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. Author manuscript; available in PMC: 2021 Mar 1.
Published in final edited form as: AIDS Care. 2019 Sep 18;32(3):302–309. doi: 10.1080/09540121.2019.1668525

Sexual Risk Behaviors in the Internet Age: The Case of Chinese Men Who Have Sex with Men

Shufang Sun 1, William T Hoyt 2, John E Pachankis 3
PMCID: PMC6980990  NIHMSID: NIHMS1540654  PMID: 31533450

Abstract

Men who have sex with men (MSM) in China, who live in a stigmatizing environment, experience high risk of HIV infection. Recently, internet has become a popular platform for MSM in China to socialize and meet sex partners. However, little is known about how MSM socialization and stigma may associate with sexual risk behaviors among MSM in China. This cross-sectional study examined a sample of sexually active Chinese MSM recruited online (N=403) to determine the associations of MSM social life (both online and in-person) and sexual identity stigma with three types of sexual risk behaviors in the past 12 months, including condom use%, number of anal intercourse partners, and number of partners engaged in condomless anal intercourse (CAI). Hierarchical regression analyses were conducted. Findings suggest that internet was a popular platform for participants to socialize and meet sex partners (69.7% met last partner via website or mobile app). More frequent use of social media was associated with higher numbers of anal intercourse partners as well as more condom use in past 12 months, but not number of CAI partners. More active in-person MSM social life was associated with higher numbers of partners in anal intercourse as well as CAI. Both perceived and enacted sexual identity stigma associated with higher numbers of CAI partners; perceived stigma was also linked to less condom use. In conclusion, social life and minority stress are relevant factors of sexual risk among MSM in China in the uptrend of internet use. The internet may be an important and promising platform for HIV prevention, and intervention efforts should consider online-based designs to promote safe sex and reduce sexual minority stigma.

Keywords: sexual risk behaviors, MSM, China, internet, stigma

Recent years in China have witnessed the rapid growth of HIV epidemic among men who have sex with men (MSM). The proportion of new HIV infections accounted for by MSM increased from 2.5% in 2006 to 25.5% in 2017 (NCAIDS, NCSTD, China CDC, 2018). The national estimated prevalence of HIV among MSM is 8% with high variability across regions (Qin et al., 2016). Injection drug use is rare among Chinese MSM and sexual risk behaviors have been identified as the primary route of HIV transmission (Zhao et al., 2016), particularly since PrEP has not yet been available. Meta-analysis estimated 34-60% MSM in China engaged in condomless anal intercourse (CAI) in the past 12 months (Yang, Zhang, Dong, Jin, & Han, 2014), highlighting the urgent need of understanding and addressing sexual risk behaviors.

Although same-sex behaviors are not criminalized, MSM in China live in a heteronormative and stigmatizing environment due to the country’s strong cultural values in marriage, procreation, and conformity to social norms (Steward, Miège, & Choi, 2013). With the advance of technology, internet-based platforms (e.g., MSM-specific dating websites and apps) provide relative privacy and have gained popularity among MSM in China to socialize with each other, build community, and find potential partners. A large cross-sectional study with MSM across 61 Chinese cities from 2008 to 2009 found 45% used internet to seek partners (Wu et al., 2013), and another study with Chinese MSM in 2014 documented this proportion increased to 57.9% (Tang et al., 2016). Internet-recruited MSM have been found to be younger and experience more sexual orientation-related stigma compared to MSM recruited from conventional venues (e.g., gay bars, bathhouses, and clubs).

The rising popularity of internet-based socialization for MSM in China warrants understanding on its role in sexual risk behaviors. Extant research has focused on comparing internet-using or recruited MSM to MSM considered as offline. Some studies found that internet-using/recruited MSM engaged in more CAI (Tang et al., 2016; Tsui & Lau, 2010) and had more sex partners in past 6 months compared to their offline-based counterparts (Lau, Kim, Lau, & Tsui, 2003), though others found no difference in CAI (Bien et al., 2015; Zhang, Bi, Lv, Zhang, & Hiller, 2008). A longitudinal study found that the accelerating rate of the HIV epidemic from 2009 to 2014 in Shenyang, China related to the increased proportion of MSM using internet, and CAI was a risk factor for HIV infection among internet-based MSM (Pan et al., 2016). Meanwhile, the internet could benefit MSM by providing information on safe sex and social support that may be difficult to receive elsewhere (Pingel, Thomas, Harmell, & Bauermeister, 2013). Given the expanded use of internet in MSM’s social life in China, research is needed on how it may be a facilitative and/or protective factor for sexual risk behaviors. Further, researchers should attend to both online- and in-person-based MSM social behaviors since they are not mutually exclusive. A more comprehensive understanding can provide clarifying information on how MSM socialization may affect risk and guide focused intervention.

Experiences of sexual identity stigma could also contribute to sexual risk behaviors, as unique stressors related to one’s sexual identity (also termed minority stress) can adversely affect health outcomes of sexual minorities (Meyer, 2003). Stigma is linked to sexual risk behaviors among Chinese MSM recruited in conventional venues: those reported more sexual stigma engaged in more frequent CAI (Choi, Hudes, & Steward, 2008). Varied types of stigma (e.g., enacted, perceived) could also affect sexual risk. Chinese MSM who perceived less in-person stigma towards their sexuality used condoms more consistently (H. Liu et al., 2011). Given that stigma may be an important aspect of the experience among Chinese MSM using internet for social life, examining stigma’s role in sexual risk behaviors, including varied types of stigma, is important. This could expand our understanding of how minority stress may inform risk behaviors in the uptrend of internet use among MSM.

Despite many public health initiatives of HIV risk reduction in China, MSM continued to experience a higher burden of HIV infections. Understanding factors associated with sexual risk behaviors in this population, especially in the age of internet use, has public health significance. Specifically, this knowledge can guide the growing interest in internet- and mobile-based health promotion efforts, which may have the potential of reaching an otherwise “hard-to-reach” population and reduce HIV risk through online-based and cost-effective methods. Therefore, with the general aim of examining potential risk factors, the current study seeks clarification on associations of MSM social life and sexual identity stigma with sexual risk behaviors among MSM in China.

Methods

Data Collection

The study was approved by Educational and Social/Behavioral Sciences Institutional Review Board. An anonymous online survey was generated using Qualtrics Survey Tool. MSM in China were recruited through advertisement on MSM and LGBT organization websites. Individuals were invited to participate if they met the following eligibility criteria: (a) age 18 or older, (b) self-identify as a Chinese man, (c) being fluent in the Chinese language, and (d) experience sexual and/or romantic attraction to men. Interested participants were directed to an internet page with a description of the survey’s contents, eligibility requirement, and informed consent. Participants were asked to read through and confirm their eligibility and consent by clicking their agreement. No incentive was provided to participants. Recruitment took place between February 2017 and April 2018.

Participants

Since this paper focuses on sexual behaviors among Chinese MSM, only sexually active (ever had sex with men in their lifetime) MSM (N = 403) who provided data on sexual risk behaviors were included. Detailed demographic information is reported in sample characteristics.

Measures

Outcome variables.

Three sexual risk outcomes were measured using American Men’s Internet Survey (AMIS), which was translated and adapted for Chinese MSM. The AMIS is a behavioral health survey developed by PRISM Health (Emory University, 2014). It is suitable for MSM and utilizes questions used in the CDC National HIV Behavioral Surveillance Survey (NHBS) (Emory University, 2014). Sexual risk behavior items inquired following behaviors in the past 12 months: (1) self-rated percentage of condom use during anal intercourse with men; (2) number of anal intercourse partners, and (3) number of sexual partners that one engaged in condomless anal intercourse (CAI) with in the past 12 months. Anal intercourse could include insertive or receptive intercourse or both.

Predictor variables.

Two predictor variables of interest are MSM social life and MSM stigma. MSM social life measured the frequency of online and in-person, MSM-specific social life using a 7-point Likert scale rating on the AMIS (0=never, 1=less than once a month, 2= once a month, 3=more than once a month, 4=once a week, 5=more than once a week, 6=once a day, 7=more than once a day). Online MSM social life assessed how often participants used the internet to meet or socialize with other MSM, including social network websites, websites directed toward gay men, dating websites, or the use of mobile social applications. Similarly, participants indicated how often they engaged in in-person MSM social life in the past 12 months by going to a place where gay men hang out, meet or socialize such as bars, clubs, social organizations, parks, gay business, bookstores, sex clubs, etc.

China MSM Stigma Scale (Neilands, Steward, & Choi, 2008) assessed experience of sexual identity stigma. The scale was originally developed from a study on sexual stigma among Latino gay and bisexual men (Diaz, Ayala, Edward, & Marin, 2001) and adapted by Neilands et al (2008) for Chinese MSM. It has 10 items and is consisted of two subscales (perceived and enacted stigma). Perceived stigma assessed perception of societal disapproval of one’s sexual orientation (e.g., “How many times have you heard that being gay/homosexual is not normal?”). Enacted stigma measured direct experiences of stigma and discrimination (e.g., physical violence, losing housing due to sexuality). This scale has been demonstrated to have adequate validity and reliability among Chinese MSM (Neilands et al., 2008). Cronbach’s α in our sample was 0.73.

Covariates.

We counted for several known risk factors as covariates. They included relative demographic and medical characteristics including age, education, income, and HIV status. Two psychological covariates were included: HIV knowledge and depression. HIV knowledge associated with less sexual risk for MSM in the United States (Raifman, Beyrer, & Arrington-Sanders, 2018) and in China (J. Liu, Qu, Ezeakile, & Zhang, 2012). Some research linked depression with increased CAI rates (Fendrich, Avci, Johnson, & Mackesy-Amiti, 2013; Storholm, Satre, Kapadia, & Halkitis, 2016) while others found a curvilinear relationship between the two (Babowitch, Mitzel, Vanable, & Sweeney, 2017; O’Cleirigh et al., 2013).

HIV knowledge was measured by the Brief HIV Knowledge Questionnaire (HIV-KQ-18) (Carey & Schroder, 2002). Participants were asked to select “true” or “false” on 18 statements about HIV. Cronbach’s α in this sample was acceptable (α=0.65). Depressive symptoms was measured by the depression subscale (13 items) of the Chinese version Symptom Checklist-90-Revised (SCL-90-R), which has been used in Chinese gay men and shown good reliability and validity (X. Liu et al., 2018). Cronbach’s α in this sample was 0.92.

Data Analysis

All analyses were performed in R (R Core Team, 2016). Missing data was relatively small (4.73%). Missing values were imputed using multivariate imputation by chained equations (Azur, Stuart, Frangakis, & Leaf, 2011). Bivariate analyses of continuous variables were conducted prior to regression analyses.

Regression analyses were conducted to examine the associations of predictor variables and covariates to sexual risk behaviors. Multiple linear regression (MLR) was used for condom use% in the past 12 months. As sexual risk count variables tend to be positively skewed (Schroder, Carey, & Vanable, 2003), negative binominal regression was performed with count variables including number of sexual partners and number of CAI partners in the past 12 months. Prevalence rate ratios (PRR) and their 95%CI were calculated. Although previous studies noted a non-linear relationship between depression and sexual risk (Babowitch et al., 2017; O’Cleirigh et al., 2013), model comparison suggests that this relationship was linear in our sample (less explained variance when using a quadratic term).

Hierarchical regression analyses were performed (Cohen, Cohen, West, & Aiken, 2003), which allows us to examine if variables of interest (MSM social life and identity stigma) are significant after accounting for covariates. Two steps were used. In the first regression analyses, demographic characteristics and psychological covariates (HIV knowledge and depression) were entered. In step 2, MSM social life, both online and in-person, and MSM stigma, both perceived and enacted stigma, were added to the model.

Results

Sample Characteristics

The majority of participants identified as gay (n=327, 81.1%), followed by bisexual (10.9%), non-labeled (5.0%), pansexual (1.7%), queer (0.7%), and other (0.5%). The average age was 26.05 (SD= 6.46). More than half (54.6%) reported younger than 25 years old (18-25). More than half reported residing in a first-tier city1 (54.6%), followed by second- or third-tier city (40.7%) and country or non-city areas (7.4%). Regarding education, 86.6% participants had either college (61.3%) or graduate degree (25.3%), followed by high school (8.4%) and without high school degree (5.0%). Most participants were single (67.2%), followed by cohabited (13.6%), in a stable romantic relationship (12.1%), in a marriage with a woman (4.5%), in a marriage with a man (2.5%), and divorced (2.2%). Most participants self-reported to be HIV-negative (78.1%), followed by status unknown (17.9%), and HIV-positive (4.0%).

Descriptive Findings of Sexual Behaviors

Table 1 presents descriptive findings on sexual behaviors with men. The average age of first oral sex and anal intercourse were 18.65 (SD=4.44) and 20.56 (SD= 4.37), respectively. Most participants engaged in both oral sex and anal intercourse with men in the past 12 months (n=343, 85.1%). On average, participants had anal intercourse with 3.65 different men (SD= 6.56, Range= [0, 80]) in the past 12 months. Self-rated condom use percentage averaged 79.98% (SD=32.34, Range= [0, 100]). The majority (n=281, 69.7%) reported that they met last partner through mobile app or internet.

Table 1.

Descriptives on sexual behaviors with men (N=403)

M SD Range
Continuous variables
Age when first had oral sex 18.65 4.44 [5, 40]
Age when first had anal intercourse 20.56 4.37 [8, 47]
In the past 12 months:
Number of men had oral sex with 4.92 7.64 [0, 100]
Number of men had anal intercourse with 3.65 6.56 [0, 80]
Number of anal intercourse partners engaged in CAI 0.86 1.97 [0, 20]
Percentage of condom use in anal intercourse 79.98 32.34 [0, 100]
Categorical variables N %
Sexual preference 51 12.7
   Top 140 34.7
   Bottom 196 48.6
   Versatile 16 4.0
Type of sex in past 12 months
   Only oral sex 51 12.7
   Only anal intercourse 6 1.5
   Oral and anal 343 85.1
   No sex in past 12 months 3 0.7
Last sexual encounter
Sexual partner type
   Main sex partner 226 56.1
   Casual sex partner 145 36.0
   Don’t know 32 7.9
Where met last sexual partner
   Work or school 57 14.1
   Friend’s party 26 6.4
   Bar 5 1.2
   Mobile app 231 57.3
   Internet 50 12.4
   Club 2 0.5
   Public sex environment 7 1.7
   Other 25 6.2
As far as you know, when you were having a sexual relationship with him, did he have sex with others?
   Definitely not 85 21.1
   Probably not 93 23.1
   Probably did 50 12.4
   Definitely did 104 25.8
   Don’t know 71 17.6
Did you have sex with others while in this sexual relationship?
   Yes 198 49.1
   No 205 50.9
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On average, participants more frequently engaged in online MSM social life (M= 4.22, about once a week) than in-person MSM social life (M= 0.88, approximately less than once a month). Regarding stigma, almost all (97.0%) reported that they at least once heard that homosexuality is not normal and most (72.5%) pretended to be heterosexual for acceptance. Half (49.1%) reported being rejected by a family member due to their sexual orientation.

Regression Analyses of Risk Behaviors

Table 2, 3, and 4 present hierarchical regression analyses results. For condom use in the past 12 months, being HIV-positive was the only significant covariate. More online social life (B= 2.63, 95%CI=[1.31, 3.96]) and less perceived sexual stigma (B= −1.53[−3.05, −0.02]) were associated with higher condom use. MSM social life and stigma explained 5% of more variance than the covariates alone, ΔR2= .05, F= 5.28, p= .0004.

Table 2.

Regression model of condom use (%) during anal intercourse in the past 12 months

Predictor Variable B 95%CI SE beta t p
Step 1 (R2= .03, model p= .34, F= 1.14)
Age −0.11 [−0.69, 0.48] 0.30 −0.02 −0.36 .72
Education (ref=college)
 Below high school −11.39 [−27.08, 4.30] 7.98 −0.08 −1.43 .15
 High school 2.88 [−9.25, 15.01] 6.17 0.02 0.47 .64
 Graduate degree 0.38 [−7.31, 8.08] 3.92 0.005 0.10 .92
Monthly income 0.48 [−2.40, 3.37] 1.47 0.02 0.33 .74
HIV Status (ref= negative)
 HIV-positive −18.09 [−34.92, −1.26] 8.56 −0.11 −2.11 .04*
 Status unknown 0.59 [−8.03, 9.21] 4.38 0.007 0.14 .89
HIV Knowledge 0.39 [−8.90, 1.67] 0.65 0.04 0.59 .55
Depression −0.06 [−0.34, 0.23] 0.14 −0.02 −0.41 .68
Step 2 (R2= .08, model p= .003, F= 2.44)
Social life
 Online MSM social 2.63 [1.31, 3.96] 0.67 0.20 3.91 .0001***
 Public MSM social 0.03 [−2.49, 2.55] 1.28 0.001 0.02 .98
MSM stigma
 Perceived stigma −1.53 [−3.05, −0.02] 0.77 −0.10 −1.99 .047*
 Enacted stigma −0.22 [−1.63, 1.18] 0.71 −0.02 −0.31 .75
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Note. * p< .05, ** p< .01, *** p< .001

Table 3.

Regression model of number of anal intercourse partners in the past 12 months

Predictor Variable B PRR 95%CI SE beta z p
Step 1 (AIC= 1917, theta= 1.04)
Age 0.04 1.04 [1.02, 1.06] 0.009 0.04 3.64 .0003***
Education (ref=college)
 Below high school 0.69 2.00 [1.18, 3.51] 0.27 0.02 2.54 .01*
 High school 0.46 1.58 [1.05, 2.45] 0.21 0.02 2.15 .03*
 Graduate degree −0.14 0.87 [0.66, 1.14] 0.14 −0.009 −1.03 .30
Monthly income 0.13 1.14 [1.03, 1.26] 0.05 0.03 2.49 .01*
HIV Status (ref= negative)
 HIV-positive 0.24 1.27 [0.75, 2.29] 0.28 0.007 0.85 .40
 Status unknown −0.46 0.63 [0.47, 0.87] 0.16 −0.03 −2.86 .004**
HIV Knowledge 0.01 1.01 [0.96, 1.06] 0.02 0.005 0.50 .62
Depression −0.007 0.99 [0.98, 1.00] 0.005 −0.01 −1.41 .16
Step 2 (AIC= 1855.6, theta= 1.32)
Social life
 Online MSM social 0.18 1.20 [1.14, 1.26] 0.02 0.07 7.74 < .001***
 Public MSM social 0.08 1.08 [1.00, 1.18] 0.04 0.02 1.99 .046*
MSM stigma
 Perceived stigma −0.008 0.99 [0.94, 1.04] 0.03 −0.003 −0.32 .75
 Enacted stigma 0.03 1.03 [0.99, 1.08] 0.02 0.01 1.37 .17
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Note. * p< .05, ** p< .01, *** p< .001

Table 4.

Regression model of number of CAI partners in the past 12 months

Predictor Variable B PRR 95%CI SE beta z p
Step 1 (AIC= 1023, theta=0.89)
Age 0.01 1.01 [0.98, 1.04] 0.01 0.03 0.71 .48
Education (ref=college)
 Below high school 0.51 1.66 [0.86, 3.28] 0.35 0.06 1.46 .14
 High school 0.03 1.03 [0.57, 1.86] 0.30 0.005 0.11 .91
 Graduate degree −0.06 0.94 [0.64, 1.36] 0.19 −0.01 −0.34 .73
Monthly income 0.06 1.06 [0.92, 1.23] 0.07 0.04 0.85 .39
HIV Status (ref= negative)
 HIV-positive 0.45 1.57 [0.79, 3.20] 0.36 0.04 1.25 .21
 Status unknown −0.33 0.72 [0.47, 1.10] 0.22 −0.06 −1.50 .13
HIV Knowledge −0.09 0.92 [0.87, 0.97] 0.03 −0.11 −2.94 .003**
Depression −0.004 1.00 [0.98, 1.01] 0.007 −0.02 −0.59 .56
Step 2 (AIC= 970.8, theta=1.66)
Social life
 Online MSM social 0.02 1.02 [0.96, 1.08] 0.03 0.02 0.54 .59
 Public MSM social 0.15 1.17 [1.06, 1.29] 0.05 0.10 3.02 .003**
MSM stigma
 Perceived stigma 0.07 1.07 [1.00, 1.15] 0.03 0.08 2.08 .04*
 Enacted stigma 0.12 1.13 [1.08, 1.19] 0.03 0.15 4.68 < .0001***
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Note. * p< .05, ** p< .01, *** p< .001. CAI= condomless anal intercourse.

Regarding number of male anal intercourse partners (past 12 months), negative binomial regression model revealed several relevant covariates, including age (PRR=1.04[1.02, 1.06]), monthly income (PRR= 1.14[1.03, 1.26]), having unknown HIV status (in comparison to HIV-negative; PRR= −0.46 [0.47, 0.87]) and lower education background. MSM social life, both online (PRR= 1.20[1.14, 1.26]) and in-person (PRR= 1.08[1.00, 1.18]) frequency, were significant predictors, but not MSM stigma. Likelihood-ratio (LR) tests suggests that the second model (including MSM social life and stigma) was superior, χ2= 69.48, p< .0001.

For number of male CAI partners (past 12 months), HIV knowledge was the only significant covariate (PRR= 0.92[0.87, 0.97]); more knowledge associated with less CAI partners. In-person MSM social life was associated with higher number of CAI partners, PRR= 1.17[1.06, 1.29]. Both types of stigma, namely perceived (PRR= 1.07[1.00, 1.15]) and enacted MSM stigma (PRR= 1.13[1.08, 1.09]), were associated with higher number of CAI partners. Likelihood-ratio (LR) tests suggests that the second model (including MSM social life and stigma) was superior, χ2= 60.64, p< .0001.

Discussion

The internet has rapidly expanded and shaped the social networking and dating life for MSM in China. This study seeks to advance our understanding on the roles of MSM social life and experiences of minority stress, specifically sexual identity stigma, in engagement of sexual risk among Chinese MSM. Findings suggest the internet as a popular platform for socializing and seeking sex partners (69.7% met last sex partner online). Casual and non-monogamous sexual relationships may also be common (e.g., half engaged in sex with other people in last sexual relationship). Both MSM social life and sexual identity stigma were associated with sexual risk, in varied ways, across three kinds of behaviors in past 12 months (self-rated condom use, number of anal intercourse partners, and number of CAI partners).

MSM social life was associated with sexual risk behaviors, yet the function of online and in-person MSM social life differed, suggesting the importance of context in studying MSM’s social life. Frequent social media use was associated with more anal intercourse partners and this association was three times stronger than in-person social life. However, only in-person social life was associated with more CAI partners. Engaging in CAI in-person MSM social life may relate to not carrying condoms and the more spontaneous nature of sex in such situations (Hart & Peterson, 2004; Ostergren, Rosser, & Horvath, 2011). On the other hand, identifying a partner via the internet might provide opportunity to prepare for the meeting and sexual encounter. The use of social media was a facilitative factor for condom use in this sample, indicating that the internet could be a useful platform for MSM to build a social community and learn about safe sex behaviors, which is not available in the current sex education in China (Shang et al., 2012). This result could also relate to the fact that those with more online social life tended to have more sex partners therefore the likelihood of using condom might increase.

Sexual identity stigma was detrimental for sexual health in Chinese MSM: perceived MSM stigma was associated with less condom use and both types of stigma (perceived and enacted) were associated with more CAI partners. The interpersonal nature of perceived stigma may affect MSM’s interpersonal communication (e.g., less assertive) and negotiation on condom use (Wang & Pachankis, 2016), particularly since condoms are perceived as interfering with sexual desire and intimacy in Chinese MSM culture (Li, Lau, Holroyd, & Yi, 2010). The strong association between enacted MSM stigma and number of CAI partners is worth noting. It is likely that individuals report enacted stigma may also experience financial hardships in part due to discrimination they undergo (e.g., losing housing or job) and as a result, increased the likelihood of engaging in situations that may involve CAI such as trading sex for money and having concurrent sex partnerships (Choi et al., 2008).

Limitations

The study has several limitations. First, the sample may only represent a subpopulation of MSM in China (gay-identifying, young MSM with higher education and live in city areas) and findings may not be generalizable to MSM who are older, with lower education and live in rural areas. Second, questions related to sexual risk, stigma, and depression are subject to social desirability due to the self-report method. Third, we cannot conclude any causal relationships among variables due to the correlational design of the study. Fourth, there could be multiple survey entries given the online data collection method, although we believe duplicate participation was minimal as the study did not offer any incentives.

Conclusion

Findings demonstrate the relevance of minority stress and social variables in understanding and addressing sexual risk among MSM in China. To meaningfully decrease risk behaviors in this population, behavioral health interventions should attend to dimensions of sexual identity and MSM social life in a nuanced fashion. In the digital age, internet has the potential to be a platform for safe sex promotion and education. In particular, research and prevention interventions with Chinese MSM aiming at promoting sexual health may benefit from culturally responsive, internet-based efforts through MSM community building, sex and HIV knowledge education, and sexual minority stigma reduction.

Acknowledgments

This research was supported by a grant from the Global Health Institute of University of Wisconsin-Madison awarded to the first author. Work by the first author was in part supported by the Providence/Boston Center for AIDS Research (P30AI042853) and National Institute of Mental Health (T32MH078788).

Footnotes

None of the authors have any conflicts of interest to report. We would like to acknowledge Beijing LGBT Center, Division of Psychology for their support.

1

The tier system has been used to classify cities in China. The first-tier cities represent most developed, metropolitan areas. The second- and third-tier cities are large, urban cities yet less connected internationally.

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