Abstract
Objectives. To examine the effects of within-neighborhood and neighboring characteristics on discrimination, stigma, mental health, and HIV outcomes among Black women living with HIV (BWLWH).
Methods. A total of 151 BWLWH in a southeastern US city provided baseline data (October 2019‒January 2020) on experienced microaggressions and discrimination (race-, gender-, sexual orientation-, or HIV-related), mental health (e.g., depression, posttraumatic stress disorder), and HIV outcomes (e.g., viral load, antiretroviral therapy adherence). Neighborhood characteristics by census tract were gathered from the American Community Survey and the National Center for Charitable Statistics. Spatial econometrics guided the identification strategy, and we used the maximum likelihood technique to estimate relationships between a number of predictors and outcomes.
Results. Within-neighborhood and neighboring characteristics (employment, education, crime, income, number of religious organizations, and low-income housing) were significantly related to intersectional stigma, discrimination, mental health, HIV viral load, and medication adherence.
Conclusions. Policy, research, and interventions for BWLWH need to address the role of neighborhood characteristics to improve quality of life and HIV outcomes. (Am J Public Health. 2022;112(S4):S433–S443. https://doi.org/10.2105/AJPH.2021.306675)
Black women living with HIV (BWLWH) are disproportionately affected by HIV and accounted for 64% of all new HIV diagnoses in 2018.1 In addition, Black or African Americans represented 43% of all deaths among people living with HIV (PLWH) in the United States in 2018.1 This may be attributable to intersectional systems of oppression that manifest at the neighborhood (e.g., employment rates, housing), interpersonal (e.g., discrimination), and individual (e.g., mental health, health behaviors) levels to have an adverse impact on the lived experiences of BWLWH.2–5 However, few scholars have used an intersectional lens incorporating neighborhood factors to improve our understanding among BWLWH. Spatial analyses provide an opportunity to examine the potential influence of factors of neighboring areas in addition to within-neighborhood factors.6
The theory of intersectionality7 states that interwoven systems act in concert to perpetuate discrimination and oppression and postulates that discrimination and oppression are best understood in the context of multiple marginalized identities and manifest differently based on the nature of this intersectionality. Furthermore, Berger conceptualized “intersectional stigma” specifically among women of color living with HIV as a process through which they face structural oppression and barriers to political participation including racism, sexism, and classism, and stigmatization of drug use, sex work, and HIV status.8,9 In addition, the social‒ecological model of health promotion posits that HIV health promotion, via increased retention and engagement in care, is affected by the complex interplay of individual-level (e.g., housing and spirituality), interpersonal-level (e.g., experiences of discrimination, microaggressions, and trauma), and community-level factors (e.g., neighborhood deprivation and poverty).10
Neighborhood context (e.g., poverty and crime rates) has been negatively associated with adverse mental and physical health outcomes (e.g., HIV viral load [VL] and depression) among PLWH.11–13 Poverty prevents access to basic needs (e.g., food, shelter) and medical care, and chronic stress from crime and poverty may make it difficult to prioritize medication adherence, which may compromise immune functioning.12 In addition, neighborhood factors related to housing reflect a legacy of structural racism—for example, through gentrification, “White flight,” the disproportionate burden of evictions on Black women, and the intentional use of public housing to segregate Black Americans historically.14,15 Housing may be a resilience resource; for instance, in the United States, programs such as the Housing Choice Voucher Program (Section 8) are prominent for their ability to provide affordable and safe housing in the private market. Religious congregations (e.g., churches) have also been a source of resilience within Black communities, and religion and spirituality have been positively associated with physical and mental health‒related quality of life among PLWH.16 Nonetheless, the direct associations between neighborhood factors and intersectional discrimination, mental health, and HIV-related outcomes among BWLWH is unknown.
Intersectional stigma,8 discrimination, and microaggressions (which are defined as subtle acts of discrimination) based on race, gender, and HIV status significantly compromise the mental and physical health of BWLWH. HIV stigma and discrimination have been positively associated with poor viral suppression, and, among BWLWH, depression and posttraumatic stress disorder (PTSD) symptoms have been linked to gendered racial microaggressions and racial-, gender-, and HIV-related discrimination.4,5 In addition, PTSD and depression are associated with increased HIV disease progression.17 However, the relationship between neighborhood-level factors and microaggressions and discrimination aimed at multiple identity axes for BWLWH has yet to be explored.
Spatial econometrics is a theoretical and empirical methodology that is designed to encapsulate the effects of potential geographic dependencies and their influences.6 Over the past 2 decades, spatial econometric methods have become increasingly important in the applications to social science, although rarely in the context of HIV and mental health. Neighborhoods influence each other, and spatial econometrics is able to incorporate these interdependencies, which are missing from previous work among BWLWH. Missing from the health literature is that the characteristics of neighboring communities (in addition to within-neighborhood factors) may be important in explaining variation in health outcomes. Accounting for these neighboring effects will help to create policies that will reduce health disparities within and across vulnerable communities.
Given the dearth of studies examining the impact of characteristics of within-neighborhood and neighboring areas on intersectional discrimination and stigma, mental health, and HIV outcomes among BWLWH, we used spatial econometrics techniques to examine neighborhood characteristics in relation to discrimination, microaggressions, mental health, and HIV outcomes. Findings have the potential to inform future research and multilevel interventions addressing intersectional discrimination and stigma and the health of BWLWH.
BWLWH residing in South Florida were recruited between October 2019 and January 2020. Recruitment included sharing flyers and posters at community health clinics and centers, hospitals, and community events. Potential participants who expressed interest completed a phone screen to determine their eligibility. Eligible participants were scheduled for an in-person baseline visit and enrolled in the study if they met the following inclusion criteria:
| 1. | aged 18 years and older; |
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| 2. | English speaking; |
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| 3. | identifying as Black (racial identity), African American (racial‒ethnic identity), or both; |
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| 4. | cis-gender female; |
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| 5. | living with HIV; |
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| 6. | owning a cell phone with text messaging and Internet capability; and |
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| 7. | capable of understanding and completing the informed consent process and procedures. |
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During the baseline visit, participants completed (1) informed consent, (2) questionnaires using the Research Electronic Data Capture, and (3) a semistructured clinical interview. Participants received a $75 stipend for the visit.
Self-report demographic information included the participants’ age, ethnicity, sexual orientation, relationship status, education level, employment status, and annual household income.
We used the Gendered Racial Microaggressions Scale for Black Women,18 a 26-item measure assessing the lifetime frequency and stress appraisal (level of stress resulting from each experience) of microaggressions encountered by Black women (e.g., “Someone accused me of being angry when I was speaking in a calm manner”). Internal reliability estimates (frequency α = 0.92; appraisal α = 0.95) for the scale have been good among samples of BWLWH.5
We used the HIV Microaggression Scale,19 a 14-item instrument measuring experiences (in past 3 months) of subtle insults stemming from HIV-related stigma (e.g., “You heard someone say, ‘I’m HIV negative; I’m clean’ ”). The internal reliability (α = 0.83) for the HIV Microaggression Scale has been good among community-based samples of PLWH.
We used the LGBT (lesbian, gay, bisexual, and transgender) People of Color Microaggression Scale,20 an 18-item instrument assessing microaggressions experienced (in the past 3 months) on the basis of being both a person of color and a sexual or gender minority (e.g., “Being rejected by potential dating or sexual partners because of your race/ethnicity”). The scale has shown good internal reliability (α = 0.89) among racially/ethnically diverse LGBT individuals.20
We used the Multiple Discrimination Scale21 to capture discrimination (in the past year) on the basis of race, sexual orientation, and living with HIV, and it was adapted to capture gender. We used 13 items to assess each type of discrimination with a total of 56 items (e.g., “In the past year, were you denied a job or did you lose a job because you are a woman?”). The scale has shown good construct validity and reliability (α for race subscale = 0.83; α for sexual-orientation subscale = 0.86; α for HIV subscale = 0.85).21
We used the 17-item Life Events Checklist22 to assess exposure to traumatic events involving actual or threatened death, serious injury, and violence. The checklist has shown good internal reliability (α = 0.78) among women living with HIV.
The 20-item PTSD Checklist23 assessed the severity of PTSD symptoms. Participants were asked to endorse symptomology (in the past month) related to their worst or most distressing traumatic event. The checklist has shown great internal reliability (α = 0.97) among PLWH.
We used the 20-item self-report Center for Epidemiological Studies Depression Scale24 to assess depressive symptoms (e.g., “I had crying spells”). The scale has shown great reliability (α = 0.88–0.98) and validity in studies focused on women living with HIV.5
We used the Mini-International Neuropsychiatric Interview for DSM-5 (Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition),25 a widely used semistructured clinical interview, to assess current major depressive disorder, PTSD, suicidality, alcohol use disorder, and substance use disorder.
At the baseline visit, blood was collected to assay for HIV viral load by using the Roche COBAS AmpliPrep/COBAS TaqMan HIV-1 Test, v2.0. A VL cutoff of less than 200 copies per millimeter was used in our analyses for viral suppression and a cutoff of less than 20 was defined as undetectable.
To capture self-reported HIV medication adherence for the past 4 weeks, we used a pre-existing item26: “Thinking about the past 4 weeks, how would you rate your ability to take all your medications as your doctor prescribed them?” Responses ranged from 1 = “very poor” to 6 = “excellent” on a 6-point Likert scale.
To gather characteristics of each participant’s neighborhood, we utilized the open-access Web sites of the American Community Survey (ACS)27 and the National Center for Charitable Statistics (NCCS) Data Archive.28 The ACS is administered by the US Census Bureau more frequently (monthly and annually) than the US Census.27 Neighborhood characteristics (e.g., employment rate, median income, education) were collected by census tract from the 2019 ACS (5-year estimates).
The NCCS collects information filed with the Internal Revenue Service (IRS) by tax-exempt nonprofit organizations. The IRS Business Master File contains descriptive information on each of these organizations, and the 2020 file28 was used to collect variables of interest (e.g., number of Christian religious institutions, number of low-income and subsidized rental housing).
Participant addresses were geocoded to latitudes and longitudes and census tract using the US Census Bureau27 Geocoder service. For each participant, the ACS and NCCS data were merged by census tract. The organizations in the IRS business file were geocoded to census tracts. To estimate the availability of potential nonprofit services for each participant, for each census tract, the number of each type of charitable entity in the NCCS data set was merged with the participant data.
Women’s responses to the National Crime Victimization Survey29 were also used as a proxy for neighborhood crime given barriers to reporting and underreporting.30 This survey assesses experiences of 7 major types of crime victimization—assault (aggravated and simple), burglary, robbery, identity theft, motor vehicle theft, rape, and sexual assault. Participants indicated if they had experienced situations related to each type of crime in the past 12 months. If a participant experienced crime victimization in the past 12 months, they were asked to indicate the frequency of the crime(s).
Maximum likelihood techniques (via Stata version 16.1; StataCorp LLC, College Station, TX) estimated all models, and we used the Wald test (χ2 distributed) to assess if the spatial correlation parameters were jointly significant. For dichotomous outcomes (e.g., diagnoses of depression, PTSD), we used the spatial probit models and assumed the spatial interactions were in the covariates and not the error terms. If spatial dependence is in the error terms for the probit model, then the multivariate normal cumulative distribution function has “n” integrals because of correlation across space and, hence, the likelihood function does not have a closed form solution—this is computationally infeasible because the number of integrals grows with the sample size.6 The unit of observation was participant nested within census tract. Participants’ longitude and latitude values (computed from addresses) were used to compute the physical distance within and across census tracts.
The spatial Durbin Error model (see Elhorst31), is given as
| (1) |
| (2) |
The variable hi represents the dependent variable for the ith individual’s outcome (e.g., discrimination, stigma, mental health, VL); xik is the kth covariate or regressor that is used to explain variations in the outcomes of the ith person. The term represents the weighted average of neighboring communities’ observations for the respective characteristics that is used to capture interdependencies among the n observations in the covariates between neighbor i and j (for i≠j). This term represents another source of exogenous variations that explain variations in the outcome variable. The term is an error term, and captures the spatial dependence in the error term—the assumption of independence is violated. This is used to capture latent variables that explain variations in outcomes via neighboring communities. Additional details are provided in Appendix A (available as a supplement to the online version of this article at http://www.ajph.org).
Among the 151 BWLWH participants, mean age was 53.5 years (range = 21–69); 64.8% had a high-school diploma or above; 85.4% identified as heterosexual; 73.5% had an annual household income of $11 999 or less; and 82.8% identified with a Christian denomination. Table 1 provides additional descriptive information on sociodemographics, intersectional discrimination, mental health, HIV outcomes, and neighborhood factors.
TABLE 1— Sociodemographics and Characteristics Among 151 Black Women Living With HIV: South Florida, October 2019‒January 2020
TABLE 1— Sociodemographics and Characteristics Among 151 Black Women Living With HIV: South Florida, October 2019‒January 2020
| Sociodemographics | Mean ±SD or No. (%) |
| Age | 53.5 ±10.5 |
| Ethnicity | |
| Non-Hispanic | 146 (96.7) |
| Hispanic | 5 (3.3) |
| Education | |
| Eighth grade or lower | 6 (4.0) |
| Some high school | 48 (32.0) |
| High-school graduate or GED | 58 (38.7) |
| Some college | 24 (16.0) |
| College graduate | 9 (6.0) |
| Some graduate school | 2 (1.3) |
| Graduate school degree | 3 (2.0) |
| Sexual orientation | |
| Heterosexual | 124 (85.5) |
| Same gender loving (gay or lesbian) | 4 (2.8) |
| Bisexual | 12 (8.3) |
| Asexual | 4 (2.8) |
| Income, $ | |
| < 5 000 | 36 (27.3) |
| 5 000‒11 999 | 61 (46.2) |
| 12 000‒15 999 | 11 (8.3) |
| 16 000‒24 999 | 11 (8.3) |
| 25 000‒34 999 | 6 (4.6) |
| 35 000‒49 999 | 5 (3.8) |
| ≥ 50 000 | 2 (1.5) |
| Housing | |
| Renting home or apartment | 84 (55.6) |
| Living in home or apartment owned by you or someone else | 19 (12.6) |
| Residential drug, alcohol, or other treatment facility | 2 (1.3) |
| Publicly subsidized housing | 26 (17.2) |
| A friend’s or relative’s home or apartment | 12 (8.0) |
| Temporary or transitional housing | 2 (1.3) |
| Homeless: sleeping in a shelter | 5 (3.3) |
| Other | 1 (0.7) |
| Employment | |
| Full-time work | 13 (8.6) |
| Part-time work | 14 (9.3) |
| Full-time or part-time in school | 1 (0.7) |
| Neither in work nor in school | 15 (9.9) |
| On disability | 101 (66.9) |
| Other | 4 (2.6) |
| I choose not to answer | 6 (4.0) |
| Religion | |
| Christian | 40 (26.5) |
| Catholic | 6 (4.0) |
| Baptist | 77 (51.0) |
| Protestant | 2 (1.3) |
| Jewish | 1 (0.7) |
| Islamic | 0 (0) |
| Other | 5 (3.3) |
| None | 13 (8.6) |
| I choose not to answer | 7 (4.6) |
Note. GED = general educational development test. A larger version of Table 1 (Table A, available as a supplement to the online version of this article at http://www.ajph.org) presents additional information.
Older age was associated with lower HIV microaggressions (b = ‒0.195; P < .05), depressive symptoms (b = ‒0.311; P < .01), PTSD symptoms (b = ‒0.479; P < .01), posttraumatic cognitions (b = ‒0.806; P < .05), and lower likelihood of diagnoses of suicidality (b = ‒0.0443; P < .01), PTSD (b = ‒0.0298; P < .05), and alcohol use disorder (b = ‒0.0309; P < .01; Tables 2–4). Women’s annual household income was not associated with microaggression, discrimination, or mental health; however, higher household income was associated with higher likelihood of having HIV viral suppression (b = 0.214; P < .01) or an undetectable VL (b = 0.263; P < .05).
TABLE 2— Microaggression and Discrimination Scores Associated With Neighborhood and Neighboring Factors: South Florida, October 2019‒January 2020
TABLE 2— Microaggression and Discrimination Scores Associated With Neighborhood and Neighboring Factors: South Florida, October 2019‒January 2020
| Appraisal GRMS, b (95% CI) | Frequency GRMS, b (95% CI) | HIV Microaggression, b (95% CI) | MDS HIV, b (95% CI) | MDS Sexual Orientation, b (95% CI) | |
| Participants’ age | −0.02 (−0.03, 0.002) | −0.02 (−0.03, 0.0002) | −0.20 (−0.35, –0.04) | −0.03 (−0.06, 0.01) | −0.02 (−0.05, 0.01) |
| Neighborhood median income | −0.0004 (−0.02, 0.01) | −0.002 (−0.02, 0.01) | 0.13 (0.001, 0.26) | 0.02 (−0.01, 0.05) | 0.05 (0.02, 0.07) |
| W_neighborhood median income | 0.002 (0.0002, 0.003) | 0.001 (−0.0001, 0.003) | 0.002 (−0.01, 0.01) | −0.001 (−0.004, 0.002) | −0.0004 (−0.003, 0.002) |
| Total crime | 0.001 (0.0001, 0.001) | 0.001 (0.00003, 0.001) | 0.002 (−0.003, 0.01) | 0.0002 (−0.001, 0.002) | −0.0001 (−0.001, 0.001) |
| W_ neighborhood educationa | −0.09 (−0.17, –0.02) | −0.07 (−0.15, 0.002) | −0.25 (−0.89, 0.39) | 0.03 (−0.14, 0.20) | 0.04 (−0.10, 0.18) |
| Neighborhood employment | −0.02 (−0.04, 0.0001) | −0.009 (−0.03, 0.01) | −0.17 (−0.33, –0.01) | −0.05 (−0.09, –0.01) | −0.02 (−0.06, 0.01) |
| Low-income housing | −0.88 (−2.06, 0.30) | −0.76 (−1.90, 0.39) | −11.90 (−22.19, –1.61) | −1.21 (−3.77, 1.35) | −0.19 (−2.30, 1.92) |
| Spatial in the error | −0.01 (−0.01, –0.001) | −0.01 (−0.01, 0.0003) | −0.01 (−0.01, 0.001) | −0.003 (−0.01, 0.004) | −0.004 (−0.01, 0.003) |
Note. CI = confidence interval; GRMS = Gendered Racial Microaggression Scale; MDS = Multiple Discrimination Scale. The coefficients are the change of microaggression and discrimination scores associated with per-unit increase in the neighborhood and neighboring factors. A larger version of Table 2 (Table B, available as a supplement to the online version of this article at http://www.ajph.org) presents variables with nonsignificant findings.
aThe variable beginning with “W_” represents neighboring areas (they provide an estimate of 
).
TABLE 3— Mental Health Outcomes Associated With Neighborhood and Neighboring Area Factors: South Florida, October 2019‒January 2020
TABLE 3— Mental Health Outcomes Associated With Neighborhood and Neighboring Area Factors: South Florida, October 2019‒January 2020
| Traumas (LEC), b (95% CI) | Depressive Symptoms (CESD), b (95% CI) | PTSD Symptoms (PCL), b (95% CI) | Posttraumatic Cognitions (PTCI), b (95% CI) | Adherence (Past 3 wk), b (95% CI) | Viral Load (Log), b (95% CI) | |
| Participants’ age | −0.04 (−0.10, 0.02) | −0.31 (−0.49, –0.13) | −0.48 (−0.81, –0.15) | −0.81 (−1.47, –0.14) | −0.004 (−0.02, 0.01) | −0.01 (−0.01, –0.03) |
| W_ neighborhood median income | 0.005 (0.0004, 0.01) | 0.01 (−0.003, 0.03) | 0.026 (−0.01, 0.06) | 0.04 (−0.01, 0.10) | −0.0003 (−0.002, 0.002) | −0.001 (−0.002, 0.0003) |
| Total crime | 0.002 (−0.0002, 0.004) | 0.01 (0.001, 0.01) | 0.005 (−0.01, 0.02) | 0.03 (0.01, 0.06) | −0.001 (−0.002, –0.0002) | 0.001 (0.00001, 0.001) |
| Neighborhood education | 5.08 (−8.72, 18.89) | −34.99 (−75.15, 5.16) | −33.95 (−109.50, 41.61) | −44.79 (−199.10, 109.53) | −3.12 (−7.38, 1.15) | −4.72 (−8.36, –1.07) |
| W_ neighborhood education | −0.43 (−0.70, –0.16) | −0.81 (−1.59, –0.02) | −2.30 (−3.93, –0.67) | −2.45 (−5.43, 0.54) | −0.02 (−0.12, 0.08) | 0.01 (−0.06, 0.79) |
| Neighborhood employment | −0.05 (−0.11, 0.02) | −0.08 (−0.26, 0.11) | −0.42 (−0.77, –0.07) | −0.22 (−0.91, 0.48) | −0.01 (−0.03, 0.01) | −0.02 (−0.03, 0.001) |
| W_ neighborhood employment | 0.002 (0.001, 0.004) | 0.004 (−0.001, 0.01) | 0.01 (0.004, 0.02) | 0.01 (−0.01, 0.03) | 0.0003 (−0.0003, 0.001) | 0.0003 (−0.0001, 0.001) |
| Christian organizations | −0.38 (−1.27, 0.52) | −2.21 (−4.83, 0.40) | −2.81 (−7.88, 2.26) | −11.60 (−21.42, –1.77) | −0.05 (−0.35, 0.25) | −0.01 (−0.25, 0.22) |
| Spatial in the error | −0.004 (−0.01, 0.01] | −0.004 (−0.01, 0.005) | 0.0002 (−0.01, 0.01) | −0.003 (−0.01, 0.01) | 0.002 (−0.01, 0.01) | −0.004 (−0.01, 0.003) |
Note. CESD = Center for Epidemiological Studies Depression Scale; LEC = Life Events Checklist; PCL = Posttraumatic Stress Disorder Checklist; PTCI = Total Post Traumatic Cognition Inventory. The variables beginning with “W_” represent neighboring areas (they provide an estimate of 
). The coefficients are the change of mental health and HIV outcomes associated with per unit increase in the neighborhood and neighboring factors. A larger version of Table 3 (Table C, available as a supplement to the online version of this article at http://www.ajph.org) presents variables with nonsignificant findings.
TABLE 4— Z-score for Binary Mental Health and HIV Conditions Associated With Neighborhood and Neighboring Factors: South Florida, October 2019‒January 2020
TABLE 4— Z-score for Binary Mental Health and HIV Conditions Associated With Neighborhood and Neighboring Factors: South Florida, October 2019‒January 2020
| Viral Load Undetectable, b (95% CI) | Viral Load Suppression, b (95% CI) | Major Depressive Disorder Current, b (95% CI) | Suicidality, b (95% CI) | PTSD Current, b (95% CI) | Alcohol Consumption (12 mo), b (95% CI) | Substance Use (12 mo), b (95% CI) | |
| Participants’ age | 0.01 (−0.01, 0.04) | 0.01 (−0.01, 0.04) | −0.01 (−0.03, 0.01) | −0.04 (−0.07, –0.02) | −0.03 (−0.06, –0.003) | −0.03 (−0.05, –0.01) | −0.02 (−0.05, 0.003) |
| Participants’ household income | 0.26 (0.03, 0.49) | 0.21 (0.04, 0.38) | −0.00004 [−0.19, 0.19) | −0.09 (−0.28, 0.10) | −0.12 (−0.33, 0.08) | −0.07 (−0.24, 0.10) | −0.23 (−0.48, 0.01) |
| Neighborhood median income | −0.02 (−0.04, –0.002) | −0.03 (−0.05, –0.001) | −0.005 (−0.02, 0.01) | 0.004 (−0.02, 0.03) | 0.01 (−0.01, 0.03) | 0.005 (−0.01, 0.02) | 0.003 (−0.02, 0.03) |
| W_ neighborhood median income | 0.003 (0.001, 0.005) | 0.001 (−0.001, 0.004) | 0.002 (−0.001, 0.004) | −0.003 (−0.01, 0.002) | 0.001 (−0.001, 0.003) | 0.0005 (−0.001, 0.002) | 0.001 (−0.001, 0.003) |
| Total crime | −0.0003 (−0.001, 0.001) | −0.001 (−0.002, 0.0001) | 0.001 (−0.00002, 0.001) | −0.002 (−0.01, 0.001) | 0.001 (0.0004, 0.002) | 0.001 (−0.001, 0.003) | 0.001 (0.0004, 0.002) |
| Neighborhood education | 6.81 (1.20, 12.43) | 6.84 (0.24, 13.40] | −5.20 (−10.91, 0.51) | −7.16 (−13.53, –0.80) | 2.52 (−3.61, 8.64) | 0.94 (−4.53, 6.42) | 0.34 (−5.52, 6.20) |
| W_ neighborhood education | −0.06 (−0.16, 0.05) | 0.002 (−0.12, 0.12) | −0.18 (−0.30, –0.05) | 0.16 (−0.05, 0.37) | −0.06 (−0.17, 0.05) | −0.06 (−0.17, 0.04) | −0.06 (−0.16, 0.05) |
| Neighborhood employment | 0.03 (0.004, 0.06) | 0.026 [−0.004, 0.06) | −0.02 (−0.05, 0.004) | 0.005 (−0.02, 0.03) | −0.02 (−0.05, 0.01) | −0.03 (−0.06, –0.01) | 0.01 (−0.01, 0.04) |
| W_ neighborhood employment | −0.001 (−0.001, 0.0001) | −0.001 (−0.002, 0.0002] | 0.001 (0.0003, 0.002) | −0.001 (−0.001, 0.0001) | 0.0003 (−0.0004, 0.001) | 0.0004 (−0.0002, 0.001) | 0.0001 (−0.001, 0.001) |
| Christian organizations | −0.14 (−0.56, 0.28) | 0.28 (−0.11, 0.67) | −0.60 (−1.01, –0.20) | 0.47 (−0.26, 1.19) | 0.15 (−0.54, 0.25) | −0.15 (−0.51, 0.21) | −0.07 (−0.46, 0.33) |
Note. The variables beginning with “W_” represent neighboring areas (they provide an estimate of 
). The estimates are probit model estimates, not the marginal effects. The coefficients are the change of z score for the mental health and HIV conditions associated with per-unit increase in the neighborhood and neighboring factors. A larger version of Table 4 (Table D, available as a supplement to the online version of this article at http://www.ajph.org) presents variables with nonsignificant findings.
Higher crime was associated with higher gendered racial microaggression (GRM) frequency (0.000652; P < .05) and appraisal (b = 0.000726; P < .05; Table 2). Higher employment was associated with lower HIV microaggressions (b = ‒0.171; P < .05) and lower HIV-related discrimination (b = ‒0.0510; P < .05). Similarly, higher number of low-income and subsidized rental housing was associated with lower HIV microaggressions (b = ‒11.90; P < .05). Conversely, higher neighborhood median income was associated with higher HIV microaggressions (b = 0.129; P < .05) and higher sexual orientation‒related discrimination (b = 0.0478; P < .01).
Higher education in neighboring areas was associated with lower GRM appraisal (b = ‒0.0918; P < .05). However, higher neighboring median income was associated with higher GRM appraisal (b = 0.00153; P < .05).
Higher crime was associated with higher depressive symptoms (b = 0.00733; P < .05) and posttraumatic cognitions (b = 0.0344; P < .01; Tables 3 and 4). Also, higher crime was associated with higher likelihood of diagnoses of PTSD (b = 0.00131; P < .01) and substance use disorder (b = 0.00126; P < .01). Higher employment was related to lower PTSD symptoms (b = ‒0.420; P < .05) and lower likelihood of alcohol use disorder (b = ‒0.0329; P < .05). Higher education was associated with lower likelihood of suicidality (b = ‒7.160; P < .05). In addition, higher number of Christian organizations was associated with lower posttraumatic cognitions (b = ‒11.60; P < .05) and lower likelihood of depression diagnosis (b = ‒0.604; P < .01).
Higher education in neighboring areas was associated with a lower number of traumas (b = ‒0.429; P < .01), lower depressive symptoms (b = ‒0.806; P < .05), lower PTSD symptoms (b = ‒2.299; P < .01), and lower likelihood of depression diagnosis (b = ‒0.177; P < .01). Higher neighboring employment was associated with higher traumas (b = 0.00241; P < .01), higher PTSD symptoms (b = 0.0138; P < .01), and higher likelihood of depression diagnosis (b = 0.00119; P < .01). Higher neighboring median income was also associated with higher traumas (b = 0.00515; P < .05).
Higher crime was associated with lower medication adherence within the past month (b = ‒0.000866; P < .05) and higher VL log (b = 0.000591; P < .05; Tables 3 and 4). Conversely, higher education was associated with lower VL log (b = ‒4.715; P < .05) and higher likelihood of HIV viral suppression (b = 6.844; P < .05) and undetectable VL (b = 6.814; P < .05). Similarly, higher employment was associated with higher likelihood of undetectable VL (b = 0.0296; P < .05). However, higher median income was associated with lower likelihood of HIV viral suppression (b = ‒0.0253; P < .05) and undetectable VL (b = ‒0.0224; P < .05).
Contrary to the direction for within- neighborhood, neighboring higher median income was associated with higher likelihood of undetectable VL (b = 0.00324; P < .01).
Analyses indicated that there was significant variation in the spatial distribution of GRM frequency (b = ‒0.00743; P < .05) and appraisal (b = ‒0.00775; P < .05), suggesting that, in addition to the neighborhood variables mentioned previously, some unknown latent characteristics of neighboring areas may influence GRM.
To our knowledge, this is the first study among BWLWH to examine how within-neighborhood and neighboring characteristics relate to intersectional discrimination and stigma, mental health, and HIV outcomes among BWLWH using spatial econometrics techniques. We present novel findings as well as results consistent with existing literature. Higher crime victimization (used as a proxy for neighborhood crime) was associated with higher GRM frequency and appraisal, suggesting that women who are facing more crime are also subjected to more GRM. In addition, higher neighborhood employment was associated with lower HIV microaggressions and HIV-related discrimination, and access to affordable housing was associated with lower HIV microaggressions. This suggests that access to jobs and housing may serve as protective factors and is consistent with literature linking unstable housing and HIV-related stigma.32
Similarly, higher education in neighboring areas was associated with lower GRM appraisal, suggesting that when people in neighboring communities are more educated, BWLWH are less distressed by GRM. However, higher within-neighborhood median income was associated with higher HIV microaggressions and higher sexual orientation‒related discrimination, and higher median income in neighboring areas was associated with higher GRM appraisal. These findings align with a recent study reporting that higher within-neighborhood median income was linked to higher internalized HIV stigma and HIV-related discrimination in health care settings.33 Higher median income may indicate income disparity, and if BWLWH have low income in a higher-income neighborhood, this may result in BWLWH facing more microaggressions. Together, these novel results suggest that socioeconomic characteristics both within neighborhoods and in neighboring areas may have an impact on microaggressions and discrimination Black women face across multiple axes of identities (HIV status, race, gender, and sexual orientation). This further echoes that individual-level experiences of discrimination are directly related to intersecting systems of oppression that manifest in the form of disparities in income and socioeconomic status.8
In addition to being associated with higher GRM, crime victimization was also associated with worse mental health (PTSD, substance use disorder, depressive symptoms) and HIV outcomes (lower medication adherence and higher VL). Consistent with existing literature,11 this indicates that higher crime exposure has negative impacts on BWLWH’s mental health and may adversely affect women’s ability to adhere to antiretroviral therapy and consequentially result in higher VL.12
However, across mental health and HIV outcomes, beneficial within-neighborhood characteristics for BWLWH were higher education, employment, and religious organizations. Higher education was associated with lower likelihood of suicidality, lower VL,12 and higher likelihood of HIV viral suppression and undetectable VL, suggesting that higher education within neighborhood may be a protective health factor, perhaps as an indication of awareness of health services. Higher within-neighborhood employment was related to lower PTSD symptoms, lower likelihood of alcohol-use disorder, and higher likelihood of undetectable VL. Higher employment within their neighborhood may serve as a buffer in 2 ways: (1) individuals are behaviorally activated (linked to better mental health) through work, and (2) work may provide access to a support system, mental health and HIV care, and resources (e.g., food, housing, car).2 In addition, a higher number of Christian organizations was associated with lower posttraumatic cognitions and likelihood of a depression diagnosis, which is consistent with literature indicating the positive effects of religious affiliation.16
In contrast to the benefits of within-neighborhood education and employment, neighborhood income was adversely associated with HIV outcomes (HIV viral suppression and undetectable VL), echoing that neighborhood-level income (perhaps in the midst of high neighborhood income disparity), may have adverse impacts on BWLWH who may have lower income. In fact, BWLWH’s own household income was associated with higher likelihood of having HIV viral suppression and undetectable VL, indicating that what matters most is higher household income for BWLWH.3
Neighboring characteristics also related to mental health and HIV outcomes in interesting ways. Higher employment and income in neighboring areas related to more mental health symptoms and diagnoses (number of traumas, PTSD symptoms, depression) and may be a proxy for income disparity that may expose BWLWH to more traumas, or a proxy for congestion or gentrification, which may have negative implications for BWLWH’s mental health.34 This reiterates the negative psychosocial implications of structural inequities in the form of housing policies.8 However, higher neighboring income related to undetectable VL may suggest proximity to services such as pharmacies.
This study presents novel findings on neighborhood factors, intersectional discrimination, and health among BWLWH; however, a few limitations need to be acknowledged. First, the cross-sectional data and sample size prevents causal and definitive conclusions. Second, our sample consisted of BWLWH in the southeastern United States and, thereby, findings may not generalize to other geographic areas. Third, women’s response to questions from the National Crime Victimization Survey were used as a proxy for their neighborhood crime; however, their self-report may be better than official statistics given barriers to reporting to police such as a history of ineffectual, uncompassionate, and sometimes deadly responses by police officers when called to serve Black individuals.29 Despite these limitations, findings presented may have important public health implications.
BWLWH bear the brunt of the HIV epidemic, and efforts should be directed to ameliorate the burden by addressing structural inequities (e.g., housing, income, and crime rates), intersectional discrimination and stigma, and mental health, and improve HIV outcomes. The HIV literature has yet to adequately examine how characteristics of within-neighborhood and neighboring areas may be associated with these factors. Our novel findings suggest that policies are needed to (1) improve the rates of neighborhood education and employment, availability of low-income housing, and access to religious organizations and (2) decrease crime. In addition, policies are needed to increase the household income of BWLWH and minimize income disparities, and human rights legislation is needed to improve their quality of life and reduce structural inequities.
ACKNOWLEDGMENTS
The research reported in this publication and the principal investigator (S. K. D.) were funded by R56MH121194 and R01MH121194 from the National Institute of Mental Health.
We would like to extend extensive gratitude to the women who participated in this study, research staff, and community stakeholders.
Note. The content of this publication is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.
CONFLICTS OF INTEREST
The authors declare that they do not have conflicts of interest.
HUMAN PARTICIPANT PROTECTION
All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. Procedures were approved by the University of Miami institutional review board and informed consent was obtained from all individual participants included in the study.
