THE MICRO-SOCIAL RISK ENVIRONMENT FOR INJECTION DRUG USE: AN EVENT SPECIFIC
MULTILEVEL ANALYSIS OF INJECTION RISK BEHAVIOR

By
Patrick Janulis

A DISSERTATION
Submitted to
Michigan State University
in partial fulfillment of the requirements
for the degree of
Psychology - Doctor of Philosophy
2014

ABSTRACT
THE MICRO-SOCIAL RISK ENVIRONMENT FOR INJECTION DRUG USE: AN EVENT SPECIFIC
MULTILEVEL ANALYSIS OF INJECTION RISK BEHAVIOR

By
Patrick Janulis
Injection drug use continues to contribute to new incidence of HIV and remains the
primary risk factor for hepatitis C virus in the United States. Accordingly, understanding the
social processes associated with injection risk behavior remains an important goal for public
health research in effort to inform interventions to reduce the frequency these behaviors.
However, previous research has largely focused on who is most likely to engage in injection risk
behavior rather than examining when, where, and with whom individuals may be at heighted risk.
The current study uses event specific data from the Sexual Acquisition and Transmission of HIV
Cooperative Agreement to examine dyadic, network, and situational characteristics associated
with injection risk behavior. Data on multiple observations nested within participants (participant
n = 784, injection episodes n = 1778) is used to examine both within and between person
variation in injection risk behavior via multilevel structural equation modeling. Results are
interpreted using Tseng and Seidman's (2007) theory of social settings.
Results indicated that injection risk behavior was lower when injecting with new
partners. While having an injection partner that is also a sexual partner was associated with
greater risk for both males and females, sexual partnership was significantly more positively
associated with injection risk for females as compared to males. Furthermore, females were at
greater risk when injecting with other females but the gender of their injection partner was not
associated with any difference in risk among males. For network characteristics, the number of
injectors in the participant's network was not significantly associated with risk behavior. Finally,

for situational characteristics no significant relationship with injection risk behavior was found
for the location of the injection episode (e.g., if the participant injected at home) but injection
risk behavior was higher when more non-injectors were present during the injection episode.
These results suggest that differences in social norms or resource availability may create
unique risk factors for female injectors as compared to males. Future studies could provide
further insight by explicitly measuring mediating social setting variables such as the availability
and control of injection resources (e.g., syringes or drugs) as well as setting level norms.
Furthermore, the results indicate that intervention and evaluations studies should continue to
develop HIV/HCV preventive interventions tailored toward sexual partners and explore the
potential for gender specific programming. While the current study provides initial insight into a
more complex view of injection risk behavior and associated dyadic, network, and situational
variables, significant within-person variability persisted after including all model variables. This
suggests that additional dyadic and situational characteristics must be identified to better predict
this unexplained variation in injection risk behavior across injection episodes. Accordingly,
future work is required to develop a more thorough understanding of social setting mechanisms
that may enhance protective behaviors and inhibit risk behavior within injection settings.

ACKNOWLEDGMENTS

As a researcher who focuses on the social context of behavior, it is abundantly clear to
me that this work was only completed due to the mentorship, guidance, patience, and support of
many individuals prior to and during my graduate studies. I am incredibly indebted to Dr.
Benjamin Hidalgo who first introduced me to community psychology and provided invaluable
advice in the early years of my graduate career. Similarly, I would certainly not be on my current
career trajectory without the mentorship provided by Dr. Greg Scott and all those who I worked
with at DePaul University's Social Science Research Center. I would also like to say thanks to
Dr. Robin L. Miller for providing many opportunities to expand my knowledge, experience, and
expertise in HIV research and Dr. James C. Anthony for introducing me to the field of
epidemiology and providing exceptional mentorship in the transition from my role as a graduate
student into an early career scholar. Finally, I am incredibly appreciative to Dr. Jennifer Watling
Neal for her flexibility in allowing me to pursue my substantive interests, eagerness to pass on
her insight and enthusiasms for social network analysis, and unwavering support for me and my
fellow graduate students.
I must also thank my family for their assistance throughout my postgraduate years. Of
particular noteworthiness, to my parents who have facilitated and encouraged my intellectual
curiosity for many years and, of course, to my partner Molly who has been instrumental to my
success through her loving support. Molly has shown endless patience during these busy and
stressful years, she has continued to encourage me to pursue my interests despite their
inconvenience to her (thanks for moving to Lansing with me!), and she has provided much

iv

needed insight and perspective that has frequently liberated me from the tunnel vision of
graduate school.

v

TABLE OF CONTENTS

LIST OF TABLES .................................................................................................................. viii
LIST OF FIGURES .................................................................................................................. ix
Introduction ................................................................................................................................1
Literature Review ........................................................................................................................5
Current Trends in HIV among IDUs .......................................................................................5
Correlates of HIV Serostatus and HIV Seroconversion. ..........................................................6
Current Trends in HCV among IDUs ......................................................................................9
Correlates of HCV and HCV seroconversion. ....................................................................... 11
Co-infection of HIV and HCV .............................................................................................. 12
Injection Risk Behavior ........................................................................................................ 13
Individual Correlates of Injection Risk Behavior. ................................................................. 14
Dyadic Correlates of Injection Risk Behavior. ...................................................................... 18
Network Correlates of Injection Risk Behavior. .................................................................... 20
Environmental and Situational Correlates of Injection Risk Behavior. .................................. 24
Interventions to Reduce Injection Risk Behavior .................................................................. 27
Limitations of Past Research................................................................................................. 30
The Social Ecology and Micro-Social Risk Environment of Injection Risk Behavior ............ 35
Social Settings Theory. ......................................................................................................... 37
The Current Study ................................................................................................................ 40
Dyadic Predictors. ................................................................................................................ 41
Network Predictors. .............................................................................................................. 43
Environmental Predictors. .................................................................................................... 43
Current Study Contributions. ................................................................................................ 45
Method ..................................................................................................................................... 55
Recruitment/Sampling .......................................................................................................... 55
Inclusion Criteria .................................................................................................................. 58
Measures .............................................................................................................................. 58
Level 2 (Individual) Measures .............................................................................................. 59
Demographics ................................................................................................................... 59
Drug Use ........................................................................................................................... 60
Network Characteristics ..................................................................................................... 60
Level 1 (Injection Episodes) Measures ................................................................................. 61
Situational Characteristics ................................................................................................. 61
Dyadic Characteristics ....................................................................................................... 62
Injection Risk Behavior ..................................................................................................... 63
Analytic Approach ............................................................................................................... 64
Multilevel Structural Equation Modeling .............................................................................. 65
Measurement Model. ......................................................................................................... 65
Model Fit........................................................................................................................... 66

vi

Hypothesis............................................................................................................................ 67
Results ...................................................................................................................................... 68
Missing Data ........................................................................................................................ 68
Preliminary Analysis ............................................................................................................ 71
Model Comparison ............................................................................................................... 73
Hypothesis............................................................................................................................ 74
Hypothesis 1 ......................................................................................................................... 75
Hypotheses 2 and 3 ............................................................................................................... 79
Other Covariates ................................................................................................................... 79
Discussion................................................................................................................................. 80
Dyadic Predictors ................................................................................................................. 80
Network Predictors ............................................................................................................... 83
Environmental Predictors ..................................................................................................... 84
Limitations ........................................................................................................................... 87
Future Directions .................................................................................................................. 89
Implications for HIV/HCV Prevention Interventions ............................................................ 91
Conclusion ................................................................................................................................ 94
APPENDICES .......................................................................................................................... 95
Appendix A. Questionnaire .................................................................................................. 96
Appendix B. Modeling Steps .............................................................................................. 101
Appendix C. Comparison of Cases with Missing Data on All Dependent Variables ............ 102
Appendix D. Association with Missing on any independent variable .................................. 103
Appendix E. Four Factor Model ......................................................................................... 104
Appendix F. Model fit using WLSMV Estimator ................................................................ 105
Appendix G. Null (Intercept Only) Model .......................................................................... 107
REFERENCES ....................................................................................................................... 108

vii

LIST OF TABLES

Table 1. Hypothesis and Tseng and Seidman's (2007) theory of social settings .......................... 50
Table 2. Demographics of Non-IDU vs. IDU Sample ................................................................ 59
Table 3. Comparison of Cases with any Missing Data on Independent Variables ....................... 70
Table 4. Kendall's Tau Correlation Coefficient for Injection Risk Behavior ............................... 72
Table 5. Factor loadings and Model Fit for Confirmatory Factor Analysis ................................. 73
Table 6. Model Parameters for Models 1 through 5 ................................................................... 76
Table 7. Modeling Steps and Freed Parameters ....................................................................... 101
Table 8. Comparison of Cases with Missing data on All Dependent Variables......................... 102
Table 9. Association with Missing on any independent variable .............................................. 103
Table 10. Model fit using WLSMV Estimator ......................................................................... 105
Table 11. Null Model .............................................................................................................. 107

viii

LIST OF FIGURES

Figure 1. Theoretical Path for Change in Social Settings ........................................................... 39
Figure 2. Model Path Diagram .................................................................................................. 47
Figure 3. Model with Modeling Steps...................................................................................... 101
Figure 4. Four Factor Model.................................................................................................... 104

ix

Introduction
Most internationally regulated drugs have multiple modes of administration (e.g.,
intranasal vs. intravenous) and the mode of administration is strongly associated with the
negative health effects of using these drugs such as risk for developing drug use disorders
(Gossop, Griffiths, Powis, & Strang, 1992) and the spread of disease associated with the these
drugs (Latkin, Knowlton, & Sherman, 2001). Of primary interest in the current study is the
elevated viral transmission risk caused by the injection of drugs for extra-medical purposes (e.g.,
to get "high"). For example, injection drug use was associated with 11% of new HIV infections
in 2011 within the United States (CDC, 2013). Given that 1% of the US population is estimated
to use injection drugs each year (Brady et al., 2008; Mathers et al., 2008; USDHHS, 2013b), this
suggests that injection drug users (IDUs)1 continue to contribute disproportionally to the new
incidence of HIV in the United States. Furthermore, IDUs remain at high risk for contracting
hepatitis C virus (HCV). For example, prevalence of HCV among IDUs in the United States was
estimated at 73.4% in 2004 (Nelson et al., 2011) and injection drug use continues to be the
greatest risk factor for contracting HCV (Alter, 2007).
Both HIV and HCV can be spread through the sharing and reuse of needles, syringes, and
other non-syringe injection paraphernalia including cookers, cottons, and rinse water (Gillies et
al., 2010; Mathei et al., 2006; J. Page et al., 2006). These behaviors spread HIV/HCV when
blood residue from a first injector is left on these objects and is subsequently exposed to an open
wound of another injector. While syringe sharing has been recognized as an HIV/HCV risk
behavior since the early days of the US HIV epidemic (Zibbell, 2012), non-syringe related

1

The use of "IDUs" in this document will refer exclusively to individuals that are injecting for non-medical drug
use, such as to get "high", and will not include individuals engage in self-injections for medical purposes such as
insulin injections.

1

paraphernalia sharing has increasingly been the focus of viral transmission, particularly for HCV
(Gillies et al., 2010; Mathei et al., 2006; Needle et al., 1998; J. Page et al., 2006). Despite
progress, many of these risk behaviors continue to be common among IDUs (Thiede et al., 2007;
USDHHS, 2013a). For example, 21.8% of IDUs reported using a syringe previously used by
another IDU during their most recent injection episode in 2012 (USDHHS, 2013a). Accordingly,
research to understand these HIV/HCV risk behaviors and interventions to prevent the spread of
these diseases among IDUs remains an urgent public health concern.
While the research examining injection risk behavior continues to focus on studies
examining individual level characteristics (e.g., age, length of injecting, gender; Rhodes, 2009), a
growing number of studies have begun to document the environmental correlates of risk
behavior such as neighborhoods, networks, and norms (Latkin, German, et al., 2013; Rhodes,
2009). Given that any single IDU may inject in a variety of circumstances over time (e.g., with
difference partners or at different physical locations), the circumstances of specific injection
episodes may increase or decrease the level of injection risk behavior. However, few studies
have examined the characteristics of settings (e.g., physical or social) for specific injection
episodes and how these characteristics may impact injection risk behavior (Latkin, German, et
al., 2013). Accordingly, understanding the situational and social circumstances of specific
injection episodes will move analysis beyond identifying who is at heighted risk to also examine
when, where, and with whom they are at risk. Those studies that have examined situational
factors associated with injection risk behavior have largely been unable to directly compare the
relative impact of individual and environmental factors because most studies either collect data
on a single injection episode or measure individual's generalized risk behavior (e.g., risk
behavior during the last 30 days) rather than risk behavior associated with a specific injection

2

episodes. Therefore, these studies have not compared risk behavior by participants in multiple
settings and/or with different injection partners.
Accordingly, this study attempts to further unpack the "micro-social" risk environment of
injection drug use (Latkin & Knowlton, 2005) by leveraging data on multiple injection episodes
nested within individuals. By analyzing this data using multilevel structural equation modeling,
this study will provide novel insight toward the associations of individual and situational
characteristics on injection risk behavior. Furthermore, this study will utilize social setting theory
(Tseng & Seidman, 2007) in attempt to model and interpret the micro-social risk environment in
a holistic manner. This theory conceptualizes setting outcomes as being derived from setting
resources, distribution of resources, and social processes. In the case of injection drug use
settings, resources include the amount of time available and physical resource requirements (e.g.,
drugs and paraphernalia) for injecting drugs, the distribution of resources describe who may
have greater or lesser amount of these resources in a specific setting, and social processes would
include paraphernalia sharing norms, participation in activities (e.g., syringe exchange program),
and relationships that lead to the setting outcome such as injection risk behavior. While many of
these theoretical components cannot be directly assessed in the current study, social setting
theory will be used to guide the understanding of the mechanisms underlying the hypothesized
associations between study variables.
The current study will explore the following research questions: 1) what characteristics of
injection partners and social/physical environment explain within person variation in injection
risk behavior, and 2) what network and individual characteristics explain between person
variation in injection risk behavior and 3) what individual characteristics explain variation in the
association between situational and dyadic characteristics and injection risk behavior? This study

3

would expand upon previous research in this area by including event specific data on multiple
injection episodes in a manner that allows for the simultaneous comparison of within and
between individual variation in injection risk behavior. By answering these research questions
and providing a more nuanced understanding of injection risk behavior, this study should directly
inform future research and interventions intended to understand and prevent HIV risk behavior
among injection drug users. For example, event specific analysis of risk behavior has been
successfully used in designing policies and interventions to promote healthier behavior during
peak risk events for engaging in alcohol (Neighbors et al., 2007) and sexual (Crosby, 2013) risk
behaviors. In this vein, a more comprehensive understanding of injection episode characteristics
that place IDUs at heightened risk would allow for interventions specifically tailored to empower
IDUs to avoid or prepare for these high risk events.

4

Literature Review
The literature review will begin by examining the current trends in HIV/HCV and
correlates of HIV/HCV seroconversion. The review will then examine the individual, dyadic,
network, and environmental correlates as well as interventions to reduce injection risk behavior.
Finally, the limitations of the existing literature will be discussed before discussing the
theoretical frame and describing the current study.
Current Trends in HIV among IDUs
HIV continues to pose a serious health challenge to IDUs through associated morbidity
and mortality (Mathers et al., 2013). The estimated prevalence of HIV was 9% in 2009 among a
large sample (n = 10,073) of IDUs in 20 metropolitan statistical areas and 45% of HIV-positive
IDUs were unaware of their HIV status (Wejnert et al., 2012). However, given that metropolitan
regions were the quickest to implement HIV preventive interventions, these estimates may not
accurately reflect the true prevalence of HIV among IDUs. The most recent prevalence estimates
of HIV among all IDUs in the United States vary from 15.5% in 2003 (Mathers et al., 2008) to 23% in 2009 (Broz et al., 2014).
Despite the continued health burden of HIV among IDUs, significant progress has been
made since the early years of the US HIV epidemic (Des Jarlais & Semaan, 2008). For example,
the incidence of HIV among IDUs, as measured per person years, has decreased by roughly 80%
since the mid 1980s (Hall et al., 2008). For example, New York City experienced a decline from
13 cases per 100 person-years in the late 1970s to mid-1980s (Des Jarlais et al., 2000) to 1 to 4
cases per 100 person-years by the mid 2000s (Des Jarlais & Semaan, 2008). Likewise, incidence
dropped from 5.5 cases per 100 person-years in 1988-1989 to 0.0 cases per 100 person-years by
the 2005-2008 among cohorts of injectors in Baltimore (Mehta et al., 2011) and similar declines

5

in incidence have been witnessed throughout the United States (Hall et al., 2008; Prejean et al.,
2011; Tempalski et al., 2009). Furthermore, prevalence of HIV decreased in 88.5% of US
metropolitan areas between 1992 and 2002 (Tempalski et al., 2009). These declines have mainly
been attributed to the proliferation of syringe exchange programs and other interventions and
policy changes that have increased access to sterile syringes (Des Jarlais & Semaan, 2008; Hall
et al., 2008). Nonetheless, given the continued health burden and disproportionate representation
of IDUs among new HIV infections, HIV remains an important concern for injection drug
research and intervention.
Correlates of HIV Serostatus and HIV Seroconversion. Sharing injection
paraphernalia, particularly syringes (Kaplan & Heimer, 1992), is a strong and consistent
predictor of HIV serostatus and HIV seroconversion among IDUs (Chaisson, Moss, Onishi,
Osmond, & Carlson, 1987; Chitwood et al., 1995; Friedman, Jose, Deren, Des Jarlais, &
Neaigus, 1995; Moss et al., 1994; David Vlahov et al., 1990) and was the primary focus of
research and intervention throughout the first two decades of HIV prevention research among
IDUs (Zibbell, 2012). For example, a study in Miami found that individuals who shared syringes
were four times more likely to seroconvert after controlling for other confounding factors
(Chitwood et al., 1995). However, aside from sharing injection paraphernalia, the most common
risk factors associated with HIV seroconversion are frequency of injection (Kozlov et al., 2006;
Spittal et al., 2002; Strathdee et al., 2001) and injecting or use of cocaine or other stimulants
(Bruneau et al., 2011; Chaisson et al., 1989; Craib et al., 2003; Kozlov et al., 2006; Patterson et
al., 2008; Spittal et al., 2002; Tavitian-Exley, Boily, & Vickerman, 2013; Tyndall et al., 2003).
These risk factors are likely associated with HIV because they mirror the level of actual exposure
to viral transmission risk (Thorpe et al., 2002). That is, frequent injectors have higher cumulative

6

exposure to HIV because more frequent injections leads to a greater number of total
opportunities for the IDU to come into contact with blood residue from other injectors with HIV,
even after controlling for the risk observed at any specific injection event. Alternatively,
injection frequency could also be associated with seroconversion because more frequent
injections require a greater amount of injection resources (e.g., syringes) and therefore IDUs may
rely on sharing injection paraphernalia if they are unable to obtain these resources (Remis,
Bruneau, & Hankins, 1998). Also, cocaine injectors tend to inject more frequently than other
injectors (Colón et al., 2001) and the frequency of injection may explain some of the increased
exposure risk. Still, in several of these studies cocaine injecting remains independently
associated with HIV seroconversion after controlling for frequency of injecting suggesting a
higher seroconversion risk is observed among these injectors in excess of the risk associated with
increased injection frequency. While the mechanism explaining this excess risk is still unclear, it
is hypothesized that cocaine injecting tends to be more common among individuals who have a
general higher profile of risk across drug and sexual risk behaviors (e.g., more frequent "binge"
drug use) and this confounding effect may explain the excess risk of seroconversion witnessed
among cocaine injectors (De, Jolly, Cox, & Boivin, 2006; Tyndall et al., 2003).
Other studies (Craib et al., 2003; Spittal et al., 2002; Strathdee et al., 2001) have
identified separate seroconversion risk factors for males and females suggesting moderation of
seroconversion risk factors by sex. For example, a study of Vancouver IDUs found that females
who needed help injecting experienced higher risk of seroconversion while this factor was not
significant for males (Spittal et al., 2002).2 Similarly, in a study of mostly African American
Baltimore IDUs, Strathdee et al. (2001) found that males who attended "shooting galleries" (i.e.,
2

While another study (O'Connell et al., 2005) found 'needing help injecting' as a significant predictor across males
and females, this study did not perform a stratified analysis or test for an interaction effect.

7

sites where IDUs congregate to inject together) and those who had a lower level of education had
higher risk for seroconversion while neither of these factors were significant predictors of
seroconversion among females. Although, the most consistent seroconversion risk factors (i.e.,
frequency of injecting) are present among both males and females (Spittal et al., 2002; Strathdee
et al., 2001). Yet, the difference risk factors observed in sex stratified analysis suggest that
different social processes may lead to seroconversion risk for males and females; these processes
will be further discussed in the later section on injection risk behavior.
Recent evidence also suggests that macro and micro structural characteristics are
independently associated with HIV seroconversion. For example, a study of Vancouver IDUs
found that location in a high-risk environment (e.g., extreme poverty and high crime rate
regions) was independently associated with seroconversion (Maas et al., 2007). Similarly,
estimated community HIV viral load significantly predicts HIV seroconversion even after
controlling for unsafe sex, paraphernalia sharing, and frequency of injecting (Wood et al., 2009).
These studies suggests that the structural characteristics such as the stage of the HIV epidemic
and concentration of high-risk behavior in local communities continue to contribute to
individual's seroconversion risk regardless of individual risk behavior. For example, individual
risk behavior differentially impacts the likelihood of seroconversion depending on the stage of
the epidemic in a given region. More specifically, in a study of 50 US cities Friedman et al.
(1995) found that in high-prevalence cities using unsterile injection equipment was a strong
predictor of seroconversion but was not a significant predictor of seroconversion in lowprevalence cities. This suggests that even risk behaviors that are very efficient at spreading viral
diseases can have weak relationships to seroconversion when controlling for the stage and extent
of the community epidemic.

8

Current Trends in HCV among IDUs
Prevalence estimates of HCV among injection drug users in the United States range
considerably from 8 to 80% (Aceijas & Rhodes, 2007). However, a systematic review found the
midrange estimate of HCV prevalence among IDUs to be 73.4% in 2002-2004 (Nelson et al.,
2011), suggesting that HCV still posed a significant health burden in this population. In the early
2000s an estimated 60% of previously acquired and 68% of newly acquired infections are
attributable to injection drug use (Alter, 2002). In fact, the decline of HCV infections attributed
to contaminated medical equipment in developed nations (Alter, 2002) has lead to an increasing
percentage of HCV infections being attributed to injection drug use in these countries (Alter,
2007; Averhoff, Glass, & Holtzman, 2012).
While sharing of syringes has long been a focus of HCV transmission, increasing
evidence suggests that sharing of non-syringe paraphernalia may also be a significant source of
HCV transmission among injection drug users (Finlinson, Colan, Negran, & Robles, 2008;
Hagan et al., 2001; Huo, Bailey, Garfein, & Ouellet, 2005; Needle et al., 1998; Pouget, Hagan, &
Des Jarlais, 2012; Thorpe et al., 2002). Injection drug use involves a complex number of steps
from combining the drugs with water, cooking the solutions, splitting the drugs (i.e., in the case
of multiple person use), injecting the drugs, often making multiple punctures, and stopping the
flow of blood from the wound. Compared to HIV, HCV can be more easily spread through
objects used to cook the drugs (i.e., "cookers"), cottons used to filter the drugs, and objects used
to clean wounds. This is because HCV can survive for longer periods outside of the human body
than HIV (Paintsil, He, Peters, Lindenbach, & Heimer, 2010), has been shown to be much more
transmittable than HIV during needle sticks (Sulkowski, Ray, & Thomas, 2002), and is therefore
more easily transmitted on non-syringe paraphernalia (Donoghoe & Wodak, 1998; Mehta et al.,

9

2011). A recent meta-analysis of 21 international studies (including 9 studies from the United
States) between 1989 and 2006 found that sharing syringes, sharing rinse water, sharing cottons,
sharing cookers, sharing miscellaneous unspecified non-syringe injecting equipment, and
backloading all had a significant pooled associations with HCV seroconversion (Pouget et al.,
2012). Accordingly, injection risk behavior for HCV clearly extends beyond direct syringe
sharing.
As the incidence and prevalence of HIV among IDUs has continued to decline in many
regions, increasing focus has been placed on the spread of HCV in this population (Madden &
Cavalieri, 2007; Zibbell, 2012). Despite progress decreasing the sharing of syringe
paraphernalia, altering the sharing of cookers, cottons, rinse water, and other non-syringe
paraphernalia has been harder to accomplish (Santibanez et al., 2006) likely due to the lower
perceived risk of these behaviors (Rhodes, Davis, & Judd, 2004), previous lack of emphasis of
reducing these behaviors in safer injecting interventions (Zibbell, 2012), and how deeply
engrained these behaviors are for social processes such as drug sharing (Grund et al., 1996).
Despite some evidence supporting the effectiveness of harm reduction interventions to reduce the
spread of HCV (Hagan, Pouget, & Des Jarlais, 2011), strong evidence for the efficacy of these
interventions has yet to be established and important questions still remain such as what level of
behavioral change is required to decrease the incidence of HCV among IDUs and to obtain
sustained reductions in prevalence (Hagan et al., 2011; Palmateer et al., 2010). Nonetheless,
some evidence suggests HCV prevalence may be decreasing among IDUs in the United States.
For example, prevalence of HCV in samples of IDUs in four major cities (i.e., Baltimore,
Chicago, Los Angeles, and New York) dropped from 65% in 1994 to 35% in 2004 (Amon et al.,
2008). However, other recent studies have estimated incidence of HCV among IDUs at ~25 per

10

1000 person years (Clatts, Colón-López, Giang, & Goldsamt, 2010; K. Page et al., 2009) which
suggests incidence remains unchanged from earlier estimates (Hahn et al., 2002). Accordingly,
further evidence is required to resolve these contradictions and uncover if there have been
changes to incidence and prevalence and, if so, what regions and individuals have experienced
these changes.
Increasing use of prescription opioids may also be contributing to a novel source of HCV
infection. Recent increases in the incidence of prescription opioids use (Compton & Volkow,
2006) may explain an increase in the incidence of prescription opioid injecting associated with a
concurrent increase in incidence of HCV among these opioid injectors (Bruneau, Roy, Arruda,
Zang, & Jutras‐ Aswad, 2012). For example, regions in the United States associated with high
rates of prescription opioid use have also seen recent increases HCV infection among young
IDUs suggesting a possible link between these two trends; accordingly, prescription opioid
injecting may be an emerging risk group for HCV infection that has not been the focus of much
previous research (Valdiserri et al., 2014).
Correlates of HCV and HCV seroconversion. Beyond sharing injection paraphernalia,
the strongest persistently identified predictor of HCV positive status among injection drug users
is the number of years injecting (Amon et al., 2008; Crofts et al., 1993; Diaz et al., 2001; Garfein
et al., 1998; Havens et al., 2013; Hope et al., 2011; Lorvick, Kral, Seal, Gee, & Edlin, 2001;
Thomas et al., 1995). This is likely because injectors face extremely high incidence rates in the
first years of injecting (Hagan et al., 2007). For example, one study (Maher et al., 2006) of urban
Australia injectors found IDUs had an incidence rate of 51.2 per 100 person years during their
first 1-3 years injecting. Similarly, another study (Hagan, Thiede, & Des Jarlais, 2004) of Seattle
IDUs found the mean weighted average time to infection at 3.4 years. Therefore, long-term

11

injectors have faced the largest cumulative risk and are most likely to be HCV seropositive but
are also least likely for seroconversion because most are already seropositive (Hagan, Pouget,
Des Jarlais, & Lelutiu-Weinberger, 2008). Just as for HIV, frequency of injecting (Amon et al.,
2008; Hagan et al., 2010; Hope et al., 2011; Thomas et al., 1995; Thorpe et al., 2002) and
cocaine injecting (C. Miller et al., 2002; Thorpe et al., 2002) are both strong predictors of HCV
seropositivity and HCV seroconversion because, again, frequency of injecting also closely
mirrors the actual level of risk for being exposed to HCV (Thorpe et al., 2002).
Co-infection of HIV and HCV
In addition to the substantial health burden both HIV and HCV independently create
among IDUs, this population also faces substantial complications cause by co-infection of HIV
and HCV (Alter, 2006; Sulkowski, 2008). Globally in the general population, HCV prevalence
rates among HIV positive individuals vary from 25-30% in western Europe and the USA (Alter,
2006) with substantially higher rates (72-95%) being observed among injection drug users
(Denis et al., 1997; Roca et al., 2003; Sulkowski & Thomas, 2003). Just as in the general
population, injection drug user is a strong predictor of HCV among HIV positive individuals. For
example, one study found injection drug use as the sole significant predictor of HCV infection
besides aspartate aminotransferase (i.e., an indicator of liver damage) among a sample of HIV
positive individuals (Staples, Rimland, & Dudas, 1999).
HIV can complicate the treatment and clearance of HCV. For example, lower rates of
spontaneous clearance have been observed among those who also have HIV (Grebely et al.,
2007). Comorbidity of HIV and HCV also increases the risk of liver damage due to highly active
antiretroviral therapy (Rockstroh & Spengler, 2004). However, the impact of HCV co-infection
among HIV positive individuals remains unclear. One study observed similar survival rates

12

among HIV/HCV co-morbid patients compared to HIV alone (Staples et al., 1999; Wright et al.,
1994) while another found decreased mortality rates among co-infected participants (El-Serag,
Giordano, Kramer, Richardson, & Souchek, 2005). However, others have found similar HIV
related mortality among co-morbid individuals but increased overall mortality due to greater
liver associated mortality (Bonacini, Louie, Bzowej, & Wohl, 2004). Contrastingly, patients with
HCV related liver disease and co-infections of HIV appear to have considerably higher rates of
mortality as compared to those with HCV alone (Pineda et al., 2005).
Clearly, HIV and HCV contribute to substantial morbidity and mortality among IDUs.
The prime focus of most research and interventions has been to reduce the frequency of injection
risk behavior among IDUs in effort to reduce the incidence of both diseases. Accordingly, next
we will examine these risk behaviors and the individual, dyadic, network, and environmental
correlates engaging in these behaviors.
Injection Risk Behavior
Since the early days of the HIV/AIDS epidemic, the majority of research on behavioral
risk factors for the spread of viral diseases among IDUs has focused on the sharing of syringes
and needles (Scott, 2011) as this behavior provided the most efficient route for the spread of HIV
among IDUs. As discussed, increasing focus has been placed on the sharing of non-syringe
paraphernalia as emphasis of research and intervention has shifted from HIV to both HIV and
HCV prevention (Zibbell, 2012). Again, the injection process involves many steps that can
include: preparation of the drug material from solid into a liquid form, splitting/sharing the
drugs, cleaning the wound, making multiple punctures, and stopping the flow of blood.
Contamination of the injection equipment or an wound can occur during many of these steps and
lead to the spread of viral diseases between individuals injecting together (Scott, 2011). The most

13

common injection risk behaviors 3 used in this research are: receptive syringe sharing (i.e.,
receiving a syringe previously used by another injector), distributive syringe sharing (i.e.,
distributing a syringe the participant has used), sharing cookers (i.e., the objects used to mix and
liquefy the drug solution), backloading (i.e., dividing the drug solution by unloading the solution
into the back end of the syringe after removing the syringe plunger), frontloading (i.e., sharing
drugs by drawing from the same source - usually the same cooker), sharing cottons (i.e., the
objects used to remove solid adulterants from the liquid drug solution), and sharing rinse water
(i.e., the water used to liquefy the drugs or to make sure the syringe is not clogged).
In 2012 an estimated 21.8% of IDUs in the United States engaged in receptive syringe
sharing (i.e., injected with a syringe previously used by someone else), 21.5% engaged in
distributive syringe sharing (i.e., let someone inject with their used syringe), and 34.6% obtained
a syringe from a source other than a syringe exchange, pharmacy, or doctor during their most
recent injection episode (USDHHS, 2013a). Furthermore, a study of non-syringe sharing
practices in 5 US cities (Chicago, Baltimore, Los Angeles, New York, and Seattle) between 2002
and 2004 estimated that 47.7% of IDUs shared cookers, 35.3% shared cottons, and 35.5% shared
water during the previous three months to participating in the study (Thiede et al., 2007). This
suggests that many IDUs continue to engage in both syringe and non-syringe related injection
risk behavior.
Individual Correlates of Injection Risk Behavior. Accordingly, a substantial body of
research has focused on understanding and predicting characteristics of individuals that engage
in injection risk behavior. Similar to correlates of seropositivity, cocaine injecting (Hudgins,
3

While not all of these behaviors place the participant at risk for contracting viruses (i.e., some behaviors place the
partner but not the participant at risk), these variable are often used to either measure the willingness of the
participant to put others at risk or used as an indicator of the participant's more general injection risk behaviors.
These variables will be further discussed in the section on latent variable modeling.

14

McCusker, & Stoddard, 1995; Mandell, Vlahov, Latkin, Oziemkowska, & Cohn, 1994; Wood,
Li, et al., 2005; Wood et al., 2002) and greater frequency of injection (Golub et al., 2007;
Mandell et al., 1994; Thiede et al., 2007) have both been found to be associated with sharing
injection equipment. This association may be due to the increased risk observed among these
IDUs is likely due to the large amount of physical resources required among frequent injectors
that leads to increased drug and paraphernalia sharing (Remis et al., 1998; Tyndall et al., 2003).
For example, syringes can only be used a certain number of times because the needles dull after
repeated use. Accordingly, frequent injectors may rely on sharing injection equipment in order to
facilitate more frequent drug use. Similarly, frequent drug use may also require greater monetary
resources to obtain a larger amount of drugs. Therefore, frequent injectors may rely on drug
sharing in order to defray the costs of frequent drug use and therefore be more likely to engage in
risk behavior during the drug splitting process (Koester, Glanz, & Barón, 2005).
Most studies have found that age has been inversely associated with injection risk
behavior with young IDUs showing higher levels of risk behavior as compared to older IDUs
(Beletsky et al., 2014; Cassin, Geoghegan, & Cox, 1998; Golub et al., 2007; Gyarmathy et al.,
2010; Thiede et al., 2007). The general higher level of risk observed among younger injectors
may be partially caused by the lower observed levels of syringe exchange program participation
among young injectors (Beletsky et al., 2014). However, one study (Lopez, Krueger, & Walters,
2010) found that, after controlling for age at first drug use, age was positively associated with
injection risk behavior. Accordingly, while older IDUs appear to be at reduced risk for engaging
in injection risk behavior, risk behavior may also be a function of time since first drug use and
the relationship between age and injection risk behavior should be further explored while taking
time since first drug use into account.

15

Other studies have examined psychological predictors of injection risk behavior. For
example, a number of studies found have found that greater levels of depression predict injection
risk behavior (Bailey et al., 2007; Hawkins, Latkin, Hawkins, & Chowdury, 1998; Metzger &
Woody, 1991; Perdue, Hagan, Thiede, & Valleroy, 2003; Stein, Solomon, Herman, Anderson, &
Miller, 2003) or moderates the relationship between network variables and injection risk
behavior (Mandell, Kim, Latkin, & Suh, 1999). For example, several studies (Bailey et al., 2007;
Hawkins et al., 1998; Perdue et al., 2003) have found that IDUs with higher diagnostic scores for
depression were more likely to engage in receptive syringe sharing. The mechanism of the
impact of depression on injection risk behavior remains unclear. However, proposed reasons that
depression could increase injection risk behavior include: depression caused fatalism over
contracting HIV and decreased attention paid to injection process due to depression (Stein et al.,
2003).
Other studies have used cognitive behavioral theories to predict risk behavior such as
protection motivation theory (Grau, Bluthenthal, Marshall, Singer, & Heimer, 2005), the AIDS
risk reduction model (Longshore, Stein, & Conner, 2004), and the health belief model (Rácz,
Gyarmathy, Neaigus, & Ujhelyi, 2007). These theories rely on properties of individuals such as
motivation, self-efficacy, and perception of risk to explain subsequent risk behavior. In their
review of cognitive behavioral studies examining injection risk behavior, Wagner, Unger,
Bluthenthal, Andreeva, and Pentz (2010) found strong support for the association between selfefficacy and perceived norms in explaining injection risk behavior. However, only mixed support
was documented for other cognitive behavioral constructs such as behavioral skills, knowledge,
and perceived susceptibility. The authors suggest that one reason for inconsistent findings
between cognitive behavioral predictors and injection risk behavior is the lack of accounting for

16

the environment of injection risk behavior that may jointly influence cognitive behavioral
predictors and HIV risk behaviors.
Sex of the injector also predicts injection risk behavior with most studies finding that
female injectors are at higher risk for injection risk behavior. For example, female injectors are
more likely to have sexual partners who are injection drug users (Evans et al., 2003; Spittal et al.,
2002), to require help injecting (Frajzyngier, Neaigus, Gyarmathy, Miller, & Friedman, 2007), to
share injection equipment with a sexual partner (Choi, Cheung, & Chen, 2006; Gollub, Rey,
Obadia, Moatti, & Group, 1998), and to share injection equipment generally (Barnard, 1993;
Beletsky et al., 2014; Evans et al., 2003; Iversen, Wand, Gonnermann, & Maher, 2010; S.
Montgomery et al., 2002; Pouget et al., 2005; Rhodes, 2009; Thiede et al., 2007). Some of these
studies used theories of social bonding (Choi et al., 2006) and gender role theory (Cruz et al.,
2007) to explain this association. For example, Cruz et al. (2007) found that traditional gender
roles were pervasive among male and female injectors in Tijuana/Juarez and these roles
influenced how participants managed risk such as male and females choosing different venues
for their injection episodes. However, most studies relied on previous research to form their
hypothesis. One study found that female IDUs who shared equipment were those with the
highest levels of depression which suggested an interaction between sex, depression, and
injection risk behavior (M. E. Johnson, Yep, Brems, Theno, & Fisher, 2002). Yet, the simplified
observation that female injectors tend to be at high risk for sharing injection equipment likely
reflects complicated social processes that are at play during injection episodes (El-Bassel, Shaw,
Dasgupta, & Strathdee, 2014). This process will be further discussed as it relates to the dyadic,
situational, and network correlates of injection risk behavior.

17

Dyadic Correlates of Injection Risk Behavior. Given that sharing injection equipment
is an inherently dyadic behavior between partners, a number of studies have attempted to move
beyond individual risk factors by examining dyadic predictors of injection risk behavior. One of
the earliest studies examining dyadic predictors of injection risk behavior (Neaigus et al., 1995)
found that having frequent contact, close relationships, or a sexual relationships with an injection
partner predicted receptive syringe sharing. Following Neaigus et al.'s (1995) early study, several
additional studies have found that having an injection partner who is also a sexual partner is a
consistent risk factor for sharing injection equipment (Bailey et al., 2007; Gyarmathy et al.,
2010; Hahn, Evans, Davidson, Lum, & Page, 2010; Hottes, Bruneau, & Daniel, 2011; Shaw,
Shah, Jolly, & Wylie, 2007; Sherman, Latkin, & Gielen, 2001; Thiede et al., 2007). Similar to
condom use in intimate relationships (C. Montgomery et al., 2008; Willig, 1997), injection
equipment sharing among sexual partners often involves explicit or implicit statements of
commitment and trust between partners (El-Bassel et al., 2014). Therefore, decisions about the
injection process are embedded in the context of these relationships and relationship concerns
may outweigh concerns about viral risk (Seear et al., 2012; Simmons, Rajan, & McMahon,
2012). These findings are also consistent with results indicating that IDUs are more likely to
share with injection partners that they see as close friends (Rhodes et al., 2004; Valente &
Vlahov, 2001) or trustworthy (Gyarmathy et al., 2010), two characteristics that many sexual
partners may have.
However, the association between dyadic predictors and injection risk behavior may also
be moderated by the gender of the injector and the gender of their injection partner. For example,
Unger et al. (2006) found both male and female participants were at heightened risk for needle
sharing when a male partner helped them inject but not when a female partner helped them

18

inject. Similarly, another study (Hahn et al., 2010) found that gender discordant partners are
more likely to share syringes relative to male-male and Gyarmathy et al. (2010) found male-male
partners were more likely to share cookers than female-female partners. However, a third study
found no significant relationship between gender concordance and sharing syringes (Sherman et
al., 2001).
Accordingly, the relationship between gender concordance/discordance and injection risk
behavior remains unclear. Nonetheless, ethnographic (Bourgois & Schonberg, 2009) and other
qualitative studies (Cruz et al., 2007) suggest that gender dynamics have some association with
injection risk behavior but the precise nature of that relationship is yet to be fully developed and
may vary across specific samples and contexts. Qualitative studies (El-Bassel et al., 2014)
suggest that the increased likelihood of males serving as the arbitrator of drug and syringe
acquisition may decrease the agency among female injectors in gender discordant injection
partnerships. While females do appear to engage in elevated levels of injection risk behavior
(Barnard, 1993; Beletsky et al., 2014; Iversen et al., 2010), researchers should be cautioned not
to oversimplify the dynamics between male-female injection partners as many female injectors
may play significant roles in acquiring drug resources and thereby retain control over the
injection process (El-Bassel et al., 2014; Syvertsen et al., 2014).
Another focus of dyadic analysis of injection drug users has been the effect of serosorting
on equipment sharing behavior. Serosorting is the process in which injectors attempt to only
share with injection partners they perceive to have the same HIV or HCV serostatus. While the
efficacy of this practice is suspect (Kim & Page, 2013) given the discordance between perceived
and actual HIV/HCV status (Stein, Maksad, & Clarke, 2001) and the possibility of HCV
clearance after an initial positive test, several studies have documented the presence of

19

serosorting among IDUs (Burt, Thiede, & Hagan, 2009; Gyarmathy et al., 2010; Hahn et al.,
2010; Mizuno et al., 2011; Smith et al., 2013; Yang, Tobin, & Latkin, 2011). For example,
participants who knew their own HCV serostatus are more likely to know their partner's
serostatus and HCV positive participants were more likely to share with HCV positive partners
(Smith et al., 2013). However, most studies examining serosorting are limited because they
examine cross-sectional data and do not directly inquire about the intentions of IDU sharing
behaviors (Kim & Page, 2013). While there is evidence of a tendency for injection partners to
have the same serostatus and for participants who know their serostatus to know their partner's
serostatus, it is not yet clear if injectors systematically chose to inject with partners they perceive
to be of the same serostatus or if another process is responsible for this association. For example,
studies that do not conduct biological tests for serostatus may simply reflect that people who
share injection equipment are more likely to perceive each other as seroconcordant (Kim & Page,
2013)
Network Correlates of Injection Risk Behavior. Looking beyond dyadic relationships,
a growing body of research has examined associations between structural network characteristics
and injection risk behavior. Research on network correlates of injection risk behavior have
generally focused on two types of networks: drug networks and injection networks. Drug
networks are usually operationalized as the number of individuals the participant uses drugs
with, often defined by using drugs in the same room at the same time. Injection networks are
operationalized as the number of individuals with whom the participant has injected drugs.
Studies have generally found that larger drug networks are associated with higher levels of risk
behavior (De et al., 2006; Latkin, Kuramoto, Davey-Rothwell, & Tobin, 2010). For more
specific examples, in studies of Baltimore, St. Petersburg, Chicago, Seattle, Los Angeles, and

20

New York, drug network size has been positively associated with increased frequency of
injection (Latkin, Mandell, Oziemkowska, et al., 1995) while drug and injector network size
have both been associated with sharing injection equipment (Cepeda et al., 2011; Latkin,
Mandell, Vlahov, et al., 1995; Latkin, Mandell, Vlahov, Oziemkowska, & Celentano, 1996;
Needle et al., 1998; Thiede et al., 2007), and injection network size has also been positively
associated with both syringe and non-syringe paraphernalia sharing (Thiede et al., 2007).
However, several studies in Baltimore, Los Angeles, Sydney, Winnipeg, Dayton, Houston, and
Rio Piedras have also found no relationship between network size and risk behaviors (Lakon,
Ennett, & Norton, 2006; Paquette, Bryant, & De Wit, 2011; Shaw et al., 2007; C. Williams, Liu,
& Levy, 2011; M. Williams et al., 1995; Yang et al., 2011). The reason for these inconsistencies
may be due to the difference of specific operational definitions of these network types, the
specific context of injection drug use for each sample, as most studies are based on samples in
specific geographic regions, or the variability in control characteristics included in multivariate
analysis. For example, many studies (e.g., Paquette, Bryant & De Wit, 2011; Thiede et al., 2007;
C. Williams, Liu, & Levy, 2011) include network size as the only network characteristic in their
model and most fail to examine interactions between network characteristics (De, Cox, Boivin,
Platt, & Jolly, 2007), such as size and composition, thus leading to potentially divergent findings
if different network characteristics are used as control variables across studies.
Furthermore, another explanation for these inconsistencies may be that association
between network size and risk behavior is likely moderated by the type of network being
measured. For example, the number of drug network members providing material support (i.e.,
members willing to lend money) has been positively associated with sharing between network
members (Suh, Mandell, Latkin, & Kim, 1997; Tobin, Davey-Rothwell, & Latkin, 2010). This

21

effect is likely due to the norms of reciprocity in sharing resources that increase the likelihood of
sharing injection equipment (Hahn et al., 2002) and the negative consequences of not sharing
paraphernalia with drug network members (Wagner et al., 2011). Conversely, larger networks
offering more pro-social connections (e.g., non-IDU) have been associated with lower risk
behavior (Cox et al., 2009). Having larger non-IDU network connections may discourage sharing
of injection equipment through stigma attached to these behaviors that are more prevalent among
non-IDUs (Mateu-Gelabert et al., 2005). This relationship could also be caused by a trade off in
network membership so that IDUs with larger non-IDU networks tend to have smaller IDU
networks. Smaller IDU networks may lead to less risk behavior given the less frequent
opportunity to share equipment with other IDUs or the inability to seek IDUs to share equipment
or drugs.
Density of IDU social networks has also been a frequent network characteristic examined
in this research. Density is the proportion of observed network connections out of all possible
connections. For example, drug social network density has been positively associated with
frequency of injection (Latkin, Mandell, Oziemkowska, et al., 1995; Trotter, Baldwin, & Bowen,
1995), sharing needles (Curtis et al., 1995; Latkin et al., 1996), and general risk behavior (Frey et
al., 1995). However, a number of studies have failed to find a significant relationship network
density and risk behavior (Knowlton, Hua, & Latkin, 2004; Koram, Liu, Li, Luo, & Nield, 2011;
Latkin et al., 2010; Suh et al., 1997). Similar to network size, the relationship between network
density likely depends on the type of network and type of risk behavior being assessed. For
example, Latkin, Mandell, Vlahov, et al. (1995) found a significant relationship between

22

personal support network4 density and sharing needles but no significant relationship between
density and attending a shooting gallery. This may be due to the norm enforcement mechanisms
of dense social networks (Latkin, Forman, Knowlton, & Sherman, 2003) that encourages
reciprocal support such as through sharing syringes. Furthermore, seeking other resources, such
as through attending shooting galleries (Celentano et al., 1991), may become unnecessary when a
dense social group is already providing injection resources when they are needed.
Finally, a smaller number of studies have also examined the relationship between
multiplexity and injection risk behavior. Multiplexity is defined as having ties to individuals in
more than one network type. For example, a relationship with Person A could be considered as
multiplex if Person A was in the participant's drug network, social support network, and sexual
network. Given that multiplex relationships may be more intimate or durable due to the multiple
roles played by these connections, its plausible that injection risk behavior could be positively
associated with multiplexity. However, the few studies examining multiplexity have generally
failed to find a significant relationship with injection risk behavior. For example, having network
members in two or more networks types (e.g., physical assistance and health information) was
not significantly associated with needle sharing (Latkin, Mandell, Vlahov, et al., 1995; Latkin et
al., 1996) frequency of injection (Latkin, Mandell, Oziemkowska, et al., 1995) or shooting
gallery attendance (Latkin, Mandell, Vlahov, et al., 1995). Although, one study (Trotter et al.,
1995) found that being in multiple networks (i.e., drug, social, and AIDS communication) was
associated with more frequent injections. As discussed, the main exception to these findings is
the consistent association between sexual and injection partnership in dyadic analysis which have

4

Personal network was defined broadly as a connection between network members in any of the six network types:
material assistance, socializing, intimate interaction, physical assistance, health information, and positive feedback.

23

not been fully elaborated within studies examining multiple structural features of drug use
networks.
Therefore, a number of network characteristics have been associated with injection risk
behavior but the precise nature of these relationships appears highly specific to certain network
types (e.g., drug network versus material support), specific network characteristics, and the types
of risk behavior because these network mechanisms likely operate through the social processes
of obtaining and injecting drugs. For example, network size may facilitate increased sharing in
networks where sharing behavior is an acceptable (e.g., IDU networks) network size may
decrease sharing in networks that this behavior is not acceptable (e.g., non-IDU networks). Due
to these complexities and continued uncertainty of relationships and mechanisms, additional
investigations are required to fully clarify the associations between network characteristics and
injection risk behavior.
Environmental and Situational Correlates of Injection Risk Behavior. In addition to
studies examining network correlates of injection risk behavior, environmental factors such as
government policy, geographic location, and neighborhood resources have been increasingly
studied as predictors of injection risk behavior (Latkin, German, et al., 2013; Rhodes, Singer,
Bourgois, Friedman, & Strathdee, 2005). Some of the earliest research on environmental
correlates of injection risk behavior focused on the physical environment in which IDUs used
drugs. For example, early studies recognized shooting galleries were as important reservoirs and
vectors of HIV transmission (Chitwood et al., 1990). Given the potential for viral transmission at
these locations, follow-up studies confirmed that injecting at shooting galleries was associated
with sharing injection equipment (Devillé, van Ameijden, & Wolffers, 2001; Klein & Levy,
2003; Latkin et al., 1994; Neaigus et al., 1994; Wood, Tyndall, et al., 2005). While there is

24

considerable variability in the type of shooting galleries (Ouellet, Jimenez, Johnson, & Wiebel,
1991), these locations may explicitly encourage equipment sharing through renting of injection
paraphernalia by individuals managing the site as well as encouraging incidental equipment
sharing among individuals requiring help to inject or those splitting drugs (Carlson, 2000).
Similar to shooting galleries, increased sharing behavior has also been documented among
injectors who inject at friend's houses relative to those who inject at their own residence (Latkin
et al., 1994). Accordingly, individuals who inject in public or semi-public places appear to be at
increased risk of sharing injection equipment. This may be explained through the increased
likelihood that they are required to share resources with those who provide the opportunity to
inject in these locations (e.g., friends or shooting gallery managers) or, alternatively, may be that
individuals that lack resources are more likely to seek out injection opportunities at these
locations because of the increased willingness of IDUs to share resources at these sites.
Furthermore, individuals who inject in public spaces are exposed to additional barriers to
hygienic injections such as police pressure that decrease carrying sterile syringes or requiring
quick injections that further exacerbate the probability of injection risk behavior. Qualitative and
ethnographic studies have documented the tendency for street policing practices to distract from
ideal injection practices due to shifts in risk attention from viral diseases toward immediate
personal safety (Aitken, Moore, Higgs, Kelsall, & Kerger, 2002; Cooper, Moore, Gruskin, &
Krieger, 2005; Maher & Dixon, 1999; Rhodes et al., 2003; Small, Kerr, Charette, Schechter, &
Spittal, 2006; Small, Rhodes, Wood, & Kerr, 2007). For example, arrest for possession of
injection paraphernalia has been associated with shooting gallery attendance and receptive
syringe sharing (Pollini et al., 2008). Similarly, being "stopped and frisked" has also been
associated with lower odds of syringe exchange attendance (Beletsky et al., 2014).

25

Finally, several recent studies have attempted to characterize the syringe coverage of
specific geographic regions in effort to predict risk behavior. Syringe coverage can be
operationalized as an individual or a population level (Burrows, 2006) but in both cases is
hypothesized to be a product of the availability of sterile syringes in the region. As a population
level characteristic, syringe coverage can reflect a percentage of the estimated number of IDUs
that have contact with syringe exchange programs or the percentage of syringe need met by SEP
and pharmacy syringe sales (Heimer, 2008). As an individual characteristic, syringe coverage
reflects a percentage of all injections made by the IDU using a sterile syringes (Sharma,
Burrows, & Bluthenthal, 2007). Many studies (Aceijas, Hickman, Donoghoe, Burrows, &
Stuikyte, 2007; Barrio et al., 2012; Tempalski et al., 2008) examining population syringe
coverage as the dependent variable assume that syringe coverage is positively associated with
reductions in risk behavior. This is because both empirical (Abdul-Quader et al., 2013; Des
Jarlais, Feelemyer, Modi, Abdul-Quader, & Hagan, 2013) and modeling studies (Vickerman,
Hickman, Rhodes, & Watts, 2006) of HIV/HCV incidence suggest that obtaining acceptable
population coverage is effective at preventing HIV/HCV. At the individual level, several studies,
have found that lower syringe coverage has been associated with re-using syringes (Iversen,
Topp, Wand, & Maher, 2012), sharing cookers, and distributive/receptive syringe sharing
(Bluthenthal, Anderson, Flynn, & Kral, 2007). While one study (Bryant, Paquette, & Wilson,
2012) failed to find this association, these results are likely explained by the high level of syringe
coverage witnessed in this population (IDUs in New South Wales) that reduced the observed
variability of risk behavior.
One of the most comprehensive studies using spatial multilevel modeling found that
spatial access to syringes significantly predicted use of an unsterile syringe (Cooper et al., 2011;

26

Cooper, Des Jarlais, Ross, et al., 2012; Cooper, Des Jarlais, Tempalski, et al., 2012). In addition,
Cooper, Des Jarlais, Ross, et al. (2012) also found that policing practices moderated the
relationship between spatial syringe access and risk behavior so that areas of high drug-related
arrest had a significantly lower slope between spatial access and risk behavior. This suggests the
impact of syringe availability and coverage on risk behavior is complex and depends on specific
local processes that may encourage or discourage obtaining and carrying sterile syringes.
Interventions to Reduce Injection Risk Behavior
Given the well-documented HIV/HCV risk faced by IDUs and the large number of
identified individual, dyadic, network, and environmental risk factors, a wide variety of
interventions have been developed in attempt to reduce the risk of acquiring HIV and HCV
among IDUs. These interventions include substance abuse treatment to reduce drug use (e.g.,
opioid replacement therapy or substance abuse counseling), behavioral interventions to reduce
risk behavior, and resource provision programs such as syringe exchange programs. Numerous
evaluation studies have established the efficacy of these interventions to reduce the incidence of
HIV seroconversion (A. Wodak & Cooney, 2005) among IDUs and to achieve sustained
reductions in the prevalence of HIV (MacDonald, Law, Kaldor, Hales, & J Dore, 2003). These
findings have been robust across countries internationally including both industrialized and
developing nations (Des Jarlais & Semaan, 2008; D. Vlahov, Robertson, & Strathdee, 2010). For
example, while the largest body of evidence exists in the United States, Western Europe, and
Australia (Mathers et al., 2010; Alex Wodak & McLeod, 2008), countries such as Bangladesh,
India, and Indonesia have all successfully implemented effective harm reduction interventions
such as syringe exchange programs (Sharma, Oppenheimer, Saidel, Loo, & Garg, 2009).

27

For example, a systematic review and meta-analysis found that opioid substitution
therapy (i.e., methadone or Buprenorphine maintenance programs) reduced the risk of HIV
infection among IDUs (MacArthur et al., 2012). Similarly, risk reduction interventions within
substance abuse treatment programs have also shown to be effective at reducing risk behavior
(Prendergast, Urada, & Podus, 2001). Finally, a comprehensive review of evidence on syringe
and needle exchange program suggested that the evidence overwhelming supported the efficacy,
safety, and cost effectiveness of these programs in reducing the spread of HIV including
evidence that six of the nine Bradford Hill causal criteria were satisfied through existing research
(A. Wodak & Cooney, 2005, 2006). The main causal mechanism behind syringe exchanges is
proposed to be the provision of sterile syringes but most modern syringe exchange programs
offer a wide variety of services including HIV/HCV and STD testing, condom distribution, nonsyringe paraphernalia distribution, referral to detoxification and substance abuse counseling, and
instruction on hygienic injection practices (Des Jarlais, McKnight, Goldblatt, & Purchase, 2009).
The evidence for the efficacy of behavioral interventions to reduce the spread of HCV is
less clear. A recent review and meta-analysis of HCV prevention interventions among IDUs
found that multi-component interventions (e.g., opioid replacement therapy combined with
syringe exchange programs) reduced risk of seroconversion by 75% (Hagan et al., 2011).
Furthermore, preventive interventions that increase the coverage of syringe programs to 50%
(i.e., an estimated 50% of syringes used IDUs are sterile and have not been previously used) may
be especially effective at reducing the prevalence of HCV (Abdul-Quader et al., 2013; Des
Jarlais et al., 2013). However, concern still exists that there is inadequate evidence to support the
effectiveness of common strategies to prevent HCV among IDUs such as syringe exchange

28

programs (MacArthur et al., 2014) particularly due to the dearth of studies examining biological
outcomes of these interventions such as HCV seroconversion (Palmateer et al., 2010).
Recently, a larger number of studies have utilized network mechanisms and peer
education to promote safer injection practices (Latkin, German, et al., 2013). While initially used
as a novel means to recruit hidden populations, networks were soon recognized as a powerful
tool to leverage social influence on injection risk behaviors (Heckathorn, Broadhead, Anthony,
& Weakliem, 1999). Network interventions generally train peer educators to intervene in their
networks and promote safer injection practices through education and, sometimes, the provisions
of sterile injection equipment (Dickson-Gomez, Weeks, Martinez, & Convey, 2006). These
interventions are largely based on theories of social influence that depend on intervention
participants to influence their larger network using cognitive behavioral training techniques
(Kelly & Kalichman, 2002). Network interventions have been effective at reducing injection risk
behavior (Latkin, Donnell, et al., 2013; Latkin, Sherman, & Knowlton, 2003; Medley, Kennedy,
O'Reilly, & Sweat, 2009; Tobin, Kuramoto, Davey-Rothwell, & Latkin, 2011). Still, the effect of
such interventions depend on the local context in which they are implemented. Latkin et al.
(2009) found that a network intervention was effective in Philadelphia, USA, but not in Chiang
Mai, Thailand. The authors speculated that local police behavior (i.e., arresting participants for
having syringes) that limited participants ability to carry sterile syringes in Chiang Mai likely
hindered the effectiveness of the intervention despite observing increases in the intervention
group for mediators of risk behavior (e.g., talking about risk reduction). Furthermore, the impact
of these interventions on incidence rates for HCV/HIV are not yet clear. For example, Garfein et
al. (2007) found no significant difference between intervention (i.e., cognitive behavioral peer

29

education training) and control arms in HCV incidence rates and were unable to compare HIV
incidence rates as no participant experienced HIV seroconversion.
Limitations of Past Research
As with all areas of research, the existing literature has a number of limitations and areas
that remain unexplored. First, most studies examining situational correlates of injection risk
behavior rely on event non-specific measures of participant behavior. For example, studies
examining injection location largely rely on generalizations about locations that participants have
injected at over a given time period. Most commonly, this variable is measured by asking if
participants have injected at a certain type of location (e.g., shooting gallery) during the last 6
months (e.g., see Tobin et al., 2010). Using this method, researchers compare individuals who
have and have not injected in that location during the time period. This approach can be
contrasted with "event specific" approaches that ask about a single or multiple specific injection
episodes. Event specific approaches can enhance the rationale for causal inference because this
approach ensures independent and dependent variables occurred during the same event (Leigh,
2002). It is also possible that this approach reduces recall bias by eliciting information about
specific events rather than general behavior (Hottes et al., 2011), although this has not been
specifically tested for injection risk behavior. An event specific approach can also examine intraindividual variation if data is collected on multiple injection episodes, allowing for analysis of
situational and dyadic predictors of injection risk behavior holding constant static individual
characteristics (Leigh & Stall, 1993). Again, event specific data with multiple observations
allows for examining when, where, and with whom IDUs may be at greatest risk. Accordingly,
studies examining intra-individual variation will provide further insight into the immediate social
mechanisms that may place individuals at higher risk for injection risk behavior.

30

While using event specific data is extremely common in related topic areas such as noninjection drug use and sexual risk behavior (Vosburgh, Mansergh, Sullivan, & Purcell, 2012),
only a handful of studies have yet to employ this technique to examine predictors of injection
risk behavior. Furthermore, most of the studies that have used this approach (Gyarmathy et al.,
2010; Hottes et al., 2011; Tortu, McMahon, Hamid, & Neaigus, 2003) have either a) collected
data on a single injection episode per participant or b) collected data on multiple injection
episodes but used a population averaged (e.g., generalized estimating equations) model to
estimate parameters that does not accurately reflect the complexity of the injection process. The
main limitation of collecting data on a single injection episode is that variation within injectors
cannot be observed across different injection environments. While this design has allowed an
accumulation of evidence determining which individuals have the highest probability of
engaging in risk behavior, it is much less informative toward the partners, locations, and
situations that might place IDUs at risk. The main limitation with using a population averaged
model is that within individual variance is treated as a nuisance parameter and not one to be
explicitly modeled, limiting flexibility in investigation of within individual factors (Hu,
Goldberg, Hedeker, Flay, & Pentz, 1998). For example, population averaged models would
likely be a more desirable approach to deal with repeated observations if the primary interest of
the study is to estimate the average effect of individual characteristics (e.g., being male or
female) across people. However, random effect models are more desirable if the primary interest
in the study is to examine how a single individual varies across different situation (e.g., when
injecting with a male or female partner). Accordingly, random effect models allow for more
successful modeling of the complex social processes that occurring across injection episodes

31

among the same individuals by providing additional flexibility in the modeling process of within
individual variability.
A second limitation in the current literature is that most studies have measured each type
of injection risk behavior as a separate outcome (e.g., sharing cookers, receptive syringe sharing,
etc.). This is problematic for a number of reasons. First, examining each outcome separately
provides a long and sometimes contradictory list of findings that may obscure important
relationships across variables. By combining conceptually related variables such as injection risk
behavior into a single score outcome variable, it becomes easier to compare results across studies
even when the precise items may vary from one study to the next (Wagner, Unger, et al., 2010).
Second, the number of injection steps that place the injector at risk during a given injection
episode is quite large and a single behavior may occur multiple times in a given episode (Scott,
2011). Therefore, single indicator assessments of injection risk behavior are unlikely to
accurately reflect the true breadth of injection risk behaviors. Finally, the goal of most IDU risk
behavior studies, both observational and experimental interventions, is to understand or reduce
the risk for contracting viral diseases. Given that many risk behaviors contribute to individual's
risk of contracting viral diseases, this risk is unlikely to be accurately modeled using a single
indicator dependent variable. However, injection risk behavior can be measured using latent
variable modeling in effort to estimate an underlying level of risk with each risk behavior being
used as an observed indicator of this risk. Using this approach, we can conceptualize each single
injection risk behavior reflecting the underlying injection risk behavior in a hierarchically
ordered manner. Therefore, IDUs who engage in risk behaviors that reflect the highest level of
underlying risk (e.g., using a syringe after multiple IDUs used it) are also likely to engage in
many other lower level risk behaviors (e.g., sharing cookers). Using all indicators, we can then

32

place IDUs on the spectrum from low to high levels of underlying risk behaviors according to
how many and which risk behaviors the IDU engaged in. Furthermore, using latent variable
modeling, these assumptions of order can be tested. For example, one study (Janulis, 2014)
found that the highest risk behaviors (e.g., sharing syringes with someone they know to be HIV
positive) were only endorsed by individuals with the highest levels of overall risk behavior and
the low risk behaviors (e.g., injecting drugs in the same room as other IDUs) tended to be more
commonly endorsed. This suggests that IDUs engage in risk behavior in a logical (i.e., ordered
hierarchically) manner and that these behaviors may be meaningfully modeling using latent
variable approaches.
While this approach has been more commonly applied to the study of sexual risk
behavior (Fendrich, Smith, Pollack, & Mackesy-Amiti, 2009; Li, Liu, Feng, & Cai, 2011;
Mattson et al., 2010; McClelland, Teplin, Abram, & Jacobs, 2002), several early studies (Darke,
Hall, & Carless, 1990; Darke, Hall, Heather, Ward, & Wodak, 1991; Fry & Lintzeris, 2003;
Petry, 2001) used factor analysis to provide latent variables scaling for injection risk behavior
but this approach has not been widely adopted by researchers in this area. More recently, a
growing number of studies (Mackesy-Amiti et al., 2013; Noor, Ross, Lai, & Risser, 2014;
Watson et al., 2013) have used latent class analysis to measure injection risk behavior using a
latent variable approach but the diversity (e.g., some include non-injection related risk behaviors)
and the small number of these studies make the relationship between IDU characteristics and
injection risk behavior as measured using latent variable modeling still unclear.
Finally, many studies examining injection risk behavior have failed to fully incorporate
theoretical frameworks to conceptualize, predict, and understand characteristics associated with
drug harms beyond the recognition of statistically significant co-variation (Friedman et al., 2013;

33

Rhodes, 2009). Of research in this area, qualitative (e.g., see Rhodes, 1997) and cognitive
behavioral (e.g., see Wagner et al., 2010) approaches have the strongest theoretical foundation.
For example, Rhodes (1997) used social action theory to challenge traditional individualistic
theories of "individual rationality" (i.e., cognitive theories about costs, risk perceptions, and
individual choice) to promote an analysis of risk behavior arising from situations, interactions
between individuals, and power disparities. Nonetheless, most studies continue to focus on
examining a single association between a specific characteristic and a specific risk behavior, an
approach that is common in public health and social epidemiological research (McKinlay &
Marceau, 2000). This process is primarily useful for the slow accumulation of data on specific
associations. However, this approach makes it increasingly difficult to build consensus without
meta-analysis or pooled data analysis across studies (Kivimäki & Kawachi, 2013). Furthermore,
this approach tends to facilitate "black-box" conceptualizations of these associations. As noted
by Galea and Link (2013), social epidemiologists have applied their methods "...with notable
success, to isolate factors that co-vary with diseased and aimed to intervene on these factors
without a good grasp on why they might matter" (p. 847). For example, the clarity of the causal
association between sharing syringes and the spread of HIV has led to rapid gains in reduction of
HIV incidence early in the US HIV epidemic among IDUs. However, these gains have slowed
considerably as the vector and precise mechanism of disease transmission has blurred for HCV
and the subsequent factors associated with HCV risk behavior have become similarly less clear.
Accordingly, an increased emphasis on mechanisms that risk factors represent and use of theories
to understand the conjunction and interaction of these mechanisms would likely facilitate
increased clarity in this research and provide firmer foundation for risk reduction interventions.

34

The Social Ecology and Micro-Social Risk Environment of Injection Risk Behavior
The intersection of dyadic, network, and environmental factors that influence injection
drug use can be understood as aspects of the social ecology (Latkin, German, et al., 2013; Latkin
& Knowlton, 2005) or risk environment (Rhodes, 2002, 2009) for injection drug use. This study
is specifically focused on those aspects of the social ecology of injection drug use that manifest
in the immediate ecological level(s) above the individual level and may change from one drug
use episode to another. While some recent focus has been placed on macro-structural
environmental elements such as syringe coverage (Mathers et al., 2010), policing strategies (C.
L. Miller et al., 2008; Rhodes et al., 2006), syringe policy (Burris, Anderson, Craigg, Davis, &
Case, 2011; O’Shaughnessy, Hogg, Strathdee, & Montaner, 2012), and the impact of place and
space on injection risk (Tempalski & McQuie, 2009), less emphasis has been placed on the
micro-social environment surrounding specific injection episodes (e.g., dyadic, network, and
situational; Latkin, German, et al., 2013) . Yet, preventive interventions often rely on the
alteration of these micro-social processes to achieve sustained change. Latkin and Knowlton
(2005) note that understanding the micro-social environment of injection drug use is the key to
shaping preventive interventions as these interventions rely on the alteration of, "...social
processes that promote and perpetuate these patterned [injection] behaviors" (p. 2).
In fact, the hypothesized causal mechanism between correlates of injection risk behavior
necessarily involves these complex micro-social processes in obtaining and using injection
equipment. For example, injection frequency is a consistent predictor of injection risk behavior
(Golub et al., 2007; Thiede et al., 2007). The hypothesized reason that injection frequency
increases risk behavior is that frequent injectors require more resources (e.g., drugs and injection
equipment). Therefore, IDUs often turn to drug and equipment sharing with other IDUs in order

35

to obtain amounts of resources they would be unable to acquire on their own (Shaw et al., 2007;
Zule, 1992). As another example, male-female injection partner disparities in injection risk
behavior can be partially explained by disadvantaged social and economic position of some
female IDUs (El-Bassel, Wechsberg, & Shaw, 2012). However, this relative disparity creates
risk through the social processes of injection (El-Bassel et al., 2014); for example, male partners
may be in charge of obtaining drugs or syringes (Tortu et al., 2003), leading to power disparities
(Zule, 1992) during the process of injecting (e.g., such female IDUs being more likely to inject
second with the same equipment). While studies examining correlates of injection risk behavior
often rely on cross-sectional snapshots of these processes, the ultimate goal of these studies
should include observing, understanding, and preventing the social processes that lead to the
spread of viral diseases, despite the frequent oversimplification in data collection and modeling
of these processes (Scott, 2011).
Accordingly, the current study is primarily concerned with the social processes that are
aspects of the micro-social risk environment of injection drug use, using Rhodes et al.'s (2003)
definition of micro-social risk environment: the "interplay of factors which taken together
influence the social norms and values surrounding HIV/AIDS and drug injecting, the nature and
structure of drug injectors’ social relationships and networks, the immediate social and physical
settings in which drugs are used, and the local neighborhoods and contexts in which drug
injectors live" (p. 50). Aspects of particular importance to this study are: network, dyadic, and
situational factors associated with these drug use episodes. To inform specific predictions about
the micro-social risk environment of injection risk behavior, this study will draw upon an area of
research cited in previous research (Latkin, German, et al., 2013; Latkin & Knowlton, 2005) as

36

important theoretical perspectives in understanding the social ecology of injection drug use:
social and behavioral settings.
Social Settings Theory. Tseng and Seidman's (2007) theory of social settings may
provide insight into the conceptual framing of injection episodes. This theory has four
components: resources, organization of resources, social processes, and outcomes. In the current
case of conceptualizing injection episodes as a social setting, resources in this setting could
include training of IDUs in safer injection practices, availability of clean injection equipment, or
external pressure on the time available to inject (e.g., due to street policing). The organization of
these resources describe how these resources are distributed throughout the setting. For example,
this would include unequal power over injection decisions or distribution of resources such as
paraphernalia or knowledge of safer injection practices. Social processes are conceptualized to
include norms, relationships, and participation in activities. Norms involve the influence of
setting member's beliefs on the participant's beliefs and behaviors. For example, norms could
dictate the acceptability of sharing equipment in a group of injectors. Relationships can be
conceptualized in multiple ways but this study is primarily interested in the impact of network
and dyadic characteristics. Participation in activities involves participation in roles such as
structured institutional activity (e.g., syringe exchange participant) as well as unstructured noninstitutional activities (e.g., "dope doctor" that helps other IDUs inject). While the settings
components were theorized to have bi-directional influence, Tseng and Seidman (2007) offered
the following theoretical path to stimulating change in social settings (Figure 1). This path
diagram suggests that resources and distribution of resources lead to social processes, which

37

mediate the impact of resources on setting outcomes. 5 In the case of injection drug use,
interventions have largely focused on increasing the human and physical resources through
providing injection material and training in safer injection practices.
The most acute example of IDU interventions to increase resources is the supervised
injection facilities that provide physical (e.g., syringes and space to inject), human (e.g., trained
staff to provide guidance in injection practices), and temporal (e.g., providing a space free from
police or other surveillance that may speed up injection episodes) resources in effort to reduce
the spread of HIV/HCV (Degenhardt et al., 2010; Kerr et al., 2006; Wood et al., 2006). However,
most injections do not take place in supervised injection facilities. Therefore, injection hygiene
interventions have attempted to promote safer injection settings in less comprehensive manner by
increasing the resources available to IDUs through the provision of sterile injection equipment
(i.e., physical resources) and training in safer injection practices (i.e., human resources).
Accordingly, the availability of these resources has been the primary focus of many studies. The
drugs themselves could also be considered a resource that is likely associated with reduced
setting level outcomes (e.g., if a large amount of available drugs lead to binge use). However,
most studies and interventions related to injection risk behavior assume that the availability of
drugs is given and therefore look at other resources to mitigate the risk caused by the availability
of these drugs.

5

Research from the network analysis paradigm also suggests that these variables could work in the opposite
direction (i.e., social processes could also determine resources and resource distribution). Furthermore, Tseng and
Seidman (2007) acknowledge that social processes likely also influence resources/resource distribution.
Accordingly, this diagram should only be viewed as one possible route to altering setting outcomes.

38

Figure 1. Theoretical Path for Change in Social Settings

Organization of resources in injection settings has also been less studied compared to the
other elements of these social settings. However, preliminary investigations have identified that
gender differences in access to drug and drug paraphernalia are often present in these settings
(El-Bassel et al., 2014; Frajzyngier et al., 2007). Similarly, a few recent studies have shown how
unequally resource distribution between geographic regions may explain racial and ethnic
disparities in injection risk behavior (Cooper et al., 2011; Cooper, Des Jarlais, Ross, et al., 2012),
reflecting unequal distributions of injecting resources available in these settings on a larger scale.
Finally, the social processes that likely mediate the relationship between resource
provision/distribution and outcomes remain much less clear in this literature (Latkin &
Knowlton, 2005; Rhodes et al., 2003). While these processes have begun to be explored through
studies of IDU norms (Latkin, Donnell, et al., 2013; Tobin et al., 2011) and networks (De et al.,
2007), most studies have not focused on the immediate social circumstances and processes
during specific injection events or how and if interventions promoting resources impact social
processes (i.e., the potential mediating role of social processes), as discussed in the limitation
section. Again, the exception to this rule is ethnographic and other qualitative approaches that
39

have documented the social processes surrounding injection episodes (e.g., see Zule, 1992).
However, these insights remain largely unconfirmed in a quantitative framework. Given that
much remains unknown about these social processes and their importance in mediating health
outcomes, these processes are the primary focus of the current study.
The Current Study
The current study is intended to expand upon preliminary investigations (Gyarmathy et
al., 2010; McMahon, Pouget, & Tortu, 2007; Unger et al., 2006) of the micro-social risk
environment for injection drug use and further explore situational, dyadic, and network
characteristics associated with injection risk behavior. This study will use Tseng and Seidman's
(2007) theory of social settings to inform predictions of specific associations, as understood in
the context of existing literature on injection drug use. By providing a more wholistic model of
individual, network, dyadic, and situational correlations, this study will further clarify the
relationships between resources, social processes, and injection risk behavior.
This study will use data from the Sexual Acquisition and Transmission of HIV
cooperative agreement program (SATHCAP) and will leverage this event specific data to
provide novel insights into the associations between injection risk behavior and these
characteristics. Due to the complex dependencies of event-specific data and the use of multiple
indicators used to measure the dependent variable, multilevel structural equation modeling
(SEM) was used to estimate model parameters. The theoretical path diagram for the expected
relationships can be seen in Figure 2. The model involves two main aspects. First, the bottom
half of the model examines "within" individual variation in injection risk behavior and
predictors. In Figure 2, injection risk behavior at each episode is measured by the 4 observed
indicators (i.e., receptive syringe sharing, using a syringe to mix drugs, sharing non-syringe

40

paraphernalia, and distributive syringe sharing). Predictors at the within level (e.g., number of
injectors, gender concordance, sexual partnership, etc.) represent characteristics of injection
episodes that can vary from one episode to the next. At this level, random intercepts are
represented by small black circles that are at the end of an arrow (i.e., y1b - y4b) while random
slopes are represented by small black circles in the middle of arrows (i.e., S1 and S2). The top
half of the model represents the "between" individual variation in injection risk behavior
measured at the between level through the latent variables created from the random intercepts of
the four within level indicators. The between level also has two latent variables that account for
the random slopes (i.e., S1 and S2) at the within level. Predictors at the between level represent
characteristics of the individual or their network that are constant for each participant (e.g.,
gender, network size, etc.). In addition, at either levels of the model interactions are represented
using an arrow that leads to the middle of another variable's arrow indicating that the originating
variable is moderating the relationship between the two variables.
All predicted relationships are based on social setting theory (Tseng & Seidman, 2007)
and previous findings of injection risk behavior literature. For further clarification, the predicted
associations expected in this study are specifically designated through the following hypotheses
and Table 1 indicates the aspect(s) of Tseng and Seidman (2007) theory of social setting to
which each hypothesis corresponds.
Dyadic Predictors. For dyadic predictors, gender concordance is expected to have a
negative relationship with risk behavior (i.e., so that concordant pairs are less likely to engage in
risk behavior; Hypothesis1a) as suggested by limited previous research (Hahn et al., 2010).
Furthermore, power and resource disparities between male and female IDUs (El-Bassel et al.,
2014) may lead to increased risk behavior in gender discordant injection partners (e.g., because

41

one partner is reliant on the other to obtain these resources) as suggested by the impact of
organization of resources in social setting theory (Tseng & Seidman, 2007). Sexual partnership is
also predicted to have a positive association with injection risk behavior (Hypothesis1b) based on
previous research (Bailey et al., 2007; Hottes et al., 2011) and the impact of the social processes
(Tseng & Seidman, 2007) caused by the intimacy and trust inherent in sexual relationships.
However, both gender concordance and sexual partnership are predicted to be moderated
by the gender of the participant (i.e., a cross level interaction with gender, level 2, predicting the
slopes of gender concordance and sexual partnership, level 1; Hypothesis1c and Hypothesis1d,
respectively). This relationship is predicted based on the unequal distribution of resources
between male and female IDUs and the subsequent power disparities previously recorded in
gender discordant pairs favoring males (El-Bassel et al., 2014; Tortu et al., 2003). I predict that
being female will increase the negative association between gender concordance and injection
risk behavior. Similarly, I predict that being female will increase the association between sexual
partnership and injection risk behavior for identical reasons. 6 Therefore, this hypothesis will
examine if the unequal distribution of resources among male and female IDUs creates additional
risk among females via the social processes associated with gender concordance and sexual
multiplex injection partnerships. Thereby, this hypothesis will examine how different aspects of
the injection setting (i.e., distribution of resources and social processes) may interact using Tseng
and Seidman's (2007) framework.
Length of injection partnership is expected to have a positive association with injection
risk behavior (Hypothesis1e) because a longer length of partnership is likely to be a proxy
variable for the closeness of the two IDUs, which itself is associated with injection risk behavior
6

These two dyadic variables are complicated by same-sex sexual relationships, particularly female-female
relationships.

42

(Valente & Vlahov, 2001). In Tseng and Seidman's (2007) framework, the social processes
inherent in long term injection partnership (e.g., accumulation of trust, greater intimacy, etc.) are
also likely to facilitate higher levels of risk behavior. Perceived concordant HIV serostatus is
also predicted to have a positive association with risk behavior (Hypothesis1f) given the current
evidence supporting serosorting between injection partners (Hahn et al., 2010; Smith et al.,
2013). That is, participants who believe they are the same HIV serostatus may be more likely to
share equipment given that sharing equipment will not change their HIV status if they only share
with same serostatus partners. In the absence of perceived risk in sharing equipment, natural
social processes (Tseng & Seidman, 2007) that promote risk behavior in these settings such as
reciprocity may promote injection risk behavior.
Network Predictors. For network variables, injection network size is predicted to have
a positive relationship with injection risk behavior (Hypothesis2a) given the previous findings
suggesting drug/injection network size was positively associated with risk behavior (Cepeda et
al., 2011; Latkin, Mandell, Oziemkowska, et al., 1995; Latkin, Mandell, Vlahov, et al., 1995). In
the social setting framework, the mechanism of this association is assumed to work through the
social processes of obtaining and injecting drugs. For example, this association could be due to
the increased opportunity to engage in risk behavior or the normative pressure to share resources
that could be enhanced among individuals with larger IDU social networks.
Environmental Predictors. For environmental or situational predictors, the number of
IDUs present at the injection episode is predicted to have a positive association with injection
risk behavior (Hypothesis3a), while the number of non-IDUs present is predicted to be negatively
associated with injection risk behavior. The number of IDUs present at the episode may limit the
per-person distribution of injection resources and thereby increase the likelihood of participants

43

engaging in risk behavior in an effort to pool the limited resources, as predicted using social
setting theory (Tseng & Seidman, 2007). It is also possible that IDUs being present at an
injection episode likely increases the social pressure and norms (i.e., facilitate social processes)
that lead to sharing injection paraphernalia. Therefore, a larger number of injectors being present
at an episode is likely to provide both the opportunity and increased normative pressure to
engage in this behavior, two social processes associated with setting outcomes (Tseng &
Seidman, 2007). Furthermore, similar to the injecting public areas (Small et al., 2007), a larger
number of injectors being present may limit the amount of time an injector has to inject behavior
(i.e., temporal resources in the Tseng and Seidman (2007) framework) due to a fear of being
disrupted by other injectors or in effort to avoid requests to share their resources. This reduction
in temporal resources could also lead to increased risk behavior. The number of non-IDUs
present at the injection episode is expected to be negatively correlated with injection risk
behavior (Hypothesis3b) and I also expect an interaction between the number of injection drug
users and non-injection drug users so that the the number of non-IDUs will reduce the
association between the number of IDUs and risk behavior (Hypothesis3c). Again, these
relationships are based on the number of non-IDUs present being associated with a decrease in
the normative social pressure to share injection equipment associated with the presence of
additional IDUs due to the fact that non-IDUs place more stigma on injection risk behavior
(Mateu-Gelabert et al., 2005). Therefore, norms in these settings are less likely to be conducive
for risk behavior which should subsequently decrease the likelihood of this behavior (Tseng &
Seidman, 2007). Finally, injecting in one's own residence is predicted to be inversely associated
with risk behavior (Hypothesis3d) given the elevated risk observed at shooting galleries and
(Koester et al., 2005) and that injecting in the participant's residence also makes it most likely

44

that the participant will have greater control over and availability of the injection resources.
Accordingly, this increased control of resources, is likely to lead to more desirable outcomes for
these individuals (Tseng & Seidman, 2007).
Current Study Contributions. By examining these hypotheses, this study would
advance current knowledge of dyadic, network, and environmental predictors of injection risk
behaviors in the following ways. First, the key contribution this study would provide is
overcoming limitations of past research that only examined a single injection episode, that used
population averaged or marginal models (e.g., generalized estimating equations) of multiple
injection episodes, or used mixed models but only examined dyadic characteristics. By doing so,
this study should provide novel insight into the social processes of injection episodes that are
associated with injection risk behavior. For example, this study will examine the number of
people using drugs at the specific episode, the physical location of the episode, as well as the
dyadic characteristics of specific partners. The ability to separately estimate network factors at
the individual level with the social circumstances of each injection episode should also provide
novel insight toward the mechanisms of social network associations with injection risk behavior.
For example, one proposed reason that drug network size is positively associated with risk
behavior is that larger drug networks tend to increase the number of individuals present during
an injection episode and thereby increase the likelihood of sharing equipment. However, this
study provides separate estimates for the number of injectors present at the episode and the
number of injectors in the participant's IDU network, allowing for a clearer picture of the
potential mechanism between network size and risk behavior.
Second, this study will overcome limitations of the previous literature by measuring
behavior by using a multiple indicator (i.e., with a latent variable) measurement model of the

45

dependent variable, injection risk behavior. The measurement of injection risk is inherently
complicated due to the multitude of risk behaviors, potential differences in time frame, and
complexity of the injection process (Samuels, Vlahov, Anthony, & Chaisson, 1992). As
discussed, most studies have examined each risk behavior as a separate dependent variable
despite the goals of these studies to understand and prevent the spread of viral diseases among
IDUs. While other types of composite scores are possible (e.g., scales weighted by experts or
sum scores), latent variable measurement provides advantages to both single-item and
composite- scoring methods in a number of ways. As noted that by McClelland et al. (2002),
three traditional approaches have been used in calculating composite scores of viral transmission
risk: counting the number of times engaging in risk behaviors, weighting behaviors based on
expert opinion, and weighting behaviors based on seroconversion. However, simple counts or
other sum scores do not provide unequal weighting for behaviors that differentially contribute to
viral risk (e.g., sharing cookers versus sharing syringes) while weights made by experts may still
not accurately reflect seroconversion risk. Weightings based on seroconversion risk would be
most desirable but precise data is not readily available on injection risk behaviors and may vary
considerably between specific contexts and based on the stage of the HIV/HCV epidemics.
Latent variable modeling provides an alternative approach that empirically derives the item
weights and provides numerous benefits over the traditional single indicator approach. For
example, latent variable measurement provides increased parsimony by offering a single
parameter estimate of each relationship between the independent variables and injection risk
behavior, explicit modeling of measurement error, incorporation of both person and item
characteristics in calculating total scores, and improved handling of missing data if the
participant only has missing data on some items (Janulis, 2014; Mattson et al., 2010). It is

46

Figure 2. Model Path Diagram

S2
Rec.
Share
(y1b)

Gender

S1
Dist.
Share
(y2b)

Injection

Injection
Risk

Network Size

Divide
Drugs
(y3b)

All Other
NonSyringe
Share
(y4b)

Demographics

Between
Within

Rec.
Share

Dist.
Share

Location

# NonInjectors

y2b
Injection
Risk

Divide
Drugs

# Injectors

y1b

S1

Gender
Concordance

S2

y3b

Sexual Partner
NonSyringe
Share

y4b
df
Serosorting

47

Length of IDU
partnership

certainly true that this modeling approach would not be ideal in all situations. For example, if the
goal of an intervention is to reduce the frequency of a specific behavior (e.g., sharing cookers)
then a study evaluating this intervention would ideally examine this outcome without pooling it
with other indicators. However, as noted, in many cases modeling injection risk behavior as a
latent variable provides multiple benefits over separately examining each outcome. For example,
studies examining injection risk behavior generally measure only a small sample of possible risk
behaviors. By pooling variables and estimating scores as latent factors, we can examine the
shared variance between items to form a measurement model that more accurately reflects the
concept of underlying viral risk. Furthermore, IDUs have been shown to respond to risk behavior
questions in a meaningful hierarchical manner, with those IDUs engaging in the highest risk
behaviors (e.g., sharing with known HIV positive injectors) generally engaging in many lower
level risk behaviors while many injectors engaging in lower level of risk behaviors (e.g., sharing
cookers) that do not engage in higher level risk behaviors. Accordingly, given the broad focus of
the current study on general injection risk and the additional information in the modeling
parameters (e.g., factor loadings), examining each indicator's contribution to the composite
score, the benefits of latent variable measurement outweigh the limitations of this approach.
Third, this study will incorporate Tseng and Seidman (2007) theory of social settings to
conceptually integrate findings on individual, situational, and dyadic predictors of injection risk
behavior. While previous research has largely overlooked theoretical frameworks for predicting
injection risk behavior, this study uses a theory of social settings to both inform predictions and
interpret these findings in a more holistic understanding of injection risk behavior. As discussed
in the outline of the hypotheses, social setting theory will be used to understand the underlying
mechanisms that may explain the expected associations in this study. For example, the

48

availability and distribution of drug use resources such as drugs and injection paraphernalia
likely drive the social processes (e.g., sharing) that create high-risk scenarios. Furthermore, the
availability and distribution of resources may be driven by the social and physical circumstances
of a given injection episode. Accordingly, we will use the available information about
individuals, networks, and situational characteristics to model predictors of injection risk
behavior while using Tseng and Seidman's (2007) theory of social settings to the guide the
understanding of how resources, the organization of resources, and social processes may give
rise to these associations.

49

Table 1. Hypothesis and Tseng and Seidman's (2007) theory of social settings
Organization
Of Resources

Resources
Hypothesis 1
a. Gender concordance will be negatively
associated with injection risk behavior.

Previous research has
found that female
IDUs tend to have less
control over injection
equipment (i.e.,
physical resources).

b. Sexual partnership will be positively
associated with injection risk behavior.

Social Processes
The resource
imbalance combined
with gender
norms/roles that place
less stigma on gender
discordant sharing will
lead to increased risk
among discordance
partners.
Sexual partners are
more likely to share
resources given
relationship aspects
that promote intimacy
and trust that
sometimes outweigh
concerns about risk.

c. The association between gender
concordance and injection risk behavior
will be modified the sex of the participant
so that being female will be associated
with a stronger negative relationship
between gender concordance and injection
risk behavior.

Given that previous
research finds that
gender discordant
physical resource
imbalances tend to
favor male IDUs, this
imbalance will
enhance the risk
witnessed among
50

In addition to resource
imbalances, gender
roles also favor males
in dominant roles (e.g.,
where the male injects
the female partner).
Therefore, male IDUs
will have increased
control over the

Table 1 (cont'd)

d. Sexual partnership will be modified by
the sex of the participant so that being
female will be associated with a stronger
positive relationship between sexual
partnership concordance and injection risk
behavior.

female IDUs with
discordance partners.

process and in effort to
mitigate their own risk
may increase the risk
of the female
participant (e.g.,
pressure the IDUs to
inject after the partner
using the same
equipment).

Again, female IDUs
tend to have less
access to injection
resources such as
paraphernalia
compared to their male
injection partners

The unequal resources
and gender norms/roles
that favor males in
positions of control
over the injection
process may greater
risk exposure among
female IDUs in dual
sexual/injection
partnerships.a

e. Length of injection partnership will be
positively associated with injection risk
behavior.

Long term injection
partners are more
likely to share
equipment due to the
trust and intimacy that
accumulates during
long term partnerships.

f. Perceived serostatus concordance will
we associated with increased risk behavior

Seroconcordant
partners are more

51

Table 1 (cont'd)
as compared to perceived discordant or
unknown HIV-status partners.

Hypothesis 2
a. Injection network size will be positively
associated to injection risk behavior.

Hypothesis 3
a. The number of injectors present during
the injection episode will be positively
associated with injection risk behavior

likely to share
equipment given that a
belief in
seroconcordance
reduces the perception
of risk in sharing and
therefore relationship
factors of reciprocity
and sharing may
overcome concerns of
risk.

While the a larger IDU
network could increase
or decrease available
physical resources at a
specific episode,
previous research has
generally found larger
networks associated
with greater risk
behavior suggesting
that networks may
strain the availability
of physical injection
resources at any given
episode.
A greater number of
injectors may limit the
temporal resources of

52

Participants with larger
IDU networks may be
exposed to more prosharing norms
regardless of the
specific injection
episode and may
participate in greater
levels of sharing.

A greater number of
injectors will likely
reduce the amount of

A greater number of
injection may increase
norms of sharing or

Table 1 (cont'd)
the participant given
that participants may
want to inject more
quickly to avoid
sharing drugs with
other injectors, similar
to the impact of
injecting in other
public spaces found in
previous research

paraphernalia per
person and thereby
make it more likely
multiple people use the
same equipment.

b. The number of non-injectors present
during the injection episode will be
negatively associated with injection risk
behavior

Non-IDU social norms
have a higher level of
stigma attached to
injection equipment
sharing and will
therefore reduce the
level of risk behavior
through this normative
pressure.

c. The number of non-injectors present
during the injection episode will moderate
the relationship between number of
injectors present and injection risk
behavior so that an increase in noninjectors will reduce the association
between the number of injectors and
injection risk behavior.

d. Injecting at the participant's home will

increase the likelihood
of being pressured to
share by any individual
IDU present (i.e.,
through relationships).
Combined with less
resources per person,
this will lead to greater
likelihood of engaging
in sharing.

The ratio of IDUs to
non-IDUs could be
considered an aspect of
the "social
organization" of
resources in the setting
that likely impact the
normative pressure on
IDUs
Participants are likely

53

Among IDUs present

Again, non-IDU social
norms look less
favorably toward
equipment sharing
behavior. Therefore, a
greater number of nonIDUs will likely
decrease the normative
effect of IDUs that are
present.
Greater access and

Table 1 (cont'd)
be associated with lower injection risk
behavior

to have more injection
paraphernalia (i.e.,
physical resources) and
greater freedom for the
amount of time
available to inject (i.e.,
temporal resources) at
their own residence as
opposed to other
locations.

at an injection episode,
participants are more
likely to have greater
control over temporal
and physical resources
because they are the
individual granting
access to the drug use
space.

control over resources
will counteract
normative and
relational pressure to
share paraphernalia.

a. Again, this relationship is complicated by same-sex female sexual partnerships and if these relationships represent a large
percentage of concurrent sexual and injection partnerships then this hypothesis may have to be re-examined

54

Method
This study used data from the Sexual Acquisition and Transmission of HIV Cooperative
Agreement Program (SATHCAP; Compton, Normand, & Lambert, 2009) . The cooperative
agreement included data collection at three U.S. sites (i.e., Chicago, IL; Los Angeles, CA; and
Raleigh Durham, NC), and one international site (i.e., St. Petersburg, Russia). However, only
data from the three U.S. sites currently available via the National Addiction and HIV Archive
Program will be used in this study. The SATHCAP project was primarily designed to examine
sexual transmission of HIV among high risk groups such as men who have sex with men (MSM)
and drug users, including both IDUs and non-IDUs. However, the sample of IDUs will be the
sole focus of this study, including both MSM and non-MSM who are IDUs, but excluding all
participants who did not inject with another individual in the previous six months. Despite the
study's emphasis on sexual transmission, the questionnaire also gathers information on
HIV/HCV drug related risk behaviors including gathering data on up to four injection episodes
that allow us to pursue these previously underdeveloped topics.
Recruitment/Sampling
The recruitment of participants for all SATHCAP sites utilized respondent driven
sampling (Iguchi et al., 2009). Respondent driven sampling is a chain referral sampling
technique that uses dual incentives intended to produce un-biased population estimates for
hidden populations (Heckathorn, 1997). This method has been commonly used to improve upon
convenience sampling among populations that traditionally cannot be sampled using probability
samples due to the low frequency of the behavior in population samples and/or strong stigma
attached to the target characteristic(s) of the target population. This method has frequently been
used when sampling IDUs (Heckathorn, Semaan, Broadhead, & Hughes, 2002) and MSM

55

(Kendall et al., 2008). The method involves providing two types of incentives. First, primary
incentives are given to participants to compensate them for their participation. However,
incentives also reward participants for recruiting additional members into the study. Seed
participants are generally gathered using non-probability convenience sampling (e.g., through
community outreach, advertising the study, or simply well known members of a given
community). Seed participants then begin the snowball mechanism of the recruitment technique,
with each wave of participants recruiting an additional wave from their social network. This
process continues until participant characteristics of the sample stabilize and new participants
can be considered independent of initial seed participants (Ramirez-Valles, Heckathorn,
Vázquez, Diaz, & Campbell, 2005).
While previous studies using respondent driven sampling targeted a single population,
SATHCAP employed an RDS recruitment technique that targeted drug users (IDU and nonIDU), MSM, and their sexual partners (both male and female). Accordingly, SATHCAP
employed a relatively novel respondent driven sampling methodology. That is, participants in the
study were provided some coupons to recruit any person who, "used heroin, methamphetamine,
cocaine, or crack, or injected another drug in the past six months", "any man who had anal sex
with another man in the past six months" as well as "any person with whom you have had sex in
the past six months" (p. S12, Iguchi et al., 2009). At the Illinois and North Carolina sites, seeds
were actively recruited through approaching community members known to meet the eligibility
criteria. At the California site, seeds were passively recruited through flyers due to institutional
review board requirements. Eligibility was screened in the following ways: 1) coupons were
examined for authenticity, 2) participants answered a short screening questionnaire to see if their
characteristics met the requirements of the coupon they obtained (e.g., if they received a higher

56

risk coupon, they were screened for being a drug user or MSM), 3) demographics were screened
for duplicate participants (e.g., same birthday and biometric features such as tattoos). All
participants, including seeds, were compensated for their time although compensation differed
across sites due to different institutional review board requirements.
Recruitment occurred in two separate phases: between September 2005 to December
2006 for phase 1 and between November 2006 to August 2008 for phase 2. During phase 1 all
participants were given three coupons to give to network members that met either of the higher
risk criteria (i.e., drug user or MSM) and three coupons to recruit any of their sex partners from
the past six months. One of the sex partner coupons was designated for individuals that were not
drug users if the participant was a drug user or for a sex partner that was a woman if the
participant was a MSM. These coupons were designed to encourage recruitment of partners that
may have roles as HIV risk "bridges" to the general population. Both sex partners and higher risk
recruits were eligible to become recruiters themselves. However, sex partners were simply given
three coupons to recruit their sex partners and these recruits (i.e., sex partners of sex partners of
higher risk groups) were ineligible to become recruiters.
Phase 2 recruitment was similar to that of phase 1 with some minor adjustments,
primarily made to adjust for under recruitment of non-drug using sex partners. All participants
from phase 1 were ineligible for recruitment in phase 2. Participants in phase 2 were given two
coupons for higher risk groups and two coupons for opposite sex partners. Higher risk
participants were always given these 4 coupons while participant recruited as opposite sex
partners were given only 2 coupons to recruit opposite sex partners. However, the number of
higher risk coupons was increased to 4 coupons midway through phase 2 in order to get
sufficient recruitment of this group.

57

Finally, participants from both phases who recruited other participants completed a final
questionnaire when they returned to obtain their secondary incentives. This questionnaire was
designed to assess the characteristics of individuals who had refused the coupon as well as the
characteristics of individuals who were given coupons but did not chose to participate in the
study.
Inclusion Criteria. The current study utilized a subsample of IDUs from the larger
sample collected in the SATHCAP study. Any participants from either phase of recruitment were
included in the current study if they injected with at least one person during the last 6 months
("with" means, "people who injected drugs at the same place and time as you"). This inclusion
criterion was necessary because individuals who have not injected in the same place and time as
another individual would not have provided data on specific injection episodes. This criteria
includes 835 total participants with 55 providing data on four injection episodes, 391 providing
data on three injection episodes, 207 providing data on a two injection episodes, and 182
providing data on a single injection episode leading to a total. Demographics of this subsample
and those of the entire sample can be found in Table 2.
Measures
The measures used in this study can be broadly organized into two groups: level 2 (i.e.,
individual) measures and level 1 (i.e., injection episode) measures. Level 2 measures include
demographics, drug use, and personal network characteristics. These variables do not change
across injection episodes. Level 1 measures included situational characteristics (i.e.,
characteristics of the injection episode), dyadic characteristics (i.e., characteristics that depend on
the participant and the injection partners), and injection risk behavior. These variables are
injection episode specific and therefore can have variability within individual participants.

58

Copies of the exact wording of the questionnaire for all measures that will be used in the study
can be found in Appendix A. Given the fact that participants were recruited using respondent
driven sampling, it is possible, and perhaps very likely, that there are dependencies in the data
(e.g., partners referenced in an injection episode that later appear as participants) not modeled
using the simple 2-level structure proposed in this study. Unfortunately, information is not
provided in the dataset to model this cross-classification. Consequently, dependency may remain
in the data even after accounted for the multilevel nesting. However, studies with similar
recruitment and analysis have also been limited by this same possibility (Ober, Shoptaw, Wang,
Gorbach, & Weiss, 2009) and this issue will remain as a similar limitation in the current study.
Table 2. Demographics of Non-IDU vs. IDU Sample
Non-IDU
n (%)
M (SD)
Age
42.79 (9.9)
Gender
Male
2410 (62.5%)
Female
1407 (36.5%)
Race
African American
3038 (78.8%)
White
567 (14.7%)
Other
105 (2.7%)
Hispanic/Latino
310 (8.0%)

IDU
n (%)

M (SD)
42.61 (10.8)

564 (67.5%)
447 (53.5%)
290 (34.7%)
17 (2.0%)
201 (24.1%)

Homeless
1549 (40.2%)
433 (51.9%)
Note. IDU refers to individuals who individuals who reported an injection partner in the past 6
months. Blank cells are censored due to the requirements of the data use agreement.
Level 2 (Individual) Measures
Demographics. The following demographic variables will be included in the study. Age
was measured in years as a continuous variable. Gender will be measured as male or female (0 =

59

Male, 1 = Female). Transgender participants will be coded as the currently identified gender. 7
Race will be coded as White, African American, and Other using two dummy coded variables
indicating if the participant was African American (0 = African American, 1 = White) or Other
(0 = African American, 1 = Other). Hispanic/Latino will be coded as a separate question as
Hispanic or non-Hispanic (0 = Non-Hispanic, 1 = Hipanic/Latino). Finally, participants will be
coded yes/no if they identify as homeless if they were homeless anytime during the past year (0
= Yes, 1 = No).
Drug Use. Participants will have two continuous indicators of frequency of crack/cocaine
or heroin/other opiates. For both drug types, drug use frequency is defined as the number of
times the participant used the respective drug during the previous 30 days. Number of years
injecting drugs will also be assessed. This will be measured by subtracting the current age of the
participant by the age at which they first injected drugs (e.g., a zero will indicate they started
using drugs within the past year). Injection frequency will also be assessed using a continuous
indicator reporting how many times the participant injected drugs during the previous 30 days.
Network Characteristics. The injection network size will be measured by the
participant’s response to the following question: "About how many different people did you
inject drugs within the past 6 months? (By "with", we mean people who injected drugs at the
same place and time as you.)". While a period of 6 months may be prone to some measurement

7

While transgender injectors are unlikely to experience the same level of risk as non transgender injectors
(Clements-Nolle, Marx, Guzman, & Katz, 2001), the small number of transgender participants that inject drugs (this
number cannot be disclosed due a small cell count and requirements of the data use agreement) made it not possible
to separately examine this group.

60

error, this interval is standard and has been used extensively in past research (Ramirez-Valles et
al., 2005; Shaw et al., 2007; Suh et al., 1997).8
Level 1 (Injection Episodes) Measures
Level 1 measures are specific to each injection episode. Data on one to four injection
episodes were elicited from each participant. Only individuals who reported that they injected in
the same place and time as another individual during the previous 6 months were asked about
specific injection partners and situations. Those who had injected with at least one individual
were asked to provide the initials of up to three individuals they had "recently injected with". In
addition, participants were also asked if one of these named partners was the partner with whom
they most frequently inject. If none of the previously named partners were their most frequent
partner, information was also elicited on their most recent injection episode with the most
frequent partner. This created the possibility of four episodes being observed (i.e., if the
participant named three partners and none of them were their most frequent partner). For each
individual named, the participants provided information about the characteristics of those
individuals and the most recent situation in which they injected with that individual.
Situational Characteristics. Two characteristics of the physical environment of the
injection episode will be included in the analysis. First, the location of the injection episode will
be categorized using a dichotomous indicator reporting if the participant injected in their own
home or injected in another location (0 = Own residence, 1 = Another residence). Second, the
location will also be categorized with two binary variable indicating if the location was the most
common location the participant injects at with that partner (0 = Most common location, 1 = Not
8

While a separate variable indicating the "non-IDU drug use network size" was originally planned to be included in
the analysis, the variable measuring the total number of drug uses in the network was not associated with the
injection network size in the expected manner. That is, the number of total drug users was frequently smaller than
the injection network. Accordingly, this variable was not included in the analysis.

61

most common location). Another binary variable indicated if data were missing on this being the
most common location (0 = Not missing, 1 = Missing). Two additional variables examined the
non-dyadic social environment of the injection episode. The first will measure how many other
individuals were injecting at the same site. The second will measure how many other individuals
where using drugs at the same site but not injecting.
Dyadic Characteristics. Several variables examined the dyadic characteristics between
the participant and the injection partner. Two variables examined gender concordance. This
variable will include one binary indicator describing if the participant and the partner identify as
the same gender (0 = Different Gender, 1 = Same Gender) and a second variable identifying if
that episode has missing data on gender (0 = Not missing, 1 = Missing). Three dummy variables
will indicate the perceived concordance of HIV status between the participant and the partner
with HIV-discordance (i.e., indicating the participant believes they do not share the same
serostatus) being used as the reference category. One variable will indicate if they are of
unknown concordance (i.e., the participant is unsure about their own or the partners HIV status;
0 = Known Concordance, 1 = Unknown Concordance). Another variable will indicate if the
participant believes the partner to be HIV-concordant (0 = Not Known to be Concordant, 1 =
Concordant). Therefore, a discordant dyad would receive two "0's", while an unknown
concordant dyad would receive a "1" on the first variable and a "0" on the second, while a
concordant dyad would receive a "0" on the first variable and a "1" on the second variable.
Finally, a third variable will indicate if data on HIV concordance is missing (0 = Not Missing, 1
= Missing). A binary variable will indicate if the injection partner is also a sex partner. Finally,
four binary variables indicate the length of the injection partnership (i.e., how long they have
been injecting together). This variable was transformed from a raw duration of injection

62

partnership to four binary indicators for the following reasons. First, the distribution was biased
to certain values due to the fact that some participants answered this question in days while
others in months or years. Therefore, when converting these values into a single metric (e.g.,
days) the variable did not approximate a continuous distribution due to overrepresentation of
certain values (e.g., 365 days if the participant indicated they had injected with that partner for 1
year). Second, a large number of participants (19.0%) could not answer this question because
this was the first time they injected with the partner. A categorical coding scheme allows for the
classification of these particular individuals’ injection partnership duration as a separate code.
Thus, the first binary indicator signified if the participants were first time injection partners (1 =
new partner, 0 = not new partner). Additionally, for other participants, three dummy codes were
used to indicate the injection partnership in quartiles. Using the lowest quartile of injection
partnership as the reference, one variable indicated the partnership was in the second quartile (0
= Not in the second quartile, 1 = in the second quartile) while two additional dummy codes
identified those in the third and fourth quartile, respectively.
Injection Risk Behavior. Injection risk behavior will be measured with four variables.
One indicator was ordinal while the other three indicators were binary. As will be discussed
further in the analysis section, these variables were measured as a single continuous latent
variable. The three-point ordinal indicator will measure if the participant engaged in receptive
syringe sharing and, if they did, whether they used bleach to clean the syringe before injecting (0
= No receptive sharing, 1 = receptive sharing with bleach 2 = receptive sharing no bleach). The
first binary indicator will measure if the participant used a used syringe to mix, measure, or
divide the drugs (0 = Did not use, 1 = Did Use). The second binary indicator will measure if the
participant shared any other non-syringe injection paraphernalia (e.g., cookers, cottons, rinse

63

water; 0 = Did not share, 1 = Did share). The third binary variable will indicate if the participant
engaged in distributive syringe sharing (i.e., if the partner used the participants syringe after the
participant injected; 0 = Did not engage in distributive sharing, 1 = Did engage in distributive
sharing).
Analytic Approach
The nature of eliciting data on multiple injection episodes for each participant requires a
modeling technique that can overcome the limitations of traditional general linear modeling. This
requirement is common to all studies of nested or repeated measure data that violate the
assumption of independence of observations (Muthén & Satorra, 1989). Multilevel models (also
known as mixed models) are a common tool to accomplish this goal (Paterson & Goldstein,
1991). However, multilevel models are not the only approach to adequately deal with nested
observation. Appropriate models such as generalized estimating equations often provide an
alternative solution that can adequately model this data (Zorn, 2001). In fact, a contentious
debate exists over the assumptions, utility, and appropriateness of generalized estimating
equations versus mixed models (e.g., see Hubbard et al., 2010). Nonetheless, for the purpose of
this study, multilevel modeling provided a more compelling solution for two reasons. First,
multilevel modeling allows for explicit modeling of the random parameters (Gardiner, Luo, &
Roman, 2009) and, as a population averaging approach, generalized estimating equation (GEE)
does not provide this opportunity. By explicitly modeling the covariance structure of nested data,
multilevel models allow examination of questions that are not possible in generalized estimating
equations (Goldstein, Browne, & Rasbash, 2002). In the current study for example, multilevel
models allow for examining how the effect of episode specific variables (e.g., sexual partnership)
on injection risk behavior may differ across individuals (e.g., males versus females). Second,

64

GEE cannot incorporate analysis of nested data in conjunction with latent variable measurement
in a single model framework. As discussed, this study used multiple indicators to create a single
latent variable measure of injection risk behavior in effort to utilize the multiple advantages of a
latent variable measurement approach (MacCallum & Austin, 2000). Accordingly, multilevel
structural equation modeling (Bovaird, 2007) was used in this study in order to exploit the
advantages of both of these statistical tools in a unified modeling approach.9
Multilevel Structural Equation Modeling
The current study used a model that includes a latent factor dependent variable measured
by four indicators and multiple observed predictors (Figure 2). This approach is sometimes
called a multilevel multiple indicator multiple cause model as well as a multilevel confirmatory
factor analysis with covariates. This measurement model will be briefly discussed before moving
on to discuss the modeling approach.
Measurement Model. As discussed, the measurement model includes four indicators
(i.e., receptive syringe sharing, dividing drugs, paraphernalia sharing, and distributive syringe
sharing) and models these variables as a single factor. While distributive syringe sharing is not a
direct causal risk factor for the participant to contract viral diseases (i.e., because it only places
the partner at risk), this variable has shown to be a good indicator when measuring injection risk
behavior using a latent variable approach (Darke, Hall, Wodak, Heather, & Ward, 1992; Janulis,
2014; Petry, 2001) and may reflect a willingness to engage in risk of other unmeasured variables.
Accordingly, distributive syringe sharing was included as an indicator of injection risk behavior
given the limited number of observed indicators for injection risk behavior in the current dataset
and the previously observed strong factor loading of this indicator in similar factor models. One
9

An alternative approach is possible using multivariate data analysis. However, this approach is less parsimonious
and was therefore not used.

65

limitations of a latent variable approach is that the measurement model implicitly assumes
measurement invariance across all covariates. That is, we assume that no covariate has an
association with the measurement indicators except through the latent variable. The accuracy of
this assumption is unclear. However, given the limited investigations into measurement
invariance in multilevel SEMs (Jak, Oort, & Dolan, 2013), the small cluster size in the data, and
the already complex nature of the model, I see this as a necessary assumption in the current
study. Furthermore, multilevel structural equation modeling also assumes that the factor structure
and loadings are consistent across each injection episode. Accordingly, a preliminary analysis
was undertaken to examine the accuracy of this assumption by performing a confirmatory factor
analysis of all four injection episodes and comparing the fit of a model with constrained factor
loadings across episodes to that of a model with factor loadings freed across episodes.
Model Fit. For the estimator, maximum likelihood with robust standard errors (MLR)
was used because this is the only estimator that allows for categorical indicators in multilevel
structural equation modeling with random slopes (Muthen & Muthen, 2012).10 Using this
estimation method, missing data on dependent variables (i.e., injection risk behavior) was
handled using full information maximum likelihood so that participants with two to four
observed injection episodes will contribute to parameter estimates in the within and between
portions of the model while participants with a single injection episode will contribute only to
the parameter estimates in the between portion of the model. However, episodes or participants
with missing data on any independent variables were excluded from analysis using listwise
deletion (number of episodes deleted = 87). As indicated in the method section, dummy codes

10

For all models, the standard integration technique was used with 7 integration points used for Models 1-4 and the
default number of integration points, 15, used to run the final model as suggested by Muthen (2010) for
computationally demanding modeling.

66

were used to minimize missing values and preliminary analysis was undertaken to examine those
cases with missing values.
Unfortunately, there is currently no method for estimating a multilevel random slope
model with categorical indicators that includes global measures of fit such as Chi-square,
RMSEA, CFI, or TLI (Geiser, Bishop, Lockhart, Shiffman, & Grenard, 2013).11 Although global
fit indices are not available when using MLR with random slopes, an iterative approach is
possible to examine if nested models significantly improve fit using the Wald test of nested
models (Muthen & Muthen, 2014). Accordingly, the modeling process will be an iterative
process that progressively frees different parameters of the model to examine if freeing these
parameters significantly improves model fit (Appendix B). The first model (i.e., Model 1)
included the full model absent of random slopes with factor loadings constrained to be equal
across the within and between levels of the model. Model 2 was the same model with all factor
loadings freed across levels (except for the fixed loading for identification). Using the best fitting
model as the reference, Model 3 freed the random slope for gender concordance and Model 4
freed the slope for sexual partner multiplexity. Finally, the last model (Model 5) included gender
as a predictor of both slopes.
Hypothesis
After establishing an acceptably fitting model, all hypotheses of direct effects of network,
dyadic, or situational predictors on injection risk behavior were tested using the p-value for the
estimated path coefficient between the given variable and risk behavior. The interactions that
were within a single level (i.e., IDUs present * non-IDUs present) were tested by examining the
p-value for the estimated path coefficient between the product term of these two variables and
11

However, in effort to provide some insight into the overall fit of the model, Models 1 and 2 were also estimated
using the weighted least squares estimator using a diagonal weight matrix (WLSMV).

67

injection risk behavior. Like all interactions, estimates of associations with random slopes must
be interpreted after taking into account the moderating variables (i.e., the participant's gender in
this case). In the current study, gender was coded so that male participants were used as the
reference group. Accordingly, default estimates of gender concordance and sexual partnership
were for male participants in the final model. Therefore, a test for simple slopes was utilized to
also provide a test for the effect of gender concordance and sexual partnership for female
participants. Finally, the effect of the gender on level-1 random slopes was evaluated by
examining the path coefficient between gender and both random slopes (e.g., latent variables S1
and S2 for gender concordance and sexual partner multiplexity, respectively).
Results
Missing Data
As discussed above, although full information maximum likelihood was used to handle
missing data on the observed dependent variables (i.e., indicators injection risk behavior),
episodes or individuals with missing data on independent variables were excluded from analysis
using listwise deletion. Accordingly, there exist two types of missing data. First, observations
were excluded if participants were missing data on all dependent variable indicators (i.e., these
participants reported the initials of an injection partner but not any data on the four injection risk
behaviors). Second, observations were excluded if they were missing data on any independent
variable. Therefore, an additional preliminary analysis was undertaken to examine the nature of
missing data in the sample.
First, 13 cases were missing data on all dependent variables representing 1.6% of all
cases in the sample while 126 injection episodes were missing data on all outcomes representing
6.3% of all episodes in the sample. Demographic comparisons between participants with missing

68

data on all dependent variables can be found in Appendix C.12 Statistical significance tests (i.e.,
chi-square for categorical and t-test for continuous variables) indicated that male, Hispanic, and
or infrequent cocaine injectors were also more likely to have this type of missing data.
Accordingly, missing data on dependent variables reduced the sample to 822 cases reporting on
1863 injection episodes (205 individuals with one episode (24.9%), 220 with two episodes
(26.8%), 370 with three episodes (45.0%), and 27 with four episodes (3.3%))
An additional 38 cases and 85 injection episodes were excluded in the primary modeling
procedures due to missing data on the independent variables leading to a final number of 1778
injection episodes being observed by 784 participants (195 individuals with one episode (24.9%),
211 with two episodes (26.9%), 351 with three episodes (44.8%), and 27 with four episodes
(3.4%)). Accordingly, the number of excluded participants was 6.1% of the entire IDU sample
while the number of excluded episodes was 10.6% of all possible episodes. Demographic and
episode specific variable comparison (for non-missing variables) can be found in Table 3
showing the percentage of each category with no missing data on any independent variable.
Furthermore, an exploratory analysis was undertaken to compare observations with any missing
independent variables. This analysis examined the bivariate association between all model
variables and being missing on any independent variable (i.e., a dummy code indicating 0 = not
missing and 1 = missing). Missingness on any variable was only associated with two
independent variables (i.e., cases with gender concordant and new partners were more likely to
have missing data) and none of the outcome variables (see Appendix D for detailed results).13

12

Comparing episode specific data across missing data groups would also be ideal. However, the vast majority
(88.1%) of episodes with missing data were missing data on all episode specific variables and therefore these
comparisons are not possible.
13
Pearson chi-square tests were used for categorical variables and t-tests were used for continuous variables. The
Benjamini–Hochberg procedure was used to control for false discovery rate in analysis of missing.

69

Accordingly, this exploratory analysis suggests that, for many of the variables of primary interest
in this study, missing and non-missing episodes were quite similar. While it is unclear if the
missing completely at random assumption is met, the relatively small number of missing cases
and the small number of associations with relevant variables suggests parameter estimates are
Table 3. Comparison of Cases with any Missing Data on Independent Variables
Variable
Most Common Location
Yes
No
Gender Concordance
Discordant
Concordant
Sexual Partners
Yes
No
HIV Concordance
Unknown Concordance
Yes
No
Concordant
Yes
No
Partnership Length
New Injection Partner
Second quartile
Third quartile
Fourth quartile
Female
Yes
No
Race
Black
Race - Other/Multiple
Hispanic
Yes
No

Non-Missing
95.3%
97.7%
93.7%
96.8%
94.8%
95.9%

96.2%
94.7%
94.1%
95.6%
92.1%
97.3%
96.6%
95.4%
96.6%
94.9%
95.0%
93.5%
93.8%
95.9%

70

unlikely to be substantially biased (Graham, 2009; McKnight, McKnight, Sidani, & Figueredo,
2007). However, this limitation will be revisited in the discussion section.
Preliminary Analysis
Before examining the full model, a preliminary analysis was performed in order to test
the assumption of equal factor loadings across the four measured injection episodes. Kendall's
tau correlation coefficients between all indicators of injection risk behavior can be found in
Table 4 while Table 5 presents the factor loadings and model fit for each four-factor model.
Again, this model assessed each episode as belonging to a separate factor with each factor being
measured by the four observed indicators of injection risk behavior (i.e., receptive sharing,
distributive sharing, diving drugs, and sharing other equipment). A graphical representation of
this factor model can be found in Appendix E. In this model, each episode is represented by a
single latent factor (E1 - E4) corresponding to the four measured injection risk indicators at that
specific episode. The first model had fixed factor loadings across injection episodes (Model P1)
while the second freed these parameters across episodes (Model P2). This allows for a test to
examine if the observed indicators have the same measurement properties across each injection
episodes, an assumption imbedded in a multilevel structural equation modeling approach. The
log-likelihood test for change in model fit indicated that freeing the factor loadings did not
significantly improve model fit (χ2(9) = 5.85, p = 0.754). Accordingly, the null model with equal
factor loadings across injection episodes was not rejected and the assumption of measurement
invariance across injection episodes for the multilevel structural equation model was not
violated.

71

Table 4. Kendall's Tau Correlation Coefficient for Injection Risk Behavior
Episode

Item
1. Rec. Share

1

2

3

4

2. Dist. Share
3. Divide
Drugs
4. Nonsyringe share
5. Rec. Share
6. Dist. Share
7. Divide
Drugs
8. Nonsyringe share
9. Rec. Share
10. Dist. Share
11. Divide
Drugs
12. Nonsyringe share
13. Rec. Share
14. Dist. Share
15. Divide
Drugs
16. Nonsyringe share

1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

.656

-

.477

.543

-

.349

.353

.433

-

.583
.485

.506
.547

.402
.408

.270
.277

.684

-

.361

.385

.598

.352

.511

.533

-

.306

.303

.396

.625

.404

.374

.529

-

.484
.457

.475
.522

.340
.356

.206
.243

.545
.518

.609
.650

.398
.350

.295
.287

.710

-

.354

.351

.544

.336

.344

.381

.579

.411

.501

.489

-

.252

.240

.336

.591

.233

.247

.365

.658

.357

.325

.524

.461
.390

.231
.402

.271
.308

.410
.436

.537
.467

.421
.491

.318
.355

.373
.436

.695
.650

.808
.671

.299
0.30

.447
.517

.693

-

.327

.389

.613

.398

.467

.483

.461

.524

.451

.474

.521

.470

.401

.492

-

.375

0.22

.393

.554

.435

.450

.302

.511

.325

.408

0.24

.653

.426

.449

.372

-

Note. Bold values indicate p-value of less than 0.05

72

-

-

-

Table 5. Factor loadings and Model Fit for Confirmatory Factor Analysis
Model P1

Model P2

Episode 1
Rec. Share
Dist. Share
Divide Drugs
Non-syringe share

1.00
1.14 (0.19)
0.67 (0.10)
0.50 (0.07)

1.00
1.31 (0.26)
0.86 (0.14)
0.56 (0.10)

Episode 2
Rec. Share
Dist. Share
Divide Drugs
Non-syringe share

1.00
1.14 (0.19)
0.67 (0.10)
0.50 (0.07)

1.00
1.05 (0.25)
0.63 (0.13)
0.51 (0.10)

Episode 3
Rec. Share
Dist. Share
Divide Drugs
Non-syringe share

1.00
1.14 (0.19)
0.67 (0.10)
0.50 (0.07)

1.00
0.84 (0.26)
0.51 (0.14)
0.37 (0.09)

Episode 4
Rec. Share
Dist. Share
Divide Drugs
Non-syringe share

1.00
1.14 (0.19)
0.67 (0.10)
0.50 (0.07)

1.00
1.60 (0.92)
0.60 (0.24)
0.53 (0.18)

AIC
7176.28
7188.96
BIC
7331.76
7386.85
Loglikelihood
-3555.14
-3552.48
Correction Factor
1.15
1.09
Note. Model P1 has all factor loadings fixed across injection episodes while
model P2 freed factor loadings across episodes.

Model Comparison
Following the preliminary analysis, models were tested using an iterative approach
beginning with the most constrained model and examining if freeing parameters significantly
improved model fit, using Wald's test (Bentler, 1990) for nested models. Results for all models

73

can be found in Table 6.14 Beginning with the first two models that excluded all random slopes,
freeing the factor loadings across the within and between levels (Model 2) did not improve fit
over the fully constrained model (Model 1), W = 5.84, df = 3, p = 0.120. Therefore, the null
model (i.e., Model 1) was retained and all subsequent models included factor loadings
constrained to be equal across the between and within levels of the model. Next, Model 3 freed
the random slope for the effect of gender concordance on injection risk behavior. Model 3
significantly improved fit over Model 1 (W = 24.08, df = 1, p < 0.001) suggesting that the
relationship between gender concordance and injection risk behavior did vary across individuals.
This was also confirmed by the significance of the variance for the random slope (σ2s1 = 2.21, p
= 0.031). Model 4 significantly improved the fit of the model over Model 3 by freeing the slope
for sexual partnership (W = 343.93, df = 1, p < 0.001), again corresponding with a significant
variability in this slope parameter (σ2s2 = 0.09, p < 0.001). Finally, Model 5 also significantly
improved model fit over Model 4, (W = 15.04, df = 2, p < 0.001), by including the participant's
gender as a predictor of both the random slopes for gender concordance as well as sexual
partnership. Accordingly, all hypotheses were examined using Model 5 with both random slopes
freed and gender used as a predictor of these slopes.
Hypothesis
Using Model 5 as the final model, each hypothesis was assessed by examining the path
coefficient between the independent variables and the latent variable, either injection risk
behavior or the random slopes. Parameter estimates and standard errors for all hypotheses can be
found under Model 5 in the far right column of Table 6.

14

To provide a rough estimate of model fit to the data, results with absolute model fit indices for models without
random slopes (i.e., Model 1 and Model 2) using WLSMV estimation can be found in Appendix F.

74

Hypothesis 1
Hypotheses 1a through 1f involved dyadic predictors of injection risk behavior. As
discussed, two estimates (i.e., one for females and one for males) were examined for the
Hypotheses 1a and 1b because these hypotheses involved parameters with random slopes. For
males, gender concordance was not a significant predictor of injection risk behavior (γ = -0.51, p
= 0.060) while sexual partnership was positively associated with risk behavior (γ = 1.07, p <
0.001). For females, both gender concordance (γ = 1.10, p = 0.001) and sexual partnership (γ =
2.21, p < 0.001) were significantly positively associated with injection risk behavior. As
suggested by the simple slopes, female participants had significantly more positive slopes for
both gender concordance (γ = 1.60, p < 0.001) and sexual partnership (γ = 1.14, p = 0.014) as
assessed by the path coefficient between gender and each random slope (Hypothesis1d and
Hypothesis1e). This indicated that females with injection partners that were female or sexual
partners were more likely to engage in risk behavior. Finally, no significant residual variability
remained in either random slope remained after accounting for gender as a predictor (σ2s1 = 1.92,
p = 0.050; σ2s2 = 0.61, p = 0.592), suggesting nearly all variability in these slopes was accounted
for by participant's gender. Moving to other dyadic variables, length of injection partnership
(Hypothesis1e) was not significantly associated with injection risk behavior when comparing the
first (γ = 0.26, p = 0.336), second (γ = 0.38, p = 0.171), and third (γ = 0.34, p = 0.213) quartile of
injection partnership to episodes in the lowest quartile of injection partnerships. However,
injecting with a first time partner was negatively associated with injection risk (γ = -1.26, p =
0.008). There was also no evidence of serosorting (Hypothesis1f) as dyads with unknown
concordance (γ = -0.42, p = 0.490) and those that were sero-concordant (γ = -0.66, p = 0.292)
showed no significant difference in level of risk as compared to those with known discordance.

75

Table 6. Model Parameters for Models 1 through 5
Model 1

Model 2

Model 3

Model 4

Model 5

-3288.64
1.20
6651.28
6854.16

-3277.68
1.25
6635.36
6854.69

-3284.55
1.19
6645.09
6853.45

-3284.50
1.15
6647.01
6860.86

-3276.34
1.18
6634.67
6859.49

-

5.84, p = 0.120

24.08
p < 0.001

343.93
p < 0.001

15.04,
p < 0.001

1.00
1.12 (0.17)
0.70 (0.11)
0.55 (0.08)

1.00
1.21 (0.20)
0.92 (0.21)
1.07 (0.30)

1.00
1.12 (0.16)
0.71 (0.11)
0.55 (0.08)

1.00
1.12 (0.16)
0.70 (0.11)
0.55 (0.08)

1.00
1.13 (0.17)
0.71 (0.11)
0.55 (0.08)

0.30 (0.20)
0.09 (0.55)
1.61 (0.26)
-

0.20 (0.16)
0.13 (0.40)
1.02 (0.31)
-

0.15 (0.21)
0.07 (0.53)
1.62 (0.26)
-

0.12 (0.21)
0.05 (0.53)
1.61 (0.22)
-

-0.51 (0.27)
1.10 (0.32)
-0.11 (0.53)
1.07 (0.28)
2.21 (0.42)

-1.20 (0.48)
0.31 (0.27)
0.43 (0.28)

-0.88 (0.37)
0.35 (0.21)
0.50 (0.23)

-1.27 (0.49)
0.30 (0.27)
0.41 (0.28)

-1.27 (0.49)
0.30 (0.27)
0.41 (0.28)

-1.26 (0.48)
0.26 (0.27)
0.38 (0.28)

Model Fit
Loglikelihood
Scaling Correction Factor
AIC
BIC
Wald Test
Parameters
Within
Factor Loadingsa
Receptive Sharing
Distributive Sharing
Divide Drugs
Non-syringe sharing
Path Coefficients
Gender Concordance
Female (Simple Slope)
Missing
Sexual Partner
Female (Simple Slope)
Partnership Length
New Partner
1st Quartile (Reference)
2nd quartile
3rd quartile

76

Table 6 (cont'd)
4th quartile
HIV Concordance
Discordant (Reference)
Unknown Concordance
Concordant
Missing
# of people injecting
# non-injectors using drugs
# of injectors * # of non-injectors
Inject at home
Location is Common Location
Missing

Model 1
0.38 (0.28)

Model 2
0.55 (0.23)

Model 3
0.36 (0.28)

Model 4
0.37 (0.28)

Model 5
0.34 (0.27)

-0.36 (0.62)
-0.60 (0.64)
-0.18 (0.62)
0.02 (0.04)
0.10 (0.04)
0.00 (0.00)
-0.26 (0.22)
-0.59 (0.36)
-0.26 (0.41)

-0.16 (0.48)
-0.36 (0.50)
-0.06 (0.47)
-0.02 (0.03)
0.08 (0.03)
0.00 (0.00)
-0.22 (0.17)
-0.48 (0.27)
-0.39 (0.30)

-0.32 (0.61)
-0.59 (0.62)
-0.13 (0.60)
0.02 (0.03)
0.10 (0.03)
0.00 (0.00)
-0.27 (0.22)
-0.54 (0.36)
-0.21 (0.42)

-0.33 (0.60)
-0.60 (0.62)
-0.14 (0.60)
0.02 (0.03)
0.10 (0.03)
0.00 (0.00)
-0.27 (0.22)
-0.54 (0.36)
-0.21 (0.42)

-0.42 (0.61)
-0.61 (0.62)
-0.20 (0.60)
0.01 (0.03)
0.10 (0.03)
0.00 (0.00)
-0.27 (0.22)
-0.50 (0.35)
-0.23 (0.41)

Residual Variance - Within
Injection Risk (Latent)

1.91 (0.51)

1.19 (0.43)

1.47 (0.47)

1.45 (0.46)

1.35 (0.45)

1.00
1.12 (0.17)
0.70 (0.11)
0.55 (0.08)

1.00
1.05 (0.16)
0.63 (0.11)
0.45 (0.07)

1.00
1.12 (0.16)
0.71 (0.11)
0.55 (0.08)

1.00
1.12 (0.16)
0.70 (0.11)
0.55 (0.08)

1.00
1.13 (0.17)
0.71 (0.11)
0.55 (0.08)

4.16 (1.06)
6.04 (1.10)
4.25 (1.27)
1.59 (0.74)

3.77 (1.08)
5.71 (1.11)
3.77 (1.23)
1.34 (0.76)

3.98 (1.05)
5.90 (1.09)
4.12 (1.23)
1.51 (0.72)

3.96 (1.05)
5.89 (1.09)
4.08 (1.22)
1.49 (0.72)

3.15 (1.04)
5.01 (1.08)
3.22 (1.21)
0.94 (0.73)

Between
Factor Loadingsa
Rec. Share
Dist. Share
Divide Drugs
Non-syringe sharing
Thresholds
Rec. Share
Shared - used bleach
Shared - did not use bleach
Dist. Share
Divide Drugs

77

Table 6 (cont'd)
Other equipment
Path Coefficients
Injection Network Size
Female
Age
Race
Black
Race - Other/Multiple
Hispanic
Homeless
Drug Use
Crack/Cocaine Frequency
Heroin/Opioids Frequency
Injection Frequency
Injection Duration
Random Slopes Predictors
Gender Concordance
Female
Sexual Partnership
Female

Model 1
0.20 (0.57)

Model 2
0.10 (0.68)

Model 3
0.14 (0.56)

Model 4
0.12 (0.55)

Model 5
-0.30 (0.57)

0.00 (0.01)
0.51 (0.30)
-0.04 (0.02)

0.00 (0.01)
0.61 (0.34)
-0.06 (0.03)

0.00 (0.01)
0.43 (0.29)
-0.04 (0.02)

0.00 (0.01)
0.42 (0.30)
-0.04 (0.02)

0.00 (0.01)
-0.86 (0.49)
-0.04 (0.02)

1.06 (0.37)
0.16 (0.53)
0.32 (0.50)
1.74 (0.34)

1.27 (0.42)
0.18 (0.61)
0.39 (0.57)
2.03 (0.40)

1.01 (0.36)
0.21 (0.53)
0.31 (0.49)
1.75 (0.34)

1.01 (0.36)
0.20 (0.53)
0.32 (0.50)
1.75 (0.34)

1.00 (0.36)
0.13 (0.52)
0.33 (0.49)
1.72 (0.34)

-0.01 (0.01)
-0.04 (0.01)
-0.00 (0.01)
0.04 (0.02)

-0.01 (0.02)
-0.05 (0.02)
-0.00 (0.01)
0.05 (0.02)

-0.01 (0.01)
-0.04 (0.01)
-0.00 (0.01)
0.05 (0.02)

-0.01 (0.01)
-0.04 (0.02)
-0.00 (0.01)
0.05 (0.02)

-0.01 (0.01)
-0.04 (0.01)
-0.00 (0.01)
0.04 (0.02)

-

-

-

-

1.60 (0.42)

-

-

-

-

1.14 (0.47)

Residual Variance - Between
Injection Risk (Latent)
9.65 (2.16)
12.49 (2.95)
8.84 (2.00)
8.85 (1.97)
8.39 (1.95)
Gender Concordance
1.92 (0.98)
2.21 (0.90)
2.34 (0.93)
Sexual Partnership
0.61 (0.72)
0.09 (0.01)
Note. Bold values indicate p-value of less than 0.05. All hypotheses were tested using path coefficients from model 5. a Factor
loadings for the first factor indicator were fixed at 1 for identification purposes.
78

Hypotheses 2 and 3
Hypotheses 2 and 3 examined network and situational predictors of injection risk
behavior. Injection network size was not significantly associated with injection risk (γ = 0.00, p
= 0.652; Hypothesis 2a). While the number of injectors at the injection episode was not
associated with risk behavior (γ = 0.04, p = 0.311; Hypothesis 3a), the number of non-injection
drug users was positively associated with risk behavior (γ = 0.09, p = 0.006), contradicting the
expected direction in Hypothesis 3b. However, there was no significant interaction between the
number of injectors and the non-injectors present during the injection episode (γ = 0.00, p =
0.990; Hypothesis 3c). Finally, injecting at one's own home was not significantly related to
injection risk behavior (γ = -0.27, p = 0.208; Hypothesis 3d).
Other Covariates
While not the main focus of the current study, the association between several variables
and injection risk behavior were also included in the model. The injection site being the most
common site of injection was not associated with risk behavior (γ = -0.50, p = 0.160). However,
both black (γ = 1.00, p = 0.006) and homeless (γ = 1.72, p < 0.001) participants witnessed higher
levels of injection risk behavior but female (γ = -0.86, p = 0.077), other race (γ = 0.13, p =
0.796), and Hispanic (γ = 0.33, p = 0.499) were not significant predictors of injection risk
behavior. Similarly, age had no association with injection risk (γ = -0.04, p = 0.083). Finally,
neither frequency of crack/cocaine use (γ = -0.04, p = 0.552) nor injection frequency (γ = -0.00,
p = 0.631) were significantly associated with risk behavior. However, injection duration was
positively associated (γ = 0.04, p = 0.009) while frequency of heroin/opioid use was negatively
associated (γ = -0.04, p = 0.001) with risk.

79

Discussion
Given the continued burden of HCV and HIV among injection drug users, understanding
the social processes associated with the spread of these diseases remains an important goal of
public health research. However, previous research has largely focused on between person
variation in injection risk behavior (Rhodes, 2009) or failed to use analytic methods idealy suited
to assess within person variability (e.g., see Montgomery et al., 2013) . This study attempted to
overcome limitations in previous research by exploiting data with multiple observations of drug
use episodes for each participant in effort to examine within person variation of injection risk
behavior in addition to modeling the traditional between person variation. The results of the
current study will first be discussed in the context of social setting theory and previous research
before moving on to study limitations and the implications for future research.
Dyadic Predictors
Contradicting Hypothesis 1a, gender concordance did not have a negative association
with risk behavior for both males and females. After accounting for the cross-level interaction
with the participant's gender (Hypothesis 1c), gender concordance had no significant association
for males but was a significant positive predictor of injection risk behavior for females, the
opposite direction of the hypothesized relationship. This suggests that gender concordant
injection partnerships represented a risk factor unique to females. In the context of social setting
theory, these findings may suggest that norms surrounding female-female injection partnerships
may facilitate increased levels of risk behavior (Latkin et al., 2010). For example, DaveyRothwell and Latkin (2007) found that perceived approval of equipment sharing was not a
significant predictor for females but was for males, suggesting different normative influences
may exist in gender concordant partnerships among females as compared to males. This finding

80

also suggests that there was no evidence of disadvantaged social locations among female with
discordant injection partners due to more restricted access to drug resources (Tortu et al., 2003)
or gender norms (Barnard, 1993; Bourgois, Prince, & Moss, 2004) that encourage females to
share equipment with male partners. However, the positive association between injection risk
behavior and gender concordant partners among females could suggest that these partnerships
exist in the contexts with reduced overall levels of available injection resources. For example,
previous studies have found that female injectors are less likely to have access to drug resources
(Tortu et al., 2003; Wagner, Lankenau, et al., 2010). Accordingly, females may be forced to
share equipment, and thereby increase risk, when injecting with other female partners due to
lower levels of overall resources available to these injectors. Finally, given the unexpected
findings in the current study and the mixed findings of previous research (Gyarmathy et al.,
2010; Hahn et al., 2010; Sherman et al., 2001), these findings may suggest that the association
between gender concordance and injection risk is highly specific to local contexts and may be
sensitive to other un-measured social processes.
As expected, there was a positive association between sexual partnership and injection
risk behavior across group males and females (Hypothesis 1b). Furthermore, as expected in
Hypothesis 1d, the association between sexual partnership and injection risk behavior was
significantly more positive for female injectors as compared to males. This concurs with findings
from several studies indicating that sexual partners were at increased probability for engaging in
injection risk behavior for both male and female injectors (Bailey et al., 2007; Hottes et al.,
2011). Furthermore, in the context of social setting theory, this may suggest that the resource
imbalances or gender norms may enhance the potential risk of sexual partnerships for female
injectors. For example, females’ sexual partners may be responsible for obtaining drugs (El-

81

Bassel et al., 2014) and, due to this responsibility, may subsequently retain greater control over
the injection process (e.g., injecting first with the same equipment to be used by the female
sexual partner). This finding also concurs with results from a number of smaller studies
indicating sexual partnerhsips may be more detrimental, in terms of injection risk, to female
injectors as compared to males (Choi et al., 2006; Gollub et al., 1998). However, by using event
specific data, the current study confirms that it is not merely individuals that inject with sexual
partners that are at higher risk, but the specific injection episodes with sexual partners that are
related to heighted risk. That is, injecting with a sexual partner was observed at the episode level
rather than the individual. Therefore, the positive assocaition between sexual partnership and risk
behavior found in this study suggests that the same individual is at greater risk when injecting
with a sexual partner compared to non-sexual partners.
As for injection partnership duration, the results of Hypothesis 1e were mixed. While new
partners were at decreased risk for injection risk behavior, there was no significant relationship
between length of partnership and injection risk behavior across the various quartiles of injection
partnership for non-new partners. This suggests that longer partnership durration was not
associated with higher levels of risk as predicted and there was no impact of the expected
increase of intimacy of long term injection partnerships on injection risk behavior. In the context
of social setting theory, this suggests the norms related to new (i.e., first time) partners may
reduce risk behavior but trust or intimacy accrued over long term partners do not appear to
impact risk, or may act primarily through other factors such as sexual partnership. Previous
research has also been mixed on the association between partnership length and injection risk
behavior. While emotional closeness between injection partners predicts injection risk behavior
in a number of studies (Barnard, 1993; Sherman et al., 2001; Tortu et al., 2003), several studies

82

specifically examining the relationship between partnership length and risk behavior found nonsignificant results (Niccolai et al., 2010; Paintsil et al., 2009). Accordingly, the current study’s
finding concur with these previous findings. However, it is possible that the coding procedure
that turned this continuous variable into discrete categories may have obscured the relationship
between these variables. Although previous studies (e.g., Morris et al., 2014) have used similar
categorical coding schemes, future work could clarify the association between injection risk
behavior and injection partnership duration using more nuanced measurement of this variable.
Similarly, no evidence of serosorting was observed in the current study (i.e., increased
likleihood to engage in risk behavior with partners perceived to have the same HIV serostatus).
Previous studies generally found support for the presence of serosorting behavior (Burt et al.,
2009; Chen, McFarland, & Raymond, 2014; Smith et al., 2013; Yang et al., 2011). However,
other studies have found no evidence of serosorting (Hagan et al., 2006) or limited evidence of
serosorting only among a minority of injectors (Mizuno et al., 2011). Accordingly, the current
results similarly suggest that injection drug users do not appear to be selecting partners based on
perceived serostatus in all situations. However, the current finding is limited by the fact that a
roughly a third of observations (33.2%) of injection episodes had missing data on the partner's
HIV status. Accordingly, the statistical test in the current study may have limited power to detect
an effect given this missing data. HIV/HCV serosorting and the conditions and settings in which
serosorting may be more or less likely remains an active area for research.
Network Predictors
Contrary to Hypothesis 2, no signficant relationship between size of the participant's
injection network and injection risk behavior was observed. This may suggest that the size of
injection networks may have little effect on injection risk behavior, as found in previous studies

83

(Paquette et al., 2011; Shaw et al., 2007). Several studies have found a positive association
between network size and injection risk (Cepeda et al., 2011; Latkin, Mandell, Vlahov, et al.,
1995; Latkin et al., 1996; Needle et al., 1998; Thiede et al., 2007). However, differences in the
current study from most previous studies (e.g., the current study used sampling population linked
to MSM and statistical models that controlled for episode specific variables) could explain why
the current findings differ from those in the past. However, the non-significant relationship
between injection network size and risk behavior may also be inaccurate due to the inherent
difficulty of recalling the number of injectors in the "same place and time" in the last 6 months
among individuals who frequently inject drugs. Accordingly, this instrument could introduce
measurement error that may have attenuated the relationship between injection network size and
risk behavior. Alternative measurement techniques that use more recent time periods, most
specific relationships, or use recall techniques that have been vetted and refined (Brewer &
Garrett, 2001; Brewer, Garrett, & Kulasingam, 1999) would likely provide improved
measurement properties.
Environmental Predictors
For Hypothesis 3, the number of injectors at an episode had no relationship and the
number of non-injectors was positively associated with injection risk behavior contradicting both
Hypothesis 3a and 3b. This suggests that alternative setting factors may be at play in these
settings that were not considered. For example, given that at least one other injector was present
at all injection episodes due to the manner in which the questionnare elicited episodes, injection
episodes with more non-injectors may be settings in which injection resources (e.g., syringes and
cookers) are more scarce. For example, injecting at a location with multiple non-injectors may
suggest that the location is not a location primarily used for injecting. Consequently, this could

84

make the availability and surplus of injection equipment less likely if injection equipment is not
frequently stored at the physical location, thereby increasing the likelihood of engaging in risk
behavior. Alternatively, due to the stigma of injection drug use, locations with many noninjectors may reduce the temporal resources available to injectors and require more hurried
injection episodes. Therefore, these episodes may facilitate higher levels of risk behavior similar
to other examples of public injecting (Small et al., 2007). Although previous studies have found
that non-IDU norms may discourage sharing behavior (Cox et al., 2009; Mateu-Gelabert et al.,
2005), the material requirements of the injection process may outweigh these norms in situations
with many non-injectors. However, studies specifically designed to test hypotheses examining
norms and resources (e.g., by measuring perceived injection norms or the number of sterile
syringes available at the injection locations) would be better equipped to examine this
relationship.
Finally, no significant relationship was observed between injecting at home and risk
behavior (Hypothesis 3d). From the perspective of social setting theory, this may suggest that
participant's injection episodes at homes had no greater resources than those at other locations or
that injecting at one's home provides no privledged control over the injection process. Previous
studies that have compared specific types of injection locations (e.g., home vs. shooting gallery
vs. outdoors) have found mixed results with some finding signficant associations (Bailey et al.,
2007; Latkin et al., 1994) while others found no relationship between injecting location (R. A.
Johnson, Gerstein, Cerbone, & Brown, 2002). Accordingly, the association between injection
location and injection risk behavior continues to remain unclear. Studies examining this
association would benefit by explicitly inclduing assessment of possible mediating variables

85

(e.g., resources and social proceses) to clarify the impact of injecting location on injection risk
behavior.
Given the novel approach of the current study that allows estimation of within person
variability across partners and settings, a particulary noteworthy finding that was not one of the
main hypotheses is that significant variability was observed in injection risk behavior across
injection episodes, as measured using the four indicator latent variable. This indicates that
participants in the current study did experience different levels of injection risk behvaior across
injection episodes. More specifically, in a null model with no within-level predictors (Appendix
G), 13.2% (ICC = 0.132) of the unexplained variability in injection risk behavior took place at
the within-person level indicating that a sizable percentage of variability existed across different
injection episodes. This finding suggests that setting and partner level characteristics are
important factors in determining the observed level of risk behavior for specific drug use
episodes. Furthermore, the significant variability of the within-level latent factor in the final
model (i.e., Model 5) suggests that significant variability continued to exist across injection
episodes after accounting for all the included explanatory variables at the within level. Clearly,
the included partner and setting level independent variables were not sufficient to account for all
setting level variability and much remains unknown about influential setting and partner level
factors. More generally, only a single setting variable (i.e., number of non-injection drug users)
was significant while several dyadic variables were signifcaint at the within level. Accordingly,
much remains unknown about setting specific variables that may impact injection risk.
Limitations of this study that may help shed light on this unexplained variability will first be
examined before moving on the implications of the current study for future research.

86

Limitations
First, the current study used pre-existing data that was not primarily designed to assess
episode specific variability in injection risk behavior or to test the theoretical application of
social setting theory to injection episodes. Accordingly, important variables related to injection
settings and social setting theory could not be included in the model. For example, the control
over the drugs used to inject is likely an important variable related to injection risk behavior.
Previous studies have found that some males may have greater control over the order of injecting
(i.e., the ability to inject first) due to their responsibility in obtaining the drugs (El-Bassel et al.,
2014). A study specifically tailored to examine the associations between event specific
characteristics (e.g., who paid for or brought the drugs into the setting), control over resources
(e.g., who chose the order of injection or who split the drugs between partners), the amount of
available resources (e.g., the number of sterile syringes), and injection risk behavior would likely
provide increased clarity of these associations and greater insight into competing hypothesis
about the mechanisms of these associations. Similarly, explicitly measuring injection norms
during specific episodes may provide greater insight into the social pressures of sharing within
specific settings.
Second, measurement of event specific data is somewhat unique and the approach of the
current study (i.e., collecting data on injection episodes up to 6 months prior to the data
collection) is likely prone to potential biases. For example, participants may suffer recall bias or
other forms of measurement error due to the difficulty of remembering injection specific
information that may have occurred weeks or months before the questionnaire is completed.
While similar approaches have been used in previous studies of sexual risk behavior and
substance use (Vosburgh et al., 2012), the novelty of this approach being applied to injection risk

87

behavior makes the validity and reliability of this measurement process uncertain. Similarly, the
current study conflated partner and setting level variables given the manner in which the
questionnaire elicited drug use events. That is, because setting level variables were collected
relating only to specific injection episodes with specific injection partners, the variability of
partner and setting are the same. Therefore, collecting observations in this manner limits the
ability to examine partner effects separately from event specific characteristics.
Third, in contrast to most multilevel studies, this study has small cluster sizes (n

4) but

a large number of clusters (n = 835). Accordingly, the study likely had substantial power to
detect between person associations but less statistical power to detect within person associations.
Accordingly, the small cluster size at the within level may have limited the power for this model
to detect associations between setting/dyadic variables and injection risk behavior. This may
partially explain the failure to find expected associations in some variables at the within level.
Future studies that provide a larger sample of injection episodes per participant would likely
provide greater insight into associations at the within level.
Fourth, the current study used a rather complex adaptation of respondent driven sampling
to recruit the study population. Furthermore, injection drug users were not the primary
characteristic for which participants were sampled. While the current sample (i.e., 784) is on the
larger size for hidden populations such as IDUs, given the duel incentives used for recruitment it
is unclear if these findings can be generalized to a larger population.
Fifth, un-modeled dependencies likely exist between observations in the current study
due to the snowball RDS recruitment procedures. For example, participants may have named
injection partners that were also participants in the study and data to identify this crossclassification was not available in the dataset. While recruitment and sampling of hidden

88

populations like injection drug users will remain a challenge for future studies, approaches
specifically tailored to eliminate or account for un-modeled dependency may provide the most
accurate parameter estimates and standard errors. For example, more accurate estimates might be
provided if event specific data were included in the injection drug use questions in traditional
nationally representative household surveys such as the NSDUH that lack the complex
dependencies inherent in RDS samples. 15 Alternatively, advanced statistical methods that can
account for complex dependencies in data analysis continue to be developed and refined. For
example, Bastos, Pinho, Codeço, and Bastos (2012) have proposed analyzing RDS data using an
error term that explicitly models the network recruitment. These methods will likely continue to
improve given the recent explosion of research using and understanding the impact of RDS
sampling methodology in social science and public health research (Gile & Handcock, 2010). As
these methods improve, the confidence in the accuracy of parameter estimates and standard
errors should also improve.
Future Directions
Given the new insights into injection settings provided by the current study and these
acknowledged limitations, this study suggests a number of avenues for future research examining
injection settings and the social processes associated with injection risk behavior. First,
significant within person variability in injection drug risk behavior remained unexplained after
including all independent predictors were included in the final model. Accordingly, uncovering
variables that more accurately explain this variability remains an important area for future
research. This work could be particularly important in identify the mediating social processes

15

While most such surveys do have issues of dependency due to complex sampling designs (e.g., survey strata),
these dependencies are relatively easily accounted for using analytic techniques appropriate for complex survey
designs.

89

relevant to the impact of syringe exchange programs and behavioral interventions among IDUs.
For example, social setting theory suggests that networks and norms are key variables that
explain setting outcomes. Network variables could be more comprehensively measured by
collecting structural data (e.g., ego network density) on multiple types of networks in injection
settings (e.g., friendship, injection, sexual, etc). Furthermore, previous studies (Davey-Rothwell
& Latkin, 2007) have utilized measures of descriptive (i.e., perceived prevalence of behavior)
and injunctive (i.e., perceived approval) norms among injection drug users. More explicitly
measuring these norms would likely clarify the processes and mechanisms at play in these
settings. As discussed, the continued burden of HCV (Nelson et al., 2011; Suryaprasad et al.,
2014) and the uncertain impact of behavioral interventions on the reduction of HCV (Gillies et
al., 2010; MacArthur et al., 2014; Palmateer et al., 2010) suggests that continued improvement in
these interventions may be required to obtain sustained reduction in HCV incidence and
prevalence among IDUs. Identifying these mediating social processes in injection settings may
assist in this process of improving interventions in effort to maximize their impact.
Second, as discussed, the current study conflates partner and setting level characteristics
due to the format of the questionnaire. Future studies could provide greater insight by collecting
data on injection settings (e.g., physical location) separate from information on each partner
present at the setting. While this data would increase the complexity of analysis given the
potential for cross-classification of partners across settings, it would allow examination of the
variability of partner related factors from those of setting related factors. This information would
be helpful for interventions to know if specific partner types (e.g., sexual partners) are the
primary episode specific factors associated with injection risk before or if setting level
characteristics that may be associated with partner types may be responsible for variability in

90

episode specific injection risk behavior. This data would also be more challenging to collect.
However, ecological momentary assessment (Shiffman, Stone, & Hufford, 2008) or other realtime data collection techniques such as coded ethnography (Scott, 2011) could provide the data
required to perform such complex analysis. Similarly, these approaches may also improve upon
the discussed measurement issues inherent in a study that inquires about specific injection
episodes up to 6 months prior to the data collection. Furthermore, these methods would allow for
researchers to easily obtain a large number of observations per participant and therefore
overcome the limitation of low statistical power at the within participant level.
Third, given that the current study adds to the accumulating evidence that IDUs respond
to questions regarding injection risk behaviors in a hierarchical and logical manner and due to
the advantages provided by latent variable measurement discussed in this study, the use of latent
variable measurement of injection risk behavior should be continued to be explored. For
example, future studies that incorporate additional questions (e.g., backloading, frontloading,
number of punctures, etc.) would likely increase the observed variability in injection risk
behavior and more accurately reflect the true breadth of these behaviors. However, additional
studies are required to evaluate the measurement properties of more comprehensive injection risk
behavior inventories. For example, studies examining the predictive validity of latent variable
measurement of injection risk behavior on seroconversion would be particularly insightful and
would provide much needed data on the degree to which this measurement approach accurately
reflects viral transmission risk.
Implications for HIV/HCV Prevention Interventions
The findings of the current study also have practical implications for IDU preventive
interventions. As discussed, the significant variability across different episodes suggests that

91

partner and setting factors impact the level of risk behavior at each event. In the current study,
participants injecting with sexual partners or non-first time partners were more likely to engage
in risk behavior. Accordingly, behavioral interventions may benefit from targeting these
relationships as particularly important for reducing risk behavior. For example, previous
interventions have attempted to increase communication about and promote self-efficacy to
engage in harm reduction practices through skill building exercises with sexual partners
(Jiwatram-Negrón & El-Bassel, 2014). While IDU interventions have begun to explore the
targeting of intimate partner relationships and communication between sexual partners (Dwyer,
Fraser, & Treloar, 2011; El-Bassel et al., 2011), this approach has been much more widely
utilized in sexual health interventions (El-Bassel et al., 2001; El-Bassel et al., 2005; Harvey et
al., 2008; Jiwatram-Negrón & El-Bassel, 2014; Witte et al., 2006). Accordingly, the full utility of
this approach has yet to be extended to injection related interventions. Future interventions and
evaluation studies should expand upon the previous work of sexual heath interventions and
examine mediating social processes between partners that may be particularly valuable for
successful interventions.
Furthermore, female injectors were more likely to be at risk when injecting with gender
concordant partners or sexual partners. Accordingly, gender specific programming may also be
beneficial in order to target circumstances that may place females at unique or increased level of
risk. For example, pre-exposure prophylaxis may provide leverage to prevent HIV among
women whose injection or sexual partners are unwilling to engage in other risk reduction
activities (Bontell & Strathdee, 2014).While the current study focused on sexual partnership and
gender concordance, other social processes are almost certainly involved in health behavior
inequities across gender groups. However, and similar to sexual partnership interventions, gender

92

specific programming has been much more common in sexual HIV risk reduction interventions
(Ehrhardt et al., 2002; Melendez, Hoffman, Exner, Leu, & Ehrhardt, 2003; S. Miller, Exner,
Williams, & Ehrhardt, 2000; Shain et al., 1999). The efficacy of gender specific programming
for reduction of injection related harms should similarly be examined. However, it is also
important to note that the complexities and diversity of experiences among female IDU and
gender specific programming should address this diversity. For example, financial situations of
female injectors differs considerably based on their specific economic situation; while financial
dependency with injector partners may place some female injectors at heighted risk, other female
injectors may have higher financial earnings that can be used to mitigate risk (El-Bassel et al.,
2014). Furthermore, to the extent that these relationships reflect unequal distribution of power or
resources, the resource distributions themselves may be a target for intervention activities. For
example, purposefully increasing access to harm reduction resources among women may
facilitate a more equal distribution of these resources (Amaro, 1995; Wingood & DiClemente,
2000).
Finally, as increased emphasis has been placed on structural and community interventions
for injection drug users (Golden, Collins, Cunningham, Newman, & Card, 2013), it remains
important to the success of these interventions to identify mediating causal variables in effort to
maximize the impact and better understand the mechanisms of action for these programs (Latkin,
German, et al., 2013). Event level data and explicit modeling of shifting social processes would
allow for more specific exploration of the potential causal mechanisms and identification of the
most important components of community interventions.

93

Conclusion
This study utilized a novel approach to analyzing event specific data of multiple
observations of drug use episodes in effort to further elucidate the partner and setting
characteristics that influence injection risk behavior. The findings confirm that both dyadic and
setting level factors appear to play a significant role explaining within person variability in
injection risk behavior. However, these associations varied for males and females in the current
study, with females having increased risk when injecting with sexual or other female partners.
Furthermore, significant variability remained unexplained and future studies are required to more
accurately understand the dyadic, networks, and environmental factors that influence injection
risk.

94

APPENDICES

95

Appendix A. Questionnaire

Level 2 Variables
Demographics

Q12. What is your date of birth? __ __ / __ __ / __ __ __ __ mm / dd / yyyy

Q13. Are you: (Choose one)
1 MALE
2 FEMALE
3 TRANSGENDER, MALE to FEMALE
4 TRANSGENDER, FEMALE to MALE
Q19. What is your race? (Please check all that apply)

__ White
__ Black/African American
__ American Indian or Alaska Native
__ Asian or Pacific Islander
__ Other
__ Refuse to Answer
Q16. Are you Spanish/Hispanic/Latino?
1 Yes
2 No Skip to Q19
-8 Refuse to Answer Skip to Q19
Q44. At any time during the past year, did you consider yourself homeless?
1 Yes
2 No
-8 Refuse to Answer
Drug Use
Q120. Did you use powder cocaine in the past 6 months?

96

1 Yes
2 No Skip to instruction before Q121a
-7 Don't Know Skip to instruction before Q121a
-8 Refuse to Answer Skip to instruction before Q121a

Q120b. How many days did you use powder cocaine (coke) by itself (other than crack) that
you injected or snorted in the past 30 days?
__ __
00 zero Skip to instruction before Q121a
-8 Refuse to Answer Skip to instruction before Q121a

Q122. Did you use heroin in the past 6 months?
1 Yes
2 No Skip to instruction before Q123a
-7 Don't Know Skip to instruction before Q123a
-8 Refuse to Answer Skip to instruction before Q123a

Q122b. How many days did you use heroin in the past 30 days?
__ __
00 zero Skip to instruction before Q123a
-8 Refuse to Answer Skip to instruction before Q12a

Q133. How old were you the first time you injected drugs?
__ __
-8 Refuse to Answer
Q136. In the past 30 days, about how many times did you inject drugs?
__ __ __
000 zero Skip to Q142a
-8 Refuse to Answer

Network Characteristics
Q147. About how many different people did you inject drugs within the past 6 months? (By
"with", we mean people who injected drugs at the same place and time as you.)
__ __ __
-8 Refuse to Answer

97

RDSM1. How many people do you know personally (i.e., you know their name, you know who
they are, and they know you, and you have seen them in the last 6 months) who use heroin,
methamphetamines, and/or powder or crack cocaine or who inject some other drug?
__ __ __
-8 = Refuse to Answer
Level 1 Variables
Situational Characteristics
Q182. Where did you last inject drugs with [INJECTION PARTNER]: (Choose one)
1 In your home
2 In your neighborhood
3 In a different neighborhood but within 20 miles from
your neighborhood
4 More than 20 miles away from your neighborhood
-8 Refuse to Answer
Q181. Is this where you most often inject with [INJECTION PARTNER]?
1 Yes
2 No
-8 Refuse to Answer
Dyadic Characteristics
Q170. Please choose one answer. Is [INJECTION PARTNER]: (Choose one)
1 Male
2 Female
3 Transgender
-8 Refuse to Answer
Q171. What race or ethnic group is [INJECTION PARTNER]? (Choose one)
01 Black (not Hispanic)
02 White (not Hispanic)
03 Hispanic
04 Asian or Pacific Islander
05 American Indian or Alaskan Native
10 Other
-7 Don't Know
-8 Refuse to Answer
Q196. What is [INJECTION PARTNER]'s HIV status? (Choose one)

98

1 I don't know or am not sure
2 I am sure she/he is HIV negative
3 I am sure she/he is HIV positive
-8 Refuse to Answer
Q175. Have you injected with [INJECTION PARTNER] more than one time?
1 Yes
2 No Skip to Q178
-8 Refuse to Answer Skip to Q178
Q176. For how long have you injected drugs with [INJECTION PARTNER]? Do you want to
answer in days, months, or years? (Choose one)
1 DAYS
2 MONTHS
3 YEARS
-8 Refuse to Answer
If Q176 is equal to "Refuse to Answer", then skip to Q178.
Q177. For how many [Response to Q176] have you injected drugs with [INJECTION
PARTNER]?
__ __ __
-8 Refuse to Answer
Injection Risk Behavior
Q189. The last time you injected with [INJECTION PARTNER], did you inject with a syringe
after [INJECTION PARTNER] had used it?
1 Yes
2 No Skip to Q191
-8 Refuse to Answer Skip to Q191
Q190. Was the syringe cleaned with bleach before you injected with it?
1 Yes
2 No
-7 Don't Know
-8 Refuse to Answer
Q191. The last time you injected with [INJECTION PARTNER], did [INJECTION PARTNER]
inject with a syringe after you used it?
1 Yes
2 No
-7 Don't Know

99

-8 Refuse to Answer
Q192. Did you mix, measure, or divide the drugs with [INJECTION PARTNER] using a single
syringe?
1 Yes
2 No Skip to Q195
-8 Refuse to Answer Skip to Q195
Q195. The last time you injected with [INJECTION PARTNER], did you share other injecting
equipment, such as cookers, cotton, rinse water, or anything else, with [INJECTION
PARTNER]?
1 Yes
2 No
-8 Refuse to Answer

100

Appendix B. Modeling Steps
Table 7. Modeling Steps and Freed Parameters
Model 2

Freed Paramaters
Freed factor loadings across less

Model 3

Random slope for gender concordance (S1)

Model 4

Random slope for sexual partnership (S2)

Model 5

Gender as a predictor of S1 (gender  S1) and S2 (gender  S2)

Figure 3. Model with Modeling Steps

101

Appendix C. Comparison of Cases with Missing Data on All Dependent Variables
Table 8. Comparison of Cases with Missing data on All Dependent Variables
Variable
Non-Missing
Missing
n (%)
n (%)
Gender
Male
1271 (92.4)
104 (7.6)
Female
592 (96.4)
22 (3.6)
Race
Black
848 (94.7)
47 (5.3)
Race - Other/Multiple
385 (91.7)
35 (8.3)
Hispanic
Yes
419 (89.0)
52 (11.0)
No
1444 (95.1)
74 (4.9)
Homeless
Yes
983 (93.5)
68 (6.5)
No
880 (93.8)
58 (6.2)

Network Size
Frequency Crack/Cocaine
Frequency Heroin/Opioids
Frequency of Injection
Injection Duration

M (SD)

M (SD)

8.04 (18.4)
8.11 (10.6)
14.41 (13.3)
22.76 (34.1)
19.07 (13.1)

6.87 (10.9)
5.09 (8.7)
11.64 (13.3)
19.56 (23.5)
18.59 (12.8)

Significance Test

11.34

3.22
3.58
23.03

0.07

0.70
3.15
2.29
1.03
0.11

Note. Significance test is a chi-square value for categorical and t-test value for continuous
variables.

102

Appendix D. Association with Missing on any independent variable
Table 9. Association with Missing on any independent variable
Statistic

Variable

χ2
2.13
1.71

Inject at home
Most Common Location
# of injectors
# of non-injectors
Gender Concordant
Sexual Partner
HIV Concordance
Unknown Concordance
Concordant

t

-0.64
0.52
10.30
1.47
2.37
2.13

Partnership Length
New Partner
Second quartile
Third quartile
Fourth quartile
Female
Age
Race
Black
Race - Other/Multiple
Hispanic
Homeless
Injection Network Size
Drug Use
Frequency of Crack/Cocaine
Frequency of Heroin/Opioids
Frequency of Injection
Injection Duration

11.24
3.83
1.62
0.08
2.79
-1.80
0.55
4.16
3.35
3.87
1.22
1.55
2.43
1.43
2.59

Receptive Sharing
1.40
Distributive Sharing
0.23
Divide Drugs
0.01
Share other equipment
1.96
Note. Bold values indicate significant value after controlling for false discovery rate using
Benjamini Hochberg correction. Statistical tests did not take into account the clustered nature of
the data but this should only bias standard errors toward increased likelihood to reject the null
hypothesis.

103

Appendix E. Four Factor Model
Figure 4. Four Factor Model

y1(1)

y1(2)

y2(1)

y2(2)
E1

E2

y3(1)

y3(2)

y4(1)

y4(2)

y1(3)

y1(4)

y2(3)

y2(4)
E3

E4

y3(3)

y3(4)

y4(3)

y4(4)

104

Appendix F. Model fit using WLSMV Estimator
Table 10. Model fit using WLSMV Estimator
Model 1

Model 2

113.49, p = 0.035
0.013
0.975
0.966

107.839, p = 0.030
0.013
0.974
0.964

1.00
1.41 (0.25)
1.41 (0.26)
0.96 (0.16)

1.00
1.68 (0.37)
1.53 (0.31)
1.01 (0.18)

-0.15 (0.15)
-0.46 (0.24)
-0.11 (0.26)
0.04 (0.03)
0.11 (0.03)
0.00 (0.01)
0.22 (0.15)
-0.07 (0.43)
1.31 (0.18)

-0.14 (0.14)
-0.43 (0.22)
-0.07 (0.40)
0.04 (0.02)
0.11 (0.03)
0.00 (0.01)
0.21 (0.14)
-0.07 (0.40)
1.23 (0.18)

-0.18 (0.61)
-0.32 (0.61)
-0.09 (0.60)

-0.16 (0.57)
-0.29 (0.57)
-0.08 (0.56)

-0.96 (0.31)

-0.89 (0.29)

0.27 (0.19)
0.31 (0.20)
0.24 (0.21)

0.25 (0.18)
0.29 (0.19)
0.22 (0.19)

1.32 (0.27)

1.16 (0.24)

Model Fit Statistics
χ2
RMSEA
CFI
TLI
Parameters
Within
Factor Loadings
Receptive Sharing
Distributive Sharing
Divide Drugs
Share other equipment
Path Coefficients
Inject at home
Location is Common Location
Missing
# of people injecting
# non-injectors using drugs
# of injectors * # of non-injectors
Gender Concordance
Missing
Sexual Partner
HIV Concordance
Unknown Concordance
Concordant
Missing
Partnership Length
New Partner
First Quartile (Reference)
Second quartile
Third quartile
Fourth quartile
Residual Variance – Within
Injection Risk Behavior (Latent)

105

Table 10 (cont'd)
Between
Factor Loadings
Rec. Share
Dist. Share
Divide Drugs
Non-syringe sharing

1.00
1.41 (0.25)
1.41 (0.26)
0.96 (0.16)

1.00
1.34 (0.27)
1.32 (0.26)
0.94 (0.16)

Thresholds
Rec. Share
Shared - used bleach
Shared - did not use bleach
Dist. Share
Divide Drugs
Non-syringe sharing

0.71 (1.14)
2.24 (1.15)
2.50 (1.40)
4.13 (1.67)
2.67 (1.23)

0.69 (1.11)
2.16 (1.11)
2.72 (1.54)
4.18 (1.71)
2.64 (1.22)

0.37 (0.21)
-0.03 (0.02)

0.38 (0.22)
-0.03 (0.02)

0.71 (0.26)
0.36 (0.34)
0.12 (0.32)
1.25 (0.23)
0.00 (0.02)

0.72 (0.27)
0.37 (0.35)
0.12 (0.34)
1.32 (0.24)
0.00 (0.01)

-0.01 (0.01)
-0.03 (0.01)
-0.00 (0.00)
0.03 (0.01)

-0.01 (0.01)
-0.03 (0.01)
-0.00 (0.00)
0.03 (0.01)

Path Coefficients
Female
Age
Race
Black
Race - Other/Multiple
Hispanic
Homeless
Injection Network Size
Drug Use
Frequency of Crack/Cocaine
Frequency of Heroin/Opioids
Frequency of Injection
Injection Duration

Residual Variance – Between
Injection Risk (Latent)
3.90 (0.84)
4.33 (0.96)
Note. Bold parameters indicate p < 0.05 and standard errors are presented in parentheses.
RMSEA = root mean squared error of approximation, CFI = Comparative Fit Index, and
TLI = Tucker Lewis Index. Chi-square statistics are not directly comparable when using
WLSMV estiamtion.

106

Appendix G. Null (Intercept Only) Model

Table 11. Null Model
Parameters
Within
Factor Loadings
Receptive Sharing
Distributive Sharing
Divide Drugs
Share other equipment

1.00
1.17 (0.19)
0.68 (0.11)
0.50 (0.07)

Residual Variance – Within
Injection Risk (Latent)

2.21 (0.56)

Between
Factor Loadings
Rec. Share
Dist. Share
Divide Drugs
Non-syringe sharing

1.00
1.17 (0.19)
0.68 (0.11)
0.50 (0.07)

Thresholds
Rec. Share
Shared - used bleach
Shared - did not use bleach
Dist. Share
Divide Drugs
Non-syringe sharing

3.45 (0.36)
5.42 (0.57)
3.70 (0.49)
1.12 (0.15)
-0.14 (0.11)

Residual Variance – Between
Injection Risk (Latent)

14.45 (3.05)

ICC
0.13
Note. ICC indicates the intraclass correlation coefficient or
(Within Variance / Within + Between Variance)

107

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