1254074461Am J Public HealthAm J Public HealthAmerican journal of public health0090-00361541-004821852650322236510.2105/AJPH.2011.300281NIHMS329096ArticleDifferences in the Social Networks of African American Men Who Have Sex With Men Only and Those Who Have Sex With Men and WomenLatkinCarlPhDYangCuiPhDTobinKarinPhDPennimanTyphanyePhD, MPH, MSWPattersonJocelynMPHSpikesPilgrimPhD, MSW, MPH, MA, MPPCarl Latkin, Cui Yang, Karin Tobin, and Typhanye Penniman are with the Johns Hopkins Bloomberg School of Public Health, Baltimore, MD. Pilgrim Spikes and Jocelyn Patterson are with the National Center for HIV/AIDS, Viral Hepatitis, STD, and TB Prevention, Centers for Disease Control and Prevention, Atlanta, GA.Correspondence should be sent to Carl Latkin, Johns Hopkins Bloomberg School of Public Health, Department of Health, Behavior and Society, Hampton House, 624 North Broadway, Room 737, Baltimore, MD 21205 (clatkin@jhsph.edu). Reprints can be ordered at http://www.ajph.org by clicking the “Reprints/Eprints” link38201218820111020111410201210110e18e23Objectives

We compared social network characteristics of African American men who have sex with men only (MSMO) with social network characteristics of African American men who have sex with men and women (MSMW).

Methods

Study participants were 234 African American men who have sex with men who completed a baseline social network assessment for a pilot behavioral HIV prevention intervention in Baltimore, Maryland, from 2006 through 2009. We surveyed the men to elicit the characteristics of their social networks, and we used logistic regression models to assess differences in network characteristics.

Results

MSMO were significantly more likely than were MSMW to be HIV-positive (52% vs 31%). We found no differences between MSMO and MSMW in the size of kin networks or emotional and material support networks. MSMW had denser sexual networks, reported more concurrent and exchange partners, used condoms with more sexual partners, and reported interaction with a larger number of sexual partners at least once a week.

Conclusions

Although there were many similarities in the social and sexual network characteristics of MSMO and MSMW, differences did exist. HIV prevention interventions should address the unique needs of African American MSMW.

Recent epidemiological data suggest that the highest rates of HIV infection in the United States are found among African American men who have sex with men (MSM).1 According to the National HIV Behavioral Surveillance survey in 2004–2005, among 5 cities studied, Baltimore, Maryland, had the highest HIV rates among African American MSM, with a prevalence of 51% and an estimated incidence rate of 8% per year.2 African American MSM are also more likely than are MSM of other racial/ethnic groups to report bisexual identity.3–9

Some studies have focused on men who have sex with men and women (MSMW) as a potential bridge group to heterosexual transmission.10,11 Several investigators have found that MSMW have lower HIV rates than do men who have sex with men only (MSMO).11–13 Some studies have found that MSMW report more sexual partners than do MSMO,11,14 although another has found that not to be true.15

Social network factors have been linked to transmission of HIV and other sexually transmitted infections.16–18 Indeed, there is evidence that network structural characteristics, such as network density (the extent to which social network members know one another) and partner concurrency, may lead to high rates of sexually transmitted infectious diseases.19–21 Network dynamics have also been used to explain the greater burden of HIV and AIDS among African Americans compared with other racial groups in the United States.22 Network characteristics such as network size, composition, and density have been found to be associated with HIV risk behaviors, such as sharing injection equipment,23–27 having multiple partners, engaging in unprotected sex, and exchanging sex for money or drugs.28–31

Social network analysis is a useful method to assess amounts, types, and sources of emotional and instrumental social support32 without assuming that social support is derived from specific role relationships, such as spouse, co-worker, main sexual partner, or friend. In different populations, specific role relationships may not exist, or the same role may provide different types of social support. Few studies have examined the social networks of MSM,33,34 and there is even less information on the social networks of African American MSM. Miller et al.35 conducted in-depth interviews with 21 African American MSM and inquired about the composition of their social networks. They found that African American MSM listed twice as many non-MSM male friends as MSM friends. Few listed MSM friends with whom they did not have sex. A study of HIV-positive men and women reported that African American MSM received more social support from friends and health care providers than they did from family members.36 It has been suggested that some African American MSM perceive that their community consists of their social network members rather than a physical location.37

We examined differences in social network composition between African American MSMO and African American MSMW. Examining social network composition is critical not only for assessing the dynamics of transmission of HIV but also for assessing the social and economic support available to people with HIV. Support issues are especially important among impoverished urban populations. Understanding social network composition is also critical for developing and sustaining appropriate HIV prevention and care programs. In these analyses we were specifically interested in examining the sources and functions of social support within participants’ social networks, the sexual risk behaviors participants engaged in with network members, and the overlap between social support networks and sexual networks. We anticipated that MSMO would report more dense social networks than MSMW but less social support.

METHODS

The Unity in Diversity (UND) study was a culturally tailored pilot randomized clinical trial of a behavioral HIV prevention intervention for at-risk African American MSM in Baltimore from 2006 through 2009. Highly trained study staff recruited 2 types of participants: primary and secondary. Primary participants were directly recruited by the study; secondary participants were those recruited by primary participants. Primary participants were recruited through street and venue-based outreach by trained field recruiters, word of mouth, advertisements in local papers, and active Internet-based recruitment on Web sites and chat rooms for African American MSM. Primary participant inclusion criteria were (1) aged 18 years or older, (2) identified as a male, (3) self-reported Black or African American race/ethnicity, (4) reported having had at least 2 sexual partners in the prior 3 months (at least 1 of whom must have been a male), (5) reported unprotected anal intercourse with a male in the prior 3 months, (6) was willing to take an HIV test if HIV status was negative or unknown, or to provide documentation of HIV-positive status, and (7) was willing to identify social network members and recruit them into the study.

We screened potential primary participants in a community-based research clinic setting using audio computer-assisted self-interview methods. Eligible primary participants who provided written informed consent were enrolled in the study and completed a baseline survey using the same methods. A trained interviewer administered a social network inventory (as described in the Measures section) and a risk survey face to face. At the end of the baseline visit, we asked primary participants to invite their social network members into the study. Inclusion criteria for secondary participants included (1) aged 18 years or older and (2) verification that they were invited to participate by a primary participant. Secondary participants who were enrolled in the study completed the same base-line procedures as the primary participants.

Of the 959 men screened for the study, 46% were screened ineligible. The majority of ineligibles were those men who answered “no” to the question “UND is a research study to improve the sexual health of African-American men who have sex with men. Does this apply to you?” A total of 261 men enrolled in the study. Of these, 188 (72.0%) were primary participants, and 73 (28.0%) were secondary participants.

Participants received $40 for completing the baseline assessments, and primary participants were given an additional $10 for the time and effort required to recruit secondary participants.

MeasuresOutcome

The primary outcome was the difference between network factors of MSMO and network factors of MSMW. In addition to reporting male partners, MSMW were defined as reporting any female sexual partners in the prior 3 months in the main survey or reporting a female sexual network member in the prior 90 days on the social network inventory. Those who only reported male sexual partners or transgender partners (direction of gender change was not specified on the survey) were categorized as MSMO.

Social network inventory

This measure was a modified version of a previously used social support and drug network inventory.38 The social network inventory contained 14 name-generator questions. The name generators asked participants to list individuals who provided specific types of functional social support to them. Different name generators elicited different domains of social support. Emotional support was assessed with this name generator: “During the last 3 months, who did you talk to about things that were personal and private or who did you get advice from?” Members of the material or financial support network were elicited with the question “During the last 3 months, who loaned or gave you some money?” The socializing support network was assessed with the question “During the last 3 months, who did you get together to hang out with or socialize?” Participants were also asked to list members of their sexual network, which was assessed by asking participants to list the individuals with whom they had had sex in the past 3 months. After participants listed their sexual partners, they were asked if they considered the partner to be a main, casual, or exchange partner (sexual partners who exchange sex for money or valuable goods). The total size of the network was the sum of the number of unique individuals listed.

Once the network had been elicited, we asked participants about a variety of characteristics of the listed network members, such as their age, gender, and role relationship (e.g., kin or friend). To measure concurrency of sexual partnerships, participants provided the dates of the first sexual episode and the most recent sexual episode with each sexual partner listed. Overlap of these dates indicated concurrency. To assess condom use with sexual partners listed on the social network inventory we asked, “What best describes your condom use with [partner]?” There were 4 response categories: “never use condoms,” “used condoms when we first started having sex but no longer use them,” “use condoms every now and then,” and “always use condoms with this partner for all types of sex.” We dichotomized these responses into “always” and “less than always.”

We assessed density of the social networks, a measure of interconnectedness of network members, by asking participants which network members knew other network members. We measured standard nondirected binary network density scores as the total number of network members who knew each other divided by the maximum possible number of network members who knew each other.24 Density scores may range from 0 (indicating that no network members knew other network members) to 1(indicating that all network members knew each other). We also assessed the density of the sexual network as the proportion of sexual network members who knew each other. We defined kin relationship as any family member, such as parent, child, or sibling. Nonkin relationships included friends, acquaintances, godparents, and neighbors. Sexual partners were a separate category.

Sociodemographic characteristics

These characteristics included age, education, current employment status, and sexual identity.

HIV serostatus

All participants who self-reported negative or unknown serostatus provided an oral specimen that we tested by using Oraquick rapid HIV antibody testing kits. Preliminary positive results were confirmed by using Western blot assay. Participants who self-reported HIV-positive serostatus were asked to provide written documentation (such as medications or clinical test results) for validation, or they provided an oral specimen for HIV antibody testing. HIV-seropositivity was defined as participants testing positive by confirmatory tests or providing documentation of HIV-positive test results.

Data Analysis

The analyses for this study were restricted to UND study participants self-identified as African American males (n=234; 90% of total sample). The majority of the sample for the current study (80.3%) were primary participants. We calculated frequency distributions to examine the distribution of the variables and to generate a profile of sample characteristics among all participants, MSMW and MSMO. The outcome of interest was differences in network characteristics of MSMW versus MSMO. We used bivariate and multivariate logistic regression models with generalized estimating equations to assess differences in network characteristics. Variables with a P value of less than .2 in the bivariate models were entered into multivariate models. We generated 4 multivariate models to compare network characteristics between MSMW and MSMO, including material and emotional support, network members knowing participants’ MSM status, partner relationships, and risk networks. We checked variance inflation factor to determine the potential multicollinearity among the independent variables. Independent variables with a variance inflation factor of greater than 4.0 were dropped in the final model. We used Stata version 10.0 (StataCorp LP, College Station, TX) to perform all analyses.

RESULTS

About two thirds of participants (66.2%) were MSMO, and one third (33.8%) were MSMW. More than half of participants (58.1%) identified themselves as “homosexual, gay or same-gender loving,” less than one third (31.6%) identified themselves as “bisexual,” and 7.3% considered themselves “straight.”

Table 1 presents sample characteristics and comparisons of sociodemographic and social network characteristics between MSMW and MSMO. Less than half of the sample had a college degree (bachelor’s, associate’s, or technical) (42.3%), less than one third were employed full or part time (27.8%), and almost half (44.9%) tested HIV positive or provided documentation of HIV-positive test results. MSMO were significantly more likely to be HIV positive than MSMW (52.3% vs 30.4%). There were no differences in size of total network, network density, number of kin, or number of network members providing emotional or material support. MSMW were older (42 vs 36 years) and less educated. Compared with MSMO, MSMW reported fewer network members with whom they socialized, fewer male network members, and more female network members.

Compared with MSMO, MSMW reported more sexual partners, fewer male partners, more exchange partners, and more partners that they always used condoms with. In both groups, the percentage of sexual partners providing material support (14% for MSMO vs 16% for MSMW) or emotional support (15% for MSMO vs 20% for MSMW) was small. MSMW had more sexual partners who provided material support and more sexual partners whom they saw at least once a week, although these differences were not statistically significant.

Tables 2, 3, and 4 present the results of multivariate models of differences in participant and network characteristics between MSMW and MSMO. The models examined the social support provided by network members (Table 2), sexual partner relationships (Table 3), and sexual risk networks as predictors of being MSMW versus being MSMO (Table 4). In the model of network social support (Table 2), for MSMW there was an independent and statistically significant association with being older, having decreased odds of HIV-positive status, and having attained higher education. MSMW were also associated with greater odds of reporting a higher number of female network members and a lower number of male network members (Table 2).

In the model of partner relationship (Table 3), reporting interacting with a greater number of sexual partners at least once a week was associated with higher odds of being MSMW than of being MSMO. Finally, in the model of risk networks (Table 4), reporting more male sexual partners was associated with lower odds of being MSMW than of being MSMO. Reporting concurrent partners, moreexchange partners, and more partners with whom one always used condoms were associated with higher odds of being MSMW than of being MSMO.

DISCUSSION

We used network analysis to examine differences in support and sexual network characteristics between African American MSMW and MSMO. Findings from the multiple logistic regression models revealed some differences between MSMW and MSMO in the number of social support network members by gender, role, and type of support function. Both groups reported that about one third of their close support network members were kin.

Our findings suggest that the African American MSMW and MSMO in our sample had diverse but relatively similar network configurations. Consequently, intervention developers should consider this diversity of sexual behaviors and social relationships when developing HIV prevention interventions. These interventions should capitalize on the range of social network relationships beyond sexual partners, such as kin, friends, and other social network members.

Some significant differences were reported in sexual network characteristics between MSMW and MSMO. Not only did MSMW report more sexual partners; they also tended to see more partners at least once a week. Having concurrent partners in their networks and a larger number of exchange partners were independently and significantly associated with being MSMW, as was always using condoms with sexual partners listed in the network inventory.

Interestingly, the majority of sexual partners were not reported as members of participants’ emotional or material support networks. This finding suggests that many sexual partners are not perceived to be key sources of emotional or material support. Consequently, African American MSMW and MSMO may be receptive to the introduction of condom use because of the informal nature of these relationships; however, the relative weakness of these social ties may also make it more difficult to establish norms of communication regarding risk reduction and support for HIV medical care. In the current study, MSMW had significantly lower rates of HIV than did MSMO, yet MSMW reported more concurrent partnerships. These findings support previous research suggesting that the rate of HIV or sexually transmitted diseases among sexual network members is a key factor determining transmission.39

Although HIV rates were much higher among MSMO than they were among MSMW, HIV rates were exceedingly high in both groups– 52.3%and 30.4% respectively. Given the levels of reported concurrent partnerships among MSMW found in this study and elsewhere,40 there is a need to develop HIV prevention interventions for African American MSMO and MSMW and their male and female sexual partners.

The high rates of sexual risk behaviors that we discovered are cause for concern. Although these findings may be attributable in part to the study selection criteria of having had at least 2 sexual partners in the past 3 months, a previous study of MSM in Baltimore aged 15 to 29 years found that the median number of lifetime male sexual partners was 10,41 and the proportion of African American MSM who reported female partners in the current study was similar to what has been found in other studies.31

Limitations

There are limitations to our findings. In addition to the selection criteria, these findings are limited by the study’s sampling procedures, self-reported data, and cross-sectional study design. We do not know if these findings generalize to African American MSMO and MSMW who did not volunteer for the study.

Because this study did not use random sampling, it is possible that the differences between MSMW and MSMO may be attributable in part to the recruitment strategy. Given the study limitations, it would be important to examine social network differences between MSMW and MSMO in other samples. Moreover, as this was an exploratory study, the numerous statistical comparisons between the 2 groups may have led us to find significant statistical associations that were attributable to chance. Social networks are not static; hence, it would be valuable to examine how these men’s social networks change over time. It would be also valuable to identify social network factors that help facilitate positive physical and mental health. Our study did not examine sociometric network composition; sociometric data may provide greater insights into the dynamics of HIV transmission and how to develop more effective social network–based interventions.

Conclusions

Given the high proportion of MSMW among African American MSM,5 future interventions need to focus on African American MSMW. MSMW may be harder to access than other MSM because of their smaller MSM networks, so special attention is warranted to recruit MSMW for HIV prevention interventions. Interventions also need to take into account the high levels of unemployment and underemployment among African American MSMW and MSMO that may lead these populations to make HIV prevention a lower priority than acquiring economic resources. Although other urban minorities are also subject to economic deprivation and often must rely on network members for economic and social support, African American MSMW and MSMO must contend with the double stigma of sexual orientation and, for many, HIV infection. These conditions suggest the importance of tailoring HIV testing, prevention, and medical care programs to the unique needs of African American MSMW and MSMO and their risk partners.

The findings and conclusions in this report are those of the authors and do not necessarily represent the views of the Centers for Disease Control and Prevention.

Contributors

C. Latkin and K. Tobin conceptualized and designed the study. C. Yang analyzed the data. C. Latkin, K. Tobin, and C. Yang wrote the initial draft of the article. T. Penniman, P. Spikes, and J. Patterson assisted with conceptualizing the study and writing the article. All authors reviewed and approved the final draft of the article.

Human Participant Protection

All study protocols were approved by the institutional review board of Johns Hopkins Bloomberg School of Public Health.

Acknowledgments

This study was supported by a cooperative agreement between Johns Hopkins University and the Centers for Disease Control and Prevention (1 UR6 PS000355-01).

The LAAMP Study Team wishes to acknowledge all the study participants who volunteered for this project and the study staff and facilitators for their commitment to the success of this project. We would also like to acknowledge and thank the Centers for Disease Control and Prevention study team: Stephen A. Flores, Heather Joseph, David Purcell, Greg Millett, Cathy Zhang, and Helen Ding.

ReferencesCenters for Disease Control and PreventionHIV Surveillance ReportVol. 21Accessed July 18, 2011Available at: http://www.cdc.gov/hiv/topics/surveillance/resources/reports. Published February 2011Centers for Disease Control and PreventionHIV prevalence, unrecognized infection, and HIV testing among men who have sex with men—five US cities, June 2004–April 2005MMWR Morb Mortal Wkly Rep2005542459760115973239MillettGMalebrancheDMasonBSpikesPFocusing “down low”: bisexual Black men, HIV risk and heterosexual transmissionJ Natl Med Assoc2005977 suppl52S59S16080458MontgomeryJPMokotoffEDGentryACBlairJMThe extent of bisexual behaviour in HIV-infected men and implications for transmission to their female sex partnersAIDS Care200315682983714617504PathelaPHajatASchillingerJBlankSSellRMostashariFDiscordance between sexual behavior and self-reported sexual identity: a population-based survey of New York City menAnn Intern Med2006145641642516983129SanchezTFinlaysonTDrakeAHuman immunodeficiency virus (HIV) risk, prevention, and testing behaviors—United States, National HIV Behavioral Surveillance System: men who have sex with men, November 2003–April 2005MMWR Surveill Summ200655611616826162SimonPAThometzEBunchJGSorvilloFDetelsRKerndtPRPrevalence of unprotected sex among men with AIDS in Los Angeles County, California 1995– 1997AIDS199913898799010371181StokesJPPetersonJLHomophobia, self-esteem, and risk for HIV among African American men who have sex with menAIDS Educ Prev19981032782929642425WohlARJohnsonDFLuSHIV risk behaviors among African American men in Los Angeles County who self-identify as heterosexualJ Acquir Immune Defic Syndr200231335436012439213PrabhuROwenCLFolgerKMcFarlandWThe bisexual bridge revisited: sexual risk behavior among men who have sex with men and women, San Francisco, 1998–2003AIDS200418111604160615238783GorbachPMMurphyRWeissREHucks-OrtizCShoptawSBridging sexual boundaries: men who have sex with men and women in a street-based sample in Los AngelesJ Urban Health200986suppl 1637619543837FloresSABakemanRMillettGAPetersonJLHIV risk among bisexually and homosexually active racially diverse young menSex Transm Dis200936532532919295470WilliamsCTMackesy-AmitiMEMcKirnanDJOuelletLJDifferences in sexual identity, risk practices, and sex partners between bisexual men and other men among a low-income drug-using sampleJ Urban Health200986suppl 19310619479381SpikesPSPurcellDWWilliamsKMChenYDingHSullivanPSSexual risk behaviors among HIV-positive Black men who have sex with women, with men, or with men and women: implications for intervention developmentAm J Public Health20099961072107819372509ZuleWABobashevGVWechsbergWMCostenbaderECCoomesCMBehaviorally bisexual men and their risk behaviors with men and womenJ Urban Health200986suppl 1486219513854FriedmanSRNeaigusAJoseBSociometric risk networks and risk for HIV infectionAm J Public Health1997878128912969279263PérisséARLangenbergPHungerfordLEgocentric network data provide additional information for characterizing an individual’s HIV risk profileAIDS201024229129819904198WardHPrevention strategies for sexually transmitted infections: importance of sexual network structure and epidemic phaseSex Transm Infect200783suppl 1i43i4917389716AdimoraAASchoenbachVJDohertyIAHIV and African Americans in the southern United States: sexual networks and social contextSex Transm Dis2006337 supplS39S4516794554ChoudhuryBRisleyCLGhaniACIdentification of individuals with gonorrhoea within sexual networks: a population-based studyLancet2006368953013914616829298MorrisMKretzschmarMConcurrent partnerships and transmission dynamics in networksSoc Networks1995173–4299318KottiriBJFriedmanSRNeaigusACurtisRDesJRisk networks and racial/ethnic differences in the prevalence of HIV infection among injection drug usersJ Acquir Immune Defic Syndr20023019510412048369BohnertASBradshawCPLatkinCAA social network perspective on heroin and cocaine use among adults: evidence of bidirectional influencesAddiction200910471210121819563564LukeDAHarrisJKNetwork analysis in public health: history, methods, and applicationsAnnu Rev Public Health200728699317222078LatkinCADaveyMAHuaWSocial context of needle selling in Baltimore, MarylandSubst Use Misuse2006416–790191316809178LatkinCMandellWVlahovDKnowltonAOziemkowskaMCelentanoDPersonal network characteristics as antecedents to needle-sharing and shooting gallery attendanceSoc Networks1995173–4219228NeaigusAFriedmanSRJoseBHigh-risk personal networks and syringe sharing as risk factors for HIV infection among new drug injectorsJ Acquir Immune Defic Syndr Hum Retrovirol19961154995098605596BarringtonCLatkinCSweatMDMorenoLEllenJKerriganDTalking the talk, walking the walk: social network norms, communication patterns, and condom use among the male partners of female sex workers in La Romana, Dominican RepublicSoc Sci Med200968112037204419356834El BasselNGilbertLWuEChangMA social network profile and HIV risk among men on methadone: do social networks matter?J Urban Health200683460261316755389HowardDLLatkinCAA bridge over troubled waters: factors associated with non-injection drug users having injection drug-using sex partnersJ Acquir Immune Defic Syndr200642332533016639349MimiagaMJReisnerSLCranstonKSexual mixing patterns and partner characteristics of Black MSM in Massachusetts at increased risk for HIV infection and transmissionJ Urban Health200986460262319466554KnowltonARInformal HIV caregiving in a vulnerable population: toward a network resource frameworkSoc Sci Med20035661307132012600367FrostSDUsing sexual affiliation networks to describe the sexual structure of a populationSex Transm Infect200783suppl 1i37i4217664363LeeSSTamDKTanYAn exploratory study on the social and genotypic clustering of HIV infection in men having sex with menAIDS200923131755176419609202MillerMSernerMWagnerMSexual diversity among Black men who have sex with men in an inner-city communityJ Urban Health2005821suppl. 1i26i3415738323WohlARGalvanFHMyersHFSocial support, stress and social network characteristics among HIV-positive Latino and African American women and men who have sex with menAIDS Behav20101451149115820107888WilliamsJKWyattGEResellJPetersonJAsuan-O’BrienAPsychosocial issues among gay- and non-gay-identifying HIV-seropositive African American and Latino MSMCultur Divers Ethnic Minor Psychol200410326828615311979LatkinCAMandellWVlahovDThe relationship between risk networks’ patterns of crack cocaine and alcohol consumption and HIV-related sexual behaviors among adult injection drug users: a prospective studyDrug Alcohol Depend19964231751818912800LaumannEOYoumYRacial/ethnic group differences in the prevalence of sexually transmitted diseases in the United States: a network explanationSex Transm Dis199926525026110333277OperarioDSmithCDArnoldEKegelesSSexual risk and substance use behaviors among African American men who have sex with men and womenAIDS Behav201115357658319572194SifakisFHyltonJBFlynnCRacial disparities in HIV incidence among young men who have sex with men—the Baltimore young men’s surveyJ Acquir Immune Defic Syndr200746334334817846561

Sample Characteristics and Unadjusted Analysis of African American Men Who Have Sex With Men: Baltimore, Maryland, 2006–2009

CharacteristicsTotal, No. (%) orMean ±SD (n = 234)MSMO, No. (%) orMean ±SD (n = 155)MSMW, No. (%) orMean ±SD (n = 79)UnadjustedOR (95% CI)P
College degree (bachelor’s, associate’s, or technical)99 (42.3)81 (52.3)18 (22.8)0.27 (0.15, 0.50)< .001
Employed (part/full time)65 (27.8)46 (29.7)19 (24.1)0.75 (0.40, 1.42).38
Annual income > $10 000110 (47.0)78 (50.3)32 (41.5)0.67 (0.38, 1.20).18
HIV positive (Oraquick result or documented status)105 (44.9)81 (52.3)24 (30.4)0.40 (0.22, 0.73).003
Having any concurrent partners in the network124 (53.0)74 (47.7)50 (63.3)1.89 (1.04, 3.40).004
Age, y38 ± 10.5936 ± 11.2042 ± 8.161.06 (1.03, 1.09)< .001
Network size8.38 ± 4.308.68 ± 4.707.77 ± 3.330.95 (0.89, 1.01).09
Kin2.11 ± 2.042.10 ± 2.022.11 ± 2.081.00 (0.88, 1.15).97
Nonkin4.54 ± 3.454.79 ± 3.744.03 ± 2.760.93 (0.86, 1.01).09
Network members providing support
   Emotional1.94 ± 1.642.08 ± 1.731.65 ± 1.410.83 (0.67, 1.04).1
   Material1.27 ± 1.091.30 ± 1.131.22 ± 1.000.93 (0.23, 1.19).54
Network members socialized with2.62 ± 2.492.88 ± 2.762.11 ± 1.730.84 (0.74, 0.98).03
Male network members5.46 ± 3.255.98 ± 3.524.43 ± 2.340.83 (0.76, 0.92)< .001
Female network members2.92 ± 2.092.70 ± 2.103.34 ± 2.011.15 (1.02, 1.31).03
Network density0.42 ± 0.260.44 ± 0.260.40 ± 0.280.56 (0.18, 1.71).36
Sexual partners3.01 ± 1.772.79 ± 1.743.44 ± 1.751.23 (1.06, 1.43).007
   Men2.40 ±1.732.70 ± 1.761.80 ± 1.510.69 (0.56, 0.85)< .001
   Women0.48 ± 0.9501.43 ± 1.13……
Transgender sexual partners0.13 ± 0.470.08 ± 0.380.22 ± 0.611.74 (0.96, 3.17).07
Exchange partners0.45 ± 1.110.25 ± 0.730.84 ± 1.551.67 (1.21, 2.29).002
Sexual partners providing support
   Material0.35 ± 0.620.30 ± 0.500.44 ± 0.801.46 (0.97, 2.19).07
   Emotional0.41 ± 0.590.38 ± 0.560.48 ± 0.641.33 (0.83, 2.12).23
Sexual partners seen at least once/wk1.46 ± 1.271.26 ± 1.011.86 ± 1.621.45 (1.18, 1.80).001
Sexual network density0.23 ± 0.320.20 ± 0.280.28 ± 0.382.16 (0.94, 4.97).07
Partners with whom condoms are always used1.11 ± 1.340.92 ± 1.231.48 ± 1.481.35 (1.09, 1.67).005
   Men0.85 ± 1.190.90 ± 1.200.77 ± 1.170.91 (0.72, 1.16).45
   Women0.21 ± 0.5800.61 ± 0.87……

Note. CI = confidence interval; MSMO= men who have sex with men only; MSMW= men who have sex with men and women; OR = odds ratio. Ellipses indicate variables not included in the adjusted model.

Bivariate and Multivariate Comparisons of Characteristics of Social Support Networks of African American MSMW versus MSMO: Baltimore, MD, 2006–2009

Network CharacteristicsOR (95% CI)PAOR (95% CI)P
Age1.06 (1.03, 1.09)< .0011.07 (1.04, 1.10)< .001
College degree (bachelor’s, associate’s, or technical degree)0.27 (0.15, 0.50)< .0010.30 (0.15, 0.59).001
HIV positive0.40 (0.22, 0.73).0030.29 (0.15, 0.59).001
No. of network members providing support
   Emotional0.83 (0.67, 1.04).10.91 (0.79, 1.14).55
   Material0.93 (0.23, 1.19).54…
No. of network members socialize with0.84 (0.74, 0.98).030.95 (0.79, 1.14).59
No. of male network members0.83 (0.76, 0.92)< .0010.86 (0.76, 0.98).024
No. of female network members1.15 (1.02, 1.31).031.31 (1.09, 1.58).004
No. of kin1.00 (0.88, 1.15).97…
No. of nonkin0.93 (0.86, 1.01).09…
Network density0.56 (0.18, 1.71).36…
Sexual network density2.16 (0.94, 4.97).071.48 (0.53, 4.09).45

Note. AOR = adjusted odds ratio; CI = confidence interval; MSMO= men who have sex with men only; MSMW = men who have sex with men and women; OR = odds ratio. Sample size was n = 234. Ellipses indicate variables not included in the adjusted model.

Bivariate and Multivariate Comparisons of Partner Relationships of African American MSMW Versus MSMO: Baltimore, MD, 2006–2009

Network CharacteristicsOR (95% CI)PAOR (95% CI)P
Age1.06 (1.03, 1.09)< .0011.08 (1.04, 1.11)< .001
College degree (bachelor’s, associate’s, or technical degree)0.27 (0.15, 0.50)< .0010.20 (0.09, 0.40)< .001
HIV positive0.40 (0.22, 0.73).0030.25 (0.12, 0.52)< .001
No. of sexual partners providing support
   Material1.46 (0.97, 2.19).071.22 (0.72, 2.05).44
   Emotional1.33 (0.83, 2.12).23……
No. of sexual partners seen at least once/wk1.45 (1.18, 1.80).0011.48 (1.12, 1.96).005
Total no. of sexual partners1.23 (1.06, 0.43).0071.14 (0.95, 1.36).15

Note. AOR = adjusted odds ratio; CI = confidence interval; MSMO = men who have sex with men only; MSMW = men who have sex with men and women; OR = odds ratio. Sample size was n = 234. Ellipses indicate variables not included in the adjusted model.

Bivariate and Multivariate Comparisons of Sexual Risk Networks of African American MSMW Versus MSMO: Baltimore, MD, 2006–2009

Network CharacteristicsOR (95% CI)PAOR (95% CI)P
Age1.06 (1.03, 1.09)< .0011.06 (1.02, 1.09).001
College degree (bachelor’s, associate’s, or technical degree)0.27 (0.15, 0.50)< .0010.28 (0.13, 0.62).002
HIV positive0.40 (0.22, 0.73).0030.25 (0.12, 0.51)< .001
Total no. of sexual partners1.23 (1.06, 1.43).23…
No. of male partners0.69 (0.56, 0.85)< .0010.41 (0.26, 0.63)< .001
Having concurrent partners in the network1.89 (1.04, 3.40).043.59 (1.35, 9.52).01
No. of exchange partners1.67 (1.21, 2.29).0022.23 (1.53, 3.25)< .001
Condom use with sexual partners1.35 (1.09, 1.66).0051.85 (1.31, 2.62)< .001

Note. AOR = adjusted odds ratio; CI = confidence interval; MSMO = men who have sex with men only; MSMW = men who have sex with men and women; OR = odds ratio. Sample size was n = 234. Ellipsis indicates a variable not included in the adjusted model.