Abstract
Background:
Understanding the course of anxiety and depressive symptoms in early opioid use disorder (OUD) treatment may inform efforts to promote positive early treatment response and reduce treatment attrition.
Methods:
Persons in treatment for nonmedical opioid use were identified from 86 addiction treatment facilities. Growth mixture modeling was used to identify trajectories of anxiety and depression symptoms during the first month of treatment among individuals who screened positive for depression (N=3016) and/or anxiety (N=2779) at intake.
Results:
A three-class solution best fit the data for anxiety symptoms and included the following trajectories: 1) persistent moderate-to-severe anxiety symptoms, 2) remitting severe anxiety symptoms, and 3) persistent minimal-to-mild anxiety symptoms. Similarly, a three-class solution best fit the data for depressive symptoms and included trajectories characterized by 1) persistent moderate-to-severe depressive symptoms, 2) persistent moderate depressive symptoms, and 3) mild/remitting depressive symptoms. Persistent moderate-to-severe anxiety and depressive symptoms were predicted by female gender and heavy past-month benzodiazepine co-use.
Limitations:
Fine grained-information about substance use was not collected. Results may not be generalizable to individuals receiving treatment outside of specialty addiction clinics.
Conclusions:
Analysis of anxiety and depression symptom trajectories in early treatment suggest that a subset of individuals entering treatment for opioid use experienced persistent and significant anxiety and depressive symptoms, whereas others experience a remission of symptoms. Interventions designed to target individuals at the greatest risk, such as women and individuals reporting opioid/benzodiazepine co-use, may help improve mental health symptoms in early OUD treatment.
Keywords: opioids, anxiety, depression, polysubstance use, trajectories, sex differences
INTRODUCTION
Rates of opioid use disorder (OUD) and opioid-related overdoses have increased substantially over the past 20 years (Martins et al., 2017; Mitchell et al., 2021; Rudd et al., 2016; Slavova et al., 2020). Although there are a number of gold-standard treatments available for opioid use disorder such as methadone, buprenorphine/suboxone, and extended-release naltrexone treatment, a substantial portion of individuals with OUD do not access these treatments (Askari et al., 2020; Hadland et al., 2020; Huhn et al., 2020), and may be at greater risk for early disengagement from treatment (Askari et al., 2020; Hadland et al., 2020). However, even among gold-standard treatments, treatment discontinuation is often high, albeit highly variable, with a recent systematic review suggesting that three-month retention in MOUD treatment ranged from 19–94% (Timko et al., 2016). Engagement and treatment of symptoms during early OUD treatment and in shorter-duration programming (i.e., 30-day residential programs) are critical in preventing premature discharge and improving quality of life.
Addressing co-occurring symptoms and affective distress may be one way to promote engagement and retention in early treatment. Anxiety and depressive symptoms are common among persons in treatment for OUD (Subramaniam and Stitzer, 2009; Webster, 2017). This comorbidity is thought to be due to a combination of factors; including shared risk factors of OUD and anxiety/depression symptoms (Levin et al., 2021), negative reinforcement (i.e., temporary alleviation of anxiety/depression symptoms through use of opioids (Koob, 2015)), and stressful life circumstances that are caused or exacerbated by OUD. These symptoms may be associated treatment attrition and drug use in some individuals (Carroll et al., 2018; Ferri et al., 2014), though explorations of the course of symptoms during the early stabilization period remain sparse. Previous work has highlighted the clinical relevance of persistent and non-remitting comorbidities. One longitudinal study during early OUD treatment classified patients in treatment for prescription OUD based on trajectories of depression and pain, as well as opioid relapse. Four subgroups were identified: 1)“Low relapse”, 2)“High depression and moderate pain”, 3)“High pain”, and 4)“High relapse. Nearly all of the patients within the latter three groups had returned to opioid use within twelve weeks, highlighting the relevance of comorbidities in OUD treatment (Vest et al., 2020). A separate study examining trajectories of stress found that individuals with moderate and stable stress had the highest rates of drug use (Burgess-Hull et al., 2021).
Further, there is a pressing need to identify demographic and substance use characteristics associated with the course of anxiety and depressive symptoms among persons in OUD treatment. For example, women present to treatment with higher incidence of co-occurring conditions, including anxiety and depressive symptoms/disorders (Huhn et al., 2019; Huhn and Dunn, 2020), but may demonstrate a more rapid decrease in symptoms (Wang et al., 2017). Additionally, a large literature has cross-sectionally linked polysubstance use, especially benzodiazepine/sedative use, with co-occurring anxiety (Ellis et al., 2020; Gressler et al., 2018; Hearon et al., 2011; McHugh et al., 2017; Yarborough et al., 2019) and depressive (Ellis et al., 2020; Saunders et al., 2012) symptoms among persons who use opioids. However, it is unclear how anxiety and depressive symptoms change over time among those showing symptoms at admission to OUD treatment.
Individuals seeking treatment for nonmedical opioid use often enter treatment in a state of distress. Early treatment response (i.e., the first four weeks) is critical in establishing rapport and stabilizing patients, and patients in residential treatment often remain inpatient for four-weeks. Persistent mood and anxiety symptoms decrease quality of life and stymie attempts at recovery; however, few studies have looked at symptom trajectories in early treatment. In this multisite, micro-longitudinal study of patients presenting to residential or outpatient treatment for nonmedical opioid use, the primary aims were to 1) identify trajectories of anxiety or depressive symptoms in the first four weeks of treatment, and 2) test the hypothesis that persons seeking treatment for nonmedical opioid use who co-use benzodiazepines are at higher risk for persistently high anxiety and depressive symptoms during early treatment. Secondary aims were to 1) explore additional demographic and substance use correlates associated with trajectories, and 2) explore relationships between membership in the trajectory subgroups and likelihood of discharging AMA.
METHOD
Participants
Individuals seeking treatment for nonmedical opioid use (N=6554) who presented for admission to one of 86 addiction treatment facilities across the U.S. were drawn from a larger dataset (N=39063) collected by a third-party treatment outcomes data collection system (Vista Research Group Inc.). Vista partners with inpatient and outpatient treatment providers to track mental health symptoms and substance use behaviors. The majority of patients were recruited from treatment providers providing highly structured and high-intensity care, including residential facilities (N=2591, 39.5%), detox treatment facilities (N=2428, 37.0%), partial hospitalization programs (N=1010, 15.4%), and intensive outpatient programs (N=445, 6.8%); however, a small number of individuals were recruited from general outpatient or other SUD programs (N=80, 1.2%).
Individuals in the dataset entered treatment between the first quarter of 2014 (January-March) and the third quarter of 2020 (July-September). Because the study team only received de-identified data, the study was submitted to and acknowledged by the Johns Hopkins School of Medicine Institutional Review Board as exempt from human subjects research. A flowchart summarizing eligibility for the present analyses is shown in Figure 1. We used the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting guidelines (Von Elm et al., 2014) for presentation of analyses. Only unique cases were used (i.e., individuals that were readmitted were not counted as new cases).
Figure 1.
Case eligibility flowchart
Inclusion criteria included: 1) opioids or heroin was identified as the patient’s primary substance at intake, 2) the patient used heroin and/or other opioids in the month preceding treatment, and indicated that they perceived their past month use to be heavy. Exclusion criteria included: 1) Heroin or opioids was described as the primary substance, but the respondent indicated that they had never used opioids or heroin on subsequent substance use questions, and 2) Reporting being transgender, due to small cell sizes (n=11). Additional inclusion criteria for the growth mixture model (GMM) analysis included complete cases on covariates of interest, screening positive (i.e., moderate symptoms or higher) for anxiety or depression symptoms at intake, and having at least one in-treatment survey between one-week post-intake and four weeks post-intake.
Procedure
Individuals were administered questionnaires at intake, and separate questionnaires during their treatment to monitor symptoms endorsed at intake. Treatment providers distributed questionnaires to participants electronically. Because items were collected as part of an individual’s ongoing treatment, treatment centers could choose the frequency with which they sought to assess individuals. Thus, the timing and frequency of survey administration differed for surveys administered within the first 30 days (M=6.42 days, SD=2.87 days, median=7). For repeated measures analyses described below, responses were binned by week (week 1=7 days +/−3 days, week 2=14 days +/−3 days, week 3=21 days +/−3 days, week 4=28 days +/−3 days). If individuals completed more than one survey in a given time period, the first survey was used.
Measures
Demographic information.
Participants reported on their age, gender, race, ethnicity, unstable housing status, and education level. Additional information is presented in the supplemental material.
Type of Treatment.
Facility-level information was also collected, and individuals were coded based on whether they attended a residential program (detox treatment and other residential treatment) or a non-residential program (partial-hospitalization day program, intensive outpatient, and general outpatient). Participants also indicated whether they were receiving opioid agonist or partial-agonist medication treatment (e.g., methadone, buprenorphine) at intake, which was collapsed into two categories for the GMM analyses (i.e., on methadone, buprenorphine, Suboxone®, or Sublocade® vs. not on one of these medications).
Substance Use.
Individuals also reported their use of substances (e.g., alcohol, marijuana, benzodiazepines, opioids, heroin) in the month before treatment. Options included 1) Never used, 2) Used in their lifetime, but not in the past month, 3) Used in the 30 days before treatment, but not heavily, and 4) Used heavily in the 30 days before treatment. For the purposes of the present study, we focused on substance use in the past month (i.e., heavy use in the past month vs. no heavy use in the past month, and non-heavy use in the past month vs. heavy use or no use in the past month). Separately, individuals were asked to identify their primary substance (defined for participants as their drug of choice, or the drug that they liked the most and used most often before treatment)). They were subsequently asked about 1) route of administration, and 2) how often they had used their primary substance (i.e., heroin or opioids) in the past month (“Not at all”, “1–3 times per month”, “1–2 days per week”, “3–4 days per week”, “4–6 times per week”, “daily or nearly every day”, “2–3 times per day”, and “4 or more times per day”). These categories were recoded into “less than daily”, “daily or nearly every day”, “2–3 times per day”, and “4 or more times per day”. Participants were not asked how frequently they used substances other than their primary substance (e.g., opioids, heroin). However, among individuals who identified opioids or heroin as their primary substance, the majority (90.3%) of those reporting heavy use of opioids or heroin daily or multiple times per day.
Anxiety.
At intake, individuals completed two pre-screening questions related to anxiety symptoms: 1) “Do you often feel anxious, nervous or on edge?” and 2) “Have you had anxiety attacks? OR “Have you been unable to stop or control worrying?” The second screening question was modified after the survey had been active, and the majority of individuals (N=5143, 78.5%) completed the modified question (see supplemental material for additional information). Participants who endorsed either of the two pre-screening items completed the Generalized Anxiety Disorder-7 (GAD-7), a widely used screening measure for anxiety symptoms at intake. The measure was shown to have strong psychometric properties in the standardization sample of individuals in primary care (Spitzer et al., 2006), and has undergone further psychometric validation in a number of populations, including individuals with substance use disorders (Bentley et al., 2021; Delgadillo et al., 2012). Individuals were grouped into 4 categories at intake based on established cutoffs (Spitzer et al., 2006), including 1) Screened negative for anxiety symptoms (0–4 on the GAD-7, or answered “no” to both pre-screening items), 2) Mild anxiety symptoms (5–9 on the GAD-7), 3) Moderate anxiety symptoms (10–14 on the GAD-7), and 4) Severe anxiety symptoms (15+ on the GAD-7). Participants who reported moderate or severe anxiety symptoms on the GAD-7 at intake were followed up during treatment. Once in treatment, individuals were continuously monitored for anxiety using the GAD-7.
Depression.
At intake, individuals who endorsed either of the first two PHQ items (i.e., feeling down, depressed, or hopeless; or losing little interest or pleasure in doing things) completed the full PHQ-9. The PHQ-9 is a widely used screening measure of depression has been shown to have high reliability and validity (Kroenke et al., 2010, 2001). The measure was initially standardized in patients receiving primary care (Kroenke et al., 2001), but has also shown good psychometric properties among individuals in substance use treatment (Bentley et al., 2021; Dum et al., 2008). Participants were grouped into five categories at intake based on established cutoffs (Kroenke et al., 2001), including 1) Screened negative for depression, 2) Mild depressive symptoms (5–9 on the PHQ-9), 3) Moderate depressive symptoms (10–14 on the PHQ-9), 4) Moderately severe depressive symptoms (15–19 on the PHQ-9), and 5) Severe depressive symptoms (20–27 on the PHQ-9). Depressive symptoms were tracked during treatment among individuals who endorsed moderate, moderately severe, or severe symptoms on the PHQ-9 at intake. Individuals were continuously monitored for depressive symptoms during treatment using the PHQ-9.
Discharge Against Medical Advice (AMA).
For each patient, centers were asked to indicate the treatment outcome for each patient. Centers could select from 20 treatment outcomes. The treatment outcome variable was re-coded into two different binary outcome variables: 1) discharge AMA vs. all other outcomes, and 2) a sensitivity analysis, coded as discharge AMA vs. categories indicating successfully completed treatment, reported in supplemental material.
Data Analysis
Demographic characteristics of the sample based on individuals’ level of anxiety and depressive symptoms at intake are presented in Tables 1 and 2. Differences between individuals with different anxiety and depression severity at intake were tested using chi-squared tests and one-way ANOVA.
Table 1.
Participant demographic information based on anxiety symptom level endorsement at intake
| No/Minimal Anxiety Symptoms (N = 1157) | Mild Anxiety Symptoms (N = 1136) | Moderate Anxiety Symptoms (N = 1422) | Severe Anxiety Symptoms (N = 2839) | χ2 or F | p-value | |
|---|---|---|---|---|---|---|
| Sex | 140.97 | <.001* | ||||
| Male | 917 (79.3%) | 850 (74.8%) | 1027 (72.1%) | 1770 (62.3%) | ||
| Female | 240 (20.7%) | 286 (25.2%) | 395 (27.8%) | 1069 (37.7%) | ||
| Race/Ethnicity | 15.22 | .436 | ||||
| White, non-Hispanic | 941 (81.3%) | 928 (81.7%) | 1196 (84.1%) | 2348 (82.7%) | ||
| African American | 64 (5.5%) | 54 (4.8%) | 52 (3.7%) | 109 (3.8%) | ||
| Hispanic or Latino | 99 (8.6%) | 96 (8.5%) | 105 (7.4%) | 221 (7.8%) | ||
| Asian | 8 (0.7%) | 9 (0.8%) | 6 (0.4%) | 25 (0.9%) | ||
| Native American | 11 (1.0%) | 11 (1.0%) | 19 (1.3%) | 33 (1.2%) | ||
| Other | 34 (2.9%) | 38 (3.3%) | 44 (3.1%) | 103 (3.6%) | ||
| Age, M(SD) | 31.81 (11.19) | 30.82 (10.07) | 30.56 (10.02) | 30.49 (9.72) | 4.97 | .002* |
| Employment Status | 97.78 | <.001* | ||||
| Employed full or part time | 820 (70.9%) | 772 (68.0%) | 910 (64.0%) | 1598 (56.3%) | ||
| Unemployed | 336 (29.1%) | 364 (32.0%) | 111 (36.0%) | 1241 (43.7%) | ||
| Education Level | 40.03 | .002* | ||||
| < High school | 116 (10.0%) | 116 (10.2%) | 147 (10.3%) | 286 (10.1%) | ||
| High school or GED | 446 (38.5%) | 426 (37.5%) | 523 (36.8%) | 1011 (35.6%) | ||
| Some college | 324 (28.0%) | 322 (28.3%) | 464 (32.6%) | 916 (32.3%) | ||
| Associate’s degree | 74 (6.4%) | 116 (10.2%) | 110 (7.7%) | 199 (7.0%) | ||
| Bachelor’s Degree | 123 (10.6%) | 97 (8.5%) | 105 (7.4%) | 260 (9.2%) | ||
| Masters or PhD | 29 (2.5%) | 27 (2.4%) | 21 (1.5%) | 54 (1.9%) | ||
| Other | 45 (3.9%) | 32 (2.8%) | 52 (3.7%) | 113 (4.0%) | ||
| Unstable Housing Status | 167 (14.4%) | 219 (19.3%) | 289 (20.3%) | 753 (26.5%) | 79.86 | <.001* |
| Marital Status | 13.53 | .035* | ||||
| Married | 229 (19.8%) | 198 (17.4%) | 239 (16.8%) | 503 (17.7%) | ||
| Single, Never Married | 802 (69.3%) | 835 (73.5%) | 1030 (72.4%) | 1988 (70.0%) | ||
| Single, Previously Married | 126 (10.9%) | 103 (9.1%) | 153 (10.8%) | 348 (12.3%) | ||
| Medication treatment | 28.28 | <.001* | ||||
| Methadone | 15 (1.3%) | 16 (1.4%) | 19 (1.3%) | 47 (1.7%) | ||
| Buprenorphine | 139 (12.0%) | 170 (15.0%) | 246 (17.3%) | 521 (18.4%) | ||
| None of these | 1003 (86.7%) | 950 (83.8%) | 1157 (81.4%) | 2271 (80.0%) |
Table 2.
Participant demographic information based on depression symptom level endorsement at intake
| No/Minimal Depressive Symptoms (N = 971) | Mild Depressive Symptoms (N = 900) | Moderate Depressive Symptoms (N = 1113) | Moderately Severe Depressive Symptoms (N = 1471) | Severe Depressive Symptoms (N = 2099) | χ2 or F | p-value | |
|---|---|---|---|---|---|---|---|
| Sex | 170.52 | <.001* | |||||
| Male | 746 (76.8%) | 707 (78.6%) | 839 (75.4%) | 1016 (69.1%) | 1256 (59.8%) | ||
| Female | 225 (23.2%) | 193 (21.4%) | 274 (24.6%) | 455 (30.9%) | 843 (40.2%) | ||
| Race/Ethnicity | 15.64 | .739 | |||||
| White, non-Hispanic | 784 (80.7%) | 737 (81.9%) | 913 (82.0%) | 1224 (83.2%) | 1755 (83.6%) | ||
| African American | 51 (5.3%) | 42 (4.7%) | 51 (4.6%) | 59 (4.0%) | 76 (3.6%) | ||
| Hispanic or Latino | 84 (8.7%) | 79 (8.8%) | 94 (8.4%) | 113 (7.7%) | 151 (7.2%) | ||
| Asian | 9 (0.9%) | 6 (0.7%) | 7 (0.6%) | 7 (0.5%) | 19 (0.9%) | ||
| Native American | 15 (1.5%) | 8 (0.9%) | 12 (1.1%) | 17 (1.2%) | 22 (1.0%) | ||
| Other | 28 (2.9%) | 28 (3.1%) | 36 (3.2%) | 51 (3.5%) | 76 (3.6%) | ||
| Age, M(SD) | 31.25 (10.57) | 31.27 (10.48) | 30.44 (9.95) | 30.44 (9.77) | 30.82 (10.12) | 1.78 | .130 |
| Employment Status | 183.11 | <.001* | |||||
| Employed full or part time | 699 (72.0%) | 625 (69.4%) | 770 (69.2%) | 922 (62.7%) | 1084 (51.6% | ||
| Unemployed | 272 (28.0%) | 275 (30.6%) | 342 (30.8%) | 548 (37.3%) | 1015 (48.4%) | ||
| Education Level | 33.62 | .092 | |||||
| < High school | 102 (10.5%) | 96 (10.7%) | 117 (10.5%) | 127 (8.6%) | 223 (10.6%) | ||
| High school or GED | 358 (36.9%) | 336 (37.3%) | 419 (37.6%) | 548 (37.3%) | 745 (35.5%) | ||
| Some college | 286 (29.5%) | 254 (28.2%) | 341 (30.6%) | 464 (31.5%) | 681 (32.4%) | ||
| Associate’s degree | 73 (7.5%) | 70 (7.8%) | 107 (9.6%) | 100 (6.8%) | 149 (7.1%) | ||
| Bachelor’s Degree | 93 (9.6%) | 93 (10.3%) | 83 (7.5%) | 135 (9.2%) | 181 (8.6%) | ||
| Masters or PhD | 25 (2.6%) | 19 (2.1%) | 18 (1.6%) | 31 (2.1%) | 38 (1.8%) | ||
| Other | 34 (3.5%) | 32 (3.6%) | 28 (2.5%) | 66 (4.5%) | 82 (3.9%) | ||
| Unstable housing status | 146 (15.0%) | 155 (17.2%) | 212 (19.1%) | 287 (19.5%) | 628 (29.9%) | 127.65 | <.001* |
| Marital Status | 18.14 | .020* | |||||
| Married | 191 (19.7%) | 162 (18.0%) | 214 (19.2%) | 245 (16.7%) | 357 (17.0%) | ||
| Single, Never Married | 687 (70.8%) | 645 (71.7%) | 796 (71.5%) | 1057 (71.9%) | 1470 (70.0%) | ||
| Single, Previously Married | 93 (9.6%) | 93 (10.3%) | 103 (9.3%) | 169 (11.5%) | 272 (13.0%) | ||
| Medication treatment | 18.52 | .018* | |||||
| Methadone | 8 (0.8%) | 10 (1.1%) | 19 (1.7%) | 26 (1.8%) | 34 (1.6%) | ||
| Buprenorphine | 143 (14.7%) | 121 (13.4%) | 184 (16.5%) | 247 (16.8%) | 381 (18.2%) | ||
| None of these | 820 (84.4%) | 769 (85.4%) | 910 (81.8%) | 1198 (81.4%) | 1684 (80.2%) |
Two GMMs were conducted; the first included individuals who screened positive for anxiety symptoms at intake, and the second included individuals who screened positive for depression symptoms at intake. These GMMs captured trajectories of anxiety and depression symptoms over time during treatment (i.e., post-intake, week 1 through week 4). The GAD-7 and PHQ-9 were analyzed as continuous measures. Not all individuals who screened positive for depressive symptoms also screened positive for anxiety symptoms, and vice versa (57.9% screened positive for both disorders). Thus, we did not examine dual symptom trajectories in the same model. Instead, for the GMM examining anxiety-symptom trajectories, we included a covariate indicating whether an individual had screened positive for co-occurring depressive symptoms at intake. For the GMM examining depression-symptom trajectories, we included a covariate indicating whether an individual had screened positive for co-occurring anxiety symptoms at intake. Missing data across timepoints ranged from 30.3–55.0%. We retained all cases and used full information maximum likelihood (FIML) to handle missingness on depression and anxiety symptoms at different timepoints (Johnson, 2021; Lang and Little, 2018).
Models were compared using the Akaike’s Information Criteria (AIC), Bayesian Information Criteria (BIC), sample size adjusted BIC, the size of the smallest class size (>5%), entropy, and the Lo-Mendell-Rubin Adjusted Likelihood Ratio Test (LRT). After initial model fitting, the Bootstrapped Likelihood Ratio Test (BLRT) test was used to compare the k and k-1 class models (Nylund et al., 2007). After model selection, covariates were added to the model to examine whether these variables influenced membership in the growth trajectories using multinomial logistic regression. Covariates included sex, age, heavy past-month benzodiazepine use, other substance use (i.e., alcohol, marijuana, stimulants), and MOUD status. We examined whether anxiety and depressive symptom trajectories were differentially predictive of discharging AMA, which adjusted for the covariates described above. P-values < .05 in bivariate tests and odds ratio 95% confidence intervals that did not contain 1 in GMM models were interpreted as statistically significant. Descriptive data analyses were conducted in SPSS version 25 (Armonk, NY; 24), and GMMs were conducted in MPlus Version 8.6 (Los Angeles, CA; 25).
RESULTS
Descriptive Information
As shown in Tables 1 and 2, anxiety and depressive symptoms at intake were common among individuals presenting for treatment for nonmedical opioid use (N = 6554). As shown in Tables 1 and 2, greater anxiety and depression severity at intake were associated with female gender, being unemployed, unstable housing status, education level, and marital status. Individuals receiving a medication for opioid use disorder (MOUD) at intake were also more likely to report more severe depression/anxiety symptoms.
Characterization of Anxiety and Depressive Symptoms Trajectories
For both anxiety and depressive symptom models, a three-class model was selected because of a combination of factors, including 1) the smallest class in the four-class model was <5%, which may indicate model overfitting, 2) the classes identified were clinically distinct (Figure 2) For the anxiety GMM, entropy decreased in the four-class model. As shown in Figure 2, three anxiety symptom trajectories were identified: 1) persistent (i.e., stable) moderate-to-severe anxiety symptoms (N=389, 14.0%), 2) remitting (i.e., decreasing) severe anxiety symptoms (N=390, 14.0%), and 3) persistent minimal-to-mild anxiety symptoms (N=2000, 72.0%). Similarly, three depressive symptom trajectories were observed: 1) persistent moderate-to-severe depressive symptoms (N=158, 5.2%), 2) persistent moderate depressive symptoms (N=501, 16.6%), and 3) remitting mild depressive symptoms (N=2357, 78.2%).
Figure 2. Anxiety and Depressive Symptom Trajectories (Weeks 1–4 in treatment).
Trajectories of anxiety and depressive symptoms one week post-intake to four weeks post-intake. The GAD-7 and PHQ-9 were included in each model as a continuous measure; however, symptom cut points (i.e., severe anxiety symptoms, moderate anxiety symptoms, mild anxiety symptoms, minimal anxiety symptoms for the anxiety model, and moderately severe depressive symptoms, moderate depressive symptoms, mild depressive symptoms, minimal depressive symptoms) are included for interpretability.
Correlates of Anxiety Trajectories
With the inclusion of covariates and discharging AMA, class sizes remained stable (class size ranged from 14.0%−72.0% without covariates and discharging AMA, and 14.0%−70.7% with covariates and discharging AMA) for the model reflecting anxiety symptom trajectories (Table 3). Model fit indices for the model with covariates and the outcome were as follows: AIC=38143.62, BIC=38511.27, SSA-BIC=38314.27, entropy=.703). Regression results are presented in Table 4. Relative to the persistent minimal-to-mild anxiety symptoms trajectory, membership in the persistent moderate-to-severe anxiety symptoms trajectory was predicted by heavy benzodiazepine use in the month before treatment and female sex. Relative to the persistent minimal-to-mild anxiety symptoms trajectory, membership in the remitting severe anxiety symptom trajectory group was predicted by heavy alcohol use in the month before treatment, and younger age.
Table 3.
Fit indices 1–5 class for anxiety and depression symptom GMM models
| Log-likelihood | AIC | BIC | SSA-BIC | Entropy | Adj. LRT Test p-value | BLRT Test p-value | Smallest class size | |
|---|---|---|---|---|---|---|---|---|
| Anxiety (N = 2779) | ||||||||
| 1 class | −18596.879 | 37191.758 | 37245.127 | 37216.531 | -- | -- | -- | -- |
| 2 class | −18344.882 | 36713.763 | 36784.921 | 36746.793 | .764 | <.001 | <.001 | 14.8% |
| 3 class | −18273.123 | 36576.245 | 36665.193 | 36617.533 | .691 | <.001 | <.001 | 14.0% |
| 4 class | −18226.515 | 36489.030 | 36595.768 | 36538.576 | .639 | <.001 | <.001 | 4.8% |
| 5 class | −18206.580 | 36455.160 | 36579.687 | 36512.963 | .659 | .0621 | <.001 | 1.4% |
| Depression (N = 3016) | ||||||||
| 1 class | −20108.596 | 40235.192 | 40289.297 | 40260.701 | -- | -- | -- | -- |
| 2 class | −19883.730 | 39791.460 | 39863.600 | 39825.471 | .803 | <.001 | <.001 | 9.2% |
| 3 class | −19798.767 | 39627.534 | 39717.709 | 39670.048 | .685 | .0458 | <.001 | 5.2% |
| 4 class | −19740.432 | 39516.864 | 39625.074 | 39567.881 | .689 | .0203 | <.001 | 4.6% |
| 5 class | −19689.235 | 39420.471 | 39546.716 | 39479.991 | .702 | .4038 | <.001 | 2.7% |
Bolded model reflects the model that was ultimately selected.
Note. AIC = Akaike information criterion, BIC = Bayesian information criterion, SSA-BIC = Sample size adjusted Bayesian information criterion, LRT = Likelihood-ratio test, BLRT = Bootstrapped-likelihood ratio test.
Table 4.
Predictors of Anxiety and Depressive Symptom Trajectories
| Adjusted OR | SE | 95% CI | Adjusted OR | SE | 95% CI | ||
|---|---|---|---|---|---|---|---|
| MODEL 1: ANXIETY SYMPTOM TRAJECTORIES (N = 2779) | MODEL 2: DEPRESSIVE SYMPTOM TRAJECTORIES (N = 3016) | ||||||
| Persistent Anxiety Symptoms 1 | Persistent Moderately Severe Depressive Symptoms 2 | ||||||
| Female Sex | 1.98 | 0.31 | 1.46–2.67* | Female Sex | 2.30 | 0.58 | 1.40–3.77* |
| Age | 1.00 | 0.01 | 0.98–1.01 | Age | 1.02 | 0.01 | 1.00–1.04 |
| Benzodiazepine use (Past month - heavy) | 1.87 | 0.35 | 1.30–2.71* | Benzodiazepine use (Past month - heavy) | 1.81 | 0.47 | 1.08–3.02* |
| Benzodiazepine use (Past month – not heavy) | 1.31 | 0.24 | 0.92–1.88 | Benzodiazepine use (Past month – not heavy) | 1.13 | 0.29 | 0.68–1.88 |
| Alcohol use (Past month - heavy) | 1.28 | 0.27 | 0.85–1.94 | Alcohol use (Past month - heavy) | 1.02 | 0.32 | 0.56–1.89 |
| Alcohol use (Past month – not heavy) | 0.97 | 0.17 | 0.68–1.37 | Alcohol use (Past month – not heavy) | 0.82 | 0.21 | 0.40–1.35 |
| Cannabis use (Past month - heavy) | 1.15 | 0.22 | 0.79–1.67 | Cannabis use (Past month - heavy) | 1.96 | 0.30 | 0.62–1.84 |
| Cannabis use (Past month – not heavy) | 0.89 | 0.16 | 0.64–1.26 | Cannabis use (Past month – not heavy) | 1.03 | 0.27 | 0.62–1.72 |
| Positive Depression Screen at Intake | 1.52 | 0.42 | 0.89–2.59 | Positive Anxiety Screen at Intake | 1.74 | 0.56 | 0.92–3.27 |
| Residential/Detox Treatment1 | 1.15 | 0.24 | 0.77–1.72 | Residential/Detox Treatment1 | 0.80 | 0.21 | 0.48–1.33 |
| On Methadone or Buprenorphine/Suboxone | 1.30 | 0.23 | 0.91–1.84 | On Methadone or Buprenorphine/Suboxone | 1.71 | 0.46 | 1.00–2.91 |
| Remitting Anxiety Symptoms 1 | Persistent Moderate Depressive Symptoms 2 | ||||||
| Female Sex | 1.34 | 0.34 | 0.82–2.21 | Female Sex | 1.32 | 0.23 | 0.94–1.85 |
| Age | 0.97 | 0.01 | 0.95–0.99* | Age | 0.99 | 0.01 | 0.97–1.00 |
| Benzodiazepine use (Past month - heavy) | 1.70 | 0.49 | 0.97–2.98 | Benzodiazepine use (Past month - heavy) | 1.56 | 0.31 | 1.06–2.30* |
| Benzodiazepine use (Past month – not heavy) | 1.07 | 0.31 | 0.61–1.88 | Benzodiazepine use (Past month – not heavy) | 0.91 | 0.18 | 0.63–1.33 |
| Alcohol use (Past month - heavy) | 2.15 | 0.56 | 1.29–3.60* | Alcohol use (Past month - heavy) | 1.10 | 0.28 | 0.66–1.82 |
| Alcohol use (Past month – not heavy) | 0.93 | 0.22 | 0.58–1.47 | Alcohol use (Past month – not heavy) | 0.89 | 0.17 | 0.61–1.29 |
| Cannabis use (Past month - heavy) | 0.94 | 0.20 | 0.61–1.43 | Cannabis use (Past month - heavy) | 1.18 | 0.25 | 0.77–1.80 |
| Cannabis use (Past month – not heavy) | 0.75 | 0.18 | 0.48–1.19 | Cannabis use (Past month – not heavy) | 1.15 | 0.22 | 0.79–1.68 |
| Positive Depression Screen at Intake | 1.76 | 0.97 | 0.60–5.18 | Positive Anxiety Screen at Intake | 1.32 | 0.27 | 0.89–1.97 |
| Residential/Detox Treatment3 | 1.34 | 0.41 | 0.74–2.42 | Residential/Detox Treatment1 | 0.96 | 0.18 | 0.66–1.39 |
| On Methadone or Buprenorphine/Suboxone | 1.06 | 0.27 | 0.64–1.76 | On Methadone or Buprenorphine/Suboxone | 0.98 | 0.23 | 0.63–1.55 |
Denotes confidence interval does not contain 1
Reference group = Persistent minimal-to-mild anxiety symptoms,
Reference group = Mild/remitting depressive symptoms,
Reference group = In outpatient or intensive outpatient.
Correlates of Depression Trajectories
In the depression models, class sizes remained stable with the inclusion of covariates and treatment attrition in the model (5.2%−78.2% without covariates and treatment attrition, and 5.3%−77.3% with covariates and treatment attrition). Model fit indices with covariates and treatment attrition as an outcome were as follows: AIC=41328.64, BIC=41701.36, SSA-BIC=41504.37, entropy=.699. As shown in Table 4, relative to the mild/remitting depressive symptom trajectory, membership in the persistent moderate-to-severe depressive symptom trajectory was predicted by heavy benzodiazepine use in the month before treatment and female sex. Membership in the persistent moderate depressive symptom trajectory was predicted by heavy benzodiazepine use in the past month.
Symptom Trajectories and Discharging AMA
Relative to the mild/remitting depressive symptom trajectory, membership in the persistent moderate depressive symptom trajectory was associated with greater likelihood of discharging AMA (OR=1.62, SE=0.38, 95% CI=1.02–2.57); however, membership in the persistent moderately-severe depressive symptoms trajectory relative to the mild/remitting trajectory was not related to discharging AMA (OR=1.06, SE=0.41, 95% CI=0.50–2.27). Membership among different anxiety symptom trajectory subgroups was unrelated to discharging AMA. The same pattern of results was observed in the sensitivity analysis, reported in supplemental material.
DISCUSSION
This study identified clinically meaningful subgroups of mental health symptom trajectories among individuals entering treatment for nonmedical opioid use and presenting with depression and/or anxiety symptoms at intake. For example, clinically significant anxiety symptoms remitted for some individuals but persisted in others, suggesting that there is substantial individual variation in the resolution of anxiety symptoms during the first four weeks of treatment. On the other hand, depressive symptoms did not change appreciably during the first four weeks of treatment after treatment, suggesting that depressive symptoms may persist for many individuals entering treatment for nonmedical opioid use. This is the first study to our knowledge to examine associations between co-occurring substance use and depression and anxiety symptom trajectories during early treatment.
Individuals who experienced non-remitting anxiety and/or depressive symptoms during early treatment were more likely to report using benzodiazepines heavily in the month before treatment, extending previous work that has cross-sectionally linked benzodiazepine and opioid co-use to anxiety and/or depressive symptoms (Ellis et al., 2020; Gressler et al., 2018; Hearon et al., 2011; McHugh et al., 2017; Saunders et al., 2012; Yarborough et al., 2019). It is possible that unpleasant, non-remitting symptoms are a motive for opioid/benzodiazepine co-use; however, this hypothesis should be further explored by explicitly assessing motives for use. Additionally, future work should explore whether non-remitting anxiety and depressive symptoms during treatment (vs. low anxiety or depression symptoms) are associated with returning to use following discharge.
Of note, membership in the remitting anxiety symptom group was also associated with heavy alcohol use in the month prior to treatment. It is possible that these patients experienced rebound anxiety upon ceasing alcohol use that resolved with sustained abstinence, or that anxiety symptoms associated with alcohol withdrawal may remit in a subset of patients with OUD. Among patients who initially report high levels of anxiety symptoms followed by an improvement of symptoms, continued monitoring following discharge from treatment is needed. For residential treatment in particular, individuals may experience a re-occurrence of anxiety symptoms when they leave treatment and return home, where significant stressors may still be present.
Results also suggested that women were at a higher risk of persistent anxiety and depression symptoms. These findings are consistent with a recent systematic review suggesting that women are more likely to present to OUD treatment with co-occurring mental health issues and specifically depressive symptoms/disorders (Huhn et al., 2019; Huhn and Dunn, 2020). These results are also partially consistent with a previous study that suggested that women reported higher depressive symptoms than men at intake and sharper subsequent declines in depressive symptoms from intake to a three-month follow-up. However, between 3 and 9 months of treatment, depression scores stayed relatively stable for women (and marginally or significantly higher than men; Wang et al., 2017). Women may also present to treatment with a number of additional clinically relevant barriers that may exacerbate or increase the likelihood of depressive symptoms, such as stigma, intimate partner violence, and challenges related to childcare and family planning (Huhn and Dunn, 2020). Thus, interventions that address these concerns among women specifically may be beneficial.
Membership in the persistent moderate depressive symptoms group relative to the mild/remitting depressive symptom trajectory was associated with greater likelihood of discharging AMA. However; membership in the other persistent symptom trajectory groups were unrelated to discharging AMA. It is possible that individuals presenting with more severe and persistent symptoms receive greater attention by providers, or that patients recognize the severity of their condition and are thus motivated to remain in treatment. In line with this hypothesis, a recent study found that individuals with major depressive disorder spent a greater proportion of follow-up months in medication treatment for OUD than those without a comorbid disorder, despite continuing to experience difficulties with substance use and psychiatric symptoms (Zhu et al., 2021). Individuals with depressive symptoms may require special attention in treatment, and future work should further explore moderators of the depressive symptoms/treatment retention relationship, such as demographic and substance use characteristics, symptom severity, and protective factors.
Additionally, future work should explore how to best address persistent symptoms in early treatment. Previous work suggests that approximately half of individuals with depressive symptoms do not perceive a need for treatment of depressive symptoms (Stein et al., 2017). Additionally, antidepressant treatment in early buprenorphine treatment did not improve depressive symptoms or increase likelihood of retention (Stein et al., 2019). Certain psychological interventions (i.e., dialectical behavior therapy) in combination with methadone treatment have some promise in reducing depressive symptoms relative to methadone treatment alone (Rezaie et al., 2021). Unfortunately, we do not have fine-grained information about how psychiatric and psychological treatment varied across centers in the present study, precluding opportunities to explore whether specific interventions are associated with better treatment response or symptom trajectories. The effect of treatment approach should be explored in future work, as depression and anxiety symptoms may be moderated by treatment type and center-level characteristics. Identifying these factors may assist with moving towards a precision medicine approach of treating psychiatric comorbidity in OUD.
Limitations
Individuals only received the full PHQ-9 or GAD-7 at intake if they endorsed initial pre-screening items related to specific anxiety and depressive symptoms; thus, it is possible that individuals who may have endorsed additional symptoms on the PHQ-9 or GAD-7 at intake were identified as negative screens and were not eligible for inclusion. Further, formal diagnostic criteria for opioid use disorder (American Psychiatric Association, 2013) and fine-grained information of non-opioid substance use in the month before treatment was not collected, and participants self-reported other drug use as heavy or not heavy. While this self-report metric is not optimal, it does represent, to some degree, the real-world perceptions of a given person’s drug use, is thus has ecological validity in the context of mental health symptom trajectories. Future work should explore symptom trajectories not only during treatment, but post-treatment.
Due to the small numbers of individuals who identified as transgender and among individuals of specific racial and ethnic groups, we were unable to fully explore the effects of being in a marginalized group on treatment outcomes, something that should be explored in future work. We do not have fine-grained information on psychiatric care provided to patients, and it is likely that centers differed with regard to mental health care practices, yet the centers were similar in that they offered some level of behavioral care (i.e., they were not primary care physicians providing only medication treatment). Thus, the results of this study are specific to persons seeking treatment in specialty addiction facilities and not primary care providers. Finally, we did not examine withdrawal or toxicology screen data in the present study, both of which may influence mood symptoms. Mood symptoms exacerbated by withdrawal may still be clinically relevant if they influence quality of life and AMA risk. However, consistent inclusion of withdrawal in future studies may help clarify the extent to which depression/anxiety is influenced by withdrawal. For example, future work should examine 1) the time course of depression and anxiety symptoms during withdrawal, and 2) associations between withdrawal severity and severity of depression and anxiety symptoms. Future work should also examine trajectories of mood symptoms during protracted withdrawal. Anhedonia (Huhn et al., 2016b) and negative mood (Welsch et al., 2020) have both been observed among individuals with opioid use disorder following withdrawal, and both low PA and high NA have been linked to craving during this period (Huhn et al., 2016a). Thus; examining symptom trajectories following withdrawal may help identify which individuals are at a high risk for persistent affective symptoms during early abstinence.
Conclusions
This study examined trajectories of anxiety and depressive symptoms during the first four weeks of treatment for nonmedical opioid use. Results suggest that although some individuals experience a remission of symptoms following presentation to treatment, a subset of individuals experienced non-remitting symptoms. Co-occurring benzodiazepine use was associated with persistent anxiety and depressive symptoms in this study. Women may also be particularly prone to persistent symptoms. This study highlights the need for tailored interventions among specific subgroups to improve mental health symptoms in OUD treatment.
Supplementary Material
Highlights.
Discharge against medical advice is common among individuals in OUD treatment.
Growth-mixture modeling was used to identify anxiety and depression symptom trajectories.
Women experienced more persistent moderate-to-severe anxiety and depression symptoms.
Benzodiazepine use was associated with persistent anxiety and depression.
Persistent moderate depression was associated with greater risk of discharge AMA.
Acknowledgement:
The authors acknowledge that the reported results are, in whole or in part, based on analyses of addiction treatment outcomes research collected by Vista Research Group, Inc. This manuscript may not represent the opinions of Vista Research Group, Inc., nor is Vista Research Group, Inc. responsible for its contents.
Role of funding source:
This study was supported by the National Institute on Drug Abuse T32 DA007209 (Bigelow) and UG3DA048734 (Huhn).
Declaration of interest:
ASH receives research funding from Ashley Addiction Treatment through his university. PHF is on the scientific advisory board for Ninnion. The other authors report no conflicts of interest.
Footnotes
Publisher's Disclaimer: This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
References
- American Psychiatric Association, 2013. Diagnostic and statistical manual of mental disorders: DSM-5, 5th ed. American Psychiatric Association., United States. [Google Scholar]
- Askari M, Martins S, Mauro P, 2020. Medication for opioid use disorder treatment and specialty outpatient substance use treatment outcomes: Differences in retention and completion among opioid-related discharges in 201. J. Subst. Abuse Treat 114, 108028. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Bentley K, Sakurai H, Lowman K, Rines-Toth L, McKowen J, Pedrelli P, Evins A, Yule A, 2021. Validation of brief screening measures for depression and anxiety in young people with substance use disorders. J. Affect. Disord 282, 1021–1029. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Burgess-Hull A, Smith K, Schriefer D, Panlilio L, Epstein D, Preston K, 2021. Longitudinal patterns of momentary stress during outpatient opioid agonist treatment: a growth-mixture-model approach to classifying patients. Drug Alcohol Depend. 108884. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Carroll K, Nich C, Frankforter T, Yip S, Kiluk B, DeVito E, Sofuoglu M, 2018. Accounting for the uncounted: Physical and affective distress in individuals dropping out of oral naltrexone treatment for opioid use disorder. Drug Alcohol Depend. 192, 264–270. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Delgadillo J, Payne S, Gilbody S, Godfrey C, Gore S, Jessop D, Dale V, 2012. Brief case finding tools for anxiety disorders: Validation of GAD-7 and GAD-2 in addictions treatment. Drug Alcohol Depend. 125, 37–42. [DOI] [PubMed] [Google Scholar]
- Dum M, Pickren J, Sobell L, Sobell M, 2008. Comparing the BDI-II and the PHQ-9 with outpatient substance abusers. Addict. Behav 33, 381–387. [DOI] [PubMed] [Google Scholar]
- Ellis J, Pittman B, McKee S, 2020. Co-occurring opioid and sedative use disorder: Gender differences in use patterns and psychiatric co-morbidities in the United States. J. Subst. Abuse Treat 114, 108012. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Ferri M, Finlayson A, Wang L, Martin R, 2014. Predictive factors for relapse in patients on buprenorphine maintenance. Am. J. Addict 23, 62–67. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Gressler L, Martin B, Hudson T, Painter J, 2018. Relationship between concomitant benzodiazepine-opioid use and adverse outcomes among US veterans. Pain 159, 451–459. [DOI] [PubMed] [Google Scholar]
- Hadland S, Bagley S, Rodean J, Silverstein M, Levy S, Larochelle M, Samet J, Zima B, 2020. Receipt of timely addiction treatment and association of early medication treatment with retention in care among youths with opioid use disorder. JAMA Pediatr. 172, 1029–1037. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Hearon B, Calkins A, Halperin D, McHugh R, Murray H, Otto M, 2011. Anxiety sensitivity and illicit sedative use among opiate-dependent women and men. Am. J. Drug Alcohol Abuse 37, 43–47. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Huhn A, Berry M, Dunn K, 2019. Sex-based differences in treatment outcomes for persons with opioid use disorder. Am. J. Addict 28, 246–261. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Huhn A, Dunn K, 2020. Challenges for women entering treatment for opioid use disorder. Curr. Psychiatry Rep 22, 1–10. [DOI] [PubMed] [Google Scholar]
- Huhn A, Harris J, Cleveland H, Lydon D, Stakoski D, Cleveland M, Deneke E, Bunce S, 2016a. Ecological momentary assessment of affect and craving in patients in treatment for prescription opioid dependence. Brain Res. Bull 123, 94–101. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Huhn A, Hobelmann J, Strickland J, Oyler G, Gergeria C, Umbricht A, Dunn K, 2020. Differences in availability and use of medications for opoid use disorder in residential treatment settings in the United States. JAMA Netw. Open 5, e1920843. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Huhn A, Meyer R, Harris J, Ayaz H, Deneke E, Stankoski D, Bunce S, 2016b. Evidence of anhedonia and differential reward processing in prefrontal cortex among post-withdrawal patients with prescription opiate dependence. Brain Res. Bull 123, 102–109. [DOI] [PMC free article] [PubMed] [Google Scholar]
- IBM Corp. Released 2017. IBM SPSS Statistics for Windows, Version 25.0. Armonk, NY: IBM Corp., n.d. [Google Scholar]
- Johnson S, 2021. Latent profile transition analyses and growth mixture models: A very non-technical guide for researchers in child and adolescent development. New Dir. Child Adolesc. Dev 175, 111–139. [DOI] [PubMed] [Google Scholar]
- Koob G, 2015. The dark side of emotion: the addiction perspective. Eur. J. Pharmacol 753, 73–87. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Kroenke K, Spitzer R, Williams J, 2001. The PHQ-9: Validity of a brief depression severity measure. J. Gen. Intern. Med 16, 606–613. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Kroenke K, Spitzer R, Williams J, Löwe B, 2010. The Patient Health Questionnaire Somatic, Anxiety, and Depressive Symptom Scales: a systematic review. Gen. Hosp. Psychiatry 32, 345–359. [DOI] [PubMed] [Google Scholar]
- Lang K, Little T, 2018. Principled missing data treatments. Prev. Sci 19, 284–294. [DOI] [PubMed] [Google Scholar]
- Levin S, Mahler S, Baram T, 2021. The developmental origins of opioid use disorder and its comorbidities. Front. Hum. Neurosci [DOI] [PMC free article] [PubMed] [Google Scholar]
- Martins S, Sarvet A, Santaella-Ternorio J, Saha T, Grant B, Hasin D, 2017. Changes in US Lifetime heroin use and heroin use disorder: Prevalence from 2001–2002 to 2012–2013 National Epidemiologic Survey on Alcohol and Related Conditions. JAMA Psychiatry 74, 445–455. [DOI] [PMC free article] [PubMed] [Google Scholar]
- McHugh R, Votaw V, Bogunovic O, Karakula S, Karakula S, Griffin M, Weiss R, 2017. Anxiety sensitivity and nonmedical benzodiazepine use among adults with opioid use disorder. Addict. Behav 65, 283–288. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Mitchell M, See K, Champlin K, Essary A, Evans M, 2021. Opioid use disorder and COVID-19: Implications for policy and practic. J. Am. Acad. PAs 34, 1–4. [DOI] [PubMed] [Google Scholar]
- Muthén LK and Muthén BO (1998–2017). Mplus User’s Guide. Eighth Edition. Los Angeles, CA: Muthén & Muthén, n.d. [Google Scholar]
- Nylund K, Asparouhov T, Muthén B, 2007. Deciding on the number of classes in latent class analysis and growth mixture modeling: A Monte Carlo simulation study. Struct. Equ. Model. Multidiscip. J 14, 535–569. [Google Scholar]
- Rezaie Z, Afshari B, Balagabri Z, 2021. Effects of Dialectical Behavior Therapy on Emotion Regulation, Distress Tolerance, Craving, and Depression in Patients with Opioid Dependence Disorder. J. Contemp. Psychother 1–10. [Google Scholar]
- Rudd R, Seth P, Scholl D, 2016. Increases in drug and opioid-involved overdose deaths: United States, 2010–2015. Morb. Mortal. Wkly. Rep 65, 1445–1452. [DOI] [PubMed] [Google Scholar]
- Saunders K, VonForff M, Campbell C, Banta-Green C, Sullivan M, Merrill J, Weisner C, 2012. Concurrent use of alcohol and sedatives among persons prescribed chronic opioid therapy: prevalence and risk factors. J. Pain 13, 266–275. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Slavova S, Rock P, Bush H, Hesinberry D, Walsh S, 2020. Signal of increased opioid overdose during COVID-19 from emergency medical services data. Drug Alcohol Depend. 214, 108176. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Spitzer R, Korenke K, Williams J, Löwe B, 2006. A brief measure for assessing generalized anxiety disorder: the GAD-7. Arch. Intern. Med 166, 1092–1097. [DOI] [PubMed] [Google Scholar]
- Stein M, Herman D, Kettavong M, Cioe P, Friedmann P, Tellioglu T, Anderson B, 2019. Antidepressant treatment does not improve buprenorphine retention among opioid-dependent persons 39, 157–166. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Stein M, Santiago Rivera O, Anderson B, Bailey G, 2017. Perceived need for depression treatment among person entering inpatient opioid detoxification. Am. J. Addict 26, 395–399. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Subramaniam G, Stitzer M, 2009. Clinical characteristics of treatment-seeking prescription opioid vs. heroin-using adolescents with opioid use disorder. Drug Alcohol Depend. 101, 13–19. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Timko C, Schultz N, Cucciare M, Vittorio L, Garrison-Dihen C, 2016. Retention in medication-assisted treatment for opiate dependence: a systematic review. Journal of addictive diseases. J. Addict. Dis 35, 22–35. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Vest N, McPherson S, Burns G, Tragresser S, 2020. Parallel modeling of pain and depression in prediction of relapse during buprenorphine and naloxone treatment: A finite mixture model. Drug Alcohol Depend. 209, 107940. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Von Elm E, Altman D, Egger M, Pocock S, Gøtzsche P, Vandenbroucke J, Strobe INitative, 2014. The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) Statement: guidelines for reporting observational studies. Int. J. Surg 12, 1495–1499. [DOI] [PubMed] [Google Scholar]
- Wang P, Lin H, Yang Y, Hsu C, Chung K, Wu H, Yen C, 2017. Gender and age effects on the trajectory of depression in opioid users during methadone maintenance treatment. Front. Psychiatry 8, 288. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Webster L, 2017. Risk factors for opioid-use disorder and overdose. Anesth. Analg 125, 1741–1748. [DOI] [PubMed] [Google Scholar]
- Welsch L, Bailly J, Darcq E, Kieffer B, 2020. The Negative Affect of Protracted Opioid Abstinence: Progress and Perspectives From Rodent Models. Biol. Psychiatry 87, 54–63. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Yarborough B, Stumbo S, Stoneburner A, Smith N, Dobscha S, Deyo R, Morasco B, 2019. Correlates of benzodiazepine use and adverse outcomes among patients with chronic pain prescribed long-term opioid therapy. Pain Med. 20, 1148–1155. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Zhu Y, Mooney L, Yoo C, Evans E, Kelleghan A, Saxon A, Curtis M, Hser Y, 2021. Psychiatric comorbidity and treatment outcomes in patients with opioid use disorder: Results from a multisite trial of buprenorphine-naloxone and methadone. Drug Alcohol Depend. 108996. [DOI] [PMC free article] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.