Abstract
Objectives. We examined the association between discrimination and mental health distress, focusing specifically on the relative importance of discrimination because of particular demographic domains (i.e., race/ethnicity, socioeconomic position [SEP]).
Methods. The research team surveyed a sample of gay and bisexual men (n = 294) at a community event in New York City. Participants completed a survey on demographics, discrimination experiences in the past 12 months, attributed domains of discrimination, and mental health distress.
Results. In adjusted models, discrimination was associated with higher depressive (B = 0.31; P < .01) and anxious (B = 0.29; P < .01) symptoms. A statistically significant quadratic term (discrimination-squared; P < .01) fit both models, such that moderate levels of discrimination were most robustly associated with poorer mental health. Discrimination because of SEP was associated with higher discrimination scores and was predictive of higher depressive (B = 0.22; P < .01) and anxious (B = 0.50; P < .01) symptoms. No other statistically significant relationship was found between discrimination domains and distress.
Conclusions. In this sample, SEP emerged as the most important domain of discrimination in its association with mental health distress. Future research should consider intersecting domains of discrimination to better understand social disparities in mental health.
In the United States, discrimination has increasingly become an important focus of scholarly inquiry in understanding social determinants of health.1–5 A growing body of empirical evidence points to the negative health consequences of social stressors for gay and bisexual males, including gay-related discrimination and prejudice.2,6–9 Studies demonstrate that gay and bisexual men experience discrimination at both structural and institutional levels, including in housing, employment, access to medical care, and legal policies,6,10 as well as at the individual level, such as harassment and violence.11,12 Previous research has also demonstrated that these discriminatory experiences operate as objective and subjective stressors in the lives of gay and bisexual males, and are significantly associated with psychiatric disorders9,13 and psychological distress14,15 in this population.
Theoretical frameworks, including the minority stress model9 and psychological mediation framework,16 have been used to explain the higher prevalence of mental health problems among sexual minority male populations as a function of both acute and chronic social stressors. In these frameworks, discrimination and stigma create stressful social environments for gay and bisexual men, contributing to elevated rates of presenting mental health problems. These theoretical frameworks for understanding mental health disparities among gay and bisexual men provide generative starting points for testable hypotheses concerning the relationship between discrimination and mental health16; however, few studies have examined the relationship between perceptions of discrimination on the basis of specific domains (such as income or socioeconomic position [SEP], race/ethnicity, HIV status, age, gender identity, sexual identity) and mental health outcomes.17 More nuanced research on the association between attributions of discrimination experiences to particular domains and mental health distress for gay and bisexual men is warranted.
We used cross-sectional data from a survey conducted at gay and bisexual community events in New York City to examine the associations between domain-specific perceptions of discrimination and mental health distress. The overarching aims of this study were 2-fold. First, we sought to examine whether experiences of discrimination were positively associated with mental health distress (depression and anxious symptom scores) in this community sample of gay and bisexual men. Second, building on previous work documenting the impact of multiple stigmatized identities,18–20 we sought to examine whether specific domains of self-reported discrimination (i.e., income or SEP, race/ethnicity, HIV status, age, gender identity, sexual identity) were differentially associated with mental health symptoms.
METHODS
The research team surveyed a community sample of 342 ethnically diverse men at 3 gay, lesbian, and bisexual (GLB) community events in New York City (NYC) in the fall of 2010 using a cross-sectional, brief-intercept sampling method,21 through the Sex and Love Study, version 8.0. At each event, the research team hosted a booth, and a member of the research team actively approached each person who passed. The response rate was high, with 76.0% of those approached consenting. Men completed a 10- to 15-minute paper-and-pencil survey, and received a free movie pass as an incentive. Forty respondents were dropped from this sample because of missing data on 1 or more of the measures, and 8 were dropped because they did not self-identify as gay or bisexual, leaving a complete sample of 294 (86%) included in analyses. There were no statistically significant differences between included and excluded participants. All study activities were approved by the institutional review board of the corresponding author.
Measures
Mental health distress.
The Brief Symptom Inventory (BSI)22 is a self-report measure of psychological distress in the past week23 that has been found to be a reliable and valid measure of emotional distress in previous studies.24,25 Participants completed 2 BSI subscales—the 6-item depression subscale (α = 0.89) and the 6-item anxiety subscale (α = 0.85).
Perceived discrimination.
We used 9 previously validated items from the Detroit Area Study Everyday Discrimination Scale26 to assess the frequency with which participants experienced various forms of interpersonal mistreatment in their day-to-day lives over the previous 12 months. The Everyday Discrimination scale has been shown to have good psychometric properties,26–28 including high levels of internal consistency among sexual minorities.17 The Cronbach’s coefficient α in our sample of urban gay and bisexual men was 0.90, with individual item-to-total correlations ranging from 0.57 to 0.72, suggesting high internal consistency reliability. Discrimination scale items adequately loaded on a single factor (eigenvalue = 4.94; proportion of variance explained = 54.9%) and we summed them to create a single scale score of frequency of discriminatory experiences with possible scores ranging from 1 to 45.
Perceived effects of discrimination.
We used 2 items from the Detroit Area Study Everyday Discrimination Scale to assess perceived effects of discrimination on participants’ lives.26 These 2 individual items were correlated 0.54, loaded onto a single factor (eigenvalue = 1.74; proportion of variance explained = 87.0%), and were summed together to create a single measure that ranged from 2 to 8.
Attributed domain(s) of discrimination.
Participants were asked to indicate the demographic domain(s) to which they attributed their discrimination experiences, including race/ethnicity, gender, age, income or SEP, HIV status and sexual orientation. We used the term socioeconomic position, instead of socioeconomic status, to explicitly consider the interplay of both objective material resources (adjusted for in these analyses) and subjective perceptions of socioeconomic ranking (attributed domain of discrimination).4 Because participants could check as many domains as they wished in their attribution of discrimination (i.e., categories were not mutually exclusive), each of the domains of discrimination was treated as a separate binary variable. Participants who indicated “yes” scored 1, and “no” scored 0.
Covariates.
Sociodemographic covariates included in the analysis were age in years (continuous); race/ethnicity (categorical): White, Black, Latino/Hispanic, or other; sexual identity (dichotomous): gay versus bisexual; educational attainment (dichotomous): higher education (≥ 4-year college degree) versus lower education (≤ 4-year degree); annual income (categorical): self-reported lower income (< $40 000), middle income ($40 000–$80 000), and higher income (> $80 000); and HIV status (dichotomous): self-reported HIV-positive serostatus versus HIV-negative or unknown serostatus.
Data Analysis
General statistical procedures.
We conducted statistical analyses by using SAS version 9.2 statistical software (SAS Institute, Cary, NC). We predetermined statistical significance at the α = 0.05 level. We obtained descriptive statistics for all variables included in the analysis, including the distribution of all scale scores, with appropriate tests for normality. We conducted bivariate analyses to provide information about the functional form of the relationship between the exposure and indicators of interest (all continuous), including appropriate diagnostic statistics for inclusion in linear models. We estimated bivariate relationships by using Pearson r correlations and t tests (pooled estimates, assuming equal variances). We investigated differences in discrimination scores by demographic subgroups, as well as by the attributed domain of discrimination.
Testing proposed hypotheses.
We utilized simple ordinary least squares linear regression (PROC REG) to fit a series of regression models. Given previous research, which has shown that the relationship between discrimination and health is curvilinear (specifically, quadratic),29,30 we conducted hierarchically nested regressions including only a linear term first (discrimination score and perceived discrimination), followed by a model that included both a linear and quadratic term (discrimination, perceived discrimination, and discrimination squared), so that we could examine incremental improvement in model prediction. Adding a quadratic term (discrimination-squared) marginally improved the model fit for depressive symptoms (F-test of the change in model fit comparing model 1 and 2: F1282 = 3.59; P = .056) and significantly improved the model fit for anxious symptoms (F-test of the change in model fit comparing model 4 and 5: F1282 = 6.38; P = .01). The sample size and analyses yielded a power of greater than 0.80 (we included a Bonferroni adjustment for dependence of hypotheses assuming correlation of discrimination and perceived discrimination scales).
RESULTS
Table 1 outlines the characteristics of the study sample (N = 294). The sample ranged in age from 18 to 83 years (mean = 42.1; SD = 11.8). The majority (94.6%) self-identified as gay. More than one third were racial/ethnic minorities (16.7% Black/African American, 12.6% Latino/Hispanic, and 9.5% other non-White race/ethnicity), 24.5% had less than a bachelor’s degree, and 28.9% earned less than $40 000 annually. More than 1 in 5 (20.8%) reported being HIV-positive. The mean discrimination score for the entire sample was 12.6 (SD = 8.2), ranging from 1 to 45. The mean perceived impact of discrimination score was 3.6 (SD = 1.6), ranging from 2 to 8. In unadjusted, bivariate regression models, Black, compared with White participants, self-reported significantly higher discrimination scores (B = 0.38; 95% confidence interval [CI] = 0.06, 0.69; P < .05) as well as higher scores on the perceived impact of discrimination scale (B = 0.53; 95% CI = 0.22, 0.84; P < .01). Earning a higher income (income > $80 000 compared with < $40 000) was associated with significantly lower discrimination scores (B = −0.37; 95% CI = −0.65, −0.08; P < .05) and lower perceived impact of discrimination scores (B = −0.44; 95% CI = −0.72, −0.15; P < .01). No other significant sociodemographic differences in discrimination or perceived discrimination were present.
TABLE 1—
Overall Characteristics of the Study Sample of Gay and Bisexual Men: New York City, 2010
| Characteristic | No. (%) or Mean ±SD |
| Sexual identity | |
| Gay or homosexual | 278 (94.6) |
| Bisexual | 16 (5.4) |
| Race/ethnicity | |
| White | 180 (61.2) |
| Black/African American | 49 (16.7) |
| Latino/Hispanic | 37 (12.6) |
| Other race/ethnicity | 28 (9.5) |
| Educationa | |
| High education | 222 (75.5) |
| Low education | 72 (24.5) |
| Income | |
| < $40 000 | 85 (28.9) |
| $40 000–$80 000 | 107 (36.4) |
| > $80 000 | 102 (34.7) |
| HIV serostatus | |
| HIV-positive | 61 (20.8) |
| HIV-negative | 233 (79.3) |
| Self-reported reason for discrimination | |
| Race/ethnicity | 111 (37.8) |
| Gender | 29 (9.9) |
| Age | 85 (28.9) |
| Sexual orientation | 184 (62.6) |
| Income or SEP | 52 (17.7) |
| HIV status | 21 (7.1) |
| Age in years (range = 18–83) | 42.1 ±11.8 |
| Discrimination (continuous) | |
| Discrimination scale score (range = 1–45) | 12.6 ±8.2 |
| Perceived impact of discrimination score (range = 2–8) | 3.6 ±1.6 |
| Internalizing symptoms (continuous) | |
| BSI depressive symptoms (range = 0–22) | 4.5 ±4.9 |
| BSI anxious symptoms (range = 0–22) | 3.9 ±4.6 |
Note. BSI = Brief Symptom Inventory; SEP = socioeconomic position. The sample size was n = 294.
High education ≥ 4-year college degree or higher; low education < 4-year degree.
Reported Domains of Discrimination
Overall, participants reported experiencing discrimination attributed to income or SEP (17.7%), race/ethnicity (37.8%), HIV status (7.1%), age (28.9%), gender (9.9%), and sexual orientation (62.6%).
Attributing discrimination to sexual orientation (B = 0.24; 95% CI = 0.0002, 0.47; P < .05) and income or SEP (B = 0.44; 95% CI = 0.15, 0.74; P < .01) was associated with higher discrimination scores. Attributing discrimination to race/ethnicity (B = 0.34; 95% CI = 0.10, 0.57; P < .01) and income or SEP (B = 0.32; 95% CI = 0.02, 0.62; P < .05) was associated with higher perceived impact of discrimination scores.
We separately fit logistic regression models that included all sociodemographic characteristics as predictors (age, race/ethnicity, sexual identity, education, income, and HIV status) with each of the domains of attributed discrimination experienced as an outcome. In logistic regression models adjusted for all sociodemographic characteristics, demographic factors patterned as expected along each of the other areas of attributed discrimination (e.g., Black and Latino participants were more likely to report experiencing racial/ethnic discrimination; older participants were more likely to report discrimination because of age), providing evidence of construct validity.
Bivariate and Multivariable Models
At the bivariate level (unadjusted; z scored), discrimination scores were positively associated with perceived impact of discrimination (r = 0.47; P < .001), depressive symptoms (r = 0.36; P < .001), and anxious symptoms (r = 0.32; P < .001).
As shown in Table 2 (column 1), attributing discrimination to income or SEP was associated with significantly higher depressive symptom scores (unadjusted B = 0.68; 95% CI = 0.39, 0.97; P < .001) and anxiety scores (unadjusted B = 0.71; 95% CI = 0.42, 0.99; P < .001). Column 2 shows models that adjusted estimated parameters for all sociodemographic characteristics, except for the domain-specific discrimination variable. For example, when we adjusted for age, race, sexual orientation, and HIV status (but not for income or education), attributing discrimination to income or SEP was associated with both higher depressive (B = 0.70; 95% CI = 0.41, 0.99; P < .001) and anxious (B = 0.70; 95% CI = 0.41, 0.99; P < .001) scores. After we adjusted for income and education along with all other demographic covariates (column 3), attributing discrimination to income or SEP was, on average, associated with two-thirds higher standard deviation difference in depressive symptoms (B = 0.66; 95% CI = 0.35, 0.97; P < .001) and anxious symptoms (B = 0.66; 95% CI = 0.35, 0.98; P < .001). Attributing discrimination to race/ethnicity, gender, age, sexual orientation, and HIV status was not statistically associated with mental health symptoms, after we adjusted for sociodemographic covariates.
TABLE 2—
Association Between Attributed Domains of Discrimination and Affective Symptoms Among Gay and Bisexual Men: New York City, 2010
| Depressive Symptoms |
Anxious Symptoms |
|||||
| Characteristic | Unadjusted B (95% CI) | Adjusteda B (95% CI) | Adjustedb B (95% CI) | Unadjusted B (95% CI) | Adjusteda B (95% CI) | Adjustedb B (95% CI) |
| Race/ethnicity | 0.002 (−0.23, 0.24) | −0.02 (−0.27, 0.22) | −0.05 (−0.36, 0.26) | −0.05 (−0.28, 0.19) | −0.06 (−0.31, 0.18) | 0.08 (−0.23, 0.39) |
| Gender | −0.25 (−0.64, 0.13) | … | −0.26 (−0.66, 0.13) | −0.20 (−0.58, 0.19) | … | −0.19 (−0.59, 0.20) |
| Age | −0.10 (−0.25, 0.15) | −0.09 (−0.35, 0.17) | −0.10 (0.36, 0.17) | −0.07 (−0.32, 0.19) | −0.05 (−0.31, 0.20) | −0.06 (−0.32, 0.21) |
| Sexual orientation | 0.05 (−0.19, 0.29) | 0.07 (−0.18, 0.32) | 0.07 (−0.18, 0.32) | 0.07 (−0.16, 0.31) | 0.05 (−0.20, 0.30) | 0.05 (−0.20, 0.30) |
| Income or SEP | 0.68*** (0.39, 0.97) | 0.70*** (0.41, 0.99) | 0.66*** (0.35, 0.97) | 0.71*** (0.42, 0.99) | 0.70*** (0.41, 0.99) | 0.66*** (0.35, 0.97) |
| HIV | 0.16 (−0.29, 0.60) | 0.13 (−0.33, 0.58) | 0.11 (−0.39, 0.62) | 0.15 (−0.30, 0.60) | 0.10 (−0.35, 0.56) | 0.08 (−0.43, 0.58) |
Note. CI = confidence interval; SEP = socioeconomic position. Z-scored parameter estimates are presented. Each domain-specific area of discrimination fit in separate unadjusted and adjusted models.
Adjusted for sociodemographic covariates except for the attributed domain-specific discrimination variable. For example, adjusted model for attributed discrimination to race/ethnicity included age, education, income, sexual orientation, and HIV status (but did not include race/ethnicity). No adjusted model was estimated for gender discrimination because all participants were men.
Adjusted for the following sociodemographic covariates: age, race/ethnicity, education, income, sexual orientation, and HIV status.
†P < .001.
Mean depressive and anxious symptom scores by race/ethnicity are presented in Table 3, comparing those who reported discrimination because of income or SEP with those who did not. The relationship between attributing discrimination to income or SEP and higher mental health symptoms behaved similarly across racial/ethnic groups in stratified analyses. However, we saw statistically significant differences in mean depressive symptom scores only for White and Latino men, and differences in mean anxious symptoms were seen only for White men.
TABLE 3—
Depressive and Anxious Symptom Scores by Those Attributing Discrimination to Income or SEP by Race/Ethnicity Among Gay and Bisexual Men: New York City, 2010
| Depressive Symptoms |
Anxious Symptoms |
|||||
| Race/Ethnicity | Income or SEP Discrimination, Mean (SD) | No Income or SEP Discrimination, Mean (SD) | t-Test Statistic (P) | Income or SEP Discrimination, Mean (SD) | No Income or SEP Discrimination, Mean (SD) | t-Test Statistic (P) |
| Black (n = 49) | 7.20 (8.07) | 4.75 (5.00) | −0.97 | 3.20 (5.22) | 3.53 (3.97) | 0.17 |
| White (n = 180) | 7.19 (6.20) | 3.71 (4.14) | -3.23 (.002) | 7.43 (6.03) | 3.36 (4.02) | -3.89 (<.001) |
| Latino (n = 37) | 9.50 (7.29) | 3.03 (4.45) | -2.92 (.006) | 5.67 (7.06) | 2.74 (3.68) | −0.99 |
| Other (n = 28) | 4.50 (1.73) | 4.46 (4.70) | −0.02 | 4.75 (2.99) | 3.75 (4.73) | −0.41 |
Note. SEP = socioeconomic position. Pooled t-test statistics (equal variances) are reported. P values are only shown for those test statistics that reached statistical significance.
Multivariable Models
Table 4 presents final fitted regression models showing the main effects of discrimination and perceived effect of discrimination on mental health (z-scored parameter estimates). We adjusted all models for age, race/ethnicity, sexual identity, education, income, and HIV serostatus.
TABLE 4—
Fitted Linear Regression Models for the Association Between Discrimination and Internalizing Mental Health Symptoms Among Gay and Bisexual Men: New York City, 2010
| Depressive Symptoms |
Anxious Symptoms |
|||||
| Variable | Model 1 | Model 2 | Model 3 | Model 4 | Model 5 | Model 6 |
| Discrimination, estimated B (95% CI) | 0.26*** (0.13, 0.38) | 0.32*** (0.18, 0.45) | 0.30*** (0.16, 0.43) | 0.20** (0.08, 0.32) | 0.28*** (0.14, 0.41) | 0.26** (0.13, 0.39) |
| Discrimination-squared (quadratic) | … | 0.07a (−0.15, 0.003) | 0.08* (−0.15, 0.002) | … | 0.10* (−0.17, 0.02) | 0.10** (−0.17, 0.03) |
| Perceived impact of discrimination | 0.23** (0.10, 0.35) | 0.23** (0.11, 0.35) | 0.21** (0.09, 0.34) | 0.28*** (0.15, 0.40) | 0.28*** (0.16, 0.40) | 0.26*** (0.14, 0.38) |
| Income or SEP discrimination | … | … | 0.50** (0.22, 0.79) | … | 0.51** (0.23, 0.80) | |
| Model fit statistics | ||||||
| F-value | 5.54 | 5.66 | 6.24 | 5.70 | 5.87 | 6.60 |
| R-square | 0.1809 | 0.1914 | 0.2245 | 0.1818 | 0.2003 | 0.2346 |
| P | < .001 | < .001 | < .001 | < .001 | < .001 | < .001 |
| SS model | 53.014 | 56.07 | 65.78 | 53.27 | 58.69 | 68.73 |
| SS error | 239.99 | 236.93 | 227.22 | 239.73 | 234.31 | 224.27 |
| df error | 282 | 281 | 280 | 282 | 281 | 280 |
Note. CI = confidence interval; SEP = socioeconomic position. All models were adjusted for age, race, ethnicity, education, income, sexual orientation, and HIV status. No sociodemographic variables were statistically significant in the fitted models shown. The sample size was n = 294.
Item approached statistical significance (P < .06).
*P < .05; **P < .01; ***P < .001.
Depressive symptoms.
In adjusted model 2, discrimination (B = 0.32; 95% CI = 0.18, 0.45; P < .001) and perceived impact of discrimination (B = 0.23; 95% CI = 0.11, 0.35; P < .01) were positively associated with higher depressive symptom scores. A quadratic term for discrimination-squared approached significance in this fitted model (B = −0.07; 95% CI = −0.15, 0.003; P = .06).
In model 3, which added a dichotomous variable indicating whether perceived discrimination was attributed to income or SEP, discrimination (B = 0.30; 95% CI = 0.16, 0.43; P < .001), perceived impact of discrimination (B = 0.21; 95% CI = 0.09, 0.34; P < .01), and income or SEP discrimination (B = 0.50; 95% CI = 0.22, 0.79; P < .01) were each significantly associated with elevated depressive symptom scores. Moreover, the quadratic term (discrimination squared) became statistically significant (B = −0.08; 95% CI = −0.15, −0.002; P < .05).
Anxiety scores.
Model 5 regressed anxious symptoms on discrimination measures and revrealed a positive association between discrimination (B = 0.28; 95% CI = 0.14, 0.41; P < .001) and the perceived impact of discrimination (B = 0.28; 95% CI = 0.16, 0.40; P < .001), when we adjusted for all other sociodemographic covariates. We fit a quadratic term (discrimination squared; B = −0.10; 95% CI = −0.17, −0.02; P < .05) in this multivariable model and it reached statistical significance at the α = 0.05 level.
In model 6, which added attribution of discrimination because of income or SEP, we found a statistically significant positive association between attributing discrimination to income or SEP (B = 0.51; 95% CI = 0.23, 0.80; P < .01) and anxious symptoms. Also significant in this final fitted model were discrimination (B = 0.26; 95% CI = 0.13, 0.39; P < .01), perceived impact of discrimination (B = 0.26; 95% CI = 0.14, 0.38; P < .001), and the quadratic term discrimination squared (B = −0.10; 95% CI = −0.17, −0.03; P < .01).
DISCUSSION
This study is the first, to our knowledge, to examine multiple types of discrimination among gay and bisexual men in an urban setting and the relations between attributions of discrimination and elevated symptoms of mental health distress. Three overarching findings emerged from the current study that may contribute to the growing body of literature on social disparities in mental health among sexual minority men. First, we found no statistically significant relationship between mental health symptoms and reports of discrimination because of race/ethnicity, gender, age, sexual orientation, or HIV. Consistent with previous research, the results illustrate that Black and Latino gay and bisexual men reported experiencing discrimination on the basis of race/ethnicity.16 However, self-reporting discrimination because of race/ethnicity was not associated with worse mental health symptoms among this group of men. It is important to note that approximately 63% and 38% of the sample attributed their discrimination to sexual orientation and race/ethnicity, respectively.
Many studies examining the impact of discrimination on mental health compared a socially disadvantaged group (e.g., sexual minorities) to a control group (e.g., heterosexuals).16,31 By contrast, this study examined within-group differences in perceived discrimination. This approach may have allowed us to identify discrimination experiences that distinguish among different subgroups of gay and bisexual men, rather than focusing on discrimination experiences that distinguish gay men from their heterosexual counterparts. As such, our findings have important implications for understanding more nuanced discrimination experiences that may take place within minority groups that may be obscured by traditional analyses. Our study did not ask participants about the source of their discrimination experiences (i.e., whether discrimination on the basis of SEP or race/ethnicity was experienced from other members of the lesbian, gay, bisexual, and transgender community, or from outside it). Discrimination that comes from inside one’s own marginalized group may be experienced as particularly damaging to mental health or self-esteem. Future research should examine the implications of both out-group and in-group discrimination experiences, which may have a differential impact on affective outcomes.
Second, self-reporting discrimination because of SEP was, on average, associated with higher discrimination scores. In addition, as shown in Figure 1, self-reporting discrimination because of income or SEP was associated with elevated depressive and anxious symptom scores, even after we adjusted for sociodemographic covariates. A significant body of literature has documented a social gradient in the prevalence of mental health disorders, such that lower SEP in the form of financial hardship is associated with an increased risk for adverse mental health outcomes.32–36 Our findings are consistent with these studies, as well as others that have shown that subjective SEP is associated with worse mental health, even after adjustment for objective indicators of SEP.37–39 This finding is particularly interesting given the high proportion of men with moderate or high SEP in this sample.
FIGURE 1—
Graphical display of final fitted nonlinear model for depressive symptoms for a prototypical participant with sociodemographic characteristics and perceived effects of discrimination set at their mean values.
Note. BSI = Brief Symptom Inventory; SEP = socioeconomic position.
Third, higher frequency of perceived discrimination was significantly associated with worse mental health symptoms, when we adjusted for all sociodemographic factors. Moreover, consistent with previous research,30 the relationship between discrimination and mental health appeared curvilinear. Figure 1 presents a graphical display of the final fitted linear model showing the relationship between discrimination scores and depressive symptoms (graph presents a “prototypical” participant from the study sample with sociodemographic characteristics and perceived effects of discrimination set at their mean values). As visually depicted, moderate levels of self-reported discrimination were most robustly associated with higher depression scores when we adjusted for discrimination on the basis of income or SEP and sociodemographic covariates. In other words, men who reported the lowest and highest levels of discrimination actually reported less psychological distress compared with participants who reported levels of discrimination in the middle of the range. We found a similar relationship for anxious symptoms. The curvilinear relationship is consistent with previous research that has illustrated that, on average, individuals who report moderate levels of discrimination have more health problems than do individuals who report lower or higher levels of discrimination.30,40,41
One possible explanation for the current findings may be found in the social–psychological literature. Crocker and Major posited that attributing negative events and rejecting situations to discrimination can have self-protective properties for members of marginalized groups.42 Consistent with social identity theory,43 members of minority groups who strongly identify and feel connected with their group membership may be more likely to attribute discrimination to external forces.42 Such externalizing of attributed discrimination has been shown to be associated with better psychological outcomes.44–46 Thus, the gay and bisexual men in our sample who reported the highest levels of discrimination may also be those who had a propensity to cope with discrimination by externalizing their attributions. An alternative explanation for these findings could be attributed to shifts in the social and political meaning of same-sex attraction from one of complete exclusion to increasing inclusion.47 As a result, research has suggested that sexual minorities who are aware of discrimination may construct “resistance” narratives,48,49 which buffer against the deleterious effects of adversity.
Although our findings lend support to the continued existence of discrimination, the variability in the psychological effects of discrimination may represent significant shifts in sociohistorical context of sexual minority populations.49,50 Thus, future research is needed to examine whether and how protective properties of group membership may be similar for perception of discrimination on the basis of SEP, alongside sexual orientation, gender, and race/ethnicity. In addition, longitudinal studies that take a life-course perspective are warranted to examine if and how “resistance” narratives are protective against discrimination for different cohorts of sexuality-minority men.
Finally, the results of this study demonstrate that participants who attributed discrimination to sexual orientation reported higher discrimination scores, and those who attributed discrimination to race/ethnicity reported higher perceived impact of discrimination scores. However, there were no statistically significant associations between the domain to which participants attributed discrimination and depressive or anxious symptoms, after we adjusted for sociodemographic factors. This was true even when the sociodemographic factor associated with attributing discrimination was not controlled (e.g., regressing depression on income or SEP discrimination, with adjustment for age, race/ethnicity, sexual orientation, and HIV status, but not for income and education).
This finding is consistent with previous research by Kessler et al. who demonstrated that perceived discrimination across multiple social statuses (e.g., age, race/ethnicity, income, or education) was common on a population level in the United States, yet the only significant association between social status and mental health problems was educational status (a proxy for an objective indicator of socioeconomic position), whereby the association between perceived discrimination and mental health distress was significantly stronger among respondents with lower levels of educational attainment.1 Taken together, these findings suggest that both objective and subjective indicators of SEP are of considerable importance in examining the association between perceptions of discrimination and mental health outcomes. Additional research is needed with samples of gay and bisexual men, including those not recruited via community-based gay events, and that includes both objective and subjective indicators of SEP to replicate these findings and provide evidence of generalizability.
Limitations
Several limitations are important to consider. First, this was a convenience sample recruited from community events, which may not be representative of the larger gay and bisexual male population. For example, this sample was comprised of men who self-identified as gay or bisexual, who were at a community event, and who were in the New York City area. As such, our findings may not generalize to other regions or sexual minorities who would not feel comfortable attending these community events. Second, this study was cross-sectional and does not allow for causal inferences because of a lack of temporal ordering between exposure and outcome. It is plausible, for example, that men had depressive and anxious symptoms before ever experiencing discrimination; thus, we cannot rule out reverse causation or how these relationships change over time.
Third, a strong assumption we made was that any missing data were missing completely at random (i.e., probability any variable was missing did not depend on any other variables in the fitted regression models).51 Fourth, many of the critiques of measurement-related issues in the research on discrimination and health are applicable to this study (see Krieger for review52). Few studies of sexual minority health have used the measures of discrimination that we utilized in this study.17 The measures we used were found to have good psychometric properties in this sample of gay and bisexual men. This finding represents an additional contribution of the current study to the research literature on measuring discrimination among sexual minority male populations. In addition, we did not assess important moderators of the relationship between discrimination and mental health distress in this study. Future research is needed to examine potential stress buffers such as group affiliation53 and community engagement.54
Conclusions
The results of the study provide support for the hypothesis that discrimination attributed to SEP is associated with mental health distress among gay and bisexual adults. Findings demonstrate the importance that mental health providers, researchers, and policymakers consider the role of SEP as a unique social stressor. Further consideration of how experiences of discrimination because of income or SEP dynamically intersect with forms of discrimination based on sexual orientation, gender identity, and race/ethnicity is warranted. Future research is needed that examines both objective and subjective measures of SEP, which require measurement to understand and document the structural determinants of health, as well as individual-level subjective experiences of discrimination. Given that SEP has been linked with health disparities on a population level in the United States,55 more refined investigations of discrimination on the basis of SEP are needed to examine its association with mental health disparities among gay and bisexual men.
Acknowledgments
The Sex and Love v8.0 (2010) study was supported by the Hunter College Center for HIV Educational Studies and Training under the direction of J. T. Parsons and S.A. Golub.
The authors acknowledge the contributions of the members of the Sex and Love v8.0 Research Team: Michael Adams, Anthony Bamonte, David Bimbi, Christian Grov, Chris Hietikko, Catherine Holder, Amy LeClair, Corina Lelutiu-Weinberger, Mark Pawson, Gregory Payton, Jonathan Rendina, Kevin Robin, Joel Rowe, Tyrel Starks, Anthony Surace, Julia Tomassilli, Andrea Vial, Brooke Wells, and the Drag Initiative to Vanquish AIDS (DIVAs).
Human Participant Protection
This study was approved by the institutional review board of Hunter College of the City University of New York.
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