substance abuse

Existing methods of screening for substance abuse (standardized questionnaires or clinician’s simply asking) have proven difficult to initiate and maintain in primary care settings. This article reports on how predictive modeling can be used to screen for

Author/s: 
Alemi, Farrokh, Avramovic, Sanja, Schwartz, Mark D.

Existing methods of screening for substance abuse (standardized questionnaires or clinician's simply asking) have proven difficult to initiate and maintain in primary care settings. This article reports on how predictive modeling can be used to screen for substance abuse using extant data in electronic health records (EHRs). We relied on data available through Veterans Affairs Informatics and Computing Infrastructure (VINCI) for the years 2006 through 2016. We focused on 4,681,809 veterans who had at least two primary care visits; 829,827 of whom had a hospitalization. Data included 699 million outpatient and 17 million inpatient records. The dependent variable was substance abuse as identified from 89 diagnostic codes using the Agency for Healthcare Quality and Research classification of diseases. In addition, we included the diagnostic codes used for identification of prescription abuse. The independent variables were 10,292 inpatient and 13,512 outpatient diagnoses, plus 71 dummy variables measuring age at different years between 20 and 90 years. A modified naive Bayes model was used to aggregate the risk across predictors. The accuracy of the predictions was examined using area under the receiver operating characteristic (AROC) curve in 20% of data, randomly set aside for the evaluation. Many physical/mental illnesses were associated with substance abuse. These associations supported findings reported in the literature regarding the impact of substance abuse on various diseases and vice versa. In randomly set-aside validation data, the model accurately predicted substance abuse for inpatient (AROC = 0.884), outpatient (AROC = 0.825), and combined inpatient and outpatient (AROC = 0.840) data. If one excludes information available after substance abuse is known, the cross-validated AROC remained high, 0.822 for inpatient and 0.817 for outpatient data. Data within EHRs can be used to detect existing or predict potential future substance abuse.

Combined Pharmacotherapy and Cognitive Behavioral Therapy for Adults With Alcohol or Substance Use Disorders: A Systematic Review and Meta-analysis

Author/s: 
Ray, L.A., Meredith, L.R., Kiluk, B.D., Walthers, J., Carroll, K.M., Magill, M.

Abstract

Importance: Substance use disorders (SUDs) represent a pressing public health concern. Combined behavioral and pharmacological interventions are considered best practices for addiction. Cognitive behavioral therapy (CBT) is a first-line intervention, yet the superiority of CBT compared with other behavioral treatments when combined with pharmacotherapy remains unclear. An understanding of the effects of combined CBT and pharmacotherapy will inform best-practice guidelines for treatment of SUD.

Objective: To conduct a meta-analysis of the published literature on combined CBT and pharmacotherapy for adult alcohol use disorder (AUD) or other SUDs.

Data sources: PubMed, Cochrane Register, MEDLINE, PsychINFO, and Embase databases from January 1, 1990, through July 31, 2019, were searched. Keywords were specified in 3 categories: treatment type, outcome type, and study design. Collected data were analyzed through September 30, 2019.

Study selection: Two independent raters reviewed abstracts and full-text articles. English language articles describing randomized clinical trials examining CBT in combination with pharmacotherapy for AUD and SUD were included.

Data extraction and synthesis: Inverse-variance weighted, random-effects estimates of effect size were pooled into 3 clinically informative subgroups: (1) CBT plus pharmacotherapy compared with usual care plus pharmacotherapy, (2) CBT plus pharmacotherapy compared with another specific therapy plus pharmacotherapy, and (3) CBT added to usual care and pharmacotherapy compared with usual care and pharmacotherapy alone. Sensitivity analyses included assessment of study quality, pooled effect size heterogeneity, publication bias, and primary substance moderator effects.

Main outcomes and measures: Substance use frequency and quantity outcomes after treatment and during follow-up were examined.

Results: The sample included 62 effect sizes from 30 unique randomized clinical trials that examined CBT in combination with some form of pharmacotherapy for AUD and SUD. The primary substances targeted in the clinical trial sample were alcohol (15 [50%]), followed by cocaine (7 [23%]) and opioids (6 [20%]). The mean (SD) age of the patient sample was 39 (6) years, with a mean (SD) of 28% (12%) female participants per study. The following pharmacotherapies were used: naltrexone hydrochloride and/or acamprosate calcium (26 of 62 effect sizes [42%]), methadone hydrochloride or combined buprenorphine hydrochloride and naltrexone (11 of 62 [18%]), disulfiram (5 of 62 [8%]), and another pharmacotherapy or mixture of pharmacotherapies (20 of 62 [32%]). Random-effects pooled estimates showed a benefit associated with combined CBT and pharmacotherapy over usual care (g range, 0.18-0.28; k = 9). However, CBT did not perform better than another specific therapy, and evidence for the addition of CBT as an add-on to combined usual care and pharmacotherapy was mixed. Moderator analysis showed variability in effect direction and magnitude by primary drug target.

Conclusions and relevance: The present study supports the efficacy of combined CBT and pharmacotherapy compared with usual care and pharmacotherapy. Cognitive behavioral therapy did not perform better than another evidence-based modality (eg, motivational enhancement therapy, contingency management) in this context or as an add-on to combined usual care and pharmacotherapy. These findings suggest that best practices in addiction treatment should include pharmacotherapy plus CBT or another evidence-based therapy, rather than usual clinical management or nonspecific counseling services.

Interventions for Substance Use Disorders in Adolescents: A Systematic Review

Author/s: 
Steele, D.W., Becker, S.J., Danko, K.J., Balk, E.M., Saldanha, I.J., Adam, G.P., Bagley, S.M., Friedman, C., Spirito, A., Scott, K., Ntzani, E.E., Saeed, I., Smith, B., Popp J., Trikalinos, T.A.

Structured Abstract

Objectives. This systematic review (SR) synthesizes the literature on behavioral, pharmacologic, and combined interventions for adolescents ages 12 to 20 years with problematic substance use or substance use disorder. We included interventions designed to achieve abstinence, reduce use quantity and frequency, improve functional outcomes, and reduce substance-related harms.

Data sources. We conducted literature searches in MEDLINE, the Cochrane CENTRAL Trials Registry, Embase, CINAHL, and PsycINFO to identify primary studies meeting eligibility criteria through November 1, 2019.

Review methods. Studies were extracted into the Systematic Review Data Repository. We categorized interventions into seven primary intervention components: motivational interviewing (MI), family focused therapy (Fam), cognitive behavioral therapy (CBT), psychoeducation, contingency management (CM), peer group therapy, and intensive case management. We conducted meta-analyses of comparative studies and evaluated the strength of evidence (SoE). The PROSPERO protocol registration number is CRD42018115388.

Results. The literature search yielded 33,272 citations, of which 118 studies were included. Motivational interviewing reduced heavy alcohol use days by 0.7 days/month, alcohol use days by 1.2 days/month, and overall substance use problems by a standardized mean difference of 0.5, compared with treatment as usual. Brief MI did not reduce cannabis use days (net mean difference of 0). Across multiple intensive interventions, Fam was most effective, reducing alcohol use days by 3.5 days/month compared with treatment as usual. No intensive interventions reduced cannabis use days. Pharmacologic treatment of opioid use disorder led to a more than 4 times greater likelihood of abstinence with extended courses (2 to 3 months) of buprenorphine compared to short courses (14 to 28 days).

Conclusions. Brief interventions: MI reduces heavy alcohol use (low SoE), alcohol use days (moderate SoE), and substance use–related problems (low SoE) but does not reduce cannabis use days (moderate SoE). Nonbrief interventions: Fam may be most effective in reducing alcohol use (low SoE). More research is needed to identify other effective intensive behavioral interventions for alcohol use disorder. Intensive interventions did not appear to decrease cannabis use (low SoE). Some interventions (CBT, CBT+MI, and CBT+MI+CM) were associated with increased cannabis use (low SoE). Both MI and CBT reduce combined alcohol and other drug use (low SoE). Combined CBT+MI reduces illicit drug use (low SoE). Subgroup analyses of interest (male vs. female, racial and ethnic minorities, socioeconomic status, and family characteristics) were sparse, precluding conclusions regarding differential effects. Pharmacological interventions: longer courses of buprenorphine (2–3 months) are more effective than shorter courses (14–28 days) to reduce opioid use and achieve abstinence (low SoE). SRs in the college settings support use of brief interventions for students with any use, heavy or problematic use. More research is needed to identify the most effective combinations of behavioral and pharmacologic treatments for opioid, alcohol, and cannabis use disorders.

Citation

Suggested citation: Steele DW, Becker SJ, Danko KJ, Balk EM, Saldanha IJ, Adam GP, Bagley SM, Friedman C, Spirito A, Scott K, Ntzani EE, Saeed I, Smith B, Popp J, Trikalinos TA. Interventions for Substance Use Disorders in Adolescents: A Systematic Review. Comparative Effectiveness Review No. 225. (Prepared by the Brown Evidence-based Practice Center under Contract No. 290-2015-00002-I.) AHRQ Publication No. 20-EHC014. Rockville, MD: Agency for Healthcare Research and Quality. May 2020. Posted final reports are located on the Effective Health Care Program search page. DOI: https://doi.org/10.23970/AHRQEPCCER225.

Keywords 
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