Patterns of Online and Offline Connectedness Among Gay, Bisexual, and Other Men Who Have Sex with Men

  • Kiffer G. Card
  • Heather L. Armstrong
  • Nathan J. Lachowsky
  • Zishan Cui
  • Julia Zhu
  • Eric A. Roth
  • Robert S. Hogg
Original Paper

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.

Keywords

Gay and bisexual Community Risk Management HIV Latent class analysis 

Resumen

Este estudio examinó los patrones de conexión entre 774 hombres gay, bisexuales, y otros hombres que tienen sexo con otros hombres, sexualmente activos (HGB), que son mayores de 16 años, reclutados con un muestreo controlado por los mismos participantes en el área de Vancouver. El análisis de clases latentes (ACL) examinó los patrones de conexión incluyendo: la asistencia a eventos y lugares gay (por e.j., bares/clubs, grupos comunitarios, desfiles de orgullo gay), el tiempo que pasaron socializando con otros HGB, el uso de aplicaciones y páginas del Web para buscar sexo, y la consumición de medios gay. Una estadística de regresión multinomial encontró una relación entre los miembros pertenecientes al grupo. Se especificaron tres clases de ACL: Clase 1 “los socialites” (38.8%) estuvieron altamente conectados a travez de todos los indicadores. Clase 2 “los tradicionalistas” (25.7%) estuvieron moderadamente conectados, con muy poco uso de aplicaciones/páginas web. Clase 3 “los techies” (35.4%) tuvieron un alto uso de internet y relativamente poco interacción en persona. En el modelado multivariable, “los socialites” tenían un colectivismo más alto que “los tradicionalistas.” “Los tradicionalistas” tenían un colectivismo más alto que “los techies.” “Los socialites” también tenían ingresos anuales más altos que los hombres asignados a otras clases. “Los techies” era más probable que “los tradicionalistas” a divulgar el sexo anal sin condones con un socio serodiscordante o con un socio del estado VIH desconocído. “Los techies” era también más probable preguntar la situación del VIH de su socio antes de tener sexo y haber recibido una prueba para el VIH. “Los tradicionalistas” por otra parte eran menos probable practicar la gestión de riesgos del VIH y tenían cuentas más bajas sobre la estigma de HIV/AIDS que “los socialites.” Además, “los tradicionalistas” eran más viejos y eran más probable parejados. “Los tradicionalistas” también divulgaron a menos parejas sexuales masculinas que hombres en otros grupos. Estos conclusions recalcan cómo los modelos de conexión se relacionan con el gestión de riesgos de HGB.

Notes

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, 143342, PJT-153139). 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). HLA is supported by a Postdoctoral Fellowship Award from the Canadian Institutes of Health Research (Grant # MFE-152443).

Compliance with Ethical Standards

Conflict of interest

Kiffer G. Card, Heather Armstrong, Nathan J. Lachowsky, Zishan Cui, Julia Zhu, Robert S. Hogg, Eric A. Roth declare that they have no conflict of interest.

Ethical Approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional research committees at Simon Fraser University, The University of British Columbia, and the University of Victoria and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. This article does not contain any studies with animals performed by any of the authors.

Informed Consent

Informed consent was obtained from all individual participants included in the study.

References

  1. 1.
    CDC. Evolution of HIV/AIDS prevention programs–United States, 1981-2006. MMWR Morb. Mortal. Wkly. Rep. 2006;55:597–603.Google Scholar
  2. 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.CrossRefPubMedPubMedCentralGoogle Scholar
  3. 3.
    Centers for Disease Control and Prevention. Condom use and fear of AIDS both declining, according to surveys. CDC AIDS Wkly. 1988;2.Google Scholar
  4. 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.CrossRefPubMedGoogle Scholar
  5. 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.CrossRefPubMedGoogle Scholar
  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.CrossRefPubMedPubMedCentralGoogle Scholar
  7. 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.Google Scholar
  8. 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.CrossRefGoogle Scholar
  9. 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.CrossRefPubMedGoogle Scholar
  10. 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.CrossRefGoogle Scholar
  11. 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.CrossRefPubMedGoogle Scholar
  12. 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.CrossRefPubMedGoogle Scholar
  13. 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.CrossRefPubMedPubMedCentralGoogle Scholar
  14. 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;3. http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4129125/ Accesssed 11 Apr 2016.
  15. 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.CrossRefPubMedGoogle Scholar
  16. 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.Google Scholar
  17. 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.CrossRefGoogle Scholar
  18. 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.CrossRefPubMedPubMedCentralGoogle Scholar
  19. 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.CrossRefPubMedPubMedCentralGoogle Scholar
  20. 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.CrossRefPubMedGoogle Scholar
  21. 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.CrossRefPubMedGoogle Scholar
  22. 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.CrossRefPubMedPubMedCentralGoogle Scholar
  23. 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.CrossRefPubMedGoogle Scholar
  24. 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.CrossRefPubMedPubMedCentralGoogle Scholar
  25. 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.Google Scholar
  26. 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.CrossRefPubMedPubMedCentralGoogle Scholar
  27. 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;44(7):2067–76.CrossRefPubMedGoogle Scholar
  28. 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.CrossRefPubMedPubMedCentralGoogle Scholar
  29. 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.CrossRefPubMedPubMedCentralGoogle Scholar
  30. 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;.  https://doi.org/10.1007/s10508-017-0948-y.PubMedGoogle Scholar
  31. 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.CrossRefPubMedGoogle Scholar
  32. 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.Google Scholar
  33. 33.
    Venkatesh V, Morris MG, Davis GB, Davis FD. user acceptance of information technology: toward a unified view. MIS Q. 2003;27:425–78.Google Scholar
  34. 34.
    Fishbein M, Ajzen I. Predicting and Changing Behavior: The Reasoned Action Approach. Taylor & Francis; 2011.Google Scholar
  35. 35.
    Douglas M. Risk Acceptability According to the Social Sciences. Routledge: Psychology Press; 2003.Google Scholar
  36. 36.
    Ordóñez CE, Marconi VC. Understanding HIV Risk Behavior from a Sociocultural Perspective. J. AIDS Clin. Res. [Internet]. 2012;3. http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3600897/. Accessed 25 Apr 2017.
  37. 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.CrossRefPubMedPubMedCentralGoogle Scholar
  38. 38.
    Hofstede G. Dimensionalizing Cultures: The Hofstede Model in Context. Online Read. Psychol. Cult. [Internet]. 2011. http://scholarworks.gvsu.edu/orpc/vol2/iss1/8.
  39. 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.PubMedGoogle Scholar
  40. 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.CrossRefGoogle Scholar
  41. 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.CrossRefGoogle Scholar
  42. 42.
    Lunjun H. The Impact of Cultural Values on Email Acceptance: Evidence from the PRC [Internet]. Lingnan University; 2003. http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.102.6161&rep=rep1&type=pdf.
  43. 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.CrossRefGoogle Scholar
  44. 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.CrossRefGoogle Scholar
  45. 45.
    Markus ML. Finding a happy medium: explaining the negative effects of electronic communication on social life at work. ACM Trans Inf Syst. 1994;12:119–49.CrossRefGoogle Scholar
  46. 46.
    Straub D, Keil M, Brenner W. Testing the Technology acceptance model across cultures: a three country study. Inf Manage. 1997;33:1–11.CrossRefGoogle Scholar
  47. 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.CrossRefGoogle Scholar
  48. 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.CrossRefPubMedPubMedCentralGoogle Scholar
  49. 49.
    In Nimmons D, Together This. J Psychol Hum Sex. 1998;10:75–87.Google Scholar
  50. 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.CrossRefGoogle Scholar
  51. 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.CrossRefPubMedGoogle Scholar
  52. 52.
    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;.  https://doi.org/10.1521/aeap.2017.29.2.154.Google Scholar
  53. 53.
    Meyer I. Minority stress and mental health in gay men. J Health Soc Behav. 1995;36:38–56.CrossRefPubMedGoogle Scholar
  54. 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.CrossRefPubMedGoogle Scholar
  55. 55.
    Ghaziani A. There goes the gayborhood? princeton. NJ: Princeton University Press; 2014.Google Scholar
  56. 56.
    Holt M. Gay men and ambivalence about “gay community”: from gay community attachment to personal communities. Cult Health Sex. 2011;13:857–71.CrossRefPubMedGoogle Scholar
  57. 57.
    Schneider B. The people make the place. Pers Psychol. 1987;40:437–53.CrossRefGoogle Scholar
  58. 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.Google Scholar
  59. 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.CrossRefPubMedPubMedCentralGoogle Scholar
  60. 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.Google Scholar
  61. 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.CrossRefPubMedPubMedCentralGoogle Scholar
  62. 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;16(3):288–301.CrossRefGoogle Scholar
  63. 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.Google Scholar
  64. 64.
    Grossberg R, Gross R. Use of pharmacy refill data as a measure of antiretroviral adherence. Curr. HIV/AIDS Rep. 2007;4:187–91.CrossRefPubMedGoogle Scholar
  65. 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.Google Scholar
  66. 66.
    Tansey J, O’riordan T. Cultural theory and risk: A review. Health Risk Soc. 1999;1:71–90.CrossRefGoogle Scholar
  67. 67.
    Collins LM, Lanza ST. Latent class and latent transition analysis: with applications in the social, behavioral, and health sciences. New York: Wiley; 2013.Google Scholar
  68. 68.
    Frost D, Goode S, Hart D. Individualist and collectivist factors affecting online repurchase intentions. Internet Res. 2010;20:6–28.CrossRefGoogle Scholar
  69. 69.
    Barrett DC, Pollack LM. Whose gay community? Social class, sexual self-expression, and gay community involvement. Sociol Q. 2005;46:437–56.CrossRefGoogle Scholar
  70. 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.Google Scholar
  71. 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.CrossRefPubMedGoogle Scholar
  72. 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.CrossRefPubMedGoogle Scholar
  73. 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.PubMedGoogle Scholar
  74. 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.CrossRefGoogle Scholar
  75. 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.CrossRefGoogle Scholar
  76. 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.CrossRefPubMedGoogle Scholar
  77. 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;20:2812–26.CrossRefPubMedPubMedCentralGoogle Scholar
  78. 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.CrossRefPubMedPubMedCentralGoogle Scholar
  79. 79.
    Douglas M. Natural symbols: explorations in cosmology. Routledge: Psychology Press; 1976.Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2017

Authors and Affiliations

  • Kiffer G. Card
    • 1
    • 2
    • 6
  • Heather L. Armstrong
    • 1
    • 3
  • Nathan J. Lachowsky
    • 1
    • 4
  • Zishan Cui
    • 1
  • Julia Zhu
    • 1
  • Eric A. Roth
    • 1
    • 5
  • Robert S. Hogg
    • 1
    • 2
  1. 1.BC Centre for Excellence in HIV/AIDSVancouverCanada
  2. 2.Faculty of Health ScienceSimon Fraser UniversityVancouverCanada
  3. 3.Faculty of MedicineUniversity of British ColumbiaVancouverCanada
  4. 4.School of Public Health and Social PolicyUniversity of VictoriaVictoriaCanada
  5. 5.Department of AnthropologyUniversity of VictoriaVictoriaCanada
  6. 6.C/O Faculty of Health Sciences8888 University DriveBurnabyCanada

Personalised recommendations