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Social network and contextual influences on substance use and HIV risk behavior among young men who have sex with men

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Social network and contextual influences on substance use and HIV risk behavior among young men who have sex with men

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SOCIAL NETWORK AND CONTEXTUAL INFLUENCES ON
SUBSTANCE USE AND HIV RISK BEHAVIOR AMONG
YOUNG MEN WHO HAVE SEX WITH MEN

by

Ian Walter Holloway

A Dissertation Presented to the
FACULTY OF THE USC SCHOOL OF SOCIAL WORK
UNIVERSITY OF SOUTHERN CALIFORNIA
In Partial Fulfillment of the
Requirements for the Degree
DOCTOR OF PHILOSOPHY
(SOCIAL WORK)

August 2012

Copyright 2012 Ian Walter Holloway

ii

AC KNOWLED GMEN TS
This research was supported by a Ruth L. Kirschstein National Research Service
Award from the National Institute on Drug Abuse of the National Institutes of Health
(1F31DA031648-01). The content is solely my responsibility and does not necessarily
represent the views of the National Institute of Drug Abuse or the National Institutes of
Health.
While my name alone appears on the title page, this work could not have been
possible without the support and guidance of many individuals. I am deeply grateful to
each member of my dissertation committee: Drs. Lawrence Palinkas, Dorian Traube, Eric
Rice, Michele Kipke and Lynn Miller. Their expertise and support were crucial in the
formulation and completion of this dissertation. I am especially grateful to Dr. Palinkas,
my dissertation committee chair and mentor during the five years I spent at the University
of Southern California School of Social Work. Dr. Palinkas has always been encouraging
of my ideas, supportive of my decisions and generous with his time – three things that have
made all of the difference in my success at USC.
The years I have spent at USC have been some of the most challenging and
fulfilling of my life. Our Program Chair, Dr. Michalle Mor Barak, and our Program
Coordinator, Malinda Sampson, have been instrumental in creating a supportive
environment where doctoral students can flourish. For that, I am extremely grateful. I also
wish to extend my thanks to the members of my doctoral cohort: Dahlia Fuentes, Rohini
iii

Pahwa, Ling Xu, Hsin-Yi Hsiao and Minkyoung Rhee. We entered the doctoral program
together and have been a constant source of support for each other. I could not imagine a
better group of people with whom I could have shared this experience.
My friends and colleagues outside USC played a key role in shaping this
dissertation. Sheree Schrager from the Division of Adolescent Medicine at Children’s
Hospital Los Angeles was always willing to consult on statistical analysis. Jeremy
Goldbach and Shannon Dunlap generously read early drafts and offered critical feedback.
Their expertise on HIV prevention and service delivery with YMSM was especially helpful
in thinking about implications of this research for social work practice and policy.
Finally, I would like to express my gratitude and love to my partner, Felipe, and my
wonderful family. My mom, dad, sister, brother and grandmother have been my primary
social support network since childhood and throughout my doctoral studies. I especially
want to acknowledge my mom, Eve Holloway, who has encouraged my love of reading
and writing from an early age. Over the past two years, she has devoted considerable time
toward helping with my dissertation, always willing to proofread a chapter or format a
Table, and for that I am eternally grateful.

iv

TABLE OF CON TEN TS
Acknowledgments ii
List of Tables vi
List of Figures viii
Abstract xi
Chapter One: Introduction
Background of the problem 1
Purpose of the study 3
Specific aims and hypotheses 3
Significance of the study 4
Methodology 5
Organization of the study 6

Chapter Two: Literature Review
Prevalence of HIV risk among YMSM 7
Correlates of HIV risk among YMSM 9
Substance use among YMSM 13
Correlates of substance use among YMSM 14
Association between substance use and HIV risk in YMSM 18
Gay venue attendance, substance use, and sexual risk behavior 20
Conclusion 27

Chapter Three: Theoretical Framework 3
Social Action Theory 28
Theory of Duality of Persons and Groups 33
Conclusion 36

Chapter Four: Methods
Data source 37
Study design 39
Statistical methods 47
Hypothesis testing 51

v

Chapter Five: Person Network Results
Description of the sample 55
Substance use 59
HIV testing and sexual behaviors 65
Social network visualizations 68
Social network metrics 72

Chapter Six: Venue Network Results
Description of venues 113
Hypothesis testing 137

Chapter Seven: Race/ethnicity Sub-Analysis
Network visualization 149
Social network metrics 153

Chapter Eight: Discussion
Person social network 161
Limitations and suggestions for further study 175
Conclusion 179

References 180

Appendix 188

vi

LIST OF TABLES
Table 1: Venues by type included in the final HYM sampling frame 38
Table 2: Social network metrics used in the present analysis 49
Table 3: Demographic characteristics of final sample 57
Table 4: Substance use behaviors of final sample 60
Table 5: Substance use indices for final sample 64
Table 6: Sexual behaviors and HIV testing characteristics of final sample 66
Table 7: Comparison of social network metrics for person network as
venue-sharing threshold is raised 72
Table 8: E-I indices based on demographic characteristics 101
Table 9: E-I indices based on substance use variables 103
Table 10: E-I indices based on sexual risk behavior and HIV testing
variables 104
Table 11: Mean homophily scores by demographic characteristics with
comparisons between groups 106
Table 12: Mean homophily scores by substance use with comparisons
between groups 108
Table 13: Mean homophily scores by sex and HIV testing variables with
comparisons between groups 111
Table 14: Venue characteristics 114
Table 15: Mean venue network centrality metrics with varying degrees of
exclusion 126
Table 16: E-I index based on venue type, location, and risk 128
Table 17: Egocentric homophily based on venue type, location and risk 129
vii

Table 18: Comparison of low K-core venues and high K-core venues
by demographic characteristics 134
Table 19: Comparison of low K-core venues and high K-core venues
by substance use characteristics 135
Table 20: Comparison of low K-core venues and high K-core venues
by sexual risk behavior and HIV testing characteristics 136
Table 21: Comparison of venues by risk type by demographic
variables 138
Table 22: Comparison of venues by risk type by substance use
variables 139
Table 23: Comparison of venues by risk type of sexual risk and HIV
testing variables 140
Table 24: Comparison of social network metrics between low-risk
and high-risk venues 148
Table 25: Social network metrics of venue network by racial/ethnic
group 154
Table 26: Comparison of top 10 most nominated venues by
racial/ethnic group 155
Table A1: Statistical comparisons on all variables by those who
responded to the name-generator versus those who did not 198
Table A2: Comparison of top 6 venues by demographic characteristics 205
Table A3: Comparison of top 6 venues by substance use variables 207
Table A4: Comparison of top 6 venues by sexual risk behavior and
HIV testing variables 209

viii

LIST OF FIGURES
Figure 1: Estimated new immunodeficiency virus (HIV) infections
by transmission category, extended back-calculation model,
50 U.S. States and the District of Columbia, 1977 – 2006 8
Figure 2: Social Action Theory: Contextual model representing
self-regulation as a subcomponent of large social and
environmental systems 30
Figure 3: Illustration of matrix manipulation using the Theory of
Duality of Persons and Groups 35
Figure 4: Person network with one or more shared venues 69
Figure 5: Person network with two or more venues shared 70
Figure 6: Person network with three venues shared 71
Figure 7: Person network by age category 78
Figure 8: Person network by race/ethnic group 79
Figure 9: Person network by residential status 80
Figure 10: Person network by currently attending school 81
Figure 11: Person network by currently working 82
Figure 12: Person network by sexual identity 83
Figure 13: Person network by relationship status 84
Figure 14: Person network by past 3-month alcohol use 86
Figure 15: Person network by past 3-month cigarette use 87
Figure 16: Person network by past 3-month marijuana use 88
Figure 17: Person network by past 3-month illicit substance use 89
Figure 18: Person network by past 3-month club drug use 90
ix

Figure 19: Person network by alcohol use index 91
Figure 20: Person network by cigarette smoking index 92
Figure 21: Person network by marijuana use index 93
Figure 22: Person network by illicit substance use index 94
Figure 23: Person network by recent multiple partner UAI 95
Figure 24: Person network by lifetime STI diagnosis 96
Figure 25: Person network by recent STI diagnosis 97
Figure 26: Person network by lifetime HIV test 98
Figure 27: Person network by recent HIV test 99
Figure 28: Venue social network 115
Figure 29: Venue social network with tie strength depicted 117
Figure 30: HYM venue network with person sharing of 1 or more 118
Figure 31: HYM venue network with person sharing of 2 or more 119
Figure 32: HYM venue network with person sharing of 5 or more 119
Figure 33: HYM venue network with person sharing of 10 or more 120
Figure 34: HYM venue network with person sharing of 30 or more 120
Figure 35: Venue network coded by venue type 123
Figure 36: Venue network coded by venue location 124
Figure 37: Venue network coded by risk venue 125
Figure 38: Venue network with K-core analysis (spring embedding) 131
Figure 39: Venue network with K-core analysis (graph by K-core
attribute on x-axis) 132

x

Figure 40: Percentage of total sample captured by high-risk venues
and low-risk venues individually 142
Figure 41: Percentage of total sample captured when low-risk and
high-risk venues are connected by 1-degree of separation 143
Figure 42: Venue network grouped by low-risk and high-risk venue type 145
Figure 43: High-risk venue network 146
Figure 44: Low-risk venue network 147
Figure 45: Venue network restricted to African American respondents 150
Figure 46: Venue network restricted to Latino respondents 151
Figure 47: Venue network restricted to white respondents 152

xi

ABSTRAC T
In the United States, men who have sex with men (MSM) remain the group most
affected by HIV with younger men at particular risk for new infection. Venue-based HIV
prevention shows promise in reducing the spread of HIV among young MSM (YMSM);
however, little research has been conducted on the social contexts where YMSM
congregate and engage in behaviors that promote HIV risk, such as substance use.
Informed by Social Action Theory (Ewart, 1991), which emphasizes the importance of
the social environment in influencing individual behavior and has been used to explain
substance use and sexual risk behavior among YMSM previously, the present study
examines YMSM socialization patterns and their association with substance use and
sexual risk behavior. Participants (N=484) nominated their top three favorite places to
socialize in Los Angeles, California; these affiliation data were transformed into two
networks: one that connected YMSM through shared venue attendance (i.e., person
network) and another that connected venues through co-nomination by YMSM (i.e.,
venue network). Social network analysis techniques were used to identify structural
properties of both networks, which were then associated with substance use and sexual
risk behavior of participants.
Over 100 venues were nominated as favorite places to socialize. Almost all
participants (99%) were connected by at least one venue and over 80% were connected
by two or more venues, indicating a high potential for social interaction among YMSM
based on shared venue attendance. Nearly all venues in the center of the venue network
xii

were bars and dance clubs classified as ―High-risk‖ by a Community Advisory Board.
However, a few key coffee shops and restaurants classified as ―Low-risk‖ were also
central. The majority of YMSM indicated recent alcohol use (94%) and large percentages
had smoked cigarettes (82%) and used marijuana (62%) in the past 3 months. Users of
these commonly available substances were more central to the person network and were
positioned next to other users more frequently than non-users were positioned next to
non-users. Illicit substance users (29%) and club drug users (23%) were evenly dispersed
throughout the person network and were more likely to be connected to non-users than
other users. YMSM who had engaged in unprotected anal intercourse with serodiscordant
or multiple partners in the past 3 months (26%) and those previously diagnosed with an
STI (25%) were also dispersed throughout the person network.
YMSM in Los Angeles are highly interconnected based on their shared
attendance at a core group of similar and geographically proximal venues. These findings
indicate the potential utility of targeted, venue-based HIV prevention that can be diffused
quickly to large numbers of YMSM who attend a core group of popular social venues.
Highly shared ―Low-risk‖ venues (e.g., coffee shops) may be particularly useful in
staging primary HIV prevention interventions that can be disseminated by YMSM who
cross over into more ―High-risk‖ venues. Findings from the present research lend support
to prior research on co-occurring health problems among YMSM and demonstrate the
ways in which HIV-related risk behavior are concentrated in popular YMSM social
contexts.

1

CHAPTER ONE: IN TRODUCTION
Background of the problem
Recent Centers for Disease Control data suggests that almost two thirds of
adolescents between the ages 13 and 29 were infected with HIV through male-to-male
sexual contact (CDC, 2011a). While the HIV incidence rate has remained relatively
stable over the U.S. population as a whole, infections among YMSM steadily increased
between 2006 and 2009 (Prejean et al., 2011). Among the most frequently examined
potential correlates of HIV risk behavior are substance use and substance abuse (Clatts,
Goldsamt & Yi, 2005; Dudley, Rostosky, Korfhage & Zimmerman, 2004; Mansergh et
al., 2001). Not only do YMSM have a higher prevalence of substance use and substance
abuse than their heterosexual counterparts (Russell, Driscoll & Truong, 2002), but
research also suggests that substance use and abuse are associated with a higher
prevalence of Sexual risk behavior among YMSM (Stueve, O'Donnell, Duran, Doval &
Geier, 2002; Waldo, McFarland, Katz, MacKellar & Valleroy, 2000).
Despite the expense and effort in developing and implementing behavioral
interventions to reduce HIV risk behaviors among YMSM, epidemiological data
documents a 34% increase in HIV infection rates among this population between 2006
and 2009 (Prejean et al., 2011). Evidence points to a disconnect between current HIV
prevention approaches and interest in those approaches among YMSM (Orellana,
Picciano, Roffman, Swanson & Kalichman, 2006). For example, surveillance and
prevention data show that among YMSM those who are older are over-represented in
2

HIV prevention programs, whether clinical or research based (Iguchi et al., 2009; Koblin
et al., 2003; Orellana et al., 2006). Some researchers have suggested that YMSM may
feel less vulnerable to HIV, which makes them less committed to changing HIV risk
behavior (Rutledge, Roffman, Picciano, Kalichman, & Berghuis, 2002). Others have
suggested that YMSM who reached sexual maturity in the age of highly active
antiretroviral therapies may feel complacency about Sexual risk behavior, since HIV can
now be managed as many other chronic diseases (Valdisseri, 2004).
A recent study using qualitative interviews to understand barriers and facilitators
to future HIV prevention approaches with YMSM in Los Angeles, CA found that YMSM
were reluctant to attend formal HIV prevention programs because they believed they
already knew the information required to protect themselves from contracting HIV and
that they were too busy to attend a formal HIV prevention program (Holloway,
Cederbaum, Ajayi, & Shoptaw, in press). When asked what would encourage them to
attend a prevention program in the future, YMSM indicated that they would be interested
in a program that was delivered through their social networks and/or delivered in contexts
that they were already attending. However, little is known about the social contexts in
which YMSM congregate and their potential utility as sites for HIV prevention efforts.

3

Purpose of the study
The present research sought to understand socialization patterns of a diverse
group of YMSM in Los Angeles, California and identify venues in which HIV prevention
efforts might be most successful with this population. Using a social network analysis
approach, this research was designed to understand how YMSM were connected to each
other through shared social spaces and whether the social spaces that YMSM share were
associated with different types of substance use and sexual risk behavior. In addition,
socialization patterns and venue popularity based on key demographic characteristics,
such as age, Race/ethnicity, and residential status were examined with the ultimate goal
of informing future HIV prevention interventions developed for and targeted to YMSM.
Specific aims and hypotheses
Specific Aim I: To describe social network patterns of YMSM by sociodemographic
characteristics, substance use, and sexual risk behavior.
Hypothesis I: Social networks of YMSM will be structured according to the
principle of homophily; that is, participants with similar demographic
characteristics and similar types of substance use and sexual risk behavior will
cluster together.
Specific Aim II: Understand factors related to the social contexts that are most popular
among YMSM and how those contexts are associated with varying levels of substance
use and sexual risk behavior.

4

Hypothesis II: Social contexts where substance use and/or Sexual risk behavior
is/are sanctioned (e.g., bars, clubs, bathhouses) will be associated with higher
levels of risk behaviors; and
Hypothesis III: YMSM who engage in substance use and sexual risk behavior will
either attend contexts that overtly promote risk behavior (e.g., bathhouses) or
contexts that do not overtly promote risk behavior (e.g., coffee shops).
Significance of the study
This study has significant implications for HIV prevention with YMSM. First,
this research will document the popularity of different types of social venues among a
large, diverse sample of YMSM. This information can be useful when deciding how to
allocate limited resources for venue-based HIV prevention in order to maximize the reach
of intervention delivery. Second, the study will demonstrate the interconnectivity of
YMSM in this sample and the venues they attend. While HIV prevention program
delivery might be difficult in a bar or club where substances are being used, this study
will identify the degree of overlap between these venues and other social spaces where
YMSM may be more amenable to intervention. Finally, this study highlights an
innovative methodology for understanding the relationship between social context and
individual behavior. This approach is useful in the field of HIV prevention but may also
be applied to other types of health behavior research where the social environment plays
a key role in health decision-making.

5

Methodology
The present research used an innovative social network analysis technique to
analyze secondary data from the Healthy Young Men’s (HYM) Study (R01DA015638), a
National Institute of Drug Abuse-funded study of YMSM in Los Angeles, California
(Kipke, Kubicek et al., 2007). YMSM were asked their three favorite gay places to
socialize. In total, 484 men nominated as least one favorite gay place to socialize; a total
of 110 unique venues were identified. The data analysis was guided by the Theory of
Duality of Persons and Groups (Breiger, 1974), a social network theory that outlines a
methodology for transforming affiliation data (person by venue) into two unique social
networks (person by person and venue by venue). In the person network, YMSM were
connected by shared social spaces; in the venue network, venues were connected by
YMSM who co-nominated multiple venues. The person network analysis was used to
address Specific Aim I and Hypothesis I. The venue network was used to address
Specific Aim II and hypotheses II and III. Both person and venue networks were
analyzed in three steps. First, networks were mapped using NETDRAW (Borgatti, 2002)
and visually inspected to determine patterns and descriptive statistics were reported.
Next, a series of social network metrics were calculated for each network in UCINET
(Borgatti, Everett & Freeman, 2002) to determine overall network properties. Finally,
inferential statistics were used to test the hypotheses outlined above.

6

Organization of the study
This dissertation consists of eight chapters. Chapter One presents the background
of the problem under study, the purpose of the study, the specific aims to be addressed,
the significance of the problem, and the methodology used. Chapter Two is a review of
the relevant literature. It addresses the following topics: prevalence and correlates of HIV
risk and substance use among YMSM, the relationship between substance use and HIV
risk among YMSM, and the role of gay venue attendance in substance use and HIV risk
in this population. Chapter Three provides the theoretical framework for the present
study, which incorporates principles from Social Action Theory (Ewart, 1991) and the
Theory of Duality of Persons and Groups (Breiger, 1974). Chapter Four presents the
methodology used in the study, including the research aims, the research design, and the
analysis plan. Chapter Five presents the findings from the person network analysis, which
was used to address Specific Aim I and Hypothesis I. Chapter Six presents the findings
from the venue network analysis, which was used to address Specific Aim II and
Hypotheses II and III. Chapter Seven presents an additional venue sub-analysis to
elucidate findings related to Race/ethnicity in the previous two chapters. Chapter Eight
presents the discussion and interpretation of study findings, implication of findings for
social work practice and policy, study limitations and future directions.
7

CHAPTER TWO: LITERA TURE REVIEW
This chapter is intended to demonstrate the public health relevance of HIV and
substance use among young men who have sex with men (YMSM). Reviewing the
existing literature on HIV risk and substance use among YMSM will also help to inform
the present study, which seeks to better understand the associations between gay
community venues, substance use and sexual risk behavior in this population in order to
inform future substance use and HIV prevention approaches with this population. This
review first provides an overview of the literature related to the prevalence and correlates
of HIV risk and substance use among YMSM followed by material on the association
between HIV and substance use in this population. The next section focuses specifically
on the connection between venue attendance, substance use, and sexual risk behavior by
YMSM. The last section presents the rationale for the present study in light of existing
literature.
Prevalence of HIV risk among YMSM
In the early 1980’s, when HIV first became a public health concern in the United States,
the disease was almost exclusively associated with the sexual behaviors of men who have
sex with men (MSM) (Engel, 2006). Today, over thirty years since the discovery of HIV,
MSM remain the group most affected by HIV in this county. Recent estimates indicate
over 60% of new infections occur among MSM (Prejean et al., 2011), an incidence rate
44 times that of their heterosexual counterparts (CDC, 2011b). As can be seen in Figure
8

1, MSM represent the only major risk group with steadily increasing rates of HIV
infection over the last decade (Hall et al., 2008).

Figure 1. Estimated new Immunodeficiency Virus (HIV) infections by transmission
category Extended back-calculation model, 50 US States and the District of Columbia,
1977 – 2006.

Note
From ―Estimation of HIV incidence in the United States‖ by Hall, H. I., Song, R., and
Rhodes, P., Prejean, J., An, Q.…Janssen, R. S, 2008, Journal of the American
Medical Association, 300(5), p.520-529. Copyright 2008 by Journal of the American
Medical Association. Reprinted with permission.

9

Young MSM (YMSM) are also disproportionately infected with HIV. Recent
CDC data suggests that almost two thirds of adolescents between the ages 13 and 29 were
infected through male-to-male sexual contact (CDC, 2011a). While the HIV incidence
rate has remained relatively stable over the U.S. population as a whole, infections among
YMSM steadily increased between 2006 and 2009 (Prejean et al., 2011). In addition, a
national study of awareness and prevention demonstrated that YMSM were 63% less
likely to be aware of their HIV status than their heterosexual counterparts (CDC, 2010).
Numerous studies have demonstrated key factors associated with HIV risk among
YMSM. The following section will outline recent research focused on this population and
identify demographic, psychological and behavioral risk factors for HIV among YMSM.
Correlates of HIV risk among YMSM
Race/ethnicity and HIV infection among YMSM
African American men have the highest incidence of HIV/AIDS when compared
to all other racial and ethnic groups and an incidence almost 8 times higher than their
White counterparts (Prejean et al., 2011). Numerous studies have demonstrated that
African American YMSM experience higher prevalence of HIV than any other racial or
ethnic group within the YMSM community (Agwu & Ellen, 2009; Celentano, Sifakis,
Hylton, Torian, Guillin & Koblin, 2005; Clerkin, Newcomb & Mustanski, 2011;
Feldman, 2010; Garofalo, Mustanski, Johnson & Emerson, 2010; Halkitis et al., 2011;
Harawa et al., 2004; Outlaw et al., 2010; Sifakis et al., 2007). However, studies have
demonstrated that African American YMSM do not exceed the behavioral risk-taking of
10

Latino and White YMSM. In a study on racial and ethnic differences associated with HIV
prevalence and risk, all substance use categories as well as condom use with oral and anal
sex were not significantly concomitant with higher HIV prevalence among African
American YMSM (Celentano et al., 2005). Furthermore, compared with Latino and
White YMSM, African American YMSM were found to have less unprotected sex and
fewer insertive and receptive anal sex partners (Garofalo et al., 2010; Harawa et al.,
2004). However, a more recent study on Race/ethnicity and HIV among YMSM found
that African American YMSM reported more receptive anal sex with High-risk partners
as compared with White and Latino YMSM (Garofalo et al., 2010).
Some have attributed high rates of HIV among African American men to the
closed nature of these men’s sexual networks (Adimora, Schoenbach & Doherty, 2006).
African American YMSM are more likely to choose partners of the same race, putting
them at risk for HIV transmission due to higher HIV prevalence within the African
American community than all other racial and ethnic groups (Bocour, Renaud, Wong,
Udeagu & Shepard, 2011; Clerkin et al., 2011; Feldman, 2010; Halkitis et al., 2011;
Harawa et al., 2004; Sifakis et al., 2007). Studies by Celentano et al. (2005) and Clerkin
et al. (2011) found African American YMSM report higher frequency of condom use
when partnered with other African American YMSM. Conflicting research, however, has
found that unprotected anal intercourse with a main male partner was higher than with
casual partners for African American YMSM (26.5% vs. 12.1% respectively) (Hurt et al.,
2010). A recent study by VanDevanter and colleagues (2011) found that infrequent
condom use among African American YMSM was influenced by multiple factors such as
11

serosorting, beliefs of partners’ responsibility to wear a condom, older partners’ refusal to
wear a condom and/or history of sex work. Although these factors may not necessarily be
a consequence of intra-racial sexual networks, it does suggest that relationship dynamics
between partners is imperative when ascertaining risk among African American YMSM.
While Latinos represent 16% of the U.S. population they constituted 20% of HIV
cases in the United States in 2009 (CDC, 2011c). Correlates of HIV infection among
Latino YMSM include factors related to culture. For example, one study demonstrated
that Latino YMSM who were less acculturated and who had less education were more
likely to engage in unprotected insertive anal intercourse (Agronick et al., 2004). Seminal
work by Diaz and Ayala (1999) on cultural factors and HIV risk among MSM, in general,
has been extended in recent years by Jarama and colleagues (2005), who found that
communication about HIV, machismo, and experiences of discrimination among Latino
MSM were all associated with Sexual risk behavior.
Residential status and HIV infection among YMSM
Other factors associated with HIV infection among YMSM are risks associated
with housing instability. Kipke, Weiss and Wong (2007) found that YMSM who had
been forced to leave their homes due to their Sexual identity were more likely to engage
in both substance use and HIV risk behaviors. In the same study, YMSM living with
family were found to be at the lowest risk for negative health outcomes. Another study
examining homelessness and housing instability found that YMSM with history of
homelessness reported more sexual partners and more occasions of forced sex or survival
sex, placing them at increased risk for HIV exposure (LaLota, Kwan, Waters, Hernandez
12

& Liberti, 2005). Both LaLota et al. (2005) and Kipke, Weiss et al. (2007) found higher
percentages of homelessness among Latinos and African Americans. Kipke, Weiss et al.
(2007) found that although African American YMSM reported increased homelessness
history, White YMSM were more likely to be kicked out of the home due to Sexual
identity. However, a study examining predictors of unprotected anal intercourse among
African American, Latino and White MSM, in general, indicated that homelessness was a
significant predictor of unprotected sex among African American YMSM because they
tended to display more attachment to their racial/ethnic community and less attachment to
the gay community, leaving them with a limited support system if homeless (Warren et
al., 2008). This attachment construct was critical as being kicked out of home due to gay
identity compounded feelings of isolation from family and racial/ethnic community
(Warren et al., 2008).
Types of relationships and HIV infection among YMSM
Differences between casual and steady partnerships may also contribute to HIV
risk behaviors among YMSM. While sex with multiple casual sexual partners presents
clear risk for HIV infection, many researchers have exposed the unique risks found
within steady and primary male partnerships. Generally, unprotected anal intercourse
among YMSM is more common in steady relationships (Davidovich, deWit & Stroebe,
2000; Dudley et al., 2004; Hart & Peterson, 2004). However, perceived safety within
primary relationships may place YMSM at increased risk in the absence of continued
communication, negotiated safety, and HIV testing. Negotiated safety is a term that has
been used in the extant literature to describe HIV-negative men in primary partnerships
13

who engage in sexual behaviors with partners other than their main partner based on a set
of rules determined by the couple (Guzman et al., 2005). A study of MSM in general
demonstrated that among the men 18-29, a higher percentage used ―negotiated safety‖
tactics compared to older men (Guzman et al., 2005). It should also be noted that among
the total sample, 29% violated their negotiated safety rule during the previous 3 months.
Research specifically with YMSM has demonstrated engagement in unprotected anal
intercourse prior to negotiated safety decisions with their primary partner (Davidovich et
al., 2000).
Substance use among YMSM
Higher rates of substance abuse exist among samples of sexual minority
individuals when compared to the general population (Marshal et al., 2008; Moon, Fornili
& Bryant, 2007). Specifically, research demonstrates that YMSM are at increased risk for
substance use and substance abuse (Kipke, Kubicek et al., 2007; Moon et al., 2007),
including the use of cigarettes (Holloway et al., 2012; Storholm, Halkitis, Siconolfi &
Moeller, 2011), alcohol and marijuana (Russel et al., 2002) cocaine, ecstasy and other
club drugs (Kipke, Weiss et al., 2007). YMSM are also more likely to use multiple
substances at the same time (Clatts et al., 2005). For example, in a study of 172 YMSM
recruited online, 48.5% endorsed using club drugs (defined here as crystal
methamphetamine, ecstasy, poppers, cocaine and Viagra) and of those, 50.6% used two
or more at the same time and 25.3% used three or more at the same time (Fernandez et
14

al., 2010). Chronic club drug users also report high rates of lifetime exposure to multiple
substances and active polysubstance use (Clatts et al. 2005).
Correlates of substance use among YMSM
A growing body of research with YMSM suggests that there are cultural elements
underpinning initiation into substance use (Kubicek et al., 2007; Mustanski, Newcomb,
Du Bois, Garcia & Grov, 2011), which typically begins in social settings (Halkitis,
Parsons & Wilton, 2003; Halkitis, Fischgrund & Parsons, 2005; Parsons, Kelly & Weiser,
2007). These studies demonstrate that factors related to gay community connection,
psychological distress, and several qualitative studies suggest that YMSMs’ use of
substances permits them to become more social and act in ways that bring about social
prestige, sexual inhibition and productivity within the gay community (Halkitis et al.,
2005; Kubicek et al., 2007). These factors associated with gay community involvement
along with psychosocial stressors, including negative life experiences are described
below.
Gay community involvement and substance use among YMSM
Previous work among YMSM has demonstrated that integration into gay
communities may lead to increased substance use patterns (Stall, Friedman & Catania,
2008). For many YMSM, substance use may afford protective capacities that stimulate
comfort in exploring Sexual identity while navigating new social situations and venues
(Kubicek et al., 2007; Mutchler et al., 2011; Rosario, Schrimshaw & Hunter, 2004). In
addition to enhancing peer connections and sociability, substance use often serves as a
15

vehicle for engagement in sexual experiences and relationships (Celentano et al., 2006;
McKay, McDavitt, George & Mutchler, 2010).
A study by Wong, Kipke and Weiss (2008) on binge drinking among YMSM
found that frequent bingers reported higher levels of Sexual identity disclosure to friends,
more friends who engaged in risk behaviors, and higher levels of gay bar/club attendance
compared to non- and light-users (Wong et al., 2008). Another study with the same group
of YMSM examining cigarette smoking (Holloway et al., 2012) found that although more
frequent gay bar/club attendance was associated with cigarette smoking, gay community
connection was not directly associated with cigarette smoking. Instead, gay community
connection was mediated by decreased internalized homophobia, which was in turn
negatively associated with cigarette smoking. Taken together these studies indicate the
complex role of the gay community with regard to substance use among YMSM. Peer
pressure to conform to perceived norms within gay-identified social scenes may facilitate
the development of detrimental health consequences associated with substance use and
substance abuse (Kubicek et al., 2007; McKay et al., 2010). However, simultaneously,
affiliation with the gay community connection may benefit YMSM by reducing
psychological distress related to sexual minority status.
However, for racial/ethnic minority YMSM, integration into the gay community
may be made more difficult due to experiences of racism within the gay community,
which may lead to substance use. For example, while African American and Latino
YMSM are less likely to report substance use than their White counterparts, those who
report illicit substance use also report higher levels of racism, measured by a composite
16

index that included physical assault due to race and experiences of racism in gay social
setting and/or sexual relationships (Wong, Kipke, Weiss & McDavitt, 2010). A recent
study of African American YMSM in the Los Angeles House and Ball Communities
found that while experiences of social sexual racism were associated with psychological
distress; however, this distress was buffered by greater involvement with the House and
Ball Communities (Wong, Schrager, Holloway, Meyer & Kipke, under review).
Psychosocial stressors and substance use among YMSM
The number of negative life events, availability of support systems, and early age
of stress experiences all impact susceptibility for drug use. Research with sexual minority
populations in general (Meyer, 2003) and sexual minority youth (Bontempo & D’Augelli,
2002) have demonstrated that stressors specific to the experience of being LGBT can
increase risk for substance use and substance abuse. While less research has been
conducted on YMSM specifically, some studies do show associations between increased
levels of gay-related stress and substance use (Rosario, Rotheram-Borus & Reid, 1996).
Exposure to stress related to Sexual identity may place YMSM at unique risk for
substance use. For example, YMSM who experienced homophobia, witnessed or were
victims of childhood violence within the home, partner violence, and/or institutional
racism had higher levels of substance abuse than those who had not (Traube, Holloway,
Schrager & Kipke, 2011; Wong et al., 2010). In research on stress experiences and
alcohol use by YMSM, it was found that non-users and light-users reported both fewer
stressful life events and increased life satisfaction than YMSM who were occasional or
frequent binge drinkers (Wong et al., 2008).
17

Sexual minority youth in general, who experience negative life events may have
limited networks of supportive adults, peers, or other community members to access for
support, and may use drugs to cope with feelings of isolation and loneliness (Espelage,
Aragon, Birkett & Koenig, 2008). Indeed, studies of MSM in general have demonstrated
that substance use may occur to cope with feelings of alienation (Cabaj, 2000; Kurtz,
2005). Among YMSM, substance use has been associated with decreasing anxiety and
increasing emotional and physical comfort during initiation into gay social settings and
sexual relationships (McKay et al., 2010; Rosario et al., 2006). This is consistent with
work on MSM in general that implicates substance use in allowing men to increase
openness and reduce anxiety related to gay sexual activity (Halkitis et al., 2005).
Peer influence on substance use among YMSM
Among YMSM, those who report having more friends who engage in risk
behaviors, including substance use, report more substance use themselves (Traube et al.,
2011; Wong et al., 2008). Specifically, Wong and colleagues (2008) found that YMSM
who were frequent binge drinkers had more friends who engaged in risk behaviors, and
Traube and colleagues (2011) found that motivations to engage in risk behavior, which
included friends’ risk behaviors were directly associated with illicit substance use. A
qualitative study by Kubicek and colleagues (2007) on club drug use (defined as ecstasy,
crystal methamphetamine and cocaine) found that real and perceived social network
norms often defined the context of use. For example, ecstasy was highly associated with
bar and club settings, whereas crystal methamphetamine was used more frequently at
home with friends or within a sexual context (Kubicek et al., 2007). Kubicek and
18

colleagues (2007) also found that ecstasy was perceived as a community drug used at
clubs or raves but less useful within sexual experiences. On the other hand, YMSM
viewed crystal methamphetamine as a substance that was negatively perceived by others
and was used primarily in sexual situations (Kubicek et al., 2007).
Association between substance use and HIV risk in YMSM
The association between substance use and HIV risk behavior are well
documented in general (Guo et al., 2002; Malow et al., 2001; Tapert, Aarons, Sedlar &
Brown, 2001) and among YMSM specifically (Clatts et al., 2005; Dudley et al., 2004;
Mansergh et al., 2001). Not only do YMSM have a higher prevalence of substance use
and substance abuse than their heterosexual counterparts (Kipke, Weiss et al., 2007;
Russell et al., 2002) but research also suggests that substance use and substance abuse are
associated with a higher prevalence of Sexual risk behavior among YMSM (Stueve et al.,
2002; Waldo et al., 2000). For example, substance use among YMSM has been correlated
to a higher number of sexual partners (Shoptaw et al., 2005) and a decrease in use of
condoms (Semple, Patterson & Grant, 2002).
Studies among MSM in general, closely link substance use and sexual risk
behavior with Sexual identity and/or expression (Lye Chng & Geliga-Vargas, 2000; Stall
& Purcell, 2000; Vicioso, Parsons, Nanin, Purcell & Woods, 2005). Crystal
methamphetamine has been given particular attention over the past decade due to its
association with a range of behaviors that increase HIV transmission, including higher
numbers of sexual partners, lower condom use, greater likelihood of exchanging sex for
19

money or drugs, and greater likelihood of having had a sexually transmitted infection
(Rusch, Lampinen, Schilder & Hogg, 2004). YMSM who used methamphetamine as their
primary drug were 6 times more likely to endorse unprotected anal sex within the past
year and 4 times more likely to endorse multiple anal sex partners within the last 90 days
(Garofalo, Mustanski, McKirnan, Herrick & Donenberg, 2007).
While substance use during sexual experiences may increase arousal and
emotional comfort, the protective strategies necessary in the prevention of STIs and HIV
are difficult to enact when using substances (Celentano et al., 2006; Clatts et al., 2005;
Garofalo et al., 2007). Those YMSM who used alcohol and/or methamphetamine prior to
or during sexual encounters were found to be two to three times less likely to use
condoms (Mutchler et al., 2011). Celentano and colleagues (2006) found that YMSMs’
reports of unprotected receptive anal intercourse and/or unprotected insertive anal
intercourse within the last six months were associated with alcohol, cocaine and
methamphetamine use. Clatts and colleagues (2005) found that during their last sexual
encounter, 59% of YMSM studied used club drugs (i.e., speed, ketamine and/or MDMA)
and 49% of their partners used club drugs. Despite increased reports of protective
strategies employed during IAI within their last sexual encounter, 26% reported never
using a condom for insertive anal intercourse and 32% reported never using a condom for
receptive anal intercourse with most frequent partner within the last month (Clatts et al.,
2005). Other studies have demonstrated that YMSM often used substances and/or
engaged in sex without a condom due to pressure from their sexual or intimate partner
(Garofalo et al., 2007; Kubicek et al., 2007).
20

Gay venue attendance, substance use, and sexual risk behavior
HIV researchers have explored the role of gay venue attendance in facilitating
both substance use and sexual risk behavior among MSM in general, and to a lesser
extent YMSM. For example, it is well documented that Sexual risk behavior is common
in gay social settings where substances are being used (Halkitis et al., 2005; Kubicek et
al., 2007; Shoptaw et al., 2005; Vicioso et al., 2005). Those venues most studied include
contexts such as bathhouses, circuit parties, and sex parties. In addition, an emerging
literature also examines how attendance of more mainstream gay venues, such as bars
and clubs, may be associated with both substance use and sexual risk behavior (Grov &
Crow, 2012). While research on the association between venues, substance use, and HIV-
risk has commonly focused on older MSM (Clatts et al., 2005; Grov, Parsons & Bimbi,
2007; Lee, Galanter, Dermatis & McDowell, 2004; Mansergh et al., 2001; Patel et al.,
2006; Ross, Mattison & Franklin, 2003; Woods et al., 2003), there are a handful of
studies that provide insights related to HIV risk in these contexts for YMSM (Binson et
al., 2001; Choi et al., 2004; Garofalo et al., 2007; Operario et al., 2006; Solomon et al.,
2011).
Bathhouses
Bathhouses date back as far as the 6th century, BC, including ancient records of
homosexuality in Greece (Debonneville, 1998). In western cultures, bathhouses were the
location of sexual activity since the late 19th century when homosexuality was illegal and
often resulted in arrest (Binson et al., 2001). Seen in the United States as early as 1888,
21

The Everard, a New York City bathhouse, was regularly frequented by gay and bisexual
men until it’s closing in 1986 (Bronstein, 1987). In general, bathhouses charge a
―membership fee‖ for entry and operate under the pretense of a private club. As such,
bathhouses have played and may continue to play an important role in the sex lives of gay
and bisexual men because they permit these men to escape from the homophobia and
heterosexism that may be experienced in their everyday lives (Binson et al., 2001;
Vicioso et al., 2005).
Studies of sexual behavior in bathhouses have demonstrated high rates of both
substance use and sexual risk behavior. A study of non-monogamous MSM in New York
and Los Angeles showed that nearly 40% of men who preferred bathhouses to other
social venues reported unprotected anal intercourse with an HIV serodiscordant or
unknown partner (Grov et al., 2007). A large study of MSM from recruited using
telephone-based sampling from four major cities across the U.S. (Stall et al., 2001)
documented that approximately half of the men surveyed and 66% of the men between
the ages of 18 – 25 attended a sex venue in the past year (Binson et al., 2001). While the
definition of sex venues in this study extended beyond bathhouses to include sex parties
and public ―cruising areas‖, this literature demonstrates the importance of examining
YMSM attendance in these social contexts. One of the few studies to specifically
examine bathhouse attendance among YMSM (16 – 24) demonstrated that 26% reported
having had sex in a bathhouse or sex club (Garofalo et al., 2007).

22

Circuit parties
Circuit parties are multi-event weekends that cater specifically to gay and
bisexual men. These parties occur annually in cities throughout the world and offer
opportunities for gay men to meet one another in gay-affirmative social spaces that allow
for self-expression (Colfax et al., 2001). Motivations for attending circuit parties may
vary; for example, a study of 1169 men attending circuit parties in three North American
cities in 1999 documented that 63% indicated their prime motivations for attendance
were to socialize (e.g., to celebrate and have fun, to be with friends, to dance and enjoy
music), while only 13% indicated that their main reasons were consistent with sensation-
seeking (e.g., to have sex, to be uninhibited and wild, to party or use drugs) (Ross et al.,
2003). Regardless of motivation, circuit parties do present opportunities for both
substance use and sexual risk behavior. One study of circuit party attending men found
that over half of the sample had used alcohol or club drugs at such an event (Mattison,
Ross, Wolfson & Franklin, 2001). Another study of circuit party attendees in Palm
Springs, California indicated that 50% reported having sexual relations over the course of
the weekend with 1.27 partners per respondent on average (Patel et al., 2006). Yet
another study of 295 gay and bisexual men documented that during their most recent
party attendance 80% of participants used ecstasy 66% used ketamine, 29% used gamma-
hydroxybutate or gamma-butyrolactone (GHB/GBL), 14% used sildenafil (Viagra), 12%
used amyl nitrates (poppers) and 53% used four or more drugs (Colfax et al., 2001).
These high rates of substance use during circuit parties have been documented in
subsequent studies as well (Lee et al., 2004).
23

The link between circuit party attendance, substance use and sexual risk behavior
has also been well documented. For example, the study by Colfax and colleagues (2001)
showed a clear link between substance use and unprotected anal intercourse in this
context. Specifically, those who used crystal methamphetamine twice as likely to have
engaged in unprotected anal intercourse; those who used Viagra were nearly four times as
likely to have engaged in unprotected anal intercourse; and those who used poppers more
than twice as likely to have engaged in unprotected anal intercourse. Studies of Asian and
Pacific Islander YMSM in San Francisco have demonstrated that those who had attended
a circuit party were over two times as likely to have used club drugs and multiple
substances and nearly 7 times more likely to be infected with HIV (Choi et al., 2004).
Another cross-sectional study among gay and bisexual men in San Francisco, CA who
had attended a circuit party in the past year indicated that almost all of the respondents
used substances during circuit parties and 25% reported a ―drug overuse incident‖,
defined as an incident where they ―passed out, needed medical assistance, or could not
take care of themselves owing to substance use‖ (Mansergh et al., 2001, p. 954) in the
past year. Among all of those in the sample, 28% reported unprotected anal intercourse
during their most recent circuit party weekend, and 9% of those engaged in unprotected
anal intercourse with a serodiscordant partner or a partner of unknown serostatus.
Sex parties
Sex parties are alternatives to bathhouse and circuit party environments. Sex
parties differ from those other social contexts in that they are not public spaces but rather
discretely advertised parties often organized in private homes or hotel rooms (Clatts et
24

al., 2005), which gay and bisexual men attend for the explicit purpose of engaging in sex
with Multiple partners. Unfortunately, the hidden nature of these venues had made it
difficult for researchers to determine patterns of substance use and sexual behaviors that
occur in these social contexts (Woods et al., 2003). The few studies of sex parties among
MSM in general have demonstrated that those who met partners at private sex parties are
a greater risk of unprotected anal intercourse compared to MSM who met partners
through other venues (Grov et al., 2007). In a study of 311 gay, bisexual and other
identified MSM in New York City found that the highest levels of unprotected anal
intercourse took place among MSM who met partners at bareback sex parties (Pollock &
Halkitis, 2009), making them particularly susceptible for STI/HIV infection. A case-
control study of syphilis among older MSM in New York City showed that cases were
more than three times as likely to have attended a sex party within the previous six
months compared to controls (Paz-Bailey et al., 2004).
One of the few studies that specifically examined sex party attendance among
YMSM used data from a large study of YMSM from a major urban center (N=540)
showed that nearly nine percent of those men had attended sex parties in the three months
prior to assessment. While there were no statistically significant relationships between
demographic characteristics and sex party attendance, those who attended sex parties
reported more lifetime and recent sex partners, more frequent unprotected anal
intercourse, and more frequent substance use (i.e., powder cocaine, crack cocaine,
inhalant nitrates, GHB, methamphetamine, benzodiazepines) compared to those who did
not attend (Solomon et al., 2011). These men also indicated limited access to lubricant
25

and condoms during sex parties further putting sex party attendees at risk for HIV
infection. These results clarify that while a small percentage of YMSM from the larger
sample reported recent sex party attendance, those who did were at heightened risk for
HIV transmission. The authors conclude that sex parties have the potential to accelerate
the spread of HIV among urban YMSM.
Gay bars and clubs
More mainstream gay social spaces, such as bars and clubs, have also been
implicated in risk for substance use and HIV infection. These social spaces may be
especially important to YMSM, who are in the developmental period termed ―emerging
adulthood‖ (Arnett, 2002). As part of this developmental stage, many YMSM are
learning to express their Sexual identity in the context of a larger society that is often
homophobic and hostile. As such, YMSM may seek out social contexts that are
accepting of their Sexual identity, which include gay bars and clubs, where there are
significant opportunities for both social support and where community norms exist that
are tolerant (and sometimes encouraging) of substance use and casual sex (Garofalo et
al., 2010; Harper, 2007; Stall et al., 2008). As YMSM are exposed to such settings, they
may learn to associate substance use with sex (Stall et al., 2001). Indeed, researchers
have demonstrated the association between gay bar attendance and cigarette smoking
(Holloway et al., 2012), alcohol (Wong et al., 2008), illicit substance use and sexual risk
behaviors (Greenwood et al., 2001). A recent study of the relationship between type of
venue attendance and HIV risk behavior among MSM accessing mobile testing services
in Massachusetts demonstrated that those who were recruited at gay bars and clubs were
26

over 8.5 times as likely to have engaged in unprotected receptive anal intercourse in the
year prior to study enrollment (Reisner et al., 2009).
To date, there are few studies that seek to examine the association between more
mainstream gay venues, substance use and sexual risk behavior among MSM and even
fewer that devote specific attention to YMSM. For example, in an extensive search of the
extant literature, only one study could be found that examined different types of gay
venue attendance and sexual risk behavior among MSM. This cross-sectional study,
Project PUMP, was conducted by Pollock and Halkitis (2009) and surveyed 311 gay and
bisexual men who were recruited at gymnasiums in New York City using both active
(e.g., handing out study information at gyms) and passive (e.g., posting flyers)
recruitment strategies. The sample was made up of approximately 50% men of color; the
Mean age was 39; and approximately one-third were HIV positive. Of those who reported
casual sex partners in the past 6 months, the most common mainstream physical venues
for meeting partners were bars (42.3%), gyms (42.3%), gay neighborhoods (41.2%),
dance clubs (28.5%), and work/school (16.4%). None of these contexts was associated
with total number of casual partners or unprotected anal intercourse. However,
attendance at these mainstream physical venues was positively correlated with attendance
at other venue types, such as bathhouses and sex parties, where MSM did engage in
higher levels of Sexual risk behavior. These findings suggest that men who engage in
Sexual risk behavior may be engaged for HIV prevention in social spaces outside of those
previously associated with the highest levels of HIV risk behavior, which is useful when
considering venue-based HIV prevention with this population.
27

Conclusion
YMSM have been identified as a high priority population in the most recent HIV-
Related Research Plan put forward by the Office of AIDS Research of the National
Institutes of Health (NIH, 2012). HIV infection rates are increasing in this community
despite substantial investments in community-based prevention interventions targeted to
YMSM (Valleroy et al., 2000). Recent reviews indicate limited availability of effective,
theoretically-based substance use and sexual risk reduction interventions for YMSM
(Johnson et al., 2002; Johnson et al., 2005; McLean, 2006; Johnson-Masotti, Weinhardt,
Pinkerton, Otto-Salaj, 2003). Interventions that are used may have limited appeal for
YMSM (Holloway, Cederbaum, Ajayi & Shoptaw, in press; Orellana et al., 2006).
The literature reviewed here demonstrates the importance of understanding social context
in relation to HIV-related risk behavior among YMSM. Social contexts may represent a
prime opportunity for targeted HIV prevention with YMSM. However, in order to engage
YMSM in venue-based HIV prevention, as has been suggested by others (Grov & Crow,
2012), researchers must better understand the socialization patterns of YMSM, their
interaction with the gay community, and the venues in which YMSM may be most
amenable to HIV prevention efforts. This study, seeks to better understand the gay
community landscape for YMSM, in order to guide future community-based intervention
efforts.

28

CHAPTER THREE: T HEORETICA L FRAMEWORK
There are two overarching theoretical frameworks that guide the present research:
Social Action Theory (Ewart, 1991) and the Theory of Duality of Persons and Groups
(Breiger, 1974). The research presented here is not meant to test either of these models;
rather each theoretical framework guides different aspects of the study. Social Action
Theory, which places particular importance on the role of the environment on individual
health behavior, provides a theoretically-grounded rationale for focusing attention on the
social venues in which YMSM congregate. The Theory of Duality of Persons and
Groups, which provides a social network analysis methodology for studying the ways in
which individuals are connected through their co-attendance in social spaces, provides a
guide for the analytic strategy, which was used in this research. In this chapter, a brief
overview of each theory is presented followed by an explanation of how the theory is
applied to the current work.
Social Action Theory
Numerous theories of health behavior have been used to explain HIV risk in a
variety of populations. These models include the Health Belief Model (Becker, 1974); the
Theory of Reasoned Action (Fishbein & Azjen, 1974); Social Cognitive Theory
(Bandura, 1986); and the Information-Motivation-Behavioral Skills Model (Fisher &
Fisher, 1992). Recently, Traube and colleagues (2011) compared these leading models of
health behavior used to explain HIV-related risk behavior and noted significant gaps in
29

each model. Social Action Theory was put forward by the authors as a useful framework
for understanding HIV risk because it includes the important individual, social and
contextual determinants of health risk behavior put forward by other leading models of
health behavior but includes a more specific and comprehensive explanation of how
social context influences HIV risk behavior (Traube, Holloway & Smith, 2011).
Subsequently, Social Action Theory has been useful in guiding research on YMSM and
illicit substance use (Traube et al., 2011; Traube, Schrager, Holloway & Kipke, under
review) and sexual risk behavior (Traube et al., under review). The figure below presents
Social Action Theory as conceptualized by Ewart (1991).

30

Figure 2. Social Action Theory: Contextual model representing self-regulation as a
subcomponent of large social and environmental systems

Note
From ―Social Action Theory for a public health psychology‖ by Ewart, C. K., 1991,
American Psychologist, 46(9), p. 931-946. Copyright 1991 by American
Psychologist. Reprinted with permission.

31

Social Action Theory is an ideal theoretical framework for the present research
because it places particular importance on the environmental antecedents (both structural
and social) to health behaviors.
The model’s three dimensions, respectively, emphasize the role of social context
in maintaining health routines or habits (action state dimensions), provide a causal
framework linking self-change processes to interpersonal environments (process
dimension), and specify macrosocial and environmental influences that empower
or constrain personal change (contextual dimension) (Ewart, 1991, p. 932).

The interplay between person and environment described by Ewart in his seminal work
on Social Action Theory is that which the present study seeks to address by
understanding the ways in which gay social environments are associated with varying
levels and patterns of personal health behaviors, specifically substance use and sexual
risk behavior. Gay social venues, the subject of study in this dissertation, would fall
within the ―action contexts‖ construct. For YMSM, social venues represent one of the
key settings in which health behaviors among YMSM are enacted.
Environmental settings and social systems [that] affect personal behavior by
channeling a person’s interpretations of events, affecting one’s biological
condition, influencing the formation of close relationships, and interacting with
physiological processes to generate mood states that bias cognition and constrain
social interaction. (p. 973)

In this regard, ―action contexts‖ can influence health behavior in two ways: (1) by
providing structural cues to behavior (e.g., highly sexualized contexts may produce
individual arousal, which may lead to engagement in Sexual risk behavior); and (2) by
providing opportunities for interaction with similar individuals (e.g., highly sexualized
contexts may promote attendance and affiliation between individuals who desire to
32

engage in Sexual risk behavior). This social interaction is one of the key domains labeled
as a ―self-change process‖ in Ewart’s original model.
Previous studies that have used Social Action Theory to explain substance use and
sexual risk behavior among YMSM have operationalized ―action contexts‖ in terms of
individuals’ previous experiences of violence, victimization and homophobia and have
operationalized ―social interaction‖ in terms of social support. For example, Traube and
colleagues (2012) used this operationalization of ―action contexts‖ in a study to explain
substance use among YMSM and found that ―action contexts‖ were indeed associated
with ―social interaction,‖ that ultimately influenced substance use through relationships
with other ―social interaction processes‖, as posited in Ewart’s original model.
In an effort to expand the conceptualization of the ―action contexts‖ and ―social
interaction‖ domains prior to further investigation of the utility of Social Action Theory
in explaining risk behavior among YMSM, the present study focused exclusively on
these processes. A more expansive definition of ―action contexts‖ that includes gay venue
attendance and ―social interaction‖ that includes opportunities to interact with other
YMSM in those contexts may be useful in increasing the explanatory power of Social
Action Theory driven models to explain YMSMs’ health behavior in the future. The
present study helps to further develop and understand how gay social venue attendance is
associated with substance use and sexual risk behavior in order to build on a previous
operationalization of the ―action contexts‖ and ―social interaction‖ domains prior to
further testing of Social Action Theory to explain health behavior in this population.

33

Theory of Duality of Persons and Groups
In order to understand the relationships between gay venue attendance, substance
use and sexual risk behavior among YMSM, the Theory of Duality of Persons and
Groups (Breiger, 1974), was employed in the present study. First published in 1972 in the
journal Social Forces, the Theory of Duality of Persons and Groups outlines a conceptual
and empirical framework for analyzing data about individuals’ membership in groups as
social network data. Based on seminal work by Erving Goffman (1971) proposing that
―the individual is linked to society through two principal social bonds: to collectivities
through membership and to other individuals through social relationships‖ (p. 181), the
Theory of Duality of Persons and Groups highlights the notion that individuals who
belong to the same group or attend the same social context are connected to one another
through their membership and/or attendance at that context. Similarly, specific social
contexts are connected to one another through individuals who spend time in multiple
social spaces. Therefore, data that includes information about where individual
participants socialize can be transformed into two types of matrices: (1) person matrices,
and (2) group-based matrices, which can then be transformed into social networks for
further analysis.
The Theory of Duality of Persons and Groups has been used by researchers in
various disciplines to understand a range of issues at both the individual and group level.
At the individual level, researchers have used the Theory of Duality of Persons and
Groups to examine patterns of infectious disease transmission based on shared social
spaces (De, Singh, Wong, Yacoub & Jolly, 2004; Fur, 2010; Wiley, Shah & Jolly, 2007).
34

At the group level, the Theory of Duality of Persons and Groups has been used to
examine interlocking directorates of international companies based on individuals’ co-
membership on those companies’ boards of directors (Levine, 1972; Robins &
Alexander, 2003; Ming, 2005), interconnected hospitals based on patient transfers
between those hospitals (Lee et al., 2011) and more recently, linked social service
organizations through the individuals who access the services of those organizations
(Schneider et al., 2012). The existing literature demonstrates the utility of this theoretical
framework in public health and social work research; however, to my knowledge, the
Theory of Duality of Persons and Groups has never been applied to behavioral health
data examining HIV-related risk behavior among YMSM.
In order to understand the application of the Theory of Duality of Persons and
Groups to data based on YMSM attendance at social venues, consider the example that
follows, accompanied by Figure 1. Let us imagine a dataset in which there are eight men
(PID
1
, PID
2
, PID
3
, PID
4
, PID
5
, PID
6
, PID
7
, PID
8
), each of whom typically socialize at
one (or more) of 4 bars (AKBAR, BILLY’S, CLUBLA, DELITE). If we place the
individuals on the rows of an 8x4 matrix and place the bars on the columns of that matrix
then we can designate an individual’s attendance at a particular bar with a 1 in the
corresponding cell and non-attendance at a bar with 0, as illustrated in the 2-mode matrix
a. With this information, we can do a series of transformations with this 2-mode matrix
that will result in two subsequent 1-mode matrices: a person by person matrix (matrix c),
where individuals are linked by shared attendance at a particular bar; and (2) a bar by bar
matrix (matrix b), where bars are connected through individuals who attend those bars;
35

these matrices can then be transformed into traditional social network diagrams (diagram
d) and (diagram e). In the next chapter, the methodology outlined by the Theory of
Duality of Persons and Groups will be applied to the Healthy Young Men Study data.

Figure 3. Illustration of matrix transformation using the Theory of Duality of Persons and
Groups.

36

Conclusion
These two theoretical frameworks, Social Action Theory (Ewart, 1991), which
provides the conceptual basis for the study of gay social contexts, and the Theory of
Duality of Persons and Groups (Breiger, 1974), which outlines the methodology for
transforming data on venue attendance into social networks that can be used to further
understand the relationships between social context and health behaviors, guide the
present study. This research may be thought of as formative in that it helps to broaden the
current conceptualization of Social Action Theory’s domains of ―action contexts‖ and
―social interaction‖ with YMSM using the methodology outlined in the Theory of Duality
of Persons and Groups. However, this research is also epidemiological in nature and is
meant to inform future HIV prevention intervention efforts with YMSM. Under the
translational science framework put forward by the National Institutes of Health, social
and behavioral health research must be dynamic and seek to move research quickly from
the laboratory to the community. While this study will expand on theory development for
prevention of HIV among YMSM, this research is also applied – by understanding the
socialization patterns of YMSM, social workers and public health practitioners can tailor
and target HIV prevention efforts to this high risk population.

37

CHAPTER FOUR: METHODS
Data source
The analyses for the present study utilized baseline data from the HYM study
(N=526). The HYM study is a National Institute of Drug Abuse (NIDA) funded
longitudinal study of substance use and sexual risk behavior among YMSM in Los
Angeles (LA), CA (Kipke, Kubicek et al., 2007; Ford et al., 2009). All participants were
male; 18-24 years old at recruitment; self-identified as gay, bisexual, or uncertain about
their sexual orientation and/or reported having sex with a man; self-identified as
Caucasian, African American, or Latino of Mexican descent; and were residents of LA
County with no expectation of living outside the County for at least six months following
recruitment. An established, context-based probability sampling approach (MacKellar,
Valleroy, Karon, Lemp & Janssen, 1996; Muhib et al., 2001) was used to obtain a
representative sample of YMSM attending gay contexts in LA County. Contexts ranged
from large special events such as gay pride festivals, to small contexts such as bars and
coffee shops. Confidential, self-administered survey data were collected using computer-
assisted interviews. Each survey (available in English and Spanish) took up to 90 minutes
to complete. Participants were given 35 dollars compensation for their time and effort.
The Committee on Clinical Investigations at Children’s Hospital Los Angeles approved
the study. Table 1 displays a complete list of the contexts from which YMSM were
recruited along with the corresponding numbers of study participants yielded from each
context.

38
Table 1
Venues by type included in the final HYM sampling frame

Number of
venues
Total eligible
Refused study
participation
Did not complete
interview
Enrolled
in cohort
n N n (%) n (%) n (%)

Social service agency

4 16 3 (19) 5 (31) 8 (50)

Ball/house party

5 66 20 (30) 27 (41) 19 (29)

Bar/club

18 605 197 (33) 183 (30) 225 (37)

Pride festival

5 139 32 (23) 32 (23) 75 (54)

Street

4 545 181 (33) 165 (30) 199 (37)

39

Study design
The Theory of Duality of Persons and Groups (Breiger, 1974) guided the design
of the present study. The Theory of Duality of Persons and Groups has been used widely
in the field of social network research to transform 2-mode social network data into 1-
mode social network data, which can be analyzed in a number of ways. Specifically, the
Theory of Duality of Persons and Groups lays out a conceptual and empirical framework
for analyzing data about individuals’ group membership as social network data. Based on
seminal work by Erving Goffman (1971), proposing that ―the individual is linked to
society through two principal social bonds: to collectivities through membership and to
other individuals through social relationships‖ (p. 181), the Theory of Duality of Persons
and Groups highlights the notion that individuals who belong to the same group or attend
the same social context are connected to one another through their membership in and/or
attendance at that context. Therefore, data that includes information about where
individual participants socialize can be transformed into two types of matrices: (1) person
matrices, which connect individuals by the social spaces they share and (2) venue
matrices, connect venues by individuals who share those social spaces.
The Theory of Duality of Persons and Groups has been used by others to
understand a range of issues at both the individual and organizational levels. At the
individual level, researchers have used the Theory of Duality of Persons and Groups to
examine patterns of infectious disease transmission (Frost, 2007). On the organizational
level, the Theory of Duality of Persons and Groups has been used to examine
40

interlocking directorates of international companies based on individuals’ co-membership
on those companies’ boards of directors (Levine, 1972; Ming, 2005; Robins &
Alexander, 2003), interconnected hospitals based on patient transfers between those
hospitals (Lee et al., 2011) and more recently, linked social service organizations through
the individuals who access the services of those organizations (Schneider et al., 2012).
The extant literature demonstrates the utility of this theoretical framework in public
health and social work research. However, to my knowledge, the Theory of Duality of
Persons and Groups has never been applied to behavioral health data examining HIV and
substance use risk.
The data transformations described in the example in the last chapter are
illustrative of those used in the present study. However, prior to data transformation, an
extensive data cleaning and organizing process was required. In the original HYM data
respondents were asked to name their ―favorite three gay places to socialize.‖ This
single-Item open-ended name generator was the basis for the original 2-mode matrix. Of
the 526 participants from the HYM study 484 (92%) provided answers to this name
generator; analyses were limited accordingly. Participants’ responses included various
types of social venues, including bars, dance clubs, bookstores, etc. Each nominated
venue was verified through the use of internet searches, which included the name of the
venue listed by the respondent and the following key words: ―gay‖ and ―Los Angeles.‖
Addresses of venues were recorded along with brief notes describing the type of venue
(e.g., restaurant, adult bookstore, etc.). When venues could not be located through the
41

internet search, I drew on the expertise of a Community Advisory Board (CAB), which
included YMSM as well as service organization and community leaders. In some
instances the names of venues nominated by participants could not be verified; these
venues were removed from the final list. In addition, venue nominations that were too
broad (e.g., West Hollywood) or too narrow (e.g., my friend’s house) were removed from
the final venue list. In total, 110 venues were included in the final analyses; each of these
venues was given a unique Numeric code.
The final 2-mode matrix (persons by venues) contained 484 rows and 110
columns. This binary adjacency matrix contained a ―1‖ in each to represent an
individual’s attendance at a particular venue and a ―0‖ to represent non-attendance at that
venue. Matrix algebra was used to transform this binary adjacency matrix into two 1-
mode matrices: a person-by-person matrix (P) and a venue-by-venue matrix (V). First the
original 2-mode matrix (A) was transposed (A
T
) so that ordinary (inner product) matrix
multiplication could be used (matrices can only be multiplied if the rows of one matrix
are equal to the columns of the second matrix). In order to create the person-by-person
matrix the following formula was used: P=A(A
T
). In order to create the venue-by-venue
matrix the following formula was used V=(A
T
)A. The resulting person matrix contained
484 rows by 484 columns. The resulting venue matrix contained 110 rows by 110
columns. In addition to connections between persons and venues being designated
dichotomously in the resulting matrices (0 for no connection, 1 for connection), valued
matrices representing the degree to which persons and venues were connected were also
42

created. In the person matrix, the theoretical range of values was 0 – 3 since the
maximum number of venues any two individuals could share based on these data were 3.
In the venue matrix, the theoretical range of values was 0 – 484, since the maximum
number of persons that connected any two venues was the maximum number of YMSM
in the sample. These valued were used to create different versions of the person- and
venue networks at different thresholds of venue and person sharing.
Measures
A wide array of data from a diverse range of topic areas was collected as part of
the HYM questionnaire. The following measures were used in the present study:
Sociodemographic characteristics. Participants were asked to report their date of
birth which was then transformed into two variables reflecting age, one continuous
(range: 18-24) and one categorical (1=―18-19 years,‖ 2=―20-21 years,‖ and 3=―22-24
years‖). A single categorical variable representing Race/ethnicity was abstracted from
recruitment data (1=―African American,‖ 2=―Latino,‖ and 3=―Caucasian‖). Place of
residence was a four category variable reflecting participants’ current living situation
(1=―with family,‖ 2=―own place/apartment/dorm,‖ 3=―with friends/partner‖ and 4=―no
regular place/other‖). This variable was then dichotomized into a single item representing
whether or not the individual lived with family (0=No, 1=Yes). A single categorical
variable was used to reflect participants’ current education and employment status (1=―In
school only,‖ 2=―In school and employed,‖ 3=―employed but not In school‖ and
4=neither In school nor employed). This variable was then transformed to reflect two
43

unique variables representing whether the respondent was currently in school (0=No,
1=Yes) and currently working (0=No, 1=Yes). Sexual identity was divided into three
categories (1=―gay,‖ 2=―other same-sex identity‖ and 3=―bisexual/straight‖). This
variable was then dichotomized to represent whether the respondent identified as gay or
another same-sex identity (0=No, 1=Yes). Participants were also asked whether or not
they were currently in a primary partner relationship (0=No and 1=Yes). Finally, a single
question asked participants to report the frequency with which they attended gay bars and
clubs in the past three months (0=―Never,‖ 1=―Once a month or less,‖ 2=―Several times a
month 3=―About once a week‖ and 4=―Several times a week or every day‖).
Substance use. Participants were asked if they had ever used alcohol, cigarettes
marijuana, and other drugs, including cocaine, crack, ecstasy, LSD, PCP, mushrooms,
crystal methamphetamine, other forms of speed, heroin, poppers, other inhalants (e.g.,
NO
2
), GHB, Ketamine, Rohypnol, and prescription drugs without a physician’s orders
(e.g., Viagra, anti-anxiety medications, opiates, etc.). Each substance was treated as an
individual dichotomous variable (0=No, 1=Yes). If participants reported having used a
particular substance, they were asked their age when they first used that drug and whether
or not they had used the substance recently (i.e., in the past 3 months). Two additional
dichotomous variables were created to determine whether YMSM had used any illicit
substance (i.e., any of those listed above except alcohol, cigarettes, and marijuana) in
their lifetime and in the past 3 months. These substances were excluded from the illicit
substance use composite variable since they are available and widely used in the general
44

young adult population; this composite variable has also been used previously in other
analyses from the HYM study (Kipke, Weiss and Wong, 2007).
In addition to individual substances, several substance use indices were created to
reflect recent frequency and intensity of use. These variables were computed by asking
respondents who indicated using substances within the past three months, whether they
had used in the past 30 days and if so, how much of each substance they had used. Four
indices were used for the present analysis: cigarettes, alcohol, marijuana, and illicit drugs.
The cigarette index had four categories (1=―lifetime non-use‖; 2=―lifetime use but not in
the past 30 days‖; 3=―light use in the past 30 days‖; 4=―frequent or heavy use in the past
30 days‖). The alcohol use index had four categories reflecting binge-drinking behavior
(1=―non-use/light use‖; 2=―frequent non-binge‖; 3=―occasional binge‖; 4=―frequent
binge‖). The marijuana use index had four categories (1= ―non-use/light use‖; 2=―less
than 1x/week‖; 3=―1 – 3 times/week‖; 4=―more than 3x/week‖). The illicit substance use
index, which also excluded alcohol, cigarettes and marijuana, had four categories
(1=―lifetime non-use‖; 2=―lifetime use but not in the past 30 days‖; 3=―light use in the
past 30 days‖; 4= ―frequent or heavy use in the past 30 days‖). All substance use indices
were treated as continuous variables in subsequent analyses.
Sexual risk behavior. Respondents were asked about their sexual activity during
the past 3 months, including number of sexual partners, if they had engaged in anal
insertive and/or receptive sex, and if they had used a condom. These variables were used
to create a composite sexual risk index with four categories (1=―no partners‖;
45

2=―protected anal intercourse‖; 3=―unprotected anal intercourse with single
seroconcordant partner‖; 4=unprotected anal intercourse with serodiscordant or Multiple
partners). YMSM who reported only engaging in seroconcordant unprotected anal
intercourse with a primary partner were excluded from this scale in order to treat this
variable as a continuous measure of sexual risk with higher scores indicating greater risk.
In addition to this Sexual risk behavior scale, a dichotomous variable representing recent
UAI with serodiscordant or multiple partners was used (0=No, 1=Yes).
Since previous STI diagnosis has been associated with increased risk for HIV,
variables regarding STIs were included in the present study. Specifically, participants
were asked whether they had ever been diagnosed with the following STIs: gonorrhea,
syphilis, Chlamydia, genital herpes, HPV/genital warts, hepatitis and scabies/crabs. Each
of these STIs was treated individually as dichotomous variables (0=No, 1=Yes) and a
composite STI index with two categories was created (0=None, 1=1 or more).
Participants who had been diagnosed with an STI previously were asked when the
diagnosis was made. Based on this variable, a single dichotomous variable representing
whether the participant had been diagnosed with an STI in the past 6 months was created
(0=No, 1=Yes).
HIV testing. All participants were asked whether or not they had ever received an
HIV test (0=No, 1=Yes) and if so, whether they had returned to obtain test results (0=No,
1=Yes). Sexually active participants reported on the timing of their most recent HIV test.
A four-category variable representing timing of most recent test was created (1=―tested
46

within 6 months,‖ 2=―tested between 6-12 months,‖ 3=―tested more than 12 months
ago,‖ and 4=―never been tested‖). This variable was then dichotomously coded to reflect
whether participants had received an HIV test within the past year (0=No, 1=Yes).
Participants were asked their HIV status based on most recent test results; a three-
category variable was created based on these responses (1=HIV positive, 2=HIV
negative, 3=don’t know).
Venue-level measures. Using data from the internet searchers and CAB
consultations described above, three descriptive venue-level measures were created to
describe the types of venues nominated by individuals and the geographic location of
those venues. Venues were given one of seven classifications: (1) Dance club, (2) Bar,
(3) Service organization, (4) Gym or recreation center; (5) Coffee shop or restaurant; (6)
Adult bookstore, or (7) Other. Other venue types included specific places that did not fall
into one of the other six categories (e.g., public parks) and specific events that could not
be associated with a specific location (e.g., gay pride festival, House/Ball event). Based
on these seven classifications, venues were grouped into those that on their face could be
associated with risk (i.e., venues where alcohol is served, drugs are available, or public
sex is known to occur) and venues that on their face did not seem to be associated with
risk (i.e., coffee shops, service organizations). Finally, using zip code information for
each of the venues that could be fixed to a specific location, a variable representing the
geographic area in which venues were located was created. Five geographic areas were
represented by this variable (1=West Hollywood, 2=Long Beach, 3=Other LA County,
47

4=Orange County, 5=Other). Venues that fell into the ―other‖ category were those that
were located in geographic areas outside of the categories listed above or those that could
not be linked with a specific geographical location (e.g., gay pride festival, House/Ball
events).
Statistical methods
Using the social network data analysis program, UCINET (Borgatti, Everett &
Freeman, 2002) the 2-mode node list described above (persons by venues) was
transformed into two 1-mode adjacency matrices (persons by persons and venues by
venues), which represented two distinct networks, which will be referred to as the
―person network‖ and the ―venue network‖ hereafter. The person network reflects
individuals who are connected by their mutual nomination of a particular social context.
The venue network reflects social venues, which are connected by the individuals who
report attending those social contexts. Once network matrices were created, both
networks were entered into NETDRAW 2.090 (Borgatti, 2002). The spring embedder
routine was used to generate network visualizations. Spring embedding is based on the
idea that two nodes in a network push and pull each other simultaneously based on the
strength of their connection. Two points located close together represent actors who have
a stronger pull (and weaker push) on each other, while distant actors have a stronger push
(and a weaker pull) on each other. The spring embedding algorithm seeks to achieve a
global optimum where there is the least amount of stress on the ―springs‖ connecting
nodes to each other (Freeman, 2000). Other visualization techniques in NETDRAW
48

(Borgatti, 2002) were also employed; these included the ―circle layout‖ function, which
arranges nodes in a circle and the ―graph by attribute‖ function, which clusters nodes
based on a specific attribute (i.e., risk venues).
Social network metrics generated in NETDRAW (Borgatti, 2002) and UCINET
(Borgatti, Everett & Freeman, 2002) were calculated for both the person and venue
networks. These metrics along with their definitions are presented in Table 2 below.

49

Table 2

Social network metrics used in the present analysis
Social network metric Definition
Node
An individual actor in a network (i.e., YMSM in the person network and venues in the
venue network)
Isolate A node not connected to any other nodes in the network.
Ties
Connections between nodes in a network (i.e., shared venues in the person network and
shared individuals in the venue network)
Components Partitions of the network in which all nodes can reach each other.
Density
The volume of connections in the network calculated by taking the number of ties between
all nodes and dividing that number by the total number of possible ties.
Average path length
The average distance between all reachable pairs of nodes in the network. Used to
understand how quickly information can flow through a network.

50
Table 2 Continued
Social network metric Definition
Bonacich eigenvector centrality

Based on the idea that a node is central if it is connected to other central nodes in the
network. Scores based on the centrality of the node and the centrality of its neighbors is
used to rank each node in the network. Used to determine prestige of nodes within a
network.

Between-ness
The degree to which a node lies on the shortest paths between all other nodes in the
network.
Closeness

The inverse of the average distance to others in the network. A node is more central the
lower its closeness score. Often used to determine how quickly information can spread
through a network.

K-core

A group of nodes that are connected to a minimum other (k) nodes. Higher K-cores
represent higher interconnectivity.

51

Hypothesis testing
The three hypotheses for the present study outlined in Chapter One were tested
using both the person and venue networks. The person network was examined to address
Hypothesis I and the venue network was used to address Hypotheses II and III. In the
person network attribute data about individual participants' demographic characteristics,
substance use and sexual risk behavior was be applied to the network diagram in order to
determine whether the principle of homophily was operating within the YMSM social
networks (i.e., to determine whether YMSM clustered by demographic characteristics,
substance use, Sexual risk behavior and HIV testing characteristics). Studies of smoking
and obesity have demonstrated that health behaviors often cluster in social networks
(Christakis and Fowler, 2007; Christakis and Fowler, 2008) and it was anticipated that
risk behavior would cluster in the interpersonal network.
To test the principle of homophily based on demographic, substance use, Sexual
risk behavior and HIV testing characteristics, the person network was restricted only to
those individuals who shared two social spaces. Since the majority of YMSM attended a
handful of popular social venues this restriction of the data was warranted to determine if
socialization patterns based on homophily were present. Homophily analyses were
conducted in UCINET using two different analysis strategies: E-I analysis and ego
homophily analysis. The E-I analysis function partitions the network based on a
particular characteristic (e.g., Race/ethnicity) and then performs a permutation test to
evaluate the significance of the partition. Specifically, the E-I index represents the
52

number of ties external to the group minus the number of ties that are internal to the
group divided by the total number of ties. The E-I index can range from -1 to 1; the
permutation test determines whether the network E-I index is significantly higher or
lower than expected when compared to a fictional network with ties evenly distributed
within and between groups. The ego homophily function generates an individual
homophily score for each person in the network, which substantively represented the
proportion of network members who are like the participant himself. This score is
calculated by counting the number of ―same‖ alters (i.e., social network members) on
individual attributes (e.g., African American Race/ethnicity) and then dividing that
number by the total number of alters in each person’s network – as a result, ego
homophily scores range between 0 and 1, with higher scores indicating greater
homophily. These scores were then tested for mean differences across demographic,
substance use, Sexual risk behavior and HIV testing groups. Analyses were conducted
using ANOVA and independent sample t-tests depending on the number of groups.
ANOVA tests were complemented by Tukey post-hoc testing to determine significant
differences between three or more groups.
The venue network was examined to test Hypotheses II and III. Hypothesis II
specified that substance use and sexual risk behavior would be more concentrated in
High-risk venues as compared to Low-risk venues. As demonstrated previously with
other vulnerable populations, network structures and the concentration of HIV-risk within
those structures may vary considerably (Rice, Milburn, Barman-Adhikari & Monro,
2012). Possible findings from the present research with regard to substance use
53

or Sexual risk behavior patterns of the YMSM group network included risk occurring in
isolated pockets among a small number of relatively disconnected venues or high levels
of risk behavior occurring in a centralized core of contexts. To determine possible
patterns of substance use and sexual risk behavior in the venue network, visual
representations of the venue network were created using different thresholds of tie
strength, which in this case represented the number of YMSM who share social contexts.
Descriptive characteristic indices were calculated for each venue based on the YMSM
who reported attending that venue. Specifically, percentages or means on demographic,
substance use, Sexual risk behavior and HIV testing characteristics were aggregated
within venues to create a composite score for the venue as a whole. These composite
scores were then compared across those venues identified by the CAB as High-risk and
low-risk. In order to test for statistically significant differences in substance use and
sexual risk behavior across venue types, non-parametric Mann-Whitney U tests were
employed. An a priori alpha-level of 0.05 was used.
Hypothesis III posited that YMSM engaging in substance use and sexual risk
behavior would share social spaces that on their face appeared High-risk and those that
appeared Low-risk. To test this hypothesis, the top 6 most popular venues that were
associated with risk (i.e., bars, clubs, adult bookstores) were isolated to determine how
much of the total sample of YMSM were captured by these venues. Next, network
analysis was used to determine the percent of the sample that could be captured when
both High-risk venues and low-risk venues were connected to each other by 1-degree of
person sharing – that is at least one person who crossed High-risk and low-risk social
54

spaces. These different percentages of YMSM who were captured when examining
different types of social spaces were graphed to compare social space sharing between
High-risk and low-risk venues.

55

CHAPTER FIVE: PERSON NETWORK RESU LTS
Description of the sample
Of the 526 men originally enrolled in the HYM study, 484 (92%) named at least
one ―favorite gay place to go to‖. The present analysis was restricted to these 484 men
since the overarching aim of the study was to elucidate associations between substance
use, HIV risk and venue attendance. Bivariate statistical comparisons using independent
sample t-tests and chi-square tests were used to determine any statistically significant
differences in demographic characteristics, substance use patterns, HIV risk and HIV
testing between HYM participants who answered the venue name generator question and
those who did not. Overall, the two groups were similar; however, a greater percentage
of men who did not respond to the name generator question (78.6%) reported attending
gay bars less than once per week when compared to men who responded to the name
generator question (52.4%, p<0.01). A complete listing of bivariate comparisons between
men who responded to the name generator and those who did not is featured in Appendix
Table A1.
Table 3 provides demographic characteristics of the final sample included in the
analysis. YMSM had a Mean age of 20.14 (SD=1.56) and the majority identified as gay
or another same-sex Sexual identity (83.3%). Slightly over one fifth identified as African
American (23.8%); the remainder of the sample was evenly split between Latinos of
Mexican descent (38.8%) and Caucasians (37.4%). The majority of respondents
56

indicated either being in school or employed (87.2%); approximately half (53.9%) lived
with their families, and approximately half (47.3%) were in relationships with primary
partners.
YMSM had the opportunity to name up to three favorite venues. Approximately
13% nominated one venue; 26% nominated two venues; and 62% nominated three
venues. Small percentages of participants indicated that they ―never‖ attended gay bars or
clubs in the past three months (9.3%) or did so ―once a month or less‖ (15.9%); the
remainder of the sample reported attending gay bars/clubs ―several times a month‖ to
―several times a week or every day‖ (74.7%).

57

Table 3
Demographic characteristics of final sample (N=484)
Variable
Categories
Mean (SD) or
n (%)
Mean (SD) 20.17 (1.56)
Age
18 – 19 years 183 (37.8)
20 – 21 years 187 (38.6)
22+ years 114 (23.6)
Race/ethnicity
African American 115 (23.8)
Mexican descent 188 (38.8)
Caucasian 181 (37.4)
Residence
Family 261 (53.9)
Own place/apartment/dorm 175 (36.2)
With friends/partner 31 (6.4)
No regular place/other 17 (3.5)

58

Table 3 Continued
Variable
Categories
Mean (SD) or
n (%)
Mean (SD) 20.17 (1.56)
Employment
In school 101 (20.9)
In school, employed 134 (27.7)
Employed, not In school 187 (38.6)
Not employed, not In school 62 (12.8)
Sexual identity
Gay 361 (74.6)
Other same-sex identity
42 (8.7)
Bisexual
81 (16.7)
Relationship status In primary partner relationship 229 (47.3)
Recent gay bar/club
attendance
1

Never
45 (9.3)
Once a month or less 77 (15.9)
Several times a month 131 (27.1)
About once a week 127 (26.3)
Several times a week or every day
103 (21.3)
# of nominated venues
1 60 (12.4)
2 126 (26.0)
3 298 (61.6)
Note
Sample sizes may vary due to missing data on individual variables

59

Substance use
Age of initiation, lifetime use, and past three-month use of alcohol, cigarettes, and
a range of illicit substances are presented in Table 4. Apart from alcohol (91.3%),
cigarettes (64.7%) and marijuana (64.3%), relatively small percentages reported lifetime
use of individual types of illicit substances. The most commonly used illicit drugs were
cocaine (23.8%), Ecstasy (22.9%), and crystal methamphetamine (19.7%). However,
when examining a composite measure of illicit substance use, which included all drugs
listed above excluding alcohol, cigarettes, and marijuana, nearly half (48.8%) of the men
reported lifetime use and of those, 28.5% (n=236) had used an illicit drug in the past three
months. A similar picture emerged when limiting analyses exclusively to club drugs (i.e.,
cocaine, crystal/methamphetamine, ecstasy, poppers, GHB, Ketamine, and other forms of
speed) where 41.3% of the total sample indicated lifetime use and 22.9% of those
(n=200) reported using at least one club drug in the past three months. Due to relatively
low percentages of individual types of illicit substances, subsequent analyses of illicit
substance use were conducted using the composite measures of illicit drug use and club
drug use.

60
Table 4
Substance use behaviors of final sample (N=484)
Drugs
Age of
initiation
Lifetime
use
Past 3 month use
M (SD) n (%) n (%)
Alcohol 16.46 (2.74) 442 (91.3) 416 (94.1)
Cigarettes 16.58 (2.51) 313 (64.7) 255 (81.5)
Marijuana (weed, pot, grass) 16.70 (2.27) 311 (64.3) 194 (62.4)
Cocaine 18.21 (2.22) 115 (23.8) 49 (42.6)
Crack 17.95 (2.35) 23 (4.8) 5 (21.7)
Ecstasy 17.87 (1.67) 111 (22.9) 39 (35.1)
LSD 16.64 (2.40) 25 (5.2) 3 (12.0)
PCP 17.29 (1.89) 8 (1.7) 2 (25.0)
Mushrooms 18.00 (1.87) 74 (15.3) 13 (17.6)
Crystal meth (tina) 18.13 (1.93) 95 (19.7) 42 (44.2)
Other forms of Speed 17.41 (2.06) 45 (9.3) 8 (17.8)

61
Table 4 Continued
Drugs
Age of
initiation
Lifetime
use
Past 3 month use
M (SD) n (%) n (%)
Heroin 18.43 (2.15) 8 (1.7) 4 (50.0)
Anti-anxiety med without prescription
(Valium, Xanax)
18.13 (2.01) 72 (14.9) 24 (33.3)
Depressants, no prescription
(Nembutal, Seconal)
17.36 (1.79) 25 (5.2) 3 (12.0)
Opiates or narcotics, no prescription
(Vicodin, Oxycontin, Codeine)
17.86 (2.06) 84 (17.4) 32 (38.1)
Attention Deficit medication, no
prescription (Adderall, Ritalin)
17.55 (3.10) 51 (10.6) 14 (27.5)
Anti-depressants/sedatives, no
prescription (Paxil, Prozac)
17.59 (1.79) 36 (7.5) 7 (19.4)
Poppers 18.65 (2.79) 75 (15.6) 29 (38.7)
Other inhalants like NO2 or paint 17.08 (1.92) 51 (10.6) 13 (25.5)

62
Table 4 Continued

Drugs
Age of
initiation
Lifetime
use
Past 3 month use
M (SD) n (%) n (%)
GHB 18.96 (1.89) 25 (5.2) 8 (32.0)
Ketamine 18.34 (1.88) 33 (6.8) 6 (18.2)
Rohypnol 20.00 (1.73) 3 (0.6) 2 (66.7)
Viagra, no prescription 19.11 (4.00) 28 (5.8) 8 (28.6)
Other drugs 16.71 (2.59) 27 (5.6) 10 (37.0)
Illicit drugs 17.44 (2.69) 236 (48.8) 138 (28.5)
Club drugs 18.30 (2.22) 200 (41.3) 111 (22.9)
Note
Sample sizes may vary due to missing data on individual variables
63

Three substance use indices representing the most commonly used substances
(i.e., cigarettes, alcohol, and marijuana) and one composite index representing any illicit
drug use were created to represent frequency and intensity of use in the past 30 days.
Over one fifth (22.1%) of YMSM reported frequent (i.e., over 15 days) or heavy (i.e.,
greater than half a pack of cigarettes per day) cigarette use in the past thirty days. Small
percentages of the sample indicated frequent binge drinking (8.7%) and frequent
marijuana use (11.8%). However, 16.1% reported frequent or heavy illicit substance use
in the past 30 days and an additional 7.9% reported light use of illicit drugs in the past 30
days. Complete distributions of these substance use indices along with mean scores can
be viewed in Table 5.

64

Table 5
Substance use indices for final sample (N=484)
Index Numeric range or category Mean (SD)
Cigarette use
(1 – 4) 2.40 (1.17)
Lifetime non-use 171 (35.3)
Lifetime use but not in past 30 days 71 (14.7)
Light use in past 30 days 135 (27.9)
Frequent or heavy use in past 30 days 107 (22.1)
Alcohol use
(1 – 4) 1.63 (1.03)
Non-use/light use 327 (67.6)
Frequent non-binge 53 (11.0)
Occasional binge 62 (12.8)
Frequent binge 42 (8.7)
Marijuana use
(1 – 4) 1.30 (1.33)
Non-use/light-use 317 (65.5)
Used less than 1x/week 70 (14.5)
Used 1-3x/week 40 (8.3)
Used more than 3x/week 57 (11.8)
Illicit drug risk
(1 – 4) 1.88 (1.09)
Lifetime non-use 248 (51.2)
Lifetime use but not in past 30 days 120 (24.8)
Light use in past 30 days 38 (7.9)
Frequent or heavy use in past 30 days 78 (16.1)
Note
Sample sizes may vary due to missing data on individual variables

65

HIV testing and sexual behaviors
Nearly three quarters of YMSM (74.1%) reported having ever been tested for
HIV and over half reported having been tested within the past year (52.7%). Among
sexually active participants, 80% indicated that they were HIV-negative while 19.6%
indicated that they were HIV-positive or did not know their HIV status either because
they did not test or did not return for their test results. The average number of sex
partners reported by respondents in the past 3 months was 3.37 (SD=7.34). The majority
of respondents indicated engaging in UAI in their lifetime with 30.6% engaging in UAI
during their most recent sexual encounter. Within the past 6 months, over a quarter of
respondents indicated having UAI with Multiple partners or partners whose HIV status
was unknown or different than their own. Nearly one fourth (23.2%) of participants
reported having been previously diagnosed with an STI by a healthcare provider and
7.8% reported an STI diagnosis within the past 6 months. The most common STIs were
gonorrhea (8.7%), scabies/crabs (10.3%) and Chlamydia (4.8%). A complete listing of
variables related to HIV testing and sexual risk behavior is listed in Table 6.

66

Table 6
Sexual behaviors and HIV testing characteristics of final sample (N=484)
Variable Category n (%)
Number of sex partners
(past 3 months)
3.37 (7.34)
Sexual risk
2

No partners 79 (19.6)
Protected AI 147 (36.5)
Single seroconcordant partner UAI 71 (17.6)
Multiple partners/serodiscordant UAI 106 (26.3)
HIV testing
Ever tested for HIV 351 (74.1)
Obtained results of last test1 324 (93.4)
Recent HIV testing
3

Tested within 6 months 159 (35.3)
Tested 6 months to 1 year ago 84 (17.4)
Tested more than 1 year ago
93 (20.6)
Never been tested 115 (25.5)
HIV status
Positive 14 (2.9)
Negative 389 (80.4)
Don’t know 81 (16.7)

67
Table 6 Continued
Variable Category n (%)
Diagnosed STI (ever)
None 362 (74.8)
1 STI 88 (18.2)
2+ STIs 34 (7.0)
Recent STI Last 6 months 38 (7.8)
Lifetime STI
Gonorrhea 42 (8.7)
Syphilis 9 (1.9)
Chlamydia 23 (4.8)
Genital herpes 4 (0.8)
HPV/Genital warts 17 (3.5)
Hepatitis 3 (0.6)
Scabies/crabs 50 (10.3)
Notes
Sample sizes may vary due to missing data on individual variables
1
Seroconcordant UAI with primary partner
2
Data beyond last 3 months
3
Sexually activity among participants
68

Social network visualizations
Sociograms representing the person network with different thresholds of
connectivity are depicted below. Figure 4 presents the person network with all ties
included. Figure 5 presents the person network with tie strengths greater than one. Figure
6 presents the person network with tie strengths greater than two.

69

Figure 4. Person network with one or more shared venues.

Note
Isolates (n=6) removed for readability

70

Figure 5.Person network with two or more venues shared.

Note
Smaller components {i.e., isolates (n=82) and three 2-node components} removed for
readability.

71

Figure 6. Person network with three venues shared.

Note
348 isolates removed for readability

72

Social network metrics
Table 7
Comparison of social network metrics for person network as venue-sharing threshold is
raised
Social network metric All ties ≥ 2 ties =3 ties
Number of nodes 478 402 136
Number of isolates 6 82 348
Number of ties 93,474 17,700 770
Density 0.410 0.110 0.042
Number of components
1
1 4 36
Size of largest component 478 396 18
Proportion of largest component
2
98.8% 81.8% 13.2%
Avg. path length
3
1.656 2.598 1.000
Distance-based cohesion
4
0.694 0.441 0.042
Normalized degree centrality 41.00 (20.20) 10.98 (9.77) 4.19 (3.948)
Normalized eigenvector centrality 5.669 (3.116) 4.551 (5.388) 4.412 (11.296)
Normalized between-ness 0.138 (0.147) 0.388 (1.09) 0.000 (0.000)
Notes
1
Isolates not counted as components
2
Of total sample (N=484)
3
Among reachable pairs
4
Range 0 to 1: Larger values indicate greater cohesiveness
73

Nearly all YMSM in the sample (98.8%) were connected by at least one venue
and the majority (83.1%) were connected by two or more venues. However, less than a
third of the sample (28.1%) were connected by three or more venues. The network
became increasingly fragmented as the threshold of venue-sharing was raised – not
counting isolates, YMSM were connected to each other in a single component when all
ties were counted, in four components when the venue-sharing threshold was raised to
two, and 36 components when the venue-sharing threshold was raised to three. The
density of the person network calculated with at least one venue (0.410) was almost four
times that of the person network calculated with two or more connections (0.110) and
almost ten times that of the person network calculated with three or more connections
(0.042). Distance-based cohesion also decreased as the venue-sharing threshold was
raised.
Centrality metrics were normalized in order to compare across networks. Like
density, degree centrality was nearly four times higher in the person network with all ties
(41.00) compared to the person network restricted to two or more ties (10.98) and nearly
ten times higher than in the person network restricted to three ties (4.19). Eigenvector
centrality also decreased as the threshold of venue-sharing was raised. Between-ness
centrality, which represents the average number of times any given node falls on the
shortest path between all other nodes, was non-existent in the person network with three
ties. Between-ness centrality was higher in the person network with two or more ties
(0.388) compared to the person network with all ties (0.138). Taken together, these
results indicate greater inclusion of YMSM in the sample and greater cohesion when the
74

venue-sharing threshold was the most relaxed. As venue-sharing increased, the
interconnectivity of YMSM decreased and the network became more and more
fragmented.
Associations between person network structure and attributes
In order to determine whether demographic characteristics, substance use, Sexual
risk behavior and HIV testing behaviors were associated with individuals’ position within
the person network, statistical testing between individual centrality measures and these
attributes were tested. Continuous measure of degree centrality, eigenvector centrality,
closeness and between-ness were computed for each respondent. Then one way analysis
of variance for multi-category attributes (e.g., Age category, Race/ethnicity), independent
sample t-tests for binary variables (e.g., Living with family, previous STI diagnosis), and
correlations for continuous variables (e.g., substance use scales) were used to test for
differences between groups.
Demographic characteristics
Age category was associated with degree centrality (F[2,481]=3.554, p-0.029) and
eigenvector centrality (F[2,481]=4.266, p =0.015). Tukey post-hoc testing revealed that
those 18 – 19 years of age (M=38.66, SD=41.37) and those 20 – 21 years of age
(M=39.71, SD=39.36) had higher degree centrality than their older counterparts
(M=28.06, SD=34.94). Similarly, those in the two younger age groups had higher
eigenvector centrality (18–19: M=0.030, SD=0.039; 20 – 21: M=0.029; SD=0.037)
compared to their older counterparts (M=0.018, SD=0.037). Race/ethnicity was also
75

statistically significantly associated with degree centrality (F[2,483]=7.413, p<0.001) and
eigenvector centrality (F[2,483]=7.154, p<0.001). African American participants had
statistically significantly lower degree centrality (M=25.38; SD=31.76) than their Latino
(M=43.08, SD=42.38) and White counterparts (M=39.92, SD=39.10). Similarly, African
American participants had lower eigenvector centrality (M=0.017, SD=0.030) than their
Latino (M=0.034, SD=0.040) and White (M=0.026; SD=0.036). There were no
statistically significantly different differences in centrality measures between residential
status, gay identification, school status or employment status variables.
Substance use
Statistically significant differences in closeness centrality between recent alcohol
users and their non-using counterparts (t=2.546, p=0.011) emerged. Those who were
recent users of alcohol had slightly lower closeness centrality scores (M=1.09; SD=0.118)
compared to recent non-alcohol users (M=1.11, SD=0.12). There was also a statistically
significant difference in closeness centrality between recent cigarette smokers and their
non-smoking counterparts (t=-2.546, p=0.012). Those who smoked had slightly higher
closeness centrality (M=1.11, SD=0.004) compared to non-smokers (M=1.08, SD=0.16).
Statistically significant differences in degree centrality (t=2.393, p=0.015) and
eigenvector centrality (t=2.718, SD=0.007) emerged between non-marijuana smokers and
others. Specifically, marijuana smokers scored lower on degree centrality (M=31.36,
SD=36.96) and eigenvector centrality (M=0.02, SD=0.03) than non-marijuana smokers
(M=40.06, SD=40.59 and M=0.03, SD=0.04). There were no statistically significant
76

differences on centrality measures on the recent illicit drug measure or any of the
composite substance use indices.
Sexual risk behavior and HIV testing
No statistically significant differences emerged on measures of Sexual risk
behavior or HIV testing.
Person network hypothesis testing
Hypothesis I: Social networks of YMSM will be structured according to the principle
of homophily; that is, participants with similar sociodemographic characteristics and
similar types of substance use and sexual risk behavior will cluster together.

In order to test this hypothesis, a three-step social network analysis process was
used. The first step was to create sociograms of the person network with demographic,
substance use, Sexual risk behavior and HIV testing attributes overlaid on the network
maps. This step was employed to achieve a basic understanding of homophily through
visual inspection. The second step was to calculate overall homophily indices for the
entire network based on demographic, substance use, Sexual risk behavior and HIV
testing characteristics using the E-I functionality in UCINET (Borgatti, Everett &
Freeman, 2002). This technique yields a global understanding of homophily in the
network as compared to a fictional network where ties between actors are evenly
distributed both within and outside groups based on the attribute of choice. Finally,
egocentric homophily calculations were used to understand the proportion of individual
77

actors’ networks that were made up of like individuals. These scores were then entered
into SPSS in order to test for statistical significance in homophily between groups of
actors based on demographic, substance use, Sexual risk behavior, and HIV testing
variables.
According to the Theory of Duality of Persons and Groups, the more venues
individuals share the more closely they are connected to one another. YMSM who
frequent the same social spaces have more opportunities to interact and be influenced by
environmental cues present in those social contexts. Therefore, it was desirable to test for
homophily based on the network in which the maximum number of social spaces were
shared. However, interconnectivity decreased dramatically when the venue-sharing
threshold was set to three – the maximum number of shared social spaces in the present
study – capturing only 28% of the total sample. The decision was made to calculate
homophily scores on the person network in which individuals shared a minimum of two
social spaces. By calculating homophily based on this version of the network, it was
possible to capture over 80% of the total sample while still taking into account multiple
venue-sharing by individuals.
Social network visualization: demographic characteristics
The first set of figures below depicts the person social network with demographic
characteristics color coded so that the reader can easily identify patterns according to Age
category, Race/ethnicity, residential status, school attendance, employment, Sexual
identity and relationship status.
78

Figure 7. Person network by age category.

18 – 19 years

20 – 21years

22+ years
79

Figure 8. Person network by racial/ethnic group.

White

Latino

African American
80

Figure 9. Person network by residential status.

Living with family

Other
81

Figure 10. Person network by currently attending school.

Yes

No
82

Figure 11. Person network by currently working.

Yes

No
83

Figure 12. Person network by sexual identity.

Gay/other same sex

Bi/straight
84

Figure 13: Person network by relationship status.

Primary partner

No primary partner
85

The next set of social network visualizations depicts substance use patterns
throughout the network using both 3-month substance use variables (i.e., alcohol,
cigarettes, marijuana, illicit drugs, club drugs) and substance use index categories.
Visualizing the network using these attribute variables allows the reader to understand
both presence vs. absence of use and frequency and intensity of use within the person
network.

86

Figure 14. Person network by past 3-month alcohol use.

Yes

No
87

Figure 15. Person network by past 3-month cigarette use.

Yes

No
88

Figure 16. Person network by past 3-month marijuana use.

Yes

No
89

Figure 17. Person network by past 3-month illicit substance use .

Yes

No
90

Figure 18. Person network by past 3-month club drug use.

Yes

No
91

Figure 19. Person network by alcohol use index.

Non-use/light use

Frequent use, non-binge

Occasional binge

Frequent binge
92

Figure 20. Person network by cigarette smoking index.

Lifetime non-use

Lifetime but not recent use

Recent light use

Recent frequent or heavy use
93

Figure 21. Person network by marijuana use index.

Non-use/light-use

Used less than 1x/week

Used 1-3x/week

Used more than 3x/week
94

Figure 22. Person network by illicit substance use index.

Lifetime non-use

Lifetime use but not recent

Recent light use

Recent frequent or heavy use
95

Figure 23. Person network by recent multiple partner UAI.

Yes

No
96

Figure 24. Person network by lifetime STI diagnosis.

Yes

No
97

Figure 25. Person network by recent STI diagnosis.

Yes

No
98

Figure 26. Person network by lifetime HIV test.

Yes

No
99

Figure 27. Person network by recent HIV test.

Yes

No
100

Homophily testing with E – I index
The first test of homophily was conducted using the E-I index functionality in
UCINET (Borgatti, Everett & Freeman, 2002), which gives a global understanding of
homophily in the network. The E-I index is computed by partitioning the network into
mutually exclusive groups (e.g., all African Americans), counting the number of ties that
are internal to the group (e.g., ties to other African Americans) and subtracting the
number of ties that are external to that group (e.g., ties to Latinos or Whites). This
number is then divided by the total number of ties in the network, which yields a score
ranging from -1 (perfect heterophily) to +1 (perfect homophily). The index is then re-
scaled to take into account the group size. Finally, the E-I index is compared to a fictional
network created by UCINET (Borgatti, Everett & Freeman, 2002) in which all of the ties
in the network have been evenly distributed within and outside of each of the groups.
This allows for a probability test to determine whether the E-I index is statistically
significantly higher or lower than expected (i.e., an E-I index of zero). Tables 8 – 10
present the E-I index calculations based on demographic characteristics, substance use,
Sexual risk behavior and HIV testing.

101

Table 8
E-I indices based on demographic characteristics
Demographics E-I Minimum Average Maximum SD p-value
Age category
18 – 19 -0.070 -0.173 -0.057 0.004 0.025 0.269
20 – 21 -0.021 -0.161 -0.050 0.002 0.023 0.908
22+*** -0.442 -0.465 -0.278 -0.135 0.044 0.001
Race/ethnicity
African
American***
-0.471 -0.461 -0.274 -0.135 0.044 0.000
Latino -0.073 -0.193 -0.061 0.002 0.026 0.299
White -0.021 -0.151 -0.048 0.012 0.023 0.896
Living with family -0.026 -0.107 -0.004 0.021 0.012 0.051
Gay self-identified -0.469 -0.620 -0.441 -0.274 0.049 0.286
In primary relationship -0.009 -0.089 -0.001 0.028 0.011 0.204
In school -0.009 -0.055 0.001 0.026 0.010 0.142
Employed -0.121 -0.218 -0.105 -0.022 0.031 0.290
Note
***p<0.001

102

Two statistically significant homophily effect based on demographic variables
were discovered through E-I index analyses: (1) Those in the oldest age group had lower
homophily scores than expected when compared to the fictional network where ties were
evenly distributed within and outside of that age group; and (2) African American
YMSM had lower homophily scores than expected when compared to a fictional network
where ties were evenly distributed within and outside the African American racial/ethnic
group. In other words, those 22 and older statistically significantly more ties to men in
the younger age groups than expected and African Americans had statistically
significantly more ties to Whites and Latinos than expected. It is interesting to note, while
all three racial/ethnic groups were heterophilous, African Americans had an E-I index six
times lower than their Latino counterparts and ten times lower than their White
counterparts.

103

Table 9
E-I indices based on substance use variables

E-I Minimum Average Maximum SD p-value

Substance use
Alcohol -0.459 -0.749 -0.515 -0.353 0.049 0.880
Cigarettes -0.078 -0.202 -0.084 0.001 0.029 0.533
Marijuana -0.059 -0.191 -0.079 -0.001 0.028 0.752
Illicit drugs** -0.031 -0.047 0.002 0.032 0.009 0.006
Club drugs -0.335 -0.470 -0.292 -0.150 0.045 0.169
Substance use
indices

Alcohol index 0.040 -0.193 0.018 0.182 0.052 0.654
Cigarette index 0.459 0.385 0.457 0.502 0.016 0.523
Marijuana index 0.453 0.398 0.487 0.553 0.022 0.070
Illicit drug index 0.330 0.168 0.295 0.400 0.034 0.852
Note
**p<0.01

104

Table 10
E-I indices based on sexual risk behavior and HIV testing variables

E-I Minimum Average Maximum SD p-value
Sexual risk
behavior
Recent High-risk sex
1
-0.330 -0.405 -0.314 -0.236 0.022 0.240
STI ever -0.232 -0.450 -0.245 -0.112 0.043 0.603
STI past 6 months -0.732 -0.859 -0.711 -0.567 0.045 0.326
Tested for HIV -0.161 -0.325 -0.173 -0.068 0.037 0.604
Recently tested for HIV 0.002 -0.022 0.001 0.011 0.003 0.566
Note
1
Refers to serodiscordant UAI with Multiple partners in the past 6 months

There was one statistically significant homophily effect based on substance use :
Illicit drug users had a lower E-I index than expected when compared to a fictional
network where ties were evenly distributed within and between groups. As we can glean
from the data within Table 10, there were no statistically significant homophily effects
based on sexual risk behavior or HIV testing variables.

105

Homophily testing using egocentric homophily
The final step in testing Hypothesis I was conducted using the ―Ego Homophily‖
function in UCINET, which generates an individual homophily score for each person in
the network. This score is calculated by counting the number of ―same‖ alters on
individual attributes (e.g., African American Race/ethnicity) and then dividing that
number by the total number of alters in each person’s network – as a result ego
homophily scores range between 0 and 1, with higher scores indicating greater
homophily. These scores can then be used to test for mean differences in homophily
scores across groups (e.g., African Americans vs. Whites vs. Latinos). These results are
presented in Table 11.

106

Table 11
Mean homophily scores by demographic characteristics with comparisons between
groups
Variable N Mean (SD) p-value
Age category
1

18 – 19 146 0.47 (0.23) 0.000
20 – 21 160 0.39 (0.19)
22+ 96 0.28 (0.21)
Race/ethnicity
2

African American 89 0.31 (0.28) 0.000
Latino 158 0.49 (0.21)
White 155 0.38 (0.19)
Living with family
No 190 0.46 (0.21) 0.000
Yes 212 0.57 (0.19)
Gay self-identified
No 65 0.12 (0.09) 0.000
Yes 337 0.84 (0.14)
In primary relationship
No 212 0.55 (0.20) 0.000
Yes 190 0.43 (0.18)
In school
No 193 0.54 (0.21) 0.000
Yes 179 0.43 (0.20)
Employed
No 116 0.32 (0.20) 0.000
Yes 256 0.70 (0.18)
Notes
1
Post-hoc testing revealed statistically significant differences (p<0.05) between all
age groups
2
Post-hoc testing revealed statistically significant differences (p<0.05) between all
racial/ethnic groups.

107

Statistically significant differences in ego homophily scores were noted between
all three age groups and racial/ethnic groups. On average, those ages 18 – 19 had nearly
half (47%) of their networks made up of other 18 – 19 year olds compared to those ages
20 – 21 and 22 and over who had 39% and 28% of their networks made up of same age
group alters. A similar trend emerged with relation to Race/ethnicity. Specifically,
Latinos had the highest homophily scores, with an average of 57% of their networks
made up by other Latinos compared to Whites and African Americans who had 38% and
31% of their networks made up of same racial/ethnic alters on average. There was a
higher level of homophily among those who lived at home with family (57%) compared
to those who lived elsewhere (46%). YMSM who identified as gay or another same-sex
Sexual identity had an average of 84% of their alters who also identified as gay compared
to 12% of non-gay identified YMSM. Those YMSM not in primary partner relationships
had a higher proportion of like alters (55%) than those in primary partner relationships
(43%) and those in school had a lower proportion of like alters in their networks (43%)
compared to those who were not attending school (54%). YMSM who were currently
working had a higher proportion of similar alters (70%) compared to those who were not
currently working (32%).

108

Table 12
Mean homophily scores by substance use with comparisons between groups
Variable N Mean (SD) p-value
Substance use
Alcohol
No 53 0.02 (0.04) 0.000
Yes 348 0.95 (0.08)
Cigarettes
No 188 0.06 (0.16) 0.000
Yes 206 0.86 (0.17)
Marijuana
No 241 0.17 (0.25) 0.000
Yes 157 0.57 (0.26)
Illicit drugs
No 289 0.67 (0.18) 0.000
Yes 113 0.32 (0.23)
Club drugs
No 309 0.74 (0.21) 0.000
Yes 93 0.28 (0.25)

109

Table 12 Continued

Variable N Mean (SD) p-value
Substance use
indices

Cigarette 0.879
Lifetime non-use 170 0.30 (0.13)
Lifetime use but not in past 30 days 71 0.27 (0.11)
Light use in past 30 days 132 0.27 (0.11)
Frequent or heavy use in past 30 days 105 0.29 (0.11)
Alcohol 0.116
Non-use/light use 323 0.49 (0.27)
Frequent non-binge 53 0.51 (0.29)
Occasional binge 62 0.48 (0.27)
Frequent binge 40 0.50 (0.27)
Marijuana 0.575
Non-use/light-use 314 0.26 (0.13)
Used less than 1x/week 69 0.28 (0.13)
Used 1-3x/week 39 0.25 (0.14)
Used more than 3x/week 56 0.25 (0.14)
Illicit drugs 0.391
Lifetime non-use 245 0.37 (0.19)
Lifetime use but not in past 30 days 118 0.36 (0.20)
Light use in past 30 days 38 0.41 (0.21)
Frequent or heavy use in past 30 days 76 0.35 (0.20)

110

Statistically significant differences emerged in homophily scores on all individual
recent substance use variables but none of the substance use indices. The majority of
alcohol users networks were made up of other alcohol users (95%) compared to non-
alcohol users, whose networks were only made up by 2% of similar alters on average.
Eighty-six percent of cigarette smokers’ networks were made up of other cigarette
smokers on average while only 6% of non-cigarette smokers’ networks were made up of
other non-smokers. A statistically significant difference emerged in the composition of
marijuana users networks versus non-marijuana users networks with 57% of marijuana
users alters also using marijuana and 17% of non-marijuana users also not using
marijuana. Those who used illicit drugs and club drugs had smaller percentages of their
networks composed of similar alters (32% and 28% respectively) compared to their non-
illicit drug using peers, whose networks were largely made up of other non-illicit
substance users (67% and 74%, respectively).

111

Table 13
Mean homophily scores by sex and HIV testing variables with comparisons between
groups
Variable N Mean (SD) p-value
UAI with Multiple partners
1

No 293 0.75 (0.06) 0.000
Yes 106 0.25 (0.05)
STI ever
No 296 0.72 (0.18) 0.000
Yes 106 0.26 (0.16)
STI past 6 months
No 369 0.91 (0.13) 0.000
Yes 33 0.06 (0.06)
Tested for HIV
1

No 93 0.30 (0.22) 0.000
Yes 289 0.72 (0.20)
Recently tested for HIV
2

No 173 0.49 (0.18) 0.332
Yes 209 0.51 (0.20)
Notes
1
Past 6 months
2
Among sexually active participants

112

Those YMSM who had not engaged in UAI with Multiple partners in the past 6
months had a statistically significantly lower proportion of like alters in their networks
(25%) compared to those who had not engaged in Sexual risk behavior (75%). Similarly,
those who had ever been infected with an STI and those who had been diagnosed in the
past six months had smaller proportions of like alters in their networks (26% and 6%
respectively) compared to those who had never been diagnosed with an STI and/or had
not been diagnosed in the past 6 months (72% and 91% respectively). Those ever tested
for HIV had a higher percentage of like alters in their networks on average (72%)
compared to those who had not been tested for HIV (30%).

113

CHAPTER SIX: VENUE NETWORK RESU LTS
Description of venues
Out of the 110 unique venues nominated by YMSM 105 were nominated by more
than 1 person (98.7%). The most popular types of venues were bars or clubs (65.4%)
followed by smaller numbers of other types of venues, such as coffee shops/restaurants
(9.1%), adult bookstores (7.3%), service organizations (7.3%), gymnasiums or recreation
centers (4.5%) or other types of venues (6.4%), which could include specific outdoor
spaces, such as public parks or yearly events, such as the gay pride festival. Venues were
located geographically in Los Angeles (39.1%), West Hollywood (30.9%), Orange
County (15.5%) or another geographic area (14.5%). Based on feedback from CAB
members, 80 of the 110 venues (72.7%) were classified as ―High-risk venues‖ – that is
venues where alcohol was sold, where public sex occurred and/or where drugs were
known to be used.

114

Table 14
Venue characteristics (N=110)
Venue Characteristic n (%)
Venue type
Club 45 (40.9)
Bar 27 (24.5)
Service organization 8 (7.3)
Gym/recreation center 5 (4.5)
Coffee shop/restaurant 10 (9.1)
Adult bookstore 8 (7.3)
Other 7 (6.4)
High-risk venues 80 (72.7)
Geographic location
West Hollywood 34 (30.9%)
Other LA County 48 (43.6%)
Long Beach 9 (8.2%)
Orange County 9 (8.2%)
Other 10 (9.1%)

Network visualization
Sociograms of the venue network were visualized using NETDRAW (Borgatti,
Everett & Freeman, 2002). The venue social network using the spring embedding
function is depicted in Figure 28. Venue names are included within the diagram.

115

Figure 28. Venue social network (N=110).

Note
Smaller components {i.e., isolates (n= 5) and one 3-node single member component}
removed for readability

116

Based on visual inspection of the venue network, it is apparent that this is a
network with a core-periphery structure – several highly connected venues are located at
the core of the network; these venues connect to a larger number of peripheral venues
that have fewer connections to each other than they do to the venues in the center of the
network. Figure 29 represents the venue network with tie strength visualized. Ties in this
network indicate the number of YMSM who share the social spaces – thicker lines
represent more person sharing; thinner lines represent less person sharing. In this
diagram, it is also apparent that there are a small number of very popular venues in the
center of the network, which are highly interconnected and a much larger number of
venues on the periphery of the network that are connected to central venues but rarely to
each other.

117

Figure 29. Venue social network with tie strength depicted (N=110).

Note
Smaller components (i.e., isolates (n= 5) and one 3-node single member component)
removed for readability

118

Another way in which to demonstrate the number of persons shared between
venues is to depict the network as a circle and progressively remove ties that do not meet
a minimum threshold of person sharing. This process is depicted in Figures 30 – 34.
Venue names are not included in these diagrams in order to aid readability.

Figure 30. HYM venue network with person sharing of 1 or more.

119

Figure 31. HYM venue network with person sharing of 2 or more.

Figure 32. HYM venue network with person sharing of 5 or more.

120

Figure 33. HYM venue network with person sharing of 10 or more.

Figure 34. HYM venue network with person sharing of 30 or more.

121

Through the process of visualizing the network with progressively increasing
thresholds of person sharing it becomes apparent that most venues share a small number
of individuals. When the person-sharing threshold is set to 1 or more, 102 venues are
included in the network (92.7% of all venues nominated). When the person-sharing
threshold is set to 2 or more, 44 venues remain in the network (40.0% of all venues
nominated). When the person-sharing threshold is set to 5 or more, 18 venues remain in
the network (16.4% of all venues nominated). When the person-sharing threshold is set to
10 or more, 11 venues remain in the network (10.7% of all venues nominated). By the
time the person-sharing threshold is set to 30 or more, only 6 venues remain in the
network (5.4% of all venues nominated). These venues: Arena, The Abbey, Rage,
Tigerheat, Mickey’s and Fiesta Cantina are those located in the center of the venue
network depicted using the spring embedding algorithm above. All of these venues are
bars or dance clubs and all are located in West Hollywood or Hollywood. Aggregate
demographic, substance use, Sexual risk behavior and HIV testing characteristics across
these most popular venues can be seen in Appendix Tables A2-4.
Most popular venues
Figure 35 depicts the distribution of different types of venues throughout the full
network. Figure 36 depicts the geographical location of venues in the network. Figure 37
represents the venues classified as ―risk venues‖. Labels in all three figures have been
removed for readability.
122

Figure 35 depicts the entire venue network with nodes color-coded according to
their venue type. Bars and clubs (red and orange nodes) are present throughout the
network but appear highly concentrated in the center of the center. Coffee
shops/restaurants, gyms/recreation centers, and service organizations fall toward the
periphery of the network. This finding is corroborated with comparisons of centrality
scores between venues which on their face are associated with ―High-risk‖ (i.e., serve
alcohol, are known to be meeting places for public sex) have statistically significantly
higher centrality scores than ―Low-risk‖. One coffee shop located toward the lower right
of the core of the network is the non-bar/club venue with the highest centrality score.
This venue is a coffee shop located in the center of West Hollywood, an area of Los
Angeles with a high concentration of LGBT individuals.
123

1
2
3

123

Figure 35. Venue network coded by venue type.
Club

Bar
Service organization
Gym/recreation center
Coffee shop/restaurant

Adult bookstore
Other
124

1
2
4

124

Figure 36. Venue network coded by venue location.
West Hollywood
Orange County
Long Beach
Other LA County
Other
125

Figure 37. Venue network coded by risk venue.
Risk venue
Non-risk venue
126

Social network metrics
Table 15
Mean venue network centrality metrics with varying degrees of exclusion
Network metric
Full
network
Venues with
2+
Venues with
5+
Venues
with 10+
Number of isolates
1
8 66 92 99
Network size 102 44 18 11
Number of ties 678 236 78 34
Density 0.0658 0.1247 0.2549 0.3091
Avg. path length
2
2.412 2.140 1.895 1.964
Distance-based cohesion
3
0.457 0.518 0.602 0.612
Degree centrality 6.58 12.47 25.49 30.91
Between-ness 1.41 2.71 5.60 10.71
Eigenvector centrality 9.97 16.68 28.72 37.03
Notes
1
For the full network, a three-venue component shared by 1 person was also excluded
2
For each pair of nodes, the algorithm finds the # of edges in the shortest path between
them.
3
Range 0 to 1; larger values indicate greater cohesiveness

127

Social network metrics reflect patterns noted through visual inspection of the
network diagrams. As the threshold of person sharing is increased, the number of isolates
(those not connected by the minimum number of people required per the threshold)
increases and the number of ties decreases. Density scores, however, increase as the
network gets smaller since high concentrations of participants share those venues as the
core of the network – these are the venues that remain as the person-sharing threshold is
raised. Distance based cohesion increases progressively, as do average normalized
centrality scores. Those venues at the center of the network are the most nominated and
therefore have the highest degree centrality – these venues also lie on shortest paths
between all other venues in the network (between-ness).
Venue network homophily
Homophily analyses on the venue network were undertaken to determine whether
venues of different types and geographic locations clustered together in the network.
Similar to the person-level analyses, these homophily analyses were undertaken in two
steps – first with the E-I index calculation and later with the egocentric homophily
calculation. Results are presented in Tables 16 and 17.

128

Table 16
E-I index based on venue type, location, and risk
E-I Min Avg Max SD p-value
Venue type 0.421 0.181 0.509 0.754 0.087 0.168
Venue location 0.450 0.058 0.397 0.690 0.044 0.741
Risk venue\ -0.287 -0.556 -0.199 0.205 0.116 0.243
129

Table 17
Egocentric homophily based on venue type, location, and risk
Variable N Mean (SD) p-value
Venue type
Club 44 0.272 (0.261) 0.897
Bar 26 0.209 (0.238)
Service organization 7 0.242 (0.306)
Gym/recreation center 4 0.125 (0.250)
Coffee shop/restaurant 10 0.198 (0.272)
Adult bookstore 7 0.304 (0.407)
Other 7 0.244 (0.275)
High-risk venues 80 0.597 (0.407) 0.508
Geographic location
West Hollywood 32 0.313 (0.259) 0.324
Other LA County 46 0.333 (0.308)
Long Beach 9 0.346 (0.227)
Orange County 9 0.168 (0.344)
Other 9 0.292 (0.217)
Note
Sample sizes may vary due to missing data on homophily scores for isolates.

130

As we can deduce from the data within Tables 16 and 17, neither E-I nor
egocentric homophily analyses yielded statistically significant results for the venue
network, venue type or geographic location.
Structural analyses of the venue network using aggregate person data
In order to understand differences between venues that fell toward to periphery of
the network and those that fell toward the center of the network with regard to
demographic characteristics, substance use, Sexual risk behavior and HIV testing patterns
of the men who nominated these venues, a series of analyses comparing aggregate scores
across these venues were conducted. To divide the network into core and periphery
venues a K-core analysis was used. K-core analyses indicate the maximum number of
venues that are all connected to some number of other venues, represented by the letter
K. Thus the 7-core of the venue network includes all venues that are connected to 7 other
venues. The number of K-cores decreases as the person-sharing threshold is raised
because there are fewer venues to which other venues can be connected as the threshold
gets higher. Visual inspection of the K-core diagram for the entire network can be
helpful in identifying those venues that are most connected to all other venues. Figures 38
and 39 below depict two different representations of the K-core analysis for the full
network. The 7-core venues are those depicted in red and are located at the center of the
network visualization using spring embedding (Figure 38) and at the far right side of the
network visualization in Figure 39, which graphs the network according to the K-core
attribute on the x-axis of the graph.

131

Figure 38. Venue network with K-core
analysis (spring embedding).

3-core
4-core
5-core
6-core
7-core

132

Figure 39. Venue network with K-core
analysis.

Note
Graph by K-core attribute on x-axis
1K-core
2K-core
3K-core
4K-core
5K-core
6K-core
7K-core
133

Inspection of the K-core figures above demonstrates that about half of the venues
fall on the periphery of the network (i.e., 0-Core to 2-Core venues) and half of the venues
fall within the center of the network (i.e., 3-Core to 7-core venues). Statistical
comparisons of venues in the outermost cores versus those in the innermost cores were
conducted on demographic, substance use, Sexual risk behavior and HIV testing
variables and are presented in Tables 18 – 20 below. Mann-Whitney U tests were used
for non-normally distributed proportions on categorical variables; independent sample t-
tests were used for continuous variables. These analyses should be interpreted with
caution as aggregate scores across venues are not independent, given that individuals who
nominated venues on the periphery of the network were sometimes the same as those
who nominated venues in the center of the network. Also note that means and standard
deviations are reported but statistical testing for categorical variables is based on tests of
distribution of proportions.

134

Table 18
Comparison of low K-core venues and high K-core venues by demographic
characteristics

High K-core
(3 – 7)
Low K-core
(0 – 2)
Mean SD Mean SD
Mean age 20.04 (0.65) 19.84 (1.35)
Age category
18 – 19 0.422 (0.228) 0.452 (0.490)
20 – 21 0.381 (0.239) 0.392 (0.479)
22+*** 0.197 (0.151) 0.156 (0.343)
Race/ethnicity
African American** 0.232 (0.219) 0.228 (0.419)
Latino 0.380 (0.233) 0.441 (0.483)
White* 0.389 (0.198) 0.330 (0.455)
Living with family 0.529 (0.233) 0.545 (0.485)
Gay self-identified 0.812 (0.167) 0.823 (0.376)
In primary relationship 0.515 (0.194) 0.528 (0.471)
In school 0.560 (0.234) 0.571 (0.471)
Employed 0.659 (0.231) 0.628 (0.465)
Notes
*p<0.05. **p<0.01. ***p<0.001

135

The distribution of those in the oldest age group was higher among venues in the
core of the network compared to venues on the periphery of the network (M=0.197,
SD=0.151) M=0.156; SD=0.343). On average, the distribution of African Americans was
higher among core venues (M=0.232, SD=0.219) compared to periphery venues
(M=0.228, SD=0.419) and a slightly higher among whites in core venues (M=0.389,
SD=0.198) compared to periphery venues (M=0.330, SD=0.455).

Table 19
Comparison of low K-core venues and high K-core venues by substance use
characteristics
High K-core
(3 – 7)
Low K-core
(0 – 2)
Mean SD Mean SD
Substance use

Alcohol*** 0.930 (0.121) 0.956 (0.198)
Cigarettes** 0.837 (0.224) 0.699 (0.458)
Marijuana** 0.641 (0.283) 0.509 (0.481)
Illicit drugs*** 0.240 (0.185) 0.171 (0.351)
Club drugs*** 0.182 (0.148) 0.077 (0.244)
Substance use
indices

Alcohol Use Index 1.60 (0.42) 1.55 (0.97)
Cigarette Use 2.37 (0.44) 2.37 (1.04)
Marijuana Use Index 1.26 (0.56) 1.32 (1.25)
Illicit Drug Use Index 1.76 (0.39) 1.63 (0.85)
Notes
**p < 0.01. ***p < 0.001

136

The distribution on recent alcohol use was slightly higher among respondents who
nominated peripheral venues (M=0.956, SD=0.198) compared to those who nominated
core venues (M=0.930, SD=0.121). However, all other recent substance use distributions
were higher among those who nominated core venues compared to peripheral venues,
indicating more substance use on average among those venues located in the center of
the venue network.

Table 20
Comparison of low K-core venues and high K-core venues by sexual risk behavior and
HIV testing characteristics
High K-core
(3 – 7)
Low K-core
(0 – 2)
Sexual risk behavior Mean or N SD or % Mean or N SD or %
Number of partners 3.22 (2.83) 3.28 (5.50)
Sex risk index 2.49 (0.48) 2.60 (1.09)
STI*ever 0.236 (0.217) 0.252 (0.413)
STI past 6 months*** 0.096 (0.177) 0.045 (0.164)
Tested for HIV** 0.776 (0.166) 0.784 (0.393)
Recently tested*** 0.625 (0.211) 0.486 (0.488)
Notes
*p < 0.05. **p < 0.01. ***p < 0.001

137

The distribution of lifetime STI infection was higher in the peripheral venues
(M=0.252, SD=0.413) than in the core venues (M=0.236, SD=0.217) yet recent STI
infection was higher in the core venues (M=0.096, SD=0.177) compared to the peripheral
venues (M=0.045, SD=0.164). While lifetime HIV testing was higher on average in the
periphery venues (M=0.784, SD=0.393) versus core venues (M=0.778, SD=0.166),
recent HIV testing was higher among the core venues (M=0.625, SD=0.211) compared to
the peripheral venues (M=0.486, SD=0.488).
Hypothesis testing
Hypothesis II: Social contexts where substance use and/or Sexual risk behavior is/are
sanctioned (e.g., bars, clubs, bathhouses) will be associated with higher levels of risk
behaviors.
In order to test Hypothesis II, the sample of venues was divided into Low-risk
(i.e., restaurants, coffee shops, service organizations, etc.) and high-risk venues (i.e., bars,
clubs, adult bookstores). Due to the small Sample size, non-parametric statistics were
used to test for differences between the two groups. Specifically, Mann-Whitney U tests
were used for non-normally distributed proportions on categorical variables and
independent sample t-tests were used for continuous variables. Results are presented in
Tables 21 – 23. These analyses should be interpreted with caution as aggregate scores
across venues are not independent, given that individuals who nominated venues on the
periphery of the network were sometimes the same as those who nominated venues in the
138

center of the network. Also note that means and standard deviations are reported but
statistical testing for categorical variables is based on tests of distribution of proportions.

Table 21
Comparison of venues by risk type by demographic variables

Low-risk
(n=30)
High-risk
(n=80)
Mean or N SD or % Mean or N SD or %
Mean age 20.24 (1.11) 19.83 (1.02)
Age category
18 – 19* 0.326 (0.382) 0.478 (0.371)
20 – 21 0.430 (0.414) 0.370 (0.360)
22+ 0.243 (0.369) 0.152 (0.208)
Race/ethnicity
African American 0.229 (0.373) 0.230 (0.216)
Latino 0.449 (0.436) 0.395 (0.353)
White 0.322 (0.385) 0.374 (0.335)
Living with family 0.539 (0.433) 0.536 (0.356)
Gay self-identification 0.858 (0.244) 0.802 (0.302)
In primary relationship 0.606 (0.402) 0.490 (0.225)
In school 0.587 (0.403) 0.558 (0.356)
Employed 0.700 (0.351) 0.623 (0.368)
Note
*p < 0.05

139

The low-risk and high-risk venue groups were similar with regard to demographic
characteristics. One statistically significant difference emerged the distribution of 18 – 19
year old YMSM was higher in the high-risk venues compared to the low-risk venues
(M=0.478, SD=0.371 versus M=0.326, SD=0.382).

Table 22
Comparison of venues by risk type by substance use variables

Low-risk
(n=30)
High-risk
(n=80)
Mean or N SD or % Mean or N SD or %
Substance use
Alcohol 0.946 (0.127) 0.941 (0.174)
Cigarettes 0.747 (0.411) 0.789 (0.326)
Marijuana 0.503 (0.455) 0.621 (0.343)
Illicit drugs 0.235 (0.329) 0.196 (0.261)
Club drugs 0.131 (0.226) 0.130 (0.200)
Substance use
indices

Alcohol use index 1.70 (0.93) 1.53 (0.65)
Cigarette use index 2.25 (0.81) 2.41 (0.78)
Marijuana use index 1.43 (0.96) 1.23 (0.95)
Illicit drug use index 1.79 (0.76) 1.66 (0.62)

140

Table 23
Comparison of venues by type of sexual risk and HIV testing variables

Low-risk
(n=30)
High-risk
(n=80)
Mean or N SD or % Mean or N SD or %
Number of partners 2.92 (4.13) 3.37 (4.40)
Sex risk index 2.79 (0.93) 2.45 (0.78)
STI ever 0.301 (0.403) 0.222 (0.293)
STI past 6 months 0.082 (0.204) 0.086 (0.208)
Tested for HIV 0.834 (0.246) 0.760 (0.315)

As we can conclude from the data displayed within Table 22, no statistically
significant differences emerged between the venue risk groups with regard to substance
use. Likewise, the data within Table 23 reflect no statistically significant differences with
respect to Sexual risk behavior or HIV testing between the low-risk and high-risk venues.
Based on these analyses, we can conclude that social contexts in which substance use and
sexual risk behavior are sanctioned are not associated with higher levels of substance use
and sexual risk behavior. In these analyses, substance use and sexual risk behavior were
distributed similarly between low-risk and high-risk venues.

141

Hypothesis III: YMSM who engage in substance use and sexual risk behavior will
either attend contexts that overtly promote risk behavior (e.g., bathhouses) or contexts
that do not overtly promote risk behavior (e.g., coffee shops).

To test Hypothesis III, the top six low-risk venues and the top six high-risk
venues (according to raw number of nominations) were isolated to determine how much
of the total sample of YMSM could be found in these venues. The top six venues in each
risk category were ordered consecutively from lowest to highest to demonstrate the
cumulative percentage of YMSM that could be found in these venues (without double
counting any of the participants). The top six high-risk venues were all dance clubs or
bars and together captured 87% of the total sample. These results emphasize the
popularity of a small handful of bars and clubs among YMSM in this sample. In contrast,
the top six low-risk venues captured only eight percent of the total sample. For an
illustration of the differences in the percentage of the sample captured by each of these
venue types individually, we turn to Figure 40.

142

Figure 40. Percentage of total sample captured by high-risk venues and low-risk venues
individually.

44%
61%
76%
80%
85% 87%
3%
5%
6%
7%
7%
8%
1 2 3 4 5 6
Percent of Sample
Top Six Venues
Risk
Non-Risk
143

To test for crossover between High-risk and low-risk venues, network analysis
was used to determine the percent of the sample that could be captured when both High-
risk venues and low-risk venues were connected to each other by 1-degree of person
sharing – that is at least one person who shared High-risk and low-risk social spaces.
Figure 41 below depicts the percentage of the total sample that can be captured when
these two types of venues are connected by at least one individual.

Figure 41. Percentage of total sample captured when low-risk and high-risk venues are
connected by 1-degree of separation.

92%
93%
94%
96%
97% 98%
28%
61%
88%
89%
93%
94%
1 2 3 4 5 6
Percent of Sample
Top 6 Venues
Risk
Non-Risk
144

These results demonstrate the high interconnectivity between low-risk and high-
risk venues. When High-risk venue one was isolated, it captured 44% of the total sample
(Figure 40); however, when YMSM who nominated that venue and all other venues
connected to that venue were counted 92% of the sample could be captured. By contrast,
when low-risk venue number one was isolated, it captured only 3% of the total sample;
however, when that venue and all other venues connected to Low-risk venue one were
counted, that venue captured 28% of the sample. Figure 41 illustrates the small number of
Low-risk venues that would need to be targeted in order to reach most of the YMSM in
the sample. This figure also demonstrates the high overlap between individuals who
attend High-risk venues and YMSM who attend Low-risk venues.
Another way to visually depict person sharing of these two types of venues is to
use the graph by attribute function in NETDRAW (Borgatti, 2002). Figure 42 groups
Low-risk and high-risk venues together and depicts the connections between these venue
types; Figures 43 and 44 depict the venue network when ties are broken between low-risk
and high-risk venues; and Table 24 presents the accompanying social network metrics.
145

Risk venues {bars; clubs; sex
venues}

Non-risk venues {coffee
shops; restaurants; service
organizations}

Figure 42. Venue network grouped by low-risk and high-risk venue type.

146

Figure 43. High-risk venue network.
147

Figure 44. Low-risk venue network.

148

Table 24
Comparison of social network metrics between low-risk and high-risk venues
Network metric Low-risk High-risk
Number of isolates 11 5
Network size 19 75
Number of ties 30 512
Number of components
1
6 1
Density 0.0877 0.3323
Avg. path length 1.417 2.317
Distance-based cohesion 0.113 0.481
Note
1
Not including isolates

It is evident from the figures above and the social network metrics below that the
high-risk venue network is much more dense and cohesive. There are 71 ties between the
low-risk and high-risk networks when the person sharing threshold is set to 1 or more; 11
ties when the person sharing threshold is set to 2 or more; 4 ties when the person sharing
threshold is set to 3 or more; and 3 ties when the person sharing threshold is set to 4 or
more.
149

CHAPTER SEVEN: RACE/ETHNICITY SUB-AN ALYSIS
In the person analyses African American participants had statistically
significantly lower centrality scores than their White and Latino counterparts, indicating
less venue-sharing among African Americans. In order to further understand differences
in venue attendance by Race/ethnicity, a separate set of venue network analyses were
conducted to determine differences in venue network structure between racial/ethnic
groups. These analyses have particular significance for HIV prevention given that
incidence rates of HIV among YMSM differ by Race/ethnicity with African American
YMSM at particular risk for new infection (Prejean et al., 2011). Differences in venue
networks by racial/ethnic group may help to inform targeted prevention strategies for
diverse racial/ethnic groups of YMSM.
Network visualization
Since ties in the person-network represent shared social spaces, a stratified
examination of the venue network by Race/ethnicity was conducted to determine whether
different social spaces emerged at the center of the venue network among African
Americans compared to Whites and Latinos. Depicted below in Figures 45 – 47 are the
networks of the three-racial/ethnic groups in the present study. Nodes in these
visualizations are sized according to degree centrality, indicating which venues received
the most nominations by YMSM in each racial/ethnic group.

150

Figure 45. Venue network restricted to African American respondents.

151

Figure 46. Venue network restricted to Latino respondents.

152

Figure 47. Venue network restricted to White respondents.

153

Overall, network visualizations between the three-racial/ethnic groups look
relatively similar on visual inspection. A small group of venues emerge at the center of
each network, surrounded by a larger number of less-shared venues that appear to be
more connected to central venues than to each other. The venue network of White
respondents seems to indicate a larger number of peripheral venues than the networks of
African Americans and Latinos. However, these network diagrams do not include isolates
– further network analysis demonstrates large numbers of isolates in the African
American and Latino venue networks compared to the White venue network. Finally, the
same five venues – Rage, The Abbey, Mickey’s, Arena, and Tigerheat appear highly
connected and central in all three networks, which seems to indicate little difference in
the most shared venues, even after stratifying by Race/ethnicity.
Social network metrics
Table 25 presents social network metric comparisons between the three venue
networks. The White YMSM venue network is only slightly larger than that of Latinos
and African Americans; however, it has over 100 more ties than either of the racial/ethnic
minority groups. The White YMSM network is also contained in a single component,
which indicates more cohesion – that is, all White YMSM nominated venues that were
also nominated by other White YMSM. The Latino and African American networks by
contrast, both contain many isolates, indicating that several of these venues are not shared
by others from the same racial/ethnic group.

154

Table 25
Social network metrics of venue network by racial/ethnic group
Social network metric African American Latino White
Number of isolates 20 27 0
Number of nodes 55 64 63
Number of ties 202 218 352
Density 0.1960 0.1528 0.1966
Avg. path length1 2.076 2.081 2.304
Distance-based cohesion2 0.218 0.179 0.481
Degree centrality 5.77 5.89 5.59
Between-ness 3.26 3.09 2.14
Eigenvector centrality 19.55 19.12 13.60
Notes
1
For each pair of nodes, the algorithm finds the # of edges in the shortest path
between them
2
Range 0 to 1; larger values indicate greater cohesiveness

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Since network visualizations indicated the same handful of venues in the core of
all three venue networks, the venues were ranked according to degree centrality across all
three networks. These rankings are listed in Table 26. The top six venues in the exact
order were present across all three venue networks, indicating that these most shared
venues were the same across racial/ethnic group.

Table 26
Comparison of top 10 most nominated venues by racial/ethnic group
Ranking African American Latino White
1 Rage Rage Rage
2 Abbey Abbey Abbey
3 Arena Arena Arena
4 Mickey’s Mickey’s Mickey’s
5 Tigerheat Tigerheat Tigerheat
6 Fiesta Cantina Fiesta Cantina Fiesta Cantina
7 Here Thrust Thrust
8 Oz Here Here
9 Motherload Oz Oz
10 Circus Circus Motherload

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These results demonstrate few differences in the most central venues across
racial/ethnic group, which indicates that YMSM of different racial/ethnic backgrounds
share these central social spaces with members of their own racial/ethnic group and
members of other racial/ethnic groups. The large number of isolates in the venue
networks of racial/ethnic minority YMSM indicates that these YMSM may be more
isolated from each other – they share the same group of core venues as White YMSM;
however, they also spend time in venues that are not shared by others of their own
racial/ethnic group. Coupled with findings from the person network, which indicated that
African American YMSM in particular were less central, these results suggest that
African American men in particular are more disconnected from the larger gay
community.

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CHAPTER EIGHT: DISCUSSION
To my knowledge, the present study is the first of its kind to use affiliation-based
social network data to analyze patterns of substance use and sexual behavior among a
diverse sample of YMSM. This research has a number of implications for both substance
use and sexual risk behavior prevention in this High-risk population. Since analyses were
conducted on both person and venue networks separately the first part of this chapter is
organized according to findings associated with each type of network. Next, results from
both types of network analysis will be integrated to discuss the three main topics of
interest for the present study: (1) demographic characteristics; (2) substance use; and (3)
Sexual risk behavior and HIV testing. Lastly, implications for social work practice and
policy are presented, outlining limitations and suggestions for future research.
Person social network
In the person social network YMSM were connected to each other through shared
attendance at social venues. The only statistically significant difference between YMSM
who nominated social venues and those who did not was the frequency of gay bar/club
attendance. YMSM who nominated venues attended gay bars and clubs more frequently
than those who did not. This finding is intuitive in that YMSM who attend gay
bars/clubs may have higher levels of gay community connection as has been shown in
previous analyses with these data (Holloway et al., 2012) and therefore, may have been
more readily able to identify favorite gay social spaces. While the results presented here
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cannot be extrapolated to YMSM who do not attend social venues, the similarities
between YMSM who nominated at least one venue and those who did not may be an
indication that gay venue attendance in general is not associated with demographic
characteristics, substance use or Sexual risk behavior.
Nearly all participants (99%) were connected by at least one venue and over 80%
were connected by two or more venues. Even after limiting the person network to
individuals who shared two or more social spaces, a dense, highly connected network
emerged, indicating the degree to which YMSM in this study may have been influenced
by similar social environments. While HYM study participants may not have known
each other personally, these data suggest their high potential for social interaction. If
connection to gay social venues can be regarded as a proxy for gay community
connection, these results suggest high connection to the gay community overall. Multiple
studies have stressed the importance of gay community connectedness in influencing
both social support and risk behavior (Holloway et al., 2012; Riggle, Whitman, Olson,
Rostosky & Strong, 2008; Stall & Purcell, 2000; Wright & Perry, 2008). The extent to
which YMSM were connected through venue-sharing has implications for disease
transmission and intervention delivery in the YMSM community. Specifically, innovative
HIV prevention interventions introduced into this network could have the potential to
spread widely and rapidly between YMSM due to the high degree of interconnectivity
based on shared venue attendance.

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Venue network
YMSM nominated a range of different social contexts in which they socialized
with other gay men. These venues were as diverse as recreation centers and social service
organizations. However, the vast majority of social contexts nominated in this study were
bars and clubs. An earlier study by Pollock and Halkitis (2009) examining the link
between venue attendance and HIV risk among older MSM found that 42% of those
interviewed met their most recent sexual partners at bars and 29% met their most recent
sexual partner at a dance club. In this research, 41% of venues nominated were dance
clubs and 25% of venues nominated were bars, which indicates greater popularity of
dance clubs among YMSM. Venues identified as ―High-risk‖ by a Community Advisory
Board because alcohol or illicit substances were known to be used in these contexts
and/or public sex was known to occur at these venues comprised the majority of
nominated venues.
All of the top six most shared venues – Rage, Mickey’s, Tigerheat, Here, Arena,
and Fiesta Cantina – were located within 2 miles of each other. Together, these six
venues captured 87% of all YMSM in the HYM sample. The high interconnectivity of
the person network is reinforced by these findings. YMSM in Los Angeles have many
venues in which to socialize; however, only a small group was nominated by most
YMSM. The result of this nomination pattern was a distinct core-periphery structure of
the venue network. A small number of venues emerged at the center of the network
surrounded by a larger number of venues that were rarely connected to other peripheral
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venues but almost always connected to one of the venues at the networks’ center. These
results suggest the ability to reach the majority of YMSM in this sample by targeting a
handful of the most shared venues. The HYM study used a complicated venue-based
sampling strategy to achieve a representative sample of YMSM in Los Angeles. The fact
that most of the YMSM in this study shared a small percentage of geographically
proximal venues has implications for future venue-based sampling with YMSM and
targeted prevention efforts, which will be discussed at length below.
Demographic characteristics
Statistically significant structural differences in the person network and venue
network were noted by Age category and racial/ethnic group. Specifically, those in the
oldest age group (22-24) were less central to the network compared to their younger
counterparts. Similarly, African American YMSM were less central to the person
network than White or Latino YMSM. However, the venue network demonstrates that a
lower percentage of older and African American YMSM nominated venues at the
periphery than venues in the core. Upon initial examination, results from the person
network and the venue network appear contradictory. However, in the person network,
individuals who are more central share more venues with others, while comparisons
between core and periphery venues in the venue analysis rely on raw proportions of
individuals who nominated venues. Therefore, it is possible that although a larger
proportion of older YMSM and African American YMSM nominated venues in the
center of the network; they shared fewer venues with other YMSM in general, making
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them less central in the person network. These results are consistent with the racial/ethnic
group sub-analyses, which demonstrated that although African American YMSM share
the same group of core venues as their White and Latino counterparts, these African
American men also nominated venues that were not shared by others.
While it is difficult to determine what factors may be driving these structural
network patterns for older and African American YMSM, the peripheral position of these
individuals may point to less interest in and/or comfort engaging with the gay
community. When these men do engage with the community, they seem to attend the
most popular community venues; however, overall older and African American men
appear to be engaging with the gay community less than their younger, White and Latino
counterparts. Older YMSM may be more engaged in primary partner relationships or
career-related activities, which may result in less or interest in venue attendance. Previous
research indicates that African American YMSM may feel more social/sexual racism
within the larger gay community (Berube, 2001) and/or may be more connected with
church or neighborhood communities, resulting in less time spent in gay community
venues. Additionally, African American YMSM are less likely to openly identify as gay
(Millett, Malebranche, Mason & Spikes, 2005), which may contribute to less engagement
with the gay community.
African American YMSM are at greater risk for HIV infection than YMSM of
other racial/ethnic groups (Prejean et al., 2011). As such, this is a population of particular
importance for HIV prevention efforts. The results of the present study indicate that
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venue-based HIV prevention in traditional gay social venues with African American
YMSM may not be the most effective strategy for reaching these men for research or
prevention efforts. Others have hypothesized that high rates of HIV infection among
African American YMSM are related to closed social networks, where higher base-rates
of HIV lead to higher infection (Harawa et al., 2004). The research presented here
indicates the need to develop innovative recruitment strategies for this population of
YMSM in order to engage these men in HIV prevention. These recruitment strategies
might include those most popular gay venues that African American YMSM attend.
However, internet or mobile-technology recruitment may be more effective ways to
target African American YMSM who rarely engage with the larger gay community.
Research using these strategies may allow prevention scientists to elucidate unique
factors leading to HIV transmission among African American YMSM and use this
knowledge to formulate targeted intervention programs.
Substance use
Network structure has been shown important in the explanation of individual risk
behavior (Rice et al., 2012). In this study there were several associations between
position in the person network, substance use and sexual risk behavior. For example,
recent users of alcohol and cigarettes had higher closeness centrality than their non-using
counterparts, and recent marijuana users had higher degree centrality than non-users.
These data indicate a slightly higher degree of venue-sharing among users of these
substances. However there were no differences between illicit drug users and non-users
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with regard to their position in the person network. One possible explanation for higher
centrality scores among those who drink, smoke and use marijuana may relate to
availability and social acceptability of these substances in the venues where YMSM
congregate. The majority of YMSM in this sample were connected by their shared
attendance at a core group of venues where alcohol is served and cigarette smoking is
known to occur. Previous studies with YMSM have demonstrated that greater gay/bar
club attendance is associated with elevated alcohol and cigarette use (Greenwood et al.,
2001; Holloway et al., 2012; Reisner et al., 2009; Wong et al., 2008); therefore, it is
understandable that YMSM who use these substances would be more central in this
network where centrality is determined by a greater degree of venue-sharing.
Individuals with similar substance use patterns were hypothesized to cluster
together based on their use of specific substances. Previous work has demonstrated
homophily by health behaviors in sociometric networks (Christakis and Fowler, 2007;
Christakis and Fowler, 2008). When examining egocentric homophily within the person
network, patterns of homophily based on substance use emerged on every dichotomous
outcome of recent substance use. Specifically, the networks of recent alcohol, cigarette
and marijuana users were largely made up of other users of those substances. Non-users
of alcohol, cigarettes, and marijuana had relatively small percentages of their networks
made up of other non-users. These data are reflective of a high prevalence of these
substances throughout the network but also of the dispersion of use throughout the
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network. Shared attendance at social spaces puts cigarette smokers, alcohol users, and
marijuana users within in close proximity to one another.
These person network results are supported by venue network results, which
demonstrate lower use of cigarettes and marijuana use in venues that fall toward the
periphery of the network. Use of these substances seems concentrated in those highly
shared social spaces. In addition, while fewer YMSM used illicit substances overall,
statistically significantly smaller percentages of YMSM attending venues at the periphery
of the network used illicit drugs and club drugs compared to those attending venues at the
center of the network. Interestingly, large proportions of alcohol-using YMSM attend
venues both at the core and periphery of the network (with a slightly higher percentage of
users in peripheral venues). These findings indicate that alcohol use is pervasive
throughout this community of YMSM. However, cigarettes, a more socially stigmatized
substance in the general population (Kim & Shanahan, 2003), and illicit substance use
are concentrated in the most central gay community venues.
Together, these data may add to existing literature on the presence of a syndemic
of intersecting health issues within the gay community. The term ―syndemic‖ was
originally used by Singer (1994) to explain the co-occurrence of substance use, HIV risk
and violence within poor urban communities. A syndemic has been defined as ―two or
more afflictions, interacting synergistically, contributing to excess burden of disease in a
population‖ (Milstein, 2008, p. 7). Evidence of a syndemic of psychosocial health
problems leading to HIV risk has been demonstrated in both MSM in general (Stall et al.,
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2003) and YMSM specifically (Mustanski, Garofalo, Herrick and Donenberg, 2007). The
Theory of Syndemic Production described by Stall and colleagues (2008) postulates that
as YMSM are introduced to the gay community they are exposed to opportunities for
both risk and resilience. The gay community offers social support, which may not have
been available in other communities of which these YMSM are a part; however, due to
the high prevalence of substance use, mental health issues, HIV and other STIs, in
addition to permissive norms related to risk taking, YMSM may also be exposed to a
range of negative health outcomes through their engagement in the gay community.
The present study may offer some insight into the ways in which syndemic health
problems among YMSM are created and/or reinforced. Through attendance at gay social
venues, YMSM are likely interacting with others who use commonly available
substances, such as cigarettes, alcohol and marijuana. Use of these drugs has been
associated with use of more illicit substances (Storholm et al., 2011) and engagement in
Sexual risk behaviors (Pollock et al., 2012) among YMSM. While it is impossible to
determine the causal chain by which YMSM are susceptible to HIV based on the data
presented here, this research connects risk-taking behavior of YMSM to the places in
which they socialize. Future studies should seek to further understand the ways in which
gay community contexts promote risk behaviors. Specifically, it would be useful to know
whether social contexts influence engagement in risk behavior through environmental
cues present in those social spaces, opportunities to engage with others who engage in
risk behaviors or both.
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Sexual risk behavior and HIV testing
There were no statistically significant differences in person network centrality
metrics based on sexual risk behavior or HIV testing patterns. However, egocentric
homophily in the person network differed based on engagement in HIV risk behavior,
previous STI diagnosis, and HIV testing. Specifically, those who had engaged in UAI
with a serodiscordant partner or multiple partners in the past 6 months had a smaller
proportion of their network made up of like others (25%) than those who had not engaged
in those behaviors (75%). Similarly, those who had been diagnosed with an STI
previously had smaller percentages of their networks made up of like alters than those
who had not been diagnosed with an STI. Those who had been tested for HIV had a
larger percentage of their networks made up of others who had tested compared to those
who had not been tested for HIV. Interestingly, venues in the core of the venue network
had a higher percentage of recent STI diagnosis.
From an epidemiological standpoint these network findings may indicate high
potential for disease transmission among YMSM. Rice and colleagues (2012) have
demonstrated the importance of position within sociometric social networks in relation to
HIV risk among homeless youth. In this person network of YMSM, ―High-risk‖
individuals are not clustered in small, disconnected components nor relegated to the
periphery of the network. Instead, they are evenly distributed throughout the network –
that is ―High-risk‖ individuals attend the same venues as ―Low-risk‖ individuals. For
example, 91% of the networks of YMSM who had been diagnosed with an STI in the
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past 6 months were made up of YMSM who had not been diagnosed with an STI during
this time frame. While these data do not allow for inference regarding YMSM sexual
networks, they do indicate that based on venue attendance, YMSM have the potential to
meet STI-infected partners. These data also indicate that YMSM may have a greater
likelihood of encountering a recently STI-infected partner in those most central venues.
Results related to HIV testing, however, also indicate that those tested for HIV
have a higher percentage of their network made up of like others than those who have
never been tested for HIV. In addition, venues in the center of the network contain a
greater percentage of individuals who have been tested for HIV recently than venues on
the periphery of the network. These results may relate to the ways in which HIV testing
has become an accepted norm within the YMSM community (Cederbaum, Holloway, &
Shoptaw, under review). However, the fact that venues in the center of the venue network
(those that are shared by more YMSM) contain a higher percentage that have been tested
for HIV may be a function of higher risk-behavior among YMSM who attend those
venues, as evidenced by greater percentage of YMSM reporting recent STI diagnosis in
those most central venues.
Implications for social work practice
Despite advances in treatment and expansions in prevention strategies targeting
High-risk communities, YMSM continue to use illicit substances and become infected
with HIV at alarmingly high rates. Reviews indicate limited availability of theoretically
based substance use and sexual risk behavior reduction interventions tailored specifically
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for YMSM despite the unique needs of this population (Johnson et al., 2002; Johnson et
al., 2005; McLean, 2006). Furthermore, studies have demonstrated waning interest in
HIV prevention programs among YMSM in general (Iguchi et al., 2009; Koblin et al.,
2003; Orellana et al., 2006). A recent study by Holloway and colleagues (in press) found
that YMSM felt that they were ―too busy‖ to devote significant time and attention to HIV
prevention programming. When asked what might prompt them to attend an HIV
prevention program in the future, these YMSM indicated that prevention programming
should be integrated into their social networks and/or delivered in the social contexts that
they were already attending.
The implications of the present study for intervention development and delivery
may be viewed in the context of a translational science approach to social work practice
(Palinkas & Soydan, 2012). Translational science has been defined as ―the
multidirectional and multidisciplinary integration of basic research, patient-oriented
research, and population-based research, with the long-term aim of improving the health
of the public‖ (Rubio et al., 2010, p. 470). This dynamic approach aims to move
innovation from the laboratory to the community as quickly as possible. The present
study provides insights about the social environment of YMSM in Los Angeles, which
may aid in the development and delivery of targeted intervention programs to reduce
HIV-related risk in this population.
It is notable that of the 484 men who nominated at least one social venue, 87%
were connected by 6 social venues. When criteria for connection between YMSM was
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expanded to include all venues connected to each of those most shared 6 social venues,
98% of YMSM in the sample were captured. These findings demonstrate the high
potential for social interaction between these men, which has significant implications for
intervention delivery. In an era where resources for public health and social work
intervention become increasingly limited, it is imperative that those resources are used
wisely. The findings presented here indicate that by targeting a small number of highly
connected venues it is possible for an intervention to reach large numbers of YMSM.
Social marketing campaigns, which have been used to raise awareness about negative
health outcomes resulting from Sexual risk behavior and substance use among MSM
previously (Nanin, Parsons, Bimbi, Grov & Brown, 2006; Vega & Roland, 2005) may be
more effective in reaching large numbers of YMSM if placed in maximally shared social
venues.
Previous research demonstrates that while venues such as bathhouses, circuit
parties, sex parties, and bars/clubs may represent prime opportunities for substance use
and sexual risk behavior among YMSM, these venues may also be appropriate locations
for HIV prevention service delivery (Mustanski, Newcomb, Du Bois, Garcia & Grov,
2011). Many HIV prevention interventions have been staged in bars and clubs
(Amirkhanian, Kelly, Kabakchieva, McAuliffe & Vassileva, 2003; Amirkhanian, Kelly &
McAuliffe, 2005; Flowers, Williamson, Frankis & Der, 2002), circuit parties (Mansergh
et al., 2001) African American House and Ball events (Holloway, Traube, Kubicek,
Weiss & Kipke, 2012), and bathhouses (Spielberg et al., 2005; Woods, Binson, Mayne,
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Gore & Rebchook, 2001) in order to bring treatment services to communities of gay men.
A study by Seal and colleagues (2000), which solicited suggestions from YMSM
regarding effective HIV prevention programming, cites venue-based interventions, such
as ―rap sessions‖ in cafes, bars, and other venues where YMSM congregate as potentially
effective in attracting YMSM. The authors suggest partnering with venue owners and
event producers to embed HIV prevention interventions in workshops on topics that
might be more compelling to YMSM, such as making safer sex enjoyable and finding the
right partner. The results presented here demonstrate that further attention to venue-
based HIV prevention among YMSM is needed. If prevention professionals aim to
reduce syndemic health problems in this population, they may have the most impact by
targeting core venues where both substance use and sexual risk behavior are highest.
It is significant that all of the most shared venues among YMSM in this study
were bars and clubs. While these may be ideal venues for social marketing interventions,
it may be more difficult to engage YMSM in one-on-one HIV prevention activities in
these settings. Bars and clubs are often noisy venues attended by individuals seeking to
socialize with friends and/or meet romantic partners; oftentimes, alcohol and other
substances are also used at these venues. However, results from this study demonstrate
high connectivity between High-risk venues (e.g., bars, clubs, adult bookstores) and low-
risk venues (e.g., gyms, coffee shops). These findings indicate that YMSM who attend
High-risk social venues also attend Low-risk social venues where they may be more
amenable to participation in HIV prevention activities. In fact, when targeting the top 6
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low-risk venues and all of the venues connected to those Low-risk venues, it was possible
to reach 94% of the total sample of YMSM in this study. These findings also indicate that
interventions staged in Low-risk venues have the potential for rapid diffusion into High-
risk venues. The diffusion of innovations literature suggests that opinion leaders may be
instrumental in disseminating interventions through social networks (Valente &
Pumpuang, 2007). The results from this study indicate that targeting opinion leaders in
Low-risk venues and asking them to disseminate HIV prevention messages in the high-
risk social spaces they attend may be a useful approach for reducing HIV among YMSM.
Popular opinion leader (POL) models have been used in HIV prevention efforts
with MSM in diverse settings since the early 1990s and have shown increases in HIV
knowledge, social norms to prevent HIV and HIV risk behavior itself (Amirkhanian et
al., 2003; Kelly et al., 1991; Kelly et al., 1992; Kelly et al., 1997; Somerville, Diaz,
Davis, Coleman & Taveras, 2006). However, all of these studies have taken place in
geographically limited areas, such as mid-size cities in the U.S. (Kelly et al., 1991), small
social networks in Bulgaria and Russia, and small communities of migrant workers
(Somerville et al., 2006). Elford and colleagues sought to employ the POL model with
MSM in London and reported no significant impact on HIV risk behaviors (Elford,
Bolding & Sheer, 2001; Elford, Hart, Sheer, Williamson & Bolding, 2002a; Elford,
Sheer, Bolding, Serle & Macguire, 2002b). However, Kelly (2004) critiqued the work of
Elford and colleagues stating that failure to see behavior change was likely due to lack of
fidelity in implementing the POL intervention (Kelly, 2004). That said, a more recent
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study of the POL model in a 5-country randomized control trial showed no additional
reductions in risk behaviors among those sites in the intervention group versus the control
group (National Institute of Mental Health Collaborative HIV/STD Prevention Trial
Group, 2010). Together, this body of literature indicates a need for further exploration of
the POL model in reducing HIV risk behavior among MSM and YMSM in particular.
The research presented here may be used to inform future POL models of HIV
prevention with YMSM. Specifically, the analytic strategy informed by the Theory of
Duality of Persons and Groups may be used to identify the most shared venues among
YMSM. Then these venues (or venues connected to these venues) can be used to stage
POL interventions. Typically, POL models of HIV prevention recruit opinion leaders
through social venues and then ask these opinion leaders to disseminate HIV prevention
messages through their interpersonal networks (Kelly, 2004). The findings from the
present study point to further exploration of POL intervention delivery in the social
venues that YMSM attend. These interventions have the potential to reach larger numbers
YMSM in the social spaces where risk behavior may occur.
Implications for social work policy
In addition to implications for practice, the present study has implications for
policy development at the community level. For example, public health practitioners may
want to explore the possibility of using data regarding YMSM socialization patterns in
combination with data on HIV infection rates. Because venues can be located in
geographic space as well as social space, the location of clusters of ―High-risk‖ venues
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may help to identify ideal locations for HIV testing sites. HIV prevention policy has
emphasized the ―test to treat‖ model of prevention, which aims to identify HIV-infected
individuals through testing and link them to treatment as quickly as possible in order to
reduce viral load within communities (Granich, Gilks, Dye, De Cock & Williams, 2009).
Biomedical interventions to prevent the spread of HIV among MSM, such as pre-
exposure prophylaxis, post-exposure prophylaxis, anal microbicide, male circumcision
and vaccine development have gained significant attention in recent years (Ramjee &
Whitaker, 2011). With the expansion of ―test to treat‖ and the development and testing of
biomedical interventions actively underway, it is necessary to consider how these
interventions might be delivered to High-risk communities. Techniques like those used in
the present research can be useful in quickly identifying concentrations of High-risk
individuals, which may inform policies about the allocation of prevention service
delivery in communities.
The methodology described here can be a useful way to identify social spaces that
have high rates of sexually transmitted infections based on the YMSM who attend those
spaces. Through further study of these particular venues, researchers may be able to
identify physical characteristics of these social contexts that promote risk behaviors, such
as darkrooms for public sex and/or drug use. Policies may be put into place for venue
owners that require them to more closely monitor these spaces for illegal activity. These
types of policy regulations must be considered with great caution given the history of
bathhouse regulation in the early years of the AIDS epidemic, which was often conducted
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without the support of empirical data (Bayer, 1989). Woods and colleagues (2003)
examined differences in bathhouse attendance by MSM across major U.S. cities to
elucidate whether bathhouse policies were associated with differences in HIV-related risk
behaviors among MSM and found few difference across cities. Furthermore, the
researchers determined that more strict regulations regarding public vs. private spaces
within bathhouses may simply encourage patrons to choose other venues in which to
engage in risk behavior. Mathematical models of the effects of bathhouse and sex club
closure on the spread of HIV indicate a potential reduction in HIV incidence if closure
resulted in a reduction in sexual activity (Faissol, Swann, Kolodzeijski, Griffin & Gift,
2007). However, others have suggested that closure of these types of social venues would
only result in risk behavior being moved to other, more hidden underground locations
where HIV prevention may be more difficult (Woods, Binson, Mayne, Gore &
Rebchook, 2000). Further exploration is needed regarding the types of policies that may
be put into place to discourage risk behavior in High-risk venues and whether these
policies are warranted to reduce the spread of HIV.
It is important to remember that in this study, no bathhouses or sex clubs were
nominated by YMSM. This is significant given that previous studies have documented
rates of bathhouse attendance in other cities between 26% and 66% among YMSM
(Binson et al., 2001; Garofalo et al., 2007). Several adult bookstores and bars in which
public sexual behavior and drug use are known to occur in the community were
nominated (personal communication, CAB member, March 25, 2012). The lack of
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nominations regarding public sex venues may be the result of the way in which the venue
name-generator question was asked. YMSM were asked their favorite places to socialize
with other gay men; not their favorite places to seek sex with other men. Future research
studies should seek to use more specific venue name-generators for different types of
behavioral health issues. This methodological limitation is discussed in more depth
below.
Limitations and suggestions for further study
The present study is subject to a number of limitations, which should be
considered when interpreting results. First, data were collected from YMSM attending
venues throughout Los Angeles limiting the generalizability of results. As a result of the
recruitment strategy, YMSM may have been more likely to nominate the venue in which
they were recruited. Since the majority of YMSM were sampled at bars and clubs, this
may be one reason for the large numbers of bars and clubs in the venue network. In
future studies investigators may want to consider alternative sampling strategies to
venue-based sampling when gathering these types of data. Sampling through the internet
or mobile phone applications, which has been useful in gathering samples of YMSM
previously (Bauermeister, 2012; Bauermeister, Giguere, Carbello-Dieguez, Ventuneac &
Eisenburg, 2010; Bauermeister, Leslie-Santana, Johns, Pingel & Eisenberg, 2011; Rice,
Holloway et al., under review), may be more effective ways to get venue data that is not
biased toward the social space in which the data was collected.
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All data are self-report, which may be subject to social desirability bias. Studies
of substance use and sexually transmitted infections have been subject to underreporting
previously (Niccolai et al., 2005; Williams & Nowatzki, 2005). The present study used
ACASI technology; these techniques, which afford participants greater privacy, have
been shown to reduce self-report bias in previous studies with High-risk populations
(Ghanem, Hutton, Zenilman, Zimba & Erbelding, 2005; Gribble et al., 2000). However, it
is possible that substance use and sexual risk behavior were underreported in this study.
Future studies with YMSM may consider incorporating biological measures of substance
use and HIV infection. These might include urine testing for recent substance use or
rapid HIV testing (Martin & Pequegnat, 2000; Richter & Johnson, 2001). These data
might give epidemiologists more a more nuanced understanding about how disease is
distributed through particular communities and venues that carry high disease burden and
possibility for HIV transmission.
The person network in particular should be understood as a map of the
connections of individuals through the social spaces that they share. Individuals were not
connected through interpersonal connections; in fact, these YMSM may never have
shared the same social spaces as others who nominated a particular venue at the same
time. These data would be strengthened by additional information on the timing of venue
attendance as many social venues hold events that cater to sub-populations of the gay
community on different nights of the week. These data might also be strengthened by
additional information on participants’ egocentric networks (i.e., networks where
177

individuals are connected based on interpersonal relationships). There is evidence in STI
prevention literature that sexual networks can be linked to venues when egocentric and
venue data is collected simultaneously (De et al., 2004); this approach may be useful in
future studies with YMSM.
YMSM were asked to nominate their top three favorite places to socialize. In
some cases, YMSM nominated places that were either too broad or too specific for
inclusion in the venue network. For example, some participants stated that the
neighborhood of West Hollywood was their favorite place to socialize. However, West
Hollywood was not included as a venue in the network because that geographical space
was too large to give a meaningful understanding of potential connections between
individuals. Similarly, some YMSM nominated places that were too specific to be
included in the network venue. For example, some YMSM nominated places such as
―my friend’s house‖ or ―house parties‖. These venues could not be included in the
network because there was no way to know if other members of the HYM study were
friends with other participants and/or if they attended the same social events. In future
studies, it would be beneficial to have a more specific name generator for YMSM’s
favorite places to socialize. While broad questions are useful because they capture the
wide range of socialization venues for YMSM, some parameters or ―prompts‖ may help
to gather more useful venue network data. For example, researchers may ask a question
that gives specific parameters about venues. In subsequent studies with YMSM,
178

researchers have sought to make the venue name generator question more specific in
order to gather more meaningful venue data (Rice, Holloway et al., under review).
The large number of statistical comparisons may have inflated the chance of
committing a Type I error; that is, rejecting a null hypothesis that is actually true. As a
result, a small proportion of the statistically significant effects may have been due to
chance alone. Future studies should incorporate the use of more robust statistical
analyses, fewer individual comparisons, and/or adjustments for multiple comparisons.
Finally, the present study did not examine connections between YMSM based on their
use of online venues. Existing literature on YMSM partner seeking suggests the
popularity of websites, message boards, and mobile applications as avenues to connect
with drug use and sexual partners (Bauermeister et al., 2010; Bauermeister et al., 2011;
Garofalo et al., 2007; Rice, Holloway et al., under review). Because online venues have
become such popular avenues for partner seeking, it is possible that social spaces
considered ―Low-risk‖ in the present study, such as coffee shops, might be places that
YMSM are seeking drugs or sexual partners online or through mobile phone applications.
Future studies must take into account internet venues as popular social spaces among
YMSM and investigate the ways in which these social contexts might promote or deter
engagement in risk behaviors. A similar process to the one enlisted here guided by the
Theory of Duality of Persons and Groups should be conducted with data on internet
venues.

179

Conclusion
Despite the limitations of the current study, this research provides a foundation
for further exploration of the ways in which social environments influence Sexual risk
behavior and substance use among YMSM. The theoretical foundation for the present
study, Ewart’s Social Action Theory (1991), postulates that social contexts provides cues
to behavioral health decision-making and allow for interpersonal interaction, which may
promote or reinforce risk behaviors. To date, expansion of Social Action Theory to
explain Sexual risk behavior and substance use among YMSM has not included variables
related to the specific social contexts where YMSM congregate. The present research
demonstrates the utility of including these variables in explanatory models of HIV risk
behavior for this population. Simultaneously, information about the venues where
YMSM socialize may be especially useful in applied HIV prevention with YMSM. Data
on where YMSM socialize can be useful in tailoring interventions to specific
communities of YMSM and targeting interventions to the social spaces where YMSM
may be most amenable to receiving HIV prevention information.
180

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198
APPENDIX
Table A1
Statistical comparisons on all variables by name generator responders versus non-responders
Demographics
Non-response
(n=42)
Response
(n=484)
Mean age 19.83 (1.68) 20.17 (1.56)
Age category
18 – 19 23 (54.8) 183 (37.8)
20 – 21 9 (21.4) 187 (38.6)
22+ 10 (23.8) 114 (23.6)
Race/ethnicity
African American 11 (26.2) 115 (23.8)
Latino 17 (40.5) 188 (38.8)
White 14 (33.3) 181 (37.4)
Born outside of U.S. 10 (24.4) 72 (14.9)
Residential Status
1

Family 20 (47.6) 261 (53.9)
Own 16 (38.1) 175 (36.2)
With friends/partner 5 (11.9) 31 (6.4)
No regular place 1 (2.4) 17 (3.5)

199
Table A1 Continued

Non-response
(n=42)
Response
(n=484)
Sexual identity
Gay 30 (71.4) 361 (74.6)
Other same sex 5 (11.9) 42 (8.7)
Bi/straight 7 (16.7) 81 (16.7)
In primary relationship 23 (54.8) 255 (52.7)
In school 20 (47.6) 235 (48.6)
Employed 22 (52.4) 321 (66.3)
Frequency of gay bar/club attendance
2
**
Less than once per week 33 (78.6) 253 (52.4)
Once per week or less 3 (7.1) 127 (26.3)
More than once per week 6 (14.3) 103 (21.3)
Substance use
Substance use (recent)
Alcohol 34 (81.0) 416 (86.0)
Cigarettes 28 (66.7) 255 (52.7)
Marijuana 16 (38.1) 194 (40.1)

200
Table A1 Continued

Non-response
(n=42)
Response
(n=484)
Cocaine 2 (4.8) 49 (10.1)
Crack 0 (0.0) 5 (1.0)
Ecstasy 2 (4.8) 39 (8.1)
LSD 0 (0.0) 3 (0.6)
PCP 0 (0.0) 2 (0.4)
Mushrooms 3 (7.1) 13 (2.7)
Crystal 3 (7.1) 42 (8.7)
Other speed 1 (2.4) 8 (1.7)
Heroin 0 (0.0) 4 (0.8)
Valium 0 (0.0) 24 (5.0)
Nembutal 0 (0.0) 3 (0.6)
Vicodin 2 (4.8) 32 (6.6)
Adderall 1 (2.4) 14 (2.9)
Paxil 2 (4.8) 7 (1.4)
Poppers 1 (2.4) 29 (6.0)

201
Table A1 Continued

Non-response
(n=42)
Response
(n=484)
Other inhalants 2 (4.8) 13 (2.7)
GHB 0 (0.0) 1 (1.7)
Ketamine 1 (2.4) 6 (1.2)
Rohypnol 2 (0.4) 0 (0.0)
Viagra 0 (0.0) 8 (1.7)
Other drugs 0 (0.0) 10 (2.1)
Illicit drugs (3 months) 18 (42.9) 242 (50.0)
Illicit drugs (recent – no marijuana) 10 (23.8) 138 (28.5)
Club drugs (recent) 6 (14.3) 111 (22.9)
Substance use indices
Cigarette use (0 – 4)*
Lifetime non-use 8 (19.0) 171 (35.3)
No use past 30 days 7 (16.7) 71 (14.7)
Light use past 30 days 20 (47.6) 135 (27.9)
Frequent/heavy use 7 (16.7) 107 (22.1)

202
Table A1 Continued

Non-response
(n=42)
Response
(n=484)
Alcohol use index (0 – 4)
Non-users/light 29 (69.0) 327 (67.6)
Frequent non-binge 5 (11.9) 53 (11.0)
Non-frequent binge 5 (11.9) 62 (12.8)
Frequent binge 3 (7.1) 42 (8.7)
Marijuana use index (0 – 5)
Non-user 16 (38.1) 173 (35.7)
No use past 30 days 12 (28.6) 144 (29.8)
Less than 1x/week 8 (19.0) 70 (14.5)
1 – 3x/week 2 (4.8) 40 (8.3)
> 3x/week 4 (9.5) 57 (11.8)
Illicit drug use index (0 – 4)
Lifetime non-use 21 (50.0) 248 (51.2)
Not past 30 days 12 (28.6) 120 (24.8)
Light use past 30 days 6 (14.3) 38 (7.9)
Frequent of heavy use 3 (7.1) 78 (16.1)

203
Table A1 Continued

Non-response
(n=42)
Response
(n=484)
Sexual risk behavior
Number of partners 3.31 (4.29) 3.37 (7.35)
Sex risk index (0 – 4)
No partners 4 (11.1) 82 (18.8)
Protected AI 19 (52.8) 197 (45.2)
Single partner UAI 0 (0.0) 45 (10.3)
2+ partners and UAI 13 (36.1) 112 (25.7)
STI ever 10 (23.8) 122 (25.2)
STI past 6 months
STI
Gonorrhea 4 (9.5) 42 (8.7)
Syphilis 0 (0.0) 9 (1.9)
Chlamydia 2 (4.8) 23 (4.8)
Herpes 0 (0.0) 4 (0.8)
HPV 2 (4.8) 17 (3.5)
Hepatitis 2 (4.8) 3 (10.3)
Scabies/Crabs 2 (4.8) 50 (10.3)

204
Table A1 Continued

Non-response
(n=42)
Response
(n=484)
HIV testing
Tested for HIV 29 (70.7) 351 (74.1)
Returned for test results 26 (89.7) 324 (93.4)
HIV testing group
Never 12 (29.3) 115 (25.1)
Not tested for > 1 yr 7 (17.1) 93 (20.3)
Tested < 1 yr 22 (53.7) 250 (54.6)
HIV status
Negative 31 (75.6) 389 (80.4)
Positive 1 (2.4) 14 (2.9)
Don’t know 9 (22.0) 81 (16.7)
Notes
Statistical testing not performed if variable for either group had a cell size less than 5 unless otherwise noted.
*p < 0.05. **p < 0.01
1
Comparison was lives with family vs. others
2
Comparison was low gay bar/club attendance vs. others

205
Table A2
Comparison of top 6 venues by demographic characteristics
Ranking 1 2 3 4 5 6
Venue Rage Arena Abbey Mickey’s Tigerheat Fiesta
Numeric code (42) (6) (2) (94) (96) (56)
Sample size N=213 N=160 N=119 N=103 N=88 N=57
Percent of total (44.0%) (33.1%) (24.5%) (21.2%) (18.2%) (11.5%)
Demographics
Mean age 20.25 (1.63) 20.22 (1.57) 20.29 (1.43) 20.19 (1.56) 20.35 (1.69) 20.18 (1.70)
Age category
18 – 19 86 (40.4) 61 (38.1) 33 (32.0) 33 (37.5) 19 (33.3) 47 (39.5)
20 – 21 67 (31.5) 58 (36.3) 45 (43.7) 32 (36.4) 21 (36.8) 41 (34.5)
22+ 60 (28.2) 41 (25.6) 25 (24.3) 23 (26.1) 17 (29.8) 31 (26.1)

206
Table A2 Continued
Ranking 1 2 3 4 5 6
Venue Rage Arena Abbey Mickey’s Tigerheat Fiesta
Race/ethnicity
African
American
52 (24.4) 44 (27.5) 23 (22.3) 22 (25.0) 11 (19.3) 32 (26.9)
Latino 80 (37.6) 64 (40.0) 36 (25.0) 38 (43.2) 15 (26.3) 29 (24.4)
White 81 (38.0) 52 (32.5) 44 (42.7) 28 (31.8) 31 (54.4) 58 (48.7)
Living with family 109 (51.2) 80 (50.0) 49 (47.6) 52 (59.1) 26 (45.6) 63 (52.9)
Gay
self-identified
159 (74.6) 129 (80.6) 73 (70.9) 56 (63.6) 45 (78.9) 96 (80.7)
In primary
relationship
108 (50.7) 75 (46.9) 50 (48.5) 44 (50.0) 33 (57.9) 62 (52.1)
In school 98 (46.0) 78 (48.8) 43 (41.7 40 (45.5) 30 (52.6) 64 (53.8)
Employed 131 (61.5) 103 64.4 65 63.1 60 68.2 43 75.4 77 64.7

207
Table A3
Comparison of top 6 venues by substance use variables
Ranking 1 2 3 4 5 6
Venue Rage Arena Abbey Mickey’s Tigerheat Fiesta
Numeric code (42) (6) (2) (94) (96) (56)
Sample size N=213 N=160 N=119 N=103 N=88 N=57
Percent of total (44.0%) (33.1%) (24.5%) (21.2%) (18.2%) (11.5%)
Substance
use

Alcohol 183 (85.9) 137 96 (93.2) 75 (85.2) 48 (84.2) (85.6) 106 (89.1)
Cigarettes 113 (53.1) 84 57 (55.3) 45 (51.1) 32 (56.1) (52.5) 65 (54.6)
Marijuana 86 (40.4) 57 45 (43.7) 35 (39.8) 27 (47.4) (35.6) 51 (42.9)
Illicit drugs 106 (49.8) 73 58 (56.3) 41 (46.6) 32 (56.1) (45.6) 65 (54.6)
Club drugs 54 (25.4) 34 29 (28.2) 20 (22.7) 16 (28.1) (21.3) 31 (26.1)

208
Table A3 Continued
Ranking 1 2 3 4 5 6
Venue Rage Arena Abbey Mickey’s Tigerheat Fiesta
Composite
indices

Cigarette use 2.39 (1.21) 2.36 2.47 (1.14) 2.38 (1.17) 2.49 (1.17) (1.21) 2.45 (1.19)
Alcohol use
index
1.62 (1.01) 1.69 1.63 (1.03) 1.81 (1.14) 1.60 (0.96) (1.06) 1.61 (1.00)
Marijuana
index
1.29 (1.36) 1.16 1.44 (1.44) 1.30 (1.32) 1.40 (1.24) (1.26) 1.36 (1.31)
Illicit drug
index
1.93 (1.12) 1.87 2.06 (1.19) 1.86 (1.14) 2.00 (1.09) (1.14) 1.96 (1.11)

209
Table A4
Comparison of top 6 venues by sexual risk behavior and HIV testing variables
Ranking 1 2 3 4 5 6
Venue Rage Arena Abbey Mickey’s Tigerheat Fiesta
Numeric code (42) (6) (2) (94) (96) (56)
Sample size N=213 N=160 N=119 N=103 N=88 N=57
Percent of total (44.0%) (33.1%) (24.5%) (21.2%) (18.2%) (11.5%)
Sexual risk
behavior

Number of
partners
3.25 (8.03) 2.82 3.33 (5.30) 3.96 (6.59) 3.87 (5.34) (4.88) 3.42 (5.74)
Multiple
partners/UAI
52 (27.4) 33 28 (30.8) 24 (30.0) 18 (32.7) (23.6) 29 (25.4)
STI ever 60 (28.2) 46 26 (25.2) 19 (21.6) 18 (31.6) (28.7) 31 (26.1)
STI past 6
months
15 (7.0) 15 8 (7.8) 3 (3.4) 9 (15.8) (9.4) 11 (9.2)
HIV testing
Tested for HIV 154 (74.0) 118 72 (71.3) 62 (70.5) 40 (71.4) (77.1) 86 (74.1)
Recently tested 106 (53.5) 81 55 (57.9) 43 (50.0) 29 (54.7) (54.7) 66 (59.5)

Abstract (if available)

Abstract In the United States, men who have sex with men (MSM) remain the group most affected by HIV with younger men at particular risk for new infection. Venue-based HIV prevention shows promise in reducing the spread of HIV among young MSM (YMSM)

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Asset Metadata

Creator Holloway, Ian Walter (author)

Core Title Social network and contextual influences on substance use and HIV risk behavior among young men who have sex with men

School School of Social Work

Degree Doctor of Philosophy

Degree Program Social Work

Publication Date 06/28/2013

Defense Date 06/06/2012

Publisher University of Southern California (original), University of Southern California. Libraries (digital)

Tag HIV,LGBT,OAI-PMH Harvest,social networks,substance use,YMSM

Language English

Contributor Electronically uploaded by the author (provenance)

Advisor Palinkas, Lawrence A. (committee chair), Kipke, Michele D. (committee member), Miller, Lynn C. (committee member), Rice, Eric R. (committee member), Traube, Dorian E. (committee member)

Creator Email ian.w.holloway@gmail.com

Permanent Link (DOI) https://doi.org/10.25549/usctheses-c3-50433

Unique identifier UC11290333

Identifier usctheses-c3-50433 (legacy record id)

Legacy Identifier etd-HollowayIa-910.pdf

Dmrecord 50433

Document Type Dissertation

Rights Holloway, Ian Walter

Type texts

Source University of Southern California (contributing entity), University of Southern California Dissertations and Theses (collection)

Access Conditions The author retains rights to his/her dissertation, thesis or other graduate work according to U.S. copyright law. Electronic access is being provided by the USC Libraries in agreement with the a...

Repository Name University of Southern California Digital Library

Repository Location USC Digital Library, University of Southern California, University Park Campus MC 2810, 3434 South Grand Avenue, 2nd Floor, Los Angeles, California 90089-2810, USA