Medicine, Faculty of
Medicine, Department of
DSpace
UBCV
Duailibe, Felipe
Master of Science - MSc
University of British Columbia
Introduction: We aimed to (1) estimate the size of the gay, bisexual, and other men who have sex with men population (GBM) in Canada, (2) identify syndemic factors associated with HIV sexual risk behaviour among HIV-negative GBM by creating a network of associations, and (3) apply syndemic theory to examine how factors influence HIV sexual risk behaviour in models of co-occurring epidemics. Methods: In study 1 we used two cycles of Canadian Community Health Survey data (2015-2016 and 2019) to estimate the size of the GBM population in Canada’s four most populous provinces and compare the sociodemographic composition of GBM and non-GBM. In study 2 we used baseline data (2017-2019) from sexually active self-reported HIV-negative GBM in Engage study sites of Montreal, Toronto and Vancouver to estimate HIV sexual risk behaviour. We explored pairwise associations between potential syndemic factors, sociodemographic characteristics and HIV sexual risk behaviour using Somers’ D statistics. We reduced the number of syndemic factors using a variable reduction method. We built logistic regression models for the mediation and product terms analyses. Results: In study 1 estimates of the GBM population were 4.0% in 2015-2016 in Alberta, Ontario, British Columbia and Quebec and 4.9% in British Columbia and Ontario in 2019. In study 2, syndemic factors associated with HIV sexual risk behaviour included childhood sexual abuse (CSA), intimate partner violence (IPV), transactional sex, polysubstance use, and sexual compulsivity. We estimated a statistically significant direct effect for CSA (c' = 0.09, 95% CI 0.01 – 0.17) and total effect of CSA (c = 0.14, 95% CI 0.06 – 0.21) on HIV sexual risk behaviour. The estimated increase in adjusted odds of HIV sexual risk behaviour was 14.0 (95% CI 5.6 – 35.2) for CSA and transactional sex combined, compared to the absence of these factors. Conclusions: In study 1 we observed sexual orientation discordance and an increase in the GBM population comparing CCHS cycles. In study 2 we found a significant multiplicative interaction between CSA and transactional sex associated with HIV risk behaviour. IPV, transactional sex, sexual compulsivity and polysubstance use mediated the effect of CSA on HIV sexual risk behaviour.
ESTIMATING THE SIZE OF THE GAY, BISEXUAL AND OTHER MEN WHO HAVE SEX WITH MEN POPULATION AND IDENTIFYING SYNDEMIC FACTORS AND THEIR EFFECT ON HIV SEXUAL RISK BEHAVIOUR by Felipe Duailibe B.Sc., Universidade Federal do Piaui, 2011 A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE in THE FACULTY OF GRADUATE AND POSTDOCTORAL STUDIES (Experimental Medicine) THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver) August 2024 © Felipe Duailibe, 2024 ii The following individuals certify that they have read, and recommend to the Faculty of Graduate and Postdoctoral Studies for acceptance, the thesis entitled: Estimating the size of the gay, bisexual and other men who have sex with men population and identifying syndemic factors and their effect on HIV sexual risk behaviour submitted by Felipe Duailibe in partial fulfillment of the requirements for the degree of Master of Science in Experimental Medicine Examining Committee: Dr. Viviane Dias Lima, Associate Professor, Department of Medicine, UBC Supervisor Dr. David M. Moore, Professor, Department of Medicine, UBC Supervisory Committee Member Dr. Mark Hull, Clinical Assistant Professor, Department of Medicine, UBC Supervisory Committee Member iii Abstract Introduction: We aimed to (1) estimate the size of the gay, bisexual, and other men who have sex with men population (GBM) in Canada, (2) identify syndemic factors associated with HIV sexual risk behaviour among HIV-negative GBM by creating a network of associations, and (3) apply syndemic theory to examine how factors influence HIV sexual risk behaviour in models of co-occurring epidemics. Methods: In study 1 we used two cycles of Canadian Community Health Survey data (2015-2016 and 2019) to estimate the size of the GBM population in Canada’s four most populous provinces and compare the sociodemographic composition of GBM and non-GBM. In study 2 we used baseline data (2017-2019) from sexually active self-reported HIV-negative GBM in Engage study sites of Montreal, Toronto and Vancouver to estimate HIV sexual risk behaviour. We explored pairwise associations between potential syndemic factors, sociodemographic characteristics and HIV sexual risk behaviour using Somers’ D statistics. We reduced the number of syndemic factors using a variable reduction method. We built logistic regression models for the mediation and product terms analyses. Results: In study 1 estimates of the GBM population were 4.0% in 2015-2016 in Alberta, Ontario, British Columbia and Quebec and 4.9% in British Columbia and Ontario in 2019. In study 2, syndemic factors associated with HIV sexual risk behaviour included childhood sexual abuse (CSA), intimate partner violence (IPV), transactional sex, polysubstance use, and sexual compulsivity. We estimated a statistically significant direct effect for CSA (c' = 0.09, 95% CI 0.01 iv – 0.17) and total effect of CSA (c = 0.14, 95% CI 0.06 – 0.21) on HIV sexual risk behaviour. The estimated increase in adjusted odds of HIV sexual risk behaviour was 14.0 (95% CI 5.6 – 35.2) for CSA and transactional sex combined, compared to the absence of these factors. Conclusions: In study 1 we observed sexual orientation discordance and an increase in the GBM population comparing CCHS cycles. In study 2 we found a significant multiplicative interaction between CSA and transactional sex associated with HIV risk behaviour. IPV, transactional sex, sexual compulsivity and polysubstance use mediated the effect of CSA on HIV sexual risk behaviour. v Lay Summary Gay, bisexual and other men who have sex with men (GBM) are disproportionately affected by HIV and STIs due to underlying risk factors, but how these factors interact to influence risk is not completely understood. To discover this, we estimated the GBM population in Canada's four most populous provinces using data from a national survey. Next, we identified the factors that influence the most HIV sexual risk behaviour in a sample of GBM from the three most populous cities in Canada. Finally, we analyzed how these factors interact, increasing the risk of HIV. In study 1 we found that 4% of the male population in 2015-2016 was GBM in Alberta, British Columbia, Ontario and Quebec, and 4.9% in 2019 in British Columbia and Quebec. In study 2 we found that childhood sexual abuse, polysubstance use, sexual compulsivity, transactional sex and violence within a relationship interact, increasing HIV sexual risk behaviour. vi Preface This study was conducted by Felipe Duailibe under the supervision of Dr. Viviane Dias Lima, with the input of members of the thesis committee (Dr. David Moore and Dr. Mark Hull). All research steps were completed with input from and under the guidance of my research supervisory committee. Data were obtained from the Canadian Community Health Survey conducted by Statistics Canada from two survey cycles (2015-1016 and 2019) and from the Engage study. Data analysis for Chapter 2 was conducted by Felipe Duailibe, with the supervision of Dr. Viviane Dias Lima, at the Research Data Centre located at Simon Fraser University. Data analysis for Chapter 3 was conducted by Tian Shen and Wendy Zhang, statisticians at the British Columbia Centre for Excellence in HIV/AIDS, For study 1, a Microdata Research Contract between the researchers and Statistics Canada was signed, and security clearance was confirmed, granting access to the Research Data Centre to conduct these analyses. Ethics approval was acquired from the Providence Health Care Research Ethics Board (H18-00949) for both studies. For study 2 ethics approval was acquired from the institutions of each principal investigator of the Engage Study. vii Table of Contents Abstract ......................................................................................................................................... iii Lay Summary .................................................................................................................................v Preface ........................................................................................................................................... vi Table of Contents ........................................................................................................................ vii List of Tables ..................................................................................................................................x List of Figures ............................................................................................................................... xi List of Abbreviations .................................................................................................................. xii Acknowledgements .................................................................................................................... xiii Chapter 1: Introduction ................................................................................................................1 1.1 Problem Statement .......................................................................................................... 1 1.2 Factors associated with HIV sexual risk behaviour ........................................................ 2 1.2.1 Sociodemographic characteristics ........................................................................... 2 1.2.2 Mental health conditions ......................................................................................... 4 1.2.3 Psychosocial factors ................................................................................................ 6 1.3 Syndemic theory, its development and application ........................................................ 8 1.4 Research Objectives and Hypotheses ........................................................................... 12 1.5 Overview of Thesis ....................................................................................................... 13 1.6 Ethics............................................................................................................................. 13 Chapter 2: Changes in the Size and Composition of the Gay, Bisexual and Other Men Who Have Sex With Men Population in Canada’s Largest Provinces: A Descriptive Study Using Data From a Population-Based Survey......................................................................................15 viii 2.1 Introduction ................................................................................................................... 15 2.2 Methods......................................................................................................................... 16 2.2.1 Data source ............................................................................................................ 16 2.2.2 Study sample and design ....................................................................................... 17 2.2.3 Statistical Analysis ................................................................................................ 20 2.3 Results ........................................................................................................................... 20 2.4 Discussion ..................................................................................................................... 27 2.5 Limitations .................................................................................................................... 31 2.6 Conclusion .................................................................................................................... 32 Chapter 3: Identifying Syndemic Factors and Their Effect on HIV Sexual Risk Behaviour Among Gay, Bisexual and Other Men Who Have Sex With Men in Vancouver, Montreal and Toronto ..................................................................................................................................34 3.1 Introduction ................................................................................................................... 34 3.2 Methods......................................................................................................................... 35 3.2.1 Study design and participant selection .................................................................. 35 3.2.2 Measures ............................................................................................................... 35 3.2.3 Statistical Analysis ................................................................................................ 39 3.3 Results ........................................................................................................................... 41 3.4 Discussion ..................................................................................................................... 53 3.5 Conclusion .................................................................................................................... 57 Chapter 4: Conclusion .................................................................................................................58 4.2 Overview ....................................................................................................................... 58 4.3 Summary of findings..................................................................................................... 58 ix 4.3.1 Changes in the Size and Composition of the Gay, Bisexual and Other Men Who Have Sex With Men Population in Canada’s Largest Provinces: A Descriptive Study Using Data From a Population-Based Survey ................................................................................. 58 4.3.2 Identifying Syndemic Factors and Their Effect on HIV Sexual Risk Behaviour Among Gay, Bisexual and Other Men Who Have Sex With Men in Vancouver, Montreal and Toronto ........................................................................................................................... 60 4.3.3 Strengths and Limitations ..................................................................................... 60 4.3.4 Public Health Implications .................................................................................... 62 4.4 Conclusion .................................................................................................................... 63 Bibliography .................................................................................................................................64 Appendices ....................................................................................................................................85 Appendix A Sensitivity analysis of sociodemographic characteristics of participants included and excluded from the study. .................................................................................................... 85 Appendix B Somers’ D syndemic factors pairwise association matrix. ................................... 89 Appendix C Variable reduction procedure (VARCLUS) applied to syndemic factors and sociodemographic characteristics associated with HIV sexual risk behaviour ........................ 91 x List of Tables Table 1 Distribution of the Gay, Bisexual and other Men who have Sex with Men population, aged 18-64 years, in Canada based on the 2015–2016 and 2019 cycles of the Canadian Community Health Survey by differing sexual orientation definition ......................................... 20 Table 2 Estimation of the size of the Gay, Bisexual and other Men who have sex with Men population, aged 18–64 years, in Alberta, British Columbia, Ontario and Quebec. .................... 22 Table 3 Demographic characteristics of the male population, aged 18–64 years, in Alberta, British Columbia, Ontario and Quebec based on the 2015–2016 Canadian Community Health Survey. .......................................................................................................................................... 23 Table 4 Demographic characteristics of the male population, aged 18–64 years, in British Columbia and Ontario based on the 2019 Canadian Community Health Survey. ........................ 25 Table 5 Distribution of gay, bisexual, and other men who have sex with men sociodemographic characteristics and syndemic indicators, stratified by HIV sexual risk behaviour status (n=1571)........................................................................................................................................................ 42 Table 6 Logistic regression showing the synergistic effect of two syndemic factors on HIV sexual risk behaviour among gay, bisexual and other men who have sex with men. ................... 53 xi List of Figures Figure 1 Framework of the proposed analysis. ............................................................................. 48 Figure 2 Significant associations between syndemic factors, sociodemographic characteristics and HIV sexual risk behaviour (as indicated by a significant Somers’ D with p-values <0.05) (n=1458). The thickness of the line is proportional to the absolute value of Somers’ D. ............ 49 Figure 3 Significant associations between syndemic factors, sociodemographic characteristics and HIV sexual risk behaviour (as indicated by a significant Somers’ D with p-values <0.05) (n=1458). The thickness of the line is proportional to the absolute value of Somers’ D. ............ 50 Figure 4 Significant associations between syndemic factors, sociodemographic characteristics and HIV sexual risk behaviour (as indicated by a significant Somers’ D with p-values <0.05) (n=1458). The thickness of the line is proportional to the absolute value of Somers’ D. ............ 51 Figure 5 Serially causal epidemics model using mediation analysis [Standardised estimates (95% CI)]. * p-value <0.05. .................................................................................................................... 52 xii List of Abbreviations ADD = attention deficit disorder ADHD = attention deficit and hyperactivity disorder AOR = adjusted odds ratio ART = antiretroviral therapy AUDIT-C = alcohol use disorder identification test CASI = computer-assisted self-interviewing CCHS = Canadian Community Health Survey CI = confidence interval CSA = childhood sexual abuse FSI = financial strain index GBM = gay, bisexual and other men who have sex with men GHB = gamma hydroxybutyrate HADS = hospital anxiety and depression scale IPV = intimate partner violence IPVP = intimate partner violence perpetration IPVV = intimate partner violence victimization LGBTQ = lesbian, gay, bisexual, transsexual and queer LSD = lysergic acid diethylamide NIT = nitrous oxide PrEP = pre-exposure prophylaxis PTSD = post-traumatic stress disorder PWID = people who inject drugs RDS = respondent-driven sampling STBBI = sexually transmitted and blood-borne infections STI = sexually transmitted infections xiii Acknowledgements I would like to thank Dr. Viviane Dias Lima for her support and mentorship since day one. When moving to another country, the last thing I expected to happen was a pandemic and to be locked down for two years. I will be forever grateful for your fantastic mentorship during my studies and your patience with all my struggles in adapting to life in a different country during a pandemic. I want to thank my thesis committee members, Dr. Moore and Dr. Hull, for their guidance throughout the program. Thank you to all my colleagues from the EMBARC research group. Sara, thank you for being a friend and understanding the struggles of studying during the pandemic. Nance, thank you for your unique insights and words of wisdom. Tian and Wendy, thank you for your help in the analysis. I could not have asked for a better pair of statisticians. You made this entire analysis process less stressful. Thank you to all of my family. I know it has been a tough time for all of us, but we will soon be reunited to celebrate. My father (in memoriam), thank you! I know you are happy to see my accomplishments. You were always proud of everything I had done and were not ashamed to share every achievement with everybody. My mother, thank you! You are my biggest inspiration. Thank you for all the sacrifices you made so that I could be here. This research was supported by funding to the Canadian Research Data Centre Network (CRDCN) from the Social Sciences and Humanities Research Council (SSHRC), the Canadian Institute for Health Research (CIHR), the Canadian Foundation for Innovation (CFI), and Statistics Canada. Although the research and analysis are based on data from Statistics Canada, the opinions expressed do not represent the views of Statistics Canada. 1 Chapter 1: Introduction 1.1 Problem Statement HIV/AIDS remains a significant global public health challenge with substantial variation across different regions and population groups.1 According to the Joint United Nations Programme on HIV/AIDS (UNAIDS), an estimated 39 million people were living with HIV globally in 2022, with approximately 1.3 million new infections reported in the same year.2 Sub-Saharan Africa remains the most heavily affected region, accounting for approximately two-thirds of all people living with HIV worldwide. Other regions, including Asia, Eastern Europe, and Latin America, also reported significant HIV burdens with varying epidemic profiles.1 The global response to HIV/AIDS has made significant strides over the past few decades, with increased access to antiretroviral therapy (ART) leading to improved health outcomes and reduced HIV-related mortality.3 However, challenges persist, including ongoing transmission among key populations such as gay, bisexual and other men who have sex with men (GBM), people who inject drugs (PWID), sex workers, and transgender individuals.3 Canada has experienced lower HIV incidence compared to many other countries.4,5 However, in 2022, 1,833 people were newly diagnosed with HIV in Canada, which corresponds to 4.7 per 100,000, a 24.9% increase over 2021.5 The epidemiological landscape of HIV/AIDS in 2022 Canada exhibits significant diversity across provinces and territories. Saskatchewan had the highest rate of new HIV diagnosis (20.3 per 100,000 population), and the Territories collectively had the lowest (1.5 per 100,000 population).5 Nevertheless, the majority of HIV cases are concentrated in urban centers, with particularly high rates observed in Vancouver, Toronto, and Montreal, where many GBM live.6,7 2 Despite advances in HIV prevention strategies, GBM remain at elevated risk of becoming infected with HIV.8 The increased risk in this population is directly associated with social, structural, biological, epidemiological and behavioural factors. These factors include, but are not restricted to, income inequality, racial/ethnical residential segregation, previous STI infection, higher prevalence of HIV among the GBM population, condomless anal sex, multiple sexual partners, substance use during sex, and sexual intercourse with serodiscordant partners.9-14 These multifaceted and interrelated factors can co-exist, creating complex networks that intensify HIV risk among GBM.10,15 Therefore, identifying the population more at risk and the factors that are associated with the increased likelihood of engaging in HIV sexual risk behaviour is essential to implement policies addressing these issues and consequently decrease the burden of HIV among GBM. 1.2 Factors associated with HIV sexual risk behaviour Multiple and interconnected factors, including sociodemographic, mental health and psychosocial factors, influence HIV sexual risk behaviour. For instance, substance use, alcohol use, anxiety, depression, childhood sexual abuse, intimate partner violence, and post-traumatic stress disorder (PTSD) are prominent factors associated with increased HIV sexual risk behaviour. 16-24 1.2.1 Sociodemographic characteristics The association between sociodemographic characteristics and HIV sexual risk behaviour among GBM is a complex and multifaceted issue that requires careful examination. Many sociodemographic characteristics can impact ones vulnerability to health-compromising 3 conditions.25. Sociodemographic characteristics, such as age, income, education, and immigration status, play significant roles in shaping patterns of HIV sexual risk behaviour. Age is one of the key sociodemographic factors associated with HIV risk among GBM.26 Some researchers believe that there is a “young age risk effect” for HIV among GBM.27,28 There is no consensus on the definition of young GBM. However, most studies use cutoff points of less than 25 or less than 30 years old to define young GBM.27,29 The HIV Incidence Risk Index for men who have sex with men (HIRI-MSM), for example, attributes the highest score for those aged between 18-28 years.26 Younger GBM may be more likely to engage in higher risk sexual behaviour, such as receptive condomless anal sex with partners of unknown HIV status, have HIV-discordant condomless receptive intercourse, and have a history of sexually transmitted infections and gonorrhea, compared to older GBM.27,28 Financial strain and education level also play important roles in GBM’s vulnerability to HIV infection. Financial hardship, characterized by economic instability, poverty or lack of resources, has been associated with increased engagement in HIV sexual risk behaviour in GBM.30 A study analyzing the relationship between social discrimination and financial hardship on sexual risk behaviour among 2,235 GBM identified that the relationship between financial hardship and condomless anal sex with a serodiscordant or serostatus-unknown partner was mediated by lack of social support.31 Another study, based on a survey with a sample of 580 GBM, identified that individuals experiencing higher levels of financial strain were more likely to have a non-HIV sexually transmitted infection (STI) and to engage in risky sexual behaviour, such as condomless anal sex or transactional sex.30 Financial hardship may also indirectly influence HIV risk behaviour through its association with mental health conditions.32 Many studies show higher levels of education are associated with greater access to HIV prevention information, health literacy and 4 awareness of safer sexual practices.33 Individuals with higher education levels may have better knowledge about HIV transmission and prevention methods; such knowledge can contribute to reduced engagement in high-risk sexual behaviour.33,34 Studies have also suggested that immigration status may influence individuals' social and economic status and access to healthcare services, including HIV prevention and testing.35 Recent immigrants may experience difficulties navigating a new sexual environment, which can affect their ability to engage in safer sexual practices and to seek appropriate care in the event of HIV infection.35 Newly arrived immigrants may experience barriers, such as cultural differences, a lack of knowledge about available resources, racism, nativism, language barriers, and a lack of familiarity with North American conceptions of gay identity and community, which can hinder their access to HIV prevention information and services.35 A systematic review that aimed to analyze studies focusing on the relationship between HIV sexual risk behaviour and ethnic minority GBM of known nativity in North America and Europe identified high rates of HIV, condomless sex, and stimulant use in foreign-born Latino samples and high rates of alcohol and club drug use in foreign-born Asian Pacific Islander samples.36 1.2.2 Mental health conditions Sexual minority men face a heightened risk of both HIV infection and mental health conditions compared to heterosexual men.37-39 Depression, anxiety, PTSD symptoms and sexual compulsivity are all associated with a greater likelihood of condomless sex sex in GBM.35,40,41 Other less prevalent mental health conditions, like bipolar and borderline disorder, are also associated with HIV sexual risk behaviour.40-43 5 Depression is one of the most prevalent mental health conditions among GBM. A recent systematic review of 62 studies found that the pooled prevalence of current depression among GBM worldwide was 35% (95% CI 31% - 39%), compared to 11.5% (95% CI: 9% to 14.6%) for men in general.44,45 Individuals with depression may experience feelings of loneliness and hopelessness for the future, which can lead to engagement in risky sexual behaviour such as condomless anal sex.46 However, different levels of depression might increase or decrease GBM’s willingness to engage in HIV sexual risk behaviour in a curvilinear association.17,47,48 Individuals with moderate levels of depression are more likely to engage in sexual risk behaviour compared to those with low or high levels of depression.17,47,48 Anxiety disorders are also highly prevalent among GBM and are associated with increased HIV sexual risk behaviour.49,50 Individuals with anxiety disorder, especially sexual minorities, might have suffered from trauma, shame, stigma, social exclusion and minority stress.51,52 These factors may contribute to anxiety management coping mechanisms such as multiple sexual partners, condomless anal sex and substance use, all of which increase HIV exposure risk.53-58 The relationship between PTSD and HIV sexual risk behaviour among GBM is a critical area of study within HIV prevention research. GBM with PTSD symptoms may experience heightened vulnerability to engaging in HIV risk behaviour due to the psychological distress and maladaptive coping strategies associated with trauma.59 GBM with PTSD are more likely to engage in condomless anal sex (insertive or receptive).60 PTSD is also associated with substance use, which, in turn, is highly associated with increased engagement in sexual risk behaviour such as condomless anal sex and having multiple sexual partners.61 Sexual compulsivity, defined as increased levels of sexual thoughts and behaviours that are difficult to control, interfering with daily activities and causing interpersonal conflicts and distress, 6 is also associated with sexual risk behaviours among GBM, including condomless anal sex, sex under the influence of club drugs and multiple sexual partners.40,62,63 Sexual compulsivity is more prevalent among GBM than among the general population.64-66 Additionally, it interacts with other conditions that further increase HIV risk, such as depression, anxiety, childhood sexual abuse, intimate partner violence, and substance use.65,66 1.2.3 Psychosocial factors The relationship between psychosocial factors and HIV sexual risk behaviour is intricate and multifaceted. Childhood sexual abuse (CSA), for example, is a significant predictor of HIV sexual risk behaviour among GBM. Individuals who have experienced CSA may be more likely to engage in condomless sex, have multiple sexual partners, and to participate in high-risk sexual activities as a result of trauma, diminished self-worth, and difficulties in establishing healthy sexual boundaries.67,68 Tomori et al. (2018) observed that among a sample of 11,788 GBM, 22.4% reported CSA.69 They found that study participants who experienced CSA had a 21% higher rate of recent HIV-related behaviour, and 2.0 higher lifetime HIV-related behaviour compared to those who did not.69 Intimate partner violence , including physical, sexual, or psychological abuse within intimate relationships, is also associated with increased HIV risk among GBM.70 Perpetrators of intimate partner violence may exert control over sexual decision-making, limit condom negotiation, and engage in coercive sexual practices, placing their partners at risk for HIV transmission.70 A study that recruited GBM through a geosocial-networking smartphone application in New York concluded that intimate partner violence victimization was associated with a higher number of sexual partners for condomless receptive and insertive intercourse.71 7 Another study found that the perpetration of physical violence was directly associated with condomless anal sex.72 Therefore, both perpetrators and victims of intimate partner violence are at higher risk of engaging in HIV sexual risk behaviour. Transactional sex, defined as the exchange of sex for goods, drugs, or money, has been documented to be associated with HIV sexual risk practices among GBM.73 GBM involved in transactional sex may experience varied power dynamics due to their social or economic status potentially leading to instances of physical or sexual violence, challenges in negotiating condom use, substance misuse, and psychological distress.74 Furthermore, engaging in transactional sex may subject GBM to heightened stigma, limiting their access to HIV prevention resources and exacerbating internalized stigma, thereby contributing to psychosocial distress.75,76 A study conducted in Vancouver demonstrated that GBM that reported transactional sex were more likely to engage in substance use (i.e., crystal methamphetamine, poppers, GHB, and non-steroid injection drugs) and higher risk sexual behaviour (i.e., more sex partners, sex party attendance, and condomless anal sex with serodiscordant or unknown HIV status partners).77 However, it is important to differentiate transactional sex from sex work, such as working as an escort. People whose primary income comes from working in the sex trade thorough agencies, websites or online applications might differ from people who engage in sex exchange occasionally for money, drugs, shelter or other needs in their abilities to negotiate safe sexual practices, including condom use.78-82 Therefore, their HIV risk profiles might differ. Similarly, early sexual debut, defined as the age at which individuals first engage in sexual activity, is associated with increased HIV risk among GBM.83 Individuals who initiate sex at a younger age may have limited sexual health knowledge, negotiation skills, and access to prevention resources. Early sexual debut is linked to higher rates of condomless sex, multiple 8 sexual partners, and engagement in transactional sex, increasing the likelihood of HIV transmission.83 The age considered as early sexual debut varies across studies. Results from a study with 363 young GBM indicated that participants with sexual debut before the age of 16 were more likely to engage in transactional sex and drug use, to have emotional or psychological problems related to substance use, and to have a history of suicide attempts, compared to participants with later sexual debuts.84 Another study identified that GBM that experienced early anal sexual debut at the age of 14 or less were more likely to be infected with HIV than those who did not.85 Sexual minority men compared to heterosexual men experience higher body dissatisfaction, including negative perceptions of one's body size, shape, or appearance 86 Body dissatisfaction, in turn, is associated with HIV sexual risk behaviour among GBM.87 A meta-analysis revealed that as body dissatisfaction increases, condom use self-efficacy decreases.88 Body dissatisfaction may contribute to low self-esteem and depression, resulting in engagement in sexual risk behaviour as a means of seeking validation or acceptance from sexual partners.87 In addition, body dissatisfaction may lead to the use of substances, further exacerbating HIV risk among GBM.89 1.3 Syndemic theory, its development and application The anthropologist Merrill Singer introduced syndemic theory to the anthropology field in 1996 when he was exploring the relationship between AIDS, violence and substance use.90 Syndemic theory posits that health issues do not occur in isolation, but rather intersect and interact within specific social and environmental contexts, exacerbating individual and community health disparities.90 Singer emphasized the interconnectedness of health issues and their roots in social, economic, and political factors.90 Over the years, syndemic theory has expanded to encompass a 9 broad array of health issues, including mental health disorders, chronic diseases, and social determinants of health.10 Numerous studies support the association of syndemic factors and the prevalence of HIV among GBM. For example, a study published by Mustanski et al. (2007) discovered that among populations of young GBM, the number of psychosocial health problems increased the odds of having multiple anal sexual partners, condomless anal sex and HIV-positive status.16 Furthermore, Stall et al. (2003) demonstrated that the greater the number of psychosocial health problems among GBM, the higher the risk of engaging in HIV sexual risk behaviour and HIV infection.91 A study by Chuang et al. (2021) applied syndemic theory to the associations between five co-occurring psychosocial health conditions and HIV infection and condomless anal sex.92 They compared community members who had experienced psychosocial health conditions to those who had never experienced any in a community sample of 1,000 Taiwanese GBM. In this study, GBM with one or more psychosocial health conditions had greater than twofold higher odds of being HIV-positive, and those with three or more had twofold higher odds of having condomless anal sex.92 Many other studies have also applied syndemic theory, claiming the addition of syndemic factors increases the nuanced understanding of HIV sexual risk behaviour and HIV exposure. For example, Biello et al. (2014) studied the association between five psychosocial conditions (i.e., depression, alcohol dependence, any recent drug use, sexual violence and CSA) and condomless anal sex in a sample of 300 male sex workers. They found that every unit increase in the number of psychosocial conditions was associated with a 25%–30% increase in odds of condomless anal sex.74 In a longitudinal study, Mimiaga et al. (2015) also concluded that, among 4,295 men, the accumulation of syndemic factors (i.e., depression, CSA, heavy alcohol use, stimulant use and polydrug use) was associated with HIV-related sexual risk behaviours and seroconversion.93 10 Furthermore, a study conducted in India analyzing the effect of depression, frequent alcohol use and violence victimization on inconsistent condom use for anal sex among 300 GBM and 300 transgender women found that, as the number of co-occurring psychosocial health problems increased, the adjusted odds ratios (aOR) also increased for sexual risk.94 Syndemic theory has shown great explanatory power when applied to complex issues such as HIV transmission. The core of syndemic theory is the interaction of multiple factors of different types, whether physical, mental, behavioural, psychosocial, cultural, environmental, or structural. Over the years, researchers have developed approaches to guide their syndemic analysis. The sum score approach was popular in the first two decades of syndemic analysis. Sum score measures syndemics as a composite score or count.10 The resulting index of the number of syndemic conditions can be used to predict associations with adverse health outcomes, including HIV sexual risk behaviour. In many studies, a higher index of syndemic factors was found to be associated with harmful health outcomes, such as suicide attempts or ideation and HIV sexual behaviour.15,91,93,95-98 The sum score approach has the advantage of simplicity and is especially useful in early research when the relative importance of factors is not established. Despite its important role in demonstrating that HIV-risk behaviour is a function of the number of syndemic conditions experienced, the sum score approach limits the information that might inform population-specific interventions, such as whether distinct combinations of syndemic conditions are associated with sexual risk behaviour more than other combinations as the sum score approach gives each syndemic condition equal weight of influence on outcomes.15 For instance, when using the sum score approach to model associations between syndemic conditions and HIV-risk behaviour, a syndemic count of two might be composed in several different ways (e.g., polydrug use and suicidality versus polydrug use and alcohol misuse versus 11 alcohol misuse and suicidality and so forth), blurring many combinations that might confer distinct risk. More importantly, this approach gives little to no information about how the syndemic factors interact, not making full use of one of the central premises of the syndemic theory, that is, the synergy between syndemic factors.15,98 To gain a more detailed understanding of relationships between syndemic factors, Tsai et al. (2016) published a methodological note criticizing the literature on syndemics and proposing statistical methods to address synergy between syndemic factors.15 Since then, and applying the concepts recommended by Tsai and colleagues, many studies have been published addressing the interplay of syndemic factors and their effect on HIV risk and sexual behaviours. For instance, Chakrapani et al. (2019) published a study examining the effects of violence victimization, frequent alcohol use and drug use on inconsistent condom use during anal intercourse.99 This study applied different syndemic models to address the interactions between syndemic factors, including path analysis, product terms and mediation analysis. In addition, they used the sum score approach to compare with previous published studies. Tomori et al. (2018) also applied a later approach to syndemic interactions and compared it with the traditional sum score approach.69 They analyzed the synergistic associations of five syndemic conditions and their effect on condomless anal sex and active syphilis infection. They used relative excess risk due to interaction (RERI) for each two-way and three-way interaction.69 The appropriate approach choice and application of the syndemic theory to analyze the complex interconnected nature of syndemic factors is paramount. Employing the appropriate methodology ensures that researchers, practitioners, planners, and policymakers can accurately identify important health problems and their interaction and even establish priorities for intervention, given that different factors affect health outcomes differently. 12 1.4 Research Objectives and Hypotheses Previous research has examined the relationship between HIV sexual risk behaviour and syndemic factors. However, there is no standard method to identify the most influential syndemic factors among GBM, and many studies do not use the optimal approach when analyzing the relationship between syndemic factors and HIV sexual behaviour. Therefore, innovative methods to identify the most influential syndemics and the studies which use the most productive analysis for examining the relationship are needed. In this thesis, as the first step, I aimed to estimate the size of the GBM population in the four most populated provinces in Canada. In addition, I identified syndemic factors, and based on these factors, I applied the syndemic theory to study their effect on HIV sexual risk behaviour. The specific research objectives of this thesis were as follows: i. To estimate the size of the GBM population in Canada based on different definitions and data from consecutive CCHS cycles, and to compare sociodemographic characteristics of GBM vs non-GBM. Hypothesis: We will observe sexual orientation discordance according to diverse definitions of the GBM population, and sociodemographic characteristics will differ between GBM and non-GBM. ii. To identify syndemic factors associated with HIV sexual risk behaviour among HIV-negative GBM by creating a network of associations. Hypothesis: Syndemic factors will be identified and show high interconnectedness and association with HIV sexual risk behaviours. 13 iii. To apply the syndemic theory to examine the syndemic factors’ influence on HIV sexual risk behaviour, according to the models of co-occurring epidemics. Hypothesis: Co-occurring syndemic factors will increase HIV sexual risk behaviour. This research will provide important information on valuable population-specific estimates to public health practitioners, policymakers and researchers to identify the population at higher risk of different diseases, and will provide denominators to estimate GBM-specific disease rates. In addition, this research will provide critical information to better understand the elevated burden of HIV among GBM and suggest effective intervention strategies addressing the syndemic factors. 1.5 Overview of Thesis In Chapter 2, I estimated the size of the GBM population in Canada's four most populous provinces and compared the sociodemographic characteristics of GBM and non-GBM. In Chapter 3, I identified the most influential syndemic factors in a sample of GBM in Vancouver, Toronto and Montreal, and applied the syndemic theory to analyze their effect on HIV sexual risk behaviour. In Chapter 4, I presented the main findings, strengths and limitations, and the public health implications of the main findings on GBM’s health. 1.6 Ethics For study 1 (Chapter 2), a Microdata Research Contract between the researchers and Statistics Canada was signed, and security clearance was confirmed, granting access to the Research Data Centre to conduct the analyses. Ethics approval was acquired from the Providence Health Care Research Ethics Board (H18-00949) for both studies. Additionally, for study 2 (Chapter 3), ethics approval was acquired from all the institutions of the principal investigators of the Engage study: 14 McGill University Health Centre REB (15-623-MUHC); Ryerson University REB (2016-113); University of Toronto REB (00033527); St. Michael’s Hospital REB (17-043); University of British Columbia REB (H16-0122). 15 Chapter 2: Changes in the Size and Composition of the Gay, Bisexual and Other Men Who Have Sex With Men Population in Canada’s Largest Provinces: A Descriptive Study Using Data From a Population-Based Survey 2.1 Introduction Sexual minority men are disproportionally affected by HIV/AIDS and other STI worldwide.100 They also experience higher rates of mental health disorders, such as depression and anxiety.101-103 Designing, developing, and implementing appropriate prevention, care, and treatment interventions targeting this key population is challenging due to the lack of reliable and representative data on its population size.104 Given the necessity of determining the underlying population size of individuals who might be more at risk of STIs and other health problems, different methods for estimating the size of gay, bisexual and other men who have sex with men (GBM) populations have been used over the years. These include methodologies, such as surveys, capture-recapture and triangulation of multiple methods.104-108 Each approach has its strengths and weaknesses, and there is no gold standard method.109 In this context, the Canadian Community Health Survey (CCHS), a national survey administered by Statistics Canada, collects information related to health status, health care utilization, and determinants of health representative of 98% of Canada’s population (aged 12 years and over) that can help us estimate the size of the GBM population.110 From 2003 to 2014, CCHS only included a single question regarding self-reported sexual identity (i.e., Do you consider yourself to be...? Ans: Heterosexual/Homosexual - that is, lesbian or gay/Bisexual), which is one of the three dimensions of sexual orientation (i.e., sexual identity, behaviour, and attraction).111-113 16 However, from a public health perspective, creating estimates of this key population based on the other dimensions of sexual orientation is critical, given that there are discordances among the dimensions of sexual orientation, and the characteristics of each dimension can influence health outcomes.114,115 For instance, men can identify as heterosexual, yet feel attracted to, and have sexual encounters with other men.116 At the same time, men can identify as gay, feel attracted to men, and never have sexual encounters with other men.111 Previous studies that have used CCHS data to develop estimates of the gay and bisexual populations in Canada, have focused on sexual identity.112,113 Therefore, these estimates based on only sexual identity may not accurately capture the population at risk of STI, and a sexual behaviour-based estimate would offer a more precise estimate of men who have sex with men.111 In 2015, Statistics Canada expanded the CCHS to include specific questions related to sexual behaviour. Since 2015, it is, therefore, now possible to use sexual identity and sexual behaviour questions separately or in combination to estimate the size of the GBM population. In this study, we estimated the size of the GBM population based on different definitions and data from different CCHS cycles and compared the demographic composition of the GBM and non-GBM in Canada. 2.2 Methods 2.2.1 Data source Data were obtained from Statistics Canada’s CCHS. The CCHS is a national, cross-sectional survey that collects information on the participants' relevant sociodemographic and health-related characteristics.110 The survey is administered to a representative sample of the Canadian population 12 years of age and over, except for persons living on reserves and other Indigenous 17 settlements in the provinces, institutionalized populations, children aged 12-17 that are living in foster care, and persons living in the Quebec health regions of Région du Nunavik and Région des Terres-Cries-de-la-Baie-James.110 Data is collected using computer-assisted in-person and telephone interviews.110 The CCHS produces an annual microdata file, which is a file combining two years of data. To provide reliable representative estimates, the two-year targeted sample size is 130,000 respondents, corresponding to 65,000 each year.117 For all provinces, the data collected in a single year is representative of that setting, except for the territories where only half of the communities are visited annually.117 Therefore, the data is only representative of the territories every two years. The territories refer to the three northern regions of Canada: Yukon, Northwest Territories, and Nunavut.118 These territories differ from provinces in governance and political structure.118 A complete description of the complex CCHS design can be found elsewhere.110 To keep the definitions across the years more consistent and comparable, we used data from the CCHS 2015–2016 and 2019 cycles, given that the CCHS 2017-2018 cycle did not include the sexual behaviour content. In the 2015–2016 biannual cycle, 110,095 individuals were included in the survey, resulting in an overall combined response rate of 59.5%.117 The CCHS 2019 cycle included 65,970 respondents, with an overall response rate of 54.4%.119 2.2.2 Study sample and design The design of this study is cross-sectional. For this analysis, we selected men aged 18 to 64 years with valid responses to sexual identity and sexual behaviour content. We defined sexual orientation by using the specific questions in the survey that comprised sexual identity and behaviour. For the 2015–2016 cycle, we used combinations of the following survey items: • Is [Respondent name] male or female? Ans: Male/Female 18 • Do you consider yourself to be...? Ans: Heterosexual/Homosexual - that is, lesbian or gay/Bisexual; • During your lifetime, have you had sex with...? Ans: Males/Females/Both; • In the past 12 months, have you had sex with a male? Ans: Yes/No. For the 2019 cycle, we combined the following survey items: • Sex at birth. Ans: Male/Female; • What is your sexual orientation? Ans: Heterosexual/Gay or lesbian/ Bisexual or pansexual/Sexual orientation, not specified elsewhere; • In the past 12 months, have you had sex with a male? Ans: Yes/No. We created three definitions for our population based on the combination of these questions: Definition 1, a sexual identity definition of “Men who identify as gay or bisexual”; Definition 2: a sexual behaviour definition of “Men who had sex with men in the last 12 months”; and Definition 3, a combination of sexual identity and behaviour “Men who identify as gay or bisexual OR who had sex with men in the last 12 months”. Since the 2019 cycle changed the sexual identity question to include a pansexual category, Definition 3 for 2019 is “Men who identify as gay, bisexual or pansexual OR who had sex with men in the last 12 months”. We defined non-GBM as men who identified as heterosexual and who did not have sex with men in the last 12 months. Note that, in this study, we are using sex at birth to define men, given that in the 2015–2016 cycle, there was no mention of gender identity in the survey. Although, in the 2019 cycle, there was a gender identity question, for comparability, we used the sex at birth question in both cycles. Therefore, the population presented here represents cis men and transgender women. 19 We estimated the size of the GBM population based on the three definitions. We used data from all of Canada for the 2015–2016 cycle and conducted a separate analysis for Canada's four most populous provinces (i.e., Ontario, Quebec, British Columbia and Alberta). In the 2019 cycle, sexual behaviour was included in the survey as optional content, in which provinces and territories included specific questions to address their public health priorities.110 Therefore, for the 2019 cycle, we included the provinces that opted for the sexual behaviour content (i.e., Ontario, British Columbia, Manitoba, Newfoundland, Labrador and Prince Edward Island), which includes the questions regarding having sex with men in the lifetime and in the previous 12 months, and conducted a separate analysis for Ontario and British Columbia. The provinces that comprised this second analysis were selected based on sample size. The less populous provinces had fewer respondents, resulting in a small unweighted sample size and unreliable estimates. The demographic characteristics presented in the second part of the analysis were marital status, household income and population centre or rural area status. Missing household income data information was imputed, where missing values resulting from the respondent's refusal or lack of knowledge of household income were replaced using various techniques, including a nearest neighbour imputation approach based on a modelled household income.117 The variable “population centre or rural area status” was comprised of five categories: rural area (less than 1,000); small population centre (1,000 to 29,999); medium population centre (30,000 to 99,999); and large urban population centre (100,000 or greater). Given the small sample size of GBM living in rural areas in some provinces, we grouped the variable into two categories: men living in areas with less than 30,000 (i.e., rural areas and small population centres) and 30,000 people or more (i.e., medium and large urban population centres).120 20 2.2.3 Statistical Analysis We used a set of 500 bootstrap weights to estimate uncertainty accounting for household and person-level non-response and calculated the sampling weight of each respondent.117 We also incorporated sampling weights into the analysis so that the CCHS estimates represented our covered population at the national, provincial and regional levels.117 In addition, we calculated coefficients of variation and confidence intervals to determine the quality of the estimates regarding sampling variability. We used the chi-squared test to compare the distribution of sociodemographic characteristics of GBM versus non-GBM. All the descriptive analyses were performed using SAS 9.4 using survey procedures, and the significance level was set at 5%. 2.3 Results Table 1 shows the weighted estimated number of GBM identified in Canada based on different definitions derived from the survey responses, including sexual identity, sexual behaviour and sexual identity and behaviour combined (reported here as Definitions 1–3, respectively). Considering Definition 1, based on sexual identity only, the proportion of GBM increased from 3.1% (95% CI 2.7–3.4) in the 2015–2016 cycle to 4.2% (95% CI 3.5–4.9) in 2019. Table 1 Distribution of the Gay, Bisexual and other Men who have Sex with Men population, aged 18-64 years, in Canada based on the 2015–2016 and 2019 cycles of the Canadian Community Health Survey by differing sexual orientation definition Definition Weighted percentage 95% CI weighted percentage CCHS 2015–2016 1: Men who identify as gay or bisexual 3.1 2.7–3.4 21 2: Men who had sex with men in the last 12 months 2.4 2.0–2.7 3: Men who identify as gay or bisexual OR who had sex with men in the last 12 months 3.8 3.4–4.2 CCHS 2019 1: Men who identify as gay, bisexual or pansexual 4.2 3.5–4.9 2: Men who had sex with men in the last 12 months 2.9 2.3–3.4 3: Men who identify as gay, bisexual or pansexual OR who had sex with men in the last 12 months 4.8 4.0–5.5 Note: CCHS=Canadian Community Health Survey. CI=Confidence interval. CCHS cycle 2015-2016 includes respondents from all provinces and territories. CCHS 2019 includes Ontario, British Columbia, Manitoba, Newfoundland and Labrador and Prince Edward Island. In the 2015–2016 cycle, restricting the analysis to Ontario, Quebec, British Columbia and Alberta and using the combination of sexual identity and behaviour (Definition 3), 4.0% (95% CI 3.5–4.4) of the male respondents were estimated to be GBM (Table 2). Quebec was the province with the largest GBM population estimate (4.8%, 95% CI 4.0–5.7)], and Alberta had the smallest estimate (2.7%, 95% CI 1.9–3.4) (Table 2). In the 2015-2016 cycle, compared to non-GBM, GBM were more likely to be single/never married (54.9% vs. 26.8%, p-value < 0.001), have an annual household income of less than $30,000 (14.3% vs. 9.1%, p-value < 0.001), live in medium and large population centres (84.5% vs. 74.2%, p-value < 0.001), and have lower mean age (38.8 years vs. 42.0 years, p-value <0.001) (Table 3). 22 Table 2 Estimation of the size of the Gay, Bisexual and other Men who have sex with Men population, aged 18–64 years, in Alberta, British Columbia, Ontario and Quebec. Weighted percentage; 95% Confidence Interval of weighted percentage (Weighted frequency) CCHS 2015–2016 Alberta British Columbia Ontario Quebec Total Men who identify as gay or bisexual, or who had sex with men in the last 12 months No 97.3; 96.5–98.1 (1,209,075) 96.5; 95.6–97.3 (1,184,003) 95.9; 95.0–96.8 (3,400,994) 95.2; 94.3–96.0 (2,252,322) 96.0; 95.5–96.4 (8,046,392) Yes 2.7; 1.9–3.4 (33,281) 3.5; 2.7–4.4 (43,328) 4.1; 3.2–4.9 (144,260) 4.8; 4.0–5.7 (114,585) 4.0; 3.5–4.4 (335,455) CCHS 2019 Men who identify as gay, bisexual or pansexual, or who had sex with men in the last 12 months No * 95.1; 93.5–96.7 (1,259,199) 95.1; 94.1–96.0 (3,770,089) * 95.1; 94.2–95.9 (5,029,288) Yes 4.9; 3.3–6.4 (64,478) 4.9; 3.9–5.9 (195,861) 4.9; 4.10–5.7 (260,339) *Province did not opt for the sexual behaviour content in the 2019 cycle; therefore, estimates were not calculated. CCHS=Canadian Community Health Survey. 23 Table 3 Demographic characteristics of the male population, aged 18–64 years, in Alberta, British Columbia, Ontario and Quebec based on the 2015–2016 Canadian Community Health Survey. Demographic characteristics Weighted percentage; 95% Confidence Interval of weighted percentage (Weighted frequency) CCHS 2015–2016 Alberta British Columbia Ontario Quebec Total p–value Marital status < 0.001 Non-GBM Single, never married 23.6; 21.6–25.6 (284,905) 27.1; 25.2–28.9 (319,662) 26.8; 25.3–8.30 (910,965) 28.3; 26.7–29.7 (636,958) 26.8; 25.9–27.7 (2,152,491) Married/common–law 70.2; 67.9–72.3 (846,290) 66.1; 64.1–68.1 (780,945) 66.3; 64.5– 68.0 (2,249,944) 65.4; 63.7–67.1 (1,474,258) 66.6; 65.5–67.6 (5,351,437) Divorced, separated, widowed 6.2; 5.3–7.0 (74,555) 6.8; 5.7–7.7 (80,028) 6.9; 6.00–7.72 (232,891) 6.3; 5.4–7.0 (141,105) 6.6; 6.07–7.08 (528,580) GBM Single, never married 52.9; 38.6–67.3 (17,637) 52.0; 40.5–63.5 (22,534) 59.0; 48.9–69.0 (84,833) 51.4; 43.1– 9.6 (58,903) 54.9; 49.3–60.5 (183,907) Married/common–law 42.9; 28.4–57.4 (14,297) 42.7; 30.8–54.5 (18,508) 34.7; 24.7–44.7 (49,980) 42.5; 34.0–50.8 (48,639) 39.2; 33.6–44.8 (131,423) Divorced, separated, widowed E E 6.3; 3.2–9.3 (9,047)C 6.1; 2.9–9.4 (7,044)C 5.9; 3.9–7.8 (19,725) Household income Non-GBM <$30000 4.7; 3.7–5.6 (56,749) 9.3; 8.0–10.6 (110,312) 9.2; 8.0–10.36 (312,958) 11.3; 10.1–12.4 (254,678) 9.1; 8.45–9.8 (734,697) <0.001 ≥$30000 95.3; 94.4–96.2 (1,152,326) 90.7; 89.3–91.9 (1,073,691) 90.8; 89.6–91.9 (3,088,035) 88.7; 87.5–89.8 (1,997,643) 90.9; 90.1–91.5 (7,311,696) GBM <$30000 E 13.0; 6.05–19.8 (5,616)C 14.6; 8.6–20.5 (21,056)C 17.4; 11.7–23.0 (19,932)C 14.3; 10.9–17.6 (47,928) ≥$30000 96.0; 91.8– 100 (31,957) 87.0; 80.1–93.9 (37,712) 85.4; 79.5–91.3 (123,204) 82.6; 76.9–88.2 (94,653) 85.7; 82.3–89.1 (287,527) Population centre status Non-GBM Rural area and small population centre 29.2; 26.9–31.4 (352,876) 24.4; 22.3–26.5 (289,338) 22.4; 21.0–23.8 (762,866) 29.8; 27.6–31.8 (670,280) 25.8; 24.8–26.7 (2,075,360) <0.001 24 Demographic characteristics Weighted percentage; 95% Confidence Interval of weighted percentage (Weighted frequency) Medium and large population centres 70.8; 68.5–73.0 (856,199) 75.6; 73.4–77.6 (894,665) 77.6; 76.1–78.9 (2,638,128) 70.2; 68.1–72.3 (1,582,041) 74.2; 73.2–75.1 (5,971,033) GBM Rural area and small population centre 17.5; 7.1–27.8 (5,823)D 17.4; 8.9–25.7 (7,518)C 10.4; 6.8–13.9 (14,933)C 20.8; 14.9–26.5 (23,796) 15.5; 12.4–18.5 (52,070) Medium and large population centres 82.5; 72.1– 92.8 (27,459) 82.6; 74.2–91.0 (35,810) 89.6; 86.1–93.2 (129,327) 79.2; 73.4–85.0 (90,789) 84.5; 81.4–87.5 (283,385) Continuous demographic characteristic Mean age Non-GBM 40.8 (40.4– 41.1) 42.2 (41.8– 42.5) 42.0 (41.7– 42.3) 42.4 (42.1– 42.7) 42.0 (41.8– 42.1) <0.001 GBM 37.2 (33.6– 40.7) 39.6 (36.8– 42.5) 38.1 (35.1– 41.0) 39.9 (37.7– 42.1) 38.8 (37.2– 40.4) CCHS=Canadian Community Health Survey. GBM=Men who identify as gay, bisexual OR who had sex with men in the last 12 months. Non-GBM=Men who identified as heterosexual and did not have sex with men in the last 12 months. C=estimates of marginal acceptability due to high sampling variability (Coefficient of Variance 0.15–0.25). D=estimates of marginal acceptability due to high sampling variability (Coefficient of Variance 0.25–0.35). E=estimates of unacceptable quality due to high sampling variability (Coefficient of Variance > 0.35). In the 2019 cycle, considering Definition 3 for British Columbia and Ontario, 4.9% (95% CI 4.1–5.7) of the male respondents were estimated to be GBM (Table 2). Compared to non-GBM, GBM were more likely to be single/never married (59.1% vs. 26.0%, p-value < 0.001), have an annual household income of less than $30,000 (12.5% vs. 6.9%, p-value < 0.001), live in medium and large population centres (87.5% vs. 76.5%, p-value <0.001), and have lower mean age (37.2 vs. 42.3 years, p-value= 0.011) (Table 4). 25 Table 4 Demographic characteristics of the male population, aged 18–64 years, in British Columbia and Ontario based on the 2019 Canadian Community Health Survey. Demographic characteristics Weighted percentage; 95% Confidence Interval of weighted percentage (Weighted frequency) CCHS 2019 British Columbia Ontario Total p–value Marital status Non-GBM Single, never married 24.6; 21.6–27.5 (308,787) 26.5; 24.6–28.4 (999,005) 26.0; 24.4–27.6 (1,307,792) <0.001 Married/common–law 69.5; 66.2–72.6 (871,548) 67.9; 65.8–70.0 (2,559,607) 68.3; 66.5–70.0 (3,431,155) Divorced, separated, widowed 5.9; 4.6–7.1 (73,903) 5.6; 4.7–6.4 (209,669) 5.7; 4.9–6.3 (283,571) GBM Single, never married 54.0; 36.7–71.3 (34,845)C 60.7; 49.8–71.5 (118,578) 59.1; 50.2–67.9 (153,423) Married/common–law 42.9; 25.5–60.2 (27,666)C 35.6; 25.0–46.1 (69,524) 37.4; 28.7–46.1 (97,190) Divorced, separated, widowed E 3.7; 1.2–6.1 (7,140)D 3.5; 1.5–5.5 (9,108)D Household income Non-GBM <$30000 7.6; 5.9–9.2 (95,516) 6.7; 5.6–7.7 (252,714) 6.9; 6.0–7.8 (348,230) <0.001 ≥$30000 92.4; 90.7–94.1 (1,163,683) 93.3; 92.2–94.3 (3,517,375) 93.1; 92.1–93.9 (4,681,058) GBM <$30000 E 11.6; 5.2–17.9 (22,745)D 12.5; 7.0–18.0 (32,645)C ≥$30000 84.647; 73.3–95.9 (54,579) 88.4; 82.0–94.7 (173,115) 87.5; 81.9–92.9 (227,694) Population centre status Non-GBM Rural area and small population centre 21.9; 19.3–24.3 (275,365) 24.1; 22.5–25.6 (908,543) 23.5; 22.2–24.8 (1,183,908) <0.001 Medium and large population centres 78.1; 75.6–80.6 75.9; 74.3–77.5 76.5; 75.1–77.7 26 CCHS=Canadian Community Health Survey. GBM=Men who identify as gay, bisexual or pansexual OR who had sex with men in the last 12 months. Non-GBM=Men who identified as heterosexual and did not have sex with men in the last 12 months. C=estimates of marginal acceptability due to high sampling variability (Coefficient of Variance 0.15–0.25). D=estimates of marginal acceptability due to high sampling variability (Coefficient of Variance 0.25–0.35). E=estimates of unacceptable quality due to high sampling variability (Coefficient of Variance > 0.35)(983,834) (2,861,546) (3,845,380) GBM Rural area and small population centre 13.5; 4.7–22.3 (8,726)D 12.2; 6.2–18.1 (23,934)C 12.5; 7.8–17.2 (32,660)C Medium and large population centres 86.46; 77.7–95.2 (55,752) 87.8; 81.8–93.7 (171,927) 87.5; 82.7–92.1 (227,679) Continuous demographic characteristic Weighted mean (95% Confidence Interval of the weighted mean) Mean age Non-GBM 42.3 (41.8–42.8) 42.3 (41.9–42.6) 42.3 (42.0–42.6) 0.011 GBM 37.3 (33.6–41.0) 37.2 (33.8–40.5) 37.2 (34.4–39.9) 27 Comparing Definition 3 estimates, which combines sexual identity with sexual behaviour British Columbia increased from 3.5% (95% CI 2.7–4.4) in 2015–2016 to 4.9% (95% CI 3.3–6.4) in 2019. However, the confidence intervals overlap (Table 2). 2.4 Discussion Our estimates, based on the CCHS, found that relying on only one domain of sexual orientation might not be sufficient to capture all the GBM population in Canada. We saw that men who identify as gay or bisexual OR who had sex with men in the last 12 months corresponded to 3.8% of the male population in Canada in 2016-2017. On the other hand, using a purely behaviour-based definition (i.e., men who had sex with men in the last 12 months), we identified that 2.4% of the male Canadian population were GBM in 2015-2016. In 2019, men who identify as gay, bisexual or pansexual OR who had sex with men in the last 12 months comprised 4.8% of the male population, and men who had sex with men in the last 12 months corresponded to 2.9%. In Ontario, Quebec, British Columbia and Alberta, in 2015–2016, 4.0% of the male respondents were estimated to be men who identify as gay or bisexual OR who had sex with men in the last 12 months. In British Columbia and Ontario, 4.9% of the male respondents in 2019 were estimated to be men who identify as gay, bisexual or pansexual OR who had sex with men in the last 12 months. Overall, GBM in these two periods were predominately single/never married, had an annual household income of more than 30,000 dollars and lived in medium and large population centres. Studies have demonstrated sexual orientation discordance in the United States, Britain and Canada.111,115,121 In our study, we showed different estimates using sexual identity and behaviour separately and combined. These estimates should be used carefully according to their specific 28 needs and assumptions made for the targeted population, knowing whether sexual identity, behaviour or both combined would be more appropriate. For instance, Definition 1 (identity-based) is more appropriate when referring to people more likely to experience stigma and discrimination.111 Definition 2 (behaviour-based) should be used when referring to the population more at risk of HIV and other STIs.111 And finally, Definition 3 (identity and behaviour) should be used when the health outcome is more present in both groups, as seen in mental health disorders such as anxiety and depression.101-103 Our findings also complement previous studies estimating the size of the GBM population in Canada. In 2016, the British Columbia Center for Disease Control estimated the men who have sex with men population in British Columbia to be 2.6% of the male population using data from the 2013-2015 CCHS cycle and adjusting for under-reporting.122 A similar estimate was found in a study conducted in Vancouver (2.9%) using multiple datasets and methods such as estimates from the CCHS, Wisdom of the Crowds and the multiplier method.106 As previously mentioned, CCHS only included the sexual behaviour question we used to create our GBM population estimates in 2015. Therefore, the higher estimate of the GBM population in British Columbia observed in our study, might be due to the capture of more GBM using the combined identity-behaviour definition. A more recent study combining data from two surveys (Sex Now 2019 and CCHS 2017, 2018 and 2019)123, created GBM estimates using sexual identity and behaviour-based questions. In this study, they produced estimates for each data source, adjusted for nondisclosure of sexual identity, and then averaged the estimates. They found that 4.1%, 6.6% and 4.8% of the adult male population identified as gay and bisexual in Ontario, Toronto and Ottawa, respectively. In addition, when they used a behaviour-based definition, only including sexually active GBM, the proportion of GBM reduced to 2.8% in Ontario.124 Comparing our results to the findings of this study, our estimates for Ontario differ in 29 the combined identity-behaviour estimates. Instead of retaining only sexually active GBM, we created an estimate that included both, sexually active (i.e., men who had sex with men in the last 12 months) and men who identified and gay or bisexual (and pansexual in 2019), what might explain the difference in the estimates. Similarly, a national study using Sex Now 2019 and CCHS 2020 estimated the sexual behaviour-based GBM population to be 2.7%, and the combined identity and behaviour-based GBM population to be 4.1% of the male population. Similarly, a national study using Sex Now 2019 and CCHS 2020 estimated the sexual behaviour-based GBM population to be 2.7%, and the combined identity and behaviour-based GBM population to be 4.1% of the male population.125 In this study, the identity-behaviour based estimate is comparable and similar to ours, given that they combined both populations, not excluding non-sexually active GBM. We observed an overall increased proportion of GBM from 2015–2016 to 2019. Although the confidence intervals overlapped, this could be an indication of the increasing GBM population. We believe that cultural and political changes in Canada might explain those differences. Historically, same-sex practices and relationships globally carry stigma on different levels, being criminalized in at least 76 countries and exposing millions of individuals to the risk of arrest, prosecution, imprisonment and the death penalty.126 In Canada, same-sex sexual activities were considered a crime punishable by imprisonment before 1969.127 Since then, legislation has changed, and, currently, sexual orientation is included in human rights legislation as prohibited grounds for discrimination with same-sex civil marriage, one of the most significant recent progressions in LGBTQ rights (Lesbian, Gay, Bisexual, Transgender and Queer), being legalized in 2005 nationwide.127,128 It is also important to note that, in 2015, a new policy agenda for LGBTQ legal reform was introduced by the Liberal government, which included the reform of the Criminal 30 Code, a government apology to LGBTQ Canadians for past discrimination, and the expungement of records of past criminal convictions for same-sex behaviour.128 Consequently, we believe that as more public policies are created and implemented to protect sexual minorities over time, GBM will be more likely to feel comfortable disclosing their sexual orientation on a government national survey; this is likely reflected in the recent estimates. Emphasizing the possible changes in the willingness to disclose sexual orientation, the proportion of Americans who self-identify as LGBTQ has been increasing over time. Gallup’s (an American analytics and advisory company) latest update on LGBTQ self-identification concluded that the LGBTQ population increased from 3.5% in 2012 to 7.2% in 2020.129 One of the main reasons for the observed change in the proportions was that younger generations are more likely to disclose themselves as LGBTQ than older generations.129 One of the main reasons for the observed change in the proportions was that younger generations are more likely to disclose themselves as LGBTQ than older generations.129 In addition, a study conducted in Canada observed that there were age disparities in the willingness to disclose sexual orientation to a government survey with older men less likely to reveal it.130 In our study we observed a lower mean age of GBM compared to non-GBM. Comparing 2015-2016 to 2019, we found that the mean age of the GBM respondents decreased from 39.6 years in British Columbia and 38.1 years in Ontario, to 37.3 and 37.2 years, respectively, what could also explain the increase in the more recent GBM estimates. We also observed that GBM were more likely to live in medium or large urban centres than their non-GBM counterparts. The migration of GBM to large urban centres is a phenomenon observed globally.131-133 Large cities often have more diverse populations and cultural landscapes with a sense of community belonging and identity affirmation for sexual minorities. Therefore, 31 GBM in large cities can express their sexuality more freely, likely impacting their willingness to disclose their sexual orientation compared to GBM. A qualitative study conducted in the United States among closeted GBM has shown that conservative perceptions of traditional masculine gender norms and unfavourable attitudes toward same-sex relationships in rural areas have influenced them against disclosing their sexual orientation.134 This finding could also reflect on the GBM population estimates of more conservative or rural provinces in Canada, underestimating the proportion of GBM. 2.5 Limitations There are some limitations to our findings. First, Definition 3 did not include men who had sex with men who reported having sex more remotely than in the most recent 12 months. However, using the definitions with recent sexual intercourse with another man was more appropriate for our study to express a more contemporary assessment of their sexual behaviour. Second, the sexual identity-based questions differ from the 2015–2016 to 2019 cycles, including the “pansexual” identity in 2019. Although this is a minor difference, it is important to mention this limitation to show the evolution of new sexual identity terminology in a nationwide survey. Third, in 2019, the estimation of the size of the GBM population using Definition 1 for Canada 2019 did not include all provinces and territories. Fourth, we intended to estimate the GBM population size for Canada's four main provinces in 2019. However, in this cycle, the sexual behaviour content was unavailable for all Canadian provinces. Fifth, as we used sex at birth to define men, we might have missed heterosexual transgender men and transexual men who have sex with men who do not identify as 32 gay, bisexual or pansexual. Finally, our estimates could be underestimated as some people are unwilling to disclose their sexual orientation. 2.6 Conclusion In conclusion, this study used sexual identity and behaviour to estimate the size of the GBM population based on a national population-based survey. We found that, in 2015-2016, the size of the GBM population, using the hybrid definition combining sexual identity and behaviour (i.e., men who identify as gay or bisexual or who had sex with men in the last 12 months), corresponded to 4.0% of the male population in Alberta, British Columbia, Ontario and Quebec, and, in 2019, the GBM population (i.e., men who identify as gay, bisexual or pansexual, or who had sex with men in the last 12 months) corresponded to 4.9% of the male respondents. We also found that compared to non-GBM, GBM were more likely to be single/never married, have an annual household income of less than $30,000, live in medium and large population centres, and have lower mean age. Additionally, we observed an increase of the GBM population comparing the 2015-2016 to the 2019 CCHS cycle, possibly due to people being more comfortable in disclosing their sexual orientation and behaviour. Our estimates are valuable to public health practitioners, policymakers and researchers since we can now provide denominators to estimate GBM-specific disease rates, such as HIV and other sexually transmitted infections, and evaluate rates of anxiety, depression and other psychological problems specifically in GBM. In addition, our behaviour-based definition could serve as a denominator to forecast GBM that need PrEP and evaluate PrEP uptake. It is important to emphasize that sexual orientation is a multidimensional concept and that there is discordance 33 across the domains. Therefore, the estimations presented here, based on different domains of sexual orientation, should be used cautiously, with a relevant and clear rationale for their use. 34 Chapter 3: Identifying Syndemic Factors and Their Effect on HIV Sexual Risk Behaviour Among Gay, Bisexual and Other Men Who Have Sex With Men in Vancouver, Montreal and Toronto 3.1 Introduction Many studies have used syndemic theory to understand how various structural and psychosocial factors may lead to an increased burden of HIV among GBM. 10,135 However, the majority of past research has used a "sum score" approach which provides limited insight into the ways in which these factors may interact to elevate the HIV burden and what factors should be prioritized for interventions.98,136 To address this gap, Tsai et al. (2018) outlined three models of co-occurring epidemics and proposed methods to operationalize them: (1) mutually causal epidemics: co-occurring epidemics that cause each other (analyzed through path analysis); (2) synergistically interacting epidemics: the joint effect of two or more epidemics exceeds their individual effect on health problems (evaluated using product terms models); and (3) serially causal epidemics: the consequences of one condition accumulate and lead to subsequent conditions (examined through mediation analysis).99,136 Although Tsai et al. (2018) proposed methodologies for applying syndemic theory more appropriately136, there is still no standardized method for the selection of syndemic factors that should be used when studying the effect of syndemics on HIV risk.15,98 Therefore, for objective 1, we aimed to identify syndemic factors associated with HIV sexual risk behaviour among HIV-negative GBM by creating a network of associations. This visual representation of all factors in the study aimed to assist in the understanding of how syndemic factors are interconnected and 35 serve as novel systematic way of selecting syndemic factors most relevant to HIV sexual risk behaviour among GBM. Objective 2 aimed to apply the syndemic theory to examine the syndemic factors’ influence on HIV sexual risk behaviour, according to the models of co-occurring epidemics that would be most appropriate to the syndemic factors identified in the first phase of the study. This research will provide critical information to better understand the elevated burden of HIV among GBM and identify areas for intervention to address the impact of syndemic factors contributing to elevated HIV risk. 3.2 Methods 3.2.1 Study design and participant selection This cross-sectional study used baseline data from the Engage study, a mixed-methods prospective cohort study of GBM residing in Vancouver, Toronto, and Montreal.137 Participants in Engage were cisgender or transgender men, at least 16 years old, who reported having sex with men in the past six months and were able to read the questionnaire in French or English. Participants were recruited between 2017 and 2019 using Respondent-Driven Sampling (RDS), a formalized chain-referral method.138 The Engage study combines data from computer-assisted self-interviewing (CASI) and biological samples to detect HIV and other selected sexually transmitted and blood-borne infections (STBBI). Further details on the study are reported elsewhere.137 We included in our analysis self-reported HIV-negative men (cisgender or transgender) with valid responses for the variables of interest. 3.2.2 Measures Outcome 36 HIV sexual risk behaviour was our outcome of interest. Participants were asked to report on their last sexual encounter with up to five of their most recent partners in the past six months. Participants reported whether condoms or HIV pre-exposure prophylaxis (PrEP) were used and, for sexual encounters with partners living with HIV, whether they knew the viral load of these partners. HIV sexual risk behaviour was defined as reporting condomless anal sex with at least one of the last five partners in the previous six months, where the partner was living with HIV with detectable viral load or had unknown HIV status, and the participant was not using PrEP. Sociodemographic Measures Potentially Associated with HIV Sexual Behaviour Sociodemographic measures included age (<30 / ≥30 years), annual household income (<$30.000/ ≥$30.000), employment status (no/yes), highest level of education (less than high school/high school or higher), born in Canada (no/yes), proud to be LGBT (Lesbian, Gay, Bisexual and Transexual) (no/yes) and Financial Strain Index (FSI). FSI was used to measure the financial stress the participants experienced.139 The following sociodemographic characteristics were used to describe the population, but were not added to the Somers’ D association matrix: city (Vancouver, Montreal and Toronto), sexual orientation (gay, bisexual, queer and other orientation), ethnic group (Canadian, European, Asian and other ethnic group) and marital status (married or common-law, separated, divorced or widowed and single). Potential Syndemic Factors Mental Health Conditions 37 In our study, we included mental health conditions that are associated with sexual risk behaviour. 19-22 Post-traumatic stress disorder (PTSD), attention deficit disorder (ADD) or attention deficit and hyperactivity disorder (ADHD), bipolar disorder and borderline disorder were assessed by respondent's answers to the question, "Have you ever been told by a physician, psychologist, or psychiatrist that you have any of the following mental health conditions?". Symptoms of anxiety and depression in the past week were measured via the Hospital Anxiety and Depression Scale (HADS).140 HADS consists of 14 questions, with seven items each for anxiety and depression on a 4-point Likert scale, ranging from one to three. The score is the sum of the seven items for each subscale, ranging from zero to 21. Responses were dichotomized with a score ≥11 expressing clinically significant anxiety/depression. Sexual compulsivity was measured via the 10-item Kalichman Sexual Compulsivity Scale.141 Responses ranged from one (not at all like me) to four (very much like me). Studies using this scale have used a cutoff score of 24 to indicate higher levels of sexual compulsivity.142-145 The mean score for each participant was calculated, with scores 2.4 and above indicating higher levels of sexual compulsivity.141 Childhood Sexual Abuse Childhood sexual abuse (CSA) was measured via the Childhood Trauma Questionnaire – Short Form using only the five questions about sexual abuse.146 Participants were classified as sexually abused during childhood if they answered "rarely", "sometimes", "often" or "very often" to any of the questions, as opposed to "never". Substance Use 38 Polysubstance use was defined as the non-medical use of two or more of the following substances in the past six months: cannabis, crack, cocaine, ecstasy, crystal, mephedrone, speed, Ritalin®, poppers, nitrous oxide (NIT), benzodiazepines, barbiturates, gamma hydroxybutyrate (GHB), ketamine, mushrooms, lysergic acid diethylamide (LSD), other hallucinogen drugs, heroin, morphine, codeine, Oxycontin, other opioids, Viagra® and steroids. Alcohol use was measured using the Alcohol Use Disorder Identification Test (AUDIT-C).147 AUDIT-C captures the frequency of alcohol use ("How often do you have a drink containing alcohol?"), quantity consumed on a typical drinking day ("How many standard drinks containing alcohol do you have on a typical day?"), and binge drinking frequency ("How often do you have six or more drinks on one occasion?"). Scores of four and above indicated alcohol misuse. Intimate Partner Violence Intimate partner violence was assessed via three questions regarding being the perpetrator of verbal, physical or sexual violence and three questions about being the victim of verbal, physical or sexual violence. Thus, three variables were created based on the status of victim, perpetrator of violence or both: intimate partner violence victimization, intimate partner violence perpetration and any intimate partner violence.148 Sexual Coercion Sexual coercion was measured by using the sexual coercion scale.149 The scale contains four items about unwanted sexual activities and uses a 3-point scale format. Therefore, scores ranged from 0 to 12. High scores indicated high levels of sexual coercion experience. Given the lack of clear cutoff points in the literature, we used the median to categorize Sexual Coercion (no/yes). 39 Sex Work and transactional sex Sex work was defined as having a history of ever working as an escort or in the sex industry. Transactional sex was assessed through questions that asked about receiving or giving money, drugs, or other goods or services in exchange for sex in the past six months.150 Early Sexual Debut Participants were asked about the age at which they first had sex with men. Early sexual debut was then defined as having sexual encounters with men before the age of 13.83 Two variables were created according to the nature of the first sexual encounter: First oral and first anal sexual encounter. Body Dissatisfaction Body dissatisfaction was measured using the Revised Male Body Attitudes Scale (MBAS-R).139,151 The scale comprises 15 questions regarding participants' perceptions about their bodies. Responses could range from one to five ("never", "rarely", "occasionally", "often", "always"), with higher scores indicating high body dissatisfaction. Given the lack of clear cutoff points in the literature, the median score was used to categorize body dissatisfaction. 3.2.3 Statistical Analysis Descriptive statistics (frequencies and proportions) were calculated for all study variables overall and stratified by HIV sexual risk behaviour status (no/yes). Fisher's exact test was used to test for statistically significant differences by HIV sexual risk behaviour status for categorical variables, 40 and the Kruskal Wallis test for continuous variables. Descriptive statistics were generated using RDS-II weighting.152 Next, to create a network of potential syndemic factor associations, we assessed the pairwise associations between all study variables using Somers' D statistic.153 Somers' D ranges from -1 to 1, with -1 suggesting that two categorical variables disagree with each other and 1 indicating that two variables perfectly agree. For the next step, we retained only variables that were significantly associated with the outcome, given that we were only interested in the effect of syndemic factors on HIV sexual risk behaviour and how they are interconnected. We then used an oblique component analysis (PROC VARCLUS) to reduce the number of variables by grouping them into non-overlapping clusters.154 PROC VARCLUS groups variables that are highly correlated with each other into the same cluster while minimizing correlations between clusters. One variable from each cluster was selected for inclusion in the models based on having a high correlation with its own cluster and a low correlation with the other clusters. The reduction of the number of variables was crucial for the feasibility of the syndemic analysis given the complexity of the models created. P-values <0.05 were taken to indicate statistically significant associations. Logistic regression models were used to evaluate associations between the syndemic factors identified using the VARCLUS method and HIV sexual risk behaviour among GBM. According to the variables identified by the variable reduction method, we created syndemics conceptual frameworks based on theory and the research literature. For instance, temporality is a necessary assumption for the serially causal epidemics model, and the theoretical possibility of mutual causality between variables is imperative for the mutually causal epidemics model. Therefore, the Tsai models that best fit the syndemic factors identified were serially causal and synergistically interacting epidemics.136 For the serially causal epidemics model, we conducted a mediation analysis using a path model based on the variables identified by the VARCLUS 41 procedure. Since CSA happened in earlier stages of the participants' lives compared to the other syndemic factors, it was determined to be the primary exposure. The other syndemic factors were included in the model as mediators of the effect of CSA on HIV sexual risk behaviour. Direct and indirect effects were calculated using the standardized estimates from the models and reported with their corresponding 95% confidence intervals (CI) and significance levels. Standardization of estimates was done by multiplying the original coefficient by the standard deviation of the independent variable and dividing it by the standard deviation of the dependent variable. Mediation effects were assessed using the Monte Carlo method.155 For the synergistically interacting epidemics model, we tested two-way interaction terms one at a time between all identified syndemic factors predicting HIV sexual risk behaviour. Then, the significant two-way interactions identified in the previous step were included in the same model. In the case of significant interactions, we reported adjusted odds ratios (aOR) for each syndemic factor and its 95% CI. RDS weights and sociodemographic characteristics (i.e., city, age, sexual orientation, ethnic group, education, marital status and income) and other syndemic factors were adjusted for in all the models. Analyses were conducted using SAS® software version 9.4 (SAS, North Carolina, United States), and figures were created using R software version 4.2.1 (R Core Team, Vienna, Austria), igraph package (v1.2.6; Csardi & Nepusz, 2006). 3.3 Results The study population consisted of 1,571 self-reported HIV-negative GBM for the descriptive analyses. Table 1 shows the distribution of participants' sociodemographic characteristics and potential syndemic factors, stratified by HIV sexual behaviour status. Participants identified predominately as Canadian (39.6%) or European (19.0%) ethnicity, gay (80.7%), had an annual 42 income of less than $30,000 (59.9%), were employed (68.9%) and had education greater than high school (84.4%). The most prevalent syndemic factor was alcohol misuse (56.6%), followed by any intimate partner violence (42.3%), intimate partner violence victimization (39.4%) and polysubstance use (38.5%). Participants in Vancouver were more likely to engage in HIV sexual risk behaviour (16.1% vs. 5.8% in Toronto and 13.4% in Montreal, p<0.001). Participants that reported income <$30,000 (14.8% vs. 9.5%, p-value 0.0021), self-identified as bisexual (17.6% vs. 12.8% for gay, 4.2% for queer and 2.5 for other sexual orientation, p-value = 0.0047), reported sexually compulsive behaviour (18.5% vs. 11.5%, p-value= 0.0015), engaged in polysubstance use (17.2% vs. 9.8%, p-value <0.0001) and transactional sex (34.0% vs. 11.0%, p-value <0.0001), and have experienced CSA (20.0% vs. 11.1%, p-value <0.0001), any intimate partner violence (15.8% vs 10.4% p-value = 0.0018), intimate partner violence perpetration (18.3% vs 10.6%, p-value <0.0001) and sexual coercion (15.1% vs. 9.0%, p-value= 0.0002) were also more likely to report HIV sexual risk behaviour. Table 5 Distribution of sociodemographic characteristics and syndemic indicators, stratified by HIV sexual risk behaviour status among self-reported HIV-negative gay, bisexual, and other men who have sex with men at enrollment (n=1571). Overall HIV sexual risk behaviour P value Characteristics No Yes RDS-II adjusted n % n % n % City Montreal 726 46.2 621 86.6 105 13.4 <0.0001 Toronto 333 21.2 305 94.2 28 5.8 Vancouver 512 32.6 443 83.9 69 16.1 Age 18-29 696 51.7 610 87.8 86 12.2 0.5901 43 Overall HIV sexual risk behaviour P value Characteristics No Yes RDS-II adjusted n % n % n % ≥30 875 48.3 759 86.8 116 13.2 Ethnic group Canadian 757 39.6 646 86.8 111 13.2 0.4013 European 314 19.0 278 90.2 36 9.8 Asian 189 15.5 168 87.2 21 12.8 Other 311 25.9 277 86.1 34 13.9 Marital status Married, common-law 334 21.2 300 85.7 34 14.3 0.3896 Separated, divorced, widowed 100 6.4 82 84.4 18 15.6 Single 1137 72.4 987 88.0 150 12.0 Annual income <$30,000 732 59.9 619 85.2 113 14.8 0.0021 ≥$30,000 839 40.1 750 90.5 89 9.5 Employment status No 339 31.1 294 88.8 45 11.2 0.2452 Yes 1232 68.9 1075 86.7 157 13.3 Highest education High school or less 190 15.6 149 86.1 41 13.9 0.5210 Greater than high school 1381 84.4 1220 87.5 161 12.5 Born in Canada No 609 46.2 544 87.9 65 12.1 0.4920 Yes 962 53.8 825 86.8 137 13.2 Sexual orientation Gay 1333 80.7 1162 87.2 171 12.8 0.0047 Bisexual 98 11.7 77 82.4 21 17.6 Queer 104 4.8 99 95.8 5 4.2 Other 36 2.8 31 97.5 5 2.5 Childhood Sexual Abuse No 1189 71.6 1059 88.9 130 11.1 <0.0001 Yes 311 20.7 248 80.0 63 20.0 Prefer not to answer 71 7.7 62 91.6 9 8.4 PTSD No 1400 88.9 1221 87.6 179 12.4 44 Overall HIV sexual risk behaviour P value Characteristics No Yes RDS-II adjusted n % n % n % Yes 149 8.5 135 87.1 14 12.9 0.8648 Prefer not to answer 22 2.6 13 78.3 9 21.7 ADD/ADHD No 1388 87.4 1215 87.0 173 13.0 Yes 159 7.6 137 89.4 22 10.6 0.4414 Prefer not to answer 24 5.0 17 90.1 7 9.9 Bipolar No 1483 91.3 1300 86.9 183 13.1 Yes 63 4.0 51 90.6 12 9.4 0.3952 Prefer not to answer 25 4.7 18 92.9 7 7.1 Borderline personality No 1500 92.4 1314 87.3 186 12.7 Yes 48 3.1 39 79.3 9 20.7 0.0976 Prefer not to answer 23 4.4 16 92.5 7 7.5 Polysubstance use No 755 61.5 671 90.2 84 9.8 <0.0001 Yes 816 38.5 698 82.8 118 17.2 Prefer not to answer 16 3.1 11 89.6 5 10.4 Proud to be gay or bisexual Disagree 160 12.2 138 90.5 22 9.5 0.1862 Agree 1357 81.8 1189 87.1 168 12.9 Prefer not to answer 54 6.0 42 83.3 12 16.7 Intimate partner violence any No 791 57.7 703 89.6 88 10.4 0.0018 Yes 780 42.3 666 84.2 114 15.8 Intimate partner violence perpetration No 1064 73.0 936 89.4 128 10.6 <0.0001 Yes 507 27.0 433 81.7 74 18.3 Intimate partner violence victimization No 833 60.6 741 89.5 92 10.5 0.0013 Yes 738 39.4 628 84.0 110 16.0 45 Overall HIV sexual risk behaviour P value Characteristics No Yes RDS-II adjusted n % n % n % Transactional sex (P6M) No 1427 92.2 1261 89.0 166 11.0 <0.0001 Yes 132 6.8 98 66.0 34 34.0 Prefer not to answer 12 1.0 10 73.4 2 26.6 Worked as an escort No 1420 93.0 1244 87.5 176 12.5 0.2684 Yes 135 6.1 110 83.6 25 16.4 Prefer not to answer 16 0.9 15 93.9 1 6.1 AUDIT-C <4 599 43.4 527 87.8 72 12.2 0.6329 ≥4 972 56.6 842 87.0 130 13.0 HADS Anxiety <11 1109 70.6 980 87.2 129 12.8 0.3647 ≥11 390 24.8 327 85.4 63 14.6 Missing 72 4.6 62 92.9 10 7.1 HADS Depression <11 1404 89.4 1225 86.6 179 13.4 0.4631 ≥11 84 5.3 71 89.2 13 10.8 Missing 83 5.3 73 91.8 10 8.2 Sexual compulsivity scale <2.4 1260 79.9 1104 88.5 156 11.5 0.0015 ≥2.4 295 18.3 250 81.5 45 18.5 Missing 16 1.8 15 94.2 1 5.8 First oral sexual encounter <13 201 10.4 169 85.1 32 14.9 0.3793 ≥13 1367 89.1 1197 87.5 170 12.5 Missing 3 0.5 3 100.0 0 First anal sexual encounter <13 53 3.3 42 88.5 11 11.5 0.7256 ≥13 1508 93.7 1317 86.9 191 13.1 Missing 10 3.0 10 100.0 0 46 Overall HIV sexual risk behaviour P value Characteristics No Yes RDS-II adjusted n % n % n % Sexual Coercion No 626 562 91.0 64 9.0 0.0002 Yes 906 781 84.9 125 15.1 Missing 39 26 68.4 13 31.6 Continuous variables n Median (Q1-Q3) n Median (Q1-Q3) n Median (Q1-Q3) Financial Strain Index 1571 6 (5, 9) 1369 7 (5, 9) 202 7 (5, 10) 0.1479 Revised Male Body Attitudes Scale 1540 21 (17, 25) 1346 21 (17, 26) 194 21 (17, 23) 0.1803 Notes: PTSD=Post-traumatic stress disorder. ADD=Attention deficit disorder. ADHD=Attention deficit and hyperactivity disorder. P6M=previous six months. Q1-Q3=25th to 75th percentile Due to missing data on variables of interested, 113 participants were excluded from the Somers’ D association matrix and the logistic regression analysis. Participants from Montreal, Asian participants, those who were single, aged 18-29 years and those with an annual income <$30,000, unemployed, with education level of high school or less, responded “other” for the sexual orientation question, who preferred not to answer the question regarding proud of being LGBT and had higher levels of financial strain, were more likely to be excluded due to missing responses to variables of interest (Appendix A, page 85). The network with all study variables' statistically significant pairwise associations is presented in Figure 2. Lines represented significant Somers` D associations. The thickness of each line corresponds to the association's strength, and the lines' colour indicates the association's direction. There was a high level of interconnectedness among the variables, with the Somers’ D statistic ranging from -0.29 (FSI - employment) to 0.93 (intimate partner violence victimization - any intimate partner violence). Figure 3 shows only the nine variables that were directly associated 47 with HIV sexual risk behaviour. The strongest Somers’ D association was polysubstance use (0.16), sexual coercion (0.15), CSA (0.14), intimate partner violence perpetration (0.14), transactional sex (0.13), any intimate partner violence (0.12), intimate partner violence victimization (0.12), income (-0.11) and sexual compulsivity (0.09). Figure 4 shows the pairwise associations of study variables after using the VARCLUS procedure. The initial set of nine variables was reduced to six after identifying clusters and selecting one variable from each cluster according to the VARCLUS procedure: CSA, polysubstance use, any intimate partner violence, transactional sex, sexual compulsivity and income. The Somers’ D matrix with all pairwise associations and the variable reduction procedure (VARCLUS) table can be found in Appendix B and C (pages 89-91), respectively. Due to the variables identified in VARCLUS and the theoretical frameworks created, we built two syndemic models based on the models of co-occurring epidemics proposed by Tsai et al. (2018), serially causal and synergistically interacting epidemics.136 We did not include a mutually causal epidemics model in the analysis, given that CSA could be the cause of the other syndemic factors yet could not be caused by any of them due to temporality. Figure 1 shows the framework of the proposed analysis. 48 Figure 1 Framework of the proposed analysis. 49 Figure 2 Significant associations between syndemic factors, sociodemographic characteristics and HIV sexual risk behaviour (as indicated by a significant Somers’ D with p-values <0.05) (n=1458). The thickness of the line is proportional to the absolute value of Somers’ D. Notes: ADD/ADHD=Attention deficit disorders/attention deficit and hyperactivity disorder. CSA=childhood sexual abuse. FSI=financial strain index. IPV=Intimate partner violence. IPVP=intimate partner violence perpetration. IPVV=intimate partner violence victimization. PSTD=Post-traumatic stress disorder. RMBAS=Revised Male Body Attitudes Scale. 50 Figure 3 Significant associations between syndemic factors, sociodemographic characteristics and HIV sexual risk behaviour (as indicated by a significant Somers’ D with p-values <0.05) (n=1458). The thickness of the line is proportional to the absolute value of Somers’ D. Notes: CSA=childhood sexual abuse. IPV=Intimate partner violence. IPVP=intimate partner violence perpetration. IPVV=intimate partner violence victimization. 51 Figure 4 Significant associations between syndemic factors, sociodemographic characteristics and HIV sexual risk behaviour (as indicated by a significant Somers’ D with p-values <0.05) (n=1458). The thickness of the line is proportional to the absolute value of Somers’ D. Notes: CSA=childhood sexual abuse. IPV=Intimate partner violence. 52 Serially Causal Epidemics All mediators were theoretically in the causal pathway between CSA and HIV sexual risk behaviour and were significantly associated with both. Figure 4 shows the serially causal epidemics model with CSA as the primary exposure and any intimate partner violence, transactional sex, polysubstance use and sexual compulsivity as mediators of the effect of CSA on HIV sexual risk behaviour. The final model showed a statistically significant direct effect (c' = 0.09, 95% CI 0.01 – 0.17) and total effect (c = 0.14, 95% CI 0.06 – 0.21) of CSA on HIV sexual risk behaviour. The mediators, any intimate partner violence, transactional sex, polysubstance use and sexual compulsivity, mediated 11.6%, 11.9%, 6.6%, and 11.0% of the effect of CSA on HIV sexual risk behaviour, respectively, corresponding to a combined mediation effect of 41.1% (Figure 4). Figure 5 Serially causal epidemics model using mediation analysis [Standardised estimates (95% CI)]. * p-value <0.05. 53 Notes: a=effect of exposure on mediator. b=effect of mediator on outcome. c=total effect of exposure and mediators on outcome. c’=direct effect of exposure on outcome. The model was adjusted by city, age, sexual orientation, ethnic group, education, marital status, income and RDS weights. Synergistically Interacting Epidemics We tested ten models with ten two-way interaction terms, one at a time. We identified two significant multiplicative interactions, an interaction between any intimate partner violence and polysubstance use and an interaction between transactional sex and CSA. When we fitted a model including both significant interaction terms, only the interaction between transactional sex and CSA remained significant. Table 6 shows the aOR of the interaction between transaction sex and CSA (reference category was none of these). Results indicated that the effect of CSA and transactional sex combined (aOR 14.0, 95% CI 5.6-35.2) had 14 times the odds of not having a history of CSA and transactional sex, adjusting for demographic characteristics and other factors. Table 6 Logistic regression showing the synergistic effect of two syndemic factors on HIV sexual risk behaviour among gay, bisexual and other men who have sex with men. Syndemic indicators aOR 95% CI Transactional sex Childhood Sexual Abuse No No reference No Yes 1.2 0.8 1.9 Yes No 1.3 0.6 2.7 Yes Yes 14.0 5.6 35.2 Note: The model was adjusted by city, age, sexual orientation, ethnic group, education, marital status, income, polysubstance use, sexual compulsivity and RDS-II weights. aOR=adjusted Odds Ratio. CI=Confidence Interval. 3.4 Discussion We found that syndemic factors among our GBM population frequently co-occurred and were highly interconnected with each other and with sociodemographic characteristics. From the 26 54 factors evaluated, we identified eight syndemic factors and one sociodemographic characteristic that were directly associated with HIV sexual risk behaviour. These included experiences of CSA, perpetration and victimization of intimate partner violence, sexual coercion, transactional sex, sexual compulsivity, polysubstance use and low income. In addition, we found evidence supporting serially causal and synergistically interacting syndemic models. Specifically, we found that polysubstance use, any intimate partner violence, transactional sex and sexual compulsivity mediated 41% of the total effect of CSA on HIV sexual risk behaviour, and we observed a synergistic interaction between transactional sex and CSA on HIV sexual risk behaviour. Consistent with other studies, we also found a high prevalence (20.7%) of GBM who reported CSA.24,135,156 Notably, CSA has been known to affect HIV risk among GBM. A 2009 study identified that CSA among GBM was associated with increased odds of engaging in condomless anal sex and serodiscordant condomless anal sex, and the effect was mediated by drug use, depression, lower self-efficacy for adopting safer sexual behaviour, poorer communication skills regarding safer sex, and poorer safer sex norms.24 Similarly, in our mediation analysis, we found that polysubstance use mediated the effect of CSA on HIV sexual risk behaviour. We observed similarities between the initial exploratory analysis of our study, in which we built a network of associations, and the results of a study that explored the associations between nine syndemic factors among a sample of HIV-negative GBM at high risk of HIV acquisition using network analysis.157 In that study, the authors found statistically significant direct relationships between suicidal ideation and depression, suicidal ideation and injection drug use, substance use and injection drug use, and social anxiety and suicidal ideation.157 Another study found significant associations between condomless anal sex and alcohol use, CSA and unstable housing, alcohol use and CSA, and substance use and intimate partner violence.135 In this study, they used network 55 analysis to show the interconnectedness among syndemic factors and condomless anal sex. The study used bidirectional polychoric correlations with the rationale that syndemic factors interact and reinforce each other.135 However, in addition to identifying potential syndemic factors, we also applied models of co-occurring epidemics to show the syndemic effect of the variables on HIV sexual risk behaviour in different ways, specifically using mediation analysis and product terms models. Regarding the syndemic models, the study that applied a methodology closer to ours was conducted among GBM in India and tested three models of co-occurring epidemics using data from a population-based national survey. In the synergistically interacting epidemics model, they found significant interaction terms between violence victimization and alcohol use, as well as between drug use and alcohol use on inconsistent condom use.99 In the serially causal epidemics model, they identified that the mediated effect of drug use and alcohol use corresponded to 15.3% of the total effect of violence victimization on inconsistent condom use.99 Our study findings suggest that transactional sex and CSA combined increased the odds of engaging in HIV sexual risk behaviour greater than their additive effects. When found together, their effect on HIV sexual risk behaviour increases, showing evidence of a strong synergistic effect. Interventions addressing both problems would be ideal. However, given the synergy observed between the syndemic factors in the synergistically interacting epidemics model, single-component interventions could also be effective in reducing HIV sexual risk behaviour. GBM that had experienced CSA may have trouble distinguishing safe situations from unsafe ones, including safe sexual practices. Therefore, cognitive-behavioral therapies that help GBM identify and change maladaptive trauma-related cognitions, can reduce the likelihood of GBM engaging in sexual risk behaviors.158,159 For instance, a clinical trial in the United States showed that cognitive-behavioural 56 therapy for trauma and self-care (CPT-TSC) among a sample of GBM with a history of CSA reduced the odds of engaging in any condomless anal sex in 60%, compared to HIV voluntary counseling and test (VCT).158 In regards to transactional sex, a possible efficient intervention to reduce the burden of HIV among GBM would be a targeted expansion of PrEP program among GBM that engage in transactional sex. A study conducted in Rhode Iland using a simulated HIV transmission model concluded that a PrEP program expansion focused on GBM that engage in transactional sex may reduce the incidence of HIV not only among sex workers, but also the overall number of HIV infections.160 Additionally, in the serially cause epidemics models, we found that CSA was associated with HIV sexual risk behaviour, mediated by polysubstance use, sexual compulsivity, transactional sex and any intimate partner violence. Therefore, addressing CSA and the mediators could also be beneficial in reducing the burden of HIV among GBM to some extent. In terms of strengths, to our knowledge, this is the first study that combined the use of a systematic methodology for the selection of multiple syndemic factors that influence HIV sexual risk behaviour among a population of GBM, and, additionally, conducted syndemic analysis using different and appropriate models of co-occurring epidemics. Another important aspect of our study is the definition of HIV sexual risk behaviour. We used event-level variables regarding the last five sexual encounters instead of measuring the outcome using a single observation or past 6 months recall. Additionally, as previously mentioned, we accounted for PrEP use and participants’ sexual encounters with HIV-positive partners with detectable viral load to express a contemporary HIV sexual risk behaviour assessment, given the current advances in PrEP expansion and HIV treatment. And, finally, results from our exploratory phase (i.e., network of associations) can help future studies to identify unexplored pathways to HIV sexual behaviour. 57 This study has some limitations. As we checked for ten two-way interaction terms, the prevalence of some co-occurring syndemic factors was low. Therefore, due to power issues, we might have failed to detect significant interaction terms. In addition, we may have underestimated CSA due to our reliance on the participants' self-report information. There are many barriers when it comes to disclosing the history of CSA in adults, including not having an accurate memory of whether what they experienced was abuse, shame, guilt or anxiety 161. Since we only used baseline cross-sectional data, we assumed temporality for the serially causal epidemics model. However, it would be important for future studies to use longitudinal data to study how syndemic factors influence sequentially HIV sexual risk behaviour. Finally, we could not find clear cutoff points in the literature for some scales used in the study (e.g., FSI, sexual coercion scale). Therefore, we might have misclassified some participants. 3.5 Conclusion In summary, in the synergistically interacting epidemics model, we found a significant multiplicative interaction between CSA and transactional sex in terms of their association with HIV risk behaviour. Additionally, we found that in the model of serially causal epidemics, the effect of CSA on HIV sexual risk behaviour was mediated by any intimate partner violence, transactional sex, sexual compulsivity, and polysubstance use. The results from this model suggest that the prevention and treatment of the trauma caused by CSA could decrease HIV sexual risk behaviour and consequently reduce the burden of HIV among GBM. Moreover, interventions addressing the mediating problems (e.g., polysubstance use) may also have a significant effect in reducing HIV risk among GBM. 58 Chapter 4: Conclusion 4.2 Overview This thesis had three main objectives: 1. To estimate the size of the GBM population in Canada based on different definitions and data from consecutive CCHS cycles and to compare sociodemographic characteristics of GBM vs non-GBM. 2. To identify syndemic factors associated with HIV sexual risk behaviour among HIV-negative GBM by creating a network of associations. 3. To apply the syndemic theory to examine the syndemic factors’ influence on HIV sexual risk behaviour, according to the models of co-occurring epidemics. To address these objectives, I conducted two independent studies using data from adult males in the Canadian Community Health Survey (CCHS) annual cycles from 2015-2016 and 2019 and from the Engage study, including data from sexually active GBM in Vancouver, Toronto and Montreal. This concluding chapter highlights the study’s key findings, strengths and limitations and public health implications. 4.3 Summary of findings 4.3.1 Changes in the Size and Composition of the Gay, Bisexual and Other Men Who Have Sex With Men Population in Canada’s Largest Provinces: A Descriptive Study Using Data From a Population-Based Survey The findings of this study show that the GBM population changed according to the dimensions of sexual orientation (i.e., sexual identity, behaviour and attraction). CCHS included only questions 59 regarding sexual identity and behaviour; therefore, we created a third variable for the second part of the analysis combining sexual identity and behaviour: men who identify as gay or bisexual OR who had sex with men in the last 12 months/men who identify as gay, bisexual or pansexual OR who had sex with men in the last 12 months. Estimating the GBM population size according to only one dimension of sexual orientation might not be enough to identify the actual population at risk of some health conditions. For instance, a definition relying purely on sexual identity would not capture all people at higher risk of engaging in particular HIV sexual risk behaviours. Although the dimensions overlap, we would have misclassified some people if they were not sexually active. We do not intend to say that the hybrid definition combining sexual identity and behaviour separately is more accurate than the identity and behaviour definitions for many purposes. We emphasize that one should be careful in research when selecting a definition of sexual orientation according to the specific objectives and hypotheses to ensure that the population captured is the right target for a specific intervention. Comparing the same provinces, we also observed an increase in the GBM population size from 2025-2016 to 2019. However, the CI overlapped. Therefore, we could not prove our hypothesis that the GBM population is increasing over time, possibly because we only used two CCHS cycles. We encourage future studies to do a temporal trend analysis of the GBM population in Canada. Stratifying the data by age groups would be an important step, as the literature indicates that younger generations are more willing to disclose their sexual orientation. 60 4.3.2 Identifying Syndemic Factors and Their Effect on HIV Sexual Risk Behaviour Among Gay, Bisexual and Other Men Who Have Sex With Men in Vancouver, Montreal and Toronto The results of our study showed that the most prevalent syndemic factors were alcohol misuse, followed by any intimate partner violence, intimate partner violence victimization, and polysubstance use. We found that syndemic factors among GBM are highly interconnected. We found nine syndemic factors associated with HIV sexual risk behaviour. We used VARCLUS to reduce the number of variables to five syndemic factors. In the synergistically interacting epidemics model, we found a significant multiplicative interaction between CSA and transactional sex. This interaction was associated with increased odds of engaging in HIV sexual risk behaviour, above the odds of CAS and transactional sex separately, indicating that interventions addressing any of the syndemic factors could significantly reduce HIV risk. Additionally, we found that any intimate partner violence, transactional sex, sexual compulsivity, and polysubstance use mediated the effect of CSA on HIV sexual risk behaviour. 4.3.3 Strengths and Limitations Strengths and limitations specific to each study are discussed in detail in Chapters 2 and 3, and some general limitations are presented here. One of the main limitations common in both studies is the cross-sectional design. Due to the studies’ designs, we could not assess changes in one person’s sexual behaviour or determine the temporality of the variables presented in the serially causal epidemics model. In addition, due to the cross-sectional design, we cannot infer any causal relationship between the variables. Another important limitation in both studies is the social desirability bias. As sexual orientation and sexual risk behaviour might be seen as sensitive 61 subjects to some people, participants might have given answers that they thought were more acceptable and less stigmatizing. Therefore, when asked if they were GBM, men might feel pressured to say “no”, not to disclose their sexual orientation, or to omit their actual sexual behaviour. The study presented in Chapter 2 was based on a representative national survey. Therefore, the results presented can be generalizable to the overall GBM population living in the specific provinces mentioned. The second study, however, focused on GBM living in urban centres. Hence, the findings presented in Chapter 3 cannot be generalized to GBM living in rural areas or small cities. Another crucial limitation when working with CCHS data is the residual disclosure risk, i.e., the potential for individuals' identities to be indirectly revealed by releasing survey data. As I used close variations of highly specific definitions, the risk of identification impacted my ability to explore the data completely and, consequently, produce results. Since, in this study, unweighted data was stratified by province and used closely related but finely distinguished definitions, we had to restrict the number of sociodemographic characteristics used and the number of possible combinations of CCHS questions regarding sexual orientation to avoid the risk of unintentionally re-identifying individuals who appeared in small categories. In addition, the number of data points in some sociodemographic characteristics cells was very small, resulting in very high coefficients of variance and producing unreliable estimates. We could not report any results for such cells. We also saw that in the synergistically interacting epidemics model, some interaction terms had low prevalence in the sample, which might have resulted in non-significant associations with the outcome or large confidence intervals, as we saw in the interaction between CSA and transactional sex. 62 4.3.4 Public Health Implications Estimating the size of a population is essential to creating programs accurately and allocating the appropriate resources for any public health strategy. We showed that different dimensions of sexual orientation produced different population size estimates. Public health policymakers should be careful when choosing which definition to use depending on their target population and objective. For example, for programs that focus on stigma towards sexual minorities, a sexual identity definition should be used, given that gay, bisexual and pansexual men are more likely to suffer from prejudice. If, however, the focus of a public program is on HIV/STI prevention/treatment, it should use a sexual behaviour definition, given that recently sexually active GBM are more likely to engage in sexual risk activities in the future as well. For instance, PrEP programs could use the behaviour-based GBM population estimates as a denominator when calculating appropriate levels of PrEP uptake. Finally, if, regardless of the dimension of sexual orientation, the GBM population is disproportionately affected by a certain condition (e.g., mood and anxiety disorders), the hybrid definition combining sexual identity and behaviour may be most useful. We saw that syndemic factors are prevalent among GBM. According to the concept of syndemic interactions, targeting one syndemic factor might substantially reduce HIV risk given that their combined effect increases the odds of engaging in HIV sexual risk behaviour above the odds of each individually. We also found that prevention of CSA would be efficient in reducing HIV sexual risk behaviour. However, CSA happened in the past. Therefore, counselling targeting adult survivors of CSA, offering appropriate treatment to help them deal with the trauma caused by experiences of sexual abuse during childhood and the coping mechanisms they might be 63 engaging in, could also have an effect in reducing HIV sexual risk behaviours. In addition, interventions should also target the mediator factors between CSA and HIV sexual risk behaviour. In this study, we found that potential mediator factors were any intimate partner violence, transactional sex, sexual compulsivity, and polysubstance use. Targeting these factors might also reduce HIV sexual risk behaviour and, consequently, the HIV burden among GBM. 4.4 Conclusion The findings of this research provide estimates of the sizes of GBM populations in the most populous provinces in Canada. 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Oct 2015;16(4):391-400. 85 Appendices Appendix A Sensitivity analysis of sociodemographic characteristics of participants included and excluded from the study. Included in analysis (N=1458) Excluded from analysis (N=113) P Value Categorical variables N % RDS% 95%CI N % RDS% 95%CI City <.0001 Montreal 662 91.2 85.4 (79.0, 91.7) 64 8.8 14.6 (8.3, 21.0) Toronto 310 93.1 91.9 (87.2, 96.6) 23 6.9 8.1 (3.3, 12.8) Vancouver 486 94.9 92.5 (86.3, 8.6) 26 5.1 7.5 (1.4, 13.7) Age 0.0021 18-29 645 92.7 87.1 (81.4, 92.7) 51 7.3 12.9 (7.2, 18.6) 30+ 813 92.9 91.2 (86.4, 96.0) 62 7.1 8.8 (4.0, 13.6) Ethnic group <.0001 Canadian 708 93.5 91.7 (87.3, 96.0) 49 6.5 8.3 (4.0, 12.6) European 294 93.6 91.1 (85.9, 96.29) 20 6.4 8.9 (3.7, 14.1) Asian 172 91.0 83.3 (70.0, 96.5) 17 9.0 16.7 (3.5, 30) 86 Included in analysis (N=1458) Excluded from analysis (N=113) P Value Categorical variables N % RDS% 95%CI N % RDS% 95%CI Other (includes Aboriginal, African/Caribbean/Black, Mixed Race and other) 284 91.3 87.0 (78.0, 96.13) 27 8.7 13.0 (3.9, 22.0) Annual income <.0001 <$30,000 655 89.5 84.5 (78.5, 90.4) 77 10.5 15.5 (9.6, 21.5) >=$30,000 803 95.7 95.9 (94.0, 97.9) 36 4.3 4.1 (2.1, 6.0) Marital status <.0001 Married (and not separated), Common-law 319 95.5 95.2 (92.2, 98.2) 15 4.5 4.8 (1.7, 7.8) Separated, Divorced, Widowed 91 91.0 91.2 (83.5, 98.9) 9 9.0 8.8 (1.1, 16.5) Single 1048 92.2 87.3 (82.47, 92.17) 89 7.8 12.7 (7.8, 17.5) Employment status 0.0068 No 299 88.2 85.4 (77.3, 93.5) 40 11.8 14.6 (6.5, 22.6) Yes 1159 94.1 90.7 (86.6, 94.7) 73 5.9 9.3 (5.2, 13.3) Highest education <.0001 87 Included in analysis (N=1458) Excluded from analysis (N=113) P Value Categorical variables N % RDS% 95%CI N % RDS% 95%CI High school or less 156 82.1 77.3 (66.3, 88.3) 34 17.9 22.7 (11.7, 33.8) Greater than high school 1302 94.3 91.2 (87.28, 95.21) 79 5.7 8.8 (4.79, 12.72) Born in Canada 0.6633 No, I was born in another country 562 92.3 88.8 (83.1, 94.4) 47 7.7 11.2 (5.6, 16.8) Yes, I was born in Canada 896 93.1 89.3 (84.2, 94.3) 66 6.9 10.7 (5.6, 15.7) Sexual orientation 0.0100 Gay 1244 93.3 88.8 (84.4, 93.2) 89 6.7 11.2 (6.8, 15.5) Bisexual 83 84.7 90.6 (83.8, 97.3) 15 15.3 9.4 (2.6, 16.2) Queer 102 98.1 98.7 (96.3, 100) 2 1.9 1.3 (0, 3.6) Other (includes Straight, Questioning, Asexual, Pansexual, Two-spirit and Other) 29 80.6 73.6 (41.8, 100) 7 19.4 26.4 (0, 58.1) Proud to be LGB 0.0179 Disagree (includes Disagree Strongly, Disagree, Disagree somewhat) 185 91.6 88.5 (80.2, 97.0) 17 8.4 11.5 (3.1, 19.8) 88 Included in analysis (N=1458) Excluded from analysis (N=113) P Value Categorical variables N % RDS% 95%CI N % RDS% 95%CI Agree (includes Agree, Agree somewhat, Agree Strongly) 1235 95.1 93.5 (90.1, 96.8) 63 4.9 6.5 (3.2, 9.8) Prefer not to answer 38 53.5 40.2 (20.4, 60.0) 33 46.5 59.8 (39.9,79.5) Continuous variable n Median (Q1-Q3) n Median (Q1-Q3) Financial Strain Index (Score) 1458 6 (5,9) 6 (5, 9) 113 8 (5, 10) 8 (5, 11) <.0001 89 Appendix B Somers’ D syndemic factors pairwise association matrix. HIV sexual risk behaviour Age Income Employment Education Born in Canada CSA PTSD ADD/ADHD Bipolar Borderline Polysubstance use HIV sexual risk behaviour 1.00 Age 0.02 1.00 Income -0.11 0.11 1.00 Employment 0.04 -0.08 0.23 1.00 Education -0.02 0.03 0.14 0.13 1.00 Born in Canada 0.03 -0.12 0.16 0.08 -0.27 1.00 CSA 0.14 0.12 -0.12 -0.12 -0.06 -0.05 1.00 PTSD 0.00 0.02 -0.01 -0.05 0.01 0.01 0.14 1.00 ADD/ADHD -0.02 -0.03 -0.04 -0.03 -0.03 0.05 0.04 0.20 1.00 Bipolar -0.01 0.04 -0.02 -0.05 0.03 0.01 0.03 0.18 0.14 1.00 Borderline 0.02 0.03 -0.04 -0.05 -0.01 0.01 0.07 0.13 0.14 0.35 1.00 Polysubstance use 0.16 -0.03 0.00 0.08 -0.04 0.11 0.06 0.23 0.12 0.02 0.31 1.00 Proud to be LGBT 0.00 -0.02 0.04 0.05 0.00 -0.02 0.02 0.03 -0.10 -0.10 -0.18 0.01 IPV 0.12 0.03 0.02 0.02 0.02 0.06 0.16 0.14 0.18 0.13 0.20 0.17 IPVP 0.14 0.04 0.04 0.00 -0.05 0.08 0.14 0.05 0.15 0.10 0.25 0.13 IPVV 0.12 0.03 0.04 0.00 0.02 0.06 0.18 0.16 0.16 0.16 0.22 0.18 Transactional sex 0.13 -0.03 -0.04 -0.01 -0.04 0.05 0.02 0.02 0.07 0.02 0.13 0.04 Worked as an escort 0.02 0.04 -0.04 -0.04 -0.14 0.06 0.10 0.06 0.10 0.02 0.01 0.03 AUDIT-C 0.02 -0.17 0.08 0.07 0.02 0.14 0.03 -0.04 0.09 0.05 -0.04 0.20 Anxiety 0.03 -0.09 -0.05 -0.02 -0.11 0.04 0.10 0.14 0.18 0.19 0.37 0.10 Depression 0.03 -0.01 -0.06 -0.06 -0.05 0.02 0.01 0.09 0.05 0.06 0.00 0.00 Sexual compulsivity 0.09 0.05 -0.05 -0.06 0.01 -0.11 0.10 0.09 0.09 0.15 0.33 0.00 First oral sexual encounter -0.02 -0.03 -0.01 -0.01 0.06 0.00 -0.22 -0.06 0.00 0.01 0.01 -0.04 First anal sexual encounter 0.00 -0.01 0.01 0.04 0.02 0.04 -0.12 -0.06 -0.02 -0.01 0.00 0.00 FSI 0.03 0.10 -0.37 -0.29 -0.20 -0.10 0.28 0.26 0.23 0.34 0.51 0.09 RMBAS -0.07 -0.18 0.01 0.05 0.11 0.04 -0.04 0.00 0.01 0.06 0.01 -0.01 Sexual coercion 0.15 -0.09 0.08 0.00 0.18 0.06 0.22 0.23 0.23 0.17 0.13 0.14 Notes: Numbers in red indicate a significant association. ADD/ADHD=Attention deficit disorders/attention deficit and hyperactivity disorder. CSA=childhood sexual abuse. FSI=financial strain index. IPV=Intimate partner violence. IPVP=intimate partner violence perpetration. IPVV=intimate partner violence victimization. PSTD=Post-traumatic stress disorder. RMBAS=Revised Male Body Attitudes Scale. 90 Proud to be LGBT IPV IPVP IPVV Transactional sex Worked as an escort AUDIT-C Anxiety Depression Sexual compulsivity First oral First anal FSI RMBAS Sexual coercion Proud to be LGBT 1.00 IPV -0.10 1.00 IPVP -0.16 0.64 1.00 IPVV -0.06 0.93 0.69 1.00 Transactional sex 0.01 0.03 0.06 0.03 1.00 Worked as an escort 0.01 0.05 0.06 0.05 0.27 1.00 AUDIT-C 0.03 0.05 0.07 0.05 0.06 0.15 1.00 Anxiety -0.09 0.19 0.19 0.20 0.08 0.06 0.05 1.00 Depression -0.05 0.03 0.02 0.04 0.02 0.04 -0.07 0.13 1.00 Sexual compulsivity -0.12 0.08 0.12 0.07 0.11 0.03 -0.08 0.12 0.18 1.00 First oral sexual encounter -0.03 -0.04 -0.06 -0.05 0.00 -0.12 -0.01 -0.06 0.02 -0.07 1.00 First anal sexual encounter -0.01 0.00 0.00 0.00 0.00 0.00 0.02 -0.02 0.00 -0.01 0.28 1.00 FSI -0.03 0.15 0.09 0.14 0.05 0.25 -0.04 0.17 0.18 0.26 -0.15 -0.17 1.00 RMBAS -0.04 -0.04 -0.01 -0.03 0.00 -0.16 0.16 0.27 0.14 0.00 0.06 0.02 -0.07 1.00 Sexual coercion -0.03 0.37 0.26 0.36 0.15 0.11 0.07 0.17 0.05 0.18 -0.15 -0.11 0.05 0.05 1.00 Notes: Numbers in red indicate a significant association. ADD/ADHD=Attention deficit disorders/attention deficit and hyperactivity disorder. CSA=childhood sexual abuse. FSI=financial strain index. IPV=Intimate partner violence. IPVP=intimate partner violence perpetration. IPVV=intimate partner violence victimization. PSTD=Post-traumatic stress disorder. RMBAS=Revised Male Body Attitudes Scale. 91 Appendix C Variable reduction procedure (VARCLUS) applied to syndemic factors and sociodemographic characteristics associated with HIV sexual risk behaviour Clusters Variable R-squared with 1-R**2 Variable Own Next Ratio Cluster Closest Cluster 1 Intimate partner violence 1 0.1966 0 Intimate partner violence perpetration 0.9428 0.1178 0.0649 Intimate partner violence victimization 0.9433 0.1992 0.0708 Cluster 2 Childhood sexual abuse 0.6951 0.0837 0.3327 Sexual coercion 0.6951 0.1654 0.3653 Cluster 3 Transactional sex 1 0.0379 0 Cluster 4 Income 1 0.0243 0 Cluster 5 Sexual compulsivity 1 0.062 0 Cluster 6 Polysubstance use 1 0.0389 0 Note: One variable from each cluster was selected (in red)
Thesis/Dissertation
eng
Experimental Medicine
Vancouver : University of British Columbia Library
Attribution-NonCommercial-NoDerivatives 4.0 International
Graduate
Lima, Viviane Dias
Estimating the size of the gay, bisexual and other men who have sex with men population and identifying syndemic factors and their effect on HIV sexual risk behaviour
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