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Opioid withdrawal symptoms, opioid use, and injection risk behaviors among people who inject drugs (PWID)

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Opioid withdrawal symptoms, opioid use, and injection risk behaviors among people who inject drugs (PWID)

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Content Copyright 2022 Kelsey A Simpson
OPIOID WITHDRAWAL SYMPTOMS, OPIOID USE, AND INJECTION RISK BEHAVIORS
AMONG PEOPLE WHO INJECT DRUGS (PWID)
By:
Kelsey A. Simpson, MA
A Dissertation Presented to the
FACULTY OF THE USC GRADUATE SCHOOL
UNIVERSITY OF SOUTHERN CALIFORNIA
In Partial Fulfillment of the
Requirements for the Degree
DOCTOR OF PHILOSOPHY
(PREVENTIVE MEDICINE (HEALTH BEHAVIOR))
December 2022
ii
Acknowledgements
This dissertation project would not have been possible without the support, mentorship,
and guidance of many important people in my life that have shown up for me throughout my
time as a doctoral student. In particular, my thanks go to:

Participants in the Change the Cycle Study and Cannabis and Opioids Study, and especially
those who sat down with me for qualitative interviews used in this dissertation. None of this
research would have been possible without your openness and willingness to share very personal
information about your lives and the challenges you’ve faced and continue to face daily. I have
been humbled by your insights and strengths and feel eternally grateful for the opportunity to
interact with you over the past 6 years of working in the field.

My primary mentor, Dr. Ricky Bluthenthal, who has consistently shown up for me throughout
the entirety of my doctoral education. His mentorship has instilled upon me what it means to be
both an excellent scientist and an excellent human being, and I will strive to uphold these values
in my personal and professional life. Dr. Bluthenthal’s unrelenting support as a mentor has been
critical to my success as a researcher, and critical in my ability to complete this dissertation
work.

The astounding team of students, staff, ungrads, graduate students, and volunteers who have
been involved in data collection on Dr. Bluthenthal’s studies over the past 6 years. I have had an
incredible time working with all of you. You have taught me everything there is to know about
harm reduction, community-based research, peer-to-peer research, and the power in treating
iii
people with compassion, dignity, and respect. I have truly learned so much from you and hope to
continue to learn from you going forward in future research endeavors. I would not have been
able to get through my program without your friendship and unwavering support.

Matthew Kirkpatrick whose mentorship, wit, and advice have been highlights of my doctoral
education. I feel so fortunate to have gotten to work with over the past few years. You are an
inspiring researcher with such a generous heart, and your kindness has helped me in so many
ways. Thank you for all you have given me.

Jess Barrington-Trimis who has modeled what it means to be a rockstar scientist who advocates
for her students no matter the circumstances. Her mentorship has opened the door to an
abundance of research opportunities that have undoubtedly shaped the trajectory of my career. I
am so appreciative of the kindness and mentorship she has lent my way and will be forever
appreciative of the personal and intellectual enrichment that has come as a result.

Jordan Davis and Junhan Cho, with whom I credit most of my understanding of the statistics
applied in this dissertation. I thank you for the countless hours of statistical support, Mplus
training, and data advising you lent me throughout this project, as well as during my F31
proposal. Your generosity and support have been crucial to the survival of this project, and I will
be forever appreciative.

I also want to thank my friends and family, especially my best friend Julia Soto for continuously
encouraging me to keep pushing forward during times where I felt stuck. I still remember the day
iv
you found out I got into USC and filled our apartment with balloons and trojan paraphernalia.
Your friendship has kept me smiling and laughing through the toughest times. I would also like
to thank Jordan Ulloa for your willingness to support me throughout this long academic
rollercoaster of successes and failures. I would not be anywhere near the position I am now
without you.

Thank you to my fellow cohort member Carol Ochoa for not only being there for me
emotionally, but for notoriously being a few steps ahead of me in our program and helping me
navigate grant applications, qualifying exams, and this dissertation work. Also, thanks to Maria
Bolshakova for allowing me to partner with you in conducting the qualitative research aspect of
this dissertation project. It has truly been a pleasure getting to work with you and I appreciate all
the time you have dedicated to this study.

v
TABLE OF CONTENTS
Acknowledgements ........................................................................................................................ ii
List of Tables ............................................................................................................................... vii
List of Figures ............................................................................................................................. viii
Glossary of Key Terms ................................................................................................................. ix
Abstract .......................................................................................................................................... x
Chapter 1: Introduction .................................................................................................................. 1
Background and Significance .................................................................................................. 1
A Brief Overview of the Origins and Evolution of the Opioid Crisis ..................................... 1
Injection as a Route of Opioid Administration ........................................................................ 3
Negative Health Outcomes Among People Who Inject Drugs ................................................ 4
Additional Economic and Structural Drivers of Harm ............................................................ 5
Opioid Withdrawal................................................................................................................... 5
The Burden of Opioid Withdrawal .......................................................................................... 7
Qualitative Evidence Linking Opioid Withdrawal to Infectious Disease Risk ....................... 7
Pharmaceutical Approaches for Treating Opioid Use Disorder .............................................. 9
Treatment Gaps ...................................................................................................................... 10
Gaps in Understanding of Long-Term Consequences of Opioid Withdrawal ....................... 10
Introduction to Dissertation Studies ...................................................................................... 11
Theoretical Framework .......................................................................................................... 12
Dissertation Aims .................................................................................................................. 13

Chapter 2: Longitudinal associations between opioid withdrawal symptoms and opioid use
frequency in PWID ..................................................................................................................... 15
Abstract ................................................................................................................................... 15
Introduction ............................................................................................................................. 17
Methods ................................................................................................................................... 18
Results ..................................................................................................................................... 24
Discussion ............................................................................................................................... 27
Conclusion ............................................................................................................................... 31

Chapter 3: Opioid withdrawal symptoms as longitudinal predictors of injection-related risk
behaviors ...................................................................................................................................... 39
Abstract ................................................................................................................................... 39
Introduction ............................................................................................................................. 41
Methods ................................................................................................................................... 43
Results ..................................................................................................................................... 48
Discussion ............................................................................................................................... 50
Conclusion ............................................................................................................................... 55
vi
Chapter 4: Characterizing Opioid Withdrawal Experiences Among a Community Sample of
PWID ........................................................................................................................................... 62
Abstract ................................................................................................................................... 62
Introduction ............................................................................................................................. 64
Methods ................................................................................................................................... 66
Results ..................................................................................................................................... 69
Discussion ............................................................................................................................... 79
Conclusion ............................................................................................................................... 85

Chapter 5: Overall Discussion ..................................................................................................... 94
Overview ................................................................................................................................. 94
Summary of Findings .............................................................................................................. 94
Implication of Findings ........................................................................................................... 97
Theoretical Implications .......................................................................................................... 97
Methodological Implications ................................................................................................... 98
Practical Implications .............................................................................................................. 99
Overall Limitations ................................................................................................................ 103
Future Research Direction ..................................................................................................... 104

References .................................................................................................................................. 106
vii
List of Tables
Table 1. Summary of somatic and affective opioid withdrawal symptoms ................................... 6

Table A1. Baseline socio-demographic characteristics of PWID in Los Angeles and San
Francisco, CA, who reported regular opioid use during at least one study visit (N=834)

.......... 33

Table A2. Final time-varying covariate model for opioid use frequency .................................... 34

Table A3. Final time-varying covariate model for opioid withdrawal frequency ....................... 35

Supplemental Table A1. Sociodemographic characteristics of participants included (vs.
excluded from) the primary analytic sample ............................................................................... 38

Table B1. Final time-varying covariate latent growth model predicting injection risk ............... 56

Table B2. Final multigroup time-varying covariate latent growth model by gender .................. 57

Table B3. Final multigroup time-varying covariate latent growth model by homelessness
status ............................................................................................................................................ 58

Table B4. Multivariable logistic regression model of factors associated with any non-fatal
overdose reported during the 12-month study period .................................................................. 59

Table B5. Bivariate correlations among predictors, covariates and injection risk behaviors
across time ................................................................................................................................... 60

Table B6. Descriptive statistics of injection risk variables at each timepoint ............................. 61

Table C1. Sociodemographic information, housing status, and MOUD history ......................... 86

Supplemental Table C1. Qualitative interview guide .................................................................. 87

viii
List of Figures
Figure 1. Visual model of dissertation studies ............................................................................. 14

Figure A1. Participant accrual flow chart .................................................................................... 36

Figure A2. Final RI-CLPM model displaying within-person cross-lagged effects ..................... 37

ix
Glossary of Key Terms
Opioid misuse – The use of opioids in any manner that is not directed by a health care provider
or prescriber including use without a prescription of one’s own, and use in greater amounts,
more often, or longer than directed.
Medication-assisted treatment (MAT) – Treatment for opioid use disorder combining the use
of medications (e.g., methadone, buprenorphine, naltrexone) with counseling or behavioral
therapy.
Opioid withdrawal – refers to the opioid-specific problematic behavior change with
physiologic and cognitive components due to the cessation of or reduction of opioid use.
Opioid use disorder (OUD) – problematic pattern of opioid use that results in significant
impairment or distress. A DSM-V diagnosis is based on fulfilling at least two of the following
criteria within a 12-month time period: unsuccessful attempts to cut down or control use; use in
larger amounts or for longer than intended; spending a great deal of time obtaining, using, or
recovering from use; cravings or strong desires to use; failure to fulfill major work or social
obligations due to use; continued use despite recurrent interpersonal problems; recurrent use in
physically hazardous situations; continued use despite knowledge of persistent physical or
psychological consequences exacerbated by the substance; tolerance; and withdrawal.
Opioid dependence – adaptation to using opioids in which symptoms of withdrawal occur
when opioids are reduced or discontinued.
x
Abstract
Injection opioid use (including use of prescription opioids, heroin, and synthetic opioids)
is a significant and growing public health concern. Globally, there are an estimated 15.6 million
people who have injected drugs (PWID), including 6 million in the United States (1-3). The
injection of illicit or prescription opioids has substantial health repercussions for PWID,
including increased risk for HIV and HCV, venous injuries, cerebral complications,
subcutaneous infections, infective endocarditis, and fatal drug overdose (1, 4-8). Injection opioid
use leads to increased levels of tolerance, and distressing withdrawal symptoms when opioids are
discontinued or dosage is reduced (9). Previous research has shown that PWID experiencing
opioid withdrawal face increased risk of blood-borne illnesses, injection-related infections, and
fatal drug overdose due to increased engagement in high-risk injection practices including
syringe sharing (10-18). Despite this evidence, most research on opioid withdrawal have been
conducted among samples of patients taken directly from clinical trials and medication-assisted
treatment programs, which are not generalizable to the majority of the PWID population.
Consequently, knowledge of how withdrawal relates to subsequent substance use and other
injection-related risk behaviors among PWID who are not currently seeking treatment is scant.
To fill these knowledge gaps, this dissertation examined: 1) longitudinal and bidirectional
associations between opioid withdrawal symptoms and opioid use, 2) prospective associations
between opioid withdrawal symptoms and injection-related risk behaviors, and 3) opioid
withdrawal experiences and mechanisms that lead to the progression of further opioid-related
harms. Study 1 revealed consistent positive associations between opioid withdrawal symptoms
and frequency of opioid use over time; however, these associations did not hold true in our
model accounting for reciprocal influences. Study 2 found an association between withdrawal
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and increased injection risk at baseline. Homelessness moderated the relationship between
withdrawal and injection risk such that for participants who reported recent or current
homelessness (prior 30 days), withdrawal symptom frequency was associated with increased
injection risk at baseline. Withdrawal did not affect injection risk for participants who were not
homeless. Baseline opioid withdrawal and past 6-month non-fatal overdose were associated with
increased odds of reporting a non-fatal overdose event during either the 6 or 12-month follow-
up. Study 3 discovered the following themes related to withdrawal experiences and perspectives:
withdrawal importance, withdrawal consequences, how withdrawal impacted daily life activities,
methods for coping with withdrawal symptoms, withdrawal and economic insecurity, fentanyl
versus heroin withdrawal, and withdrawal in the context of buprenorphine treatment.
Collectively, the results of these three studies demonstrate the profound importance of the opioid
withdrawal syndrome and the complexity of drug and health related consequences associated
with such symptoms.
1
Chapter 1: Introduction
Background and Significance
The opioid overdose death crisis has caused immense loss of life among Americans, with
over 93,000 deaths occurring in 2020 (19). Approximately 400,000 Americans died from
overdoses involving any illicit or prescribed opioid between 1999 and 2017, with death rates
exceeding those caused from motor vehicle accidents, gun violence, and deaths by any other
drugs or drug classes, with the exception of alcohol (1, 19-21). The rise in opioid use has
contributed to increased rates of people who inject drugs (PWID) in the US, with an estimated 6
million PWID to date (3, 5, 22). Injection opioid use is a significant source of morbidity and
mortality, with national overdose mortality deaths involving opioids, heroin, and other synthetic
narcotics (primarily fentanyl or fentanyl analogs) increasing nearly six-fold in the past two
decades (6, 23). The rising burden of opioid overdose deaths and associated consequences on the
health care system, labor market, and criminal justice system were recently estimated to cost
upwards of $500 billion dollars annually (20), indicating clear social and economic need for
action and improved solutions.

A Brief Overview of the Origins and Evolution of the Opioid Crisis
The opioid overdose death crisis originated from several developments. Beginning in the
1990s, the Joint Commission, American Pain Society, American Academy of Pain, Federation of
State Medical Boards, and other patient groups began to advocate for better identification and
treatment of pain (24). This movement prompted the launch of the ‘Pain: The Fifth Vital Sign’
campaign which encouraged the use of opioid analgesics for the treatment of chronic non-cancer
pain (21). Shortly thereafter, standards for assessing pain were reconfigured in hospitals and
2
health care programs to enforce this notion, which resulted in major increases in the medical
prescribing of opioids (21, 24, 25).
Following shifts in the medicalization of opioids came the release of OxyContin to the
US marketplace. OxyContin is a sustained-release formulation of oxycodone manufactured by
Purdue Pharma designed to provide pain relief for 8 to 12 hours (26). Oxycodone was marketed
as an effective and non-addictive treatment for pain due to its slow-release delivery properties
(26, 27), and quickly became the country’s most prescribed opioid only two years after its debut
(28). Such commercial success was attributed to Purdue Pharma’s strategic marketing approach
which targeted geographic locations known to have the highest rates of physical injury and
unemployment in the workforce (e.g., Maine, West Virginia, Kentucky, Virginia, and Alabama)
(21, 27). These tactics resulted in a nearly ten-fold increase in OxyContin prescriptions between
1997 (670,000) and 2002 (6.2 million) (21).
The growing popularity and widespread availability of prescription opioids led to many
disastrous health consequences including increased rates of opioid misuse (defined as use
without a prescription, or in a manner or dose other than what is directed by a doctor), with 3.1
million Americans reporting OxyContin misuse in 2004 (26, 29). Emergency room visits for
opioid use, opioid-related overdose deaths, and hospital admissions for opioid use disorder
(OUD) also rose dramatically during this time (1, 28, 30). In effort to mitigate these harms,
federal agencies and pharmaceutical companies thereby launched several initiatives including
prescription drug monitoring programs, abuse-deterrent formulations of oxycodone, and
restrictive policies surrounding prescribing practices to reduce the number of opioids prescribed
and combat misuse (21, 28). These restrictions made it increasingly difficult to obtain
3
prescription opioids and simultaneously drove up the cost, sparking the widespread shift in
domestic heroin use, a cheaper and more accessible alternative to prescription opioids (1, 5).

Injection as a Route of Opioid Administration
It is well established that transitions from prescription opioids to heroin also involved a
more potent route of administration—opioid injection (1, 5, 8). In the United States, heroin is
primarily self-administered by route of injection, which introduces a host of additional lethal
health consequences (1, 5, 8). Further, the differences in potency and onset of effects among
snorted and orally ingested opioids and injected opioids place a person making the switch away
from oral routes at much higher risk for overdose, risk of dependence, and route-specific health
complications (28, 31). This is due to the direct route of consumption into the bloodstream,
which bypasses the metabolic processes in which orally administered drugs are ingested,
increasing the speed of delivery to the brain (31). This induces a rapid set of drug responses (e.g.,
strong feelings of euphoria, relief from pain) that can be strongly rewarding and increase the
likelihood for repeated use (31, 32). The mechanism by which opioid injection can lead to future
drug-seeking can be further understood by reinforcement models of behaviorism, which posit
that behaviors are determined by learned consequences or ‘reinforcements’(33).
Broadly, behaviorism refers to the psychological framework in which changes in
behaviors are thought to result from an individual’s learned (or conditioned) interactions with the
environment (33). A fundamental component of behaviorism is reinforcement, which is the
repetition of a particular response based off a relationship with a stimulus (33, 34). Reinforcers
can be classified as positive (when a stimuli is added) or negative (when a stimuli is taken away),
and the temporal association between a behavior and an immediately following reward can
4
increase the probability that a behavior would be repeated. In the context of substance use, drugs
themselves can serve as robust reinforcers due to the intrinsically positive effects they can elicit.
Furthermore, the immediate and biological potent positive responses of opioid injection can
reinforce future drug usage.

Negative Health Outcomes Among People Who Inject Drugs
In addition to increased likelihood for continued use, PWID are also subject to
disproportionate burdens of chronic diseases and health vulnerabilities that cause extensive
morbidity and mortality. For instance, PWID experience a higher prevalence of fatal and non-
fatal drug overdoses compared to non-injection drug users, with 30-45% of PWID experiencing
at least one lifetime non-fatal overdose (compared to 3.5-13% of non-injection opioid users) (35-
37). Rates of blood-borne and sexually transmitted pathogens including the human
immunodeficiency virus (HIV), and hepatitis B and C are all higher among PWID than the
general population, and an uncontrolled opioid use order complicates management of chronic
illness (1, 4-8, 21, 38-42). According to a recent systematic review, nearly one-fifth of PWID are
known to be living with HIV, 52% with hepatitis C, and 9.1% with hepatitis B (43). Regional
and nationwide increases in injection-related bacterial infections including abscesses, cellulitis,
skin and soft tissue infections, and hospitalizations for infective endocarditis have also been
shown (4, 44, 45). High-risk practices such as sharing syringes and other injection equipment,
frequent injection, reusing needles repeatedly, and not cleaning one’s skin before injecting all
pose increased risk for bloodborne infections with severe downstream health consequences (12,
42, 46, 47).

5
Additional Economic and Structural Drivers of Harm
The opioid overdose death crisis has been fueled by the widening income gap and
increased poverty, with socioeconomically disadvantaged communities being hit with the highest
overdose death rates and infectious disease outbreaks (21, 48). For example, rural communities
including central Appalachia and Scott County, Indiana have been particularly struck by opioid-
related harms, with 181 newly reported HIV cases in Scott County in 2015 (21). HIV outbreaks
among PWID have also occurred outside of rural settings such as Massachusetts and Seattle,
with elevated rates of poverty and overdose-related mortality (45, 49). In a study of PWID from
two urban cities in Boston, PWID who were newly infected with HIV all reported past year
homelessness (45). These communities are deemed ‘highly susceptible’ to injection-related
harms primarily due to their limited access to medications for opioid use disorder, low physician
availability, minimal addiction treatment services, and few or no harm-reduction programs (i.e.,
syringe exchange programs, overdose education, naloxone distribution) (21, 38, 48). These
findings highlight how structural and community contexts can influence health and vulnerability
for drug-related harms and emphasize the need for targeted prevention efforts geared towards
PWID with compounding vulnerabilities.

Opioid Withdrawal
Physical dependence is an inherent byproduct of repeated use of opioids by route of
injection (9, 20, 50-52). Physical dependence, as defined by the Diagnostic and Statistical
Manual of Mental Disorders, Fifth Edition (DSM-V), refers to the drug-induced stage in which
withdrawal symptoms occur in response to periods of drug abstinence or dose reduction (53).
Abstinence from opioid-dependence states induce a host of adverse psychobiological symptoms
6
such as nausea, vomiting, muscle spasms, abdominal cramps, anxiety/restlessness, tachycardia,
hypertension, hot flashes/chills, insomnia, and depression; with type and severity varying widely
(20, 52, 54). A comprehensive list of somatic and affective withdrawal symptoms is presented
below (Table 1).

Opioid withdrawal symptoms can occur as quickly as a few hours after a missed dose,
and are known to be more severe among drug injectors compared to other opioid using
subgroups (55). The typical onset and time course of opioid withdrawal symptoms is dependent
on the amount, and half-life of the specific type of opioid used (20). Withdrawal symptoms for
dependent users of short-acting opioids (e.g., heroin, hydrocodone, oxycodone) usually begins
within 12 hours of last usage, peaks within 36-72 hours, and persists for 7-10 days (50). Fentanyl
(another short-acting opioid) has a similar withdrawal profile, with symptoms occurring between
8 and 16 hours after discontinuation, peaking within 36-72 hours, and lasting 5-8 days in
Table 1. Summary of somatic and affective opioid withdrawal symptoms
Somatic Affective
• Bone, joint, muscular pain, aches, spasms,
tension
• Hyperalgesia (enhanced pain sensitivity)
• Insomnia
• Lacrimation
• Tremor
• Nausea, vomiting, abdominal cramps, diarrhea
• Changes in body temperature
• Ptosis (drooping eyelids) and pupillary dilation
• Tachycardia, arrhythmias, hypertension
• Teeth chattering
• Weakness
• Yawning
• Rhinorrhea
• Mydriasis
• Anxiety/restlessness
• Depression
• Irritability
• Dysphoria
• Alexithymia
7
duration (9). Withdrawal from long-acting opioids (e.g., buprenorphine, methadone) is typically
less severe than short-acting opioids, begins within 30 hours of last exposure, peaks at 72-96
hours, and persists in duration for 2 weeks or longer in some cases (9).

The Burden of Opioid Withdrawal
The clinical manifestations of opioid withdrawal can involve severe fluid loss and
electrolyte abnormalities that can result in hemodynamic instability, seizures, and even death if
abrupt or managed improperly (54, 56, 57). Among PWID, these symptoms are characterized as
‘debilitating,’ (58) with most finding it an almost insurmountable condition, dreading
withdrawal, or having unrelenting craving for relief from the withdrawal state (16, 32, 58). In
these circumstances, the severe discomfort of withdrawal often becomes the primary
motivational force driving continued drug-taking (16, 59). This mechanism for sustained drug-
seeking aligns with behavioral models of reinforcement in that opioid administration can serve as
a powerful means of negative reinforcement by taking away or alleviating withdrawal symptoms
(34). In this manner, the learned associated between opioid use and relief of aversive withdrawal
states becomes reinforced through ongoing opioid use (32). Further, highly addictive opioids
(i.e., heroin and fentanyl), with a faster onset and shorter half-life, create a high frequency of
vacillating states of intoxication and withdrawal, providing more opportunities to solidify learned
associations and driving addiction susceptibility (9).

Qualitative Evidence Linking Opioid Withdrawal to Infectious Disease Risk
As a person’s route of opioid administration shifts to drug injection, the disparity between
the dosage needed to avoid withdrawal and available money to buy drugs often leads to periods
8
of forced withdrawal among PWID (16). These periods of sickness place PWID in contexts of
exceptional vulnerability to infectious disease risk. Moreover, as a person experiences
withdrawal, they develop an immediate urgency to alleviate such symptoms, which drives users
to resort to their most immediate sources for either injection equipment or drugs, despite the
consequences (12, 60). Previous qualitative research has illustrated that PWID are more likely to
engage in high-risk injection practices when experiencing opioid withdrawal (11, 16, 61-63). For
instance, in looking at reasons for sharing injection equipment, Ross et al., (1994) discovered
PWID to experience increased urgency to inject during withdrawal events. Similarly, PWID in
withdrawal demonstrated increased feelings of desperateness and were more willing to share
syringes/needles (16, 63). Studies examining perceived barriers to not using a clean syringe for
every injection or not cleaning one’s skin prior to injection also listed withdrawal as a major
contributing factor (18, 59, 64, 65). Among homeless injection drug users diagnosed with
hepatitis C, acute withdrawal symptoms were discussed in reference to reusing filters and cottons
and reusing or sharing drug paraphernalia (63).
Adverse withdrawal scenarios have also been discussed in literature examining barriers to
health care utilization among PWID. For instance, experience of, aversion to, and concerns about
withdrawal were major motivations for delaying or avoiding treatment for skin and soft tissue
infections among injection heroin users in Boston and Sacramento (58). In another study,
obtaining and using drugs to prevent opioid withdrawal were seen as a time-consuming
competing priority to all other health care needs, despite the high personal risk and cost (66).
Furthermore, the intense anxiety and preservation when anticipating withdrawal, combined with
knowledge that administering opioids can immediately relieve withdrawal symptoms, drives
PWID to avoid settings in which adequate opioids are not available.
9
Pharmaceutical Approaches for Treating Opioid Use Disorder
Pharmaceutical management of opioid withdrawal symptoms is currently considered the
gold standard for treating individuals with opioid use disorder (OUD), and a necessary first step
in effectively stabilizing patients over abrupt cessation (9, 20, 50). In fact, evidence indicates that
less than 10% of people achieve long-term abstinence without receiving some form of
withdrawal-attenuating medication (67). At present, there are four US Food and Drug
Administration approved medications available for the treatment of opioid use disorder:
methadone, buprenorphine, extended-release naltrexone, and lofexidine (21, 50, 54). Methadone
is a full opioid agonist that has been around since the 1950’s through federally approved opioid
treatment programs that provide medication dispensation under strict and highly regulated
policies. Methadone is formulated for oral administration and is offered in pill and liquid form
taken once daily.
Buprenorphine is a partial opioid agonist that attaches and partially activates opioid
receptors, but less strongly than full opioid agonists such as methadone (9). Buprenorphine
formulations include daily sublingual tablets or films and long-acting formulations that are
injected monthly or implanted subdermally (68). Extended-release naltrexone is a full opioid
antagonist dispensed in the form of a monthly injection, with administration requiring abstinence
from opioids for 7-10 days prior to treatment initiation (54, 69). Lofexidine is a non-opioid based
medication designed to target autonomic withdrawal symptoms related to noradrenergic
hyperactivity (i.e., elevated blood pressure, irritability, sweating). Lofexidine is typically
dispensed to patients undergoing acute opioid withdrawal, and administration is in the form of
0.18 mg tablets ingested orally for up to 14 days (50).

10
Treatment Gaps
Medications for opioid use disorder are associated with reductions in frequency of opioid
use, fewer injections, and lower rates of HIV prevalence and incidence (68, 70-74). Through
reductions in injection drug use, opioid medications have the potential to limit the spread of HIV
and HCV, yet treatments are primarily offered to patients in clinical settings such as medical
taper programs and drug detoxification centers (21, 50, 54, 75). Further, there is a paucity of
access and use of these medications among out of treatment populations of PWID, who face
greater health disparities and adverse consequences of injection drug use (76). In a study of
PWID with OUD in Seattle, the majority of participants (70%) reported no past year methadone
or buprenorphine treatment (76). This treatment gap underscores the need for future research
efforts geared towards understanding individual and community-level barriers to treatment
utilization among PWID who use opioids.

Gaps in the Understanding of Long-Term Consequences of Opioid Withdrawal in PWID
While opioid withdrawal is widely understood as a major aspect of opioid use disorder,
no previous studies have attempted to characterize how withdrawal experiences may influence
long-term opioid use patterns in PWID. This is important for two reasons. First, prior work
indicate that severity and duration of withdrawal is greater among injectors than non-injection
opioid users (55, 77, 78). Second, few studies have explored how important economic and
structural risk factors (e.g., homelessness, SES) may influence stability and change in opioid use
and withdrawal patterns over time. Data on how withdrawal symptoms relate to prospective
opioid use patterns will help to provide a clearer understanding of the mechanisms by which
opioid withdrawal may potentiate the progression, maintenance, or cessation of opioid use.
11
While previous research has documented a potential link between opioid withdrawal and
heightened injection-related risk practices (11-18), no empirical studies have examined the
longitudinal associations between withdrawal and opioid-related health outcomes in PWID.
Thus, critical information regarding the possible causal direction of established associations
remains unclear. Given the recent spike in incident HIV and HCV cases in PWID (1), and
increasing rates of mortality due to opioid overdose, understanding how withdrawal from opioids
can increase infectious disease and overdose risk over time is critical in preventing future
bloodborne disease transmission among PWID throughout the United States.

Introduction to Dissertation Studies
To address these gaps in literature, the current research investigates the prospective
associations of opioid withdrawal symptoms with frequency of opioid use, and injection-related
risk behaviors over a 12-month follow-up period among a diverse cohort of community-recruited
PWID. Qualitative data from a subset of participants was collected to further explain and
describe opioid withdrawal experiences and mechanisms. Data for this project was drawn from
two data sources: the Change the Cycle (CTC) efficacy trial study (R01DA038965; MPI
Bluthenthal [contact]/Kral) a completed study among PWID in Los Angeles and San Francisco,
CA, and the Cannabis and Opioids Study (R01DA046049; MPIs Bluthenthal [contact]/Corsi), an
ongoing prospective cohort study of street recruited PWID in community settings in Los
Angeles, California, and Denver, Colorado. Qualitative interviews were conducted among a
subsample of participants who resided in Los Angeles in the Cannabis and Opioids study to
better understand the individual, social, and structural factors that influence the onset, frequency,
12
and severity of opioid withdrawal symptoms and how PWID experience and manage such
symptoms in their everyday lives.

Theoretical Framework
The theoretical frameworks guiding my dissertation studies applies different aspects of
Zinberg’s Drug, Set, and Setting theory (79), and the Risk Environment Framework (80). The
Drug, Set, and Setting theory propagates the idea that drug use is influenced by three primary
factors: (1) Drug: the pharmacological effect of the drug itself (i.e., effects produced by the
drug’s quantity, potency, and the mode of administration); (2) Set: individual-level
characteristics of the user (i.e., age, race, medical history); and (3) Setting: interpersonal,
environmental, and systemic level influences (i.e., perceived norms, homelessness status,
neighborhood SES, proximity to syringe exchange services) on drug use behaviors. The Risk
Environment Framework (80) states that environment, or “Setting,” has physical, social, and
structural dimensions that all interact to produce individual susceptibility to drug-related harm.
There is growing recognition of the importance of environmental factors on drug use patterns
and injection risk (61, 81). Coverage of harm reduction programs (structural), social norms and
perceptions of medications for opioid use disorder (social), and homelessness (physical) are
environmental factors capable of influencing drug use patterns and opioid-related health
outcomes and were applied to the understanding of withdrawal impacts among PWID. We have
integrated this into our conceptualization of setting.

13
Dissertation Aims
Study 1: To evaluate longitudinal, bidirectional associations between opioid
withdrawal symptoms and opioid use in the CTC cohort (N=834). We evaluate: (1a)
whether high levels of opioid withdrawal symptoms are positively associated with increased
frequency of opioid use over 6-month follow-up assessments; and (1b) whether greater
frequency of opioid use is associated with increased withdrawal frequency. We also formally
tested (1c) the bidirectional associations of opioid withdrawal symptoms and opioid use using a
random-intercepts cross-lagged panel model.

Study 2: To evaluate longitudinal associations between opioid withdrawal
symptoms and injection-related risk behaviors in the CTC cohort (N=834). We examine:
(2a) whether frequency of opioid withdrawal symptoms is associated with contemporaneous
injection risk; (2b) the moderating effects of gender and homelessness on associations between
withdrawal and injection risk; and (2c) associations between opioid withdrawal and non-fatal
overdose risk.

Study 3: To collect new qualitative data characterizing how PWID experience,
react to, and assert control over symptoms of opioid withdrawal in their everyday lives.
This was achieved using semi-structured qualitative interviews with a sample of opioid using
PWID (N=22) to better characterize experiences of opioid withdrawal using interview prompts
incorporating theoretical principles of behavioral pharmacology to document experiences of
opioid withdrawal and the social environment, how PWID cope with withdrawal symptoms, and
the impact of withdrawal experiences on treatment utilization.
14

Figure 1. Visual model of dissertation studies

Opioid use
Opioid withdrawal
symptoms
Study 1
(Longitudinal and Cross-
Lagged)
Study 2
(Longitudinal and Moderation
Analysis)
Injection risk behaviors
Cumulative injection risk
Non-fatal overdose
Study 3
(Qualitative Interviews)
15
Chapter 2: Longitudinal Associations Between Opioid Withdrawal Symptoms and Opioid
Use Frequency in PWID

Abstract
Background: While opioid withdrawal is widely understood to be a major consequence of
opioid use disorder, empirical data illustrating the relationship between opioid withdrawal
symptoms and prospective patterns of opioid use among people who inject drugs (PWID) in
community settings is scant. In the following, we examined longitudinal and bidirectional
associations between opioid use and opioid withdrawal symptoms while controlling for
demographic and socioeconomic covariates.

Methods: The current study leveraged data from a prospective longitudinal cohort study of
opioid using PWID in Los Angeles and San Francisco, California (2016-2018). Opioid use and
opioid withdrawal frequency was assessed at three separate time points (baseline, 6-month, 12-
month). Latent growth curve models with time-varying and time-invariant covariates estimated
contemporaneous associations between opioid withdrawal and opioid use frequency. A random-
intercept cross-lagged panel model (RI-CLPM) was used to estimate concurrent and subsequent
associations between the two constructs.

Results: Overall, 834 PWID were evaluated (23.6% female, Mean [SD] age at baseline = 42.13;
[12.13] range=18-76). In our model for opioid use, opioid withdrawal frequency and
homelessness were consistently positively associated with opioid use over time. Recent
methadone use was associated with a decrease in opioid use at 6- and 12-months. In our model
16
for opioid withdrawal, both opioid use and homelessness were positively associated with
withdrawal symptom frequency, and recent methadone use was negatively associated with
contemporaneous withdrawal frequency. Associations between withdrawal and opioid use did
not hold true in our RI-CLPM accounting for reciprocal influences.

Conclusion: Findings underscore the importance of opioid withdrawal symptoms as a predictor
of opioid use patterns. This study is the first to demonstrate a significant temporal relationship
between withdrawal and opioid use in a non-clinical sample of opioid users. Treatment initiatives
that target withdrawal symptoms may be a successful way to reduce overall rates of illicit opioid
use and improve drug cessation. Further, these results should inform future prevention efforts
designed to reduce the consequences of opioid dependence for opiate users in community
settings.

17
Introduction
Increased tolerance and physiological dependence are an inherent byproduct of chronic or
repeated use of opioids by route of injection (9, 20, 50-53). Physiological dependence
necessitates the continued administration of opioids to prevent the occurrence of painful
withdrawal symptoms when opioids are abruptly discontinued or dosage is reduced (9, 20, 52).
Such symptoms involve a host of adverse psychobiological consequences (e.g. nausea, vomiting,
muscle spasms, abdominal cramps, anxiety/restlessness, tachycardia, hypertension, hot
flashes/chills, insomnia, and depression) that cause significant amounts of pain and distress to
the user (52, 82). Pharmacological management of opioid withdrawal symptoms with opioid
agonist therapy (e.g., methadone or buprenorphine) during acute drug detoxification is
universally recommended as a first line step in providing comprehensive care to patients with
opioid use disorder (OUD) (20). Failure to recognize and adequately address withdrawal
symptoms can play a significant role in driving relapse behaviors and inhibiting cessation efforts
altogether (9, 20). Furthermore, proper withdrawal treatment during initial stages of OUD
treatment can serve as an effective bridge to longer-term treatment and overall reductions in rates
of illicit opioid use among patient samples (83).
Opioid withdrawal symptoms are alarmingly common among opioid-using people who
inject drugs, with estimated prevalence ranging as high as 85% in recent samples (12, 84). While
opioid withdrawal is widely understood as a major aspect of OUD, no previous studies have
attempted to characterize how withdrawal experiences may influence long-term opioid use
patterns in PWID. Furthermore, most of the research involving opioid withdrawal has been
limited to samples of patients taken directly from clinical trials and medication-assisted treatment
programs, which are not generalizable to the majority of the PWID population. Consequently,
18
knowledge of how withdrawal relates to subsequent opioid use patterns is lacking. This is
unfortunate as PWID in community settings face greater health disparities and adverse
consequences of injection drug use (55). Examining variations and patterns of opioid use and
opioid withdrawal in PWID who are not currently enrolled in any type of substance or health-
care treatment are needed to delineate the real-world consequences of withdrawal in the general
population. Such information may offer additional insight into the mechanisms by which opioid
withdrawal may potentiate the progression, maintenance, or cessation of opioid use and inform
treatment outcomes.
Accordingly, this study sought to evaluate the longitudinal and bidirectional associations
between opioid withdrawal symptoms and frequency of opioid use in a diverse cohort of
community recruited PWID using three separate analyses. First, we examine whether opioid
withdrawal symptoms are associated with frequency of opioid use over subsequent 6-month
follow-up assessments (Aim 1). Based on behavioral models of negative reinforcement (34) and
evidence that highly addictive opioids (i.e., heroin and fentanyl) can create increased opportunity
for vacillating states of intoxication and withdrawal (9), we hypothesized that greater frequency
of opioid withdrawal symptoms will be positively associated with frequency of opioid use over
time. Second, we determine whether greater frequency of opioid use is associated with greater
withdrawal frequency over time (Aim 2). Lastly, we investigate the causal relations (which could
potentially be bidirectional) between opioid withdrawal and opioid use frequency using a random
intercepts cross-lagged panel model (Aim 3).

Methods
Participants and procedures
19
Participants were enrolled in a large prospective cohort study of PWID in community
settings residing in Los Angeles and San Francisco, California between 2016 and 2018 (NIDA
grant#RO1DA046049). Participants in the parent study were originally recruited by community
outreach workers using a targeted sampling approach (85-87). Inclusion criteria for the baseline
interview were (1) recent injection of illicit drugs (past 30 days), (2) age 18 years or older, and
(3) ability to provide informed consent. Participation involved a total of three study visits
(baseline, 6-month, and 12-month) over the course of 12-months. During each study visit,
participants completed computer-based quantitative surveys that were administered face-to-face
by trained research assistants. All participants provided written informed consent prior to
participation. The University of Southern California Internal Review Board approved all study
procedures.
Consistent with prior literature on opioid withdrawal in PWID (84, 88), the analytic
sample for this study was restricted to PWID who reported at least 12 instances of opioid use
(i.e., heroin, prescription opioids, opioids in combination with methamphetamine [goofball] or
cocaine [speedball]) in the prior 30 days during any one of the three study visits. Information on
accrual and inclusion in the study’s analytic sample is depicted in Figure A1.

Measures
Opioid use
Opioid use was assessed using a series of questions regarding frequency of use of four
different types of opioid products in the past 30-days, either by injection or non-injection routes.
Opioid products included any positive endorsement of the following types of opioids: heroin,
speedballs [cocaine and heroin mixed], goofballs [methamphetamine and heroin mixed]), and
20
any non-prescribed opioid medication [e.g., Vicodin, OxyContin] which has been used to
classify opioid use in previous studies involving this population (88, 89). Frequency of total
opioid use was calculated by creating a sum score of total number of times used any
heroin/opioid product by route of injection or non-injection in the past 30-days. Opioid use
frequency was transformed into a categorical variable with the following groups based on times
of use per day: less than once a day (0-29 times), once or twice a day (30-60 times), 3 to 5 times
a day (61-150), 5 to 10 times a day (151-300), and greater than 10 times a day (>300 times).

Opioid withdrawal
Consistent with prior research (56, 89), opioid withdrawal items were only asked to
individuals who reported regular opioid use, which was defined as 12 or more instances of opioid
use (by route of injection or non-injection) in the prior 30 days. Participants who positively
endorsed regular opioid use were asked a single-item survey question consisting of Diagnostic
and Statistical Manual of Mental Disorders —5th Edition (DSM-V) diagnostic criterion for opioid
withdrawal to determine past 6-month history of opioid withdrawal (53). Specifically,
participants were asked, “In the last 6 months, have you experienced restlessness, bone or muscle
aches, runny nose, sweating, cold or hot flashes, anxiety, teary eyes, stomach cramps, nausea,
diarrhea, vomiting, or other symptoms due to withdrawal from heroin or opiates?” Participants
who responded “yes” to experiencing opioid withdrawal were then asked about frequency of
withdrawal episodes using the single-item question, “In the last 6 months, how many times have
you experienced heroin or opiate withdrawal or withdrawal symptoms?” Responses were
recorded as numerical continuous values indicating the total frequency of self-reported
withdrawal episodes. The average frequency of opioid withdrawal episodes was 46.53 (Standard
21
Deviation [SD] = 72.74; median = 12; Interquartile Range [IQR] = 56). Due to this highly
skewed distribution, opioid withdrawal frequency was reclassified by percentiles with the
following categories: 0-4, 5-12, 13-60, 61-180, and >180 times.

Covariates
We included several socio-demographic, economic vulnerability, and substance use
treatment variables as covariates in our analysis based on associations with opioid withdrawal in
previous literature (16, 90). Gender identity (male vs female) was included as a time-invariant
covariate. Time-varying covariates included past 30-day homelessness status (homeless vs not
homeless; yes/no), any past 6-month methadone use (including both prescribed and non-
prescribed methadone use; yes/no), and any past 6-month buprenorphine use (including
prescribed and non-prescribed; yes/no). Intervention assignment was also included as a control
variable in all analyses.

Statistical analysis
Analysis: Aims 1 & 2
To analyze Aims 1 and 2, we conducted a series of latent growth curve models (LGCM)
in a structural equation modeling framework with time-varying and time-invariant covariates
using Mplus Version 8 (91). LGCM is a useful approach for longitudinal data analysis in
circumstances where incomplete and unbalanced data are present, between-subject variation may
change over time, various sources of heterogeneity should be considered (i.e., within-subject and
between-subject variation), and within-subject variations are not statistically independent of each
other (92). Furthermore, LGCM can be used when respondents have been measured at least three
22
times, and the sample size is adequate enough to permit the detection of person-level effects
(92).
For Aims 1 & 2, we began by first fitting an unconditional latent growth model for
opioid use and opioid withdrawal frequency to determine the functional form of the data. This
was evaluated by testing models with random versus fixed slopes and determining best fit using
negative two log-likelihood tests. Next, a time-invariant covariate (female) and time-varying
covariates (frequency of opioid withdrawal, homelessness status, past 6-month buprenorphine
use, past 6-month methadone use) were regressed onto the contemporaneously observed
frequency of opioid use and opioid withdrawal variables to determine the effects of each
covariate over time. To do this, we first built a model with all time-varying covariates as freely
estimated predictors (freely estimated variance) and tested this model against one where time-
varying covariates were constrained systematically (effects constrained to be equal). Differences
in negative two log likelihood tests were used to determine whether a constrained or freely
estimated model best fit the data. After testing each time-varying covariate, final models
included parameter effects for each time-varying covariate specified as constrained or
unconstrained. Standard fit statistics were also considered when determining model fit and
included the Comparative Fit Index (CFI; >0.90 where larger is better fit), Root Mean Square
Error of Approximation (RMSEA; <0.08 where lower is better fit), and Standardized Root Mean
Square Residual (SRMR; <0.08 where lower is better fit) (93).

Analysis: Aim 3
We used a random intercepts cross-lagged panel model (RI-CLPM)(94) to test whether
opioid withdrawal symptoms predicted opioid use over time and, conversely, whether opioid use
23
predicted opioid withdrawal symptoms in one simultaneous model. The RI-CLPM is an
extension of the traditional cross-lagged panel model that accounts for both temporal stability
(between person) as well as trait-like, time-invariant (within-person) differences in the data
through the inclusion of a random intercept (94). In other words, each latent variable of opioid
withdrawal and opioid use can be broken down into a stable, trait-like component (an
individual’s personal norm), and a state-like part component (deviations within individuals) at
each measurement wave.
The RI-CLPM was performed following procedures suggested by Hamaker et al. (2015)
in which observed opioid use and withdrawal measures were regressed on their own latent factor
(each loading constrained at 1) (95). Six additional latent factors (i.e., one for opioid use and
opioid withdrawal at each of the three time points) were used to identify autoregressive paths
(e.g., baseline opioid use → 6-month opioid use), cross-lagged coefficients (e.g., baseline opioid
use → 6-month opioid withdrawal), and cross-sectional associations (baseline correlation and
correlated change). The residual variances of the observed variables were constrained at zero to
ensure the latent factor structure to capture within and between-person variance. Random
intercepts were generated for opioid use and opioid withdrawal to represent stable, trait-like
differences between individuals. Correlations between random intercepts reflected how between-
person differences in opioid use were associated with stable between-person differences in
withdrawal (Figure A2).
RI-CLPM was tested with Mplus 8 (Version 1.6). Satorra-Bentler scaled χ2 difference
tests (96) were used to compare a fully constrained base model to a series of models with freely
estimated equality constraints in repeated parameters estimates. Significant chi-square
differences between models (p<.05) suggested that the model with lifted constraints fit the model
24
significantly worse than the previous model, and corresponding parameters should remain
constrained. Results yielding non-significant differences were freely estimated in our final
model.

Missing Data
Missing data were handled through the full information maximum likelihood (FIML)
estimator. FIML is a process conducted in Mplus which treats all observed predictors as a single-
item latent variable whereby each individual person contributes to the data they have available at
each time point and no individuals are removed through listwise deletion (91). This methodology
has been shown to be preferrable over listwise deletion under circumstances where data is
missing at random. Opioid use and withdrawal frequency were first assessed in the baseline CTC
survey (baseline; total surveyed=979). Data from the 6-month follow up (N=594), and 12-month
follow-up (N=532) were also included in this study. Attrition analyses were conducted to
determine sociodemographic differences between participants included (vs. excluded from) the
primary analytic sample.

Results
Our analytic sample consisted of 834 PWID who reported regular opioid use (use on 12
or more occasions in the past 30 days) during any of the three assessment surveys (baseline, 6-
month, 12-month). Participants excluded from the sample had a lower proportion of
Hispanic/Latino participants, and a higher proportion of White participants and people reporting
an income of less $1,401 in the prior month at baseline (Supplemental Table A1). The analytic
sample (M[SD] age = 43.12 years; 23.6% female) was socio-demographically diverse, with 40%
25
who self-identified as White, 21% Black, 25% Hispanic or Latino, 7% Native American, and 7%
as Mixed Race or another race/ethnicity.

Aim 1 Results: Opioid Use Model
Initial models for opioid use indicated that a random slope fit the data best (CFI = 0.89,
RMSEA = 0.16, SRMR = 0.05). Our unconditional model for frequency of opioid use indicated a
significant intercept (𝛼 = 2.92, SE = 0.04, p < .001) and a decrease in opioid use over the 12-
month assessment period (𝜇 = -0.37, SE = 0.03, p < .001). Results from model constraint testing
of our time-varying covariates indicated a final model in which the effects of opioid withdrawal
frequency, homelessness status, and past 6-month buprenorphine use were constrained to be
equal, and past 6-month methadone use was freely estimated. This model provided excellent
model fit (CFI = 0.98, RMSEA = 0.01, SRMR = 0.03)(Table A2).
Opioid withdrawal frequency had a positive and stable association with contemporaneous
opioid use frequency (𝑏 = 0.13, 95% CI [0.07, 0.18]). That is, for every unit increase in
withdrawal, opioid increased by 0.13 units at each subsequent time point. Current homelessness
had a consistent, positive association with contemporaneous opioid use frequency (𝑏 = 0.46,
[0.34, 0.59]). Interestingly, past 6-month methadone use (freely estimated) was not associated
with frequency of opioid use at baseline (𝑏 = 0.03, p = 0.68); however, was associated with a
decrease in frequency of opioid use at 6 months (𝑏 = -0.48, [-0.64, -0.32]), and 12 months (𝑏 = -
0.27, [-0.46, -0.09]). To understand if there is a difference between baseline and 12-month
methadone use, we used Wald test of parameter constraints. Results supported significant
difference between baseline and 12-month methadone use (Wald test of parameter constraints =
6.86(1), p = 0.01) indicating that, as time progressed, methadone use had an increasingly
26
negative effect on opioid use. Past 6-month buprenorphine use was not associated with opioid
use in our final model (𝑏 = 0.12, p = 0.10).

Aim 2 Results: Opioid Withdrawal Model
The unconditional growth model for opioid withdrawal frequency signified a random
intercept and fixed slope to provide the best model fit (CFI = 1.00, RMSEA = 0.00, SRMR =
0.03). Results of our unconditional growth model indicated an average starting point (i.e.,
intercept) of 2.44, and a significant negative slope (-0.09) indicating decreasing withdrawal
symptomology over time. Next, after entering all time-invariant and time-varying covariates and
testing constraints, results indicated constraining all time-varying covariates to be equal resulted
in the best model fit (Table A3). The final model had excellent fit (CFI = 1.00, RMSEA = 0.00,
SRMR = 0.03).
Frequency of opioid use had a positive, stable association with opioid withdrawal
frequency at each time point (𝑏 = 0.15, [0.09, 0.22]). Past 30-day homelessness was also
consistently, positively associated with contemporaneous frequency of withdrawal symptoms (𝑏
= 0.31, [0.13, 0.48]). Past 6-month methadone use had a significant negative association with
withdrawal frequency (𝑏 = -0.17, [-0.31, -0.04]). Past 6-month buprenorphine use did not emerge
as a significant predictor of withdrawal symptomatology in our final model.

Aim 3 Results: RI-CLPM
A fully constrained baseline RI-CLPM was first estimated which included random
intercepts, autoregressive paths, within-time correlations, cross-lagged effects, as well as female
gender and intervention assignment as covariates. A significant difference was found between
27
this initial model and a model with freely estimated autoregressive paths for opioid use (𝜒 2
=
329.784, df = 21, p < 0.001, RMSEA = 0.00, CFI = 1.00, SRMR = 0.02; 𝜒 2
= 4.95, df = 1, p
= 0.03). No other significant differences were found between the fully constrained model and
subsequent models with freely estimated parameters. Given these results, a final model was built
where the autoregressive paths for opioid use were constrained, but the within-time associations
and cross-lagged paths were free to vary across time (Figure A2).
The overall model fit of the final RI-CLPM was good (CFI = 0.99, RMSEA = 0.02,
SRMR = 0.03). The autoregressive paths for opioid use were significant, indicating that within-
person deviations in opioid use were significantly associated with prior within-person differences
in opioid use in the prior survey. No other significant effects were found in the RI-CLPM.

Discussion
This article comprehensively examined longitudinal, and bidirectional associations
between opiate withdrawal symptoms and opioid use among PWID in community settings in two
California cities. Using latent growth curve modeling, we found (1) consistent positive
associations between opioid use frequency and opioid withdrawal frequency over time, (2)
associations between past 6-month methadone use and decreased opioid use and withdrawal
symptom frequency, and (3) associations between current homelessness and increased frequency
of opioid use and withdrawal symptomatology. This study is the first to demonstrate a significant
temporal relationship between withdrawal and opioid use in a non-clinical sample of opioid
users. These results can inform future prevention efforts designed to reduce the consequences of
opioid dependence for opiate users in community settings.
28
Over the years, research concerning opioid withdrawal has appeared mostly in clinical
settings, with the construction of scales measuring quantity and severity of symptoms prior to
and after receiving medications for opioid use disorder (MOUD) (52). Given consistent positive
associations found between opioid withdrawal and frequency of opioid use in our sample, our
findings highlight the value of monitoring withdrawal behaviors outside of clinical
environments. Opioid-related withdrawal symptoms are an overwhelming obstacle to successful
treatment of opioid use disorder, with fear of withdrawal being cited as the largest barrier to
voluntary opioid discontinuation among patients receiving chronic opioid therapy for pain
management (97, 98). Given the highly addictive nature of illicit opioids (i.e., heroin and
fentanyl) (9), increased opportunities to intervene and track opioid withdrawal symptoms as
means for reducing overall rates of opioid use among PWID at the community level are
warranted. Future harm reduction efforts may benefit by adopting brief screening such as the
Clinical Opiate Withdrawal Scale (COWS) (52) to identify PWID who present more frequent
withdrawal symptomatology. Such measures could be incorporated in settings where PWID
physically congregate such as syringe service programs or be implemented by physicians who
provide direct medical care for PWID, such as street medicine teams.
The present study aimed to add scientific evidence to the discourse by investigating the
longitudinal reciprocal relationships between opioid withdrawal and opioid use. The focus in our
study was on the within-person effects, and how opioid use patterns and withdrawal behaviors
reinforce each other over time. To investigate these effects, we applied the RI-CLPM, which
allows us to disentangle within-person from between-person effects. At the within-person level,
we found within-person fluctuations of opioid use at baseline and 6-month follow-up to
significantly predict opioid use in the subsequent 6-months. However, we did not discover
29
bidirectional associations between opioid withdrawal symptoms and opioid use in the RI-CLPM.
To make strong statistical conclusions regarding temporality, it is important to consider the
stability of both constructs and their concurrent association in the same model as directionality
(94). As such, it is possible that a relationship between these behaviors and consequences may
have been masked by the many structural and economic disadvantages this community faces
(this sample being 84% homeless, 83% food insecure, 70% reporting past 30-day income of less
than $1401). Given the scarcity in research focused on opioid-related withdrawal symptoms as a
precursor to opioid use behaviors, and even more limited research on opioid use as an antecedent
of withdrawal outcomes, continued study of transactional relationships between withdrawal and
opioid use is needed.
Methadone use was associated with lower rates of opioid use and withdrawal symptoms
over time. This finding is consistent with previous evidence illustrating the effectiveness of
methadone programs in reducing heroin use, and drug injection among people with OUD (69,
76, 99, 100). One possible explanation for these findings may be due to our inclusion of both
prescribed and non-prescribed methadone use. Non-prescribed methadone use (also known as
nonmedical methadone use) refers to the use of methadone without a prescription and/or outside
of its prescribed indication (101). While previous research has typically analyzed these two
behaviors separately (102), methadone stockpiling and self-prescribed “split dosing” have been
noted in British (103), and Australian studies of opioid users (104). According to Harris and
Rhodes, non-prescribed methadone use can serve as a protective strategy to allow individuals
with opioid dependence to gain control over their drug use, improve their social relations, and
reduce the transmission of blood-borne viruses on account of reduced drug injection (105). This
evidence draws attention to the harm reducing capacities of non-adherent methadone diversion
30
practices. Continued access and monitoring of methadone use behaviors as a potential tool for
facilitating greater agency and assistance in withdrawal management is needed.
Importantly, homelessness was associated with increased rates of opioid use and
withdrawal over time. This finding falls in line with ecological models of health behavior (106)
that posit that health outcomes emerge from the interaction of individual, community, and
institutional level factors. Our findings draw attention to the multiple determinants of drug use,
particularly emphasizing the role of social and economic conditions in driving opioid use
patterns and related consequences. Reducing the morbidity and mortality associated with the
ongoing opioid crisis can only be achieved by simultaneously addressing the conditions that
drive opioid use. Increased solutions to improve material conditions such as long-term
supportive housing and increased jobs security are critical to reducing the burden of opioid use
disorder for PWID.
Findings from this study should be considered in lieu of the following limitations. First,
all our measures were self-report, which pose the inherent risk for participants to report biases
that can potentially influence the data. For example, participants’ desire to be viewed positively
may have resulted in an artificially low prevalence of reporting substance use and withdrawal
information. Second, our results may have been subject to recall biases due to the duration of
time frames asked in key questionnaire survey items (e.g., past 6-month withdrawal episodes,
past 30-day substance use behaviors). Future studies should consider incorporating measures
with shorter time durations (e.g., past 24 hours, past 7 days) to improve the accuracy of recalling
withdrawal episodes and related substance use behaviors. Next, our study included a total of
three measurement waves. Due to recent advancements in statistical methodologies requiring
four or more waves of data (i.e., latent difference score, latent curve model with structured
31
residuals), future studies with more frequent data collections and a longer time span might
further unravel the relationship between opioid withdrawal and opioid use in this population.
Lastly, it is worth noting that this study is part of intervention study which potentially opens the
door to biases due to retention. Although neither the experimental intervention (focused on
decreasing assisted injection initiation) nor attention control (focused on improving protein and
water intake) were related to outcomes used in this analysis, we included intervention assignment
as a control variable in all models to safeguard against possible effects.
Despite limitations, this study is the first to quantitatively explore how withdrawal is
associated with stability and transitions in opioid use patterns in PWID. This is a substantial
asset, as research on this topic has predominantly involved patients from drug-use treatment
programs and clinical trials, ignoring PWID in community settings who face greater health
disparities and adverse consequences of injection drug use. These results provide valuable
information about drug use experiences and how episodes of opiate withdrawal play out in the
natural ecology amongst the general population. Future research should incorporate opioid
withdrawal measures as means for understanding mechanisms that underly the development of
opioid dependence, addiction, and other opioid-related complications. Given the lack of literature
on opioid withdrawal in observational samples of opioid users, more research is needed to
substantiate these findings in the context of PWID.

Conclusion
This is the first article to longitudinally examine prospective associations between opiate
withdrawal symptoms and frequency of opioid use among a non-clinical sample of people who
use opioids. Together, finding underscores the value in adequately considering opioid
32
withdrawal symptoms when considering an individual’s opioid use consumption. Results from
this article extend current knowledge of how opioid withdrawal symptoms contribute to future
opioid use behaviors. Treatment initiatives that target withdrawal symptoms may be a successful
way to reduce overall rates of illicit opioid use and improve drug cessation.

33
Table A1. Baseline socio-demographic characteristics of PWID in Los Angeles and San
Francisco, CA, who reported regular opioid use during at least one study visit (N=834)
1

Note.
1
Available (non-missing) data Ns range based on missing responses for each variable;
2
Mixed race/other includes participants who selected ‘American Indian,’ ‘Native Hawaiian or
Pacific Islander,’ ‘Multiethnic/Multiracial,’ or ‘other race/ethnicity.’

Variable N (%)
Study site
Los Angeles 401 (48.1%)
San Francisco 433 (51.9%)
Gender identity
Male 627 (75.2%)
Female 197 (23.6%)
Transgender/other 6 (0.7%)
Age, in years—mean (standard deviation) 42.13 (12.31)
Age
Less than 30 years old 165 (19.8%)
30-39 years old 211 (25.3%)
40-49 years old 196 (23.5%)
Age 50 or older 262 (31.4%)
Race/ethnicity
Latinx 209 (25.1%)
Black/African American 173 (20.7%)
White 337 (40.4%)
Native American 55 (6.6%)
Mixed race/other
2
60 (7.2%)
Gay, lesbian, or bisexual
Yes 134 (16.1%)
No 699 (83.8%)
Educational attainment
High school or GED 599 (71.8%)
Less than high school 235 (28.2%)
Current homelessness, last 30 days
Yes 697 (83.6%)
No 137 (16.4%)
Income amount, last 30 days
Less than $1401 575 (68.9%)
$1401 or more 257 (30.8%)
Food insecurity, last 30 days

Yes 690 (82.7%)
No 137 (16.4%)
Currently on probation
Yes 220 (26.4%)
No 607 (72.8%)
Currently on parole
Yes 34 (4.1%)
No 796 (95.4%)
34
Table A2. Final time-varying covariate model for opioid use frequency
Note. Effects are presented as unstandardized beta regression coefficients and standard errors
[b(SE)]; Bold values indicate significant contemporaneous effects (p < 0.05); Time-varying
covariates were tested as constrained versus fixed effects; b, beta regression coefficient; SE,
standard error; CFI, Comparative Fit Index; RMSEA, Root Mean Square Error of
Approximation; SRMR, Standardized Root Mean Squared Residual; T1=baseline, T2=6-month
follow-up, T3=12-month follow-up.

Variable 𝑏 (SE)
p-value
Time-varying covariates

Opioid withdrawal frequency (T1) 0.13 (.03)
<.001
Opioid withdrawal frequency (T2) 0.13 (.03)
<.001
Opioid withdrawal frequency (T3) 0.13 (.03)
<.001
Current homelessness (T1) 0.46 (.06)
<.001
Current homelessness (T2) 0.46 (.06)
<.001
Current homelessness (T3) 0.46 (.06)
<.001
Buprenorphine use, last 6 months (T1) 0.12 (.07)
0.10
Buprenorphine use, last 6 months (T2) 0.12 (.07)
0.10
Buprenorphine use, last 6 months (T3) 0.12 (.07)
0.10
Methadone use, last 6 months (T1) 0.03 (.07)
0.68
Methadone use, last 6 months (T2) -0.48 (.08)
<.001
Methadone use, last 6 months (T3) -0.27 (.09) 0.003
Growth parameters
Intercept 2.20 (0.10)
<.001
Slope -0.27 (0.05)
<.001
Variance

Intercept
0.45 (0.09)
<.001
Slope
0.17 (0.05)
0.001
Model Fit

CFI
0.98
--
RMSEA
0.01
--
SRMR
0.03
--
35
Table A3. Final time-varying covariate model for opioid withdrawal frequency
Note. Effects are presented as unstandardized beta regression coefficients and standard errors
[b(SE)]; Bold values indicate significant contemporaneous effects (p < 0.05); Time-varying
covariates were tested as constrained versus fixed effects; b, beta regression coefficient; SE,
standard error; CFI, Comparative Fit Index; RMSEA, Root Mean Square Error of
Approximation; SRMR, Standardized Root Mean Squared Residual; T1=baseline, T2=6-month
follow-up, T3=12-month follow-up.

Variable 𝑏 (SE) p-value
Time-varying covariates

Opioid use frequency (T1) 0.15 (0.03)
<.001
Opioid use frequency (T2) 0.15 (0.03)
<.001
Opioid use frequency (T3) 0.15 (0.03) <.001
Current homelessness (T1) 0.31 (.09)
0.001
Current homelessness (T2) 0.31 (.09)
0.001
Current homelessness (T3) 0.31 (.09)
0.001
Buprenorphine use, last 6 months (T1) .09 (.09)
0.31
Buprenorphine use, last 6 months (T2) .09 (.09)
0.31
Buprenorphine use, last 6 months (T3) .09 (.09)
0.31
Methadone use, last 6 months (T1) -0.17 (.07)
0.01
Methadone use, last 6 months (T2) -0.17 (.07)
0.01
Methadone use, last 6 months (T3) -0.17 (.07) 0.01
Growth parameters
Intercept 1.78 (.14)
<.001
Slope 0.003 (.06)
0.96
Variance

Intercept
0.48 (.06)
<.001
Slope
--
--
Model Fit

CFI
1.00
--
RMSEA
0.00
--
SRMR
0.03
--
36
Figure A1. Participant accrual flow chart

Note. Analytic sample consists of 834 PWID surveyed from 2016-2018 who reported regular
opioid use (defined as at least 12 total times [injection or non-injection] of opioid use in the prior
30 days) during at least one study visit.

984 Completed CTC baseline
interview
979 Total analytic sample in
CTC baseline survey
145 Did not report regular opioid use (use of
heroin, or prescription opioids) 12 or more times
in past month) at baseline, 6-month, or 12-month
follow-up
834 Analytic sample (met
regular opioid use requirement
at baseline, 6-month, or 12-
month follow-up)
5 Did not meet inclusion criteria for
injection drug use
37

Figure A2. Final RI-CLPM model displaying within-person cross-lagged effects.

Note. Effects are presented as unstandardized beta regression coefficients and standard errors; Bold line indicate significant effect,
dashed line represent non-significant effect; WS = within subject, BS = between subject; T1 = Baseline, T2 = 6-month follow up, T3 =
12-month follow up; **p < .001.

WS opioid
use
(T1)
WS opioid
use
(T2)
WS opioid
use
(T3)
WS opioid
withdrawal
(T1)
WS opioid
withdrawal
(T2)
.34(.07)**
.34(.07)**
WS opioid
withdrawal
(T3)
.08(.08)
.16(.08)
-.01(.12)
.03(.13)
.08(.09)
.05(.11)
-.01(.10) .09(.10)
Opioid use
(T1)
Opioid use
(T2)
Opioid use
(T3)
Opioid
withdrawal
(T1)
Opioid
withdrawal
(T3)
BS opioid
use
BS opioid
withdrawal
Opioid
withdrawal
(T2)
.17(.10)
.12(.07)
38

Supplemental Table A1. Sociodemographic characteristics of participants included (vs. excluded from) the primary analytic sample
Note. Abbreviations: N = sample size; M = mean; SD = standard deviation; Available (non-missing) data Ns range based on missing
responses for each variable; Categorical variable differences calculated using the Chi-squared test; Continuous variable differences
calculated with t-tests; Mixed race/other includes participants who selected ‘American Indian, ’ ‘Native Hawaiian or Pacific Islander, ’
‘Multiethnic/Multiracial, ’ or ‘other race/ethnicity. ’

(1) Sample who did
not meet
requirements for
regular opioid use
during any study
session (N=145)
(2) Primary analytic
sample of PWID with
regular opioid use
during any study visit
(N=834)
Test of overall group
differences P-value
Female, N(%) 26 (18.1%) 197 (23.7%) 0.134
Age, M(SD) 43.29 (11.31) 42.13 (12.31) 0.29
Race/ethnicity, N(%)
Latinx 20 (13.8%) 209 (25.1%) 0.003*
Black/African American 24 (16.6%) 173 (20.7%) 0.25
White 75 (51.7%) 337 (40.4%) 0.01*
Native American 13 (9.0%) 55 (6.6%) 0.30
Mixed race/other

13 (9.0%) 60 (7.2%) 0.45
Current homelessness, last 30 days, N(%) 117 (80.7%) 697 (83.6%) 0.39
Income less than $1,401, last 30 days, N(%) 116 (80.0%) 575 (69.1%) 0.008*
Food insecurity, last 30 days, N(%) 124 (85.5%) 690 (83.4%) 0.53
39
Chapter 3: Opioid Withdrawal Symptoms as Longitudinal Predictors of Injection Risk
Behaviors

Abstract
Background: People who inject drugs (PWID) are disproportionately impacted by infectious
disease and drug overdose. Few studies have investigated how opioid withdrawal symptoms
contribute to longitudinal patterns of injection-related health risk.

Methods: The analytic sample was derived from a prospective cohort study of PWID recruited
from Los Angeles and San Francisco, California who completed quantitative surveys. Data were
collected across three measurement waves (baseline, 6-month, 12-month). Latent growth curve
modeling (LGCM) with time-varying covariates examined contemporaneous associations
between opioid withdrawal symptoms and injection risk behaviors (receptive syringe sharing,
distributive syringe sharing, sharing a cooker, sharing rinse or mix water, and sharing a filter or
cotton). Injection risk was treated as a summed score (count) of responses to the five injection
risk variables (range 0-5) with higher scores indicating greater injection risk. Multivariate
logistic regression was used to evaluate baseline variables associated with likelihood of reporting
a non-fatal overdose at either the 6-month or 12-month follow-up.

Results: In our overall model for injection risk, opioid withdrawal was associated with increased
injection risk at baseline. Gender was not a moderator of associations between withdrawal and
injection risk. In our multi-group model for homelessness, we found a significant effect of opioid
withdrawal frequency on injection risk at baseline for people reporting current homelessness;
40
however, opioid withdrawal did not influence injection risk for people were not homeless.
Logistic regression analysis revealed baseline withdrawal symptom frequency and past 6-month
non-fatal overdose to be associated with increased odds of reporting a non-fatal overdose event
during the 12-month study period.

Conclusion: Injection risk behaviors are shaped in part from increases in opioid withdrawal
frequency. Findings highlight the importance of early intervention efforts to treat opioid
withdrawal symptoms as a front-line prevention strategy to reduce infectious disease outcomes
and overdose risk among this community.

41
Introduction
People who inject drugs (PWID) face disproportionate rates of chronic diseases and
health vulnerabilities that cause extensive morbidity and mortality with rates of blood-borne
pathogens including the human immunodeficiency virus (HIV), and hepatitis B and C all higher
than the general population (1, 4-8, 21, 36, 38-42). According to meta-analytic data, nearly 20%
of PWID are known to be living with HIV, over 50% with hepatitis C, and 9% with hepatitis B
(43). PWID experience a higher prevalence of fatal and non-fatal drug overdoses compared to
non-injection drug users, with 30-45% experiencing at least one lifetime non-fatal overdose
(compared to 3.5-13% of non-injection opioid users) (35-37). Such infectious disease
consequences and injection-related harms have posed a substantial burden on US health systems
and communities of PWID.
Previous qualitative research has described how opioid withdrawal symptoms can
increase vulnerability to HIV/HCV and overdose risk through unsafe injection practices (16, 59,
62, 65, 80). Moreover, as a person experiences withdrawal, they develop an immediate urgency
to alleviate such symptoms, which drives users to resort to their most immediate sources for
either injection equipment or drugs, despite the consequences (12, 60). While previous literature
has laid the foundation for a potential link between withdrawal and injection risk (11-18),
quantitative research on this topic is sparse. In fact, only two articles demonstrating the cross-
sectional impact of opioid withdrawal symptoms among PWID exist today. The first article
among heroin injectors (aged 25-44) in New York City revealed a significant association
between nonfatal overdose and having at least one episode of serious opioid withdrawal in the
prior two months (12). The second article which utilized baseline data from the Change the
42
Cycle cohort, found opioid withdrawal symptoms to be associated with increased odds of
receptive syringe sharing and nonfatal overdose risk (84).
Despite the above knowledge, several key gaps in the literature regarding the relationship
between opioid withdrawal and injection-related risk behaviors remain. The first gap is that the
temporal nature of the relationship between opioid withdrawal symptoms and infectious disease
risk is unknown. While information gleaned from previous research has been useful in
elucidating circumstances in which withdrawal can heighten the likelihood of unsafe injection
practices, the mechanism by which opioid withdrawal relates to injection risk over time has not
yet been substantiated. Such information can build upon the current evidence base by
understanding whether established cross-sectional associations persist longitudinally. The second
gap is that previous studies have not yet explored the longitudinal impact of opioid withdrawal
on overdose risk. Data on how opioid overdose events vary over time based on experiences of
opioid withdrawal have important implications for improving HIV prevention and overall health
initiatives to better meet the needs of PWID. The last gap is that few studies have attempted to
characterize withdrawal symptom patterns over time and how such patterns relate to underlying
injection risk. Given recent spikes in incident HIV and HCV cases in PWID (1), and increasing
rates of mortality due to opioid overdose, understanding how withdrawal from opioids can
increase infectious disease and overdose risk over time is critical in preventing future bloodborne
disease transmission among out of treatment samples of opioid users throughout the United
States.
Accordingly, the current study sought to evaluate longitudinal associations between
opioid withdrawal symptoms, injection risk behaviors, and nonfatal overdose among PWID
using three separate aims. Given the success of syringe availability programs in rendering access
43
to clean needles and thereby reducing overall rates of syringe sharing (47, 73, 107), this study
examined the impact of withdrawal on a wider range of injection risk practices found to
influence infectious disease transmission (42, 107). These behaviors include the sharing of
“cookers” (containers used to mix and heat drugs), cottons (used to filter out particles as the drug
is drawn into the syringe), filters, and rinse or mix water (107). Based on cross-sectional
evidence demonstrating associations between withdrawal and receptive syringe sharing, we
hypothesize that higher frequency of opioid withdrawal symptoms will be associated with
increased injection risk over the subsequent 6-months. Secondarily, this analysis examined the
moderating effects of gender and homelessness on associations between withdrawal and
injection risk using multi-group latent growth curve models. Lastly, this study examined baseline
factors associated with likelihood of reporting a non-fatal overdose during 6-month or 12-month
follow-up.

Methods
Participants and procedures
This longitudinal study uses data collected from a prospective cohort study evaluating a
behavioral intervention in reducing injection initiation and related behaviors in adults who inject
drugs in Los Angeles and San Francisco, California (108). Between 2016 and 2018, adult PWID
aged 18 or older who had injected drugs in the past 30 days were recruited from community
settings using targeted sampling strategies (85, 87, 109). Recruitment sources included street
outreach, needle exchange program referrals, and word of mouth. All data were collected via
computer-based quantitative surveys administered in person by trained research assistants. All
participants provided written informed consent prior to completing the baseline survey.
44
Participants were compensated $20 upon completion of the baseline interview, $30 for the 6-
month, and $40 for the 12-month follow ups. Participants were also renumerated an additional
$10 each month for checking into the field site to update their contact information for purposes
of study retention. The research protocol was approved by the University of Southern California
Institutional Review Board.

Measures
Opioid withdrawal symptoms
Any past 6-month opioid withdrawal was assessed using the item,” In the last 6 months,
have you experienced restlessness, bone or muscle aches, runny nose, sweating, cold or hot
flashes, anxiety, teary eyes, stomach cramps, nausea, diarrhea, vomiting, or other symptoms due
to withdrawal from heroin or opiates?” with yes/no response options. Respondents who reported
“yes” to experiencing opioid withdrawal were then asked about frequency of withdrawal
episodes using the question, “In the last 6 months, how many times have you experienced heroin
or opiate withdrawal or withdrawal symptoms?” Responses were recorded as numerical
continuous values indicating the total frequency of self-reported withdrawal episodes. The
average frequency of opioid withdrawal episodes was 46.53 (Standard Deviation [SD] = 72.74;
median = 12; Interquartile Range [IQR] = 56). Due to this highly skewed distribution, opioid
withdrawal frequency was reclassified by percentiles with the following categories: 0-4, 5-12,
13-60, 61-180, and >180 times.

Injection risk behaviors
45
Respondents were asked whether they engaged in several injection drug use associated
risk behaviors within the preceding six months. These included five separate indicators of risk
behaviors related to the sharing of injection equipment. These included: (1) receptive syringe
sharing (injecting with a needle that someone else had used), (2) distributive syringe sharing
(giving a used syringe to someone else to inject with) (3) sharing a cooker, (4) sharing rinse or
mix water, and (5) sharing a filter or cotton in the last 6 months. These items were asked at each
assessment survey (baseline, 6-mo, 12-mo). A summed score (count) of injection risk was
created by adding responses to the five dichotomous variable outcomes (range 0-5) with higher
scores indicating greater injection risk. This summative approach has been used in previous
research representing the cumulative burden of injection risk behaviors among PWID (110).

Non-fatal overdose
This measure was assessed during all three measurement waves using the single-item
question, “In the last 6 months, have you overdosed?” participants responded with yes/no
options.

Covariates
To address moderation analyses, baseline factors shown to be previously associated with
injection risk behaviors and opioid withdrawal were included as covariates (16, 90). This
included self-reported gender (male, female) and past 30-day homelessness status (yes/no).
Intervention assignment was also included as a control variable in all analyses.

Statistical analysis
46
Descriptive statistics
We calculated descriptive statistics (means, frequencies) for all outcome variables (Table
B6) and bivariate correlations between opioid withdrawal symptoms and injection-related risk
behaviors at each time point were conducted (Table B5). Descriptive analyses were carried out
using SPSS 28.0 (SPSS Inc., Chicago, IL).

Analysis: Aim 1
To analyze associations between opioid withdrawal symptoms and injection risk
behaviors, we used latent growth curve modeling (LGCM) with time-varying covariates (92). In
LGCM, two latent factors extracted from observations were modeled: baseline injection risk sum
(intercept) and change in injection risk over time (slope). We estimated a linear slope and
allowed both intercept and slope variance to be freely estimated (i.e., random). Time varying co-
variates were entered into the model as constrained (equal) predictors of contemporaneous
injection risk behaviors. Model fit was assessed with fit indices including the comparative fit
index (CFI), Tucker-Lewis index (TLI), root mean square error of approximation (RMSEA), and
standardized root mean square residual (SRMR). For the CFI and TLI, values close to 1 are
considered an indication of better model fit. Values close to 0 for RMSEA and SRMR indicate
satisfactory fit. Within LGCM, robust maximum likelihood (MLR) estimation was used to
handle missing data.

Analysis: Aim 2
To test the moderating effects of demographic and economic variables on associations
between opioid withdrawal symptoms and injection risk, we estimated a series of multi-group
47
latent growth curve models (MG-LGCM) with self-reported gender and homelessness as our
grouping variables. MG-LGCM is a modeling approach that allows for growth models to be
specified simultaneously for two groups and tests the equality of intercepts and slopes across
groups using a Wald test of parameter constraints. Prior to all analyses, an unconditional multi-
group latent growth model was estimated for injection risk. we then tested equality constraints
for initial injection risk (intercepts) and change in injection risk (slopes) to determine the
functional form of the data within each multi-group model. We used negative two log likelihood
ratio tests to determine best model fit. After the best fitting model was determined, we then
introduced time-varying covariates and socio-demographic control variables into the models. We
used negative two log likelihood ratio tests to determine best model fit. After the best fitting
model was determined, the same model building process was repeated for models for
homelessness and income. Significant differences in parameter estimates in final multi-group
models were tested using Wald tests (111). All models were estimated using Mplus Version 8.1
(112).

Secondary analysis
To test the association of baseline opioid withdrawal and covariates on likelihood of
reporting a non-fatal overdose during 6-month or 12-month follow-up, we used multivariate
logistic regression. Gender (male/female), baseline opioid use frequency, non-fatal overdose
history at baseline, opioid withdrawal frequency at baseline, and past 30-day income at baseline
were entered as explanatory variables in our model. We also tested interactions between opioid
withdrawal and our socio-demographic covariates. Adjusted odds ratios (AORs) and 95%
48
confidence intervals (95% CI’s) are reported.

Results
Participant accrual, sample size, and exclusions from the analytic sample are consistent
with the sample reported in Study 1 of this dissertation project (See Figure A1). Following
previously established work (56, 84), the present study sample was restricted to PWID who self-
reported opioid use (i.e., heroin, prescription opioids, opioids in combination with
methamphetamine [goofball] or cocaine [speedball]) 12 or more times in the prior 30 days during
at least one of the three assessment interviews (N=834). The average injection risk score was
1.95 at baseline, 1.35 at 6-months, and 1.09 at 12-month follow-up.

Results of Overall Latent Growth Model Predicting Injection Risk (Aim 1)
Results of our unconditional growth model indicated an average starting point (i.e.,
intercept) of 1.95 at baseline, and a significant negative slope (-0.45), indicating a decrease in
injection risk over the study period. Results of our model fitting process indicated a model where
the effects of opioid withdrawal frequency and opioid use frequency were freely estimated
(versus constrained to be equal) to fit the data best (CFI = 0.96, RMSEA = 0.02, SRMR = 0.04).
In our final unstratified model predicting injection risk (Table B1), opioid withdrawal frequency
was positively associated with injection risk at baseline (𝑏 = 0.19, 95% CI [0.10, 0.29]), but these
effects became non-significant in the 6-month and 12-month follow-ups (6-month: 𝑏 = 0.04, p =
0.53; 12-month: 𝑏 = -0.10, p = 0.10). That is, there was an effect of opioid withdrawal early on,
but these effects became non-significant in the 6-month and 12-month follow-ups.

49
Multigroup Modeling Results (Aim 2)
Multigroup differences by gender
To address moderation effects by gender, we tested a multi-group time varying covariate
model where self-reported gender was used as our grouping variable. Log likelihood tests
between constrained versus unconstrained models indicated a model where the effects of opioid
use frequency and opioid withdrawal frequency were freely estimated to provide the best fit. The
final model (Table B2) had excellent model fit (CFI = 0.94, RMSEA = 0.03, SRMR = 0.05).
Individuals who self-identified as male had significant starting values of injection risk (𝛼 = 0.81,
SE = 0.24, p = 0.001), but a non-significant change in injection risk over the study period (𝜇 = -
0.26, SE = 0.16, p = 0.11). Those who self-identified as female did not have a significant
intercept or slope. Wald tests of parameter constraints indicated no significant differences in
starting injection risk values (Wald = 0.004, df = 1, p = 0.95) or slopes (Wald = 0.63, df = 1, p =
.43) between groups.
For both males and females, frequency of opioid withdrawal symptoms was associated
with greater injection risk in the baseline interview (Females: 𝑏 = 0.21, [0.02. 0.40]; Males: 𝑏 =
0.19, [0.07, 0.30]). However, frequency of opioid withdrawal symptoms was not associated with
injection risk at 6- or 12-month follow-up for males or females. Wald tests indicated no
differences in the effects of opioid withdrawal at baseline between males and females (Wald =
0.06, df = 1, p = 0.81).

Multigroup differences by homelessness status
For homelessness, our model building process indicated similar constraints as the gender
based multigroup model, yielding a final model with excellent fit (CFI = 0.93, RMSEA = 0.03,
50
SRMR = 0.05). Individuals who were not homeless at baseline had significant intercept injection
risk scores (𝛼 = 0.93, SE = 0.38, p = 0.01), but no significant change in injection risk over time
(𝜇 = -0.18, SE = 0.27, p = 0.50). However, those who were homeless had both significant
intercepts (𝛼 = 1.22, SE = 0.21, p < .001), and a significant decrease in injection risk over the 12-
month study period (𝜇 = -0.37, SE = 0.14, p = 0.01). There were no significant differences in
intercept values between groups (Wald = 1.42, df = 1, p = 0.23).
In our final multigroup model (Table B3), we found a significant effect of opioid
withdrawal frequency on injection risk at baseline for people who were currently homeless, such
that opioid withdrawal frequency was associated with greater injection risk (𝑏 = 0.21, [0.10,
0.31]). Opioid withdrawal did not influence injection risk at any time point for people who were
not homeless.

Secondary Analysis Results
Multivariate logistic regression analysis (Table B4) revealed greater frequency of opioid
withdrawal symptoms at baseline to be associated with greater odds of non-fatal overdose
(adjusted odds ratio [AOR] = 1.43; [1.11, 1.85]) during the 12-month data collection period.
PWID reporting any non-fatal overdose at baseline (versus PWID who did not report any non-
fatal overdose at baseline) was associated with increased odds of reporting a non-fatal overdose
in the subsequent interviews (AOR = 9.29; [5.40, 15.97]). All other socio-demographic control
variables and interaction terms were not significantly associated with non-fatal overdose in final
models.

Discussion
51
This is the first longitudinal study to investigate the time-specific effects of opioid
withdrawal on important injection-related health outcomes among PWID. In the overall model
predicting injection risk sum, results revealed a significant effect of opioid withdrawal frequency
on injection risk sum at baseline, but this effect became non-significant in 6-month and 12-
month follow-ups. Gender did not moderate associations between withdrawal and injection risk
in multigroup models. We found a significant effect of opioid withdrawal frequency on injection
risk at baseline for people reporting current homelessness; however, opioid withdrawal did not
influence injection risk at any follow-up for people were not homeless. Logistic regression
analysis revealed baseline opioid withdrawal and prior history of non-fatal overdose to be
associated with increased odds of non-fatal overdose during the 12-month CTC data collection
period. Lastly, we found significant associations between withdrawal frequency and greater
injection risk at baseline across all models. In general, these results follow past research reports
of opioid withdrawal consequences among people with opioid dependence (11, 12, 16, 18, 58-60,
63, 65, 66). Findings iterate the importance of early intervention efforts to treat opioid
withdrawal symptoms as a prevention tool to reduce infectious disease outcomes and
contaminated drug scenarios among PWID.
This article included five separate indicators of behaviors related to the sharing or use of
contaminated injection equipment to measure injection risk. These included: receptive syringe
sharing, distributive syringe sharing, using an unclean cooker, using unclean rinse or mix water,
and using an unclean filter or cotton. This methodology was informed by prior knowledge of the
importance in studying drug preparation equipment as critical in shaping infectious disease
transmission (42, 47, 73, 107). Furthermore, recent cohort data show that sharing drug
preparation equipment is more common than sharing syringes (47). While increased efforts to
52
expand programs such as needle exchange programs, naloxone distribution, and supervised
consumption sites are underway, new health initiatives that provide opioid withdrawal care to
people with opioid dependence could also lead to reductions in injection drug use related harm.
Current harm reduction recommendations that include access to sterile syringes and drug
preparation equipment are also strongly encouraged. Safe injection programs should train users
on opioid withdrawal consequences and how to avoid exposure to potentially contaminated drug
preparation equipment, as well as potentially contaminated syringes, when sharing drugs.
Gender did not moderate associations between withdrawal and injection risk in our
sample. This evidence runs contrary to previous work highlighting the unique risks of opioid
withdrawal in females (84, 113, 114). In a recent study of PWID in California, females had
higher odds of reporting very or extremely painful withdrawal symptoms compared to males
(84). Given that our study did not explicitly explore gender-specific variations in withdrawal
pain and severity of symptoms, continued exploration in this area is warranted. Altogether, little
systematic investigation of gender differences in opioid dependence has been undertaken.
Understanding how men and women differ in the clinical presentation of withdrawal symptom
characteristics may aid in enhancing assessment and treatment planning practices.
We found that for people who were homeless, opioid withdrawal symptoms were
associated with increased injection risk at baseline; however, these associations did not persist
for people who were not homeless. These findings are consistent with previous work
demonstrating a link between socioeconomic immiseration and increased HIV risk (115-118).
For example, unhoused PWID with low incomes often need to sell ‘washes’ (leftover drug
residue on cookers and filters after injection) to generate income to meet basic material needs
(119). In a study among active drug injectors in Vancouver, homelessness was associated with
53
failure to cook or filter drugs, and unsafe disposal of injection paraphernalia (120). Increased
syringe sharing due to confiscation of sterile syringes by police (120) and lack of income (121)
has also been documented. Up to this point, no previous studies have looked at the relationship
between withdrawal and injection risk over time, and how such associations vary by
homelessness status. Our findings emphasize the importance in considering structural
determinants of health as drivers of opioid-related harms. Given the consequences of poverty and
homelessness on overall population health, and myriad of health vulnerabilities related to drug
injection, it is likely that uncontrolled withdrawal symptoms further complicate the ability to
manage chronic illness (1, 4-8, 21, 38-42). Thus, current approaches for the treatment of opioid
use disorder should be altered so that withdrawal is considered a top priority. Furthermore, rapid
efforts to distribute opioid withdrawal medications to PWID experiencing multiple forms of
discrimination and social exclusion are needed to prevent further disease burden and improve the
HIV-care continuum for this community. Greater understanding of the influence of homelessness
on opioid dependence over time, and how policies and public health interventions can be tailored
to prevent homelessness, has the potential to improve drug recovery and other health outcomes.
Our findings add to the nascent literature on opioid withdrawal consequences by
documenting how increased withdrawal symptomatology increases odds of nonfatal overdose
within a 6-month time span. This finding confirms prior research findings where any withdrawal
symptoms were associated with nonfatal overdose at the cross-sectional level (84). Past 6-month
history of non-fatal overdose at baseline was associated with 9.25 times the odds of reporting a
non-fatal overdose in subsequent interviews in our analysis. Higher overdose rates are known to
be associated with prior history of experiencing an overdose (40). Interventions designed to
reduce the risk of overdose may be more effective if they simultaneously treat opioid withdrawal
54
symptoms and target individuals who have already experienced an overdose. Other potential
solutions include expanded supervised consumption sites which provide environments safe from
harm and are staffed with trained practitioners who respond with oxygen and/or naloxone in the
event of an overdose. Oral and injectable opioid agonist therapies may also reduce reliance on
the unregulated drug supply and enable people to have more options to take control over their
drug use by providing relief from opioid withdrawal symptoms.
Limitations of the current study include our use of self-reports to measure constructs in
our analysis. This approach subjects our results to potential biases due to social desirability.
However, items measuring HIV risk behaviors, and self-reported recent substance use have been
shown to be valid and reliable in previous studies with similar samples (90, 122, 123). Next, our
use of a single-item measure to assess opioid withdrawal limits our ability to determine how
specific symptoms may lead to increased injection risk. While this approach has been used in
previous samples of PWID (90), future studies encompassing separate items of withdrawal
criterion are warranted. Additionally, because fentanyl use measures were added as part of the
12-month follow-up survey, our analyses were unable to examine the differential effects of
fentanyl on injection risk. Lastly, because of inconsistencies in time frames used in questionnaire
items (e.g., past 30-day drug use, past 6-month drug treatment), it is possible that responses may
have been influenced by recall bias. Nonetheless, this study has implications for overdose
prevention and outreach initiatives. Overdose deaths have surged to unprecedented levels in
2020 indicating critical need to improve the unintended consequences of OUD. Expanding
MOUD treatment to PWID in community settings is an important next step in addressing gaps in
treatment capacity and lowering transmission of HIV-related illness.

55
Conclusion
This is the first study to explore how opioid-related withdrawal symptoms relate to
injection risk behaviors and nonfatal overdose over time. Our results indicate that withdrawal
symptoms are a predictor of increased injection risk at baseline. However, opioid withdrawal
frequencies do not vary much overtime and so the effect on injection risk behaviors diminished
over other observation points. We also conclude that the effect of this exposure varies by
homelessness. The paper fills numerous knowledge gaps by adding to our understanding of the
longitudinal consequences of opioid withdrawal in relation to injection risk behaviors and non-
fatal overdose. Together, these findings suggest that early intervention efforts to treat opioid
withdrawal may be the most successful at preventing infectious disease consequences and
overdose risk. Incorporating discussion of ways to overcome withdrawal as a risk reduction
strategy for PWID in community settings is strongly needed.
56
Table B1. Final time-varying covariate latent growth model predicting injection risk
Note. Effects are presented as unstandardized beta regression coefficients and standard errors
[b(SE)]; Bold values indicate significant contemporaneous effects (p < 0.05); Time-varying
covariates were tested as constrained versus fixed effects; b, beta regression coefficient; SE,
standard error; CFI, Comparative Fit Index; RMSEA, Root Mean Square Error of
Approximation; SRMR, Standardized Root Mean Squared Residual; T1=baseline, T2=6-month
follow-up, T3=12-month follow-up; Model adjusted for gender, baseline homelessness status,
and intervention assignment.

Variable b (SE)
p-value
Time-varying covariates

Opioid withdrawal frequency (T1) 0.19 (.05)
<.001
Opioid withdrawal frequency (T2) 0.04 (.06)
0.53
Opioid withdrawal frequency (T3) -0.10 (.06) 0.10
Opioid use frequency (T1) 0.09 (.05)
0.07
Opioid use frequency (T2) 0.18 (.05)
<.001
Opioid use frequency (T3)
0.34 (.06) <.001
Growth parameters
Intercept 0.72 (.21)
0.001
Slope -0.19 (.14)
0.18
Variance

Intercept
1.45 (.15)
<.001
Slope
0.10 (.06)
0.15
Model Fit

CFI
0.96
--
RMSEA
0.02
--
SRMR
0.04
--
57
Table B2. Final multigroup time-varying covariate latent growth model by gender
Note.

Effects are presented as unstandardized beta regression coefficients and standard errors
[B(SE)]; Bold values indicate significant contemporaneous effects (p < 0.05); Time-varying
covariates were tested as constrained versus fixed effects; b, beta regression coefficient; SE,
standard error; CFI, Comparative Fit Index; RMSEA, Root Mean Square Error of
Approximation; SRMR, Standardized Root Mean Squared Residual; T1=baseline, T2=6-month
follow-up, T3=12-month follow-up; Models adjusted for baseline homelessness status and
intervention assignment.

Variable b (SE) p-value
Females

Time-varying covariates

Opioid withdrawal frequency (T1) 0.21 (.10)
0.03
Opioid withdrawal frequency (T2) -0.16 (.12)
0.18
Opioid withdrawal frequency (T3) -0.12 (.13) 0.38
Opioid use frequency (T1) -0.02 (.10)
0.83
Opioid use frequency (T2) 0.27 (.10)
0.01
Opioid use frequency (T3) 0.28 (.11) 0.01
Growth parameters
Intercept 0.76 (.41)
0.06
Slope -0.05 (.26)
0.84
Variance

Intercept 1.41 (.27)
<.001
Slope 0.15 (.10)
0.15
Males

Time-varying covariates
Opioid withdrawal frequency (T1) 0.19 (.06) 0.001
Opioid withdrawal frequency (T2) 0.09 (.06) 0.18
Opioid withdrawal frequency (T3) -0.09 (.07) 0.18
Opioid use frequency (T1) 0.12 (.06) 0.04
Opioid use frequency (T2) 0.17 (.06) 0.003
Opioid use frequency (T3) 0.37 (.06) <.001
Growth parameters
Intercept 0.81 (.24) 0.001
Slope -0.26 (.16) 0.11
Variance
Intercept 1.42 (.16) <.001
Slope 0.06 (.07) 0.36
Model Fit
CFI 0.94 --
RMSEA 0.03 --
SRMR 0.05 --
58
Table B3. Final multigroup time-varying covariate latent growth model by homelessness status
Note.

Effects are presented as unstandardized beta regression coefficients and standard errors
[B(SE)]; Bold values indicate significant contemporaneous effects (p < 0.05); Time-varying
covariates were tested as constrained versus fixed effects; b, beta regression coefficient; SE,
standard error; CFI, Comparative Fit Index; RMSEA, Root Mean Square Error of
Approximation; SRMR, Standardized Root Mean Squared Residual; T1=baseline, T2=6-month
follow-up, T3=12-month follow-up; Models adjusted for gender and intervention assignment;
Slope for the non-homeless group was fixed at zero due to non-positive residual covariance.
Variable 𝑏 (SE) p-value
Homeless
Time-varying covariates
Opioid withdrawal frequency (T1) 0.21 (.05) <.001
Opioid withdrawal frequency (T2) 0.04 (.06) 0.50
Opioid withdrawal frequency (T3) -0.10 (.07) 0.15
Opioid use frequency (T1) 0.08 (.05) 0.13
Opioid use frequency (T2) 0.18 (.05) 0.001
Opioid use frequency (T3) 0.38 (.06) <.001
Growth parameters
Intercept 1.22 (.21) <.001
Slope -0.37 (.14) 0.01
Variance
Intercept 1.52 (.15) <.001
Slope 0.18 (.07) 0.01
Not homeless
Time-varying covariates
Opioid withdrawal frequency (T1) 0.14 (.12) 0.25
Opioid withdrawal frequency (T2) 0.04 (.15) 0.81
Opioid withdrawal frequency (T3) -0.07 (.15) 0.62
Opioid use frequency (T1) 0.09 (.10) 0.39
Opioid use frequency (T2) 0.16 (.13) 0.22
Opioid use frequency (T3) 0.13 (.14) 0.37
Growth parameters
Intercept 0.93 (.38) 0.01
Slope -0.18 (.27) 0.50
Variance
Intercept 1.20 (.23) <.001
Slope -- --
Model Fit
CFI 0.93 --
RMSEA 0.03 --
SRMR 0.05 --
59
Table B4. Multivariable logistic regression model of factors associated with any non-fatal
overdose reported at either 6 or 12-month follow-up
Note.

AOR = Adjusted Odds Ratio, CI = Confidence Interval. Model adjusted for intervention
assignment.
Variable AOR (95% CI) p-value
Opioid withdrawal frequency, baseline 1.43 (1.11-1.85) 0.02
Opioid use frequency, baseline 0.94 (0.71-1.24) 0.64
Female 0.64 (0.34-1.18) 0.07
Any non-fatal overdose, baseline 9.29 (5.40-15.97) 0.001
Current homelessness, baseline 1.82 (0.83-3.93) 0.26
Income less than $1401, baseline 1.02 (0.54-1.88) 0.99
60
Table B5. Bivariate correlations among predictors, covariates and injection risk behaviors across time

Association estimates by variable number
Variable 1 2 3 4 5 6 7 8 9
1. Withdrawal frequency, baseline 1
2. Withdrawal frequency, 6 months 0.40 1
3. Withdrawal frequency, 12 months 0.35 0.37 1
4. Injection risk, baseline 0.18 0.07 0.12 1
5. Injection risk, 6 months 0.16 0.05 0.12 0.48 1
6. Injection risk, 12 months 0.10 -0.05 -0.04 0.42 0.45 1
7. Opioid use frequency, baseline 0.15 0.01 0.07 0.09 0.10 0.11 1
8. Opioid use frequency, 6 months 0.15 0.16 0.05 0.15 0.23 0.25 0.35 1
9. Opioid use frequency, 12 months 0.15 0.12 0.21 0.15 0.20 0.29 0.27 0.50 1
61

Table B6. Descriptive statistics of injection risk variables at each timepoint
Note.

Available (non-missing) data Ns range based on missing responses for each variable;
T1=baseline, T2=6-month follow-up, T3=12-month follow-up.
Variable N (%)
Nonfatal overdose, last 6 months (T1) 231 (27.7%)
Nonfatal overdose, last 6 months (T2) 83 (10.0%)
Nonfatal overdose, last 6 months (T3) 81 (9.7%)
Mean injection risk (SD) (T1) 1.98 (1.7)
Mean injection risk (SD) (T2) 1.35 (1.5)
Mean injection risk (SD) (T3) 1.09 (1.5)
62
Chapter 4: Characterizing Opioid Withdrawal Experiences Among a Community Sample
of PWID

Abstract
Background: Greater understanding of opioid withdrawal experiences is needed to reduce
injection-related health disparities and improve treatment outcomes. In the following, we used
qualitative interviews to explore opioid withdrawal circumstances that lead to the progression of
opioid-related harms among people who inject drugs (PWID). Additionally, we examined use of
medications for opioid use disorder (MOUD) and differences in fentanyl versus heroin
withdrawal symptomatology.

Methods: Semi-structured qualitative interviews were conducted with 22 PWID (aged 27-63
years) in Los Angeles, CA between May 2021 and May 2022. Participants self-reported opioid
and injection drug use in the last 30 days. Interviews were audio-recorded and transcribed
verbatim. We employed an iterative, modified grounded theory approach to systematically code
and synthesize textual interview data.

Results: Participants experienced withdrawal symptoms frequently, with many going to great
lengths to avoid it. Withdrawal pain was described as incapacitating, and interfered with PWID’s
ability to sustain regular employment, and ensure stable housing. Avoiding withdrawal was
described as the single most influential factor driving the decision to continue using opioids
despite persistent negative consequences. Mechanisms for coping with withdrawal were using
other substances, moderating daily opioid use, and securing a constant supply of opioids. PWID
63
who transitioned from heroin to fentanyl use revealed more frequent, painful, and a faster onset
of withdrawal symptoms. Adverse withdrawal scenarios in the context of buprenorphine use
served as a barrier to buprenorphine treatment initiation and facilitator of increased overdose
risk. Social-structural inequities (e.g., homelessness, poverty) contributed to poorer withdrawal
outcomes.

Conclusion: Withdrawal symptoms among PWID increase health risk and socioeconomic
marginalization. Improved strategies for expanding and rendering accessible medications to treat
opioid withdrawal are urgently needed. Solutions such as safe supply and intentional opioid
withdrawal interventions (educational trainings, withdrawal comfort kits) are needed to improve
withdrawal management and reduce overdose risk.

64
Introduction
For individuals with opioid dependence, one of the most difficult aspects of their drug
use is opioid withdrawal symptoms (16, 89). On the street, these symptoms are colloquially
referred to as “dope sickness” or “getting sick,” and are described as a combination of physically
unpleasant and subjectively distressing symptoms including chills, sweats, pains, diarrhea,
vomiting, and anxiety (54, 56, 57). Among people who inject drugs (PWID), these symptoms are
characterized as ‘debilitating,’ (58) with most finding it an almost insurmountable condition,
dreading withdrawal, or having unrelenting craving for relief from the withdrawal state (16, 32,
58).
Previous qualitative research has documented several important risks related to opioid
withdrawal among people who inject opioids. For example, accounts of needle sharing among
PWID have described a scarcity in needles and the pooling of scarce resources in order to avoid
withdrawal as circumstances that perpetuate high-risk injection behaviors (121). Along these
lines, opioid withdrawal has been identified as a barrier to using clean needles and sterile
injection equipment (11, 16, 61-63). In a study with PWID in New York City, adverse
withdrawal scenarios were shown to increase HIV/HCV risk by undermining willingness to
inject safely, raising the number of injection partners, and driving people to seek additional
partners for drug and needle sharing (16). Among homeless PWID in North England, withdrawal
symptoms were discussed as a reason for reusing filters and cottons and reusing or sharing drug
paraphernalia (63). Studies examining barriers to not using a clean syringe for every injection or
not cleaning one’s skin prior to injection also listed withdrawal as a major contributing factor
(18, 59, 64, 65).
65
Opioid withdrawal symptoms have also been shown to impact decisions surrounding
healthcare utilization. For instance, PWID in Northeast US, despite feeling the need to improve
their health, had difficulty prioritizing their primary health care needs over the competing
demands of earning money to purchase drugs in order to avoid withdrawal (66). Furthermore,
untreated withdrawal symptoms have been described as a significant reason for delaying or
avoiding medical care altogether and leaving hospital care prematurely among PWID (16, 58, 63,
66, 124). PWID undergoing hospitalizations report insufficient receipt of withdrawal
medications (i.e., methadone, buprenorphine), as well as overall lack in concern and
acknowledgement of withdrawal as an extenuating health circumstance (58). Furthermore, the
profound anxiety and physical suffering caused by opioid withdrawal drives PWID to avoid
settings in which adequate opioids are not available, despite the personal cost on their health.
While previous work has unearthed important aspects about opioid withdrawal in
connection to health care utilization and contaminated injection scenarios, understanding
withdrawal experiences and mechanisms has not been the primary purpose of previous research.
Accordingly, the main objective of the present work was to expand upon the current literature by
gathering lived experiences and perspectives of withdrawal from PWID who use opioids in
community settings. Specifically, we sought to understand how PWID experience, react to, and
assert control over symptoms of opioid withdrawal in their everyday lives, and develop
knowledge of withdrawal circumstances and contexts that lead up to the progression of opioid-
related harms (i.e., overdose). Drug user experiences, withdrawal mechanisms, and how social
and contextual influences may alter acute and long-term drug responses have not been previously
characterized in this population. This will overcome limitations of previous studies by
facilitating the understanding of how episodes of opiate withdrawal play out in natural
66
environments. Given recent reports of increased fentanyl use as a preferred opioid product of use
among PWID in San Francisco and Los Angeles (125, 126), this article also aimed to assess the
impacts of the changing opioid drug market and growing fentanyl availability on withdrawal
outcomes. This information has not yet been collected in community samples of opioid using
PWID. Lastly, given the paucity of access and utilization of medications for opioid use disorder
(MOUD) among out-of-treatment samples of PWID (76), this study aimed to examine barriers
and experiences with MOUD, and the impact of MOUD on withdrawal.

Methods
Participants and procedures
Participants in this study were drawn from the Cannabis Use and Health Outcomes Study
(R01DA046049; Bluthenthal[contact]/Corsi), an ongoing prospective cohort study investigating
whether changes in cannabis use are associated with changes in opioid-related health outcomes
among PWID in Los Angeles, CA. Eligibility criteria for the cohort study were: (1) self-reported
injection opioid use in the prior 30 days (i.e., heroin, prescription opioids, opioids in combination
with methamphetamine [goofball] or cocaine [speedball]), (2) at least 18 years of age, and (3)
ability to provide written informed consent. Qualitative data collection was conducted among a
subset of participants who completed the baseline survey of the parent study (between 5/2021-
5/2022) and had returned to the field site during data collection hours for a follow-up visit. Prior
to obtaining written informed consent, participants were informed of the study purpose and
components of the study and were specifically assured that all responses would be de-identified
and recordings would be stored on a password-protected site. All subjects were compensated $40
67
upon completion of the interview. Procedures for this study were reviewed and approved by the
Institutional Review Board at the University of Southern California.
One-on-one qualitative interviews were conducted by two research team members in two
separate field site locations in Los Angeles (Boyle Heights, Hollywood). All interviews occurred
in-person and followed a semi-structured interview script which allowed participants to freely
introduce new topics of interest as they emerged during the interview process. Interviews
followed an ethnographic orientation to data collection—meaning that lived-experience and
meaning-centered narratives were the focus of interviews to permit the holistic understanding of
withdrawal experiences and perspectives (127). This methodological approach has been shown
to have utility with populations who have intersecting vulnerabilities or are stigmatized, such as
people who use drugs and whose range of experience may not be adequately represented in the
original semi-structured interview questionnaire (127).

Semi-structured qualitative interviews
Qualitative interviews included questions in three main content domains: 1) drug use
factors, 2) individual-level factors, and 3) community, social, and structural level factors. Items
related to pharmacological effects of drugs themselves included drug type, dose level (i.e.,
estimated average dose, estimated range of doses), route of administration (i.e., injection,
smoked), drug potency (fentanyl vs heroin use), fentanyl use as a preferred opioid use product,
and use of medications for opioid use disorder (MOUD) (methadone and buprenorphine).
Questions pertaining to individual-level characteristics of the user included medical history,
withdrawal experiences, withdrawal importance in the context of daily life activities, withdrawal
coping strategies, and changes in withdrawal over time, withdrawal pain and severity of
68
symptoms, mental health history, and injection history. Lastly, community, social, and structural
level questionnaire items included norms and attitudes about MOUD, access to syringe exchange
services, general healthcare access, current living situation, and homelessness status which have
been known to contribute to patterns of opioid-related harms (80). The interview was iteratively
modified as interviews progressed to allow for further exploration of emerging topics that came
out of earlier interviews. A full breakdown of content domains and interview questions is
provided in Supplemental Table C1.

Data analysis
Sessions were audio-recorded with participants’ permission and transcribed verbatim,
first using a digital audio transcription service (Otter.ai), and then reviewed for accuracy by
trained research assistants. Transcripts were then imported onto NVivo (Version 12.5) (QSR
International [Americas] Inc., Burlington, MA) where they were systematically organized and
analyzed using an iterative, multi-step procedure. First, two research team members
independently reviewed a set of transcripts to document initial memos and preliminary codes
based on significant “clusters of meaning”, or “themes” derived from the data (128). Team
members then met to assemble an initial codebook based on the review of transcripts. Next, team
members independently tested the established set of codes on a set of additional transcripts and
then met to discuss coding progress, assess coding discrepancies, and agree upon codebook
revisions. This process was repeated several times until team members reached consensus on the
final set of codes, sub-codes, and code definitions. Finally, all content related to each individual
code was compiled, and the coded text segments were analyzed thematically to develop an
interpretation of results.
69
Results
Participant characteristics
The analytic sample included 22 participants (Table C1). In the sample, the median age
was 46.5 years (interquartile range [IQR]: 39-61). The sample was 63.6% male, and 36.4%
female, with 7 individuals who self-identified as Caucasian (31.8%), 1 who self-identified as
Black (4.5%), 2 as Native American (9.1%), 11 as mixed race or another race/ethnicity, and 14
as having Hispanic or Latino descent (63.6%). Being unhoused in the prior 3 months was
reported by 36.4% of participants, and 72.7% made less than $1,401 in the past 30 days. In terms
of MOUD experiences, participants described diverse patterns of MOUD utilization, with all but
two mentioning current or past methadone use, and 32% (7/22) reporting having ever tried
buprenorphine or suboxone.

Thematic analysis results
Interview participants provided rich descriptions of their withdrawal experiences. The
major themes were withdrawal importance, withdrawal consequences, how withdrawal impacted
daily life activities, methods for coping with withdrawal symptoms, withdrawal and economic
insecurity, fentanyl versus heroin withdrawal, and withdrawal in the context of buprenorphine
treatment. We provide findings paired with illustrative quotes in the text below. Pseudonyms are
used to protect participant anonymity.

Withdrawal importance
For most participants, avoiding withdrawal was described as the single most influential
factor underlying the decision to continue using opioids despite persistent negative costs. This
70
opinion was repeatedly voiced in response to the question: “On a scale of 1 to 10, with 1 being
not important at all and 10 being extremely important, how important is tending to withdrawal
symptoms in reference to all of your other daily life activities?” For example, John (ID14, male,
age 43) responded: “I'd say it's a 10 priority, where I'm not doing anything else until I do that.”
Similarly, another participant (Chris, ID21, male, age 27) explained how securing enough drugs
to stave off withdrawal symptoms was the “focus of his existence,” and took precedence over all
other matters in his life. As James (ID10, male, age 57) stated: “That's the main thing, when
you're strung out, that’s all that matters. You need to get that fix. It completely takes over your
life. That's all. You're hustling every day for that next fix.”

Withdrawal consequences
In conversations regarding the physical severity and impact of withdrawal symptoms,
many participants described scenarios in which they were so incapacitated that they were
physically unable to function. For example, Jason (ID23, male, age 44) explained how he could
only “lay down in the fetal position” until being able to use heroin again and “get well.” Another
participant (Damon, ID05, male, age 69) described an instance of physical immobility on
account of withdrawal, stating: “I can't move around. I just stay in one spot. I cannot move.”
Altogether, withdrawal events were colloquially described as “intense” and “debilitating” and
associated with tremendous amounts of physical suffering on the body. In the following excerpt,
Michael (ID20, male, age 45) encapsulates the amount of pain and humiliation ascribed to his
various withdrawal experiences:
“I mean there is nothing you can do. You have no control over anything, bodily
functions, anything. It's taken away your muscles, power… It's like, I can't even clench
my ass cheeks to stop from shitting myself. Because that's what withdrawal is. You shit
yourself, like a child, like a two year old.”
71

For many PWID, the consequences of opioid withdrawal were linked to a wider range of
societal and economic hardships. During acute withdrawal events, PWID explained needing to
engage in unsafe injection behaviors and participate in illegal income generating activities to
secure the necessary resources to manage such symptoms. These scenarios resulted in further
deterioration of PWIDs’ economic standing and likelihood of being incarcerated. For example,
Antoine (ID01, male, age 27) described having to steal bottles of alcohol from the grocery store
to cope with withdrawal and oftentimes getting arrested as a result. Damon (ID05, male, age 69)
explained similar circumstances and needing to “rob stores, break glass, and do whatever you
have to do to get it [heroin].”
Others expressed feelings of hopelessness surrounding the “vicious cycle” of their drug
use which centered around the desire to prevent withdrawal. Furthermore, this “vicious cycle”
was explained as a daily process of waking up in the morning and feeling “dope-sick,” struggling
to earn money to obtain drugs, and repeating that process as soon as their high wore off. One
man (David, ID06, male, age 63) who hadn’t experienced withdrawal symptoms in a few years
reflected on this manner of life:
“I got tired of going through the hassle of hustling and getting money just for that
[withdrawal]. I wouldn't even buy something to eat. And then once you're sick, and you
put that needle in your arm, the minute you pull it out, you're already feeling better. It's like
you never got sick. Now you're getting hungry, and you gotta go through this again just to
get something to eat.”

Other participants recognized the need to engage in high-risk injection behaviors
whenever undergoing withdrawal episodes. For example, Linda (ID08, female, age 53) described
how the immediate pressure of needing to cure dope sickness by injecting fentanyl when she
typically smokes fentanyl put her at increased risk for overdose. She explained:
72
“If I’m withdrawing, guaranteed the first thing I need to do is do a shot, which I don’t like
to do because I don’t want to overdose because there’s so many times where I get too
powerful of stuff…and the stuff is just laced with fentanyl…it’s just meant to kill you.”

Withdrawal as a barrier to other basic human needs
Throughout interviews, withdrawal was repeatedly seen as a barrier to other basic human
needs including the ability to secure housing, obtain proper healthcare, keep steady employment,
and even eat and sleep regularly. Moreover, the time allotted to making money to obtain drugs
often consumed the entirety of participants’ time, and any disruptions to that process would
inevitably conclude in withdrawal. For example, one participant (Damon, ID05, male, age 69)
explained how this made it increasingly difficult for him to keep basic appointments. He shared:
“If I have an appointment with social security or something like that, I just won't go.
Because I won't go sitting down jumping like this.”

Damon also explained how withdrawal interfered with his ability to get sleep when not
having access to heroin, stating: “When you're sick you can't sleep. You won't be able to when
you don't have heroin, and you can't get it.” Another participant (Jim, ID06, male, age 63) shared
a similar experience where he was unable to eat or sleep during an acute withdrawal event: “I
can't keep nothing down. You know, you get the runs which goes with that, you know, then you
don't eat. You can't sleep so you're exhausted and you're so hungry that then you're too burnt out,
you can't eat.”
In other cases, withdrawal symptoms directly interfered with participant’s perceived
capacity to work, with many describing instances where withdrawal rendered vocational
responsibilities impossible to execute. For example, one female participant (Lauren, ID18, age
39) remarked how a recent episode of dope sickness impaired her to the point where she was
physically unable to go to work and make money:
73
“A couple of days ago I was dope sick. And I had a couple seizures. Like through the
night. And I wasn't able to go to work to make any money because of it…. so, I was dope
sick for a couple days because of it. It always sets me back."

Withdrawal coping
When asked about strategies and tactics for coping with withdrawal, participants often
discussed circumstances in which they had to resort to using other substances (i.e., Xanax,
antihistamines, heavy alcohol use) to provide temporary relief from withdrawal until being able
to use again. For example, Antoine (ID01, male, age 27) described how the sedative properties of
antihistamines were able to assist with his acute withdrawal pain:
“The only way I'll was able to cope with them (withdrawal symptoms) was by taking an
antihistamine. It got rid of the runny nose, itchiness and desire. And I slept all the time
though. And then when I finally woke up, and I started to get sick again, I didn't have any
antihistamine, I want to go fix.”

Raymond (ID13, male, age 37) described a similar circumstance where he took multiple
Xanax to try and “sleep through” his sickness. He remarked: “The only thing you can do is like,
pray for death. And then if that doesn't come just take a couple of Xanax.” Another participant
(Antoine, ID01, male, age 27) talked about drinking copious amounts of alcohol to the point of
loss of consciousness in effort to deal with his withdrawal, which inevitably placed him in
greater risk for personal harm. He explained:
“If it’s already too late, I try to if I can, get a bottle of something and pound it, just so I
blackout, pass out. Like a big bottle, something that I know is too much, and will try to
down it in one shot, just like one drink. You know like a good bottle of grey goose vodka,
that little thing, and if I can’t drink it all in one shot, I’ll finish it in the next swig, and then
within like 5 minutes, I’m blacked out. I wake up, I pass out, I drink an entire bottle, biggest
bottle I can get I drink it, pound it, and then I, as long as no one found my other bottle, I’ll
drink another half of it.”

Several participants in the study had not experienced withdrawal symptoms recently
despite being regular opioid users. These people were questioned about the methods they used to
74
avoid instances of withdrawal. One participant Linda, (ID08, female, age 53) reported limiting
her daily amount of heroin to what was only necessary to avoid withdrawal. Furthermore, Linda
explained how she and her partner stick to a very strict routine of using one gram of heroin per
day to ensure they do not get sick:
“I think we just do it (use opioids) to avoid it. We don't do it to get high. That's why we
have been consistent. It's been consistent... One gram a day for the last two years straight.
We haven't increased it at all. We've decreased it at some times, but we've never
increased it. We keep ourselves at that level. Because it's just enough to keep the sickness
away. Which is very, very important.”

Another strategy for withdrawal prevention was ensuring stable means of income to
enable a constant supply of opioids on hand. For example, David (ID06, male, age 63) explained:
“My main thing is to get the money to make sure I always have a fix. So I don't become sick. So
it's always a hustle.” Jeffrey (ID06, male, age 63) expressed a similar narrative: “When I go
through the sickness, I go through it, but I'm not going to do this again. I'm gonna keep myself so
I have some money in my pocket some way somehow.”

Withdrawal and economic insecurity
Many participants discussed the severity of their symptoms as contingent upon their
financial circumstances, with economic insecurity leading to poorer withdrawal outcomes.
Furthermore, an emergent discussion point was the influence of economic and structural
vulnerabilities as compounding factors that contribute to worsening withdrawal conditions. For
example, when asked about the specific circumstances in which participants noticed having the
worst withdrawal symptoms, many participants noted times where their employment status and
housing status played a role. For example, Damon (ID05, male, age 69) identified being
75
homeless and without a job as a period in his life when he noticed having the worst case of
withdrawal symptoms:
Interviewer: What kind of circumstances have you noticed that you've had the worst
withdrawal?

Participant: Downtown when I was homeless. I was bad. I didn't have nothing. I didn't have
no money. Broke. I didn't have a real job. You can't even work without the heroin.

Not having enough money was also perceived as a key facilitator of adverse withdrawal
outcomes, with many participants explaining how material security influenced their ability to
both prevent withdrawal and better cope with withdrawal. This idea is portrayed in the following
excerpt involving participant ID05 (Damon, male, age 69).
Interviewer: Do you feel like there's anything that helps your withdrawal?
Participant: Your money

Participants frequently described an internal conflict between their income-related
responsibilities and the management of drug dependency. Furthermore, participants commonly
expressed frustrations surrounding the avoidance of withdrawal leading to what seemed like an
inappropriate use of time and material resources. For one participant (Pete, ID17, male, age 60),
the constant anxiety and preservation associated with “looking for money and trying to figure out
how to get your heroin” resulted in his decision to enroll in a methadone maintenance program
for purposes of clearing more space in his life to devote to his personal vocational interests
instead of needs.

Fentanyl versus heroin withdrawal
Several participants reported recently transitioning from using heroin to using fentanyl as
their preferred opioid product. These participants described important differences between heroin
and fentanyl withdrawal symptomatology. For example, Michael, (ID20) a 45-year-old male who
76
smokes fentanyl daily noted: “Fentanyl is 1,000 times worse. Oh my god. I wish I was dope sick
from heroin.” Others shared similar opinions as John and noted more frequent and painful
withdrawal symptoms since switching to fentanyl. For example, a 39-year-old female (Lauren,
ID18) disclosed how withdrawal from fentanyl appeared to be more intense, stronger, and took
increased effort to ‘get well’ compared to heroin. She stated:
“Yeah, it’s (using fentanyl vs heroin) made it (withdrawal) like way worse. Like way worse.
Like, it's more intense. It's stronger. It comes on quicker. It seems like it takes more to get
well…it just really sucks. Actually heroin --I miss heroin withdrawal. Heroin withdrawal was
like easy.”

Others also noticed a faster onset and increased severity of withdrawal symptoms when
using fentanyl, with one participant (John, ID14, male, age 43) saying: “Yeah, it seems like it's
more intense [fentanyl withdrawal], but it also seems like it may be a more faster moving process
as well.” John also discussed how his withdrawal sickness lasted longer in duration than before
and attributed this change to his increased tolerance to opioids. He explained:
“Fentanyl will just boost my tolerance up to such ridiculous levels that like I'll get dope sick
immediately when I come down. I remember about two months ago, I was dope sick I think
for a month straight. Yeah, it was pretty rough.”

Withdrawal in the context of buprenorphine use
When discussing current and previous experiences with MOUD, PWID repeatedly shared
strong opinions about precipitated withdrawal from buprenorphine. This was brought up by
people who had never tried buprenorphine, and by those who had tried it. Furthermore,
conversations about precipitated withdrawal occurred within two overall contexts: 1) as a
primary deterrent for initiating buprenorphine treatment based on knowledge gathered from
those within their immediate social network, and 2) as a reason for discontinuing buprenorphine
treatment altogether due to adverse withdrawal events occurring within non-hospital settings.
77
PWID who had never tried buprenorphine discussed pre-emptive hesitations to try
buprenorphine due to anticipations of precipitated withdrawal based on friends and family
experiences. For example, John (ID14, male, age 43) stated: “I hear that the suboxone can make
you dope sick immediately, so that sounds really scary when you're trying to avoid that.”
Similarly, James (ID10, male, age 57) claimed: “From what I understand, if you went and you
were high, and you took the suboxone, you get viciously sick. So yeah, that was one thing I
never wanted to even get near.” For other participants, the fear of going through the initial stages
of withdrawal required prior to buprenorphine induction was the key reason for not wanting to
try it. Natasha, a 43-year-old female (ID11) articulated this sentiment succinctly: “From what I
was told, you have to be really sick to get on it (buprenorphine) and I don't want to be really sick.
I don't want to feel that. So that was not for me.”
One female participant used heroin just moments before entering the hospital and stated,
“they didn't know at that time that you have to be clean three days.” Consequently, she
proceeded to go through two days of miserable withdrawal symptoms in which she described: “I
almost died. I thought I was dying. I was crying and screaming. And I wanted to kill myself.”
For those who had tried buprenorphine, several participants shared very painful and
traumatic occurrences of precipitated withdrawal that barred them from wanting to continue
treatment altogether. For example, Heather (ID07, female, age 61) recounted two agonizing days
she spent undergoing excruciating withdrawal after being administered suboxone at a local
hospital. She had used heroin just moments before walking into the hospital and claimed how
hospital staff “did not know at that time that you have to be clean three days” prior to initiating
treatment.
78
Participant: I tried Suboxone one time... I went to [local hospital]…They didn't know at
that time that you have to be clean three days. She told me are you getting sick? I go,
Yeah.
Interviewer: Wait so let me take it back. So you tried the Suboxone?
Participant: Okay, and it got me roasted.
Interviewer: And you hadn't waited three days because?
Participant: I had just fixed before I got there.
Interviewer: So then you got sick?
Participant: Yes, I went total withdrawals. It was out of my system like this. And I go
what am I gonna do? He was putting me [giving more suboxone] every hour. And I was
still sick. So I told him stop. I only lasted two days and I took off. I almost died. I thought
I was dying. I was crying and screaming. And I wanted to kill myself. Like I'm gonna
hang myself right now.

Another participant, Laura (ID18, female, age 39) who has been homeless for the past
two years recounted a similarly painful withdrawal episode when taking suboxone on the street.
She shared:
“I took suboxone too early. I waited till I was dope sick. But like, apparently, I didn't wait
long enough. I waited, like 36 hours. But apparently you have to wait 72 hours. So yeah,
that's apparently how long it takes for fentanyl to get entirely out of your body. And it was
like the worst sick I've ever been. Yeah, it was like, I wouldn't wish that on my worst
enemy. I was in Silver Lake, actually. Under the silver lake bridge, just crying. It's sucked
so bad.”

Laura went on to express her interest in trying MOUD again, but not suboxone “Because
it's too hard to go cold turkey…and that precipitated withdrawal sucks.” Furthermore, within the
current context of her life, which was being homeless without medical insurance, suffering the
time it takes to go through the beginning stages of withdrawal to properly begin suboxone
treatment was undoable for her.

Other risks associated with out-of-treatment buprenorphine use
Participants described other high-risk injection drug-use associated scenarios that
occurred while using buprenorphine in non-treatment settings. Many of these situations occurred
where PWID were seeking to feel the effects of opioids but were unable to due to the blocking
79
properties of buprenorphine. In these scenarios, PWID used larger amounts of opioids than they
would normally use to try and “override the blocker.” It was reported that is led to several
overdose episodes. For example, Linda, a 53-year-old female (ID08) illustrated how a friend of
hers overdosed and died when using opioids on top of buprenorphine. She shared:
“I had a friend overdose because he was using (opioids) and Suboxone. He was taking
more (opioids) to get the effects, so when the Suboxone wore off, he overdosed and died.”

Another participant (ID01, male, age 27) spoke about similar dangers when revealing
how his peers recommended him to keep a couple of “back-up shots ready” when undergoing
precipitated withdrawal from buprenorphine. He explained:
“But a couple of dope friends would be like, dude, yeah you get sick and then you have
to have a couple of back-up shots ready, basically saying—say you were slamming a
gram, now you have to slam three grams, which is way fucking dangerous for you, I
mean just because you got that [buprenorphine] or whatever people say it’s okay, it does
not mean you cannot overdose.”

Gina, a 48-year-old female participant (ID16) provided a first-hand account of a time in
which she was driven to inject three times the amount of heroin she typically used to alleviate
acute withdrawal pain due to buprenorphine. She shared:
Participant: So I take half a strip waited 20 minutes. I felt worse. And then I took the
other half of the strip. I waited 10 minutes. I felt even worse. So, on and on, three strips
down and umm it's been about another 15 minutes and I'm like, panic attack....like crazy.
And calling everybody I know to give me a ride to go get heroin. And I have 100 dollars
I don't care. I'm going to buy heroin with it. And umm, getting there was horrible.
Getting it was horrible. Trying to use it was horrible. I couldn't. I couldn't do anything.
I couldn't function. I kept dropping everything. I ended up injecting a whole gram of
heroin.

Interviewer: How did that go? Did you feel it?

Participant: No. You can't feel anything. It's a blocker. I took three times the amount I
was supposed to take. So I didn't feel anything. And I kept using it, and using it, and
using it and somebody said," You're gonna die and you're not gonna know anything
happened because you're not able to feel it." And I said, "Okay, well, that's alarming
because I just took a gram and I haven't used in a month."

80
Discussion
This study adds to the existing qualitative literature elucidating opioid withdrawal
consequences. The myriad of experiences shared by participants in this study highlight the
profound importance of the withdrawal syndrome in the lives of PWID. These findings align
with previous work suggesting opioid withdrawal symptoms to be among one of the most salient
chronic health conditions experienced by PWID with opioid dependence (89). For many PWID,
avoiding withdrawal was described as the single most influential factor driving the decision to
continue using opioids despite persistent negative costs. In many cases, withdrawal was
described as a hindrance to other basic human needs including the ability to keep and hold a
steady job. Mechanisms for coping with withdrawal were using other substances, moderating
daily opioid use, and securing stable income to ensure a constant supply of opioids. PWID who
transitioned from heroin to fentanyl use revealed more frequent, painful, and a faster onset of
withdrawal symptoms. Lastly, PWID revealed several important narratives related to withdrawal
within the context of buprenorphine use, with such experiences serving as a barrier for future
buprenorphine treatment initiation, as well as a reason for discontinuing buprenorphine treatment
altogether.
Study results revealed the breadth of ways in which withdrawal overwhelms the lives of
PWID with opioid dependence. Qualitative interviews signified that withdrawal occurs all the
time for PWID, and for people who are not experiencing withdrawal, just the possibility of being
in withdrawal drives the unrelenting need to continue using opioids at all costs. Furthermore, we
found that the physiological consequences of withdrawal are so debilitating that they interfere
with the capacity to engage in basic activities that deem a person a functional member of society
including the ability to secure a stable job, obtain housing, and even show up for social security
81
appointments. For many participants, the demand of needing to engage in activities every day to
make enough money to purchase opioids completely occupied their day. In an ethnography of
addicted, pregnant, and poor women in San Francisco (114), this repetitious cycle of seeking and
scoring drugs is referred to as “addict time,” and this routine limits people with compounding
structural vulnerabilities and substance use disorder with little opportunity to do much of
anything else with their lives other than continue to use drugs. To begin to break this cycle, there
is a critical need for expanded solutions to reduce opioid withdrawal burden among marginalized
communities. This is especially imperative given the toxicity of the current opioid drug supply
and escalating rate of overdose death fatalities that continues to devastate Americans.
The concerns of participants surrounding precipitated withdrawal from buprenorphine
arose from both first-hand experiences and stories shared by people within their immediate social
environment. For those who had tried buprenorphine, several shared disparaging accounts of
precipitated withdrawal occurring inside and outside of hospital settings. Within the hospital,
PWID attributed getting dope sick to improper training of hospital staff in instructing patients of
the need to be in mild or moderate withdrawal prior to buprenorphine induction. A lack of
knowledge of consequences of buprenorphine use while opioids are still in the body was also
expressed by people reporting precipitated withdrawal outside the hospital. For these
participants, the only viable solution for responding to acute withdrawal pain was to try and
“override the buprenorphine” by self-administering more opioids. This led to dangerous risk
scenarios where PWID consumed large amounts of opioids and resulted in overdose and even
death in one case. Lack of education, knowledge, and confidence in providing treatment to
patients with opioid use disorder are major barriers to MOUD utilization that can further
exacerbate feelings of distrust with healthcare providers (58, 129). However, these barriers
82
appear to be modifiable, potentially remedied with increased training of providers in accurately
assessing withdrawal stages before buprenorphine induction and creating standardized protocols
that include educating patients about precipitated withdrawal and the consequences of using
opioids in conjunction with buprenorphine. In the general population, physician treatment and
feeling that a physician appreciates patient concerns are shown to be protective against avoiding
medical care (130), thus such actions may facilitate changes in PWID’s willingness to continue
buprenorphine treatment as well as alter negative perceptions of buprenorphine in the
community. While buprenorphine has been considered the “gold standard” of biomedical
interventions used to treat opioid use disorder (50), findings inform us that the current delivery
approach is not as efficient or helpful to this population and can lead to further injection related
harms for certain people.
Participants described using other substances including Xanax, antihistamines, and
alcohol to the point of sedation to cope with withdrawal. This method of withdrawal
management poses danger to the user and is not as effective as other approaches. This evidence
signals the need for improved strategies to prevent and reduce overall withdrawal occurrences.
At present, there are four FDA-approved medications with established efficacy in treating opioid
withdrawal symptoms: methadone, buprenorphine, extended-release naltrexone, and lofexidine
(21, 50, 54). However, these medications are vastly underutilized amongst most people who use
opioids (68, 70-74, 76, 131). Efforts to expand access to these opioid treatment medications to
better reach PWID in community settings is highly warranted. For instance, a phone hotline was
developed to connect patients with mild to severe opioid use disorder to a buprenorphine
provider and outpatient treatment in Rhode Island (132). A mobile health clinic dispensing low-
threshold buprenorphine to PWID from over 10 neighborhoods in Baltimore is currently being
83
piloted (133, 134). Innovations have also been made in buprenorphine delivery systems, with
protocols using “micro-induction” (whereby small doses of buprenorphine are administered more
frequently to minimize precipitated withdrawal) being piloted in hospital settings (131, 135).
These programs may contribute to improvements in opioid withdrawal outcomes among PWID,
and therefore are worthy of tracking in future research studies.
A preference for smoking fentanyl as opposed to injecting heroin was expressed by
several participants who reported injection opioid use in the past. Notably, these participants
identified key differences in the duration, onset, and severity of withdrawal symptoms since
switching to fentanyl. To the best of our knowledge, this study is the first to examine the
influence of fentanyl on opioid withdrawal outcomes. It is abundantly clear that illicitly
manufactured fentanyl remains highly prevalent in illicit drug supplies. Given our findings that
fentanyl appears to exacerbate the withdrawal condition, alternative solutions to support PWID
as they navigate their drug use and withdrawal are paramount. Harm reduction programs (i.e.,
needle exchange programs, safe injection facilities, street outreach teams) should consider
implementing educational trainings to improve knowledge and awareness of fentanyl impacts in
the community.
The current study exists within a wider framework of community-based participatory
science that seeks to involve community members in the identification and resolution of public
health problems (136). Treating people who use drugs with relevant experience and expertise
with the drug types, consumption techniques, and environment being investigated has been
shown to improve the quality of scientific research by producing results that are grounded in
lived realities and thus more applicable and relevant (137, 138). Drug user unions like the North
Carolina Survivors Union (NCSU) have demonstrated this success through their participation in
84
several research collaborations with people who use drugs that have led to the establishment of
harm reductions interventions including needle-exchange programs (139), peer-delivered
naloxone (140), and community-based drug checking (141). Results from this study add to this
growing body of literature and provide additional validation for the utility in working alongside
people with lived experience as means for better understanding factors that influence drug use
behaviors and negative health outcomes.
The following limitations should be considered when interpreting study findings. First,
our sample only included PWID residing in two Los Angeles neighborhoods; thus, the themes
and concepts disclosed by participants may not be generalizable to PWID in different geographic
locations of the United States. Next, because interviews were conducted at locations that also
offer syringe service programs and/or are situated within walking distance of a methadone clinic,
experiences regarding opioid medication treatment access and withdrawal may differ from those
of PWID in rural parts of the US without access to such services. However, due to the
differences in demographics and access to MOUD treatment between East Los Angeles and
Hollywood, our sample captures diverse withdrawal experiences that can improve current
MOUD approaches and promote increased access to withdrawal services in community settings.
Lastly, due to the qualitative design of this study, we cannot make statistical conclusions
regarding causality or temporality. Nevertheless, this study is the first to use qualitative
interviews to facilitate the understanding of how episodes of opiate withdrawal play out in the
natural ecology. This knowledge has the potential to enhance safe behaviors, improve treatment
initiatives, and prevent injection-related disease consequences in this medically underserved
population.

85
Conclusion
In summary, the current study utilized qualitative interviews to describe opioid
withdrawal experiences and impacts among Los Angeles PWID. Results revealed a wide range
of insights regarding the ways in which withdrawal overwhelms the lives of PWID with opioid
dependence. We also gained information about how buprenorphine prescribing practices can be
improved to better suit the needs of PWID in community settings. Withdrawal symptoms among
PWID increase health risk and socioeconomic marginalization. Solutions such as safe
administration of prescription opioids and intentional opioid withdrawal interventions such as
withdrawal comfort kits are needed to improve withdrawal management and reduce overdose
risk.
86

Table C1. Sociodemographic information, housing status, and MOUD history
ID Site Age Gender Race/ethnicity Housing status MOUD history
01 ELA 27 M Caucasian Housed Has tried methadone, Suboxone, and Subutex, currently using
methadone (daily)
02 ELA 63 F Hispanic/Latino Housed, living in an
apartment
Currently using methadone (daily), never tried any other MOUD
03 ELA 62 F Hispanic/Latino Housed Currently using methadone (daily), never tried any other MOUD
04 ELA 39 M Hispanic/Latino Not homeless Currently using methadone (daily), never tried any other MOUD
05 ELA 69 M Hispanic/Latino Not homeless Currently using methadone (daily), never tried any other MOUD
06 ELA 63 M Hispanic/Latino Homeless Current methadone user, has tried Suboxone in the hospital
07 ELA 61 F Hispanic/Latino Not homeless Current methadone user, tried Suboxone one time in the past
08 HW 53 F Caucasian Not homeless Previously used Suboxone and methadone, no current MOUD use
09 ELA 62 M Hispanic/Latino Not homeless Current methadone user, tried Suboxone once in the past
10 ELA 57 M Hispanic/Latino SRO Section 8 housing Current methadone user, never tried any other MOUD
11 ELA 43 F Hispanic/Latino SRO Section 8 housing Current methadone user, never tried any other MOUD
12 ELA 35 M Hispanic/Latino Housed, apartment with
wife and child
Has tried suboxone and methadone, no current MOUD use
13 HW 37 M Black Housed, own apartment Never used any MOUD
14 ELA 43 M Caucasian Homeless Current methadone user
16 HW 48 F Caucasian SRO Previously used methadone and buprenorphine, no current MOUD
use
17 ELA 60 M Hispanic/Latino Not homeless Current methadone user, never tried any other MOUD
18 HW 39 F Caucasian Homeless Has tried methadone, buprenorphine, and Subutex, not currently
using MOUD
19 ELA 40 F Hispanic/Latino Homeless Has used methadone and Suboxone, current methadone user
20 HW 45 M Caucasian Homeless Has used methadone and Subutex, no current MOUD use
21 ELA 27 M Hispanic/Latino Not homeless Has tried suboxone and methadone, not currently using MOUD
22 ELA 54 M Mixed race Housed, apartment Current methadone user
23 ELA 44 M Caucasian Homeless Current methadone user, never tried any other MOUD
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Supplemental Table C1. Qualitative interview guide
Question Probe
BACKGROUND
During a typical day, when do you start
using opioids?
• Opioids first or methadone clinic/bup first?
• As soon as you wake up, later in the
morning, afternoon?
• How often do you use throughout the day
and what does that look like
What other drugs do you use on a
daily/weekly basis?
• Combination of substances
• Frequency
• Dose
How long have you used opioids? • Which route(s) do you use and has that
changed over time
What is your relationship with opioids right
now?
• Are you comfortable with your current use,
or would you ideally like to reduce your use
or quit all together?
What is your current housing situation? • What kind of social support do you have?
MEDICATIONS FOR OPIOID USE DISORDER
General use of MOUD
Have you ever used or currently use any
form of MOUD (buprenorphine, methadone
or naltrexone) whether prescribed or
obtained from friends/on the street?

If participant has NEVER tried any
MOUD (not including being initiated on
buprenorphine while in the hospital), skip
to “NEVER USED MOUD”.

If participant has not used any MOUD in
a long time or does so very infrequently,
ask questions in past tense.
If a participant has tried more than one, do you
have a preference? Which one and why?

Differences in:
• For Bup: Subutex (just bup) vs Suboxone
(bup + naloxone) vs Sublocade (monthly
injection)
• Managing withdrawal symptoms
• Overall effect on health
• Overall effect on mental well-being
• Feasibility of obtaining medication
(including transportation and costs)
• Effect on abstinence from other opioids
• Effect on still being able to use other
opioids/get high
How do you generally feel about the MOUD
you’ve used most frequently?

• How often do you use it?
• How long have you used it for?
• For what reasons do you use it? (harm
reduction, preventing withdrawal,
preventing cravings, trying to reduce/stop
use, getting high, etc)
• How has your life changed since starting
using MOUD?
• Have you reduced your use of opioids
because of MOUD?
88
• Where do you obtain it now?
• How is your quality of life with or without
MOUD?
• Positive effects?
• Negative effects?
• Would you recommend this MOUD to
someone struggling with OUD, and what
would you say to them about it?
Do you have a preference for a certain
treatment structure or dose/delivery method?
• Have you ever had to settle for MOUD that
was not your number one choice? – i.e.,
buying street bup or methadone instead of
going to the clinic or having a prescription
• For methadone- take homes, clinic hours
(open in the afternoon or evening)
• For Bup- Subutex, Suboxone, Sublocade
• Microdosing bup (initiating small doses of
buprenorphine while still being able to be
on other opioids such as heroin, and
gradually (7-10 days) transitioning to
buprenorphine only)?
Have you stopped using MOUD or use it
less regularly than before?
• Tapering of methadone
• Why did you stop using it or use it less
frequently?
• What happened when you stopped? Both
physical effects, emotional, life
consequences, etc.
Have you had any problems accessing
MOUD when you’ve needed it?
• Transportation
• Time
• Insurance/financial
• Can’t find prescriber (for Bup)
Has using MOUD effected any co-occurring
mental health disorders, either positively or
negatively?
• What about physical disorders or
symptoms?
First experiences of MOUD
How did you first find out about MOUD? • Who first told you about it (friend, family,
doctor, saw an ad, etc)?
• What did that person say about MOUD
• Did their views on MOUD impact your
views or desire to try it?
• Ask about both if needed
What was your perspective on MOUD
before using it?
Ask about both if needed
What was your primary reason for trying
MOUD for the first time? Why then?
Ask about both if needed
89
Where/how did you obtain your first
MOUD?
Ask about both if needed
Social/structural impact on MOUD
Did you try any other treatments such as
support groups, counseling, and rehab
facilities for opioid abuse before or after
trying MOUD?
• Are you in any other treatments now?
Why/why not?
Do you know anyone who’s also been on/is
currently using MOUD?
How do you think they feel about MOUD?
• Partner (do they use with you, go to the
methadone clinic with you)
• Friends – including friends at methadone
clinic, ask about support network there
Have you faced stigma from family, friends,
doctors, support groups or rehab facilities
for using MOUD?
• Ask about both if needed
Have you ever been in prison or jail? • Did they offer SUD treatment or anything
to help with withdrawal, including
counseling or MOUD?
• If they didn’t—what happened and how did
you feel?
Have you ever ended up in the emergency
room or hospital as a result of your drug use
(overdose, abscesses, etc)?
• Were you initiated on buprenorphine while
in the hospital?
• Did any of the providers speak with you
about your substance use?
• Who and what did they say?
• Did anyone (nurse, doctor, social worker)
refer you to any kind of treatment?
• Did anyone talk to you specifically about
MOUD or give you a prescription for
MOUD?
• Did you feel stigmatized or discriminated
against by the healthcare workers or social
workers in any way?
• Did you trust your provider and have good
communication with them?
• Is there anything you wish the healthcare
staff could have done differently?
d
FOR BUP USERS ONLY
Have you ever attempted to receive a legal
prescription for any type of buprenorphine?
If no, why not?

If yes:
• Did you know ahead of time what
formulation you wanted (Subutex,
Suboxone, Sublocade)? Why that one?
90
• What made you want to receive a
prescription?
• How difficult was it to find a bup provider?
• What barriers did you face in finding a bup
provider?
• Describe the process of finding a provider
who prescribed you bup?
• Were there problems with insurance,
finding providers, pharmacy?
• Overall was a positive or negative
experience?
• What do you think could be done better for
someone who wants to get on bup but
doesn’t know where to start?
If you’ve tried buprenorphine on the street,
has that influenced your desire to obtain a
prescription for it?

NEVER USED MOUD
What is your perspective on MOUD?

Ask participant to discuss differences in
buprenorphine and methadone
throughout.
• Are there differences between how you
view buprenorphine, methadone, and
naltrexone?
• Has your view on MOUD always been this,
or has it changed over the years?
o Why did it change?
What are the primary reasons for you never
having tried MOUD?
• Have you ever thought to use it just to
manage withdrawal symptoms?
• Have you ever wanted to try MOUD but
were unable to?
• If mentioning that using Suboxone makes
you have withdrawal and/or not able to get
high with other opioids, ask how they
would feel about using Subutex (if it was
free)
Can you recall when you first learned about
MOUD? Who was the first person who
spoke with you about it (family, friend,
healthcare worker, etc).
• Who first told you about it (friend, family,
doctor, saw an ad, etc)?
• What did that person say about MOUD?
• Did their views on MOUD impact your
views or desire to try it?
Do you feel overall knowledgeable about
MOUD, why people use it, and its effects?

Have you heard any ‘myths’ about MOUD? • What are they and how truthful do you
think they are?
Do you feel that there’s stigma around using
MOUD?
• Do you think you would feel stigmatized if
you used MOUD regularly?
91
Have you tried any other treatments such as
support groups and rehab facilities for
opioid abuse?
• How did those go?

Have you ever ended up in the emergency
room or hospital as a result of your drug use
(overdose, abscesses, etc)?
• Were you initiated on buprenorphine while
in the hospital?
• Did any of the providers speak with you
about your substance use?
• Who and what did they say?
• Did anyone (nurse, doctor, social worker)
refer you to any kind of treatment?
• Did anyone talk to you specifically about
MOUD or give you a prescription for
MOUD?
• Did you feel stigmatized or discriminated
against by the healthcare workers or social
workers in any way?
• Did you trust your provider and have good
communication with them?
• Is there anything you wish the healthcare
staff could have done differently?
d
Have you heard about microdosing
buprenorphine? That is, initiating small
doses of buprenorphine while still being
able to be on other opioids such as heroin,
without experiencing precipitated
withdrawal, and gradually (7-10 days)
transitioning to buprenorphine only?
• Do you think this would work for you, is it
something you’d be interested in trying?
Would you recommend MOUD to someone
who is also struggling with opioid use?
• Is there a difference for someone who
wants to quit altogether vs reduce their use
vs just use for harm reduction and
prevention of withdrawal symptoms?
WITHDRAWAL (EVERYONE)
General withdrawal questions
How often do you experience withdrawal
due to opioids?
• In a day? In a week? Month?
What types of symptoms do you
experience?

• How painful are those symptoms?
• Nausea? Trouble sleeping
How are your day-to-day activities affected
by withdrawal symptoms?

What time of the day do you normally
experience withdrawal symptoms?
• Morning, afternoon, nighttime, middle of
the night?
• Has it always been this way?
92
• How does that time of day fit in with when
you’re using other substances?
How long after using substance(s) does it
take for you to typically experience
withdrawal symptoms?
• How long does it usually take for you to
start experiencing withdrawal?
• When do they peak?
• How long does it last?
Have you noticed any factors to have an
impact on your withdrawal symptoms? How
could you tell?
• Sleep, eating a good meal, etc
If you wanted to quit the drug, was your
ability to quit the drug limited by your
withdrawals? Walk me through your thought
process.

Coping with withdrawal
If you can rate the importance of tending to
withdrawal symptoms in the aspect of your
daily life activities on a scale of 1 to 10,
with 1 being not at all important, and 10
being extremely important, how would you
rate them?
• Think about all the important things you
need to do in a day….What are some of
those things?
• Now think about managing your
withdrawal in context to those other
things… What’s more important?
• So you said a X … why not lower or
higher? What would make it a higher
number?
How do you cope with withdrawal
symptoms?

• Using other substances
• Hospital treatment
• Trying to find other substances
• What helps/makes the symptoms worse?
If you don’t get “dope sick” or experience
withdrawal, how do you avoid that from
happening?
• What do you do to make sure you don’t get
sick?

Describe your process for managing
withdrawal symptoms?

What would be the worst possible scenario
for you to withdraw?

How severe do your symptoms get until you
need to use again?

Do you feel like you need to use a larger
amount of a substance(s) or more than you
normally use to relieve yourself from feeling
withdrawal symptoms?
• Do you experience more reward or relief
when you use a substance after being in a
period of withdrawal?
Changes in withdrawal symptoms over time
Do you feel your symptoms have gotten
better or worse over time?
How can you tell?
Do you feel the severity and painfulness of
your withdrawal symptoms changes

93
according to the amount/frequency of
substance(s) you take?
What other factors impact your experiences
of withdrawal?
• Environment
• Social network
• Housing status
• Income
If you could run your own methadone clinic (for those who have tried methadone) or MOUD
treatment, how would you do it? What would you do differently?

Is there anything else you would like me to know about your views regarding MOUD or
withdrawal that we haven’t covered?
94
Chapter 5: Overall Discussion
Overview
The goal of the current dissertation was to deepen the scientific understanding of opioid
withdrawal consequences among PWID. Findings are especially relevant in the context of the
ongoing North American overdose and drug poisoning crisis. In a large, diverse sample of people
who inject drugs recruited from community settings in Los Angeles, this dissertation examined:
(1) longitudinal and bidirectional associations between opioid withdrawal symptoms and
frequency of opioid use, (2) associations between opioid withdrawal and injection-related risk
behaviors, and (3) opioid withdrawal experiences and mechanisms that lead to the progression of
opioid-related harms. Studies 1 and 2 were secondary data analyses of an existing dataset. Study
3 involved original data collection to provide a broader understanding of the withdrawal
experience and to extend knowledge of the quantitative studies and spark new areas of inquiry.
Results hold implications for future research directions and interventions designed to improve
the health and quality of life of PWID in community settings.

Summary of findings
In Study 1, we found consistent positive associations between opioid withdrawal and
opioid use across latent growth curve models. This finding was supported by the study’s original
hypothesis that greater withdrawal symptomatology would be associated with increased opioid
use and vice versa. Past 30-day homelessness also had a positive and stable association with
contemporaneous opioid use and withdrawal symptom frequency. Past 6-month methadone use
was inversely associated with opioid use and withdrawal symptom frequency across models. Past
6-month buprenorphine use did not influence contemporaneous withdrawal or opioid use
95
frequency. In our random-intercept cross-lagged panel model accounting for reciprocal
influences, we did not observe a bidirectional relationship between withdrawal and opioid use.
However, results indicated a slight decrease in overall withdrawal symptom frequency (-0.09)
and opioid use frequency (-0.37) over the 12-month study period. Findings underscore the value
in adequately considering opioid withdrawal symptoms when considering an individual’s opioid
use consumption. Treatment initiatives that target withdrawal symptoms may be a successful
way to reduce overall rates of illicit opioid use and improve drug cessation.
In Study 2, we found a significant association between withdrawal frequency and
increased injection risk at baseline, but these effects did not persist in 6-month or 12-month
follow-up. These findings are partially supported by the study’s hypothesis, as participants
reporting greater withdrawal frequency had higher cumulative injection risk scores in the
baseline interview. In moderation analysis, we found that gender did not affect associations
between withdrawal and injection risk. However, we identified differences in associations
between withdrawal and injection risk by baseline homelessness status. Specifically, for
participants who reported recent or current homelessness (prior 30 days), withdrawal symptom
frequency was associated with increased injection risk at baseline. Withdrawal did not affect
injection risk for participants who were not homeless. Finally, in multivariable regression
analysis, frequency of opioid withdrawal symptoms and recent nonfatal overdose history (past 6
months) were associated with increased odds of reporting a non-fatal overdose event at 6 or 12-
months. These findings improve our empirical understanding of the influence of opioid
withdrawal symptoms on injection risk behaviors. People who use drugs in the United States are
facing concurrent crises of overdose deaths and HIV infections. While needle exchange
programs and medications to reverse opioid overdoses are becoming more widely available in
96
recent years, targeted interventions to treat opioid withdrawal may also be an effective method of
resource allocation.
Study 3 used semi-structured qualitative interviews to gather further information about
opioid withdrawal experiences and consequences. This research supplements quantitative
analyses with the perspectives and experiences of withdrawal in a predominantly vulnerable
population of opioid users. Participants shared a myriad of withdrawal experiences highlighting
the profound importance of the withdrawal syndrome in this community. Physiological
consequences of withdrawal were described as debilitating to the extent that they interfered with
participants’ ability to eat, sleep, and physically function. In many cases, withdrawal was
discussed as a factor that constrained the ability to locate and capitalize on economic
opportunities, and lowered capacity and motivation to undertake work and secure stable housing.
Participants described using other substances (Xanax, antihistamines, and alcohol) with sedative
properties as a strategy to cope with acute withdrawal pain, which posed risk for additional legal
and physical consequences. Participants who transitioned from heroin to fentanyl use noted more
frequent, painful, and a faster onset of withdrawal symptoms since making the switch. Several
important themes surrounding withdrawal in the context of buprenorphine use were also
unveiled. For PWID who had tried buprenorphine, several shared disparaging accounts of
precipitated withdrawal events that took place inside and outside of hospital settings. Within the
hospital, participants attributed their withdrawal to improper training of medical staff in
providing guidance of the need to abstain from opioids for 6-12 hours prior to buprenorphine
induction. For PWID who used buprenorphine outside of the hospital, several described high-risk
injection scenarios resulting from the consumption of large quantities of opioids in attempt to
97
override the buprenorphine. Results from this study contribute to the literature by documenting
how opiate withdrawal episodes play out in the natural ecology.
Collectively, the results of these three studies highlight the importance of the opioid
withdrawal syndrome among PWID. Findings from Study 1 illustrate a longitudinal relationship
between opioid withdrawal and opioid use frequency. Study 2 findings demonstrate how
withdrawal symptoms relate to overall injection risk practices and highlight the intersecting role
of homelessness as a moderator of withdrawal and injection risk. Study 3 revealed insights on
how withdrawal overwhelms the lives of PWID with opioid dependence and offers knowledge of
how buprenorphine prescribing practices can be improved to better suit the needs of PWID in
community settings.

Implication of findings
Theoretical implications
This dissertation was guided by principles of Zinberg’s Drug, Set, and Setting
Framework (79), and the Risk Environment Framework (80). The Risk Environment framework
demonstrates how social, structural, and environmental factors interact to increase individual
susceptibility and vulnerability to drug-related harms (61, 80). This theoretical framework
emphasizes the role of endogenous risk factors (i.e., drug injecting locations, homelessness,
prisons and incarceration, policy and law governing syringe access) rather than endogenous (e.g.,
age, sex, race, genetic composition) as means for producing drug-related harm (80). For
example, in the field of HIV/AIDS, personal and social networks have been shown to be key
influencers of HIV transmission among adult sex workers, men who have sex with men, and
injection drug using populations (81, 142). Findings from the current article break new ground
98
by delineating how individual-level factors related to the drug itself, specifically the biological
and psychological nature of opioid addiction, can also lead to substantial risk. This finding is
consistent Zinberg’s concept of “drug” in the Drug, Set, and Setting Framework, which
propagates that drug use is partially influenced by factors related to the pharmacological effects
of the drug itself (79). Future research efforts should be made to integrate these findings into the
current conceptualization of the Risk Environment Framework to improve health outcomes and
prevent further drug-related harms among PWID. Examining the withdrawal experiences
through the lens of human behavioral pharmacology (a discipline that seeks to understand the
acute and chronic physiological, affective, cognitive, and behavioral consequences of drug use)
could provide a more nuanced, complex characterization of individual differences in the severity
and breadth of withdrawal symptoms.

Methodological implications
Four analytical methods were used to address the three aims of this dissertation project,
which included Latent Growth Curve Modeling (LGCM) with time-varying and time-invariant
covariates, a Random-Intercept Cross-Lagged Panel Model (RI-CLPM), Multi-Group Latent
Growth Curve Models, Logistic Regression Modeling, and Thematic Analysis. LGCM is
considered a robust way of analyzing longitudinal data in circumstances where incomplete and
unbalanced data are present, and when respondents have been measured at least three times (92).
The RI-CLPM is an extension of the traditional cross-lagged panel model that accounts for both
temporal stability (between person) as well as trait-like, time-invariant (within-person)
differences in the data through the inclusion of a random intercept (94). Due to recent
advancements in statistical methodologies requiring four or more waves of data (i.e., latent
99
difference score, latent curve model with structured residuals), future studies with additional
measurement waves might be useful in further delineating the reciprocal relationship between
opioid withdrawal and opioid use frequency in this population. As this project was the first to
examine the longitudinal impact of opioid withdrawal symptoms on subsequent opioid use and
injection-related risk behaviors, future studies would benefit by replicating these analyses to
confirm results. The analytical approach used for qualitative interviews provided insights
regarding withdrawal consequences that were not explicitly explored in the quantitative studies.
Findings support the involvement of marginalized members of the community as means for
addressing complex conditions and diseases disproportionately affecting health disparity
populations. Ultimately, our knowledge of longitudinal consequences of withdrawal was
advanced by our analytic methods and the novel methodology of utilizing a diverse cohort of
opioid users who were not taken from any form of drug abuse trial or health treatment program
at the time of data collection.

Practical implications
The significance of the broader environmental context in both quantitative and qualitative
analyses points to the importance of integration interventions that minimize exposure to
environment-based risk and harm. For example, ensuring adequate housing and other basic needs
are fulfilled must be a prioritized component of future public health interventions designed to
address the ongoing overdose death crisis. Meta-analytic data reveal that PWID who are
homeless or unstably housed experience higher frequency of non-fatal overdose events compared
to PWID who are housed (35). Given the astounding consequences of poverty and homelessness
on overall population health, efforts to improve the material conditions PWID face will likely
100
lead to better adaptation strategies to cope or avoid withdrawal events and afford more time to
allocate towards seeking treatment for opioid use disorder.
While opioid withdrawal itself may not be fatal for all people, the behaviors that come
with it substantially increase harm. To begin to break the endless cycle of seeking and scoring
drugs to avoid withdrawal, increased medical interventions designed to treat withdrawal
symptoms are urgently needed. Programs that take away the panic and need to secure the next hit
can create space in a person’s life to start to address other health care and basic human needs.
Harm reduction policies that support safer drug use facilities, and low-barrier opioid distribution
programs may be crucial in this regard (143). Furthermore, the prescribing of pharmaceutical
grade opioids to opioid users at high risk of overdose may disrupt the toxicity of the illicit opioid
drug market and reduce overdose fatalities.
Results from this project signal the need for several national and state level policy
changes regarding treatment and access to medications for opioid use disorder. Considering the
myriad of health vulnerabilities experienced by PWID, it is incumbent to create public health
solutions that facilitate easier access to MOUD at the critical moment when an individual decides
to seek treatment. Buprenorphine and methadone are two types of MOUD with demonstrated
efficacy in treating opioid withdrawal symptoms. At present, both buprenorphine and methadone
are highly regulated by the federal government, and long-standing regulations have required in-
person visits with a prescriber for buprenorphine users, and in-person appointments to an opioid
treatment center to receive methadone. New emergency policies waiving the requirement for an
initial in-person appointment to initiate buprenorphine treatment have been enacted by the HHS
in response to the COVID-19 pandemic (144). These policy changes also allowed state
regulatory authorities to request blanket exceptions for patients to be able to access take-home
101
methadone medication doses for up to 28 days for “stable” patients, and up to 14 days for “less
stable” patients (145). A patient’s “stability” as defined by the federal government, is determined
based upon factors including recent history of substance use (other than MOUD), length of time
in MOUD program, and ability to safely store take-home medications (145). While these policies
have bolstered continuity of care systems for people receiving formal treatment for opioid use
disorder, such expanded access should be extended to individuals outside of formal treatment
programs. Given findings that MOUD is associated with reductions in frequency of opioid use,
fewer injections, and lower rates of HIV prevalence and incidence (68, 70-74), increasing
utilization of MOUD to PWID in community settings is a critical health promotion strategy that
could save lives.
Given the highly addictive nature of illicit opioids (i.e., heroin and fentanyl) (9),
increased opportunities to intervene and track opioid withdrawal symptoms as means for
reducing overall rates of opioid use among PWID at the community level are needed. Future
harm reduction efforts may benefit by adopting brief screening such as the Clinical Opiate
Withdrawal Scale (COWS) (52) to identify PWID who present more frequent withdrawal
symptomatology. Such measures could be incorporated in settings where PWID physically
congregate such as syringe service programs or be implemented by physicians who provide
direct medical care for PWID, such as street medicine teams.
Overdose deaths have surged to unprecedented levels in 2020 indicating critical need to
improve the unintended consequences of OUD. Expanding MOUD treatment to PWID in
community settings is an important next step in addressing gaps in treatment capacity and
lowering transmission of HIV-related illness. Furthermore, current approaches for the treatment
of OUD should be altered so that withdrawal is treated as a top priority. Behavioral interventions
102
on withdrawal coping and management offered in community settings could also be beneficial.
Interventions conducted in partnership with local syringe exchange programs which provide
sterile syringes and other critical health services for people who inject drugs, are a key venue to
reach out-of-treatment opioid users. In fact, PWID who attend needle exchange programs often
request referrals for MOUD treatment, but structural barriers to care, such as waiting lists or
transportation needs, limit engagement in treatment (76, 134). Thus, onsite MOUD treatment
paired with a short behavioral intervention designed to educate PWID on ways to avoid
withdrawal harms could be an innovative and generalizable strategy to increase MOUD
engagement and improve withdrawal outcomes. Studies enacting similar community-oriented
approaches have been piloted across the US in cities such as San Francisco, Baltimore, and
Seattle. For example, in Baltimore, a mobile health clinic was used to dispense low-threshold
buprenorphine treatment to over 500 PWID with opioid use disorder (133, 134), while a street
medicine approach was used to engage nearly 100 homeless heroin users into buprenorphine
services in San Francisco (146).
Another potential intervention strategy would be to disseminate knowledge of the
importance of withdrawal management in preventing overdoses and infectious disease
transmission within harm reduction and MOUD treatment settings. For example, trainings on
safe injection education and naloxone distribution that occur at syringe exchange programs could
be expanded to incorporate withdrawal management as a primary prevention tool to reduce
infections. Furthermore, a potential intervention could adapt an existing infection control model
(e.g., skin cleaning before injection) to include specific withdrawal contexts that enhance
potential infectious disease risk, link these contexts to specific infection control strategy, and
103
collect data regarding the effectiveness of an educational curriculum for improving provider
comfort and knowledge about infection prevention strategies for PWID.
Lastly, at the treatment level, clinicians, addiction specialists, and counselors who
provide care to patients receiving medication-assisted treatment services should be trained to
establish more open lines of communication with patients about withdrawal and provide
guidance with how to manage such symptoms within the context of their current living
situations. Furthermore, directed attention should be given to providers working in geographic
areas marked by increased homelessness, poverty, and overdose rates such as the BAART clinic
in East Los Angeles. Given important conversations surrounding opioid withdrawal in the
context of buprenorphine use drawn from qualitative interviews, such training could lead to
significant improvements in treatment outcomes and drug cessation. This empirically supported
information could also strengthen lines of communication between patients and providers, build
trust, and foster accepting and compassionate environments whereby drug users feel safe and
advocated for. Standardized protocols for MOUD administration and increased patient education
surrounding MOUD could also improve negative perceptions of MOUD in the community.

Overall limitations
These dissertation studies are not without limitations. Study 1 and 2 were secondary
analyses of the Change the Cycle study, meaning that there were limitations in variables of
interest explored and included in models. Attrition analyses revealed sociodemographic
differences between participants who were included versus excluded from the primary analytic
sample, with Hispanic or Latino participants and those reporting past 30-day income amount of
less than $1401 at baseline being more likely to be included. These sample biases were included
104
in each study’s limitation section. Additionally, we experienced low retention rates in the 6-
month and 12-month follow up interviews which limited the number of covariates available for
inclusion opens the door to potential biases due to retention. This difficulty in retaining PWID in
longitudinal research studies is common and increasingly difficult to eliminate because of the
nature of this population and the compounding structural and health vulnerabilities they face
(147, 148). Data from studies 1 and 2 of this dissertation were collected as part of a larger
randomized control trial examining the effects of a behavioral intervention focused on decreasing
assisted injection initiation. While intervention assignment was not associated with our primary
outcome variables of interest at the bivariate level, we included intervention assignment as a
control variable in all models to safeguard against potential effects.

Future research direction
To better track fluctuations in withdrawal in relation to contemporaneous opioid use and
injection risk, future research should utilize samples of novel injection initiates. This would offer
the unique opportunity to detect temporal and periodic fluctuations in withdrawal outcomes and
simultaneous injection risk during a developmental period in which opioid dependence has not
yet been established. In our sample of mature injection drug users, temporal changes in
withdrawal did not change underlying risk profiles over time, which may have been because
withdrawal outcomes and injection risk practices are already well established. Further
investigation is needed to characterize opioid withdrawal patterns over time and trajectories of
risk. Data on how withdrawal symptoms relate to prospective opioid use patterns in recent
injection initiates can help to provide a clearer understanding of the mechanisms by which opioid
withdrawal may potentiate the progression, maintenance, or cessation of opioid use.
105
Current findings can inform the development of future quantitative measures that are
better tailored to PWIDs’ specific needs and experiences. Given the absence of validated
withdrawal measures in diverse community samples of opioid using PWID, this is urgently
needed. Furthermore, I suggest the following recommendations for future quantitative studies
examining withdrawal in this population. First, independent questionnaire items measuring both
the frequency and severity of individual DSM-V withdrawal symptomatology are needed.
Furthermore, restlessness, bone or muscle aches, nausea, diarrhea, runny nose, sweating,
vomiting, stomach cramps, and teary eyes should be assessed individually, and pertain to
withdrawal experiences within the past 7 days as opposed to the past 6 months. This will
overcome limitations of previous studies by minimizing potential bias due to recall. Quantitative
protocols should also include questions on withdrawal coping. Based on findings from
qualitative interviews, studies should specifically inquire about the use of other substances (i.e.,
alcohol, prescription tranquilizers, cannabis) as a form of withdrawal management. Lastly,
surveys should include items on precipitated withdrawal experiences due to buprenorphine. This
could be assessed using a single-item question ascertained after assessing lifetime buprenorphine
use, and be phrased,” Have you ever experienced precipitated withdrawal symptoms due to
buprenorphine/suboxone (i.e., the onset of acute opioid withdrawal symptoms prompted by
buprenorphine/suboxone use)?”
106
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Abstract (if available)

Abstract Injection opioid use is a significant and growing public health concern. Globally, there are an estimated 15.6 million people who inject drugs (PWID), including 6 million in the United States. Injection opioid use leads to increased levels of tolerance, and distressing withdrawal symptoms when opioids are discontinued or dosage is reduced. Previous research show PWID experiencing opioid withdrawal to face increased risk of blood-borne illnesses, injection-related infections, and fatal drug overdose. However, most research concerning opioid withdrawal symptoms have been limited to clinical samples of opioid users. Consequently, knowledge of how withdrawal relates to subsequent substance use and other injection-related risk behaviors among PWID who are not currently seeking treatment is scant.
To fill these knowledge gaps, this dissertation examined: 1) longitudinal and bidirectional associations between opioid withdrawal symptoms and opioid use, 2) prospective associations between opioid withdrawal symptoms and injection risk behaviors, and 3) qualitative interview data on opioid withdrawal experiences and mechanisms. Study 1 revealed positive associations between opioid withdrawal and frequency of opioid use over time. Study 2 found an association between withdrawal and increased injection risk at baseline and a moderating effect of homelessness status. Study 3 discovered the following major themes related to opioid withdrawal: withdrawal importance, withdrawal consequences, withdrawal impacts on daily life activities, withdrawal coping, fentanyl versus heroin withdrawal, and withdrawal in the context of buprenorphine use. Results from these studies demonstrate the profound importance of the opioid withdrawal syndrome and the complexity of drug and health related consequences that accompany it.

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Asset Metadata

Creator Simpson, Kelsey Anne (author)

Core Title Opioid withdrawal symptoms, opioid use, and injection risk behaviors among people who inject drugs (PWID)

School Keck School of Medicine

Degree Doctor of Philosophy

Degree Program Preventive Medicine (Health Behavior)

Degree Conferral Date 2022-12

Publication Date 12/07/2022

Defense Date 11/11/2022

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

Tag injection risk behaviors,OAI-PMH Harvest,opioid use,opioid withdrawal symptoms,people who inject drugs

Format theses (aat)

Language English

Contributor Electronically uploaded by the author (provenance)

Advisor Bluthenthal, Ricky (committee chair), Barrington-Trimis, Jessica (committee member), Cho, Junhan (committee member), Davis, Jordan (committee member), Kirkpatrick, Matthew (committee member)

Creator Email kasimpso@usc.edu,kasimpso10@gmail.com

Permanent Link (DOI) https://doi.org/10.25549/usctheses-oUC112617321

Unique identifier UC112617321

Identifier etd-SimpsonKel-11347.pdf (filename)

Legacy Identifier etd-SimpsonKel-11347

Document Type Dissertation

Format theses (aat)

Rights Simpson, Kelsey Anne

Internet Media Type application/pdf

Type texts

Source 20221207-usctheses-batch-994 (batch), University of Southern California (contributing entity), University of Southern California Dissertations and Theses (collection)

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Repository Name University of Southern California Digital Library

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

Repository Email cisadmin@lib.usc.edu