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Sex talks: an examination of young Black women's social networks, sexual health communication, and HIV prevention behaviors

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Sex talks: an examination of young Black women's social networks, sexual health communication, and HIV prevention behaviors

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Sex Talks: An Examination of Young Black Women’s Social Networks, Sexual Health
Communication, and HIV Prevention Behaviors

By
Jaih B. Craddock, MA
August 2019
Doctor of Philosophy (Social Work)
University of Southern California

Dissertation Guidance Committee:
Eric Rice, PhD (chair)
Julie Cederbaum, PhD, MSW
Ricky Bluthenthal, PhD
FACULTY OF THE USC GRADUATE SCHOOL

SOCIAL NETWORKS, SEXUAL HEALTH, AND HIV PREVENTION ii
Table of Contents
List of Figures and Tables.............................................................................................................. iv
Dedication ....................................................................................................................................... v
Acknowledgment ........................................................................................................................... vi
Chapter 1: Introduction ................................................................................................................... 1
Overview of the Chapters ......................................................................................................... 2
Chapter 2: Literature Review .......................................................................................................... 4
HIV in the United States ........................................................................................................... 4
Black Women and HIV ............................................................................................................. 5
Culture, Relationship Dynamics, and Power ...................................................................... 6
Condom Use and Risk Behaviors ....................................................................................... 8
HIV Testing ...................................................................................................................... 11
Interest in PrEP ................................................................................................................. 13
Social Networks ...................................................................................................................... 14
Network Structures ........................................................................................................... 15
SNM Characteristics ......................................................................................................... 17
Sexual Networks and HIV Risk .............................................................................................. 18
Sexual Health Communication and Young Black Women..................................................... 19
Social Media Use .............................................................................................................. 20
Conclusion .............................................................................................................................. 21
Chapter 3: Theoretical Framework ............................................................................................... 23
Social Cognitive Theory ......................................................................................................... 24
Social Exchange Theory ......................................................................................................... 27
Social Diffusion Theory .......................................................................................................... 28
Chapter 4: Data Collection Procedures ......................................................................................... 30
Community Advisory Committee ........................................................................................... 31
Sampling and Recruitment ...................................................................................................... 32
Respondent-Driven Sampling ........................................................................................... 32
Recruitment ....................................................................................................................... 35
Screening and Enrollment ................................................................................................. 37
Procedures ............................................................................................................................... 39
Informed Consent.............................................................................................................. 39
Data Collection ....................................................................................................................... 39
Power Calculation ............................................................................................................. 41
Chapter 5: Measurements, Statistical Analysis and Results ......................................................... 42
Associations Between YBW’s Social Media Use, Sexual Health Communication, and
Condom Use, HIV Testing, and Interest in PrEP ....................................................... 42
Measurements ................................................................................................................... 42
Statistical Analysis .......................................................................................................... 459
Results ............................................................................................................................... 46
Associations of Social Network Characteristics and Dynamics and Individual Risk Factors
with HIV Testing, Condom Use, and Interest in PrEP Use among YBW ................ 522
Measurements ................................................................................................................. 522
Statistical Analysis ............................................................................................................ 59
Results ............................................................................................................................. 611
SOCIAL NETWORKS, SEXUAL HEALTH, AND HIV PREVENTION iii
Profiles of Young Black Women Based on Sexual Risk Factors and Sexual Health
Communication: A Latent Class Analysis ................................................................ 744
Measurements ................................................................................................................. 744
Statistical Analysis .......................................................................................................... 755
Results ............................................................................................................................. 777
Chapter 6: Discussion ................................................................................................................. 833
A Discussion of the Associations between YBW’s Social Media Use, Sexual Health
Communication, and Condom Use, HIV Testing, and Interest in PrEP ................... 833
A Discussion of the Associations of Social Network Characteristics and Dynamics and
Individual Risk Factors with Condom Use, HIV Testing, and Interest in PrEP ....... 866
A Discussion of Young Black Women Profiles Based on Sexual Risk Factors and Sexual
Health Communication ............................................................................................. 922
Classes with High Probability of Sexual Activity .......................................................... 922
Classes with Low Probability of Sexual Activity ........................................................... 955
A Comprehensive Discussion ............................................................................................... 988
Overall Findings: A Recap from Aims 1, 2, and 3 ......................................................... 988
Limitations ...................................................................................................................... 999
Future Survey Research ............................................................................................ 100100
Implications and Conclusion........................................................................................... 100
References ................................................................................................................................. 1033

SOCIAL NETWORKS, SEXUAL HEALTH, AND HIV PREVENTION iv
List of Figures and Tables
Figure 3.1. A Socioecological Perspective Drawing from Social Cognitive, Social Exchange, and
Social Diffusion Theories ................................................................................................. 24
Figure 4.1. Recruitment and Participation of YBW ..................................................................... 38
Table 5.1. Descriptive Statistics for Social Media Use, Sexual Health Communication and HIV
Prevention Behaviors ...................................................................................................... 488
Table 5.2. Univariable Logistic Regressions of Condom Use, HIV Testing, and Interest in PrEP
....................................................................................................................................... 5050
Table 5.3. Multivariable Logistic Regressions of Condom Use (n = 142) ................................. 511
Table 5.4. Multivariable Logistic Regressions of HIV Testing (n = 87) .................................... 511
Table 5.5. Multivariable Logistic Regressions of Interest in PrEP (n = 172) ............................. 522
Table 5.6. Individual-Level Variables: Demographics, Sexual Health Risk, and HIV Prevention
Behaviors (n = 180) ........................................................................................................ 622
Table 5.7. Social Network-Level Descriptive Statistics (n = 180) ............................................. 655
Table 5.8. Univariable Logistic Regressions of Individual-Level Variables and Condom Use,
HIV Testing, and Interest in PrEP .................................................................................. 688
Table 5.9. Univariable Logistic Regressions of Social Network Variables and Condom Use, HIV
Testing, and Interest in PrEP .......................................................................................... 699
Table 5.10. Multivariable Logistic Regressions of Condom Use and Interest in PrEP ............ 7171
Table 5.11. Multivariable Logistic Regressions of HIV Testing (n = 162) ................................ 722
Table 5.12. Multivariable Logistic Regressions of HIV Testing Continued (n = 162) .............. 733
Table 5.13. Multivariable Logistic Regressions of HIV Testing Continued (n = 162) .............. 744
Table 5.14. Fit Statistics for Latent Classes ................................................................................ 777
Table 5.15. Latent Class Analysis: Five-Class Model ................................................................ 800
Table 5.16. Five-Class Models with Predictor Variables ......................................................... 8282

SOCIAL NETWORKS, SEXUAL HEALTH, AND HIV PREVENTION v
Dedication
This dissertation is dedicated to several people, if not for whom I would not have made it
this far in my academic career. First, I would like to dedicate this dissertation to my husband and
daughter, who have been nothing but supportive, understanding, team players, inspiring, and
encouraging when it came to every aspect of my program and working on this dissertation. I love
you both so much! To my mom, dad, two sisters (Rana and Dena), and my favorite and only
brother, Jaron, thank you all for your love and support and for allowing me the space to think and
step outside of what was seen as typical. You all made me the person I am today. This
dissertation is also dedicated to the memory of my mother-in-law, Diane Craddock, who was
brilliant and inspiring and taught me to never let anyone take my smile away. We miss you
greatly, and you continue to inspire me. To my friend and Soror Leslye Tinson, who although I
haven’t spoken to in a while, has been influential during key moments in my life that have led
me to this point of completing my dissertation. I am forever grateful. Last, there were so many
others who have been supportive, encouraging, inspirational, understanding, and motivating.
This dissertation is dedicated to you! I thank you all!

SOCIAL NETWORKS, SEXUAL HEALTH, AND HIV PREVENTION vi
Acknowledgment
I would like to express my deepest appreciation to my committee members, Dr. Eric
Rice, Dr. Julie Cederbaum, and Dr. Ricky Bluthenthal, for their unwavering guidance, valuable
advice, and constructive criticism. Their feedback and guidance helped me think more critically
about my ideas and how I interpreted or understood various conceptions and theories, taking my
dissertation to a new level. Thank you. I would also like to extend my deepest gratitude to my
community advisory board and research assistants. Their feedback and dedication to the success
of this study was invaluable. Without them, this study would not have been what it is today. I
cannot begin to express my thanks to all the women who expressed interest and participated in
this study; without them, none of this would be possible. Thank you. I’m extremely grateful to
Dr. Norweets Milburn, who met with me and provided mentorship and guidance on my National
Institute of Minority Health and Health Disparities (NIMHD) F31 training grant proposal, which
funded this study. Without your mentorship and guidance, this dissertation would not be
possible. I would also like to extend my sincerest thanks to Dr. Jamilia Stockman for her
valuable contributions and helpful advice on my NIMHD F31 training grant proposal and
throughout the process of my dissertation. I am also grateful to Dr. Suzanne Wenzel for her
guidance and assistance in writing my first “really” rough draft of my NIMHD F31 application
and for connecting with program officers at NIMHD to talk about my proposal ideas. This
experience was critical to my ability to successfully write and submit a F31 proposal down the
line. I also wish to thank Dr. Aun Gomez for her unwavering support and guidance over the
years, and my sister Rana McReynolds for reading through and editing my dissertation. Thank
you both. Special thanks to Eric Lindberg for assisting me with the final edits of this dissertation
study and for editing almost all my papers during my time in the PhD program. Last but not
SOCIAL NETWORKS, SEXUAL HEALTH, AND HIV PREVENTION vii
least, I would like to thank everyone at the USC Suzanne Dworak-Peck School of Social Work
for their love, support and encouragement and NIMHD for seeing the value in my work and
funding this project. Without the funding from NIMHD and support from the USC Suzanne
Dworak-Peck School of Social Work, this dissertation would not be possible.

SOCIAL NETWORKS, SEXUAL HEALTH, AND HIV PREVENTION 1

Chapter 1: Introduction
A stark disparity in rates of HIV/AIDS exists for Black Americans, who represent 61% of
new HIV infections among women in the United States (Centers for Disease Control and
Prevention [CDC], 2018a). Although rates of new HIV diagnosis among Black women decreased
between 2008 and 2016 (Kaiser Family Foundation, 2019), HIV/AIDS continues to be the
fourth-leading cause of health-related deaths for Black women aged 20 to 24 and the fifth-
leading cause of health-related deaths for Black women aged 25 to 34 (CDC, 2018b).
Epidemiological and behavioral researchers have indicated that several factors significantly
affect HIV risk behaviors among Black women

(Adimora et al., 2003; Sharpe et al., 2012; T. K.
Smith & Larson, 2015; Wyatt et al., 2013). However, many of these highly cited HIV prevention
studies had limited representation of young Black women (YBW) aged 18 to 24, the age range
that has the second highest rate of new HIV diagnoses (CDC, 2019a).
More recently, increased attention has been given to sexual risk and prevention behaviors
among YBW; however, most of these studies have focused on individual-level risk factors
(Chandler, Anstey, Ross, & Morrison-Beedy, 2016; Cheong, Tucker, & Chandler, 2018; W. J.
Hall & Tanner, 2016; Sales & Sheth, 2018), failing to examine the associations between YBW’s
social network characteristics and dynamics (e.g., communication, size, relationship type, social
support, communication channels) and individual risk and protective factors (i.e., condom use,
HIV testing, interest in pre-exposure prophylaxis [PrEP]). Individual-level risk and protective
behaviors are often shaped by whom a person knows (social network members [SNMs]), the
relationships and dynamics among the people they know (e.g., social network characteristics),
and the communication that occurs between that person and their SNMs (Craddock, 2019). With
the aim of current HIV/AIDS priorities to decrease incidence of HIV among high-risk groups
SOCIAL NETWORKS, SEXUAL HEALTH, AND HIV PREVENTION 2

(National Institutes of Health [NIH], 2015), it is critical to consider social networks of YBW,
along with individual risk factors for HIV prevention behaviors among YBW, given the interplay
among individual, social, and structural factors (Baral, Logie, Grosso, Wirtz, & Beyrer, 2013).
Additionally, the CDC (2019b) has indicated several prevention strategies that can be used to
decrease new cases of HIV among YBW (i.e., condom use, HIV testing, and use of PrEP), yet
few studies have examined their associations with social network and individual risk factors.
This dissertation is the first in-depth exploration of how YBW’s individual risk, social
network structures, social media use, specific types of SNM relationships (e.g., parents, partners,
friends), social support (e.g., emotional, instrumental, informational), and SNM communication
are associated with their HIV prevention behaviors (specifically condom use, HIV testing, and
interest in PrEP). Three aims guide this study: (a) to explore YBW’s social media use (e.g.,
amount of time, type of social media, purpose of social media use) and sexual health
communication association with condom use, HIV testing, and interest in PrEP among YBW; (b)
to investigate how social networks may be associated with HIV testing, condom use, and interest
in PrEP use among YBW; and (c) to use latent class analysis (LCA) to identify subgroup profiles
of YBW based on individual- and social network-level risk factors and investigate HIV testing,
condom use, and interest in PrEP as key indicators of those subgroup profiles.
Overview of the Chapters
This dissertation is divided into six chapters: (1) Introduction, (2) Literature Review, (3)
Theoretical Framework, (4) Data Collection Procedures, (5) Measurements, Statistical Analysis,
and Results, and (6) Discussion. This introduction chapter (Chapter 1) provided a brief overview
of HIV among Black women, gaps in the literature, and the aims of this dissertation study.
Chapter 2, the literature review, provides an overview of literature focused on HIV in the United
SOCIAL NETWORKS, SEXUAL HEALTH, AND HIV PREVENTION 3

States, HIV risk among Black women, and social network characteristics and dynamics. The
literature review presents a case for why HIV prevention among YBW is important in the efforts
to decrease new incidents of HIV and why it is critical to include social network-level
determinants of HIV acquisition when examining factors associated with HIV prevention
behaviors among YBW. Chapter 3, the theoretical framework, describes the three theories (social
cognitive, social exchange, and social diffusion theories) used to conceptualize and shape the
examination of the three aims examined in this study. Chapter 4 outlines data collection
procedures, detailing the overall methods of this dissertation study, including sampling,
recruitment, and data collection. Data analyses for each aim are included in the relevant analysis
sections in Chapter 5, which is broken down in three analysis sections—one for each study aim.
Each analysis section includes a discussion of the measurements, analyses, and results, along
with tables of descriptive statistics and models. The first section explores the associations among
YBW’s social media use (e.g., amount of time, type of social media, purpose of social media
use), sexual health communication, and condom use, HIV testing, and interest in PrEP among
YBW (Aim 1). The second section investigates how social networks are associated with HIV
testing, condom use, and interest in PrEP use among YBW (Aim 2). The third section uses LCA
to identify subgroup profiles of YBW based on individual- and social network-level risk factors
and investigate HIV testing, condom use, and interest in PrEP as key indicators of those
subgroup profiles (Aim 3). A brief deliberation of each aim’s results is included in Chapter 6,
which also includes an overall discussion section, limitations of the study, recommendations for
implementation, and a conclusion.
SOCIAL NETWORKS, SEXUAL HEALTH, AND HIV PREVENTION 4

Chapter 2: Literature Review
HIV in the United States
Since the 1980s, when AIDS first appeared (HIV.gov, 2018), scientist and researchers
have been striving to intervene and prevent the spread of HIV and AIDS (the final stage of HIV).
Despite numerous recordings of HIV/AIDS in populations of women, children, and heterosexual
identified men, reports surrounding the idea that the HIV/AIDS was a “gay man’s disease,”
(HIV.gov, 2018), and particularly a “White gay man’s disease,” led many to believe they were
not at risk of HIV (Wilson et al., 2016).
Thirty-eight years after the first reports of HIV/AIDS, the United States, along with many
other countries, continue to face challenges with addressing and preventing HIV (CDC, 2016a;
World Health Organization, 2018). In an effort to decrease HIV acquisition, many
epidemiological, behavioral research, and intervention studies have been conducted with men
who have sex with men (MSM; Figueroa, Johnson, Verster, & Baggaley, 2015; Garcia et al.,
2016; Maulsby et al., 2013; Wilson et al., 2016). These studies indicated several factors are
associated with HIV acquisition and barriers to HIV prevention, including stigma from family,
friends, and church members (Garcia et al., 2016), intersecting identities and ideas of masculinity
(Fields, Morgan, & Sanders, 2016; Wilson et al., 2016), heteronormative gender roles (Wilson et
al., 2016), untreated or diagnosed STI (Maulsby et al., 2013), and lack of disclosure (Wilson et
al., 2016).
HIV-related stigma has also shown to impede communication about sexual health and
sexuality. Studies of Black MSM (BMSM) and BMSM who also have sex with women highlight
many of the challenges with disclosures of sexuality, sexual activity, and HIV status (M. R.
Friedman et al., 2019; Joseph et al., 2018; Malebranche, Arriola, Jenkins, Dauria, & Patel, 2010;
SOCIAL NETWORKS, SEXUAL HEALTH, AND HIV PREVENTION 5

Maulsby et al., 2013). Studies examining sexual networks of BMSM, BMSM who have sex with
women, and Black women have all highlighted how Black sexual networks tend to have racial
homogeneity and are isolated from other non-Black sexual networks (Adimora & Schoenbach,
2013; Amirkhanian, 2014; Newsome & Airhihenbuwa, 2013; Wilson et al., 2016), resulting in
networks where a few people’s high-risk behaviors increase the risk level for the entire group
(Adimora et al., 2003; Laumann & Youm, 1999; Newsome & Airhihenbuwa, 2013).
Black Women and HIV
Despite Black women making up only 7% of the United States population (U.S. Census
Bureau, 2019b), approximately 10% of new HIV diagnoses in the United States and 61% of new
HIV diagnoses among American women are among Black American women (CDC, 2016c).
Although there were reports of women, including Black women, diagnosed with HIV/AIDS in
the first several years of the HIV epidemic, Black women were often hidden faces of the HIV
epidemic. The CDC has recommended several strategies to help prevent HIV among Black
women (i.e., condom use, HIV testing, and use of PrEP; CDC, 2019b); however, many cultural
and social factors have shown to be barriers to implementing preventive behaviors (Adimora et
al., 2003; DiClemente & Wingood, 1995; Jemmott, Jemmott, Hutchinson, Cederbaum, &
O’Leary, 2008; Laumann & Youm, 1999).
In the 1990s and 2000s, although a majority of HIV-related research focused on MSM
communities, a handful of studies examined individual-level determinants for HIV acquisition
and HIV risk among Black women (Fullilove, Fullilove, Haynes, & Gross, 1990; Mays &
Cochran, 1988; Nyamathi, 1992). Some of these now-dated studies were critical in identifying
several important individual risk factors significantly associated with HIV risk among Black
women (Adimora et al., 2003; DiClemente & Wingood, 1995; Jemmott et al., 2008; Laumann &
SOCIAL NETWORKS, SEXUAL HEALTH, AND HIV PREVENTION 6

Youm, 1999), such as negotiation power (Amaro, 1995; Bowleg, Belgrave, & Reisen, 2000;
Bowleg, Valera, Teti, & Tschann, 2010; Caldwell & Mathews, 2015; DiClemente & Wingood,
1995; N. M. Hall & Pichon, 2014; Pulerwitz, Amaro, De Jong, Gortmaker, & Rudd, 2002;
Wingood & DiClemente, 1998), condom use self-efficacy (Crosby et al., 2013; DiClemente &
Wingood, 1995; Javier, Abrams, Moore, & Belgrave, 2018; Jemmott et al., 2008), low
perception of risk (Caldwell & Mathews, 2015; DiClemente & Wingood, 1995; Jemmott et al.,
2008) exchange sex (Neblett, Davey-Rothwell, Chander, & Latkin, 2011), sex under the
influence (W. J. Hall & Tanner, 2016; Hutton et al., 2015; Lewis, Hutton, Agee, McCaul, &
Chander, 2015), and partner concurrency (Adimora et al., 2003; Laumann & Youm, 1999;
Newsome & Airhihenbuwa, 2013). Like BMSM, stigma in Black communities regarding HIV
impeded communication about HIV and increased the potential HIV risk among Black women
(Darlington & Hutson, 2017). Members of support networks and religious institutions in Black
communities were often reluctant to talk about HIV-related topics (J. Smith, Simmons, & Mayer,
2005) and often centered their focus on heteronormative ideals of relationship dynamics between
Black men and Black women (W. L. Collins & Perry, 2015).
Culture, Relationship Dynamics, and Power
The U.S. Census highlights a dramatic Black sex-ratio imbalance not found in other
racial groups, reporting that for every 10 Black women there are nine Black men in the U.S.
population (U.S. Census Bureau, 2018b). Additionally, it is estimated that 1 in 3 Black men will
go to prison (Nowotny, Rogers, & Boardman, 2017). If incarcerated men are removed from these
nine men, the ratio decreases considerably to approximately six men to every 10 women (Davis
& Tucker-Brown, 2013; S. R. Friedman, Cooper, & Osborne, 2009), affecting the availability of
men in Black social and sexual networks.
SOCIAL NETWORKS, SEXUAL HEALTH, AND HIV PREVENTION 7

Despite the shortage of available Black men, Black women are still expected to get
married or have a stable partner (Kyomugisha, 2006). Mays and Cochran (1988) found that
African American women’s “cultural ideals make committed unions with men essential for
achieving status and attaining happiness” (p. 153). By presenting themselves as having attracted
a “loyal, upstanding partner in a perfect intact union,” women build status and self-esteem. For
these women, the need and desire to secure a partner creates the belief that they behave
differently than others who are at risk of HIV (Sobo, 1995).
Many Black women believe that entering into a stable relationship or marriage decreases
both theirs and their partner’s risky health behaviors (drinking, drug use, and concurrent sexual
partnerships; Ali & Ajilore, 2011) and increases mental health (happiness and self-esteem; Hill,
Reid, & Reczek, 2012). Extensive literature on marriage and Black Americans provides support
for those beliefs, consistently showing married adults are generally healthier, happier, and have
improved self-esteem compared to their unmarried counterparts (Ali & Ajilore, 2011; Hill et al.,
2012; Mandara, Johnston, Murray, & Varner, 2008). Nonetheless, the limited number of Black
men for every Black woman continues to create challenges for Black women when trying to
secure a stable and healthy relationship.
The disproportionate number of Black women to Black men has created a dynamic
wherein Black men tend to hold more power in relationships; Black men in high demand by
Black women may have more power to negotiate the type of relationship they desire due to their
scarcity (Alleyne & Gaston, 2010; Newsome & Airhihenbuwa, 2013). This limited pool of
potential partners creates competition when trying to find Black men interested in making
marital and family commitments (Newsome & Airhihenbuwa, 2013; Wyatt, Forge, & Guthrie,
1998), placing many Black women in vulnerable positions. Caldwell and Mathews’ (2015)
SOCIAL NETWORKS, SEXUAL HEALTH, AND HIV PREVENTION 8

qualitative study with 23 Black women aged 25 to 45 found that women reported feeling
disadvantaged and powerless in their dating experiences due to challenges finding Black men
who wanted to be in monogamous relationships. Women reported experiencing and observing a
gendered power imbalance that affected their and their peers’ ability to negotiate condom use or
monogamy in their sexual relationships (Caldwell & Mathews, 2015). Additionally, N. M. Hall,
Lee, and Witherspoon (2014) found in their study examining the sex-ratio imbalance among
Black female college students that Black women often engaged in nonmonogamous or casual
relationships with Black men because of lack of interest in commitment among Black men. This
occurred even when Black women expressed interest in a committed relationship (Alleyne &
Gaston, 2010; Ferguson, Quinn, Eng, & Sandelowski, 2006; N. M. Hall, Lee, et al., 2014; W. J.
Hall & Tanner, 2016). The combination of cultural perceptions of stable partners and power
dynamics generated by sex-ratio imbalances places some Black women who want to practice
safer sex at risk of HIV. HIV prevention among heterosexual couples using male condoms
requires both partners be willing to use this HIV prevention method. However, Black women,
who recognize the sex-ratio imbalance and hold relationships with a Black man at a high value,
may relinquish negotiation power in their relationships and be more likely to settle for less
desirable partners, accept infidelity, and agree to engage in unprotected sex (Newsome &
Arihihenbuwa, 2013).
Condom Use and Risk Behaviors
The goal of many HIV prevention interventions has been to increase condom use
negotiation skills and condom use among at-risk populations (e.g., Black women; Boekeloo et
al., 2015; Fogarty et al., 2001; Sales et al., 2012; Wenzel et al., 2016), yet condom use among at-
risk populations, such as YBW, and their partners remain low (Fogarty et al., 2001). Several
SOCIAL NETWORKS, SEXUAL HEALTH, AND HIV PREVENTION 9

barriers to condom use among U.S. populations have been linked to lack of access to condoms,
perceived stigma when buying condoms, and lack of comfort (Sarkar, 2008). Studies exploring
barriers to condom use in Black communities overwhelmingly highlighted how unprotected sex
could represent trust, love, and faithfulness between partners (Garcia, 2016; Newsome &
Airhihenbuwa, 2013).
Sex under the influence of alcohol or other drugs is another barrier to condom use (W. J.
Hall & Tanner, 2016; Hutton et al., 2015; Lewis et al., 2015). In a qualitative study examining
alcohol use and sexual risk behaviors among Black women (mean age = 24.5), many women
reported various reasons for having sex under the influence. A majority felt that drinking
stimulated their sex drive and increased their sexual satisfaction; they believed it expanded the
variety of sexual activities they engaged in, including riskier sex behaviors (Hutton et al., 2015).
Some women reported that drinking increased their participation in types of “sexual activity they
would not have engaged in while sober,” like oral sex, anal sex, group sex, and “rough” sex
(Lewis et al., 2015, p. 455). Women also identified several consequences of sex under the
influence, including having sex with new partners (often strangers) or individuals they would not
have had sex with when sober and being less concerned about unprotected sex (Lewis et al.,
2015).
Informal exchange of sex for resources such as money, housing, food, or clothes is also
associated with condomless sex. Neblett and colleagues (2011) found that exchange sex was
“part of a personal economic strategy for at-risk women” (p. 61), with their findings indicating
that women may seek partners to access resources or financial support. Strongly tied to exchange
sex is economic insecurity, which has been shown to hinder condom use negotiation and increase
some Black women’s vulnerability to HIV acquisition (Caldwell & Mathews, 2015; Chatterjee,
SOCIAL NETWORKS, SEXUAL HEALTH, AND HIV PREVENTION 10

Hosain, & Williams, 2006; Kyomugisha, 2006). Raiford and colleagues (2014) found that YBW
who reported being homeless or had low prospects for obtaining education or employment were
3 times more likely to ever participate in exchange sex. In a study examining low-resourced
YBW, participating in exchange sex in the past 12 months was associated with acquiring HIV
(Reilly et al., 2013). Because many YBW are in a transitional age at which they are often
expected to provide and gain access to their own resources, they may be susceptible to seeking
resources through other means, such as exchange sex (Raiford et al., 2014), making exchange
sex a potential factor in HIV prevention critical to understanding HIV acquisition in Black
communities.
Concurrent sexual partnerships among Black women’s sexual networks have been
documented in the literature, highlighting that concurrent sexual partnerships in these small or
isolated sexual networks results in networks wherein a few people’s high-risk behaviors increase
the risk level for the entire group (Adimora et al., 2003; Laumann & Youm, 1999; Morris et al.,
2009; Newsome & Airhihenbuwa, 2013). Thus, a YBW’s sexual partner may place her at risk of
HIV, despite not participating in any behaviors that may be perceived as risky (e.g., sex under
influence, sex exchange, concurrent sexual partners).
Aligned with cultural expectations, some Black women perceive that entering into a
stable relationship will protect them from HIV risk (Kyomugisha, 2006), thus reducing their
need to use condoms if they are using other means of birth control. In a qualitative study,
McLaurin-Jones, Lashley, and Marshall (2017) explored factors that contributed to low condom
use among Black college women. They found that women were less interested in condom use
because pregnancy was more of a serious concern than acquiring a sexually transmitted infection
(STI) or HIV, and YBW were often using other methods for pregnancy prevention. Lower
SOCIAL NETWORKS, SEXUAL HEALTH, AND HIV PREVENTION 11

condom use among these YBW was associated with testing for HIV, hormonal contraception,
and a sense of trust in their relationship. Low perceived risk of HIV was also found to be a
barrier for condom use among Black women (McLaurin-Jones et al., 2017; Payne et al., 2006);
because many Black women continue to perceive their risk of HIV as low, need to use condoms
with sexual partners was low (Nunn et al., 2011). One of the few factors shown to encourage
women to use condoms was acquiring an STI from a nonmonogamous partner (Lima et al.,
2018).
Based on evidence in the literature, consistent condom use continues to be a challenge
even though condoms have been shown to be most effective against the transmission of STIs,
including HIV (CDC, 2019b). In its compendium of effective interventions, the CDC puts forth
several interventions geared toward increasing condom use among MSM, Black men, and Black
women (CDC, 2018c). Many of these culturally tailored interventions focus on key factors
associated with increased condom use, such as condom use self-efficacy and sexual health
communication with peers, family members, or partners, and aim to decrease stigma (Crosby et
al., 2013; Hays, Rebchook, & Kegeles, 2003; Jemmott, Jemmott, & O’Leary, 2007; K. T. Jones
et al., 2008; O’Donnell, Stueve, Joseph, & Flores, 2014). However, even with these effective
interventions, rates of condom use continue to be low in populations of YBW. Thus, it is
important to continue to assess individual risk factors associated with condom use among YBW.
In addition to the goal of increasing condom use as a HIV prevention method, the CDC (2019b)
also recommends routine screening for HIV and the use of PrEP for at-risk populations.
HIV Testing
HIV testing has been shown to help prevent the spread of HIV by diagnosing those who
may not have been aware of their HIV-positive status, allowing them to seek treatment and
SOCIAL NETWORKS, SEXUAL HEALTH, AND HIV PREVENTION 12

decrease the spread of HIV (Stein et al., 2017). However, several barriers prevent women from
getting tested. Some of those barriers include HIV-related stigma, concerns about confidentiality,
beliefs around testing and perceptions of risky behavior, signaling distrust or infidelity to
partners, fear of being diagnosed with HIV, and concerns about access to care if results come
back positive (Cheong et al., 2018; De Jesus, Carrete, Maine, & Nalls, 2015; McDougall,
Dalmida, Foster, & Burrage, 2016). In a qualitative study examining barriers to HIV testing
among young Black adults, N. M. Hall, Lee, and colleagues (2014) found that lack of HIV
communication and fear were major barriers to getting tested for HIV. In another study
examining barriers to HIV risk reduction among Black female college students, participants
reported a desire to get tested regularly for HIV but also indicated that affordability, access to
testing, and stigma were barriers to using HIV testing services (Chandler et al., 2016). Although
barriers to HIV testing among YBW are known, recent studies have found that YBW report high
HIV testing rates (Craddock, 2016; McElrath, Stana, Taylor, & Johnson-Arnold, 2017; Moore &
Belgrave, 2019). Dyson, Mobley, Harris, and Randolph (2018) found in their qualitative study
that many YBW decided to test for HIV because it was offered during their yearly clinic visits
(e.g., pap smears, immunizations). Thus, YBW may have higher HIV testing rates if they opt
into testing during clinical visits. Social support from SNMs have also been highlighted as
facilitators of HIV testing among YBW (H. M. Scott et al., 2014; Williams, Pichon, Latkin, &
Davey-Rothwell, 2014), indicating that the examination of barriers and facilitators of HIV testing
at the network level is essential to understanding what is associated with HIV testing among
YBW.
SOCIAL NETWORKS, SEXUAL HEALTH, AND HIV PREVENTION 13

Interest in PrEP
PrEP is a biomedical prescription medication that can help decrease the risk of acquiring
HIV (CDC, 2018c). PrEP has been used with MSM and in several countries highly affected by
the HIV epidemic (Koechlin et al., 2017; Liu et al., 2014). Although PrEP use was mainly
targeted toward high-risk populations of MSM in the United States when it was approved (e.g.,
discordant serostatus relationships, MSM whose sex partners status is unknown; Hoff et al.,
2015), recently the U.S. Public Health Service (USPHS) suggested that PrEP is appropriate for
HIV-negative heterosexual women who have condomless sex, have a recent acquisition of a STI,
have a high number of sex partners, participate in exchange sex, or live in high HIV burden areas
(CDC–USPHS, 2018; Sales & Sheth, 2018). To assess interest in PrEP among YBW in Atlanta,
Georgia, Sales and Sheth (2018) collected data from 500 YBW aged 18–24. Of them, 43%
reported that they would be very likely to use PrEP, whereas only 20% reported being very
unlikely to use PrEP. Sales and Sheth (2018) also found a significant positive association
between perceived risk of HIV/STI and interest in PrEP, indicating that women who perceived
being at risk of HIV were more willing to use PrEP. These findings are promising, because
several barriers have been documented to PrEP acceptance and adherence among MSM,
including stigma related to HIV (Garcia et al., 2016). Although these findings are promising,
Atlanta is a city heavily burdened by HIV (CDC, 2017), and much work has been done on
decreasing HIV-related stigma in the Atlanta area (AID Atlanta, 2019). Thus, it is hard to
conclude that these findings about interest in PrEP among YBW can be generalized to other U.S.
cities. Furthermore, this is one of the few studies examining interest in PrEP among YBW (Sales
& Sheth, 2018), and it did not examine the impact of social networks on interest in PrEP use.
SOCIAL NETWORKS, SEXUAL HEALTH, AND HIV PREVENTION 14

Thus, it is essential to further explore the associations between interest in PrEP among YBW
who live in cities across the United States and social networks dynamics and characteristics.
Social Networks
Social networks are networks of social interactions and personal relationships

salient in
influencing communication (Valente, 2010).

Social network analysis allows for an examination
of differences in relationship dynamics and how those differences in networks of YBW are
associated with communication about sex and sexual health (Craddock, Rice, Rhoades, &
Winetrobe, 2016). This approach has been successfully used to understand and reduce HIV risk
behaviors in other high-risk populations (e.g., MSM, homeless youth, HIV positive individuals)
and their networks (Cederbaum, Rice, Craddock, Pimentel, & Beaver, 2017; Craddock et al.,
2016; Holloway, Pulsipher, Gibbs, Barman-Adhikari, & Rice, 2015). Social networks can be
sources of communication, observational learning, knowledge acquisition, and HIV risk behavior
reinforcement (Bandura, 2006). Through various channels of communication (e.g., in-person
conversations, phone calls, social media, text messaging), behaviors can be transmitted in a
mutuality of influences (Bondi, Craddock, Funke, Legendre, & Tiwari, 2018). The assumption is
that women exchange sexual health information with their SNMs, in turn influencing each
other’s behaviors.
There are two types of social network data, egocentric and sociocentric (whole).
Sociocentric networks are networks wherein every node (individual) is a participant, resulting in
data collection from a complete network of individuals (J. Scott, 2013). Sociocentric networks
are typically used to examine boundary-specific problems (e.g., social media-based groups,
classrooms, student organizations, homeless youth at a drop-in center). The main assumption is
that structures in these settings matter (Perry, Pescosolido, & Borgatti, 2018). Like with any
SOCIAL NETWORKS, SEXUAL HEALTH, AND HIV PREVENTION 15

research method, there are limitations to the boundaries set by sociocentric network research. For
instance, by focusing on one specific population (e.g., YBW) in one domain (e.g., a college),
other important interactions (e.g., parents, partners, friends outside the college) will be omitted.
Thus, using a network method that allows for the inclusion of all a YBW’s important SNMs
would be more beneficial in understanding the impact of important influencers sexual health
decision-making.
Egocentric social networks, or personal networks, are networks in which the focus is
placed on individual participants and SNMs directly linked to the individual (Perry et al., 2018),
accounting for important interactions missed in sociocentric networks. The idea is that each
person (e.g., YBW) lives in a personal community, and that the structure and composition of the
community influences her (Perry et al., 2018). The main goal of egocentric network research is
to predict ego-level outcomes. This is done based on variables that describe how an ego is
connected to her alters (i.e., SNMs), the characteristics of her alters, and the characteristics of the
patterns of connections among her alters. A limitation in using egocentric network analysis is
that the chains of indirect influences on behaviors (e.g., friends of friends, SNMs of family
members) cannot be examined. Thus, it cannot be determined how people who are indirectly
linked to YBW may affect sexual health decision-making and behaviors. However, compared to
sociocentric networks, egocentric networks are more conducive in making inferences from
samples to populations (Perry et al., 2018).
Network Structures
The structure of a social network is associated with how information is shared and
acquired in social networks of at-risk populations (Valente, 2010), and it can provide information
regarding how a person is situated in the network (Uchino, 2004). Three main network elements
SOCIAL NETWORKS, SEXUAL HEALTH, AND HIV PREVENTION 16

can be used to examine the structures of egocentric networks: size, density, and homophily.
Network size—the number of people in a given social network—is an important structural
property (Valente, 2010). In a sociocentric network, size is calculated by counting the total
number of nodes in the whole network, whereas in an egocentric network, network size would be
considered the number of nominated alters. Considering the size of a network is important
because size may affect the number of resources or knowledge available to a YBW in her
network. A YBW with a large network may have more access to information and more SNMs to
seek advice from, compared to YBW with a smaller network. Density is the total number of
direct links or connections in a network divided by the total possible links or connections in a
network (Valente, 2010). Assessing density in an egocentric network allows for the examination
of how connectedness between SNMs is associated with information seeking and sexual health
communication. A YBW with a high-density network might have more similar information
being shared in her network, due to the connectedness and potential shared viewpoints among
her SNMs, whereas a YBW with a low-density network may have more variety in shared
information in her network, due to low amounts of connectedness and potential differing
viewpoints among her SNMs.
Homophily is when two or more individuals who interact have similar attributes, such as
beliefs, sexual behaviors, or education (McPherson, Smith-Lovin, & Cook, 2001; Rogers, 2003).
Individuals are generally more likely to select or communicate with others (e.g., partners and
friends) who are similar (McPherson et al., 2001). Thus, considering homophily is important
when trying to understand whether YBW are speaking with SNMs who are similar or different
from themselves, and will allow for the examination of whether having more or fewer SNMs
SOCIAL NETWORKS, SEXUAL HEALTH, AND HIV PREVENTION 17

who have similar HIV prevention behaviors is associated with HIV prevention behaviors among
YBW.
SNM Characteristics
Social support. Social support is a powerful influence in social networks, with several
studies indicating that having social support can improve a person’s overall well-being, access to
services and resources, and knowledge (Amirkhanian, 2014; Craddock, 2019; Goldenberg &
Stephenson, 2015; Heaney & Isreal, 2008; Nguyen, Chatters, Taylor, & Mouzon, 2016; Wohl et
al., 2010). Social support can be measured as perceived and actual support and typically has four
components (i.e., emotional, instrumental, informational, and appraisal; Zimet, Dahlem, Zimet,
& Farley, 1988). Studies examining actual and perceived support have found that perceived
support is more important when it comes to understanding what influences behavioral changes
(Uchino, 2004).
Emotional support typically comes from SNMs who provide love or are perceived as
caring and nurturing (Perry et al., 2018). Emotional support is considered to be advantageous
because it gives a person a sense of acceptance or importance (Uchino, 2004). Informational
support, on the other hand, could refer to an SNM who provides advice or guidance (Heaney &
Isreal, 2008). For example, an SNM who provides informational support would be a person
whom a YBW may seek for answers to questions regarding testing for HIV. An SNM who
provided informational support may be a powerful influence on a YBW, helping the YBW make
a decision regarding her sexual health or shaping her thoughts on HIV risk prevention. Another
measurement of social support is instrumental (or tangible) support. Instrumental supporters are
SNMs who offer practical resources or help (Perry et al., 2018; Uchino, 2004). These are SNMs
SOCIAL NETWORKS, SEXUAL HEALTH, AND HIV PREVENTION 18

from whom YBW could borrow money or who can offer a place to stay or give them a ride.
These may also be sexual partners with whom YBW exchange sex for instrumental support.
Sexual Networks and HIV Risk
Black women’s sexual behaviors are not significantly different than those of White or
Latina women (CDC, 2017). The sexual behaviors of their partners and the structures of their
sexual networks, however, place Black women at higher risk of HIV (CDC, 2017). Sexual
networks have been utilized to better understand how STIs, including HIV, spread through
sexual networks (groups of individuals that are either directly or indirectly connected through
sexual contact; Adimora et al., 2003). Black sexual networks are more racially segregated than
those of non-Black peers; Blacks are also more likely to choose sexual partners who are also
Black (Laumann & Youm, 1999). As a result, these networks become smaller and more isolated,
allowing STIs and HIV to spread more rapidly (Newsome & Airhihenbuwa, 2013).
Little to no literature has examined the associations between YBW’s sexual risk
behaviors at the individual level and social network characteristics and dynamics. A majority of
the research examining social networks of Black women has focused on contraceptive decision-
making (Yee & Simon, 2010), HIV-positive Black women (Cederbaum et al., 2017;
Vyavaharkar et al., 2011; Wohl et al., 2010), sexual networks (Adimora & Schoenbach, 2013;
Adimora, Schoenbach, & Doherty, 2006; Grieb, Davey-Rothwell, & Latkin, 2012), Black
women outside of the United States (Kebebe, 2012), sex workers (Shushtair et al., 2018), drug
use (Rudolph, Linton, Dyer, & Latkin, 2013), or samples including few or no YBW aged 18 to
25 (Grieb et al., 2012; Neblett et al., 2011). Although these studies vary in topics, each
emphasized the importance of considering the influence of social and sexual network members
on decision making and behaviors. Therefore, it is essential to examine the associations between
SOCIAL NETWORKS, SEXUAL HEALTH, AND HIV PREVENTION 19

social and sexual network members and social network dynamics, along with individual-level
risk factors, when aiming to understand HIV prevention behaviors among YBW.
Sexual Health Communication and Young Black Women
Sexual health communication of YBW and their SNMs is underexamined in the literature
(Craddock, 2016; Fletcher et al., 2015). A majority of the literature examining peer (e.g., friends)
influence on sexual risk behaviors has focused on Black youth aged 13 to 18 and perceived
sexual norms, sexual beliefs, and activities of peers (Fletcher et al., 2015; Trinh, 2016; Trinh &
Ward, 2016), rather than specifically examining communication. Among Black women aged 18
or older, the existing literature has examined sexual and romantic partner communication and
HIV risk behaviors (Trinh, 2016; Trinh & Ward, 2016). However, little attention has been given
to sexual communication beyond partnerships. Very few studies have examined sexual
communication between YBW and peers, family members, and other SNMs that may be
associated with sexual risk behaviors, although studies have examined these associations among
MSM, homeless youth, and other high-risk populations (Amirkhanian, 2014; Cederbaum et al.,
2017; Craddock, Barman-Adhikari, Massey Combs, Fulginiti, & Rice, 2019; Holloway et al.,
2015; Widman, Choukas-Bradley, Helms, Golin, & Prinstein, 2014). One of the few studies
examining sexual health communication among YBW and their SNMs indicated that YBW are
more likely to speak to their friends and sexual partners about condom use when accounting for
family members and other types of SNMs (Craddock, 2019). In another study examining sexual
communication of Black adolescents and young adults aged 16 to 22 (M = 18), Fletcher and
colleagues (2015) found that parents and peers provided differing yet valuable types of sexual
discussions. Results from these studies emphasize that various SNMs can be sources of distinct
types of sexual health communication (e.g., sex and relationships, condom use, or HIV testing)
SOCIAL NETWORKS, SEXUAL HEALTH, AND HIV PREVENTION 20

and influence. Considering all SNMs as possible influencers of sexual risk behavior is important,
due to changes in with whom people communicate as they move through adolescence into
emerging adulthood and beyond (Trinh & Ward, 2016). Thus, it is necessary to gain an
understanding of with whom YBW aged 18 to 24 speak regarding sex, condom use, and HIV
testing to determine the impact of those conversations on condom use, HIV testing, and interest
in PrEP.
Social Media Use
Social media-based communication between YBW and their SNMs has dramatically
increased due to a rapidly changing social media landscape (Noar & Willoughby, 2012). Black
youth and young adults are using social media websites and apps at higher rates than any other
American group (Lenhart, 2015; A. Smith, 2014). Of all American youth, Black youth are most
likely to have a smartphone; about 85% of Black youth have access to a smartphone.
Furthermore, 96% of Black American internet users aged 18–29 use a social networking site of
some kind, and a majority of Black youth willing to share sexual health information with friends
through social media technology (Divecha, Divney, Ickovics, & Kershaw, 2012; Lenhart, 2015;
Pequegnat et al., 2007; A. Smith, 2014). A study examining usage of social media-based
platforms among 965 young adults in 2016 revealed that about 82% were on Snapchat, 81%
were on Instagram, 79% were on Facebook, and 78% were on Twitter (Villanti et al., 2017). Due
to the high frequency of social media use among young adults, several interventionists have used
social media platforms to implement HIV interventions (Cao et al., 2017; Taggart, Grewe,
Conserves, Gliwa, & Isler, 2015), with the aim of meeting populations “where they are” and
reducing new incidents of HIV. Considering that social media has increasingly become a primary
mode of communication for young Black adults (A. Smith, 2014) and may be related to HIV
SOCIAL NETWORKS, SEXUAL HEALTH, AND HIV PREVENTION 21

protective behaviors (Payton, Kvasny, & Kiwanuka-Tondo, 2014), it is essential to understand
how social media use (e.g., time, type, purpose) is associated with sexual health communication
with SNMs and HIV protective behaviors (i.e., condom use, HIV testing, interest in PrEP)
among YBW.
In addition to individual risk behaviors and social network characteristics and dynamics,
specific demographic factors must be considered to examine factors associated with sexual risk
behaviors of YBW, including age, education, and living in high HIV prevalence areas. Older age
is associated with increased HIV testing among Black women (Moore & Balgrave, 2019).
Higher levels of education are also associated with increased condom use and HIV testing among
Black women (Reece et al., 2010). Living in high HIV prevalence areas has been associated with
an interest in using PrEP and increased HIV testing (Sales & Sheth, 2018). Because these
demographic factors are important for HIV risk and protective behaviors among Black women,
including these factors in this dissertation study is vital when examining individual- and social
network-level factors associated with HIV prevention behaviors.
Conclusion
The CDC (2019b) has recommended several strategies to help HIV prevention: condom
use, HIV testing, and use of PrEP; however, among women, HIV continues to affect Black
women disproportionately. This review revealed several cultural (e.g., relationship dynamics),
social (e.g., HIV-related stigma, sex-ratio power dynamics), and individual-level risk factors
(e.g., sex under the influence, concurrent sexual relationship, and exchange sex) associated with
condom use, HIV testing, and interest in PrEP among Black women. This review also revealed
several studies have examined social network dynamics and their association with various risk
behaviors among, but mostly excluded YBW. No studies have examined social network and
SOCIAL NETWORKS, SEXUAL HEALTH, AND HIV PREVENTION 22

individual factors associated with condom use, HIV testing, and interest in PrEP among YBW.
YBW aged 18 to 24 have the second highest rates of new HIV diagnoses among Black
women after the age group of 25 to 30 (CDC, 2019a). Therefore, this is a vital population to
focus on for HIV prevention. This dissertation seeks to fill a gap and contribute to the literature
by increasing our understanding of specific individual-level and social network-level factors
associated with HIV prevention behaviors among YBW. Specifically, the work examines how
social network dynamics (e.g., size, density, homophily) and characteristics (e.g., social support,
relationship type), social media use (e.g., time, type, purpose), and sexual health communication
with SNMs are associated with condom use, HIV testing, and interest in PrEP among YBW.

SOCIAL NETWORKS, SEXUAL HEALTH, AND HIV PREVENTION 23

Chapter 3: Theoretical Framework
Given the interplay among individual, social, and structural factors (Baral et al., 2013)
and the current NIH HIV/AIDS priorities aimed at decreasing incidence of HIV among high-risk
groups (NIH, 2015), it is critical to consider social networks, along with individual risk factors,
for HIV prevention behaviors among YBW. The literature review revealed several factors at the
individual level (e.g., relationship dynamics, sex-ratio power dynamics, concurrent sexual
relationship, and exchange sex) and social network level (e.g., size, density, homophily, social
support, relationship type) that must be examined to understand HIV prevention behaviors
among YBW. Thus, a socioecological perspective (Latkin & Knowlton, 2005) was used to
interpret the associations among individual, social, and structural factors examined in this study.
Many health behavioral theories aim to understand and promote behavioral changes in
populations. However, no one theory captures the multifaceted processes and interactions that
occur in each YBW (individual learning), between YBW and their SNMs (social interactions),
and their environment (network structures). To better examine these multifaceted processes and
interactions (how YBW’s HIV risk behaviors are influenced by their social environments), this
study drew on both psychosocial and structural behaviorist theories (i.e., social cognitive theory
[SCT], social exchange theory [SET], and social diffusion theory [SDT]). These theories were
selected based on their shared assumption that behavioral decision-making and change is the
result of learning new ideas and gaining access to valued resources from social interactions (see
Figure 3.1).
Based on this model, individual observations of SNMs are shaped by both SCT and SDT,
encompassing the psychological process of intrapersonal influence. Both theories assume that if
YBW observe peers performing a rewarding behavior, they will also adopt that behavior in
SOCIAL NETWORKS, SEXUAL HEALTH, AND HIV PREVENTION 24

efforts to receive the reward. The level of relationships in social networks is shaped by both SET
and SDT, indicating that YBW exchange with SNMs (depending on their relationships) to
achieve a reward they cannot get on their own (SET), using various forms of communication
channels (e.g., in-person, text messaging, social media; SDT). The examination of social network
structures is solely based on SDT, highlighting the importance of size and density in behavior
adoption. These three theories combined to assume that depending on the structure and dynamics
of YBW’s social networks, YBW can exchange sexual health information with their SNMs, give
meaning by mutual feedback to the sexual health information exchanged, gain an understanding
of each other’s points of views, and influence each other’s behaviors.
Figure 3.1. A Socioecological Perspective Drawing from Social Cognitive, Social Exchange, and
Social Diffusion Theories
Social Cognitive Theory
SCT assumes that behaviors are dynamic interactions of personal, behavioral, and
environmental influences and that people are products and producers of their social systems
(Bandura, 2001, 2006). The assumption is that personal agency operates in a broad network of
SOCIAL NETWORKS, SEXUAL HEALTH, AND HIV PREVENTION 25

sociostructural influences and that a person’s self-development, adoptation of new behaviors,
and behavioral changes are embedded in their social systems (Bandura, 2001). Bandura (2001)
highlighted several components that make up the learning process described by SCT (e.g., self-
efficacy, observational learning). Observational learning and self-efficacy, or perceived self-
efficacy, was used to understand the relationship between factors and HIV prevention behaviors
among YBW.
Observational learning implies that people can learn or adopt behaviors from watching
and observing the behaviors of others. Concepts such as symbolism and vicarious capabilities are
key elements of observational learning. Bandura’s (1998) concept of vicarious capabilities
implies that people can learn from talking about or watching the experiences of others. For
example, a YBW’s SNM tells a story about how she (SNM) was exposed to HIV by a trusted
partner. This YBW can learn from the SNM’s experience and change her (YBW) beliefs (e.g.,
condom use and HIV testing are good for her health) and behaviors (e.g., use condoms, get tested
for HIV) based on her ability to vicariously live through the experiences of her SNM. The
concept of symbolism implies that people can take a symbol they have seen or learned about in
their environment (e.g., romantic relationships), give meaning to it (e.g., love and trust), and use
it as a guideline for action and judgment (e.g., sex without a condom; Bandura, 2001). Hence,
SCT suggests that a YBW’s belief that being in a romantic relationship symbolizes trust and
love—and thus condoms are not needed, as often found in the literature (Newsome &
Airhihenbuwa, 2013; Garcia, 2016)—is derived through symbolism from her environment.
Another key component of SCT is perceived self-efficacy. Perceived self-efficacy refers
to the belief of individuals that they have the skills, ability, or power to change their behavior.
Perceived self-efficacy is a major driver of behavioral change (Bandura, 1990, 1998; Fishbein,
SOCIAL NETWORKS, SEXUAL HEALTH, AND HIV PREVENTION 26

2000) and is included in many behavioral change models (Head, Noar, Iannarino, & Harrington,
2013; Kaufman, Cornish, Zimmerman, & Johnson, 2014; Tarkang & Zotor, 2015). The idea
behind perceived self-efficacy is that unless people believe that they have the skills, ability, and
power to make the desired changes, they have little capacity to make the desired changes
(Bandura, 1998). In his work, Bandura highlighted tools to help develop self-efficacy in
individuals, which are often used in HIV interventions, including: mastery experience, vicarious
experiences, and social persuasion (Bandura, 1998). Mastery of an experience is acquired when a
person can complete the desired behavior repeatedly (Bandura, 1998). For instance, if the goal of
an intervention is to increase condom use, the intervention may require that participants learn
how to use condoms and provide practice opportunities for putting a condom on an anatomical
model until they are able to do so correctly. This increases condom skills and the likelihood of
condom use during sex. Vicarious experiences, on the other hand, are when a person sees
someone like them succees in completing a desired behavior, allowing the observer to believe
that they, too, possess the capabilities to achieve the desired behavior (modeling and mimicry;
Bandura, 1998). For instance, if the goal of an intervention is to increase HIV testing, a vicarious
experience might be hearing a YBW share why she decided to get tested for HIV and her
experiences getting tested (modeling). This can lead other YBW to feel they have the capability
to get tested, resulting in those YBW getting tested for HIV as well (mimicry). Bandura also
emphasized that for a modeled behavior to be mimicked, the observer has to perceive the
observed behavior as valuable, and thus the observer mimics the modeled behavior to receive a
reward (Bandura, 1998).
Social persuasion can be seen as a form of empowerment. The ideological basis of social
persuasion is that “people who are persuaded verbally that they possess the capabilities to
SOCIAL NETWORKS, SEXUAL HEALTH, AND HIV PREVENTION 27

master given activities are likely to mobilize greater effort and sustain it” (Bandura, 1998, p.
626). For instance, if a YBW is continuously encouraged by her friends that she can talk to her
sexual partner(s) about condom use, she may be empowered to talk with her sexual partner(s)
about safer sex.
These sources of influence are not only components in interventions geared toward
behavioral changes, but can be found in people’s social networks and social and cultural
environments (Bandura, 2001). Individuals use observational and vicarious learning to acquire
new behaviors, give meaning to experiences, and make decisions. These SCT concepts imply
that a YBW’s decisions are influenced by communication with her SNMs, the actions and beliefs
of her SNMs, and her perceived ability to make a behavioral change.
Social Exchange Theory
SET is a theoretical perspective of social interaction and social structures (Cook,
Cheshire, Rice, & Nakagawa, 2013). According to Emerson (1976), SET is not a theory at all,
but instead a frame of reference in which theories can interact with one another. The SET
framework is a cost-and-benefit-based framework founded on the works of George Homans,
Peter Blau, and Richard Emerson (Cook et al., 2013; Monge, Contractor, & Contractor, 2003)
and is derived from utilitarianism and behaviorism (Cook et al., 2013). These two theories
together formulate the thought process behind SET—people behave in a way that maximizes
happiness or benefits them, as long as those behaviors are positively reinforced (Cook et al.,
2013). The assumption is that self-interest (rewards) and interdependence (reciprocity) are
essential to exchange processes (Lawler & Thye, 1999).
Emerson (1976) saw power as a function of social interaction in social networks.
Particularly, he felt power in dyadic relationships stemmed from person A being dependent
SOCIAL NETWORKS, SEXUAL HEALTH, AND HIV PREVENTION 28

(having to pay a cost) on person B for a resource (to receive a reward) that person A cannot
obtain alone. For instance, a YBW obtaining a committed partner (reward) may be dependent on
her agreeing with her partner about condom use during sex (cost). Power is a product of these
relationships when scarcity comes in to play (Emerson, 1976), assuming that negotiation power
is obtained by controlling a scarce resource. In other words, the lack of available Black men
(scarcity) increases the value of relationships (reward) and decreases the value of safer sex for
Black women (cost). Thus, Black men may possess higher negotiation power due to their
scarcity in their communities compared to Black women. SET as a framework assumes that
Black men would be less likely to negotiate in a manner that would reduce their rewards and
increase their cost (e.g., using condoms with partners). Similarly, regarding sexual health
communication, SET implies that disclosing HIV-related risk behaviors to a SNM (cost) may be
dependent on SNMs being able to provide her with emotional support (reward). If there is a
scarcity in emotionally supportive SNMs, then the value (higher reward vs. cost) of those SNMs
will increase. Thus, implying that the fewer emotionally supportive SNMs a YBW has (scarcity),
the more valued (reward vs. cost) and influential (powerful) those SNMs are to the disclosure
process of YBW. Thus, hypothetically, YBW who have fewer emotionally supportive SNMs in
their network may rely on those SNMs more for sexual health communication, compared to
YBW who may have more emotionally supportive SNMs.
Social Diffusion Theory
SDT has been used to guide the understanding of how information spreads throughout
social networks (Bondi et al., 2018). Like SCT and SET, SDT has some behaviorism aspects,
including the idea that sources of behavior reside in a person’s environment. SDT assumes
interpersonal communication with SNMs about a new behavior drives the diffusion process of
SOCIAL NETWORKS, SEXUAL HEALTH, AND HIV PREVENTION 29

that behavior, and social network structures and communication channels influence how that new
behavior is deiminated and acquired by other individuals in the network (Rogers, 2010). Rogers
(2010) stated that key components to behavioral change are influenced by social ties and the
ability to receive and share new information in one’s network. Thus, SDT argues, like SET and
SCT, that behavioral change in YBW is a result of communication with SNMs. However, SDT
also emphases the impact of social network structure, like size, density, and homophily, on
whether communication about the new behaviors can be received and acquired by YBW in
communication channels through which new behaviors are communicated (e.g., face-to-face, text
messaging, social media), influencing whether that behavior can be diffused in social networks
(Bandura, 2001; Rogers, 2010). Thus, in the context of this study, SDT was used to examine the
various elements associated with HIV prevention behaviors among YBW by considering three
factors. First, have similar SNMs already adopted those behaviors or communicated with YBW
about those HIV prevention behaviors? This factor represents an overlap of Bandura’s and
Rogers’ theories, encompassing the concepts of vicarious learning and symbolism for new HIV
behavior to be adopted and disseminated in a YBW’s social network. The other two factors:
structure of YBW’s networks (e.g., size, density, homophily) and communication channels used
to disseminate or communicate information about HIV prevention behaviors (e.g., face-to-face,
text messaging, social media), are unique to SDT.
Collectively, SET, SCT, and SDT provide insight into how individual-level variables
(e.g., condom use self-efficacy, concurrent sexual partners, sex under the influence) and SNM-
level variables (e.g., social support, communication, relationship type) may be associated with
HIV prevention behaviors (Figure 3.1).

SOCIAL NETWORKS, SEXUAL HEALTH, AND HIV PREVENTION 30

Chapter 4: Data Collection Procedures
Examining sexual health communication in social networks of YBW is essential to
understanding how these networks can be harnessed to decrease HIV. This dissertation is the
first in-depth exploration of how individual risk, social network structures, social media use,
specific types of SNM relationships (e.g., parents, partners, friends), social support (e.g.,
emotional, instrumental, informational), and SNM communication are associated with condom
use, HIV testing, and interest in PrEP.
Egocentric social network data were collected from 200 YBW aged 18 to 24 using
respondent-driven sampling (RDS) to identify how social media use (e.g., time, type, purpose),
individual-level variables (e.g., condom use self-efficacy, concurrent sexual partners, sex under
the influence), and SNM-level variables (e.g., social support, communication, relationship type)
were associated with condom use, HIV testing, and interest in use of PrEP. Thus, three aims
guided this study: to (1) explore YBW’s social media use (e.g., time, type, purpose ) and sexual
health communication association with condom use, HIV testing, and interest in PrEP among
YBW; (2) investigate how social networks may be associated with HIV testing, condom use, and
interest in PrEP use among YBW; and (3) use LCA to identify subgroup profiles of YBW based
on individual- and social network-level risk factors and investigate HIV testing, condom use, and
interest in PrEP as key indicators of those subgroup profiles. Based on previous literature, it is
hypothesized that SNM social support (e.g., emotional support and informational support), SNM
relationship types (e.g., friend, family, sexual partner), and sexual health communication
between YBW and SNMs (e.g., communication about condom use, HIV testing) would be
significantly associated with HIV testing, condom use, and interest in PrEP.
SOCIAL NETWORKS, SEXUAL HEALTH, AND HIV PREVENTION 31

This chapter details the methods used in this dissertation, including the community
advisory committee, sampling and recruitment of participants, study procedures, and data
collection. Specific details regarding data analyses for each aim are included in Chapter 5.
Community Advisory Committee
An advisory committee was recruited to assist in ensuring the cultural relevance and
appropriateness of this dissertation study. Recruitment of advisory committee members took
place via a university email listserv, referrals from community organizations (e.g., Black Women
for Wellness, California Black Women’s Health Project), and by invite from the principal
investigator (PI). An advisory committee of four YBW aged 19 to 24 from the Los Angeles area
was formed and assisted with survey development, participant recruitment, and data collection.
YBW who made up the community advisory committee had varied backgrounds, education
levels, and experiences as Black women. Characteristics of the community advisory committee
members (CACM) are as follows:
CACM 1: Age 19, undergraduate student, identified as African American mixed, lived in
Los Angeles for 2 years, and grew up as middle socioeconomic status.
CACM 2: Age 22, high school graduate, employed part-time, identifids as African
American, native to Los Angeles, and grew up as low socioeconomic status.
CACM 3: Age 23, college graduate, working part-time, identified as African American
with a specific African descent, lived in Los Angeles for 5 years, and grew up as low to middle
socioeconomic status.
CACM 4: Age 24, graduate student, identified as Black and Latina, lived in Los Angeles
for 1 year, experienced homelessness as a youth, and grew up as low socioeconomic status.
SOCIAL NETWORKS, SEXUAL HEALTH, AND HIV PREVENTION 32

The CACM were not connected to one another prior to their involvement in this study.
Thus, these women did not have the same network of connections and affiliations, allowing
recruitment to take place in diverse locations and networks.
During the survey development stage of the study, CACM met with the PI four times to
provide feedback on the study’s questionnaire and suggestion on topics that should be included
(e.g., contraceptive use) or removed from the survey. Topics often discussed during meeting
included relevance of survey questions, new trends in social media use, online dating,
recruitment of YBW, and participant engagement. During these meeting, the CACM developed
the language for the recruitment fliers, which focused on sexual health and relationship
dynamics. Before the survey opened to participants, CACM assisted with piloting the survey and
providing feedback on redundancies and errors, and during the recruitment stage, all four CACM
assisted with recruitment of the initial participants (seed YBW) via community organizations and
community and social media outreach. As part of community engagement, CACM were
recruited and trained as research assistants to assist with collecting data. Four 4-hour data
collection trainings took place in May and June 2018, which covered data collection procedures
(e.g., consenting participants, forms to complete, setting up computers and surveys, providing
incentives, and properly storing documents) and allowed CACM to conduct mock interviews.
Each CACM shadowed the PI before independently collecting data in the field.
Sampling and Recruitment
Respondent-Driven Sampling
A majority of research studies that have successfully recruited and retained Black women
in HIV and sexual health-related studies have occurred in areas with large percentages of Black
populations (26% to 73% vs. 8.2% in Los Angeles), making convenience sampling an effective
SOCIAL NETWORKS, SEXUAL HEALTH, AND HIV PREVENTION 33

and efficient method of recruitment. However, due to the smaller population of YBW aged 18 to
24 in Los Angeles County (0.44%; Los Angeles County Commission for Women, 2016; U.S.
Census Bureau, 2018b), YBW can be defined as a hard-to-reach population (Sydor, 2013).

Finding, recruiting, and engaging a representative sample of YBW at risk of HIV in Los Angeles
is a challenge for researchers and service providers interested in the sexual health of YBW
(Being Alive, 2015; N. Harawa, personal communication, November 2015; T. Montgomery,
personal communication, January 10, 2016; G. Wyatt, personal communication, July 2015),
given they are often no longer in school and there are few locations where large clusters of YBW
can be found in Los Angeles (e.g., community-based organizations). With YBW only making up
0.44% of the Los Angeles County population, limited statistics exist regarding central locations
where YBW can be found. Thus, general statistics regarding Black communities in Los Angeles
County were used to select neighborhoods (e.g., Baldwin Hills, Crenshaw, Leimert Park, and
Inglewood; Los Angeles Times, 2019) and colleges and universities from which to recruit. To
connect with this hard-to-reach and hard-to-engage population required methods involving social
network-based recruitment, such as RDS.
RDS assumes that “those best able to access members of hidden [or hard-to-reach]
populations are their own peers” (Heckathorn, 1997, p. 178). RDS involves a dual incentive
system (i.e., incentives for participation and referrals) and uses both monetary and symbolic
(excitement around the ability to contribute to an important problem) rewards (Heckathon,
1997). Heckathorn (2011) stated that RDS is not a single method, but a series of methods that
can transform chain-referral sampling into a sampling method of good estimability. RDS
involves several steps. The following are the main recruitment features used in RDS methods
(Heckathon, 1997). First, the research team recruits a specific number of initial participants
SOCIAL NETWORKS, SEXUAL HEALTH, AND HIV PREVENTION 34

(seeds). Second, once seeds complete the study, they are offered a financial incentive to recruit
their peers into the study. These seeds are given several recruitment coupons (referral tickets)
and told to pass out the referral tickets to peers that meet the study’s qualifications. If their peers
participate, referrers are paid an incentive for each recruited peer. Third, all new recruits are
offered the same dual incentives for participation and referrals. Fourth, sampling is ended when
the minimum target size is reached or the target community is saturated.
Prior to this dissertation study, a pilot study was conducted by the author to assess the
feasibility of using RDS to recruit YBW—a hard-to-engage and -find population in the Los
Angeles area. However, traditional RDS methods (i.e., referral tickets) were not effective for
recruiting YBW (Craddock, 2016).

To better address these challenges, RDS was adapted for
YBW in the feasibility pilot study.
In the pilot study, five seeds (initial participants) were recruited from two beauty salons
in Los Angeles County (Craddock, 2018). In the first method, two seeds were given five tickets
and asked to invite at least three other YBW using the traditional RDS ticketing methods. In the
text message-enhanced RDS method, three participants were asked to invite at least three other
YBW via text message. During a 3-month period, the traditional ticketing RDS method did not
produce any referrals. However, the text message-based RDS method produced referrals for 99
women in a 2-month period (Craddock, 2018). The text-enhanced version of RDS proved to be
highly effective in recruiting YBW in a short period for the pilot study compared to the
traditional RDS recruitment outcomes. This dissertation study used the text-enhanced RDS
method used in the pilot study for the first step of the recruitment process.
SOCIAL NETWORKS, SEXUAL HEALTH, AND HIV PREVENTION 35

Recruitment
Fourteen YBW (seeds) were recruited from various community sources (e.g., schools,
community events, peers) in the Los Angeles area and on Twitter and Facebook. Specifically, six
YBW were recruited from university listservs, five were recruited from community outreach and
peers, two were recruited from Facebook, and one was recruited from Twitter. Recruitment and
data collection occurred from June 4, 2018, to December 2, 2018. YBW with various risk levels
were targeted as seeds to diversify social networks examined in the study. YBW were informed
about the study through emails via community organizations, outreach, and social media (i.e.,
Twitter and Facebook). Flyers and posts contained the study’s contact information for further
information and screening. This dissertation study employed the text-enhanced RDS method
used in the pilot study for recruitment of 42 participants who completed their surveys in person.
The remaining 159 participants, who completed their surveys online, were asked to refer other
YBW using a computer-based RDS referral system that allowed participants to enter names,
emails, and phone numbers of up to seven other YBW for referrals. The change from an in-
person to an online participation and recruitment strategy was due to an observed difference in
the geographical location of YBW’s referral networks (outside of the Los Angeles area). Eleven
of 14 seeds met in person, and the other three seeds recruited from social media participated
online. Stemming from these 14 seeds, 427 referrals were made, with 42 YBW participating in
person and 159 YBW participating online (see Figure 4.1).
In-person recruitment procedures. The 11 seeds who met in person for the study
received the first part of the dual incentives once they completed the study. After they received
the incentive for participating, they received a scripted recruitment text message from the
researcher to forward to other YBW. These YBW were asked to forward the message to at least
SOCIAL NETWORKS, SEXUAL HEALTH, AND HIV PREVENTION 36

three other YBW who would be eligible to participate in the study. After the referring YBW sent
out the text message, she received the referral incentive from the researcher before leaving the
study’s. Forty-one of the 42 YBW referred at least three other YBW to the study. Referred
women who were interested in participating in the study reached out to the researcher for further
information and screening and could participate in person if they were in Los Angeles County or
online if they were outside of the county.
Online recruitment procedures. The procedures were slightly different for the online
participants. Three of the 14 seed participants were recruited from Facebook and Twitter. YBW
who contacted the researcher and expressed interested in the study received a very descriptive
email regarding the study and its procedures. The email provided details on the topics of the
survey, the approximate time needed to complete the survey, and information about the
computer-based referral system at the end of the survey. Because these participants completed
the study online, the RDS methods used for the 42 in-person participants could not be executed.
Thus, instead of having participants send text messages to other YBW, online participants were
informed prior to participating in the study about the computer-based referral system and were
prompted to inform any YBW they planned to refer to the study. Similar to the in-person
participants, online participants completed the survey first. Once the survey was complete, the
online system redirected participants to a referral form separate from the survey. The referral
form asked participants to list the names and email addresses or phone numbers of at least three
but no more than seven other YBW. Once participants completed the referral form, the
computer-based form sent out a scripted email or text message to referred YBW inviting them to
participate in the study. Interested YBW could then reply to the researcher regarding their
interest in the study. If interested YBW were eligible for the study, the PI sent them the survey
SOCIAL NETWORKS, SEXUAL HEALTH, AND HIV PREVENTION 37

link with instructions. Most contacted women had already heard of the study from the YBW who
referred them, and in some cases, had contacted the researcher before the referring YBW
completed the referral form. Once online participating YBW completed both the survey and
referral form, they receive an incentive for the completion of the survey and an incentive for
referring at least three other YBW. If a YBW did not refer other YBW, she only received an
incentive for completing the survey. Of 159 YBW who participated online, 110 of them referred
at least three other YBW (see Figure 4.1).
For both in-person and online participants, referring YBW could not receive incentives
for each of the referred YBW who participated, due to IRB requirements. Thus, referring YBW
received a $10 incentive for referring at least three other YBW, whether or not those women
participated.
Screening and Enrollment
To qualify to be a seed participant, women had to (a) be 18 to 24 years of age, (b)
identify as a Black or African American woman, and (c) ever be sexually active. As part of the
RDS, participants were asked to invite at least three eligible YBW (i.e., friends, family, or
acquaintances) to participate in the study, until 200 women had participated in the study. To
qualify as an invited participant, YBW had to meet the same eligibility criteria as seed
participants and be invited by a current study participant. When an invited person received two
or more referrals from different study participants, those network links were connected using the
invited participant’s ID during the data cleaning and coding process (n = 53). Participants
received a $10 incentive for inviting other YBW to participate in the study via text message or
the computer-based form at the end of their survey. Participants were not informed of which
invited women participated in the study to ensure confidentiality.
SOCIAL NETWORKS, SEXUAL HEALTH, AND HIV PREVENTION 38

Figure 4.1. Recruitment and Participation of YBW
Note. Recruitment started with 14 seeds: six from university listservs, five from community outreach and peer
recruitment, two from Facebook, and one from Twitter. These 14 seeds led to 427 referrals. Referrals plus seeds
were divided into online and in-person contacts (n = 365 and n = 76, respectively). Of the 76 YBW interested in
participating in the study, 51 in-person surveys were scheduled, 42 were completed, and 41 YBW referred other
YBW. Of the 365 YBW who were interested in participating in the study online, 146 never followed up after first
contact, seven were ineligible and 212 received the survey with instructions. A total of 159 participants completed
the survey online, of whom 110 referred other YBW. Final total sample size was 201 YBW. Of these participants,
21 answered no to the survey question “Have you ever had sex?”; thus, these YBW were not included in the
analysis.
14 Seeds
427 Total Referrals
Online
Contacts 365
Ineligible 7
Survey Sent 212
Completed 159
96 Did Not Refer
110 Refered
Not Completed 53
In-Person
Contacts 76
Not Scheduled 25
Scheduled
51
Completed 42
1 Did Not Refer
41 Refered
Cancelled
9
SOCIAL NETWORKS, SEXUAL HEALTH, AND HIV PREVENTION 39

Procedures
Informed Consent
For study participants who met in person, a paper version of the informed consent form
was reviewed with the participant and signed before the survey began. For participants who did
not meet in person, the informed consent form was digitalized and included as a criterion that
must be read and agreed to before participating in the online study. The informed consent form
in person and online provided the study’s description, topics covered in the survey,
confidentiality, risk and benefits, and compensation. A certificate of confidentiality was secured
to protect participant’s confidential information, and this information was provided to
participants. Participants were informed that their information would be deidentified, and people
outside of the research team would not have access to their information. Additionally, all
participants were informed that the study was optional and they could stop whenever they prefer.
Participant IDs were kept separate from all other participant information in an encrypted
document on a password-secured computer. For social network data, SNM names were removed
and separate SNM IDs were used.
Data Collection
To achieve the study’s aims, two types of data were collected from participants via an
online, self-administered survey (Qualtrics): (a) individual-level data and (b) social network-
level data. Qualtrics surveys took approximately 45 minutes to complete, and participants
received $15 for completing the online survey.
The individual-level portion of the survey included six main topic areas: demographics;
HIV risk behaviors; condom use self-efficacy and attitudes; PrEP knowledge, attitudes, and use;
relationship dynamics; and social media use. The questionnaire featured several skip patterns.
SOCIAL NETWORKS, SEXUAL HEALTH, AND HIV PREVENTION 40

Only questions applicable to each participant, based on previous responses, were asked. Once the
individual-level portion of the survey was completed, participants were prompted to start the
social network-level portion of the survey, which provided a detailed introduction of the type of
questions it featured.
Qualtrics was also used to collect social network-level data. The social network portion
of this survey collected the names of participants’ SNMs to produce standard egocentric network
data. Qualtrics turned each SNM name into an answer option, similar to categorical answers to a
multiple-choice question. For example, participants were asked, “Who do you talk to about using
condoms? Please select all that apply.” The names of all nominated network members were
listed as answer options, and the participant could select individuals corresponding to that
question. The social network portion of the survey operated in three stages (Matzat & Snijders,
2010). First, names and demographic information of each participants’ network members were
collected using a name generator (e.g., “Thinking about the last month, list all the people you
have communicated with”). Participants listed between three and 20 names of people they had
spoken to in the last 30 days. Names did not have to be exact; they could use nickname or
initials, but participants had to be able to recognize the name when it was presented to them
again. Second, questions were asked about the types of relationships and communication
participants had with each SNM they mentioned (e.g., “Which of these people do you speak with
several times a week?”). For each of these questions, participants received the names they listed
in the name generator and could select all the names relevant to the question. Third, network
structure was assessed by asking the participants about who knows whom in their network. For
each listed name, participants could select the names of other people that person may know,
providing the ability to assess the structure of each participant’s network. Study procedures were
SOCIAL NETWORKS, SEXUAL HEALTH, AND HIV PREVENTION 41

approved by the institutional review board at the University of Southern California in Los
Angeles, California.
Power Calculation
With a sample size of 201, this dissertation had 80% statistical power to detect a small to
medium effect (OR = 2.16–2.99) in a one-tailed logistic regression at a .05 significance level,
assuming the probability under the null hypotheses ranges between .10 and .30.

SOCIAL NETWORKS, SEXUAL HEALTH, AND HIV PREVENTION 42

Chapter 5: Measurements, Statistical Analysis and Results
Chapter 5 is divided into three sections. The first section is titled “Associations between
YBW’s Social Media Use, Sexual Health Communication, and Condom Use, HIV Testing, and
Interest in PrEP” (Aim 1) and explores YBW’s social media use (e.g., time, type, purpose) and
sexual health communication associated with condom use, HIV testing, and interest in PrEP
among YBW. Section 2 is titled “Associations of Social Network Characteristics and Dynamics
and Individual Risk Factors with HIV Testing, Condom Use, and Interest in PrEP use among
YBW” (Aim 2) and investigates how social networks may be associated with HIV testing,
condom use, and interest in PrEP use among YBW. The third section is titled “Profiles of Young
Black Women Based on Sexual Risk Factors and Sexual Health Communication: A Latent Class
Analysis” (Aim 3) and identifies class profiles of YBW based on individual- and social network-
level risk factors and investigates HIV testing, condom use, and interest in PrEP as key
indicators of those subgroup profiles. Together, these three statistical analysis sections provide
new insight and highlight important factors associated with HIV prevention behaviors among
YBW.
Associations Between YBW ’s Social Media Use, Sexual Health Communication, and
Condom Use, HIV Testing, and Interest in PrEP
Measurements
Sociodemographic characteristics. Sociodemographic variables used in this analysis
included age, level of education, and region. Age of YBW was assessed by the following
question: “How old are you today?” Responses ranged from 18 to 26 and were used as a
continuous variable in the analysis. To assess level of education, YBW were asked, “What is the
highest grade in school or year of college that you have completed and got credit for?” The five
SOCIAL NETWORKS, SEXUAL HEALTH, AND HIV PREVENTION 43

educational categories (i.e., high school graduate or GED, some college, two-year degree, four-
year degree, professional degree) were each measured as dichotomized variables wherein 1 = yes
and 0 = no. Due to having no participants who reported “less than high school” or “doctorate,”
these two variables were dropped from the analysis. Also due to low responses for two-year
degree, some college and two-year degree were combined to create a new variable: some college
or two-year degree. The final dichotomized variables used for level of education in this analysis
were: (a) high school graduate or GED, (b) some college or two-year degree, (c) four-year
degree, and (d) professional degree.
Current region of residence was determined by asking “What is your current zip code?”
Zip codes were then entered into the U.S. Postal Service’s (2019) Look Up Zip Code program.
Once YBW’s current states were determined, the U.S. Census Bureau (2015) guidelines for
regions was used to determine in which region each YBW resided. Regions included Northeast,
Midwest, South, and West. These regions were coded as dichotomous variables, with 1 = current
region and 0 = not current region.
Social media use. Social media use was assessed through standardized questions on the
self-administered questionnaire and follow-up questions on the network assessment. To assess
health information seeking via social media, two binary questions were asked, “Do you use
social media sites, such as Instagram, Facebook, or Twitter, to seek information about improving
your health?” and “Are you using social networking technology for seeking health information?”
(1 = yes, 0 = no). Responses to these two questions were combined to create the variable of
health information seeking via social media. If participants responded yes to either question, they
were recorded as seeking health information via social media. If participants responded no to
both questions, they were recorded as not seeking health information via social media. To assess
SOCIAL NETWORKS, SEXUAL HEALTH, AND HIV PREVENTION 44

hours spent on social networking or social media sites, participants were asked, “On average,
how much time do you spend daily on social networking sites?” (1 = less than one hour, 2 = one
to four hours, 3 = four to eight hours, 4 = eight or more hours). Based on the frequency of
responses in less than one hour and eight or more hours, the variable was dichotomized as 1 (four
or less hours per day) and 0 (more than four hours per day). To assess the use of health tracker
apps, participants were asked, “Do you use any health tracker apps on your phone? (e.g., iPeriod,
Eve, Glow, Myfitpal, Fitbit, RunTracker).” Responses were binary (1 = yes, 0 = no).
Sexual health communication via communication channel. In addition to individual-
level social media use, social media use was assessed at the social network level in the form of
communication with SNM on specific social media platforms (i.e., Instagram, Snapchat, Twitter,
and Facebook; Heaney & Israel, 2008; Noar & Willoughby, 2012; Rice, Monro, Barman-
Adhikari, & Young, 2010). The following variables measuring communication on social media
were assessed using a scale (i.e., 1 = once or more a day, 2 = several times a week, 3 = about
once a week, 4 = about once a month, 5 = a couple times a year, 6 = not in the past year): (a)
Talk on Instagram: “How often do you exchange direct messages on Instagram with these
individuals?”; (b) Talk on Snapchat: “How often do you exchange direct messages on Snapchat
with these individuals?”; (c) Talk on Twitter: “How often do you exchange messages with or
mention these individuals on Twitter?; (d) Talk on Facebook: “How often do you exchange
direct messages on Facebook with these individuals?”; (e) Talk about sex on Instagram: “How
often do you exchange direct messages on Instagram about sex, sexual experiences, or
relationships with these individuals?”; (f) Talk about sex on Snapchat: “How often do you
exchange direct messages on Snapchat about sex, sexual experiences, or relationships with these
individuals?”; (g) Talk about sex on Twitter: “How often do you exchanged messages with or
SOCIAL NETWORKS, SEXUAL HEALTH, AND HIV PREVENTION 45

mention these individuals on Twitter about sex, sexual experiences, or relationships?”; and (h)
Talk about sex on Facebook: “How often do you exchange direct messages on Facebook about
sex, sexual experiences, or relationships with these individuals?” Based on qualitative interviews
with YBW regarding how often they communicated with their most influential SNM
(unpublished qualitative data collected during this study), sexual health communication via
communication channel variables were dichotomized as 1 = frequent communication (i.e., once
or more a day, several times a week, and about once a week) and 0 = infrequent communication
(i.e., about once a month, a couple times a year, and not in the past year).
Statistical Analysis
The objective for this analysis was to use egocentric network analysis to explore YBW’s
social media use (e.g., time, type, purpose) and sexual health communication association with
condom use, HIV testing, and interest in PrEP among YBW. Egocentric network analysis allows
for the inclusion of social network variables in standard statistical models, such as multivariate
logistic regression. Independent variables constructed from SNM data were created in SAS and
merged with individual-level survey data.
Statistical analysis proceeded in two stages: (a) descriptive statistics and their social
media use were calculated, and (b) logic regression modeling was conducted. Univariable
logistic regression models were run with each independent variable and each outcome variable.
The outcome variables were treated as dichotomous outcomes and regressed on individual-level
measures (i.e., measures that only varied across participants) and social network-level measures
(i.e., measures that varied across SNMs). Individual-level measures were created based on
standard individual responses to survey items. Covariates found to be significant (p < .05) in the
univariable models were entered into a multivariable model. All multivariable logistic regression
SOCIAL NETWORKS, SEXUAL HEALTH, AND HIV PREVENTION 46

analyses were restricted to participants without missing data for the variables included in the
models. Therefore, the sample sizes for the multivariable multilevel logistic regression models
were smaller than the study’s total sample size (i.e., condom use: n = 142, HIV testing: n = 87,
interest in PrEP: n = 172). All analyses were carried out in SAS 9.4.
Results
Descriptive statistics.
Demographics. YBW ranged in age from 18 to 24 years (M = 21.15). All participants
reported completing high school (N = 180); 48.31% had completed some college or a two-year
degree, 34.83% had a bachelor’s degree, and 4.49% had a professional degree. Many YBW
currently lived in the South (42.13%), followed by the West (41.01%). YBW from the Northeast
only made up 8.99% of the sample, whereas YBW from the Midwest made up 7.87% of the
sample (see Table 5.1).
Social media use. In this sample, 97.67% of YBW were members of at least one social
networking site. Use of a health app was reported by 55.00%, and 52.22% sought health
information via social media. Daily use of social media was reported at 4 hours or less by
74.71% of the sample.
Social media communication with SNMs. Ninety-five percent of YBW spoke with at
least one SNM using a combination of social media platforms (i.e., Instagram, Snapchat, Twitter,
and Facebook). Specifically, 82.22% of YBW direct messaged with at least one SNM on
Instagram; of those, YBW 38.51% direct messaged about sex with at least one of their SNMs on
Instagram. Additionally, 78.89% of YBW direct messaged with at least one SNM on Snapchat;
of those, 50.70% direct messaged about sex with at least one of their SNMs on Snapchat. Direct
messaging with at least one SNM on Twitter was reported by 50.56% of YBW; of those, 62.64%
SOCIAL NETWORKS, SEXUAL HEALTH, AND HIV PREVENTION 47

direct messaged about sex with at least one of their SNMs on Twitter. Last, direct messaging
with a SNM on Facebook was reported by 41.67% of YBW; of those, 42.67% direct messaged
about sex with at least one if their SNMs on Facebook.
Outcome variables. Among these YBW, 72.22% reported having ever been tested for
HIV, 40.22% reported using a condom at last sex, and 36.11% reported being interested in using
PrEP for HIV prevention.
SOCIAL NETWORKS, SEXUAL HEALTH, AND HIV PREVENTION 48

Table 5.1. Descriptive Statistics for Social Media Use, Sexual Health Communication and HIV
Prevention Behaviors
n or M % or SD
Demographics
Age (range 18–24; n = 200) 21.15 1.73
Housing (n = 179) 178 99.44
Education (n = 178)
High school 25 12.36
Some college or two-year degree 86 48.31
Bachelor’s degree 62 34.83
Professional degree 8 4.49
Current region (n = 178)
Northeast 16 8.99
Midwest 14 7.87
South 75 42.13
West 73 41.01
Social media use
Member of social networking site (n = 180) 174 96.67
Use health apps (n = 180) 99 55.00
Seek health information via social media (n = 180) 94 52.22
Hours on social media (n = 174)
4 hours or less 130 74.71
More than 4 hours 44 25.29
Talk with SNMs (n = 180)
All social media 171 95.00
Instagram 148 82.22
Snapchat 142 78.89
Twitter 91 50.56
Facebook 75 41.67
Talk with SNMs about sex
All social media (n = 180) 102 56.67
Instagram (n = 148) 57 38.51
Snapchat (n = 142) 72 50.70
Twitter (n = 91) 57 62.64
Facebook (n = 75) 32 42.67
Outcomes
Interested of PrEP (n = 180) 65 36.11
Ever tested for HIV (n = 180) 130 72.22
Condom use at last sex (n = 179) 72 40.22
SOCIAL NETWORKS, SEXUAL HEALTH, AND HIV PREVENTION 49

Univariable logistic regression models.
Condom use. Two significant variables were associated with condom use in this analysis.
YBW had 73% decreased odds of using condoms if they spent more than 4 hours on social
media per day (OR = 0.27; 95% CI = 0.12, 0.61), compared to YBW who spent 4 hours or less
on social media. YBW also had 52% decreased odds of using condoms if they direct messaged
about sex on Instagram with at least one of their SNMs (OR = 0.48; 95% CI = 0.24, 0.97),
compared to YBW who did not direct message about sex on Instagram with SNMs (see Table
5.2).
HIV testing. Education was significantly associated with HIV testing. YBW who
completed some college or a two-year degree had 72% decreased odds of getting tested for HIV
(OR = 0.28; 95% CI = 0.13, 0.56), compared to YBW who did not complete some college or a
two-year degree. YBW who had a bachelor’s degree had 4.29 times the odds of getting tested for
HIV (OR = 4.29; 95% CI = 1.79, 10.29), compared to YBW who did not have a bachelor’s
degree.
Interest in PrEP. At the univariable level, three demographic variables had a significant
association with YBW’s interest in PrEP. YBW who currently lived in the South had 48%
decreased odds of being interested in PrEP (OR = 0.52; 95% CI = 0.28, 0.99), compared to YBW
living in other regions of the United States. However, YBW who spent more than 4 hours per
day on social media had 3.44 times the odds of being interested in PrEP (OR = 3.44; 95% CI =
1.69, 6.99), compared to those who spent 4 hours or less per day on social media. YBW had 2
times the odds of being interested in PrEP if they sought health information via social media (OR
= 2.00; 95% CI = 1.07, 3.73), compared to those who do not seek health information via social
media.
SOCIAL NETWORKS, SEXUAL HEALTH, AND HIV PREVENTION 50

Table 5.2. Univariable Logistic Regressions of Condom Use, HIV Testing, and Interest in PrEP
Condom Use
a
HIV Testing Interest in PrEP
OR 95% CI OR 95% CI OR 95% CI
Age 1.01 0.85, 1.21 1.51*** 1.21, 1.87 1.01 0.85, 1.22
Education
High school 0.58 0.21, 1.56 0.76 0.29, 2.00 1.02 0.40, 2.58
Some college or two-year degree 1.00 0.55, 1.83 0.28*** 0.13, 0.56 1.11 0.60, 2.05
Bachelor’s degree 1.29 0.69, 2.42 4.29** 1.79, 10.29 0.87 0.46, 1.66
Professional degree 0.91 0.21, 3.95 -- -- 1.07 0.25, 4.64
Current region
Northeast 0.67 0.22, 2.02 1.72 0.47, 6.31 0.77 0.26, 2.33
Midwest 0.39 0.11, 1.45 0.66 0.21, 2.08 1.83 0.61, 5.47
South 1.30 0.71, 2.40 1.21 0.62, 2.37 0.52* 0.28, 0.99
West 1.12 0.61, 2.06 0.80 0.41, 1.56 1.70^ 0.92, 3.16
Hours on social media
4 hours or less 3.71** 1.65, 8.34 0.71 0.32, 1.59 0.29*** 0.14, 0.59
More than 4 hours 0.27** 0.12, 0.61 1.40 0.63, 3.13 3.44*** 1.69, 6.99
Use health apps 1.12 0.61, 2.04 1.18 0.62, 2.27 0.84 0.46, 1.55
Seek health information via social media 0.96 0.53, 1.75 1.26 0.66, 2.43 2.00* 1.07, 3.73
Communication
All social media 1.37 0.33, 5.65 2.18 0.56, 8.45 2.04 0.41, 10.13
Instagram 1.61 0.71, 3.63 1.23 0.54, 2.82 1.56 0.67, 3.60
Snapchat 1.04 0.50, 2.16 1.26 0.58, 2.75 1.29 0.60, 2.77
Twitter 0.74 0.41, 1.35 0.83 0.43, 1.59 0.84 0.46, 1.54
Facebook 0.81 0.44, 1.49 1.39 0.71, 2.72 0.66 0.35, 1.24
Sex communication
All social media 0.65 0.35, 1.18 1.30 0.68, 2.50 1.67 0.90, 3.12
Instagram 0.48* 0.24, 0.97 0.92 0.44, 1.93 1.51 0.77, 2.98
Snapchat 0.62 0.31, 1.21 1.04 0.49, 2.19 1.65 0.83, 3.28
Twitter 0.73 0.31, 1.77 2.37^ 0.94, 5.95 0.92 0.38, 2.24
Facebook 0.63 0.24, 1.65 1.68 0.55, 5.09 1.05 0.39, 2.83
a
n = 179.
^p < .10. *p < .05. **p < .01. ***p < .001.
Multivariable logistic regression models.
Condom use. Multivariable results indicated that YBW had 70% decreased odds of using
condoms (OR = 0.30; 95% CI = 0.13, 0.69) if they spent more than 4 hours on social media per
day, compared to YBW who spent 4 hours or less on social media per day. A marginal
significance was found between direct messaging about sex on Instagram with at least one SNM
and condom use; YBW had 46% decreased odds of using condoms if they direct messaged about
sex on Instagram with at least one SNM (OR = 0.54; 95% CI = 0.26, 1.10), compared to YBW
who do not direct message on Instagram about sex with a SNM (see Table 5.3).
SOCIAL NETWORKS, SEXUAL HEALTH, AND HIV PREVENTION 51

Table 5.3. Multivariable Logistic Regressions of Condom Use (n = 142)
Condom Use
OR 95% CI
4 hours or less on social media per day Ref
More than 4 hours on social media per day 0.30* 0.13, 0.69
Talk about sex on Instagram 0.54 0.26, 1.11
*p < .01.
HIV testing. Regarding HIV testing, no significant relationships were found. One
marginally significant relationship was found: direct messaging about sex on Twitter with at
least one SNM. YBW had 2.33 times the odds of getting tested for HIV if they direct messaged
about sex on Twitter with at least one SNM (OR = 2.33; 95% CI = 0.89, 6.08), compared to
YBW who do not direct message about sex on Twitter with a SNM (see Table 5.4).
Table 5.4. Multivariable Logistic Regressions of HIV Testing (n = 87)
HIV Testing
OR 95% CI
Age 1.08 0.72, 1.65
Education
High school Ref
Some college or two-year degree 0.68 0.18, 2.50
Bachelor’s degree 1.42 0.27, 7.30
Talk about sex on Twitter 2.30 0.86, 6.13
Interest in PrEP. Two significant relationships were found in the multivariable models
assessing interest in PrEP use: spending more than 4 hours on social media per day and seeking
health information via social media. YBW who spent more than 4 hours on social media per day
had 3.14 times the odds of being interested in PrEP (OR = 3.14; 95% CI = 1.52, 6.49), compared
to YBW who spent 4 hours or less on social media per day. YBW who sought health information
via social media had 1.99 times the odds of being interested in PrEP use (OR = 1.99; 95% CI =
1.02, 3.89), compared to YBW who did not seek health information via social media (see Table
5.5).
SOCIAL NETWORKS, SEXUAL HEALTH, AND HIV PREVENTION 52

Table 5.5. Multivariable Logistic Regressions of Interest in PrEP (n = 172)
Interest in PrEP
OR 95% CI
Region
South 0.62 0.31, 1.22
4 hours or less on social media per day Ref
More than 4 hours on social media per day 3.14** 1.52, 6.49
Seek health information via social media 1.95* 1.01, 3.78
*p < .05. **p < .01.

Associations of Social Network Characteristics and Dynamics and Individual Risk Factors
with HIV Testing, Condom Use, and Interest in PrEP Use among YBW
Measurements
Sociodemographic characteristics. Sociodemographic variables age (continuous), level
of education (high school diploma or GED [reference group], some college or two-year degree,
bachelor’s degree, professional degree), and region (Northeast [reference group], Midwest
[reference group], South, West) were used in these analyses.
Individual-level sexual risk and prevention behaviors. Sexual risk and prevention
behaviors were assessed by asking questions regarding sexual activity, condom use during the
prior 30 days, concurrent sexual partners, exchange sex, sex under the influence, birth control
use, STI and HIV testing, and PrEP. These questions were derived from the Lifetime Sex Risk
questions from the CDC’s Youth Risk Behavior Survey and have been rigorously tested for
reliability and validity (Brener et al., 2002; CDC, 2016). Additional sex risk questions are based
on those used by Rice and colleagues in prior studies (Rice, 2010; Rice et al., 2010; Wenzel et
al., 2016). These behaviors were assessed by asking questions regarding sexual activity, i.e.,
“Have you ever had sex?” (1 = yes, 0 = no) and “When was the last time you had sex (oral,
vaginal, or anal)?” (1 = within the last week, 2 = 1–4 weeks ago, 3 = 1–3 months ago, 4 = 3–6
SOCIAL NETWORKS, SEXUAL HEALTH, AND HIV PREVENTION 53

months ago, 5 = 6–12 months ago, 6 = 1 year ago or longer). To create the variable of sex in the
prior 30 days, responses were dichotomized as 1 (within the previous 4 weeks) or 0 (more than 4
weeks ago). Condom use was measured by asking, “The last time you had sex, what kinds of sex
did you have?” (1 = anal sex with a condom, 2 = anal sex with no condom, 3 = oral sex with a
condom or dental dam, 4 = oral sex without a condom or dental dam, 5 = vaginal sex with a
condom, 6 = vaginal sex with no condom). Responses were dichotomized as 1 (condom use) or 0
(no condom use). Oral sex was excluded from analyses due to research indicating that the risk of
acquiring HIV is significantly low when participating in oral sex (CDC, 2016).
To assess the association of other forms of pregnancy prevention with condom use, HIV
testing, and interest in PrEP, participants were asked “The last time you had vaginal sex, what
method(s) did you or your partner use to prevent pregnancy? Check all that apply.” (1 = none; 2
= birth control pills; 3 = condoms; 4 = Depo-Provera (or any injectable birth control), Nuva
Ring (or any birth control ring), Implanon (or any implant), or any IUD; 5 = pulling out or
withdrawal; or 6 = some other method). A separate variable was created for each response, and
then each variable was dichotomized as 1 = yes or 0 = no. New variables included: (a) no
methods used to prevent pregnancy, (b) birth control pills, (c) condoms, (d) nonpill hormonal
birth control (i.e., Depo-Provera or any injectable birth control, Nuva Ring or any birth control
ring, Implanon or any implant, or any IUD), (e) pulling out or withdrawal and (f) some other
method. A new variable for any hormonal birth control methods was created based on the
variables of birth control pill and nonpill hormonal birth control. If participants responded yes to
birth control pills or non-pill hormonal birth control, then their responses were recorded as yes
for any hormonal birth control method. Based on this question, the variable of any hormonal
birth control methods was used in these analyses.
SOCIAL NETWORKS, SEXUAL HEALTH, AND HIV PREVENTION 54

The following sexual risk variables were assessed in a yes-or-no format (1 = yes, 0 = no):
(a) concurrent sexual partners: “In the past 12 months, did you ever have sex (vaginal or anal
sex) with one partner, sex with a different partner, and then sex with the first partner again,
within a week?”; (b) exchange sex: “Have you ever exchanged sex (oral, vaginal, or anal) for
money, a place to stay, food or meals, or anything else?”; (c) sex under the influence: “Did you
drink alcohol or use drugs before you had sex (vaginal or anal sex) the last time?”; (d) sex with
someone met online: “Have you ever had sex (vaginal or anal) with someone you met online?”;
(e) STI testing: “ Have you ever been tested for a sexually transmitted infection, or STI or STD,
for example, chlamydia, gonorrhea, syphilis, genital warts?”; (f) ever tested positive for a STI:
“Did you test positive for any STDs?”; (g) HIV testing: “Have you ever been tested for
HIV/AIDS?”; and (h) interest in PrEP use: “Does PrEP sound like something you would be
interested in taking to help prevent you from getting HIV?” Additionally, participants were asked
if they have ever attended an HIV education program: “Have you ever participated in a HIV/STI
educational or prevention program outside of school?”
Social Network Variables
To create social network variables, standard egocentric network data were collected
(Matzat & Snijders, 2010). Each participant was asked to list the names of their SNMs using a
name generator: “Thinking about the last month, list all the people you have communicated
with.” Participants listed between eight and 20 names of people they have spoken with during the
last 30 days. Once participants finished listing the names of their SNMs, demographic
information of each participants’ SNMs were collected.
SNM demographics. The following SNM demographic information was collected: each
SNM’s sex (1 = male, 2 = female), each SNM’s race and ethnicity (1 = White, 2 = Black, 3 =
SOCIAL NETWORKS, SEXUAL HEALTH, AND HIV PREVENTION 55

Latino/a, 4 = Asian, 5 = other), and the participants’ relationship with each person (How would
you best describe your relationship to each person? e.g., friend, mom, dad, sister, coworker, etc.).
To create the variable for family member, any SNM listed as a family member was recorded as a
new binary family member variable (1 = family member, 0 = not a family member).
SNM relationships and social support. After demographic information was collected,
each SNM name was turned into an answer option, similar to categorical answers to a multiple-
choice question. The names of all nominated network members were listed as answer options,
and the participant could select individuals corresponding to that question. Each question was
binary: 1 = selecting an SNM and 0 = no selection. To assess which SNMs were sexual partners,
participants were asked “Who have you ever had sex (anal, vaginal, or oral sex) with?” to assess
which SNMs were friends, participants were asked “Who on this list would you call a friend?”
To assess social support in YBW social network, several questions were combined (1 =
yes, 0 = no). To assess emotional support, participants were asked “Who provides emotional
support?” and ”Who makes you feel liked or loved?” If a participant selected a SNM for either
item, then that SNM was considered someone who provided emotional support. If an SNM was
not selected for either question, then that SNM was recorded as someone who did not provide
emotional support. To assess instrumental support, participants were asked “Who could you
borrow $100 from if you needed it?” and “Who helps you with completing forms or application
or with editing your resume?” If a participant selected a SNM for either item, then that SNM was
considered someone who provided instrumental support. If an SNM was not selected for either
question, then that SNM was recorded as someone who did not provide instrumental support. To
assess informational support, participants were asked “Who do you go to when you need help or
advice?” and “Who can you count on to listen to you when you need to talk, or is someone you
SOCIAL NETWORKS, SEXUAL HEALTH, AND HIV PREVENTION 56

can confide in?” If a participant selected an SNM for either item, then that SNM was considered
someone who provided informational support. If a SNM was not selected for either question,
then that SNM was recorded as someone who did not provide informational support.
Sexual health communication with SNMs. For communication with SNMs, each SNM
name was turned into an answer option, like categorical answers to a multiple-choice question.
The names of all nominated network members were listed as answer options, and the participant
could select individuals corresponding to that question. Each question was binary: 1 = selecting
an SNM and 0 = no selection. Unidirectional communication questions were combined to assess
bidirectional sexual health communication among YBW and their SNMs. The variable for
talking about sex was created using the following questions: “Who have you ever talked to about
your sexual experiences?” and “Who talks to you about their sexual experiences?” If a
participant selected an SNM for either item, then that SNM was considered someone YBW talks
with about sexual experiences. If an SNM was not selected for either question, then that SNM
was recorded as someone who YBW did not talk to about sexual experiences.
The variable for talking about condoms was created using the following questions: “Who
have you ever talked to about condoms, or practicing safer sex?” and “Who has ever talked to
you about condoms, or practicing safer sex?” If a participant selected an SNM foreither item,
then that SNM was considered someone YBW talks with about condoms. If an SNM was not
selected for either question, then that SNM was recorded as someone who YBW did not talk to
about condoms.
The variable for talking about STI testing was created using the following questions:
“Who have you ever talked to about getting an STI test?” and “Who has ever talked to you about
getting a STI test?” If a participant selected an SNM for either item, then that SNM was
SOCIAL NETWORKS, SEXUAL HEALTH, AND HIV PREVENTION 57

considered someone YBW talks with about STI testing. If an SNM was not selected for either
question, then that SNM was recorded as someone who YBW did not talk with about STI testing.
The variable for talking about HIV testing was created using the following questions:
“Who have you ever talked to about getting an HIV test?” and “Who has ever talked to you
about getting an HIV test?” If a participant selected an SNM for either item, then that SNM was
considered someone YBW talks with about HIV testing. If an SNM was not selected for either
question, then that SNM was recorded as someone who YBW did not talk with about HIV
testing.
Interaction variables of talking about sex, condom use, HIV testing, or STI testing with a
family member, friend, or sex partner were created to examine the differences between talking
with various types of SNMs about each of the four topics. These variables were created before
the individual and social network data were merged to capture the variations in communication
and SNMs, and they were binary (1 = yes, 0 = no). The interaction variable of talking to a family
member about sex was created if an SNM was listed as a family member and someone with
whom the YBW talks about sex. Similar interaction variables were created for talking with a
family member about condom use, HIV testing, and STI testing. This same process was repeated
for SNMs listed as friends or sex partners.
Sexual health communication via communication channel. Nonsocial media
communication channels (i.e., text messaging, phone, and in person) were assessed using a scale
(i.e., 1 = once or more a day, 2 = several times a week, 3 = about once a week, 4 = about once a
month, 5 = a couple times a year, 6 = not in the past year). The following questions were used to
assess communication in person, via phone, and via text messages: (a) talk in person: “How
often do you hang out with, chill with, party with, or have in-person conversations with these
SOCIAL NETWORKS, SEXUAL HEALTH, AND HIV PREVENTION 58

individuals?; (b) talk to about sex in person: “How often do you talk about sex, sexual
experiences, or relationships with these individuals in person?”; (c) talk on phone: “How often
do you talk on the phone with these individuals? (this can also include Skype, video chat, etc.)”;
(d) talk about sex on phone: “How often do you talk on the phone about sex, sexual experiences,
and relationships with these individuals?”; (e) talk via text messaging: “How often do you text
with these individuals?”; and (f) talk about sex via text messaging: “How often do you text about
sex, sexual experiences, and relationships with these individuals?” Based on qualitative
interviews with YBW regarding how often they communicated with their most influential SNMs
(unpublished qualitative data collected during this study), sexual health communication via
communication channel variables were dichotomized as 1 = frequent communication (i.e., once
or more a day, several times a week, and about once a week) and 0 = infrequent communication
(i.e., about once a month, a couple times a year, and not in the past year).
Interaction variables for talking about condom use, HIV testing, or STI testing via text,
phone, or in person were created to examine the differences between talking on various
communication platforms about each otopic. These variables (e.g., talking about condom use via
text, talking about HIV testing via text, talking about STIs via text, talking about condom use on
the phone, etc.) were created before the individual and social network data were merged to
capture variations in sexual health communication and communication channels, and they were
binary variables (1 = yes, 0 = no). The interaction variable of talking about condom use via text
was created if an SNM was listed as someone with whom YBW spoke to via text and someone
with whom the YBW talked about condom use.Similar interaction terms were created for talking
with an SNM about condom use on the phone and in person. This same process was repeated for
talking about HIV testing and talking about STI testing.
SOCIAL NETWORKS, SEXUAL HEALTH, AND HIV PREVENTION 59

Social network structure. To determine structural elements of YBW’s social networks,
size and density were assessed. The size of each network was equal to the number of SNMs each
YBW nominated (listed). Thus, if a YBW listed eight SNMs, the size of her network was eight.
If a YBW listed 15 SNMs, then the size of her network was 15. To calculate the density of each
YBW’s social network, YBW were asked which members know each other within her network.
For each SNM in her network, she was asked, “Who does [SNM’s name] know?” The names of
all other nominated network members were listed as answer options, and the participant could
select whom that SNM knew from the list. This was repeated for each listed SNM. Density was
calculated by computing actual ties in each YBW’s network and dividing actual ties by possible
ties. Density was calculated as:
𝐷 =
𝚤 𝑛 (𝑛 − 1)

wherein 𝚤 is the number of ties in the network and n is the network size (Valente, 2010).
Variables for homophily and its inverse heterogeneity were created to examine
homophily in race and sex. To create the race homophily variable, SNMs’ race and ethnicity (1 =
White, 2 = Black, 3 = Latino/a, 4 = Asian, 5 = other) variable was used. If an SNM was listed as
Black, then race homophily was recorded as 1; if Black was not selected, then race homophily
was recorded as 0. To create the sex homophily variable, SNMs’ sex (1 = male, 2 = female) was
used. If an SNM was listed as female, then sex homophily was recorded as 1; if male was
selected, then sex homophily was recorded as 0. The inverse of these variables represented
heterogeneity variables (i.e., heterogeneity in race and ethnicity and heterogeneity in sex).
Statistical Analysis
The objective of this analysis was to investigate how social networks may be associated
with HIV testing, condom use, and interest in PrEP use among YBW. Egocentric network
SOCIAL NETWORKS, SEXUAL HEALTH, AND HIV PREVENTION 60

analysis was conducted using SAS to determine the associations among social support (i.e.,
emotional, information, instrumental); SNM relationship types (e.g., friend, family member,
sexual partner, etc.); sexual health communication between YBW and SNMs (e.g., about
condom use, HIV testing); and HIV testing, condom use, and interest in PrEP. Egocentric
network analysis allowed for the inclusion of social network variables in standard statistical
models, such as multivariate logistic regression. Independent variables constructed from SNM
data were created in SAS and merged with individual-level survey data.
Statistical analysis proceeded in two stages: (a) descriptive statistics of the YBW, their
sexual risk factors, their sexual health communication, and social network dynamics were
calculated and (b) logic regression modeling was conducted. Univariable logistic regression
models were run with each independent variable and each outcome variable. The outcome
variables were treated as dichotomous outcomes and regressed on individual-level measures (i.e.,
measures that only varied across participants) and social network-level measures (i.e., measures
that varied across SNMs). Individual-level measures were created based on standard individual
responses to survey items. Covariates found to be significant (p < .05) in the univariable models
were entered into a multivariable model. Due to multicollinearity between the social network-
level variables, a multivariable model was run for each social network variable found to the
significant at the univariable level. Each model controlled for demographic characteristics and
included all significant individual-level variables. All multivariable logistic regression analyses
were restricted to participants without missing data for the variables included in the models.
Therefore, the sample sizes for the multivariable multilevel logistic regression models were
smaller than the study’s total sample size (i.e., condom use: n = 161, HIV testing: n = 160,
interest in PrEP: n = 160).
SOCIAL NETWORKS, SEXUAL HEALTH, AND HIV PREVENTION 61

Additionally, structural features of networks, such as network size, density, and
homophily, were assessed using SAS 9.4. All network structure variables used as independent
variables were added to the individual-level and SNM data prior to standard statistical modeling
strategies, then standard multivariable logistic regression modeling techniques described by
Hosmer and Lemeshow (2000) were followed. All final logistic regression analyses were carried
out in SAS 9.4.
Results
Descriptive statistics.
Individual-level variables.
Sexual risk factors. Of the 180 YBW, 20.56% reported ever participating in an HIV or
STI program or intervention outside of school. Among YBW, 53.07% reported sexual activity in
the prior 30 days. Risky sexual risk behaviors included: (a) having sex with someone they met
online (37.13%), (b) having sex under the influence of alcohol or drugs (31.40%), (b) concurrent
sexual partners (10.71%), and (d) participating in exchange sex (3.33%). Among participants
73.33% reported having ever been tested for STIsl of those who had been tested, 17.56% had
ever tested positive for an STI. YBW were also asked about methods used to prevent pregnancy.
Slightly more than half (51.79%) reported using a hormone-based birth control method for
pregnancy prevention (see Table 5.6 for demographics and sexual risk factors).
Outcome variables. Among the YBW, 72.22% reported having ever been tested for HIV,
40.22% reported using a condom during last sex, and 36.11% expressed interest in using PrEP
for HIV prevention (see Table 5.6).
SOCIAL NETWORKS, SEXUAL HEALTH, AND HIV PREVENTION 62

Table 5.6. Individual-Level Variables: Demographics, Sexual Health Risk, and HIV Prevention
Behaviors (n = 180)
n or M % or SD
Demographics
Age (range: 18–24; n = 177) 21.15 1.69
Housing (n = 179) 178 99.44
Education (n = 178)
High school 22 12.36
Some college or two-year degree 86 48.31
Bachelor’s degree 62 34.83
Professional degree 8 4.49
Current region (n = 178)
Northeast 16 8.99
Midwest 14 7.87
South 75 42.13
West 73 41.01
Sexual risk factors
Participated in HIV/STI program (n = 180) 37 20.56
Sex in prior 30 days (n = 179) 95 53.07
Sex under the influence (n = 172) 54 31.40
Concurrent sexual partners (n = 169) 18 10.71
Sex with someone met online (n = 167) 62 37.13
Exchange sex (n = 180) 6 3.33
Ever tested for STIs (n = 180) 132 73.33
Ever tested positive for an STI (n = 131) 23 17.56
Birth control during last vaginal sex (n = 168)
Hormonal (n = 180) 87 51.79
Outcomes
Interested of PrEP (n = 180) 65 36.11
Ever tested for HIV (n = 180) 130 72.22
Condom use at last sex (n = 179) 72 40.22
Social network-level variables.
Relationship type. Three types of relationships were examined in this study: family
members, friends, and sex partners. Ninety-nine percent of YBW listed at least one friend in their
social network, with YBW listing four friends on average (range: 0–19). Eighty-eight percent of
YBW listed at least one family member, with YBW listing two family members on average
(range: 0–12). Regarding sexual partners, 67.00% of YBW listed at least one sexual partner their
social network, and most women listed one or no sexual partners (M = 0.57; SD = 0.71; range: 0–
SOCIAL NETWORKS, SEXUAL HEALTH, AND HIV PREVENTION 63

4). Regarding heterogeneity in race and ethnicity and sex of SNMs, 64.00% of YBW reported
having at least one SNM of a different race or ethnicity and 98.00% reported having at least one
male in their social network (see Table 5.7).
Relationship type by social support. Social support was assessed using three variables:
emotional, instrumental, and informational support. Reports of emotional support were high,
with 95.00% of YBW reporting having at least one friend, 82.78% reporting having at least one
family member, and 61.67% reporting having at least one sex partner who provided emotional
support. Reports of instrumental support were also high, but slightly less prevalent: 82.22% of
YBW reported having at least one friend, 80.56% reported having at least one family member,
and 42.22% reported having at least one sex partner who provided instrumental support.
Informational support was also prevalent, with 92.78% reporting having at least one friend,
88.33% reporting having at least one family member, and 42.22% reporting having at least one
sex partner who provided informational support.
Sexual health communication topics.
Talk about sex. Ninety-eight percent of YBW reported having spoken to at least one of
their SNMs about sex. Modes of communication were reported as follows: 85% spoke via text
message, 83.89% spoke on the phone, and 94.44% spoke in person with at least one of their
SNMs about sex. Communication about sex was least likely to occur with family members
(52.78%), compared to friends (92.78%) or sex partners (63.33%; see Table 5.7).
Talk about condom use. Condom use communication was also high, with 92.22% of
YBW reporting having spoken to at least one SNM about condom use. Modes of communication
were reported as follows: 76.11% reported having spoken to at least one of their SNMs about sex
via text message, 72.78% via phone, and 84.44% in person. Communication about condoms was
SOCIAL NETWORKS, SEXUAL HEALTH, AND HIV PREVENTION 64

least likely to occur with family members (60.56%), compared to a friend (81.11%) or a sex
partner (77.69%).
Talk about STI testing. Reports of STI communication with SNMs were less common
than talking about condom use or talking about sex. In this sample, 79.44% YBW reported
having spoken with at least one of their SNMs about STI testing, with 65.56% communicating
via text messages, 62.78% speaking on the phone, and 73.89% speaking in person. Of YBW who
reported speaking about STIs, 33.33% had communicated with a family member, 67.78% had
spoken to a friend, and 42.15% spoke with a sex partner about STIs.
Talk about HIV testing. HIV testing was the least talked-about sexual health topic
assessed in this study. More than half (57.78%) of YBW reported having spoken with at least
one of their SNMs about HIV testing; 47.78% reported that communication occurred via text,
46.67% via phone, and 51.67% in person. YBW were least likely to report speaking to a family
member about HIV testing (23.33%), followed by a friend (48.33%) or sexual partner (61.98%;
see Table 5.7).
SOCIAL NETWORKS, SEXUAL HEALTH, AND HIV PREVENTION 65

Table 5.7. Social Network-Level Descriptive Statistics (n = 180)
n
a
%
a
M
b
SD
b
Range
b

Total number of SNMs 2,200 100 6.65 4.38 1–20
Network density 180 100 0.51 0.28 0–1
Race heterogeneity 115 63.89 2.57 3.19 0–16
Sex heterogeneity 176 97.78 3.46 1.95 0–10
Friend 178 98.89 4.09 3.35 0–19
Family member 159 88.33 2.18 1.92 0–12
Sex partner 121 67.22 0.57 0.71 0–4
Emotional support
Friend 171 95.00 3.22 2.97 0–19
Family member 149 82.78 1.86 1.77 0–12
Sex partner 111 61.67 0.47 0.60 0–3
Instrumental support
Friend 148 82.22 1.57 1.78 0–12
Family member 145 80.56 1.35 1.30 0–6
Sex partner 76 42.22 0.31 0.47 0–2
Informational support
Friend 167 92.78 2.35 2.32 0–16
Family member 159 88.33 2.18 1.92 0–12
Sex partner 87 48.33 0.36 0.52 0–3
Talk about sex 176 97.78
Text 153 85.00 2.31 2.36 0–14
Phone 151 83.89 2.14 2.34 0–14
In person 170 94.44 3.08 2.62 0–16
Friend 167 92.78 2.59 2.29 0–15
Family member 95 52.78 0.70 1.09 0–12
Sex partner 114 63.33 0.49 0.61 0–3
Talk about condom use 166 92.22
Text 137 76.11 1.60 1.94 0–8
Phone 131 72.78 1.52 1.90 0–14
In person 152 84.44 1.97 1.99 0–14
Friend 146 81.11 1.88 2.26 0–15
Family member 109 60.56 0.85 1.06 0–7
Sex partner 94 77.69 0.41 0.61 0–3
Talk about STIs 143 79.44
Text 118 65.56 1.09 1.49 0–11
Phone 113 62.78 0.99 1.31 0–9
In person 133 73.89 1.29 1.52 0–9
Friend 122 67.78 1.69 1.69 0–14
Family member 60 33.33 0.40 0.77 0–5
Sex partner 51 42.15 0.33 0.54 0–3
Talk about HIV 104 57.78
Text 86 47.78 0.64 1.08 0–6
Phone 84 46.67 0.61 1.08 0–7
In person 93 51.67 0.76 1.24 0–8
SOCIAL NETWORKS, SEXUAL HEALTH, AND HIV PREVENTION 66

Friend 87 48.33 0.65 1.20 0–9
Family member 42 23.33 0.24 0.59 0–3
Sex partner 75 41.67 0.21 0.43 0–2
a
Averages based on having at least one SNM in each YBW’s social network (n =
180).
b
Based on sums of SNMs across all networks of YBW.
Univariable logistic regression models.
Condom use models. At the univariable level, two individual-level variables were
significantly associated with condom use at last sex (see Table 5.8). YBW who reporting having
had sex in the last 30 days had 51% decreased odds of reporting condom use during their last
sexual encounter (OR = 0.49; 95% CI = 0.27, 0.90), compared to YBW who reported not having
sex in the last 30 days. YBW who reported using hormonal birth control methods had 61%
decreased odds of using a condom during last sex (OR = 0.39; 95% CI = 0.21, 0.73), compared
to those who did not use hormonal birth control methods. No significant relationships were
associated with condom use at the social network level.
HIV testing models. At the univariable level, four individual-level variables were
significantly associated with HIV testing (i.e., some college or two-year degree, bachelor’s
degree, participating in an HIV or STI program, and ever being tested for STIs; see Table 5.8).
YBW who reported some college or a two-year degree had 72% decreased odds of getting tested
for HIV (OR = 0.28; 95% CI = 0.13, 0.56), compared to YBW who did not report some college
or a two year-degree. YBW who had a bachelor’s degree had more than 4 times the odds of
getting tested for HIV (OR = 4.29; 95% CI = 1.79, 10.29) than YBW who did not have a
bachelor’s degree. There was no significant relationship between YBW whose highest level of
education was high school and getting tested for HIV (OR = 0.76; 95% CI = 0.29, 2.00). YBW
who participated in an HIV or STI prevention program outside of school had nearly 3 times the
odds of being tested for HIV (OR = 2.94; 95% CI = 1.07, 8.04), compared to YBW who did not
SOCIAL NETWORKS, SEXUAL HEALTH, AND HIV PREVENTION 67

participate in an HIV STI prevention program. Furthermore, YBW who had ever been tested for
STIs had 21 times the odds of being tested for HIV (OR = 21.00; 95% CI = 9.13, 48.31),
compared to YBW who have never been tested for STIs (see Table 5.8).
Regarding social network-level variables, the seven variables found to be significantly
associated with HIV testing are reported in Table 5.9. YBW who reported having at least one
friend who provided instrumental support had 2.4 times the odds of being tested for HIV (OR =
2.42; 95% CI = 1.10, 5.35), compared to YBW who did not report having a friend who provided
instrumental support. YBW who spoke in person with at least one SNM about condom use had
73% decreased odds of being tested for HIV (OR = 0.27; 95% CI = 0.08, 0.93), compared to
YBW who did not report talking in person with SNMs about condom use. YBW who spoke on
the phone about HIV testing with at least one SNM had 2.3 times the odds of being tested for
HIV (OR = 2.33; 95% CI = 1.17, 4.63), compared to YBW who did not report talking to SNMs
about HIV testing on the phone. YBW who reported talking to at least one family member about
HIV testing and about STI testing had 4.8 times (OR = 4.75; 95% CI = 1.60, 14.12) and 3.5 times
(OR = 3.50; 95% CI = 1.52, 8.05) the odds of having been tested for HIV, respectively,
compared to YBW who did not report talking about HIV testing or STI testing with a family
member. YBW who reported talking to at least one sex partner about HIV testing and STI testing
had 3.1 times (OR = 3.14; 95% CI = 1.31, 7.56) and 2.3 times (OR = 2.27; 95% CI = 1.12, 4.61)
the odds of having been tested for HIV, respectively, compared to YBW who did not report
talking with their sex partners about HIV testing and STI testing.
SOCIAL NETWORKS, SEXUAL HEALTH, AND HIV PREVENTION 68

Table 5.8. Univariable Logistic Regressions of Individual-Level Variables and Condom Use,
HIV Testing, and Interest in PrEP
Condom Use
a
HIV Testing Interest in PrEP
OR 95% CI OR 95% CI OR 95% CI
Demographics
Age 1.01 0.85, 1.21 1.51*** 1.21, 1.87 1.01 0.85, 1.22
Education
High school 0.58 0.21, 1.56 0.76 0.29, 2.00 1.02 0.40, 2.58
Some college or two-year degree 1.00 0.55, 1.83 0.28*** 0.13, 0.56 1.11 0.60, 2.05
Bachelor’s degree 1.29 0.69, 2.42 4.29** 1.79, 10.29 0.87 0.46, 1.66
Professional degree 0.91 0.21, 3.95 -- -- 1.07 0.25, 4.64
Current region
Northeast 0.67 0.22, 2.02 1.72 0.47, 6.31 0.77 0.26, 2.33
Midwest 0.39 0.11, 1.45 0.66 0.21, 2.08 1.83 0.61, 5.47
South 1.30 0.71, 2.40 1.21 0.62, 2.37 0.52* 0.28, 0.99
West 1.12 0.61, 2.06 0.80 0.41, 1.56 1.70^ 0.92, 3.16
Sexual risk factors
Participated in HIV STI program 0.88 0.42, 1.86 2.94* 1.07, 8.04 1.46 0.70, 3.05
Sex in last 30 days 0.49* 0.27, 0.90 1.06 0.55, 2.04 1.35 0.73, 2.49
Sex under the influence 1.43 0.75, 2.75 0.77 0.37, 1.61 0.77 0.39, 1.53
Concurrent sexual partners 0.63 0.23, 1.77 5.97^ 0.77, 46.37 2.50^ 0.93, 6.73
Sex with someone met online 1.38 0.73, 2.61 1.98^ 0.89, 4.41 2.46** 1.27, 4.74
Exchange sex 0.74 0.13, 4.13 -- -- 3.70 0.66, 20.79
Ever tested positive for an STI 0.81 0.32, 2.07 -- -- 1.86 0.75, 4.59
Method of birth control
Hormonal methods 0.39** 0.21, 0.73 1.03 0.51, 2.08 1.36 0.72, 2.56
Outcomes
Interested in PrEP 0.81 0.43, 1.51 1.29 0.64, 2.57 -- --
HIV testing 0.77 0.40, 1.49 -- -- 1.29 0.62, 2.57
Condom use -- -- 0.77 0.40, 1.49 0.81 0.43, 1.51
a
n = 179.
^p < .10. *p < .05. **p < .01. ***p < .001.
SOCIAL NETWORKS, SEXUAL HEALTH, AND HIV PREVENTION 69

Table 5.9. Univariable Logistic Regressions of Social Network Variables and Condom Use, HIV
Testing, and Interest in PrEP
Condom Use HIV Testing Interest in PrEP
OR 95% CI OR 95% CI OR 95% CI
Network density 0.57 0.19, 169 0.58 0.18, 1.93 0.65 0.22, 1.96
Race heterogeneity 1.08 0.58, 2.01 0.78 0.39, 1.55 1.17 0.62, 2.21
Sex heterogeneity 2.05 0.21, 20.05 -- -- 0.56 0.08, 4.05
Emotional support
Family member 0.92 0.42, 2.01 0.57 0.22, 1.49 1.23 0.54, 2.80
Friend 5.74 0.70, 46.90 0.73 0.15, 3.65 2.04 0.41, 10.13
Sex partner 0.66 0.36, 1.22 0.77 0.39, 1.53 1.10 0.59, 2.06
Instrumental support
Family member 0.65 0.31, 1.38 0.73 0.31, 1.73 1.10 0.51, 2.40
Friend 1.15 0.52, 2.53 2.42* 1.10, 5.35 1.30 0.57, 2.95
Sex partner 0.81 0.44, 1.49 0.65 0.34, 1.25 1.17 0.63, 2.15
Informational support
Family member 0.84 0.42, 1.65 1.19 0.57, 2.48 1.08 0.53, 2.18
Friend 2.37 0.63, 8.94 0.45 0.96, 2.11 1.30 0.38, 4.38
Sex partner 0.79 0.43, 1.43 0.91 0.48, 1.75 1.06 0.58, 1.94
Talk about sex
Text 0.57 0.25, 1.31 0.71 0.27, 1.87 1.74 0.70, 4.38
Phone 1.61 0.69, 3.77 1.21 0.51, 2.87 1.96 0.79, 4.88
In person 0.43 0.12, 1.57 -- -- 5.43 0.67, 43.90
Family member 0.61 0.34, 1.12 0.93 0.49, 1.80 1.75 0.94, 3.24
Friend 4.01 0.86, 18.66 0.20 0.03, 1.59 -- --
Sex partner 0.64 0.35, 1.19 0.75 0.38, 1.50 1.21 0.64, 2.29
Talk about condom use
Text 1.18 0.58, 2.39 0.52 0.22, 1.21 1.89 0.88, 4.07
Phone 2.01 0.99, 4.09 0.92 0.44, 1.92 1.40 0.69, 2.82
In person 1.51 0.64, 3.56 0.27* 0.08, 0.93 1.85 0.74, 4.63
Family member 0.63 0.34, 1.15 1.30 0.67, 2.52 1.61 0.85, 3.05
Friend 1.29 0.60, 2.82 0.50 0.19, 1.28 3.17* 1.23, 8.15
Sex partner 1.16 0.64, 2.11 0.72 0.38, 1.40 0.75 0.41, 1.39
Talk about HIV testing
Text 0.98 0.54, 1.79 1.74 0.89, 3.38 1.46 0.80, 2.70
Phone 1.40 0.77, 2.55 2.33* 1.17, 4.63 1.29 0.70, 2.38
In person 1.00 0.55, 1.82 1.92 0.99, 3.73 1.54 0.83, 2.84
Family member 0.59 0.28, 1.24 4.75** 1.60, 14.12 1.28 0.63, 2.59
Friend 1.04 0.57, 1.89 1.42 0.74, 2.75 2.09* 1.13, 3.89
Sex partner 0.69 0.35, 1.37 3.14* 1.31, 7.56 0.95 0.48, 1.87
Talk about STI testing
Text 0.81 0.43, 1.51 1.77 0.90, 3.46 1.45 0.75, 2.78
Phone 1.22 0.65, 2.26 1.87 0.96, 3.63 1.26 0.67, 2.37
In person 0.62 0.32, 1.21 1.31 0.64, 2.71 1.29 0.63, 2.61
Family member 0.58 0.30, 1.11 3.50** 1.52, 8.05 1.59 0.84, 3.01
SOCIAL NETWORKS, SEXUAL HEALTH, AND HIV PREVENTION 70

Friend 1.28 0.67, 2.45 0.99 0.49, 1.98 1.56 0.80, 3.06
Sex partner 0.57 0.31, 1.06 2.27* 1.12, 4.61 1.10 0.59, 2.03
*p < .05. **p < .01.
Interest in PrEP models. At the univariable level, two individual-level variables were
significantly associated with interest in PrEP (see Table 5.8). YBW who reported currently living
in the South had 46% decreased odds of being interested in PrEP (OR = 0.52; 95% CI = 0.28,
0.99), compared to those who lived in other regions. YBW who reported having sex with
someone they met online had 2.5 times the odds of being interested in PrEP (OR = 2.46; 95% CI
= 1.27, 4.74), compared to YBW who did not report having sex with someone they met online.
Only two social network-level variables were associated with interest in PrEP (see Table
5.9). YBW who reported having spoken about condom use with at least one friend had 3.2 times
the odds of being interested in PrEP (OR = 3.17; 95% CI = 1.23, 8.15), compared to YBW who
did not speak to a friend about condom use. YBW who reported having spoken to at least one
friend about HIV testing had 2.1 times the odds of being interested in PrEP (OR = 2.09; 95% CI
= 1.13, 3.89), compared to YBW who did not talk with a friend about HIV testing.
Multivariable logistic regression models.
Condom use. Two individual-level variables were included in the multivariable condom
use models (i.e., sex in the prior 30 days and hormonal birth control methods). However, only
one variable was significant after controlling for demographics and other individual-level
characteristics (see Table 5.10). YBW who reported using a hormonal birth control method had
62% decreased odds of using condoms during their last sexual encounter (OR = 0.38; 95% CI =
0.19, 0.79), compared to YBW who did not report using a hormonal birth control method. No
social network-level variables were included in the multivariable condom use models due to the
lack of significance at the univariable level.
SOCIAL NETWORKS, SEXUAL HEALTH, AND HIV PREVENTION 71

Interest in PrEP. Two individual and two social network variables were included in the
multivariable logistic regression models for interest in PrEP (see Table 5.10). Of those variables,
only one individual-level variable was significantly associated with interest in PrEP across the
two models. YBW who reported having sex with someone they met online had more than 2
times the odds of being interested in PrEP (OR = 2.19; 95% CI = 1.02, 4.72; OR = 2.25; 95% CI
= 1.05, 4.38), compared to YBW who did not report having sex with someone they met online.
Table 5.10. Multivariable Logistic Regressions of Condom Use and Interest in PrEP
Condom Use
a
Interest in PrEP &
Talk to Friend about
Condom Use
b

Interest in PrEP &
Talk to Friend about
HIV
b

OR 95% CI OR 95% CI OR 95% CI
Age 0.90 0.71, 1.15 1.06 0.83, 1.36 1.04 0.81, 1.34
Education
Some college or two-year degree 1.92 0.71, 5.19 0.90 0.34, 2.40 0.89 0.33, 2.37
Bachelor’s degree 2.52 0.82, 7.78 0.87 0.31, 2.45 0.82 0.29, 2.28
Current region
South 1.58 0.55, 4.50 0.81 0.28, 2.35 0.71 0.24, 2.01
West 1.61 0.55, 4.70 1.68 0.58, 4.89 1.34 0.46, 3.89
Participated in HIV or STI program 1.11 0.46, 2.71 1.73 0.73, 4.12 1.59 0.65, 3.90
Sex in last 30 days 0.52 0.26, 1.06 1.27 0.61, 2.65 1.31 0.62, 2.77
Concurrent sexual partners 0.56 0.17, 1.86 1.67 0.54, 5.15 1.90 0.60, 6.04
Sex with someone met online 1.55 0.74, 3.24 2.19* 1.02, 4.72 2.25* 1.05, 4.83
Hormonal birth control methods 0.38** 0.19, 0.79 1.51 0.72, 3.20 1.35 0.62, 2.94
Race heterogeneity 0.96 0.46, 2.04 1.46 0.66, 3.20 1.48 0.66, 3.29
Sex heterogeneity - - 0.17 0.01, 2.23 0.12 0.01, 1.62
Talk to friend about condom use -- -- 2.13 0.75, 6.02 -- --
Talk to friend about HIV -- -- -- -- 1.97 0.94, 4.13
R
2
.17 .17 .17
a
n = 161.
b
n = 160.
*p < .05. **p < .01.
HIV testing. Four individual-level and seven social network-level variables were
examined in the multivariable logistic regression HIV testing models (see Tables 5.11, 5.12, and
5.13). Due to multicollinearity, each social network variable was run independently in its own
model, controlling for demographics and individual-level risk factors. Across all models,
participating in an HIV or STI prevention program significantly increased YBW’s odds of
SOCIAL NETWORKS, SEXUAL HEALTH, AND HIV PREVENTION 72

having been tested for HIV by 5.3 to 7.6 times (OR = 5.27; 95% CI = 1.02, 27.30 and OR = 7.57;
95% CI = 1.50, 38.34), compared to those who did not participate in an HIV prevention program.
Talking in person about condoms with an SNM decreased the odds that a YBW had been tested
for HIV by 91% (OR = 0.09; 95% CI = 0.01, 0.78), compared to not talking in person with a
SNM about condom use. However, YBW who spoke to at least one family member about STI
testing had 4.1 times the odds of having been tested for HIV (OR = 4.12; 95% CI = 1.40, 12.05),
compared to YBW who did not speak to at least one family member about STI testing. YBW had
7.3 times the odds of having been tested for HIV if they spoke to at least one family member
about HIV testing (OR = 7.33; 95% CI = 1.69, 31.79), compared to YBW who did not speak to
at least one family member about HIV testing. Speaking to sexual partners about STI or HIV
testing was not significantly associated with HIV testing in the multivariable models.
Table 5.11. Multivariable Logistic Regressions of HIV Testing (n = 162)
HIV Testing &
Instrumental Support
from Friend
HIV Testing & Talk
about Condom Use
in Person
HIV Testing & Talk
about HIV Testing
on Phone
OR 95% CI OR 95% CI OR 95% CI
Age 1.34 0.98, 1.82 1.36 1.00, 1.86 1.32 0.97, 1.81
Education
Some college or two-year degree 0.45 0.14, 1.43 0.32 0.10, 1.02 0.41 0.13, 1.31
Bachelor’s degree 1.46 0.31, 6.86 1.14 0.24, 5.34 1.42 0.30, 6.64
Current region
South 0.90 0.21, 3.76 0.92 0.23, 3.73 1.05 0.27, 4.16
West 0.67 0.16, 2.84 0.58 0.14, 2.45 0.74 0.18, 3.00
Participated in HIV or STI program 6.49* 1.30, 32.51 7.57* 1.50, 38.34 6.41* 1.30, 31.67
Sex in last 30 days 0.63 0.26, 1.54 0.67 0.26, 1.75 0.62 0.25, 1.53
Concurrent sexual partners 7.23 0.80, 65.07 8.49 0.90, 80.10 7.10 0.79, 63.77
Sex with someone met online 1.60 0.58, 4.41 2.02 0.72, 5.70 1.72 0.63, 4.68
Hormonal birth control methods 0.88 0.36, 2.16 0.79 0.32, 1.96 0.85 0.35, 2.08
Race heterogeneity 1.10 0.42, 2.92 1.19 0.46, 3.01 1.37 0.53, 3.52
Instrumental support from friend 2.13 0.75, 6.07
Talk about condom use in person 0.09* 0.01, 0.78
Talk about HIV testing on phone 1.27 0.52, 3.11
R
2
.31 .35 .30
*p < .05.
SOCIAL NETWORKS, SEXUAL HEALTH, AND HIV PREVENTION 73

Table 5.12. Multivariable Logistic Regressions of HIV Testing Continued (n = 162)
HIV Testing & Talk
about Condom Use In
Person
HIV Testing & Talk
about HIV Testing
On Phone
OR 95% CI OR 95% CI
Age 1.19 0.87, 1.63 1.28 0.94, 1.76
Education
Some college or two-year degree 0.40 0.12, 1.32 0.42 0.13, 1.33
Bachelor’s degree 1.93 0.40, 9.37 1.48 0.31, 6.98
Current region
South 1.37 0.32, 5.87 1.01 0.24, 4.09
West 0.86 0.19, 3.80 0.72 0.17, 3.00
Participated in HIV or STI program 5.27* 1.02, 27.30 5.28* 1.05, 26.61
Sex in last 30 days 0.51 0.20, 1.32 0.54 0.21, 1.38
Concurrent sexual partners 9.03 0.96, 85.46 6.60 0.73, 59.70
Sex with someone met online 2.13 0.74, 6.10 0.54 0.21, 1.38
Hormonal birth control methods 0.62 0.24, 1.63 0.91 0.37, 2.25
Race heterogeneity 1.83 0.67, 4.98 1.39 0.54, 3.57
Instrumental support from friend -- --
Talk about condom use in person 7.33** 1.69, 31.79
Talk about HIV testing on phone 2.36 0.80, 7.03
R
2
.36 .31
*p < .05. **p < .01.
SOCIAL NETWORKS, SEXUAL HEALTH, AND HIV PREVENTION 74

Table 5.13. Multivariable Logistic Regressions of HIV Testing Continued (n = 162)
HIV Testing & Talk
about Condom Use in
Person
HIV Testing & Talk
about HIV Tesing on
Phone
OR 95% CI OR 95% CI
Age 1.27 0.93, 1.74 1.36 1.00, 1.84
Education
Some college or two-year degree 0.43 0.13, 1.37 0.43 0.14, 1.36
Bachelor’s degree 1.94 0.40, 9.32 1.37 0.29, 2.95
Current region
South 1.23 0.29, 5.13 1.02 0.25, 4.14
West 0.78 0.18, 3.32 0.71 0.17, 2.95
Participated in HIV or STI program 5.32* 1.04, 27.20 6.47* 1.30, 32.13
Sex in last 30 days 0.60 0.24, 1.53 0.51 0.20, 1.34
Concurrent sexual partners 7.28 0.80, 66.52 6.94 0.78, 61.91
Sex with someone met online 1.45 0.52, 4.05 1.88 0.69, 5.15
Hormonal birth control methods 0.70 0.28, 1.80 0.80 0.32, 1.99
Race heterogeneity 1.40 0.53, 3.70 1.30 0.51, 3.36
Instrumental support from friend -- -- -- --
Talk about condom use in person 4.12** 1.40, 12.05 -- --
Talk about HIV testing on phone -- -- 1.96 0.76, 4.89
R
2
.35 .31
*p < .05. **p < .01.

Profiles of Young Black Women Based on Sexual Risk Factors and Sexual Health
Communication: A Latent Class Analysis
Measurements
Individual-level variables selected for the LCA models were sexual risk variables found
to be significantly associated with condom use, HIV testing, and interest in PrEP in the
univariable logistic regression models from the previous analysis section. Additionally, social
network-level variables selected for the latent class models were communication about sex via
text, phone and in person and communication about condom use, HIV testing, and STI testing
with family members, friends, or sex partners. These variables were also selected based on their
SOCIAL NETWORKS, SEXUAL HEALTH, AND HIV PREVENTION 75

associations with HIV testing and interest in PrEP in the univariable logistic regression models
from the previous analysis section.
Individual-level sexual risk and prevention behaviors. The following sexual risk
variables were assessed in a yes-or-no format (1 = yes, 2 = no) for this analysis: (a) condom use
during the prior 30 days, (b) concurrent sexual partners, (c) ever tested positive for an STI, (d)
using hormonal birth control methods, (e) sex with someone met online, and (f) ever attended an
HIV or AIDS education program.
Condom use, HIV testing, and interest in PrEP (1 = yes, 2 = no) were used as predictor
variables in the multivariable model, along with sociodemographic variables of age, level of
education, and region.
Social network variables. The following social network variables were used for the
latent class model using a yes-or-no format (1 = yes, 2 = no): (a) talk about sex via text, (b) talk
about sex on the phone, (c) talk about sex in-person, (d) talk about condom use with at least one
friend, (e) talk about condom with at least one family member, (f) talk about condom use with at
least one sex partner, (g) talk about STI testing with at least one friend, (h) talk about STI testing
with at least one family member, (i) talk to about STI testing at least one sex partner, (j) talk
about HIV testing with at least one friends, (k) talk about HIV testing with at least one family
member, and (l) talk about HIV testing with at least one sex partner.
Statistical Analysis
The purpose of performing LCA using variables is to identify subgroups of a population
(classes) that represent response patterns in data and provide a sense of prevalence of each
subgroup (L. M. Collins & Lanza, 2010). LCA provides estimates of class membership
(subgroup) probabilities (e.g., sexual risk classes) and behavioral probabilities estimates in each
SOCIAL NETWORKS, SEXUAL HEALTH, AND HIV PREVENTION 76

class (e.g., class-specific patterns; Connell, Gilreath, & Hansen, 2009). The goal of this analysis
was to identify subgroup profiles of YBW based on individual- and social network-level risk
factors and investigate HIV testing, condom use, and interest in PrEP as key indicators of those
subgroup profiles. To identify profiles of YBW and investigate HIV testing, condom use, and
interest in PrEP as key predictors of those subgroups, LCA models were ran using Proc LCA in
SAS 9.4.
To determine the appropriate number of classes for the sexual risk model, an initial series
of class models were run (e.g., two-class model, three-class model, four-class model). The initial
series started with a two-class model and was repeated until the six-class model was reached.
Selection of the optimal model was based on the Bayesian information criterion (BIC) and the
Akaike information criterion (AIC) fit statistics, as shown in Table 5.14. AIC is an estimate of
the relative distance between the unknown true likelihood function of the data plus a constant
and the fitted likelihood function of the model. Thus, a lower AIC means a model is considered
to be closer to the truth (Burnham & Anderson, 2004; Methodology Center, 2019b). “BIC is an
estimate of a function of the posterior probability of a model being true, under a certain Bayesian
setup, so that a lower BIC means that a model is considered to be more likely to be the true
model” (Methodology Center, 2019b). Based on the literature, there is no agreed upon standard
for the best fit criteria (Nylund, Asparouhov, & Muthén, 2007; Tein, Coxe, & Cham, 2013).
Typically, researchers use a combination of AIC and BIC, along with other fit criteria, to
determine the number of latent classes to be selected (Nylund et al., 2007). Information-theoretic
methods (e.g., AIC and BIC), likelihood ratio statistical test methods (e.g., G
2
), and the entropy-
based criterion are the most common methods used for deciding the number of classes for LCA
SOCIAL NETWORKS, SEXUAL HEALTH, AND HIV PREVENTION 77

models. For this study, AIC, BIC, and entropy were used to determine the appropriate number of
classes for this analysis.
Results
As shown in Table 5.14, four- and five-class unrestricted models provided the best
overall fit to the data for YBW sexual prevention behaviors. Based on Penn State University
methodology (the developers of the Proc LCA software and experts in LCA modeling), the only
time the AIC and BIC disagree is when the AIC indicates that a larger model is a better fit; in
this case, the interpretability of the model should be used when determining the best fit (L. M.
Collins & Lanza, 2010). Based on AIC, BIC, entropy, and model interpretation, the five-class
model was selected. After the selection of the five-class model, the model was re-run with
covariates as a multivariable multinomial logistic regression model. The membership classes
were regressed on condom use, HIV testing, interest in PrEP, education level, age, and current
region. Odds ratio estimates indicated the effect of the given covariate on the probability of class
membership relative to the reference class. Latent classes are mutually exclusive; thus, the sum
of the class prevalence is 100%, and each participant belongs to only one class (L. M. Collins &
Lanza, 2010).
Table 5.14. Fit Statistics for Latent Classes
Classes Likelihood ratio G
2
df AIC BIC Entropy
2 1,683.08 524,248 1,761.08 1,885.61 0.84
3 1,558.34 524,228 1,796.34 2,176.31 0.90
4 1,489.05 524,208 1,647.05 1,899.29 0.89
5 1,410.06 524,188 1,608.06 1,924.17 0.89
6 1,385.39 524,168 1,623.39 2,003.36 0.93
Results of this model in Table 5.14 were used to summarize conditional probabilities for
sexual health communication based on class membership (see Table 5.15). Class 1 accounted for
14.09% of YBW. Members of this class reported some HIV risk behaviors (e.g., withdrawal
SOCIAL NETWORKS, SEXUAL HEALTH, AND HIV PREVENTION 78

methods) and talking mainly to sex partners about sexual health topics. Class 2 accounted for
10.35% of YBW. Members of this class reported higher amounts of HIV risk behaviors (e.g.,
withdrawal methods and testing positive for STIs) and talking to both friends and family
members about sexual health topics. Class 3 accounted for 27.26% of YBW. Members of this
class reported high HIV risk behaviors (e.g., withdrawal methods and testing positive for STIs),
but also reported participating in HIV or STI prevention programs and speaking to friends,
family members, and sex partners about sexual health topics. Class 4 accounted for 29.51% of
YBW. Members of this class reported some risk behaviors (e.g., withdrawal methods) and
talking to friends and sex partners about sexual health topics. Last, Class 5 accounted for 18.79%
of YBW. Members of this class reported low sexual activity, meaning they had the lowest
probability of having sex in the last 30 days (7%), and talking only to friends about sexual health
topics.
Class 1: Some risk and communication with sex partner. Members of this class were
less likely to talk to family members or friends about condom use (13% and 0%, respectively),
HIV testing (0% and 0%, respectively), or STI testing (0% and 9%, respectively), but were more
likely to talk with sex partners about condom use (45%), HIV testing (27%) or STI testing
(46%). This group had an 88% probability of getting tested for STIs and a 10% probability of
testing positive for an STI. Compared to the other groups, Class 1 had the lowest probability of
having sex with someone they met online (18%) and the lowest probability of talking about sex
via phone (49%), text (57%), and in person (76%).
Class 2: High risk and no communication with sex partner. Members of this class
were more likely to talk with friends and family members about condom use (100% and 90%,
respectively), STI testing (100% and 100%, respectively), and HIV testing (93% and 72%,
SOCIAL NETWORKS, SEXUAL HEALTH, AND HIV PREVENTION 79

respectively), but less likely to talk with sex partners about condom use (25%), HIV testing
(0%), and STI testing (0%). Compared to the other groups, Class 2 had the highest probability of
getting tested for HIV (100%), being on hormonal birth control methods (67%), and having sex
with someone they met online (54%).
Class 3: High risk and high sexual health communication. Members of this class were
more likely to talk with friends, family members, and sex partners about condom use, HIV
testing, and STI testing. Regarding condom use, YBW were likely to talk with friends (88%), sex
partners (88%), and family members (85%). Regarding STI testing, YBW were likely to speak
with sex partners (92%), friends (87%), and family members (74%). Talking about HIV testing
had the lowest probabilities (friends: 77%; sex partners: 76%; family members: 58%). Class
members had the highest probability of having sex in the last 30 days (74%), testing positive for
an STI (29%), and participating in an HIV or STI program (30%).
Class 4: Some risk and low sexual health communication. Members of this class were
likely to talk with some friends and some but not many sex partners about STI testing (67% and
35%, respectively) and HIV testing (31% and 13%), but less likely to talk with family members
about HIV testing (0%) and STI testing (7%). Regarding condom use, YBW in this class had a
very high probability of talking to their friends (95%). Regarding sexual risk, YBW in this class
had the lowest probability of participating in an HIV or STI program (15%), the highest
probability of using the withdrawal method for pregnancy prevention (44%), and a high
probability of having sex in the prior 30 days (70%).
Class 5: Not sexually active and communication with friends. Members of this class
were more likely to talk with friends about condom use (100%), STI testing (67%), and HIV
testing (46%). They had a lower probability of talking with family members about condom use
SOCIAL NETWORKS, SEXUAL HEALTH, AND HIV PREVENTION 80

(46%), HIV testing (0%), and STI testing (3%) and with sex partners about condom use (12%),
HIV testing (0%), and STI testing (0%). They also had the lowest probability of having sex in
the last 30 days (7%), using hormonal birth control methods (28%), using the withdrawal method
for pregnancy prevention (5%), getting tested for STIs (51%), and testing positive for an STI
(0%). But they had the second highest probability of participating in an HIV or STI program
(20%) and having sex with someone they met online (53%) when they did have sex.
Table 5.15. Latent Class Analysis: Five-Class Model
Class 1 Class 2 Class 3 Class 4 Class 5
14.09% 10.35% 27.26% 29.51% 18.79%
Individual factors
Sex in last 30 days 0.54 0.31 0.74 0.70 0.07
Sex with someone met online 0.18 0.54 0.34 0.35 0.53
Hormonal birth control methods 0.40 0.67 0.59 0.59 0.28
Withdrawal method 0.37 0.22 0.26 0.44 0.05
Ever tested for STIs 0.88 1.00 0.88 0.58 0.51
Ever tested positive for an STI 0.10 0.27 0.29 0.12 0.00
Participated in HIV or STI program 0.16 0.19 0.30 0.15 0.20
Social network factors
Talk about sex
Text 0.57 0.88 0.96 0.96 0.70
Phone 0.49 0.84 0.96 1.00 0.67
In person 0.76 0.84 0.98 1.00 1.00
Talk about condom use
Friend 0.00 1.00 0.88 0.95 1.00
Family member 0.13 0.90 0.85 0.60 0.46
Sex partner 0.45 0.25 0.88 0.57 0.12
Talk about STI testing
Friend 0.09 1.00 0.87 0.67 0.67
Family member 0.00 1.00 0.74 0.07 0.03
Sex partner 0.46 0.00 0.92 0.35 0.00
Talk about HIV testing
Friend 0.00 0.93 0.77 0.31 0.46
Family member 0.00 0.72 0.58 0.00 0.00
Sex partner 0.27 0.00 0.76 0.13 0.00
Multivariable multinomial logistic regression model. In multinomial logistic
regression models, two types of contrast take place for each variable in the model, one for
independent variables (e.g., YBW who have been tested for HIV relative to YBW who have
SOCIAL NETWORKS, SEXUAL HEALTH, AND HIV PREVENTION 81

never been tested for HIV) and one for dependent variables (e.g., Class 2 compared to Class 3).
Thus, results were written to account for independent and dependent comparisons examined in
the multinomial logistic regression models. Condom use, HIV testing, and interest in PrEP, along
with demographic factors, were used to examine the association with class membership. Class 3
was used as the reference class in the multinomial multivariable logistic regression model.
The multinomial multivariable logistic regression model revealed that YBW in Class 1
had 86% decreased odds of being interested in PrEP than Class 3, compared to YBW who were
not interested in PrEP use (OR = 0.14; 95% CI = 0.04, 0.46). YBW who reported condom use
had 3.05 times the odds to be classified in Class 5 than Class 3 (95% CI = 1.33, 7.01), relative to
YBW who did not report condom use. YBW who had been tested for HIV had 90% decreased
odds of being classified in Class 4 than Class 5, compared to those who had not been tested for
HIV (OR = 0.10; 95% CI = 0.04, 0.25).
Education was associated with class membership. YBW who completed some college or
a two-year degree had decreased odds of being classified in Class 2 or Class 4 than Class 3,
compared to YBW who completed high school only (OR = 0.19; 95% CI = 0.06, 0.66 and OR =
0.29; 95% CI = 0.10, 0.86, respectively). YBW who had completed their bachelor’s degree had
decreased odds of being classified in Class 2 than Class 3, relative to YBW who had only
completed high school (OR = 0.12; 95% CI = 0.02, 0.60).
Only one significant relationship involved age and class membership. YBW who were 21
years old had 7.22 times the odds of being classified in Class 4 than Class 3, relative to those
who were 18 (95% CI = 1.13, 40.01). No significant relationships were found between current
region and membership in a class.
SOCIAL NETWORKS, SEXUAL HEALTH, AND HIV PREVENTION 82

Table 5.16. Five-Class Models with Predictor Variables

Class 1 Class 2 Class 4 Class 5
OR 95% CI OR 95% CI OR 95% CI OR 95% CI
Interest in PrEP 0.14* 0.04, 0.46 0.96 0.34, 2.67 1.01 0.49, 2.09 0.75 0.33, 1.74
Condom use 0.95 0.38, 2.37 0.83 0.30, 2.28 1.04 0.49, 2.19 3.05* 1.33, 7.01
HIV testing 1.89 0.47, 7.48 1.17 0.52, 4.34 0.10* 0.04, 0.25 0.43 0.14, 1.32
Education
Some college 1.17 0.24, 5.76 0.19* 0.06, 0.66 0.29* 0.10, 0.86 0.30 0.08, 1.07
Bachelor’s degree 2.02 0.41, 9.97 0.12* 0.02, 0.60 0.67 0.20, 2.20 1.13 0.30, 4.21
Age
19 0.23 0.00, 12.28 10.00 0.37, 267.46 1.24 0.20, 7.84 0.23 0.01, 9.26
20 0.29 0.01, 17.82 0.51 0.05, 4.73 1.59 0.23, 11.24 0.07 0.00, 2.76
21 0.28 0.01, 13.64 0.93 0.11, 7.61 7.22* 1.13, 40.01 0.56 0.02, 20.61
22 0.22 0.00, 11.75 0.93 0.10, 9.00 3.70 0.55, 25.16 0.32 0.01, 12.52
23 0.37 0.01, 19.61 13.20 0.47, 367.72 3.02 0.44, 20.64 0.41 0.01, 16.22
24 0.95 0.02, 58.70 6.46 0.55, 76.43 5.33 0.75, 37.90 0.75 0.02, 30.67
Region
Midwest 0.68 0.01, 39.58 0.07 0.00, 1.85 2.32 0.45, 11.89 1.12 0.15, 8.37
South 0.10 0.01, 1.71 0.26 0.01, 6.65 1.35 0.43, 4.40 0.76 0.19, 2.98
West 0.08 0.00, 1.41 0.19 0.01, 4.91 2.79 0.84, 9.20 0.96 0.25, 3.79
Note. Class 3 was the referent for class; high school was the referent for education; 18 was the referent for age; and
Northeast was the referent for region.
*p < .05.
SOCIAL NETWORKS, SEXUAL HEALTH, AND HIV PREVENTION 83

Chapter 6: Discussion
This dissertation is the first in-depth exploration of how YBW’s individual risk, social
network structures, social media use, specific types of SNM relationships (e.g., parents, partners,
friends), social support (e.g., emotional, instrumental, informational), and SNM communication
are associated with their HIV prevention behaviors (condom use, HIV testing, and interest in
PrEP). Three aims guided this study: (1) to explore how YBW’s social media use (e.g., time,
type, purpose) and sexual health communication are associated with condom use, HIV testing,
and interest in PrEP among YBW; (2) to investigate how social networks are associated with
HIV testing, condom use, and interest in PrEP use among YBW; and (3) to use LCA to identify
subgroup profiles of YBW based on individual- and social network-level risk factors and
investigate HIV testing, condom use, and interest in PrEP as key indicators of those subgroup
profiles. Several notable findings were revealed in this dissertation study. This chapter is divided
into four sections. The first three sections detail the findings regarding each of the study aims.
The fourth section discusses cross-cutting themes that emerged from these data, future research
ideas, and implementation.
A Discussion of the Associations between YBW ’s Social Media Use, Sexual Health
Communication, and Condom Use, HIV Testing, and Interest in PrEP
Findings from this exploratory work indicate that YBW were very active on social media,
with 95% of them speaking with at least one SNM via Instagram, Snapchat, Twitter, and
Facebook; this is consistent with most estimates (Lenhart, 2015; Stevens et al., 2017). Previous
research has indicated that youth and young adults who use social media tend to seek out and
encounter sexual content via social media (e.g., information about sex, sexual health, and sexual
norms) and use social networking sites to communicate about sex (Stevens, Dunaev, Malven,
SOCIAL NETWORKS, SEXUAL HEALTH, AND HIV PREVENTION 84

Bleakley, & Hull, 2016). However, based on an examination of the literature, very few studies
examined social media use and actual sex risk behaviors among young adults (Broaddus et al.,
2015; Stevens et al., 2016) and specifically among YBW. The goal of Aim 1 was to explore
associations between social media use, sexual health communication on social media, and HIV
prevention behaviors (i.e., condom use, HIV testing, and interest in PrEP) among YBW.
Results from this study show no direct relationships between using or talking about sex
on Facebook, Instagram, Snapchat or Twitter and condom use, HIV testing, and interest in PrEP.
One of the few studies examining social media use and sexual risk behaviors among BMSM
indicated that use of social media was associated with condomless sex (Broaddus et al., 2015).
Although direct links between use of and communication on specific social media platforms and
HIV prevention behaviors were not found, the results did indicate that YBW who reported
spending more than 4 hours per day on social media were associated with reporting more interest
in PrEP use and having condomless sex during their last sexual encounter. Broaddus and
colleagues (2015) found that BMSM who reported using social media spent an average of 34
hours per week on social media, which would equate to more than 4 hours a day. Although they
did not examine or control for time spent on social media in their HIV risk behaviors models,
their findings along with Aim 1 findings—spending more than 4 hours on social media was
associated with interest in PrEP and having condomless sex—indicate that it may not be the use
of social media per se that is associated with HIV risk and protective behaviors, but rather the
time spent on social media sites.
In examining the literature to try to understand this relationship between time spent on
social media and sexual risk behaviors, very little research about sexual risk and protective
behaviors was found. The literature did reveal associations between time spent social media and
SOCIAL NETWORKS, SEXUAL HEALTH, AND HIV PREVENTION 85

body image. Studies have shown that time spent on social media can lead to lower self-esteem,
lower appearance satisfaction, and other issues with body image among young women
(Fardouly, Pinkus, & Vartanian, 2017; Fardouly & Vartanian, 2016; Tiggemann & Zaccardo,
2015); this effect is stronger via social media than in person (Fardouly et al., 2017). Thus, based
on the literature and findings from Aim 1, it is possible YBW who spend more time on social
media are more likely to compare themselves to other YBW. Using the framework from
exchange theory (Cook et al., 2013), if a YBW sees herself as less valuable compared to other
YBW, she may be less likely to negotiate condom use with a sex partner (cost) to maintain her
relationship with that partner (benefit); she might also be more interested in PrEP, because it
provides an acceptable way to protect against HIV (Flash et al., 2014) without relying solely on
condom negotiation with a sex partner.
YBW who sought health information via social media were also interested in PrEP.
Social media has been shown to be an important source of HIV and STI prevention information
(Stevens et al., 2017), and research has indicated that social media platforms comprise digital
neighborhoods (e.g., Black Twitter, Instagram communities), where women post about and share
outcomes of sexual risk behaviors such as HIV, STIs, or pregnancy (Stevens, Bonett, Kenyatta,
Chittamuru, & Bleakley, 2019). It is possible that YBW who are seeking sexual health
information and spending more time on social media are members of digital neighborhoods
where they discuss sexual risk behaviors, which increases their interest in PrEP use. It is hard to
make conclusions based on these data alone without knowing to which communities YBW
belong or in what activities they participate on social media, but time spent on social media may
be an indicator of differences in for what purposes YBW are using social media or exposure to
sexual health information on social media platforms. Further research should assess differences
SOCIAL NETWORKS, SEXUAL HEALTH, AND HIV PREVENTION 86

in how YBW are using social media (e.g., information seeking, inspiration, partner seeking) and
types of digital neighborhoods to which YBW belong (e.g., Black Twitter, Instagram
communities).
A Discussion of the Associations of Social Network Characteristics and Dynamics and
Individual Risk Factors with Condom Use, HIV Testing, and Interest in PrEP
Aim 2 examined individual and social network factors associated with condom use, HIV
testing, and interest in PrEP. Social support from and sexual health communication with family
members, friends, and sex partners were examined at the social network level, and HIV risk
behaviors (e.g., exchange sex, concurrent partner, ever testing positive for an STI) and protective
behaviors (i.e., participating in an HIV or STI prevention program) were examined. Based on
previous literature (Barman-Adhakari, Craddock, Bowen, Das, & Rice, 2018; Cederbaum et al.,
2017; Craddock, 2019; Craddock et al., 2019; Craddock et al., 2016), it was hypothesized that
SNM social support (e.g., emotional and informational support), SNM relationship types (e.g.,
friend, family, sexual partner), and sexual health communication between YBW and SNMs (e.g.,
communication about condom use, HIV testing) would be significantly associated with HIV
testing, condom use, and interest in PrEP.
Not surprisingly, almost all YBW in this study spoke to at least one of their SNMs about
sex (98%). Most YBW spoke to at least one friend about sex and sexual experiences, and about
half spoke to at least one family member or sex partner. Talking to SNMs about sex and sexual
health-related topics has been shown in the literature to be associated with HIV risk and
protective behaviors (Barman-Adhakari et al., 2018; Cederbaum et al., 2017; Craddock, 2019;
Craddock et al., 2019; Craddock et al., 2016; Widman, Choukas-Bradley, Noar, Nesi, & Garrett,
2016). However, talking about sex was not associated with any HIV prevention behaviors
SOCIAL NETWORKS, SEXUAL HEALTH, AND HIV PREVENTION 87

examined (i.e., condom use, HIV testing, interest in PrEP) in this study, although communication
regarding sex was high. Thus, just talking about sex with a SNM was not an important factor
regarding YBW’s personal prevention behaviors. However, talking about sex with a SNM could
be seen as a gateway to talking about more sensitive sexual health topics (e.g., condom use and
HIV testing), and may be a good way to engage YBW in interventions geared toward HIV by
framing the intervention as a program focused on sex. CACM suggested using the phrase “sex
and relationships” to help break the ice for talking about HIV. Thus, although talking about sex
in general was not associated with HIV prevention behaviors, being about to talk about sex with
SNMs may open the door to talking about condom use and HIV testing with those SNMs.
Notably, 92% of YBW reported speaking to at least one SNM about condom use, even
though less than half of YBW reported using condoms during their last sexual encounter. This is
promising, because numerous HIV prevention interventions for Black women have focused on
increasing condom negotiation and promoting behaviors and attitudes that encourage effective
condom use (Chandler et al., 2016; Javier et al., 2018). About 78% of YBW reported that they
have spoken with at least one of their sex partners specifically about condom use, indicating that
condom use negotiation may be taking place between YBW in this sample and their sex partners,
even if the actual use of condoms remains low. This is consistent with findings from McLaurin-
Jones, Lashley, and Marshall (2016) study on condom use and condom use negotiation strategies
among Black college women. In their study, women described taking active and assertive roles
in condom use negotiation and discussions with their partners, but also reported inconsistent
condom use. McLaurin-Jones and colleagues (2017) also found that birth control was often
discussed during condom use negotiations.
SOCIAL NETWORKS, SEXUAL HEALTH, AND HIV PREVENTION 88

Use of hormonal birth control was significantly associated with condom use. Findings
revealed that YBW were significantly less likely to use a condom during their last sexual
encounter if they reported using hormonal birth control (e.g., pill, IUD, patch, Implanon). This
finding aligns with prior research that found YBW are more concerned about avoiding
pregnancy then preventing STIs and HIV (Anakaraonye, Mann, Annang Ingram, & Handerson,
2019). Thus, if YBW are using a hormonal birth control method, condoms may not be seen as
necessary. Prior work has suggested that the female partner often does not want to use condoms
when she is on birth control (McLaurin-Jones et al., 2016). It could be possible that condom use
negotiation may be harder when YBW are on birth control, for both partners (McLaurin-Jones et
al., 2017). For instance, partner A (male) may feel pressure from partner B (female) to not use a
condom if partner B (female) is on birth control, or vice versa. The literature also highlights that
people tend to decrease condom use once they enter a monogamous relationship or agree to be
monogamous (Flash et al., 2014; McLaurin-Jones et al., 2016; Nesoff, Dunkle, & Lang, 2016).
McLaurin-Jones et al. (2016) found that YBW who reported not using condoms were more likely
to also report being in a monogamous relationship. Additionally, Nesoff et al. (2016) found that
college women who reported main partners only were less likely to use condoms when compared
to those who reported only casual partners. Because this study did not examine whether YBW
were having sex with a monogamous partner, based on the literature, it could be possible that
YBW were talking about and negotiating condom use with their sex partners, and those who
agreed with their partners to be in a monogamous relationship also decided to use birth control
instead of condoms as the primary method of pregnancy prevention. Many studies examining
condom use negotiation and pregnancy prevention among YBW did not include their partner’s
perspectives (Bond, Frye, Cupid, Lucy, & Koblin, 2018; McLaurin-Jones et al., 2016; Nesoff et
SOCIAL NETWORKS, SEXUAL HEALTH, AND HIV PREVENTION 89

al., 2016). To better understand the context of these conversations about condom use with sex
partners, how these conversations occur, and how decisions regarding condom use are made,
qualitative studies examining condom use communication and decision making with YBW and
their partners are needed.
Talking with family members about STI testing and HIV testing were the only two social
network-level factors associated with getting tested for HIV. YBW were 4 times more likely to
get tested for HIV if they spoke about STI testing with a family member and 7 times more likely
to get tested for HIV if they spoke to a family member about HIV testing. These are large effect
sizes. This finding is supported by the literature, which has highlighted the impact of talking with
family about STIs or HIV on HIV prevention behaviors (Bouris et al., 2017; Craddock et al.,
2016; Fletcher et al., 2015). Fletcher et al. (2015) found that young Black college student whose
parents discussed relational sex with them were more likely to be sexually assertive, had greater
condom use self-efficacy, and felt better about their sexuality. Craddock et al. (2016) found that
Black homeless youth who reported speaking to a parent in their social network about sex were
more likely to be tested for HIV. Bouris and colleagues (2017), in a study assessing the
effectiveness of an intervention for young BMSM and the association of SNM social support on
outcomes, found that people who did not drop out of an HIV intervention tended to have a family
member as a support confidant, compared those who had a friend or sex partner as a support
confidant. Although these studies did not focus on YBW specifically, they highlight the
important role family members may play in HIV prevention behaviors among young Black
adults. Additionally, these findings also reveal that although YBW speak to their friends and sex
partners about STI and HIV testing at higher rates than with their family members, those
conversations do not have the same association with behavioral outcomes.
SOCIAL NETWORKS, SEXUAL HEALTH, AND HIV PREVENTION 90

Across all models, participating in an HIV or STI prevention program was significantly
associated with YBW’s likelihood of getting tested for HIV, after controlling for all other
demographic, sexual risk, and social network factors. The relationship between participating in
an HIV or STI prevention program and HIV testing is notable, because HIV testing is often a
sought-after outcome for many prevention programs (Diallo et al., 2010; Jiwatram-Negrón & El-
Bassel, 2014; Maulsby et al., 2013). This study provided evidence that participating in these
intervention programs is associated with higher odds of being tested for HIV. It could be that as
part of an HIV prevention program YBW were tested for HIV (Wingood et al., 2013), and thus
they were more likely to report having been tested for HIV. It is also possible that YBW are
getting tested for HIV as a result of participating in an HIV intervention (Diallo et al., 2010).
Because specifics about the HIV or STI prevention program content were not assessed and this
was a cross-sectional study, it is difficult to draw specific conclusions about the temporal
relationship between testing for HIV and participating in a prevention program.
Reporting sex with someone met online was the only significant relationship found
regarding interest in PrEP in Aim 2. Only about 37% of YBW reported having sex with someone
they met online, and although meeting a long-term or serious partner online (N. M. Hall, Lee, et
al., 2014; A. Smith & Anderson, 2016; Wiederhold, 2015) is not unusual, it can also be a way to
find casual partners and “hook-ups” (Bryant & Sheldon, 2017). The association between having
sex with someone met online and being interested in PrEP may indicate that YBW may view
their sexual behaviors as riskier, and thus PrEP may be a good way of protecting themselves
from the perceived risk of HIV. Sales and Sheth (2018) found that interest in PrEP among YBW
was associated with perceived behavioral risk of HIV and that YBW who perceived their HIV
risk level to be higher were more likely to be interested in PrEP than YBW who perceived their
SOCIAL NETWORKS, SEXUAL HEALTH, AND HIV PREVENTION 91

HIV risk levels to be lower. It is possible that these YBW may not know the HIV status of the
partners they met online in the past. Thus, PrEP may be a way to protect against HIV (Flash et
al., 2014), without involving their sex partners.
A large majority of YBW reported at least one SNM who provided them with emotional,
instrumental, or informational support, indicating social support was high in the social networks
of YBW. Previous research showed that social support is associated with communication about
sexual health and HIV-related topics (Cederbaum et al., 2017; Craddock, Rice, Cederbaum, &
Fulginiti, 2015; Craddock, 2019; Williams, Pichon, Davey-Rothwell, & Latkin, 2016). Craddock
(2019) found that YBW were significantly more likely to talk with an SNM about sex, condom
use, and HIV testing if that SNM was emotionally supportive. Thus, it was hypothesized that
SNM social support (e.g., emotional and informational support) would be significantly
associated with condom use, HIV testing, and interest in PrEP. However, the current study
indicated that the relationship between social support and sexual health communication did not
translate to behavioral outcomes (i.e., using condoms, getting tested for HIV, or interest in
PrEP), even when accounting for the type of person who provided support (i.e., friend, family
member, sex partner). In the literature regarding social support, social support is talked about as
a means of decreasing HIV-related stigma (Buzi, Weinman, Smith, Loudd, & Madanay, 2018;
Earnshaw, Bogart, Dovidio, & Williams, 2015; Williams et al., 2016), and not actual HIV-
related behaviors, which may be why no association was found between social support and HIV
prevention behaviors. Further qualitative research should examine the context of these
conversations with SNMs who provide social support to understand what is being discussed
during these conversations (e.g., HIV-related stigma) and explore YBW perceptions of the
impact of these conversations on their actual behavioral decisions.
SOCIAL NETWORKS, SEXUAL HEALTH, AND HIV PREVENTION 92

A Discussion of Young Black Women Profiles Based on Sexual Risk Factors and Sexual
Health Communication
No identified studies have examined varying profiles of YBW based on HIV protective
and risk behaviors. As such, Aim 3 sought to identify classes of YBW based on individual- and
social network-level HIV-related behaviors. Five distinctive classes of YBW were found in this
sample of YBW. The examination of each of the profiles provided a nuanced understanding of
various factors associated with HIV prevention and risk behaviors among YBW. Results
indicated that based on class membership, HIV intervention prevention methods and training in
communication skill may need to be tailored to the specific needs of women in each class,
because each profile had various risk and sexual health communication behaviors.
Classes with High Probability of Sexual Activity
Three classes of YBW had a higher probability of reporting having sex in the prior 30
days (i.e., Class 1, Class 3, and Class 4). Due to their sexually active status, these three classes
would be typical targets for HIV prevention programs (Brawner, Jemmott, Wingood, Lozano, &
Hanlon, 2019; Montgomery et al., 2018; Wingood et al., 2013). Although members of these
classes were generally sexually active, there are meaningful differences among the classes
regarding risk behaviors, previous participation in HIV or STI prevention programs, and sexual
health communication that should be considered when developing effective HIV interventions
targeting YBW. Compared to all other classes, Class 3 had the highest probability of
participating in an HIV or STI prevention program, and members tended to talk with all SNM
types assessed (i.e., friends, family members, and sex partners) about all sexual health topics
examined (i.e., condom use, HIV testing, and STI testing). Classes 1 and 4 had the lowest
probability of participating in an HIV or STI prevention program, with Class 1 tending to speak
SOCIAL NETWORKS, SEXUAL HEALTH, AND HIV PREVENTION 93

only with sex partners about HIV prevention behaviors, whereas Class 4 had very low to no
communication with SNMs. However, when these YBW in Class 4 did talk, they tended to speak
with their friends. Interpersonal communication is a key component in many HIV prevention
interventions, with research indicating that interpersonal communication skills are an HIV
intervention outcome with lasting effects (Covey, Rosenthal-Stott, & Howell, 2016; Johnson et
al., 2009). Based on prior studies, it is possible that these sexual health communication
differences found between Class 3 and Classes 1 and 4 may imply that YBW in Class 3 feel less
stigma (Bond et al., 2018) and more interpersonal communication efficacy around talking about
HIV-related topics because of their previous participation in HIV prevention programs (Covey et
al., 2016; Johnson et al., 2009), compared to other classes in which less sexual health
communication is taking place.
Additionally, Class 3 had the highest probability of ever testing positive for a STI
compared to all other classes. Thus, according to the USPHS, these YBW are at higher risk of
HIV compared to the other classes (CDC–USPHS, 2018; Sales & Sheth, 2018). The USPHS has
suggested that PrEP would be appropriate for these YBW, based on their history of testing
positive for STIs (CDC–USPHS, 2018). Notably, YBW in Class 3 were more likely to be
interested in PrEP compared to YBW in Class 1. Interest in PrEP has been shown to be related to
perceived risk of HIV (Sales & Sheth, 2018). Class 1 and Class 4 had relatively low probabilities
of testing positive for STIs, which may lower their perceived risk of HIV and explain Class 1’s
lack of interest in PrEP. Additionally, members of Classes 1 and 4 may not feel the need to
consult or speak with all their SNMs about HIV prevention if they feel they are not at high risk
of HIV. Class 3 also had a higher odds of being tested for HIV compared to Class 4. Aim 2
analyses revealed that YBW who participated in HIV prevention programs were more likely to
SOCIAL NETWORKS, SEXUAL HEALTH, AND HIV PREVENTION 94

be tested for HIV; thus, these findings offer further support for the relationship between
participating in an HIV prevention program and ever being tested for HIV. Because this was a
cross sectional study, it could not be determined if Class 3’s risk behaviors preceded their
participation in an HIV prevention program or came after participating in the HIV prevention
program.
Class 4 had the highest probability of reporting using the withdrawal method for
pregnancy prevention, followed by Class 1 and Class 3. Both Class 1 and Class 4 were classified
as having some risk, because they had a lower probability of testing positive for an STI in the
past, although they tended to use the withdrawal method for pregnancy prevention. Research has
indicated that using withdrawal methods and condoms are not mutually exclusive (R. K. Jones,
Lindberg, & Higgins, 2014); thus, YBW may be using both condoms and withdrawal methods
with their partners, potentially decreasing their risk of HIV. R. K. Jones and colleagues (2014)
found in their sample of 3,276 women aged 18 to 39 that 41% of young women aged 18–24 used
withdrawal methods, but only 10% solely relied on this method. They found that 34% of women
who reported using withdrawal also reported using condoms, with 38% reporting using them at
the same time and 42% switching between the methods (R. K. Jones et al., 2014). Additionally,
literature regarding withdrawal methods has indicated that relationship context (e.g., being
monogamous and increased intimacy) is an important factor influencing why young women and
their partners used withdrawal methods (Arteaga & Gomez, 2016). Because YBW in Classes 1
and 4 had a high probability of having sex in the prior 30 days, it is possible that these YBW
may be in relationships that they consider monogamous, accounting for their high probability of
using the withdrawal method. However, a study by Towner, Dolcini, and Harper (2015) found
SOCIAL NETWORKS, SEXUAL HEALTH, AND HIV PREVENTION 95

that perceptions of relationship status (monogamous vs. open) differ between female and male
partners; thus, these YBW may still be at risk of HIV even if they perceived their risk as low.
Classes with Low Probability of Sexual Activity
Classes 2 and 5 differed from the other three more sexually active classes. The biggest
difference between these two classes of YBW (Classes 2 and 5) and the other classes is not only
the lower probability of having sex in the prior 30 days, but a higher probability of having sex
with someone met online. Although meeting and dating people using online platforms has
become a common way of meeting romantic partners (N. M. Hall, Peterson, & Johnson, 2014; A.
Smith & Anderson, 2016; Wiederhold, 2015), as discussed in relation to Aim 2, having sex with
someone met online can also be an indicator of risk behaviors (Bryant & Sheldon, 2017).
Because this study did not examine if these sex partners met online were causal partners or
partners with whom these YBW were in a serious or monogamous relationship, it is difficult to
argue that just because they are having sex with someone they met online, these YBW are
participating in risky sexual behaviors, hence their low-risk classification. However, regarding
HIV interventions, it be important to know how YBW are meeting their sexual partners and if
there are differences in risk behaviors (e.g., communication, condom use negation, relationship
expectations) between sexual partners met online versus sexual partners met through other
methods (e.g., bars, friends, school).
Although Classes 2 and 5 share some similarities in sexual behaviors, there are notable
differences between the two classes that would be important for intervention development. One
difference is that all members of Class 2 had been tested for STIs (100%), whereas Class 5 had
the lowest probability of ever being tested for STIs. Additionally, Class 2 and Class 5 also
differed in to whom they speak regarding sexual health communication. YBW in Class 2 were
SOCIAL NETWORKS, SEXUAL HEALTH, AND HIV PREVENTION 96

more likely to talk with friends and family members, whereas Class 5 members tended to speak
only with their friends. This may be due to the fact that YBW in Class 5 had a low probability of
having sex in the prior 30 days; thus, they may not talk to sexual partners about HIV prevention
behaviors because they may not be in a current serious or monogamous relationship. However,
even if YBW from Classes 2 and 5 are not in a current relationship, testing for STI and HIV is
important to preventing HIV. Class 2 findings regarding testing behaviors and low probability of
testing positive for STIs indicate that their HIV intervention needs are different than YBW in
Class 5. YBW in Class 5 likely need an HIV intervention that encourages and provides them
with the knowledge (e.g., testing locations, why testing is important) to get tested for STIs, along
with testing for HIV. Additionally, like Classes 1 and 4, members of Classes 2 and 5 need
training in interpersonal communication regarding sexual health communication with SNMs;
Class 2 particularly needs additional training on condom use negation with potential sex partners,
whereas YBW in Class 5 were more likely to report condom use during their last sexual activity.
YBW have been identified as a hidden face of the HIV epidemic, with both shared and
unique factors that place them at risk of HIV (Pittman, Kaur, & Eyler, 2019). One of the
challenges for providers when identifying women at substantial risk of HIV infection is that
many women do not report any behavioral risk factors for HIV, even those who tested positive
for an STI (Sales & Sheth, 2018; Sales et al., 2018). Results for these five classes of YBW
present similar issues in determining HIV risk levels for each class. Instead of assessing HIV risk
levels to determine who would most benefit from HIV prevention programs, these findings
indicate that all YBW would benefit from HIV prevention programs. However, interventions
should be tailored to each profile of YBW to help increase and strengthen their HIV prevention
skills (Bauermeister et al., 2015).
SOCIAL NETWORKS, SEXUAL HEALTH, AND HIV PREVENTION 97

For instance, Class 2 and Class 5 would benefit from an intervention focused on talking
about sexual health with sex partners and staying safe while having sex online. Where these two
groups differ in intervention needs is that Class 5 needs an emphasis on getting tested for both
STIs and HIV, as its members had the lowest probability of testing, whereas Class 2 had the
highest. Class 2 needs specific training in condom use and condom use negations compared to
Class 5, whose members were 3 times more likely to report condom use during last sexual
activity. Class 1 and Class 4 would benefit from an intervention that emphasizes interpersonal
communication about sexual health topics with SNMs, because both classes had relatively low
sexual health communication with SNMs. Additionally, both classes of YBW would benefit
from discussions of pregnancy prevention along with HIV prevention methods, because both had
a higher probability of using withdrawal methods for pregnancy prevention. These classes’
intervention training needs differ regarding information needed around testing for STIs. YBW in
Class 3 would possibly be great peer leaders in a peer-led social network-based intervention,
because they seemed most comfortable talking to SNMs about HIV prevention topics (condom
use and STI and HIV testing), and they already participated in HIV prevention programs.
It is important to consider the variations in groups of YBW for HIV prevention, because
training needs may differ and the relevance of an intervention to the target population is
important. Without assessing subgroup differences, an highly effective intervention may not be
effective for a seemingly similar population. Future studies should further assess how
intervention research can explore class membership profiles before implementing an
intervention.
SOCIAL NETWORKS, SEXUAL HEALTH, AND HIV PREVENTION 98

A Comprehensive Discussion
Overall Findings: A Recap from Aims 1, 2, and 3
Considering the three aims together further reveals connections among social media use,
sexual risk behaviors, social network dynamics, and HIV prevention behaviors. First,
associations emerged among time spent on social media (Aim 1), having sex with someone met
online (Aim 2), and interest in PrEP. Although this study did not examine online partner-seeking
behaviors, Broaddus et al. (2015) found that BMSM who sought sex partners via social media
spent more time online. Thus, examining time spent on social media, seeking partners online,
having sex with partners met online, and interest in PrEP is needed.
In line with these findings, two classes of YBW had the highest probability of having sex
with someone they met online (Aim 3), low probabilities of speaking with sex partners about
condom use, and did not talk with sex partners about STI and HIV testing. These results indicate
that YBW who spend more time online (Aim 1) are seeking partners online and having sex with
those partners (Aim 2), yet may not feel empowered to speak with their sex partners about
condom use and STI and HIV testing (Aim 3), hence their interest in PrEP. These two classes
unexpectedly diverged from the previous findings, and their members were not more or less
likely to be interested in PrEP compared to the high risk and high sexual health communication
class, which had a lower probability of having sex with someone met online (Aim 3). A possible
important factor not included in the LCA models was seeking health information via social
media (Aim 1). YBW were also significantly more likely to report being interested in PrEP if
they sought health information via social media (Aim 1); thus, these relationships with social
media use, health seeking via social media (Aim 1), and having sex with someone met online
SOCIAL NETWORKS, SEXUAL HEALTH, AND HIV PREVENTION 99

(Aim 2) should be examined in more detail to understand the nuances between these factors and
interest in PrEP.
Moreover, YBW were significantly more likely to have been tested for HIV if they
participated in an HIV or STI prevention program and if they spoke to their family members
about STI and HIV testing (Aim 2). The LCA models agreed with these results (Aim 3),
indicating that YBW who belonged to the class with the highest probability of participating in
HIV prevention programs also had a higher probability of talking to a wide spectrum of network
members and were more likely to get tested for HIV than Class 4. This suggests that talking with
SNMs about HIV prevention topics and participating in an HIV intervention program is highly
associated with getting tested for HIV. Longitudinal research following participation in an HIV
prevention program should be conducted to gain an understanding of how HIV testing and
communication with SNMs about HIV prevention topics continue over time.
Limitations
Although this dissertation study revealed notable findings, there are several limitations to
be considered. This study was cross-sectional, which limited the ability to make causal links
between HIV risk and protective factors. Data were self-reported, which may increase social
desirability bias. This sample cannot be considered a representative sample of the United States,
because a majority of participants were currently living in the South and West regions of the
country. Additionally, although this study was respondent driven, YBW self-selected to
participate in the study, and difference in behaviors between participants and nonparticipants
cannot be assessed.
SOCIAL NETWORKS, SEXUAL HEALTH, AND HIV PREVENTION 100

Future Survey Research
This study indicates several future research survey question ideas. Future research should
consider asking more specifically about YBW’s sexual networks. Instead of only asking YBW to
list up to 20 people to whom they have spoken in the last 30 days, more detailed information that
provides more insight into risk engagement could be collected if, for example, YBW were asked
about their last three sexual partners. This would explicate different relationship dynamics
among YBW and their sexual partners, and any partner concurrency that may occur.
Additionally, specific questions regarding sex partners YBW met online should be asked (e.g., if
they were hook-up, casual, or romantic partners, where and how they met online, who contacted
whom, and the duration of the relationship). These questions would assist with assessing the risk
level of having sex with someone met online. Another question to consider asking in the future
would be about topics covered in HIV prevention programs. By assessing these topics, we may
be able to better dissect which components are the most effective for prolonged engagement in
HIV testing and communication regarding HIV prevention behaviors. Future research should
also inquire about when YBW participated in a prevention program to assess if duration since
participating in the program is associated with HIV testing behaviors. Understanding how YBW
learned about PrEP and if and with whom YBW spoke about PrEP would extend the limited
literature exploring interest in PrEP use among heterosexual Black women in the United States.
Additionally, further examination of perceived ability to use and talk about condom use with
partners, and whether YBW want to use condoms with sex partners, should be further examined.
Implications and Conclusion
Findings from this study support previous research that has underscored the importance
of sexual health communication among at-risk populations and their SNMs (Barman-Adhakari et
SOCIAL NETWORKS, SEXUAL HEALTH, AND HIV PREVENTION 101

al., 2018; Cederbaum et al., 2017; Craddock, 2019; Craddock et al., 2019; Craddock et al., 2016;
Widman et al., 2016). Aim 1 analyses found that social media is not a risk factor for HIV
acquisition, but it is a mode through which communication about HIV risk and protective
behaviors occur (Craddock, 2018). A large majority of the sample spoke to their SNMs on at
least one form of social media, with Instagram being used the most to communicate with SNMs.
Additionally, a large majority of YBW spoke with their SNMs about sex, with most of them
speaking with at least one SNM in person, followed by text message. By assessing the channels
of communication YBW are using to speak with their SNMs, we can better determine the most
optimal, efficient, and effective ways of disseminating HIV prevention information to YBW and
their SNMs. Outside of communication channels, there were significant relationships found in
Aim 2 analyses regarding sexual health communication among YBW and their SNMs that
should be considered when developing and adapting HIV intervention programs in the future.
These results revealed that YBW were about 7 times more likely to get tested for HIV if they had
spoken about HIV testing with a family member or participated in an HIV prevention program.
This further highlights the importance of HIV prevention communication (e.g., condom use, STI
testing, and HIV testing) with family members (Widman et al., 2016) and the possible value of
including family members in HIV prevention interventions geared toward YBW. The subgroup
with a higher probability of talking about HIV prevention topics with friends, family members,
and sex partners also had the highest probability of participating in an HIV prevention program
and was more likely to get tested for HIV and be interested in PrEP.
Recently, the USPHS suggested that PrEP is appropriate for HIV-negative heterosexual
women who have condomless sex, recently acquired an STI, have a high number of sex partners,
participate in exchange sex, or live in high HIV burden areas (CDC–USPHS, 2018; Sales &
SOCIAL NETWORKS, SEXUAL HEALTH, AND HIV PREVENTION 102

Sheth, 2018). However, only about 51% of YBW in this study were aware of PrEP before this
study, and about 36% of YBW were interested in PrEP use. Thus, HIV interventions focused on
increasing effective HIV risk communication among women and their SNMs regarding PrEP and
developing women-centered and women-inclusive PrEP awareness campaigns are needed to
improve knowledge of PrEP among YBW (Bradley & Hoover 2019; Sales et al., 2018).
This study indicated YBW have unique HIV risk factors that are often overlooked in the
literature because they do not fit the model of traditional risk indicators (e.g., high drug use, low
income, sex work; Pittman et al., 2019). Findings from this study have the potential to improve
interventions geared toward YBW, by highlighting the need for population-tailored HIV
interventions. Tailoring an intervention toward YBW participating in an HIV intervention study
may improve the overall effectiveness of the intervention (Bauermeister et al., 2015; Hawkins,
Kreuter, Resnicow, Fishbein, & Dijkstra, 2008; Kreuter, Strecher, & Glassman, 1999), and
involves using individual data to customize the content of an intervention based on variations in
individual risk profiles (Bauermeister et al., 2015). Future research should examine how best to
tailor HIV interventions to meet the needs of YBW by addressing content that is relevant to their
risk (e.g., behaviors and communication) and how to use the most optimal communication
platforms to further disseminate HIV prevention information in YBW’s social networks.

SOCIAL NETWORKS, SEXUAL HEALTH, AND HIV PREVENTION 103

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Asset Metadata

Creator Craddock, Jaih B. (author)

Core Title Sex talks: an examination of young Black women's social networks, sexual health communication, and HIV prevention behaviors

School Suzanne Dworak-Peck School of Social Work

Degree Master of Social Work / Doctor of Philosophy

Degree Program Social Work

Publication Date 07/12/2019

Defense Date 05/09/2019

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

Tag black women,HIV prevention,HIV risk behaviors,OAI-PMH Harvest,sexual health communication,social networks

Format application/pdf (imt)

Language English

Contributor Electronically uploaded by the author (provenance)

Advisor Rice, Eric (committee chair), Bluthenthal, Ricky (committee member), Cederbaum, Julie (committee member)

Creator Email Jaih.craddock@ssw.umaryland.edu,jaihcraddock@gmail.com

Permanent Link (DOI) https://doi.org/10.25549/usctheses-c89-182327

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Dmrecord 182327

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