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J Urban Health. Dec 2011; 88(6): 1052–1062.
Published online Sep 1, 2011. doi:  10.1007/s11524-011-9611-4
PMCID: PMC3232415
A Comparison of the Social and Sexual Networks of Crack-Using and Non-Crack Using African American Men who Have Sex with Men
Karin Elizabeth Tobin,corresponding author1 Danielle German,1 Pilgrim Spikes,2 Jocelyn Patterson,2 and Carl Latkin1
1Department of Health, Behavior and Society, Johns Hopkins Bloomberg School of Public Health, 2213 McElderry Street, Second floor, Baltimore, MD 21205 USA
2Division of HIV/AIDS Prevention–Prevention Research Branch, Centers for Disease Control and Prevention, Atlanta, GA USA
Karin Elizabeth Tobin, Phone: +1-410-5025368, Fax: +1-410-5025385, ktobin/at/jhsph.edu.
corresponding authorCorresponding author.
The role of crack cocaine in accelerating the HIV epidemic among heterosexual populations has been well documented. Little is known about crack use as an HIV risk factor among African American men who have sex with men (AA MSM), a group disproportionately infected with HIV. We sought to compare the social and sexual network characteristics of crack-using and non-crack using AA MSM in Baltimore, MD, USA and to examine associations of crack use with sexual risk. Participants were recruited using street-based and internet-based outreach, printed advertisements, word of mouth. Inclusion criteria were being aged 18 years or older, African American or of black race/ethnicity, and have self-reported sex with another male in the prior 90 days. Crack use was operationalized as self-report of crack in the prior 90 days. Logistic regression was used to identify variables that were independently associated with crack use. Of 230 enrolled AA MSM, 37% (n = 84) reported crack use. The sexual networks of crack-using AA MSM were composed of a greater number of HIV-positive sex partners, exchange partners, and partners who were both sex and drug partners and fewer networks with whom they always use condoms as compared to non-crack using AA MSM. Crack use was independently associated with increased odds of bisexual identity and networks with a greater number of exchange partners, overlap of drug and sex partners, and lesser condom use. Results of this study highlight sexual network characteristics of crack-smoking AA MSM that may promote transmission of HIV. HIV interventions are needed that are tailored to address the social context of crack-smoking AA MSM risk behaviors.
Keywords: African American men who have sex with men, Crack use, Social networks, HIV risk
The role of crack in accelerating the HIV epidemic, especially among the heterosexual population, has been well documented.14 Use of crack cocaine is strongly associated with having greater number of sexual partners,57 including exchange sex partners, increased sexually transmitted diseases (STIs), and decreased condom use.8,9 Disparities in HIV incidence, prevalence, and disease progression by race among men who have sex with men (MSM) have been well documented and are persistent,1015 with African American MSM (AA MSM) having higher HIV rates compared to white MSM (W MSM). These disparities are not explained by riskier sexual or drug-use behaviors.16
Overall rates of substance use among AA MSM have been reported to be lower, as compared to W MSM.16 In a meta-analysis comparing drug use by race, use of club drugs (e.g., poppers) and methamphetamine were greater among W MSM.17 However, a number of studies have reported that rates of crack and cocaine use among AA MSM are higher compared to W MSM.5,1719 In a study among older AA MSM in Massachusetts, crack use before or during sexual episodes was associated with unprotected anal or vaginal sex with a female partner.20 Although methamphetamine has received considerable attention as an HIV risk factor among white gay men,21 little is known about the relationship between crack use and HIV risk, especially among AA MSM. Insight into the social context of crack use among AA MSM is needed to understand the potential of HIV and STI risk exposure and transmission to sexual partners.
Social networks have been found to be powerful influences on individuals’ behaviors, through social interactions that provide opportunities to meet sex partners and through social norms and social support.2225 Network characteristics such as number of drug users and density have been found to be associated with HIV risk behaviors.2630 Studies among non-MSM populations show that crack users’ social networks include more drug users (injection and non-injection) and HIV-positive individuals, and crack use among network members is associated with multiple partners.31
The aim of the present study is to compare the social and sexual networks of AA MSM crack smokers (CSs) to AA MSM who were not crack smokers (NCSs) in Baltimore, MD, USA and to examine associations of crack use with sexual risk behavior.
The Unity in Diversity study was a culturally tailored HIV prevention intervention designed for AA MSM. The study was conducted in Baltimore from August 2008 to June 2009. Two types of participants were recruited: Indexes and Networks. Index participants were recruited using a variety of methods including street-based outreach by trained field recruiters, word of mouth, advertisements in the local papers, and active internet-based recruitment on websites and chat rooms for AA MSM. Index inclusion criteria were: 18 years old or older, identify as a male, self-report black or African American race/ethnicity, report having at least two sex partners in the prior 3 months (at least one of which must be a male), report unprotected anal sex with a male in prior 3 months, willingness to take an HIV test, and identify and recruit social network members into the study. Eligible Index participants provided written informed consent and completed a baseline survey using audio computer-assisted self-interview (ACASI) concerning HIV risk behaviors, sociodemographics (age, employment status, highest educational level attained, and history of incarceration), and use of drugs and alcohol. A social network inventory was then administered face-to-face by a trained research assistant. The inventory consisted of a set of 18 questions to elicit the names of individuals from whom they received and provided support, with whom they have used drugs (drug network) or had sex in the past 3 months (sex network). The total size of the network was the sum of the number of people listed. Once the network was elicited, participants were asked about characteristics of each listed network member such as their age, race, employment status, type and frequency of drug use, and perceived serostatus. Participants were asked to indicate the network members whom they are “not on good terms with or disagree or fight [with]” as a measure of conflict network. Participants were also asked to indicate who on their network list “knew they had sex with men.” Density of a network is a measure of connectedness among network members. Density scores range from 0 indicating that no network members know others to 1.00 indicating that all network members know each other. At the end of the baseline visit, Index participants were asked to invite network members listed on their inventory into the study.
Inclusion criteria for Network participants were: 18 years old or older and invited by his Index. Network participants who were enrolled in the study completed the same baseline procedures as the Index participants (e.g., ACASI risk assessment and social network inventory).
Data for the present study are from Index (n = 187) and Network (n = 43) male participants who completed the baseline visit, reported black or African American race/ethnicity, and had at least one male sex partner in the prior 90 days.
Measures
Outcome
Participants were asked about use of marijuana, powdered cocaine, crack, heroin, club drugs (GHB, ecstacy), methamphetamine, poppers, and Viagra/Levitra in the prior 90 days. Responses were recoded to indicate no use versus any use in the prior 90 days. Among those who reported use of any drug, frequency was assessed (monthly or less, 1–3 days a week; ≥4 days a week).
Sexual Identity
To measure sexual identity, participants were asked, “Do you consider yourself to be: heterosexual or straight; bisexual; queer, homosexual, gay, same-gender loving; not sure/questioning?” One nominal variable was created for descriptive and bivariate analysis where 0 = gay/homosexual/same-gender loving, 1 = bisexual/not sure/questioning, and 2 = heterosexual or straight. For use in the multivariate model, two indicator nominal variables were created so that comparisons could be made between gay and bisexual categories and gay and heterosexual categories.
HIV Serostatus
Participants who self-reported negative or unknown serostatus provided an oral specimen to be tested using Oraquick rapid HIV antibody testing kits (Orasure technologies). Preliminary positive results were confirmed using Western blot assay. Participants who self-reported HIV-positive serostatus were asked to provide documentation such as medications or clinical test results for validation or to provide an oral specimen for HIV antibody testing. HIV-seropositive status was defined if participants tested positive by confirmatory tests or provided documentation of an HIV-positive test result.
Sex Network Characteristics Type of sex partner (main, casual, or exchange) and condom use were assessed. To measure concurrency of sex partnerships, participants provided the date of first and most recent sexual episode for each sex network member listed. Overlap of these dates indicated concurrency.32 Sex partners who were also listed as a drug use partner were categorized as multiple risk network members. For each sex partner listed, participants were asked to describe their condom use as never using condoms, used condoms at first but no longer, use condoms every now and then, and always use condoms for all types of sex.
Analysis
T tests and chi-square statistics were used to evaluate bivariate differences in network characteristics between CS MSM and NCS MSM. Variables that were statistically significant in the bivariate models (p < 0.05) were entered into a multivariate model that used backward stepwise selection (p < 0.20) with forced inclusion of the sex identity nominal indicator variables (gay versus heterosexual and gay versus bisexual) considered to be theoretically important to include in the model to assess independent associations with crack use. The “number of network members who knows participant was MSM” variable was not included in the multivariate model due to collinearity with the sexual identity variable. All study procedures were approved by the Johns Hopkins Bloomberg School of Public Health and the Centers for Disease Control and Prevention Institutional Review Boards.
The mean age of the sample was 37.8 years (Table 1). The majority had 12 years or General Educational Development (GED) level education and were not working. Approximately half were HIV-seropositive (46%). Of these, 91% self-reported HIV positive status and 9% (n = 9) tested HIV positive with confirmed Western Blot. Over one-half reported using marijuana in the prior 90 days. A minority reported use of ecstasy (10%), methamphetamine (3%), poppers (10%), and Viagra (11%). Over one-third reported use of crack in the prior 90 days (n = 84; 37%). Among crack users, frequency of use was high with 42% using 1–3 days a week and 20% using ≥4 days a week (data not shown). CS MSM were older (mean age 44 versus 34 years; p < 0.001) and a greater proportion identified as bisexual compared to NCS (44% versus 28%, p = 0.01).
Table 1
Table 1
Bivariate comparison of sociodemographic characteristics and drug use of crack-smoking versus non-crack-smoking AA MSM, Baltimore, MD, USA (2007–2010)
Bivariate Comparisons of the Social Network Characteristics
Table 2 presents comparisons of the social network characteristics of CS and NCS. The total size of CS social network was smaller compared to NCS MSM (mean number of network members 7.63 versus 8.78 network members; p = 0.05) and older (mean = 44.3 versus 36.9 years, p < 0.01). CSs reported a greater number of drug-using network members (2.08 versus 0.55; p < 0.001). More network members of NCS knew about the same-sex behavior of the participant and themselves sought sex partners on the internet (data not shown). There were no differences between groups in the density of the networks or number of network members with whom there was conflict.
Table 2
Table 2
Bivariate comparisons of social network characteristics of crack-smoking versus noncrack-smoking AA MSM, Baltimore, MD, USA (2007–2010)
Bivariate Comparisons of the Sexual Network Characteristics
There were no statistical differences between groups in the total size of the sex networks (mean CS sex network = 3.02 versus mean NCS sex network = 3.03, p = 0.97) or of the number of female sex network members (CU mean number female sex network=0.39 versus NCU=0.62; p = 0.09) (Table 3). The sex network members of CSs had a greater number of HIV seropositives (0.81 versus 0.47, p = 0.02), sex exchange partners (0.87 versus 0.23, p < 0.001) and network members that were both sex and drug partners (1.40 versus 0.47, p < 0.001) compared to NCSs. CSs also reported more network members on whom they were dependent for food and/or shelter.
Table 3
Table 3
Bivariate comparisons of sex network characteristics of crack-smoking versus noncrack-smoking AA MSM, Baltimore, MD (2007–2010)
NCS sex networks had a greater number of sex partners with whom the participant always used condoms (1.33 versus 0.74, p = 0.001) compared to CSs. Over half of the entire sample (53%) reported overlapping (concurrent) sexual partnerships within their network. No group differences of concurrency were observed.
Multivariate Logistic Regression of Network Characteristics on Crack Use
Crack use was independently associated with increased odds of bisexual identity, older-aged networks, greater number of drug-using network members, exchange-sex network members, and multiple-risk network members (Table 4). In the same model, crack use was associated with condom use with fewer sex partners in the network.
Table 4
Table 4
Independent associations with crack use in prior 3 months among African American men who have sex with men (2007–2010)
This study focused on the social and sexual networks of crack-using AA MSM, particularly older crack-using AA MSM, a potentially high-risk subgroup that has not been well-described in the literature. Findings reveal that sex networks of crack-using AA MSM were diverse, consisting of both male and female members and main and exchange partners. Moreover, crack use was independently associated with increased odds of having drug-using and sexual networks overlap. As Baltimore ranks among the most burdened city with gonorhea, syphilis, and chlamydia among African American women33 and HIV incidence among AA MSM,34 the present findings are a cause for concern. These sexual mixing patterns suggest a potential role that crack-using AA MSM have in bridging networks that may vary in prevalence of HIV and other STIs, and underscores the importance of sexual transmission of HIV even among drug users.3538 Consistent with results from a study in Los Angeles where Gorbach et al.39 describes a “concentration of HIV risk” among a sample of low-income, minority drug-using men who have sex with men and women, our findings suggest potential capacity of these sexual networks to facilitate spread of HIV to other sexual and drug-using networks. Moreover, overlap of social ties (e.g., sex and drug ties) has been shown to increase risk of HIV.40
It is well established in the literature that crack use is associated with increased sexual risk behaviors, namely exchanging sex and decreased condom use. In the present study, we report that after controlling for sex exchange and main sex partners, condom use with fewer partners remained associated with crack use. Interventions that have been implemented for predominately white, methamphetamine-using MSM have focused on increasing condom use self-efficacy and lowering drug-and-alcohol-influenced sex.41,42 While these interventions would require tailoring for crack-using AA MSM, they may offer a future direction for decreasing sexual risk. For example, given that the sexual networks consisted of both male and female sex partners and multiple-risk partners, interventions tailored for drug-using AA MSM should include activities to develop proper male and female condom use skills in both drug-involved and non-involved contexts. Research on prevention norms about condom use among crack-using AA MSM may also suggest potential targets for intervention.25
AA MSM in this sample were older and two-fifths did not identify as gay. Although bisexual identity was strongly and independently associated with crack use, there were no differences in sizes of male and female sexual networks after adjusting for numerous network characteristics. We did not measure the level of social integration of CSs with other AA MSM within the larger community and how crack use may influence their identities.
It was surprising that the HIV rates did not vary by crack use. This lack of difference may be due to the high prevalence of HIV among AA MSM. Although no difference was observed between CS and NCS groups, it is also concerning that over half of the full sample reported sexual concurrency (53%; i.e., overlap in the time period of their sex partnerships). Concurrency is a significant factor associated with rapid transmission of HIV and STIs in sexual networks32,43 and has been hypothesized as a contributing factor to the disparity in HIV among African Americans compared to white samples.44 Promoting consistent and proper condom use in all sexual partnerships is a critical message in addition to encouraging frequent HIV and STI testing.
Limitations of this study should be noted. The sample is comprised of older men living in Baltimore who reported having unprotected sex in the prior 90 days. Generalizability of the findings to younger men and AA MSM in other areas of the country is limited. Additionally, analysis was conducted using cross-sectional data which limits our ability to draw causal inference from the results.
These limitations notwithstanding, findings suggest that crack use is an HIV risk factor among AA MSM and networks of crack-using AA MSM would be optimal targets for testing and behavioral interventions that focus on increasing skills for HIV risk reduction and HIV and STI testing. Crack-using AA MSM in this study was embedded within high-risk networks consisting of other drug users. Given these social contacts, crack-using MSM may be an ideal source to recruit other high-risk networks for HIV testing and risk-reduction interventions. Considerations for implementing these interventions should include a range of settings such as drug treatment centers and other venues where non-gay-identified AA MSM frequent.
Acknowledgments
The Latino and African American Mens Project (LAAMP) Study Team also wishes to acknowledge all of 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 like to acknowledge and thank the CDC Study Team: Stephen A. Flores, Heather Joseph, David Purcell, Greg Millett, Cathy Zhang, and Helen Ding. This research was funded through a grant from the Centers for Disease Control and Prevention, 1UR6PS000355.
Footnotes
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.
1. Marx R, Aral SO, Rolfs RT, Sterk CE, Kahn JG. Crack, sex, and STD. Sex Transm Dis. 1991;18(2):92–101. doi: 10.1097/00007435-199118020-00008. [PubMed] [Cross Ref]
2. Inciardi JA, Chitwood DD, McCoy CB. Special risks for the acquisition and transmission of HIV infection during sex in crack houses. J Acquir Immune Defic Syndr. 1992;5(9):951–952. [PubMed]
3. Edlin BR, Irwin KL, Faruque S, et al. Intersecting epidemics—crack cocaine use and HIV infection among inner-city young adults. Multicenter Crack Cocaine and HIV Infection Study Team. N Engl J Med. 1994;331(21):1422–1427. doi: 10.1056/NEJM199411243312106. [PubMed] [Cross Ref]
4. Chiasson MA, Stoneburner RL, Hildebrandt DS, Ewing WE, Telzak EE, Jaffe HW. Heterosexual transmission of HIV-1 associated with the use of smokable freebase cocaine (crack) AIDS. 1991;5(9):1121–1126. doi: 10.1097/00002030-199109000-00011. [PubMed] [Cross Ref]
5. Ober A, Shoptaw S, Wang PC, Gorbach P, Weiss RE. Factors associated with event-level stimulant use during sex in a sample of older, low-income men who have sex with men in Los Angeles. Drug Alcohol Depend. 2009;102(1–3):123–129. doi: 10.1016/j.drugalcdep.2009.02.002. [PMC free article] [PubMed] [Cross Ref]
6. Campsmith ML, Nakashima AK, Jones JL. Association between crack cocaine use and high-risk sexual behaviors after HIV diagnosis. J Acquir Immune Defic Syndr. 2000;25(2):192–198. doi: 10.1097/00042560-200010010-00015. [PubMed] [Cross Ref]
7. Bachmann LH, Grimley DM, Chen H, et al. Risk behaviours in HIV-positive men who have sex with men participating in an intervention in a primary care setting. Int J STD AIDS. 2009;20(9):607–612. doi: 10.1258/ijsa.2009.009030. [PubMed] [Cross Ref]
8. Feist-Price S, Logan TK, Leukefeld C, Moore CL, Ebreo A. Targeting HIV prevention on African American crack and injection drug users. Subst Use Misuse. 2003;38(9):1259–1284. doi: 10.1081/JA-120018483. [PubMed] [Cross Ref]
9. Harzke AJ, Williams ML, Bowen AM. Binge use of crack cocaine and sexual risk behaviors among African-American, HIV-positive users. AIDS Behav. 2009;13(6):1106–1118. doi: 10.1007/s10461-008-9450-9. [PMC free article] [PubMed] [Cross Ref]
10. Voelker R. Studies illuminate HIV’s inequalities. JAMA. 2008;299(3):269–275. doi: 10.1001/jama.299.3.269. [PubMed] [Cross Ref]
11. Koblin BA, Torian LV, Guilin V, Ren L, MacKellar DA, Valleroy LA. High prevalence of HIV infection among young men who have sex with men in New York City. AIDS. 2000;14(12):1793–1800. doi: 10.1097/00002030-200008180-00015. [PubMed] [Cross Ref]
12. Harawa NT, Greenland S, Bingham TA, et al. Associations of race/ethnicity with HIV prevalence and HIV-related behaviors among young men who have sex with men in 7 urban centers in the United States. J Acquir Immune Defic Syndr. 2004;35(5):526–536. doi: 10.1097/00126334-200404150-00011. [PubMed] [Cross Ref]
13. Celentano DD, Sifakis F, Hylton J, Torian LV, Guillin V, Koblin BA. Race/ethnic differences in HIV prevalence and risks among adolescent and young adult men who have sex with men. J Urban Health. 2005;82(4):610–621. doi: 10.1093/jurban/jti124. [PMC free article] [PubMed] [Cross Ref]
14. Catania JA, Osmond D, Stall RD, et al. The continuing HIV epidemic among men who have sex with men. Am J Public Health. 2001;91(6):907–914. doi: 10.2105/AJPH.91.6.907. [PubMed] [Cross Ref]
15. Valleroy LA, MacKellar DA, Karon JM, et al. HIV prevalence and associated risks in young men who have sex with men. Young Men’s Survey Study Group. JAMA. 2000;284(2):198–204. doi: 10.1001/jama.284.2.198. [PubMed] [Cross Ref]
16. Millett GA, Peterson JL, Wolitski RJ, Stall R. Greater risk for HIV infection of black men who have sex with men: a critical literature review. Am J Public Health. 2006;96(6):1007–1019. doi: 10.2105/AJPH.2005.066720. [PubMed] [Cross Ref]
17. Hatfield LA, Horvath KJ, Jacoby SM, Simon Rosser BR. Comparison of substance use and risky sexual behavior among a diverse sample of urban, HIV-positive men who have sex with men. J Addict Dis. 2009;28(3):208–218. doi: 10.1080/10550880903014726. [PMC free article] [PubMed] [Cross Ref]
18. Mimiaga MJ, Reisner SL, Fontaine YM, et al. Walking the line: stimulant use during sex and HIV risk behavior among Black urban MSM. Drug Alcohol Depend. 2010;110(1–2):30–37. doi: 10.1016/j.drugalcdep.2010.01.017. [PubMed] [Cross Ref]
19. Halkitis PN, Jerome RC. A comparative analysis of methamphetamine use: Black gay and bisexual men in relation to men of other races. Addict Behav. 2008;33(1):83–93. doi: 10.1016/j.addbeh.2007.07.015. [PMC free article] [PubMed] [Cross Ref]
20. Mimiaga MJ, Reisner SL, Cranston K, et al. Sexual mixing patterns and partner characteristics of black MSM in Massachusetts at increased risk for HIV infection and transmission. J Urban Health. 2009;86(4):602–623. doi: 10.1007/s11524-009-9363-6. [PMC free article] [PubMed] [Cross Ref]
21. Mansergh G, Shouse RL, Marks G, et al. Methamphetamine and sildenafil (Viagra) use are linked to unprotected receptive and insertive anal sex, respectively, in a sample of men who have sex with men. Sex Transm Infect. 2006;82(2):131–134. doi: 10.1136/sti.2005.017129. [PMC free article] [PubMed] [Cross Ref]
22. Rothenberg RB, Long DM, Sterk CE, et al. The Atlanta Urban Networks Study: a blueprint for endemic transmission. AIDS. 2000;14(14):2191–2200. doi: 10.1097/00002030-200009290-00016. [PubMed] [Cross Ref]
23. Friedman SR, Neaigus A, Jose B, et al. Sociometric risk networks and risk for HIV infection. Am J Public Health. 1997;87(8):1289–1296. doi: 10.2105/AJPH.87.8.1289. [PubMed] [Cross Ref]
24. Adimora AA, Schoenbach VJ, Doherty IA. HIV and African Americans in the southern United States: sexual networks and social context. Sex Transm Dis. 2006;33(7 Suppl):S39–S45. [PubMed]
25. Peterson JL, Rothenberg R, Kraft JM, Beeker C, Trotter R. Perceived condom norms and HIV risks among social and sexual networks of young African American men who have sex with men. Health Educ Res. 2009;24(1):119–127. doi: 10.1093/her/cyn003. [PubMed] [Cross Ref]
26. Tobin KE, Davey-Rothwell M, Latkin CA. Social-level correlates of shooting gallery attendance: a focus on networks and norms. AIDS Behav. 2010;14(5):1142–1148. doi: 10.1007/s10461-010-9670-7. [PMC free article] [PubMed] [Cross Ref]
27. Bohnert AS, Bradshaw CP, Latkin CA. A social network perspective on heroin and cocaine use among adults: evidence of bidirectional influences. Addiction. 2009;104(7):1210–1218. doi: 10.1111/j.1360-0443.2009.02615.x. [PMC free article] [PubMed] [Cross Ref]
28. Latkin CA, Mandell W, Oziemkowska M, et al. Using social network analysis to study patterns of drug use among urban drug users at high risk for HIV/AIDS. Drug Alcohol Depend. 1995;38(1):1–9. doi: 10.1016/0376-8716(94)01082-V. [PubMed] [Cross Ref]
29. Kottiri BJ, Friedman SR, Neaigus A, Curtis R, Des Jarlais DC. Risk networks and racial/ethnic differences in the prevalence of HIV infection among injection drug users. J Acquir Immune Defic Syndr. 2002;30(1):95–104. [PubMed]
30. Neaigus A, Friedman SR, Jose B, et al. High-risk personal networks and syringe sharing as risk factors for HIV infection among new drug injectors. J Acquir Immune Defic Syndr Hum Retrovirol. 1996;11(5):499–509. doi: 10.1097/00042560-199604150-00011. [PubMed] [Cross Ref]
31. Latkin CA, Mandell W, Vlahov D. The relationship between risk networks’ patterns of crack cocaine and alcohol consumption and HIV-related sexual behaviors among adult injection drug users: a prospective study. Drug Alcohol Depend. 1996;42(3):175–181. doi: 10.1016/S0376-8716(96)01279-3. [PubMed] [Cross Ref]
32. Doherty IA, Schoenbach VJ, Adimora AA. Condom use and duration of concurrent partnerships among men in the United States. Sex Transm Dis. 2009;36(5):265–272. doi: 10.1097/OLQ.0b013e318191ba2a. [PMC free article] [PubMed] [Cross Ref]
33. Centers for Disease Control and Prevention (CDC). Sexually transmitted disease surveillance, 2000. 2001. http://www.cdc.gov/std/stats/.
34. Centers for Disease Control and Prevention (CDC) HIV prevalence, unrecognized infection, and HIV testing among men who have sex with men—five U.S. cities, June 2004–April 2005. MMWR Morb Mortal Wkly Rep. 2005;54(24):597–601. [PubMed]
35. Rhodes F, Deren S, Wood MM, et al. Understanding HIV risks of chronic drug-using men who have sex with men. AIDS Care. 1999;11(6):629–648. doi: 10.1080/09540129947550. [PubMed] [Cross Ref]
36. Millett GA, Flores SA, Peterson JL, Bakeman R. Explaining disparities in HIV infection among black and white men who have sex with men: a meta-analysis of HIV risk behaviors. AIDS. 2007;21(15):2083–2091. doi: 10.1097/QAD.0b013e3282e9a64b. [PubMed] [Cross Ref]
37. Tieu HV, Murrill C, Xu G, Koblin BA. Sexual partnering and HIV risk among black men who have sex with men: New York City. J Urban Health. 2010;87(1):113–121. doi: 10.1007/s11524-009-9416-x. [PMC free article] [PubMed] [Cross Ref]
38. Berry M, Raymond HF, McFarland W. Same race and older partner selection may explain higher HIV prevalence among black men who have sex with men. AIDS. 2007;21(17):2349–2350. doi: 10.1097/QAD.0b013e3282f12f41. [PubMed] [Cross Ref]
39. Gorbach PM, Murphy R, Weiss RE, Hucks-Ortiz C, Shoptaw S. Bridging sexual boundaries: men who have sex with men and women in a street-based sample in Los Angeles. J Urban Health. 2009;86(Suppl 1):63–76. doi: 10.1007/s11524-009-9370-7. [PMC free article] [PubMed] [Cross Ref]
40. Sherman SG, Latkin CA. Intimate relationship characteristics associated with condom use among drug users and their sex partners: a multilevel analysis. Drug Alcohol Depend. 2001;64(1):97–104. doi: 10.1016/S0376-8716(00)00236-2. [PubMed] [Cross Ref]
41. Mausbach BT, Semple SJ, Strathdee SA, Zians J, Patterson TL. Efficacy of a behavioral intervention for increasing safer sex behaviors in HIV-positive MSM methamphetamine users: results from the EDGE study. Drug Alcohol Depend. 2007;87(2–3):249–257. doi: 10.1016/j.drugalcdep.2006.08.026. [PMC free article] [PubMed] [Cross Ref]
42. Garfein RS, Metzner M, Cuevas J, Bousman CA, Patterson T. Formative assessment of ARM-U: a modular intervention for decreasing risk behaviors among HIV-positive and HIV-negative methamphetamine-using MSM. Open AIDS J. 2010;4:105–115. doi: 10.2174/1874613601004030105. [PMC free article] [PubMed] [Cross Ref]
43. Morris M. Concurrent partnerships and syphilis persistence: new thoughts on an old puzzle. Sex Transm Dis. 2001;28(9):504–507. doi: 10.1097/00007435-200109000-00005. [PubMed] [Cross Ref]
44. Adimora AA, Schoenbach VJ, Doherty IA. Concurrent sexual partnerships among men in the United States. Am J Public Health. 2007;97(12):2230–2237. doi: 10.2105/AJPH.2006.099069. [PubMed] [Cross Ref]
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