95% confidence intervals.

FIGURE 1-Differences in body mass index associated with community racial/ethnic composition by individual race/ethnicity: 2002-2007 Medical Expenditure Panel Survey, Agency for Healthcare Research and Quality.

Our multivariate models tested the extent to which the differences shown in Table 2 were attributable to the individual and community characteristics shown in Table 1. Figure 1 shows that living in a community where at least 25% of the residents were Hispanic was associated with a 0.55 increase in BMI for Hispanics, and a 0.42 increase in BMI for non-Hispanic Whites. Living in a community where at least 25% of the residents were non-Hispanic Asian was associated with a 0.68 decrease in BMI for non-Hispanic Whites. This finding, however, was statistically significant only for men; the estimate for women was in the same direction but did not attain statistical significance at P < .05. We detected no other significant associations.

The logistic regression model on obesity showed a pattern of results similar to that for BMI (Figure 2). Compared with those living in nonminority communities (i.e., communities where no racial/ethnic group made up more than 25% of the population except for non- Hispanic Whites), the odds of being obese were higher for individuals living in communities where at least 25% of the residents were Hispanic. The exception to this was for non- Hispanic Blacks. Specifically, living in a community where 25% or more of the residents were Hispanic was associated with 21% higher odds of obesity for Hispanics, 23% higher odds for non-Hispanic Whites, and 39% higher odds for non-Hispanic Asians. Among non- Hispanic Whites, living in a community where 25% or more of the residents were non- Hispanic Asian was associated with 28% lower odds of being obese. As with the results for BMI, this finding was statistically significant only for men.

It is interesting that, even though non-Hispanic Blacks were the most likely to be obese and had the highest average BMI, living in a community with a high concentration of non-Hispanic Blacks was not significantly associated with higher BMI or higher odds of being obese, even for non-Hispanic Black individuals. Moreover, among non-Hispanic Black men, those living in communities with a high concentration of non-Hispanic Blacks actually had significantly lower BMI, on average, than non-Hispanic Black men living in other nonminority communities.

Note. BMI = body mass index (defined as weight in kilograms divided by the square of height in meters). These estimates represent odds ratios for being obese associated with living in communities where 25% or more of the residents are of the specified race/ethnicity relative to those living in communities where no minority group makes up more than 25% of the population, adjusted for all variables in shown in Table 1. Lines represent 95% confidence intervals.

FIGURE 2-Odds ratios for being obese associated with community racial/ethnic composition by individual race/ethnicity: 2002-2007 Medical Expenditure Panel Survey, Agency for Healthcare Research and Quality.

We analyzed US nationally representative data and found evidence of an association between community racial/ethnic composition and obesity, net of a variety of individual and community-level characteristics. The pattern of associations, however, was not simple. Individuals who lived in communities where there was a high concentration of Hispanics were more likely to be obese, but only if they were themselves Hispanic, non-Hispanic White, or non-Hispanic Asian. Living in a community with more Asians was associated with a lower risk of obesity, but only among non- Hispanic White men. In contrast, though obesity is most prevalent among non-Hispanic Blacks, individuals who lived in communities with a high concentration of non-Hispanic Blacks were no more likely to be obese than those living in other communities, regardless of their own race/ethnicity. These findings suggest that, in addition to the built environment, community characteristics related to racial/ ethnic composition may have an important effect on residents' weight status.

This study had several limitations. Although our research questions were framed on the basis of the idea that norms related to body weight may give rise to an association between community racial/ethnic composition and obesity, we have no direct measure of such norms. There could be unmeasured individual or environmental characteristics that account for the patterns of obesity we observed across community racial/ethnic composition. In Hispanic communities, for example, outlets that offer unhealthy food selection may appeal to Hispanics and non-Hispanic Whites but not to non-Hispanic Blacks or non-Hispanic Asians. It is also possible that unmeasured differences in the composition of individuals in Hispanic communities may explain our findings. For example, non-Hispanic Whites who live in Hispanic communities may differ from those who live in other communities in characteristics that are also associated with weight and obesity. Our analysis, however, controls for a large number of individual and community characteristics, encompassing most risk factors for obesity identified by previous research. We therefore believe that any omitted variable bias is minimal. Nevertheless, these confounding mechanisms should be considered carefully when one is interpreting our results. Direct measures of norms and food preferences would strengthen research in this area but are currently unavailable.

Our study was limited by several measurement issues. First, like many studies on obesity, we used self-reported height and weight. Second, community characteristics may not be measured at substantively meaningful levels. Block groups, for example, are not geographic units that necessarily encompass "neighborhoods" or "social networks" in which body norms might develop. In a similar way, zip codes do not necessarily correspond to "food market" areas. Finally, we measured block group characteristics as of the 2000 decennial census whereas the MEPS data covered the period from 2002 to 2007. These instances of measurement error may attenuate our estimates of the effects of racial/ethnic composition, thus making our findings conservative.

Despite these limitations, this article shows that community-level racial/ethnic composition is associated with BMI and obesity net of a variety of individual and community characteristics. Further research is needed to explain why Hispanic communities are associated with higher body weight and increased risk for obesity, whereas Black communities are not, especially given that obesity is more prevalent among non-Hispanic Blacks. It is tempting to posit that there are cultural attributes in Hispanic communities that promote obesity. But why, then, does this effect only appear for non- Hispanic Whites, Hispanics, and non-Hispanic Asians but not for non-Hispanic Blacks? The nature of residential segregation in the United States is complex; there may be segregation within Hispanic communities such that non- Hispanic Blacks have less contact with Hispanics than do non-Hispanic Whites. These issues and others raised by our study represent opportunities to better understand ethnic disparities in the US obesity epidemic.

Reprints can be ordered at http://www.ajph.org by clicking the "Reprints" link.

This article was accepted September 2, 2012.

Note. The views expressed in this article are those of the authors, and no official endorsement by the Agency for Healthcare Research and Quality or the Department of Health and Human Services is intended or should be inferred.

The study was supported in part by the National Institutes of Health, National Institute of Diabetes and Digestive and Kidney Diseases (grant R01DK81335-01A1).

We thank the anonymous reviewers for their thorough reviews, constructive comments, and suggestions.

This study was covered under Chesapeake Institutional Review Board Agency for Healthcare Research and Quality protocol, Secondary Analysis of Confidential Data From the Medical Expenditure Panel Survey (CRRI 0504015).

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By James B. Kirby, PhD; Lan Liang, PhD; Hsin-Jen Chen, MS and Youfa Wang, MD, PhD, MS

James B. Kirby is with the Agency for Healthcare Research and Quality, Rockville, MD. Agency for Healthcare Research and Quality, 540 Gaither Rd, Rockville, MD 20850 (e-mail: jkirby@ahrq.gov).

Lan Liang is with the Agency for Healthcare Research and Quality, Rockville, MD.

Hsin-Jen Chen is with the Department of International Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD.

Youfa Wang is with the Johns Hopkins Global Center for Childhood Obesity, Johns Hopkins Bloomberg School of Public Health.

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