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Logo of nihpaAbout Author manuscriptsSubmit a manuscriptNIH Public Access; Author Manuscript; Accepted for publication in peer reviewed journal;
 
Am J Public Health. Author manuscript; available in PMC Mar 2, 2013.
Published in final edited form as:
PMCID: PMC3518367
NIHMSID: NIHMS442381
Network mixing and network influences most linked to HIV infection and risk behavior in a Black men who have sex with men HIV epidemic
John A. Schneider, Benjamin Cornwell, David Ostrow, Stuart Michaels, Phil Schumm, Edward Laumann, and Samuel R. Friedman
John A. Schneider, Departments of Medicine and Health Studies, University of Chicago, Chicago IL;
Corresponding Author John A. Schneider MD, MPH; Department of Medicine and Health Studies; 5841 South Maryland Avenue MC 5065, Chicago IL, 60637; jschnei1/at/medicine.bsd.uchicago.edu
Objectives
We evaluated network mixing and likely influences exerted by network members upon black men who have sex with men (BMSM).
Methods
We conducted separate social and sexual network mixing analyses of study participants to determine the degree of mixing on risk behaviors such as unprotected anal intercourse (UAI). Logistic regression was used to assess the association between a network “enabler” (network member who would not disapprove of the respondent’s behavior) presence and respondent behavior.
Results
Across the network sample (n=1,187) network mixing on risk behaviors was more assortative (like with like) in the sexual network (rsex, 0.37–0.54) compared to the social network (rsocial, 0.21–0.24). Minimal assortativity (heterogeneous mixing) among HIV-infected men on UAI was evident. BMSM reporting an enabler in the social network, were also more likely to practice UAI [adjusted odds ratio (AOR, 4.06; 95% CI 1.64–10.05) a finding that was not observed in the sex network (AOR, 1.31; 95% CI 0.44–3.91).
Conclusions
Different mixing on risk behavior was evident with more disassortativity among social compared to sexual networks. Enabling effects of social network members may be a mechanism through which social contacts affect risky behavior. Additional attention to the social networks of high-risk populations is needed for development of effective and sustained HIV prevention interventions.
The HIV epidemic among men who have sex with men (MSM) has not only grown to alarming levels overall, but is one that demonstrates significant and marked racial disparities. In 2008, 28% of MSM with new HIV infection were black and among MSM ages 13–29, the number of new infections in black MSM (BMSM) was nearly twice that of white MSM.1,2
Traditional epidemiologic approaches have made limited headway in explaining these findings because they tend to focus on the role of individual risk behaviors in shaping rates of HIV infection. The higher rates of HIV among BMSM may not be explained by individual-level risk behaviors alone, and instead may be due in part to social and sexual network factors.3,4 But efforts to further illuminate these factors have been largely unsuccessful as they have often used sampling methodologies that can distort accurate measurement of existing networks of these MSM (e.g., lack of weighting and focus on last sex partner).5,6 Furthermore, up until now, network analyses have not examined BMSM’s non-sexual social networks; such networks may contribute to the disparities observed (e.g., lack of embedded social network members7) and might provide opportunities for future interventions.
Some research has explained disparities in HIV rates by examining sexual network mixing patterns within and between racial sub-groups.8,9 Previously, we demonstrated that higher rates of sexually transmitted infections (STI) within the African-American community were related to sexual network mixing patterns.10 Higher levels of disassortative mixing – core high-risk groups mixing with peripheral low-risk groups – within the African-American community, combined with limited inter-racial mixing, was a major contributor for the disproportionately higher rates of STIs among blacks when compared to whites. Similar sexual network mixing explanations have been demonstrated among blacks in the Southeastern United States.11 Drug use behavior was found to be highly assortative (like behavior with like); while sex behavior in the form of concurrent (or simultaneous) partnerships was minimally assortative.
In contrast to the attention devoted to sexual1217 and drug-use networks1823, comparatively little research has been conducted on how non-risk social networks comprised of MSM’s close friends and family members can affect STI and HIV transmission, with a few notable exceptions.7,24,25 Social learning and differential association theories26,27 hold that risky behaviors, including rationalizations for them, diffuse through social networks of close ties. Furthermore, network members influence high-risk behavior by virtue of the behavioral examples they provide, the normative pressures they exert, and MSM’s perceptions of these influences.2830 Research has shown in a variety of contexts that risky sexual and substance use behavior is affected by individuals’ perceptions of what their network members do, regardless of whether those perceptions are accurate.3133 Studying BMSM’s normative contexts may help researchers identify not only those social conditions that facilitate risky behavior, but also potential network influences that can be exploited or modified to encourage the spread of HIV prevention behavior through modification of a social network. To date, most work that has examined the indirect role of social networks on the spread of HIV has focused primarily on the role of having social network ties in general, but has not specified the mechanisms through which social network ties affect the risk behavior of MSM.34,35
Formal social network analysis of high risk populations has focused on MSM and injecting drug users in general and not specifically on BMSM.25,36 One recent pilot study37 demonstrated that sex partners of BMSM were mostly introduced through friends. Known risk behaviors associated with HIV infection and that could be “transmitted” through a social network include sex-drug use (SDU)38 and unprotected anal intercourse (UAI). Moreover, group sex (GS) has also recently gained increased attention as an important risk practice39,40 which can complicate network analysis.41 Important influences and practices such as these, however, have not been previously explored through social network analysis within BMSM despite this population’s position as a group with the highest risk of HIV infection in the United States. Furthermore, network patterns that potentially confer risk, such as disassortative social mixing have also not been explored within this population as opposed to the larger Black community.10,42 In this paper we conduct a detailed analysis of close social and sexual networks of BMSM in order to determine the salient properties and components of these networks that are most related to HIV risk and preventive behavior among these men.
Setting
Between January and June of 2010, BMSM were recruited in Chicago using respondent-driven sampling (RDS)43 to participate in the study. All interviews took place at partnering community-based organizations by BMSM community members trained by the University of Chicago Survey Lab. HIV voluntary counseling and testing was conducted according to procedures and protocols at each organization. Procedures and protocols were approved by institutional review boards at the University of Chicago, Howard Brown Health Center and the National Opinion Research Center. Informed consent was obtained from all respondents and waived for network members listed by respondents.
Study Participants
Eligibility Criteria
Study participants include both study respondents who were interviewed, and the network members about whom they reported. Study respondents were eligible for the study if they 1) self-identified as African American or Black, 2) identified as male, 3) were age 18 years or older, 4) reported anal intercourse with a man within the past 12 months, and 5) were willing and able to provide informed consent at the time of the study visit. Network members were eligible if they were named by respondents during the interview.
Recruitment
Respondent-driven-sampling (RDS) has been widely applied to study hard-to-reach populations such as injecting drug users, sex workers, and MSM.4447 Recent theoretical and empirical work has assessed the strengths and weakness of RDS.44,48,49 This work has emphasized the importance of careful selection of “seeds” from diverse sources and sufficient iterative rounds of recruitment to penetrate further reaches of the larger social networked population being studied – “recruits”.
We recruited 21 seeds from four venues: 1) Four seeds were recruited from a local Federally Qualified Health Center; 2) Eight seeds were referred from existing Effective Behavioral Intervention (EBI) prevention programs50, 3) Four seeds were recruited from a substance use treatment program; and 4) Five seeds were recruited through fliers posted at an LGBT care center. Seeds were asked to refer up to four recruits who were MSM from their social networks, with each subsequent recruit doing the same using vouchers.
Survey Instruments
Social Network Assessment
In designing our Men’s Assessment of Social and Risk Networks (SRN Instrument), we followed an established method of gathering egocentric network data51 that is used in several large national surveys, including the General Social Survey52 (GSS), the National Health and Social Life Survey53 (NHSLS), and the National Social Life, Health, and Aging Project54 (NSHAP). We asked a “name generator” question during the course of the face-to-face interviews to elicit from each respondent a set of social network members who may indirectly affect the respondent’s risky behaviors. The name generator was selected to identify network “confidants”55 who have opportunities, through everyday interactions with the respondent, to exercise normative pressure or informal control, and to exchange information or advice regarding risky behavior: “Let’s make a list of your closest associates with whom you may share information about yourself, your physical and mental health, and your social and sexual lifestyles”. These names were entered into a roster that was recorded for future reference. We then followed up with a series of “name interpreter” questions about each network member’s attributes (e.g., age) and the best descriptor of the nature of his or her relationship with the respondent (e.g., friend) from a list of 18 possible categories. Research has shown that five network members is optimal for time and effort to field egocentric network surveys.56
This analysis focuses on two types of confidant sub-networks: 1) social (non-sexual) and 2) sexual – in order to identify different mechanisms of mixing and influence likely to be driving risk behavior. These networks were determined by a name interpreter that asked respondents to characterize each network member (e.g., friend, acquaintance, sex partner) in the roster. Because respondents were asked to characterize each network member using one descriptor, these networks were non-overlapping. The sexual network includes the respondent and his nominated network members who were classified into one of four categories, (e.g., non-primary partner). The social network included the respondent and his nominated network members who were classified into one of the other 14 non-sex partner categories (e.g., friend, relative) (Table 1).
Table 1
Table 1
Respondent (n=204) and network member (n=983) attribute and tie characteristics.
Sociodemographic, Attitude and Behavior Measures
Demographic and HIV status items were adapted from the Centers for Disease Control and Prevention’s National HIV Behavioral Surveillance Survey, MSM Cycle57 and the visit 51 Core Behavioral survey of the MACS (available at www.jhsph/epi/MACS.edu). As in previous work, serodiscordant UAI was measured in response to the item “In the past six months, have you had unprotected anal sex with a male partner of unknown or different HIV-status?”, and SDU “Have you ever used any of these substances [from a hand-card of 12 categories] as “sex drugs,” that is to make sex easier, better, last longer, or something similar?”.38,58 Group sex (GS) was measured as “having sex with two or more individuals at the same time”. Behavioral measures were assessed in frequency terms over the past year and were coded for these analyses as present if they were reported as at least monthly. HIV testing and counseling were offered onsite and HIV-infected clients were referred to appropriate services.
Analysis
Respondent Driven Sampling
Our initial analysis was directed toward evaluating the plausibility of assumptions such as random and reciprocal referral, evaluating the degree of homophily with respect to various risk-related outcomes, and developing estimates of the inclusion probabilities for use as weights. Throughout all of our substantive analyses, we utilized the RDS weights and computed design-based standard errors using analytic methods59, and compared these results to those obtained without the weights and assuming independent observations.
Mixing Analysis
Mixing analysis can inform public health researchers as to how behaviors (e.g., SDU) or statuses (e.g., HIV status) are distributed between multiple pairs of individuals within a network. The extent that this network exhibits assortative (e.g., like with like) social or sexual mixing patterns with respect to a given classification can be quantified.42,60 We calculated an assortativity coefficient to describe the mixing patterns in our sample.60 The assortativity coefficient is calculated from the mixing matrix—the proportion of total ties in a cross-tabulation of partnerships between people who do and do not have a risk attribute. This coefficient is defined as:
equation M1
where r is the assortativity coefficient, Tr e is the trace of the matrix and e is the matrix whose elements are the cell values, eij, of the mixing matrix. This formula gives r=1 when there is perfect assortative mixing and all partnerships are concordant for the characteristic of interest (e.g., SDU). When the coefficient is 0, this indicates random mixing on the characteristic. This random mixing at r=0, however, is closer to a perfectly disassortative network and can thus be interpreted in this way.60 Based upon previous work, coefficients ≥ 0.35 were characterized as assortative, 0.26–0.34 moderately assortative and 0.15–0.25 minimally assortative.42 We computed assortativity coefficients based upon mixing matrices for the presence or absence of a characteristic for the respondent and up to five network members for the social and sexual networks respectively. Characteristics included, SDU, UAI, GS and HIV status. Separate coefficients were calculated according to the HIV status for HIV infected and uninfected respondents.
Risk Network effect analysis
The primary outcomes of this study were risk-related behaviors among respondents: SDU, UAI and GS. We initially examined these outcomes individually according to the following generalized linear model.61
equation M2
where Y is a measure of risk, X are one or more variables characterizing the respondent’s network, and Z are the additional covariates; individual sociodemographics, interview site, and personal network size. Our parameter of interest is β, which describes the association between network characteristics and risk. Two models were created for each risk behavior outcome. The first examined whether the respondent has at least one network member who engages in the risk behavior – a measure of homophily. The second explored the effect of at least one network member who would not disapprove of the respondent’s SDU, UAI or GS – an “enabler” - adjusting for all variables, including homophily, from the first model.
Respondent Driven Sampling
Twenty-one seeds generated the study respondent sample with each chain averaging 5.8 new individuals (range 0–42) and up to 9 waves were completed. With subsequent waves of recruitment, the study respondents became younger, were more HIV negative, reported less SDU and UAI, and were less likely to report network confidants who disapproved of SDU and UAI. RDS weights and computed design-based standard errors were determined using analytic methods59 and comparing these results to those obtained without the weights yielded weights of 1 divided by the square root of the respondent’s network degree. These weights were used for all regression analyses.
Sample Network Characteristics
The sample included a total network (n=1,187) generated from 21 seeds and included respondents (n=204) and other listed network members (n=983) as demonstrated in Figure 1. Attributes and tie characteristics of study participants are depicted in Table 1.
Figure 1
Figure 1
Total network sample and sub-network generation (n=983).*
Social and sexual network mixing
Table 2 depicts the distribution of risk characteristics for respondents and sex/social network members by self-reported HIV status of respondents.
Table 2
Table 2
Distribution of risk characteristics for respondents and sex partner and social network members by self-reported HIV status of respondents.
Mixing Analysis
Separate social and sexual network mixing models were generated according to three separate risk behaviors and stratified by HIV status (Figure 2). Overall, network mixing on three risk behaviors was generally more assortative (like with like) with respect to sex network members (rsex, 0.37–0.54; σr, 0.28–0.58) than it was with respect to non-sexual social network members (rsocial, 0.21–0.24; σr, 0.15–0.28). This suggests that BMSM maintain ties with non-sexual social network members who do not demonstrate the same risk behavior profiles. When social and sexual networks were stratified by HIV status, one risk behavior – UAI – demonstrated minimal assortativity among the HIV-infected men to a degree where the coefficients of the distinct sexual and social network overlapped; rsex = 0.21 (σrlow-hi 0.09–0.33) and rsocial =0.17 (σrlow-hi 0.08–0.26) respectively.
Figure 2
Figure 2
Mixing within social* and sexual† networks by risk behavior and HIV status
Network influences on risk behavior
We measured the association of network members’ risk behavior with respondent’s behavior and the association between existence of at least one enabling network member (not entirely disapproving of risk behavior) and respondent’s behavior. Results are consistent with our expectations in that they suggest that BMSM whose network members participate in a given behavior are significantly more likely to engage in a particularly risky behavior (Table 3). For example, as shown in the first multivariate model in column one, MSM respondents who have at least one network member in their social network who uses drugs to enhance sexual experience are more than five and half times as likely to use sex-drugs than men who do not have such a network member (OR, 5.87; p<0.01). There is also a potentially stronger association between network members’ attitudes towards respondent’s behavior. For example, BMSM who report that at least one network member is not entirely disapproving of SDU, an enabler, are over 6 times as likely to use sex-drugs than men who do not have such a network member (OR, 6.35; p<0.001) and the previous point estimate of the association between network members’ SDU and respondents’ SDU decreases (OR, 3.70; p<0.05).
Table 3
Table 3
Two multivariable models each for social and sexual network relationships with respondents by sex-drug use and unprotected anal intercourse among Black Men who have Sex with Men in Chicago (ego n=204).*
It is often supposed that interventions to halt the sexual transmission of HIV must engage and work within sexual networks because of the mixing of high- and low-risk individuals.10,62,63 Ties between high-risk individuals, however, are also critical to disease transmission64 and interventions at the sex network level may require behavior change for both members of a high-risk encounter to reduce spread of disease through a network.65 Our study participants demonstrated sexual network mixing that was assortative (r≥0.35)42 on specific behaviors and statuses (like with like); contact between high- and low-risk individuals, however, was most apparent within social networks and was found to be minimally assortative (r=0.15–0.25).42. In further analysis, these minimally assortative social networks demonstrated heterogeneous social influences on behavior; homophily in the network was evident for some behaviors (e.g. SDU) but not others (e.g. UAI). In the case of UAI, however, the presence of an “enabler” within the social network who condoned this behavior increased the likelihood of the respondent practicing UAI, an influence not evident in the sexual networks.
While these results may not seem surprising - sex ties are more likely to be based on shared risk behaviors than are social ties – they point to non-sexual social networks as an important medium for interventions targeting high-risk individuals, given the observed behavioral heterogeneity within these networks. These diverse networks exhibited mixing with others of different levels of risk across a behavior (minimally assortative) – but with similar levels of this minimally assortative mixing across behaviors. One notable mixing pattern that differed from other behavioral mixing patterns within social networks, was that of HIV status. Assortative mixing on serostatus within social networks was evident and considerably higher than mixing on other risk behaviors within the social network; yet mixing on serostatus within the social network was still less assortative than mixing on serostatus within the sexual network (e.g. serosorting – limiting unprotected sex activity to partners of same serostatus66).
Social influence as measured by the presence of at least one enabler within BMSM’s networks was apparent with respect to social network members. But this influence varied across behaviors and networks. In the example of UAI, presence of an enabler was a significant contributor to an individual’s behavior in the social network, but not in the sexual network, whereas for SDU the enabler was important for both network typologies. This suggests that enablers within networks that are more disassortative – people with different behaviors - may be more consequential to the behavior of individuals who are embedded in such networks. The threshold of one enabler out of five not entirely disapproving was a low one in our study. Interventions directed at BMSM networks must grapple with the finding that one person, an enabler, may have an impact on an individual’s behavior despite disapproval from other network members. This finding has implications for existing theoretical models of HIV risk behavior such as the AIDS Risk Reduction Model or the Theory of Reasoned Action where an enabler may be additive to existing social norm conceptualizations within such models. If networks were rewired – a controversial network intervention67- would the respondent change behavior to conform with network norms (social influence)? Or would he shift to networks not entirely disapproving of the behavior (selection)? Longitudinal social network analysis is needed to help disentangle the contributions to homophily of social influence68 and network selection69 in order to develop effective social network interventions.70
Interestingly, network members with whom the respondent speaks more frequently about sex were more likely to disapprove of UAI (data not shown). This is somewhat surprising, given that one might have expected more discussion about sex with those who are less likely to disapprove in line with Parson’s theory of deviant behavior71 and the stress that this deviance could produce in the respondent. It is also worth pointing out that most respondents reported that the majority of their network members disapproved of these behaviors. Thus, it is not as if respondents’ projection bias – projecting their own attitudes/behaviors onto other network members - was widespread. In fact, respondents appear to have been fairly conservative in their estimates of who was an “enabler.”
Assortative mixing on behaviors and HIV status in the sex network were evident, with some variability across behaviors. SDU was the least assortative and mixing on HIV status (serosorting) demonstrated the highest assortativity. This suggests that strong and protective behaviors exist within our sampled sex network. Compared to black heterosexuals in the Southeastern United States42, assortativity coefficients on the drug use variables were similar, however, coefficients of sex behavior variables in our data were higher. Additionally, Doherty et al, found no differences in assortativity by risk behavior when the sample was stratified by HIV status. In our sex network analysis we found similar findings for SDU, but for sex behavior variables, marked differences were evident with HIV positive individuals demonstrating highly assortative mixing compared to HIV negative individuals. Cautious interpretation of these differences between studies is necessary given the different sex behaviors measured and the very different populations and settings studied. These findings suggest, however, that in contrast to the larger black community, BMSM may be strategically mixing based on serostatus and sex behavior and is in line with recent findings that mixing on serostatus is unlikely to be a reason for disparities between white and black MSM.72,73
There is, however, one alarming finding from the sex mixing pattern with respect to UAI among HIV-infected respondents in our sample: minimally assortative (disassortative) mixing was evident for this sub-group. In fact, the lack of assortativity on UAI in this group was comparable to the social network assortativity for UAI. While most current efforts targeting BMSM suggest increasing awareness of HIV status (e.g., HIV positive unaware74), these findings suggest that additional secondary prevention efforts such as prevention for positives programs for BMSM are warranted.75 Currently there are no specific secondary prevention interventions targeting this population as existing secondary prevention interventions target MSM in general76,77 or older BMSM78 (for a recent review, see79).
There are several potential limitations to this study. Chicago has the highest proportion of African Americans among the 5 largest cities in the US80 and may not be representative of mixing in other cities which have lower numbers of African Americans in total or as a proportion. For example, the possibility of mixing between races as a reason for higher rates of HIV among BMSM has been raised in other settings81, but has not been found to be the case in Chicago10, and the formal mixing analysis conducted here confirms earlier findings of assortative mixing within African Americans.10 Without longitudinal data, it is impossible to disentangle direct social influence by network members, or their self-selection into enabling social environments.70 Additionally, all data was self-reported by respondents (typical of egocentric analysis) and thus susceptible to projection bias.82 If study respondents accurately report on their perceptions of alters’ behavior, however, this could be even more important than alters’ actual behavior.83 Individuals may to some extent, however, justify their actions by believing that others support and encourage those actions.84 We focused on descriptors of network members that best describe them, recognizing that overlapping categories might exist. We also focused on strong ties by utilizing confidants who may not be representative of all sex partners. Strong ties, however, have the greatest effect on influence85 rather than information flow which is more apparent through weak ties.86 Finally, the seeds and recruits may not reflect the larger population of BMSM and the small sample size decreases the precision of our estimates.
This study is the first published quantitative network analysis of social and sexual mixing patterns among BMSM. Our findings highlight important enabling effects of social network members as a mechanism through which social contacts may affect risky behavior, above and beyond participants’ perceptions of network members’ own risk-related behaviors. Additionally, different levels of mixing assortativeness according to network typology was evident with social networks on the whole being more disassortative than sexual networks across a diverse set of risk behaviors and HIV statuses. Notably, however, there was much more disassortative sexual mixing for UAI as practiced by HIV-infected men. Future HIV prevention interventions may be made more effective by incorporating and potentially altering social networks. This might include additional focus on the norms of the network as well as fostering relationships with specific individuals within the network. Additionally, incorporating secondary prevention strategies, such as prevention for spread of HIV from positive to uninfected BMSM, may complement the numerous existing HIV prevention programs for uninfected BMSM.
Acknowledgments
Funding from the NIH (U54 RR023560, R03 DA026089, R01DA033875, R34MH097622). Versions of this work were presented at the 6th IAS Conference on HIV Pathogenesis, Treatment and Prevention, Rome, Italy July 17–20 2011, the International Network of Social Network Analysis 32nd Annual Meeting, Redondo Beach, California, March 12–18 2012 and the XIX International AIDS Conference, Washington DC, July 22–27. We would also like to thank Rachel McFadden and Don Fette for figure and manuscript preparation
Footnotes
Contributor Statement
Origination of Study: JS, BC, DO, EL, SF
Data Collection: JS, DO
Data Analysis: JS, BC, SM, PS
Interpretation of Results: JS, BC, SM
Writing: JS, BC, SM, SF
.
Ethics Statement
This work was approved by Institutional Review Boards at the University of Chicago, National Opinion Research Center and Howard Brown Health Center
Contributor Information
John A. Schneider, Departments of Medicine and Health Studies, University of Chicago, Chicago IL.
Benjamin Cornwell, Department of Sociology, Cornell University, Ithaca NY.
David Ostrow, Ostrow and Associates Inc., Chicago IL.
Stuart Michaels, National Opinion Research Center, Chicago IL.
Phil Schumm, Department of Health Studies, University of Chicago, Chicago IL.
Edward Laumann, Department of Sociology, University of Chicago, Chicago IL.
Samuel R. Friedman, National Development Research Institute, New York NY.
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