Abstract
National data indicate that Black men have higher rates of obesity than White men. Black men also experience earlier onset of many chronic conditions and premature mortality linked to obesity. Explanations for these disparities have been underexplored, and existing national-level studies may be limited in their ability to explicate these long-standing patterns. National data generally do not account for race differences in risk exposures resulting from racial segregation or the confounding between race and socioeconomic status. Therefore, these differences in obesity may be a function of social environment rather than race. This study examined disparities in obesity among Black and White men living in the same social and environmental conditions, who have similar education levels and incomes using data from the Exploring Health Disparities in Integrated Communities-SWB (EHDIC-SWB) study. The findings were compared with the 2003 National Health Interview Survey (NHIS). Logistic regression was used to examine the association between race and obesity adjusting for demographics, socioeconomic status, and health conditions. In the NHIS, Black men had a higher odds of obesity (odds ratio = 1.29, 95% confidence interval = 1.12-1.49) than White men. However in the EHDIC-SWB, which accounts for social and environmental conditions of where these men live, Black men had similar odds of obesity (odds ratio = 1.06, 95% confidence interval = 0.70-1.62) compared with White men. These data highlight the importance of the role that setting plays in understanding race disparities in obesity among men. Social environment may be a key determinant of health when seeking to understand race disparities in obesity among Black and White men.
Introduction
Obesity is a major public health problem with considerable health consequences (Griffith, Johnson-Lawrence, Gunter, & Neighbors, 2011; Malnick & Knobler, 2006). Over the past decade, there have been persistent and increasing disparities in the prevalence of obesity between genders and between people of different race/ethnic groups (Flegal, Carroll, Ogden, & Curtin, 2010; Ogden, Carroll, Kit, & Flegal, 2013). National data indicate that non-Hispanic Black (hereafter referred to as Black) men have higher rates of obesity than non-Hispanic White (hereafter referred to as White) men (Flegal et al., 2010). The prevalence of obesity among Black men is 37.1% compared with 32.4% for White men and second only to Hispanic men at 40.1% (Ogden, Carroll, Kit, & Flegal, 2014). Black men also experience earlier onset of many chronic conditions and premature mortality linked to obesity (Warner & Hayward, 2006). Race disparities in obesity among men are a topic that has received little attention (Newton, Griffith, Kearney, & Bennett, 2015).
The majority of the information on race disparities in obesity among men is based on national data, which may be suboptimal in documenting and understanding obesity disparities among men for two reasons. First, when examining obesity disparities among men using national data, race and socioeconomic status (SES) are confounded (Bruce et al., 2011; Griffith et al., 2011; LaVeist, Thorpe, Mance, & Jackson, 2007; LaVeist 2005; Thorpe et al. 2015). That is, obesity varies by race (Ogden et al., 2014). Also, obesity varies by SES (McLaren, 2007; Sobal & Stunkard, 1989; Stunkard & Sørenson, 1993). Because minority men tend to belong to the lower SES groups, there is a huge overlap between race and SES that complicates our efforts to determine whether it is the independent association of race or SES or the interaction between race and SES that drives racial disparities in obesity among men (LaVeist et al., 2007; LaVeist, Pollack, Thorpe, Fesahazion, & Gaskin, 2011).
The second problem with using national data to understand race disparities in obesity among men is the confounding of race and segregation. Where men live in the United States can contribute to their obesity rates (Black& Macinko, 2008;Dwyer-Lindgren et al., 2013; Ezzati, Martin, Skjold, Vander Hoorn, & Murray, 2006; Le et al., 2014). This is largely because national obesity estimates do not account for obesogenic environments that may be facilitated by segregation. Obesogenic environments may include greater access to high fat and high sugar foods, lower access to opportunities to be physically active in safe environments, and few healthy resources to mitigate stress (Griffith, Schulz, Johnson, & Herbert, 2010; Schulz et al., 2005; Schulz, Williams, Israel, & Lempert, 2002; Zenk et al., 2006; Zenk, Schulz, Hollis-Neely, et al., 2005; Zenk, Schulz, Israel, et al., 2005; Powell, Slater, Chaloupka & Harper, 2006; Bowie, Thorpe, Rohde & Gaskin, 2014). It is this differential risk exposure to obesogenic environments that can limit a man’s choices for health promotion and can accelerate the progression of overweight or beginning of obesity among heavy men. These two reasons highlight problems with using national-level data sets to describe race disparities in obesity among men.
Failing to account for the social and environmental conditions where men live can lead to potentially incorrectly ascribing the findings to behavior alone rather than to differences in social and environmental conditions. This is a particularly important problem in men’s health research because gender differences in health outcomes are primarily attributed to differences in health behavior between men and women and not to the differential impact of structural forces (Lohan, 2007). There is a burgeoning body of work that has demonstrated that differences in social and environmental conditions where people live accounts for a meaningful proportion of race disparities in health outcomes (Bleich, Thorpe, Sharif-Harris, Fesahazion, & LaVeist, 2010; Gaskin, Price, Brandon, & LaVeist, 2009; LaVeist et al., 2008; LaVeist, Thorpe, Galarraga, Bower, & Gary-Webb, 2009; Thorpe, Brandon, & LaVeist, 2008; Thorpe, Wilson-Frederick, et al., 2013; Thorpe et al., 2015). This evidence supports the fact that one’s social and environmental conditions remains an important, but understudied, determinant of health (LaVeist et al., 2007; LaVeist et al., 2009; Williams & Collins, 2001).
Studies relating racial segregation to obesity among men are inconclusive, leaving the question of the role of place or setting in obesity among men unanswered (Boardman, Saint Onge, Rogers, & Denney, 2005; Chang, Hillier, & Mehta, 2009; Grafova, Freedman, Kumar, & Rogowski, 2008; Kershaw, Albrecht, & Carnethon, 2013). However, to date, no study has focused on the social and environmental conditions of where men live as a possible explanation for the obesity disparities among Black men and White men. The Exploring Health Disparities in Integrated Communities Study (EHDIC) provides a unique opportunity to examine race disparities in the absence of two vexing problems in health disparities research—confounding of race and SES, and race and segregation (LaVeist et al., 2008). The purpose of this study is to investigate whether race disparities in obesity among men persist in a community of Black and White people living in similar social and environmental conditions.
Method
Study Population
EHDIC is an ongoing multisite study of race disparities within communities where Blacks and Whites live together and where there are no race differences in SES, as measured by median income. This analysis is based on data from the first EHDIC study site in Southwest Baltimore, Maryland (EHDIC-SWB), a low-income urban area.
EHDIC-SWB was a cross-sectional face-to-face survey of the adult population (aged 18 years and older) of two contiguous census tracts collected between June and September 2003. In addition to being economically homogenous, the study site was also racially balanced and well integrated, with almost equal proportions of Black and White residents. In the two census tracts, the racial distribution was 51% Black and 44% White, and the median income for the study area was $24,002 and did not differ by race. The census tracts were block listed to identify every occupied dwelling in the study area. During block listing, 2,618 structures were identified. Of those, 1,636 structures were determined to be occupied residential housing units (excluding commercial and vacant residential structures). Adults 18 years of age and older who provided proof that they lived in the census tracts were eligible to participate in the study. Up to five attempts were made to contact an eligible adult in 1,244 occupied residential housing units. A total of 65.8% of the occupied housing units were enrolled in the study. This resulted in 1,489 study participants (41.9% of the 3,555 adults living in these two census tracts recorded in the 2000 Census). Because our survey had similar coverage across each census block group including the study area, the bias to geographic locale and its relationship with SES should be minimal (LaVeist et al., 2008).
Comparisons to the 2000 Census for the study area indicated that the EHDIC-SWB sample included a higher proportion of Blacks and women, but was otherwise similar on other demographic and socioeconomic indicators (LaVeist et al., 2008). Our sample was 59.3% Black and 44.4% male, whereas the 2000 Census data identified the population was 51% Black and 49.7% male. Age distributions in our sample and 2000 Census data were similar with a median age range of 35 to 44 years for both samples. The lack of race difference in median income in the census, $23,500 (Black) versus $24,100 (White), was replicated in EHDIC with $23,400 (Black) versus $24,900 (White).
The survey was administered in person by a trained interviewer and consisted of a structured questionnaire, which included demographic and socioeconomic information, self-reported height and weight, self-reported health behaviors and chronic conditions, and three blood pressure (BP) measurements. The EHDIC study has been described in greater detail elsewhere (LaVeist et al., 2008). The study was approved by the Committee on Human Research at the Johns Hopkins Bloomberg School of Public Health. These analyses are based on the 628 Black and White men in the EHDIC-SWB sample.
The National Health Interview Survey (NHIS) is a cross-sectional survey conducted annually by the National Center for Health Statistics via in-home interviews administered by U.S. Census Bureau. NHIS uses a nationally representative sample of the U.S. non-institutionalized civilian population from all 50 states and the District of Columbia, with oversampling of Blacks and Hispanics (National Center for Health Statistics, 2004). Data from the 2003 NHIS Sample Adult file were used for this study. Participants are surveyed regarding their demographic characteristics, health behaviors and conditions, functional limitations, cancer screening, and health care access and utilization. Detailed information regarding NHIS can be found elsewhere (National Center for Health Statistics, 2004). Men 18 years of age and older who reported their race to be either Black only or White only and responded no to the question regarding Hispanic ethnicity were included in our analyses. This strategy resulted in a sample size of 10,455 male adults, of whom 1,551 are Black and 8,904 are White.
Measures
To facilitate comparison across each survey, questions from the NHIS were replicated in the EHDIC-SWB study. Each measure included in these analyses was coded similarly in both data sets. Body mass index (BMI) was calculated by dividing self-reported weight in kilograms by self-reported height in meters squared (kg/m2). Men whose BMI ≥ 30 kg/m2 were considered obese (1 = yes; 0 = no).
The primary independent variable was race. Men self-reported their race as Black/African American or White. Covariates included demographic and health-related characteristics known to be associated with race or overweight/obesity. Demographic variables included age (years), married (1 = yes; 0 = no), income level (<$35,000, $35,000-75,000, ≥$75,000), and education level (0 = less than high school graduate; 1 = high school graduate/GED; 2 = more than high school graduate).
Health-related variables included health insurance (1 = yes; 0 = no), physical inactivity (1 = yes; 0 = no), smoking and drinking status (0 = never; 1 = former; 2 = current), self-reported health status, and health conditions. Men reported whether their health as excellent, very good, good, fair, or poor. A binary variable was constructed to classify men who reported their health as fair or poor. Health conditions were based on physician diagnoses of the following: hypertension, diabetes, stroke, and heart disease. A binary variable was created for each health condition to indicate whether the men had been diagnosed by a physician with that particular health problem.
Statistical Analyses
Student’s
Results
The distribution of select demographic variables of men who participated in NHIS or EHDIC-SWB by race is reported in Table 1. There were 10,455 Black (14.8%) and White men in NHIS. Black men were, on average, 4 years younger than White men. There was a smaller proportion of Black men who reported being married or having income greater than $75,000 relative to White men. There were a larger proportion of Black men who reported less than a high school education compared with White men.
Select Demographics and Health-Related Characteristics by Data Set, NHIS 2003 and EHDIC-SWB.
In EHDIC-SWB, there were 628 men with 60.6% identifying as Blacks. Black men were on average 4 years younger than White men. In addition, a smaller proportion of Black men reported being married and reported less than a high school education compared with Whites. There were no differences in income level between the groups of men.
The distribution of age-adjusted proportions for health-related characteristics and weight status variables of the men in NHIS and EHDIC-SWB are reported in Table 2. Among men in NHIS, smaller proportions of Black men had health insurance, and were current drinkers compared with White men. However, larger proportions of Black men were physically inactive; never or former smokers; never, former, or current drinkers; reported fair/poor health; reported being diagnosed with hypertension, diabetes, having a stroke; or being obese than White men. Among men in the EHDIC-SWB, a larger proportion of Blacks had health insurance and reported current smoking compared with White men. There were a smaller proportion of Black men who reported being a former smoker than White men. There were no differences between Black and White men with being physically inactive; being a never smoker; being a never, former, or current drinker; being obese; or reporting fair/poor health, hypertension, diabetes, or heart disease.
Age-Adjusted Distribution of Health Related Characteristics Among Men in EHDIC-SWB and NHIS 2003.
Obese is defined as BMI ≥ 30.
The association between Black and White men with being obese is presented in Table 3 for NHIS and EHDIC-SWB. After adjusting for age, marital status, insurance, income, education, fair/poor health, physical inactivity, smoking and drinking status, hypertension, diabetes, stroke and heart disease, Black men in NHIS had greater odds of being obese (odds ratio [OR] = 1.29, 95% confidence interval [CI] = 1.12, 1.49) than White men. In our EHDIC-SWB sample that accounts for the social and environmental conditions in which these men live, Black men had similiar odds of being obese (OR = 1.06, 95% CI = 0.70, 1.62) compared with White men.
Association Between Black and White Men With Being Obese in 2003 NHIS and EHDIC-SWB a .
White adults are the reference category.
Only models that contained variables in both EHDIC and NHIS 2003 data sets were conducted. All estimates using NHIS 2003 data account for the stratified, multistage probability sampling design by applying the appropriate weights and strata variables.
Obese is defined as BMI ≥ 30.
Discussion
In this study, race disparities in obesity were examined among Black and White men who live in similar social and environmental conditions. These findings were compared with the NHIS 2003. Our study of obesity among men living in a racially integrated low-income urban area produced results identifying that Black and White men had similar odds of obesity. These findings differ considerably from studies using national-level data and highlight the importance of place as a key social determinant of health among men.
Black men are disproportionately represented among those living and working in economically and socially challenging environments (Xanthos, Treadwell, & Holden, 2010). In addition, poverty, substandard educational resources, family disruption, and segregation are often part of their social landscape; however, few studies have assessed the degree to which these factors have implications for non-Black men. Our findings present new potential areas of exploration.
While literature on social determinants of health disparities tends to separate individual behavior from the social, built, and economic environment where it occurs, our findings illustrate the importance of recognizing the interdependence of these factors for understanding health and health disparities (Bruce et al., 2009; Griffith, Metzl, & Gunter, 2011; Jackson & Knight, 2006; LaVeist, Gaskin, & Trujillo, 2011; Mezuk et al., 2010; Thorpe, Bowie, Wilson-Frederick, Coa, & LaVeist, 2013). Particularly in the context of men’s health disparities, this article emphasizes the importance of considering other factors that intersect with gender to determine how and where to intervene to improve men’s health (Griffith, 2012; Thorpe et al. 2013).
Race and ethnicity remain useful markers of one’s exposure to health-harming environments and substances, social disadvantage, and health-promoting resources (LaVeist, 2000). Understanding the poor status of men’s health and premature death includes considering how racialized and gendered social determinants of health independently and interactively shape men’s lives and experiences, particularly through economic and environmental factors (Pease, 2009; Treadwell & Braithwaite, 2005; Young, Meryn, & Treadwell, 2008). What the EHDIC-SWB study illustrates, however, is that it is important to recognize that racial comparisons using national-level data often obscure how individual and neighborhood poverty, poor educational opportunities, underemployment and unemployment, and multiple forms of discrimination can vary by place and influence the capacity of men to achieve and maintain good health (LaVeist et al., 2007; LaVeist, Gaskin et al., 2011; Treadwell & Braithwaite, 2005). The fact that some racial groups are more likely to live in poverty, work in low-paying and dangerous occupations, reside in closer proximity to polluted environments, be exposed to toxic substances, experience threats and realities of crime, and live with cumulative worries about meeting basic needs, highlights the importance of considering both gendered and nongendered aspects of their environments, identities, and experiences (Treadwell & Braithwaite, 2005; Xanthos et al., 2010; Young et al., 2008).
There are several caveats of EHDIC-SWB study that may affect interpretations. EHDIC-SWB does not account for race differences in work exposures which may contribute to disparities in obesity. For instance, men in low-activity occupations have a higher probability of being obese relative to men in high-activity occupations (King et al., 2001). In addition, work-related stress has been associated obesity (Kivimäki et al., 2006; Schulte et al., 2007). The EHDIC-SWB data were collected in a low-income urban population and these findings may not generalize to other minority groups, rural and suburban areas, and higher SES groups. However, these findings may be generalizable to other urban settings that have similar social and environmental characteristics as EHDIC-SWB. Both the EHDIC-SWB and NHIS data sets are cross-sectional; therefore, the ability to make causal inferences is substantially limited. Height and body weight were self-reported in both EHDIC-SWB and NHIS. When using self-report anthropometric measures, it is worth noting that participants tend to overestimate height and underestimate weight (Bowring et al., 2012; Gunnare, Silliman, & Morris, 2013), thereby potentially producing self-report height and weight bias. Nevertheless, self-reported height and body weight bias do not vary by race/ethnicity (Ezzati et al., 2006; Jackson et al., 2013; Li et al., 2012).
In addition to measuring BMI, it may be more important to measure body fat, waist circumference, and other measures that take race and gender into account and that are more strongly associated with long-term health risks (Sumner, Ricks, Sen, & Frempong, 2007). For Black men, some investigators have reported that years of life lost as a result of obesity does not occur until men reach a BMI of 32 kg/m2, with greatest longevity occurring between 23 kg/m2 and 30 kg/m2 in Black women and men (Fontaine, Redden, Wang, Westfall, & Allison, 2003). Furthermore, there are limitations to BMI as a measurement of obesity, particular when discussing patterns of obesity across gender, racial, and ethnic groups. Racial and ethnic differences between BMI and percent body fat are well documented (Deurenberg, Deurenberg-Yap, & Guricci, 2002; Deurenberg, Yap, & van Staveren, 1998; Wagner & Heyward, 2000). Blacks tend to have a higher BMI than Whites at similar levels of percent fat, thus overestimating the risk of obesity in Black men and women (Deurenberg et al., 1998; Wagner & Heyward, 2000). Additionally, BMI thresholds that define obesity are not gender-specific, which poses particular problems for estimating true rates of obesity among Blacks, whose gender differences in body fat content is greater than Whites (Hill et al., 1999).
These data provided an opportunity to examine race disparities in obesity among men when the confounding of race, segregation, and SES is significantly minimized. Study designs, such as EHDIC, that account for these types of confounding are rare. After accounting for the social and environmental conditions in which low-income urban men live, race differences in obesity were no longer apparent in EHDIC-SWB. These findings add to a small but critical line of men’s health research. Further multidimensional investigation is urgently needed to better address and reduce problems associated with the burden of obesity among Black and White men. Health promoting strategies and interventions should consider the role that social and environmental conditions play in obesity disparities among men in the United States.
Footnotes
Declaration of Conflicting Interests
The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
Funding
The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: Research conducted by the first author was supported by a grant from the National Institute on Minority Health and Health Disparities (P60MD000214) and a grant from Pfizer, Inc.