OBJECTIVE—Among individuals with diabetes, a comparison of HbA1c (A1C) levels between African Americans and non-Hispanic whites was evaluated. Data sources included PubMed, Web of Science, the Cumulative Index to Nursing and Allied Health, the Cochrane Library, the Combined Health Information Database, and the Education Resources Information Center.
RESEARCH DESIGN AND METHODS—We executed a search for articles published between 1993 and 2005. Data on sample size, age, sex, A1C, geographical location, and study design were extracted. Cross-sectional data and baseline data from clinical trials and cohort studies for African Americans and non-Hispanic whites with diabetes were included. Diabetic subjects aged <18 years and those with pre-diabetes or gestational diabetes were excluded. We conducted a meta-analysis to estimate the difference in the mean values of A1C for African Americans and non-Hispanic whites.
RESULTS—A total of 391 studies were reviewed, of which 78 contained A1C data. Eleven had data on A1C for African Americans and non-Hispanic whites and met selection criteria. A meta-analysis revealed the standard effect to be 0.31 (95% CI 0.39–0.25). This standard effect correlates to an A1C difference between groups of ∼0.65%, indicating a higher A1C across studies for African Americans. Grouping studies by study type (cross-sectional or cohort), method of data collection for A1C (chart review or blood draw), and insurance status (managed care or nonmanaged care) showed similar results.
CONCLUSIONS—The higher A1C observed in this meta-analysis among African Americans compared with non-Hispanic whites may contribute to disparity in diabetes morbidity and mortality in this population.
Ethnic minorities in the U.S. are disproportionately affected by most diabetes-related complications, including diabetic retinopathy, lower-extremity amputation, and end-stage renal disease (1–4). Although diabetes has a major adverse impact on life-years and quality-adjusted life-years in all U.S. subpopulations, the impact is even greater among minority individuals, including African Americans and Hispanics (5). Specifically, many diabetes complications are experienced at a higher rate in African Americans than in non-Hispanic whites (6). For example, the prevalence and severity of diabetic retinopathy is 46% higher in African Americans than in non-Hispanic whites (2), and African Americans with diabetes are more likely to develop kidney disease and kidney failure requiring dialysis than non-Hispanic whites (7,8). Although racial disparities in complications are somewhat less marked in populations receiving uniform access to care, disparities in HbA1c (A1C) level among African Americans, Asians, and Latinos have been shown compared with non-Hispanic whites (9). Improvements in glycemic control have been shown to prevent microvascular complications, and large trials have demonstrated the need for glucose control among patients with diabetes (10,11). Literature has suggested that A1C control may be poorer among minority populations than among nonminority populations (6). A number of factors may drive differences in A1C control: biological, socioeconomic, and quality-of-care factors have been suggested (9,12). Lack of access to health care may also affect diabetes care among minority individuals (13). African Americans report lower rates of health insurance than non-Hispanic whites. This barrier to care can lead to delayed diagnosis and increased years of exposure to untreated diabetes (14). Other studies have found that African Americans are less likely to have prescription drug coverage, which limits their ability to afford medications once they have been diagnosed (15). Differences in the frequency of obtaining common preventive care measures related to diabetes also have been implicated in the quality-of-care disparity between African Americans and non-Hispanic whites (16). Of special concern is the suggestion that minority populations receive less optimal diabetes care even after they access the health care system (17,18).
A recent review of studies reported overall poorer glycemic control in U.S. adults with diabetes as measured by A1C (19). The consistency of a higher A1C across comparative studies of African Americans and non-Hispanic whites with diabetes has not been examined. To get a better representation of whether differences in A1C levels exist between African Americans and non-Hispanic whites with diabetes, we reviewed the literature (1993–2005) for which comparisons between populations were made and conducted a meta-analysis using standardized statistical methods. This time period was selected because the A1C measurement has become more standardized over the past 10 years.
RESEARCH DESIGN AND METHODS
Identification of studies
We conducted a Medline search in PubMed, the Cumulative Index to Nursing and Allied Health, the Combined Health Information Database, the Cochrane Library, and the Web of Science using Medical Subject Heading (MeSH) and free text forms for the period 1993 through 2005. We used the search terms, “Diabetes Mellitus”[MeSH] AND “U.S.”[MeSH] AND “Hemoglobin A, Glycosylated”[MeSH] OR glycemia OR glycemic control OR A1C with Limits: All Adult: 19+ years and English language. We initially retrieved 1,596 abstracts. The search was further limited to articles containing race or ethnicity (305 total articles). The literature accepted had to include patients with diabetes and contain comparative data for both African Americans and non-Hispanic whites. We rejected abstracts that included patients with gestational diabetes or pre-diabetes. We collected additional references from bibliographies of reviews, original research articles, and other articles of interest.
To conduct as broad an analysis as possible, we included any study design that was statistically valid. For retrospective chart review studies, we evaluated the most recent A1C data. We included observational data on A1C control. For four studies (20–23) in which sample size and A1C summary statistics were not provided in the original publication, we were able to retrieve this information from the authors. We only accepted author-reported data if we received written validation that the information was obtained from the original computerized dataset. If the SD of the A1C was not reported or otherwise obtained, we did not include the study in the meta-analysis. We did not exclude studies that failed to categorize the type of diabetes, as the diabetic population primarily consists of people with type 2 diabetes; this is especially true among African Americans.
Data extraction
Two investigators (J.K.K. and R.A.B.) independently reviewed each study for the following data: 1) sample size, 2) mean (±SD) participant age, 3) number of male and female subjects, 4) mean (±SD) A1C, 5) geographic location of the research, and 6) study design.
We initially retrieved 1,581 abstracts of English-language studies conducted in the U.S. from PubMed and found 15 additional abstracts in other data sources, resulting in a total of 1,596 abstracts. We rejected 1,151 abstracts because they did not meet the inclusion criteria. Of the 445 articles initially considered, review of the full citation resulted in 55 being excluded for not meeting the inclusion criteria. Of the 391 articles that were ultimately evaluated, 78 studies contained glycemia data for minority populations. Eighteen of the studies contained glycemia data for African Americans and non-Hispanic whites. If mean (±SD) glycemic information was not available, the authors were contacted to provide these data. Because of lab variability, we only analyzed A1C values, resulting in elimination of three studies that reported glycosylated hemoglobin (24–26). Four of the remaining studies reported comparative A1C data for African Americans but were not included for a variety of reasons: not providing SD for A1C (27), using pooled A1C data from multiple studies (28), and not defining the breakdown of an ethnic group (29); an additional intervention trial was excluded due to the possibility of patient selection bias in participant recruitment (30). Eleven studies that reported A1C were represented in our final analysis, including 3 prospective cohort studies (9,20,31) and 8 cross-sectional studies (12,21–23,32–35). Only a minor portion of patients in the studies reviewed had type 1 diabetes, with 15 reported in one study (9), and another study did not discuss any breakdown of patients by diabetes type (21).
Statistical analysis
A primary meta-analysis was conducted on the 11 studies (9,12,20–23,31–35) that met the inclusion criteria (Table 1). To judge the sensitivity of the results and justify the conclusions of the primary analysis, individual meta-analyses were conducted on subsets including study type (cohort and cross-sectional studies), managed care or nonmanaged care, and method of data collection for A1C (chart review or blood draw). Characteristics of the 11 studies are summarized in the table. An effect size (mean difference in A1C divided by the pooled SD) was calculated for the difference in A1C measurements between African Americans and non-Hispanic whites. For each study, a 95% CI for the effect size was also calculated.
Homogeneity of the effect sizes across studies was first assessed using a χ2 test to determine whether a fixed- or random-effects approach should be implemented. A fixed-effects approach considers the set of studies as homogenous and representing all potential studies of interest, whereas the random-effects approach treats the studies as heterogeneous and considers them to be a sample from a population of comparable studies. With the exception of the managed care subset, the homogeneity test results led to the use of a random-effects model to pool effect-size estimates and compute a treatment effect. All tests of effect were two sided, and P < 0.05 was considered statistically significant. Results are reported for the entire dataset and then stratified by sex, study design, and data collection type for A1C.
RESULTS
Variability existed in the age of participants across studies, but most studies included patients aged >50 years. The sample sizes also varied widely across studies; that variability, however, was accommodated through the analysis for heterogeneity/homogeneity. Of the 11 studies in our meta-analysis, significant differences between African Americans and non-Hispanic whites were originally reported in 5 studies (12,31–33,35), no significant differences were reported in 1 study (34), and 1 study did not test for significant differences between ethnic groups (9). For four of the studies included in this meta-analysis, we contacted the authors for A1C data and tests for differences between African Americans and non-Hispanic whites that were not provided (20–23).
Through the meta-analysis, 10 of 11 studies indicated significantly higher A1C levels in African Americans than in non-Hispanic whites (Fig. 1). The summary effect size was −0.32 (P < 0.0001), which indicated that African Americans had A1C values that were, on average, 0.32 SD above those of non-Hispanic whites. This corresponds to an estimated 0.65% A1C difference. To evaluate the potential bias in the results due to sex, a meta-analysis conducted to compare the A1C levels between men and women, independent of race, had an estimate effect of 0.0024 (P = 0.9882).
The effects were similar regardless of study design. The cross-sectional studies had an estimated effect of 0.30 (P < 0.0001), and the prospective cohort studies had an estimated effect of −0.42 (P < 0.0010). Similarly, when studies were divided into two groups according to data collection type, the effects were consistent with the results from the summary analysis. Studies in which the A1C values were collected from chart reviews had an estimated effect of −0.31 (P < 0.0001), and studies in which the values were obtained from baseline blood draws had an estimated effect of −0.34 (P = 0.0041). When studies were divided into managed care or nonmanaged care, the effects were again consistent with the primary results. Studies in which the patients had managed care insurance had an estimated effect of −0.27 (P < 0.0001), and studies in which the patients did not have managed care insurance had an estimated effect of −0.38 (P < 0.0001).
A meta-analysis conducted for the comparison between the two ethnic groups among men had an estimated effect of −0.29 (P = 0.0026). Individually, four of the six studies providing sex-specific information would not have found a strong significant difference in men. However, the combination of the six studies through meta-analytic techniques demonstrates the significant difference between African Americans and non-Hispanic whites (Fig. 2). Likewise, the meta-analysis conducted for women showed a significant difference (P < 0.0001) between the ethnic groups with an estimated effect of −0.36 (Fig. 3).
CONCLUSIONS
This meta-analysis shows that African Americans have elevated A1C compared with non-Hispanic whites. The effect size estimate for this difference translates into an ∼0.65% difference (pooled SD for A1C across studies of 2.1) in A1C levels between African Americans and non-Hispanic whites. Our findings confirm those of Trivedi et al. (36), who, in a recent analysis of 1.8 million individual-level observations from 183 health plans, reported disparities between African Americans and non-Hispanic whites in glucose control.
Poor glycemic control is an important risk factor for diabetes complications, particularly microvascular complications (10). The difference between racial groups found in this meta-analysis represents a potentially significant increase in the micro- and macrovascular complications associated with diabetes. The U.K. Prospective Diabetes Study found a 21% reduction in any outcome for every 1% reduction in A1C (37). Thus, the difference found in this meta-analysis represents an ∼15% reduction in risk for vascular complications among non-Hispanic whites. However, this does not explain the magnitude of difference in diabetes complications between African Americans and non-Hispanic whites. Although A1C control among African Americans likely contributes to their elevated risk of complications, it accounts for only a portion of their excess risk. The need to control A1C, blood pressure, and cholesterol risk factors among patients with diabetes has been previously reviewed (19).
The consistency of the findings is notable: 10 of 11 studies showed significance such that the range of effect sizes did not include zero. Furthermore, all analyses provided the same general results and therefore strongly support the conclusion that there are significant differences in A1C between African Americans and non-Hispanic whites. In addition, two of the largest studies indicated similar point estimates for the standard effect size (Fig. 1) (20,22).
The strengths of this analysis are its inclusion of a variety of study designs, the ability to examine A1C differences by sex and study type, and the use of previously unpublished data (20–23). The results of this meta-analysis, however, depend in part on the accuracy and reliability of the A1C measurement across studies. Variability in tests of glycemia has been an issue in the field, and standardization of A1C was not widespread until the last decade (38). In more recent years, the International Federation of Clinical Chemistry and the National Glycohemoglobin Standardization Program have been working toward a certification of diagnostic equipment as being traceable to the Diabetes Control and Complications Trial reference method (38,39). In addition, persistence of hereditary fetal hemoglobin may cause a spurious elevation of A1C (40). However, type 1 diabetes and insulin treatment seem to be more associated with fetal hemoglobin production (41).
Another limitation of the analysis is publication bias; however, we performed numerous searches on this topic and contacted multiple investigators to retrieve the data for A1C means and SDs. The heterogeneity of the studies adds to the limitations of the analysis. Nevertheless, results are likely generalizable to African-American and non-Hispanic white adult patients with type 2 diabetes because the data included a broad range of patient ages, geographic settings, and study types. An additional limitation of this meta-analysis is that despite the comprehensive review of abstracts, the potential for omission exists if an abstract initially reviewed through our search process did not specifically address racial disparities.
The cause of the disparity in glucose control is multifactorial. Previous studies that have examined differences by ethnicity have found significant variation in rates of medical insurance coverage (42). Other studies of patients with diabetes have found that most have some form of medical insurance and that African Americans and non-Hispanic whites have similar rates of coverage (43). We were unable to obtain data on insurance status for eight studies included in the meta-analysis; however, two of the largest studies were conducted in managed care settings where all subjects were insured (9,23). In these studies, all of the patients had access to health care, but, even then, disparity in A1C levels was seen for minority groups.
Differences in the intensity of treatment may also account for a portion of the variation in A1C control. Determining bias is difficult, and we were unable to examine it in this study. Patient preferences for a type of treatment or comprehension of the disease and its treatment may account for some of the differences. Studies examining acceptance of cardiac procedures and renal transplants have found variation by ethnicity and race; however, differences in preference probably contribute only to a small portion of the variation in care (44,45).
This is the first meta-analysis of racial and ethnic differences in glycemic control among patients with diabetes. Although the studies included used a variety of designs, a consistency in the degree of disparity of glycemic control was found regardless of study type. Multiple separate meta-analyses were conducted across study types and sex with the same outcome. African Americans with diabetes have an ∼0.65% higher A1C than non-Hispanic whites; this difference may explain a portion of the excess microvascular complications in this population nationally. We need to understand more fully why this disparity in glycemic control exists and act to eliminate the factors that are modifiable (e.g., improved access to and delivery of care). Further research is needed to elucidate why African Americans with diabetes have poorer glycemic control than non-Hispanic whites and to identify interventions that might prevent or reduce the disparities.
Standard effect size summary for the difference between A1C in African Americans and non-Hispanic whites. *Cross-sectional study; †data obtained from chart review; ‡A1C sample from study-initiated blood draw; §prospective cohort study or clinical trial; ‖managed care; ¶nonmanged care.
Standard effect size summary for the difference between A1C in African Americans and non-Hispanic whites. *Cross-sectional study; †data obtained from chart review; ‡A1C sample from study-initiated blood draw; §prospective cohort study or clinical trial; ‖managed care; ¶nonmanged care.
Standard effect size for the difference between A1C in African-American and non-Hispanic white men.
Standard effect size for the difference between A1C in African-American and non-Hispanic white men.
Standard effect size for the difference between A1C in African-American and non-Hispanic white women.
Standard effect size for the difference between A1C in African-American and non-Hispanic white women.
Characteristics of 11 studies among African Americans and non-Hispanic whites
| Study group (ref. no.) | Non-Hispanic whites | African Americans | Site | Study design | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| n | Age (years) | A1C (%) | n | Age (years) | A1C (%) | |||||||
| Bell et al. (21)* | 105 | 55 ± 15 | 7.9 ± 1.7 | 103 | 54 ± 14 | 9.1 ± 2.6 | Medical record data from outpatient clinics across NC | Retrospective chart review | ||||
| Bonds et al. (20)* | 144 | 62 ± 8 | 7.4 ± 1.8 | 102 | 61 ± 7 | 8.0 ± 2.0 | IRAS cohort in CO, TX, and two sites in CA | Prospective cohort study | ||||
| Brown et al. (23) | 2,787 | 61 ± 13 | 7.8 ± 1.7 | 965 | 59 ± 13 | 8.2 ± 2.1 | TRIAD study of diabetes in managed care, nationwide | Prospective cohort study with cross-sectional analysis | ||||
| Cook et al. (32) | 328 | 54 ± 11 | 8.6 ± 1.8 | 4,014 | 53 ± 13 | 9.3 ± 2.5 | Registry data from outpatient diabetes clinic in GA | Retrospective chart review | ||||
| de Rekeneire et al. (31) | 194 | 74 ± 3 | 7.4 ± 1.2 | 274 | 74 ± 3 | 8.4 ± 1.7 | Health ABC study in PA and TN | Prospective cohort study | ||||
| Gary et al. (12) | 374 | ≥30 | 8.5 ± 2.2 | 732 | ≥30 | 9.1 ± 2.9 | Medical claims from university-affiliated managed care organization on east coast | Cross-sectional study of average A1C | ||||
| Harris et al. (22)* | 486 | ≥20 | 7.6 ± 1.9 | 335 | ≥20 | 8.1 ± 2.2 | NHANES III data | Cross-sectional study | ||||
| Karter et al. (9) | 40,025 | 61 ± 13 | 8.4 ± 1.8 | 8,496 | 59 ± 13 | 8.9 ± 1.8 | Northern California Kaiser Permanente patients | Prospective cohort study | ||||
| Sharma and Pavlik (33)* | 111 | 63 ± 13 | 9.4 ± 2.9 | 209 | 64 ± 33 | 11.4 ± 2.6 | Community clinic patients in TX | Retrospective chart review | ||||
| Summerson et al. (34) | 168 | 59 ± 17 | 7.4 ± 1.8 | 140 | 58 ± 17 | 7.6 ± 2.2 | Academic family practice and community health center in NC | Cross-sectional study | ||||
| Weatherspoon et al. (35) | 279 | 60 ± 13 | 7.3 ± 1.7 | 249 | 56 ± 14 | 7.7 ± 2.1 | Public clinics accessible to low-income patients in central FL | Retrospective chart review | ||||
| Study group (ref. no.) | Non-Hispanic whites | African Americans | Site | Study design | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| n | Age (years) | A1C (%) | n | Age (years) | A1C (%) | |||||||
| Bell et al. (21)* | 105 | 55 ± 15 | 7.9 ± 1.7 | 103 | 54 ± 14 | 9.1 ± 2.6 | Medical record data from outpatient clinics across NC | Retrospective chart review | ||||
| Bonds et al. (20)* | 144 | 62 ± 8 | 7.4 ± 1.8 | 102 | 61 ± 7 | 8.0 ± 2.0 | IRAS cohort in CO, TX, and two sites in CA | Prospective cohort study | ||||
| Brown et al. (23) | 2,787 | 61 ± 13 | 7.8 ± 1.7 | 965 | 59 ± 13 | 8.2 ± 2.1 | TRIAD study of diabetes in managed care, nationwide | Prospective cohort study with cross-sectional analysis | ||||
| Cook et al. (32) | 328 | 54 ± 11 | 8.6 ± 1.8 | 4,014 | 53 ± 13 | 9.3 ± 2.5 | Registry data from outpatient diabetes clinic in GA | Retrospective chart review | ||||
| de Rekeneire et al. (31) | 194 | 74 ± 3 | 7.4 ± 1.2 | 274 | 74 ± 3 | 8.4 ± 1.7 | Health ABC study in PA and TN | Prospective cohort study | ||||
| Gary et al. (12) | 374 | ≥30 | 8.5 ± 2.2 | 732 | ≥30 | 9.1 ± 2.9 | Medical claims from university-affiliated managed care organization on east coast | Cross-sectional study of average A1C | ||||
| Harris et al. (22)* | 486 | ≥20 | 7.6 ± 1.9 | 335 | ≥20 | 8.1 ± 2.2 | NHANES III data | Cross-sectional study | ||||
| Karter et al. (9) | 40,025 | 61 ± 13 | 8.4 ± 1.8 | 8,496 | 59 ± 13 | 8.9 ± 1.8 | Northern California Kaiser Permanente patients | Prospective cohort study | ||||
| Sharma and Pavlik (33)* | 111 | 63 ± 13 | 9.4 ± 2.9 | 209 | 64 ± 33 | 11.4 ± 2.6 | Community clinic patients in TX | Retrospective chart review | ||||
| Summerson et al. (34) | 168 | 59 ± 17 | 7.4 ± 1.8 | 140 | 58 ± 17 | 7.6 ± 2.2 | Academic family practice and community health center in NC | Cross-sectional study | ||||
| Weatherspoon et al. (35) | 279 | 60 ± 13 | 7.3 ± 1.7 | 249 | 56 ± 14 | 7.7 ± 2.1 | Public clinics accessible to low-income patients in central FL | Retrospective chart review | ||||
Data are means ± SD.
A1C mean (±SD) received following communication with author. ABC, Aging and Body Composition; IRAS, Insulin Resistance Atherosclerosis Study; NHANES III, Third National Health and Nutrition Examination Survey; TRIAD, Translating Research Into Action for Diabetes.
Article Information
The authors thank Carol Hildebrandt for her expertise in assembling the references and editing the manuscript.
References
This publication was made possible through a cooperative agreement between the Centers for Disease Control and Prevention (CDC) and the Association of Teachers of Preventive Medicine (ATPM) (award no. TS-0778). Its contents are the responsibility of the authors and do not necessarily reflect the official views of CDC or ATPM.
A table elsewhere in this issue shows conventional and Système International (SI) units and conversion factors for many substances.
