HIV Incidence among Men Who Have Sex with Men Using Geosocial Networking Smartphone Application in Beijing, China: an Open Cohort Study

doi:10.21203/rs.3.rs-110379/v1

Download PDF

Research Article

HIV Incidence among Men Who Have Sex with Men Using Geosocial Networking Smartphone Application in Beijing, China: an Open Cohort Study

https://doi.org/10.21203/rs.3.rs-110379/v1

This work is licensed under a CC BY 4.0 License

Journal Publication

published 02 Apr, 2021

Read the published version in Infectious Diseases of Poverty →

You are reading this latest preprint version

Background Sexual transmission among men who have sex with men (MSM) is the dominant route of HIV transmission in China. Extensive use of geosocial networking (GSN) smartphone application (app) have dramatically changed the patterns of sexual behaviors and HIV risk among MSM, but data on HIV incidence and changed risk behaviors of GSN app using MSM are limited.

Methods We constructed an open cohort which was constructed and maintained using a GSN app to assess the HIV incidence among app-using MSM, recruited from June 2017 to December 2018. MSM completed an online questionnaire of their sociodemographic characteristics, sexual behaviors, recreational drug use and sexually transmitted infections status. Then each man had an HIV test, and those testing negative were enrolled into the cohort. Participants completed follow-ups with additional HIV tests though the app during the study period, and were censored at HIV seroconversion or study end date. HIV incidence were calculated by dividing the sum of observed HIV seroconversions by the observed person-time. Univariate (Chi-square test and Fisher’s exact test) and multivariate (proportional hazards regression) analyses were used to examine correlates of HIV incidence.

Results A total of 6957 HIV negative MSM were enrolled in the open cohort, 37 seroconversions occurred among 1937 men contributing 1065 observed person-years: HIV incidence was 3.47 per 100 person-years (95%CI 2.37–4.57). HIV seroconversion was associated with the following factors in the preceding six months: more than five sexual partners (HR = 2.65 95%CI 1.04–6.67), and sex with HIV positive partners (HR = 3.82 95%CI 1.16–12.64). Consistent condom use for anal sex (HR = 0.27 95%CI 0.07–0.96), and reporting insertive anal sex only (HR = 0.23 95%CI 0.08–0.62) were protective factors for HIV seroconversion.

Conclusions Tailored interventions targeting app-using MSM are urgently needed given their high risk of HIV. As a new tool for accessing MSM at higher HIV risk, GSN smartphone app-based interventions could play an important role in HIV control among MSM using combinations of biomedical and behavioral interventions.

Health Economics & Outcomes Research

Health Policy

Infectious Diseases

Men who have sex with men

Geosocial networking application

HIV

Incidence

Sexual risk behavior

Men who have sex with men (MSM) are a key population for HIV prevention and control. The Global AIDS report released by UNAIDS estimated that almost one quarter (23%) of new adult HIV infections were among MSM. MSM had a high risk of acquiring HIV: 26 times higher than the general population[1]. Sexual transmission among MSM is the dominant route of HIV transmission in China[2]. Compared with other key populations which have decreasing HIV prevalence over time, MSM are the only key population with increasing HIV prevalence and have the highest incidence for HIV in China[2]. Data from 1888 sentinel sites established within 300 cities across 31 provinces in China showed an increase of HIV prevalence among MSM from 5.73% in 2010 to 7.75% in 2014; however HIV prevalence among drug users declined from 4.48–3.29%, and female sex workers from 0.24–0.17%[3]. Data from some provinces in China also reported an increasing incidence of HIV among MSM and decreasing or modest change among other key affected populations[4].

Although there are robust data on HIV prevalence among MSM in China, data for incidence is scarce. Conducting a prospective cohort study is recommended to estimate HIV incidence, where HIV-negative individuals are followed for a period of time until enough seroconversions are observed. It is challenging to maintain cohorts for MSM in China due to their fear of stigma and discrimination and perceived lack of confidentiality. Most reports about HIV in China come from cross-sectional studies. The MSM cohort studies published in China so far might not be completely reliable due to their small sample sizes (n = 155–547), short follow-up period (mostly 3–6 months) and high loss of follow-up rate (28.5–56.6%)[5–10].

The limited data from cohort studies show that HIV incidence varies widely. A meta-analysis estimated HIV incidence among Chinese MSM to be 3.5% (95% CI, 1.7–5.3%) from cohort studies and 6.7% (95% CI, 4.8–8.6%) from cross-sectional studies[11]. In 2014, one cohort study observed HIV incidence among MSM to be as high as 13.6/100 person-years in a city of Jiangsu province[12]. A study with data from eight Chinese cities from 2016–2017 reported an incidence rate of 15.6/100 person-years[13]. HIV incidence has geographical variations among different regions in China[11]: higher HIV incidence is reported in metropolitan cities compared to smaller cities[3]. Therefore, Beijing, the capital of China -- experiencing a large influx of MSM every year -- is expected to have the highest HIV incidence, but this has not corresponded with the current literature. For example, a cohort study with 525 MSM in Beijing 2008–2009 observed an HIV incidence of 3.4/100 person-years with 457 MSM completing the 12-month follow-up[14]. An cohort with 1003 MSM recruited in 31 months in Beijing 2009–2012 observed an HIV incidence of 6.0/100 person-years (95% CI: 4.2–8.4) with only 699 participants completing the follow-up (loss-to-follow-up rate: 30.3%)[15]. However, given the small number of participants and the observed person years, the results of these cohort studies may be inaccurate and unreliable.

It is challenging to construct mutual trust between clinical professionals, public health practitioners and MSM due to their fear of stigma and discrimination, which also makes it difficult to maintain an MSM cohort. Therefore, new strategies to recruit and maintain a cohort of Chinese MSM is needed. MSM were the early adopters of the Internet[16], and the Internet has become primary sources of social support for MSM[16, 17]. Internet-based recruitment can be more efficient and cost-effective in terms of recruitment than field-based recruitment among population with high risk behaviors[17, 18], opening the possibility for constructing an MSM cohort.

Constructing an online cohort of Chinese MSM also has the advantage of reaching a relatively high-risk group of MSM. With the popularity of smartphones–manifested by the proliferation of GSN smartphone apps, such as Blued, Grindr and Jack’d–increasing use of these apps among MSM has facilitated the ease of finding casual sexual partners, resulting in unsafe sexual behaviors among MSM. Previous studies have reported that MSM who use apps tend to have more sexual encounters, more frequent anal intercourse, more unprotected anal intercourse, and have a larger number of sexual partners living with HIV[19–24], thus increasing their risk for HIV and sexually transmitted infections (STIs), compared with MSM who did not use apps to seek sex partners[20, 25–28]. However, results about risk sexual behaviors and HIV/STIs prevalence among app-using MSM of these studies are inconsistent. Some studies suggested that app-using MSM were more likely to practice safer sex with their partners than are non-users[29, 30], have better HIV testing behaviors[26], and that use of apps was not associated with increased risk for HIV or STIs infection and transmission[31–33]. Therefore, the patterns of sexual partner seeking behaviors among MSM have changed dramatically from venue-based to Internet-based, especially GSN smartphone app-based, which could facilitate sex partner seeking and result in casual sex. But the influence of risky sexual behaviors on HIV incidence caused by this change has not been clearly proven, and the current data on HIV incidence among GSN smartphone app using MSM in China are insufficient to understand the current HIV epidemic and risk sexual behaviors. The only one paper we searched reporting HIV incidence among GSN app using MSM found that new HIV infections were independently associated with ever using GSN apps, but their sample size was small (276 app using MSM and 185 nonusers) and app using behavior was self-reported[34]. So reliable data on the HIV incidence and changed risk behaviors of GSN app using MSM are urgently needed.

To address these gaps, our study aims to: (1) construct the first gay GSN app using MSM cohort in China; (2) accurately characterize HIV incidence and its correlates among gay GSN app using MSM; and (3) summarize the characteristics of GSN app using MSM. To our knowledge there are no other published cohort studies in China that describe HIV incidence and sexual risk behaviors among app-using MSM.

Study site and study design

We conducted an open cohort study in Beijing, China. As the political, economic and cultural center of China, Beijing has a population of 21.54 million people, among which 7.65 million are internal immigrants[35]. Beijing has a large influx of MSM every year[36–39]. We constructed an open cohort of MSM during June 2017, in Beijing; inviting visitors to Blued, a gay GSN app with 40 million users worldwide, to participate in the cohort. After completing an online informed consent, participants completed a self-administrated questionnaire via the app which collected data on sociodemographic characteristics, sexual behaviors, recreational drug use and STIs status. MSM could then make an appointment for an HIV test in one of the four HIV screening sites set up by Blued in Beijing: two were in Chaoyang District, one was in Xicheng District and one was in Changping District. HIV screening was conducted by trained peer testers from Blued with finger-prick blood. We used a rapid HIV test (Colloidal Gold Device) manufactured by Beijing Wantai Biological Pharmacy Enterprise Co., Ltd, which was accepted for the WHO list of prequalified in vitro diagnostics. MSM with positive screening results were referred to the appropriate district center for disease control (CDC) in Beijing for HIV confirmatory test using Western blot test. The online informed consent and questionnaire were stored in Blued, and the HIV test (including confirmatory test) results were also stored and linked to the questionnaire by the user identification number (UID) registered on Blued.

A baseline survey was completed when a participant finished the online survey and received an offline HIV test through Blued for the first time during the study period; MSM with questionnaires completed within one minute and incomplete questionnaires were excluded. Subsequently, additional HIV testing together with the completion of the online survey by the same study subject was regarded as the completion of a follow-up visit. There were no incentives offered for participation in our survey; but in the study period, participants received private messages, messages on the startup screen, advertisement banners, and invitations to live streaming broadcasts through Blued where MSM were encouraged to test for HIV. We use the data up to Dec. 31, 2018 and participants were censored at HIV seroconversion or Dec. 31, 2018, whichever came first. The follow-up time of those who received more than two HIV tests by Blued during the study period was calculated as the time interval between the latest HIV test and the baseline survey. MSM included in the cohort who tested positive for HIV during follow-up were counted as one positive seroconversion of HIV. Our study protocol was approved by Nankai University Health Research Ethics Committee and electronic individual informed consent was obtained before the online survey.

Participant eligibility criteria

Men who met the following criteria were recruited into our cohort: (1) Born biologically male; (2) Age ≥ 18 years; (3) Ever had sex with men; (4) Resided in Beijing with a Blued account registered in Beijing; (5) Finished the online survey and offline HIV test during the study period; and (6) Voluntarily participated in this study and signed the online informed consent. MSM were excluded if they: (1) tested HIV positive at baseline; (2) completed questionnaires within 30 seconds or had incomplete questionnaires.

Outcome variables

The primary outcome was HIV seroconversion. We also collected information on the sexual behaviors in the preceding six months, including having anal sex intercourse (yes, no), number of sex partners (one, two to five, six or more), knowledge of their sex partners’ HIV status (yes, no), having sex partners who were living with HIV (yes, no, not sure), frequency of condom use during anal sex (never, sometimes, every time), role during anal sex (exclusively receptive, exclusively insertive, versatile), heterosexual intercourse (yes, no), frequency of condom use during heterosexual intercourse (never, sometimes, every time), participation in group sex (yes, no), diagnosed with other STIs (yes, no) and recreational drug use (yes, no). We also collected participants’ sociodemographic characteristics, including age (years), highest level of education (high school or below, college/undergraduate, postgraduate), current employment status (worker, service sector, civil servant, student, company employee, freelancer, other), and duration of residence in Beijing (less than 6 months, 6 months to 11 months, 12 months to 23 months, 24 months or more, and local resident).

Statistical analysis

Individual observation time was calculated as the interval between the participant’s baseline survey and the latest HIV test during the study period. HIV incidence was calculated by dividing the sum of observed HIV seroconversions by the observed person-time. The Kaplan–Meier cumulative probability of HIV seroconversion is presented using a survival curve. We used descriptive statistics to summarize the participants’ sociodemographic characteristics, sexual behaviors, recreational drug use and past diagnosis of STIs in the last six months before their latest HIV test. Frequencies and percentages were used to describe categorical variables. Median and interquartile range (IQR) was presented for age, as it was non-normally distributed. Quantitative variables like age, duration of residence in Beijing, number of sex partners during the last six months before the interview were converted to categorical variables. Sociodemographic characteristics, sexual behaviors, creational drug use and being diagnosed with STIs were used as independent variables, while HIV seroconversions used as the dependent variable to examine correlates of HIV incidence using Chi-square test and Fisher’s exact test. Statistically significant independent variables identified from the univariate analysis with P < 0.10 were included in a proportional hazards regression model for multivariate analysis to identify the correlates of HIV seroconversion; the final model contained covariates with P < 0.05. Statistical analysis was performed using Stata (Version 12, College Station, TX, USA).

The cohort and HIV seroconversion

In total, 7252 MSM submitted the online informed consent and questionnaire. After excluding one man for an incomplete survey, one for completing the survey within one minute, one for duplicated survey, and 322 who tested HIV positive, we enrolled 6957 HIV negative men in the cohort. After excluding another five seroconversions which occurred within the window period (≤ 45 days), our cohort identified 37 HIV seroconversions among 1,937 HIV negative MSM who reported two or more episodes of HIV testing during the study period. Figure 1 shows the selection of our study population. The follow-up encounters ranged from 1 to 10, while the follow up interval ranged from 1 to 18 months with a median of 5.9 months (IQR 3.0-15.9). The total person-time observed was 1,065 person-years. The HIV incidence rate was 3.5 per 100 person-years (95%CI 2.4–4.6). Figure 2 shows the Kaplan Meier cumulative probability of HIV-free survival.

Demographic characteristics of the cohort members (Table 1)

Table 1

Demographic characteristics of the participants and the results of the univariate analysis of demographic characteristics and HIV seroconversion.
		HIV seroconversion		P
	All	Yes	No
	n (%)	n (%)	n (%)
Age (years)				0.91
18~	954 (49.3)	17 (45.9)	937 (49.3)
27~	775 (40.0)	16 (43.3)	759 (40.0)
37 ~ 60	208 (10.7)	4 (10.8)	204 (10.7)
Education level				0.15
High school or below	253 (13.1)	7 (18.9)	246 (12.9)
College & undergraduate	1304 (67.3)	27 (73.0)	1277 (67.2)
Postgraduate	380 (19.6)	3 (8.1)	377 (19.9)
Duration of residence in Beijing				0.47
< 6 months	169 (8.7)	5 (13.5)	164 (8.6)
6 months~	132 (6.8)	1 (2.7)	131 (6.9)
12 months~	877 (45.3)	17 (46.0)	860 (45.3)
2 years~	590 (30.5)	9 (24.3)	581 (30.6)
Local resident	169 (8.7)	5 13.5)	164 (8.6)
Occupation ^*				0.07
Workers	44 (2.3)	1 (2.7)	43 (2.3)
Service sector	105 (5.4)	2 (5.4)	103 (5.4)
Civil servants	154 (7.9)	1 (2.7)	153 (8.1)
Students	186 (9.6)	3 (8.1)	183 (9.6)
Company employees	471 (24.3)	3 (8.1)	468 (24.6)
Freelancers	116 (6.0)	4 (10.8)	112 (5.9)
Other	861 (44.5)	23 (62.2)	838 (44.1)
*Hypothesis testing of rate/proportion differences using Fisher’s exact test.

The median age of cohort participants was 27.1 (IQR 23.7–31.2) years. Participants who were 18–35 years old accounted for 87.7% of the total. The majority (86.9%) of participants completed college or university education. Company employees (24.3%) were the most common occupation. Most (91.3%) participants immigrated from another province, and 60.8% lived in Beijing for less than two years.

Sexual behaviors, recreational drug use and being diagnosed with STIs in the last 6 months before the latest HIV test (Table 2)

Table 2

Sexual behaviors, STIs and recreational drug use of the participants in the last six months and univariate analysis of sexual behaviors and HIV seroconversion.
		HIV seroconversion		χ²	P
	All	Yes n (%)	No n (%)
	n (%)	Yes n (%)	No n (%)
Number of sexual partners				8.72	0.01
0–1	1217 (62.8)	17 (45.94)	1200 (63.16)
2–5	603 (31.1)	14 (37.84)	589 (31.00)
≥ 6	117 (6.1)	6 (16.22)	111 (5.84)
HIV-positive partners				8.12	0.02
None	915 (47.2)	10 (27.03)	905 (47.63)
Yes	87 (4.5)	4 (10.81)	83 (4.37)
Not sure	935 (48.3)	23 (62.16)	912 (48.00)
Anal sex				1.37	0.24
Yes	1869 (96.5)	37 (100.00)	1832 (96.42)
No	68 (3.5)	0 (0.00)	68 (3.58)
Using condom for anal sex				11.79	< 0.01
Never	68 (3.6)	3 (8.11)	65 (3.55)
Sometimes	601 (32.2)	20 (54.05)	581 (31.71)
Every time	1200 (64.2)	14 (37.84)	1186 (64.74)
Sex role for anal sex				9.55	0.01
Exclusively receptive	215 (26.4)	15 (40.54)	497 (26.16)
Exclusively insertive	723 (37.3)	5 (13.51)	718 (37.79)
Versatile	702 (36.2)	17 (45.95)	685 (36.05)
Group sex				0.34	0.56
Yes	159 (8.2)	4 (10.8)	155 (6.9)
No	1778 (91.8)	33 (89.2)	1745 (93.1)
Heterosexual sex				0.35	0.55
Yes	638 (51.7)	10 (58.82)	628 (51.56)
No	597 (48.3)	7 (41.18)	590 (48.44)
Using condom for heterosexual sex				9.63	0.01
Never	83 (13.0)	4 (40.00)	79 (12.58)
Sometimes	156 (24.5)	4 (40.00)	152 (24.20)
Every time	399 (62.5)	2 (20.00)	397 (63.22)
Diagnosed with a sexually transmitted infection				4.88	0.03
Yes	75 (3.9)	4 (10.81)	71 (3.74)
No	1862 (96.1)	33 (89.19)	1829 (96.26)
Recreational drug use				0.51	0.48
Yes	382 (19.7)	9 (24.32)	373 (19.63)
No	1555 (80.3)	28 (75.68)	1527 (80.37)

In the preceding six months before the latest HIV test, 37.2% of participants reported two or more sexual partners, 4.5% reported having HIV-positive sex partners, while 48.3% did not know the HIV status of their sexual partners, and those who never or occasionally used condoms during anal sex accounted for 35.8%. The percentage of MSM engaging in receptive anal sex exclusively and MSM engaging in insertive anal sex exclusively was 26.4% and 37.3%, respectively. Among 51.7% who ever had heterosexual sex, 37.5% reported inconsistent condom use. A minority (8.2%) reported engaging in group sex, 3.9% reported being diagnosed with STIs, and 19.7% reported recreational drug use in the preceding six months.

Univariate analysis of risk factors associated with HIV seroconversion (Table 2)

There were no statistically significant differences in sociodemographic characteristics between men with incident HIV and those who stayed HIV-negative (P > 0.05, Table 1). Sexual behaviors in the past six months including higher number of sexual partners, having a HIV-positive partner, inconsistent condom use during anal sex, engaging in exclusively receptive or both insertive and receptive anal sex, inconsistent condom use during heterosexual intercourse, and being diagnosed with STIs were associated with HIV incident infection (P < 0.05). Having anal sex, participation in group sex, engaging in heterosexual intercourse, and recreational drugs use were not associated with HIV incidence (P > 0.05).

Multivariate analysis of risk factors of HIV seroconversion (Table 3)

Table 3

Factors associated with HIV seroconversion identified using a proportional hazards regression model.
	HR (95% CI)	P
Number of sexual partners
0–1	-	-
2–5	1.30(0.64–2.67)	0.47
≥ 6	2.65(1.04–6.67)	0.04
Using condom for anal sex
Never	-	-
Sometimes	0.72(0.20–2.56)	0.61
Every time	0.27(0.07–0.96)	0.04
HIV-positive partners
No	-	-
Yes	3.82(1.16–12.64)	0.03
Not sure	2.02(0.95–4.30)	0.07
Sex role for anal sex
Exclusively receptive	-	-
Exclusively insertive	0.23(0.08–0.62)	< 0.01
Versatile	0.93(0.46–1.88)	0.84
HIV Incidence among Men Who Have Sex with Men Using Geosocial Networking Smartphone Application in Beijing, the Capital of China: an Open Cohort Study

Proportional hazards regression model was conducted with statistically significant variables identified by univariate analysis (P < 0.10) as independent variables: occupation, the number of sexual partners, whether having HIV positive partners or not, frequency of condom use during anal sex, sex role during anal sex, frequency of condom use during heterosexual intercourse, frequency of condom use during heterosexual intercourse and diagnosed with STIs within the past six month before the latest HIV test. Having more than five sexual partners (hazard ratio [HR] = 2.65 95%CI 1.04–6.67), sex with HIV positive partners (HR = 3.82 95%CI 1.16–12.64) were risk factors for HIV seroconversion. Consistent condom use for anal sex (HR = 0.27 95%CI 0.07–0.96), and being exclusively insertive for anal sex (HR = 0.23 95%CI 0.08–0.62) were protective factors for HIV seroconversion.

This is the first and largest online cohort study constructed and implemented through a GSN app globally that reports HIV incidence and its correlates. We add to current literature[14, 15, 40–45] by providing more robust estimates of HIV incidence, especially to the limited literature on app-using MSM[46]. Using this innovative methodology of GSN apps to maintain a cohort has advantages of reaching high-risk MSM and overcomes barriers related to fear of stigma and discrimination associated with offline cohort studies. Moreover the app-assisted questionnaire may improve the data quality of sexual and highly stigmatized behaviors research among MSM. Data quality does not only depend on the accurate recall of facts but also depends on the degree of peoples’ self-disclosure, which is commonly influenced by an individual’s inherent need to create and maintain favorable impressions of oneself in the eyes of others. Therefore, MSM tend to misrepresent their true behaviors to avoid the stigma of homosexuality and the resulting discrimination in research with traditional methods. A bulk of studies demonstrated that increased self-disclosure of sensitive information were found with decreased personal interactions with an interviewer[47, 48]. We conducted the research using a popular GSN app that is trusted by Chinese MSM; and surveys could be completed using the participants’ own smartphone, avoiding face-to-face interaction with research or healthcare staff. This strategy contributed to the acceptability of the study and consequently low drop-out rate.

The HIV incidence we observed was 3.47/100 person year (95%CI 2.37–4.57), which is lower than 5.36/100 person year (95%CI 2.19–8.53) reported in a 2011 study[42] and 6.0/100 person year (95%CI 4.2–8.4) reported in a 2012 study[15]. After 2012, there was no other cohort reporting HIV incidence among MSM in Beijing. Considering the large 95%CI of the incidence rates reported by these former cohort studies due to their smaller sample sizes (348[42], 699[15]), the HIV incidence rate we estimated was not statistically significantly different from these studies from 2011 and 2012, which might indicate that despite various HIV prevention interventions, HIV incidence among MSM in Beijing has not significantly declined in recent years.

The factors associated with HIV seroconversion in our study were mostly consistent with other literature. Having multiple sexual partners as a risk factor is consistent with other studies from Beijing, even independent of condom use during sex[42, 43]. The number of sexual partners is widely recognized as a predictor of the likelihood of acquiring STIs, including HIV[49]. Reporting multiple sex partners is high among GSN apps using MSM in China [50] and other countries[51, 52]; 37.2% of our participants reported having two or more sex partners in the preceding 6 months. Carrying their smartphone with the global positioning system imbedded app at all times, GSN smartphone app users could quickly and conveniently locate potential sex partners nearby, leading to more casual sex,greater number of sex partners༌thus facilitate app users engaging in unprotected anal sex[53, 54]. We found consistent condom use for anal sex reduced the probability of HIV incidence with hazard ratio of 0.27, which was inconsistent with other studies from MSM in Beijing with smaller sample sizes (348[42], 574–769[43]). This inconsistency may be due to their smaller sample size restricting their power to detect the independent effect of condoms use[42, 43].

Our study also found that 48.3% of our participants were not aware of the HIV status of their partners; though only 4.5% reported have HIV positive partners, sex with HIV positive partners was a significant predictor of HIV incidence. Previous research demonstrated that GSN smartphone app users were more likely to have a greater number of sexual partners known to have HIV and other STIs[55]; this increases their risk for HIV and STIs acquisition or transmission, compared with MSM who seek sex partners using other ways[56, 57]. Though seroadaptive practices (choosing HIV-negative partners or practicing only insertive anal sex with potentially discordant partners) among HIV-negative MSM is associated with a lower HIV incidence[58], the low disclosure of a partners’ HIV status have increased the HIV transmission among GSN smartphone app using MSM. Further, among MSM in China, the lifetime HIV testing rates remains at only 47%, and the annual HIV testing rates is even lower (38%)[59], therefore 62 to 87% of MSM living with HIV remains undiagnosed[60, 61].

We also found that MSM exclusively practicing insertive anal sex had a reduced risk of HIV (HR = 0.23), consistent with other studies[62–65]. In the United States, men who only had insertive anal sex had a lower risk of acquiring HIV (HR = 0.55, 95%CI 0.36–0.84)[58]. Though some studies found sociodemographic characteristics like age and immigration status as correlates of HIV infection[43, 66–69], we did not find the same associations. Some reasons might be due to different sampling methods[5, 70–72], different sample sizes[5, 70–72] and geographical difference[5, 70, 72].

Though China has scaled up its responses to the HIV pandemic[73], our findings suggest that the HIV incidence among MSM in Beijing has not decreased. The possible reason may be the patterns of HIV risk behaviors among MSM are changing with the use of the Internet and GSN smartphone apps as the main tool for partner seeking, while efforts to control HIV transmission in this population still focused on individual-level behaviors, such as consistent condom use and regular HIV testing. Many studies demonstrated the importance of contextual factors–such as where MSM meet their partners–may have on the risk of HIV transmission[51, 74–76]. The GSN smartphone apps could facilitate partner seeking, thus result in more casual and unprotected sex. Moreover, with the shift of partner seeking behaviors from in person venues to GSN apps, traditional intervention strategies designed for gay-centered venues, such as gay bar and public bathroom, may not be effective in reaching MSM anymore. Our findings suggest that further strategies and implementation of new interventions are urgently needed to curb the HIV epidemic among MSM in China.

The main strength of our study is in its innovative use of GSN app to construct and maintain a cohort of MSM in China. Though there are studies among MSM recruited using social networking applications in China, the majority of them are cross-sectional studies[77, 78]. To our best knowledge, this study is the first cohort study constructed and implemented through a GSN app globally. With fear of stigma, MSM prefer to access information about HIV and testing services through the Internet because the process is convenient, anonymous and private[79, 80]. With the development of network technology and the spread of Internet access, more MSM seek health services, especially HIV-related services, through the Internet[81, 82]; there is great potential for future use of GSN app in HIV research and intervention. In addition, we constructed an open cohort to estimate HIV incidence among app-using MSM, participants could complete a follow-up whenever they visited the GSN app for an HIV test, and new participants were enrolled whenever they visited the app to have an HIV test. Therefore, we obtained a cohort with participants staying natural and with better representativeness.

Our study is subject to several limitations. First, our study population were mostly young men who used social network application for partner seeking and health services, so our findings are not generalizable to MSM who do not use social network applications. Second, some participants might test for HIV in other places but not in Blued sites. For example, they could directly go to the CDC to get an HIV test, therefore the HIV incidence we calculated may be underestimated.

Though China has scaled up its responses to the HIV epidemic[73], the HIV incidence among MSM has not significantly decreased. Given the pervasive use of GSN smartphone app among MSM and the changed patterns of sexual risk behaviors, further strategies and implementation of new interventions are urgently needed to curb the HIV epidemic among MSM in China. Our study demonstrated the strength of GSN smartphone app in conducting research among MSM, indicating that tailored interventions based on GSN smartphone app using can be more targeted and individualized, therefore may provide us with novel opportunities to decrease HIV infection and transmission of MSM.

HIV incidence among GSN smartphone app using MSM in Beijing was high. Interventions tailored to this population should strengthened. As a new tool for accessing MSM at higher risk for HIV/STIs transmission, GSN smartphone app-based interventions could potentially play an important role in HIV control among MSM by combining biomedical and behavioral interventions.

MSM: Mem who have sex with men

HIV: Human immunodeficiency virus

AIDS: Acquired immunodeficiency syndrome

GSN: Geosocial networking

App: Application

CDC: Center for Disease Control and Prevention

STIs: Sexually transmitted infections

UID: User identification number

Ethics approval and consent to participate

The study protocol was approved by Nankai University Health Research Ethics Committee and electronic individual informed consent was obtained before the online survey.

Consent for publication

Not applicable.

Availability of supporting data

The datasets upon which our findings are based belong to Blued and Nankai University, and the study is still ongoing. For confidentiality reasons, the datasets are not publicly available. However, the datasets can be availed upon reasonable request from the corresponding author and with permission from Blued and Nankai University.

Competing interests

The authors declare that they have no competing interests.

Funding

This study was funded by a grant from Beijing Municipal Science and Technology Project [D17110700670000], the funding body had no other input into the research. The views expressed are those of the authors and not necessarily those of the Foundation.

Authors’ contributions

All authors have contributed substantially to the study. Minying Zhang and Guodong Mi have designed, supervised and oversaw the study implementation. Minying Zhang and Binbin Zhang have written the manuscript. Binbin Zhang conducted the analysis of the data. Fei Yu coordinated and managed the field work. Xianlong Ren, Yaqi Fan, Chunjun Li, Mianzhi Zhang and Fenghua Guo participated in the investigation. Jason Ong revised the work critically for important intellectual and scientific content before submission. All authors read and approved the final manuscript.

Acknowledgements

The study was performed though Blued. We sincerely thank all the study participants and the staff involved in facilitating and running the study.

Authors' information

Affiliations

Danlan Beijing Media Limited, 32 Baiziwan Road, Chaoyang District, Beijing 100020, China

Guodong Mi, Fei Yu

School of Medicine, Nankai University, 94# Weijin Road, Nankai District, Tianjin, 300071, China

Minying Zhang, Binbin Zhang, Yaqi Fan, Fenghua Guo

Beijing Center for Diseases Control and Prevention, 16# Hepinglizhong Road, Dongcheng District, Beijing, 100013, China

Xianlong Ren

Central Clinical School, Monash University, Melbourne, Australia, and Department of Clinical Research, London School of Hygiene and Tropical Medicine, London, United Kingdom

Jason J. Ong

Tianjin People’s Hospital, 190# Jieyuan Road, Hongqiao District, Tianjin, 300121, China

Chunjun Li

Dongfang Hospital, Beijing University of Chinese Medicine, 6# First District of Fangxingyuan, Fangzhuang, Fengtai District, Beijing,100078, China

Mianzhi Zhang

Corresponding author

Correspondence to Minying Zhang.

UNAIDS. Global HIV & AIDS statistics - 2020 fact sheet. 2020. https://www.unaids.org/en/resources/fact-sheet.
Lei Z, Eric P F C, Jun J, et al. HIV prevalence in China: integration of surveillance data and a systematic review. The Lancet Infectious diseases. 2013;13(11). https://doi.org/10.1016/S1473-3099(13)70245-7.
Cui Y, Guo W, Li D, et al. Estimating HIV incidence among key affected populations in China from serial cross-sectional surveys in 2010-2014. J Int AIDS Soc. 2016;19(1):20609. https://doi.org/10.7448/IAS.19.1.20609.
Yang H, Su L, Ye L, et al. Incidence rates of epidemics among 4 high risk groups in Sichuan province during 2011-2015. Chin J Epidemiol. 2017;38(12):1649-1654.
Li X, Luo Y, Zhang X, et al. Incidence and influencing factors for HIV infection among men who have sex with men in Hangzhou. Prev Med. 2018;30(10):982-986.
Ma J, Li Y, Zhang R, et al. HIV incidence among men who have sex with men in Kunming city by a cohort study during 2012 to 2016. Chin J AIDS STD. 2017;23(08):755-757.
Wang Y, Li L, Zhang G, et al. Prospective cohort retention and its influencing factors of MSM in Mianyang City. Occup and Health. 2014;30(24):3538-3542.
Wang Y, Li L, Zhang G, et al. Follow-up Visit Group-keeping and Its Influencing Factors in MSM in Mianyang. J Prev Med Inf. 2013;29(01):5-11.
Liu Z, Wu G, Xu J, et al. Factors associated with cohort retention among men who have sex with men. Chin J AIDS STD. 2011;17(06):646-650.
Zhou J, Zhen S, Wang J, et al. Factors associated with cohort retention among men who have sex with men. Chin J Public Health. 2013;29(08):1162-1165.
Li H, Peng R, Li J, et al. HIV Incidence among Men Who Have Sex with Men in China: A Meta-Analysis of Published Studies. PLOS ONE. 2011;6(e234318). https://doi.org/10.1371/journal.pone.0023431.
Yang H, Tang W, Xiao Z, et al. Worsening Epidemic of HIV and Syphilis Among Men Who Have Sex With Men in Jiangsu Province, China. CLINICAL INFECTIOUS DISEASES. 2014;58(12):1753-1759. https://doi.org/10.1093/cid/ciu175.
Huang W, Wang Y, Lu H, et al. High HIV Incidence Among Men Who Have Sex With Men in 8 Chinese Cities: Results From a Trial. Open forum infectious diseases. 2020;7(5):a147. https://doi.org/10.1093/ofid/ofaa147.
Li S, Zhou Z, Jiang S, et al. Incidence and risk factors of HIV and syphilis seroconversion among men who have sex with men in Beijing. Chin J Prev Med. 2011(02):118-122.
Liu G, Lu H, Wang J, et al. Incidence of HIV and Syphilis among Men Who Have Sex with Men (MSM) in Beijing: An Open Cohort Study. PLOS ONE. 2015;10(10):e138232. https://doi.org/10.1371/journal.pone.0138232.
Grov C, Breslow AS, Newcomb ME, et al. Gay and bisexual men's use of the Internet: research from the 1990s through 2013. J Sex Res. 2014;51(4):390-409. https://doi.org/10.1080/00224499.2013.871626.
Amirkhanian YA. Social networks, sexual networks and HIV risk in men who have sex with men. Curr HIV/AIDS Rep. 2014;11(1):81-92. https://doi.org/10.1007/s11904-013-0194-4.
Parsons JT, Vial AC, Starks TJ, et al. Recruiting drug using men who have sex with men in behavioral intervention trials: a comparison of internet and field-based strategies. AIDS Behav. 2013;17(2):688-699. https://doi.org/10.1007/s10461-012-0231-0.
Holloway IW, Pulsipher CA, Gibbs J, et al. Network Influences on the Sexual Risk Behaviors of Gay, Bisexual and Other Men Who Have Sex with Men Using Geosocial Networking Applications. AIDS Behav. 2015;19 Suppl 2:112-122. https://doi.org/10.1007/s10461-014-0989-3.
Hong H, Xu J, Mcgoogan J, et al. Relationship between the use of gay mobile phone applications and HIV infection among men who have sex with men in Ningbo, China: a cross-sectional study. Int J STD AIDS. 2018;29(5):491-497. https://doi.org/10.1177/0956462417738468.
Tang W, Tang S, Qin Y, et al. Will Gay Sex-Seeking Mobile Phone Applications Facilitate Group Sex? A Cross-Sectional Online Survey among Men Who Have Sex with Men in China. PLoS One. 2016;11(11):e167238. https://doi.org/10.1371/journal.pone.0167238.
Hull P, Mao L, Prestage G, et al. The use of mobile phone apps by Australian gay and bisexual men to meet sex partners: an analysis of sex-seeking repertoires and risks for HIV and STIs using behavioural surveillance data. Sex Transm Infect. 2016;92(7):502-507. https://doi.org/10.1136/sextrans-2015-052325.
Chow JY, Konda KA, Calvo GM, et al. Demographics, Behaviors, and Sexual Health Characteristics of High Risk Men Who Have Sex With Men and Transgender Women Who Use Social Media to Meet Sex Partners in Lima, Peru. Sex Transm Dis. 2017;44(3):143-148. https://doi.org/10.1097/OLQ.0000000000000566.
Card KG, Lachowsky NJ, Cui Z, et al. Exploring the role of sex-seeking apps and websites in the social and sexual lives of gay, bisexual and other men who have sex with men: a cross-sectional study. Sex Health. 2017;14(3):229-237. https://doi.org/10.1071/SH16150.
Zou H, Fan S. Characteristics of Men Who Have Sex With Men Who Use Smartphone Geosocial Networking Applications and Implications for HIV Interventions: A Systematic Review and Meta-Analysis. Arch Sex Behav. 2017;46(4):885-894. https://doi.org/10.1007/s10508-016-0709-3.
Sanchez TH, Sineath RC, Kahle EM, et al. The Annual American Men's Internet Survey of Behaviors of Men Who Have Sex With Men in the United States: Protocol and Key Indicators Report 2013. JMIR Public Health Surveill. 2015;1(1):e3. https://doi.org/10.2196/publichealth.4314.
Elford J, Bolding G, Sherr L. Seeking sex on the Internet and sexual risk behaviour among gay men using London gyms. AIDS. 2001;15(11):1409-1415. https://doi.org/10.1097/00002030-200107270-00010.
Evans AR, Wiggins RD, Mercer CH, et al. Men who have sex with men in Great Britain: comparison of a self-selected internet sample with a national probability sample. Sex Transm Infect. 2007;83(3):200-205, 205. https://doi.org/10.1136/sti.2006.023283.
Eric R, Ian H, Hailey W, et al. Sex Risk among Young Men who have Sex with Men who use Grindr, a Smartphone Geosocial Networking Application. AIDS & Clinical Research. 2012;S4(Suppl 4).
Winetrobe H, Rice E, Bauermeister J, et al. Associations of unprotected anal intercourse with Grindr-met partners among Grindr-using young men who have sex with men in Los Angeles. AIDS Care. 2014;26(10):1303-1308. https://doi.org/10.1080/09540121.2014.911811.
Phillips GN, Magnus M, Kuo I, et al. Use of geosocial networking (GSN) mobile phone applications to find men for sex by men who have sex with men (MSM) in Washington, DC. AIDS Behav. 2014;18(9):1630-1637. https://doi.org/10.1007/s10461-014-0760-9.
Beymer MR, Weiss RE, Bolan RK, et al. Sex on demand: geosocial networking phone apps and risk of sexually transmitted infections among a cross-sectional sample of men who have sex with men in Los Angeles County. Sex Transm Infect. 2014;90(7):567-572. https://doi.org/10.1136/sextrans-2013-051494.
Lee SS, Lam AN, Lee CK, et al. Virtual versus physical channel for sex networking in men having sex with men of sauna customers in the City of Hong Kong. PLoS One. 2012;7(2):e31072. https://doi.org/10.1371/journal.pone.0031072.
Xu J, Yu H, Tang W, et al. The Effect of Using Geosocial Networking Apps on the HIV Incidence Rate Among Men Who Have Sex With Men: Eighteen-Month Prospective Cohort Study in Shenyang, China. J Med Internet Res. 2018;20(12):e11303. https://doi.org/10.2196/11303.
Xh M, Yin D, Hong X. RESEARCH REPORT ON POPULATION DEVELOPMENT IN BEIJING （2019）. 2019.
Huang D, Wang J, Yang T. Mapping the Spatial-Temporal Distribution and Migration Patterns of Men Who Have Sex with Men in Mainland China: A Web-Based Study. Int J Environ Res Public Health. 2020;17(5). https://doi.org/10.3390/ijerph17051469.
Liu Y, Vermund SH, Ruan Y, et al. HIV testing and sexual risks among migrant men who have sex with men: findings from a large cross-sectional study in Beijing, China. AIDS Care. 2018;30(1):86-94. https://doi.org/10.1080/09540121.2017.1381331.
Mi G, Ma B, Kleinman N, et al. Hidden and Mobile: A Web-based Study of Migration Patterns of Men Who Have Sex With Men in China. Clin Infect Dis. 2016;62(11):1443-1447. https://doi.org/10.1093/cid/ciw167.
Hu M, Xu C, Wang J. Spatiotemporal Analysis of Men Who Have Sex With Men in Mainland China: Social App Capture-Recapture Method. JMIR Mhealth Uhealth. 2020;8(1):e14800. https://doi.org/10.2196/14800.
Mao H, Ma W, Lu H, et al. High incidence of HIV and syphilis among migrant men who have sex with men in Beijing, China: a prospective cohort study. BMJ Open. 2014;4(9):e5351. https://doi.org/10.1136/bmjopen-2014-005351.
Chen Q, Li G, Sun Y, et al. HIV infection and syphilis among men who have sex with men in Beijing:a survey in 2012. Chin J Dermatol. 2014;47(05):349-350.
Qi X, Xu J, Zhang Z, et al. Prevalence and incidence of HIV and syphilis among men who have sex with men in Beijing. Chin Prev Med. 2013;14(06):407-412. https://doi.org/10.16506/j.1009-6639.2013.06.016.
Li D, Gao Y, Yu M, et al. Study on the incidence of HIV and associated risk factors through a prospective cohort among men who have sex with men in Beijing, China. Chin J Epidemiol. 2012(07):663-666. https://doi.org/10.3760/cma.j.issn.0254-6450.2012.07.005.
Chen Q, Li Y, Su X, et al. Epidemiological analysis on recent infected HIV-1 patients among newly reported HIV cases in Beijing, from 2009 to 2011. Chin J Epidemiol. 2014;35(1):53-56. https://doi.org/10.3760/cma.j.issn.0254-6450.2014.01.013.
Chen Q, Sun Y, Sun W, et al. Trends of HIV incidence and prevalence among men who have sex with men in Beijing, China: Nine consecutive cross-sectional surveys, 2008-2016. PLoS One. 2018;13(8):e201953. https://doi.org/10.1371/journal.pone.0201953.
Lewnard JA, Berrang-Ford L. Internet-based partner selection and risk for unprotected anal intercourse in sexual encounters among men who have sex with men: a meta-analysis of observational studies. Sex Transm Infect. 2014;90(4):290-296. https://doi.org/10.1136/sextrans-2013-051332.
Perlis TE, Des Jarlais DC, Friedman SR, et al. Audio-computerized self-interviewing versus face-to-face interviewing for research data collection at drug abuse treatment programs. Addiction. 2004;99(7):885-896. https://doi.org/10.1111/j.1360-0443.2004.00740.x.
Caldwell DH, Jan G. Computerized assessment facilitates disclosure of sensitive HIV risk behaviors among African Americans entering substance abuse treatment. Am J Drug Alcohol Abuse. 2012;38(4):365-369. https://doi.org/10.3109/00952990.2012.673663.
Holmes KK, Sparling PF, Stamm WE, et al. Sexually transmitted diseases. 4th ed New York, NY: McGraw-Hill. 2008.
Hong H, Xu J, Mcgoogan J, et al. Relationship between the use of gay mobile phone applications and HIV infection among men who have sex with men in Ningbo, China: a cross-sectional study. Int J STD AIDS. 2018;29(5):491-497. https://doi.org/10.1177/0956462417738468.
Holloway IW, Pulsipher CA, Gibbs J, et al. Network Influences on the Sexual Risk Behaviors of Gay, Bisexual and Other Men Who Have Sex with Men Using Geosocial Networking Applications. AIDS Behav. 2015;19 Suppl 2:112-122. https://doi.org/10.1007/s10461-014-0989-3.
Zou H, Fan S. Characteristics of Men Who Have Sex With Men Who Use Smartphone Geosocial Networking Applications and Implications for HIV Interventions: A Systematic Review and Meta-Analysis. Arch Sex Behav. 2017;46(4):885-894. https://doi.org/10.1007/s10508-016-0709-3.
Grosskopf NA, Levasseur MT, Glaser DB. Use of the Internet and mobile-based "apps" for sex-seeking among men who have sex with men in New York City. Am J Mens Health. 2014;8(6):510-520. https://doi.org/10.1177/1557988314527311.
Landovitz RJ, Tseng CH, Weissman M, et al. Epidemiology, sexual risk behavior, and HIV prevention practices of men who have sex with men using GRINDR in Los Angeles, California. J Urban Health. 2013;90(4):729-739. https://doi.org/10.1007/s11524-012-9766-7.
Card KG, Lachowsky NJ, Cui Z, et al. Exploring the role of sex-seeking apps and websites in the social and sexual lives of gay, bisexual and other men who have sex with men: a cross-sectional study. Sex Health. 2017;14(3):229-237. https://doi.org/10.1071/SH16150.
Elford J, Bolding G, Sherr L. Seeking sex on the Internet and sexual risk behaviour among gay men using London gyms. AIDS. 2001;15(11):1409-1415. https://doi.org/10.1097/00002030-200107270-00010.
Evans AR, Wiggins RD, Mercer CH, et al. Men who have sex with men in Great Britain: comparison of a self-selected internet sample with a national probability sample. Sex Transm Infect. 2007;83(3):200-205, 205. https://doi.org/10.1136/sti.2006.023283.
Vallabhaneni S, Li X, Vittinghoff E, et al. Seroadaptive practices: association with HIV acquisition among HIV-negative men who have sex with men. PLoS One. 2012;7(10):e45718. https://doi.org/10.1371/journal.pone.0045718.
Zou H, Hu N, Xin Q, et al. HIV testing among men who have sex with men in China: a systematic review and meta-analysis. AIDS Behav. 2012;16(7):1717-1728. https://doi.org/10.1007/s10461-012-0225-y.
Chow EP, Wilson DP, Zhang L. The next era of HIV in China: rapidly spreading epidemics among men who have sex with men. J Acquir Immune Defic Syndr. 2010;55(4):e32-e33, e33-e34. https://doi.org/10.1097/QAI.0b013e3181f3d3c1.
Hsieh YH, Ruan Y, Chen CW, et al. HIV prevalence and underreporting of men who have sex with men in Beijing. Int J STD AIDS. 2012;23(8):606-607. https://doi.org/10.1258/ijsa.2012.011393.
Zhou C, Raymond HF, Ding X, et al. Anal sex role, circumcision status, and HIV infection among men who have sex with men in Chongqing, China. Arch Sex Behav. 2013;42(7):1275-1283. https://doi.org/10.1007/s10508-012-0008-6.
Chen YJ, Lin YT, Chen M, et al. Risk factors for HIV-1 seroconversion among Taiwanese men visiting gay saunas who have sex with men. BMC Infect Dis. 2011;11:334. https://doi.org/10.1186/1471-2334-11-334.
Wei C, Raymond HF. Preference for and maintenance of anal sex roles among men who have sex with men: sociodemographic and behavioral correlates. Arch Sex Behav. 2011;40(4):829-834. https://doi.org/10.1007/s10508-010-9623-2.
Meng X, Zou H, Fan S, et al. Relative Risk for HIV Infection Among Men Who Have Sex with Men Engaging in Different Roles in Anal Sex: A Systematic Review and Meta-analysis on Global Data. AIDS Behav. 2015;19(5):882-889. https://doi.org/10.1007/s10461-014-0921-x.
Wang QQ, Chen XS, Yin YP, et al. HIV prevalence, incidence and risk behaviours among men who have sex with men in Yangzhou and Guangzhou, China: a cohort study. J Int AIDS Soc. 2014;17:18849. https://doi.org/10.7448/IAS.17.1.18849.
Goldenberg SM, Strathdee SA, Perez-Rosales MD, et al. Mobility and HIV in Central America and Mexico: a critical review. J Immigr Minor Health. 2012;14(1):48-64. https://doi.org/10.1007/s10903-011-9505-2.
He S, Sun Y, Li G, et al. HIV infection and its influencing factors among men who have sex with men in Beijing from 2009 to 2013. Chin Prev Med. 2014;15(05):161-164.
Dobra A, Barnighausen T, Vandormael A, et al. A method for statistical analysis of repeated residential movements to link human mobility and HIV acquisition. PLoS One. 2019;14(6):e217284. https://doi.org/10.1371/journal.pone.0217284.
Yu M, Jiang G, Dou Z, et al. HIV infection incidence among men who have sex with men in common bathing pool in Tianjin: a cohort study. Chin J Epidemiol. 2016;37(3):362-366. https://doi.org/10.3760/cma.j.issn.0254-6450.2016.03.014.
Li D, Li S, Liu Y, et al. HIV incidence among men who have sex with men in Beijing: a prospective cohort study. BMJ Open. 2012;2(6). https://doi.org/10.1136/bmjopen-2012-001829.
Wang Y, Li L, Xu J, et al. Analysis on the HIV infections and factors of men who have sex with men in Mianyang city,Sichuan province. Chin J Prev Med. 2015;49(01):66-70.
Wu Z, Sullivan SG, Wang Y, et al. Evolution of China's response to HIV/AIDS. Lancet. 2007;369(9562):679-690. https://doi.org/10.1016/S0140-6736(07)60315-8.
Lewnard JA, Berrang-Ford L. Internet-based partner selection and risk for unprotected anal intercourse in sexual encounters among men who have sex with men: a meta-analysis of observational studies. Sex Transm Infect. 2014;90(4):290-296. https://doi.org/10.1136/sextrans-2013-051332.
Lee SS, Lam AN, Lee CK, et al. Virtual versus physical channel for sex networking in men having sex with men of sauna customers in the City of Hong Kong. PLoS One. 2012;7(2):e31072. https://doi.org/10.1371/journal.pone.0031072.
Wang H, Zhang L, Zhou Y, et al. The use of geosocial networking smartphone applications and the risk of sexually transmitted infections among men who have sex with men: a systematic review and meta-analysis. BMC Public Health. 2018;18(1):1178. https://doi.org/10.1186/s12889-018-6092-3.
Luo Q, Chen Z, Ma Y, et al. Risk of HIV infection and its factors among men who have sex with men: a geosocial networking application based survey in Beijing of China, 2017. Chin J Prev Med. 2018;52(12):1220-1224.
Cao B, Zhao P, Bien C, et al. Linking young men who have sex with men (YMSM) to STI physicians: a nationwide cross-sectional survey in China. BMC Infect Dis. 2018;18(1):228. https://doi.org/10.1186/s12879-018-3145-2.
Morahan-Martin JM. How internet users find, evaluate, and use online health information: a cross-cultural review. Cyberpsychol Behav. 2004;7(5):497-510. https://doi.org/10.1089/cpb.2004.7.497.
Powell J, Inglis N, Ronnie J, et al. The characteristics and motivations of online health information seekers: cross-sectional survey and qualitative interview study. J Med Internet Res. 2011;13(1):e20. https://doi.org/10.2196/jmir.1600.
Buhi ER, Daley EM, Oberne A, et al. Quality and accuracy of sexual health information web sites visited by young people. J Adolesc Health. 2010;47(2):206-208. https://doi.org/10.1016/j.jadohealth.2010.01.002.
Hooper S, Rosser BR, Horvath KJ, et al. An online needs assessment of a virtual community: what men who use the internet to seek sex with men want in Internet-based HIV prevention. AIDS Behav. 2008;12(6):867-875. https://doi.org/10.1007/s10461-008-9373-5.

Download PDF

Journal Publication

published 02 Apr, 2021

Read the published version in Infectious Diseases of Poverty →

Review #2 received at journal
22 Dec, 2020
Editorial decision: Minor revision
22 Dec, 2020
Reviewer #2 agreed at journal
19 Dec, 2020
Review #1 received at journal
06 Dec, 2020
Reviewer #1 agreed at journal
17 Nov, 2020
Reviewers invited by journal
16 Nov, 2020
Editor assigned by journal
15 Nov, 2020
Submission checks completed at journal
15 Nov, 2020
Editor invited by journal
15 Nov, 2020
First submitted to journal
14 Nov, 2020

You are reading this latest preprint version

HIV Incidence among Men Who Have Sex with Men Using Geosocial Networking Smartphone Application in Beijing, China: an Open Cohort Study

Status:

Journal Publication

Version 1

Abstract

Figures

Background

Methods

Results

Discussion

Conclusions

List Of Abbreviations

Declarations

References

Status:

Journal Publication

Version 1