Skip to main content
NCBI home page
NIHPA Author Manuscripts logoLink to NIHPA Author Manuscripts
. Author manuscript; available in PMC: 2021 Nov 1.
Published in final edited form as: Drug Alcohol Depend. 2020 Aug 18;216:108242. doi: 10.1016/j.drugalcdep.2020.108242

WITHDRAWAL-ASSOCIATED INJURY SITE PAIN PREVALENCE AND CORRELATES AMONG OPIOID-USING PEOPLE WHO INJECT DRUGS IN VANCOUVER, CANADA

Launette Marie Rieb a, Kora DeBeck b,c, Kanna Hayashi c,d, Evan Wood c,e, Ekaterina Nosova c, M-J Milloy c,e,*
PMCID: PMC7850369  NIHMSID: NIHMS1663408  PMID: 32861135

Abstract

Background

Pain can return temporarily to old injury sites during opioid withdrawal. The prevalence and impact of opioid withdrawal-associated injury site pain (WISP) in various groups is unknown.

Methods

Using data from observational cohorts, we estimated the prevalence and correlates of WISP among opioid-using people who inject drugs (PWID). Between June and December 2015, data on WISP and opioid use behaviours were elicited from participants in three ongoing prospective cohort studies in Vancouver, Canada, who were aged 18 years and older and who self-reported at least daily injection of heroin or non-medical presciption opioids.

Results

Among 631 individuals, 276 (43.7 %) had a healed injury (usually pain-free), among whom 112 (40.6 %) experienced WISP, representing 17.7 % of opioid-using PWID interviewed. In a multivariable logistic regression model, WISP was positively associated with having a high school diploma or above (Adjusted Odds Ratio [AOR] = 2.23, 95 % Confidence Interval [CI]: 1.31–3.84), any heroin use in the last six months (AOR = 2.00, 95 % CI: 1.14–3.57), feeling daily pain that required medication (AOR = 2.06, CI: 1.18–3.63), and negatively associated with older age at first drug use (AOR = 0.96, 95 % CI: 0.93–0.99). Among 112 individuals with WISP, 79 (70.5 %) said that having this pain affected their opioid use behaviour, of whom 57 (72.2 %) used more opioids, 19 (24.1 %) avoided opioid withdrawal, while 3 (3.8 %) no longer used opioids to avoid WISP.

Conclusions

WISP is prevalent among PWID with a previous injury, and may alter opioid use patterns. Improved care strategies for WISP are warrented.

Keywords: Pain, Substance withdrawal syndrome, Opioid, Opioid dependence, Hyperalgesia, Observational cohort study

1. INTRODUCTION

According to the United Nations Office on Drugs and Crime, opioid use disorder (OUD) contributes to a growing burden of morbidity and mortality globally, especially among individuals who inject illicit opioids (UNODC, 2020). Among people with OUD, pain is a common comorbidity, has been identified as a risk factor for continued non-medical opioid use, and may impact treatment prognosis (Becker et al., 2009; Dennis et al., 2015; Edwards et al., 2011; Groenewald et al., 2019; LeBlanc et al., 2015; Rosenblum et al., 2003; Voon et al., 2018). However, few studies have delineated the components of pain that may contribute to the overall pain experience and influence opioid use behaviour among people who inject drugs (PWID). Abnormal increased pain sensitivity, also termed opioid-induced hyperalgesia, has been shown to be common in those with OUD as well as those with chronic noncancer pain on long-term opioid medication (Arout et al., 2015; Compton et al., 2012; Lee et al., 2011; Mao, 2002; Rivat and Ballantyne, 2016). Once opioid use is abruptly stopped, the underlying pronociceptive forces are further revealed and combine with the effect of catacholamine release to result in withdrawal-induced hyperalgesia that can take weeks or months to resolve (Celerier et al., 2001; Prosser et al., 2008; Treister et al., 2012).

In addition to the generalized myalgias and arthralgias, we described in a previous mixed methods study how even healed and typically pain-free injury sites can temporarily become recurrently painful during opioid withdrawal (Rieb et al., 2016). To improve diagnosis and care, we named the phenomenon withdrawal-associated injury site pain (WISP). In that study, 44 % of participants with WISP reported that this emotionally-aversive pain experience caused them to relapse to opioid use. Some participants reported neuropathic features of WISP, including shooting pain and sensitivity to touch (allodynia). We suggested a variety of possible causal mechanisms, for example, linking WISP with central sensitization, opioid-induced hyperalgesia and withdrawal-induced hyperalgesia (Hutchinson et al., 2007; Rieb et al., 2016; Rivat and Ballantyne, 2016; Woolf, 2011). We later published a case report that described these features (Rieb et al., 2018). Non-opioid medications that have previously been identified to help relieve WISP include gabapentin and non-steroidal anti-inflammatories (NSAIDS) (Rieb et al., 2018, 2016). However, the prevalence and clinical significance of WISP in various populations have yet to be reported. To address this knowledge gap, we undertook a study of WISP among a community-recruited sample of opioid-using PWID to determine its prevalence, its self-reported impacts on opioid use behaviours, and estimate its relationship with relevant sociodemographic, structural, clinical, and behavioral factors.

2. MATERIALS AND METHODS

2.1. Cohorts and participants

The current analyses used data from three ongoing prospective cohort studies involving people who use illicit drugs in Vancouver, Canada: The Vancouver Injection Drug Users Study (VIDUS), the At-Risk Youth Study (ARYS), and the AIDS Care Cohort to evaluate Exposure to Survival Services (ACCESS). The participants in these cohorts have been recruited through self-referral, word-of-mouth, and street outreach. These cohorts, including their specific eligibility criteria, have been described in detail previously (Milloy et al., 2016; Strathdee et al., 1997; Wood, 2006). In brief, VIDUS consists of individuals aged at least 18 years who injected drugs ≥ 1 time in the 30 days prior to recruitment, and were HIV-negative at the time of enrollment. ACCESS is a cohort of HIV-positive adults who have recently used an illicit drug other than or in addition to cannabis (which was illegal during the study period) in the month prior to enrollment. Similarly, ARYS consists of street-involved youth aged between 14 and 26 years at baseline who have used illicit drugs other than or in addition to cannabis in the month prior to enrollment. In addition, all individuals must have resided in the greater Vancouver region and provided written informed consent to be eligible for the study.

2.2. Questionnaire

At baseline and semi-annually, participants completed a harmonized interviewer-administered questionnaire that elicited information on socio-demographic characteristics, drug use patterns, involvement in addiction treatment, and other relevant exposures and outcomes. Additionally, at each study visit, participants provided blood samples for HIV and HCV serologic testing and HIV disease monitoring, as appropriate. Participants were compensated for each study visit ($30 CDN). The VIDUS, ACCESS, and ARYS studies were approved by the University of British Columbia/ Providence Health Care Research Ethics Board.

Our original mixed methods study on WISP explored in detail multiple aspects of the WISP phenomenon and allowed participants to freely express the experience in their own words. In the current study, we employed structured questions to investigate the objectives, i.e., estimate the prevalence of the WISP phenomenon and its self-reported relationship to illicit drug use patterns. For the current study, questions on the presence of WISP were added to these cohort questionnaires and administered by trained research nurses in one-on-one interviews (Fig. 1). These questions were based on findings from our previous study (Rieb et al., 2016). The questions were vetted for meaning and clarity by a focus group of the research nurses who administer the pain-related section of the questionnaire. We chose to screen out people with only minor injuries since in our previous study most of the people reporting WISP had fractures or other significant trauma (Rieb et al., 2016).

Figure 1.

Figure 1.

Open in a new tab

WISP-specific screening questions

2.3. Inclusion criteria

For the current study, we included all adult (i.e., 18 years old and older) participants interviewed between June 1 and November 30, 2015, who had, at their baseline interview, reported ever injecting drugs, and reported injecting heroin or illicit prescription opioids at least daily during the study period or in the past.

2.4. Explanatory variables

To better understand WISP, we developed a set of socio-demographic, structural, clinical, and behavioral explanatory variables we hypothesized might be correlated to the phenomenon of WISP. For example, homelessness can affect mental health and nutritional status both of which can influence pain perception. Similarly, sex differences in pain thresholds and tolerance exist so we added this variable to see if it was important for WISP. Another example of variable choice rationale is the inclusion of speedballs (co-injection of heroin and cocaine) since catecholamines can influence pain perception. The full list of variables chosen is as follows: age (continuous, per year older); sex (male vs. female); ethnicity/ancestry (white vs. nonwhite); homelessness, defined as having no fixed address, sleeping on the street, couch surfing, or staying in a shelter or hostel (yes vs. no); education (high school diploma or above vs. less than high school diploma); regular employment, defined as having a regular job, temporary work, or being self-employed (yes vs. no); and HIV status (positive vs. negative). Drug use-related variables were the following: any injection or non-injection heroin use (yes vs. no); injection speedball (i.e., a mixture of heroin and cocaine) use (yes vs. no); injection morphine use (yes vs. no); age of first illicit drug injection (continuous, per year older); and previous non-fatal overdose (yes vs. no). Other explanatory variables included: ever diagnosed with mental health illness (yes vs. no); having depressive symptoms, defined as a Centre for Epidemiologic Studies Depression (CES-D) summed score of ≥16 at baseline. Pain-related variables included: having any major or persistent pain (other than minor headaches, sprains, etc.) (yes vs. no); feeling some form of pain that required any pain medication (illicit or prescribed, opioid or non-opioid) each and every day (yes vs. no); ever been diagnosed with chronic pain (yes vs. no); ever been diagnosed with chronic neuropathic pain (yes vs. no); ever been diagnosed with chronic inflammatory pain (yes vs. no); ever been diagnosed with chronic muscle pain (yes vs. no); ever been diagnosed with chronic bone / mechanical / compressive pain (yes vs. no); ever been diagnosed with chronic headache / migraine (yes vs. no); taking any drugs (illicit or prescribed) for pain (yes vs. no); taking gabapentin and/or non-steroidal anti-inflammatories for pain (yes vs. no); managing pain on their own (yes vs. no); and for the sub-analysis (Table 3) of individuals, we included additional explanatory variable which measured whether individuals have requested or continued a prescription for any pain medication (yes vs. no).

Table 3.

Bivariable and multivariable Logistic regression analysis of factors associated with pain impacting the use of heroin and other opioids among people who experienced the temporary return of pain to their injury site during opioid withdrawal (n = 112).

Unadjusted Adjusted
Characteristic Odds ratio (95 % CI) p value Odds ratio (95 % CI) p value
Age, per year 0.99 (0.95–1.02) 0.545
Sex, male 1.68 (0.71–3.95) 0.232
White race 0.73 (0.31–1.70) 0.477
Homelessnessb 0.79 (0.04–6.45) 0.842
High school completion or higher 0.74 (0.31–1.69) 0.481
Any employmenta 0.97 (0.39–2.49) 0.940
Any heroin useda 2.50 (1.05–5.99) 0.039
Injection speedball (heroin and cocaine) usea 4.11 (0.73–77.48) 0.188
Injection morphine usea 1.43 (0.53–4.27) 0.498
Non-fatal overdoseb 0.70 (0.25–1.78) 0.466
Age when first illicit drug injected, per 10 years older 1.04 (0.60–1.89) 0.903
Depression score equal or over 16 on CES-D 1.30 (0.44–3.62) 0.627
HIV seropositivitya 2.00 (0.77–5.92) 0.177
Been diagnosed with a mental illnessb 0.72 (0.27–1.78) 0.489
Been on methadone treatmentb 1.03 (0.21–3.98) 0.969
Taking gabapentin and/or non-steroidal anti-inflammatoriesa 0.97 (0.25–4.74) 0.969
Have requested or continued a prescription for pain medicationa 2.96 (1.20–8.11) 0.025 2.91 (1.02–9.26) 0.055
Managed pain on your owna 1.08 (0.40–2.76) 0.868
Diagnosed with a chronic neuropathic painb 0.29 (0.10–0.80) 0.017 0.25 (0.08–0.72) 0.012
Diagnosed with a chronic inflammatory painb 0.50 (0.16–1.40) 0.203
Diagnosed with a chronic muscle painb 1.22 (0.33–5.94) 0.778
Diagnosed with a chronic bone / mechanical / compressive painb 2.21 (0.79–6.11) 0.126
Diagnosed with a chronic headache / migraineb 2.54 (0.40–49.43) 0.401
Open in a new tab
a

During the last six months

b

Ever experienced

All variables except for age, age of first illicit drug injection, sex, education, HIV status, and ethnicity/ancestry referred to the past six months, except if indicated otherwise.

2.5. Analysis

First, we compared characteristics of participants with an old healed injury that was typically pain free stratified by whether or not they had WISP using the Pearson’s χ2 test for binary explanatory variables and the Wilcoxon rank-sum test for continuous explanatory variables.

Second, we estimated the relationships between WISP with the socio-demographic, clinical, structural and behavioral variables defined above using bivariate and multivariate logistic regression analysis. For the multivariable models, we used an a priori-defined backward model selection procedure based on examination of Akaike Information Criterion (AIC) to fit a multivariable model (Burnham, 2002). In brief, we constructed a full model including all variables that were associated with the outcome at p < 0.10 in bivariable analyses. After examining the AIC of the model, we removed the variable with the largest p-value and built a reduced model. We continued this iterative process until we reached the lowest AIC score.

Third, among participants with confirmed WISP, we investigated whether or not they identified that WISP impacted the use of heroin and other opioids using descriptive statistics and logistic regression analyses.

Fourth, we assessed the association between neuropathic pain and explanatory variables among individuals who had a previous injury but did not develop WISP using logistic regression analyses.

Fifth, we looked at the correlation between pain-related variables using mean square contingency coefficient (or ɸ coefficient). We used the full sample of individuals to analyze correlation between the following variables: 1) Any major or persistent pain in the last six months; 2) Taken any drugs for your pain in the last six months; 3) Felt daily some form of pain that required medication in the last six months; 4) Have requested or continued a prescription for pain medication in the last six months; 5) Managed pain on your own in the last six months; and 6) Ever been diagnosed with a chronic pain condition. We also ran the same analysis using the sample restricted to individuals who reported ever been diagnosed with a chronic pain condition.

All p-values were two-sided. All statistical analyses were performed using R, version 3.5.0 (R Foundation for Statistical Computing, Vienna, Austria).

3. RESULTS

3.1. Prevalence of WISP

Between June 1, 2015 and November 30, 2015, 1298 PWID were recruited and completed a study interview, of whom 631 (48.6 %) reported at least daily injection of opioids during the study or in the past and were included in these analyses. Among the 631 PWID, 276 participants (43.7 %) reported that they had an injury that was healed and usually pain-free. Among this analytic sample of 276 PWIDs the median age was 48 years (Interquartile Range [IQR]: 37–54), 190 (68.8 %) self-identified as male, and 168 (60.9 %) reported white ethnicity. Among these individuals 112 (40.6 %) experienced the temporary return of pain to their injury site during opioid withdrawal (thus had WISP) representing 17.7 % of all opioid-using PWID included in these analyses.

Table 1 provides baseline characteristics of the 276 PWID who have an old healed injury that is typically pain-free stratified by the presence or absence of WISP.

Table 1.

Baseline characteristicsa of participants with a healed injury that was typically pain free stratified by the occurrence of WISP (n=276).

Presence of WISP
Yes (%) (n = 112) No (%) (n = 164) p Value
Age (Median, IQRd) 46 (36–53) 48 (38–55) 0.166
Male sex (n, %) 77 (68.8) 113 (68.9) 0.979
White ancestry (n, %) 69 (61.6) 99 (60.4) 0.836
Homelessnessc (n, %) 108 (96.4) 157 (95.7) 0.771
≥ High school diploma (n, %) 65 (58.0) 67 (40.9) 0.006
Employmentb (n, %) 30 (26.8) 48 (29.3) 0.653
Any heroin usedb (n, %) 80 (71.4) 99 (60.4) 0.059
Any speedball injectionb (n, %) 10 (8.9) 12 (7.3) 0.627
Any morphine injectionb (n, %) 25 (22.3) 23 (14.0) 0.074
Non-fatal overdosec (n, %) 83 (74.1) 123 (75.0) 0.867
Age at first injection (Median, IQRd) 17 (15–22) 19 (16–25) 0.004
CES-De score ≥ 16 (n, %) 68 (60.7) 104 (63.4) 0.760
HIV seropositivityb (n, %) 30 (26.8) 46 (28.1) 0.853
Diagnosed with a mental illnessc (n, %) 79 (70.5) 101 (61.6) 0.101
Been on methadone treatmentc 102 (91.1) 143 (87.2) 0.317
Major painb (n, %) 80 (71.4) 97 (59.2) 0.037
Taken any drugs for your painb (n, %) 79 (70.5) 88 (53.7) 0.005
Felt daily pain that required medicationb (n, %) 70 (62.5) 61 (37.2) <0.001
Taking gabapentin and/or non-steroidal anti-inflammatoriesb (n, %) 10 (8.9) 12 (7.3) 0.627
Managed pain on their ownb (n, %) 86 (76.8) 110 (67.1) 0.081
Diagnosed with chronic painc 81 (72.3) 91 (55.5) 0.005
Diagnosed with a chronic neuropathic painc 26 (23.2) 23 (14.0) 0.322
Diagnosed with a chronic inflammatory painc 51 (45.5) 50 (30.5) 0.286
Diagnosed with a chronic muscle painc 12 (10.7) 15 (9.2) 0.764
Diagnosed with a chronic bone / mechanical / compressive painc 56 (50.0) 59 (36.0) 0.550
Diagnosed with a chronic headache / migrainec 7 (6.3) 4 (2.4) 0.256
Have requested or continued a prescription for pain medicationb 42 (37.5) 58 (35.37) 0.717
Open in a new tab
a

Characteristics reported at time of study enrollment

b

During the last six months

c

Ever experienced

d

Inter-quartile range

e

Center for Epidemiological Studies—Depression

3.2. WISP associated factors

In a multivariable logistical regression model, WISP was positively associated with having a high school diploma or above (Adjusted Odds Ratio [AOR] = 2.23, 95 % Confidence Interval [CI]: 1.31–3.84); any heroin use (AOR = 2.00, CI: 1.14–3.57); and feeling daily pain that required medication (AOR = 2.06, CI: 1.18–3.63). Older age at first drug injection was negatively associated with having WISP (AOR = 0.96, CI: 0.93–0.99) (Table 2). Of note, having WISP was not associated with any specific type of chronic pain (including headache, muscle, bone, neuropathic or inflammatory pain) (p > 0.05).

Table 2.

Bivariable and multivariable logistic regression analysis of factors associated with having WISP among participants with a previously healed injury that is typically pain free (n=276).

Unadjusted Adjusted
Characteristic Odds ratio (95 % CI) p value Odds ratio (95 % CI) p value
Age, per year 0.99 (0.97–1.01) 0.216
Male sex 0.99 (0.59–1.68) 0.979
White ancestry 1.05 (0.64–1.73) 0.836
Homelessnessb 1.20 (0.36–4.69) 0.772
≥High school diploma 1.98 (1.22–3.25) 0.006 2.23 (1.31–3.84) 0.003
Any employmenta 0.88 (0.51–1.51) 0.653
Any heroin useda 1.64 (0.99–2.77) 0.060 2.00 (1.14–3.57) 0.017
Injection speedball (heroin and cocaine) usea 1.24 (0.51–2.99) 0.628
Injection morphine usea 1.76 (0.94–3.31) 0.076
Non-fatal overdoseb 0.95 (0.55–1.67) 0.867
Age when first illicit drug injected, per 10 years older 0.64 (0.44–0.90) 0.014 0.96 (0.93–0.99) 0.023
Depression score equal or over 16 on CES-D 1.10 (0.60–2.05) 0.760
HIV seropositivitya 0.95 (0.55–1.63) 0.853
Been diagnosed with a mental illnessb 1.54 (0.92–2.60) 0.102
Been on methadone treatmentb 1.50 (0.69–3.45) 0.319
Major paina 1.73 (1.04–2.91) 0.038
Taken any drugs for your paina 2.07 (1.25–3.47) 0.005
Felt daily pain that required medicationa 2.81 (1.72–4.65) <0.001 2.06 (1.18–3.63) 0.012
Taking gabapentin and/or non-steroidal anti-inflammatoriesa 1.24 (0.51–2.99) 0.628
Managed pain on your owna 1.62 (0.95–2.83) 0.082
Diagnosed with chronic painb 2.10 (1.26–3.54) 0.005 1.68 (0.93–3.06) 0.087
Diagnosed with a chronic neuropathic painb 1.40 (0.72–2.73) 0.323
Diagnosed with a chronic inflammatory painb 1.39 (0.76–2.58) 0.287
Diagnosed with a chronic muscle painb 0.88 (0.38–2.01) 0.764
Diagnosed with a chronic bone / mechanical / compressive painb 1.22 (0.64–2.31) 0.550
Diagnosed with a chronic headache / migraineb 2.06 (0.60–8.11) 0.264
Open in a new tab
a

During the last six months

b

Ever experienced

3.3. WISP affect on opioid use behaviours

Among those with WISP, 79 (70.5 %) said that having this pain affected their opioid use behaviour : 57 (72.2 %) used more opioids; 19 (24.1 %) avoided opioid withdrawal; while 3 (3.8 %) no longer used opioids to avoid WISP.

3.4. Factors associated with using more opioids when WISP was present

Table 3 contains bivariate and multivariable analyses looking at the association between explanatory variables and whether pain at the old healed injury site impacted participant’s use of heroin or other opioids, among 112 participants who reported WISP. In a multivariable analysis, having neuropathic pain was the only factor significantly associated with WISP and it was seemingly protective (AOR 0.25, 95 % CI: 0.08–0.72).

3.5. Factors associated with having neuropathic pain in those with a previous injury but without WISP

In Table 4, results are shown of the association between neuropathic pain and explanatory variables, among 164 PWID who had a previous injury but did not develop WISP. A statistically significant positive association was found with having neuropathic pain without WISP and taking NSAIDs and gabapentinoids (AOR = 4.06; CI: 1.13–15.09).

Table 4.

Bivariable and multivariable logistic regression analysis of factors associated with having neuropathic pain among participants with a previous injury who did not develop WISP (n = 164).

Unadjusted Adjusted
Characteristic Odds ratio (95 % CI) p value Odds ratio (95 % CI) p value
Taking gabapentin and/or non-steroidal anti-inflammatoriesa 3.81 (1.15–12.75) 0.027 4.06 (1.13–15.09) 0.032
Age, per year 1.05 (1.00–1.11) 0.086
Sex, male 0.65 (0.24–1.78) 0.389
White race 2.68 (0.94–8.86) 0.080 2.29 (0.76–7.88) 0.157
Homelessnessb 0.20 (0.03–1.30) 0.092 0.23 (0.03–1.67) 0.148
High school completion or higher 1.41 (0.54–3.69) 0.481
Any employmenta 0.54 (0.16–1.56) 0.282
Any heroin useda 0.63 (0.24–1.66) 0.350
Injection speedball (heroin and cocaine) usea 3.14 (0.36–27.53) 0.267
Injection morphine usea 1.83 (0.56–5.58) 0.296
Non-fatal overdoseb 0.76 (0.27–2.26) 0.610
Age when first illicit drug injected, per 10 years older 0.94 (0.48–1.73) 0.837
Depression score equal or over 16 on CES-D 1.62 (0.50–6.27) 0.443
HIV seropositivitya 2.55 (0.95–6.86) 0.061 2.72 (0.94–8.02) 0.064
Been diagnosed with a mental illnessb 2.52 (0.89–8.35) 0.100
Been on methadone treatmentb 0.48 (0.14–1.75) 0.244
Managed pain on your owna 0.85 (0.28–2.94) 0.785
Diagnosed with a chronic inflammatory painb 1.38 (0.53–3.73) 0.510
Diagnosed with a chronic muscle painb 0.40 (0.06–1.63) 0.257
Diagnosed with a chronic bone / mechanical / compressive painb 1.02 (0.39–2.86) 0.965
Open in a new tab
a

During the last six months

b

Ever experienced

3.6. Correlations between pain-related variables and chronic pain

Among the full sample of 631 individuals all pairs of pain-related variables had positive moderate (ɸ: 0.30 – 0.47) to high (ɸ: 0.53 – 0.67) degree of correlation, except a single case of “Managed pain on your own” had a low correlation (ɸ = 0.17) with “requesting or continuing a prescription for pain medication”. Among the restricted sample of 368 individuals, none of the chronic pain related variables were correlated (the largest coefficient was ɸ = 0.08), except one case: Bone / mechanical / compressive pain had negative low correlation (ɸ: =−0.21) with Inflammatory pain.”

4. DISCUSSION

In this study, 40.6 % of opioid using PWID reported a temporary return of pain to old healed injury sites during opioid withdrawal, thus had WISP. This represents almost one in six of all opioid-using PWID (with and without previous injury) in our sample. To our knowledge, this is the first time the prevalence of the WISP phenomenon has been estimated.

In a multivariable analysis, having WISP was not positively associated with any social, environmental, mental or physical health issue, using any other category of substance beyond opioids, nor with having chronic pain (N.B. we did not investigate changes to chronic pain in this study). Having WISP was associated with heroin use in the last six months. This observation is not surprising given that heroin is the primary drug used among illicit opioid users in Vancouver, although in recent years it has been supplanted with fentanyl (Baldwin et al., 2018). Multiple heroin withdrawal episodes, as well as high opioid doses, have been shown to increase opioid-induced hyperalgesia and withdrawal-induced hyperalgesia (Angst et al., 2003; Celerier et al., 2001, 2000; Dunbar and Pulai, 1998).

We found an association between WISP and feeling daily pain that required medication (either illicit or prescribed). Some participants may have seen their heroin use as a pain treatment, while others might have been taking prescription pain medications by mouth, insufflation or injection, possibly adding to their hyperalgesia (Celerier et al., 2000; Dunbar and Karamian, 2003; Hooten et al., 2010). Of note, methadone use (ever) was not protective against WISP in this study. Two other previous publications of former heroin users on methadone found they were abnormally sensitive on cold pressor testing (still hyperalgesic), though a different study implied mitigation of hyperalgesia at very low methadone doses in palliative patients (the low opioid dose and use of halperodol may have confounded their results for applicability to our sample) (Compton et al., 2001; Craig, 2013; Doverty et al., 2001).

In our current study, older age at first drug injection was negatively associated with WISP, indicating individuals younger at the time of their first non-medical drug injection had a higher risk of having WISP. This added sensitivity might be due to adaptations in the nervous system when exposed to opioids during adolescence or early adulthood, or from length of exposure to the opioid contributing to pain sensitivity. Studies have found chronic pain in adolescence, as well adolescent non-medical use of opioids have both been associated with OUD in adulthood (Groenewald et al., 2019; McCabe et al., 2016).

The majority of participants in this study identified that having WISP impacted their opioid use behavior, primarily by causing them to take more opioids to avoid withdrawal. We found in our previous mixed methods study that participants often described WISP not only as severely painful, but also as emotionally aversive and stressful, in keeping with the concept that substance use, pain and/or trauma can activate both pain and anxiety sensitization, heightening the chance of reinitiation of drug use (Egli et al., 2012; Uhl et al., 2019). This finding is in alignment with other research indicating that withdrawal even from non-opioid substances like alcohol and nicotine can also increase pain sensitivity (Apkarian et al., 2013; Baiamonte et al., 2014; Egli et al., 2012).

In the multivariable subanalysis, neuropathatic pain was negatively associated with taking more opioids to avoid WISP, initially implying that it was protective. People who inject drugs are at increased risk of contracting blood borne viruses like the human immunodeficecy virus (HIV), and thus to have HIV neuropathy and take medications to treat it. Nerve injury can predispose one to the formation of tolerance and opioid-induced hyperalgesia, while opioid use can perpetuate neuropathic pain (Hutchinson et al., 2008). If WISP is a clinical correlate of opioid-induced hyperalgesia then those with HIV neuropathy abruptly stopping opioids may be predisposed to experience WISP. Therefore, a subanalysis was done among PWID with a previous injury but without WISP which revealed that having neuropathic pain was associated with being on NSAIDS and gabapentin (Table 4). This finding points to the possibility that these medications may be the reason (confounder) that having neuropathic pain appeared protective against taking more opioids in the presence of WISP.

Gabapentinoids and NSAIDs, among other medications, have been shown in clinical and pre-clinical trials to reduce opioid-induced hyperalgesia and withdrawal-induced hyperalgesia through a variety of mechanisms, and were named as mitigators of WISP by participants in our previous research (Arout et al., 2015; Compton et al., 2010; Dunbar et al., 2007; Kang et al., 2002; Lee et al., 2013; Ramasubbu and Gupta, 2011; Rieb et al., 2018, 2016). Catecholamine release can cause neuroimmune changes leading to neuroinflammation, and has been implicated as one of the mechanisms of opioid withdrawal hyperalgesia (Arout et al., 2015; Drummond, 2001; Martelli et al., 2014; Pongratz and Straub, 2014; Raghavendra et al., 2002). It is thus theoretically possible that there is a neuroinflammatory componant to WISP that NSAIDS and gabapentinoids may reduce in those with neuropathy. Mitigating the pronociceptive changes induced by opioid use and withdrawal can enhance treatment options for both pain and OUDs (Arout et al., 2015; Puig and Gutstein, 2017; Volkow and McLellan, 2016). There may not have been enough people taking these medications to report a clinically significant difference in experiencing WISP or altering opioid use behaviour in the presence of WISP in our overall sample of PWIDs with an old healed injury that is typically pain-free (Table 2, Table 3).

Our study adds to evidence to support the view that substance use disorders and pain may be bidirectional risk factors, i.e., one can increase the likelihood of the other through dysregualtion of the nervous system (Becker et al., 2009; Edwards et al., 2011; Egli et al., 2012; LeBlanc et al., 2015; Rivat and Ballantyne, 2016; Voon et al., 2018; Watkins et al., 2009). Also the brain in pain comes to look like the addicted brain and vice versa through reorganization of the mesocorticolimbic circuitry including dysregulation of the nucleus accumbens, amygdala and prefrontal cortex, while the latter can show loss of gray matter in both conditions (Apkarian, 2011; Apkarian et al., 2013, 2004; Barroso et al., 2020; Borsook et al., 2016, 2018; Elman and Borsook, 2016). There are other lines of research showing how both pain and pain relief can be reinforcing through activation of reward circuitry (Borsook et al., 2016; Elman and Borsook, 2016; Hutchinson et al., 2012; Navratilova et al., 2012). This may help to explain why the opioid use behavior is repeated despite such emotionally aversive pain experiences, including WISP: the opioid, the pain, and the pain relief may each contribute to the addiction.

There are a number of potential limitations to our study. The first limitation has to do with generalizability. The people surveyed were primarily white and Indigenous PWID in their mid-40 s. We do not know if other population groups, age groups, non-injection opioid users, and those who use opioids for chronic noncancer pain alone will have the same prevalence of WISP, in particular those with refractory dependence on opioid analgesics (Ballantyne et al., 2019). These data may not be generalizable to PWID in other settings since our cohorts are not random samples of PWID. There is the potential for recall error and socially-desirable responding in self-reported data. However, previous studies have supported the validity and reliability of self-reported drug use data (Darke, 1998).

In the current study we are unable to know if WISP was more prevalent in those with more recently experienced injuries. Another limitation is that we could not ask about buprenorphine-naloxone use in this population. This medication came on the market in Canada late compared to availability is the US and the three cohort studies we added our questions to did not ask about it during the study period. Thus left unanswered is whether or not buprenorphine would be a mitigator of WISP in the current study population as indicated by some but not all other studies on pain in opioid users (Compton et al., 2012; Daitch et al., 2014, 2012; Ravn et al., 2013; Rieb et al., 2018).

An additional limitation to the study is that we did not ask details about their injury sites that always hurt (i.e., their sites of chronic pain), how much more these sites might hurt during opioid withdrawal, and if any added pain fades once withdrawal is over. Thus, the prevalence of WISP in our current study may just be one component of pain induced by opioid withdrawal. Another potential source of underestimation of WISP is that we included only people with “a serious physical injury”, thus potentially leaving out people with minor injuries or incisional pain that may also have experienced WISP.

Future studies could test the relationship between WISP and opioid-induced hyperalgesia, neuropathic pain and focus on potential treatments.

5. CONCLUSION

We found that over 40 % of a community-recruited sample of opioid-using PWID with previous injuries had pain return temporarily to those injury sites upon opioid cessation. Withdrawal-associated injury site pain was identified as contributing to ongoing opioid use in the vast majority of those with this type of pain experience. Thus WISP may be one of the hidden drivers of continued opioid use in PWID. Further research is needed to better understand WISP and inform future clinical trials on pharmacological and non-pharmacological treatments to mitigate pain experienced during withdrawal from opioids.

HIGHLIGHTS.

  • Pain can return to previously pain-free injury sites during opioid withdrawal

  • This pain is highly prevalent among opioid using people who inject drugs

  • Having this type of pain perpetuates opioid use behavior

  • Withdrawal-associated injury site pain is clinically relevant

Acknowledgements

The authors thank the study participants for their contribution to the research, as well as current and previous research staff and investigators.

Role Of Funding Source And Contributors

The study was supported by the US National Institutes of Health (NIH) (U01DA038886, U01DA021525). At the time of data collection, Dr. Rieb received funding through a US National Institute on Drug Abuse (NIDA)-sponsored Canadian Addiction Medicine Research Fellowship (R25 DA037756-02). Dr. Wood was supported in part by a Tier 1 Canada Research Chair in Addiction Medicine. Dr. Milloy is supported in part by the United States National Institutes of Health (U01-DA051525), the Canadian Institutes of Health Research (CIHR), the Michael Smith Foundation for Health Research (MSFHR). The University of British Columbia has received an unstructured gift from NG Biomed Ltd, a private firm seeking a license to produce medical cannabis, to support him. He is the University of British Columbia’s Canopy Growth professor of cannabis science, a position created by arms’ length gifts to the university by Canopy Growth, a licensed producer of cannabis, and the Government of British Columbia’s Ministry of Mental Health and Addictions. He is also supported by a CIHR New Investigator Award and a MSFHR Scholar Award. Dr. Hayashi was supported by a CIHR New Investigator Award (MSH-141,971), a MSFHR Scholar Award and the St. Paul’s Foundation. Dr. DeBeck is supported by a CIHR New Investigator Award and a MSFHR/St. Paul’s Hospital Foundation Scholar Award. All funders had no role in the design and conduct of this study; collection, management, analysis, and interpretation of the data; and preparation, review, or approval of the manuscript.

Footnotes

Declaration of Competing Interest

The authors have no conflicts of interest to declare.

REFERENCES

  1. Angst MS, Koppert W, Pahl I, Clark DJ, Schmelz M, 2003. Short-term infusion of the mu-opioid agonist remifentanil in humans causes hyperalgesia during withdrawal. Pain 106 (1–2), 49–57. [DOI] [PubMed] [Google Scholar]
  2. Apkarian AV, 2011. The brain in chronic pain: clinical implications. Pain Manag. 1 (6), 577–586. [DOI] [PMC free article] [PubMed] [Google Scholar]
  3. Apkarian AV, Sosa Y, Sonty S, Levy RM, Harden RN, Parrish TB, Gitelman DR, 2004. Chronic back pain is associated with decreased prefrontal and thalamic gray matter density. J. Neurosci. 24 (46), 10410–10415. [DOI] [PMC free article] [PubMed] [Google Scholar]
  4. Apkarian AV, Neugebauer V, Koob G, Edwards S, Levine JD, Ferrari L, Egli M, Regunathan S, 2013. Neural mechanisms of pain and alcohol dependence. Pharmacol. Biochem. Behav. 112, 34–41. [DOI] [PMC free article] [PubMed] [Google Scholar]
  5. Arout CA, Edens E, Petrakis IL, Sofuoglu M, 2015. Targeting opioid-induced hyperalgesia in clinical treatment: neurobiological considerations. CNS Drugs 29 (6), 465–486. [DOI] [PubMed] [Google Scholar]
  6. Baiamonte BA, Valenza M, Roltsch EA, Whitaker AM, Baynes BB, Sabino V, Gilpin NW, 2014. Nicotine dependence produces hyperalgesia: role of corticotropin-releasing factor-1 receptors (CRF1Rs) in the central amygdala (CeA). Neuropharmacology 77, 217–223. [DOI] [PMC free article] [PubMed] [Google Scholar]
  7. Baldwin N, Gray R, Goel A, Wood E, Buxton JA, Rieb LM, 2018. Fentanyl and heroin contained in seized illicit drugs and overdose-related deaths in British Columbia, Canada: An observational analysis. Drug Alcohol Depend. 185, 322–327. [DOI] [PMC free article] [PubMed] [Google Scholar]
  8. Ballantyne JC, Sullivan MD, Koob GF, 2019. Refractory dependence on opioid analgesics. Pain 160 (12), 2655–2660. [DOI] [PubMed] [Google Scholar]
  9. Barroso J, Vigotsky AD, Branco P, Reis AM, Schnitzer TJ, Galhardo V, Apkarian AV, 2020. Brain gray matter abnormalities in osteoarthritis pain: a cross-sectional evaluation. Pain 02. [DOI] [PMC free article] [PubMed] [Google Scholar]
  10. Becker WC, Fiellin DA, Gallagher RM, Barth KS, Ross JT, Oslin DW, 2009. The association between chronic pain and prescription drug abuse in veterans. Pain Med. 10 (3), 531–536. [DOI] [PubMed] [Google Scholar]
  11. Borsook D, Linnman C, Faria V, Strassman AM, Becerra L, Elman I, 2016. Reward deficiency and anti-reward in pain chronification. Neurosci. Biobehav. Rev. 68, 282–297. [DOI] [PubMed] [Google Scholar]
  12. Borsook D, Youssef AM, Simons L, Elman I, Eccleston C, 2018. When pain gets stuck: the evolution of pain chronification and treatment resistance. Pain 159 (12), 2421–2436. [DOI] [PMC free article] [PubMed] [Google Scholar]
  13. Burnham KPADR, 2002. Model Selection and Multimodel Inference: A Practical Information-Theoretic Approach, 2nd ed. Springer, New York. [Google Scholar]
  14. Celerier E, Rivat C, Jun Y, Laulin JP, Larcher A, Reynier P, Simonnet G, 2000. Long-lasting hyperalgesia induced by fentanyl in rats - Preventive effect of ketamine. Anesthesiology 92 (2), 465–472. [DOI] [PubMed] [Google Scholar]
  15. Celerier E, Laulin JP, Corcuff JB, Le Moal M, Simonnet G, 2001. Progressive enhancement of delayed hyperalgesia induced by repeated heroin administration: a sensitization process. J. Neurosci. 21 (11), 4074–4080. [DOI] [PMC free article] [PubMed] [Google Scholar]
  16. Compton P, Charuvastra VC, Ling W, 2001. Pain intolerance in opioid-maintained former opiate addicts: effect of long-acting maintenance agent. Drug Alcohol Depend. 63 (2), 139–146. [DOI] [PubMed] [Google Scholar]
  17. Compton P, Kehoe P, Sinha K, Torrington MA, Ling W, 2010. Gabapentin improves cold-pressor pain responses in methadone-maintained patients. Drug Alcohol Depend. 109 (1–3), 213–219. [DOI] [PMC free article] [PubMed] [Google Scholar]
  18. Compton P, Canamar CP, Hillhouse M, Ling W, 2012. Hyperalgesia in heroin dependent patients and the effects of opioid substitution therapy. J. Pain 13 (4), 401–409. [DOI] [PMC free article] [PubMed] [Google Scholar]
  19. Craig DS, 2013. Very-low-dose methadone for the prevention of opioid hyperalgesia. J. Palliat. Med. 16 (10), 1172–1173. [DOI] [PubMed] [Google Scholar]
  20. Daitch J, Frey M, Silver D, Mitnick C, Daitch D, Pergolizzi J, 2012. Conversion of chronic pain patients from full-opioid agonists to sublingual buprenorphine. Pain Physician 15 (3), ES59–ES66. [PubMed] [Google Scholar]
  21. Daitch D, Daitch J, Novinson D, Frey M, Mitnick C, Pergolizzi J, 2014. Conversion from high-dose full-opioid agonists to sublingual buprenorphine reduces pain scores and improves quality of life for chronic pain patients. Pain Med. 15 (12), 2087–2094. [DOI] [PubMed] [Google Scholar]
  22. Darke S, 1998. Self-report among injecting drug users: a review. Drug Alcohol Depend. 51 (3), 253–263 discussion 267–258. [DOI] [PubMed] [Google Scholar]
  23. Dennis BB, Bawor M, Naji L, Chan CK, Varenbut J, Paul J, Varenbut M, Daiter J, Plater C, Pare G, Marsh DC, Worster A, Desai D, Thabane L, Samaan Z, 2015. Impact of chronic pain on treatment prognosis for patients with Opioid Use Disorder: a systematic review and meta-analysis. Subst. Abus. Res. Treat. 9, 59–80. [DOI] [PMC free article] [PubMed] [Google Scholar]
  24. Doverty M, White JM, Somogyi AA, Bochner F, Ali R, Ling W, 2001. Hyperalgesic responses in methadone maintenance patients. Pain 90 (1–2), 91–96. [DOI] [PubMed] [Google Scholar]
  25. Drummond PD, 2001. The effect of sympathetic activity on thermal hyperalgesia in capsaicin-treated skin during body cooling and warming. Eur. J. Pain 5 (1), 59–67 [Erratum appears in Eur J Pain 2002; 6(2):177]. [DOI] [PubMed] [Google Scholar]
  26. Dunbar SA, Karamian IG, 2003. Periodic abstinence enhances nociception without significantly altering the antinociceptive efficacy of spinal morphine in the rat. Neurosci. Lett. 344 (3), 145–148. [DOI] [PubMed] [Google Scholar]
  27. Dunbar SA, Pulai IJ, 1998. Repetitive opioid abstinence causes progressive hyperalgesia sensitive to N-methyl-D-aspartate receptor blockade in the rat. J. Pharmacol. Exp. Ther. 284 (2), 678–686. [PubMed] [Google Scholar]
  28. Dunbar SA, Karamian I, Zhang HH, 2007. Ketorolac prevents recurrent withdrawal induced hyperalgesia but does not inhibit tolerance to spinal morphine in the rat. Eur. J. Pain 11 (1), 1–6. [DOI] [PubMed] [Google Scholar]
  29. Edwards RR, Wasan AD, Michna E, Greenbaum S, Ross E, Jamison RN, 2011. Elevated pain sensitivity in chronic pain patients at risk for opioid misuse. J. Pain 12 (9), 953–963. [DOI] [PMC free article] [PubMed] [Google Scholar]
  30. Egli M, Koob GF, Edwards S, 2012. Alcohol dependence as a chronic pain disorder. Neurosci. Biobehav. Rev. 36 (10), 2179–2192. [DOI] [PMC free article] [PubMed] [Google Scholar]
  31. Elman I, Borsook D, 2016. Common brain mechanisms of chronic pain and addiction. Neuron 89 (1), 11–36. [DOI] [PubMed] [Google Scholar]
  32. Groenewald CB, Law EF, Fisher E, Beals-Erickson SE, Palermo TM, 2019. Associations between adolescent chronic pain and prescription opioid misuse in adulthood. J. Pain 20 (1), 28–37. [DOI] [PMC free article] [PubMed] [Google Scholar]
  33. Hooten WM, Mantilla CB, Sandroni P, Townsend CO, 2010. Associations between heat pain perception and opioid dose among patients with chronic pain undergoing opioid tapering. Pain Med. 11 (11), 1587–1598. [DOI] [PubMed] [Google Scholar]
  34. Hutchinson MR, Bland ST, Johnson KW, Rice KC, Maier SF, Watkins LR, 2007. Opioid-induced glial activation: mechanisms of activation and implications for opioid analgesia, dependence, and reward. The scientific world journal 7, 98–111. [DOI] [PMC free article] [PubMed] [Google Scholar]
  35. Hutchinson MR, Zhang Y, Brown K, Coats BD, Shridhar M, Sholar PW, Patel SJ, Crysdale NY, Harrison JA, Maier SF, Rice KC, Watkins LR, 2008. Non-stereoselective reversal of neuropathic pain by naloxone and naltrexone: involvement of toll-like receptor 4 (TLR4). Eur. J. Neurosci. 28 (1), 20–29. [DOI] [PMC free article] [PubMed] [Google Scholar]
  36. Hutchinson MR, Northcutt AL, Hiranita T, Wang X, Lewis SS, Thomas J, van Steeg K, Kopajtic TA, Loram LC, Sfregola C, Galer E, Miles NE, Bland ST, Amat J, Rozeske RR, Maslanik T, Chapman TR, Strand KA, Fleshner M, Bachtell RK, Somogyi AA, Yin H, Katz JL, Rice KC, Maier SF, Watkins LR, 2012. Opioid activation of toll-like receptor 4 contributes to drug reinforcement. J. Neurosci. 32 (33), 11187–11200. [DOI] [PMC free article] [PubMed] [Google Scholar]
  37. Kang YJ, Vincler M, Li XH, Conklin D, Eisenach JC, 2002. Intrathecal ketorolac reverses hypersensitivity following acute fentanyl exposure. Anesthesiology 97 (6), 1641–1644. [DOI] [PubMed] [Google Scholar]
  38. LeBlanc DM, McGinn MA, Itoga CA, Edwards S, 2015. The affective dimension of pain as a risk factor for drug and alcohol addiction. Alcohol 49 (8), 803–809. [DOI] [PMC free article] [PubMed] [Google Scholar]
  39. Lee M, Silverman S, Hansen H, Patel V, Manchikanti L, 2011. A comprehensive review of opioid-induced hyperalgesia. Pain Physician 14 (2), 145–161. [PubMed] [Google Scholar]
  40. Lee C, Lee HW, Kim JN, 2013. Effect of oral pregabalin on opioid-induced hyperalgesia in patients undergoing laparo-endoscopic single-site urologic surgery. Korean Journal Anesthesiol 64 (1), 19–24. [DOI] [PMC free article] [PubMed] [Google Scholar]
  41. Mao J, 2002. Opioid-induced abnormal pain sensitivity: implications in clinical opioid therapy. Pain 100 (3), 213–217. [DOI] [PubMed] [Google Scholar]
  42. Martelli D, Yao ST, McKinley MJ, McAllen RM, 2014. Reflex control of inflammation by sympathetic nerves, not the vagus. J Physiol (Lond) 592 (Pt 7), 1677–1686. [DOI] [PMC free article] [PubMed] [Google Scholar]
  43. McCabe SE, Veliz P, Schulenberg JE, 2016. Adolescent context of exposure to prescription opioids and substance use disorder symptoms at age 35: a national longitudinal study. Pain 157 (10), 2173–2178. [DOI] [PMC free article] [PubMed] [Google Scholar]
  44. Milloy MJ, Wood E, Kerr T, Hogg B, Guillemi S, Harrigan PR, Montaner J, 2016. Increased prevalence of controlled viremia and decreased rates of HIV drug resistance among HIV-Positive people who use illicit drugs during a community-wide treatment- as-Prevention initiative. Clin. Infect. Dis. 62 (5), 640–647. [DOI] [PMC free article] [PubMed] [Google Scholar]
  45. Navratilova E, Xie JY, Okun A, Qu CL, Eyde N, Ci S, Ossipov MH, King T, Fields HL, Porreca F, 2012. Pain relief produces negative reinforcement through activation of mesolimbic reward-valuation circuitry. Proc. Natl. Acad. Sci. U.S.A. 109 (50), 20709–20713. [DOI] [PMC free article] [PubMed] [Google Scholar]
  46. Pongratz G, Straub RH, 2014. The sympathetic nervous response in inflammation. Arthritis Res. Ther. 16 (6), 504. [DOI] [PMC free article] [PubMed] [Google Scholar]
  47. Prosser JM, Steinfeld M, Cohen LJ, Derbyshire S, Eisenberg DP, Cruciani RA, Galynker II, 2008. Abnormal heat and pain perception in remitted heroin dependence months after detoxification from methadone-maintenance. Drug Alcohol Depend. 95 (3), 237–244. [DOI] [PubMed] [Google Scholar]
  48. Puig S, Gutstein HB, 2017. Opioids: keeping the good, eliminating the bad. Nat. Med. 23 (3), 272–273. [DOI] [PMC free article] [PubMed] [Google Scholar]
  49. Raghavendra V, Rutkowski MD, DeLeo JA, 2002. The role of spinal neuroimmune activation in morphine tolerance/hyperalgesia in neuropathic and sham-operated rats. J. Neurosci. 22 (22), 9980–9989. [DOI] [PMC free article] [PubMed] [Google Scholar]
  50. Ramasubbu C, Gupta A, 2011. Pharmacological treatment of opioid-induced hyperalgesia: a review of the evidence. J Pain Pall Care Pharmacother 25 (3), 219–230. [DOI] [PubMed] [Google Scholar]
  51. Ravn P, Secher EL, Skram U, Therkildsen T, Christrup LL, Werner MU, 2013. Morphine- and buprenorphine-induced analgesia and antihyperalgesia in a human inflammatory pain model: a double-blind, randomized, placebo-controlled, five-arm crossover study. J. Pain Res. 6, 23–38. [DOI] [PMC free article] [PubMed] [Google Scholar]
  52. Rieb LM, Norman WV, Martin RE, Berkowitz J, Wood E, McNeil R, Milloy MJ, 2016. Withdrawal-associated injury site pain (WISP): a descriptive case series of an opioid cessation phenomenon. Pain 157 (12), 2865–2874. [DOI] [PMC free article] [PubMed] [Google Scholar]
  53. Rieb L, Norman WV, Martin RE, Berkowitz J, Wood E, Milloy MJ, McNeil R, 2018. Linking opioid-induced hyperalgesia and withdrawal-associated injury site pain: a case report. Pain Rep. 3 (e648), 1–4. [DOI] [PMC free article] [PubMed] [Google Scholar]
  54. Rivat C, Ballantyne J, 2016. The dark side of opioids in pain management: basic science explains clinical observation. Pain Rep. 1 (2), e570. [DOI] [PMC free article] [PubMed] [Google Scholar]
  55. Rosenblum A, Joseph H, Fong C, Kipnis S, Cleland C, Portenoy RK, 2003. Prevalence and characteristics of chronic pain among chemically dependent patients in methadone maintenance and residential treatment facilities. Jama-Journal of the American Medical Association 289 (18), 2370–2378. [DOI] [PubMed] [Google Scholar]
  56. Strathdee SAPD, Currie SL, Cornelisse PG, Rekart ML, Montaner JS, Schechter MT, O’Shaughnessy MV, 1997. Needle exchange is not enough: lessons from the Vancouver injecting drug use study. AIDS July 11 (8), F59–65. [DOI] [PubMed] [Google Scholar]
  57. Treister R, Eisenberg E, Lawental E, Pud D, 2012. Is opioid-induced hyperalgesia reversible? A study on active and former opioid addicts and drug naive controls. J. Opioid Manag. 8 (6), 343–349. [DOI] [PubMed] [Google Scholar]
  58. Uhl GR, Koob GF, Cable J, 2019. The neurobiology of addiction. Ann. N. Y. Acad. Sci. 1451 (1), 5–28. [DOI] [PMC free article] [PubMed] [Google Scholar]
  59. UNODC, 2020. The World Drug Report 2020. United Nations Office on Drugs and Crime. [Google Scholar]
  60. Volkow ND, McLellan AT, 2016. Opioid abuse in chronic pain–Misconceptions and mitigation strategies. N. Engl. J. Med. 374 (13), 1253–1263. [DOI] [PubMed] [Google Scholar]
  61. Voon P, Greer AM, Amlani A, Newman C, Burmeister C, Buxton JA, 2018. Pain as a risk factor for substance use: a qualitative study of people who use drugs in British Columbia, Canada. Harm Reduct. J 15 (1), 35. [DOI] [PMC free article] [PubMed] [Google Scholar]
  62. Watkins LR, Hutchinson MR, Rice KC, Maier SF, 2009. The “toll” of opioid-induced glial activation: improving the clinical efficacy of opioids by targeting glia. Trends Pharmacol. Sci. 30 (11), 581–591. [DOI] [PMC free article] [PubMed] [Google Scholar]
  63. Wood ESJ, Montaner JS, Kerr T, 2006. Evaluating methamphetamine use and risks of injection initiation among street youth: the ARYS study. Harm Reduct. J. 24 (3), 18 May. [DOI] [PMC free article] [PubMed] [Google Scholar]
  64. Woolf CJ, 2011. Central sensitization: implications for the diagnosis and treatment of pain. Pain 152 (3), S2–S15. [DOI] [PMC free article] [PubMed] [Google Scholar]

RESOURCES