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. Author manuscript; available in PMC: 2024 Aug 1.
Published in final edited form as: Stigma Health. 2023 Jan 9;8(3):372–380. doi: 10.1037/sah0000435

Associations of past-year stigma and psychosocial syndemic conditions: Considerations for intersectional stigma measures among Black Sexual Minority Men

Cristian J Chandler 1,9, Qimin Liu 2, Andre L Brown 3,9, Derrick D Matthews 4, Alexander C Tsai 5, Leigh A Bukowski 6, Lisa A Eaton 7, Ronald D Stall 9, M Reuel Friedman 8,9
PMCID: PMC10545331  NIHMSID: NIHMS1858533  PMID: 37789829

Abstract

This secondary analysis of a mixed serostatus sample of Black sexual minority men (BSMM) used conditional inference tree methods to explore associations of past-year experienced stigma and psychosocial syndemic conditions. Experienced stigmas were attributed to race, sexuality, socioeconomic status, HIV status or some “other” reason. Psychosocial syndemic conditions studied included physical assault, intimate partner violence, polysubstance use, and depression symptomology. Data are from Promoting Our Worth, Equality and Resilience (POWER), a serial, cross-sectional study conducted between 2014–2017 (N=4430). Experiences of multiple stigmas were reported by n=938 (22.1%) of BSMM. Conditional inference tree results revealed that HIV-related stigma and its intersection with “other” stigma showed the greatest variance in psychosocial condition prevalence. Our findings suggest that when developing intercategorical intersectional analyses with BSMM, there are important stigmas for BSMM beyond those attributed to race, sexuality, and SES, particularly intersecting with HIV-related stigma. Conditional inference tree analysis shows promise in quantitative explorations of intersectional stigma with BSMM, but will benefit from the inclusion of additional forms of stigma, which should be considered by the field moving forward.

Keywords: stigma, Black men who have sex with men, syndemics, conditional inference, HIV, intersectional stigma

Introduction

Black sexual minority men (BSMM) continue to experience a disproportionate burden of HIV in the United States (Centers for Disease Control and Prevention, 2020). From 2015 to 2019, BSMM continued having the highest percentage of new infections among men (36%), and accounted for greater than half of all new infections in sexual minority men under 25 (Centers for Disease Control and Prevention, 2020). Extant literature notes that despite these disparities, BSMM have historically reported similar or less individual HIV risk for transmission and acquisition of HIV (e.g., condomless sex) when compared to White men (Maulsby et al., 2014). More recent analyses of data suggest that the social context, such as experiences of stigma, influence their HIV risk behavior, HIV care utilization, and HIV prevention uptake (Elopre et al., 2018; Maulsby et al., 2014). Ongoing exploration of experiences of stigma may elucidate methods of alleviating the excess burden of HIV currently carried by BSMM when compared to other racial and ethnic counterparts.

Theorizing the relationship of stigma and psychosocial syndemic conditions for exploring intersectional stigma among BSMM

The Minority Stress Theory (MST) is a useful tool for guiding explorations of how these social contexts relate to BSMM’s HIV and related outcomes. MST posits that there is a psychosocial link between the stress that minoritized individuals experience for having a non-majority identity (e.g., being a sexual minority and/or racial minority) and their health outcomes, particularly mental health (Meyer, 2003). Minority stress is strongly driven by stigma including experiences of discrimination (Quinn et al., 2019).

Stigma, or the process of marginalization and/or discrediting leading to status loss based on minoritized social, behavioral, or physical attributes was initially described by sociologist Erving Goffman (Goffman, 1963). Importantly, this marginalization is rooted in a power differential by those considered to have violated social norms due to possession of minoritized attributes (Herek, 2002). Because stigma is rooted in power differentials, groups and individuals experience stigma at several levels (e.g., individual, group, community, and structural) based on the power, privilege, and opportunities for oppression (e.g., classism, racism, homophobia) that may be associated with co-occurring identities, including sexuality, race, gender, socioeconomic status, and HIV status (Bauer, 2014). The desire to understand the interplay among the social positions, power, stigma and health related outcomes has led to the conceptualization of intersectional stigma.

The term intersectional stigma is drawn from Black feminist theory (Bowleg, 2012; Crenshaw, 1990) and was coined by Michele Tracy Berger to describe experiences of women of color who experienced ill treatment beyond the confluence of race, class, and gender due to their status as sex workers, substance users, and women living with HIV (Berger, 2004). Intersectional stigma provides a lens to examine the way that stigma drives the marginalization of groups and individuals with minoritized attributes (e.g., living with HIV, sexual minority status), requiring the examination of social positioning and power. The status loss associated with stigma and the marginalization present contribute to health inequities and ongoing social disparities (Alvidrez et al., 2021; Herek, 2002; Quinn et al., 2019). Acknowledging and understanding the experiences of individuals living with multiple minority identities is critical when studying the HIV continuum among BSMM (Arnold et al., 2014; Bauer, 2014; English et al., 2020; Quinn et al., 2019). Stigma, particularly HIV-related stigma, continues to contribute significantly to interruptions in HIV testing, preexposure prophylaxis (PrEP) uptake, retention in HIV care, and antiretroviral therapy (ART) adherence (Elopre et al., 2018; Turan et al., 2017). The National Institutes of Health (NIH) recognizes that in order to achieve the goals of Ending the HIV Epidemic (EHE): A plan for America, addressing HIV-related stigma is a priority (Greenwood et al., 2021; Nelson, 2020).

An additional theory useful to addressing the health of sexual minority men and their health outcomes is syndemic theory. The theory of syndemic production posits that a syndemic is the co-occurrence of two or more biological and ecosocial epidemics that are mutually reinforcing to worsen health outcomes (Singer, 1996; Singer et al., 2017). Broadly, studies of psychosocial syndemic conditions among sexual minority men most often include measures of mental health, violence, and substance use beyond HIV risk (Friedman et al., 2019; Tsai & Burns, 2015). Syndemic conditions have been associated with poor HIV prevention and care continua outcomes (Dyer et al., 2012; Friedman et al., 2018; Friedman et al., 2015). Recently, the theory has been used to motivate studies of syndemics among BSMM and their outcomes along the HIV prevention and care continua (Dyer et al., 2012; Friedman et al., 2019; Friedman et al., 2018; Godley & Adimora, 2020; Leblanc et al., 2021). A recent study of syndemic impacts to HIV care engagement among Black men living with HIV found that intersecting stigmas related to race, sexuality, and HIV status were particularly salient in health seeking behavior (Quinn et al., 2018). The authors noted that finding methods to increase social support may aid in minimizing the impacts of these intersecting stigmas (Quinn et al., 2018).

Measures and methods for quantifying the constellations, interactions, and contributions of intersectional stigma to HIV-related health disparities are still in development (Earnshaw et al., 2022). While much of the current intersectional literature is qualitative, researchers across several disciplines seek to capture the complexity of intersectional stigma. McCall (2005) describes multiple forms of complexity in considering intersectional research. She includes intracategorical complexity, which seeks to study complexity within a single social category; while intercategorical complexity seeks to study inequity across social intersections (McCall, 2005). To date, stigma has generally been measured singularly, using an intracategorical approach to assess discrete stigmatized groups; although we note here that an intercategorical approach to assess multiple dimensions of stigma has recently been developed and shown to be predictive of psychosocial distress (Scheim & Bauer, 2019). Intracategorical measures consistent with Goffman’s original theory (Goffman, 1963) and with Earnshaw’s Stigma Framework (Earnshaw et al., 2013) have heretofore focused on assessing separate discrete stigmas (e.g., HIV stigma) rather than measuring co-occurring, intersecting stigmas. Moreover, since these scales are often deployed only within populations affected by these stigmas (e.g., people living with HIV), it is not possible via existing measures to assess the extent to which individuals subject to these stigmas differ from counterparts (e.g., HIV-negative individuals), or how co-occurring stigmas may be prioritized based on health outcomes. However, sexual minorities are frequently subject to multiple, intersecting stigmas that reflect their affiliation with more than one stigmatized group (e.g., older gay Black men living with HIV).

The present analysis

We conducted a secondary data analysis with a large, multi-regional, mixed-serostatus sample of BSMM. An exploratory objective of this analysis was to find any interactions between attributes of stigmatization that may be associated with compound risk for psychosocial syndemic conditions, seeking to improve conceptualizations of intercategorical intersectional stigma. Conditional inference techniques examined the heterogeneity in reported psychosocial syndemic conditions within experienced stigma across five attributes. Examining the heterogeneity in prevalence of psychosocial syndemic conditions by recently experienced stigma can provide refinement of future configurations of intersectional stigma.

Methods

In collaboration with the Center for Black Equity, a serial cross-sectional study called Promoting Our Worth, Equality and Resilience (POWER), recruited a sample of Black sexual minority men (BSMM) and transgender women (TGW) at Black Pride events between 2014 and 2017. Recruitment sites were in Atlanta, GA; Detroit, MI; Houston, TX; Memphis, TN; Philadelphia, PA and Washington, DC. Participants were sampled at official Black Pride events using time location sampling as described in published literature (Magnani et al., 2005). Consenting participants completed tablet-based behavioral surveys using the audio computer-assisted self-survey (ACASI) software. Surveys lasted approximately 20 minutes. Participants were compensated $10 for survey completion. Participants were then offered rapid HIV testing to confirm HIV status in the study. Participants were offered confidential testing by local community-based organizations (CBO) that could link new cases to care later. CBO partners used Clearview STAT-PAK (Alere Inc., Waltham, MA), INSTI (bioLytical Laboratories, Richmond, BC), or OraQuick (OraSure Technologies, Inc., Bethlehem, PA) tests. Participants who declined confidential HIV testing were offered anonymous HIV testing using an OraQuick test kit (OraSure Technologies, Inc., Bethlehem, PA) by POWER staff. Participants who completed either type of HIV testing were compensated an additional $10. More details about study procedures may be found elsewhere (Bukowski et al., 2018; Matthews et al., 2016). All study procedures were approved by the University of Pittsburgh Institutional Review Board (IRB).

Participants

In this analysis, we included participants if they were: (1) age 18 or older; (2) reported being assigned male at birth; (3) currently identified as male; (4) identified as Black or African American; and (5) reported being sexually active with a man in the past year. All records were screened for duplicate responses using a unique identifier code as described in previous literature (Hammer et al., 2003). Of the total completed surveys in POWER (N=5,858), we removed 51 surveys with unconfirmed age; 167 surveys of transgender participants; 11 surveys from intersex participants; 244 surveys of participants who did not self-identify as Black or African American; 654 surveys for lack of sexual activity with a man in the past year; and 301 duplicate records. The analytical sample was N=4,430. Participants had an average age of 30.7 years; 19.4% of the sample had income less than $10,000 annually; 17.3% of the sample had past-year bisexual behavior; 3.3% of the sample identified as Hispanic, and 38% of the sample was living with HIV. Further demographic data about the sample may be found elsewhere (Friedman et al., 2019).

Measures

Experienced stigma.

An adaptation of the Experiences of Discrimination Scale originally developed by Krieger and colleagues (Krieger, 1990; Krieger et al., 2005) was used to identify past-year experienced stigma which participants attributed to any of five attributes: race, sexual minority status (sexuality), socioeconomic status (SES), HIV status, or some other reason. For each attribution, participants could answer: “No”, “Yes”, “Don’t know” and “Refuse to answer.” Each stigmatized attribute (e.g., race) was dichotomized (i.e., no stigmatization because of race vs. any stigmatization because of race). Participants were asked about past-year experienced stigma attributed to race using the question: “In the past year, have you experienced discrimination, been prevented from doing something, or been hassled or made to feel inferior because of your race?” Duplicates of this question assessed stigma attributed to sexual minority status, SES, HIV status or “some other reason.”

Psychosocial syndemic conditions.

For this analysis, the syndemic conditions cluster included conditions of violence (physical assault, intimate partner violence), substance use (polydrug use in the past three months) and depression symptomology (past week likelihood of moderate to depression based on symptomology) as identified in this data previously (Chandler et al., 2020). For these analyses, results were coded dichotomously for each category (e.g., no IPV vs. IPV) in order to create a latent psychosocial syndemic variable, which has been used previously in published literature (Chandler et al., 2022; Chandler et al., 2020).

Physical assault.

Past-year physical assault was assessed by a single question: “In the past year have you been physically assaulted (hit, kicked, beat up or in any other way physically harmed)?”

Intimate partner violence (IPV).

Past-year IPV was assessed by a single question: “In the past year, have you been in a relationship with a partner who has ever hit, kicked, slapped, beaten or in any other way physically assaulted you?”

Polydrug use.

Polydrug use was assessed by reported experiences of using two or more substances at least monthly (amphetamines, cocaine powder, crack cocaine, GHB, heroin, inhalant “poppers”, marijuana, MDMA/ecstasy or opioids [not prescribed to the participant]) in the previous three months

Depression symptomology.

Past-week moderate to severe depressive symptoms, assessed with the Centers for Epidemic Study of Depression 10-item instrument (CESD-10) (Andresen et al., 1994). The measure was dichotomized at 10 points or greater on the scale with a range of 0–30; alpha was 0.913.

Analytical approach

Analyses were completed using R 4.0.2 (The R Foundation). To better understand how experienced intersectional stigmas could contribute to intersectional models, a multivariable conditional inference tree (CTree) analysis was performed using R 4.0.2. Decision trees have been used in computational statistics, and are used with R package partykit (Hothorn, Hornik, Van De Wiel, et al., 2006; Hothorn, Hornik, & Zeileis, 2006). CTree is a machine learning approach to dividing data based on outcome variables and is beginning to be used in studies of intersectionality, particularly in descriptive intersectional analyses. An important reason for this usage is that CTree, one of several decision tree analytic methods, can further differentiate between group differences. CTree allows for the discovery of co-occurring stigma at each iterative partition of the sample, allowing for theorizing about stigma intersections and their impact on the outcome variables. Mahendran et al. (2022) explains, “We define variable selection for descriptive intersectional research as a tool to refine a set of social positions to include in analysis, by identifying the variables most quantitatively relevant to the outcome, a goal that differs from hypothesis testing” (Mahendran et al., 2022, p. 398). Data from simulated estimations in their 2022 analysis noted that CTree was among the promising quantitative methods for use in descriptive intersectional analyses, particularly for samples under 50,000 (Mahendran et al., 2022).

In this analysis, a multivariable CTree was used to explore heterogeneity in the sample and identify homogenous subgroups among attributed stigmas that differed by reported psychosocial syndemic conditions (outcome), iteratively dividing the sample into homogenous subgroups. In other words, the CTree performed divisions of the sample based on the prevalence of psychosocial syndemic conditions according to the stigma attribute with the most variance, identifying groups that are the most similar into terminal nodes for comparison. Each of these terminal nodes represented a specific subgroup from the previous grouping. Post-hoc between-subgroup pairwise comparisons on individual psychosocial syndemic conditions were conducted via general linear and logistic regression models, adjusting for sociodemographic variables. Post-hoc analyses used the glht function from the multcomp package in R, which corrects for family-wise error (Bretz et al., 2016; Hothorn, Hornik, & Zeileis, 2006; Hsu, 1996). The post-hoc analyses compared the terminal nodes (subgroups) in each of the psychosocial syndemic conditions for significant differences by condition.

Results

In the sample, n=938 BSMM (22.1%) reported experiencing multiple stigmas in the previous year. Bisexually behaving, HIV-positive, low-income, and younger (< 40 years old) men were significantly more likely than their respective counterparts to report experiencing multiple stigmas. Race/ethnicity stigma (21.1%) and sexuality stigma (21.3%) were the most common stigma attributions reported, and the most common stigma co-occurrence (13.4%).

Conditional inference tree

Figure 1 displays the results of the CTree analysis which identified six meaningful homogenous subgroups, also known as terminal nodes (Nodes 5–8; 10–11), along with mean estimates of psychosocial syndemic conditions within each node. In the multivariable analysis, heterogeneity in the sample was first identified by stigma attributed to HIV status (Node 1), explaining the most variance in the sample. “Other” forms of stigma (Node 9) further differentiated the sample among those who had experienced HIV status stigma. Among the remaining four groups, experienced stigma attributed to socioeconomic status (Node 2) explained heterogeneity, followed by stigma attributed to race (Node 3) and sexuality (Node 4) compared to a subgroup of participants who had experienced none of the included stigmas (Node 5).

Figure 1.

Figure 1.

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Conditional Inference Tree analysis of experienced stigma by psychosocial syndemic conditions among BSMM, 2014–2017 (N=4430)

Notes:

0 = No (i.e., these participants did not identify this stigma attribution), 1=Yes (i.e., these participants identified this stigma attribution); Nodes 5–8, 10–11 are subgroups for analysis (e.g., Node 11 identifies participants who attributed stigma to the intersection of HIV status and some other stigma, while Node 10 contains participants who only attributed stigma to HIV stigma).

Terminal Nodes:

Node 5: No stigma attributions

Node 6: stigma attributed to sexuality (gay) only

Node 7: Stigma attributed to race only

Node 8: Stigma attributed to SES only

Node 10: Stigma attributed to HIV status only

Node 11: Stigma attributed to HIV status and some “other” stigma

CESD: Depression symptomology

Drug: Polydrug use

IPV: Intimate Partner Violence

Assault: Physical assault

In post-hoc analyses (Figure 2), pairwise comparisons of the six discrete-attributed stigma subgroups (Nodes 5–8; 10–11) identified differences in prevalence of psychosocial syndemic outcomes (i.e., assault, IPV, polydrug use, depression symptomology) controlling for covariates. Supplement 1 contains all post-hoc comparison mean differences.

Figure 2.

Figure 2.

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Post-hoc pairwise comparisons of conditional inference nodes by Assault, Intimate Partner Violence, Polydrug use and Depression symptomatology in BSMM, 2014–2017 (N=4430).

Notes: Y-axis: Identified nodes for subgroup analysis; X-axis: mean differences among subgroups; all analyses completed with a 95% confidence interval.

Assault.

Among reports of past-year assault, those who attributed experienced stigma to HIV status only, the combination of HIV and some other stigma, racial stigma only, or SES only (all p<0.001), and sexuality stigma (p=0.002), all differed significantly from participants who reported no experienced stigma attributions. Additionally, compared to those who attributed experienced stigma to the combination of HIV and some other stigma, those who attributed stigma to sexuality only, race only or SES only differed with significantly less reports of past-year assault (all p<.001). In pairwise comparisons of experienced stigma attributions, attributions to SES stigma only when compared to sexuality stigma only differed significantly (p=0.027); and attributions to SES stigma only when compared to racial stigma only also differed significantly (p=0.041).

IPV.

Among reports of IPV (Figure 2), those who attributed enacted stigma to HIV status only, HIV and some other stigma, sexuality only, race only and SES only (all p<0.001) differed significantly when compared to those who had no stigma attribution. Participants with HIV status and some other stigma attribution differed significantly from those who had attributed stigma to HIV status only in reporting IPV (p<0.001). Participants who attributed experienced sigma only to race when differed significantly when compared to participants who attributed stigma to HIV status only (p=0.011). Compared to those who attributed enacted stigma to the combination of HIV status and some “other” (unspecified) stigmatized attribute, those who reported sexuality stigma only, racial stigma only and socioeconomic stigma only reported significantly less IPV (all p<0.001).

Polydrug use.

Figure 2 also notes that participants differed in their reporting of polydrug use by experienced stigma attribution. Participants who attributed experienced stigma to HIV status, the combination of HIV and some other stigma, and SES (all p<0.001) differed significantly from those who reported no stigma attributions. Similarly, those attributing experienced stigma to race only differed from those reporting no stigma in reports of polydrug use (p=0.030). Participants who attributed stigma to HIV status only differed significantly in their reports of polydrug use compared to those with the combination of HIV and some other stigma (p=0.012). Compared to men who attributed stigma to the combination of HIV status and some other stigma, those who had attributed stigma to their sexuality only or race only had significantly less reports of polydrug use (both p<0.001).

Depression symptomatology.

Lastly, Figure 2 notes that past-week depression symptomatology also differed by experienced stigma attribution. Participants who attributed stigma to HIV status only, HIV status and some other stigma, sexuality only, race only, or SES only all differed significantly from those who reported no stigma (all p<0.001). Participants with HIV and some other stigma attributions differed significantly from those who attributed stigma to HIV status only (p<0.001). Participants who attributed experienced stigma to race only had significantly less reports of depression symptoms compared to those with stigma attributions to HIV only (p=0.035) Compared to those who attributed experienced stigma to HIV status and some other stigma, participants who attributed stigma to sexuality, race or socioeconomic status only reported significantly less past-week depression symptomology (all p<0.001). Lastly, those who attributed stigma to socioeconomic status only compared to race only, differed significantly (p=0.004).

Discussion

Our findings show robust associations of experienced stigma and psychosocial conditions among BSMM in the sample. Previous data has noted poorer health outcomes for sexual and gender minorities who experience intersecting stigmas, as well as those who experience psychosocial syndemic conditions (Chakrapani et al., 2017; Quinn et al., 2018), but this has not been sufficiently explored among BSMM. In the CTree analysis, the interaction of stigma attributed to HIV status and “some other reason” explained the most variance in psychosocial conditions (Node 11). The fact that HIV-related stigma accounted for the most variance in psychosocial outcomes when controlling for HIV status builds on the considerations noted by Bauer (2014) by better integrating intersectionality in population health research to generate new hypotheses and pathways of quantitative inquiry. This finding is consistent with the prioritization of HIV-related stigma by the NIH (Greenwood et al., 2021) and further explains, in a novel manner, how stigma is related to psychosocial conditions. Findings also point to the need to better understand stigma attributions as “some other reason” which emerged as an important factor in our CTree. The stigma attributions (race, sexuality, HIV status, socioeconomic status) included in our analysis are frequently cited in the literature, however, it is possible that researchers are missing a critical area of attribution or that participants find it challenging to articulate a specific attribution. In either case, this area warrants further investigation.

Our CTree analysis provides a more robust method to study interactions among stigmas in future analyses. Our results add innovative assessment and analytical approaches, building on work by Bowleg (2012), Bauer (2014), Earnshaw et al. (2015), and Turan et al. (2017). As researchers query additional forms of stigma, CTree is a method to find intersections among stigmas for more targeted intervention development. As noted by Scheim and Bauer (2019), using discrete categories of stigma is limited by a priori categories, but can aid in the discovery of intersections and considerations for prioritizing types of stigma in future analyses. A requisite step to building future intercategorical intersectional stigma analyses may be to ensure that the current stigma attributes (e.g., race, socioeconomic status) are related to the outcomes of psychosocial syndemic conditions, and what, if any, co-occurrences should be prioritized in future analyses. As intersectional research continues to mature, mixed methods studies will likely qualitatively identify additional forms of intersecting stigma among groups within BSMM (e.g., bisexually identified men, men engaged in sex work, substance using men), and these stigmas can be explored quantitatively once identified.

Limitations

There are limitations to this study. These data were collected at Black Pride events and, therefore, are non-representative of the larger population of BSMM. For example, BSMM who experience greater stigma, or have identified less social support may be less likely to attend pride events; as such, we may be underestimating experienced stigma among other BSMM. Additional considerations are that experienced intersectional stigma was operationalized by relying on recent experienced (rather than internalized/perceived/anticipated) stigma measures and these reports are subject to recognition and recall biases. It should be noted that intersectional stigma cannot rely solely upon the experiences at the individual level but must also seek to understand the power structures that undergird these stigmas as suggested by Smith et al. (2022), which was not possible in this secondary analysis. For example, participants were from across the country, however, policies that may undergird stigmas faced, such as laws related to HIV transmission, have been suggested as sources of stigma. The patchwork nature of these laws across the country may have a differential impact on participants that could not be accounted for in this analysis. As this study was focused on intersecting attributions for experienced stigma, we did not assess the impact on psychosocial health outcomes of intersecting sources and settings where stigma takes place and call for future research to explore intersections between sources of stigma, settings of stigma, structural factors, and their impact on HIV-related psychosocial health outcomes in BSMM. Recall periods for psychosocial syndemic conditions varied based on common practice in previous literature, but future longitudinal analyses may improve upon this limitation by assessing over time. The cross-sectional nature of this analysis allows for the discovery of simultaneous relationships but limits the assessment of causal modeling. We hope that future analyses evaluate stigma longitudinally to better understand the nature of intersectional stigma and stigma intervention.

Conclusion

The findings of this analysis confer the urgent need to address the multiple stigma attributions experienced at the individual level noted here, while offering encouraging future directions for intersectional approaches. This study used CTree to demonstrate how researchers will be able to identify intersections among forms of stigma in future analyses, and calls on researchers to consider stigmas beyond race, class, sexual orientation, and HIV status; although, these stigmas are of great import. In developing interventions for BSMM, a critical factor is the ability to examine the intercategorical stigmas present at multiple levels of the social ecology (i.e., interpersonal, institutional, structural) and across groups within BSMM. Some research points to engaging men in resilience-building strategies (Dulin et al., 2018), however, intersectional approaches require that researchers and communities address the power structures that create and perpetuate the stigma undergirding these sub-optimal health outcomes. Future work may benefit from a more advanced study of intersectional internalized, experienced, and anticipated stigmas. An additional field of exploration is stigma related to PrEP use among BSMM. Although PrEP has been approved for more than a decade, stigma around PrEP use is understudied and may contribute to intersectional stigma, as “PrEP stigma is inextricably linked to HIV stigma” (Golub, 2018, p. 191). We further renew the call of Bauer (2014) and Turan et al. (2017) in suggesting longitudinal study of intersectional stigma and pathways to alleviate stigma. We conclude that stigma reduction interventions may be more successful if they contain resilience-boosting approaches at the intra- and inter-personal levels but must contend with understanding how these social positions are related to power and oppression, particularly regarding minority stress. The integration of quantitative techniques like CTree alongside qualitive explorations is key to uncovering intersecting stigmas impacting BSMM and the development of a suite of interventions necessary to make sustained efforts in eliminating HIV and other health disparities impacting BSMM.

Supplementary Material

Table

Acknowledgements:

We thank the Center for Black Equity and local Black Pride organizations for partnering with us to implement POWER, the community based organizations who performed onsite HIV testing on the study’s behalf, the thousands of study participants who volunteered their time to contribute to this research, and members of the POWER Study Team who made data collection possible. The local Black Pride organizations are as follows: D.C. Black Pride, Detroit’s Hotter than July, Houston Splash, In the Life Atlanta, Memphis Black Pride, and Philadelphia Black Pride. The community based organizations who performed onsite HIV testing are as follows: Atlanta: AID Atlanta, AIDS Health Care Foundation, NAESM; Detroit: Community Health Awareness Group, Horizons Project, Unified; Houston: Avenue 360, Houston AIDS Foundation, Positive Efforts; Memphis: Friends for Life; Philadelphia: Access Matters, Philadelphia FIGHT; Washington, D.C.: Us Helping Us.

Footnotes

Declarations: A portion of this analysis appeared in a substantially different form at the International AIDS Conference 2020 (poster PED0944). The authors have no other declarations.

Conflicts of interest: ACT reports receiving a financial honorarium from Elsevier, Inc. for his role as Co-Editor in Chief of the Elsevier-owned journal SSM-Mental Health.

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