Objectives. We investigated whether racial disparities in the prevalence of type 2 diabetes exist beyond what may be attributable to differences in socioeconomic status (SES) and other modifiable risk factors.

Methods. We analyzed data from 34331 African American and 9491 White adults aged 40 to 79 years recruited into the ongoing Southern Community Cohort Study. Participants were enrolled at community health centers and had similar socioeconomic circumstances and risk factor profiles. We used logistic regression to estimate the association between race and prevalence of self-reported diabetes after taking into account age, SES, health insurance coverage, body mass index, physical activity, and hypertension.

Results. Multivariate analyses accounting for several diabetes risk factors did not provide strong support for higher diabetes prevalence rates among African Americans than among Whites (men: odds ratio [OR]=1.07; 95% confidence interval [CI]=0.95, 1.20); women: OR=1.13, 95% CI=1.04, 1.22).

Conclusions. Our findings suggest that major differences in diabetes prevalence between African Americans and Whites may simply reflect differences in established risk factors for the disease, such as SES, that typically vary according to race.

Members of racial and ethnic minority groups in the United States, including African Americans, suffer disproportionately from many chronic diseases, including type 2 diabetes (hereafter “diabetes”).13 Prevailing statistics suggest that African American adults are 50% to 100% more likely to have diabetes than are Whites,38 with evidence that diabetes precursors may even be more common in African American than in White children.9,10 Reasons for racial disparities in diabetes prevalence are not clear, but behavioral, environmental, socioeconomic, physiological, and genetic contributors have all been postulated.3,8,11

Because of the high prevalence of diabetes in the African American community, it has been suggested that African Americans may be more susceptible to the disease compared with Whites through direct genetic propensity or unfavorable gene–environment interactions.11 The fact that diabetes prevalence rates among Whites exceeded those among African Americans through at least the first half of the 20th century12 has led to the hypothesis that modern lifestyle factors (especially those that promote obesity) may have a greater effect on African Americans than on Whites.11,13

However, treating race as an etiological factor has been the subject of debate,1416 and it has been argued that despite some genotypic delineations, race largely represents a complex mixture of behavioral, environmental, and social exposures.17,18 In comparison with Whites, African Americans often are poorer, have less education, are more likely to live in distressed households and communities, are less able to access quality health care, and have a less favorable risk factor profile for many diseases.1820 Because socioeconomic (and associated environmental) differences between racial groups are so pervasive, attempts to isolate an effect of race will typically involve substantial confounding,16 resulting in difficulty estimating the relative contributions of genetic and environmental factors.

There have been several attempts to evaluate whether the disparity between African Americans and Whites with regard to diabetes can be attributed to factors other than racial background.7,13,2127 Studies involving nationally representative sampling frames7,21,2325,27 provided the platform for many of these analyses, which poses a challenge in that the average African American is of substantially lower socioeconomic status (SES) than the average White American. Because racial disparities persisted in these studies after adjustment for known diabetes risk factors, including some measures of SES, a possible genetic explanation has been invoked for the residual association, although the precise biological mechanisms remain speculative. Many of the studies conducted to evaluate the underlying reasons for racial disparities in diabetes prevalence have included fewer than 1000 each of African American men and women.7,13,21,23,25

Using the study population from the ongoing Southern Community Cohort Study (SCCS), which includes large numbers of African Americans and members of other racial/ethnic groups from generally similar socioeconomic circumstances, we had a unique opportunity to evaluate racial disparities in diabetes in a context in which confounding by extraneous factors related to race and SES would be limited by design. If racial disparities are driven by SES, one would expect little racial difference in diabetes prevalence rates within this population. We addressed the question of whether differences in diabetes prevalence between African Americans and Whites can be fully explained by SES or by adjustment for other correlates of diabetes risk.

Study Population

The SCCS is a prospective epidemiological cohort study with ongoing participant enrollment across the southeastern United States.28 For the present analysis, we included cohort members enrolled from the beginning of the study (March 2002) until January 2006. These participants were enrolled in person at 48 community health centers located in both urban and rural areas across the states of Alabama, Arkansas, Florida, Georgia, Kentucky, Louisiana, Mississippi, North Carolina, South Carolina, Tennessee, Virginia, and West Virginia. Community health centers are government-funded health care facilities that provide basic health services primarily to low-income individuals.29 Nationally, approximately 70% of community health center patients live at or below the federal poverty level. African American and White participants were enrolled at the same community health centers.

The strategy used to enroll participants was to randomly approach people entering the community health centers (e.g., patients, individuals accompanying patients, community residents seeking other services offered by the community health centers) who appeared to be between the ages of 40 and 79 years and determine their eligibility for and interest in study participation. In addition to the age criterion, participants were required to speak English and to not have undergone treatment for cancer within the preceding year. The primary focus of the SCCS is determining reasons for racial disparities in cancer risk.

Data Collection

Participants completed a comprehensive, in-person, baseline interview covering various aspects of health and behavior, including personal and family medical history, diet, exercise, tobacco use, medication use, social support, psychological well-being, and access to health services. During this interview, participants were asked, “Has a doctor ever told you that you have had diabetes or high blood sugar?” Participants responding yes were asked follow-up questions regarding their age at first diagnosis and use (and names) of prescription medications taken to manage their diabetes. Women were specifically asked not to include gestational diabetes in their reporting.

Socioeconomic Status and Other Potential Confounders

The variables used to estimate SES were total household income in the previous year (less than $15000, $15000–$24999, $25 000–$49999, $50 000–$99999, $100 000 or more), highest level of education completed, and type of job held for the longest period of time during the participant’s adult life (reported in 20 broad categories, including “never worked” and “housewife”). We used Nam–Powers–Boyd (NPB) occupational status scores30 (on a scale from 1 [lowest] to 100 [highest], representing the socioeconomic standing of an occupation) for the occupational categories by assigning each category the average of the scores for its individual job examples. In the case of a small number of participants (n = 371; 0.8%) for whom we were unable to code longest-held job with our categories, or for whom this information was missing, we substituted the score for their current occupation.

Participants self-reported their current weight, their weight at age 21 years, the most they had ever weighed (not including weight during pregnancy), and their height. Because current weight may reflect weight adjustments (in either direction) after a diagnosis of diabetes,31 we chose to focus on participants’ reports of the most they had ever weighed and their weight at age 21 years (to account for long-term overweight or obesity). We calculated body mass index (BMI; weight in kilograms divided by height in meters squared) for each of these 2 weight measures; we defined overweight as a BMI of 25 kg/m2 or higher, obesity as a BMI of 30 kg/m2 or higher, and extreme obesity as a BMI of 40 kg/m2 or higher32 (categories are not mutually exclusive).

Participants also reported on leisure time physical activities they had engaged in during their 30s (amount of time per week). These activities included both moderate-level sports (e.g., bowling, dancing, golfing, and softball) and vigorous sports (e.g., jogging, aerobics, bicycling, tennis, swimming, weightlifting, and basketball).

Statistical Analysis

We included in our cross-sectional analyses participants who identified themselves as either only African American or only White (n = 43 899; 97% of the cohort). We excluded an additional 77 participants (0.2%) who were missing self-reported information on diabetes status, leaving 43 822 (34331 African American, 9491 White) participants to serve as our study population.

We used multivariate logistic regression analyses to estimate measures of association (odds ratios [ORs] and 95% confidence intervals [CIs]). The following factors were selected a priori as covariates and modeled via the categories shown in Table 1: age at interview, educational level, total household income, NPB score, health insurance coverage, current BMI, highest BMI, BMI at age 21 years, and hypertension. Physical activity (in minutes), also selected a priori, was modeled with continuous variables. The (approximate) quartiles used for NPB scores were gender specific, with cutoffs for men of 22, 30, and 42.5 and cutoffs for women of 22, 30, and 56.

We evaluated additional variables as potential confounders, but they were found not to alter the main results by more than 5% and were not included in the final model. These variables were marital status, smoking status, and 2 measures of social support (participants’ reports of how many close friends or relatives would help with their emotional problems if needed and how many people they could ask for help in an emergency or with lending them money). Income adjusted for household size (determined by dividing the midpoint of the reported income category by the total number of people reported to be living in a given household) was also computed and substituted in the final model for the income variable, but it was not found to alter the results.

Participants’ mean age at enrollment was 51.2 years (SD = 8.7). The majority (61%) reported a household income below $15000 per year, and one third reported less than 12 years of schooling (Table 1). At the time of the baseline interview, 73% of participants were overweight, 44% were obese, and 11% were extremely obese. The prevalence of obesity was significantly (P< .001) higher among women than among men and significantly (P< .001) higher among African American than among White women. On the basis of the participants’ highest reported weight (and computed highest BMI), we found that a large percentage of each group (43% of African American men, 54% of White men, 69% of African American women, and 61% of White women) had been obese at some point in their lives. In general, we observed that the socioeconomic and other factors included in Table 1 had relatively similar race-specific distributions within each gender.

Overall, 9223 (21%) of the participants reported having been diagnosed with diabetes, and of these individuals, 86% reported taking diabetes medication, including insulin (Table 1). Differences in the reported prevalence of diabetes between African Americans and Whites were modest. Among women, African Americans were more likely to report diabetes than were Whites (24% vs 21%), whereas the converse was true for men (20% for Whites vs 17% for African Americans).

Diabetes prevalence rates in relation to factors previously shown to have significant associations with the disease (age, educational level, income, BMI) are reported in Table 2. As expected, the prevalence of diabetes increased with increasing age and BMI, and with decreasing education and income. Diabetes prevalence rose 8-fold from a low of 5% among participants whose highest BMI was less than 25 kg/m2 to 40% among those whose highest BMI was 40 kg/m2 or greater. Among participants who had ever been obese, the prevalence of diabetes varied little according to race or gender (30% among African American women, 29% among White women, 28% among African American men, and 30% among White men).

The prevalence of diabetes was inversely related to educational level, particularly among women, and overall it was 1.6 times higher among participants with less than 9 years of education than among those who had graduated from college (Table 2). Similarly, among participants in the lowest income category (less than $15000 per year), the prevalence of diabetes was 1.4 times higher than among participants with a household income of $50 000 per year or more; however, there were variations in the relationship between income and diabetes in each gender–race stratum, and the general trend of prevalence rising with decreasing income did not hold for African American men.

After adjustment for age, we observed no association between race and diabetes among men (for African Americans relative to Whites, OR = 0.92; 95% CI = 0.83, 1.01) and a modest excess among African American women in comparison with White women (OR = 1.39; 95% CI = 1.29, 1.49; Table 3). After further adjustment for educational level, income, NPB score, health insurance coverage, highest BMI, BMI at age 21, hypertension, and physical activity, there was still no significant difference between African American and White men, and the difference for women had been attenuated and remained only marginally significant (OR = 1.13; 95% CI = 1.04, 1.22). To avoid any potentially biasing effects of including individuals with type 1 diabetes, we repeated the analyses excluding participants who reported their age at first diagnosis as younger than 30 years. The results (Table 3) were nearly identical to those of our main analyses.

Table 4 shows the strong association between (highest) BMI and diabetes estimated from multivariate regression models run separately for each of the 4 gender and race groups. The strong trend of increasing risk across increasing categories of BMI was seen in all groups but tended to be more enhanced among Whites, although race × BMI interaction terms in gender-specific regression models were statistically significant only for women. We used regression models containing these interaction terms to estimate the effect of race on diabetes prevalence at various levels of BMI. Among women, the odds ratio for the effect of race was highest at the lowest BMI level (ORs = 2.03, 1.41, 1.26, 1.19, and 0.85 for women whose highest BMI was less than 25 kg/m2, 25–29.99, 30–34.99, 35–39.99, and 40 kg/m2 or higher, respectively), but no significant interaction was observed among men (ORs = 1.03, 1.42, 1.34, 0.97, and 0.60 for highest BMI of less than 25 kg/m2, 25–29.99 kg/m2, 30–34.99 kg/m2, 35–39.99 kg/m2, and 40 kg/m2 or higher, respectively).

In this large study of adults with similar socioeconomic circumstances and risk factor profiles, we found little evidence of a higher prevalence of diabetes among African Americans than among Whites. Even before we had controlled for BMI and other known determinants of the disease, we observed only a modest excess of diabetes among African Americans and only among women.

A social gradient in diabetes risk has been well documented both in the United States5,3336 and in other Westernized countries.3740 The factors underlying this gradient may include fetal or infant malnutrition,4143 chronic stress,44,45 depression and other psychosocial factors,37,38,46 obesity,24,47 inactivity,24 and lack of access to preventive health care. Controlling for SES is problematic because SES stands as a proxy for a myriad of (often unmeasured) confounders, is difficult to quantify, and is prone to a high level of measurement error.16 Because confounding by SES can be intractable in the analysis phase of a study, strategies designed to limit it in the design phase may be more effective.

Despite our advantage of having a study design that produced a population closely “matched” across racial groups in terms of SES and our efforts to quantify SES, residual confounding by SES was still, in all likelihood, a factor in our findings, possibly accounting for the small residual racial effect we observed among women. Racial disparities in diabetes are often reported to be stronger among women than among men,3,6,22,23,25 and it may be that SES is a stronger confounder among women than among men. We found some evidence for the latter possibility, with education and income showing a stronger relation with diabetes among women, both crudely (Table 2) and in our final multivariate regression models (data not shown).

Our overall finding of nearly equal rates of diabetes among African Americans and Whites is contrary to the results of practically all published epidemiological studies on this subject.7,13,2127 Although the findings from these previous studies are somewhat mixed, a common conclusion has been that racial differences in diabetes prevalence cannot be fully explained by established risk factors. We believe, however, that few investigations have overcome the confounding inherent in studies of race and disease. Indeed, these studies noted striking differences in several important confounders (e.g., measures of obesity or central adiposity, education, income, occupational status, or physical activity) between African Americans and Whites in their samples,7,13,2126 and adjustment for SES often involved adjustment for education only.7,2225

In a recent investigation that undertook a more comprehensive evaluation of the effects of confounding by SES and other variables, an initial African American excess in diabetes prevalence among women of 76% was eliminated (OR=1.04) after adjustment for poverty income ratio (i.e., income divided by the federal poverty line for a given family size), a number of examination-related variables (e.g., length of fast, time of day), body size variables, and measures of physical activity, diet, smoking, and alcohol consumption.21 In the same study, however, adjusting for the identical set of variables did not negate the effect of race among men.21

It has been suggested that, in terms of diabetes risk, obesity may have a more detrimental effect among African Americans than among Whites.7,23,25 Such a finding was reported in a pair of investigations involving data from the National Health and Nutrition Examination Survey (NHANES)7,25; in one of these studies, the strongest effect of obesity was observed among African American women.7 We did not find supportive evidence for an interaction in this direction, and in fact we observed the effect of obesity to be greater among White women than among African American women. Our finding is consistent with clinical evidence indicating that upper body obesity is more strongly associated with a diabetes-promoting metabolic profile among nondiabetic White women than among African American women.4850

Our results raise the possibility that any racial differences in diabetes among women may be greatest at low BMI levels, with the racial gap disappearing as BMI increases. This has been noted elsewhere,23 but others have reported the opposite7 (i.e., similar diabetes prevalence rates among African Americans and Whites at ideal body weights, with a racial disparity growing with increasing percentage of desirable weight). One interpretation of our finding would be that if there is a race-based disparity among women, it may be more pronounced in women of normal weight; the reason may be that obesity has a greater effect on Whites, as we observed, or that the disparity is overshadowed in general by the strong influence of obesity on diabetes. Given the large number of comparisons made in this analysis, it is also possible that our finding of an interaction between race and BMI among women arose by chance.

The prevalence of diabetes in the SCCS is higher than the prevalence in the general population of the southeastern states. According to the Centers for Disease Control and Prevention’s Behavioral Risk Factor Surveillance System, 2004 age-specific diabetes prevalence rates in the 12 SCCS enrollment states were in the range of 7% to 12% in the 45- to 54-year age group, 13% to 19% in the 55- to 64-year age group, and 14% to 22% among individuals 65 years or older51; by contrast, we found rates of 19%, 31%, and 34%, respectively. We believe that the reason for the high prevalence of diabetes in the SCCS is that participant enrollment takes place in impoverished communities and within community health centers in which people seek care for their diabetes. Thus, our prevalence estimates do not lend themselves to generalization outside of our study population, but internal comparisons between subgroups of participants (in this case, by race) remain valid.


Systematic inaccuracies in reporting of diabetes diagnoses may have obscured actual racial differences in our study, but our collection of data in a standardized fashion across racial groups should have minimized this possibility. Nondifferential misclassification can also dampen true differences in reported outcomes but would have to be substantial to generate a null finding. Some confounders included as covariates in our analyses (e.g., participants’ weight at age 21 years and physical activity in their 30s) involved recall over a long period of time, but we would not expect race-specific differences in associated reporting errors.

Although exclusion of undiagnosed disease was a limitation of our study, it is not a likely reason for our null findings, given that self-reports have been used in other studies documenting strong racial disparities.4,5,26 An estimated 29% to 44% of diabetes cases in the United States are undiagnosed.4,6 However, African Americans in our study should not have been less likely than Whites to be diagnosed because of differential access to health care, because recruitment within community health centers ensured that all cohort members had essentially equal access to primary health care, and there were few racial differences in income level or type of health insurance coverage. Moreover, a recent analysis of NHANES (1999–2000) data revealed similar rates of undiagnosed diabetes among African Americans and non-Hispanic Whites according to fasting plasma glucose test results.4


On the basis of familial aggregation, twin studies, and recent advances in identifying molecular markers of risk, it is clear that diabetes is a genetically influenced disease.52 However, success in identifying genetic underpinnings of racial disparities in diabetes prevalence has been elusive. Although the existence of a “thrifty genotype” (a genetic adaptation to feast and famine cycles) has been posited since the 1960s,53 it has yet to be characterized. Furthermore, the notion that the thrifty genotype would affect African Americans more than other groups such as European Americans has been called into question.54 Genome-wide scans have uncovered some candidate markers of diabetes risk in affected African American families,55,56 and racial variations in the adiponectin57 and other genes58,59 involved in insulin sensitization or resistance have been noted; however, the contribution of these polymorphisms to racial disparities, or to diabetes risk in general, has not been firmly established.

Our results suggest that major differences in diabetes prevalence between African Americans and Whites are unlikely to be tied to race per se; rather, it is likely that they are linked to differences in established risk factors for diabetes that typically vary between African Americans and Whites. Our findings do not discount the possibility of race-specific differences in the pathogenesis or pathophysiological characteristics of diabetes6062 or the possibility of racial differences in the molecular etiology of diabetes, but they do seem to refute the position that there is an intractable diabetes excess among African Americans unexplainable by nongenetic risk factors. Curtailing rising trends in obesity and improving the economic conditions of disadvantaged groups in the United States may be the key to controlling diabetes across all racial groups.

TABLE 1— Prevalence of Diabetes and Distribution of Diabetes Risk Factors, by Gender and Race: Southern Community Cohort Study, 2002–2006
TABLE 1— Prevalence of Diabetes and Distribution of Diabetes Risk Factors, by Gender and Race: Southern Community Cohort Study, 2002–2006
 Men, No. (%)Women, No. (%)
CharacteristicAfrican AmericanWhiteAfrican AmericanWhite
Total, No.14 236 (100)3 165 (100)20 095 (100)6 326 (100)
Age at interview, y
    40–444 236 (29.8)805 (25.4)5 275 (26.3)1 297 (20.5)
    45–493 730 (26.2)700 (22.1)4 742 (23.6)1 346 (21.3)
    50–542 871 (20.2)550 (17.4)3 892 (19.4)1 106 (17.5)
    55–591 582 (11.1)414 (13.1)2 543 (12.7)1 020 (16.1)
    60–64948 (6.7)342 (10.8)1 684 (8.4)776 (12.3)
    65–69482 (3.4)193 (6.1)1 022 (5.1)409 (6.5)
    ≥70387 (2.7)161 (5.1)937 (4.7)372 (5.9)
Educational level
    Less than 9th grade1 306 (9.2)390 (12.3)1 590 (7.9)594 (9.4)
    9th–11th grade3 720 (26.1)656 (20.7)4 864 (24.2)1 315 (20.8)
    High school/vocational school5 955 (41.8)1 174 (37.1)7 924 (39.4)2 540 (40.2)
    Some college or junior (2-year) college2 301 (16.2)597 (18.9)3 909 (19.5)1 197 (18.9)
    College681 (4.8)227 (7.2)1 283 (6.4)454 (7.2)
    Graduate school271 (1.9)120 (3.8)519 (2.6)225 (3.6)
    Unknown2 (0.0)1 (0.0)6 (0.0)1 (0.0)
Total annual household income, $
    < $15 0009 002 (63.2)1 910 (60.4)12 280 (61.1)3 724 (58.9)
    15 000–24 9993 126 (22.0)694 (21.9)4 753 (23.7)1 245 (19.7)
    25 000–49 9991528 (10.7)376 (11.9)2 168 (10.8)779 (12.3)
    50 000–99 999382 (2.7)140 (4.4)567 (2.8)399 (6.3)
    ≥100 00064 (0.5)32 (1.0)75 (0.4)105 (1.7)
    Unknown134 (0.9)13 (0.4)252 (1.3)74 (1.2)
Nam–Powers–Boyd occupational status scorea
    Quartile 14 992 (35.1)967 (30.6)4 090 (20.4)1 388 (21.9)
    Quartile 22 511 (17.6)363 (11.5)5 608 (27.9)1 322 (20.9)
    Quartile 32 620 (18.4)694 (21.9)4 397 (21.9)1 866 (29.5)
    Quartile 43 230 (22.7)929 (29.4)4 640 (23.1)1 433 (22.7)
    Unknown883 (6.2)212 (6.7)1 360 (6.8)317 (5.0)
Health insurance coverage
    None7 065 (49.6)1 513 (47.8)7 703 (38.3)2 654 (42.5)
    Any private insurance2 503 (17.6)449 (14.2)4 653 (23.2)1 475 (23.3)
    Medicaid only1 756 (12.3)341 (10.8)3 618 (18.0)789 (12.5)
    Medicare only1 205 (8.5)426 (13.5)1 942 (9.7)679 (10.7)
    Military only483 (3.4)89 (2.8)95 (0.5)35 (0.6)
    Other combinations1 162 (8.2)340 (10.7)1 963 (9.8)655 (10.4)
    Unknown62 (0.4)7 (0.2)121 (0.6)39 (0.6)
Current body mass index, kg/m2
    < 20663 (4.7)120 (3.8)589 (2.9)298 (4.7)
    20–24.994 836(34.0)885 (28.0)2 827 (14.1)1 309 (20.7)
    25–29.994 906 (34.5)1 076 (34.0)5 068 (25.2)1 642 (26.0)
    30–34.992 359 (16.6)620 (19.6)4 970 (24.7)1 377 (21.8)
    35–39.99939 (6.6)265 (8.4)3 293 (16.4)828 (13.1)
    ≥40479 (3.4)192 (6.1)3 127 (15.6)825 (13.0)
    Unknown54 (0.4)7 (0.2)221 (1.1)47 (0.7)
Highest body mass index,b kg/m2
    < 20156 (1.1)24 (0.8)172 (0.9)67 (1.1)
    20–24.992 812 (19.8)436 (13.8)1 715 (8.5)879 (13.9)
    25–29.995 058 (35.5)1 002 (31.7)4 159 (20.7)1 460 (23.1)
    30–34.993 540 (24.9)887 (28.0)5 257 (26.2)1 442 (22.8)
    35–39.991 611 (11.3)434 (13.7)3 834 (19.1)1 036 (16.4)
    ≥401 010 (7.1)378 (11.9)4 797 (23.9)1 412 (22.3)
    Unknown49 (0.3)4 (0.1)161 (0.8)30 (0.5)
Body mass index at age 21 years, kg/m2
    < 202 360 (16.6)509 (16.1)5 542 (27.6)2 139 (33.8)
    20–24.997 154 (50.3)1 561 (49.3)8 935 (44.5)2 793 (44.2)
    25–29.993 207 (22.5)790 (25.0)2 986 (14.9)713 (11.3)
    30–34.99723 (5.1)175 (5.3)1 080 (5.4)328 (5.2)
    35–39.99183 (1.3)51 (1.6)349 (1.7)136 (2.2)
    ≥40103 (0.7)32 (1.0)263 (1.3)116 (1.8)
    Unknown506 (3.6)47 (1.5)940 (4.7)101 (1.6)
    No7 346 (51.6)1 602 (50.6)7 431 (37.0)3 077 (48.6)
    Yes6 879 (48.3)1 562 (49.4)12 658 (63.0)3 246 (51.3)
    Unknown11 (0.1)1 (0.0)6 (0.0)3 (0.1)
Moderate sports activityd in 30s, h/wk
    04 917 (34.5)1 354 (42.8)8 098 (40.3)2 932 (46.4)
    0.01–2.002 739 (19.2)597 (18.9)4 255 (21.2)1 204 (19.0)
    2.01–4.992 724 (19.1)480 (15.2)3 596 (17.9)958 (15.1)
    ≥53 748 (26.3)716 (22.6)3 897 (19.4)1 182 (18.7)
    Unknown108 (0.8)18 (0.6)249 (1.2)50 (0.8)
Vigorous sports activitye in 30s, h/wk
    04 520 (31.8)1 499 (47.4)11 038 (54.9)3 486 (55.1)
    0.01–2.003 096 (21.8)611 (19.3)3 977 (19.8)1 151 (18.2)
    2.01–4.002 397 (16.8)357 (11.3)2 131 (10.6)620 (9.8)
    > 44 120 (28.9)683 (21.6)2 728 (13.6)1 029 (16.3)
    Unknown103 (0.7)15 (0.5)221 (1.1)40 (0.6)
    No11 858 (83.3)2 546 (80.4)15 178 (75.5)5 017 (79.3)
    Yes2 378 (16.7)619 (19.6)4 917 (24.5)1 309 (20.7)
Currently taking diabetes medication
    No12 217 (85.8)2 642 (83.5)15 736 (78.3)5 276 (83.4)
    Yes2 018 (14.2)523 (16.5)4 356 (21.7)1 050 (16.6)
    Unknown1 (0.0). . .3 (0.0). . .

aScore range = 1 (lowest socioeconomic ranking) to 100 (highest socioeconomic ranking); gender-specific approximate quartiles.

bBody mass index is weight in kilograms divided by height in meters squared; calculated from participants’ self-reports of their highest weight.

cMeasure was self-reported (“Has a doctor ever told you that you have had high blood pressure?”).

dDefined to participants with the examples of bowling, dancing, golfing, and softball.

eDefined to participants with the examples of jogging, aerobics, bicycling, tennis, swimming, weightlifting, and basketball.

fMeasure was self-reported (“Has a doctor ever told you that you have had diabetes or high blood sugar?”).

TABLE 2— Diabetes Prevalence Rates in Relation to Age, Education, Income, and Body Mass Index: Southern Community Cohort Study, 2002–2006
TABLE 2— Diabetes Prevalence Rates in Relation to Age, Education, Income, and Body Mass Index: Southern Community Cohort Study, 2002–2006
 Men, %Women, % 
CharacteristicAfrican AmericanWhiteAfrican AmericanWhiteTotal, %
Age, y
Educational level
    Less than 9th grade24.322.136.526.829.4
    9th–11th grade16.820.027.023.322.5
    High school/vocational school15.419.023.320.820.0
    Some college or junior college15.320.420.817.718.7
    College or higher18.116.419.915.518.3
Total annual household income, $
    < 15 00016.221.126.923.422.4
    15 000–24 99916.718.021.920.419.8
    25 000–49 99918.518.118.214.617.7
    ≥50 00019.512.218.29.915.6
Highest body mass index,a kg/m2

aBody mass index is weight in kilograms divided by height in meters squared; calculated from participants’ self-reports of their highest weight.

TABLE 3— Results of Logistic Regression Analyses of Associations Between Race and Diabetes: Southern Community Cohort Study, 2002–2006
TABLE 3— Results of Logistic Regression Analyses of Associations Between Race and Diabetes: Southern Community Cohort Study, 2002–2006
 Age Adjusted,OR (95% CI)Multivariate,a OR (95% CI)Multivariatea: Participants Diagnosed at 30 Years or Older,b OR (95% CI)
    White (Ref)111
    African American0.92 (0.83, 1.01)1.07 (0.95, 1.20)1.04 (0.92, 1.18)
    White (Ref)111
    African American1.39 (1.29, 1.49)1.13 (1.04, 1.22)1.14 (1.05, 1.24)

Note. OR = odds ratio; CI = confidence interval.

aAdjusted for age, educational level, household income, Nam–Powers–Boyd occupational status score, health insurance coverage, highest body mass index, body mass index at age 21, hypertension, time per week engaged in moderate sports in 30s, and time per week engaged in vigorous sports in 30s. See “Methods” section for details about measures used.

bLogistic regression model included only participants reporting an age at diagnosis of diabetes of 30 years or older and participants not reporting a diagnosis of diabetes.These restrictions were applied to exclude probable cases of type 1 diabetes.

TABLE 4— Multivariate Adjusted Odds Ratios (AORs) Illustrating the Association Between Body Mass Index (BMI) and Diabetes, by Gender and Race: Southern Community Cohort Study, 2002–2006
TABLE 4— Multivariate Adjusted Odds Ratios (AORs) Illustrating the Association Between Body Mass Index (BMI) and Diabetes, by Gender and Race: Southern Community Cohort Study, 2002–2006
Highest BMI,a kg/m2African American Men,AOR (9% CI)White Men,AOR (9% CI)African American Women,AOR (9% CI)White Women,AOR (9% CI)
< 25 (Ref)1111
25–29.992.09 (1.69, 2.60)1.50 (0.87, 2.61)1.75 (1.40, 2.18)2.58 (1.72, 3.89)
30–34.994.12 (3.31, 5.14)3.29 (1.92, 5.62)2.89 (2.33, 3.57)4.71 (3.16, 7.02)
35–39.996.92 (5.46, 8.76)7.93 (4.52, 13.90)4.29 (3.46, 5.33)7.15 (4.76, 10.75)
≥408.26 (6.36, 10.73)16.17 (8.98, 29.11)5.92 (4.76, 7.36)12.30 (8.18, 18.49)

Note. CI = confidence interval. Odds ratios were estimated from separate logistic regression models run for each of the 4 gender and race groups. They were adjusted for age, educational level, household income, Nam–Powers–Boyd occupational status score, health insurance coverage, body mass index at age 21, hypertension, time per week engaged in moderate sports in 30s, and time per week engaged in vigorous sports in 30s. See “Method” section for details about measures used.

aBody mass index is weight in kilograms divided by height in meters squared; calculated from participants’ self-reports of their highest weight.

The Southern Community Cohort Study was supported by the National Cancer Institute (NCI; grant R01 CA92447). D. G. Schlundt, M. S. Buchowski, and M. K. Hargreaves received partial funding from the NCI (grants P60-DK20593, HL67715, and 5P20-MD000516, respectively).

Human Participant Protection The Southern Community Cohort Study was approved by the institutional review boards of Vanderbilt University and Meharry Medical College. All participants provided written informed consent.


1. Wong MD, Shapiro MF, Boscardin WJ, Ettner SL. Contribution of major diseases to disparities in mortality. N Engl J Med. 2002;347:1585–1592. Crossref, MedlineGoogle Scholar
2. Levine RS, Foster JE, Fullilove RE, et al. Black-White inequalities in mortality and life expectancy, 1933–1999: implications for Healthy People 2010. Public Health Rep. 2001;116:474–483. Crossref, MedlineGoogle Scholar
3. Carter JS, Pugh JA, Monterrosa A. Non-insulin-dependent diabetes mellitus in minorities in the United States. Ann Intern Med. 1996;125:221–232. Crossref, MedlineGoogle Scholar
4. Centers for Disease Control and Prevention. Prevalence of diabetes and impaired fasting glucose in adults—United States, 1999–2000. MMWR Morb Mortal Wkly Rep. 2003;52:833–837. MedlineGoogle Scholar
5. Mokdad AH, Ford ES, Bowman BA, et al. Prevalence of obesity, diabetes, and obesity-related health risk factors, 2001. JAMA. 2003;289:76–79. Crossref, MedlineGoogle Scholar
6. Harris MI, Flegal KM, Cowie CC, et al. Prevalence of diabetes, impaired fasting glucose, and impaired glucose tolerance in US adults: the Third National Health and Nutrition Examination Survey, 1988–94. Diabetes Care. 1998;21:518–524. Crossref, MedlineGoogle Scholar
7. Cowie CC, Harris MI, Silverman RE, Johnson EW, Rust KF. Effect of multiple risk factors on differences between blacks and whites in the prevalence of non-insulin-dependent diabetes mellitus in the United States. Am J Epidemiol. 1993;137:719–732. Crossref, MedlineGoogle Scholar
8. Harris MI. Non-insulin-dependent diabetes mellitus in black and white Americans. Diabetes Metab Rev. 1990;6:71–90. Crossref, MedlineGoogle Scholar
9. Gower BA, Fernandez JR, Beasley TM, Shriver MD, Goran MI. Using genetic admixture to explain racial differences in insulin-related phenotypes. Diabetes. 2003;52:1047–1051. Crossref, MedlineGoogle Scholar
10. Lindquist CH, Gower BA, Goran MI. Role of dietary factors in ethnic differences in early risk of cardiovascular disease and type 2 diabetes. Am J Clin Nutr. 2000;71:725–732. Crossref, MedlineGoogle Scholar
11. Abate N, Chandalia M. The impact of ethnicity on type 2 diabetes. J Diabetes Complications. 2003;17:39–58. Crossref, MedlineGoogle Scholar
12. Roseman JM. Diabetes in black Americans. In: Harris MI, Hamman RF, eds. Diabetes in America. Bethesda, Md: US Department of Health and Human Services; 1985:1–24. Google Scholar
13. Brancati FL, Whelton PK, Kuller LH, Klag MJ. Diabetes mellitus, race, and socioeconomic status: a population-based study. Ann Epidemiol. 1996;6:67–73. Crossref, MedlineGoogle Scholar
14. Karter AJ. Race and ethnicity: vital constructs for diabetes research. Diabetes Care. 2003;26:2189–2193. Crossref, MedlineGoogle Scholar
15. Lin SS, Kelsey JL. Use of race and ethnicity in epidemiologic research: concepts, methodological issues, and suggestions for research. Epidemiol Rev. 2000;22:187–202. Crossref, MedlineGoogle Scholar
16. Kaufman JS, Cooper RS, McGee DL. Socioeconomic status and health in blacks and whites: the problem of residual confounding and the resiliency of race. Epidemiology. 1997;8:621–628. Crossref, MedlineGoogle Scholar
17. Shields AE, Fortun M, Hammonds EM, et al. The use of race variables in genetic studies of complex traits and the goal of reducing health disparities: a transdisciplinary perspective. Am Psychol. 2005;60: 77–103. Crossref, MedlineGoogle Scholar
18. Williams DR. Race and health: basic questions, emerging directions. Ann Epidemiol. 1997;7:322–333. Crossref, MedlineGoogle Scholar
19. Krieger N, Rowley DL, Herman AA, Avery B, Phillips MT. Racism, sexism, and social class: implications for studies of health, disease, and well-being. Am J Prev Med. 1993;9:82–122. Crossref, MedlineGoogle Scholar
20. Jones CP. Levels of racism: a theoretical framework and a gardener’s tale. Am J Public Health. 2000; 90:1212–1215. LinkGoogle Scholar
21. Robbins JM, Vaccarino V, Zhang H, Kasl SV. Excess type 2 diabetes in African-American women and men aged 40–74 and socioeconomic status: evidence from the Third National Health and Nutrition Examination Survey. J Epidemiol Community Health. 2000;54:839–845. Crossref, MedlineGoogle Scholar
22. Brancati FL, Kao WHL, Folsom AR, Watson RL, Szklo M. Incident type 2 diabetes mellitus in African American and White adults. JAMA. 2000;283: 2253–2259. Crossref, MedlineGoogle Scholar
23. Resnick HE, Valsania P, Halter JB, Lin X. Differential effects of BMI on diabetes risk among Black and White Americans. Diabetes Care. 1998;21:1828–1835. Crossref, MedlineGoogle Scholar
24. Winkleby MA, Kraemer HC, Ahn DK, Varady AN. Ethnic and socioeconomic differences in cardiovascular disease risk factors: findings for women from the Third National Health and Nutrition Examination Survey, 1988–1994. JAMA. 1998;280:356–362. Crossref, MedlineGoogle Scholar
25. Lipton RB, Liao Y, Cao G, Cooper RS, McGee D. Determinants of incident non-insulin-dependent diabetes mellitus among Blacks and Whites in a national sample. Am J Epidemiol. 1993;138:826–839. Crossref, MedlineGoogle Scholar
26. O’Brien TR, Flanders WD, Decoufle P, Boyle CA, DeStefano F, Teutsch S. Are racial differences in the prevalence of diabetes in adults explained by differences in obesity? JAMA. 1989;262:1485–1488. Crossref, MedlineGoogle Scholar
27. Bonham GS, Brock DB. The relationship of diabetes with race, sex, and obesity. Am J Clin Nutr. 1985; 41:776–783. Crossref, MedlineGoogle Scholar
28. Signorello LB, Hargreaves MK, Steinwandel MD, et al. The Southern Community Cohort Study: establishing a cohort to investigate health disparities. J Natl Med Assoc. 2005;97:972–979. MedlineGoogle Scholar
29. Hargreaves M, Arnold C, Blot WJ. Community health centers: their role in the treatment of minorities and in health disparities research. In: Satcher D, Pamies R, eds. Multicultural Medicine and Health Disparities. New York, NY: McGraw-Hill International Book Co; 2006:485–494. Google Scholar
30. Nam CB, Boyd M. Occupational status in 2000: over a century of census-based measurement. Popul Res Policy Rev. 2004;23:327–358. CrossrefGoogle Scholar
31. Mudaliar S, Edelman SV. Insulin therapy in type 2 diabetes. Endocrinol Metab Clin North Am. 2001;30: 935–982. Crossref, MedlineGoogle Scholar
32. Flegal KM, Carroll MD, Ogden CL, Johnson CL. Prevalence and trends in obesity among US adults, 1999–2000. JAMA. 2002;288:1723–1727. Crossref, MedlineGoogle Scholar
33. Steenland K, Henley J, Thun M. All-cause and cause-specific death rates by educational status for two million people in two American Cancer Society cohorts, 1959–1996. Am J Epidemiol. 2002;156:11–21. Crossref, MedlineGoogle Scholar
34. Robbins JM, Vaccarino V, Zhang H, Kasl SV. Socioeconomic status and type 2 diabetes in African American and non-Hispanic white women and men: evidence from the Third National Health and Nutrition Examination Survey. Am J Public Health. 2001;91:76–83. LinkGoogle Scholar
35. Cowie CC, Eberhardt MS. Sociodemographic characteristics of persons with diabetes. In: Diabetes in America. 2nd ed. Bethesda, Md: National Institutes of Health; 1995. NIH publication 95–468. Google Scholar
36. Leonetti DL, Tsunehara CH, Wahl PW, Fujimoto WY. Educational attainment and the risk of non-insulin-dependent diabetes or coronary heart disease in Japanese-American men. Ethn Dis. 1992;2:326–336. MedlineGoogle Scholar
37. Kumari M, Head J, Marmot M. Prospective study of social and other risk factors for incidence of type 2 diabetes in the Whitehall II Study. Arch Intern Med. 2004;164:1873–1880. Crossref, MedlineGoogle Scholar
38. Agardh EE, Ahlbom A, Andersson T, et al. Explanations of socioeconomic differences in excess risk of type 2 diabetes in Swedish men and women. Diabetes Care. 2004;27:716–721. Crossref, MedlineGoogle Scholar
39. Connelly V, Unwin N, Sherriff P, Bilous R, Kelly W. Diabetes prevalence and socioeconomic status: a population based study showing increased prevalence of type 2 diabetes mellitus in deprived areas. J Epidemiol Community Health. 2000;54:173–177. Crossref, MedlineGoogle Scholar
40. Evans JM, Newton RW, Ruta DA, MacDonald TM, Morris AD. Socio-economic status, obesity and prevalence of type 1 and type 2 diabetes mellitus. Diabet Med. 2000;17:478–480. Crossref, MedlineGoogle Scholar
41. Hales CN, Barker DJ. The thrifty phenotype hypothesis. Br Med Bull. 2001;60:5–20. Crossref, MedlineGoogle Scholar
42. Rich-Edwards JW, Colditz GA, Stampfer MJ, et al. Birthweight and the risk for type 2 diabetes mellitus in adult women. Ann Intern Med. 1999;130:278–284. Crossref, MedlineGoogle Scholar
43. Hales CN, Barker DJ, Clark PM, et al. Fetal and infant growth and impaired glucose tolerance at age 64. BMJ. 1991;303:1019–1022. Crossref, MedlineGoogle Scholar
44. Mooy JM, de Vries H, Grootenhuis PA, Bouter LM, Heine RJ. Major stressful life events in relation to prevalence of undetected type 2 diabetes: the Hoorn Study. Diabetes Care. 2000;23:197–201. Crossref, MedlineGoogle Scholar
45. Bjorntorp P, Holm G, Rosmond R. Hypothalamic arousal, insulin resistance and type 2 diabetes mellitus. Diabet Med. 1999;16:373–383. Crossref, MedlineGoogle Scholar
46. Musselman DL, Betan E, Larsen H, Phillips LS. Relationship of depression to diabetes types 1 and 2: epidemiology, biology, and treatment. Biol Psychiatry. 2003;54:317–329. Crossref, MedlineGoogle Scholar
47. Baltrus PT, Lynch JW, Everson-Rose S, Raghunathan TE, Kaplan GA. Race/ethnicity, life-course socioeconomic position, and body weight trajectories over 34 years: the Alameda County Study. Am J Public Health. 2005;95:1595–1601. LinkGoogle Scholar
48. Dowling HJ, Pi-Sunyer FX. Race-dependent health risks of upper body obesity. Diabetes. 1993;42: 537–543. Crossref, MedlineGoogle Scholar
49. Dowling HJ, Fried SK, Pi-Sunyer FX. Insulin resistance in adipocytes of obese women: effects of body fat distribution and race. Metabolism. 1995;44:987–995. Crossref, MedlineGoogle Scholar
50. Karter AJ, Mayer Davis EF, Selby JV, et al. Insulin sensitivity and abdominal obesity in African-American, Hispanic, and non-Hispanic White men and women: the Insulin Resistance and Atherosclerosis Study. Diabetes. 1996;45:1547–1555. Crossref, MedlineGoogle Scholar
51. US Department of Health and Human Services. Prevalence data—diabetes 2004. Available at: http://apps.nccd.cdc.gov/brfss/list.asp&?cat=DB&yr=2004&qkey=1363&state=All. Accessed February 16, 2006. Google Scholar
52. O’Rahilly S, Barroso I, Wareham NJ. Genetic factors in type 2 diabetes: the end of the beginning? Science. 2005;307:370–372. Crossref, MedlineGoogle Scholar
53. Neel JV. Diabetes mellitus: a thrifty genotype rendered detrimental by progress? Am J Hum Genet. 1962; 14:353–362. MedlineGoogle Scholar
54. Paradies YC, Montoya MJ, Fullerton SM. Racialized genetics and the study of complex diseases: the thrifty genotype revisited. Perspect Biol Med. 2007;50: 203–227. Crossref, MedlineGoogle Scholar
55. Sale MM, Freedman BI, Langefeld CD, et al. A genome-wide scan for type 2 diabetes in African-American families reveals evidence for a locus on chromosome 6q. Diabetes. 2004;53:830–837. Crossref, MedlineGoogle Scholar
56. Ehm MG, Karnoub MC, Sakul H, et al. Genetics of NIDDM. Am J Hum Genet. 2000;66:1871–1881. Crossref, MedlineGoogle Scholar
57. Wang H, Zhang H, Jia Y, et al. Adiponectin receptor 1 gene (ADIPOR1) as a candidate for type 2 diabetes and insulin resistance. Diabetes. 2004;53: 2132–2136. Crossref, MedlineGoogle Scholar
58. Karim MA, Wang X, Hale TC, Elbein SC. Insulin promoter factor 1 variation is associated with type 2 diabetes in African Americans. BMC Med Genet. 2005; 6:37. Crossref, MedlineGoogle Scholar
59. Gibbons GH. Physiology, genetics, and cardiovascular disease: focus on African Americans. J Clin Hypertens. 2004;6:11–18. CrossrefGoogle Scholar
60. Banerji MA. Diabetes in African Americans: unique pathophysiologic features. Curr Diabetes Rep. 2004;4:219–223. Crossref, MedlineGoogle Scholar
61. Buthelezi EP, van der Merwe MT, Lonnroth PN, Gray IP, Crowther NJ. Ethnic differences in the responsiveness of adipocyte lipolytic activity to insulin. Obes Res. 2000;8:171–178. Crossref, MedlineGoogle Scholar
62. Haffner SM, D’Agostino R, Saad MF, et al. Increased insulin resistance and insulin secretion in non-diabetic African Americans and Hispanics compared with non-Hispanic Whites: the Insulin Resistance Atherosclerosis Study. Diabetes. 1996;45:742–748. Crossref, MedlineGoogle Scholar


No related items




Lisa B. Signorello, ScD, David G. Schlundt, PhD, Sarah S. Cohen, MS, Mark D. Steinwandel, BBA, Maciej S. Buchowski, PhD, Joseph K. McLaughlin, PhD, Margaret K. Hargreaves, PhD, and William J. Blot, PhDLisa B. Signorello, Joseph K. McLaughlin, and William J. Blot are with the International Epidemiology Institute, Rockville, Md, and the Department of Medicine, Vanderbilt University, Nashville, Tenn, and the Vanderbilt-Ingram Cancer Center, Nashville. David G. Schlundt is with the Department of Psychology, Vanderbilt University, Nashville. Sarah S. Cohen and Mark D. Steinwandel are with the International Epidemiology Institute, Rockville, Md. Maciej S. Buchowski is with the Department of Medicine, Vanderbilt University, Nashville, and the Department of Family and Community Medicine, Meharry Medical College, Nashville. Margaret K. Hargreaves is with the Department of Internal Medicine, Meharry Medical College, Nashville. “Comparing Diabetes Prevalence Between African Americans and Whites of Similar Socioeconomic Status”, American Journal of Public Health 97, no. 12 (December 1, 2007): pp. 2260-2267.


PMID: 17971557