Skip to main content
NCBI home page
NIHPA Author Manuscripts logoLink to NIHPA Author Manuscripts
. Author manuscript; available in PMC: 2017 Jun 1.
Published in final edited form as: J Community Health. 2016 Jun;41(3):574–583. doi: 10.1007/s10900-015-0132-z

Discrimination and Hate Crimes in the Context of Neighborhood Poverty and Stressors Among HIV-positive African-American Men Who Have Sex with Men

Sannisha K Dale 1, Laura M Bogart 2, Frank H Galvan 3, Glenn J Wagner 4, David W Pantalone 5, David J Klein 6
PMCID: PMC4842332  NIHMSID: NIHMS747558  PMID: 26696119

African-American men in the United States represent the largest portion of all new HIV diagnoses among men and women in comparison to other racial/ethnic groups.1 Male-male sex is the primary route of HIV transmission, and African-American men who have sex with men (MSM) are at the highest risk for HIV acquisition compared to both White MSM and other racial/ethnic minority MSM.1 In addition, African-American MSM who are HIV-positive have higher rates of HIV medication (antiretroviral therapy or ART) nonadherence and viral ART failure (when ART fails to suppress viral load) than Whites MSM.2 Research suggests that sociocultural and environmental factors, such as discrimination, violence, and poverty3–6 may contribute to the sexual risk behavior of African-Americans, as well as the health disparities experienced by African-American MSM in comparison to MSM of other racial/ethnic groups. African-American MSM stand at the crossroads of multiple identities—race, gender, and sexual orientation—and, also, are exposed to macro-level social inequities such as neighborhood-related stressors and poverty.

Theories of intersectionality7 describes how such co-occurring social identities (e.g., race, gender, sexual orientation) are experienced as interlocking (e.g. “Black man” versus “Black” and “man”) at the individual level, and reflect larger social inequities and oppression at the macro-level (e.g., racism, heterosexism, poverty). Intersectionality theory holds that these identities and inequities exist together, and that a deeper understanding of societal issues and marginalized populations can be obtained by moving beyond examining each identity in isolation. From an intersectionality perspective, there is significant value added by examining intersecting identities in conjunction with social inequities compared to examining identities without taking into consideration social context and inequities.7,8

HIV-positive African-American MSM are subjected to discrimination, stigma, and hate crimes related to their multiple identities—racial identity, HIV status, and sexual orientation—each of which are associated with worse health outcomes for this group. For example, research on a convenience sample of HIV-positive African-American MSM found high frequencies of all three types of discrimination experiences (race/ethnicity, 40%; HIV status, 38%; and sexual orientation, 33%) and, further, that greater perceived discrimination was significantly related to lower HIV medication adherence over six months, greater AIDS symptoms, lower likelihood of having an undetectable viral load, and higher likelihood of having visited a hospital emergency department.4,9 A recent study of HIV-positive African-American and Latino MSM found that perceived HIV stigma and sexual minority stigma (in terms of internalized stigma, as well as perceived discrimination events) were significantly associated with depression10 and, among HIV-positive men and women, HIV stigma was linked to greater HIV-related symptoms and HIV medication nonadherence.11 Similarly, reports of sexual orientation-related hate crimes have been associated with depression, posttraumatic stress disorder (PTSD), and anxiety among gay men and lesbians.12,13

HIV-positive African-American MSM experience discrimination and stigma within a greater system of structural oppression and inequity at the macro-level.14–16 Structural inequities are visible in neighborhood characteristics, such as quality housing,17 access to employment and adequate wages,18 availability of good schools,19 crime,10 and poverty.14 HIV-positive African-American MSM often reside within disenfranchised neighborhoods with high poverty15, 21, 22 and significant neighborhood-related stressors23 (e.g., negative neighborhood conditions such as vandalism and litter). A qualitative study24 highlighted that Black MSM’s lives are impacted by unemployment, racial discrimination, police surveillance/harassment, and incarceration. These researchers25 also noted additional themes in the lives of Black MSM, including environmental stress (i.e. “The stress of the streets”).

Literature on neighborhood characteristics and discrimination echoes the importance of examining neighborhood level variables as a context for discrimination. Dailey and colleagues39 found that, for African-American women, residing in socioeconomically disadvantaged neighborhoods and having 12 years or less of education were associated with fewer reports of racial discrimination, which may be a consequence of fewer opportunities to interact with non-African Americans in their communities. Similarly, Crawford and colleagues40 found that experiencing less drug use-related discrimination was associated with neighborhoods (where participants spent most of their time) with a higher proportion of Black residents. This small body of literature suggests that neighborhood factors may be associated with both lower and higher levels of discrimination.

In addition to the socio-demographic composition of the neighborhood, neighborhood-related stressors (e.g. litter, vandalism) and poverty may also relate to discrimination among HIV-positive African-American MSM. Neighborhoods with higher poverty and crime may be patrolled more frequently by police officers, who may racially profile and harass African-American men and could further discriminate if an individual’s sexual orientation or HIV status are revealed during the course of the interaction or while in police custody.24,41

In sum, prior research suggests that HIV-positive African-American MSM are negatively impacted by neighborhood-related stressors and poverty at the macro-level, which may set the stage for discrimination and hate crimes related to three intersecting, targeted identities at the individual level. However, no research exists examining how neighborhood factors—such as poverty and neighborhood-related stressors—relate to perceived discrimination among HIV-positive African-American MSM. Thus, in the present study, we examined neighborhood factors that may be associated with perceived chronic discrimination, as well as instances of discrimination-related violence (i.e., hate crimes), in a sample of HIV-positive African-American MSM living in Los Angeles, CA. Greater understanding of these associations may have important implications for public health and community-level interventions to reduce the stigma faced by HIV-positive African-American MSM.

METHODS

In our sample of HIV-positive African-American MSM recruited from Los Angeles, CA, we examined the associations between poverty and neighborhood related stressors with perceived discrimination and hate crimes based on race, sexual orientation, and HIV status. Given that the existing literature suggests that neighborhood factors may be associated with both lower and higher levels of discrimination, we explored whether, and in what ways, poverty and neighborhood-related stressors were significantly associated with higher perceived discrimination and hate crimes.

The sample consisted of 214 HIV-positive African-American men recruited by staff who handed out and posted fliers advertising a study of “HIV treatment, attitudes, and behaviors” at HIV medical clinics and community social service agencies in Los Angeles, CA, from January 2007 through February 2009. Potential participants were phone screened to determine if they met the five inclusion criteria: (1) age 18 years or older; (2) Black/African-American racial/ethnic identity; (3) self-identification as male; (4) HIV-positive serostatus; and (5) prescribed ART. This study was approved by the institutional review boards at RAND Corporation, Charles Drew University of Medicine and Science, and Boston Children’s Hospital, and the study data were protected by a federal Certificate of Confidentiality issued by the National Institutes of Health. All participants provided informed consent and received a $30 honorarium. At the study visit, participants provided data (via an audio computer assisted interview [ACASI]) on socio-demographic variables, perceived discrimination, neighborhood-related stressors, and residential ZIP codes.

Measures

Perceived discrimination

The 30-item Multiple Discrimination Scale (MDS)9,13 was used to capture perceived discrimination in the past year based on African-American/Black race/ethnicity (MDS-Black), HIV-serostatus (MDS-HIV), and sexual orientation (MDS-Gay). Participants were asked to respond “yes” or “no” to questions about whether they experienced any of 10 different discriminations events including interpersonal discrimination, institutional discrimination, or violence. Sample items include, “In the past year, were you denied a job or did you lose a job because you are Black/African-American?” (tapping racial discrimination), “In the past year, were you ignored, excluded, or avoided by people close to you because someone knew or suspected that you are HIV-positive?” (tapping HIV-related discrimination), and “In the past year, did someone insult or make fun of you because someone thought you were gay?” (tapping gay-related discrimination). Parallel items were used for each subscale (10 items each, summed separately, possible score range 0–10), which have shown good reliability13 and construct validity (associated with mental health symptoms, AIDS symptoms, and HIV medication nonadherence) in prior samples.4,9,13 Cronbach’s alpha for the subscales were .91 (Black-related), .86 (HIV-related), and .85 (gay-related).

Hate crimes

Based on adaption of selected items from a published Anti-Gay Violence and Victimization scale,12 12 questions were used to capture hate crimes experienced within the past year. Although the measure was originally developed specifically for gay-related victimization, we took four of the questions and created parallel items to assess hate crimes related to African-American/Black race/ethnicity, HIV-positive serostatus, and gay sexual orientation. Questions asked about police harassment (e.g., “During the past year, were you harassed by police because you are Black or African-American?”), physical attack (e.g., “During the past year, were you hit, beaten, or physically attacked because someone thought you were gay?”), property damage (e.g., “During the past year, was your personal property damaged or destroyed because you have HIV?”), and sexual assault (“During the past year, were you raped or sexually assaulted because you are Black or African-American?”), with response options 0 = no, 1 = yes. The four questions for each victimization type were summed separately (possible score range 0–4). Cronbach’s alpha coefficients were .57 (Black-related), .80 (HIV-related), and .77 (gay-related).

Poverty

Participants’ residential ZIP codes and U.S. Census data were used to determine neighborhood poverty rates. Residential ZIP codes were geocoded and linked to census tracts. Prior research studies have utilized census tract poverty levels and have found significant associations with health outcomes.26,31 We used the U.S. Census Bureau to download poverty data (i.e., number of residents living in poverty) for each participant’s census tract. For poverty rates, we used 5-year averages (2006–10) from the U.S. Census Bureau’s American Community Survey (ACS); because the ACS samples a fraction of residents and some tracts have sparse data, 5-year averages were used. Poverty rates (i.e., proportion of people in poverty) were derived by dividing the number of residents living in poverty in the neighborhood by the total number of residents in the neighborhood.

Neighborhood-related stressors

Participants reported information on seven perceived neighborhood-related stressors (vandalism, litter, burglary, vacant housing, people selling drugs, people getting robbed on the street, groups of teenagers hanging out on the street). For each stressor, response options included “not a problem,” “somewhat of a problem,” or “a big problem.” Items were adapted from two existing validated scales.42,43 Participants’ responses to the seven items were averaged and possible total average scores ranged from 0 to 2. Cronbach’s alpha in the present study was .90.

Socio-demographic characteristics

On a self-report measure, participants provided information on their age (derived from date of birth), education level (dichotomized into < high school diploma versus ≥ high school diploma), annual household income (dichotomized into < $5,000 versus ≥ $5,000), employment (dichotomized into two groups of employed full/part-time versus other), sexual orientation (dichotomized into heterosexual versus other [i.e., bisexual, gay/same-gender loving, something else, not sure or in transition, or don’t know]), and housing status dichotomized into stable (rent or own apartment or home; subsidized housing) or unstable (residential treatment facility, temporary/transitional housing, living rent-free with relative/friend, homeless). Men were also asked whether they had ever had sex (oral or anal) with another man in their lifetime; affirmative responses were taken to indicate MSM status for the purpose of this analysis.

Statistical Analyses

We used SAS version 9.3 to conduct all analyses. We first generated descriptive statistics for socio-demographics, neighborhood factors, and perceived discrimination and hate crime experiences, and computed Pearson correlations to assess bivariate relations between poverty and neighborhood-related stressors and between the types of perceived discriminations and hate crimes. Multivariate linear regressions controlling for age, education, employment status, housing situation, and income were run with poverty and neighborhood-related stressors as predictors and perceived discrimination and hate crime experiences as outcomes.

RESULTS

Of the total sample of 214 African-American HIV-positive men, 192 provided residential ZIP code data. A total of 162 of the 192 men identified as MSM (i.e., ever having sex with men) and, thus, comprise our analytic sample. Among those 162 men, the average age was 43.7 (SD = 8.2); 38% earned an annual income below $5000; 84% were unemployed; and 21% had less education than a high school diploma (see Table 1). In our sample of MSM, 67% identified as gay/same-gender loving and 18% as bisexual. The majority of participants reported stable housing situations (44% rented or owned their apartment or home; 12% lived in subsidized housing) and 43% reported unstable housing. Frequencies of perceived discrimination and hate crimes were low and participants indicated moderate to high rates of poverty and neighborhood-related stressors (see Table 1).

TABLE 1.

Sample Socio-demographic Characteristics, Neighborhood Factors, and Discrimination/Hate Crimes Experiences

Sample Characteristics Mean (SD)/N(%)
Socio-demographics
  Age 43.7 (8.3)
  Education
    Less than high school diploma 33 (21%)
    Completed high school 96 (60%)
    Some college 32 (20%)
  Income
    Below $5,000 61 (38%)
    $5,001-$11,999 55 (35%)
    $12,001-$15,999 19 (12%)
    $16,000 or more 24 (15%)
  Unemployed 136 (84%)
  Sexual orientation
    Heterosexual 19 (12%)
    Gay/same-gender loving 108 (67%)
    Bisexual 29 (18%)
    Not sure or in transition, something else, or don’t know 5 (3%)
  Housing status
    Own or rent home or apartment 72 (44%)
    Subsidized housing 19 (12%)
    Homeless 11 (7%)
    Residential treatment facility 11 (7%)
    Living rent-free with friend/relative 21 (13%)
    Temporary/transitional housing 25 (15%)
    Other 3 (2%)
Neighborhood factors
  Neighborhood poverty rate 29% (17%)
  Neighborhood-related stressors 0.48 (0.54)
Discrimination and hate crime experiences
  Discrimination
    HIV-related 1.11 (1.79)
    Black-related 1.36 (1.86)
    Gay-related 1.30 (1.94)
  Hate crimes
    HIV-related 0.25 (0.79)
    Black-related 0.69 (0.91)
    Gay-related 0.37 (0.94)
Open in a new tab

Note. The sample size was n = 162.

As shown in Tables 2 and 3, results of the multivariate linear regressions showed that higher neighborhood poverty was significantly related to more frequent experiences with gay-related hate crimes (b = 1.15, SE = .43, p = .008). Across all three identities, higher perceived neighborhood-related stressors were significantly related to more frequent perceived discrimination events (Black-related: b = .91, SE = .28, p = .001; gay-related: b = .71, SE = .29, p = .01; and HIV-related: b = .65, SE = .28, p = .02) and hate crimes (gay-related: b = .48, SE = .13, p < .001; and Black-related: b = .28, SE = .14, p <.05).

TABLE 2.

Multivariable Regression Analyses of the Associations Between Neighborhood Factors and Discrimination

Neighborhood Factors and Discrimination Unstandardized
Estimate		 Standard
Error		 P value
Black-related Discrimination
    Poverty −.57 .89 .52
    Neighborhood-related stressors .91 .28 .001
    Age −.03 .02 .06
    Low education −.06 .36 .87
    Employment −.85 .41 .04
    Stable housing −.33 .30 .27
    Low income −.26 .31 .41
Gay-related Discrimination
    Poverty −.25 .93 .79
    Neighborhood-related stressors .71 .29 .01
    Age −.05 .02 .01
    Low education −.27 .38 .48
    Employment −.41 .43 .34
    Stable housing −.48 .32 .13
    Low income −.37 .33 .26
HIV-related Discrimination
    Poverty −.25 .88 .78
    Neighborhood-related stressors .65 .28 .02
    Age −.01 .02 .69
    Low education .10 .36 .77
    Employment −.14 .41 .74
    Stable housing −.49 .30 .10
    Low income −.08 .31 .79
Open in a new tab

Note. The sample size was n = 162.

TABLE 3.

Multivariable Regression Analyses of the Associations Between Neighborhood Factors and Hate Crimes

Neighborhood Factors and Hate Crimes Unstandardized
Estimate		 Standard
Error		 P value
Black-related Hate crimes
    Poverty .75 .44 .09
    Neighborhood-related stressors .28 .14 <.05
    Age −.01 .01 .10
    Low education −.01 .18 .93
    Employment −.04 .20 .86
    Stable housing −.15 .15 .32
    Low income −.02 .15 .90
Gay-related Hate crimes
    Poverty 1.15 .43 .008
    Neighborhood-related stressors .48 .13 <.001
     Age −.02 .01 .03
    Low education −.06 .17 .74
    Employment −.27 .20 .17
    Stable housing .06 .15 .70
    Low income .12 .15 .42
HIV-related Hate crimes
    Poverty .62 .39 .11
    Neighborhood-related stressors .12 .12 .32
    Age .00 .01 .69
    Low education −.02 .16 .89
    Employment −.05 .18 .78
    Stable housing −.01 .13 .94
    Low income .24 .14 .08
Open in a new tab

Note. The sample size was n = 162.

Neighborhood-related stressors were not significantly related to HIV-related hate crimes. In addition, poverty was not significantly associated with Black- or HIV-related hate crimes nor associated with Black-, HIV-, or gay-related perceived discrimination.

DISCUSSION

Among HIV-positive African-American MSM residing in Los Angeles, CA, we found that neighborhood-related stressors were significantly associated with perceived discrimination and hate crime experiences related to the men’s HIV-positive status, Black racial identity, and gay identity (or perceptions thereof). Also, neighborhood poverty was associated with gay-related hate crimes. To our knowledge, this is the first study to report associations between neighborhood-related stressors and poverty with perceived discrimination and hate crimes, and results are notable given that HIV-positive African-American MSM often live in neighborhoods with high poverty rates and neighborhood-related stressors.11–14 Our results also suggest that neighborhood-related stressors, poverty, and discrimination are facets of oppression that occur together and, thus, need to be addressed systemically across levels by policies, programs, and interventions.

Prior research has found significant associations between (a) discrimination and negative mental and physical health outcomes and behaviors, including mental health symptoms (e.g., PTSD and depression),10,12 AIDS symptoms, and HIV medication nonadherence,4,11 and (b) poverty and environmental stressors with mental health 32,34,36 and physical health.29,31,33 Our study extends this prior work by emphasizing that perceived discrimination and hate crimes are important factors to include in discussions about environmental factors and poor health. Some previous findings, published by Dailey and colleagues,39 detail an inverse association between disadvantaged neighborhoods and racial discrimination among 1249 African-American women. These findings were not replicated in our sample, that is, we found no association between poverty and racial discrimination in our sample of African-American MSM, perhaps due to our smaller sample size compared to this prior research study.

Poverty was associated with gay-related hate crime experiences, but not Black- or HIV-related hate crimes. High poverty environments may have a larger concentration of African-American persons, which contributes to high levels of residential racial segregation.44 People who reside in segregated communities may be less likely to interact with individuals of different races (especially in their social circles) and, thus, may be more likely to experience hate crimes unrelated to race, such as gay-related hate crimes. Further, although high levels of homophobia have been reported in African-American communities, this is not necessarily unique in comparison to other racial communities in the U.S.45,46 In a large sample (7,000 Blacks and 43,000 Whites), Lewis45 found that African Americans reported greater disapproval of homosexuality than Whites, but African-Americans were more likely to support laws prohibiting antigay discrimination than Whites perhaps due to their historical experience with legally sanctioned discrimination based on race.

Neighborhood-related stressors were significantly associated with higher Black- and gay-related discrimination and hate crimes, as well as HIV-related discrimination, but not HIV-related hate crimes. Neighborhoods with higher crime may be patrolled more frequently by police officers, who may racially profile and harass African-American individuals.41 In addition, HIV status is the most concealable of the three identities (known primarily through disclosure)47 because race is usually perceived based on appearance (although not always accurately)48,49 and sexual orientation can be known by partners, and may be assumed by others either by viewing interactions between partners or by observing other gender nonconforming behaviors.50 The higher visibility of race and sexual orientation compared to HIV status may, in part, explain the significant associations between environmental stressors with gay-related and Black-related hate crimes, but not with HIV-related hate crimes. Future research is needed to improve our understanding regarding inconsistent associations between environmental stressors and hate crimes.

Implications

Additional research with larger samples is needed to assess the mechanisms through which poverty and environmental stressors relate to discrimination and hate crime experiences among HIV-positive African-American MSM. Understanding the pathways by which neighborhood level factors are linked to discrimination and hate crimes could provide insights about how better to address these issues to reduce the frequency of their occurrence or to ameliorate their impact. More sophisticated research designs or analyses could also examine the geo-spatial (incorporates geographical and spatial features) associations among neighborhood factors and discrimination experiences. Geospatial analyses can incorporate aspects of the adjacent neighborhoods (and their features, such as poverty rates) to participant’s neighborhoods, to improve our understanding of whether the characteristics of adjacent neighborhoods relate to participant’s discrimination experiences. For example, in racially segregated cities, individuals may reside in a majority African-American community with high poverty that borders a community that is middle- to upper-class and majority White. Thus, their racial discrimination experiences might be low within the confines of their own community (consistent with findings by Dailey et al.39), but high when they interact with members of the neighboring community. Future studies are also needed to look at potential moderators, mediators, and protective factors of the relationships between poverty/neighborhood-related stressors and discrimination/hate crimes, and to examine these associations in other geographic regions’ cities as well as whether similar patterns emerge for more suburban/rural areas.

To inform future individual-level interventions, qualitative research with African-American MSM with HIV might help us to (a) understand the complex ways in which different neighborhood contexts (both inside and outside their own neighborhood) may be linked to discrimination experiences related to their intersecting identities; and (b) to learn about strategies that might be used to cope effectively with discrimination and neighborhood inequalities. To address macro-level community factors, intervention efforts are needed to reduce structural inequalities such as neighborhood poverty and stressors, and decrease structural discrimination (such as discrimination from law enforcement). Efforts at the community/neighborhood level can address structural inequalities (i.e., poverty, environmental stressors, unemployment), discrimination, and hate crimes. Programs (e.g., job creation and access to affordable housing) that promote individual- and community-level socioeconomic empowerment may help to alleviate some structural inequalities. Discrimination and hate crimes may be perpetrated by a wide variety of individuals, including members of communities where African-American MSM reside, individuals outside the communities, providers working in the communities, and institutions such as law enforcement and employers. Thus, interventions should be designed to decrease discrimination and hate crimes across domains. Interventions may include enforcement of discrimination statutes when crimes are committed, holding law enforcement officials accountable when they engage in discriminatory practices, and creating/delivering mandatory and recurring training programs on discrimination for providers and employers. Sustainable changes at the institutional and structural level could take generations to occur and, ideally, the target of intervention and prevention efforts should be individuals and systems engaging in discriminatory practices. However, in the meantime, interventions also are needed for HIV-positive African-American MSM themselves, to (a) enhance coping strategies to deal with discrimination and hate crimes and (b) provide case management and resources to obtain employment, a stable income, and housing.

Limitations

As with any individual study, there are several limitations to our methods and findings. This study is a cross-sectional design that prevents the ability to draw causal conclusions. In addition, our sample of HIV-positive African-American MSM was based in the Los Angeles, CA, area, which may limit the generalizability of our findings to other geographical locations. The Cronbach’s alpha for the Black-related hate crimes subscale was relatively low (alpha = .57) but, given that the scale was a broad list of distinct hate crime experiences, it is not concerning that the list of experiences would not be highly correlated (e.g., someone who experienced police harassment due to race may not have experienced rape as a result of race). Further, while we examined how neighborhood factors and perceived discrimination/hate crimes were related, discrimination or hate crime events may have occurred outside of the participant’s community and, therefore, may reflect how HIV-positive African-American MSM coming from poor neighborhoods are disenfranchised in the larger societal context. The noted findings between neighborhood factors and discrimination/hate crimes might reflect co-occurring structural disadvantages faced by African-American MSM with HIV and does not imply that neighborhood factors cause discrimination for this group. Also, the U.S. Census data collection and estimates (used to derive poverty rates) have methodological weaknesses, including undercounting of ethnic minorities, the absence of individual-level data (e.g., household size), sampling error linked to data being gathered from a proportion of the U.S. population instead of the entire population, and the potential double counting of some households who receive and complete more than one census form.51,52 In addition, zip codes were utilized to capture neighborhood poverty and some may correspond to very large areas with both high- and low- income neighborhoods. Reports of perceived discrimination and hate crimes were low in our overall sample, although hate crimes and more overt forms of discrimination (as assessed by the MDS) can be expected to be lower frequency than daily microaggressions (i.e., exchanges that send demeaning messages to individuals based on their group membership).53 Finally, the literature is divided on how best to capture intersectionality quantitatively, and our approach of capturing perceived discrimination among African-American MSM with HIV by using parallel measures for three identities differed from another approach that may have used one quantitative measure to assess discrimination across all three identities together.54,55 However, a psychometrically valid measure assessing discrimination across all three identities together (Black-, HIV-, and gay-related) is not available in the published literature. We also advance that the process, inquiry, and focus on multiple identities is an important aspect of doing intersectionality research, irrespective of the particular method that is utilized.56

Conclusions

Our study reports novel findings for a sample of HIV-positive African-American MSM recruited at HIV medical clinics and community social service agencies in Los Angeles, CA, showing that neighborhood factors were associated with more experiences of perceived discrimination and hate crimes related to HIV status, African-African ethnicity/race, and being perceived as gay. Thus, programs, policies, and interventions need to address neighborhood-related stressors, poverty, and discrimination among HIV-positive African-American MSM.

Our study also provides important implications for future research that can build on the foundation of our knowledge in this area and inform prevention and intervention efforts at the community level, such as the enforcement of anti-discrimination policies and laws, and interventions at the individual level to increase adaptive strategies to cope with discrimination and other forms of oppression.

Acknowledgements

This research was funded by the National Institute of Mental Health [R01MH072351, PI: Laura M. Bogart]. Some of Sannisha K. Dale’s time was covered by 1K23MH108439-01 from the National Institute of Mental Health. We would like to express gratitude to Charisma Acey, Denedria Banks, E. Michael Speltie, and Kellii Trombacco for their assistance; Charles Hilliard, PhD, and the staff and clients of SPECTRUM at the Charles Drew University of Medicine and Science for their contribution to the project; as well as the staff of AIDS Project Los Angeles, Minority AIDS Project, and OASIS for their support.

Footnotes

Contributors

S. K. Dale conducted the literature review, conceptualized the data analysis plan in collaboration with L. M. Bogart and D. J. Klein, and led the writing of this article. L. M. Bogart led the primary study design, and helped with the data analysis conceptualization, results interpretation, and drafting of the manuscript. F. H. Galvan and G. J. Wagner assisted in designing the primary study and interpreting the results. D. W. Pantalone helped to interpret the results. D. J. Klein managed the dataset, conducted the statistical analyses, and helped to interpret the results. All authors reviewed and provided feedback on the draft article and approved the final version.

Contributor Information

Sannisha K. Dale, Department of Psychiatry, Massachusetts General Hospital, Boston, MA and Harvard Medical School, Boston, MA..

Laura M. Bogart, Health Unit, RAND Corporation, Santa Monica, CA..

Frank H. Galvan, Department of Research and Evaluation, Bienestar Human Services, Inc., Los Angeles, CA..

Glenn J. Wagner, Health Unit, RAND Corporation, Santa Monica, CA..

David W. Pantalone, University of Massachusetts Boston and The Fenway Institute of Fenway Health, Boston, MA..

David J. Klein, RAND Corporation, Santa Monica, CA..

References

  • 1.Centers for Disease Control and Prevention. [Accessed March 22, 2013];HIV Surveillance - Epidemiology of HIV Infection (through 2011) 2013 http://www.cdc.gov/hiv/topics/surveillance/resources/slides/general/index.htm.
  • 2.Beer L, Oster AM, Mattson CL, Skarbinski J Medical Monitoring, Project. Disparities in HIV transmission risk among HIV-infected black and white men who have sex with men, United States, 2009. AIDS. 2014;28(1):105–114. doi: 10.1097/QAD.0000000000000021. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 3.Land Helen, Linsk Nathan. HIV Stigma and Discrimination: Enduring Issues. Journal of HIV/AIDS & Social Services. 2013;12(1):3–8. doi: 10.1080/15381501.2013.765717. [Google Scholar]
  • 4.Bogart LM, Wagner GJ, Galvan FH, Klein DJ. Longitudinal relationships between antiretroviral treatment adherence and discrimination due to HIV-serostatus, race, and sexual orientation among African-American men with HIV. Annals of Behavioral Medicine. 2010;40(2):184–190. doi: 10.1007/s12160-010-9200-x. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 5.Mayer KH, Wang L, Koblin B, Mannheimer S, Magnus M, Del Rio C Team Hptn Protocol. Concomitant Socioeconomic, Behavioral, and Biological Factors Associated with the Disproportionate HIV Infection Burden among Black Men Who Have Sex with Men in 6 U.S. Cities. PLoS One. 2014;9(1):e87298. doi: 10.1371/journal.pone.0087298. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 6.Bowleg L, Neilands T, Tabb L, Burkholder G, Malebranche D, Tschann JM. Neighborhood Context and Black Heterosexual Men’s Sexual HIV Risk Behaviors. AIDS and Behavior. 2014:1–12. doi: 10.1007/s10461-014-0803-2. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 7.Crenshaw KW. Demarginalizing the intersection of race and sex: A Black feminist critique of antidiscrimination doctrine, feminist theory, and antiracist politics. University of Chicago Legal Forum. 1989;(139):139–167. [Google Scholar]
  • 8.Collins PH. Black feminist thought: Knowledge, consciousness, and the politics of empowerment. New York: Routledge; 1991. [Google Scholar]
  • 9.Bogart LM, Landrine H, Galvan FH, Wagner GJ, Klein DJ. Perceived discrimination and physical health among HIV-positive Black and Latino men who have sex with men. AIDS and Behavior. 2013;17(4):1431–1441. doi: 10.1007/s10461-012-0397-5. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 10.Wohl A, Galvan F, Carlos J-A, et al. A Comparison of MSM Stigma, HIV Stigma and Depression in HIV-Positive Latino and African American Men who have Sex with Men (MSM) AIDS and Behavior. 2013;17(4):1454–1464. doi: 10.1007/s10461-012-0385-9. [DOI] [PubMed] [Google Scholar]
  • 11.Vanable PA, Carey MP, Blair DC, Littlewood RA. Impact of HIV-Related Stigma on Health Behaviors and Psychological Adjustment Among HIV-Positive Men and Women. AIDS and Behavior. 2006;10(5):473–482. doi: 10.1007/s10461-006-9099-1. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 12.Herek GM, Berrill KT. Hate Crimes: Confronting Violence Against Lesbians and Gay Men. Newbury Park, CA: Sage Publications; 1992. [Google Scholar]
  • 13.Bogart LM, Wagner GJ, Galvan FH, Landrine H, Klein DJ, Sticklor LA. Perceived discrimination and mental health symptoms among Black men with HIV. Cultural Diversity and Ethnic Minority Psychology. 2011;17(3):295–302. doi: 10.1037/a0024056. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 14.Massey Douglas S, Gross Andrew B, Eggers Mitchell L. Segregation, the concentration of poverty, and the life chances of individuals. Social Science Research. 1991;20(4):397–420. doi: http://dx.doi.org/10.1016/0049-089X(91)90020-4. [Google Scholar]
  • 15.Raymond HF, Chen YH, Syme SL, Catalano R, Hutson MA, McFarland W. The role of individual and neighborhood factors: HIV acquisition risk among high-risk populations in San Francisco. AIDS and Behavior. 2014;18(2):346–356. doi: 10.1007/s10461-013-0508-y. [DOI] [PubMed] [Google Scholar]
  • 16.Malebranche DJ, Bowleg L. Beyond Gay, Bisexual, or DL: Structural Determinants of HIV Sexual Risk among Black Men in the United States. In: Treadwell HM, Xanthos C, Holden KB, editors. Social determinants of health among African-American men. John Wiley & Sons; 2012. p. 161. [Google Scholar]
  • 17.Schill Michael H, Wachter Susan M. Housing market constraints and spatial stratification by income and race. Housing Policy Debate. 1995;6(1):141–167. [Google Scholar]
  • 18.Cohen Philip N. Black Concentration Effects on Black-White and Gender Inequality: Multilevel Analysis for U.S. Metropolitan Areas. Social Forces. 1998;77(1):207–229. [Google Scholar]
  • 19.Orfield Gary, Bachmeier Mark D, James David R, Eitle Tamela. Deepening Segregation in American Public Schools: A Special Report from the Harvard Project on School Desegregation. Equity & Excellence in Education. 1997;30(2):5–24. [Google Scholar]
  • 20.Shihadeh Edward S, Steffensmeier Darrell J. Economic Inequality, Family Disruption, and Urban Black Violence: Cities as Units of Stratification and Social Control. Social Forces. 1994;73(2):729–751. [Google Scholar]
  • 21.Sullivan PS, Peterson J, Rosenberg ES, et al. Understanding Racial HIV/STI Disparities in Black and White Men Who Have Sex with Men: A Multilevel Approach. PLoS One. 2014;9(3):e90514. doi: 10.1371/journal.pone.0090514. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 22.Diez-Roux AV, Kiefe CI, Jacobs DR, et al. Area characteristics and individual-level socioeconomic position indicators in three population-based epidemiologic studies. Annals of Epidemiology. 2001;11(6):395–405. doi: 10.1016/s1047-2797(01)00221-6. [DOI] [PubMed] [Google Scholar]
  • 23.Bruce MA, Beech BM, Sims M, Brown TN, et al. Social environmental stressors, psychological factors, and kidney disease. Journal of Investigative Medicine. 2009;57(4):583–589. doi: 10.231/JIM.0b013e31819dbb91. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 24.Bowleg L, Teti M, Malebranche DJ, Tschann JM. “It’s an Uphill Battle Everyday”: Intersectionality, Low-Income Black Heterosexual Men, and Implications for HIV Prevention Research and Interventions. Psychology of Men & Masculinity. 2013;14(1):25–34. doi: 10.1037/a0028392. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 25.Bowleg L, Teti M, Malebranche DJ, Tschann JM. An uphill battle everyday: Exploring the implications of structural factors for sexual HIV risk among Black heterosexual men; Denver, CO. 138th American Public Health Association Annual Meeting and Exposition.2010. [Google Scholar]
  • 26.Krieger N, Chen JT, Waterman PD, Soobader MJ, Subramanian SV, Carson R. Geocoding and monitoring of US socioeconomic inequalities in mortality and cancer incidence: does the choice of area-based measure and geographic level matter?: the Public Health Disparities Geocoding Project. American Journal of Epidemiology. 2002;156(5):471–482. doi: 10.1093/aje/kwf068. [DOI] [PubMed] [Google Scholar]
  • 27.Lopez-De Fede A, Stewart JE, Hardin JW, Mayfield-Smith K, Sudduth D. Spatial visualization of multivariate datasets: an analysis of STD and HIV/AIDS diagnosis rates and socioeconomic context using ring maps. Public Health Reports. 2011;126(Suppl 3):115–126. doi: 10.1177/00333549111260s316. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 28.Springer Yuri P, Samuel Michael C, Bolan Gail. Socioeconomic Gradients in Sexually Transmitted Diseases: A Geographic Information System-Based Analysis of Poverty, Race/Ethnicity, and Gonorrhea Rates in California, 2004–2006. American Journal of Public Health. 2010;100(6):1060–1067. doi: 10.2105/AJPH.2009.172965. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 29.Schootman M, Jeffe DB, Baker EA, Walker MS. Effect of area poverty rate on cancer screening across US communities. Journal of Epidemiology & Community Health. 2006;60(3):202–207. doi: 10.1136/jech.2005.041020. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 30.Schootman M, Jeffe DB, Lian M, Gillanders WE, Aft R. The role of poverty rate and racial distribution in the geographic clustering of breast cancer survival among older women: a geographic and multilevel analysis. American Journal of Epidemiology. 2009;169(5):554–561. doi: 10.1093/aje/kwn369. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 31.Subramanian SV, Chen JT, Rehkopf DH, Waterman PD, Krieger N. Racial disparities in context: a multilevel analysis of neighborhood variations in poverty and excess mortality among black populations in Massachusetts. American Journal of Public Health. 2005a;95(2):260–265. doi: 10.2105/AJPH.2003.034132. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 32.Nandi A, Glass TA, Cole SR, et al. Neighborhood poverty and injection cessation in a sample of injection drug users. American Journal of Epidemiology. 2010;171(4):391–398. doi: 10.1093/aje/kwp416. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 33.Volkova N McClellan W, Klein M, et al. Neighborhood poverty and racial differences in ESRD incidence. Journal of the American Society of Nephrology. 2008;19(2):356–364. doi: 10.1681/ASN.2006080934. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 34.Bennett T. Perceived Health Effects of Litter and Trash by Inner City Residents. (Honors), The Ohio State University; 2012. Retrieved from http://hdl.handle.net/1811/51932. [Google Scholar]
  • 35.Linares LO. Social Connection to Neighbors, Multiple Victimization, and Current Health Among Women Residing in High Crime Neighborhoods. Journal of Family Violence. 2004;19(6):347–358. [Google Scholar]
  • 36.Doyle S, Kelly-Schwartz A, Schlossberg M, Stockard J. Active Community Environments and Health: The Relationship of Walkable and Safe Communities to Individual Health. Journal of the American Planning Association. 2006;72(1):19–31. [Google Scholar]
  • 37.Frye V Koblin B, Chin J, et al. Neighborhood-level correlates of consistent condom use among men who have sex with men: a multi-level analysis. AIDS and Behavior. 2010;14(4):974–985. doi: 10.1007/s10461-008-9438-5. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 38.Buttram ME, Kurtz SP. Risk and protective factors associated with gay neighborhood residence. American Journal of Mens Health. 2013;7(2):110–118. doi: 10.1177/1557988312458793. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 39.Dailey AB, Kasl SV, Holford TR, Lewis TT, Jones BA. Neighborhood- and individual-level socioeconomic variation in perceptions of racial discrimination. Ethnicity & Health. 2010;15(2):145–163. doi: 10.1080/13557851003592561. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 40.Crawford ND, Borrell LN, Galea S, Ford C, Latkin C, Fuller CM. The influence of neighborhood characteristics on the relationship between discrimination and increased drug-using social ties among illicit drug users. Journal of Community Health. 2013;38(2):328–337. doi: 10.1007/s10900-012-9618-0. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 41.Weatherspoon Floyd D. Racial Profiling of African-American Males: Stopped, Searched, and Stripped of Constitutional Protection. John Marshall Law Review. 2004;38:439. [Google Scholar]
  • 42.Ewart CK, Suchday Sonia. Discovering how urban poverty and violence affect health: Development and validation of a neighborhood stress index. Health Psychology. 2002;21(3):254–262. doi: 10.1037//0278-6133.21.3.254. [DOI] [PubMed] [Google Scholar]
  • 43.Steptoe Andrew, Feldman Pamela J. Neighborhood problems as sources of chronic stress: Development of a measure of neighborhood problems, and associations with socioeconomic status and health. Annals of Behavioral Medicine. 2001;23(3):177–185. doi: 10.1207/S15324796ABM2303_5. [DOI] [PubMed] [Google Scholar]
  • 44.Iceland J, Sharpe C, Steinmetz E. Class differences in African American residential patterns in US metropolitan areas: 1990–2000. Social Science Research. 2005;34(1):252–266. doi: http://dx.doi.org/10.1016/j.ssresearch.2004.02.001. [Google Scholar]
  • 45.Lewis GB. Black-White Differences in Attitudes toward Homosexuality and Gay Rights. Public Opinion Quarterly. 2003;67(1):59–78. [Google Scholar]
  • 46.Poindexter CC, Linsk NL, Warner RS. “He Listens… and Never Gossips:” Spiritual Coping without Church Support among Older, Predominantly African-American Caregivers of Persons with HIV. Review of Religious Research. 1999;40(3):230. [Google Scholar]
  • 47.Derlega VJ, Winstead BA, Greene Kathryn, Serovich Julianne, Elwood William N. Perceived HIV-related Stigma and HIV Disclosure to Relationship Partners after Finding Out about the Seropositive Diagnosis. Journal of Health Psychology. 2002;7(4):415–432. doi: 10.1177/1359105302007004330. [DOI] [PubMed] [Google Scholar]
  • 48.Campbell ME, Troyer L. The Implications of Racial Misclassification by Observers. American Sociological Review. 2007;72(5):750–765. [Google Scholar]
  • 49.Herman MR. Do You See What I Am?: How Observers’ Backgrounds Affect Their Perceptions of Multiracial Faces. Social Psychology Quarterly. 2010;73(1):58–78. [Google Scholar]
  • 50.Johnson KL, Gill S, Reichman V, Tassinary LG. Swagger, sway, and sexuality: Judging sexual orientation from body motion and morphology. Journal of Personality and Social Psychology. 2007;93(3):321. doi: 10.1037/0022-3514.93.3.321. [DOI] [PubMed] [Google Scholar]
  • 51.Ronzio CR. Ambiguity and discord in U.S. Census data on the undercount, race/ethnicity and SES: Responding to the challenge of complexity. DataCrítica: International Journal of Critical Statistics. 2007;1(1):11–17. [Google Scholar]
  • 52.Brooks T, Khatiwada S, Vargas J, McCurry M. The U.S. census Bureau and American Community Survey: Advantages, Uses, and Limitations. Rural Life Census Data Center Newsletter. 2008 http://pubstorage.sdstate.edu/AgBio_Publications/articles/rs3-08.pdf.
  • 53.Sue DW. Microaggressions in Everyday Life: Race, Gender, and Sexual Orientation. Hoboken, New Jersey: John Wiley & Sons, Inc.; 2010. [Google Scholar]
  • 54.Hancock A-M. When multiplication doesn’t equal quick addition: examining intersectionality as a research paradigm. Perspective on Politics. 2007;5(1):63–79. [Google Scholar]
  • 55.Bauer GR. Incorporating intersectionality theory into population health research methodology: Challenges and the potential to advance health equity. Social Science & Medicine. 2014;110:10e17. doi: 10.1016/j.socscimed.2014.03.022. [DOI] [PubMed] [Google Scholar]
  • 56.Hancock A-M. Intersectionality as a Normative and Empirical Paradigm. Politics & Gender. 2007;3 [Google Scholar]

RESOURCES