Injection drug use has been shown to increase the risk of HIV transmission and acquisition through both unsafe injection practices and risky sexual behavior.1–4 According to the 2014 World Drug Report, 12.7 million people inject drugs globally, about 1.7 million of whom are living with HIV.5 The population of people who inject drugs (PWID) in South East and East Asia is estimated to be nearly 4 million.6 In China and Thailand, injection drug use is rampant due to illicit opium production.2 In China, PWID accounted for approximately 39% of new HIV infections between 2005 and 2009,7,8 whereas in Thailand, HIV prevalence among PWID in 2010 ranged between 11% and 24%.9
HIV and other infections have the potential to spread within the population of PWID and to the general population through sexual networks. In the region particularly in China, there has been a change in HIV transmission patterns among PWID, from being predominantly due to unsafe injection practices to heterosexual transmission.10–12 It is therefore important to determine whether long-term medication-assisted treatment (LT-MAT) with buprenorphine/naloxone (BUP/NX) of injection opiate dependency will lead to changes in risky sexual behavior in this region. Studies conducted in the United States suggest that LT-MAT with BUP/NX may lead to a reduction in risky sexual behavior in PWID.13,14 BUP/NX has fewer clinical and regulatory barriers as well as comparable clinical effectiveness with methadone.15–19
Methadone maintenance treatment is widely used in PWID in the region through community-based treatment programs implemented by the Chinese and Thai governments, where opiate-dependent patients can be admitted.20–25 Buprenorphine/naloxone treatment for opiate dependence was not yet licensed for treatment in Asia at the time of conducting the HIV Prevention Trials Network (HPTN) 058 study.26 Policies on injection drug use management in the region are evolving, from being punitive through compulsory detoxification programs implemented through labor centers to these community-based rehabilitation.21 These changes therefore increase the relevance of exploring other treatment options for injection drug use management such as buprenorphine/naloxone and further to determine the effect of LT-MAT on risky sexual behavior.
Risky sexual behavior has been defined in other studies to include multiple sexual partners, unprotected sexual acts, sexual frequency, having sex while under the influence of alcohol or drugs, and transactional sex.4,27 Risky sexual behaviors among PWID are common, in particular, condomless sex, multiple partners, and transactional sex.10,13,28–31 This makes this population vulnerable to both transmission and acquisition of sexually transmitted infections including HIV. Using data from the HPTN 058 study, it is important to determine predictors of risky sexual behavior to derive sexual risk prevention strategies.
HPTN 058 was a randomized controlled trial (RCT) of LT-MAT vs. short-term medication-assisted treatment (ST-MAT) among PWID in China and Thailand. The primary aim of the original study was to evaluate the effect of randomized treatment on the rates of new HIV infection and mortality. The study was stopped early at the recommendation of Data Safety Monitoring Board (DSMB) in October 2011 due to lower than expected overall HIV incidence.32
The purpose of this study is to evaluate if receipt of LT-MAT vs. ST-MAT influenced risky sexual behavior longitudinally among HIV-uninfected PWID and to identify other predictors of risky sexual behavior in this population. These analyses can provide new information about risky sexual behaviors among HIV-uninfected PWID globally, which may be helpful for designing behavioral intervention strategies to reduce risky sexual behavior among this population, which in turn impacts the general population.
HPTN 058 recruited HIV-uninfected PWID who were older than 18 years of age from 4 centers across China and Thailand.32 The participants had to meet Diagnostic and Statistical Manual of Mental Disorders (DSM IV) criteria for opiate dependence, with positive urine test for opiates at time of enrollment, admit to injecting opiates at least 12 times in the previous 28 days, and either not be of reproductive potential or be willing to use contraception. Exclusion criteria included pregnancy, breastfeeding, enrollment for methadone treatment, and significant medical conditions. Between May 2007 and October 2011, 1251 participants were enrolled and randomized. Of the 1250 evaluable participants; 623 were randomized to LT-MAT and 627 randomized to ST-MAT. Participants randomized to LT-MAT received BUP/NX thrice weekly for 48 weeks coupled with 21 sessions of risk-reduction counseling, and followed by dose tapering.32 Participants randomized to ST-MAT received BUP/NX detoxification for 15 days together with 21 sessions of risk-reduction counseling. Participants were followed for a minimum of 52 weeks to accommodate the time required for the LT-MAT plus dose tapering. We will refer to this 52-week period as the treatment phase.
The 5 primary endpoints for this analysis were binary indicators of risky sexual behaviors in the previous month: (1) any condomless sex with a primary partner, (2) any condomless sex with a nonprimary partner, c) multiple sexual partners (>1), and d) more than 3 sex acts in a month. Data regarding self-reported risky sexual behavior were collected through structured interview at baseline and again every 26 weeks.
The primary predictor for this analysis was type of opiate dependency treatment. Treatment was defined in 3 ways: (1) for the descriptive statistics and intention-to-treat (ITT) analyses, treatment was defined dichotomously as randomized arm (LT-MAT vs. ST-MAT), (2) for the first as-treated analysis, treatment was defined as percent adherence (proportion of doses taken vs. total expected doses) to BUP/NX in the past 28 days (participants in the ST-MAT arm were coded as having 0% adherence), and (3) for the second as-treated analysis, treatment was defined categorically with 3 levels based on cumulative percent adherence to BUP/NX during the treatment period (LT-MAT ≥75% cumulative adherence, LT-MAT <75% cumulative adherence, and ST-MAT). Participant BUP/NX dosing was assessed weekly during the treatment period, primarily through direct observation by study clinicians. Percent adherence refers to the proportion of completed dosing in the previous 28-day period.
To describe other risk factors and effect modifiers for risky sexual behavior by treatment arm, demographics and risky injection behavior covariates at baseline were included as other potential predictors of risky sexual behavior: age (10-year increment), sex, minority ethnicity (participants who did not identify as Han in China or Thai in Thailand), marital status (married/living with partner vs. not), years of education, income (>$1000 annual income vs. not), employment (employed vs. unemployed), history of incarceration (any vs. none referring to past 6 months), alcohol use (any vs. none referring to the past 6 months), noninjection drug use (any vs. none referring to past 6 months), number of days injected (referring to past 6 months), average number of times per day injected (referring to the past month), mixing of different drugs (any vs. none referring to past 6 months), using front- or back-loaded syringes (any vs. none referring to past 6 months), passing (lending) drug injection needles after use (any vs. none, number of times, and number of people passed to referring to past 6 months), and sharing (receptive of) drug injection needles after others (any vs. none, number of times, and number of people shared with referring to past 6 months).
Study participant characteristics were summarized at baseline using frequencies and percentages for categorical variables and medians with interquartile ranges for continuous variables.
To describe risky sexual behaviors over time by treatment group, the proportion of participants with each risk behavior were plotted by treatment arm at each visit from baseline to week 104. Then, for each endpoint, odds ratios (ORs) associated with treatment were estimated at each time point. Each model included an interaction between treatment arm and visit, the main effects for treatment and visit, and adjustment for site. Differences between ORs at baseline and weeks 52 and 104 were evaluated using Wald tests.
To determine if LT-MAT (vs. ST-MAT) was associated with risky sexual behaviors, ITT and as-treated analyses were completed. For the ITT, ORs associated with treatment (as randomized) were estimated. Two models were used (both adjusted for site). Model 1 was used to test for an interaction between treatment arms and visit (an interaction indicating that the treatment effect was visit-dependent). If the interaction was nonsignificant (per Wald test), model 2 was used to estimate an overall OR.
Two as-treated analyses were conducted. First, to evaluate the effectiveness of treatment on risky sexual behavior during the treatment period, outcome data were limited to weeks 26 and 52, and ORs associated with 10 percentage points higher adherence in the past 28 days were estimated. Second, to evaluate the effectiveness of the treatment on risky sexual behavior after the treatment ended, outcome data were limited to weeks 52, 78, and 104, and ORs associated with cumulative adherence were estimated. Models were adjusted for site and visit.
Finally, to explore risk factors for risky sexual behaviors, ORs associated with each risk factor were estimated. Two models were fit for each endpoint. Model 1, a partially adjusted model, adjusted for site, treatment (as randomized), baseline vs. follow-up, and an interaction between treatment and baseline vs. follow-up (to allow for any treatment effects). Model 1 was run for each potential risk factor. Model 2 was a fully adjusted model. In addition to all Model 1 adjustment terms, Model 2 included any model 1 risk factors that had P < 0.1.
All ORs were estimated using generalized estimating equations using logistic regression to account for the binary endpoints and exchangeable correlation structures to account for the repeated measures. We used exchangeable covariance structure with the assumption that the correlation between visits for the participant is constant.
Institutional review boards/ethics committees at each of the 4 sites in China and Thailand approved the HPTN 058 trial. Written informed consent was obtained from all study participants. The HPTN ethical committees approved this data analysis.
Recruited participants were active opiate drug injectors across 3 sites in China (n = 161 in Guangxi, Nanning; n = 411 in Heng County; and n = 477 in Xinjiang, Urumqi); and n = 202 in Chiang Mai, Thailand.
Of the 1250 participants included in the analysis, 92% were male, with a median age of 34 years (interquartile range, 28–39). Baseline characteristics were similar in the randomized arms (Table 1).
At baseline, referring to the past month, 36% of participants reported condomless sex with their primary partner, 4% reported condomless sex with a nonprimary partner, 6% reported multiple sex partners, and 30% reported more than 3 sex acts.
Adherence Over Time
Adherence to the induction phase of the study ranged from 88% (ST-MAT) to 91% (LT-MAT). Eighty percent (n = 502) participants completed the detoxification phase.
Risky Sexual Behavior Over Time
Figure 1 shows the proportion of participants in each treatment arm with a given risky sexual behavior at each study time point. As you can see from the plots, rates of risky sexual behaviors were fairly consistent over the course of the study. Although there were slight differences between treatment groups at various time points, ORs associated with treatment were nonsignificant at every visit including baseline for all endpoints (Table 2).
Effect of Treatment on Risky Sexual Behaviors
In the ITT analysis, the interaction between treatment and time was nonsignificant for all endpoints. This allowed us to estimate overall treatment effects for each risky sexual behavior. Supplemental Digital Content Table S1, http://links.lww.com/QAI/B140, presents the overall OR for each endpoint, all of which were nonsignificant. Similarly, the as-treated analyses did not provide evidence of an association between medication-assisted treatment and risky sexual behaviors. Specifically, ORs associated with 10 percentage points higher 28-day adherence during the treatment period were all nonsignificant with point estimates extremely close to one (see Supplemental Digital Content Table S2, http://links.lww.com/QAI/B140). The ORs associated with cumulative adherences had more variation in terms of point estimates (see Supplemental Digital Content Table S3, http://links.lww.com/QAI/B140) however, they were also nonsignificant for all endpoints.
Other Predictors of Risky Sexual Behaviors
We then looked at other potential predictors of each of the risky sexual behavior endpoints (Table 3).
Statistically significant (P < 0.05) predictors associated with higher odds of condomless sex with a primary partner in the fully adjusted model (adjusted for site, treatment, baseline vs. follow-up and interaction between treatment and baseline vs. follow-up) were being married/living with partner: adjusted odds ratio (AOR) = 4.34 [95% confidence interval (CI): 3.61 to 5.23], being employed: AOR = 1.22 (95% CI: 1.05 to 1.42), and alcohol use: AOR = 1.45 (95% CI: 1.24 to 1.70). Significant predictors associated with lower odds of condomless sex with a primary partner were incarceration: AOR = 0.75 (95% CI: 0.54 to 0.82) and mixing different drugs: AOR = 0.69 (95% CI: 0.47 to 0.85).
Significant predictors associated with higher odds of condomless sex with a nonprimary partner were incarceration: AOR = 1.62 (95% CI: 1.08 to 2.42) and noninjection drug use: AOR = 1.92 (95% CI: 1.40 to 2.64). Significant predictors associated with lower odds of condomless sex with a nonprimary partner were older age: AOR = 0.69 (95% CI: 0.54 to 0.87) and being married/living with partner: AOR = 0.65 (95% CI: 0.46 to 0.91).
Significant predictors associated with higher odds of having multiple sex partners were more years of education: AOR = 1.09 (95% CI: 1.01 to 1.16), annual income >$1000: AOR = 2.11 (95% CI: 1.32 to 3.39), noninjection drug use: AOR = 1.80 (95% CI: 1.33 to 2.43), and number of people with whom needles were shared after use: AOR = 1.25 (95% CI: 1.04 to 1.50). Significant predictors associated with lower odds of having multiple sex partners were older age: AOR = 0.71 (95% CI: 0.55 to 0.91) and being married/living with partner: AOR = 0.01 (95% CI: 0.46 to 0.90).
Finally, significant predictors associated with higher odds of more than 3 sexual acts were being married/living with partner: AOR = 2.71 (95% CI: 2.25 to 3.27), being employed: AOR = 1.25 (95% CI: 1.07 to 1.46), alcohol use: AOR = 1.32 (95% CI: 1.12 to 1.56), and number of people to whom drug injection needles were passed to after use: AOR = 1.21 (95% CI: 1.08 to 1.36). Significant predictors associated with lower odds of more than 3 sexual acts were older age: AOR = 0.72 (95% CI: 0.64 to 0.81), incarceration: AOR = 0.75 (95% CI: 0.58 to 0.97), number of days drugs were injected: AOR = 0.99 (95% CI: 0.98 to 1.00), and sharing of needles after use: AOR = 0.59 (95% CI: 0.44 to 0.81).
This study was the first large RCT of opiate dependence treatment among PWID. In this analysis of risky sexual behavior of PWID enrolled into HPTN 058, we found that long-term BUP/NX treatment was not significantly associated with different risky sexual behavior than short-term treatment among PWID. This is consistent with previous smaller RCT and behavioral studies which have shown long-term BUP/NX to be significantly associated with a reduction in drug injection risk, but not leading to a reduction in risky sexual behavior.33–36 Based on this finding, it is important that PWID on opiate dependence treatment be provided with sexual risk reduction counseling in addition to injection drug use risk-reduction interventions. Risky sexual behavior is an additional risk for HIV and hepatitis B and C transmission among PWID.
PWID have generally been known to have greater risky sexual behavior compared with general population.2–4,28,30 In contrast to what has been previously reported in studies of injecting drug users, we found lower rates of risky sexual behavior at baseline and at all time points.4,30,31,35 This could be because the trial offered additional sexual behavior risk-reduction counseling together with drug taking risk-reduction counseling at least monthly. This could also indicate effectiveness of sexual behavioral interventions in HIV prevention over time.
One factor significantly associated with lower risky sexual behaviors among this HIV-uninfected PWID population was older age. This could be due to less experimentation and risk taking of older PWID compared with those of younger persons.34,37
Being married or living with a partner was significantly associated with higher frequency of condomless sex with the primary partner, higher frequency of sexual acts, as well as lower reports of condomless sex with a nonprimary partner and fewer sexual partners. Being married or living with a partner was therefore associated with a protective effect as there were significantly fewer reports of multiple sex partners and unprotected sex with a nonprimary partner. This is consistent with previous studies.31,38,39 This could be because greater trust and caring have been shown to be characteristic of relationships with primary partners.39,40 These findings also show that in sexual risk–reduction interventions, it is important to ascertain the nature of sexual relationships as there is a difference in behavior between primary and nonprimary partners among PWID, consistent with other studies.40
PWID in the HPTN 058 who also admitted to noninjection drug abuse were noted to be at significantly higher risk of unprotected sex with a nonprimary partner as well as higher risk of multiple partners. These findings are consistent with previously published literature,30,38,41,42 and are of public health relevance for future education and intervention among HIV-uninfected PWID.
Income of >$1000/yr was significantly associated with higher risk of condomless sex with a nonprimary partner and with having multiple partners, consistent with previous reports in which higher socioeconomic status was a predictor of unsafe sex.43,44 More research on higher income PWID might be necessary to further understand the social dynamics that might contribute to risky sexual behaviors among this population. This would be critical in tailoring prevention interventions among this group.
Alcohol use was significantly associated with unprotected sex acts with the primary partner as well as higher frequency of sex among HIV-uninfected PWID. This is consistent with existing literature.40,45–49 Interventions to reduce alcohol consumption are important in HIV-uninfected PWID because they have a potential to reduce risky sexual behavior, which in turn reduces sexually transmitted infections including HIV. Furthermore, condom use messaging should be intensified in this population.
In this analysis, we were able to show the association of injection drug taking risk with the different risky sexual behaviors. The number of days that a participant injected opiate drugs was noted to be significantly associated with lower sexual acts and this could be because high levels of intoxication have a potential to reduce sexual activity.50–52 The number of people with whom drug injection needles were used after others was significantly associated with higher risk of multiple sexual partners. It is important to target these specific populations in sexual risk–reduction interventions.
All data on risky sexual behaviors were collected retrospectively by self-report, potentially resulting in possible recall bias and social desirability bias. Studies have shown that sensitive information is more likely to be underreported.53–56 In this study, this was minimized by having counselors not being involved in intervention acceptability assessments.
There was a possibility of selection bias; individuals who were more comfortable participating in this research project may not be representative of HIV-uninfected PWID in the general population. Finally, trial participants were mostly male (92%) and, therefore, results may not be generalizable to female PWID.
In conclusion, LT-MAT did not significantly modify risky sexual behavior among HIV-uninfected PWID. Significant predictors of low frequency of unprotected sex with nonprimary partner and low odds of multiple partners among this population included older age and being married/living with a partner, whereas significant predictors of unprotected sex and multiple partners were incarceration, concomitant noninjection drug use, alcohol use, and needle sharing. Interventions that may lead to reduction in risky sexual behaviors should target these populations.
The authors thank HPTN 058 investigators and study teams, HPTN 058 study participants, HPTN Scholars Program team, Botswana Harvard AIDS Institute Partnership; Dr J. Makhema, Dr S. Moyo, and Lucy Mupfumi for mentorship and critical review of concept. Spouse Dr Bhekiqiniso Shava for the encouragement and moral support.
1. World Health Organization UNOoDaC, Joint United Nations Programme on HIV/AIDS. WHO/UNODC/UNAIDS Position Paper. Geneva, Switzerland; 2004.
2. Crime UNOoDa. World Drug Report. Vol. 20182011.
3. Gowing L. Mitigating the risk of HIV infection with opioid substitution treatment. 2012.
4. Booth RE, Kwiatkowski CF, Chitwood DD. Sex related HIV risk behaviors: differential risks among injection drug users, crack smokers, and injection drug users who smoke crack. Drug Alcohol Depend. 2000;58:219–226.
5. UNAIDS. The Gap Report 2014. 2014.
6. Society IA. People Who Inject Drugs (PWID) -Fact Sheet. 2014.
7. Needle RH, Zhao L. HIV Prevention Among Injection Drug Users; Strengthening U.S. Support for Core Interventions; a Report of the CSIS Global Health Policy Center April 2010. 2010.
8. The NSDUH Report: HIV/AIDS and Substance Use, Substance Use and Mental Health Services Administration. Rockville, MD. 2010.
9. Organization WH. ATLAS of Substance Use Disorders. 2010.
10. Yao Y, Wang N, Chu J, et al. Sexual behavior and risks for HIV infection and transmission among male injecting drug users in Yunnan, China. Int J Infect Dis. 2009;13:154–161.
11. Office SCAWC. A Joint Assessment of HIV/AIDS Prevention, Treatment and Care in China; 2008.
12. National Center for AIDS/STD control and Prevention CC. Chronology of NCAIDS. Available at: http://ncaids.chinacdc.cn/english/aboutncaids/ancon/
. Accessed February 04, 2018.
13. Chaudhry AA, Botsko M, Weiss L, et al. Participant characteristics and HIV risk behaviors among individuals entering integrated buprenorphine/naloxone and HIV care. J Acquir Immune Defic Syndr. 2011;56(suppl 1):S14–S21.
14. Woody GE, Bruce D, Korthuis PT, et al. HIV risk reduction with buprenorphine-naloxone or methadone: findings from a randomized trial. J Acquir Immune Defic Syndr. 2014;66:288–293.
15. Sullivan LE, Fiellin DA. Buprenorphine: its role in preventing HIV transmission and improving the care of HIV-infected patients with opioid dependence. Clin Infect Dis. 2005;41:891–896.
16. Mauger SFR, Gill K. Utilizing Buprenorphine-Naloxone to Treat Illicit and Prescription-Opioid Dependence. Dove Press; 2014 2013;Volume 2014:10:Pages 587–598.
17. Ottawa (ON): Canadian Agency for Drugs and Technologies in Health. Buprenorphine/naloxone versus methadone for the treatment of opioid dependence: a review of comparative clinical effectiveness, cost-effectiveness and guidelines. 2016. PMID:27656728.
18. Wesson DR, Smith DE. Buprenorphine in the treatment of opiate dependence. J Psychoactive Drugs. 2010;42:161–175.
19. Barnett PG, Zaric GS, Brandeau ML. The cost-effectiveness of buprenorphine maintenance therapy for opiate addiction in the United States. Addiction. 2001;96:1267–1278.
20. Ruan Y, Liang S, Zhu J, et al. Evaluation of harm reduction programs on seroincidence of HIV, hepatitis B and C, and syphilis among intravenous drug users in southwest China. Sex Transm Dis. 2013;40:323–328.
21. Qian HZ, Schumacher JE, Chen HT, et al. Injection drug use and HIV/AIDS in China: review of current situation, prevention and policy implications. Harm Reduct J. 2006;3:4.
22. Li J, Ha TH, Zhang C, et al. The Chinese government's response to drug use and HIV/AIDS: a review of policies and programs. Harm Reduct J. 2010;7:4.
23. Csete J, Wolfe D. Seeing through the public health smoke-screen in drug policy. Int J Drug Policy. 2017;43:91–95.
24. Thailand OotNCBo. National Narcotics Control Policy on Kingdom's Unity for Victory over Drugs Strategy. 2011.
25. World Health Organization. Resources for the Prevention and Treatment of Substance Use Disorders (SUD); Country Profile: THAILAND. 2010.
26. Lucas GM, Young A, Donnell D, et al. Hepatotoxicity in a 52-week randomized trial of short-term versus long-term treatment with buprenorphine/naloxone in HIV-negative injection opioid users in China and Thailand. Drug Alcohol Depend. 2014;142:139–145.
27. Eaton DK, Kann L, Kinchen S, et al. Youth risk behavior surveillance—United States, 2009. MMWR Surveill Summ. 2010;59:1–142.
28. Zhao M, Du J, Lu GH, et al. HIV sexual risk behaviors among injection drug users in Shanghai. Drug Alcohol Depend. 2006;82(suppl 1):S43–S47.
29. Chen YH, McFarland W, Raymond HF. Risk behaviors for HIV in sexual partnerships of San Francisco injection drug users. AIDS Care. 2014;26:554–558.
30. Tyndall MW, Patrick D, Spittal P, et al. Risky sexual behaviours among injection drugs users with high HIV prevalence: implications for STD control. Sex Transm Infect. 2002;78(suppl 1):i170–i175.
31. Huang J, Jiang J, Li JZ, et al. Prevalence and correlates of sexual risk behaviors among drug users in western China: implications for HIV transmission. AIDS Res Hum Retroviruses. 2013;29:673–680.
32. Metzger DS, Donnell D, Celentano DD, et al. Expanding substance use treatment options for HIV prevention with buprenorphine-naloxone: HIV prevention trials network 058. J Acquir Immune defic Syndr. 2015;68:554–561.
33. Sullivan LE, Moore BA, Chawarski MC, et al. Buprenorphine/naloxone treatment in primary care is associated with decreased human immunodeficiency virus risk behaviors. J Subst Abuse Treat. 2008;35:87–92.
34. Meade CS, Weiss RD, Fitzmaurice GM, et al. HIV risk behavior in treatment-seeking opioid-dependent youth: results from a NIDA clinical trials network multisite study. J Acquir Immune Defic Syndr. 2010;55:65–72.
35. Meade CS, McDonald LJ, Weiss RD. HIV risk behavior in opioid dependent adults seeking detoxification treatment: an exploratory comparison of heroin and oxycodone users. Am J Addict. 2009;18:289–293.
36. Vlahov D, Robertson AM, Strathdee SA. Prevention of HIV infection among injection drug users in resource-limited settings. Clin Infect Dis. 2010;50(suppl 3):S114–S121.
37. Turner CF, Ku L, Rogers SM, et al. Adolescent sexual behavior, drug use, and violence: increased reporting with computer survey technology. Science. 1998;280:867–873.
38. Schumacher CM, Go VF, Nam le V, et al. Social injecting and other correlates of high-risk sexual activity among injecting drug users in northern Vietnam. Int J Drug Policy. 2009;20:352–356.
39. Rusch ML, Farzadegan H, Tarwater PM, et al. Sexual risk behavior among injection drug users before widespread availability of highly active antiretroviral therapy. AIDS Behav. 2005;9:289–299.
40. Rosengard C, Anderson B, Stein MD. Intravenous drug users' HIV-risk behaviors with primary/other partners. Am J Drug Alcohol Abuse. 2004;30:225–236.
41. Roxburgh A, Degenhardt L, Breen C. Drug use and risk behaviours among injecting drug users: a comparison between sex workers and non-sex workers in Sydney, Australia. Harm Reduct J. 2005;2:7.
42. Vlahov D, Safaien M, Lai S, et al. Sexual and drug risk-related behaviours after initiating highly active antiretroviral therapy among injection drug users. AIDS. 2001;15:2311–2316.
43. Assari S, Yarmohamadivasel M, Moghani Lankarani M, et al. Having multiple sexual partners among Iranian intra-venous drug users. Front Psychiatry. 2014;5:125.
44. Assari S, Yarmohmmadi Vasel M, Tavakoli M, et al. Inconsistent condom use among Iranian male drug injectors. Front Psychiatry. 2013;4:181.
45. Stueve A, O'Donnell LN. Early alcohol initiation and subsequent sexual and alcohol risk behaviors among urban youths. Am J Public Health. 2005;95:887–893.
46. Anderson JE, Mueller TE. Trends in sexual risk behavior and unprotected sex among high school students, 1991-2005: the role of substance use. J Sch Health. 2008;78:575–580.
47. Des Jarlais DC, Arasteh K, McKnight C, et al. Using hepatitis C virus and herpes simplex virus-2 to track HIV among injecting drug users in New York City. Drug Alcohol Depend. 2009;101:88–91.
48. Arasteh K, Des Jarlais DC. HIV testing and treatment among at-risk drinking injection drug users. J Int Assoc Physicians AIDS Care (Chic). 2009;8:196–201.
49. Arasteh K, Des Jarlais DC. At-risk drinking and injection and sexual risk behaviors of HIV-positive injection drug users entering drug treatment in New York City. AIDS Patient Care STDS. 2009;23:657–661.
50. Palha AP, Esteves M. A study of the sexuality of opiate addicts. J Sex Marital Ther. 2002;28:427–437.
51. Mirin SM, Meyer RE, Mendelson JH, et al. Opiate use and sexual function. Am J Psychiatry. 1980;137:909–915.
52. Smith S. Drugs that cause sexual dysfunction. Pschiatry. 2007:112–114.
53. Catania JA. A Framework for conceptualizing reporting bias and its Antecedents in interviews assessing human sexuality. J Sex Res. 1999;36.
54. Catania JA. Methodological problems in AIDS behavioural research: influences on measurement error and participation bias in studies of sexual behaviour. Psychol Bull. 1990;108:339–362.
55. Latkin CA. Outreach in natural settings: the use of peer leaders for HIV prevention among injecting drug users' networks. Public Health Rep. 1998;113(suppl 1):151–159.
56. Coates RA. Validity of sexual histories in a prospective study of male sexual contacts of men with AIDS or an AIDS-related condition. Am J Epidemiol. 1988;128:719–728.