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. Author manuscript; available in PMC: 2015 Nov 1.
Published in final edited form as: Drug Alcohol Depend. 2014 Sep 18;144:178–185. doi: 10.1016/j.drugalcdep.2014.09.009

Is residential treatment effective for opioid use disorders? A longitudinal comparison of treatment outcomes among opioid dependent, opioid misusing, and non-opioid using emerging adults with substance use disorder

Zev Schuman-Olivier 1,2,3,*, M Claire Greene 2, Brandon G Bergman 1,2, John F Kelly 1,2
PMCID: PMC4253677  NIHMSID: NIHMS634086  PMID: 25267606

Abstract

Background

Opioid misuse and dependence rates among emerging adults have increased substantially. While office-based opioid treatments (e.g., buprenorphine/naloxone) have shown overall efficacy, discontinuation rates among emerging adults are high. Abstinence-based residential treatment may serve as a viable alternative, but has seldom been investigated in this age group.

Methods

Emerging adults attending 12-step-oriented residential treatment (N=292; 18–24yrs, 74% Male, 95% White) were classified into opioid dependent (OD; 25%), opioid misuse (OM; 20%), and no opiate use (NO; 55%) groups. Paired t-tests and ANOVAs tested baseline differences and whether groups differed in their during-treatment response. Longitudinal multilevel models tested whether groups differed on substance use outcomes and treatment utilization during the year following the index treatment episode.

Results

Despite a more severe clinical profile at baseline among OD, all groups experienced similar during-treatment increases on therapeutic targets (e.g., abstinence self-efficacy), while OD showed a greater decline in psychiatric symptoms. During follow-up relative to OM, both NO and OD had significantly greater Percent Days Abstinent, and significantly less cannabis use. OD attended significantly more outpatient treatment sessions than OM or NO; 29% of OD was completely abstinent at 12-month follow-up.

Conclusions

Findings here suggest residential treatment may be helpful for emerging adults with opioid dependence. This benefit may be less prominent, though, among non-dependent opioid misusers. Randomized trials are needed to compare more directly the relative benefits of outpatient agonist-based treatment to abstinence-based, residential care in this vulnerable age-group, and to examine the feasibility of an integrated model.

Keywords: opioid dependence, emerging adult, 12-step residential, office-based opioid treatment, youth, abstinence

1. INTRODUCTION

Rates of chronic prescription opioid and heroin use have risen dramatically in the past 15 years and are significantly higher among 18–25 year old emerging adults (7.4 per 1,000 compared with 5.0 and 4.0 per 1,000 among 26–34 and 35–49 year olds, respectively; Jones, 2012). More emerging adults also are seeking treatment for opioid use disorders. From 1998 to 2008, admissions to treatment for prescription opioid dependence increased by 350% among 18–25 year olds, a disproportionately greater rise than among all other age groups (TEDS, 2009). Opioids, in particular, carry greater mortality risk due to acute respiratory depression, and opioid analgesic overdose deaths have now surpassed all other forms of drug poisoning deaths in the United States (Warner et al., 2011). Furthermore, many prescription opioid users progress to injection heroin use over time (Cicero et al., 2012) and this increases risk of contracting Human Immunodeficiency Virus (HIV) and Hepatitis C (HCV) (Mathers et al., 2008; Wang et al., 2011). In a recent opioid treatment trial of 16–21 year olds, 18% were infected with HCV at entry, despite only an average 1.5 years of opioid dependence (Subramaniam et al., 2009). Additionally, following a decade of decreasing new infections, incidence of HCV increased from 2010–2011 by 50% among 20–29 years olds (CDC, 2013).

Opioid replacement therapy (e.g., buprenorphine/naloxone [B/N]) improves outcomes for many people with opioid dependence and this has emerged as a first-line treatment; however, emerging adults with opioid dependence draw less long-term benefit than older adults from office-based B/N treatment (Schuman-Olivier et al., 2014). Extended treatment with B/N has been demonstrated to be more effective for opioid dependence than detoxification or placebo (Fudala et al., 2003; Weiss et al., 2011). The wide-spread introduction of office-based B/N maintenance treatment for opioid dependence, has been largely successful with more than 14,000 prescribers nationwide (SAMHSA, 2013) and 9.3 million prescriptions were dispensed in 2012 alone (DEA Fax 2013 from IMS Health™ National Prescription Audit Plus). Yet, younger age and emerging adulthood in particular, is associated with higher rates of illicit drug use and much greater attrition during B/N treatment (Dreifuss et al., 2013; Hillhouse et al., 2013; Marsch et al., 2005; Schuman-Olivier et al., 2014; Soeffing et al., 2009), which, in turn, is associated with relapse (Zhang et al., 2003) and increased overdose risk (Clausen et al., 2008; Davoli et al., 2007). While short-term extended B/N treatment for youth has been shown to be more effective than rapid detoxification (Woody et al., 2008), a long-term study of B/N treatment comparing older adults to emerging adults demonstrated a substantially lower proportion of emerging adults remaining in treatment at 12 months (17% versus 45%) and emerging adults were significantly more likely to test positive for illicit opioids, relapse, or drop out of treatment (Schuman-Olivier et al., 2014). Therefore, more research is needed to identify effective long-term treatments for emerging adults with opioid dependence.

Residential treatment may be an important treatment alternative for emerging adults. Residential programs generally offer the potential for opioid detoxification, coping skills development, and facilitated involvement in the 12-step recovery community (Borkman et al.,, 2007; Kelly et al., 2013). Relapse is very common among the opioid-dependent after abstinence-based inpatient treatment, leading to a perceived lack of efficacy of residential treatment (Smyth et al., 2010). Yet, some studies have demonstrated promising long-term outcomes after residential treatment for opioid dependence (Gossop et al., 1989). In a naturalistic study of 28-day residential treatment for 18–25 year olds with mixed substance use disorders, nearly 89% of emerging adults were attending AA/NA three months later, and 12-step involvement was associated independently with increasingly greater abstinence in the year following treatment (Kelly et al., 2013). These data about outcomes for emerging adults after residential treatment is compelling, but we are aware of no studies comparing residential treatment outcomes based on opioid use status among emerging adults.

Given the lackluster outcomes for office-based B/N among emerging adults, it is particularly important to investigate whether emerging adults with opioid dependence can benefit from the common Minnesota model residential treatment (Anderson et al., 1999; McElrath, 1997) in a similar manner as emerging adults with other substance use disorders. Also, it is vital to understand the differential effects of residential treatment on emerging adults with opioid dependence compared to those with opioid misuse or other substance use disorders. Since B/N was indicated only for treatment of DSM-IV-TR opioid dependence, many non-dependent opioid misusers have been directed to residential treatment programs. We need to examine how non-dependent opioid misusers fare during and after residential treatment compared to opioid dependent individuals and those with other substance use disorders. Research investigating the effectiveness of residential treatment among emerging adults with opioid dependence and misuse would inform placement guidelines and novel treatment strategies for emerging adults with opioid-related problems.

The aims of the current study were to compare patients with opioid dependence, non-dependent opioid misuse, and no opioid misuse on: 1) demographic and clinical variables at treatment entry; 2) during-treatment changes in clinical target variables (e.g., abstinence self-efficacy; recovery motivation; abstinence-focused coping skills); and 3) substance use outcomes and treatment utilization in the year following discharge from residential treatment.

2. METHODS

2.1. Participants

Participants were 292 emerging adults (18–24 years old) undergoing residential treatment and enrolled in a naturalistic study with 12-month follow-up. At admission, participants were 20.3 years old on average (SD = 1.6). Most were Caucasian (94.9%), male (73.6%), and all were single. At admission, 41.2% were employed full- or part-time, and 33.1% were students. Overall, 43.4% had a high school diploma and 45.6% reported involvement in the criminal justice system.

Forty-five percent reported opioid use in the 90 days prior to admission. Specifically, 25% (n=73) had an active opioid dependence (OD) diagnosis at admission, 20% (n=58) reported opioid misuse (OM) in the past 90 days but did not meet criteria for opioid dependence, and 55% (n=161) reported no history of opioid dependence nor any opioid misuse in the past 90 days (NO). Four participants with opioid dependence reported a buprenorphine prescription prior to entering residential treatment episode, while no participants reported methadone maintenance treatment.

2.2. Treatment

Treatment was youth-specific, included integrated psychiatric assessment and treatment when appropriate, and was based on 12-step Minnesota Model treatment philosophy (McElrath, 1997). Motivational enhancement, cognitive-behavioral and family-based therapeutic approaches were used to facilitate problem recognition, treatment engagement, and recovery. Participants remained in treatment for an average of 25.5±5.7 days (range 4–35 days) and 83.9% were discharged with staff approval (Kelly et al., 2013), indicating a high rate of treatment completion. Opioid detoxification was conducted primarily with buprenorphine. Dosing was determined by severity and Clinical Opiate Withdrawal Scale (Wesson and Ling, 2003); tapers averaged 7 days in length. Neither buprenorphine nor methadone maintenance were offered at discharge from residential treatment.

2.3. Procedure

In this study, 607 emerging adults were admitted to treatment during the recruitment period (10/2006-3/2008) (Kelly et al., 2013). To ensure sufficient representation of all ages within the target range (18–24 years old), a stratified sampling procedure was used such that all patients aged 21 to 24 years and every second patient aged 18 to 20 were approached for the study. Of those approached (n = 384), 64 declined to participate, 17 participants withdrew prior to data collection, and 1 individual was excluded due to a complication with informed consent. Of the remaining 302 (78.6% of those approached), in order to fulfill the study’s primary aims, we excluded individuals who had incomplete or unclear substance use assessments (n=3) and those who had a lifetime history of opioid dependence but were in some form of remission with no opioid use in the past 90 days (n=7), which resulted in a final sample of 292.

Research staff conducted assessments, including staff-administered and self-report measures, at baseline and end of treatment, 3-, 6-, and 12-months post-discharge. Participants were compensated a maximum of $180 for completing all assessments. The targeted length of stay was roughly 30 days; the observed average length of stay was 26.37 (8.32) days. Post-discharge, study retention rates were 81.2% (n = 237) at 3-month follow-up, 72.6% (n = 212) at 6-month follow-up, and 70.9% (n = 207) at 12-month follow-up. At each follow-up time point, at least 98% of retained participants completed interviews and at least 75% of retained participants completed all surveys. Relative to those with post-secondary education, those with a high school education or less were more likely to be missed at follow-up assessments, both during and after treatment. Opioid use status was not significantly associated with missing assessment at any follow-up point.

The study was conducted in accordance with the Institutional Review Board at Schulmann Associates IRB, an independent review board. All participants signed informed consent documents.

2.4. Measures

2.4.1 Demographics

Participants provided information about their gender, age, race and ethnicity, educational attainment, and employment and student status as part of the routine intake process.

2.4.2 Structured Clinical Interview for DSM-IV-TR (SCID)

The SCID (First et al., 2002) is a semi-structured interview and was administered by trained personnel at baseline to assess primary DSM–IV Axis I diagnoses and to establish an opioid dependence diagnosis. To ensure inter-rater reliability, supervisory reviews of audio-taped SCID interviews were conducted.

2.4.3 Leeds Dependence Severity (LDQ)

The LDQ is a brief measure of dependence severity that is not specific to particular substances. The 10 items address the frequency of symptom experience, rated from never (0) to nearly always (3). Responses are summed with higher scores indicating greater dependence severity. The measure demonstrates high internal consistency (α = .93) and good construct validity (Kelly et al., 2010; Lennings, 1999; Raistrick et al., 1994).

2.4.4 Brief Symptom Inventory-18 (BSI-18)

The BSI-18 assesses psychiatric symptomatology, yielding a global severity index (GSI). Participants rated symptomatic distress for 18 items on a 5-point scale from 0 (not at all) to 4 (extremely), and raw summary scores were converted to T scores, with higher scores indicating greater distress. The measure has demonstrated adequate internal consistency, test–retest reliability, convergent and discriminant validity among substance users (Derogatis, 2001; Wang et al., 2010).

2.4.5 Form-90

The Form-90 (Miller and Del Boca, 1994; Project MATCH Research Group, 1993) is an interview-based measure capturing substance use (alcohol and nine classes of illegal or prescription drugs), use of psychiatric medications, criminal justice system and treatment involvement, past-year hospitalizations for substance use, and consequences related to work or school. Opioid agonist treatments (e.g., buprenorphine, methadone) were recorded under “other medications”. Modifications were made to subsequent assessments to capture the entire time period elapsed since the previous interview (i.e., averaging 60 days for the 3-month follow-up, 90 days for the 6-month follow-up, and 180 days for the 12-month follow-up), and if a session was missed, then the next session would include the combination of the recent and previous time periods. We calculated percent days abstinent (PDA) by dividing the number of days with abstinence from all nine assessed substances (except nicotine) by the number of days in the assessment period. Primary clinical outcome measures derived from this instrument included PDA, presence/absence of heavy drinking or use of each of the nine assessed substances, and subsequent treatment. The Form-90 has been tested with adult and adolescent samples and has demonstrated test-retest reliability and validity (Slesnick and Tonigan, 2004; Tonigan et al., 1997).

2.4.6 Inventory of Drug Use Consequences—Recent (InDUC-2R)

The InDUC-2R (Tonigan and Miller, 2002) is a 50-item measure assessing substance use consequences. Items are rated in terms of their frequency of occurrence in the past 90 days, from never (0) to daily or almost daily (3), (e.g., “I have felt guilty or ashamed because of my drinking or drug use.”). Raw scores are summed to provide a total score (potential range = 0–135). The InDUC-2R has been shown to be sensitive to change and has good-to-excellent test–retest reliability.

2.4.7 Abstinence Self-Efficacy

A single item asked participants “How confident are you that you will be able to stay clean and sober in the next 90 days, or 3 months?” on a 10-point scale ranging from not confident (1) to very confident (10). This single-item measure of self-efficacy has been validated and found to be a strong predictor of relapse, superior to longer gold standard measures (Hoeppner et al., 2011).

2.4.8 Commitment to Sobriety (CSS)

The CSS is a brief validated five-item measure that assesses level of client commitment to alcohol and drug use cessation and continued abstinence (e.g., “I am totally committed to staying off alcohol/drugs”). Each is rated on a 6-point Likert scale from strongly disagree (1) to strongly agree (6). This scale has shown excellent internal consistency (α ≥ 0.89) and predictive validity (Kelly and Greene, 2013).

2.4.9 Multidimensional Measure of Mutual-Help Activity Scale

This is a 32-item, interview-based index assessing seven dimensions of involvement in 12-step groups. Participants reported 12-step participation based on their engagement at AA, NA, and “other” 12-step organizations during the time since last interview. Percentage of days attending a meeting was determined by dividing the total sum of 12-step meetings attended by the total days in the assessment period. The measure has shown excellent psychometric properties among youth (Kelly et al., 2002).

2.4.10 Twelve-Step Expectancies Questionnaire (TSEQ)

The TSEQ (Kahler et al., 2006) examines attitudes and expectancies about participating in 12-step groups. The intentions to attend 12-step variable was a single reverse-coded item included in the twelve step expectancies questionnaire, stating “I do not want to go to any AA or NA meetings.”

2.4.11 Adolescent Relapse Coping Questionnaire (ARCQ)

The ARCQ is an interview-based, 34-item measure evaluating temptation and coping skills for relapse to drug and alcohol use. Respondents are presented a commonly encountered hypothetical relapse situation (i.e., a party with alcohol and drugs), followed by six appraisal items assessing abstinence self-efficacy, perceived difficulty, and importance of abstinence (each rated on a 10-point Likert scale). Remaining items assess the likelihood of using 28 specific coping strategies, each rated on a scale from 1 (definitely would not use) to 7 (definitely would use this strategy). This measure has shown good internal consistency (α = .78–.82) and predictive validity among youth (Myers and Brown, 1996).

2.5. Biochemical Verification

To verify self-reported abstinence from alcohol and other drugs, oral fluid testing (Cone et al., 2002), which assessed tetrahydrocannabinol, cocaine metabolites, opiates, amphetamines, and phencyclidine, was administered on a subsample of participants that lived within 50 miles of the treatment facility and could attend follow-up interviews in-person. Among this subsample, there were 54 times that oral fluid testing was conducted and a subject reported abstinence during the previous time period; abstinence was confirmed in 98% of these situations, with one subject inaccurately reporting abstinence at 3 months.

2.6. Analysis Plan

To compare differences between OD, OM, and NO groups at baseline, we used one-way ANOVA’s and Chi-square tests. Significant between-group differences were further characterized using post-hoc tukey tests (continuous variables) and contrast statements in logistic regression models (categorical variables), respectively. We evaluated within- and between-group treatment changes in clinical target variables using paired samples t-tests and ANCOVAs (controlling for baseline level of clinical target variable), respectively. Lastly, to investigate the effect of opioid group status on post-discharge substance use outcomes and further treatment utilization in the year following this index episode, we constructed multilevel models using Proc Mixed (continuous outcomes) and Proc Genmod (binary and count outcomes). These models included education (predictor of attrition), the baseline level of the dependent variable, time (in months) and opioid group as independent variables. The outcomes, inpatient treatment and number of outpatient treatment sessions, did not have corresponding baseline variables that could be included in the model. Continuous dependent variables included percent days abstinent (PDA) and substance use consequences. To adhere to models’ statistical assumptions, we transformed PDA using a negative log transformation and substance use consequences using a square root transformation. We further transformed percent days abstinence for each of the nine substances assessed during follow-up into binary variables for each time period, because they were all positively skewed and responded poorly to linear transformations. Given the skew/kurtosis, inpatient treatment was also included as a binary dependent variable. Number of outpatient sessions was included as a count variable and was modeled using the Poisson distribution. Appropriate covariance structures were selected using the likelihood ratio test for continuous outcomes and comparison of QIC values for the binary and count outcomes.

In order to compare with rates reported in a prior study of emerging adults with opioid dependence receiving buprenorphine maintenance (Schuman-Olivier et al., 2014), we also used chi-squared tests to examine between-group differences in complete abstinence during follow-ups measured at 6-month as past 90 days abstinence, and at 12-month as past 180 days complete abstinence. In order to be conservative, missing a follow-up assessment was assumed to represent non-abstinent/relapsed status. We also used chi-squared tests to examine between-group differences in subsequent inpatient/residential treatment across the entire follow-up period. All analyses were generated using SAS Version 9.2.

3. RESULTS

3.1. Baseline group differences

The three groups were similar on all demographic characteristics (Table 1). Regarding clinical variables at treatment admission, OD presented with significantly higher levels of dependence severity than OM and NO, and significantly higher levels of substance use consequences (p < 0.01), substance-related hospitalizations in the past year (p < 0.05), and psychiatric symptoms (p < 0.01) than NO. Both opioid use groups reported significantly lower abstinence rates than NO. Compared with NO, OD was significantly more likely to meet criteria for hallucinogen use disorder, but significantly less likely to meet criteria for alcohol use disorder. OD were significantly more likely to meet criteria for cocaine and anxiolytic use disorders, while OM was significantly more likely to meet criteria for DSM polysubstance dependence than each of the other groups (p < 0.001). OD were more likely to report heroin as their only form of recent opioid use, while OM were more likely to report only prescription opioid use (p < 0.001). No significant differences emerged in psychiatric diagnoses, prior year arrests, medication use, or pre-treatment 12–step attendance.

Table 1.

Demographic and pre-treatment characteristics by opioid use group (n=292)

Opioid
Dependence
(n=73)		 Opioid
Misuse
(n=58)		 No Opioid
Use
(n=161)		 F/χ2 p
Demographic
Age 20.44±1.53 20.24±1.66 20.34±1.58 0.25 0.776
Male 58 (79.45) 45 (77.59) 112 (69.57) 3.11 0.211
White 70 (95.89) 58 (100.00) 149 (92.55) 5.07 0.079
Unmarried 72 (100.00) 58 (100.00) 161 (100.00) -- --
Has HS diploma 61 (83.56) 45 (77.59) 137 (85.63) 2.00 0.368
At least some college 33 (45.21) 18 (31.03) 65 (40.63) 2.79 0.247
Employed 20 (38.46) 13 (38.24) 46 (43.40) 0.50 0.781
Current Student 21 (28.77) 24 (41.38) 50 (32.05) 2.49 0.288
Clinical
Prior year SUD hospitalization3 29 (39.73)a 17 (29.31) 34 (21.12)a 8.88 0.012
Percent days abstinent3 16.10±21.08a 17.01±24.43b 29.03±30.07ab 7.78 0.001
Substance use consequences4 72.17±19.46a 67.46±26.40 61.59±24.94a 5.06 0.007
Dependence severity1 21.69±6.33a 19.66±8.79b 15.71±8.14ab 15.92 0.000
Self-efficacy5 6.99±2.50 6.36±3.00 7.13±2.48 1.89 0.154
Psychiatric medication use3 33 (45.21) 30 (51.72) 72 (44.72) 0.88 0.643
Psychiatric symptoms2 66.04 ± 8.31a 64.81 ± 11.03 61.55 ± 10.14a 5.97 0.003
1+ comorbid psychiatric dx6 30 (41.10) 29 (50.00) 76 (47.20) 1.17 0.558
12-step attendance7 32 (43.84) 20 (34.48) 51 (31.68) 3.27 0.195
Opioid use patterns3
   Heroin & prescription opioid use 30 (41.10) 12 (20.69) ------ 9.88 0.002
   Heroin use only 23(31.51) 2 (3.45) ------ 16.48 0.000
   Prescription opioid use only 20(27.40) 44 (75.86) ------ 30.38 0.000
Substance use disorders (lifetime) 6
   Alcohol 46 (63.01)a 44 (75.86) 132 (81.99)a 9.92 0.007
   Cannabis 55 (75.34) 37 (63.79) 118 (73.75) 2.59 0.274
   Cocaine 54 (73.97)ab 20 (34.48)a 59 (36.65)b 31.79 0.000
   Anxiolytic 21 (28.77)ab 8 (13.79)a 14 (8.70)b 16.16 0.000
   Amphetamine 16 (21.92) 15 (25.86) 32 (19.88) 0.91 0.635
   Hallucinogenic 17 (23.29)a 9 (15.52) 17 (10.56)a 6.52 0.039
   Poly-substance 6 (8.22)a 21 (36.21)ab 10 (6.21)b 36.41 0.000
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Notes Number and proportion (%) of sample reported for categorical variables, mean ± standard deviation reported for continuous variables; χ2 reported for categorical variables and opioid use patterns, F statistic reported for continuous variables.

a/b

Significant post-hoc between-group differences (p<.05).

Source Measures:

1

Leeds Dependence Severity (LDQ),

2

Brief Symptom Inventory-18 (BSI-18),

3

Form-90,

4

Inventory of Drug Use Consequences (InDUC-2R),

5

Abstinence Self-Efficacy,

6

Structured Clinical Interview for DSM-IV-TR (SCID),

7

Multidimensional Measure of Mutual-Help Activity Scale

3.2. Changes during treatment

During the residential treatment, all groups demonstrated statistically significant increases in levels of commitment to sobriety, abstinence self-efficacy, coping skills, and intentions to attend 12-step mutual help organizations, while simultaneously experiencing significant decreases in psychiatric symptomatology (Table 2). There were a couple of exceptions to the similar gains made during treatment in that OD maintained their initially high commitment to sobriety and experienced a significantly greater decline in psychiatric symptomatology relative to NO. Groups did not differ in rates of treatment completion; specifically, 76% of OM, 81% of OD, and 88% of NO, successfully completed residential treatment.

Table 2.

Treatment changes within and between opioid use group

Within-Group Treatment Changes
 Between-Group
Differences
Baseline End of
Treatment		 t p F/χ2 p
Commitment to sobriety1 0.28 0.759
   Opioid dependence 25.40 ± 4.10 26.18 ± 3.91 1.62 0.111
   Opioid misuse 22.95 ± 5.52 25.54 ± 4.41 3.31 0.002
   No opioid use 23.78 ± 5.44 25.41 ± 4.61 4.58 0.000
Self-efficacy2 0.77 0.466
   Opioid dependence 6.99 ± 2.50 8.21 ± 1.93 3.71 0.001
   Opioid misuse 6.36 ± 3.00 8.13 ± 2.07 3.04 0.004
   No opioid use 7.13 ± 2.48 8.50 ± 1.76 6.87 0.000
Coping Skills3 0.76 0.467
   Opioid dependence 127.21 ± 27.29 133.86 ± 22.38 2.45 0.017
   Opioid misuse 121.71 ± 22.97 137.57 ± 21.34 3.82 0.000
   No opioid use 127.45 ± 27.36 137.09 ± 23.14 5.76 0.000
Intentions to attend 12-step4 0.46 0.633
   Opioid dependence 0.43 ± 0.28 0.50 ± 0.26 2.57 0.013
   Opioid misuse 0.34 ± 0.26 0.51 ± 0.28 4.48 0.000
   No opioid use 0.36 ± 0.26 0.47 ± 0.27 5.06 0.000
Psychiatric symptoms5 3.56 0.030
   Opioid dependencea 66.04 ± 8.31 53.05 ± 8.03 −12.80 0.000
   Opioid misuse 64.81 ± 11.03 53.91 ± 8.91 −8.06 0.000
   No opioid usea 61.55 ± 10.14 53.49 ± 9.03 −10.62 0.000
Treatment Completion 5.49 0.064
   Opioid dependence -- 59 (80.82) -- --   
   Opioid misuse -- 44 (75.86) -- --
   No opioid use -- 142 (88.20) -- --
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*

p<0.05;

**

p<0.01;

***

p<0.001;

a

Significant post-hoc between-group differences (p<.05)

Note: Test statistic for within-group comparisons is t (paired t-test). Between-group differences were calculated using an ANCOVA controlling for baseline levels of the treatment variable.

Source Measures:

1

Commitment to Sobriety (CSS),

2

Abstinence Self-Efficacy,

3

Adolescence Relapse Coping Questionnaire (ARCQ),

4

Twelve-Step Expectancies Questionnaire (TSEQ),

5

Brief Symptom Inventory-18 (BSI-18).

3.3. Substance use outcomes

OM generally had more substance use and lower abstinence rates than the other two groups across the 12-month follow-up (Table 3). Specifically, OM had significantly lower abstinence rates than NO and, in parallel, significantly higher likelihood of cannabis as well as general alcohol use and heavy drinking. Despite similar abstinence rates relative to NO, OD had significantly greater likelihood of heroin use as expected. Use of each substance and related consequences increased significantly over time, suggesting a waning effect of treatment over time, though no group differences in consequences were identified.

Table 3.

Multilevel models examining associations between opioid use status and substance use, treatment utilization and other health-related outcomes at 3−, 6− and 12−months

Percent Days Abstinent (PDA)
 Any Heroin Use
 Any Narcotic Opioid Use
β SE F p β SE χ2 p β SE χ2 p
Education 0.287 0.223 1.66 0.200 −0.111 0.571 0.04 0.849 −0.250 0.552 0.19 0.664
Baseline DV 0.113 0.292 0.15 0.700 0.276 0.347 0.64 0.425 0.987 0.328 8.03 0.005
Time −0.077 0.012 38.80 0.000 0.164 0.035 12.00 0.001 0.085 0.040 3.70 0.054
Opioid Misuse −0.436 0.215 4.10 0.044 0.536 0.611 0.71 0.400 0.852 0.682 1.54 0.215
Opioid Dependence −0.169 0.194 0.76 0.384 1.851 0.671 3.99 0.046 0.820 0.696 1.31 0.252
Any Cannabis Use
 Any Alcohol Use
 Any Heavy Drinking
β SE χ2 p β SE χ2 p β SE χ2 p
Education −0.588 0.346 2.55 0.110 −0.462 0.277 2.83 0.092 −0.791 0.288 6.71 0.010
Baseline DV 1.329 0.323 14.16 0.000 −0.116 0.384 0.09 0.763 0.970 0.421 4.41 0.036
Time 0.111 0.020 25.72 0.000 0.115 0.017 40.00 0.000 0.135 0.051 34.81 0.000
Opioid Misuse 0.819 0.316 5.77 0.016 0.568 0.281 3.91 0.048 0.552 0.276 3.64 0.056
Opioid Dependence 0.601 0.328 2.95 0.086 0.067 0.284 0.02 0.897 0.137 0.304 0.20 0.652
Number of Outpatient Sessions
 Return to Inpatient Treatment
 Substance Use Consequences
β SE χ2 p β SE χ2 p β SE F p
Education 0.183 0.402 0.23 0.631 0.078 0.324 0.06 0.808 −0.756 0.526 2.06 0.152
Baseline DV -- -- -- -- -- -- -- -- 0.006 0.008 0.58 0.447
Time −0.051 0.045 1.39 0.239 −0.236 0.033 59.38 0.000 0.069 0.030 5.40 0.021
Opioid Misuse 0.032 0.432 0.01 0.942 0.501 0.278 3.04 0.081 0.663 0.482 1.89 0.170
Opioid Dependence 0.818 0.338 4.69 0.030 0.457 0.251 3.24 0.072 0.263 0.453 0.34 0.562
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Note: Comparison group is no opioid misuse

3.4. Treatment utilization outcomes

OD patients attended significantly more outpatient sessions than NO (Table 3). A small proportion of participants reported a buprenorphine prescription at 6 or 12 months follow-up (OD: 4.1%, OM: 5.2%). At least 40% of study participants (116 out of 292) reported subsequent residential/inpatient SUD treatment or continuing care within 3 months post-discharge (likelihood of further inpatient treatment declined significantly over time) (Table 4). Likelihood of further inpatient SUD treatment or residential continuing care during follow-up was lowest among NO (OD: 54.8%; OM: 51.7%; NO: 39.8%) but group differences were not significant.

Table 4.

Comparisons of complete abstinence and subsequent inpatient treatment over follow-up period (N=292)

Opioid Dependent Opioid
Misuse		 No Opioid
Misuse		 χ2 p
N=73 (%) N=58 (%) N=161 (%)
Complete abstinence (6 mo)1 31 (42.5) 18 (31.0) 68 (42.2) 2.46 0.292
Complete abstinence (12 mo)1 21 (28.8) 13 (22.4) 52 (32.3) 2.03 0.363
Complete abstinence (6 & 12 mo) 1 20 (27.4) 10 (17.2) 46 (28.6) 2.94 0.230
Inpatient treatment (3 mo) 35 (53.9) 23 (54.8) 58 (44.6) 2.17 0.338
Inpatient treatment (6 mo) 13 (25.0) 10 (23.3) 20 (17.1) 1.69 0.430
Inpatient treatment (12 mo) 7 (15.2) 7 (16.7) 9 (7.6) 3.53 0.171
Inpatient treatment (3,6, or 12 mo) 40 (54.8) 30 (51.7) 64 (39.8) 5.57 0.062
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1

Missing data for percent days abstinent was recoded as not completely abstinent (assumed relapse)

3.5. Abstinence rates

While OM had the smallest proportion achieving complete abstinence at 6- and 12-months, groups were not significantly different (Table 4). Importantly, rates of complete abstinence for OD were 42.5% at 6 months and 28.8% at 12 months. Among OD abstinent patients at 6 months (n = 31) and 12 months (n = 21), 10 and 0, respectively, attended further residential treatment during that assessment window.

4. DISCUSSION

This investigation of the relationship between opioid use status and residential treatment response and outcomes among emerging adults had several key findings. First, complete abstinence rates for emerging adults with opioid dependence were substantially higher at 12 month follow-up (29%) than was expected given results from a prior study of emerging adults with opioid dependence enrolled in outpatient B/N maintenance treatment, which had only 17% 12-month retention (Schuman-Olivier et al., 2014). Even though self-reported “abstinence” and treatment “retention” can differ, within opioid maintenance treatment programs, non-retention is strongly associated with dropout and relapse to illicit opioid use (Schuman-Olivier et al., 2014; Zhang et al., 2003). Notably, 6-month complete abstinence rates after this residential treatment (42.5%) were similar to 6-month retention rates in buprenorphine maintenance treatment (38%; Schuman-Olivier et al., 2014). If future research confirms these findings, it would suggest that 28-day residential treatment plus strong linkage to residential and outpatient continuing care could be considered as a first-line option among emerging adults with opioid dependence with long-term outcomes similar to or better than B/N maintenance treatment.

Second, participants with opioid misuse, but not dependence, had poorer substance use outcomes (i.e., more alcohol and cannabis) than those with opioid dependence or no opioid misuse. They had the lowest rates of treatment completion and despite similar rates of subsequent inpatient treatment, they were less likely to engage in outpatient care relative to those with opioid dependence. The absence of opioid dependence may foster a perception among opioid misusers of lower addiction severity that results in poorer compliance with recommendations for continuing care (Finney and Moos, 1995).

Finally, residential treatment enhanced crucial recovery attitudes and skills in the current sample of emerging adults, such as commitment to sobriety, abstinence self-efficacy, coping skills, and intention to participate in 12- step mutual-help organizations irrespective of participants’ opioid-related diagnostic status. Substantial reductions in psychiatric symptoms are also demonstrated in all groups, but especially among those with opioid dependence. While psychiatric symptoms often improve early in office-based B/N treatment for opioid dependence (Dean et al., 2004; Kosten et al., 1990), integral recovery attitudes and skills (e.g., abstinence self-efficacy) may not be acquired as rapidly in outpatient treatment.

Further research is needed to compare residential treatment alone with outpatient B/N maintenance among emerging adults. Residential treatment options for opioid dependence have been reduced in recent years with barriers for participation based on socioeconomic status (Saloner and Le Cook, 2013). Sample characteristics across the two studies of emerging adults are not identical; consequently, more direct experimental/quasi-experimental tests are needed. Nevertheless, this study suggests that the option of residential treatment plus strong linkage with continuing care may be a first-line choice among emerging adults with long-term abstinence outcomes at least comparable to outpatient B/N treatment. A major concern about residential treatment for people with opioid dependence is the increased risk of overdose death directly following residential treatment (Ravndal and Amundsen, 2010). Researchers have conducted studies of long-acting depot naltrexone among opioid dependent youth (Fishman et al., 2010); however, patients are often reluctant to accept this modality (Goodman, 1997). Therefore, future studies among emerging adults with opioid dependence might also examine the combination of stabilization on buprenorphine during abstinence-based residential treatment followed by strong linkage to outpatient continuing care with B/N maintenance. Finally, this study also suggests that alternatives to residential treatment could be considered for patients with opioid misuse, who are not yet opioid dependent, as many may not be ready to benefit from the substantial investment in residential treatment.

4.1. Limitations

Since this study was designed prior to wide-spread adoption of B/N treatment, the primary limitation of this study is that B/N treatment was included under “other medication” and was not assessed in a standardized manner with specific inquiry about buprenorphine. Additional limitations are that HIV and HCV status were not obtained during treatment or follow-up, and biochemical verification of abstinence was only conducted on a subsample of participants who remained living in the area surrounding the facility. Follow-up in this study was based on residential discharge date, making the follow-up periods actually one month later than the long-term follow-up referred to in the comparator study of B/N treatment for emerging adults (Schuman-Olivier et al., 2014). Importantly, this study included no direct comparison of B/N and residential treatment. The ability to compare this study and the comparator B/N study is limited by use of differing samples of emerging adults and treatment outcome measures.

4.2. Conclusions

Residential treatment plus strong linkage to and engagement with ongoing continuing care may be helpful for emerging adults with opioid dependence. This benefit may be less prominent among non-dependent opioid misusers who had fewer days abstinent during follow-up than emerging adults with no opioid misuse. Randomized trials are needed to compare more directly the relative benefits of outpatient agonist-based treatment to abstinence-based, residential care in this vulnerable age-group, and to examine the feasibility of an integrated model.

Study Highlights.

  • N=292 (18–24 yr olds) residential treatment with strong linkage to continuing care.

  • 29% of emerging adults with opioid dependence were abstinent at 12 months follow-up.

  • Emerging adults with opioid misuse, but not dependence, had fewest days abstinent.

  • Opioid misusers were more likely to use alcohol and cannabis during follow-up.

  • Opioid misusers were less likely to engage in outpatient care during follow-up.

Acknowledgements

This research was supported by grant funding from the National Institute of Alcohol Abuse and Alcoholism (R21AA018185-02) and by anonymous donations to the Hazelden Foundation. This work is solely the responsibility of the authors and the funding agencies had no role in the conception or execution of this work nor in the report presented here.

Role of Funding Source

This research was supported by grant funding from the National Institute of Alcohol Abuse and Alcoholism (R21AA018185-02) and by anonymous donations to the Hazelden Foundation. Dr. Kelly’s effort on the study was funded through NIAA Grant R21AA018185-02. No funding sources had any role in study design; in the collection, analysis and interpretation of data; in the writing of the report; or in the decision to submit the paper for publication.

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 citable 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.

Contributors

All authors were involved in the design of the study and contributed the protocol. Zev Schuman-Olivier managed the literature searches and summaries of previous related work. Zev Schuman-Olivier managed the study design for this analysis and wrote the first draft of the manuscript. M. Claire Greene managed the database and undertook the statistical analysis and the design of tables and figures. John Kelly managed the overall process of data collection and directed the implementation of the analyses and manuscript development. All authors contributed to and have approved the final manuscript.

Conflict of Interest

None.

REFERENCES

  1. Anderson DJ, McGovern JP, DuPont RL. The origins of the Minnesota model of addiction treatment-- a first person account. J. Addict. Dis. 1999;18:107–114. doi: 10.1300/J069v18n01_10. [DOI] [PubMed] [Google Scholar]
  2. Borkman TJ, Kaskutas LA, Owen P. Contrasting and converging philosophies of three models of alcohol/other drugs treatment: Minnesota Model, Social Model, and addiction Therapeutic Communities. Alcohol. Treat. Q. 2007;25:21–38. [Google Scholar]
  3. CDC. Viral Hepatitis Surveillance and Statistics. 2013 http://www.cdc.gov/hepatitis/Statistics/2011Surveillance/Slide4.2.htm.accessed on.
  4. Cicero TJ, Ellis MS, Surratt HL. Effect of abuse-deterrent formulation of OxyContin. N. Engl. J. Med. 2012;367:187–189. doi: 10.1056/NEJMc1204141. [DOI] [PubMed] [Google Scholar]
  5. Clausen T, Anchersen K, Waal H. Mortality prior to, during and after opioid maintenance treatment (OMT): a national prospective cross-registry study. Drug Alcohol Depend. 2008;94:151–157. doi: 10.1016/j.drugalcdep.2007.11.003. [DOI] [PubMed] [Google Scholar]
  6. Cone EJ, Presley L, Lehrer M, Seiter W, Smith M, Kardos KW, Fritch D, Salamone S, Niedbala RS. Oral fluid testing for drugs of abuse: positive prevalence rates by Intercept immunoassay screening and GC-MS-MS confirmation and suggested cutoff concentrations. J. Anal. Toxicol. 2002;26:541–546. doi: 10.1093/jat/26.8.541. [DOI] [PubMed] [Google Scholar]
  7. Davoli M, Bargagli AM, Perucci CA, Schifano P, Belleudi V, Hickman M, Salamina G, Diecidue R, Vigna-Taglianti F, Faggiano F. Risk of fatal overdose during and after specialist drug treatment: the VEdeTTE study, a national multi-site prospective cohort study. Addiction. 2007;102:1954–1959. doi: 10.1111/j.1360-0443.2007.02025.x. [DOI] [PubMed] [Google Scholar]
  8. Dean AJ, Bell J, Christie MJ, Mattick RP. Depressive symptoms during buprenorphine vs. methadone maintenance: findings from a randomised, controlled trial in opioid dependence. Eur. Psychiatry. 2004;19:510–513. doi: 10.1016/j.eurpsy.2004.09.002. [DOI] [PubMed] [Google Scholar]
  9. Derogatis LR. Administration, Scoring, And Procedures Manual. Minneapolis, MN: NCS Pearson; 2001. BSI-18: Brief Symptom Inventory 18. [Google Scholar]
  10. Dreifuss JA, Griffin ML, Frost K, Fitzmaurice GM, Potter JS, Fiellin DA, Selzer J, Hatch-Maillette M, Sonne SC, Weiss RD. Patient characteristics associated with buprenorphine/naloxone treatment outcome for prescription opioid dependence: results from a multisite study. Drug Alcohol Depend. 2013;131:112–118. doi: 10.1016/j.drugalcdep.2012.12.010. [DOI] [PMC free article] [PubMed] [Google Scholar]
  11. Finney JW, Moos RH. Entering treatment for alcohol abuse: a stress and coping model. Addiction. 1995;90:1223–1240. doi: 10.1046/j.1360-0443.1995.90912237.x. [DOI] [PubMed] [Google Scholar]
  12. First MB, Spitzer RL, Gibbon M, Williams JBW. (SCID-I/P) New York: Biometrics Research, New York State Psychiatric Institute; 2002. Structured Clinical Interview for DSM-IV-TR Axis I Disorders, Research Version, Patient Edition. [Google Scholar]
  13. Fishman MJ, Winstanley EL, Curran E, Garrett S, Subramaniam G. Treatment of opioid dependence in adolescents and young adults with extended release naltrexone: preliminary case-series and feasibility. Addiction. 2010;105:1669–1676. doi: 10.1111/j.1360-0443.2010.03015.x. [DOI] [PubMed] [Google Scholar]
  14. Fudala PJ, Bridge TP, Herbert S, Williford WO, Chiang CN, Jones K, Collins J, Raisch D, Casadonte P, Goldsmith RJ, Ling W, Malkerneker U, McNicholas L, Renner J, Stine S, Tusel D. Office-based treatment of opiate addiction with a sublingual-tablet formulation of buprenorphine and naloxone. N. Engl. J. Med. 2003;349:949–958. doi: 10.1056/NEJMoa022164. [DOI] [PubMed] [Google Scholar]
  15. Goodman CAR, Harwood R, Ringel D, Savage K, Mendelson D, Rubin R. [accessed on 7/22/2014 2014>];Market Barriers to the Development of Pharmacotherapies for the Treatment of Cocaine Abuse and Addiction: Case Studies: LAAM, Naltrexone, Clozapine, and Nicorette. 1997 http://aspe.hhs.gov/health/reports/cocaine/4cases.htm.
  16. Gossop M, Green L, Phillips G, Bradley B. Lapse, relapse and survival among opiate addicts after treatment. A prospective follow-up study. Br. J. Psychiatry. 1989;154:348–353. doi: 10.1192/bjp.154.3.348. [DOI] [PubMed] [Google Scholar]
  17. Hillhouse M, Canamar CP, Ling W. Predictors of outcome after short-term stabilization with buprenorphine. J. Subst. Abuse. Treat. 2013;44:336–342. doi: 10.1016/j.jsat.2012.08.016. [DOI] [PMC free article] [PubMed] [Google Scholar]
  18. Jones CM. Frequency of prescription pain reliever nonmedical use: 2002 – 2003 and 2009 – 2010. Arch. Intern. Med. 2012;172:1265–1267. doi: 10.1001/archinternmed.2012.2533. [DOI] [PubMed] [Google Scholar]
  19. Kahler CW, Kelly JF, Strong DR, Stuart GL, Brown RA. Development and initial validation of a 12-step participation expectancies questionnaire. J. Stud. Alcohol. 2006;67:538–542. doi: 10.15288/jsa.2006.67.538. [DOI] [PubMed] [Google Scholar]
  20. Kelly JF, Magill M, Slaymaker V, Kahler C. Psychometric validation of the Leeds Dependence Questionnaire (LDQ) in a young adult clinical sample. Addict. Behav. 2010;35:331–336. doi: 10.1016/j.addbeh.2009.11.005. [DOI] [PMC free article] [PubMed] [Google Scholar]
  21. Kelly JF, Myers MG, Brown SA. Do adolescents affiliate with 12-step groups? A multivariate process model of effects. J. Stud. Alcohol. 2002;63:293–304. doi: 10.15288/jsa.2002.63.293. [DOI] [PMC free article] [PubMed] [Google Scholar]
  22. Kelly JF, Stout RL, Slaymaker V. Emerging adults' treatment outcomes in relation to 12-step mutual-help attendance and active involvement. Drug Alcohol Depend. 2013;129:151–157. doi: 10.1016/j.drugalcdep.2012.10.005. [DOI] [PMC free article] [PubMed] [Google Scholar]
  23. Kosten TR, Morgan C, Kosten TA. Depressive symptoms during buprenorphine treatment of opioid abusers. J. Subst. Abuse Treat. 1990;7:51–54. doi: 10.1016/0740-5472(90)90035-o. [DOI] [PubMed] [Google Scholar]
  24. Lennings C. An evaluation of the Leeds Dependence Questionnaire. J. Child Adolesc. Subst. Abuse. 1999;8:73–87. [Google Scholar]
  25. Marsch LA, Stephens MA, Mudric T, Strain EC, Bigelow GE, Johnson RE. Predictors of outcome in LAAM, buprenorphine, and methadone treatment for opioid dependence. Exp. Clin. Psychopharmacol. 2005;13:293–302. doi: 10.1037/1064-1297.13.4.293. [DOI] [PubMed] [Google Scholar]
  26. Mathers BM, Degenhardt L, Phillips B, Wiessing L, Hickman M, Strathdee SA, Wodak A, Panda S, Tyndall M, Toufik A, Mattick RP. Global epidemiology of injecting drug use and HIV among people who inject drugs: a systematic review. Lancet. 2008;372:1733–1745. doi: 10.1016/S0140-6736(08)61311-2. [DOI] [PubMed] [Google Scholar]
  27. McElrath D. The Minnesota Model. J. Psychoactive Drugs. 1997;29:141–144. doi: 10.1080/02791072.1997.10400180. [DOI] [PubMed] [Google Scholar]
  28. Miller WR, Del Boca FK. Measurement of drinking behavior using the Form 90 family of instruments. J. Stud. Alcohol Suppl. 1994;12:112–118. doi: 10.15288/jsas.1994.s12.112. [DOI] [PubMed] [Google Scholar]
  29. Myers MG, Brown SA. The Adolescent Relapse Coping Questionnaire: psychometric validation. J. Stud. Alcohol. 1996;57:40–46. doi: 10.15288/jsa.1996.57.40. [DOI] [PubMed] [Google Scholar]
  30. Project MATCH Research Group. Project MATCH (Matching Alcoholism Treatment to Client Heterogeneity): rationale and methods for a multisite clinical trial matching patients to alcoholism treatment. Alcohol. Clin. Exp. Res. 1993;17:1130–1145. doi: 10.1111/j.1530-0277.1993.tb05219.x. [DOI] [PubMed] [Google Scholar]
  31. Raistrick D, Bradshaw J, Tober G, Weiner J, Allison J, Healey C. Development of the Leeds Dependence Questionnaire (LDQ): a questionnaire to measure alcohol and opiate dependence in the context of a treatment evaluation package. Addiction. 1994;89:563–572. doi: 10.1111/j.1360-0443.1994.tb03332.x. [DOI] [PubMed] [Google Scholar]
  32. Ravndal E, Amundsen EJ. Mortality among drug users after discharge from inpatient treatment: an 8- year prospective study. Drug Alcohol Depend. 2010;108:65–69. doi: 10.1016/j.drugalcdep.2009.11.008. [DOI] [PubMed] [Google Scholar]
  33. Saloner B, Le Cook B. Blacks and Hispanics are less likely than whites to complete addiction treatment, largely due to socioeconomic factors. Health Aff. (Millwood) 2013;32:135–145. doi: 10.1377/hlthaff.2011.0983. [DOI] [PMC free article] [PubMed] [Google Scholar]
  34. SAMHSA. [accessed on 5/19/2013];Buprenorphine physician and program treatment locator. 2013 http://buprenorphine.samhsa.gov/bwns_locator/dr_search.htm.
  35. Schuman-Olivier Z, Hoeppner BB, Borodovsky J, Albanese MJ. Emerging adult age status predicts poor buprenorphine treatment retention. J. Subst. Treat. 2014;47:202–212. doi: 10.1016/j.jsat.2014.04.006. [DOI] [PMC free article] [PubMed] [Google Scholar]
  36. Slesnick N, Tonigan JS. Assessment of alcohol and other drug use by runaway youths: a test-retest study of the Form 90. Alcohol. Treat. Q. 2004;22:21–34. doi: 10.1300/J020v22n02_03. [DOI] [PMC free article] [PubMed] [Google Scholar]
  37. Smyth BP, Barry J, Keenan E, Ducray K. Lapse and relapse following inpatient treatment of opiate dependence. Ir. Med. J. 2010;103:176–179. [PubMed] [Google Scholar]
  38. Soeffing JM, Martin LD, Fingerhood MI, Jasinski DR, Rastegar DA. Buprenorphine maintenance treatment in a primary care setting: outcomes at 1 year. J. Subst. Abuse. Treat. 2009;37:426–430. doi: 10.1016/j.jsat.2009.05.003. [DOI] [PubMed] [Google Scholar]
  39. Subramaniam GA, Fishman MJ, Woody G. Treatment of opioid-dependent adolescents and young adults with buprenorphine. Curr. Psychiatry Rep. 2009;11:360–363. doi: 10.1007/s11920-009-0054-5. [DOI] [PubMed] [Google Scholar]
  40. Tonigan JS, Miller WR. The inventory of drug use consequences (InDUC): test-retest stability and sensitivity to detect change. Psychol. Addict. Behav. 2002;16:165–168. [PubMed] [Google Scholar]
  41. Tonigan JS, Miller WR, Brown JM. The reliability of Form 90: an instrument for assessing alcohol treatment outcome. J. Stud. Alcohol. 1997;58:358–364. doi: 10.15288/jsa.1997.58.358. [DOI] [PubMed] [Google Scholar]
  42. Wang J, Kelly BC, Booth BM, Falck RS, Leukefeld C, Carlson RG. Examining factorial structure and measurement invariance of the Brief Symptom Inventory (BSI)-18 among drug users. Addict. Behav. 2010;35:23–29. doi: 10.1016/j.addbeh.2009.08.003. [DOI] [PMC free article] [PubMed] [Google Scholar]
  43. Wang X, Zhang T, Ho WZ. Opioids and HIV/HCV infection. Journal of neuroimmune pharmacology : the official journal of the Society on NeuroImmune Pharmacology. 2011;6:477–489. doi: 10.1007/s11481-011-9296-1. [DOI] [PMC free article] [PubMed] [Google Scholar]
  44. Warner M, Chen LH, Makuc DM, Anderson RN, Minino AM. Drug poisoning deaths in the United States, 1980 – 2008. NCHS data brief. 2011:1–8. [PubMed] [Google Scholar]
  45. Weiss RD, Potter JS, Fiellin DA, Byrne M, Connery HS, Dickinson W, Gardin J, Griffin ML, Gourevitch MN, Haller DL, Hasson AL, Huang Z, Jacobs P, Kosinski AS, Lindblad R, McCance-Katz EF, Provost SE, Selzer J, Somoza EC, Sonne SC, Ling W. Adjunctive counseling during brief and extended buprenorphine-naloxone treatment for prescription opioid dependence: a 2-phase randomized controlled trial. Arch. Gen. Psychiatry. 2011;68:1238–1246. doi: 10.1001/archgenpsychiatry.2011.121. [DOI] [PMC free article] [PubMed] [Google Scholar]
  46. Wesson DR, Ling W. The Clinical Opiate Withdrawal Scale (COWS) J. Psychoactive Drugs. 2003;35:253–259. doi: 10.1080/02791072.2003.10400007. [DOI] [PubMed] [Google Scholar]
  47. Woody GE, Poole SA, Subramaniam G, Dugosh K, Bogenschutz M, Abbott P, Patkar A, Publicker M, McCain K, Potter JS, Forman R, Vetter V, McNicholas L, Blaine J, Lynch KG, Fudala P. Extended vs short-term buprenorphine-naloxone for treatment of opioid-addicted youth: a randomized trial. JAMA. 2008;300:2003–2011. doi: 10.1001/jama.2008.574. [DOI] [PMC free article] [PubMed] [Google Scholar]
  48. Zhang Z, Friedmann PD, Gerstein DR. Does retention matter? Treatment duration and improvement in drug use. Addiction. 2003;98:673–684. doi: 10.1046/j.1360-0443.2003.00354.x. [DOI] [PubMed] [Google Scholar]

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