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. Author manuscript; available in PMC: 2024 Apr 18.
Published in final edited form as: J Subst Abuse Treat. 2022 Oct 1;143:108892. doi: 10.1016/j.jsat.2022.108892

Longitudinal associations between pain and substance use disorder treatment outcomes

Erin Ferguson a,b, Ben Lewis c, Scott Teitelbaum c, Gary Reisfield c, Michael Robinson a,b, Jeff Boissoneault a,b,*
PMCID: PMC11025107  NIHMSID: NIHMS1981827  PMID: 36228338

Abstract

Introduction:

Pain is commonly reported among those in treatment for substance use disorders (SUD) and is associated with poorer SUD treatment outcomes. The current study examined the trajectory of pain over the course of SUD treatment and associations with substance use outcomes.

Methods:

This observational study included adults seeking treatment for alcohol, cannabis, or opioid use disorders (N = 811). Participants completed a battery of assessments at treatment admission, 30 days post admission, and at discharge, including measures of demographics, pain, quality of life, abstinence self-efficacy, and craving.

Results:

Analyses indicated linear reductions in pain intensity and interference over time. Significant interactive effects were observed for opioid use disorder (OUD) and time, such that participants with OUD had greater reductions in pain intensity and interference over time compared to those without OUD. Elevated pain intensity was associated with negative treatment outcomes, including reduced quality of life and abstinence self-efficacy, and greater craving and negative affect.

Conclusions:

Reductions in pain occur over the course of SUD treatment, particularly for those with OUD. Greater pain was also associated with adverse SUD treatment outcomes. Results suggest that treatment and associated abstinence may be beneficial for those with co-occurring pain and SUD, highlighting an additional benefit of improving access to SUD treatment for patients and health care systems. Future research should replicate these findings among diverse samples and further characterize the trajectory of pain during and after SUD treatment.

Keywords: Pain, Substance use, Substance use disorder, Treatment

1. Introduction

Pain is an aversive experience associated with substantial physical, psychosocial, and economic burden (Dahlhamer et al., 2018; Institute of Medicine (US) Committee on Advancing Pain Research, Care, and Education, 2011; Turk et al., 2016). It also represents one of the most common reasons that individuals pursue medical treatment (Finley et al., 2018; Sauver et al., 2013). Notably, scientific interest in interrelations between pain and hazardous substance use is growing, as both are highly prevalent and frequently co-occur (Alford et al., 2016; John & Wu, 2020; Morasco et al., 2011). Both substance use and pain are significant public health concerns associated with synergistic consequences, including increased health care costs, lost productivity, and reduced quality of life (Ditre et al., 2019; Dueñas et al., 2016; Kawai et al., 2017).

Research suggests that 52–74 % of individuals with a substance use disorder (SUD) experience chronic pain (Boissoneault et al., 2019; Ilgen et al., 2010; John & Wu, 2020). Pain and substance use likely interact reciprocally, resulting in a greater risk of SUD in individuals with pain and vice versa (Ditre et al., 2019; Ferguson et al., 2020). This connection is supported by current literature, which suggests that substance use is more common among individuals with chronic pain, and pain is highly prevalent among those reporting substance use (Brennan & SooHoo, 2013; Kosiba et al., 2020; Larson et al., 2007; McDermott et al., 2018; Zvolensky et al., 2011). Further, pain can provoke substance use, as self-medicating pain via substance use is common and represents a growing public health concern (Alford et al., 2016; Fales et al., 2019; Ferguson et al., 2020; Riley & King, 2009). Indeed, experimental studies suggest that acute pain increases urge and motivation to use alcohol (Moskal et al., 2018; Stennett et al., 2021).

Pain presents a unique challenge within SUD treatment, as the experience of pain and substance use appear to exacerbate and maintain one another through physiological and psychological mechanisms, such as negative reinforcement processes and withdrawal-induced hyperalgesia (Ditre et al., 2019). Systematic study of biopsychosocial mechanisms underlying pain as a proximal antecedent for substance use is largely lacking. Nonetheless, evidence suggests that pain-related negative affect is likely a critical mediator of the effect of pain on substance use (Ditre et al., 2019). Pain-related beliefs (e.g., pain catastrophizing), pain-related outcome expectancies, and personality factors like negative urgency (the tendency to act rashly to attenuate a negative mood state) may also be critical vulnerability factors (Ferguson et al., 2020). For individuals with high levels of pain catastrophizing or pain-related anxiety, strong beliefs that substance use will relieve pain, and high negative urgency, substance use may become a preferred method of pain self-management. Because chronic heavy substance use (especially of alcohol and opioids) can lead to a hyperalgesic state (Egli et al., 2012), self-management of pain via substance use can further exacerbate pain in the long term, leading to a feed-forward cycle with significant personal and societal consequences.

Notably, pain has reciprocal interactions with both opioid and nonopioid substances, including alcohol, cannabis, and tobacco. Individuals in SUD treatment commonly report experiencing pain within the past year (Boissoneault et al., 2019; Caldeiro et al., 2008; Potter et al., 2008; Price et al., 2011; Rosenblum et al., 2003; Trafton et al., 2004), and persistent pain predicts poorer SUD-related treatment outcomes, including reduced treatment retention and abstinence rates, and greater service utilization and treatment costs (Caldeiro et al., 2008). Compared to treatment-seeking patients without pain, those with pain are also more likely to report weekly substance use and greater depressive symptoms (Boissoneault et al., 2019; Potter et al., 2008). Further, among individuals with alcohol use disorder (AUD), pain is prospectively associated with risk of post-treatment alcohol consumption (Larson et al., 2007) and heavy drinking lapses during and after treatment (Witkiewitz, Vowles, et al., 2015). Additionally, individuals seeking AUD treatment report that pain contributes to their drug and alcohol use (Boissoneault et al., 2019), with many (26 %) identifying pain as the reason for substance use initiation (Sheu et al., 2008).

Although research has shown pain to complicate SUD treatment, few studies have investigated relations between pain reduction and substance use treatment outcomes. Emerging literature suggests that interventions designed to concurrently target pain and substance use reduce pain intensity and promote abstinence (Barrett & Chang, 2016; Barry et al., 2019; Ilgen et al., 2016). However, these interventions have not yet been widely implemented across treatment centers, and the trajectory of pain over the course of SUD treatment remains poorly characterized. Improvements in pain during treatment may promote better substance use outcomes, and improvements in SUD symptomatology may predict decreases in pain. Greater understanding of these interactions during treatment can inform treatment center services, including timing and modality of pain management interventions. Therefore, the purpose of this longitudinal and observational study was to characterize the experience of pain over the course of residential substance use treatment, as well as potential impacts of SUD diagnosis on patients’ pain trajectory. This study also examined associations between pain and substance use treatment outcomes, including quality of life, mood, abstinence self-efficacy, and craving. We hypothesized that pain intensity and pain-related interference would decrease by the time of treatment discharge, given proposed reciprocal interactions between pain and substance use (Apkarian et al., 2013; Ditre et al., 2019). We also hypothesized that reductions in pain would be associated with improved quality of life, mood, and abstinence self-efficacy, as well as reduced craving.

2. Methods

2.1. Study design

Individuals entering residential treatment completed self-report assessments related to substance use and mental health. Assessments required approximately one hour, and trained research staff administered the assessments. The study repeated assessments after 30 days in treatment and at treatment discharge. All individuals who consented to have their data used for research and were older than 18 years of age were eligible for this study. Eight hundred and eleven (811) participants completed informed consent and at least one assessment time point. However, a subset of participants did not complete assessments at either the 30-day time point or at discharge (n = 190), which may have been due to leaving treatment prior to the 30-day time point, declining to repeat the assessment, or leaving the premises of the treatment center after discharge before assessments could be re-administered. The current analysis included all participants (N = 811) who completed assessments between August 2017 and May 2021. Analyses included all participants, rather than only those who reported recent pain at baseline, given the potential for changes in pain status during treatment. Approximately 29 % of people who reported no pain at baseline developed pain over the course of treatment. The University of Florida Institutional Review Board approved all study procedures and materials.

The treatment program from which the sample was drawn is a national and regional hub for medical and other professionals seeking treatment for SUD. Patients are admitted to treatment following an intake process with a board-certified addiction medicine physician, and they receive both individual and group therapy. Patients spend approximately 17–20 h per week in therapy (e.g. 12-step groups, tobacco cessation groups, individual sessions). Uncomplicated and acute pain symptoms were managed by primary care providers at the treatment facility. More complex, chronic pain concerns were addressed through several avenues: 1) consultation with medical specialty physicians at the University of Florida (e.g., neurology, rheumatology); 2) treatment from the pain medicine physician associated with the University of Florida Division of Addiction Medicine; 3) cognitive behavioral treatment with a psychologist on site; and 4) physical therapy through the University of Florida. Patients were prescribed analgesic medications (both opioid and nonopioid) as needed. The treatment team determined discharge based on patient progress, and it did not occur at a set time point.

2.2. Measures

2.2.1. Demographics

Participants completed a demographics measure upon admission to treatment. This questionnaire assessed age, gender, education level, employment, race/ethnicity, and previous SUD treatment. Data regarding admission type (voluntary or quasi-compulsory) and opioid medication status during treatment (i.e., prescribed buprenorphine, morphine, or other opioid analgesic vs. not prescribed) was also collected.

2.2.2. Pain

Pain intensity and pain interference were measured using scales from the Patient Reported Outcomes Measurement Information System (PROMIS; Amtmann et al., 2010; Cella et al., 2010). For pain intensity, participants completed three items to rate the intensity of their current and past-7-day average and worst levels of pain. Response options ranged from 1 (no pain) to 5 (very severe), and ratings were summed for analyses. For pain interference, participants completed four items related to the extent to which pain interfered with their day-to-day activities, work around the home, ability to engage in social activities, and completion of household chores over the past 7 days. Response options ranged from 1 (not at all) to 5 (very much), and the study used their sum for analyses. Pain-related scales from the PROMIS have good psychometric properties (Amtmann et al., 2010; Revicki et al., 2009; Stone et al., 2016), and pain intensity and pain interference scales demonstrate good and acceptable convergent validity with the Brief Pain Inventory, respectively (Cella et al., 2010; Cook et al., 2015). Further, studies have called for the use of PROMIS instruments in addiction research for patient outcome monitoring (Clarke et al., 2021; Hilton & Pilkonis, 2015), and these pain-related scales have been administered in several studies with SUD populations (Vowles et al., 2020; Wiest et al., 2014; Wilson et al., 2018).

2.2.3. Quality of life

This study assessed quality of life using the abbreviated World Health Organization quality of life scale (WHOQOL-BREF). The WHOQOL-BREF includes 26 items examining four quality of life domains: psychological, physical, social, and environmental (World Health Organization, 1996). It is well-validated in many countries and among the general population, as well as clinical samples (Skevington et al., 2004). The utility of this measure for SUD populations has been previously demonstrated (Feelemyer et al., 2014; Laudet, 2011). Analyses for this study used the mean of quality of life domain scores as a continuous measure, with higher scores reflecting higher quality of life.

2.2.4. Mood

Participants completed the Patient Health Questionnaire-9 (PHQ-9) and the Generalized Anxiety Disorder 7-item Scale (GAD-7), which measure symptoms of depression and anxiety, respectively. The PHQ-9 is a brief, 9-item measure used to assess frequency of depressive symptoms over the past two weeks using a four-point Likert scale (Kroenke et al., 2001). Higher scores reflect more severe depressive symptomology. Similarly, the GAD-7 is a self-report measure of anxiety that assesses frequency of symptoms over the past two weeks (Spitzer et al., 2006). Higher total scores indicate more severe levels of anxiety. Both measures are well-validated and have strong psychometric properties (Kroenke et al., 2001; Löwe et al., 2008; Martin et al., 2006; Spitzer et al., 2006).

2.2.5. Substance use and related measures

SUD diagnosis was obtained by medical chart review following treatment admission; diagnoses were made or confirmed by physicians at the treatment center. The current study included those with alcohol use disorder (AUD), cannabis use disorder (CUD), or opioid use disorder (OUD), alone or in combination, given their relevance to pain outcomes (Ditre et al., 2019; Ferguson et al., 2020). This study assessed abstinence self-efficacy, or belief in one’s ability to abstain from substance use, using the Alcohol Abstinence Self-Efficacy Scale (AASE) and the Drug Abstinence Self-Efficacy Scale (DASE). The AASE includes 20 items comprising four subscales examining alcohol use cues related to Negative Affect, Social/Positive, Physical and Other Concerns, and Craving/Urges (DiClemente et al., 1994). Respondents answered items using a five-point Likert-type response scale, with response options ranging from 1 (not at all) to 5 (extremely). The DASE is identical to the AASE (Hiller et al., 2000), but modified to reflect drug vs. alcohol use. Scores range from 20 to 100, with higher scores indicating greater abstinence self-efficacy. Both measures have demonstrated good reliability in samples reporting substance use (DiClemente et al., 1994; Hiller et al., 2000).

The Penn Alcohol Craving Scale (PACS) was used to measure craving for substances. This scale has high internal consistency and has been found to predict relapse (Flannery et al., 1999; Kavanagh et al., 2013). The PACS is a 5-item self-report measure of thoughts about drinking, average craving, and perceived ability to resist drinking if alcohol were available, with response options ranging from 0 to 6. The current study modified the PACS to assess craving for both alcohol and drugs, with higher total scores indicating greater craving for substances.

2.3. Statistical analysis

All analyses were conducted in IBM SPSS 25. We used descriptive analyses to characterize sample demographics and examine potential covariates and predictor and outcome variables. This study used multilevel modeling (MLM using full maximum likelihood) to examine changes in pain intensity and pain interference over the treatment period. Predictors included SUD diagnoses (AUD; OUD; CUD), interactions between these diagnoses, and interactions between these diagnoses and time. We included SUD diagnoses as between-subjects factors, which allowed for multiple combinations of diagnoses in the models (e.g., comorbid AUD and OUD). To reduce multicollinearity, mean centering was used with all interaction terms.

MLM was also used to examine relationships between pain intensity levels and treatment outcomes over time. Separate models for each treatment outcome (craving, alcohol and drug self-efficacy, depression, anxiety, and quality of life domains) were conducted. These models included level 2 pain intensity (varied between participants, only one value per participant) and level 1 pain intensity (varied within participants, up to 3 values per participant). Interactive effects of time and average pain intensity levels investigated whether rates of change in treatment outcomes were moderated by average pain intensity levels.

Level 2 pain intensity was grand-mean centered, and the study added fixed effect to the models. Level 1 pain intensity was person-mean centered, and fixed and random effects were added to the models. All analyses included age and gender as covariates given documented associations with pain (Bartley & Fillingim, 2013; Brennan et al., 2005; Edwards, 2006). Alpha was set to p < .05, random effects of time were removed from all models due to Hessian errors.

Given that opioid analgesic medications may affect the trajectory of pain intensity and interference across treatment, we also conducted exploratory MLMs to determine if opioid medication status moderated patterns of change for pain outcomes across treatment among those with OUD. These models included opioid medication status, time, and the interaction between these factors. Opioid medication status was included in models as a dichotomous, time-varying covariate.

3. Results

3.1. Sample characteristics

Most participants were male (63.5 %) and identified as White (94.1 %) and non-Hispanic (92 %). The average age was 40.75 years (SD = 13.93), and completion of a doctoral-level degree was common (26.4 %). Slightly >89 % (89.1 %) of the sample was employed, with approximately 39.7 % reporting employment in the medical profession. On average, participants spent 66.62 (SD = 33.44) days in treatment. For nearly half (42.7 %), treatment was quasi-compulsory (i.e., required to avoid sanction by a professional licensing board). Approximately half (52.7 %) of participants had engaged in prior substance use disorder treatment. Overall, 76 participants (9.4 %) were prescribed an opioid analgesic (i.e. buprenorphine, morphine, or other opioid medication) during treatment. Of those diagnosed with OUD (n = 233), 21 participants were prescribed an opioid analgesic.

Across the sample, average pain intensity at the three time points was: baseline (M = 5.98, SD = 3.27), 30-day follow-up (M = 5.21, SD = 2.74), and discharge (M = 4.84, SD = 2.63). Average pain interference at each assessment was: baseline (M = 7.16, SD = 4.64), 30-day follow-up (M = 5.81, SD = 3.32), and discharge (M = 5.39, SD = 2.84). Pain in the past week was reported by 53.5 % of the sample at baseline, 47.3 % at 30-day follow-up, and 39.6 % at discharge. Most participants were diagnosed with AUD (69.3 %). Among participants, 28.7 % and 24.9 % were diagnosed with OUD and CUD, respectively. Table 1 presents sociodemographic information.

Table 1.

Selected demographics of participants (N = 811).

Variable N (% of total) or M(SD)
Age 40.75 (13.93)
Gender
 Female 296 (36.5)
Race
 White 763 (94.1)
 Black 26 (3.2)
 Asian 24 (3.0)
 American Indian 17 (2.1)
 Other 15 (1.8)
Ethnicity
 Non-Hispanic 746 (92.0)
Employment
 Yes 723 (89.1)
Admission type
 Quasi-compulsory 346 (42.7)
 Voluntary 417 (51.4)
Education
 High school diploma/GED or less 79 (9.7)
 Some college/technical or vocational school 179 (22.1)
 Associate degree 86 (10.6)
 Bachelor’s degree 186 (22.9)
 Master’s degree 67 (8.3)
 Doctoral degree 214 (26.4)
SUD Diagnosis
 AUD 562 (69.3)
 OUD 233 (28.7)
 CUD 202 (24.9)
Pain intensity (baseline) 5.98 (3.27)
Pain intensity (30-day) 5.21 (2.74)
Pain intensity (discharge) 4.84 (2.63)
Pain interference (baseline) 7.16 (4.64)
Pain interference (30-day) 5.81 (3.32)
Pain interference (discharge) 5.39 (2.84)
Days spent in treatment 66.62 (33.44)
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3.2. Changes in pain outcomes over time

Separate MLMs were built to assess changes in pain intensity and pain interference over time and examine the impact of SUD diagnosis on these changes. Of note, the missing at random assumption was violated given that missing data at follow-up and discharge were associated with greater pain intensity and interference. However, we proceeded with use of the full sample given that the pattern and significance of results remained unchanged when conducting analyses with only those who completed assessments at all three timepoints. Further, we employed full maximum likelihood estimation in the models to address missing data.

3.2.1. Pain intensity

Results indicated that there was a significant pattern of linear decline in pain intensity over time (b = −0.57, SE =0.05; p < .001). There was a significant fixed effect of OUD (b = 0.47, SE =0.12; p < .001), such that those with OUD had higher pain intensity compared to those without OUD. There was also a significant time x OUD (b = −0.28, SE = 0.06; p < .001) interaction, suggesting that this diagnosis moderated rates of change in pain intensity. Those with OUD demonstrated greater linear decline in pain intensity over time compared to those without OUD (Fig. 1). Main effects and interactive terms of other SUD diagnoses were nonsignificant (ps > .05). Overall, the model accounted for 53.40 % of within-person variation and 46.60 % of between-person variation in pain intensity over the treatment period. Summary statistics for the final model are presented in Table 2.

Fig. 1.

Fig. 1.

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A linear decline in pain intensity over time was detected across the sample (b = −0.57, SE = 0.05; p < .001). However, individuals with OUD had greater pain intensity across all three time points (b = 0.47, SE = 0.12; p < .001), and experienced greater declines in pain over the course of treatment than those without OUD (b = −0.28, SE = 0.06; p < .001).

Table 2.

Summary statistics for the Multilevel Model 1 predicting pain intensity.

Predictor B SE df t p Lower bound Upper bound
Linear time −0.57 0.05 842.01 −10.81 <.001 −0.67 −0.46
Quadratic time −0.05 0.08 642.37 −0.63 .53 −0.22 0.11
AUD −0.03 0.12 1596.27 −0.29 .77 −0.27 0.20
OUD 0.47 0.12 1606.02 3.88 <.001 0.23 0.71
CUD 0.00 0.13 1556.66 0.03 .98 −0.25 0.26
AUD_OUD 0.07 0.12 1606.24 0.55 .58 −0.17 0.31
AUD_CUD 0.22 0.14 1638.33 1.58 .12 −0.05 0.49
OUD_CUD −0.04 0.14 1642.08 −0.31 .76 −0.31 0.22
Linear time*AUD 0.11 0.06 854.48 1.90 .06 0.00 0.22
Linear time*OUD −0.28 0.06 847.99 −4.74 <.001 −0.39 −0.16
Linear time*CUD −0.02 0.06 829.95 −0.30 .76 −0.13 0.10
Quadratic time*AUD −0.07 0.09 649.27 −0.71 .48 −0.25 0.12
Quadratic time*CUD 0.13 0.10 638.70 1.39 .17 −0.06 0.33
Quadratic time*OUD 0.02 0.09 643.26 0.22 .83 −0.16 0.21
Linear time*AUD_CUD −0.06 0.07 825.56 −0.99 .32 −0.19 0.06
Linear time*AUD_OUD 0.04 0.06 843.27 0.72 .47 −0.07 0.16
Linear time*OUD_CUD 0.11 0.06 839.56 1.63 .10 −0.02 0.23
Quadratic time*AUD_CUD −0.08 0.11 630.69 −0.73 .47 −0.29 0.13
Quadratic time*AUD_OUD −0.13 0.10 639.73 −1.34 .18 −0.31 0.06
Quadratic time*OUD_CUD 0.01 0.11 644.30 0.12 .91 −0.20 0.22
Age 0.04 0.01 792.99 6.51 <.001 0.03 0.06
Gender 1.06 0.18 795.16 6.01 <.001 0.72 1.41
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3.2.2. Pain interference

Similar results were observed for pain interference. There was a significant fixed effect of linear time (b = −0.86, SE = 0.07; p < .001), suggesting linear decline in pain interference over time across participants. There was also a significant fixed effect of OUD (b = 0.56, SE = 0.16; p < .001) and a significant time x OUD interaction (b = −0.31, SE = 0.08; p < .001). Individuals with OUD had higher pain interference and demonstrated greater linear decline in pain interference over time compared to those without OUD (Fig. 2). Other main effects and interactive terms of SUD diagnoses were nonsignificant (ps > .05; see Table 3 for summary statistics of final model). Overall, the model accounted for 35.80 % of within-person variation and 64.20 % of between-person variation in pain interference over the treatment period.

Fig. 2.

Fig. 2.

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Like for pain intensity, pain interference declined over time across the sample (b = −0.86, SE = 0.07; p < .001). Individuals with OUD had greater pain interference across all three time points (b = 0.56, SE = 0.16; p < .001), and experienced greater declines in pain interference over the course of treatment than those without OUD (b = −0.31, SE = 0.08; p < .001).

Table 3.

Summary statistics for the Multilevel Model 2 predicting pain interference.

Predictor B SE df t p Lower bound Upper bound
Linear time −0.86 0.07 852.89 −11.89 <.001 −1.01 −0.72
Quadratic time 0.15 0.11 616.84 1.34 .18 −0.07 0.37
AUD 0.04 0.16 1663.52 0.27 .79 −0.27 0.35
OUD 0.55 0.16 1673.58 3.49 <.001 0.24 0.87
CUD 0.23 0.17 1622.27 1.37 .17 −0.10 0.57
AUD_CUD 0.09 0.16 1673.70 0.57 .57 −0.22 0.41
AUD_OUD 0.09 0.18 1707.87 0.51 .61 −0.26 0.45
CUD_OUD −0.10 0.18 1711.78 −0.58 .56 −0.45 0.25
Linear time*OUD 0.05 0.08 866.27 0.64 .52 −0.11 0.21
Linear time*AUD −0.31 0.08 859.40 −3.79 <.001 −0.47 −0.15
Linear time*CUD 0.06 0.08 839.90 0.72 .47 −0.10 0.22
Quadratic time*OUD −0.15 0.12 623.68 −1.22 .22 −0.39 0.09
Quadratic time*AUD −0.06 0.13 613.25 −0.47 .64 −0.31 0.19
Quadratic time*CUD 0.05 0.13 617.66 0.38 .70 −0.20 0.29
Linear time*AUD_CUD −0.17 0.09 835.16 −1.85 .07 −0.35 0.01
Linear time*AUD_OUD 0.02 0.08 854.26 0.23 .82 −0.14 0.18
Linear time*CUD_OUD −0.10 0.09 850.27 −1.09 .27 −0.28 0.08
Quadratic time*AUD_CUD 0.14 0.14 605.29 0.97 .33 −0.14 0.42
Quadratic time*AUD_OUD −0.18 0.13 614.22 −1.39 .16 −0.43 0.07
Quadratic time* CUD_OUD 0.25 0.14 618.80 1.76 .08 −0.03 0.53
Age 0.05 0.01 781.65 5.80 <.001 0.03 0.07
Gender 1.04 0.23 783.96 4.57 <.001 0.60 1.49
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3.3. Associations between changes in pain intensity and SUD treatment outcomes

A series of MLMs were conducted to examine relationships between pain variability and treatment outcomes (abstinence self-efficacy, quality of life, mood, and craving). We also examined whether rates of change in these outcomes were moderated by average pain intensity levels. Summary statistics for all models are presented in Supplementary Materials.

3.3.1. Abstinence self-efficacy

For alcohol self-efficacy, results revealed a significant pattern of change in self-efficacy over time (b = 0.63, SE =0.02; p < .001), and this pattern was quadratic (b = −0.42, SE = 0.04; p < .001), indicating that there was a general increase in alcohol self-efficacy over treatment, but this effect attenuated from follow-up to discharge. Average pain intensity levels moderated linear rates of change in self-efficacy (b = 0.02, SE = 0.01; p = .04), indicating greater improvements in alcohol self-efficacy over time among individuals with higher average pain intensity.

For drug self-efficacy, there were significant linear (b = 0.60, SE = 0.03; p < .001) and quadratic (b = −0.41, SE = 0.04; p < .001) trends in self-efficacy over time, indicating that a general increase in drug self-efficacy occurred over the course of treatment, but this effect attenuated from follow-up to discharge. There was a significant fixed level 2 effect of mean pain intensity (b = −0.07, SE = 0.02; p < .001), suggesting that individuals with greater average pain intensity levels had lower drug self-efficacy. However, mean pain intensity levels did not moderate quadratic rates of change in self-efficacy (ps > .05). Fixed (b = −0.05, SE = 0.01; p < .001) and random (b = 0.01, SE = 0.01; p = .04) effects of level 1 pain intensity were significant, suggesting individual differences in the effect of pain intensity levels on drug self-efficacy.

3.3.2. Craving

Results indicated significant linear (b = −0.64, SE = 0.03; p < .001) and quadratic (b = 0.46, SE = 0.04; p < .001) trends in craving over time. These trends reflect a decline in craving over treatment that leveled off from follow-up to discharge. The fixed level 1 effect of pain intensity was significant (b = 0.07, SE = 0.02; p < .001), indicating that at timepoints with higher than usual pain, craving was also higher than usual. All other effects were nonsignificant (ps > 0.05).

3.3.3. Mood

For depression, there were significant linear (b = −0.43, SE = 0.01; p < .001) and quadratic (b = 0.28, SE = 0.02; p < .001) patterns of change over time. Depression decreased over the course of treatment, particularly from baseline to follow-up, and increased slightly from follow-up to discharge. Significant fixed effects of level 2 average pain intensity (b = 0.06, SE = 0.01; p < .001) and level 1 pain intensity (b = 0.05, SE = 0.01; p < .001) were observed, suggesting that greater average pain intensity levels were positively associated with depression, and at treatment timepoints with higher than usual pain, depression was also higher than usual for individuals.

In the anxiety model, results also indicated significant patterns of linear (b = −0.48, SE = 0.02; p < .001) and quadratic (b = 0.31, SE = 0.02; p < .001) change over time, which reflected an initial decrease in anxiety during treatment from baseline to follow-up and a slight increase from follow-up to discharge. There were also significant fixed effects of Level 2 average pain intensity (b = 0.07, SE = 0.01; p < .001) and Level 1 pain intensity (b = 0.04, SE = 0.01; p < .001). This suggests that greater average pain intensity predicted higher anxiety, and at treatment timepoints with higher than usual pain, anxiety was also higher than usual.

3.3.4. Quality of life

For psychological quality of life, results indicated significant linear (b = 1.64, SE = 0.06; p < .001) and quadratic (b = −0.86, SE = 0.08; p < .001) trends over time. Significant fixed effects of level 2 average pain intensity (b = −0.19, SE = 0.04; p < .001) and level 1 pain intensity (b = −0.13, SE = 0.03; p < .001) were observed. There was also a significant random effect of level 1 pain intensity (b = 0.05, SE = 0.02; p = .04), which indicated individual differences in the effect of pain intensity on psychological quality of life. Overall, these results suggest that psychological quality of life generally increased during treatment, although this effect leveled off from follow-up to discharge. Greater average pain intensity predicted lower quality of life, and at treatment timepoints with higher than usual pain, quality of life was lower than usual.

For social quality of life, a significant linear increase occurred over time (b = 1.13, SE = 0.06; p < .001). Results also indicated significant fixed effects of level 2 average pain intensity (b = −0.19, SE = 0.05; p < .001) and level 1 pain intensity (b = −0.15, SE = 0.04; p < .001), suggesting that greater average pain intensity and pain intensity variability during treatment predicted reduced quality of life.

Results for the physical quality of life model were similar. There were significant patterns of linear (b = 1.20, SE = 0.05; p < .001) and quadratic (b = −0.75, SE = 0.07; p < .001) change over time, such that quality of life increased from baseline to follow-up and decreased slightly from follow-up to discharge. There was a significant fixed level 2 effect of average pain intensity (b = −0.49, SE = 0.04; p < .001), and average pain intensity levels moderated linear rates of change (b = 0.07, SE = 0.02; p < .001). This indicated greater increases in physical quality of life over time among individuals with higher average pain intensity. Further, significant fixed (b = −0.26, SE = 0.03; p < .001) and random (b = 0.04, SE = 0.02; p = .03) effects of level 1 pain intensity were observed, suggesting individual differences in the effect of pain intensity on quality of life.

For environmental quality of life, there was a significant linear increase over time (b = 0.53, SE = 0.05; p < .001). There was a significant fixed level 2 effect of average pain intensity (b = −0.17, SE = 0.04; p < .001), suggesting that higher average levels of pain during treatment predicted lower quality of life. A significant fixed effect of level 1 pain intensity was also detected (b = −0.15, SE = 0.03; p < .001), indicating that at treatment timepoints with higher than usual pain, environmental quality of life was lower than usual.

3.4. Exploratory analyses

Exploratory MLMs assessed the effect of opioid medication status on changes in pain intensity and interference among individuals with OUD. There was a significant fixed effect of opioid medication status on pain interference (b = 1.06, SE = 0.38; p = .01), such that those who were prescribed opioid medications had greater levels of pain interference. No other significant effects were detected for pain intensity or pain interference, including interactions between time and opioid medication status (ps > .05). See Supplementary Materials for further details on summary statistics of these exploratory models.

4. Discussion

This study aimed to address gaps in the current literature on pain and substance use by examining the trajectory of pain over the course of SUD treatment and associations between changes in pain and SUD treatment outcomes. Results indicated linear reductions in pain intensity and interference over time from admission to discharge across the sample. However, results also indicated that participants with OUD had greater reductions in pain intensity and pain interference over time than those without OUD, which is likely related to reporting higher pain at baseline. Higher pain at baseline in this group may have been due to opioid-induced hyperalgesia, opioid withdrawal, or accidents/injuries occurring as a result of activities related to pursuit of opioids. Effects of other SUD diagnoses were nonsignificant, which may suggest that treatment-related changes in pain are most salient for those with OUD. Of note, we considered whether opioid medication status moderated rates of change in pain outcomes for those with OUD and did not find evidence of this effect. This finding suggests that receipt of opioid analgesics (i.e., buprenorphine, morphine, other medications) during SUD treatment did not account for individual differences in rates of change in pain outcomes among this sample.

Overall, there were patterns of change in treatment outcomes over time, such that abstinence self-efficacy and quality of life increased and craving and mood symptoms decreased. In contrast to pain intensity and interference, changes in these measures were quadratic such that improvements from baseline to 30-day follow-up were greater than from follow-up to discharge. Notably, greater average pain intensity tended to predict poorer treatment outcomes (i.e., lower drug self-efficacy and quality of life, greater negative affect), and within-person increases in pain were also related to negative outcomes (i.e., lower drug self-efficacy and quality of life, higher craving and negative affect). Further, there were greater improvements in certain treatment outcomes over time (i.e., alcohol self-efficacy, physical quality of life) among individuals reporting higher average levels of pain intensity. Given that pain intensity was positively associated with negative affect and craving and inversely associated with alcohol/drug abstinence self-efficacy and quality of life across assessment points, findings suggest that reductions in pain intensity over the course of treatment may benefit a wide variety of treatment-related outcomes.

To our knowledge, this study is the first to assess pain across multiple time points during SUD treatment, and findings contribute to a growing literature on pain–substance use interactions (Ditre et al., 2019; Ferguson et al., 2020; John & Wu, 2020; Wyse et al., 2021). Consistent with prior work, we found nearly half of the sample (48.8 %) reported pain in the past 7 days at admission (Boissoneault et al., 2019; Caldeiro et al., 2008; Potter et al., 2008; Price et al., 2011; Rosenblum et al., 2003; Trafton et al., 2004). The present study expanded on previous findings by demonstrating that pain reductions occur during SUD treatment, particularly for those with OUD. Additionally, increased pain levels were linked to poorer SUD treatment outcomes, which builds upon prior literature documenting associations between persistent pain and negative treatment outcomes, including poor treatment retention, abstinence, and relapse (Caldeiro et al., 2008; Jakubczyk et al., 2016; Larson et al., 2007; Witkiewitz, Vowles, et al., 2015). Taken together, findings suggest that SUD treatment may be beneficial for managing comorbid pain and substance use.

Several potential explanations exist for the pain reductions observed throughout SUD treatment in this study. For example, abstinence from substances may have contributed to reduced pain levels. Participants in this study were required to undergo detoxification prior to entering residential treatment and to maintain abstinence from substances throughout treatment. Previous literature has identified chronic or heavy substance use as a risk factor for pain (Ditre et al., 2011; Kosiba et al., 2020, p. 20; Lawton & Simpson, 2009; Sugiyama et al., 2010), and imaging studies suggest that substance use cessation may reduce pain by disrupting shared neural circuitry of pain and reward pathways (Apkarian et al., 2013; Petre et al., 2015). Further, cessation of opioids has been associated with long-term reductions in pain levels (Goesling et al., 2019; Nicholas et al., 2020; Tardif et al., 2021). This evidence is consistent with our findings that, relative to baseline, pain was significantly reduced over the course of treatment.

Another potential explanation for changes in pain observed in this analysis may be related to the content of the SUD treatment. During treatment , participants received psychotherapeutic services, with the goal of developing relapse prevention skills and improving psychological, social, and adaptive functioning (Kleber et al., 2007). These services may be particularly beneficial, as mental health conditions are frequently comorbid with substance use (Asmundson & Katz, 2009; Grant et al., 2004; Merikangas et al., 1998; Morasco et al., 2013) and pain (Gureje et al., 2008; Hooten, 2016; IsHak et al., 2018). Notably, the self-medication hypothesis suggests that individuals may use substances to cope with negative affect (Khantzian, 1997), and studies suggest that negative affect may at least partially mediate the relationship between pain and substance use (Ditre & Brandon, 2008; Moskal et al., 2018; Witkiewitz, McCallion, et al., 2015). Given this literature, it is possible that pain reductions in this study may have occurred as a result of receiving psychotherapeutic services over the course of treatment and subsequent improvements in mood. These psychotherapeutic services may have also reduced transdiagnostic factors implicated in substance use and pain, such as distress intolerance and pain catastrophizing (Ditre et al., 2019; Kosiba et al., 2018, 2020). Such factors can be successfully addressed with cognitive behavioral interventions (McHugh et al., 2014; Smeets et al., 2006), which are commonly implemented in treatment centers and have demonstrated efficacy in SUD treatment (Magill et al., 2019; Substance Abuse and Mental Health Services Administration, 2020b). Overall, future research is needed to characterize mechanisms underlying observed pain reductions and further characterize the trajectory of pain during and after SUD treatment.

The current findings highlight SUD treatment as a potential resource to manage pain among people with SUDs. As such, it remains critical that timely SUD treatment be accessible to those in need. Existing data suggest that gaps exist between those experiencing SUD symptoms and receipt of treatment services (Substance Abuse and Mental Health Services Administration, 2020a), and racial/ethnic disparities in SUD treatment access, utilization, and retention have also been consistently documented (Acevedo et al., 2018; Bluthenthal et al., 2007; Matsuzaka & Knapp, 2020; Pinedo, 2021; Pinedo et al., 2018). Our sample was predominantly White and non-Hispanic, which may be reflective of these treatment disparities and suggests the need for additional research in this area with sociodemographically diverse samples. Nonetheless, increasing the availability of SUD treatment services should remain an important priority for health care systems and treatment centers given the frequent co-occurrence of SUD and chronic pain. It may also be beneficial for treatment centers to incorporate integrated treatments for pain and substance use (Barrett & Chang, 2016; Barry et al., 2019; Ilgen et al., 2016) to facilitate continued improvements in overall treatment outcomes. Future research should explore barriers to the successful integration of pain and SUD treatment and potential solutions to address them.

Several limitations and avenues for future research should be considered for the current study. First, our sample was not representative of typical treatment-seeking populations. Approximately 90 % of our sample identified as White and non-Hispanic. In addition, given the status of the treatment center as a national hub for professionals in substance use recovery, many had completed a doctoral degree. While our data provide an important opportunity to examine trajectories of pain and treatment-related psychosocial outcomes in a unique population, future studies should examine these relationships in more diverse samples (e.g., racial/ethnic groups, other education levels) to improve generalizability of the current findings. Second, information regarding current substance use, substance use history, and severity of SUD diagnosis was not collected during the treatment assessments. Data related to substance use patterns and diagnosis would be helpful to further contextualize the pain trajectory throughout SUD treatment. Additionally, lapses or relapses in substance use during treatment were possible and may have affected pain and other treatment outcomes measured in this study. Regarding pain, measurements of pain intensity and pain interference were also limited to the past 7 days and did not include information regarding pain duration or etiology. Future work should attempt to frequently measure substance use and pain throughout SUD treatment to more closely characterize this relationship and identify potential moderators, such as severity of SUD diagnosis and pain type and duration. Objective measures of substance use may also be valuable to corroborate self-report in future research efforts. Finally, our study was unable to assess the long-term clinical significance of the pain reductions observed. Reductions in pain have been associated with lower risk of relapse following alcohol treatment (Jakubczyk et al., 2016), and additional research should seek to understand if pain reductions are also associated with lower risk of relapse and post-treatment outcomes (e.g., abstinence self-efficacy, craving) for other substances. It may also be important for future studies to focus on individuals whose pain increased during SUD treatment to examine factors influencing their pain trajectories and associations with treatment outcomes.

In summary, the current results provide evidence that pain reductions occur during SUD treatment, particularly for those with OUD. Increases in pain were also related to poorer treatment outcomes, including reduced quality of life and abstinence self-efficacy, and greater craving and negative affect. Broadly, these findings demonstrate the importance of SUD treatment for pain management and suggest a need for increased accessibility of treatment. Continued research is warranted to examine potential mechanisms of the observed effects and further characterize the trajectory of pain and substance use during and after treatment.

Supplementary Material

Supplemental Material

Acknowledgements

This research was funded by the Arthur Tabb Fitch Hardy Fund for Research in Addiction Psychiatry. Support for the research team was provided by National Institute on Alcohol Abuse and Alcoholism grants F31AA028696 (EF), R01AA025337 (JB), and R21AA026805 (JB); and the University of Florida Pottash Professorship in Psychiatry and Neuroscience (ST).

Footnotes

Declaration of competing interest

The authors declare no conflicts of interest.

CRediT authorship contribution statement

Erin Ferguson: Conceptualization, Methodology, Formal analysis, Data curation, Writing – original draft, Visualization. Ben Lewis: Methodology, Investigation, Resources, Data curation, Writing – review & editing, Supervision, Project administration. Scott Teitelbaum: Methodology, Investigation, Resources, Writing – review & editing, Supervision, Project administration. Gary Reisfield: Methodology, Investigation, Resources, Writing – review & editing, Supervision, Project administration. Michael Robinson: Writing – review & editing. Jeff Boissoneault: Conceptualization, Methodology, Formal analysis, Data curation, Writing – review & editing, Visualization, Supervision.

Appendix A. Supplementary data

Supplementary data to this article can be found online at https://doi.org/10.1016/j.jsat.2022.108892.

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