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Background and Objectives: Tobacco smoking and alcohol use disorder (AUD) are highly prevalent among individuals receiving medication for opioid use disorder (MOUD) treatment, yet their combined impact on treatment outcomes remains underexplored. This study investigates the differences in clinical profiles and treatment outcomes based on smoking and AUD status among individuals initiating MOUD.
Methods: This secondary analysis utilized data from a multi-site randomized clinical trial (CTN-0027) evaluating the hepatotoxicity during 24 weeks of buprenorphine or methadone treatment. Participants were categorized into four groups based on baseline smoking and AUD status: Non-AUD/Non-smoker, Smoker Only, AUD Only, and AUD+Smoker. Clinical profiles and treatment outcomes were compared across groups.
Results: Among 973 participants (68.6% male, 70.5% White, mean age 37.5 years), 50% were Smoker Only, 16% AUD+Smoker, 8% AUD Only, and 27% Non-AUD/Non-smoker. Smoking prevalence was high (66%), while AUD prevalence was lower (24%). AUD+Smoker and AUD Only groups had significantly higher rates of additional substance use disorders (p < .01). However, treatment outcomes—measured by urinalysis results, retention, and completion—did not differ significantly across groups.
Discussion and Conclusions: Smoking and AUD status were not associated with poorer MOUD outcomes, but the high prevalence of smoking, and the clustering of additional substance use disorders among individuals with AUD suggest the need for integrated care. These findings support inclusion of adjunctive behavioral and public health interventions within MOUD programs.
Related protocols: CTN-0027
Background: The Tobacco, Alcohol, Prescription Medication, and Other Substance (TAPS) tool is a screening and brief assessment instrument to identify unhealthy tobacco, alcohol, drug use, and prescription medication use in primary care patients. This secondary analysis compares the TAPS tool to the Alcohol Use Disorders Identification Test-Consumption (AUDIT-C) for alcohol screening.
Methods: Adult primary care patients (1124 female, 874 male) completed the TAPS tool followed by AUDIT-C. Performance of each instrument was evaluated against a reference standard measure, the modified World Mental Health Composite International Diagnostic Interview, to identify problem use and alcohol use disorder (AUD). Area under the curve (AUC) appraised discrimination, and sensitivity and specificity were calculated for Youden optimal score thresholds.
Results: For identifying problem use: On the AUDIT-C, AUC was 0.90 (95% Confidence Interval: 0.86-0.92) for females and 0.91 (0.89-0.93) for males. Sensitivity and specificity for females were 0.89 (0.83-0.93) and 0.78 (0.75-0.80), respectively, and for males were 0.84 (0.79-0.88) and 0.82 (0.79-0.85). On the TAPS tool, AUC was 0.82 (0.79-0.86) for females and 0.81 (0.78-0.84) for males. Sensitivity and specificity for females were 0.78 (0.72-0.84) and 0.78 (0.75-0.81), respectively, and for males were 0.76 (0.71-0.81) and 0.76 (0.72-0.79). For AUD: On the AUDIT-C, AUC was 0.90 (0.88-0.93) for both females and males. Sensitivity and specificity for females were 0.83 (0.74-0.90) and 0.83 (0.80-0.85), respectively, while for males, they were 0.81 (0.74-0.87) and 0.84 (0.81-0.87). On the TAPS tool, AUC was 0.84 (0.80-0.89) for females and 0.82 (0.78-0.86) for males. Sensitivity and specificity for females were 0.73 (0.63-0.81) and 0.85 (0.83-0.88), respectively, while for males, they were 0.75 (0.68-0.81) and 0.84 (0.81-0.86).
Conclusions: The AUDIT-C performed somewhat better than the TAPS tool for alcohol screening. However, the TAPS tool had an acceptable level of performance for alcohol screening and may be advantageous in practice settings seeking to identify alcohol and other substance use with a single instrument.
Related protocols: CTN-0059
Objective: This secondary analysis evaluated the validation results of the Tobacco, Alcohol, Prescription Medication, and Other Substance Use (TAPS) tool for older adults.
Methods: Researchers performed a subgroup analysis of older adults aged =65 (n = 184) from the TAPS tool validation study conducted in 5 primary care clinics (CTN-0059). They compared the interviewer and self-administered versions of the TAPS tool at a cutoff of =1 for identifying problem use with a reference standard measure, the modified World Mental Health Composite International Diagnostic Interview.
Results: The mean age was 70.6 ± 5.9 years, 52.7% were female, and 49.5% were non-Hispanic Black. For identifying problem use, the self-administered TAPS tool had sensitivity of 0.91 (95% CI: 0.75–0.98) and specificity of 0.91 (95% CI: 0.85–0.95) for tobacco; sensitivity of 0.68 (95% CI: 0.45–0.86) and specificity of 0.88 (95% CI: 0.82–0.93) for alcohol; and sensitivity 0.86 (95% CI: 0.42–1.00) and specificity 0.94 (95% CI: 0.90–0.97) for cannabis. The interviewer-administered TAPS tool had similar results. Researchers were unable to evaluate its performance for identifying problem use of individual classes of drugs other than cannabis in this population due to small sample sizes.
Conclusions: While the TAPS had excellent sensitivity and specificity for identifying tobacco use among older adults, the results for other substances lack precision, and we were unable to evaluate its performance for prescription medications and individual illicit drugs in this sample. This analysis underlines the critical need to adapt and validate screening tools for unhealthy substance use, specifically for older populations who have unique risks.
Related protocols: CTN-0059
A trial comparing extended-release naltrexone and sublingual buprenorphine-naloxone demonstrated higher relapse rates in individuals randomized to extended-release naltrexone. The effectiveness of treatment might vary based on patient characteristics. We hypothesized that causal machine learning would identify individualized treatment effects for each medication.
This is a secondary analysis of a multicenter randomized trial (CTN-0051, X:BOT) that compared the effectiveness of extended-release naltrexone versus buprenorphine-naloxone for preventing relapse of opioid misuse. Three machine learning models were derived using all trial participants with 50% randomly selected for training (n = 285) and the remaining 50% for validation. Individualized treatment effect was measured by the Qini value and c-for-benefit, with the absence of relapse denoting treatment success. Patients were grouped into quartiles by predicted individualized treatment effect to examine differences in characteristics and the observed treatment effects.
The best-performing model had a Qini value of 4.45 (95% confidence interval, 1.02–7.83) and a c-for-benefit of 0.63 (95% confidence interval, 0.53–0.68). The quartile most likely to benefit from buprenorphine-naloxone had a 35% absolute benefit from this treatment, and at study entry, they had a high median opioid withdrawal score (P < 0.001), used cocaine on more days over the prior 30 days than other quartiles (P < 0.001), and had highest proportions with alcohol and cocaine use disorder (P = 0.02). Quartile 4 individuals were predicted to be most likely to benefit from extended-release naltrexone, with the greatest proportion having heroin drug preference (P = 0.02) and all experiencing homelessness (P < 0.001).
Conclusions: Causal machine learning identified differing individualized treatment effects between medications based on characteristics associated with preventing relapse.
Related protocols: CTN-0051
Opioid and alcohol use disorders are increasingly being addressed in primary care, yet how medications to treat these disorders are prescribed in rural regions is unknown.
For this study, a secondary analysis of EHR data gathered as part of CTN-0102, researchers determined prevalence, types, and duration of medication prescription for opioid and/or alcohol use disorder among adult patients in rural primary clinics. The sample included 1874 adult patients who visited one of six rural primary care sites in the Northeastern and Northwestern United States at least once from October 2019 to January 2021 and had a diagnosis code for opioid use disorder (OUD), alcohol use disorder (AUD), or co-occurring opioid and alcohol use disorder (OUD + AUD) during that time.
Patients with OUD + AUD were more likely to be prescribed medication for at least one of these disorders (85.3 %) than patients with OUD only (63.7 %) or AUD only (10.3 %). Further, the OUD + AUD group had the highest number of days on medication (M = 264.7), followed by OUD only (M = 220.5), then the AUD only group (M = 62.5). Only 8.8 % of patients with OUD + AUD were prescribed naltrexone or medication for OUD + AUD to treat both substance use disorders.
Conclusions: Medications for treating AUD as well as OUD are available, but few patients with OUD + AUD and even fewer with AUD received pharmacological treatment for AUD. The current work highlights the need for rural clinicians to consider medications for AUD as an important treatment method for patients with AUD only or OUD + AUD.
Related protocols: CTN-0102
Screening for substance use in rural primary care clinics faces unique challenges due to limited resources, high patient volumes, and multiple demands on providers. To explore the potential for electronic health record (EHR)-integrated screening in this context, researchers conducted an implementation feasibility study with a rural federally-qualified health center (FQHC) in Maine. This was an ancillary study to a NIDA Clinical Trials Network study of screening in urban primary care clinics (CTN-0062).
Researchers worked with stakeholders from three FQHC clinics to define and implement their optimal screening approach. Clinics used the Tobacco, Alcohol, Prescription Medication, and Other Substance (TAPS) Tool, completed on tablet computers in the waiting room, and results were immediately recorded in the EHR. Adult patients presenting for annual preventive care visits, but not those with other visit types, were eligible for screening. Data were analyzed for the first 12 months following implementation at each clinic to assess screening rates and prevalence of reported unhealthy substance use, and documentation of counseling using an EHR-integrated clinical decision support tool, for patients screening positive for moderate-high risk alcohol or drug use.
Screening was completed by 3749 patients, representing 93.4% of those with screening-eligible annual preventive care visits, and 18.5% of adult patients presenting for any type of primary care visit. Screening was self-administered in 92.9% of cases. The prevalence of moderate-high risk substance use detected on screening was 14.6% for tobacco, 30.4% for alcohol, 10.8% for cannabis, 0.3% for illicit drugs, and 0.6% for non-medical use of prescription drugs. Brief substance use counseling was documented for 17.4% of patients with any moderate-high risk alcohol or drug use.
Conclusions: Self-administered EHR-integrated screening was feasible to implement, and detected substantial alcohol, cannabis, and tobacco use in rural FQHC clinics. Counseling was documented for a minority of patients with moderate-high risk use, possibly indicating a need for better support of primary care providers in addressing substance use. There is potential to broaden the reach of screening by offering it at routine medical visits rather than restricting to annual preventive care visits, within these and other rural primary care clinics.
Related protocols: CTN-0062-Ot
Included among the significant risk factors for opioid overdose is concomitant use of other central nervous system depressants, particularly alcohol. Given the continued expansion of community pharmacy in the continuum of care, it is imperative to characterize alcohol use among pharmacy patients dispensed opioids in order to establish a foundation for identification and intervention in these settings.
This secondary analysis utilized data from a one-time, cross-sectional health assessment conducted among patients dispensed opioid medications in 19 community pharmacies in Indiana and Ohio (CTN-0093, PHARMSCREEN). Adult, English speaking, patients not receiving cancer care who were dispensed opioid medications were asked to self-report alcohol and substance use, behavioral and physical health, and demographic information. Descriptive and logistic regression analyses were employed to characterize alcohol use/risky alcohol use and patient characteristics associated therewith.
The analytical sample included 1494 individuals. Participants were on average 49 years of age (Standard Deviation=14.9)-with 6% being persons of color (n = 89). Weekly drinking was reported by 18.1% (n = 204) and daily drinking was reported by 6.8% (n = 77) of the study sample, with a total of 143 (9.6%) participants reporting moderate/high risk drinking. Males (Adjusted Odds Ratio [AOR]=1.94, 95% CI=1.3,2.9), those with higher pain interference (AOR=1.44, 95% CI=1.0,2.0), overdose history (AOR=1.93, 95% CI=1.1,3.5), sedative use (AOR=2.11, 95% CI=1.3,3.5), and tobacco use (AOR=2.41, 95% CI=1.6,3.7) had increased likelihood of moderate/high risk alcohol use (all p < 0.05).
Conclusions: Medication labeling and clinical guidelines clearly indicate that patients should abstain from concomitant use of opioids and alcohol. This study has identified rates and associated risk factors of risky alcohol use among a clinical sample of community pharmacy patients dispensed opioid medications. Continuing this line of research and potential clinical service development has the ability to improve patient safety through addressing a significant gap within the current opioid epidemic.
Related protocols: CTN-0093
Traditional treatments for substance use disorders (SUDs) rely heavily on face-to-face interactions, which pose substantial limitations for patients. A clinical trial of a digital therapeutic (DT) delivering behavioral therapy demonstrated safety and efficacy in a population including patients with opioid use disorder (OUD) not treatment with buprenorphine, which is not a guideline-recommended approach. This study re-analyzed the data excluding patients with OUD to more closely approximate real-world patient populations.
The study was a secondary analysis of data from CTN-0044 (“Web-Delivery of Evidence-Based, Psychosocial Treatment for Substance Use Disorders;” n=399 after patients with OUD were excluded). Patients received 12 weeks of outpatient treatment-as-usual (TAU; n=193) or TAU with reduced counseling plus a digital therapeutic (DT; n=206) providing computerized cognitive behavioral therapy and contingency management (the reSET, a commercial version of the Therapeutic Education System (TES)). Primary outcomes were abstinence in weeks 9-12 and retention in treatment.
The 399 patients in the analysis (206 in the DT group and 193 in the TAU group) reported substance use disorders related to alcohol, cannabis, cocaine, or other stimulants (e.g. methamphetamine). Demographic and baseline characteristics including age, sex, race, education, and reported primary substance use disorder were balanced between treatment groups. Abstinence was significantly higher in the DT group compared to the TAU group (40.3 vs. 17.6%), as was retention in therapy (76.2 vs. 63.2%).
Conclusions: These results demonstrate that use of a digital therapeutic safely increased abstinence (reduced substance use) and retention in treatment among patients with substance use disorders related to alcohol, cannabis, cocaine, or other stimulants (including methamphetamines).
Related protocols: CTN-0044
Despite advances in antiretroviral therapy, chronic immune activation continues to be observed among individuals with well-controlled HIV viral loads, and is associated with non-AIDS defining morbidities among people living with HIV. Alcohol use disorder impacts a significant proportion of individuals living with HIV, and alcohol exposure is known to damage the intestinal epithelium which may increase translocation of pathogens and their molecular products, driving systemic immune activation and dysregulation. The aim of this study was to determine if adults living with HIV with well-controlled viral loads, who also suffer from alcohol use disorder with and without hepatitis C virus co-infection (n=23), exhibit evidence of advanced systemic immune activation, intestinal damage, and microbial translocation, as compared to adults living with HIV who are not exposed to chronic alcohol or other substances of abuse (n=29). The impact of a 1-month intervention to treatment alcohol use disorder was also examined.
Alcohol use disorder was associated with evidence of advanced innate immune activation, alterations in monocyte phenotype including increased expression of Toll-like receptor 4, increased burden of stimulatory ligands for Toll-like receptor 4, and alterations in plasma cytokine signature, most notably elevations in soluble CD40 ligand and transforming growth factor beta. Alcohol-associated immune activation was more pronounced among individuals with hepatitis C virus co-infection.
Conclusions: Although the 1-month intervention to treat alcohol use disorder did not result in significant reductions in the interrogated indicators of immune activation, these findings suggest that chronic alcohol exposure is a major modifiable risk factor for chronic immune activation and dysregulation among people living with HIV.
Related protocols: CTN-0055
This study examined the associations of polysubstance use, mood disorders, and chronic conditions with the history of anxiety disorder among patients with opioid use disorder (OUD). Researchers performed a secondary analysis of the baseline data from NIDA-CTN-0027 (“Starting Treatment with Agonist Replacement Therapies (START)”), a clinical trial that included 1,645 individuals with OUD, of which 513 had anxiety disorder. Substance use disorder (SUDs) included alcohol, amphetamines, cannabis, cocaine, and sedative use disorders. Mood disorders included major depressive disorder (MDD) and bipolar disorder (BD). Chronic conditions were allergies, gastrointestinal problem(s), skin problem(s), and hypertension. Sedative use disorder, MDD, BD, skin problems, and hypertension were significantly associated with anxiety disorder. Additionally, more than two SUDs, two mood disorders, and more than two chronic conditions were significantly associated with anxiety disorder.
Conclusions: These findings highlight the comorbid mental health and physical health problems in individuals with OUD, as well as the need for integrated multidisciplinary treatment plans. Future areas of research should focus on not only OUD, but also patients presenting with other comorbidities to identify more vulnerable groups, discover effective solutions, and reduce the prevalence of OUD.
Related protocols: CTN-0027
The randomized X:BOT trial (CTN-0051) showed that following induction, sublingual agonist (buprenorphine-naloxone, BUP-NX) or antagonist injection (extended release naltrexone, XR-NTX) produced similar benefits for reducing opioid relapse in injection users with opioid use disorder. Given that XR-NTX reduces drinking in alcohol use disorder (AUD), researchers completed a secondary analysis of the X:BOT sample of patients successfully inducted onto treatment to determine if XR-NTX (n=204) was superior to BUP-NX (n=270) to reduce drinking or heavy drinking in patients with opioid use disorder.
Researchers examined timeline follow-back recorded standard drink units consumed and used mixed-models regression to analyze monthly frequencies of any drinking or heavy drinking over 6 months of treatment and proportional hazard survival examined time to first drink.
Both treatment groups reduced drinking from baseline to post-treatment (small to medium effect), but no differences between groups were detected. However, only 29% (n=136) of the sample had AUD and 19% (n=26/136) of those were abstinent before treatment. Analysis of a subsample enriched for possible drinking included n=136 with an AUD diagnosis plus n=43 who did not have AUD, but reported at least one day of heavy drinking prior to study. Even so, this subsample still reported only 32% of days of any drinking with a median of only 13% of days designated as “heavy”. Within this subsample, the BUP-NX group reported greater mean drinks per drinking day than did the XR-NTX group at baseline (p=0.03); however, there were no other significant group differences in drinking observed before, during, or at the end of treatment.
Conclusions: An overall improvement in drinking occurred for treatment of OUD using both agonist and antagonist approaches indicating that the hypothesis that XR-NTX would be superior to BUP-NX was not supported. The study is limited by low levels of comorbid AUD or heavy drinking observed in X:BOT trial participants seeking treatment for opioid use disorder.
Related protocols: CTN-0051
Cannabis use is common among individuals with pain who are prescribed opioids, occurring in approximately 10% of this population. This study aimed to explore the relationship between non-medical cannabis use and other health risks among individuals filling opioids at community pharmacies.
This study was an exploratory secondary data analysis of a National Drug Abuse Treatment Clinical Trials Network (CTN)-sponsored study, Validation of a Community Pharmacy-Based Prescription Drug Monitoring Program Risk Screening (CTN-0093), examining the relationship between risky cannabis use and depressive symptoms, pain, overdose, and other substance misuse among individuals filling opioid prescriptions in community pharmacies (N = 1440).
Participants reporting moderate- to high-risk compared to low-risk cannabis use were more likely to report depressive symptoms (adjusted OR = 1.67, 95% CI = 1.11–2.56), history of overdose (adjusted OR = 2.15, 95% CI = 1.34–3.44), and moderate- to high-risk use of alcohol (adjusted OR = 2.10, 95% CI = 1.28–3.45), opioids (adjusted OR = 2.50, 95% CI = 1.67–3.76), sedatives (adjusted OR = 2.58, 95% CI = 1.72–3.86), stimulants (adjusted OR = 4.79, 95% CI = 2.83–8.01), and tobacco (adjusted OR = 3.60, 95% CI = 2.47–5.24).
Conclusion: Community pharmacies may be valuable sites for identifying, studying, and intervening with substance use problems.
Related protocols: CTN-0093
Primary care settings provide salient opportunities for identifying patients with problematic substance use and addressing unmet treatment needs. The aim of this study was to examine the extent and correlates of problematic substance use by substance-specific risk categories among primary care patients to inform screening/intervention efforts.
This secondary analysis of CTN-0059 (TAPS Tool) looked at data from 2000 adult primary care patients aged 18 or older (56% female) across 5 clinics in the eastern U.S. Participants completed the Alcohol, Smoking, and Substance Involvement Screening Test (ASSIST). Prevalence and ASSIST-defined risk-level of tobacco use, alcohol use, and nonmedical/illicit drug use was examined. Multinomial logistic regression models analyzed the demographic correlates of substance use risk-levels.
Among the total sample, the prevalence of any past 3-month use was 53.9% for alcohol, 42% for tobacco, 24.2% for any illicit/Rx drug, and 5.3% for opioids; the prevalence of ASSIST-defined moderate/high-risk use was 45.1% for tobacco, 29% for any illicit/Rx drug, 14.2% for alcohol, and 9.1% for opioids. Differences in the extent and risk-levels of substance use by sex, race/ethnicity, and age group were observed. Adjusted logistic regression showed that male sex, white race, not being married, and having less education were associated with increased odds of moderate/high-risk use scores for each substance category; older ages (versus ages 18-25 years) were associated with increased odds of moderate/high-risk opioid use.
Conclusions: Intervention need for problematic substance use was prevalent in this sample. Providers should maintain awareness and screen for problematic substance use more consistently in identified high risk populations.
Related protocols: CTN-0059
Abstinence from drinking represents the primary treatment target for alcohol use disorders (AUD) in youth, but few adolescents who engage in problematic drinking seek treatment. A reduction in World Health Organization (WHO) drinking risk level has been established as a valid and reliable non-abstinent treatment target for AUD in adults but remains unstudied in youth.
The present study used data from the NIDA-CTN-0028 trial (Osmotic-Release Methylphenidate for ADHD in Adolescents with Substance Use Disorders) to examine associations between reductions in WHO drinking risk level and changes in global functioning and attention-deficit hyperactivity disorder (ADHD) symptoms during treatment in a sample of adolescents (ages 13-18) with ADHD and comorbid substance use disorder (SUD) (n=297, 61% with AUD) receiving a 16-week intervention that combined ADHD pharmacotherapy (OROS-methylphenidate vs. placebo) and drug-focused cognitive-behavioral therapy.
Shifts in drinking risk level during treatment were highly variable in adolescents treatment for ADHD/SUD and influenced by AUD diagnostic status. In the total sample, 15% of participants had a 2-level or greater reduction in WHO drinking risk level, with 59% and 24% showing no change or an increase in risk-level during treatment respectively. Achieving at least a 2-level change in WHO drinking risk level during treatment was associated with greater reduction in ADHD symptoms and better functional outcomes.
Conclusions: These findings parallel the adult AUD literature and provide preliminary support for the use 2-level reductions in WHO risk levels for alcohol use as a clinical valid non-abstinent treatment outcome for youth with ADHD and comorbid AUD.
Substance use disorder (SUD) management by medical providers may be important for patients with comorbid health conditions exacerbated by SUD. This study, supported by protocol CTN-0080 (Medication Treatment for Opioid Use Disorder in Expectant Mothers), evaluated potential associations of SUD with morbidity and mortality in a large sample of hypertensive patients.
Analysis of a limited data set was obtained through IBM Watson Health Explorys, a platform integrating data from electronic health records. Matched controls were defined for each of five SUDs: tobacco use disorder (TUD), alcohol use disorder (AUD), cocaine use disorder (SOUD), opioid use disorder (OUD), and cannabis use disorder (CUD) using Mahalanobis distance within propensity score calipers. All patients were from the MetroHealth System (Cleveland, OH) and had diagnosed hypertension. SUD group participants had diagnosed abuse/dependence for the substance of interest. Controls for each SUD group had no diagnosis code related to the substance of interest and were selected to match the SUD patients on several factors. Total sample sizes for each SUD-control comparison ranged from 3,176 (CUD) to 49,696 (TUD); proportions of female patients ranged from 31.7% (AUD) to 51.2% (TUD). Outcomes were diagnosis (yes/no) of the following: cerebrovascular accident, myocardial infarction, renal failure, and all-cause mortality.
Logistic regressions revealed that SUD was significantly associated with cerebrovascular accident, renal failure, myocardial infarction, and mortality.
Conclusions: Among patients with hypertension, those with SUDs appear to have significantly greater risk for morbidity and mortality, suggesting the importance of managing SUD in hypertensive patients.