Despite evidence of a recent reduction in overall HIV incidence in the United States,1 disparities in rates of HIV infection continue to exist among blacks, Latinos, and men who have sex with men. In particular, black men who have sex with men (BMSM) disproportionately represent incident HIV cases when compared with white MSM. Recent estimates indicate that BMSM accounted for 39% of new infections in MSM in 2015, and BMSM between the ages of 13 and 24 experienced an 87% increase in new HIV cases between 2005 and 2014.2 Previous research indicates that individual risk factors such as condomless anal sex or substance use within the context of sexual activity do not provide sufficient explanations for these disparate infection rates,3 although there is evidence that decreased HIV testing frequency contributes to increased HIV incidence in this population.4
In recent years, structural factors such as unemployment, lower income, economic hardships, incarceration, and limited access to health care/antiretroviral treatment (eg, being uninsured or underinsured) have been highlighted as contributors to HIV infection disparities and decreased HIV testing access in BMSM.5–9 There is also evidence that BMSM may be more likely to select sexual partners from communities of BMSM, which can lead to an increased potential for HIV exposure because of a higher background prevalence of HIV.6 Although recent modeling research demonstrated that multiple sources significantly contribute to this disparity (including HIV care cascade challenges, partnership selection, and HIV-related stigma), several possible risk factors remain underexplored.10
A person's social relationships may significantly affect one's HIV risk in a myriad of ways. Friends, romantic partners, family members, or others who frequently interact with a person11 may influence behavioral choices. In particular, social networks can influence one's likelihood of engaging in sexual risk behavior.12,13 Although several studies have examined how peer connections or social networks influence HIV-related risk behaviors such as condomless anal sex,12,13 HIV testing and prevention services,14,15 or injection drug use,16 there is little to no published information on whether social networks directly affect the risk of HIV seroconversion.17
To examine the association between social network characteristics and seroconversion in BMSM, this study used an egocentric network approach to examine data previously collected from the HIV Prevention Trials Network (HPTN) 061, the largest prospective cohort study among BMSM in 6 US cities. This analysis explored whether network factors, such as the presence of social support, were associated with risk of seroconversion. Based on previous studies of social support, the study hypothesis was that increased levels of social support would be associated with a decreased risk of seroconversion.
This study analyzed data from the BROTHERS (HPTN 061) study, which has been previously described in other publications.18,19 Between 2009 and 2011, men across 6 US cities (Atlanta, GA, Boston, MA, Los Angeles, CA, New York City, NY, San Francisco, CA, and Washington, DC) participated in a feasibility and acceptability study for an intervention to reduce HIV infections in self-identified BMSM. Study participants were at least 18 years of age and reported condomless anal sex with a man within the past 6 months. The study intervention included HIV/sexually transmitted infection (STI) screening at study sites both at baseline and at follow-up visits (at 6 and 12 months) while also offering peer health navigation to assist men in obtaining needed medical or psychosocial services. HIV seroconversion cases were confirmed retrospectively at the HPTN Laboratory Center (Baltimore, MD). Study participants provided demographic information (including age, education history, employment status, annual household income, relationship status, and sexual orientation), and interviewers used an in-person social network questionnaire to gather data about each participant's social contacts based on a previously validated survey instrument.20 To ascertain a person's social support network, interviewers asked a series of questions beginning with the following: “If you wanted to talk to someone about things that are very personal and private is there anybody you could talk to?” If a participant indicated that there were network members who met this criterion, the interviewer subsequently asked him to name up to 5 people by using an identifier such as initials, nicknames, or a combination of letters to help each man remember the specified network member at future study visits. Then, the interviewer would repeat this process for 3 additional support criteria (ie, 2. “Is there anybody who would go to a medical appointment with you?,” 3. “Is there anybody you know who you would ask to lend you $100 or more if you need it?,” and 4. “Is there anybody that you get together with, spend time talking, relaxing or just hanging out with?”). Men could name network members from earlier questions or provide new names (up to a maximum of 5 for each question). Therefore, network members were capable of providing 4 types of social support: (1) personal/emotional, (2) medical, (3) financial, and (4) social participation. Respondents answered additional questions about their network contacts such as to which age category the person belonged. Participants completed the social network questionnaire at baseline and 6-month follow-up visits; network data referred to the 6 months before each assessment.
Univariate statistics and bivariate tests including χ2 and Wilcoxon rank-sum test were completed to describe and compare participant demographic characteristics and social network variables across seroconversion categories. For univariate and bivariate analyses, network variables were averaged within participants across time points before calculating overall sample mean. Network variables included social network size (the total number of people who provided any type of support), network composition measures that examined the proportion of network contacts who fit each of the 4 specific social support criteria described above, and whether the network contact was less than or equal to 30 years of age at the time of the questionnaire. This age classification of network contacts is similar to previous studies that have delineated young vs. old MSM.18,21 We present both absolute counts and proportions for these variables in Table 1.
The primary analyses for this article were a series of Cox proportional hazard regressions on time to HIV seroconversion with time-dependent network covariates. The first set of regressions examined each covariate alone, and the second set examined network covariates while controlling for participant age at enrollment and study site. As increasing sexual partner age has been shown to increase seroconversion risk,22–24 we tested for a similar relationship between participant age and the proportion of older social network members. We operationalized this relationship by testing an interaction between participant age (as a continuous variable) and the proportion of network members older than 30 years. Participants were considered to have seroconverted at the time they first tested positive at a follow-up visit and did not contribute further follow-up time, even if they attended future visits. Participants who did not seroconvert were censored at last follow-up; if a participant missed the first follow-up visit but attended the second, missing data were handled by carrying baseline values forward. To reduce the impact of variation in overall support network size on our analyses, composition measures accounted for that variation using proportions. All analyses were completed using SAS v.9.3 at a 2-tailed level of significance of P < 0.05.
In total, 1167 BMSM were enrolled in this study who tested HIV negative at baseline and were eligible for follow-up. However, 167 of those men did not contribute any follow-up data (ie, did not attend any study visits beyond baseline) and were not included in the sample. The remaining 1000 men were 37.1 ± 12.3 years of age on average. Just under half (46%) had attended or completed postsecondary education, 21% were currently students, and 35% were currently working either full-time or part-time. Fifty-eight percent reported an annual household income of less than $20,000. Most men in this study were single (89%), 48% identified as homosexual or gay, and 42% as bisexual. The average follow-up time in this sample was 0.92 ± 0.22 person-years. Of these 1000 men, 28 seroconverted during the study period, and the average follow-up time until seroconversion was 0.44 ± 0.27 person-years.
Table 1 presents descriptive social network data by seroconversion status. Participants who seroconverted were younger (median age: 22.5 vs. 39; P value < 0.001) than those who did not seroconvert, had larger social networks (median size: 5 vs. 4; P value = 0.045), and had a lower proportion of older network members (median proportion: 33.3% vs. 75%; P value < 0.001). By contrast, nonseroconverting participants reported higher proportions of network members providing personal (45.1% vs. 33.3%; P value = 0.006), medical (35% vs. 23.8%; P value = 0.039), financial (35% vs. 23.6%; P value = 0.021), and social participation support (66.7% vs. 50.8%; P value < 0.001).
Table 2 presents the results of Cox regression analyses on time to seroconversion. Controlling for age at study entry and study site, increased personal/emotional, medical, and social participation support in the past 6 months were protective against seroconversion. Adjusted hazard ratios (HRs) for 5-percent increases in network members providing personal/emotional, medical, and social support were 0.92 [95% confidence interval (CI): 0.85 to 0.99], 0.92 (95% CI: 0.85 to 0.99), and 0.91 (95% CI: 0.86 to 0.97), respectively, and were statistically significant. These findings indicate that, for example, each 5% increase in network members providing personal/emotional support was associated with a 9% reduction in the risk of seroconversion. In addition, an interaction between participant age and percent of network members older than 30 years was also significant (P = 0.026). Figure 1 presents this interaction graphically by plotting the adjusted HRs and CIs for a 5% increase in network members older than 30 years as a function of participant age at study entry. As can be seen in the figure, seroconversion risk steadily rises with increased participant age as social networks become increasingly composed of older men.
These findings suggest that increasing proportions of network members who provide personal/emotional, medical, or social participation support are associated with a greater delay in seroconversion when controlling for age and study site in a sample of BMSM at risk of HIV infection. This is one of the first studies to date that has measured the impact of social networks on HIV seroconversion in BMSM. Although this study did not explore the mechanisms that may contribute to this protective relationship, previous studies have found that social support has positive benefits for health by affecting a person's coping mechanisms or increasing his engagement in health-promoting behaviors.25 Although social rejection from key networks such as family or religious institutions can contribute to sexual risk behavior in BMSM,26 there is also evidence that social support can mitigate minority-related stress and stigma that can contribute to HIV infection risk.27,28 Being affiliated with a ballroom house or independent gay family community was recently linked to a greater likelihood of protective health behaviors such as pre-exposure prophylaxis (PrEP) awareness and increased health care access.29 Having a higher proportion of social network members who provide personal, medical, or social support could possibly contribute to more exposure to health-promoting interactions; there is evidence that specific network affiliations can affect HIV treatment access or PrEP knowledge for BMSM.15 The increased depth of one's social support network may correspond with better overall sexual health, which can contribute to the success of social network strategies that target HIV testing in BMSM.30,31 Such social network factors could act indirectly to reduce HIV transmission by mitigating contextual factors that can contribute to increased HIV risk such as incarceration, reduced access to health care, or financial instability that can lead to sex work, exchange sex, or homelessness.7 Although the BROTHERS study ended before FDA approval of PrEP in 2012, it is possible that our medical support finding could have implications for PrEP dissemination within a social network. For example, having a person who could accompany a man at high HIV seroconversion risk to a medical appointment may lead to subsequent discussions about PrEP that could facilitate his decision to start this medication. Network-focused interventions could broadly consider both sexual and nonsexual network members (such as close friends or family members) as targets for improved PrEP uptake. Future research should examine how social network support directly or indirectly reduces HIV risk in BMSM, which could lead to greater insight into social network characteristics that may mitigate or contribute to HIV risk behavior. This additional clarity could contribute to more specific targeting of BMSM through a functional support screener that could identify who may have greater HIV infection risk because of a lack of social network support and could benefit from initiating PrEP. This screener could lead to an increased emphasis on methods that can increase social network support, lead to improved HIV prevention strategies based on changing peer norms about biomedical prevention, or increase the number of PrEP users within a subset of a social network, thereby increasing the prevalence of PrEP use within the entire network.
The interaction between participant age and the percentage of network members older than 30 years suggests that having an older demographic makeup within a person's social network increased risks of seroconversion. Having an older social network may have increased the likelihood that BMSM in this study encountered older sex partners (who would have a higher possibility of being HIV positive). Although past research has been inconclusive on how much age disassortativity contributes to disproportionate HIV infection rates,32,33 this finding may provide further credence to previous studies that suggested older dating partners may contribute to HIV disparities in BMSM.22–24 Additional research can determine how network member age factors into seroconversion risk of BMSM.
This study has some limitations. As previously noted by Koblin et al,18 various factors in the BROTHERS study including the availability of HIV/STI testing, referrals for additional care (including medical, supportive, and HIV/STI services), and peer counseling as an additional form of social support could have contributed to a decreased estimate of seroincidence. However, the rate of new HIV infections in this sample was still significantly high when compared with the national HIV incidence during the study's period. The social network questionnaire assessed each participant's perception of whether network members would provide support in the 4 functional domains; study findings may have differed if the questionnaire had measured instances of enacted support. There was also a limit on the number of network members that could be named by study participants. Study measures did not provide specific information about the dynamics or processes that compose social network relationships, which limited our ability to determine how social network support may directly or indirectly contribute to seroconversion risk. Qualitative data could have further clarified the mechanisms that link social network support to HIV infection. Sample selection was limited to 6 urban areas, which limits the results generalizability, and it is unclear how these findings would apply to rural BMSM, who are at particularly high risk of delayed HIV diagnosis.34 It is also unclear how our network support findings may influence or could be impacted by the availability of PrEP given the study's timing. Despite these limitations, this study has several strengths including the size of the study sample, the multisite design that led to geographical variability, and the prospective nature of the analysis.
This study demonstrated that significant associations exist between the proportion of social network members who provide emotional, medical, and social participation support and risk of seroconversion in a sample of BMSM at risk of HIV infection. Our findings suggest that increased levels of support in one's social network may be protective factor against acquiring HIV. Although the exact mechanisms of these findings remain unclear, future research should ascertain how social network support directly or indirectly influences seroconversion risk, which could lead to strategies that improve and subsequently mobilize social support to improve HIV testing frequency, PrEP dissemination, emotional resilience, or health care access while also reducing financial insecurity or other structural barriers to contribute to HIV infection disparities.
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