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J Am Acad Child Adolesc Psychiatry. Author manuscript; available in PMC Sep 30, 2013.
Published in final edited form as:
PMCID: PMC3786351
NIHMSID: NIHMS501645
Predictors of Abstinence: NIDA Multi-site Buprenorphine/Naloxone Treatment Trial in Opioid Dependent Youth
Geetha A. Subramaniam, M.D., Diane Warden, Ph.D., M.B.A., Abu Minhajuddin, Ph.D., Marc J. Fishman, M.D., Maxine L. Stitzer, Ph.D., Bryon Adinoff, M.D., Madhukar Trivedi, M.D., Roger Weiss, M.D., Jennifer Potter, Ph.D., Sabrina A. Poole, MS, and George E. Woody, MD.
Dr. Subramaniam is with the National Institute on Drug Abuse. Drs. Subramaniam, Fishman, and Stitzer are with Johns Hopkins University School of Medicine. Drs. Warden, Minhajuddin, Adinoff, and Trivedi are with the University of Texas Southwestern Medical Center. Drs. Weiss is with Harvard Medical School and McLean Hospital. Dr. Potter is with University of Texas Health Science Center. Ms. Poole and Dr. Woody are with the University of Pennsylvania and Treatment Research Institute. Dr. Adinoff is also with the Veterans Affains North Texas Health Care System. Dr. Fishman is also with Mountain Manor Treatment Center.
Correspondence to: Geetha Subramaniam, M.D., Division of Clinical Neuroscience and Behavioral Research, National Institute on Drug Abuse, 6001 Executive Blvd, Rm 3173, MSC 9593, Bethesda, MD 20892, Tel. 301-435-0974, Fax.301-443-6814, geetha.subramaniam/at/nih.gov
Objective
To examine predictors of opioid abstinence in buprenorphine/naloxone (Bup/Nal) assisted psychosocial treatment for opioid dependent youth
Method
Secondary analyses of data from 152 youth (ages 15–21) randomly assigned to 12 weeks of extended Bup/Nal therapy or up to 2 weeks of Bup/Nal detoxification, both with weekly individual and group drug counseling. Logistic regression models were constructed to identify baseline and during-treatment predictors of opioid positive urines (OPU) at week-12. Predictors were selected based on significance or trend toward significance (i.e. p<0.1) and backward stepwise selection was used, controlling for treatment group, to produce final independent predictors at p ≤ 0.05.
Results
Youth presenting to treatment with past 30-day injection drug use (IDU) and more active medical/psychiatric problems were less likely to have a week-12 OPU. Those with early treatment opioid abstinence (i.e. weeks 1 and 2); and those who received additional non-study treatments during the study were less likely to have a week-12 OPU; and those not completing 12 weeks of treatment were more likely to have an OPU.
Conclusions
Youth with advanced illness (i.e. reporting IDU and additional health problems), and those receiving ancillary treatments to augment study treatment were more likely to have lower opioid use. Treatment success in the first 2 weeks and completion of 12 weeks of treatment were associated with lower rates of OPU. These findings suggest that youth with advanced illness respond well to Bup/Nal treatment, and identify options for tailoring treatment for opioid-dependent youth presenting at community-based settings.
Keywords: treatment predictors, opioid dependent youth, buprenorphine treatment
Prescription opioids are second only to marijuana as the most commonly used illicit substances among high school seniors1. During the past 10 years, annual use prevalence of non-heroin opioids among 12th graders has risen from 6 to 9%, while heroin use has hovered around 1%1. Treatment-seeking youth with opioid use disorders2 and opioid problem use (added to cannabis/alcohol problem use)3 have complex needs, presenting with higher rates of psychiatric and medical (HIV-risk and Hepatitis-C infection) conditions, polysubstance use, legal problems, and greater risk for school-drop out compared to youth with marijuana and/or alcohol use disorders/problem use. Opioid analgesic-related Emergency Department visits among youth under 21 have increased from 17,267 to 53,668 between 2004 and 2008, reflecting the enormity of this problem4 . Despite these trends, effective treatments for opioid dependent youth are just emerging.
Methadone and buprenorphine, both medications with opioid-agonist actions, are highly effective in treating opioid dependent adults (see Cochrane reviews56) but only a few uncontrolled studies from the 1970’s have been published providing limited evidence for the efficacy of methadone maintenance in youth. Further, methadone is often not a feasible option for youth under 18 due to regulatory restrictions and the need to dispense it only at specialized opioid treatment programs7. Buprenorphine, a partial mu-agonist, has shown promise in treating opioid dependent youth in two recent NIDA-funded randomized controlled outpatient trials. The first study with opioid dependent youth (n=36, ages 13–18 years), conducted at a single-site, showed that a 28-day treatment episode with buprenorphine was associated with significantly less opioid use and better treatment retention when compared to clonidine8. A more recent multi-site trial funded by the NIDA Clinical Trials Network (CTN) randomized opioid dependent youth (ages 15–21 years) to a 14-day buprenorphine/naloxone (Bup/Nal) detoxification (DETOX) or 12-week extended Bup/Nal therapy (BUP) with a dose taper beginning at week 9 and ending during week 129. The BUP group fared significantly better than the DETOX group on the primary outcome of opioid use, and almost all secondary outcomes including better treatment retention, less injection drug use (IDU), and less cocaine and marijuana use. At week 12, 53 in the DETOX group had opioid positive urine results (51%; 95% CI=35%–67%) vs. 49 in the BUP group (43%; 95% CI=29%–57%). At week-12, 16 of 78 DETOX patients (20.5%) remained in treatment vs. 52 of 74 BUP (70%; Χ2 =32.90, P_.001).
These efficacy studies are important as they provide mean results for the overall sample, but do not illustrate the heterogeneity of the sample or identify specific subgroups of patients that may have greater benefits. An exploration of baseline and during-treatment factors of better outcomes may provide additional information to help physicians tailor these interventions to individual patients1011. In studies with adult opioid dependent patients treated with Bup/Nal, socio-demographic factors such as female gender, fewer days of paid employment, illness severity (as indicated by high depressive symptoms and greater opioid withdrawal), and during-treatment factors such as higher medication dose and better adherence were associated with greater abstinence1215. Concomitant use of cocaine or marijuana was not related to outcome, while tobacco use was associated with poorer outcome15 or unrelated to outcome16 In the only related study, Motamed and colleagues17 conducted secondary analyses of the Marsch et.al. study8 to determine if outcomes were different for prescription opioid-dependent (n=17) vs. heroin-dependent (n=19) youth; they found no differences in opioid abstinence or treatment retention..
Given the lack of information on predictors of outcome for Bup/Nal treatment of opioid dependent youth, we conducted secondary analyses of the larger (N=152) multi-site Bup/Nal study9 of opioid dependent youth in an attempt to identify baseline and during-treatment predictors associated with lower opioid use that might provide insights into potential mechanisms of improvement and/or information to help guide patient management and clinical decision-making in this population.
Participants and Study Procedures
The study methods were approved by the institutional review boards of all participating institutions and reported in the primary outcome paper9. Participants were treatment-seeking youth 15–21 years old who met DSM-IV criteria for opioid dependence with physiologic features and were recruited from 6 community-based treatment sites from July 2003 to December 2005. Participants (N=152) were randomized to either 1) 12-weeks of buprenorphine/naloxone (BUP) with a dose taper beginning in week-9 and ending in week-12, or 2) up to 2 weeks of buprenorphine/naloxone (DETOX). Both groups were offered one weekly individual and one group counseling session during the 12-week active study phase guided by the individual and group drug counseling manuals which encouraged making positive relationships and stopping drug use, taking medication as prescribed, tolerating stressful events without using drugs, keeping appointments, teaching ways to avoid drug-using situations, educating about addiction, giving positive feedback for achieving goals, referring for treatment of associated problems, and participating in age-appropriate self-help groups18. The participants were assessed at baseline, and weeks 4, 8, and 12 (active treatment phase).
Study Treatment
Details of medication dosing and administration and counseling are described elsewhere9, 1920. Participants were asked to abstain from opioids for 6 or more hours and present with opioid withdrawal prior to their first dose of Bup/Nal. Dosing was given under direct observation 5–7days/week, depending on the site. Participants were inducted on day 1 with a maximum dose of 8 mg. On days 2 and 3, patients received the total dose from the previous day unless they were overmedicated, and as clinically needed received additional doses, as needed. The maximum dose was determined a priori as 14mg for DETOX and 24mg for BUP. A dose taper was begun in weeks 1 or 2 in DETOX and completed by day 14; and was begun in week-10 and completed by the end of week-12 in the BUP patients. In addition, all participants were asked to attend weekly manual-guided individual and group drug counseling for 12 weeks18.
Baseline Demographic and Clinical Predictors
The NIDA CTN Baseline Demographics Form was used to collect information on gender; race (white/non-white), age, years of education, employment; and use of substances (alcohol, cannabis, cocaine, and cigarettes) in the past month (days) and lifetime (years). At baseline, the Substance Dependence Severity Scale Lite, (SDSS lite21) documented opioid dependence with physiologic features and provided information on the type of opioid identified as the main problem (i.e. heroin, prescription opioids, or both). The Risk Behavior Survey (RBS,2223) provided information on past 30-day HIV-risk behaviors (e.g. injection drug use of opioids, cocaine and/or amphetamine, sexual-risk behaviors, etc.). The Youth Self-Report and the Young Adult Self-Report (YSR and YASR2425) provided baseline information on internalizing and externalizing problems in the past 90 days. The participant’s health status was assessed at baseline and at week-12: the medical history provided information on lifetime and current medical and psychiatric conditions; liver enzyme levels (i.e. AST, ALT, GGT, LDH) and serum Hepatitis B and C titers were obtained at baseline and week 12. Having active (current) medical and/or psychiatric problems was defined as reporting such problems at the time of baseline medical history. Non-study treatments (e.g. additional outpatient visits, hospitalizations, etc.) and medications taken prior to study entry and during the study were documented, including names, doses and duration of administration.
During-Treatment Predictors
Daily logs recorded the Bup/Nal dose prescribed and taken and medication adherence for each day. In the current analyses, we defined medication adherence as taking at least 5 out of 7 doses per week. Doses in the BUP group were categorized into low (<12 mg), moderate (12–16mg) and high (17–24 mg), the latter including one participant who received a max dose of 32mg. Doses in the DETOX group where the maximum recommended dose was 14 mg were categorized into low (<10mg), moderate (10– <14mg), and high (14mg or higher), the latter including one participant whose maximum dose was 20mg. Different dosing categories were used for the two groups since the recommended max doses were different. The number and type of counseling appointments kept were documented. Treatment completion was defined as continuing in study treatment for 77 or more days (i.e. 12 weeks). Information on medical, psychiatric and addiction services that the participant received outside of the assigned treatment condition was collected at baseline (prior 30 days) and weekly during the study. Signs and symptoms of withdrawal severity during the first 2 dosing weeks were measured by the Short Opiate Withdrawal Scale (SOWS)26. Detailed information on adverse events (AE) and Serious Adverse Events (SAE) was collected weekly during the first 12 weeks. Urinalyses for drugs of abuse were performed on-site using the SureStep drug screen card (which tests all opioids except oxycodone) and the Rapid One OXY which tests for oxycodone.
Data Analyses
As reported previously10, the primary outcome for these secondary analyses was the presence of opioid positive urine (OPU) at week-12. Preliminary bivariate analyses were conducted from a list of baseline and during-treatment variables to determine those associated with an OPU. Comparisons between those with OPU on baseline and during-treatment factors were performed using Student t-tests for continuous measures and Chi-square tests for categorical variables. If the cell frequencies were too low for the chi-square approximation to be valid, Fisher’s exact test was used. Analyses included all participants randomized to treatment, consistent with an intent-to-treat approach. Two separate logistic regression models were constructed to identify independent predictors of opioid use while controlling for treatment group assignment. In the first model, we entered five baseline factors that were significant or approaching significance (p ≤ 0.10) in preliminary bivariate analyses; the second model was constructed using six during-treatment factors with similar significance (p ≤ 0.10) We excluded the number of active medical/psychiatric problems in the second model because these data were only collected at the end of treatment (i.e. the week-12 visit), and the small sample size limited the number of variables that could be included in the model. Backward stepwise selection was used to refine the model with a threshold p-value of 0.05 for including variables in the final predictive model.
Urine drug screen (UDS) results were available for 59% of participants at week-12. An intent-to-treat sample was used with missing urine tests imputed as positive. Since there were no significant differences between BUP and DETOX in percent samples available or in the baseline characteristics (described above) between those who submitted a urine sample (i.e. the completer sample) versus those who did not (except for mean scores on internalizing symptoms), we merged the two samples for analyses. Data were also re-analyzed including only those participants who provided a urine sample at week-12. Analyses were performed using SAS statistical software version 9.227.
Study Sample Characteristics
The age range was 15–21yrs with sample mean of 19.2 years (SD 1.5); 17% were less than 18 yrs of age; 74% were Caucasian, 24% were Hispanic and 26% of another race; 40% reported past 30-day heroin only use, 24% opioid analgesics/other opioids only and 36% both types of opioids; 16% reported past 30-day IDU. Mean years of education was 11.2 (SD 1.6); and 75% reported being employed in the past three years. Mean maximum doses of Bup/Nal were 15.1 (SD 4.9) mg in the BUP group and 11.5 (SD 2.9) mg in the DETOX group. Only 44% of the study sample completed 12 weeks of treatment.
Baseline Predictors (Table 1)
Among the socio demographic characteristics examined, age, race, past 30-day employment and years of education were not related to outcome, but there was a trend for females [χ2 (df =1) = 3.92, p=0.06] to have lower rates of OPU at week-12. Neither past 30-day nor lifetime use (latter results not shown) of any substance (including type of opioid) was related to outcome. Among the clinical factors examined, reporting past 30-day IDU of opioids, cocaine or amphetamines [χ2(df =1) = 7.27, p=0.007], experiencing more active medical and/or psychiatric problems [t(df =150)= 2.22, p= 0.028] and higher mean scores on the internalizing problem subscale [t(df =141) = 2.24, p= 0.027] were linked to lower rates of OPU. There was a trend towards significance for having an elevated liver enzyme and lower rates of OPU [χ2 (df =1) =3.37, p=0.067]. Neither non-study medications nor treatment services received in the 30 days prior baseline (results not shown) were associated with outcome.
Table 1
Table 1
Bivariate Comparison of Baseline Factors with Week 12 Opioid Positive Urine Tests
During-Treatment Characteristics (Table 2)
Early treatment markers (Weeks-1 and 2)
Having opioid negative urine [χ2(df =1) = 6.85, p=0.009], attending at least one study therapy session [χ2(df =1) = 8.45, p= 0.004] and being medication adherent [Fisher’s exact test, p < 0.0001] were associated with lower rates of OPU at week-12. However, markers related to distress such as the number of moderate to severe study-related SAEs or number of withdrawal symptoms in the first two weeks was not associated with treatment outcome. Across 12 weeks of treatment markers. Treatment characteristics that were significantly associated with lower rates of week-12 OPUs consisted of having an elevated liver enzyme [χ2(df =1) = 11.1, p=0.001]; receiving any non-study treatment services [χ2(df =1) = 17.5, p < 0.0001] or concomitant medications [χ2(df =1) = 12.06, p= 0.0005]; attending more study counseling sessions [t (df =148) = 4.93, p < 0. 0001]; having more active medical and/or psychiatric problems at the week-12 visit [t(df =150) = 5.43, p < 0.0001]; and completing 12 weeks of treatment [χ2(df =1) =9.62, p= 0.002]. Maximum dose of Bup/Nal, high/intermediate/low Bup/Nal dosing ranges, and reports of SAEs (study medication-related or not) were unrelated to outcome.
Table 2
Table 2
Bivariate Comparisons of During-Treatment Factors with Week 12 Opioid Positive Urine Tests
Independent Baseline and During Treatment Characteristics and Opioid Abstinence
In the first logistic regression model (controlling for treatment group assignment) the following 5 baseline factors that were significant or approached significance were included: past 30-day IDU (yes/no), number of active comorbid medical or psychiatric problems, gender (female/male), elevated liver enzymes (yes/no) and number of internalizing problems. In the final model (Table 3), those reporting past 30-day IDU [Odds Ratio (OR) = 0.32, 95% confidence interval (CI): 0.13, 0.80] and having more active medical/psychiatric problems [OR=0.77, 95%CI: 0.60, 0.98] were less likely to have an OPU.
Table 3
Table 3
Multivariate Models: Independent Predictors of Week 12 Opioid Positive Urine Tests
In the second logistic regression model, (also controlling for treatment group assignment) the following 6 during-treatment factors that were significant or approached significance were included: opioid negative urines at weeks 1 and 2 (yes/no), any study therapy attendance at wks 1 and 2 (yes/no), medication compliance during weeks 1 and 2, receiving any non-study medication (yes/no), dropping out prior to 12th week of treatment (yes/no), and receiving any non-study treatment services (yes/no). In the final model (Table 3), those having a negative urine test in weeks 1 and 2 (OR: 0.24, 95% CI: 0.09, 0.66); those receiving non-study treatment services (OR: 0.11, 95% CI: 0.03, 0.43) and receiving any non-study medications (OR: 0.11, 95% CI: 0.03, 0.41) were less likely to have an OPU. Participants who dropped out of treatment prior to 12th week (OR: 4.71, 95% CI: 1.16, 19.06) were more likely to have an OPU.
Logistic Regression analyses were repeated using the completer sample (i.e. those who provided a UDS result at week 12). Among baseline factors, past 30-day IDU remained significant (OR: 0.28, 95% CI: 0.08, 0.93). Among the during-treatment factors, only two factors significantly reduced the risk of having an OPU at week 12: providing opioid negative urines in weeks 1 and 2 (OR: 0.22, 95% CI: 0.07, 0.70) and receiving any non-study medications during treatment (OR: 0.10, 95% CI: 0.03, 0.41).
This paper presents new information on baseline and during-treatment factors related to treatment success in this outpatient, community-based, multi-site trial of Bup/Nal treated opioid dependent adolescents and young adults.
Injection drug use (IDU) at baseline was a significant and independent predictor of lower rates of OPU at week 12. This finding is consistent with prior research that injection drug users had better Bup/Nal outcomes or were more likely to believe that it would be helpful15, 2829 lending further support for the role of Bup/Nal as a potentially effective tool in reducing HIV and Hepatitis-C infections/risk28. Having more active medical and/or psychiatric problems at treatment-entry also emerged as a significant independent predictor of lower rates of OPU. Higher mean baseline scores on self-reported internalizing problems, was associated with better opioid use outcomes (in bivariate analyses) but was not a significant independent predictor of lower rates of OPU. This finding is concordant with reports of adult patients treated with buprenorphine showing that higher levels of depression was associated with better opioid outcomes1213, 15. For youth, the association between IDU and high distress levels and better treatment outcome may be explained by their awareness of being in a downward spiral and being tired of devoting so much time and resources to obtaining and using drugs to the exclusion of prosocial activities. This could be a motivating factor for treatment..
The third significant finding was that Bup/Nal treatment led to similar rates of opioid abstinence regardless of the type of opioid they reported using (i.e. heroin, non-heroin prescription opioid analgesic or both), consistent with findings from another adolescent opioid treatment study17. Also non-significant were baseline predictors such as race, education/employment status, and concomitant cocaine, tobacco or marijuana use with a trend towards significance for gender and elevated liver enzymes, in contrast to the results from adult treatment studies1216.
The during-treatment factors associated with treatment success spanned three areas: non-study medications and other non-study treatment services; early treatment phase opioid abstinence; and study treatment completion. The significance of receiving ancillary treatments and medications (to ease withdrawal symptoms, insomnia, pain and co-occurring psychiatric symptoms) to augment Bup/Nal interventions was not supported with adult patients15, 30. It is likely that the improved outcomes for patients that received non-study medications and/or treatment reflect benefits of treating other medical/psychiatric disorders while in the study (as shown in studies with adults12) and the important benefits of receiving additional treatment elsewhere for co-occurring problems even after dropping out of the study.
Achieving opioid abstinence during the early treatment phase may serve as an important marker of treatment success, as in a previous study with adults31, and may have been driven by motivation to get well among those severely addicted resulting in adherence to medication and counseling. Similarly, it was no surprise that treatment completion was associated with better outcomes since longer periods of treatment participation have consistently been linked to better buprenorphine and other substance abuse treatment outcomes3234.
This study was not adequately powered to detect a clinically significant interaction with all the baseline and during-treatment factors examined. While the lack of statistical significance in the results may be a result of sample size, our analyses were meant to generate hypotheses for future study. Future, adequately powered studies are needed to replicate these findings, re-evaluate the non-significant findings from this study, evaluate specific co-occurring psychiatric disorders, consider study-endpoints/outcomes other than end-of-study urine results and include other predictors e.g. biological and genetic markers that were not examined in this study. Although we did not find that youth < 18 years (n=26, 18%) fared differently from those who were older, this finding may be an artifact of the small sample size. The low rates of study treatment completion/retention may have adversely affected the outcomes. Consistent with the primary study, we, conservatively, imputed positive results for missing urine results, which may have incorrectly estimated those who achieved recovery.
The primary study demonstrated the efficacy of 12 weeks of Bup/Nal in reducing opioid use and improving treatment retention among opioid dependent youth ages 15–21 years. The current study has contributed new and important clinical information on baseline and during-treatment factors that were linked to lower rates of OPU. Patients presenting with an advanced opioid use pattern (i.e. IDU and other health issues) were more likely to have lower rates of OPU at week-12, suggesting that they can respond well to outpatient Bup/Nal treatment. Those that were able to achieve early opioid abstinence, receive supplemental treatment services and medications outside of the study and remain in treatment for the entire 12 weeks of treatment were more likely to have better opioid outcomes. These new findings have the potential to facilitate tailored treatments for opioid dependent youth and inform the design of future treatment trials for this largely understudied population.
Acknowledgments
This study was supported by the following grants from the National Institutes on Drug Abuse: U10-DA13043 and KO5 DA-17009 (GEW) to University of Pennsylvania, U10-013034 (MLS) and K12-DA000357 (GAS, Paula Riggs, MD) to Johns Hopkins University, U10-DA020024 (BA) to UT Southwestern Medical Center at Dallas and U10-DA015831 (RW) and K24-DA022288 (RW) to McLean Hospital.
Disclosure: Dr. Warden holds stock in Pfizer and Bristol-Myers Squibb. He has received funding from the National Alliance for Research on Schizophrenia and Depression. Dr. Fishman is the medical director of Mountain Manor Treatment Center (MMTC), one of multiple research sites in this study. He is a beneficiary of the trust which owns MMTC. He serves on the governing board of the trust and the board of directors of MMTC. The terms of Dr. Fishman’s potential conflict of interest in research are managed by Johns Hopkins University in accordance with its conflict of interest policies. Dr. Weiss has received grants from the National Institute of Drug Abuse (NIDA). He has served as a consultant to Titan Pharmaceuticals. Dr. Adinoff has received grant support from the National Institute of Alcohol Abuse and Alcoholism, NIDA, and the Department of Veterans Affairs. He has served as a consultant to Shook, Hardy, and Bacon LLP; and Paul J. Passante, P.C. He has received honoraria from the University of New Mexico, the Medical University of South Carolina, the American Institute of Biological Sciences, the American Academy of Addiction Psychiatry, the Methodist Medical Center, Vanderbilt University, the University of North Texas Health Care System, John Peter Smith Hospital, and Texas Tech University. Dr. Trivedi has served as a consultant for Abbott, Alkermes, AstraZeneca, Axon Advisors, Bristol-Myers Squibb, Cephalon, CME Institute of Physicians, Eli Lilly and Co., Evotek, Forest, GlaxoSmithKline, Johnson and Johnson, Lundbeck, MedAvante, Neuronetics, Ostuka, Pamlab, Pfizer, PgxHealth, Rexahn, Shire, Takeda, Tal Medical/Puretech Venture, and Transcept. He has served on the speakers’ bureau for Axon Advisors, Bristol-Myers Squibb, CME Institute of Physicians, Eli Lilly and Co., Forest, GlaxoSmithKline, Lundbeck, MedAvante, Otsuka, Pamlab, Pfizer, PgxHealth, Rexahn, and Takeda. He has received research support from the Agency for Healthcare Research and Quality, the National Institute of Mental Health, NIDA, Naurex, Targacept, and Valient. Dr. Woody has served on the advisory board of the Researched Abuse, Diversion, and Addiction-Related Surveillance (RADARS) System. Drs. Subramaniam, Minhajuddin, Stitzer, and Potter, and Ms. Poole report no biomedical financial interests or potential conflicts of interest.
Footnotes
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1. Johnston LD, O’Malley PM, Bachman JG, Schulenberg JE. Monitoring the Future national results on adolescent drug use: Overview of key findings, 2009. Bethesda, Maryland: National Institute on Drug Abuse; 2009. Publication No. 10-7583.
2. Subramaniam GA, Stitzer MA, Woody GE, Fishman MJ, Kolodner K. Clinical Characteristics of Treatment-Seeking Adolescents with Opioid Versus Cannabis/Alcohol Use Disorders. Drug Alcohol Depend. 2009;99:141–149. [PMC free article] [PubMed]
3. Subramaniam GA, Ives ML, Stitzer ML, Dennis ML. The added risk of opioid problem use among treatment-seeking youth with marijuana and/or alcohol problem use. Addiction. 2010 Apr;105(4):686–698. [PMC free article] [PubMed]
4. [Accessed February 1, 2011];(DAWN) DAWN. Detailed Tables: National Estimates, Drug-Related Emergency Department Visits for 2004–2009. 2009 https://dawninfo.samhsa.gov/data//
5. Mattick RP, Kimber J, Breen C, Davoli M. Buprenorphine maintenance versus placebo or methadone maintenance for opioid dependence. Cochrane Database Syst Rev. 2008;2:CD002207. [PubMed]
6. Mattick RP, Breen C, Kimber J, Davoli M. Methadone maintenance therapy versus no opioid replacement therapy for opioid dependence. Cochrane Database Syst Rev. 2009;3:CD002209. [PubMed]
7. Parrino M. State methadone treatment guidelines. Rockville, MD: 1993. Vol TIPS Series 1. DHHS Publication No. (SMA) 93-1991.
8. Marsch LA, Bickel WK, Badger GJ, et al. Comparison of pharmacological treatments for opioid-dependent adolescents: a randomized controlled trial. Arch Gen Psychiatry. 2005 Oct;62(10):1157–1164. [PubMed]
9. Woody GE, Poole SA, Subramaniam G, et al. Extended vs short-term buprenorphine-naloxone for treatment of opioid-addicted youth: a randomized trial. JAMA. 2008 Nov 5;300(17):2003–2011. [PMC free article] [PubMed]
10. Kraemer HC, Frank E, Kupfer DJ. Moderators of treatment outcomes: clinical, research, and policy importance. JAMA. 2006 Sep 13;296(10):1286–1289. [PubMed]
11. Kravitz RL, Duan N, Braslow J. Evidence-based medicine, heterogeneity of treatment effects, and the trouble with averages. Milbank Q. 2004;82(4):661–687. [PubMed]
12. 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 Nov;13(4):293–302. [PubMed]
13. Gerra G, Borella F, Zaimovic A, et al. Buprenorphine versus methadone for opioid dependence: predictor variables for treatment outcome. Drug Alcohol Depend. 2004;75(1):37–45. [PubMed]
14. Fiellin DA, Pantalon MV, Chawarski MC, et al. Counseling plus buprenorphine-naloxone maintenance therapy for opioid dependence. N Engl J Med. 2006 Jul 27;355(4):365–374. [PubMed]
15. Ziedonis DM, Amass L, Steinberg M, et al. Predictors of outcome for short-term medically supervised opioid withdrawal during a randomized, multicenter trial of buprenorphine-naloxone and clonidine in the NIDA clinical trials network drug and alcohol dependence. Drug Alcohol Depend. 2009 Jan 1;99(1–3):28–36. [PMC free article] [PubMed]
16. Torrington M, Domier CP, Hillhouse M, Ling W. Buprenorphine 101: treating opioid dependence with buprenorphine in an office-based setting. J Addict Dis. 2007;26(3):93–99. [PubMed]
17. Motamed M, Marsch LA, Solhkhah R, Bickel WK, Badger GJ. Differences in Treatment Outcomes between Prescription Opioid-Dependent and Heroin-Dependent Adolescents. Journal of Addiction Medicine. 2008;2(3):158–164. 110.1097/ADM.1090b1013e31816b31812f31884. [PubMed]
18. Mercer DE, Woody GE. In: An Individual Drug Counseling Approach to Treat Cocaine Addiction: The Collaborative Cocaine Treatment Study Model. NIDA, editor. Rockville, MD: 1999. Vol NIH Pub. No. 99-4380.
19. Chakrabarti A, Woody GE, Griffin ML, Subramaniam G, Weiss RD. Predictors of buprenorphine-naloxone dosing in a 12-week treatment trial for opioid-dependent youth: secondary analyses from a NIDA Clinical Trials Network study. Drug Alcohol Depend. 2010 Mar 1;107(2-3):253–256. [PMC free article] [PubMed]
20. Meade CS, Weiss RD, Fitzmaurice GM, et al. HIV risk behavior in treatment-seeking opioid-dependent youth: results from a NIDA clinical trials network multisite study. J Acquir Immune Defic Syndr. 2010 Sep 1;55(1):65–72. [PMC free article] [PubMed]
21. Miele GM, Carpenter KM, Smith Cockerham M, Dietz Trautman K, Blaine J, Hasin DS. Concurrent and predictive validity of the Substance Dependence Severity Scale (SDSS) Drug Alcohol Depend. 2000 Apr 1;59(1):77–88. [PubMed]
22. Needle R, Fisher DG, Weatherby N, Chitwood D, Brown B, Cesari H, Booth R, Williams ML, Watters J, Andersen M, Braunstein M. Reliability of self-reported HIV risk behaviors of drug users. Psychology of Addictive Behaviors. 1995 Dec 199;Vol 9(4):242–250.
23. NIDA. Risk Behavior Survey. 3rd Edition. Rockville, MD: National Institute on Drug Abuse, Community Research Branch; 1993.
24. Achenbach TM. Manual for the YSR and 1991 Profile. Burlington, VT: University of Vermont, Department of Psychiatry; 1991.
25. Achenbach T. Manual for the Young Adult Self-Report and 1997 Profiles. Burlington, VT: University of Vermont, Department of Psychiatry; 1997.
26. Gossop M. The development of a Short Opiate Withdrawal Scale (SOWS) Addict Behav. 1990;15(5):487–490. [PubMed]
27. SAS. SAS. 9.2 edition. Cary, NC: SAS Institute; 2010.
28. Sullivan LE, Fiellin DA. Buprenorphine: its role in preventing HIV transmission and improving the care of HIV-infected patients with opioid dependence. Clin Infect Dis. 2005 Sep 15;41(6):891–896. [PubMed]
29. Bachireddy C, Bazazi AR, Kavasery R, Govindasamy S, Kamarulzaman A, Altice FL. Attitudes toward opioid substitution therapy and pre-incarceration HIV transmission behaviors among HIV-infected prisoners in Malaysia: Implications for secondary prevention. Drug Alcohol Depend. 2011 Jul 1;116(1-3):151–157. [PMC free article] [PubMed]
30. Hillhouse M, Domier CP, Chim D, Ling W. Provision of ancillary medications during buprenorphine detoxification does not improve treatment outcomes. J Addict Dis. 2010 Jan;29(1):23–29. [PMC free article] [PubMed]
31. Stein MD, Cioe P, Friedmann PD. Buprenorphine retention in primary care. J Gen Intern Med. 2005 Nov;20(11):1038–1041. [PMC free article] [PubMed]
32. Kakko J, Svanborg KD, Kreek MJ, Heilig M. 1-year retention and social function after buprenorphine-assisted relapse prevention treatment for heroin dependence in Sweden: a randomised, placebo-controlled trial. Lancet. 2003 Feb 22;361(9358):662–668. [PubMed]
33. Katz EC, Schwartz RP, King S, et al. Brief vs. extended buprenorphine detoxification in a community treatment program: engagement and short-term outcomes. Am J Drug Alcohol Abuse. 2009;35(2):63–67. [PMC free article] [PubMed]
34. Villafranca SW, McKellar JD, Trafton JA, Humphreys K. Predictors of retention in methadone programs: a signal detection analysis. Drug Alcohol Depend. 2006 Jul 27;83(3):218–224. [PubMed]