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. Author manuscript; available in PMC: 2026 Aug 11.
Published in final edited form as: Drug Alcohol Depend. 2025 Jun 14;274:112756. doi: 10.1016/j.drugalcdep.2025.112756

Cognitive-behavioral interventions for co-occurring substance use and mental health disorders: A meta-analysis of consumption and psychosocial functioning

Molly Magill 1, Emily C Helminen 1, Blaine Lynch-Gadaleta 1, Kari Allen 1, Brian D Kiluk 2, Lara A Ray 3
PMCID: PMC13455035  NIHMSID: NIHMS2199046  PMID: 40543363

Abstract

The current meta-analysis provides an overview of cognitive-behaviorally-based interventions (CBI) that treat co-occurring alcohol and other drug use (AOD) and one or more mental health disorders. A literature search was conducted through October of 2024. All published outcome data were extracted and categorized into one of thirteen different outcome types (e.g., abstinence, quantity, mental health symptoms, quality of life) and two broader outcome classes (i.e., consumption; psychosocial). The study sample included 47 randomized trials (K = 101 publications; 912 effect sizes). The studies were primarily polydrug-focused and targeted samples where diagnostic criteria for a use disorder were met. CBIs targeting co-occurring disorders provided benefit over usual care and control comparators for consumption, but not psychosocial outcomes. The opposite pattern of effects was observed when CBI was added to usual care and compared to usual care alone. When a CBI treatment was compared to a CBI treatment targeting AOD or mental health only, significant effects were not observed. These results suggest modest efficacy of CBI interventions in this sample of studies targeting a range of substances and co-occurring mental health conditions.

Keywords: Alcohol Treatment, Cognitive-Behavioral, Co-occurring Disorders, Dual Diagnosis, Meta-Analysis

Graphical abstract

Cognitive Behavioral Interventions for Co-Occurring AOD/MHD: Between and Within Group Effect Sizes by Outcome Type

Note. k = number of studies; Kes = number of effect sizes; I2 = I squared measure of heterogeneity in the pooled estimate. p-values are marked to indicate statistical significance: *** <0.0001, ** <0.001, * <0.01, and † <0.05.

graphic file with name nihms-2199046-f0004.jpg

1. Introduction

Mental health conditions show high co-occurrence rates with alcohol or other drug use disorders (AOD), often leading to more complex clinical presentations than observed with AOD alone. Among adults in the United States aged 18 or older, 33% percent (or 84.5 million people) had either a mental health disorder, alcohol use disorder, or a substance use disorder in the past year (National Household Survey on Drug Use and Health [NHSDUH], 2023). Adults with AOD report roughly 40% co-occurrence rates with any mental health condition, and among those with any mental health condition, roughly 30% report AOD (NHSDUH, 2023). The COVID-19 pandemic has also exacerbated the prevalence of co-occurring disorders, underscoring the continued need for effective mental health and substance use treatments (Czeisler et al., 2020).

Overtime, treatment for AOD with one or more co-occurring mental health disorders (AOD/MHD) has evolved from targeting either condition in isolation or in a pre-determined sequence (e.g., identifying one condition as primary and targeting remission prior to addressing the secondary condition) to considering integrated care as best practice. Integrated treatment models, that combine mental health and substance use services, have been shown to improve outcomes by addressing both disorders concurrently (Yule & Kelly, 2019). These methods range from integrated care programs (e.g., Assertive Community Treatment; Phillips et al., 2001) to specific treatment models. Regarding the latter, cognitive-behavioral therapy (CBT) may be particularly promising because it is highly flexible and has demonstrated support for many relevant conditions such as alcohol use disorder, substance use disorders, mood disorders, and post-traumatic stress disorder (PTSD; Carroll, 2004; McGovern et al., 2009; McHugh et al., 2010).

The efficacy of cognitive-behavioral therapy has led to increased utilization, evolution, and integration with other modalities suggesting a larger umbrella term such as cognitive-behaviorally-based intervention (CBI) may be a useful way to frame the growing applications of CBT (Magill et al., 2023). In prior meta-analyses, CBIs targeting co-occurring disorders have demonstrated efficacy for substance use and mental health symptom outcomes when compared to interventions targeting a substance use disorder or mental health condition only, but not when added to existing services and compared to those services alone (Mehta et al., 2021; 2024; k = 15 studies with k = 12 included in the current review). Effect sizes were small in magnitude and varied by the specific conditions targeted with the most promising effects observed in studies targeting alcohol use disorder and PTSD (Mehta et al., 2021; 2024). However, other studies do not unanimously support these conclusions. For example, Torchalla and colleagues (2012) examined 12 studies of co-occurring substance use and PTSD treatment (k = 2 included in the current review) and did not find differential benefit of dual compared to single disorder modalities. A review examining integrated CBT for substance use and depression suggested efficacy over no treatment controls, but not other therapies (Hides et al., 2010; k = 12 studies with k = 3 included in the current review). Therefore, the above literature on best treatment methods for AOD/MHD is mixed with potentially important differences in treatment effect when considering experimental comparator and the specific AOD or mental health condition targeted.

1.1. Meta-analysis Purpose and Aims

The following meta-analysis examines CBI for AOD/MHD in a sample of 47 clinical trials. We aggregated two classes of outcome related to CBI efficacy – substance consumption and broader, psychosocial functioning. The goal of this study was to provide an overview of CBI efficacy and effectiveness, while also examining the role of comparison condition and primary substance or mental health condition targeted in determining pooled effect size significance and magnitude. Additional sensitivity analyses were conducted, including risk of bias assessment, publication bias assessment, and influential studies assessment.

2. Method

2.1. Primary Study Literature Search

A librarian-facilitated search1 was conducted that sought studies for a larger meta-analysis project focused on CBIs for AOD. This included, a title, abstract, and keyword search by treatment (e.g., “cognitive behavioral therapy”), outcome (e.g., “alcohol”), and methodological (e.g., “randomized clinical trial”) terms conducted in the PubMed database via MedLine (search string can be found in Table 1). Using the same or a similar search string, additional databases were searched including Cochrane CENTRAL and EBSCO (i.e., CINAHL, APA PsychINFO, and SocINDEX). The study team used Covidence (www.covidence.org) for abstract screening where two raters were required for each study and consensus decisions were made by the lead study investigator. Because various modes of CBI delivery were of interest, the literature search yielded a large initial pool of studies (K = 1386 primary and secondary reports) that were then divided into sub-groups for meta-analysis (e.g., CBI for AOD delivered via technology, CBI for AOD delivered face-to-face, CBI targeting a dual disorder population [current report]). An additional search strategy for the current report was a bibliographic review of articles related to co-occurring substance use and mental health disorders (Baker et al., 2010a; 2012ab; Cleary et al., 2008; Hides et al., 2010; Hobbs et al., 2011; Horsefall et al., 2009; Hunt et al., 2013; McHugh, 2015; Prisciandaro & Myrick, 2010; Pennay et al., 2011; Riper et al., 2013; Simpson et al., 2017). Study inclusion flow can be found in Figure 1 and the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines were followed (Supplement Aa/b). The final meta-analytic sample included K = 47 studies (k = 54 related reports; N = 5,535 individuals) of CBIs for AOD/MHD.

Table 1.

Sample Search Strategy - Medline

((“Cognitive Behavioral Therapy”[MeSH Terms] OR “Secondary Prevention”[MeSH Terms] OR “Behavior Therapy”[MeSH Terms:noexp] OR “Behavior Therapy”[Text Word] OR “behaviour therapy”[Text Word] OR “behavioral therapy”[Text Word] OR “behavioral therapies”[Text Word] OR “behavioural therapy”[Text Word] OR “behavioural therapies”[Text Word] OR “CBI”[Text Word] OR “CBT”[Text Word] OR “cognitive therapy”[Text Word] OR “cognitive therapies”[Text Word] OR “cognitive behavioral”[Text Word] OR “cognitive behavioural”[Text Word] OR (“relapse prevention”[Text Word] OR “Secondary Prevention”[Text Word]) OR (“coping skills”[Text Word] OR “coping behavio*”[Text Word] OR “social skills training”[Text Word])) AND (“Substance-Related Disorders”[MeSH Terms] OR “Illicit Drugs”[MeSH Terms] OR “Designer Drugs”[MeSH Terms] OR “Heroin”[MeSH Terms] OR (“alcohol”[Text Word] OR “cocaine”[Text Word] OR “methamphetamine”[Text Word] OR “stimulant”[Text Word] OR “opiate”[Text Word] OR “opioid”[Text Word] OR “Heroin”[Text Word] OR “marijuana”[Text Word] OR “cannabis”[Text Word] OR “illicit drug*”[Text Word] OR “illegal drug*”[Text Word] OR “polysubstance”[Text Word])) AND ((“randomized controlled trial”[Publication Type] OR “controlled clinical trial”[Publication Type] OR “randomized”[Title/Abstract] OR “placebo”[Title/Abstract] OR “clinical trials as topic”[MeSH Terms] OR (“randomly”[Title/Abstract] OR “trial”[Title])) NOT (“animals”[MeSH Terms] NOT “humans”[MeSH Terms]))) AND (1970/1/1: 3000/12/12[pdat])
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Figure 1.

Figure 1.

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Study Flow

2.2. Study Inclusion

Study inclusion criteria were: 1) English language publication, 2) peer-reviewed publication, 3) publication date between 1970 and 2024, 4) randomized controlled trial, 5) adult patient population (mean age ≥ 18), 6) patient population meeting criteria for an alcohol or other substance use disorder as well as a co-occurring mental health disorder (i.e., DSM III-R through 5-TR; American Psychiatric Association, 1987, 1994, 2000, 2013, 2022)2, and 7) a cognitive-behavioral (e.g., Carrol, 1998; Marlatt & Gordon, 1985; Monti et al., 1989; Kadden et al., 1992) or combined cognitive-behavioral (i.e., combined with elements of other evidence-based treatments such as motivational interviewing) intervention; see Supplement B for details on each CBI intervention and control conditions.

2.3. Effect Size Sub-Group Variables and Other Descriptors

Effect sizes were pooled by the two outcome classes overall and by the following between-group comparison types: 1) CBI compared to usual care or a control condition (e.g., a condition controlling for non-specific factors such as information or contact time), 2) CBI added to usual care compared to usual care only, and 3) CBI targeting a co-occurring condition compared to CBI targeting a substance use or mental health condition only3. Because between-group effect sizes provide minimal information about the extent of change when a given treatment is received, we additionally calculated within-group effect sizes for the CBI experimental treatment effect at follow-up in reference to baseline.

Study characteristic variables were organized by demographic, clinical, treatment, and study method factors. Demographic variables included mean age of participants, percent women participants, percent of participants belonging to a racial (e.g., Asian, Black, white) or ethnic (percent Hispanic/Latine) group, percent of participants with at least some college education, and percent of participants that are full- or part-time employed. Clinical variables included the primary drug targeted (i.e., alcohol, cannabis, opioid, polydrug, other) and primary mental health condition targeted (i.e., anxiety, mood, personality, psychotic, post-traumatic, or multiple disorders). Treatment variables were CBI format (i.e., in-person CBI only versus in-person CBI combined with other evidence-based treatment elements), CBI combined with substance use or mental health pharmacotherapy (i.e., yes versus no), and treatment length (i.e., number of sessions). Study methodological factors included study sample size, publication year, context (i.e., community sample, specialty substance use or mental health clinic, other setting [e.g., primary care clinic]), publication country (i.e., United States, other country), and study-level risk-of-bias score (Higgins et al., 2011). Data extraction guidelines were detailed in a study codebook available, upon request, from the first author. Data were extracted using consensus methods between the third and fourth authors (agreement rate 95.74%). The first author resolved disagreements, with input from members of the full study team.

2.4. Effect Size and Outcomes

The effect size for this study is Hedges’ g with formulae for the standardized mean difference and small sample correction below (Hedges, 1994).

gi=Mti−Mcispi∗[f],wheref=1−34∗(df−1),andspi=(nti−1)sti2+(nci−1)sci2nti+nci−2

In this project, the literature search process sought all publications related to a given primary study sample. The purpose of this was to extract all available outcome datapoints related to substance consumption, mental health, and psychosocial functioning. Specifically, all available outcomes and their exact measures were identified and categorized into one of thirteen outcome types (i.e., abstinence, use frequency, heavy use frequency, peak consumption, quantity, dependence severity, other drug use, consequences, cognitive outcomes [e.g., motivation, self-efficacy], coping outcomes, mental health symptoms, general health, quality of life). As a result, each study could contribute multiple effect sizes by comparison type, outcome type, and follow-up timepoint (i.e., data extracted for the latest data point within four ranges [0 months, 1 to 3 months, 4 to 6 months, 7 months or later]). Effect sizes were reverse scored as needed (e.g., number of days drank) such that a positive effect size indicated a positive treatment outcome for the CBI condition (see Supplement C for a list of study measures and harmonization procedures) and effect magnitude was assessed with a combination of Cohen’s (1988) generic benchmarks (i.e., .2 = “small”, .5 = “medium”, .8 = “large”) and U3 (i.e., the % of treated individuals showing improvement above the average in the control group). Finally, when data from publications were insufficient for effect size calculation (i.e., missing variance estimates, statement of “no significant difference between conditions”), raw data were sought from authors (response rate 100%)4.

2.5. Effect Size Pooling and Sensitivity Analysis

Effect sizes were pooled using inverse variance weighting and a random effects model. Inverse variance weighting allows larger sample studies more weight in the overall pooled effect estimate (Hedges & Olkin, 1985). A random effects model assumes a distribution, rather than a single, population effect size (Hedges & Vevea, 1998). For hypothesis testing, sources of dependency from multiple effect sizes within studies were handled via robust standard error estimates (Fisher et al., 2017). The within study correlation between dependent effect sizes was assumed to be constant (i.e., default rho of .8) and sensitivity analyses were conducted that varied the correlation weight. Robust standard errors become more precise as the sample size increases, but a small-sample correction provides stable estimates at K =10 primary studies (Tipton, 2015). Meta-analysis was performed in the ROBUMETA package for R (Fisher & Tipton, 2015) and pre-registration can be found at https://osf.io/kqmgf.

The random effects, pooled effect sizes in the current study were calculated for two outcome classes: consumption (i.e., abstinence, use frequency, heavy use frequency, peak consumption, quantity, dependence severity, and other drug use) and psychosocial functioning (i.e., consequences, cognitive outcomes, coping outcomes, mental health symptoms, general health, and quality of life). Outcome as well as comparator type were the primary sub-group moderators in this study. In cases of significant residual between-study heterogeneity5, regression-based moderator analyses were undertaken (i.e., by timepoint, and primary substance and mental health condition targeted). Additional sensitivity analysis included effect size pooling for each outcome type, analyses using traditional meta-analytic methods (i.e., average dependent effect sizes to the study level and then calculate random effects pooled effect sizes and corresponding hypothesis test statistics), and influential studies assessment6. To test for potential publication bias, the relationship between error and effect size was examined (Begg & Mazumdar, 1994). Finally, risk of bias results are summarized for all studies.

3. Results

3.1. Primary Study Descriptive Characteristics

This meta-analysis included 47 randomized trials (K = 101 publications; 912 effect sizes) of CBIs for AOD/MHD. The median sample size in these studies was 91 participants with a minimum of 23 (Linehan et al., 2002) and a maximum of 353 (Hien et al., 2009). The mean age across study samples was 37.8 (SD = 6.8; k = 46), samples were 44.6% women (SD = 29.0%; k = 47) and some studies had men (Boden et al., 2012; Rezaie et al., 2021) or women (Linehan et al., 1999; 2002; Pearson et al., 2019; Zlotnick et al., 2009) only samples. Among studies that reported racial and ethnic data, the samples were 63.3% white (SD = 27.6%; k = 32), 26.4% Black (SD = 23.2%; k = 25), 4.4% Asian or Pacific Islander (SD = 6.7%; k = 7), 20.5% Native American/Indigenous person (SD = 39.0%; k = 6; with Pearson et al., 2019 at 100% Indigenous person), and 15.6% multi-race (SD = 21.8%, k = 6). The samples were 10.9% (SD = 9.0%; k = 18) Latine ethnicity.

The studies were primarily polydrug-focused (61.7%) and targeted samples where diagnostic criteria for a substance use disorder were met (82.6%). A range of mental health conditions were targeted in these studies including mood or anxiety (40.4%) and PTSD (28.3%). While the description of each study intervention can be found in Supplemental Table B, the length of treatment was roughly 16 sessions (SD = 10.5; k = 45), and the majority of interventions included elements of other evidence-based therapies such as motivational interviewing (68.1%, e.g., Baker et al., 2010b; Buckner et al., 2019; Jones et al., 2019) as well as inclusion of individuals receiving some form of pharmacotherapy (74.1%, e.g., Linehan et al., 2002; Lydecker et al., 2010). Participants were primarily recruited from specialty mental health or addictions treatment facilitates (40.4%) and studies most frequently took place within the United States (59.6%). Finally, study-level risk of bias results showed the majority of studies had at least one major criterion rated as high risk (see Supplement Figure A for summary). All sample-level study characteristics are summarized in Table 2, while characteristics at the individual study level are in Table 3.

Table 2.

Primary Study Characteristics – sample level

Demographic K (47 total) M (SD) Percentage
Study N 47 117.77 (1.80)
Study Date 47 2013 (5.70)
Age 46 37.77 (6.82)
% Female 47 44.61 (28.97)
% White 32 63.27 (27.61)
% Black 25 26.35 (23.22)
% Hispanic/Latine 18 10.88 (9.04)
% Native American/Indigenous 6 20.55 (39.00)
% Asian/Pacific Islander 7 4.44 (6.73)
% Multi-race 6 15.59 (21.85)
% Some College 18 53.16 (22.72)
% Employed 28 38.66 (19.66)
Clinical K (47 total) M (SD) Percentage
Drug Type
Alcohol 12 25.50
Cannabis 4 8.50
Opioid 2 4.30
Polydrug 29 61.70
Dependence
No 8 17.00
Yes 38 80.90
Mental Health Disorder Type
Anxiety Disorders 7 14.90
Mood Disorders 12 25.50
General Mental Health 6 12.80
Other (ADHD) 1 2.10
Personality Disorders 2 4.30
Psychotic Disorders 6 12.80
PTSD 13 27.70
Treatment K (47 total) M (SD) Percentage
Integrative Treatment
No 15 31.90
Yes 32 68.10
Pharmacotherapy
No 7 14.90
Yes 20 42.60
Number of Sessions
Number 46 16.07 (10.46)
Study Method K (47 total) M (SD) Percentage
Context
College setting 1 2.10
Community sample 14 29.80
Medical setting 4 8.50
Other/More Than One setting 9 19.10
Specialty setting 19 40.40
Publication Country
US 28 59.60
Non US 18 38.30
Mixed US and Non US 1 2.10
Overall Risk of Bias
High Risk 38 80.90
Unclear 9 19.10
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Note. ADHD = attention-deficit hyperactivity disorder; PTSD = posttraumatic stress disorder; US = United States. Percent of transgender participants and those identifying with a sexual orientation other than heterosexual were data points collected with this study, but no studies reported these data.

Table 3.

Primary Study Characteristics – study level

First author (date) N1 Intervention and Comparison Conditions Timepoint Substance Outcomes (Consumption) Mental health symptoms Condition Outcomes (Psychosocial)
CBI compared to usual care or control treatment
Alegria (2019) 341 Integrated Intervention for Dual Problems and Early Action (IIDEA) vs Enhanced Usual Care 6mo Polydrug Abstinence, dependence General Mental health symptoms Mental health symptoms
Baker (2006) 130 CBT + MI vs Usual Care Baseline, post-tx, 6mo, 12mo Polydrug Frequency Non-acute psychotic disorders Mental health symptoms, quality of life
Brown (2006) 66 Integrated CBT vs Twelve-Step Facilitation Therapy Baseline, post-tx, 3mo, 6mo Polydrug Frequency Depression Mental health symptoms
Brown (2011) 166 CBT for Depression vs Relaxation Training Baseline, 3mo, 6mo, 12mo Alcohol Frequency, quantity Depression Mental health symptoms
Delgadillo (2015) 50 Behavioral Activation vs Guided Self-Help Baseline, post-tx, 6mo Polydrug Frequency Depression Mental health symptoms
Eack (2015) 33 Cognitive Enhancement Therapy vs Usual Care Post-tx Polydrug Frequency Schizophrenia Mental health symptoms
Garland (2016) 180 Mindfulness-oriented Recovery
Enhancement (MORE) vs Usual Care		 Baseline, post-tx Polydrug --- Multiple disorders2 Cognitive, coping, Mental health symptoms
Hien (2009) 353 Seeking Safety vs Women’s Health Education Baseline, post-tx Polydrug Abstinence, frequency PTSD Mental health symptoms
Hunter (2012) 73 CBT for Depression and Substance Use vs Usual Care Baseline, 3mo, 6mo Polydrug Frequency, quantity Depression Consequences, Mental health symptoms
Kushner (2013) 344 Hybrid CBT vs Progressive Muscle Relaxation Training Baseline, post-tx, 4mo Alcohol Frequency, binge/peak, quantity Anxiety disorders Mental health symptoms
Linehan (1999) 28 DBT vs Usual Care Baseline, 4mo, 16mo Polydrug Abstinence, frequency Borderline personality disorder Quality of life
Linehan (2002) 24 DBT vs Comprehensive Validation Therapy and 12-Step Facilitation Post-tx, 4mo Opioids Abstinence, other drug Borderline personality disorder --
Lydecker (2010) 206 Integrated CBT + Standard Pharmacotherapy vs Twelve Step Facilitation Therapy + Standard Pharmacotherapy Baseline, post-tx, 6mo, 12mo Polydrug Frequency, dependence Depression Mental health symptoms
Madigan (2013) 88 Group Psychological Intervention vs Usual Care Baseline, 3mo, 12mo Cannabis Frequency Psychosis Cognitive, Mental health symptoms, quality of life
McHugh (2010) 36 Women’s Recovery Group vs Group Drug Counseling Baseline, post-tx, 3mo, 6mo Polydrug --- Multiple Disorders Mental health symptoms
Morley (2016) 37 Specialized Integrated Treatment for Alcohol and Comorbid Anxiety and/or Mood Disorder vs Treatment for Alcohol Baseline, 3mo Alcohol Frequency, heavy frequency, quantity, dependence Anxiety and depression Cognitive, Mental health symptoms
Pearson (2019) 73 Culturally Adapted Cognitive Processing Therapy vs Waitlist Baseline, post-tx, 3mo Polydrug Frequency, dependence PTSD Consequences, Mental health symptoms
Ruglass (2017) – Arm 1 110 Concurrent Treatment of PTSD and SUD using Prolonged Exposure (COPE) vs Assessment and Monitoring Baseline, post-tx, 3mo Polydrug Frequency, dependence PTSD Mental health symptoms
Simpson (2022) – Arm 2 101 Cognitive Processing Therapy vs Assessment Only Post-tx Alcohol Frequency, heavy frequency PTSD Mental health symptoms
Weiss (2007) 62 Integrated Group Therapy vs Group Drug Counseling Post-tx Polydrug Frequency, use severity Bipolar disorder ---
Weiss (2009) 61 Brief Integrated Group Therapy vs Group Drug Counseling Baseline, post-tx, 3mo Polydrug Frequency, dependence Bipolar disorder Mental health symptoms
CBI as an add-on to Usual Care compared to Usual Care alone
Barrowclough (2010) 327 Integrated CBT-MI + Standard
Psychiatric Care vs Standard
Psychiatric Care		 Baseline, 6mo, 24mo Polydrug Abstinence, frequency Non-affective psychotic disorders Consequences, Cognitive, Mental health symptoms, general health, quality of life
Barrowclough (2014) 110 CBT-MI + Standard Care vs Standard Care Baseline, 4.5mo, 18mo Cannabis Abstinence, frequency, quantity, other drug Non-affective psychotic disorders Mental health symptoms, quality of life
Boden (2012) 117 Seeking Safety + Clinic Usual Care vs Clinic Usual Care 3mo, 6mo Polydrug Frequency, dependence PTSD Mental health symptoms
Daughters (2008) 44 Life Enhancement Treatment for Substance Use (LETS Act!) + Usual Care vs Usual Care Post-tx Polydrug --- Depression Coping, Mental health symptoms
Hjorthøj (2013) 103 CapOpus + Usual Care vs Usual Care Baseline, post-tx, 4mo Cannabis Abstinence, frequency, quantity Schizophrenia spectrum psychosis Mental health symptoms, quality of life
Jones (2019) 44 MI-CBT + Usual Care vs Usual Care Baseline, 3mo, 6mo, 11.5mo, 12mo Alcohol Frequency, binge/peak, quantity Bipolar disorder Cognitive, Mental health symptoms
McGovern (2011) 53 Integrated CBT + Individual Addiction
Counseling vs Individual Addiction
Counseling		 Baseline, post-tx, 6mo Polydrug Frequency, dependence PTSD Mental health symptoms
McGovern (2015) – Arm 1 221 Integrated CBT + Individual Addiction
Counseling vs Individual Addiction
Counseling		 Baseline, post-tx Polydrug Abstinence, frequency, dependence PTSD Mental health symptoms
McGovern (2015) – Arm 2 221 Integrated CBT + Standard Clinic Care vs Standard Clinic Care Baseline, post-tx Polydrug Abstinence, frequency, dependence PTSD Mental health symptoms
Mills (2012) 103 Concurrent Treatment of PTSD and Substance Use Disorders using Prolonged Exposure (COPE) + Usual Care vs Usual Care Baseline, post-tx, 6mo Polydrug Abstinence, dependence PTSD Mental health symptoms
Morley (2014) 185 Opportunistic Cognitive Behavioral Intervention Package + Standard Clinic Care vs Standard Clinic Care Baseline, post-tx Polydrug Quantity Suicidality Cognitive, Mental health symptoms
Rezaie (2021) 50 DBT + Methadone Maintenance Therapy vs Methadone Maintenance Therapy Baseline, post-tx, 3mo Opioids --- Multiple disorders Cognitive, coping, Mental health symptoms
Schadé (2005) 96 Anxiety-focused CBT + Usual Care vs Usual Care 5.5mo Alcohol Abstinence Phobic disorders ---
Schäfer (2019) – Arm 1 343 Seeking Safety + Usual Care vs Usual Care Baseline, post-tx, 3mo, 6mo Polydrug Frequency, dependence PTSD Coping, Mental health symptoms
Schmitz (2002) 46 CBT + Medication Monitoring vs Medication Monitoring Baseline, post-tx Polydrug Abstinence, frequency Bipolar disorder Mental health symptoms
Worden (2017) 41 Anxiety Sensitivity Intervention + Usual Care vs Usual Care Baseline, post-tx, 3mo Polydrug Frequency Anxiety disorders Mental health symptoms
Zlotnick (2009) 49 Seeking Safety + Usual Care vs Usual Care Baseline, 6mo Polydrug Frequency, dependence PTSD Mental health symptoms, quality of life
CBI for co-occurring disorders compared to CBI targeting substance use or Mental health symptoms only
Back (2019) 81 Concurrent Treatment of PTSD and Substance Use Disorders Using Prolonged Exposure (COPE) vs Relapse Prevention Baseline, post-tx Polydrug Abstinence, frequency, quantity, dependence PTSD Mental health symptoms
Baker (2010) - Arm 1 284 Integrated Focus MI-CBT vs Alcohol-focused MI-CBT Baseline, post-tx Alcohol Frequency, quantity Depression Mental health symptoms
Baker (2010) - Arm 2 284 Integrated Focus MI-CBT vs Depression-focused MI-CBT Baseline, post-tx Alcohol Frequency, quantity Depression Mental health symptoms
Buckner (2019) 55 Integrated Cannabis and Anxiety Reduction Treatment (ICART) vs Motivation Enhancement Therapy/CBT for cannabis use disorders Baseline, post-tx Cannabis Abstinence, quantity Anxiety disorders Consequences, Mental health symptoms
Pedrelli (2020) 94 CBT for depression and brief MI vs CBT for depression Baseline, post-tx, 1mo Alcohol Heavy frequency Depression Consequences, Mental health symptoms
Randall (2001) 110 Integrated cognitive behavioral therapy vs CBT for alcohol only Baseline, post-tx, 3mo Alcohol Frequency, heavy frequency Social anxiety disorder Mental health symptoms
Ruglass (2017) - Arm 2 110 Concurrent Treatment of PTSD and SUD using Prolonged Exposure (COPE) vs Relapse prevention therapy for SUD Baseline, post-tx Polydrug Frequency, dependence PTSD Mental health symptoms
Sannibale (2013) 62 Integrated CBT for PTSD and AUD vs Alcohol Support Baseline, post-tx, 5mo, 9mo Alcohol Quantity, dependence PTSD Consequences, Mental health symptoms
Schäfer (2019) – Arm 2 343 Seeking Safety vs Relapse Prevention Therapy Baseline, post-tx, 3mo, 6mo Polydrug Frequency, dependence PTSD Coping, Mental health symptoms
Simpson (2022) – Arm 1 101 Cognitive Processing Therapy vs Relapse Prevention Therapy Baseline, post-tx, 3mo, 12mo Alcohol Frequency, heavy frequency PTSD Mental health symptoms
Soder (2019) 37 Treatment of Integrated Posttraumatic Stress and Substance Use (TIPSS) vs CBT for SUD Baseline, post-tx Polydrug Frequency PTSD Mental health symptoms
Stapinski (2021) 117 Integrated CBT and motivational enhancement for SAD and AUD vs Integrated CBT and motivational enhancement for AUD only Baseline, post-tx, 3mo, 6mo Alcohol Quantity, dependence Social anxiety disorder Mental health symptoms, quality of life
Toneatto (2015) 82 Alcohol and anxiety treatment (ALCANX) vs Alcohol treatment only Baseline, post-tx, 10mo Alcohol Frequency, quantity Anxiety disorders Mental health symptoms
vanEmmerik-vanOortmersen (2019) 119 Integrated Cognitive Behavioral Therapy vs CBT for SUD Baseline, post-tx, 2mo Polydrug Heavy frequency ADHD Mental health symptoms, quality of life
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Notes. K = 47 with 54 study arms.

1

Total number of participants randomized.

2

Study inclusion targeted more than one mental health disorder.

ADHD = attention-deficit hyperactivity disorder, AUD = alcohol use disorder, CBT = cognitive behavioral therapy, DBT = dialectical behavioral therapy, MI = motivational interviewing, PTSD = posttraumatic stress disorder, SUD = substance use disorder.

3.2. CBI Effect Size by Comparison and Outcome Type

Primary study effect sizes were pooled by comparison and outcome sub-groups. When effects sizes were pooled across all between-condition comparisons, the overall pooled effect for consumption outcomes was not statistically significant (g = 0.05: 95% CI = −0.02, 0.13, p = .159; tau2 = .05, I2 = 50.7%; k = 43; kes = 224). Psychosocial outcomes, however, were significant, small in magnitude and heterogeneous (0.15: 95% CI = 0.06, 0.25, p = .002; tau2 = .08, I2 = 64.2%; k = 44; kes = 250). Next, these outcomes are examined by type of comparison condition.

3.3. CBI for co-occurring disorders compared to usual care or control treatment

Studies of CBI compared to minimal treatment, usual care, or a non-CBI control treatment showed a significant effect for consumption (g = 0.20: 95% CI = 0.04, 0.36, p = .018; tau2 = .07, I2 = 59.8%; k = 19; kes = 75), but not psychosocial (g = 0.12: 95% CI = −0.01, 0.25, p = .063; tau2 = .04, I2 = 52.7%; k = 19; kes = 91)7 outcomes over follow-up. Heterogeneity estimates were slightly above our thresholds, suggesting the potential value of further moderator analysis. Findings were consistent across correlation weights and like those based on traditional meta-analytic methods (see Supplement D). For assessment of bias due to publication status, Supplement Figures B–C suggest potential publication bias for the consumption (B) but not psychosocial (C) pooled effect size.

3.4. CBI for co-occurring disorders as an addition to usual care

Studies of CBI as an addition to usual care, compared to usual only, did not show a significant effect for consumption outcomes over follow-up (g = 0.06: 95% CI = −0.03, 0.16, p = .173; tau2 = .04, I2 = 42.3%; k = 14; kes = 85)8. In contrast, psychosocial effects were positive and significant (g = 0.29: 95% CI = 0.05, 0.53, p = .022; tau2 = .19, I2 = 78.1%; k = 15; kes = 83). Heterogeneity for psychosocial outcomes was above our threshold, suggesting the potential value of further moderator analysis. Findings were consistent across correlation weights and like those based on traditional meta-analytic methods (see Supplement D). For assessment of bias due to publication status in studies testing CBI as an addition to usual care, Supplement Figures D–E do not suggest bias.

3.5. CBI for co-occurring disorders compared to CBI targeting a single disorder

A number of studies examined CBI for a co-occurring condition in contrast to CBI targeting substance use or a mental health condition only. These studies did not suggest a differential benefit of the experimental conditions tested for consumption (g = −0.11: 95% CI = −0.24, 0.02, p = .085; tau2 = .01, I2 = 15.8%; k = 13; kes = 64)9 and psychosocial (g = 0.10: 95% CI = −0.06, 0.26, p = .182; tau2 = .04, I2 = 46.1%; k = 13; kes = 77)10 outcomes over follow-up. Heterogeneity data did not suggest the potential utility of further moderation analysis. Findings were consistent across correlation weights and like those based on traditional meta-analytic methods (see Supplement D). For assessment of bias due to publication status, Supplemental Figures F–G do not suggest bias.

3.6. CBI for co-occurring disorder effectiveness as measured by change from baseline

While between-group effect sizes offer a measure of intervention effect compared to some form of experimental control, within-group effect sizes provide information on the extent of change that may occur when a given treatment is received. For consumption outcomes over follow-up, the effect was large, positive, statistically significant, and heterogeneous (g = 0.62: 95% CI = 0.47, 0.77, p < .0001; tau2 = .21, I2 = 91.6%; k = 36; kes = 153). Psychosocial outcomes were slightly larger in magnitude, but the pattern of results was similar (g = 0.79: 95% CI = 0.65, 0.93, p < .0001; tau2 = .22, I2 = 91.4%; k = 40; kes = 221). Heterogeneity data suggested the utility of further moderation analyses. Findings were consistent across correlation weights and like those based on traditional meta-analytic methods (see Supplemental D).

3.7. Moderation of Heterogenous Effect Sizes

In the current study, we examined the effects of CBI treatments targeting a range of co-occurring disorders with sub-group pooled effects by three between-condition contrasts, within-treatment, and by two groups of outcomes – consumption and psychosocial. Some of these pooled effect sizes suggested a high proportion of systematic relative to random variability, which suggests that effect estimates could be further defined by examining study-level moderators. These sub-groups were CBI compared to usual care or a control treatment (consumption and psychosocial outcomes), as an addition to usual care (psychosocial outcomes), and when within CBI effect sizes were estimated (consumption and psychosocial outcomes). In these post hoc analyses, outcome time point (i.e., early = 0-3 months and later = 4 months or later), primary drug targeted (i.e., alcohol, polydrug, other), and primary mental health condition targeted (e.g., mood or anxiety disorders, PTSD, psychotic disorders, personality disorders) were selected as study-level moderators in multivariate, correlated effects meta-regression models11. These analyses did not yield additional effect modifiers except in the case of within CBI effects on psychosocial outcomes where studies targeting psychotic disorders (k = 6) or personality disorders (k = 2) had smaller effect sizes than studies targeting mood disorders (k = 11), anxiety disorders (k = 7), or general mental health (k = 6; b = −0.49, p = .003) populations.

3.8. Pooled Effect Sizes for Each Outcome Type

In the current meta-analysis, 13 types of outcomes reported across 47 clinical trials of CBI for AOD/MHD were grouped into consumption and psychosocial pooled effect sizes. In this final sensitivity analysis, we examined pooled effects for each outcome type. The results of these analyses are organized by between- and within-group effect sizes and are shown in Figures 2 and 3. Figure 2 (between condition effect sizes) shows that across all outcome types, only mental health symptoms were statistically significant. In contrast, Figure 3 shows that most effect sizes within CBI condition were statistically significant and in the moderate to large range of magnitude. The exception was abstinence, binge/peak consumption, and coping outcomes, which were non-significant. Estimates of heterogeneity remained above moderate for roughly half of between-group and all within-group effect sizes.

Figure 2.

Figure 2.

Open in a new tab

Between Group Effect Sizes by Outcome Type

Note. p-values are marked to indicate statistical significance: *** <0.0001, ** <0.001, * <0.01, and † <0.05.

Figure 3.

Figure 3.

Open in a new tab

Within Group Effect Sizes by Outcome Type

Note. p-values are marked to indicate statistical significance: *** <0.0001, ** <0.001, * <0.01, and † <0.05.

4. Discussion

This meta-analysis examined the effects of CBIs for populations with co-occurring AOD/MHD on consumption and psychosocial outcomes across 101 reports of 47 clinical trials. Between-group effect sizes were small overall and varied by contrast condition and outcome type. Compared to minimal care, usual care, or a control treatment, CBIs demonstrated efficacy for consumption, but not psychosocial outcomes. In contrast, compared to usual care alone, CBIs in addition to usual care demonstrated efficacy for psychosocial outcomes, but not consumption. Compared to CBIs for a single disorder, integrated CBIs did not demonstrate efficacy for either outcome class. However, within-group effect sizes estimating change from baseline to follow-up after CBI treatment were significant and large for both consumption and psychosocial outcomes. Most pooled between- and within-group effects demonstrated heterogeneity that was not accounted for in moderation analyses, suggesting that the sources of heterogeneity were beyond the tested moderators. Overall, these results suggest preliminary and modest efficacy of CBIs across studies targeting a range of co-occurring AOD/MHDs.

Results from the current meta-analysis partially align with previous meta-analytic work in this area. The most relevant prior meta-analysis examined the effects of CBIs for people with co-occurring AOD/MHD on substance use and primary mental health outcomes (e.g., depression symptoms for trials targeting co-occurring AOD and depression; Mehta et al., 2021; 2024). They similarly found no significant effects on AOD outcomes for trials comparing CBIs plus usual care to usual care alone, but they did find that integrated CBIs demonstrated small and significant effects compared to single-disorder therapies (Mehta et al., 2021; 2024). The current review examined this contrast only among studies that provided a direct test of integrated CBIs compared to single disorder CBIs. This is arguably a more stringent test because the evidence-based therapy foundation is constant across experimental conditions. This is an important ongoing question to dual disorder science and practice. The results of the current study suggest that future clinical trials should consider where, if, and for whom integrated AOD/MHD interventions offer an added benefit when the comparator is an evidence-based, albeit single-disorder-focused, intervention.

The present meta-analysis also found that CBIs added to usual care outperformed usual care alone for psychosocial outcomes. Therefore, efficacy on outcome type could vary by implementation context where consumption effects can be observed when these interventions are delivered in isolation, but psychosocial, recovery effects may require a more comprehensive approach such as CBI combined with traditional community-based programing. This interpretation warrants caution as there was evidence that non-significant findings in this study were not stable. Influential outlier analyses indicated that removing certain studies (Barrowclough et al., 2014; Morley et al., 2016; Toneatto et al., 2015) resulted in statistically significant effect sizes for consumption outcomes. Given heterogeneity and instability of pooled effects in the present study, our results should be considered preliminary.

Although including all clinical trial endpoints in the present study may have contributed to heterogeneity for pooled consumption and psychosocial effects, it also allowed us to examine preliminary efficacy of CBIs for populations with co-occurring AOD/MHDs with more outcome specificity than has occurred in the literature to date. This is important given recent emphasis in our field on measures of recovery and harm reduction (Hagman et al., 2022). Moreover, understanding treatment effects on indicators of psychosocial well-being are likely of equal interest to practitioners as are indicators of consumption. There was one statistically significant between-group effect size, which was mental health outcomes, and all effects except one (i.e., other substance use) were in the expected, positive direction. A portion of these comparisons may have also been underpowered when few studies were included within the pooled estimate. In contrast, within-group effects by specific outcome were moderate to large and significant, suggesting that 65 to 79% of treated participants had improvements above the pre-treatment mean in abstinence, use frequency, heavy use frequency, quantity, dependence severity, consequences, cognitive outcomes, mental health symptoms, and quality of life. This suggests that the CBIs in these studies improved behavioral health and psychosocial functioning, but the extent to which they outperform strong comparators (e.g., single-disorder CBIs) remains undetected without larger samples and more trials measuring each specific outcome.

The current study examined moderation of heterogeneous effect sizes by length of follow-up, primary drug targeted, and primary mental health condition. In these analyses, the pooled within-group effect size for psychosocial outcomes was moderated. Specifically, CBIs produced larger improvements in psychosocial outcomes for those with mood and anxiety disorders or in general mental health samples compared to studies targeting psychotic or personality disorders. This suggests that CBIs may be more effective at improving psychosocial outcomes for some co-occurring AOD/MHDs compared to others. Yet, future meta-analytic work may benefit from reducing heterogeneity and further clarifying these effects by examining CBI efficacy among specific sub-populations with co-occurring AOD/MHD. In a recent review targeting PTSD, heterogeneity values were lower, but the findings were, nevertheless consistent with those observed here (Simpson et al., 2021).

4.1. Strengths, Limitations and Conclusions

This meta-analysis provides a comprehensive view of cognitive-behaviorally-based interventions targeting one or more substance and co-occurring mental health disorders. A unique feature of this work was extraction all available data from a group of clinical trials, including data collected from all primary and secondary study reports. The extent of effect size information provided to scientists and practitioners is significant. However, there are limitations to consider, and the first is the variability within this sample of studies that included a range of substances, mental health conditions, and experimental comparators. Given this variability, we provide sub-group effect sizes defined by key variables, but this approach often creates a trade-off with sample size. Power for detecting significance for sub-group effect sizes as well as in detecting regression moderators may have been limited in some cases. Limitations in sample size also led to the grouping of measures such as motivation and self-efficacy or depression and anxiety symptoms into single outcome types (i.e., cognitive outcomes, mental health outcomes, respectively) when there are important conceptual differences between these indicators. This limitation underscores the importance of future studies not only reporting all outcome data in descriptive format but also reporting all primary and secondary outcomes. This is a force of open science that is in motion and will result in more stable and informative pooled effect sizes in the future. At present, bias due to publication of significant findings cannot be ruled out and sensitivity analysis suggested this was the case with one of six between-group effects examined. Finally, some pooled estimates contained residual heterogeneity and influential studies, which suggests the need for caution when interpreting these results.

With the noted limitations in mind, the results of this meta-analysis suggest a modest benefit of CBIs for AOD/MHD over a generally stringent set of comparators. Effect sizes were small and this finding is consistent with prior reviews (Hides et al., 2010; Mehta et al., 2021, 2024; Simpson et al., 2021; Torchalla et al., 2012) in this complex area of the literature. Given the extent of impairments these conditions can cause in the lives of many affected individuals, however, small effects still offer public health impact at scale (Carey et al., 2023). Most surprisingly, the dual disorder CBIs examined did not show benefit over a cognitive-behavioral condition targeting substance use or mental health only. Within condition effect sizes showed moderate to large positive effects, suggesting that 65 to 79% of treated AOD/MHD individuals will show improvement across varied consumption and recovery measures.

Supplementary Material

1
2
3
4
5
6
7
8

Highlights.

  • The literature on CBIs for co-occurring AOD/MHD is heterogeneous.

  • Compared to control treatment, CBIs show efficacy for consumption outcomes.

  • Compared to usual care, CBIs plus usual care show efficacy for recovery outcomes.

  • Compared to CBIs for a single disorder, integrated CBIs did not show efficacy.

  • Within CBI effects suggest 65 to 79% of treated individuals will show improvement.

Acknowledgements:

This research is supported by National Institute on Alcohol Abuse and Alcoholism grant R01 AA029703 (Magill). Emily Helminen’s contribution to the manuscript was supported by the National Institute on Drug Abuse (T32DA016184). The authors would like to thank the primary study authors who collected the original data and responded to requests for data. Author BDK receives personal fees from CBT4CBT LLC, outside the submitted work. All other authors have no conflict of interests to declare.

Footnotes

Publisher's Disclaimer: This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

1

The project search was conducted to October 2022, and then updated to October 2024 prior to final analysis.

2

Studies could also meet inclusion if the study intervention explicitly targeted substance use and specific mental health symptoms, even if the full sample did not meet diagnostic criteria.

3

These contrast groupings differ from the preregistration due to the observed distribution of studies across groupings (i.e., a low number of studies within specific sub-groups). A priori groupings were experimental CBI compared to 1) another dual disorder treatment, 2) a single disorder AOD treatment, 3) a single disorder MHD treatment, 4) minimal treatment.

4

Data request procedures involved attempts to reach each primary study author in two-week intervals. Therefore, if the first/contact author did not respond to two attempts at contact (two weeks apart), the second author was contacted. This procedure was followed for at least first three authors in the author string.

5

The criteria for moderator analyses were: 1) substantial or greater heterogeneity (≥ 50% Hedges & Vevea, 1998) and df ≥ 4 (Fisher & Tipton, 2015).

6

An influential study was defined as a study, if removed, would change the statistical significance of the pooled estimate.

7

The pooled estimate was significant and positive (g = 0.13: 95% CI 0.002, 0.264) with Morley et al., 2016 removed.

8

The pooled estimate was significant and positive (g = 0.09: 95% CI 0.01, 0.17) with Barrowclough et al., 2014 removed.

9

The pooled estimate was significant and negative (g = −0.15: 95% CI −0.27, −0.02) with Baker et al., 2010b removed.

10

The pooled estimate was significant and positive (g = 0.141, 0.01, 0.28) with Toneatto et al., 2015 removed.

11

These analyses collapsed some groupings due to sample sizes across timepoint, and primary substance and mental health condition. See Table 2.

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Supplementary Materials

1
NIHMS2199046-supplement-1.docx (26KB, docx)
2
NIHMS2199046-supplement-2.docx (33.3KB, docx)
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NIHMS2199046-supplement-3.docx (50.9KB, docx)
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NIHMS2199046-supplement-4.docx (48.5KB, docx)
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NIHMS2199046-supplement-5.docx (16.6KB, docx)
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NIHMS2199046-supplement-6.docx (23.6KB, docx)
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NIHMS2199046-supplement-7.docx (156.6KB, docx)
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NIHMS2199046-supplement-8.docx (129.3KB, docx)

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