EPIDEMIOLOGIC FACTORS CONTRIBUTING to prevalence and incidence of behaviorally transmitted infections, such as STIs and HIV, are not equally distributed in a risk population. 1 Infections follow paths of behavioral interactions that correspond with individual partner choices. However, individuals do not mix uniformly or homogeneously within a population, and these patterns of mixing have an effect on the dynamics of a behavioral disease epidemic. All populations demonstrate assortative mixing (individuals choosing like partners) and disassortative mixing (individuals choosing unlike partners). Assortative mixing produces a quick epidemic restricted to the subgroups in which a pathogen is introduced, while disassortative mixing produces a sustained epidemic that progresses slowly and widely throughout a general population. 2,3 The importance of disassortative mixing is that it provides a bridge of infection from one population group to another. 1,4
Sociodemographic characteristics are population factors that tend to act as natural mixing boundaries. 5,6 Age cohorts are one boundary. Most sexual relationships occur within age cohorts. Mixing across age cohorts has the potential to expose one cohort to pathogens prevalent in another age cohort. Sexual mixing across age cohorts has been implicated in HIV transmission from older to younger age cohorts in blacks and gay men. 7–10 Needle sharing across age cohorts has also been found to be a route of HIV transmission from older to younger drug injectors. 11 Sexual orientation and gender are also mixing boundaries. Sexual mixing across sexual orientations and genders by bisexual men has been implicated as a method of HIV transmission between homosexual men and heterosexual women. 12–15
Although not usually thought of as a mixing boundary, routes of drug ingestion may be relevant to HIV transmission, particularly in combination with other sociodemographic characteristics. Mixing across groups of drug injectors who have sex with men and heterosexual injectors has been proposed as a path to groups of heterosexual injectors. 16,17 Sexual mixing across groups of injecting drug users and nonusers has also been proposed as a pathway for HIV to cross from one group to another. 18–20
Drug-using male sex workers (DUMSWs) are known to have large numbers of partners with whom they inject drugs and have sex. 21–23 The purpose of this study was to describe the behavioral mixing patterns of DUMSWs that are relevant to the transmission of HIV infection. Respondent/partner pairs were examined in two behavioral groups: pairs in which the respondent/partner injected drugs and pairs in which the respondent/partner had sex. Mixing patterns were examined for each type of pair by gender, race/ethnicity, age cohort, and participation in sex work.
The study was conducted between August 2001 and January 2002 in Houston and was nested in a larger study intended to determine the efficacy of an intervention to increase condom use by drug-using street-based male sex workers. A university committee for the protection of human subjects reviewed and approved study procedures. Procedures for this study were consistent with those of the larger study.
The sample for the original study was created with use of a targeted sampling plan. 24,25 Key informant interviews were used to identify neighborhoods where male sex workers solicited money for sex. Street solicitation was confirmed by direct observation. The neighborhood in which most men trade sex for money has historically been identified with the gay community. The neighborhood's population is diverse, comprising whites, blacks, and Hispanics of all economic strata. The neighborhood's many restaurants and entertainment spots attract large numbers of people from all areas of the city. Solicitation is largely conducted on streets that surround gay bars and clubs, which are distributed throughout the neighborhood.
After identifying and confirming areas where solicitation was taking place, a trained outreach worker contacted some of the men believed to be soliciting sex for money. As a means of introduction, the outreach worker offered condoms, HIV prevention information, and business cards. Contacts were then engaged in conversation, and they were asked to go to a storefront data collection center to be screened for eligibility.
Men who presented for screening were informed of the intent of the study, that participation was voluntary, and that if they qualified and decided to participate they could refuse to answer specific questions. Participation was limited to male sex workers who were age 16 years and older, self-identified themselves as male, had exchanged sex for money in the previous week, and had smoked crack cocaine or injected an illicit substance in the 48 hours before being screened.
Once written informed consent was obtained, participants provided a urine sample that was tested for cocaine or opioid metabolites to confirm recent drug use. In addition, to confirm participation in sex work, men were asked a series of questions about soliciting money for sex. Responses were evaluated for consistency with current information provided by key informants. If a urine sample was negative or if information about street solicitation was inconsistent, the individual was not eligible for this study.
Trained research assistants conducted interviews in a storefront center in private offices. Participants were paid $20 for their time and the costs of transportation to the storefront center.
Data were collected with a modified version of the Bridge Group Assessment Questionnaire. 26 The questionnaire collects three types of egocentric network data on a respondent and up to six recent drug-using partners. Data were recorded directly into computer files. The data entry program was designed to flag questionable responses for clarification and to preclude out-of-range responses.
Data were collected on the following: the respondent, the partner, and the relationship between the respondent and the partner, as reported by the respondent. A recent drug-use partner was defined as an individual with whom the respondent had smoked crack cocaine or had injected a drug in the previous 30 days. Data on the respondent included sociodemographic characteristics, drug use, and sexual behaviors.
Data on the partner were drug use, gender, race/ethnicity, age, and participation in sex work. Drug use was measured as the type of drug use and whether the drug was injected. Gender was measured as male/female. Race/ethnicity was measured as white, black, Hispanic, and other. Age of the partner was recorded as 19 years or less, 20 to 29 years, or 30 years or older. The partner's participation in sex work was measured as a dichotomous (trade/no trade) response. We expected that respondents might not know the age of the partner or if he or she was engaged in sex work. For these two measures, a “don't know” response was allowed.
Relationship measures were constructed for the drug use and the sexual pair. Drug use by the pair was measured as smoking crack cocaine or injecting a drug. Sexual behaviors were measured as vaginal sex, insertive anal sex, receptive anal sex, and condom use during vaginal or anal sex.
Analyses were conducted on respondent/partner pairs. Eighty-nine men provided data on 353 drug-using partners. Pairs included in the analyses were limited to 49 pairs who injected drugs, 118 pairs who had sex, and 51 pairs had both injected drugs and had sex. These pairs were chosen for analysis because drug injection and vaginal/anal sex directly transmit HIV. One hundred thirty-five pairs that smoked crack cocaine but did not inject drugs or have sex were excluded from the analyses.
Analyses were conducted separately for drug injection and sex pairs. Respondent and partner characteristics were examined to determine how like (concordant) or dislike (discordant) they were on gender, trading sex for money, race/ethnicity, and age. Pairs with “don't know” responses on the variables of partner trading sex for money or partner age were excluded from the relevant analyses. The distributions of concordant/discordant pairs across race/ethnicity and age were assessed with Pearson chi-square statistics. The proportion of concordant pairs, calculated as the percentage of the total pairs possessing the same characteristic, was used as indicative of assortative needle use or sex partner mixing. The proportion of discordant pairs, calculated as the percentage of the total partner pairs possessing discordant characteristics, was considered indicative of disassortative mixing.
Drug Injection Pairs
Table 1 shows drug injection pairs by gender, trading sex for money, race/ethnicity, and age cohort. Less than a quarter of the 100 drug injection pairs (22%) were gender-concordant. A quarter of injection pairs (25%) were sex trading–discordant, where the partner was not engaged in trading sex for money. More than a quarter of male/male injection pairs (27%) were trading sex for money–discordant. Of the male/female injection pairs, about a fifth (18%) were trading sex for money–discordant.
About a third (30%) of drug injecting pairs were race/ethnicity-discordant. There were significant differences in the distribution of concordant/discordant pairs across race/ethnicity (P < 0.000). Most Hispanic respondents (91%) had injection partners who were race/ethnicity-discordant. Two fifths (40%) of black injection pairs were race/ethnicity-discordant, as were about a tenth (14%) of the partners of white respondents.
Respondents reported the ages of 90 of the 100 injecting partners. Twenty-nine injection pairs (35%) were age-discordant. The distribution of concordant/discordant pairs significantly differed across age categories (P < 0.000). Almost three quarters (70%) of respondents 19 years or younger had age-discordant injecting partners. A third (33%) of respondents aged 20 to 29 years and about a fourth (23%) of respondents aged 30 years or older had age-discordant injecting partners.
As shown in Table 2, 169 pairs had sex. A quarter of sex pairs (26%) were gender-discordant. More than two thirds of sex pairs (66%) were trading sex for money–discordant. Almost two thirds (62%) of the male/male sex pairs were trading sex–discordant. More than four fifths (83%) of male/female pairs were trading sex for money–discordant.
About a third (30%) of sex pairs were race/ethnicity-discordant. There were significant differences in concordant/discordant pairs across race/ethnicity (P < 0.000). Hispanic respondents had the highest rate of race/ethnicity-discordant sex partners (86%). More than a third (37%) of black respondents and a fifth (19%) of white respondents had sex partners who were race/ethnicity-discordant.
All but nine respondents reported the age category of their sex partners. About half (47%) of the sex pairs were age-discordant. There were significant differences in discordant pairs across age categories (P < 0.000). Three quarters (76%) of respondents aged 19 years or younger had age-discordant sex partners. About half (49%) of respondents aged 20 to 29 years had age-discordant sex partners, as did more than a third (35%) of respondents aged 30 years or older.
The data on the drug injection and sexual partner choices of DUMSWs presents considerable patterns of mixing, as suggested by discordance in respondent/partner characteristics. Mixing is evident for drug injection and sex, both behaviors that have the potential to transmit HIV. Substantial rates of mixing are evident across genders and—by inference—sexual orientations, since by definition DUMSWs who have sex with female partners also have sex with men. The majority of both male and female sex partners were not engaged in sex work, a circumstance suggesting considerable mixing between respondents engaged in trading sex for money and partners not engaged. The rate of discordance in trading sex for money was particularly notable for male/female sexual pairs. The increased risk with this type of mixing is for the partner not trading sex for money.
For both drug injection and sex, mixing across age cohorts is high. The results suggest a negative linear mixing pattern associated with age, because discordant partner choice decreases as age increases. Mixing across age cohorts may be especially relevant in understanding risk as it relates to younger DUMSWS. It is assumed that younger members of a group are least likely to be infected with an STI or HIV. 7 If this assumption is correct, then younger DUMSWs or younger partners of DUMSWs are at greater risk of infection than their older counterparts. DUMSWs 19 years of age and younger have likely just begun trading sex for money and, if doing so, injecting drugs. Consequently, mixing across ages may provide a means for linking older groups of injectors and sex partners to younger age cohorts of male sex workers.
The data demonstrated that mixing across race/ethnicity is also high, especially for minority drug-using male sex workers. Most injection or sex partners of Hispanic DUMSWs were whites or blacks. More than a third of the injection or sex partners of blacks were whites or Hispanics. These findings may be noteworthy for two reasons. First, Laumann and Youm 6 found high rates of sexual mixing between STD core and periphery groups among blacks. Second, studies have consistently shown that blacks have disproportionately higher rates of HIV than whites, especially young black men who have sex with men. 27,28 Mixing by DUMSWs across racial/ethnic groups may provide a mechanism for linking the high-risk or core individuals of one racial/ethnic group with individuals of another group on the periphery.
The data are not without limitations, and these need to be considered when interpreting the results of the study. The study is limited because it is cross-sectional, and we are unable to assess how mixing patterns might change over time or causality. The sample used for the study was a convenience sample, and the results cannot be generalized to other populations of DUMSWs. Generalizability is further affected by the relatively small sample size, especially African and Hispanic DUMSWs, and local conditions, such as the neighborhood and city in which the data were collected.
All data were self-reported by the respondent for both the respondent and the partner. Identification as a male sex worker was by self-report. Men were questioned about prevailing conditions related to sex work, such as the prices charged for specific sex acts, and these responses were compared to information obtained from key informants. However, the veracity of claims to be engaged in sex worker was not further verified. Like reports of sexual behaviors, activities that would definitively confirm sex work are not usually observable.
Furthermore, while self-report measures of drug use and sexual behaviors of a respondent are reliable and valid, the reliability and the validity of data reported by a respondent about a drug injection or sex partner are unknown. We attempted to attenuate error associated with recall of a partner's characteristics and behaviors by limiting reporting to the previous 30 days. We also tried to limit recall error by eliminating data on paid sex partners, as paid partners are those about whom DUMSWs are least likely to be knowledgeable. The number of recent partners on whom data were collected may also limit the findings. Some DUMSWs likely had more than six recent drug-using partners, and some might have had sex partners in the 30 days before the interview with whom they did not use drugs. An additional limitation is that we were unable to collect HIV/STI biologic markers. Therefore, although we can infer risk, we were unable to directly assess HIV risk posed by the mixing patterns.
These limitations notwithstanding, the study provides useful data on the drug injection and sexual mixing patterns of an important HIV risk group. It has been suggested that male sex workers act as epidemiologic bridges between risk groups. 21–23 From these data, we can infer that DUMSWs might bridge gender, sexual orientation, age, racial/ethnic, and sex work groups. The potential for DUMSWs to bridge networks is particularly evident when the partner, although a drug user, is not engaged in trading sex for money. Elaborating the mechanisms by which DUMSWs might act as an epidemiologic bridge is complex and involves more variables than were explored in this study. Nevertheless, these findings suggest that such a study would be worthwhile.
More research remains to be done before the HIV risk dynamics of DUMSWs can be completely described and before more effective control measures can be fully understood and implemented. For example, since all partners in this study had injected drugs or had had sex with a DUMSW within the previous 30 days, the partners are concurrent. 3,29 The relevance of focusing on concurrency in future studies is that having concurrent partners increases the risk of transmitting STIs and HIV among the partners. An investigation of rates of disassortative mixing across concurrent partners of DUMSWs could greatly enhance our understanding of risk dynamics of this at-risk group and the groups with which they interact. As of yet there have been no study reports describing the types of partner concurrency among DUMSWs, the rates of mixing among concurrent partners, and the impact that different types of concurrency might have on HIV/STI transmission.
1. Aral S, Hughes J, Stoner B, et al. Sexual mixing patterns in the spread of gonococcal and chlamydial infections. Am J Public Health 1999; 89: 825–833.
2. Rothenberg R, Baldwin J, Trotter R, Muth S. The risk environment for HIV transmission: results from the Atlanta and Flagstaff network studies. J Urban Health 2001; 78: 419–432.
3. Ford K, Sohn W, Lepkowski J. Am adolescents: sexual mixing patterns, bridge partners, and concurrency. Sex Transm Dis 2002; 29: 13–19.
4. Rothenberg R, Potterat J, Woodhouse D. Personal risk taking and the spread of disease: beyond core groups. J Infect Dis 1996; 174: S144–S149.
5. Rothenberg, R, Potterat J. Temporal and social aspects of gonorrhea transmission: the force of infectivity. Sex Transm Dis 1987; 15: 88–92.
6. Laumann E, Youm Y. Racial/ethnic differences in the prevalence of sexually transmitted diseases in the United States: a network explanation. Sex Transm Dis 1999; 26: 250–261.
7. Blower, S, McLean A. Mixing ecology and epidemiology. Proc R Soc London 1991; 245: 187–192.
8. Morris M, Zavisca J, Dean L. Social and sexual networks: their role in the spread of HIV/AIDS among young gay men. AIDS Ed Prev 1995; 7: 24–35.
9. Mansergh G, Marks G. Age and risk of HIV infection in men who have sex with men. J Acquir Immun Defic Syndr 1998; 12: 1119–1128.
10. DiClemente R, Wingood G, Crosby R, et al. Sexual risk behaviors associated with having older sex partners: a study of black adolescent females. Sex Transm Dis 2002; 29: 20–24.
11. Kral A, Lorvick J, Edlin B. Sex-, drug-related risk among populations of younger and older injection drug users in adjacent neighborhoods in San Francisco. J Acquir Immun Defic Syndr Hum Retrovirol 2000; 24: 162–167.
12. Wood R, Krueger L, Pearlman T, Goldbaum G. HIV transmission: women's risk from bisexual men. Am J Public Health 1993; 83: 1757–1759.
13. Doll L, Beeker C. Male bisexual behavior and HIV risk in the United States: synthesis of research with implications for behavioral interventions. AIDS Ed Prev 1996; 8: 205–225.
14. Wold C, Seage G, Lenderking W, et al. Unsafe sex in men who have sex with both men and women. J Acquir Immun Defic Syndr Hum Retrovirol 1998; 17: 361–367.
15. Craib K, Weber A, Cornelisse P, et al. Comparison of sexual behaviors, unprotected sex, and substance use between two independent cohorts of gay and bisexual men. AIDS 2000; 14: 303–311.
16. Battjes R, Pickens R, Amsel Z. Introduction of HIV infection among intravenous drug abusers in low prevalence areas. J Acquir Immun Defic Syndr 1989; 2: 533–539.
17. Williams M, Zhao Z, Bowen A, Freeman R, Elwood W. Introduction of HIV into drug injection networks outside AIDS epicenters. Int J STD AIDS 1997; 8: 629–635.
18. Deren S, Estrada A, Stark M, Williams M, Goldstein M. A multisite study of sexual orientation and injection drug use as predictors of HIV serostatus in out-of-treatment male drug users. J Acquir Immun Defic Syndr Hum Retrovirol 1997; 15: 289–295.
19. Lewis D, Watters J. Sexual behavior and sexual identity in male injection drug users. J Acquir Immun Defic Syndr Hum Retrovirol 1994; 7: 190–198.
20. Molitor F, Ruiz J, Flynn N, Mikanda J, Sun R, Anderson R. Methamphetamine use and sexual and injection risk behaviors among out-of-treatment injection drug users. Am J Alcohol Abuse 1999; 25: 475–493.
21. Elifson K, Boles J, Sweat M. Risk factors associated with HIV infection among male prostitutes. Am J Public Health 1993; 83: 79–83.
22. Parker M, Ward H, Day S. Sexual networks and the transmission of HIV in London. J Biosocial Sci 1998; 30: 63–83.
23. Morse E, Simon P, Osofsky H, Balson P, Gaumer H. The male street prostitute: a vector for transmission of HIV infection into the heterosexual world. Soc Sci Med 1991; 5: 535–539.
24. Watters J, Biernacki P. Targeted sampling: options for the study of hidden populations. Social Problems 1989; 36: 416–430.
25. Heckathorn D, Semaan S, Broadhead R, Hughes J. Extensions of respondent-driven sampling: a new approach to the study of injection drug users aged 18–25. AIDS Behavior 2002; 6: 55–67.
26. Williams M, Trotter R, Zhuo Z, Siegal H, Robles R, Jones A. An investigation of the HIV risk behaviors of drug use networks. Connections 1995; 18: 58–72.
27. Rosenberg P, Biggar R. Trends in HIV incidence among young adults in the United States. JAMA 1998; 279: 1894–1899.
28. Catania J, Osmond D, Stall R, et al. The continuing HIV epidemic among men who have sex with men. Am J Public Health 2001; 91: 907–914.
29. Gorbach P, Stoner B, Aral S, Whittington W, Holmes K. “It takes a village”: understanding concurrent sexual partnerships in Seattle, Washington. Sex Transm Dis 2002; 29: 444–452.