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\begin{document}

\title{Assessing Pain in Persons with Opioid Use Disorder: Approaches,
Techniques, and Special Considerations}



\author[1]{Julio Nunes}%
\author[2]{Gabriel Costa}%
\author[1]{Jeremy  Weleff}%
\author[1]{Michael Rogan}%
\author[3]{Peggy Compton}%
\author[1]{Joao P. De Aquino	}%
\affil[1]{Yale University}%
\affil[2]{Universidade de Ribeirao Preto}%
\affil[3]{University of Pennsylvania}%


\vspace{-1em}



  
  \date{May 26, 2024}


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\selectlanguage{english}
\begin{abstract}
Pain and opioid use disorder (OUD) are inextricably linked, as the
former can be a risk factor for the development of the latter, and over
a third of persons with OUD suffer concomitant chronic pain. Assessing
pain among people with OUD is challenging, be-cause ongoing opioid use
brings changes in pain responses and most pain assess-ment tools have
not been validated for this population. In this narrative review, we
discuss the fundamentals of pain assessment for populations with OUD.
First, we de-scribe biological, psychological, and social aspects of the
pain experience among people with OUD, as well as how opioid-related
phenomena and healthcare dispari-ties in this population may contribute
to the pain experience. Second, we review meth-ods to assess pain
including: (1) traditional self-reported methods, such visual analog
scales, and structured questionnaires; (2) behavioral observations and
physiological indicators; (3) and laboratory-based approaches, such as
functional brain imaging, electroencephalography, and quantitative
sensory testing. These methods are consid-ered from a perspective that
encompasses both pain and OUD. Finally, we discuss strategies for
improving pain assessment in persons with OUD and implications for
future research, including educational strategies for multidisciplinary
teams. Substan-tial gaps persist in our knowledge, particularly
regarding the applicability of current pain assessment methods to
persons with OUD, as well as the generalizability of the existing
results from adjacent populations. As research linking pain and OUD
evolves, considering the needs of diverse populations with complex
psychosocial back-grounds, we will be better equipped to reduce these
gaps.%
\end{abstract}%



\sloppy


\textbf{1. INTRODUCTION}

The convergence between pain and opioid use disorder (OUD) continues to
be a complex problem and significant public health concern. Pain is a
pervasive issue, with over half of the U.S. adult population reporting
some form of pain within the last three months\textsuperscript{1}. More
specifically, chronic pain, defined as persisting pain lasting over
three months, affects over 100 million adults in the U.S., and chronic
low back pain ranks as one of the 10 leading causes of reduction in
disability-adjusted life years\textsuperscript{2, 3}. Considering health
care, forensic, production loss, and life loss costs, the economic
burden of chronic pain has exceeded \$600 billion
annually\textsuperscript{4}.

In parallel, the consequences of the OUD continue to escalate amidst the
opioid crisis, as we witness consecutive yearly records for fatal opioid
overdoses and opioid-related hospitalizations in the U.S., now above
60,000 opioid-involved overdose deaths per year\textsuperscript{5}. Pain
is pivotal to this crisis, particularly as the initial wave of the
opioid epidemic was the result of inadequately prescribed opioids for
chronic pain\textsuperscript{6}. An added layer of complexity is the
pervasive stigma associated with OUD, often resulting in labeling
patients with pain as ''opioid-seeking.'' This hampers appropriate
medical care in the context of acute and chronic
pain\textsuperscript{7}. Such is the need for specialized care that
considers the consequences of co-occurring pain and OUD, for which
clinicians and other stakeholders have been attempting to develop
innovative strategies, including specialized clinics that are capable of
jointly managing pain and OUD\textsuperscript{2, 3, 8, 9}. A persistent
challenge, however, is distinguishing between pain-related and
OUD-related phenomena, due to their clinical and neurobiological
similarities\textsuperscript{10}.

Several approaches have been used to assess both acute and chronic pain
among persons with OUD. For example, Delorme and colleagues recently
conducted a meta-analysis to evaluate the prevalence of chronic pain
among persons with OUD receiving buprenorphine or
methadone\textsuperscript{11}. The analysis included 23 studies, using a
variety of pain assessment tools, with the Brief Pain Inventory (BPI)
most frequently used (n=12). Four studies relied on a simple binary
question, asking about the presence or absence of pain. One study
utilized a pain numerical rating scale\textsuperscript{12, 13}, while
another combined the BPI questionnaire with a numerical rating
scale\textsuperscript{14}. A unique approach was taken in one study,
where a custom-made questionnaire was used to gauge pain
levels\textsuperscript{15}. The other two determined the presence of
chronic pain based on the prescription or dispensation of analgesic
medication longitudinally. This wide variation in assessment tools
illustrates the lack of consensus in methods used to understand and
quantify the experience of chronic pain among patients with OUD,
highlighting the need for empirically-supported consensus in this area.

In this narrative review, we discuss the fundamental approaches to pain
measurement in persons with OUD. First, we describe biological,
psychological, and social aspects of the pain experience among people
with OUD. Our discussion ranges from molecular opioid-related phenomena
to healthcare disparities. Second, we review methods to assess pain
including: (1) traditional self-reported methods, such as visual analog
scales and structured questionnaires; (2) behavioral observations (e.g.,
antalgic and pain-avoidant behaviors) and physiological indicators
(e.g., heart rate, blood pressure); (3) and laboratory-based approaches
such as functional brain imaging, electroencephalography, and
quantitative sensory testing. We discuss the influence of relevant
clinical phenomena in the assessment of pain among persons with OUD,
including tolerance, heightened pain sensitivity (i.e., hyperalgesia),
and pain exacerbated by opioid withdrawal. Finally, we will outline
strategies for improving pain assessment in OUD and implications for
future research.

\textbf{2. THE COMPLEXITY OF PAIN ASSESSMENT IN OPIOID USE DISORDER}

\textbf{Biological aspects of pain in persons with opioid use disorder}

\emph{2.1.1 The normal physiology of acute pain}

Pain is a complex and multifaceted experience, intertwining sensory,
affective, cognitive, and behavioral dimensions. Nociception is the
sensory nervous system process of encoding noxious or tissue-damaging
stimuli, involving the activation of peripheral nociceptors, which in
turn communicate through synapses with the central nervous
system\textsuperscript{16}. Acute pain can produce autonomic responses,
frequently leading to increases in blood pressure, cardiac output,
respiratory and heart rate. Thus, the experience of acute pain involves
a visceral sympathetic reaction that informs the organism it must avoid
or withdraw from a source of discomfort or damage\textsuperscript{17}.
Ascending neural pathways carry sensory information from the periphery
through the spine to corticolimbic regions; descending pathways,
conversely, modulate the pain experience as they relay physical and
emotional signals down the spinal cord\textsuperscript{18}.

\emph{2.1.2 Pain chronification from a biological perspective}

As pain becomes chronic, the persistent activation of neural circuitry
responsible for emotional processing can lead to further psychological
and physical consequences, thereby worsening the perception of pain and
its functional impact over time\textsuperscript{18}. Neurobiologically,
pain chronification requires disruptions in multiple complex pathways
crucial for the neural processing of the pain experience. The mechanisms
behind these disruptions are diverse\textsuperscript{19-21}. At the
cellular level, they involve glutamate excitotoxicity and reductions in
inhibitory gamma-aminobutyric acid (GABA) levels, intensifying pain
sensitivity in the periphery\textsuperscript{22}. At the central nervous
system level, shifts occur in the activation patterns of brain sensory
cortical areas, gradually stressing limbic areas over
time\textsuperscript{22}. Eventually, this progression leads to
neuroplastic changes, manifesting as a pathological rewiring of brain
and spinal cord circuitry\textsuperscript{22}. Notably, the activation
of limbic areas plays a pivotal role in driving the development and
escalation of chronic pain\textsuperscript{22}. This process occurs
through emotional activation, triggering the conversion of an acute
sensory experience into an emotional, chronic and, debilitating one.
Consequently, the individual's experience of reality and decision-making
become heavily impacted by the pain experience\textsuperscript{21}. The
fundamental link between pain and OUD stems from disruptions in the
endogenous opioid system, which significantly influence the cellular and
central nervous system changes described, with inappropriate neuronal
rewiring and neuroplastic changes as a result, leading to perpetuation
of pain and reward system imbalances\textsuperscript{19, 20}.

\emph{2.1.3 The role of opioids in modifying the experiencing of pain}

Opioids have profound analgesic properties, reliably reducing both
physical pain and psychological distress\textsuperscript{23}.
Molecularly, they bind to G-protein coupled opioid receptor
subtypes(e.g., mu-\selectlanguage{greek}μ, \selectlanguage{english}delta-\selectlanguage{greek}δ, \selectlanguage{english}and kappa-\selectlanguage{greek}κ\selectlanguage{english}) in multiple brain and spinal
regions\textsuperscript{23}. Each receptor type activates different
cellular pathways, leading to varied physiological effects. The \selectlanguage{greek}μ
-\selectlanguage{english}opioid receptor is the primary target for most clinically used opioids
and is chiefly responsible for their analgesic effects. The activation
of the \selectlanguage{greek}μ \selectlanguage{english}receptor generally leads to a decrease in the release of
certain neurotransmitters including substance P, glutamate, and
GABA\textsuperscript{23}. In pain pathways, this results in the
hyperpolarization of post-synaptic neurons, which thereby reduces
synaptic activity and the inter-neuron communication of pain
signals\textsuperscript{23}. Secondary effects include reductions in
blood pressure, heart rate, respiratory rate, as well as
drowsiness\textsuperscript{24}. Centrally, opioids agonists' actions at
the opioid receptor level have euphoria-inducing and anxiolytic
properties limiting one's awareness or appreciation of painful
stimuli\textsuperscript{25}.

Continued opioid use, whether motivated by pain or OUD, can lead to the
neuroadaptive developments of tolerance and physical dependence,
necessitating higher doses to achieve the same effects over time and
resulting in withdrawal symptoms upon drug cessation. Chronic opioid
exposure triggers modifications in the quantity and responsiveness of
opioid receptors, a process known as receptor downregulation and
desensitization\textsuperscript{26}. As a consequence, receptors become
internalized or less reactive, contributing to the phenomenon of
tolerance\textsuperscript{26}.

Human studies utilizing pain laboratory models have shown that
individuals maintained on full-agonist opioids such as methadone for the
treatment of OUD exhibit increased sensitivity and decreased tolerance
to painful stimuli, as evidenced by studies conducted by the
investigative teams of Clark, Compton, and
Wachholtz\textsuperscript{27-29}. Interestingly, Athanasos and
colleagues found that despite inducing respiratory depression in some
participants, high doses of morphine failed to enhance pain tolerance in
methadone-maintained patients\textsuperscript{30}. Additionally, Compton
and colleagues reported that neither buprenorphine nor methadone
treatments improved pain sensitivity for participants with
OUD\textsuperscript{28}. In a systematic review of 225 participants on
opioid agonist therapy for OUD, De Aquino and colleagues found that the
majority of participants do not experience analgesia despite receiving
opioid doses up to 20 times greater than those used to treat acute pain
in opioid-na\selectlanguage{ngerman}ïve participants\textsuperscript{31}. Conversely, they
remained vulnerable to respiratory depression despite receiving
medications for OUD --- suggesting tolerance to analgesic effects cannot
be equated with tolerance to adverse effects from opioids. This
intricate interplay of physiological changes underscores the
complexities associated with opioid-induced neuroadaptations and pain
management in individuals with OUD.

\textbf{Psychological aspects of chronic pain in opioid use disorder}

The presence of pain can significant worsen one's quality of life. As a
multifaceted phenomenon, it also brings wide-ranging consequences. For
instance, mobility, sleep, concentration, mood, and overall physical
functioning are negatively impacted by ongoing pain. Various
psychological factors can worsen the pain experience, including negative
expectancy (i.e., behaving in an avoidant manner as if expecting the
pain to worse) or perceived controllability (i.e., sense of lack of
control over their pain increases the perception of intensity). These
factors bring additional repercussions, such as social isolation and
avoiding physical activities or \emph{kinesiophobia} (from the Greek
terms ``kinesis'' {[}movement{]} and phobia {[}fear{]}). Collectively,
these components of the pain experience can converge, and the individual
may refrain from usual enjoyable activities and roles, contributing to
depression, anxiety, and lower quality of life\textsuperscript{32, 33}.

Physical pain and emotional pain intersect and can synergistically
influence not only the overall experience of pain itself, but also
influence co-occurring psychopathology (e.g., mood disorders and
trauma-related disorders)\textsuperscript{34}. For example, mood
disorders predict both non-medical opioid use and the increased
likelihood for developing chronic pain conditions\textsuperscript{35,
36}. Persons with chronic pain are also more likely to be diagnosed with
mood disorders and may be at higher risk of developing
OUD\textsuperscript{37, 38}; although there are conflicting data in the
literature regarding the risk of progression to OUD\textsuperscript{39}.
However, despite the considerable overlap between these conditions, the
influence of co-occurring psychopathology on the assessment of pain
among people with OUD remains largely unaccounted for in most clinical
settings.

Other important psychological factors that contribute to the pain
experience in persons with chronic pain include pain catastrophizing and
attentional bias\textsuperscript{40}. Pain catastrophizing involves
ruminative thoughts about pain, and a sense of hopelessness regarding
pain improvement resulting in an amplification of
pain\textsuperscript{41}. Studied in both acute pain (e.g., whiplash
injury after motor vehicle accidents) and chronic
pain\textsuperscript{42-44} (e.g., fibromyalgia\textsuperscript{45}, low
back pain\textsuperscript{46, 47}), pain catastrophizing is a risk
factor for poorer pain treatment prognosis and outcomes in persons with
OUD, as well as a predictor of pain chronicity\textsuperscript{48}.
Attentional bias refers to a cognitive fixation in which attention is
automatically captured by pain- or opioid-related cues, serving as
motivation for further medication use\textsuperscript{49, 50}. In other
words, as patients with chronic pain engage in re-occurring opioid use,
pain (e.g., experiencing external or interoceptive painful stimuli) and
opioid-related cues (e.g., pill bottles) can trigger craving and
perceived worsening of pain. Clinically, it has been suggested that
attentional bias may precede drug use in persons with OUD and be an
early warning signal of return to non-medical opioid
use\textsuperscript{51}.

Research shows that persons with OUD tend to experience pervasive
anhedonia and dysphoria with consequences such as increased sensitivity
to social rejection, reward deficiency, and heightened pain
experience\textsuperscript{52-54}, contributing to opioid craving and
further non-medical opioid use\textsuperscript{53, 55, 56}. This
dysphoria or \emph{hyperkatifeia} (from the Greek term ``katifeia''
{[}dejection{]}) is referred to as encompassing negative emotional
symptoms such as irritability, anxiety, and unease that derive from
dysregulation of brain reward and stress systems, and has been
demonstrated to worsen during protracted abstinence and seems to
facilitate relapse\textsuperscript{57}.

Further exemplifying the clinical relevance of these chronic
pain-related psychological factors, emerging evidence demonstrates that
interventions addressing both the physical consequences of pain and
long-term opioid use, and the maladaptive psychological patterns, such
as pain catastrophizing, produce superior clinical
outcomes\textsuperscript{58}. As an example from an adjacent long-term
opioid use population, Martinson and colleagues studied 77 veterans with
multiple chronic pain conditions in the primary care setting and offered
six, 50-minute sessions of cognitive behavioral therapy for
pain\textsuperscript{59}. Approximately 52\% of participants had
long-term opioid use. They suggest that this psychological behavioral
intervention significantly improves pain symptoms, physical function,
family stability, sleep quality, satisfactions with outcomes of care,
pain-related anxiety, generalized anxiety, pain catastrophizing, and
depressed mood. As seen in most studies encompassing pain and long-term
opioid use, a limitation of this study is that it does not formally
assess for OUD, thus, we suggest careful extrapolation of these findings
from long-term opioid use populations to those living with OUD.

In summary, the psychological consequences of both chronic pain and OUD,
especially when compounded by negative coping strategies and thought
patterns, can make pain feel overwhelming for persons with co-occurring
OUD and chronic pain. The perception of pain in these patients is
influenced by these significant psychological factors, which can be
accurately assessed for and are amenable to effective interventions.

\textbf{Social aspects influencing pain assessment in persons with OUD}

As the fields of pain research and treatment have progressed towards an
adoption of a biopsychosocial model as an alternative to a purely
biomedical approach, social aspects in clinical evaluations and pain
assessments have garnered growing interest\textsuperscript{60}. This
model argues that social factors (e.g., racial-ethnic disparities,
social support networks, access to health care) are often as important
as biological determinants in the origin, exacerbation, and maintenance
of pain.

\emph{2.3.1 The role of social support networks}

Social support, defined as the perception of availability of other
people in one's social networks, appears to play a central role in one's
ability to cope with pain\textsuperscript{61}. For instance, people with
chronic pain who report high levels of social support experience less
clinical pain intensity, distress related to pain, and less
mood-disorder comorbidities than those with less
support\textsuperscript{61-63}. Moreover, higher levels of social
support have been shown to reduce the occurrence of pain catastrophizing
in a cohort of 74 persons with spinal cord injury\textsuperscript{64}.
Likewise, another study of 168 older adults with various forms of
chronic pain showed that high social support, as measured with the
Formal Social Support for Autonomy and Dependence in Pain Inventory,
positively impacts not only the pain experience itself and decrease
pain-related disability, but also favors higher levels of function
autonomy and independence\textsuperscript{65}.

Although social connections appear to positively influence the outcomes
of OUD treatment\textsuperscript{66}, (i.e., increased medication
adherence, time in treatment, number of drug-free urine samples), thus
far there are no specific studies specifically investigating the effect
of social-support networks for patients with co-occurring OUD and
chronic pain. However, given the clear benefits resulting from high
levels of social support for people with pain or OUD, it is likely to be
a benefit for those with both conditions.

Methods for assessment of social support networks have been well
described by Bryant and colleagues\textsuperscript{67} and interactive
tools such as the Columbia Social Support Network Map have been created
to simplify this assessment\textsuperscript{68}. Social workers are
well-equipped with knowledge and skills to assess levels of social
support, highlighting the importance of multidisciplinary care for both
chronic pain and OUD.

\emph{2.3.2 The role of racial-ethnic disparities}

Barnett and colleagues used 2016-2019 Medicare claims data to identify
racial-ethnic differences in the prescription and rates of use of
medications to treat OUD (buprenorphine, intramuscular extended-release
naltrexone) and prevent opioid overdose deaths (naloxone), as well as
high-risk prescription medications (e.g., opioid analgesics and
benzodiazepines)\textsuperscript{69}. They found that Black persons are
less likely to access buprenorphine and naloxone than non-Hispanic White
(NHW) populations. This is particularly concerning, in that Black
persons have experienced greater increases in opioid-involved overdose
deaths than any other racial group, growing by a factor of 7.7 between
2010 and 2020\textsuperscript{70}.

Noted disparities in OUD treatment are mirrored by disparities in
chronic pain treatment in minoritized patients living with OUD. Black
and, to a degree, Hispanic adults have been shown to experience greater
clinical pain severity and pain-related disability than NHW
adults\textsuperscript{71-73}. In laboratory models of pain, Black
persons have been found to exhibit lower pain thresholds and lower
tolerance of pain\textsuperscript{74-80}. Growing evidence highlights
that these racial differences in pain perception may result from the
harmful effects of health-care disparities and societal racism in
general.

At the mechanistic level, racism-related stress, or pervasive emotional
distress caused by racial discrimination, may negatively affect pain
perception through sleep disturbance and corticolimbic disfunction, as
demonstrated by Letzen and colleagues\textsuperscript{81}. The authors
assessed the effects of race-related stress on the corticolimbic system,
using positron emission tomography (PET) to evaluate the binding
potential of \selectlanguage{greek}µ-\selectlanguage{english}opioid receptors; actigraphy sleep variables were also
measured\textsuperscript{81}. An association was demonstrated between
levels of exposure to racism and pain sensitivity through mechanisms of:
(1) race-related stress, (2) sleep disturbances associated with
race-related vigilance, and (3) corticolimbic opioid-receptor modulation
changes. These findings emphasize the role of multi-disciplinary
trauma-informed approach to pain treatment in racialized populations, as
the experience of racism may worsen the experience of pain and be
evident beyond physical domains\textsuperscript{82}.

In summary, biological, psychological, and social factors converge to
impact the experience of chronic pain, challenging holistic pain
assessments. For persons with OUD, this assessment is further
complicated by opioid-related disruptions of pain pathways. For optimal
clinical outcomes, it is imperative to undertake a comprehensive,
multidisciplinary pain evaluation that can account for this complexity
(\textbf{Figure 1} ).

\textbf{SPECIAL CONSIDERATIONS}

\textbf{Tolerance and hyperalgesia}

Pain assessment in individuals with OUD can be complicated by the
phenomena of tolerance and hyperalgesia, which are opioid-induced
changes to pain systems that can result in increased analgesic demand by
persons with pain\textsuperscript{26}. Tolerance is characterized by a
decreased response to an opioid over repeated administration,
necessitating dose increases to achieve the previous magnitude of
analgesic effects\textsuperscript{26}. Opioid-induced hyperalgesia (OIH)
is a paradoxical state of heightened pain sensitivity that is distinct
from and superimposed on the painful condition\textsuperscript{26, 83}.
As people with chronic pain continue to deteriorate, increasing need for
opioids at higher doses can indicate several diagnostic hypotheses.
First, it may reflect untreated or inadequately treated pain that could
indeed benefit from higher dose adjustments\textsuperscript{84, 85};
second, it may signal the development of tolerance to the analgesic
effects of the current opioid regimen\textsuperscript{86, 87}; or third
and alternatively, the dose escalation itself may be triggering OIH, in
which case higher doses might, in fact, be
detrimental\textsuperscript{26, 83}.

Multiple mechanisms likely underlie the development of tolerance and
OIH. In some ways similar to mechanisms for pain chronification, these
include NMDA-receptor activation, neuroadaptations in descending pain
modulatory pathways, increased excitatory neuropeptides, and glial cell
activation\textsuperscript{85, 87, 88}. However, the precise etiology
remains incompletely understood. Clinically, distinguishing between
under-treated pain, tolerance to analgesic effects of opioids, and
hyperalgesia is crucial yet challenging in persons with OUD. A
comprehensive phenotyping of the pain experience exploring
characteristics, timing, triggers, and radiation can determine if pain
represents disease progression or an opioid-related effect. A trial of
opioid dose reduction may also clarify if OIH is
present\textsuperscript{87}. Carefully weighing these pain-related
factors helps clinicians to identify the source(s) of pain and optimize
pain management in this complex clinical population.

\textbf{3.2 Physical dependence and opioid withdrawal}

In addition to tolerance and OIH, pain in patients with OUD can be
complicated by the consequences of physical dependence, namely opioid
withdrawal hyperalgesia\textsuperscript{89-91}. Opioid withdrawal is
commonly assessed using standardized scales such as the Clinical Opiate
Withdrawal Scale (COWS)\textsuperscript{92}, the Subjective Opioid
Withdrawal Scale\textsuperscript{93}, and the Objective Opioid
Withdrawal Scale (OOWS)\textsuperscript{93}. These tools evaluate
self-reported symptoms and observer-rated signs of opioid withdrawal,
including anxiety or irritability, perspiration or sweating, tearing or
lacrimation, runny nose or rhinorrhea, goosebumps or piloerection,
restlessness, as well as the presence of specific types of pain itself
(e.g., abdominal pain and cramps).

However, there is considerable overlap between the symptoms of opioid
withdrawal and those associated with poorly treated chronic pain. For
example, feelings of irritability\textsuperscript{94},
anxiety\textsuperscript{95}, and restlessness\textsuperscript{96} can be
associated with chronic pain as well as opioid withdrawal. This
overlapping symptomatology makes it challenging to differentiate
etiology, potentially leading to inadequately treated pain in clinical
practice. Hence, clinicians must consider the possibility of
undertreated pain when evaluating opioid withdrawal, especially in
patients with known OUD. \textbf{Figure 2} provides clinical pain
assessment parameters to distinguish between opioid hyperalgesia,
tolerance, and withdrawal. Such differential assessment can prevent
premature diagnostic closures that negatively affect pain-related
functioning.

\textbf{4. METHODS OF PAIN ASSESSMENT}

Pain is a wholly subjective experience; thus, objective assessment is
hindered. Traditionally, pain has been evaluated using three main
approaches: patient self-report, behavioral observations, and
physiologic indicators. In addition, there is growing evidence for the
use of psychophysical, neurophysiological, and neuroimaging techniques
to objectively complement those assessments \textbf{(Table 1).} However,
accurately applying these modalities in patients with OUD requires
nuanced understanding of their respective strengths and limitations,
particularly considering the biopsychosocial and opioid-related
variables of interest.

Notably, the pain assessment tools utilized in various observational and
randomized controlled trials evaluating both acute and chronic forms of
pain in individuals with OUD are often inconsistent, as there is limited
evidence to justify the choice of one tool over another, reflecting the
need to develop consensus on optimal evaluation methods for this complex
population. Additionally, although the psychometric properties of the
assessment self-report scales have been widely
reported\textsuperscript{97-99}, providing evidence of reliability and
validity, psychometric evaluation is often absent for populations of
persons with both pain and OUD.

\textbf{4.1 Self-report measures}

\emph{4.1.1 Visual Analogue Scale}

The visual analogue scale (VAS) was first described by Hayes and
colleagues\textsuperscript{100} as an instrument to quantify pain
intensity. It features a linear scale ranging from ''no pain'' to
''worst pain ever'', and the individual marks the intensity of their
pain on a 100 mm line. This scale has been commonly used in various
populations (e.g., children\textsuperscript{101}, patients with chronic
non-cancer pain\textsuperscript{102}, individuals with juvenile
idiopathic arthritis\textsuperscript{103}). In patients with OUD,
several studies have utilized the VAS to assess
pain\textsuperscript{104-106}. Notably, similar VAS tools have been used
to measure other opioid-related clinical phenomena, such as opioid
craving and withdrawal\textsuperscript{107, 108}.

Several studies have examined the use of the VAS to assess pain in
patients with OUD undergoing opioid switching or taper. For example,
Muriel and colleagues conducted an observational study in 138 patients
with OUD and co-occurring chronic pain undergoing a 6-month opioid
taper\textsuperscript{104}. They examined whether CYP2D6 (an enzyme
involved in opioid metabolism) phenotypes (poor vs. extensive vs.
ultrarapid metabolizers) affected the severity of opioid withdrawal
symptoms and pain using VAS and the Opioid Withdrawal Scale (OWS). In
the context of significant opioid tapering, CYP2D6 ultrarapid
metabolizers demonstrated more severe opioid withdrawal symptoms and
higher VAS pain scores compared to extensive and poor metabolizers. This
suggests that the VAS may be used to quantify the severity of chronic
pain experienced during opioid tapering\textsuperscript{104}.

Veldman and colleagues conducted an observational study examining the
effects of switching 43 persons with OUD and co-occurring chronic pain
from full \selectlanguage{greek}μ-\selectlanguage{english}opioid receptor agonists to
buprenorphine-naloxone\textsuperscript{106}. Using the VAS, pain levels
were measured at baseline, while on full agonists, and again two months
after switching to buprenorphine-naloxone. Change scores indicated that
patients showed a significant reduction in pain scores on the VAS
following the transition; further, they also demonstrated increased
pressure and electrical pain thresholds and tolerance, suggesting
reduced hyperalgesia\textsuperscript{106}.

Taken together, these studies demonstrate that the VAS appears to be an
acceptable and valid method for evaluating pain severity in populations
with co-occurring OUD and chronic pain. Notably, the unidimensional
nature of the VAS pain assessment portends significant limitations in
elucidating the multifaceted experience of chronic pain in persons with
OUD.

In an additional adjacent study, Nielsen and colleagues also employed
the VAS to measure perceived pain severity in a randomized trial
comparing ketamine to placebo for acute postoperative pain in 147
patients with chronic pain patients with a history of daily opioid
use\textsuperscript{105}. Evaluating VAS scores, the investigators found
that ketamine reduced the need for opioids during the 24 hours after
surgery compared to placebo. A follow-up pain assessment conducted six
months post-surgery continued to show that patients treated with
ketamine had greater improvements in pain relief as measured by the VAS
compared to those who received the placebo\textsuperscript{105}. Of
note, it is unclear if these patients actually met diagnostic criteria
for OUD, so caution is suggested in generalizing these findings to this
population.

\emph{4.1.2 Numeric Rating Scale}

The Numeric Rating Scale (NRS), first described by Downie and
colleagues\textsuperscript{109}, is a numeric version of the VAS that
asks individuals to choose a number from zero to 10 (or 20 or 100) to
communicate their pain severity, with zero representing ``no pain'' and
the higher number representing the ``worst pain
imaginable''\textsuperscript{110}. Much like the VAS, the NRS is broadly
utilized in both clinical and research settings due to its simplicity,
and is a unidimensional operationalization of pain. Notably, as it only
accepts discrete numerical responses, the NRS offers a less detailed
pain gradation in comparison to the VAS\textsuperscript{111}. In short,
while the NRS, due to its simplicity, has demonstrated ease of
application in some studies\textsuperscript{112}, its inherent
limitations necessitate careful interpretation in persons with chronic
pain and OUD.

Despite its widespread use, literature supporting the use of NRS for
patients with comorbid OUD and chronic pain is sparse. Much like the
VAS, the NRS relies on self-report, which must be appreciated in
patients with OUD while also considering the potential for altered pain
perception and tolerance due to neurobiological changes from long-term
opioid use\textsuperscript{113}. Opioid-induced hyperalgesia and the
overlap between pain and withdrawal can also complicate
assessment\textsuperscript{87}.

In an example of using the NRS for assessing pain in patients with OUD,
Latif and colleagues conducted a cross-sectional study to assess the
prevalence and characteristics of chronic pain among 560 persons with
OUD receiving buprenorphine or methadone therapy in
Norway\textsuperscript{114}. An 11-point NRS was used to assess pain
intensity in addition to a survey that captured pain duration, onset,
triggers, sites, persistence, radiation, migration, triggers and
medication effects. Chronic pain was reported by 55\% of patients, and
those with higher NRS pain scores were more likely to describe their
pain as constant, migrating, not improved with analgesics, and triggered
by stress and exercise. The study supports evidence that chronic pain is
highly prevalent in persons with OUD and that the NRS has been used to
successfully measure the severity of their pain.

\emph{4.1.3 Brief Pain Inventory}

The Brief Pain Inventory (BPI) was developed in 1983 to assess pain in
individuals with cancer\textsuperscript{115}. Unlike the unidimensional
VAS and NRS tools, not only does it capture pain severity (derived from
the average of four NRS pain intensity questions)\textsuperscript{116},
but also calculates an accompanying pain interference score, derived
from seven items which evaluate how pain affects ability to participate
in activities of daily living. Together, these scores produce an overall
rating between zero and 70, reflecting the intensity, chronicity, and
functional impact of pain\textsuperscript{115}.

With a completion time of approximately five minutes, and
multidimensional approach to pain assessment\textsuperscript{117}, the
BPI is particularly valuable when assessing individuals with
co-occurring OUD and chronic pain\textsuperscript{114}. Notably, it has
been endorsed by the Initiative on Methods, Measurement, and Pain
Assessment in Clinical Trials (IMMPACT) for use in chronic pain trials
due to its ``reliable, validated assessment of pain's impact on physical
functioning''\textsuperscript{118}.

With respect to its applicability to persons with co-occurring OUD and
chronic pain, the BPI was used by Hall and colleagues to assess the
relationship between pain interference and central sensitization (an
abnormal state of responsiveness of the nociceptive
system)\textsuperscript{119} in 141 patients with
OUD\textsuperscript{120}. Nearly 90\% of participants reported chronic
widespread pain, often meeting diagnostic criteria for fibromyalgia, and
those with higher levels of central sensitization (assessed by the
American College of Rheumatology Fibromyalgia Survey), were more likely
to report BPI pain interference as a reason for delaying OUD treatment,
continuing and escalating opioid use, and returning to non-medical
opioid use. In short, the BPI assesses multiple dimensions of pain,
providing a more comprehensive characterization than unidimensional
intensity scales like the VAS or NRS as is appropriate for chronic, as
opposed to acute, pain.

\emph{4.1.4 McGill Pain Questionnaire}

The McGill Pain Questionnaire (MPQ) prompts persons with chronic pain to
rate (0 = not at all, 1 = mild, 2 = moderately, and 3 = severe) the
degree that they feel certain types of pain sensations (throbbing,
tiring, heavy, stabbing, etc.)\textsuperscript{121}. With three
subscores (affective pain, sensory pain, and total pain), the MPQ
attempts to capture how an individual's pain experience is divided into
affective and sensory components\textsuperscript{121}.

De Aquino and colleagues used a 15-item version of the MPQ to assess the
sensory and affective dimensions of an acute pain experience among
methadone-maintained persons with OUD in a randomized,
placebo-controlled study to investigate the acute analgesic effects of
10 mg or 20 mg of delta-9-tetrahydrocannabinol
(THC)\textsuperscript{122}. Participants reported significant relief on
the MPQ to an experimental pain stimulus in the THC conditions, with
predominant effects on sensory rather than affective components of the
pain experience.

In another randomized trial conducted by Latif and colleagues, a
Norwegian version of the Short-Form MPQ was used to evaluate chronic
pain in 143 individuals with OUD randomized to either 12 weeks of
naltrexone or buprenorphine\textsuperscript{123}. No differences in MPQ
chronic pain reports were found after patients transitioned from
non-prescribed opioid use to either buprenorphine or naltrexone;
additionally, a 36-week follow-up found no increase in MPQ pain scores
for those continuing naltrexone or those switching from buprenorphine to
naltrexone. These studies provide preliminary evidence of the utility of
the MPQ in assessing pain in persons with OUD.

\textbf{4.2 Behavioral Observation}

Behavioral observations assess pain through the visual inspection of
patients' responses or actions. These techniques are especially useful
for those who may have difficulty self-reporting their pain, such as
children\textsuperscript{124}, the critically ill\textsuperscript{125},
sedated patients\textsuperscript{126}, and individuals with cognitive
impairment\textsuperscript{127}. Other pain-related behavioral findings
identified in the literature include changes in facial expressions,
affect, agitation, irritability, and the use of self-soothing or
distraction techniques\textsuperscript{128}. For those with OUD, shifts
in behavior related to pain may include social withdrawal, which has
been identified as a potential consequence of chronic pain that may
contribute to worsening OUD symptoms\textsuperscript{129}. However, the
literature specifically examining the role of these techniques in
assessing pain among persons with OUD is insipient.

\emph{4.2.1 The role of technologies for behavioral observation in OUD}

In addition to simple observation of pain-related behaviors, more
objective tools have been employed to quantify several behaviors
commonly associated with pain in individuals with OUD. Of note, an
important limitation is that the following studies did not specifically
assess pain, and therefore, these results need to be extrapolated with
caution.

In a human laboratory study, Teeters and colleagues randomized 39
individuals with OUD to either a 15-minute laboratory stress or
no-stress condition followed by exposure to opioid
cues\textsuperscript{130}. Opioid craving was measured, using a craving
VAS before and after exposure to opioid cues, and sleep duration using
the Pittsburgh Sleep Quality Index\textsuperscript{131} and actigraphy.
The study found that participants in the no-stress control group who
reported shorter average nightly sleep duration had higher levels of
opioid craving following opioid cue exposure. These suggest that poor
sleep increases vulnerability to opioid craving, which could increase
the perceived need for opioids to manage pain. As a final example,
Salgado Garc\selectlanguage{ngerman}ía and colleagues analyzed biosensor data from 46 patients
who underwent dental surgery and received opioids after extraction.
Based on metrics such as skin conductance and accelerometry, machine
learning models could identify periods in which patients were using
opioids with an accuracy of up to 83.7\%\textsuperscript{132}.

Lambert and colleagues used sternal accelerometers to monitor
involuntary movements in 23 patients undergoing opioid withdrawal, which
has substantial clinical overlap with the pain
experience\textsuperscript{133}. The study revealed that patients
exhibiting sinusoidal wave patterns in their accelerometry data,
indicative of periodic leg bouncing and foot tapping, had worsening
withdrawal symptoms, measured using the COWS. This finding suggests that
accelerometry can identify those at risk of worsening opioid withdrawal.
Bertz and colleagues also utilized actigraphy watches and electronic
diaries to assess the sleep of 37 patients with OUD undergoing methadone
or buprenorphine treatment\textsuperscript{134}. Their findings
suggested that patients experienced shorter sleep periods and delayed
sleep timing during periods when they used non-prescribed opioids and
cocaine based on urine drug screens. This suggests the potential for
actigraphy to be useful in detecting return to non-medical substance
use. Therefore, the ability to monitor behaviors quantitatively over
time may also provide insights about pain trajectories and volatility,
which have also been found to predict non-medical opioid use among
persons with OUD\textsuperscript{135}.

\textbf{4.3. Physiological indicators}

Physiologic indicators of pain refer to measurable changes in the body
that occur in response to acute painful stimuli. Some indicators include
changes in vital signs (e.g., heart rate and blood
pressure)\textsuperscript{136}, skin conductance, pupil
dilation\textsuperscript{137}, and neurophysiological
activity\textsuperscript{138}. Similar to behavioral observation, these
physiological changes have been shown to be useful in patients unable to
adequately communicate pain, such as individuals receiving invasive
forms of mechanical ventilation in critical care
settings\textsuperscript{139, 140}. For populations with OUD, they may
be particularly helpful in the acute care setting, as these variables
may offer clues into needs for higher opioid dosages in the setting of
acute pain, particularly as people with OUD usually experience high
opioid tolerance.

Respiratory rate is a commonly used physiological indicator of pain. A
large observational study including 19,908 patients who called for
emergency medical service due to pain, found that respiratory rate had
the strongest correlation with patients' self-reported pain intensity
compared to other vital signs\textsuperscript{141}. This suggests that
increased respiratory rate is a useful indicator of acute pain. It
should be noted, however, that opioids directly depress respiratory
activity by acting on opioid receptors in the
brainstem\textsuperscript{142}, therefore, pain-induced increases in
respiratory rate might be masked by an underlying opioid-induced
respiratory depression. As such, careful interpretation of respiratory
rate is required when using it to assess pain in this population, and it
may be an unreliable indicator in isolation.

One of the most studied physiological indicators for acute pain
assessment is heart rate variability (HRV). HRV refers to the variation
in the time interval between consecutive
heartbeats\textsuperscript{143}. It is influenced by the autonomic
nervous system, which regulates the body's response to pain and
stress\textsuperscript{144}. A study has shown that patients with OUD
have lower resting-state high-frequency heart rate variability when
compared to patients without OUD, suggesting a disturbed autonomic
flexibility in the former\textsuperscript{145}. Another study found that
opioid withdrawal might induce a reduction in cardiac vagal tone,
resulting in increased systolic blood pressure, heart rate, and
decreases in heart rate variability\textsuperscript{146}. Therefore, the
autonomic sequelae of OUD might confound the interpretation of HRV for
pain assessment. Still considering cardiovascular markers, blood
pressure becomes an additional confounder in the assessment of pain
among those using opioids chronically. Because of opioids vasodilating
effects, we may not observe pain-related arterial hypertension
frequently associated with acute pain\textsuperscript{147, 148}.

Thus, physiological indicators may not accurately reflect acute pain in
patients with OUD due to opioid-induced physiological alterations. It's
critical, therefore, to consider these potential confounders in pain
assessment; integrating these indicators with self-report measures may
offer a more comprehensive and precise pain assessment in this unique
population.

\textbf{4.4 Psychophysical, neurophysiological, and neuroimaging
techniques}

\emph{4.4.1 Quantitative Sensory Testing}

Quantitative sensory testing (QST) of pain refers to a series of
standardized techniques to quantify sensory experiences through various
pain inducing assays. QST-induced nociceptive stimuli can include heat,
cold, mechanical, or pressure\textsuperscript{149}. Through controlled
and calibrated administration of nociceptive stimuli, QST aims to
reliably quantify pain and detect abnormalities in pain processing
systems. Commonly used QST measures include single-point or static
paradigms to a single stimulus, such as threshold (the weakest stimulus
sufficient to cause pain) and tolerance (the maximum stimulus tolerated
before pain becomes unbearable)\textsuperscript{149}.

In addition, multiple point or dynamic paradigms can measure central
nervous system pain processing, by applying a supra-threshold pain
stimuli and closely assessing the pain responses over a defined period
of time\textsuperscript{150}. Examples of dynamic QST include temporal
summation and conditioned pain modulation. Temporal summation involves
increased pain perception to repetitive noxious stimuli and reflects
augmented spinal cord facilitation, while conditioned pain modulation
refers to decreased pain from one stimulus due to a second simultaneous
painful stimulus, reflecting descending inhibition\textsuperscript{151}.
By quantifying these processes, the QST can identify abnormalities in
ascending and descending pain pathways. A full QST profile (e.g.,
batteries containing various modalities of sensory inputs) can typically
be completed within an hour and brief (20-minute) batteries have been
developed\textsuperscript{149}.

Results from a study by Prosser and colleagues show that patients with a
history of OUD had higher heat and pain thresholds compared to healthy
controls, indicating reduced sensitivity to noxious
stimuli\textsuperscript{152}. These abnormal heat and pain perceptions
persisted even after remission from opioids, which suggests that there
may exist subgroups of individuals whose endophenotypes (e.g., aberrant
pain modulatory systems) are associated with OUD\textsuperscript{152}.
Other studies have attempted to correlate QST's detection of
hyperalgesia with genetic risks for the development of OUD, further
verifying QST's clinical relevance\textsuperscript{153-155}.
Collectively, these studies support the notion that QST can help us gain
insights into the multifaceted nature of pain.

Edwards and colleagues demonstrated that QST may be a predictive tool
for identifying patients at high risk for OUD or those with OUD at risk
for a worsening prognosis. A total of 91 participants were chronically
prescribed at least 50 mg daily morphine milligram equivalents (MME) of
non-specified full agonist opioids. Although rates of formally diagnosed
OUD were not described in the study, several participants presented with
opioid craving and tolerance, and some had already started non-medical
opioid use, potentially qualifying for at least mild OUD. In this
longitudinal study, participants classified as high-risk for non-medical
opioid use exhibited increased pain sensitivity and decreased pain
threshold and tolerance across multiple pain modalities, regardless of
whether or not they already used opioids
non-medically\textsuperscript{156}. The high-risk group patients also
presented with higher rates of hyperalgesia.

Echoing Edwards' findings, Compton and
collaborators\textsuperscript{157} identified differences in QST
responses between patients with chronic pain who developed OUD after
starting prescribed opioid therapy (n=20) and those who did not (n=20).
In this cross-sectional study, they demonstrated worsened temporal
summation results and increased pain sensitization among those patients
who developed OUD. These results indicate that QST can identify pain
phenotypes associated with a higher risk for the development of OUD.

Although QST has the potential to become a practical clinical tool for
measuring pain responses in patients with OUD, it is not yet widely
clinically available. Much of the existing research with QST and
abnormal pain profiles provides strong associations with a risk of OUD,
but there is little mechanistic understanding of these
associations\textsuperscript{149}. There are also several documented
instances of interpersonal and intrapersonal variables that affect QST
responses, such as age, gender, diet, mood, sleep,
etc.\textsuperscript{149} Further research into characterizing and
understanding how QST is related to these variables is necessary before
it can be implemented as a widely available clinical tool.

\emph{4.4.2 Functional Magnetic Resonance Imaging}

Functional magnetic resonance imaging (fMRI) captures changes in blood
flow (as a proxy for brain activity) within the brain during a variety
of states to provide insight into how the brain responds to certain
stimuli and tasks\textsuperscript{158}; it has also been used as a proxy
of neural correlates of pain in the human brain\textsuperscript{159}.
Multiple studies have found that painful stimulation activates regions
involved in the so-called ``pain matrix'' of the brain, including the
primary and secondary somatosensory cortices, anterior cingulate cortex
(ACC), and insula\textsuperscript{160, 161}. Similarly, fMRI studies
reveal not only alterations in brain activity associated with pain
states but also specific abnormalities in regions related to reward and
emotion regulation --- such as the thalamus, striatum, and
prefrontal\textsuperscript{162, 163}.

Focusing specifically on persons with OUD, a prospective, non-blinded,
single-arm pilot study by Faraj and colleagues aimed to examine the
effects of a 12-week virtual reality (VR) meditative intervention on
chronic pain in 15 patients with OUD receiving
methadone\textsuperscript{164}. The VR-based intervention incorporated
therapist-guided martial arts movements, breathing techniques, and
meditation exercises using narration and VR technology. Patients
completed 30-minute biweekly sessions that taught relaxation through
coordinated upper body movements and breathing. During fMRI scans,
patients first had a 10-minute resting state scan with their eyes
closed. They then watched a 5-minute video designed to evoke mental
states related to physical pain, as well as control, social, and
mentalizing conditions. Before and after each biweekly intervention
session, patients also rated their baseline chronic pain (BPI) and
opioid craving on a 0-10 VAS. Results showed VAS ratings of pain, opioid
craving, anxiety, and depression decreased significantly after each
session compared to pre-session. The fMRI showed that after the 12-week
meditation intervention, patients showed reduced activity in the
postcentral gyrus, a region involved in processing physical pain
sensations, when watching the two video tasks and also exhibited reduced
postcentral gyrus connectivity with some other key pain neuromatrix
regions, like the anterior cingulate cortex. This provides evidence for
the usefulness of the fMRI in assessing the pain neuromatrix activation
in individuals with OUD.

In an adjacent population, an experimental pilot study conducted by
Dowdle and colleagues evaluated the pattern and amplitude of neural
activity associated with acute pain in patients with chronic
non-alcoholic pancreatitis who had been using prescription opioids daily
for at least six months, compared to gender-matched non-opioid using
healthy controls\textsuperscript{165}. Twenty-eight participants
underwent fMRI and completed the BPI and Current Opioid Misuse Measure
to assess pain and opioid misuse. An individualized painful thermal
stimulus equivalent to a pain rating of 7/10 was determined using a
thermode on capsaicin-sensitized skin. During functional MRI scanning,
participants underwent 3 runs of 14-second blocks of the personalized
painful thermal stimulus alternating with 19-second blocks of a
non-painful 32°C stimulus. Relative to controls, the patient group
reported significantly higher pain scores on the BPI and showed
significantly greater activity during acute pain in somatosensory
cortex, anterior cingulate cortex, and occipital regions. The amplitude
of ACC response correlated positively with opioid dose. In summary, this
fMRI study demonstrated that compared to healthy controls, patients with
chronic pain using prescription opioids have an amplified neural
response to acute experimental pain, likely related to hyperalgesia,
particularly in pain processing regions like somatosensory and cingulate
cortex. Despite not studying people with OUD, as chronic prescription
opioid use is different than the disorder, the authors suggest that the
fMRI technique helped identify targets for future targeted-treatments
pain among people with chronic opioid usage, including OUD.

As demonstrated in this section, few studies have used fMRI studies in
patients with OUD have assessed pain as an outcome. Despite that, fMRI
has the potential to elucidate brain dysfunction for OUD patients,
allowing for a better understanding of their symptoms and experiences,
which allows for the future development of treatments that target these
symptoms and help maintain remission from opioids. It is important to
note that fMRI may be particularly helpful for the measurement of
pain-correlates, but not of pain experience itself, which is
fundamentally subjective. It is limited in its ability to provide
clinically relevant results for the understanding and treatment of OUD
in its current state. Development of improved imaging techniques in the
future is required to make substantial conclusions on pain and OUD
treatment.

\emph{4.4.3 Electroencephalogram}

Electroencephalogram (EEG) is a technique that measures changes in
activity within brain systems and has been used to assess pain
associated with evoked potentials (measurable electrical signals in the
nervous system that originate from a controlled stimulus) and
resting-state EEG\textsuperscript{166-168}. EEG is readily available and
relatively easy to use, although its uses in studies of OUD are
limited\textsuperscript{169}. EEG has been used to and evaluate OUD
symptoms (e.g., impulsivity, emotional dysregulation, and reward
sensitivity)\textsuperscript{170-172} and co-occurring chronic pain, as
well as assess who may benefit most from
analgesics\textsuperscript{173}.

As sleep quality directly influences wellbeing and poor sleep worsens
the experience of pain\textsuperscript{174}, the use of EEG for
assessment of pain in patients with OUD presents as a promising, largely
unexplored opportunity. For example, Lewis and
colleagues\textsuperscript{175} demonstrated, using EEG techniques, that
heroin use suppressed REM sleep as well as deep non-REM sleep; notably,
these findings were extended to methadone and morphine by Dimsdale and
collaborators\textsuperscript{176}. Discovery and validation of these
abnormal sleep patterns in OUD patients through EEG provoked research
into ameliorating these issues with novel treatments, subsequently
reducing future risks of patient relapse\textsuperscript{177}. EEG has
proven to be a powerful tool in preclinical, human laboratory, and
clinical research and has the potential to be a useful tool in
diagnostics and risk assessment for patients with OUD in a clinical
setting. The use of EEG in investigating how these measurements apply to
pain for patients with OUD is limited and presents an opportunity for
future biomarker research.

\textbf{5. STRATEGIES FOR IMPROVING PAIN ASSESSMENT IN OPIOID USE
DISORDER}

\textbf{5.1 Multidisciplinary pain assessment}

The enduring division of healthcare professionals who typically manage
chronic pain and those who treat substance use disorders is a
contributing factor to the potentially suboptimal care received by
people living with co-occurring OUD and chronic
pain\textsuperscript{82}. Both pain and OUD treatment present complex
clinical challenges that often coexist, necessitating a multifaceted
approach to care. Thus, it is expected that the collaboration among
professionals from various disciplines (e.g., physicians, nurses,
psychologists, social workers, and addiction specialists) results in
care that optimizes treatment outcomes and addresses the various facets
of these intricate illnesses.

Despite this expectation, there are few studies examining the impact of
multidisciplinary teams on pain and OUD outcomes, as well as the quality
of the pain assessment provided, with most focusing only on pain-related
outcomes but not addiction-related ones. Interdisciplinary pain clinics
have been described for various pain
conditions\textsuperscript{178-183}, including patients with co-morbid
OUD, and have shown some evidence for reduced daily opioid requirements,
pain intensity, and disability\textsuperscript{184-186}. When integrated
into primary care, these approaches appear to be supported by team
members who see them as particularly helpful for comprehensive pain care
and improving confidence and self-efficacy\textsuperscript{187}.
Similarly, specialized opioid treatment programs have integrated pain
management as part of their OUD treatment, with positive preliminary
results regarding treatment adherence, patient satisfaction, mood, and
pain intensity\textsuperscript{188, 189}.

In cases where clinics have embraced interdisciplinary teams for
assessing pain in persons OUD, they commonly integrate a comprehensive
biopsychosocial evaluation and diverse, patient-centric treatment
strategies, prioritizing functional outcomes and
safety\textsuperscript{183, 190}. By taking into account the
individual's physical, psychological, and social dimensions,
multidisciplinary teams can gain a comprehensive understanding of the
patient's pain condition and needs, as well as related co-morbid opioid
considerations, thereby facilitating multimodal assessments. This
comprehensive approach enables the identification and management of pain
triggers related to substance use, as well as addressing any barriers to
treatment adherence or recovery. Moreover, the interdisciplinary nature
of the team ensures that patients receive integrated care, potentially
minimizing fragmentation in treatment plans and potentially improving
treatment outcomes.

\textbf{5.2 Training and education for healthcare providers}

The significance of interprofessional education on pain assessment
cannot be overstated. Particularly as assessing pain in individuals with
OUD requires not only efficient, validated tools, but also professionals
knowledgeable in the nuanced interactions between the two conditions.
For a comprehensive review, it would be impossible to discuss
assessments without considering the education of those doing the
assessment,

Despite calls for action within undergraduate medical education to
address pain as a multidimensional construct and to address biases in
pain assessment\textsuperscript{191}, along with a strong desire to
acquire pain-management and addiction-management skills, as indicated in
stakeholder analyses\textsuperscript{192}, there have been limited
formal evaluations of the effectiveness of these interventions in
improving pain outcomes. Moreover, there is a scarcity of studies
specifically designed to investigate the intersection between pain and
OUD. From a students' perspective, clinical skill simulation
laboratories have illuminated that medical students find cases involving
these two interconnected diagnoses intricate and
demanding\textsuperscript{193}.

A recurrent criticism against numerous interprofessional continuing
education initiatives is the dearth of assessments measuring tangible
improvements in patient outcomes\textsuperscript{194}. It is evident
that medical students, for example, frequently receive inadequate
instruction on pain and addiction management during their medical
schooling\textsuperscript{195, 196}, although there exist several
published model curricula that can be employed for both pain and OUD
education\textsuperscript{197-201}. For example, Stevens and colleagues
developed a pain assessment and management curriculum for second-year
medical students and compared them with the previous class which did not
have access to the curriculum\textsuperscript{202}. At the end of third
year, both cohorts underwent a clinical skills examination considering
different types of pain cases (acute, chronic, and terminal). More
intervention students obtained basic (87.2\% vs. 76.0\%, p=.028) and
comprehensive (75.2\% vs. 60.9\%, p=.051) descriptions of acute pain
than control students. Students exposed to the curriculum more often
asked about the impact of pain on functioning (40.7\% vs. 25.8\%,
p=.027), advised changes of medication (97.3\% vs. 38.7\%,
p\textless{}.001), and provided additional medication counseling (55.0\%
vs. 27.0\%, p\textless{}.001).~However, the authors did not comment on
their curricula applications to OUD, exemplifying the scarcity of
integrating the two issues.

Educational interventions such as the one described generally lead to
enhancements in pain documentation, improvements in patient
self-reported pain scores, and pain satisfaction
levels\textsuperscript{203}. Regrettably, while individual studies and
resources exist for addressing pain and OUD separately, few have delved
into the co-occurrence of these two conditions. To our knowledge, the
scarcity of curricula considering both pain and OUD is pervasive
throughout health-care professions beyond medicine, as we also could not
find published examples in nursing, psychology, or social work
literature.

A significant obstacle to effective pain management in persons with OUD
is the prevailing stigma within healthcare systems and among individual
providers towards this patient population, impacting patient encounters
and treatment outcomes for chronic pain and OUD\textsuperscript{204,
205}. Concerning in the previously referenced study by Sobel and
colleagues is the early emergence of certain forms of stigma towards
patients with OUD at an early stage of medical
training\textsuperscript{193}. Brief educational interventions hold the
potential to significantly reduce stigmatizing beliefs, particularly
regarding OUD, among medical professionals\textsuperscript{201, 206}. By
addressing stigma through education, healthcare providers may adopt an
inclusive, patient-centered approach to evaluating and managing pain and
OUD, enhancing patient engagement and outcomes.

\textbf{7. FUTURE RESEARCH DIRECTIONS}

In striving towards a more comprehensive and patient-centered approach
to pain assessment, thereby improving pain treatment and fostering
improved quality of life and outcomes for individuals with pain and OUD,
we set out a brief research agenda for next steps on this topic.

First, validating pain scales tailored specifically to individuals with
OUD remains an essential undertaking. Understanding how opioid use
impacts pain perception is critical to developing accurate and reliable
pain assessment tools that consider the unique characteristics of this
population (including opioid-pain phenomena such as hyperalgesia,
tolerance, and withdrawal. An additional direction includes the use of
the data points obtained from these scales to inform patients and shared
decision-making regarding medication changes and dose increases, as well
as potentially quantifying the analgesic potential of each drug using
multimodal methods.

Validating these assessments would involve evaluating their content
validity to ensure they measure key pain domains relevant to this
population, and its criterion validity by correlating each tool's scores
against other pain measures. Construct validity would be assessed by
correlating scores with related factors like depression and disability.
Its responsiveness, or ability to detect changes over time or with
treatment, must also be analyzed. Finally, these tools should
demonstrate inter-rater reliability, ensuring consistent scores between
different raters, and test-retest reliability to assess score
consistency upon repeated administrations.

Second, assessing and validating various technologies and tools for the
assessment of pain (e.g., QST, EEG, fMRI) and how they may impact
clinical outcomes is needed. QST allows for a more precise evaluation of
sensory perception and pain responses. Functional MRI and EEG can offer
insights into the neural mechanisms underlying pain and chronic opioid
effects, and how they intersect in OUD, with varying degrees of spatial
and temporal resolution. Future research should investigate the
neurobiological mechanisms underlying pain modulation in OUD and explore
potential alterations in pain processing pathways, as these could inform
objective methods of pain assessment, as well as using these
technologies to expand the role of potential non-opioid analgesic
strategies\textsuperscript{207}. Integrating these cutting-edge
technologies into pain and OUD care may someday allow for
mechanistic-based treatment of pain rather than symptom-based
management, the current panorama.

Third, we highlight the necessity to advance pain assessment approaches
in minoritized populations. Historically biased approaches to pain
assessments among these populations have repeatedly resulted in worse
clinical outcomes\textsuperscript{208}. Recognizing the impact of social
stress and racism on the pain experience is essential to address
disparities in treatment outcomes. Future research should delve into the
social determinants of pain experiences, considering racism-related
stress, cultural factors, social support networks, and stigma. By
understanding these dynamics, healthcare professionals can develop
culturally sensitive pain interventions that acknowledge and respect the
diversity of experiences within these communities. The groundwork and
methodological considerations for the elaboration of anti-racist pain
research have been thoughtfully described in the three-part work by
Morais and colleagues\textsuperscript{208}.

Finally, the expansion of pain assessment education for healthcare
professionals and the use of multidisciplinary care for pain and
addiction management present promising avenues for future research.
Particularly refining the training of the professionals involved in the
assessment of pain among patients with OUD is as important as improving
the assessments themselves. Multidisciplinary education is necessary to
guarantee that the various forms of assessments discussed in this
manuscript are correctly and widely used.

\textbf{CONCLUSION}

Pain and OUD are complex clinical conditions with consequences that go
beyond the biomedical into psychological and societal realms. When
combined, the challenge of assessing and properly addressing pain in
persons with OUD is magnified, as their pathophysiology, signs, and
symptoms overlap and modify each other. In this review, we discussed how
pain is a multidimensional biopsychosocial entity, adding nuance to the
clinical presentation of OUD. Opioids are capable of modulating pain but
can also produce phenomena that challenge their assessment such as
hyperalgesia, tolerance, and withdrawal symptoms. For a substantial
proportion of individuals with OUD, pain and opioid use become
inexorably connected, thus, clinical, and experimental assessments of
pain deserve special considerations. Pain inventories, scales,
behavioral and physiological findings, as well as technology-based
assessments have to be considered carefully, as opioid use and its
phenomena reshape traditional assessment of pain in patients without
OUD.

Unfortunately, research considering how pain and OUD are interconnected
and how these assessments can be clinically used remains relatively
undeveloped. For many of the pain assessment methods discussed in this
review, there are few clinical trials exploring their applications for
persons with OUD. Furthermore, an upstream deficit exists in medical
education and multidisciplinary clinical approaches for the
co-management of pain and addiction, with very few programs in the
country discussing these combined issues. The ongoing opioid crisis
demands more than passive acknowledgment; it calls for proactive,
informed action. Beyond serving as a review of the available literature
on the topic, we present this paper as a call-to-action, as the gaps in
knowledge regarding pain assessment in patients with OUD are alarming.
As research linking these two areas evolves, considering the needs of
diverse populations with complex psychosocial backgrounds, and
understanding the role that such psychosocial variables may play in the
worsening of pain and OUD, we will be better equipped to reduce these
gaps. Given the profound overlap between chronic pain and OUD---and the
fact that the opioid epidemic's initial surge is closely tied to
inadequate treatment of chronic pain---it becomes clear: our united
commitment is essential. Together, pain and addiction clinicians and
scientists must strive to improve the assessment of pain in persons with
OUD, an important step to curtail the spiraling opioid crisis.

\textbf{TABLE 1.} Methods of pain assessment and considerations in
patients with opioid use disorder: an overview.\selectlanguage{english}
\begin{longtable}[]{@{}llllll@{}}
\toprule
\begin{minipage}[b]{0.16\columnwidth}\raggedright\strut
\textbf{Instrument}\strut
\end{minipage} & \begin{minipage}[b]{0.16\columnwidth}\raggedright\strut
\textbf{Description}\strut
\end{minipage} & \begin{minipage}[b]{0.16\columnwidth}\raggedright\strut
\textbf{Advantages}\strut
\end{minipage} & \begin{minipage}[b]{0.16\columnwidth}\raggedright\strut
\textbf{Disadvantages}\strut
\end{minipage} & \begin{minipage}[b]{0.16\columnwidth}\raggedright\strut
\textbf{Considerations in OUD}\strut
\end{minipage} & \begin{minipage}[b]{0.16\columnwidth}\raggedright\strut
\textbf{Study} \textbf{Examples}\strut
\end{minipage}\tabularnewline
\midrule
\endhead
\begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
\textbf{Self-Report Instruments}\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
\textbf{Self-Report Instruments}\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
\textbf{Self-Report Instruments}\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
\textbf{Self-Report Instruments}\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
\textbf{Self-Report Instruments}\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
\textbf{Self-Report Instruments}\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
\textbf{Visual Analogue Scale (VAS)} \textbf{1921}\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
A 10-cm line ranging from ''no pain to ''worst pain imaginable''. The
patient marks a point on the line to indicate their pain level\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
- Allows for a wide range of responses, and can be used for non-verbal
communication of pain - Sensitive to small
changes\textsuperscript{209}\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
- Relies on patients' subjective perception - Cannot be administered
verbally - Difficult conceptualization for some patients. -
Unidimensional\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
Subjective pain reporting may be affected by tolerance and
hyperalgesia\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
Muriel et al. (2023)\textsuperscript{104} Nielsen et al.
(2017)\textsuperscript{105} Veldman et al.
(2022)\textsuperscript{106}\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
\textbf{Numeric Rating Scale (NRS)} \textbf{1978}\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
Patients rate their pain on a scale from 0 (no pain) to 10 (worst
possible pain)\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
- Simple and easy to use - Can be performed verbally in telephone
interviews\textsuperscript{210}\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
- Relies on patients' subjective perception - May lack sensitivity due
to restricted range\textsuperscript{211} - Unidimensional\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
Restricted range may fail to capture nuances for patients with altered
pain perception\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
Latiff et al. (2021)\textsuperscript{114}\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
\textbf{Brief Pain} \textbf{Inventory (BPI)} \textbf{1982}\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
Questionnaire that measures both intensity of pain (sensory dimension)
and interference of pain (reactive dimension)\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
- Multidimensional - Assesses functionality impairments - Short and
simple format\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
- Longer to complete than the VAS or NRS\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
Useful for capturing multidimensional nature of pain in OUD patients,
including functional impairment\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
Hall et al. (2022)\textsuperscript{120}\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
\textbf{McGill Pain} \textbf{Questionnaire} \textbf{1999}\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
Questionnaire that measures multiple pain domains\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
- Multidimensional - Assesses impairments in function, mood, social
life, and sleep\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
- Longer to complete than the BPI\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
Useful for capturing details regarding the nature of pain in OUD
patients, beyond functional impairments\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
De Aquino et al. (2023)\textsuperscript{122} Latif et al.
(2019)\textsuperscript{123}\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
\textbf{Clinical, Psychophysical, Neurophysiological, and Neuroimaging
Techniques}\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
\textbf{Clinical, Psychophysical, Neurophysiological, and Neuroimaging
Techniques}\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
\textbf{Clinical, Psychophysical, Neurophysiological, and Neuroimaging
Techniques}\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
\textbf{Clinical, Psychophysical, Neurophysiological, and Neuroimaging
Techniques}\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
\textbf{Clinical, Psychophysical, Neurophysiological, and Neuroimaging
Techniques}\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
\textbf{Clinical, Psychophysical, Neurophysiological, and Neuroimaging
Techniques}\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
\textbf{Behavioral} \textbf{Observations}\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
Observable pain-related behaviors such as facial expressions, body
language, changes in interpersonal interactions and changes in activity
level as measured by actigraphy and pedometers\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
- Not self-reported - Useful for patients unable to communicate\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
- Behaviors can be ambiguous - Difficult to quantify - Prone to observer
bias\textsuperscript{126}\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
Opioid-induced hyperalgesia can complicate interpretation of behavioral
reactions to pain stimuli\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
Teeters et al. (2021)\textsuperscript{130} Salgado Garc\selectlanguage{ngerman}ía et al.
(2022)\textsuperscript{132} Lambert et al. (2022)\textsuperscript{133}
Bertz et al. (2019)\textsuperscript{134}\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
\textbf{Physiological} \textbf{Indicators}\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
Objective measures of bodily function such as heart rate, blood
pressure, and respiratory rate\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
- Not self-reported - Useful for patients unable to communicate\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
- Nonspecific - Many factors can influence physiological responses -
Equipment required\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
Opioid-induced physiological changes, and withdrawal may confound
interpretation\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
Roberts et al. (2022)\textsuperscript{145} Levin et al.
(2019)\textsuperscript{146}\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
\textbf{Quantitative} \textbf{Sensory Testing (QST)}\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
Series of standardized tests that quantify sensory experiences. Can use
heat, cold, mechanical, or pressure stimuli\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
- QST is useful for pain phenotyping, assessing threshold, tolerance,
habituation, and summation - Helpful to diagnose hyperalgesia\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
- Not yet optimized for daily clinical usual - Clinical usability data
is incipient\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
QST is a rising tool for assessing opioid phenomena, but its use is yet
limited to research settings\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
Prosser et al. (2018)\textsuperscript{152} Edwards et al.
(2011)\textsuperscript{156} Compton et al.
(2020)\textsuperscript{157}\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
\textbf{Functional} \textbf{MRI}\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
Brain imaging that captures changes in blood flow as a proxy for brain
activity.\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
- Can provide insight into which brain areas respond to certain stimuli
and tasks - May serve for validation of therapy responses\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
- Correlation of brain activation does not necessarily imply a causal
relationship - Less available, higher costs\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
Limited clinically relevant results for the understanding and treatment
of OUD in its current state. Findings may be used as biomarkers in the
future.\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
Faraj et al. (2021)\textsuperscript{164}\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
\textbf{Electroencephalogram (EEG)}\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
Technique using measurable electrical signals in the nervous system that
originate from a controlled stimulus\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
- Relatively cheap and easy to employ, readily available - Can be used
with other measures in a singular session (e.g., actigraphy)\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
- Does not provide the same spatial resolution and anatomical
localization as neuroimaging - Gathers superficial cortical electrical
activation, not inclusive of deeper brain structures.\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
Emerging use as a biomarker in OUD studies, for example as a predictor
of opioid analgesic response\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
Huhn et al. (2022)\textsuperscript{177}\strut
\end{minipage}\tabularnewline
\bottomrule
\end{longtable}



\textbf{REFERENCES}

1. Lucas, J.W., E.M. Connor, and J. Bose, \emph{Back, Lower Limb, and
Upper Limb Pain Among U.S. Adults, 2019.} NCHS Data Brief, 2021(415): p.
1-8.

2. Burgess, D.J., et al., \emph{Shifting the National Consciousness
about Pain Treatment: The Critical Need for a National Public Education
Campaign.} J Pain, 2021. \textbf{22} (10): p. 1129-1133.

3. Humphreys, K., et al., \emph{Responding to the opioid crisis in North
America and beyond: recommendations of the Stanford-Lancet
Commission.}Lancet, 2022. \textbf{399} (10324): p. 555-604.

4. Florence, C., F. Luo, and K. Rice, \emph{The economic burden of
opioid use disorder and fatal opioid overdose in the United States,
2017.} Drug Alcohol Depend, 2021. \textbf{218} : p. 108350.

5. Centers for Disease Control and Prevention. \emph{Multiple Cause of
Death} . 2021.

6. Centers for Disease Control and Prevention. \emph{Opioid Data
Analysis and Resources} . 2022; Available from:
https://www.cdc.gov/opioids/data/analysis-resources.html.

7. Morley, G., E. Briggs, and G. Chumbley, \emph{Nurses' Experiences of
Patients with Substance-Use Disorder in Pain: A Phenomenological
Study.}Pain Manag Nurs, 2015. \textbf{16} (5): p. 701-11.

8. Juurlink, D.N., \emph{Rethinking ''doing well'' on chronic opioid
therapy.} Cmaj, 2017. \textbf{189} (39): p. E1222-e1223.

9. Powell, V.D., et al., \emph{Evaluation of Buprenorphine Rotation in
Patients Receiving Long-term Opioids for Chronic Pain: A Systematic
Review.} JAMA Network Open, 2021. \textbf{4} (9): p. e2124152-e2124152.

10. Berna, C., et al., \emph{Opioid-Independent and Opioid-Mediated
Modes of Pain Modulation.} J Neurosci, 2018. \textbf{38} (42): p.
9047-9058.

11. Delorme, J., et al., \emph{Systematic Review and Meta-Analysis of
the Prevalence of Chronic Pain Among Patients With Opioid Use Disorder
and Receiving Opioid Substitution Therapy.} J Pain, 2023.\textbf{24}
(2): p. 192-203.

12. Barry, D.T., et al., \emph{Relations among psychopathology,
substance use, and physical pain experiences in methadone-maintained
patients.} J Clin Psychiatry, 2009. \textbf{70} (9): p. 1213-8.

13. Barry, D.T., et al., \emph{Pain and associated substance use among
opioid dependent individuals seeking office-based treatment with
buprenorphine-naloxone: a needs assessment study.} Am J Addict,
2013.\textbf{22} (3): p. 212-7.

14. Stein, M.D., et al., \emph{Chronic Pain and DepressionAmong Primary
Care Patients Treated with Buprenorphine.} Journal of General Internal
Medicine, 2015. \textbf{30} (7): p. 935-941.

15. Jamison, R.N., J. Kauffman, and N.P. Katz, \emph{Characteristics of
methadone maintenance patients with chronic pain.} J Pain Symptom
Manage, 2000. \textbf{19} (1): p. 53-62.

16. Basbaum, A.I., et al., \emph{Cellular and molecular mechanisms of
pain.} Cell, 2009. \textbf{139} (2): p. 267-84.

17. Jessell, T.M., E.R. Kandel, and J.H. Schwartz, \emph{Principles of
neural science} . 2000.

18. Lumley, M.A., et al., \emph{Pain and emotion: a biopsychosocial
review of recent research.} J Clin Psychol, 2011. \textbf{67} (9): p.
942-68.

19. De Aquino, J.P., et al., \emph{Pharmacological treatment of pain
among persons with opioid addiction: A systematic review and
meta-analysis with implications for drug development.} Addiction
Biology, 2021. \textbf{26} (4): p. e12964.

20. Cahill, C.M. and A.M.W. Taylor, \emph{Neuroinflammation---a
co-occurring phenomenon linking chronic pain and opioid
dependence.}Current Opinion in Behavioral Sciences, 2017. \textbf{13} :
p. 171-177.

21. Kathleen T. Brady, M.D., Ph.D. , and and Rajita Sinha,
Ph.D.,\emph{Co-Occurring Mental and Substance Use Disorders: The
Neurobiological Effects of Chronic Stress.} American Journal of
Psychiatry, 2005. \textbf{162} (8): p. 1483-1493.

22. Garland, E.L., et al., \emph{The downward spiral of chronic pain,
prescription opioid misuse, and addiction: cognitive, affective, and
neuropsychopharmacologic pathways.} Neurosci Biobehav Rev,
2013.\textbf{37} (10 Pt 2): p. 2597-607.

23. Ossipov, M.H., et al., \emph{Antinociceptive and nociceptive actions
of opioids.} J Neurobiol, 2004. \textbf{61} (1): p. 126-48.

24. Wachholtz, A., S. Foster, and M. Cheatle, \emph{Psychophysiology of
pain and opioid use: implications for managing pain in patients with an
opioid use disorder.} Drug Alcohol Depend, 2015. \textbf{146} : p. 1-6.

25. Berrocoso, E., et al., \emph{Opiates as antidepressants.} Curr Pharm
Des, 2009. \textbf{15} (14): p. 1612-22.

26. Mercadante, S., E. Arcuri, and A. Santoni, \emph{Opioid-Induced
Tolerance and Hyperalgesia.} CNS Drugs, 2019. \textbf{33} (10): p.
943-955.

27. Clark, D.J., \emph{Comment on: Doverty et al, Hyperalgesic responses
in methadone maintenance patients (Pain 2001;90;91-6).} Pain,
2002.\textbf{99} (3): p. 608-609.

28. Compton, P., et al., \emph{Hyperalgesia in heroin dependent patients
and the effects of opioid substitution therapy.} J Pain,
2012.\textbf{13} (4): p. 401-9.

29. Wachholtz, A. and G. Gonzalez, \emph{Pain sensitivity and tolerance
among individuals on opioid maintenance: Long-term effects.} Drug and
Alcohol Dependence, 2015. \textbf{146} : p. e14.

30. Athanasos, P., et al., \emph{Methadone maintenance patients are
cross-tolerant to the antinociceptive effects of very high plasma
morphine concentrations.} Pain, 2006. \textbf{120} (3): p. 267-275.

31. De Aquino, J.P., et al., \emph{Opioid-induced analgesia among
persons with opioid use disorder receiving methadone or buprenorphine: A
systematic review of experimental pain studies.} Drug Alcohol Depend,
2021. \textbf{228} : p. 109097.

32. van Tulder, M.W., et al., \emph{Behavioral treatment for chronic low
back pain: a systematic review within the framework of the Cochrane Back
Review Group.} Spine (Phila Pa 1976), 2000. \textbf{25} (20): p.
2688-99.

33. Smit, T., et al., \emph{Anxiety sensitivity and pain intensity
independently predict opioid misuse and dependence in chronic pain
patients.} Psychiatry Res, 2020. \textbf{294} : p. 113523.

34. Raja, S.N., et al., \emph{The revised International Association for
the Study of Pain definition of pain: concepts, challenges, and
compromises.} Pain, 2020. \textbf{161} (9): p. 1976-1982.

35. Jones, C.M. and E.F. McCance-Katz, \emph{Co-occurring substance use
and mental disorders among adults with opioid use disorder.} Drug
Alcohol Depend, 2019. \textbf{197} : p. 78-82.

36. Rizvi, S.J., W. Gandhi, and T. Salomons, \emph{Reward processing as
a common diathesis for chronic pain and depression.} Neurosci Biobehav
Rev, 2021. \textbf{127} : p. 749-760.

37. Pereira, F.G., et al., \emph{Prevalence and clinical profile of
chronic pain and its association with mental disorders.} Rev Saude
Publica, 2017. \textbf{51} : p. 96.

38. Morasco, B.J., et al., \emph{Systematic review of prevalence,
correlates, and treatment outcomes for chronic non-cancer pain in
patients with comorbid substance use disorder.} Pain, 2011.\textbf{152}
(3): p. 488-497.

39. Vowles, K.E., et al., \emph{Rates of opioid misuse, abuse, and
addiction in chronic pain: a systematic review and data synthesis.}Pain,
2015. \textbf{156} (4): p. 569-576.

40. Chaves, J.F. and J.M. Brown, \emph{Spontaneous cognitive strategies
for the control of clinical pain and stress.} J Behav Med,
1987.\textbf{10} (3): p. 263-76.

41. Quartana, P.J., C.M. Campbell, and R.R. Edwards, \emph{Pain
catastrophizing: a critical review.} Expert Rev Neurother,
2009.\textbf{9} (5): p. 745-58.

42. Vangronsveld, K.L.H., et al., \emph{The influence of fear of
movement and pain catastrophizing on daily pain and disability in
individuals with acute whiplash injury: a daily diary study.} Pain,
2008. \textbf{139} (2): p. 449-457.

43. Buitenhuis, J., et al., \emph{Catastrophizing and causal beliefs in
whiplash.} Spine (Phila Pa 1976), 2008. \textbf{33} (22): p. 2427-33;
discussion 2434.

44. Rivest, K., et al., \emph{Relationships between pain thresholds,
catastrophizing and gender in acute whiplash injury.} Man Ther,
2010.\textbf{15} (2): p. 154-9.

45. Jensen, I., et al., \emph{Coping with long-term musculoskeletal pain
and its consequences: is gender a factor?} Pain, 1994. \textbf{57} (2):
p. 167-172.

46. Larivi\selectlanguage{ngerman}ère, C., et al., \emph{Poor back muscle endurance is related
to pain catastrophizing in patients with chronic low back pain.} Spine
(Phila Pa 1976), 2010. \textbf{35} (22): p. E1178-86.

47. Kovacs, F.M., et al., \emph{The correlation between pain,
catastrophizing, and disability in subacute and chronic low back pain: a
study in the routine clinical practice of the Spanish National Health
Service.} Spine (Phila Pa 1976), 2011. \textbf{36} (4): p. 339-45.

48. Martinez-Calderon, J., et al., \emph{Pain Catastrophizing, Opioid
Misuse, Opioid Use, and Opioid Dose in People With Chronic
Musculoskeletal Pain: A Systematic Review.} J Pain, 2021.\textbf{22}
(8): p. 879-891.

49. MacLean, R.R., et al., \emph{Attentional bias in opioid users: A
systematic review and meta-analysis.} Drug Alcohol Depend,
2018.\textbf{191} : p. 270-278.

50. Garland, E.L., et al., \emph{Attentional bias for prescription
opioid cues among opioid dependent chronic pain patients.} J Behav Med,
2013. \textbf{36} (6): p. 611-20.

51. Field, M., R. Marhe, and I.H. Franken, \emph{The clinical relevance
of attentional bias in substance use disorders.} CNS Spectr,
2014.\textbf{19} (3): p. 225-30.

52. Gradin, V.B., et al., \emph{Abnormal brain activity during a reward
and loss task in opiate-dependent patients receiving methadone
maintenance therapy.} Neuropsychopharmacology, 2014. \textbf{39} (4): p.
885-94.

53. Zale, E.L., J.M. Powers, and J.W. Ditre, \emph{Cognitive-Affective
Transdiagnostic Factors Associated With Vulnerability to Alcohol and
Prescription Opioid Use in the Context of Pain.} Alcohol Res,
2021.\textbf{41} (1): p. 08.

54. Bach, P., et al., \emph{Effects of social exclusion and physical
pain in chronic opioid maintenance treatment: fMRI correlates.} Eur
Neuropsychopharmacol, 2019. \textbf{29} (2): p. 291-305.

55. Havassy, B.E., S.M. Hall, and D.A. Wasserman, \emph{Social support
and relapse: Commonalities among alcoholics, opiate users, and cigarette
smokers.} Addictive Behaviors, 1991. \textbf{16} (5): p. 235-246.

56. Zargar, F., et al., \emph{Effectiveness of Emotion Regulation Group
Therapy on Craving, Emotion Problems, and Marital Satisfaction in
Patients with Substance Use Disorders: A Randomized Clinical Trial.}Iran
J Psychiatry, 2019. \textbf{14} (4): p. 283-290.

57. Koob, G.F., \emph{Neurobiology of Opioid Addiction: Opponent
Process, Hyperkatifeia, and Negative Reinforcement.} Biol Psychiatry,
2020. \textbf{87} (1): p. 44-53.

58. Glenn, B. and J.W. Burns, \emph{Pain self-management in the process
and outcome of multidisciplinary treatment of chronic pain: evaluation
of a stage of change model.} J Behav Med, 2003. \textbf{26} (5): p.
417-33.

59. Martinson, A., J. Craner, and J. Clinton-Lont, \emph{Outcomes of a
6-week Cognitive-Behavioral Therapy for Chronic Pain Group for veterans
seen in primary care.} Transl Behav Med, 2020. \textbf{10} (1): p.
254-266.

60. Andrasik, F., H. Flor, and D.C. Turk, \emph{An expanded view of
psychological aspects in head pain: the biopsychosocial model.} Neurol
Sci, 2005. \textbf{26 Suppl 2} : p. s87-91.

61. Jensen, M.P., et al., \emph{Cognitions, coping and social
environment predict adjustment to phantom limb pain.} Pain,
2002.\textbf{95} (1-2): p. 133-42.

62. Manne, S.L. and A.J. Zautra, \emph{Spouse criticism and support:
their association with coping and psychological adjustment among women
with rheumatoid arthritis.} J Pers Soc Psychol, 1989. \textbf{56} (4):
p. 608-17.

63. Waltz, M., W. Kriegel, and P. van't Pad Bosch, \emph{The social
environment and health in rheumatoid arthritis: marital quality predicts
individual variability in pain severity.} Arthritis Care Res,
1998.\textbf{11} (5): p. 356-74.

64. Giardino, N.D., et al., \emph{Social environment moderates the
association between catastrophizing and pain among persons with a spinal
cord injury.} Pain, 2003. \textbf{106} (1-2): p. 19-25.

65. Matos, M., S.F. Bernardes, and L. Goubert, \emph{Why and when social
support predicts older adults' pain-related disability: a longitudinal
study.} Pain, 2017. \textbf{158} (10): p. 1915-1924.

66. Kumar, N., et al., \emph{The role of social network support in
treatment outcomes for medication for opioid use disorder: A systematic
review.} J Subst Abuse Treat, 2021. \textbf{127} : p. 108367.

67. Bryant, B.K., \emph{Assessment of social support, social network,
and social capital} , in \emph{The Cambridge handbook of environment in
human development.} 2012, Cambridge University Press: New York, NY, US.
p. 626-654.

68. Work, C.U.S.o.S., \emph{Social Support Network Map} .

69. Barnett, M.L., et al., \emph{Racial Inequality in Receipt of
Medications for Opioid Use Disorder.} N Engl J Med, 2023.\textbf{388}
(19): p. 1779-1789.

70. Friedman, J.R. and H. Hansen, \emph{Evaluation of Increases in Drug
Overdose Mortality Rates in the US by Race and Ethnicity Before and
During the COVID-19 Pandemic.} JAMA Psychiatry, 2022. \textbf{79} (4):
p. 379-381.

71. Boissoneault, J., J.R. Bunch, and M. Robinson, \emph{The roles of
ethnicity, sex, and parental pain modeling in rating of experienced and
imagined pain events.} J Behav Med, 2015. \textbf{38} (5): p. 809-16.

72. Gagnon, C.M., et al., \emph{Ethnicity and interdisciplinary pain
treatment.} Pain Pract, 2014. \textbf{14} (6): p. 532-40.

73. Trost, Z., et al., \emph{Examining Injustice Appraisals in a
Racially Diverse Sample of Individuals With Chronic Low Back Pain.} J
Pain, 2019. \textbf{20} (1): p. 83-96.

74. Campbell, C.M., R.R. Edwards, and R.B. Fillingim, \emph{Ethnic
differences in responses to multiple experimental pain stimuli.} Pain,
2005. \textbf{113} (1-2): p. 20-6.

75. Campbell, C.M., et al., \emph{Ethnic differences in diffuse noxious
inhibitory controls.} J Pain, 2008. \textbf{9} (8): p. 759-66.

76. Edwards, R.R., et al., \emph{Ethnic differences in pain tolerance:
clinical implications in a chronic pain population.} Psychosom Med,
2001. \textbf{63} (2): p. 316-23.

77. Kim, H.J., et al., \emph{Racial and ethnic differences in
experimental pain sensitivity: systematic review and
meta-analysis.}Pain, 2017. \textbf{158} (2): p. 194-211.

78. Mechlin, M.B., et al., \emph{African Americans show alterations in
endogenous pain regulatory mechanisms and reduced pain tolerance to
experimental pain procedures.} Psychosom Med, 2005. \textbf{67} (6): p.
948-56.

79. Rahim-Williams, F.B., et al., \emph{Ethnic identity predicts
experimental pain sensitivity in African Americans and Hispanics.} Pain,
2007. \textbf{129} (1-2): p. 177-84.

80. Sheffield, D., et al., \emph{Race and sex differences in cutaneous
pain perception.} Psychosom Med, 2000. \textbf{62} (4): p. 517-23.

81. Letzen, J.E., et al., \emph{Preliminary Evidence for the
Sequentially Mediated Effect of Racism-Related Stress on Pain
Sensitivity Through Sleep Disturbance and Corticolimbic Opioid Receptor
Function.} J Pain, 2023. \textbf{24} (1): p. 1-18.

82. Edens, E.L., I. Gafni, and J. Encandela, \emph{Addiction and Chronic
Pain: Training Addiction Psychiatrists.} Acad Psychiatry,
2016.\textbf{40} (3): p. 489-93.

83. Chang, G., L. Chen, and J. Mao, \emph{Opioid tolerance and
hyperalgesia.} Med Clin North Am, 2007. \textbf{91} (2): p. 199-211.

84. King, T., et al., \emph{Is paradoxical pain induced by sustained
opioid exposure an underlying mechanism of opioid antinociceptive
tolerance?} Neurosignals, 2005. \textbf{14} (4): p. 194-205.

85. DuPen, A., D. Shen, and M. Ersek, \emph{Mechanisms of opioid-induced
tolerance and hyperalgesia.} Pain Manag Nurs, 2007. \textbf{8} (3): p.
113-21.

86. Bannister, K., \emph{Opioid-induced hyperalgesia: where are we
now?}Curr Opin Support Palliat Care, 2015. \textbf{9} (2): p. 116-21.

87. Lee, M., et al., \emph{A comprehensive review of opioid-induced
hyperalgesia.} Pain Physician, 2011. \textbf{14} (2): p. 145-61.

88. Mao, J., \emph{Opioid-induced abnormal pain sensitivity.} Current
Pain and Headache Reports, 2006. \textbf{10} (1): p. 67-70.

89. Tzabazis, A.Z. and W. Koppert, \emph{Opioid-induced hyperalgesia or
opioid-withdrawal hyperalgesia?} European Journal of Anaesthesiology,
2007. \textbf{24} (9): p. 811-812.

90. Compton, P., P. Athanasos, and D. Elashoff, \emph{Withdrawal
hyperalgesia after acute opioid physical dependence in nonaddicted
humans: a preliminary study.} J Pain, 2003. \textbf{4} (9): p. 511-9.

91. Weber, L., D.C. Yeomans, and A. Tzabazis, \emph{Opioid-induced
hyperalgesia in clinical anesthesia practice: what has remained from
theoretical concepts and experimental studies?} Curr Opin Anaesthesiol,
2017. \textbf{30} (4): p. 458-465.

92. Wesson, D.R. and W. Ling, \emph{The Clinical Opiate Withdrawal Scale
(COWS).} J Psychoactive Drugs, 2003. \textbf{35} (2): p. 253-9.

93. Handelsman, L., et al., \emph{Two new rating scales for opiate
withdrawal.} Am J Drug Alcohol Abuse, 1987. \textbf{13} (3): p. 293-308.

94. Jha, M.K., et al., \emph{Cross-Sectional Associations Among Symptoms
of Pain, Irritability, and Depression and How These Symptoms Relate to
Social Functioning and Quality of Life: Findings From the EMBARC and
STRIDE Studies and the VitalSign6 Project.} J Clin Psychiatry,
2021.\textbf{82} (3).

95. Mullins, P.M., R.J. Yong, and N. Bhattacharyya, \emph{Associations
between chronic pain, anxiety, and depression among adults in the United
States.} Pain Pract, 2023. \textbf{23} (6): p. 589-594.

96. Goulart, L.I., R.N. Delgado Rodrigues, and M.F. Prieto
Peres,\emph{Restless legs syndrome and pain disorders: what's in
common?} Curr Pain Headache Rep, 2014. \textbf{18} (11): p. 461.

97. Modarresi, S., et al., \emph{A Systematic Review and Synthesis of
Psychometric Properties of the Numeric Pain Rating Scale and the Visual
Analog Scale for Use in People With Neck Pain.} Clin J Pain,
2021.\textbf{38} (2): p. 132-148.

98. Kapstad, H., B. Rokne, and K. Stavem, \emph{Psychometric properties
of the Brief Pain Inventory among patients with osteoarthritis
undergoing total hip replacement surgery.} Health Qual Life Outcomes,
2010. \textbf{8} : p. 148.

99. Kahl, C. and J.A. Cleland, \emph{Visual analogue scale, numeric pain
rating scale and the McGill pain Questionnaire: an overview of
psychometric properties.} Physical Therapy Reviews, 2005.\textbf{10}
(2): p. 123-128.

100. Hayes, M., \emph{Experimental development of the graphic rating
method.} Psychological Bulletin, 1921. \textbf{18} : p. 98-99.

101. Margeti\selectlanguage{polish}ć, \selectlanguage{english}B., et al., \emph{Depression, anxiety and pain in
children with juvenile idiopathic arthritis (JIA).} Eur Psychiatry,
2005. \textbf{20} (3): p. 274-6.

102. Cheatle, M.D., et al., \emph{Development of the Revised Opioid Risk
Tool to Predict Opioid Use Disorder in Patients with Chronic
Nonmalignant Pain.} J Pain, 2019. \textbf{20} (7): p. 842-851.

103. Hagglund, K.J., et al., \emph{Predicting pain among children with
juvenile rheumatoid arthritis.} Arthritis Care Res, 1995. \textbf{8}
(1): p. 36-42.

104. Muriel, J., et al., \emph{Impact of CYP2D6 genotype on opioid use
disorder deprescription: an observational prospective study in chronic
pain with sex-differences.} Front Pharmacol, 2023. \textbf{14} : p.
1200430.

105. Nielsen, R.V., et al., \emph{Intraoperative ketamine reduces
immediate postoperative opioid consumption after spinal fusion surgery
in chronic pain patients with opioid dependency: a randomized, blinded
trial.} Pain, 2017. \textbf{158} (3): p. 463-470.

106. Veldman, S., et al., \emph{Effects of opioid rotation to
buprenorphine/naloxone on pain, pain thresholds, pain tolerance, and
quality of life in patients with chronic pain and opioid use
disorder.}Pain, 2022. \textbf{163} (5): p. 955-963.

107. Boyett, B., et al., \emph{Assessment of craving in opioid use
disorder: Psychometric evaluation and predictive validity of the opioid
craving VAS.} Drug Alcohol Depend, 2021. \textbf{229} (Pt B): p. 109057.

108. Kleykamp, B.A., et al., \emph{Craving and opioid use disorder: A
scoping review.} Drug Alcohol Depend, 2019. \textbf{205} : p. 107639.

109. Downie, W.W., et al., \emph{Studies with pain rating scales.} Ann
Rheum Dis, 1978. \textbf{37} (4): p. 378-81.

110. Breivik, H., et al., \emph{Assessment of pain.} Br J Anaesth,
2008.\textbf{101} (1): p. 17-24.

111. Haefeli, M. and A. Elfering, \emph{Pain assessment.} Eur Spine J,
2006. \textbf{15 Suppl 1} (Suppl 1): p. S17-24.

112. Chiarotto, A., et al., \emph{Measurement Properties of Visual
Analogue Scale, Numeric Rating Scale, and Pain Severity Subscale of the
Brief Pain Inventory in Patients With Low Back Pain: A Systematic
Review.} J Pain, 2019. \textbf{20} (3): p. 245-263.

113. Bommersbach, T., D.A. Ross, and J.P. De Aquino, \emph{Perpetual
Hunger: The Neurobiological Consequences of Long-Term Opioid Use.} Biol
Psychiatry, 2020. \textbf{87} (1): p. e1-e3.

114. Latif, Z.H., et al., \emph{Chronic Pain Among Patients With an
Opioid Use Disorder.} Am J Addict, 2021. \textbf{30} (4): p. 366-375.

115. Daut, R.L., C.S. Cleeland, and R.C. Flanery, \emph{Development of
the Wisconsin Brief Pain Questionnaire to assess pain in cancer and
other diseases.} Pain, 1983. \textbf{17} (2): p. 197-210.

116. Poquet, N. and C. Lin, \emph{The Brief Pain Inventory (BPI).} J
Physiother, 2016. \textbf{62} (1): p. 52.

117. Cleeland, C.S. and K.M. Ryan, \emph{Pain assessment: global use of
the Brief Pain Inventory.} Ann Acad Med Singap, 1994. \textbf{23} (2):
p. 129-38.

118. Dworkin, R.H., et al., \emph{Core outcome measures for chronic pain
clinical trials: IMMPACT recommendations.} Pain, 2005.\textbf{113}
(1-2): p. 9-19.

119. Latremoliere, A. and C.J. Woolf, \emph{Central sensitization: a
generator of pain hypersensitivity by central neural plasticity.} J
Pain, 2009. \textbf{10} (9): p. 895-926.

120. Hall, O.T., et al., \emph{Central sensitization in opioid use
disorder: a novel application of the American College of Rheumatology
Fibromyalgia Survey Criteria.} Pain Rep, 2022. \textbf{7} (4): p. e1016.

121. Katz, J. and R. Melzack, \emph{MEASUREMENT OF PAIN.} Surgical
Clinics of North America, 1999. \textbf{79} (2): p. 231-252.

122. De Aquino, J.P., et
al.,\emph{Delta-9-tetrahydrocannabinol~modulates pain sensitivity among
persons receiving opioid agonist therapy for opioid use disorder: A
within-subject, randomized, placebo-controlled laboratory
study.}Addiction Biology, 2023. \textbf{28} (9): p. e13317.

123. Latif, Z.E., et al., \emph{No increased pain among opioid-dependent
individuals treated with extended-release naltrexone or
buprenorphine-naloxone: A 3-month randomized study and 9-month
open-treatment follow-up study.} Am J Addict, 2019. \textbf{28} (2): p.
77-85.

124. Arif-Rahu, M., D. Fisher, and Y. Matsuda, \emph{Biobehavioral
measures for pain in the pediatric patient.} Pain Manag Nurs,
2012.\textbf{13} (3): p. 157-68.

125. G\selectlanguage{ngerman}élinas, C., et al., \emph{The Behavior Pain Assessment Tool for
critically ill adults: a validation study in 28 countries.} Pain,
2017.\textbf{158} (5): p. 811-821.

126. Pande, R.K., \emph{Behavioral Pain Assessment Tool: Yet Another
Attempt to Measure Pain in Sedated and Ventilated Patients!} Indian J
Crit Care Med, 2020. \textbf{24} (8): p. 617-618.

127. Snow, A.L. and J.L. Shuster, Jr., \emph{Assessment and treatment of
persistent pain in persons with cognitive and communicative
impairment.}J Clin Psychol, 2006. \textbf{62} (11): p. 1379-87.

128. Morrison, R., et al., \emph{Racial/Ethnic Differences in
Staff-Assessed Pain Behaviors Among Newly Admitted Nursing Home
Residents.} J Pain Symptom Manage, 2021. \textbf{61} (3): p. 438-448.e3.

129. Bannon, S., et al., \emph{The role of social isolation in physical
and emotional outcomes among patients with chronic pain.} Gen Hosp
Psychiatry, 2021. \textbf{69} : p. 50-54.

130. Teeters, J.B., et al., \emph{Sleep moderates the relationship
between stress and craving in individuals with opioid use disorder.} Exp
Clin Psychopharmacol, 2021. \textbf{29} (4): p. 418-426.

131. Buysse, D.J., et al., \emph{The Pittsburgh Sleep Quality Index: a
new instrument for psychiatric practice and research.} Psychiatry Res,
1989. \textbf{28} (2): p. 193-213.

132. Salgado García, F.I., et al., \emph{Using wearable technology to
detect prescription opioid self-administration.} Pain, 2022.\textbf{163}
(2): p. e357-e367.

133. Lambert, T.P., et al., \emph{Leveraging Accelerometry as a
Prognostic Indicator for Increase in Opioid Withdrawal
Symptoms.}Biosensors (Basel), 2022. \textbf{12} (11).

134. Bertz, J.W., et al., \emph{Sleep reductions associated with illicit
opioid use and clinic-hour changes during opioid agonist treatment for
opioid dependence: Measurement by electronic diary and actigraphy.} J
Subst Abuse Treat, 2019. \textbf{106} : p. 43-57.

135. Worley, M.J., et al., \emph{Pain volatility and prescription opioid
addiction treatment outcomes in patients with chronic pain.} Exp Clin
Psychopharmacol, 2015. \textbf{23} (6): p. 428-35.

136. Erden, S., et al., \emph{Vital signs: Valid indicators to assess
pain in intensive care unit patients? An observational, descriptive
study.} Nurs Health Sci, 2018. \textbf{20} (4): p. 502-508.

137. Gunn, J., et al., \emph{An Analysis of Biomarkers in Patients with
Chronic Pain.} Pain Physician, 2020. \textbf{23} (1): p. E41-e49.

138. Korving, H., et al., \emph{Physiological Measures of Acute and
Chronic Pain within Different Subject Groups: A Systematic Review.} Pain
Res Manag, 2020. \textbf{2020} : p. 9249465.

139. Chen, H.J. and Y.M. Chen, \emph{Pain assessment: validation of the
physiologic indicators in the ventilated adult patient.} Pain Manag
Nurs, 2015. \textbf{16} (2): p. 105-11.

140. Relland, L.M., A. Gehred, and N.L. Maitre, \emph{Behavioral and
Physiological Signs for Pain Assessment in Preterm and Term Neonates
During a Nociception-Specific Response: A Systematic Review.} Pediatr
Neurol, 2019. \textbf{90} : p. 13-23.

141. Andersson, J.-O., et al., \emph{The intensity of pain in the
prehospital setting is most strongly reflected in the respiratory rate
among physiological parameters.} The American Journal of Emergency
Medicine, 2019. \textbf{37} (12): p. 2125-2131.

142. Takita, K., et al., \selectlanguage{greek}\emph{Actions of opioids on respiratory
activity via activation of brainstem μ-, δ- and κ-receptors; an in vitro
study.}\selectlanguage{english} Brain Research, 1997. \textbf{778} (1): p. 233-241.

143. Forte, G., et al., \emph{Heart Rate Variability and Pain: A
Systematic Review.} Brain Sci, 2022. \textbf{12} (2).

144. Cowan, M.J., \emph{Measurement of Heart Rate Variability.} Western
Journal of Nursing Research, 1995. \textbf{17} (1): p. 32-48.

145. Roberts, R.L. and E.L. Garland, \emph{Association between opioid
use disorder and blunted heart rate variability among opioid-treated
chronic pain patients.} Addict Biol, 2022. \textbf{27} (6): p. e13230.

146. Levin, C.J., et al., \emph{Changes in cardiac vagal tone as
measured by heart rate variability during naloxone-induced opioid
withdrawal.} Drug and Alcohol Dependence, 2019. \textbf{204} : p.
107538.

147. Chen, A. and M.A. Ashburn, \emph{Cardiac Effects of Opioid
Therapy.} Pain Medicine, 2015. \textbf{16} (suppl\_1): p. S27-S31.

148. Frew, A.K. and P.D. Drummond, \emph{Opposite effects of opioid
blockade on the blood pressure--pain relationship in depressed and
non-depressed participants.} Pain, 2009. \textbf{142} (1): p. 68-74.

149. M\selectlanguage{ngerman}ücke, M., et al., \emph{Quantitative sensory testing (QST).
English version.} Schmerz, 2021. \textbf{35} (Suppl 3): p. 153-160.

150. Marcuzzi, A., et al., \emph{The long-term reliability of static and
dynamic quantitative sensory testing in healthy individuals.} Pain,
2017. \textbf{158} (7): p. 1217-1223.

151. Mackey, I.G., et al., \emph{Dynamic Quantitative Sensory Testing to
Characterize Central Pain Processing.} J Vis Exp, 2017(120).

152. Prosser, J.M., et al., \emph{Abnormal heat and pain perception in
remitted heroin dependence months after detoxification from
methadone-maintenance.} Drug Alcohol Depend, 2008. \textbf{95} (3): p.
237-44.

153. Zahari, Z., et al., \emph{Relationship between CYP2B6*6 and cold
pressor pain sensitivity in opioid dependent patients on methadone
maintenance therapy (MMT).} Drug Alcohol Depend, 2016. \textbf{165} : p.
143-50.

154. Zahari, Z., et al., \emph{ABCB1 Polymorphisms and Cold Pressor Pain
Responses: Opioid-Dependent Patients on Methadone Maintenance
Therapy.}Nurs Res, 2017. \textbf{66} (2): p. 134-144.

155. Heddini, U., et al., \selectlanguage{greek}\emph{A118G polymorphism in the μ-opioid
receptor gene and levels of β-endorphin are associated with provoked
vestibulodynia and pressure pain sensitivity.}\selectlanguage{english} Scand J Pain,
2014.\textbf{5} (1): p. 10-16.

156. Edwards, R.R., et al., \emph{Elevated Pain Sensitivity in Chronic
Pain Patients at Risk for Opioid Misuse.} The Journal of Pain,
2011.\textbf{12} (9): p. 953-963.

157. Compton, P.A., T.E. Wasser, and M.D. Cheatle, \emph{Increased
Experimental Pain Sensitivity in Chronic Pain Patients Who Developed
Opioid Use Disorder.} Clin J Pain, 2020. \textbf{36} (9): p. 667-674.

158. Logothetis, N.K., \emph{What we can do and what we cannot do with
fMRI.} Nature, 2008. \textbf{453} (7197): p. 869-78.

159. Wager, T.D., et al., \emph{An fMRI-based neurologic signature of
physical pain.} N Engl J Med, 2013. \textbf{368} (15): p. 1388-97.

160. Fomberstein, K., S. Qadri, and R. Ramani, \emph{Functional MRI and
pain.} Curr Opin Anaesthesiol, 2013. \textbf{26} (5): p. 588-93.

161. Iannetti, G. and A.V. Apkarian, \emph{498499fMRI and Human Pain
Perception} , in \emph{The Oxford Handbook of the Neurobiology of Pain}
, J.N. Wood, Editor. 2020, Oxford University Press. p. 0.

162. McConnell, P.A., et al., \emph{Impaired frontostriatal functional
connectivity among chronic opioid using pain patients is associated with
dysregulated affect.} Addict Biol, 2020. \textbf{25} (2): p. e12743.

163. Wey, H.-Y., et al., \emph{Simultaneous fMRI--PET of the opioidergic
pain system in human brain.} NeuroImage, 2014. \textbf{102} : p.
275-282.

164. Faraj, M.M., et al., \emph{A Virtual Reality Meditative
Intervention Modulates Pain and the Pain Neuromatrix in Patients with
Opioid Use Disorder.} Pain Med, 2021. \textbf{22} (11): p. 2739-2753.

165. Dowdle, L.T., et al., \emph{Sensitized brain response to acute pain
in patients using prescription opiates for chronic pain: A pilot
study.}Drug Alcohol Depend, 2019. \textbf{200} : p. 6-13.

166. Pinheiro, E.S., et al., \emph{Electroencephalographic Patterns in
Chronic Pain: A Systematic Review of the Literature.} PLoS One,
2016.\textbf{11} (2): p. e0149085.

167. Ploner, M. and E.S. May, \emph{Electroencephalography and
magnetoencephalography in pain research-current state and future
perspectives.} Pain, 2018. \textbf{159} (2): p. 206-211.

168. Ploner, M., C. Sorg, and J. Gross, \emph{Brain Rhythms of
Pain.}Trends Cogn Sci, 2017. \textbf{21} (2): p. 100-110.

169. Davis, K.D., et al., \emph{Discovery and validation of biomarkers
to aid the development of safe and effective pain therapeutics:
challenges and opportunities.} Nature reviews. Neurology,
2020.\textbf{16} (7): p. 381-400.

170. Corace, K., et al., \emph{Resting State EEG Activity Related to
Impulsivity in People with Prescription Opioid Use Disorder.} Psychiatry
Res Neuroimaging, 2022. \textbf{321} : p. 111447.

171. Hudak, J., et al., \emph{Neurophysiological Deficits During
Reappraisal of Negative Emotional Stimuli in Opioid Misuse.} Biol
Psychiatry, 2022. \textbf{91} (12): p. 1070-1078.

172. Miller, D.K., et al., \emph{Acute Intravenous Synaptamine Complex
Variant KB220 ``Normalizes'' Neurological Dysregulation in Patients
during Protracted Abstinence from Alcohol and Opiates as Observed Using
Quantitative Electroencephalographic and Genetic Analysis for Reward
Polymorphisms: Part 1, Pilot Study with 2 Case Reports.} Postgraduate
Medicine, 2010. \textbf{122} (6): p. 188-213.

173. Koyama, S., et al., \emph{An Electroencephalography Bioassay for
Preclinical Testing of Analgesic Efficacy.} Scientific Reports,
2018.\textbf{8} (1): p. 16402.

174. Brooks Holliday, S., et al., \emph{Do Sleep and Psychological
Distress Mediate the Association Between Neighborhood Factors and
Pain?}Pain Med, 2019. \textbf{20} (2): p. 278-289.

175. Lewis, S.A., et al., \emph{Heroin and human
sleep.}Electroencephalogr Clin Neurophysiol, 1970. \textbf{28} (4): p.
374-81.

176. Dimsdale, J.E., et al., \emph{The effect of opioids on sleep
architecture.} J Clin Sleep Med, 2007. \textbf{3} (1): p. 33-6.

177. Huhn, A.S., et al., \emph{Suvorexant ameliorated sleep disturbance,
opioid withdrawal, and craving during a buprenorphine taper.} Sci Transl
Med, 2022. \textbf{14} (650): p. eabn8238.

178. Danilov, A., et al., \emph{Interdisciplinary approach to chronic
pain management.} Postgrad Med, 2020. \textbf{132} (sup3): p. 5-9.

179. Ghafouri, N., et al., \emph{Effects of interdisciplinary pain
rehabilitation programs on neuropathic and non-neuropathic chronic pain
conditions - a registry-based cohort study from Swedish Quality Registry
for Pain Rehabilitation (SQRP).} BMC Musculoskelet Disord,
2023.\textbf{24} (1): p. 357.

180. Goldenberg, D.L., \emph{Multidisciplinary modalities in the
treatment of fibromyalgia.} J Clin Psychiatry, 2008. \textbf{69 Suppl 2}
: p. 30-4.

181. Scascighini, L., et al., \emph{Multidisciplinary treatment for
chronic pain: a systematic review of interventions and
outcomes.}Rheumatology (Oxford), 2008. \textbf{47} (5): p. 670-8.

182. Sarzi-Puttini, P., et al., \emph{Multidisciplinary approach to
fibromyalgia: what is the teaching?} Best Pract Res Clin Rheumatol,
2011. \textbf{25} (2): p. 311-9.

183. Becker, W.C., et al., \emph{Management of patients with issues
related to opioid safety, efficacy and/or misuse: a case series from an
integrated, interdisciplinary clinic.} Addict Sci Clin Pract,
2016.\textbf{11} (1): p. 3.

184. Kwon, E., et al., \emph{A Comprehensive, Multimodal,
Interdisciplinary Approach to Chronic Non-Cancer Pain Management in a
Family Medicine Clinic: A Retrospective Cohort Review.} Perm J,
2021.\textbf{25} .

185. Dobscha, S.K., et al., \emph{Collaborative care for chronic pain in
primary care: a cluster randomized trial.} Jama, 2009. \textbf{301}
(12): p. 1242-52.

186. Mailis, A., A. Deshpande, and S.F. Lakha, \emph{Long term outcomes
of chronic pain patients attending a publicly funded community-based
interdisciplinary pain program in the Greater Toronto area: results of a
practice-based audit.} J Patient Rep Outcomes, 2022. \textbf{6} (1): p.
44.

187. Purcell, N., et al., \emph{The Integrated Pain Team: A
Mixed-Methods Evaluation of the Impact of an Embedded Interdisciplinary
Pain Care Intervention on Primary Care Team Satisfaction, Confidence,
and Perceptions of Care Effectiveness.} Pain Med, 2018. \textbf{19} (9):
p. 1748-1763.

188. DiMeola, K.A., et al., \emph{A Pilot Investigation of
Nonpharmacological Pain Management Intervention Groups in Methadone
Maintenance Treatment.} J Addict Med, 2022. \textbf{16} (2): p. 229-234.

189. Barry, D.T., et al., \emph{The feasibility and acceptability of
groups for pain management in methadone maintenance treatment.} J Addict
Med, 2014. \textbf{8} (5): p. 338-44.

190. Edmond, S.N., et al., \emph{Optimizing Interdisciplinary Virtual
Pain Care and Buprenorphine Initiation During COVID-19: A Quality
Improvement Study.} Pain Med, 2022. \textbf{23} (6): p. 1043-1046.

191. Mardian, A.S., et al., \emph{Flipping the hidden curriculum to
transform pain education and culture.} Frontiers in Pain Research,
2023.\textbf{4} .

192. Tellier, P.P., et al., \emph{Improving undergraduate medical
education about pain assessment and management: a qualitative
descriptive study of stakeholders' perceptions.} Pain Res Manag,
2013.\textbf{18} (5): p. 259-65.

193. Sobel, H.G., et al., \emph{Assessment of an observed clinical
skills exam in undergraduate medical education: a patient with opioid
use disorder and chronic pain.} J Addict Dis, 2023: p. 1-6.

194. Gillan, C., et al., \emph{The evaluation of learner outcomes in
interprofessional continuing education: a literature review and an
analysis of survey instruments.} Med Teach, 2011. \textbf{33} (9): p.
e461-70.

195. Muzyk, A., et al., \emph{Substance Use Disorder Education in
Medical Schools: A Scoping Review.} Acad Med, 2019. \textbf{94} (11): p.
1825-1834.

196. Shipton, E.E., et al., \emph{Systematic Review of Pain Medicine
Content, Teaching, and Assessment in Medical School Curricula
Internationally.} Pain Ther, 2018. \textbf{7} (2): p. 139-161.

197. Hern\selectlanguage{ngerman}ández-Sánchez, S., et al., \emph{Dealing with Chronic Pain:
Tips to Teach Undergraduate Health Sciences Students.} J Pain Res,
2022.\textbf{15} : p. 3223-3232.

198. Shipton, E., C. Steketee, and E. Visser, \emph{The Pain Medicine
Curriculum Framework-structured integration of pain medicine education
into the medical curriculum.} Front Pain Res (Lausanne), 2022.\textbf{3}
: p. 1057114.

199. Tookes, H.E., et al., \emph{Opioid Use Disorder Curriculum:
Medicine Clerkship Standardized Patient Case, Small-Group Activity, and
Patient Panel.} MedEdPORTAL, 2022. \textbf{18} : p. 11248.

200. Vargovich, A.M., et al., \emph{Difficult Conversations: Training
Medical Students to Assess, Educate, and Treat the Patient with Chronic
Pain.} Acad Psychiatry, 2019. \textbf{43} (5): p. 494-498.

201. Renner, J.A., Jr., \emph{Counteracting the Effect of Stigma on
Education for Substance Use Disorders.} Focus (Am Psychiatr Publ),
2019.\textbf{17} (2): p. 134-140.

202. Stevens, D.L., et al., \emph{Medical students retain pain
assessment and management skills long after an experiential curriculum:
a controlled study.} Pain, 2009. \textbf{145} (3): p. 319-324.

203. Drake, G. and C.W.A.C. de, \emph{Nursing Education Interventions
for Managing Acute Pain in Hospital Settings: A Systematic Review of
Clinical Outcomes and Teaching Methods.} Pain Manag Nurs,
2017.\textbf{18} (1): p. 3-15.

204. Bean, D.J., et al., \emph{The Determinants and Effects of Chronic
Pain Stigma: A Mixed Methods Study and the Development of a Model.} J
Pain, 2022. \textbf{23} (10): p. 1749-1764.

205. Earnshaw, V.A., \emph{Stigma and substance use disorders: A
clinical, research, and advocacy agenda.} Am Psychol, 2020.\textbf{75}
(9): p. 1300-1311.

206. Countey, H., C. Steinbronn, and S.E. Grady, \emph{Changing student
attitudes and perceptions toward opioid use disorder.} Ment Health Clin,
2018. \textbf{8} (5): p. 222-226.

207. Oliveira, D., et al., \emph{Developing non-opioid therapeutics to
alleviate pain among persons with opioid use disorder: a review of the
human evidence.} International Review of Psychiatry, 2023: p. 1-20.

208. Morais, C.A., et al., \emph{Confronting Racism in Pain Research: A
Call to Action.} J Pain, 2022. \textbf{23} (6): p. 878-892.

209. Kersten, P., P.J. White, and A. Tennant, \emph{Is the pain visual
analogue scale linear and responsive to change? An exploration using
Rasch analysis.} PLoS One, 2014. \textbf{9} (6): p. e99485.

210. Von Korff, M., M.P. Jensen, and P. Karoly, \emph{Assessing global
pain severity by self-report in clinical and health services
research.}Spine (Phila Pa 1976), 2000. \textbf{25} (24): p. 3140-51.

211. van Dijk, J.F., et al., \emph{The diagnostic value of the numeric
pain rating scale in older postoperative patients.} J Clin Nurs,
2012.\textbf{21} (21-22): p. 3018-24.



\textbf{FIGURE LEGENDS}

\textbf{Figure 1.} Biopsychosocial considerations in the assessment of
pain for persons with opioid use disorder.

Pain and opioid use disorder are multidimensional entities.
Biologically, ascending pain pathways carry neural signals from the
spine and periphery to the corticolimbic system, which in turn, through
descending pathways, modulate the actual physical and emotional
experience of pain. Opioids may disrupt such pathways, increasing pain
perception (hyperalgesia) and hindering modulatory input. Psychological
aspects may alter these perceptions and lead to behaviors and thoughts
patterns which in turn, can worsen or improve that experience. Finally,
social support networks are a cornerstone of this assessment, as support
can improve or worsen outcomes for both OUD and pain treatment. It is
also important to acknowledge the role that healthcare disparities may
play both in the perception of pain as well as on its treatment.

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\textbf{Figure 2.} Opioid-induced hyperalgesia, tolerance, and
withdrawal as important considerations for pain assessment in opioid use
disorder.

The illustrated table describes the hallmarks to differentiate
opioid-related phenomena. Patients with hyperalgesia tend to experience
pain symptoms that are different than their original presentation,
worsening with increased opioid use. Tolerance leads to worsening pain
due to desensitization, but the pain is often similar to the initial
symptom and tends to improve with additional opioids. Finally, those
patients experiencing withdrawal develop certain signs and symptoms
otherwise described in opioid withdrawal assessment instruments, such as
the Clinical Opioid Withdrawal Scale (COWS).\selectlanguage{english}
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