OBJECTIVE

Individuals with diabetes are at higher risk for depression than the general population. Although depression can be treated with antidepressant medications, patients with diabetes and comorbid depression often go untreated. The goal of this study was to examine racial disparities in the treatment of depression with antidepressant medication in the southeastern U.S.

RESEARCH DESIGN AND METHODS

Cross-sectional data were collected at baseline from 69,068 participants (71% African American, 60% female, and 82% with incomes <$25,000) recruited from community health centers and enrolled in the Southern Cohort Community Study (SCCS). The SCCS is a prospective epidemiological cohort study designed to explore causes of health disparities in adults aged 40–79 years. Binary logistic regression was used to identify factors associated with antidepressant use among those with diabetes (n = 14,279).

RESULTS

Individuals with diagnosed diabetes (14,279) were classified with no depressive symptoms (54.7%), or with mild (24.2%), moderate (12.8%), or severe depressive symptoms (8.3%). After controlling for sex, age, insurance, income, education, BMI, smoking status, alcohol consumption, and level of depression, African Americans with diabetes were much less likely to report taking antidepressant medication than whites (adjusted odds ratio 0.32 [95% CI 0.29–0.35], P < 0.0001).

CONCLUSIONS

Antidepressant use is much less common among African Americans than among whites with diabetes. Reasons for racial disparities in treatment of depressive symptoms are unclear but may include a combination of differential diagnosis and treatment by health professionals as well as cultural differences in seeking help for emotional distress.

Depression is one of the most common psychiatric disorders. Almost one of every five people in the U.S. will experience a major depressive episode during their lifetime. Individuals with diabetes are as much as twice as likely as those without diabetes to experience depression (1). Despite its relevance to the course of diabetes, it is estimated that depression is recognized and treated in <25–50% of individuals with diabetes who are depressed (2). Women are more likely to be recognized (1), as are those with panic attacks, frequent primary care visits, and poorer health (3). Less is known about the identification of depression in specific racial and ethnic groups with diabetes. Variation in the identification of depression might explain noted racial/ethnic disparities in diabetes outcomes (i.e., why, on average, African Americans have worse control of their disease, and experience more diabetes-related complications and mortality than whites do).

In diabetes, comorbid depression has been associated with difficulty performing diabetes self-care behaviors (4), worse glycemic control (5,7), and adverse health outcomes (8). In large cohort studies, comorbid depression was associated with a 36–38% increased risk in all-cause mortality compared with that in individuals with diabetes alone (9); in a separate cohort study, it was associated with a 1.3-fold increase risk in all-cause mortality (10). The human toll of diabetes and depression translates into a 50% increase in health care costs compared to the cost of treating diabetes alone (8).

The successful treatment of depression may reduce health care costs (11) by improving glycemic control, overall functioning, and the quality of life of individuals with diabetes (2). Studies show that psychotherapeutic and pharmacological treatment is effective for patients with diabetes and comorbid depression (12). Such treatment has been associated with a reduction in depressive symptoms (6) and improvements in glycemic control (5,7) and may ultimately reduce the risk of adverse health outcomes associated with depression and diabetes.

In an effort to reduce and eventually eliminate well-documented racial/ethnic disparities in diabetes outcomes, it is important to know whether certain racial/ethnic groups with diabetes and comorbid depression are differentially identified and/or treated for depression. In this study, we investigated racial variation in self-reported 1) physician-diagnosed diabetes, 2) the presence and severity of depressive symptoms, and 3) the treatment for depression in a large cohort of low-income adults with diabetes.

The Southern Community Cohort Study (SCCS) is a prospective cohort study designed to identify causes of health disparities between African Americans and whites in the incidence and mortality rates of several cancers and other chronic diseases (13). The study was approved by the institutional review boards at Vanderbilt University and Meharry Medical College. Participants were enrolled in person at Community Health Centers (CHCs) in the states of Alabama, Arkansas, Florida, Georgia, Kentucky, Louisiana, Mississippi, North Carolina, South Carolina, Tennessee, Virginia, and West Virginia. Participants were enrolled by approaching people entering the CHCs (patients and individuals accompanying patients) and screening them for eligibility and interest in study participation. Participants were required to be between the ages of 40 and 79, to speak English, and to not have been treated for cancer (excluding nonmelanoma skin cancer) within the past year. After providing informed consent, trained interviewers collected baseline data using a computer-assisted personal interview. The baseline interview contained questions on demographic information, medical history, family history, environmental and occupational exposures, diet, physical activity, and psychosocial variables.

Measures

Depression.

Depressive symptoms were assessed using a 10-item version of the Centers for Epidemiological Studies Depression (CES-D) scale, a valid and reliable measure of the presence and severity of depressive symptoms (14). Cutoffs of 10, 15, and 20 define mild, moderate, and severe levels of depressive symptoms (14). The CES-D has demonstrated measurement equivalency in samples with differential characteristics including race and sex (15).

Antidepressant use.

Participants were asked if they were taking any antidepressant medication or antianxiety medication and then asked for the name(s) of all medications of this type being taken.

Selection of subjects for analysis

The initial dataset consisted of 69,068 individuals. Individuals with missing values for BMI (n = 1,575), education (n = 45), income (n = 736), smoking status (n = 100), marital status (n = 8), insurance (n = 336), and alcohol consumption (n = 310) and those who were aged <40 years (n = 2), had a depressive symptoms score (n = 99), did not answer the question about having diabetes or hypertension (n = 53), or did not answer the question about using antidepressants (n = 37) were excluded, resulting in 65,767 individuals selected for analysis. Participants with diabetes were identified using the question, “Has a doctor ever told you that you have diabetes?” Participants who answered “yes” to this question were selected for analysis, consisting of 21.7% of the original sample or 14,279 individuals. In two validation studies, one involving a review of medical records and the other assessing A1C percentages, the self-reported diagnoses were confirmed for 98% of those reporting diabetes (16).

Statistical analysis

Data were analyzed using means, frequencies, and cross-tabulations to calculate descriptive statistics. The main study hypothesis was tested using binary logistic regression analysis. Antidepressant use was coded as absent = 0 and present = 1 and served as the dependent variable. The model was built in three steps. In step 1, sex, age, insurance status, income, education, BMI, smoking status, and alcohol consumption were entered. In step 2, level of depressive symptoms (no depressive symptoms, mild depressive symptoms, moderate depressive symptoms, or severe depressive symptoms) was entered. In the final step, race was coded into three categories (whites, African Americans, and others/mixed race) and was entered into the regression model, with whites used as the reference group for calculating odds ratios (ORs). In the same model, two-way interactions between race and the other predictor variables were also examined. This analytic strategy tested whether rates of antidepressant use differed for African Americans and others/mixed race relative to whites after controlling for sex, age, insurance status, income, education, BMI, smoking status, alcohol consumption, and level of depressive symptoms. All analyses were done using SPSS (version 17.0; SPSS, Chicago, IL).

Description of the sample

The mean ± SD age of the sample with diabetes was 54.5 ± 9.0, with African Americans being slightly but significantly younger (P < 0.0001) than whites (54.2 vs. 55.4 years). Table 1 provides a description of the 14,279 participants who reported a diabetes diagnosis stratified by race. Level of income reported in this sample was very low, with ≥61% of each race/ethnicity subgroup reporting <$15,000 per year in family income. There were significant racial differences in the crude (i.e., unadjusted) demographic variables. Specifically, insurance status, income, education, BMI, smoking status, alcohol consumption, depressive symptoms, and antidepressant use differed by race. African Americans were more likely to report having health insurance, incomes of <$15,000, dropping out of high school, lower BMI levels, never having smoked, and consuming more alcohol on a daily basis than whites or others/mixed race. African Americans were more likely to have no depressive symptoms, whereas whites and others/mixed race were more likely to have moderate and severe depressive symptoms. Finally, among participants reporting diabetes, 17.4% of African Americans reported using antidepressant medications compared with 39.0% of whites and 31.3% of individuals of other/mixed race.

Table 1

Characteristics of 14,279 participants in the SCCS reporting a diagnosis of diabetes

Variable and categoriesWhite only
African American only
Others/mixed race
Frequency%Frequency%Frequency%
n 3,552 10,164 563 
Sex (P < 0.057)       
    Male 1,136 32.0 3,419 33.6 205 36.4 
    Female 2,416 68.0 6,745 66.4 358 63.6 
Age (P < 0.0001)       
    40–49 years 1,072 30.2 3,469 34.1 176 31.3 
    50–59 years 1,304 36.7 3,954 38.9 235 41.7 
    60–69 years 919 25.9 2,046 20.1 119 21.1 
    70–79 years 257 7.2 695 6.8 33 5.9 
Insured (P = 0.001)       
    Yes 2,246 63.2 6,771 66.6 365 64.8 
    No 1,306 36.8 3,393 33.4 198 35.2 
Income (P < 0.0001)       
    <$15,000 2,241 63.1 6,673 65.7 343 60.9 
    $15,000–$24,999 720 20.3 2,165 21.3 127 22.6 
    $25,000–$49,999 416 11.7 1,026 10.1 62 11.0 
    ≥$50,000 175 4.9 300 3.0 31 5.5 
Education (P < 0.0001)       
    Less than high school 1,188 33.4 3,936 38.7 188 33.4 
    High school or GED 1,415 39.8 3,826 37.6 166 29.5 
    Some college or votech school 636 17.9 1,650 16.2 147 26.1 
    Graduated from college 229 6.4 527 5.2 43 7.6 
    Graduate or professional school 84 2.4 225 2.2 19 3.4 
BMI category (P < 0.0001)       
    Underweight (<18.5) 45 1.3 149 1.5 0.9 
    Normal (18.5–24.9) 300 8.4 1,031 10.1 55 9.8 
    Overweight (25–29.9) 743 20.9 2,415 23.8 136 24.2 
    Obese-I (30–34.9) 934 26.3 2,665 26.2 150 26.6 
    Obese-II (35–39.9) 700 19.7 1,920 18.9 109 19.4 
    Obese-III (≥40) 830 23.4 1,984 19.5 108 19.2 
Smoking status (P < 0.0001)       
    Never 1,286 36.2 4,368 43.0 183 32.5 
    Former smoker 1,160 32.7 2,704 26.6 197 35.0 
    Current smoker 1,106 31.1 3,092 30.4 183 32.5 
Alcohol consumption (P < 0.0001)       
    Never 2,448 68.9 6,339 62.4 356 63.2 
    <1 drink/day 909 25.6 2,577 25.4 156 27.7 
    1–3 drinks/day 108 3.0 742 7.3 32 5.7 
    ≥4 drinks/day 87 2.4 506 5.0 19 3.4 
Depressive symptoms (P < 0.0001)       
    None 1,764 49.7 5,785 56.9 264 46.9 
    Mild 808 22.7 2,502 24.6 142 25.2 
    Moderate 551 15.5 1,191 11.7 89 15.8 
    Severe 429 12.1 686 6.7 68 12.1 
Antidepressant (P < 0.0001)       
    Present 1,385 39.0 1,770 17.4 176 31.3 
    Absent 2,167 61.0 8,394 82.6 387 68.7 
Variable and categoriesWhite only
African American only
Others/mixed race
Frequency%Frequency%Frequency%
n 3,552 10,164 563 
Sex (P < 0.057)       
    Male 1,136 32.0 3,419 33.6 205 36.4 
    Female 2,416 68.0 6,745 66.4 358 63.6 
Age (P < 0.0001)       
    40–49 years 1,072 30.2 3,469 34.1 176 31.3 
    50–59 years 1,304 36.7 3,954 38.9 235 41.7 
    60–69 years 919 25.9 2,046 20.1 119 21.1 
    70–79 years 257 7.2 695 6.8 33 5.9 
Insured (P = 0.001)       
    Yes 2,246 63.2 6,771 66.6 365 64.8 
    No 1,306 36.8 3,393 33.4 198 35.2 
Income (P < 0.0001)       
    <$15,000 2,241 63.1 6,673 65.7 343 60.9 
    $15,000–$24,999 720 20.3 2,165 21.3 127 22.6 
    $25,000–$49,999 416 11.7 1,026 10.1 62 11.0 
    ≥$50,000 175 4.9 300 3.0 31 5.5 
Education (P < 0.0001)       
    Less than high school 1,188 33.4 3,936 38.7 188 33.4 
    High school or GED 1,415 39.8 3,826 37.6 166 29.5 
    Some college or votech school 636 17.9 1,650 16.2 147 26.1 
    Graduated from college 229 6.4 527 5.2 43 7.6 
    Graduate or professional school 84 2.4 225 2.2 19 3.4 
BMI category (P < 0.0001)       
    Underweight (<18.5) 45 1.3 149 1.5 0.9 
    Normal (18.5–24.9) 300 8.4 1,031 10.1 55 9.8 
    Overweight (25–29.9) 743 20.9 2,415 23.8 136 24.2 
    Obese-I (30–34.9) 934 26.3 2,665 26.2 150 26.6 
    Obese-II (35–39.9) 700 19.7 1,920 18.9 109 19.4 
    Obese-III (≥40) 830 23.4 1,984 19.5 108 19.2 
Smoking status (P < 0.0001)       
    Never 1,286 36.2 4,368 43.0 183 32.5 
    Former smoker 1,160 32.7 2,704 26.6 197 35.0 
    Current smoker 1,106 31.1 3,092 30.4 183 32.5 
Alcohol consumption (P < 0.0001)       
    Never 2,448 68.9 6,339 62.4 356 63.2 
    <1 drink/day 909 25.6 2,577 25.4 156 27.7 
    1–3 drinks/day 108 3.0 742 7.3 32 5.7 
    ≥4 drinks/day 87 2.4 506 5.0 19 3.4 
Depressive symptoms (P < 0.0001)       
    None 1,764 49.7 5,785 56.9 264 46.9 
    Mild 808 22.7 2,502 24.6 142 25.2 
    Moderate 551 15.5 1,191 11.7 89 15.8 
    Severe 429 12.1 686 6.7 68 12.1 
Antidepressant (P < 0.0001)       
    Present 1,385 39.0 1,770 17.4 176 31.3 
    Absent 2,167 61.0 8,394 82.6 387 68.7 

χ2 test for categorical variables. GED, general education degree. Votech, vocational/technical.

Disparities in antidepressant use

Figure 1 presents a bar graph of the rates of antidepressant use stratified by race. There were disparities in antidepressant use at all levels of depressive symptoms. Whites and others/mixed race were more likely to report using antidepressants than African Americans, regardless of the severity of depressive symptoms.

Figure 1

Depressive symptoms and antidepressant use by racial group.

Figure 1

Depressive symptoms and antidepressant use by racial group.

Close modal

Table 2 presents the results (ORs and 95% confidence limits) of the logistic regression analysis predicting antidepressant use. Sex and health insurance status were strong predictors of antidepressant use, with women and insured individuals being approximately twice as likely to be taking antidepressants as men and uninsured individuals. Usage tended to decrease with advancing age and increasing income but rose with education and (slightly) with increasing BMI. Use of antidepressant medications was also positively associated with smoking, with former smokers being 1.21 times and current smokers being 1.46 times more likely to report antidepressant use than participants who had never smoked, but inversely associated with alcohol consumption. As expected, use of antidepressant medications was strongly associated with the level of depressive symptoms. Participants with severe symptoms were 6.55 times more likely to be taking antidepressants than those with the lowest level of depressive symptoms.

Table 2

Logistic regression predicting antidepressant use from sex, age, insurance status, income, education, BMI, smoking status, alcohol consumption, severity of depressive symptoms, and race

PredictorOR (95% CI)
Female (male)* 1.93 (1.74–2.14) 
Age 0.98 (0.97–0.98) 
Uninsured (insured) 0.54 (0.49–0.60) 
Income (<$15,000)  
    $15,000–$25,000 0.72 (0.65–0.81) 
    $25,000–$50,000 0.62 (0.53–0.72) 
    ≥$50,000 0.48 (0.36–0.63) 
Education (dropout)  
    High school or GED 1.14 (1.03–1.27) 
    Some college or votech school 1.62 (1.43–1.84) 
    Graduated from college 1.77 (1.45–2.16) 
    Graduate or professional school 1.77 (1.29–2.44) 
BMI 1.01 (1.00–1.01) 
Smoking status (never)  
    Former smoker 1.21 (1.08–1.35) 
    Current smoker 1.46 (1.31–1.63) 
Alcohol consumption (never)  
    <1 drink/day 0.99 (0.90–1.10) 
    1–3 drinks/day 0.74 (0.60–0.90) 
    ≥4 drinks/day 0.83 (0.65–1.05) 
Depressive symptoms (none)  
    Mild 2.18 (1.96–2.42) 
    Moderate 4.24 (3.75–4.78) 
    Severe 6.55 (5.68–7.56) 
Race (white only)  
    African American only 0.32 (0.29–0.36) 
    Other/mixed race 0.63 (0.51–0.78) 
PredictorOR (95% CI)
Female (male)* 1.93 (1.74–2.14) 
Age 0.98 (0.97–0.98) 
Uninsured (insured) 0.54 (0.49–0.60) 
Income (<$15,000)  
    $15,000–$25,000 0.72 (0.65–0.81) 
    $25,000–$50,000 0.62 (0.53–0.72) 
    ≥$50,000 0.48 (0.36–0.63) 
Education (dropout)  
    High school or GED 1.14 (1.03–1.27) 
    Some college or votech school 1.62 (1.43–1.84) 
    Graduated from college 1.77 (1.45–2.16) 
    Graduate or professional school 1.77 (1.29–2.44) 
BMI 1.01 (1.00–1.01) 
Smoking status (never)  
    Former smoker 1.21 (1.08–1.35) 
    Current smoker 1.46 (1.31–1.63) 
Alcohol consumption (never)  
    <1 drink/day 0.99 (0.90–1.10) 
    1–3 drinks/day 0.74 (0.60–0.90) 
    ≥4 drinks/day 0.83 (0.65–1.05) 
Depressive symptoms (none)  
    Mild 2.18 (1.96–2.42) 
    Moderate 4.24 (3.75–4.78) 
    Severe 6.55 (5.68–7.56) 
Race (white only)  
    African American only 0.32 (0.29–0.36) 
    Other/mixed race 0.63 (0.51–0.78) 

Data are OR (95% CI). GED, general education degree.

*Reference group used to calculate ORs is in parentheses. Votech, vocational/technical.

After controlling for these variables, race was significantly associated with antidepressant use (P < 0.0001). African Americans were 0.32 times as likely and others/mixed race were 0.63 times as likely to be taking antidepressants than whites. There were significant interactions for race and income (P = 0.004), race and smoking status (P = 0.001), and race and level of depressive symptoms (P = 0.009). An examination of these interactions showed that the direction of the effect was always consistent, with only a modest change in the magnitude of the effect across the various strata.

We investigated the relationship between racial status and antidepressant use in a large low-income cohort of individuals with diabetes. Although rates of antidepressant use increased as levels of depressive symptoms increased, a large number of individuals with moderate and severe depressive symptoms were not being treated with antidepressant medication. There was a racial disparity in the use of antidepressants at all levels of depressive symptoms. After adjustment for confounders, for every 10 white participants only 3 African American participants were likely to receive antidepressant medications. The prevalence of antidepressant use was consistently lowest for African Americans, even after controlling for factors that interacted with racial status (e.g., income, smoking, and severity of depressive symptoms).

The present study was conducted among a diverse sample of adults receiving care from CHCs in the southeastern U.S. Two other studies performed in other regions of the U.S. also reported racial disparities in antidepressant medication use among persons with diabetes (17,18). However, to our knowledge, our study is the first to show this disparity in a large low-income cohort of older adults with diabetes and depressive symptoms. Although socioeconomic status has been an important determinant of many health disparities (19), socioeconomic status measured by self-reported household income and education was associated with antidepressant use in this study, but the patterns were conflicting, with lower income and higher education being associated with greater antidepressant use. The disparity between African Americans and whites in this sample is unlikely to be explained by differences in income or education, as the difference persisted after adjustment for these factors.

The reasons for a disparity in antidepressant use are not easily apparent and probably represent a combination of factors including the recognition and diagnosis of depression in African Americans, as well as access and attitudes toward antidepressant medication in this population. In some primary care settings, providers recognize depression and recommend treatment for African Americans at rates equal to those for whites (20). However, even when depression is recognized, African Americans were less likely to take antidepressant medications than whites. Attitudes toward medications, particularly antidepressant medication, may contribute to this disparity. African Americans are less likely than whites to accept antidepressant treatment (20) and have been shown to prefer counseling over antidepressant medication (21). African Americans may also use spirituality and religion as a coping mechanism more frequently than whites.

There are several study limitations to acknowledge. First, because this is a cross-sectional study, inferences regarding causal relationships cannot be made. Second, diagnosis of diabetes was self-reported and not based on chart review or biological testing. In a separate validation study, 124 medical records of SCCS participants reporting a prior diagnosis of diabetes were reviewed, and 98% of the self-reports were validated based on criteria including ICD-9 codes, physician notes or prescriptions, and/or A1C or blood glucose measurements from the medical records (16). Third, antidepressant use may have been differentially reported because of differences in beliefs, attitudes, and preferences for treatment (20,21). Fourth, unmeasured differences in presenting depression-related complaints were unaccounted for, as were physicians' treatment priorities. Providers may be less likely to discuss depression treatment with African Americans who are often sicker when seeking care and present with more somatic attributions (22). Finally, although the CES-D has been validated in various populations, including multiethnic groups with diabetes (23), it may not be psychometrically equivalent across race-ethnicity or immigrant generations (24) and has demonstrated false-positive rates in low-income CHC settings. In one study, the standard cutoff for major depressive disorder yielded a sensitivity of 0.95 and specificity of 0.70, but more than two-thirds of those who screened positive did not actually meet the criteria for major depressive disorder (25). Thus, it is possible that depression would not be diagnosed in some study participants who reported mild and moderate depressive symptoms or they would not be considered good candidates for treatment after a clinician conducted a careful diagnostic interview.

Despite these limitations, our findings suggest a sizable racial disparity in antidepressant use in a large, low-income cohort of individuals with diabetes and depressive symptoms. Given this is a population with large numbers of people with diabetes and with limited access to health care services, our findings provide important evidence of the need for appropriate management of depression as part of regular diabetes care. Treatment for depression in individuals with diabetes may improve glycemic control, overall functioning, and quality of life (2), thereby reducing one's risk of adverse health outcomes associated with untreated depression. Conversely, untreated depression might contribute to racial disparities in diabetes outcomes, complications, and premature death. The mechanisms underlying the association between racial status and antidepressant use is likely to be multifactorial. Prospective studies are needed to more closely examine the association between racial status and depression treatment in low-income, ethnic minority, adults with diabetes.

The costs of publication of this article were defrayed in part by the payment of page charges. This article must therefore be hereby marked “advertisement” in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.

This research was funded with support by the National Institutes of Health (Grants R01-CA-92447, HL-67715, and P01-DK-20593). C.Y.O. is supported by a Diversity Supplement Award, National Institute of Diabetes and Digestive and Kidney Diseases (P60-DK-020593).

No potential conflicts of interest relevant to this article were reported.

Parts of this study were presented in poster form at the 31st annual meeting of the Society for Behavioral Medicine, Seattle, Washington, 7–10 April 2010.

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