Point 'Oh!' Five

News

Patterns of Online and Offline Connectedness among Gay and Bisexual Men

Submitted Draft; Final Version in AIDS and Behavior

ABSTRACT

This study examined patterns of connectedness among 774 sexually-active gay, bisexual, and other men who have sex with men (GBM), aged >16 years, recruited using respondent-driven sampling in Metro Vancouver. Latent class analysis examined patterns of connectedness including: attendance at gay venues/events (i.e., bars/clubs, community groups, pride parades), social time spent with GBM, use of online social and sex seeking apps/websites, and consumption of gay media. Multinomial regression identified correlates of class membership. A three-class LCA solution was specified: Class 1 “Socialites” (38.8%) were highly connected across all indicators. Class 2 “Traditionalists” (25.7%) were moderately connected, with little app/website-use. Class 3 “Techies” (35.4%) had high online connectedness and relatively lower in-person connectedness. In multivariable modelling, Socialites had higher collectivism than Traditionalists, who had higher collectivism than Techies. Socialites also had higher annual incomes than other classes. Techies were more likely than Traditionalists to report recent serodiscordant or unknown condomless anal sex and HIV risk management practices (e.g., ask their partner’s HIV status, get tested for HIV). Traditionalists on the other hand were less likely to practice HIV risk management and had lower HIV/AIDS stigma scores than Socialites. Further, Traditionalists were older, more likely to be partnered, and reported fewer male sex partners than men in other groups. These findings highlight how patterns of connectedness relate to GBM’s risk management.

INTRODUCTION

Background

Beginning in the early 1980’s, the HIV epidemic swept through gay communities. In response, community action campaigns focused on promoting safe sex to prevent HIV transmission within gay communities [1]. By the mid-1990’s, however, highly active antiretroviral therapies (HAART) led to significant declines in HIV-related morbidity, mortality, and transmission [2], fundamentally reshaping the epidemic and its consequences for people living with HIV [3]. After the advent of antiretroviral therapy, reports began to emerge that condom use was declining among gay, bisexual, and other men who have sex with men (GBM; [4], leading some to believe that treatment optimism was contributing to greater HIV risk in this population [5]. However, wider cultural changes taking place in the 1990’s made it difficult to parse out whether treatment optimism was the primary driver of these changes.

Indeed, during this period internet-mediated technologies (i.e., apps and websites) have also emerged as an important social and sex seeking venues for GBM  and researchers have demonstrated that apps and websites have had a profound impact on the gay community [6]. Comparisons between online and offline samples and other approaches assessing internet use among GBM show that online samples are younger [7], less likely to identify as gay [8], and exhibit lower community attachment and social embeddedness [8–11], suggesting important differences between GBM connected online and those who are not. Further, online sex seeking has been associated with having more sexual partners [12–16], differing patterns of HIV risk management [17–19], and higher odds of condomless anal sex [20–22]. With the arrival of sex seeking apps in 2009, these trends continue to merit significant attention from health researchers [23–25].

However, some of the differences between internet and non-internet users simply arise from the ways that the use of internet-mediated technologies has been operationalized. For example, Jenness et al. found that, on average, men who meet partners both online and offline had significantly more partners than men who reported meeting partners only online or only offline [26]. Further, these studies have generally shown that GBM who meet partners both online and offline are the most likely to engage in condomless anal sex (CAS) [23,27–31]. Similarly, Grov et al. reported that men who preferred meeting partners online had significantly more partners than men who preferred meeting partners at bars, but not more than those who preferred meeting partners at bathhouses—highlighting heterogeneity linked to venue preference [10]. Similar associations have also been documented when exploring so called “scenes” as “micro-cultures” [32]. With respect to these scenes, we also note that not all internet-mediated locales are the same: not only can distinctions be drawn between websites and apps as such, but even within these two domains there is a significant amount of variation [24,25].

Theoretical Framework

Despite these observations, it is unlikely that the observed differences between online and offline groups can be attributed to methodology alone. Information systems researchers studying the uptake of new technologies argue that they diffuse in predictable patterns, based on specific user characteristics (e.g., age, social attitudes, etc.). For example, technology acceptance models [33] posit that four key constructs predict technology uptake: (i) performance expectancy, (ii) effort expectancy, (iii) social influence, and (iv) facilitating conditions. Similarly, the Reasoned Action Approach [34], applied to sexuality, suggests that sexual behavior can be predicted by one’s (i) behavioral beliefs, (ii) normative beliefs, (iii) control beliefs, and (iv) actual controls over one’s behavior. Together, these behavioral models highlight the central role of cognitive dispositions in shaping human agency. However, they also raise an important question: How do these dispositions come to be?

In the canons of risk theory, rational choice models emphasize the central importance of beliefs, but they do not speak well to how and why individuals come to believe the things they do [35]. Addressing this limitation, sociocultural theory can be used to help us understand the social genesis of personally-held beliefs and worldviews and thereby explain better why people do the things they do [36]. Sociocultural theory therefore seeks to liberate individuals from psychological flowcharts and conceptualize them instead as social animals embedded within social environments. In other words, sociocultural theory expands the discourse about risk into examinations of social interactions, relationships, and situations [37], thereby reorienting academic inquiry towards not just one’s objective knowledge about risk, but also their cultural interpretations of these risks.

Among many potentially relevant constructs evaluated in the social sciences, the tensions between individualism (i.e., self-determination, reliance, and definition) and collectivism (i.e. group-structured determination, reliance, and definition) has garnered widespread attention since its use by Hofstede to describe national cultural dimensions [38,39]—though they were considered earlier by others [40]. In person-level analyses, collectivism and individualism are perceived as two dimensions of a related construct, with individuals within strongly bound social groups having stronger collectivism and loosely networked groups possessing stronger individualism. Examining the relationship between collectivism and technology, collectivism has been associated with slower uptake of new technologies [41,42] and a tendency to only embrace technology on the proviso that it is compatible with group norms [43]. Dake further distinguishes between egalitarian and hierarchical collectivists [44], suggesting that egalitarian collectivists tend to perceive new technologies as threats to their social structure [45,46], while hierarchical collectivists are more likely to perceive them as tools by which thy can expand their social dominance [44].

In a similar fashion, culture also appears to shape sexual behavior. For example, Lo et al. found that collectivism was associated with maintenance of sexual norms, while individualism was associated with more liberal beliefs regarding sexual behavior [47]. Similarly, collectivism has also been shown to impact the level of stigma that sexual minorities feel, with higher collectivism contributing to higher stigma when an individual is in violation of group norms [48]. Another, related concept known as sexual altruism (i.e., prioritization of collective-interest over self-interest) has also been shown to predict GBM’s sexual behavior [49–51]. Our own research on the covariates of sexual risk among GBM shows that collectivism and altruism act as protective factors for online-initiated sexual events [52]. Complicating this picture, Douglas and Calvez [40] insist that the group norms of egalitarian collectivists are usually grounded in community-experience, while those of hierarchical collectivists usually rely on established authorities. Therefore, altruistic and collectivistic motivations may take on very different forms depending on the ways groups respond to one another and to scientific authority—highlighting the influence of cultural collectivism on the formation of subjective-risk perceptions.

Among GBM, community and culture have long been reflected in the ways they connect with one another (both socially and sexually). Beginning in the 1960’s gay men began to organize themselves in neighborhoods where they could find a greater sense of safety and security [53]. As such, gay neighborhoods, complete with gay bars, clubs, bookstores, publications, and many other amenities, have become a common feature of major urban centers [54]. However, the internet and other socio-economic forces have attracted GBM to other neighborhoods [55], decreasing the centrality of these in-person venues in the lives of GBM [56]. Further, theories of social behavior suggest that individuals seek out forms of social participation that are consistent with their social and cultural values [57,58]—creating so called ‘micro-cultures,’ or what are colloquially referred to as ‘scenes’ [32].

Objective

Together, the evidence and theory summarized above suggest that patterns of community connectedness may reflect, to some degree, the underlying cultural values of gay men, and thus may also play a role in shaping their sexual behavior. However, there is limited research exploring the role of collectivism in the context of GBM’s health and HIV prevention. Therefore, the present analysis explores patterns of online and offline connectedness among GBM and examines their relationship to GBM’s prevention-related beliefs and behavior.

METHODS

Study Protocol

Baseline cross-sectional data were analyzed from participants enrolled in the Momentum Health Study, a longitudinal prospective cohort based in Metro Vancouver, Canada. In brief, participants were recruited if they met the following eligibility criteria: (i) identified as a man, (ii) reported sex with another man in the past six months, (iii) were 16 years of age or older, (iv) could complete a questionnaire in English, and (v) were recruited using respondent-driven sampling (RDS) between February 2012 and February 2015. Initially, 30 seeds were selected and given 6 coupons each. However, due to under-productive chain sampling, additional online and offline-recruited seeds (n = 89) were added throughout the recruitment process to achieve equilibrium across key indicators. Each eligible participant recruited by RDS-seeds received 6 vouchers which they were encouraged to give to other eligible participants. Additional information regarding formative research and RDS-sampling procedures for this study are provided elsewhere [59–62]. Upon receipt of informed consent, bio-behavioral and psychosocial data were collected using a computer-administered questionnaire. After completion of the questionnaire a study nurse administered a point-of-care HIV test to HIV-negative individuals and viral-load and CD4+ cell count tests for HIV-positive individuals. Serological screens for hepatitis C and syphilis were also performed; screenings for gonorrhea and chlamydia were optional. Participants were reimbursed an honorarium of $50 CAD at each study visit and $10 CAD for each participant they recruited into the study. Additional details regarding study protocol are reported elsewhere [61]. Ethics approval for this study was granted by the Research Ethics Boards at Simon Fraser University, the University of British Columbia, and the University of Victoria.

Independent Variables

Independent variables included participant demographics, prevention-related behavior, Treatment as Prevention (TasP) awareness, sexual altruism, collectivism, and perceptions of treatment optimism and HIV/AIDS stigma. Demographic measures included age, sexual orientation (gay vs. other), disclosure of sexual orientation (out, coming out, non-gay identified [including bisexual]), race (white, Indigenous, other), annual income in Canadian dollars ( $30,000, $30,000), geographic residence (downtown Vancouver, elsewhere in Vancouver, outside Vancouver) and the participant’s self-reported HIV-status (negative, positive, unknown). HIV/STI testing was assessed by whether they had ever (yes versus no) or recently been tested for HIV (Recently = Past 24 months; yes versus no) and recently tested for other STIs (past six months [P6M], yes versus no). Questions regarding sexual behavior assessed the participant’s relationship status (monogamous/married, open/partially open relationship, no regular partner), how many male sex partners they had in the past six months, and whether they engaged in CAS with a serodiscordant or unknown status partner in the past six months (yes versus no). Seroadaptive strategies were assessed by asking HIV-positive and HIV-negative men whether they engaged in any of the following strategies: consistent condom use (yes versus no), strategic positioning for CAS (yes versus no), anal sex avoidance (yes versus no), serosorting for CAS (yes versus no), viral load sorting for CAS (yes versus no), withdrawal for CAS (yes versus no), or always asking partners their HIV status (yes versus no) [63]. These questions were assessed using different wording for HIV-positive and HIV-negative men (e.g., to assess strategic positioning HIV-positive men were asked if they bottomed for CAS to avoid transmission and HIV-negative men were asked if they topped for CAS to avoid HIV-acquisition). Questions regarding TasP assessed whether they had ever heard of TasP (yes versus no) and whether they learned about TasP from any of the following sources: their sex partners (yes versus no), a community agency (yes versus no), their doctor or another healthcare professional (yes versus no), gay news/media (yes versus no), or other media (yes versus no). Consent to data linkage with the British Columbia Centre for Excellence in HIV/AIDS’s Drug Treatment Program was used to determine viral load (<50 copies/ml, 51 – 200 copies/ml, >200 copies/ml) and CD4 cell counts (>500 cells/µL, 200 – 499 cells/µL, <200 cells/µL) of HIV-positive participants, whether they were currently on treatment (yes versus no), and if they were adherent to HAART (pharmacy refill, < 95% vs. >95%; [64]. Finally, treatment optimism, HIV/AIDS stigma, altruism, and collectivism were assessed using four scales:

The Treatment Optimism-Skepticism scale (study α = 0.82) was used to assess GBM’s risk perceptions regarding HIV in the HAART era. Response scores, ranging from 0 – 36, were summed across 12 items (e.g., “HIV is less of a threat because the epidemic is on the decline.”) with Likert-based response options (i.e., strongly disagree, disagree, agree, strongly agree), with higher scores representing higher treatment optimism [5].

The HIV/AIDS Stigma Scale (study α = 0.81; allowed respondents to rate their level of agreement, using a four-point Likert scale (i.e., strongly disagree, disagree, agree, strongly agree), for 6 items assessing perceptions of HIV/AIDS stigma in the gay community (e.g., “HIV-negative men treat a guy differently when they know he is HIV-positive”). Scores ranged from 6 – 24 with higher scores indicating higher perceived stigma [65].

The Sexual Altruism scale is comprised of two subscales which assessed personal altruism (study α = 0.82; 7 items; e.g., “Having safer sex shows I care about my partner”; “I have a responsibility to stop my partner from doing something risky.”) and communal altruism (study α = 0.88; 6 items; e.g., “Having safer sex is what gay men should do for each other”; “I have safer sex because I want the gay community to survive.”). Each subscale was scored separately by dividing the summed Likert response item scores (i.e., disagree strongly, disagree somewhat, neither agree or disagree, agree somewhat, agree strongly) by the number of questions answered. Scores ranged from 0 – 4, with higher scores representing higher altruistic motivations for safer sex [51].

A scale measuring collectivist attitudes towards the gay community (herein referred to as collectivism; study α = 0.81, range = 0 – 12) was created from four items: (i) “Being part of the gay/bisexual/queer community is an important reflection of who I am;” (ii) “Belonging to the gay/bisexual/queer community is not a good thing for me (reverse scored);” (iii) “Being part of the gay/bisexual/queer community has a lot to do with how I feel about myself;” and (iv) “How important is it to you to be connected to and involved in the gay community?”. Items 1 through 3 were scored on a four-point Likert scale measuring agreement (i.e., strongly disagree, disagree, agree, strongly agree) and item 4 was scored on a four-point Likert scale measuring importance (i.e., not at all important, not very important, somewhat important, very important). Total scores ranged from 0 to 12, with higher scores representing higher collectivism. We acknowledge that although this scale might not measure a general sense of psychological collectivism, reviews have repeatedly highlighted the problem of operationalizing cultural values on an individual-level and one review suggests that quantitative indices “need to be tailored for the social contexts under examination” [66]. As such, this is the approach we have taken in constructing the present scale.

Dependent Variable

Recognizing that simple dichotomies of connectedness, such as “online versus offline,” are unlikely to provide sufficient insight into understanding the relationship between GBM’s social and sexual behavior, the present study explored patterns of connectedness using a person-centered analytic approach known as latent class analysis (LCA; [67]). This approach is a sub-type of structural equation modelling that leverages measured variables, or indicators, to identify unmeasured latent constructs. In this way, it is similar to factor analysis, but instead of exporting a continuous latent variable, LCA constructs are categorical. These LCA categories, are referred to as classes and emphasize differences in quality, not just quantity. For this reason, LCA has been lauded as a potentially useful analytic strategy for describing social and behavioral phenomena [67].

Statistical Analysis

In the present analysis, nine indicator variables were modeled using the PROC LCA procedure in SAS v.9.4. These variables assessed whether participants read gay news/media (P6M, yes versus no), participated in gay group meetings (P6M, yes versus no), patronized gay bars and clubs (P6M, yes versus no), played on gay sports team (P6M, yes versus no), attended the most recent gay pride parade (yes versus no), spent more than 50% of their social time with other GBM (>50% versus <50%), had a Facebook page (yes versus no), used websites to seek sex (P6M, yes versus no), and used apps to seek sex (P6M, yes versus no). LCA model selection was made with consideration to statistical fit of the models (i.e., AIC, BIC, CAIC, aBIC) and the interpretability and differentiation of LCA classes (See Figure 1). The two-class, three-class, and four-class models each had high conceptual interpretability and distinguishability. AIC was minimized in the six-class model, although BIC, CAIC, and aBIC were minimized in the three-class LCA model.  Therefore, the three-class solution was selected (See Figure 2). Each participant was then probabilistically assigned to an LCA class based on the vector of classification probabilities generated for each participant based on their observed item-responses. Due to our analytic approach, statistics were not adjusted using RDS weights.

Using the PROC LOGISTIC procedure, univariable and multivariable multinomial logistic regression models were then used to identify the relationship between dependent and independent factors. Three pairwise comparisons were conducted: Class 1 versus Class 2, Class 1 versus Class 3, and Class 2 versus Class 3. Models were specified using a backwards selection method, wherein variables with a univariate p-value greater than 0.20 were initially included. Variables with the lowest Type-III p-value at each step were then omitted in a step-wise fashion until the model fit statistic (i.e., AIC) was minimized. Univariate p-values and multivariable adjusted odds ratios (AOR) with 95% confidence intervals are reported. Given the relatively open time-frame for sampling using RDS, multivariable models were adjusted for time of recruitment.

RESULTS

Sample Description

A total of 774 GBM were recruited. The median age of this sample was 34 years (Q1, Q3: 26, 47). Most participants were HIV-negative men (n =551, 71.2%), white (n = 585, 75.6%), single (n = 477, 61.6%), had annual incomes of < $30,000 CAD (n = 485, 62.7%), identified as gay (n = 655, 84.6 %), and were “out” (n = 611 78.9%). The plurality of men lived in downtown Vancouver (n = 382, 49.4%) or elsewhere in the municipality (n = 240, 31.0%). The sample also reported high levels of connectedness: 82.7% (n = 640) read gay news media, 82.6% (n = 639) had a Facebook account; 79.7% (n = 617) patronized gay bars or clubs, 62.8% (n = 486) attended the most recent gay pride parade, 63.1% (n = 488) sought sex on websites, 54.4% (n = 421) sought sex on apps, 50.3% (n = 389) spent more than half of their social time with other GBM, 38.8% (n = 300) attended gay group meetings, and 11.6% (n = 90) played on gay sports teams.

Latent Class Analysis

The first latent class, described herein as “Socialites” due to relatively high in-person and internet-based connectedness, made up 38.8% of the sample. Approximately one-quarter (25.8%) of the sample was assigned to the second latent class, which was characterized by moderately high in-person connectedness and low internet-based connectedness. These individuals are described as “Traditionalists.” Finally, class 3, which comprised 35.4% of the sample, is described as “Techies” due to relatively lower in-person connectedness but high internet-based connectedness.

The median date of recruitment for “Socialites,” “Traditionalists” and “Techies,” were respectively: April 9th, 2013 (Q1, Q3: September 19th, 2012 – September 11th, 2013), April 25th, 2013 (Q1, Q3: October 23rd, 2012 – September 7th, 2013), and May 21st, 2013 (Q1, Q3: January 8th, 2013 – October 8th, 2013). These data suggest that “Techies” were on average, recruited significantly later than “Socialites” (p = 0.011) or “Traditionalists” (p = 0.002), though there was no significant difference between “Traditionalists” and “Socialites” (p = 0.145).

Multinomial Logistic Regression

Socialites had higher collectivism scores, higher annual incomes, and were more likely to identify as gay compared to men in the other two classes. Compared with Socialites, Traditionalists were more likely to be older, Indigenous, married or monogamous, and perceived lower HIV/AIDS stigma in the gay community, while Techies were more likely to be coming out and report lower communal and personal sexual altruism scores. On average, compared with Traditionalists, Techies tended to report lower collectivism, were younger, less likely to be white, and less likely to live in downtown Vancouver (vs. elsewhere in Vancouver). However, Techies were more likely to be single, closeted, and have higher HIV/AIDS Stigma scores.

Traditionalists were less likely than Socialites to have always used condoms, always asked their partner’s HIV status prior to sex, and recently been tested for HIV, recently been tested for other STIs, ever been tested for HIV, and ever been tested for other STIs. Further, they had fewer sexual partners and were more likely to self-identify as HIV-positive or to not know their HIV status. Techies were less likely than Socialites to have heard of TasP, particularly from a community agency. Comparing Techies with Traditionalists, Techies were more likely to have had a recent STI test, have had a recent HIV test, been HIV-negative, have asked their partner’s HIV status consistently, have had engaged in any recent serodiscordant or unknown CAS, and had more sexual partners in the past six months. Across all three groups, there were no significant differences in whether they heard about TasP from sex partners, health professionals, or the media, nor were their significant differences in strategic positioning (i.e. anal sex avoidance, serosorting, viral load sorting, withdrawal). For HIV-positive men, there were no significant differences in treatment status, treatment adherence, viral load, and CD4 counts.

Techies were significantly more likely to be recruited later than Socialites and Traditionalists, though no difference was observed between Socialites and Traditionalists. Further, Socialites were more likely than Traditionalists and Techies to have higher collectivism scores and an annual income >$30,000. Compared with Socialites, Traditionalists were older, less likely to be single, had lower HIV/AIDS stigma scores, had fewer sexual partners, and were less likely to have ever tested for HIV. Comparing Techies and Traditionalists, Techies had lower collectivism and were younger, and they were more likely to be single, to have ever been tested for HIV, to ask their partner’s HIV status, to engage in CAS with a serodiscordant or unknown status partner, and to have more sexual partners in the past six months.

DISCUSSION

In the present analysis, we sought to examine how collectivist attitudes (i.e., collectivism, altruism), risk perceptions (e.g., HAART optimism, HIV/AIDS stigma, TasP awareness), and behavior (i.e., HIV/STI testing history, prevention strategies, serosorting) loaded onto nuanced patterns of social and community connectedness among GBM. In doing so, we used latent class analysis to characterize GBM’s patterns of online and offline attachment to the gay community. The results of this analysis highlight diversity in the ways GBM connect to their communities. Differences in group participation primarily arose from the intensity of their connectedness and from their uptake of socio-sexual networking apps and websites. Further, class membership was strongly associated with age-cohort effects (i.e., age), socioeconomic status, sexual attitudes and behaviors, altruistic motivations for safe sex, and collectivism among GBM. Class membership was also associated with time of recruitment, with Techies being more likely to be recruited later in the study. This is consistent with technology acceptance models that have shown that technology uptake is a culturally embedded process [33,43,44,46] — highly correlated with age cohort effects, social attitudes towards technology uptake, and the diffusion processes of these technologies themselves. Similar patterns of culturally-dependent diffusion have been documented with respect to the emergence of email [42] and online shopping [41,68].

Of course, patterns of connectedness (as operationalized in the present analysis) are predicted by more than just cultural dispositions. We observed that class membership was also associated with practical motivators, evidenced by person-level characteristics. For example, compared with Socialites, Traditionalists were more likely to be older (suggesting an age-cohort effect), to be in a relationship, and to have fewer partners. Similarly, Socialites had higher annual incomes than both Traditionalists and Techies, and Techies were younger, had more sexual partners, and were more likely to be single than Traditionalists. These associations highlight the practical implications of community connectedness. For example, the associations between class membership and income likely reflects the importance of disposable time and resources — with more affluent gay men being able to afford greater connectedness [69]. Similarly, the association between relationship status and class membership is likely reflective of the impact that having a regular partner has on one’s motivations for online sex seeking. Further, the association between online sex seeking and partner number highlights an important function of apps and websites—namely that they allow GBM to find more sexual partners. Together these factors highlight the diverse drivers that underlie the ways GBM connect with their communities [70,71].

Connecting these patterns of connectedness and their relationship with HIV risk management, our findings offer several relevant implications. For example, our univariable and multivariable comparison of Socialites and Traditionalists point to a greater tolerance of HIV risk among Traditionalists. For example, on the univariable level, Traditionalists (vs. Socialites) had lower HIV/AIDS stigma, were more likely to be HIV positive or not know their HIV status, and were less likely to have had an HIV/STI test, use condoms consistently, or ask their partner’s HIV status prior to sex. On the multivariable level, lifetime lack of HIV testing and decreased likelihood of inquiry of partner serostatus persisted even after accounting for other factors. This limited HIV aversion among Traditionalists might be the result of social norms within these networks that are more inclusive of people living with HIV [72]. Alternatively, these findings may highlight the role of the internet in promoting HIV stigma among GBM [73]. For example, because the internet better facilitates the disclosure of individual’s HIV status, HIV-positive internet users may experience more frequent sero-discrimination from potential partners who use serosorting strategies to prevent HIV acquisition [17,73].

However, comparing the behavior of Techies and Traditionalists, our findings also suggests that Techies may be more willing to engage in higher risk behaviors (i.e. serosorting for CAS, more sexual partners). Considering this within the context of lower collectivism scores, we note that previous research has shown that individualism has been associated with both the uptake of the internet [42,43,74] and more adventurous sexual norms [40,47]. With that said, Techies were also more likely than Traditionalists to have ever tested for HIV and to ask their partner’s HIV status before sex—suggesting increased willingness to engage in unproven, entrepreneurial HIV prevention strategies which strike a new balance between risk and pleasure. Again, previous research has shown that greater individualism is associated with the uptake of novel risk prevention strategies [40]. These observed interrelationships between collectivism, connectedness, and risk behavior may lend indirect support to [40] cultural theory of HIV risk among GBM, which aims to describe the role of social and cultural factors in shaping risk perceptions and behavior among GBM [44,75]. However, our findings suggest that the patterns of community connectedness we have measured here are more strongly indicative of HIV attitudes (HIV/AIDS Stigma), health service uptake (i.e., HIV/STI testing), and, potentially, high risk sexual behavior (i.e., CAS with serodiscordant or unknown status partners); and less indicative of patterns of sexual behaviors (i.e., seroadaptation). In fact, apart from a few key behaviors (i.e., serodiscordant sex, number of partners), sexual behavior did not vary according to patterns of social connectedness—at least as operationalized in the present study. This may suggest that sexual practices in the gay community are amenable to a variety of cultural orientations (i.e., the socially constructed inclinations of individuals towards specific ways of thought, behavior, or emotion) or modes of community participation.

In light of research by Rodger et al [76], which suggests that a person with a suppressed viral load is unlikely to pass on HIV to their sexual partners, GBM now have a variety of community-grounded and scientifically supported options for safe sex [77]; it appears that these options have diffused across the patterns of online and offline connectedness examined in the present study. Consistent with these findings, health promotion efforts, specifically those targeting HIV attitudes and health service uptake, may need to better account for the influence that cultural and social factors have in shaping the diffusion and uptake of these efforts. Of course, further research is needed to explicitly examine how collectivism and other cultural orientations might specifically influence prevention-related beliefs and behaviors. This is particularly important for emerging prevention strategies when their uptake is dependent on GBM’s attitudes towards them, such as may be the case with pre-exposure prophylaxis (PrEP). Future studies should explore how cultural dispositions might shape the uptake of emerging risk reduction and HIV-prevention strategies.

LIMITATIONS

Although the present study provides insight into the social and sexual behavior of GBM, readers should be aware of several important limitations. First, this data was collected using RDS and may therefore be biased towards greater social engagement and community altruism. However, considering that the present study focused on the venues GBM attend, RDS is likely preferable to internet- or venue-based sampling. Further, because RDS weights were not used in the present analysis our sample is analogous to other non-adjusted chain-referral samples—where recruitment conditions may influence salient factors considered in our analysis. As such, we recognize that the large number of seeds (n = 119), unproductive recruiters, and diverse motivations for participating in the study (e.g., financial, altruistic, etc.) may further bias our sample [60]. Further, with the emergence of apps in 2009, it is likely that the use of apps and websites by GBM was not in a state of equilibrium across our recruitment period (i.e., February 2012 – February 2015). Therefore, it is possible that individuals recruited earlier in our study were less likely to use apps than those recruited later in our study, simply as a mechanism of when they were recruited into our study. Alternatively, our recruitment of online seeds later in the study period may have also biased GBM recruited later in the study towards increased app use [59]. Either way, additional research and analyses are needed to understand how app use has impacted patterns of connectedness among GBM. Second, the variables used to assess app and website use specifically dealt with sex seeking, while other connectedness variables did not assess intent of use. As some GBM may use apps and websites without the intent to find sexual partners [78], the present study may underestimate app or website use among for these individuals. Third, although patterns of connectedness were correlated with our measures assessing collectivism, LCA classes did not directly measure the cultural orientations hypothesized by Douglas [79]. Although latent classes might indirectly measure elements of culture, acting as potential signals supporting the importance of cultural biases, our findings highlight significant overlap in the ways diverse GBM connect to their communities. Other authors have conducted analyses which highlight greater heterogeneity in the cultural orientations of GBM [32]. The associations presented here may therefore average over participant characteristics, obscuring important and relevant findings. This is particularly true when considering our use of LCA. Future studies should directly test these latent constructs using measures designed specifically for this purpose. Indeed, the results of the present study are merely suggestive of underlying latent constructs and additional work is needed to define, explore, and operationalize these constructs. Finally, with respect to the seroadaptive strategies assessed in the present study, dichotomous measures of period prevalence may not sufficiently capture the frequency or consistency of use as they are employed by GBM. Future studies should examine how patterns of connectedness relate to within-person variance in the sexual behaviors these GBM employ.

CONCLUSIONS

The present study highlights important similarities and differences in the ways GBM connect to their communities, which reflects the heterogeneity in the cultural and social attitudes of GBM (as measured by HIV/AIDS stigma and collectivism scores). Further, acknowledging previous literature drawing an association between collectivism and risk perception [40,44,47], the present study shows that patterns of connectedness (measured in the present study using LCA) were associated with GBM’s attitudes toward HIV/AIDS (as measured by HIV/AIDS stigma scores) and their HIV/STI testing behavior. Meanwhile, we did not capture a significant association between patterns of connectedness and seroadaptation (e.g., serosorting, strategic positioning, viral load sorting). These findings may provide insight into the diffusion of specific HIV prevention strategies, and reflect how cultural values held by individuals in different settings may influence which prevention strategies successfully diffuse into the repertoires of GBM.

ACKNOWLEDGEMENTS

The authors would like to thank Kirk J. Hepburn for editing this manuscript prior to publication; the r/translator community for assistance in Spanish language translation; the Momentum Study participants, office staff and community advisory board; and our community partner agencies: Health Initiative for Men, YouthCO HIV and Hep C Society, and Positive Living Society of BC. Momentum is funded through the National Institute on Drug Abuse (R01DA031055-01A1) and the Canadian Institutes for Health Research (MOP-107544). NJL was supported by a CANFAR/CTN Postdoctoral Fellowship Award. DMM is supported by a Scholar Award from the Michael Smith Foundation for Health Research (#5209). JSGM is supported with grants paid to his institution by the British Columbia Ministry of Health and by the US National Institutes of Health (R01DA036307).

REFERENCES

1. CDC. Evolution of HIV/AIDS prevention programs--United States, 1981-2006. MMWR Morb. Mortal. Wkly. Rep. 2006;55:597–603.

2. Montaner JSG, Lima VD, Harrigan PR, Lourenço L, Yip B, Nosyk B, et al. Expansion of HAART Coverage Is Associated with Sustained Decreases in HIV/AIDS Morbidity, Mortality and HIV Transmission: The “HIV Treatment as Prevention” Experience in a Canadian Setting. PLoS ONE. 2014;9:e87872.

3. Centers for Disease Control and Prevention. Condom use and fear of AIDS both declining, according to surveys. CDC AIDS Wkly. 1988;2.

4. Adam BD, Husbands W, Murray J, Maxwell J. AIDS optimism, condom fatigue, or self-esteem? Explaining unsafe sex among gay and bisexual men. J. Sex Res. 2005;42:238–48.

5. Van de Ven P, Crawford J, Kippax S, Knox S, Prestage G. A scale of optimism-scepticism in the context of HIV treatments. AIDS Care. 2000;12:171–6.

6. Grov C, Breslow AS, Newcomb ME, Rosenberger JG, Bauermeister JA. Gay and bisexual men’s use of the Internet: research from the 1990s through 2013. J. Sex Res. 2014;51:390–409.

7. Allman D, Meyers T, Xu K, Steele S. The social technographics of gay men and other men who have sex with men (MSM) in Canada: Implications for HIV research, outreach and prevention. Digit. Cult. Educ. 2012;4:126–44.

8. Grov C, Rendina HJ, Parsons JT. Comparing three cohorts of MSM sampled via sex parties, bars/clubs, and Craigslist.org: implications for researchers and providers. AIDS Educ. Prev. Off. Publ. Int. Soc. AIDS Educ. 2014;26:362–82.

9. Fernández MI, Warren JC, Varga LM, Prado G, Hernandez N, Bowen GS. Cruising in cyber space: comparing Internet chat room versus community venues for recruiting Hispanic men who have sex with men to participate in prevention studies. J. Ethn. Subst. Abuse. 2007;6:143–62.

10. Grov C. HIV Risk and Substance Use in Men Who Have Sex with Men Surveyed in Bathhouses, Bars/Clubs, and on Craigslist.org: Venue of Recruitment Matters. AIDS Behav. 2011;16:807–17.

11. McKirnan D, Houston E, Tolou-Shams M. Is the Web the culprit? Cognitive escape and Internet sexual risk among gay and bisexual men. AIDS Behav. 2007;11:151–60.

12. Abara W, Annang L, Spencer SM, Fairchild AJ, Billings D. Understanding internet sex-seeking behaviour and sexual risk among young men who have sex with men: evidences from a cross-sectional study. Sex. Transm. Infect. 2014;90:596–601.

13. Broaddus MR, DiFranceisco WJ, Kelly JA, St Lawrence JS, Amirkhanian YA, Dickson-Gomez JD. Social Media Use and High-Risk Sexual Behavior Among Black Men Who Have Sex with Men: A Three-City Study. AIDS Behav. 2015;19 Suppl 2:90–7.

14. Hernandez-Romieu AC, Sullivan PS, Sanchez TH, Kelley CF, Peterson JL, del Rio C, et al. The Comparability of Men Who Have Sex With Men Recruited From Venue-Time-Space Sampling and Facebook: A Cohort Study. JMIR Res. Protoc. [Internet]. 2014 [cited 2016 Apr 11];3. Available from: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4129125/

15. Phillips G, Magnus M, Kuo I, Rawls A, Peterson J, Jia Y, 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;18:1630–7.

16. White JM, Mimiaga MJ, Reisner SL, Mayer KH. HIV sexual risk behavior among black men who meet other men on the internet for sex. J. Urban Health Bull. N. Y. Acad. Med. 2013;90:464–81.

17. Berry M, Raymond HF, Kellogg T, McFarland W. The Internet, HIV serosorting and transmission risk among men who have sex with men, San Francisco. AIDS Lond. Engl. 2008;22:787–9.

18. Holloway IW, Dunlap S, Del Pino HE, Hermanstyne K, Pulsipher C, Landovitz RJ. Online Social Networking, Sexual Risk and Protective Behaviors: Considerations for Clinicians and Researchers. Curr. Addict. Rep. 2014;1:220–8.

19. Noor SWB, Rampalli K, Rosser BRS. Factors influencing HIV serodisclosure among men who have sex with men in the US: an examination of online versus offline meeting environments and risk behaviors. AIDS Behav. 2014;18:1638–50.

20. 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;90:290–6.

21. Liau A, Millett G, Marks G. Meta-analytic examination of online sex-seeking and sexual risk behavior among men who have sex with men. Sex. Transm. Dis. 2006;33:576–84.

22. Yang Z, Zhang S, Dong Z, Jin M, Han J. Prevalence of unprotected anal intercourse in men who have sex with men recruited online versus offline: a meta-analysis. BMC Public Health. 2014;14:508.

23. Jenness SM, Neaigus A, Hagan H, Wendel T, Gelpi-Acosta C, Murrill CS. Reconsidering the Internet as an HIV/STD Risk for Men Who Have Sex with Men. AIDS Behav. 2010;14:1353–61.

24. Horvath KJ, Rosser BRS, Remafedi G. Sexual risk taking among young internet-using men who have sex with men. Am. J. Public Health. 2008;98:1059–67.

25. Hull P, Mao L, Prestage G, Zablotska I, Wit J de, Holt M. 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;sextrans-2015-052325.

26. Kerr ZY, Pollack LM, Woods WJ, Blair J, Binson D. Use of Multiple Sex Venues and Prevalence of HIV Risk Behavior: Identifying High-Risk Men Who Have Sex with Men. Arch. Sex. Behav. 2014;44:443–51.

27. Leung KK, Poon CM, Lee SS. A Comparative Analysis of Behaviors and Sexual Affiliation Networks among Men Who Have Sex With Men in Hong Kong. Arch. Sex. Behav. 2014;

28. Noor SWB, Rampalli K, Rosser BRS. Factors influencing HIV serodisclosure among men who have sex with men in the US: an examination of online versus offline meeting environments and risk behaviors. AIDS Behav. 2014;18:1638–50.

29. Wei C, Lim SH, Guadamuz TE, Koe S. Virtual vs. physical spaces: which facilitates greater HIV risk taking among men who have sex with men in East and South-East Asia? AIDS Behav. 2014;18:1428–35.

30. Noor SW, Adam BD, Brennan DJ, Moskowitz DA, Gardner S, Hart TA. Scenes as Micro-Cultures: Examining Heterogeneity of HIV Risk Behavior Among Gay, Bisexual, and Other Men Who Have Sex with Men in Toronto, Canada. Arch. Sex. Behav. 2017;

31. 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;8:510–20.

32. Tang W, Best J, Zhang Y, Liu F, Tso LS, Huang S, et al. Gay mobile apps and the evolving virtual risk environment: a cross-sectional online survey among men who have sex with men in China. Sex. Transm. Infect. 2016;sextrans-2015-052469.

33. Venkatesh V, Morris MG, Davis GB, Davis FD. User Acceptance of Information Technology: Toward a Unified View. MIS Q. 2003;27:425–78.

34. Fishbein M, Ajzen I. Predicting and Changing Behavior: The Reasoned Action Approach. Taylor & Francis; 2011.

35. Douglas M. Risk Acceptability According to the Social Sciences. Psychology Press; 2003.

36. Ordóñez CE, Marconi VC. Understanding HIV Risk Behavior from a Sociocultural Perspective. J. AIDS Clin. Res. [Internet]. 2012 [cited 2017 Apr 25];3. Available from: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3600897/

37. Lippman SA, Treves-Kagan S, Gilvydis JM, Naidoo E, Khumalo-Sakutukwa G, Darbes L, et al. Informing Comprehensive HIV Prevention: A Situational Analysis of the HIV Prevention and Care Context, North West Province South Africa. PLOS ONE. 2014;9:e102904.

38. Hofstede G. Dimensionalizing Cultures: The Hofstede Model in Context. Online Read. Psychol. Cult. [Internet]. 2011;2. Available from: http://scholarworks.gvsu.edu/orpc/vol2/iss1/8

39. Slovic P. Trust, emotion, sex, politics, and science: surveying the risk-assessment battlefield. Risk Anal. Off. Publ. Soc. Risk Anal. 1999;19:689–701.

40. Douglas M, Calvez M. The self as risk taker: a cultural theory of contagion in relation to AIDS. Sociol. Rev. 1990;38:445–64.

41. Lim KH, Leung K, Sia CL, Lee MK. Is eCommerce boundary-less? Effects of individualism–collectivism and uncertainty avoidance on Internet shopping. J. Int. Bus. Stud. 2004;35:545–59.

42. Lunjun H. The Impact of Cultural Values on Email Acceptance: Evidence from the PRC [Internet]. Lingnan University; 2003. Available from: http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.102.6161&rep=rep1&type=pdf

43. Abbasi MS, Tarhini A, Elyas T, Shah F. Impact of individualism and collectivism over the individual’s technology acceptance behaviour: A multi-group analysis between Pakistan and Turkey. J. Enterp. Inf. Manag. 2015;28:747–68.

44. Dake K. Orienting Dispositions in the Perception of Risk: An Analysis of Contemporary Worldviews and Cultural Biases. J. Cross-Cult. Psychol. 1991;22:61–82.

45. Markus ML. Finding a Happy Medium: Explaining the Negative Effects of Electronic Communication on Social Life at Work. ACM Trans Inf Syst. 1994;12:119–149.

46. Straub D, Keil M, Brenner W. Testing the Technology Acceptance Model Across Cultures: A Three Country Study. Inf Manage. 1997;33:1–11.

47. Lo V, So CYK, Zhang G. The influence of individualism and collectivism on Internet pornography exposure, sexual attitudes, and sexual behavior among college students. Chin. J. Commun. 2010;3:10–27.

48. Liu H, Feng T, Ha T, Liu H, Cai Y, Liu X, et al. Chinese Culture, Homosexuality Stigma, Social Support and Condom Use: A Path Analytic Model. Stigma Res. Action. 2011;1:27–35.

49. Nimmons D. In This Together. J. Psychol. Hum. Sex. 1998;10:75–87.

50. Nimmons D, Folkman S. Other-Sensitive Motivation for Safer Sex Among Gay Men: Expanding Paradigms for HIV Prevention. AIDS Behav. 1999;3:313–24.

51. O’Dell BL, Rosser BRS, Miner MH, Jacoby SM. HIV prevention altruism and sexual risk behavior in HIV-positive men who have sex with men. AIDS Behav. 2008;12:713–20.

52. Card KG, Lachoswky NJ, Cui M, Shurgold S, Armstrong H, Rich AJ, et al. An Event-level Analysis of the Social and Situational Factors associated with Condomless Anal Sex among Gay, Bisexual, and Other Men who have Sex with Men (GBM) with Online-met Partners. Manuscr. Submitt. Publ. AIDS Educ. Prev. 2016;

53. Meyer I. Minority stress and mental health in gay men. J. Health Soc. Behav. 1995;36:38–56.

54. Kelly BC, Carpiano RM, Easterbrook A, Parsons JT. Sex and the community: the implications of neighbourhoods and social networks for sexual risk behaviours among urban gay men. Sociol. Health Illn. 2012;34:1085–102.

55. Ghaziani A. There Goes the Gayborhood? Princeton, NJ: Princeton University Press; 2014.

56. Holt M. Gay men and ambivalence about “gay community”: from gay community attachment to personal communities. Cult. Health Sex. 2011;13:857–71.

57. Schneider B. The People Make the Place. Pers. Psychol. 1987;40:437–53.

58. Schneider B, Brent D, Goldstein HW. Attraction–selection–attrition: Toward a person–environment psychology of organizations. In: Walsh WB, Craik KH, Price RH, editors. Pers. Psychol. New Dir. Perspect. 2nd Ed. Mahwah, NJ, US: Lawrence Erlbaum Associates Publishers; 2000. p. 61–85.

59. Lachowsky NJ, Lal A, Forrest JI, Card KG, Cui Z, Sereda P, et al. Including Online-Recruited Seeds: A Respondent-Driven Sample of Men Who Have Sex With Men. J. Med. Internet Res. 2016;18:e51.

60. Forrest JI, Lachowsky NJ, Lal A, Cui Z, Sereda P, Raymond HF, et al. Factors Associated with Productive Recruiting in a Respondent-Driven Sample of Men who Have Sex with Men in Vancouver, Canada. J. Urban Health Bull. N. Y. Acad. Med. 2016;93:379–87.

61. Moore DM, Cui Z, Lachowsky NJ, Raymond HF, Roth E, Rich A, et al. HIV Community Viral Load and Factors Associated With Elevated Viremia Among a Community-Based Sample of Men Who Have Sex With Men in Vancouver, Canada: JAIDS J. Acquir. Immune Defic. Syndr. 2016;72:87–95.

62. Forrest JI, Stevenson B, Rich A, Michelow W, Pai J, Jollimore J, et al. Community mapping and respondent-driven sampling of gay and bisexual men’s communities in Vancouver, Canada. Cult. Health Sex. 2014;

63. Card KG, Lachowsky NJ, Cui Z, Carter A, Armstrong H, Shurgold S, et al. A Latent Class Analysis of Seroadaptation Among Gay and Bisexual Men. Arch. Sex. Behav. 2016;1–12.

64. Grossberg R, Gross R. Use of pharmacy refill data as a measure of antiretroviral adherence. Curr. HIV/AIDS Rep. 2007;4:187–91.

65. Courtenay-Quirk C, Wolitski RJ, Parsons JT, Gómez CA, Seropositive Urban Men’s Study Team. Is HIV/AIDS stigma dividing the gay community? Perceptions of HIV-positive men who have sex with men. AIDS Educ. Prev. Off. Publ. Int. Soc. AIDS Educ. 2006;18:56–67.

66. Tansey J, O’riordan T. Cultural theory and risk: A review. Health Risk Soc. 1999;1:71–90.

67. Collins LM, Lanza ST. Latent Class and Latent Transition Analysis: With Applications in the Social, Behavioral, and Health Sciences. John Wiley & Sons; 2013.

68. Frost D, Goode S, Hart D. Individualist and collectivist factors affecting online repurchase intentions. Internet Res. 2010;20:6–28.

69. Barrett DC, Pollack LM. Whose Gay Community? Social Class, Sexual Self-Expression, and Gay Community Involvement. Sociol. Q. 2005;46:437–56.

70. Miller B. “They’re the modern-day gay bar”: Exploring the uses and gratifications of social networks for men who have sex with men. Comput. Hum. Behav. 2015;51, Part A:476–82.

71. Ross MW, Rosser BRS, McCurdy S, Feldman J. The Advantages and Limitations of Seeking Sex Online: A Comparison of Reasons Given for Online and Offline Sexual Liaisons by Men Who Have Sex with Men. J. Sex Res. 2007;44:59–71.

72. Burnham KE, Cruess DG, Kalichman MO, Grebler T, Cherry C, Kalichman SC. Trauma symptoms, internalized stigma, social support, and sexual risk behavior among HIV-positive gay and bisexual MSM who have sought sex partners online. AIDS Care. 2016;28:347–53.

73. Smit PJ, Brady M, Carter M, Fernandes R, Lamore L, Meulbroek M, et al. HIV-related stigma within communities of gay men: A literature review. AIDS Care. 2012;24:405–12.

74. Lee W-N, Choi SM. The Role of Horizontal and Vertical Individualism and Collectivism in Online Consumers’ Responses Toward Persuasive Communication on the Web. J. Comput.-Mediat. Commun. 2005;11:317–36.

75. Peters E, Slovic P. The Role of Affect and Worldviews as Orienting Dispositions in the Perception and Acceptance of Nuclear Power1. J. Appl. Soc. Psychol. 1996;26:1427–53.

76. Rodger A, Cambiano V, Brunn T, Vernazza P, Collins S, van Lunzen J, et al. Sexual activity without condoms and risk of hiv transmission in serodifferent couples when the hiv-positive partner is using suppressive antiretroviral therapy. JAMA. 2016;316:171–81.

77. Otis J, McFadyen A, Haig T, Blais M, Cox J, Brenner B, et al. Beyond Condoms: Risk Reduction Strategies Among Gay, Bisexual, and Other Men Who Have Sex With Men Receiving Rapid HIV Testing in Montreal, Canada. AIDS Behav. 2016;

78. Goedel WC, Duncan DT. Geosocial-Networking App Usage Patterns of Gay, Bisexual, and Other Men Who Have Sex With Men: Survey Among Users of Grindr, A Mobile Dating App. JMIR Public Health Surveill. 2015;1:e4.

79. Douglas M. Natural Symbols: Explorations in Cosmology. Psychology Press; 1976.

 

PublicationsKiffer Card