Impact of Comorbid Psychological Distress on Stimulant Use Disorder Treatment Outcomes

Abstract

Background:

Psychological distress is common among individuals with stimulant use disorder (StUD) and may affect treatment outcomes, with potential differences in men and women. We examined the impact of comorbid psychological distress on StUD treatment outcomes across behavioral interventions, with a focus on differences by sex.

Methods:

Data were pooled from 4 randomized controlled trials of behavioral interventions (eg, motivational interviewing, 12-step, and smoking-cessation) for StUD (n = 1858) from the National Institute on Drug Abuse Data Share. Psychological distress was assessed using the Addiction Severity Index-psychiatric problems subscale (≥0.246 cutoff). Outcomes included (a) any reduction in stimulant use frequency from baseline to the end of trial, (b) stimulant-positive urine test, and (c) other drug-positive urine tests at the end of trial. We used individual participant data meta-analysis with regression models, adjusting for sociodemographic factors, treatment arms and trial duration, and clustering by trials. We used inverse probability weighting to address missing data. Analyses were conducted on the full sample and stratified by sex.

Results:

Overall, 57.1% of women and 38.7% of men experienced psychological distress. Among men, psychological distress was associated with decreased odds of reducing stimulant use (adjusted odds ratio [aOR] = 0.70, 95% confidence interval [CI] = 0.49, 0.99), and increased odds of having a stimulant-positive urine test (aOR = 1.23, 95% CI = 1.03, 1.47). Among women, distress was not significantly associated with reduced stimulant use or with having a stimulant-positive urine test. However, psychological distress moderated the treatment effect: women with psychological distress who received behavioral interventions were more likely to reduce stimulant use than those without psychological distress.

Conclusion:

Psychological distress was associated with poorer treatment outcomes for men but appeared to enhance treatment response for women. Screening and addressing psychological distress may improve StUD treatment. Sex differences highlight the need for sex-specific approaches to managing psychological distress in StUDs.

Keywords

stimulant use disorder comorbidity behavioral interventions psychological distress stimulant use disorder treatment

Highlights

Nearly half of individuals with stimulant use disorder (StUD) experienced psychological distress.

Women reported a higher prevalence of comorbid psychological distress than men.

Among men, psychological distress was associated with poorer StUD treatment outcomes overall.

Among women, psychological distress moderated the treatment effect and improved treatment outcomes.

Introduction

Stimulant use disorders (StUDs), including cocaine, amphetamine, and methamphetamine use disorders, remain major public health concerns affecting approximately 4.3 million people in the United States.¹ StUDs frequently co-occur with mental health disorders,^2-4 particularly psychological distress, defined as a state of emotional suffering characterized by mental and somatic symptoms that are associated with normal fluctuations of mood.⁵ Psychological distress often manifests as symptoms of anxiety, depression, and emotional dysregulation. Many individuals with StUDs experience significant but subthreshold symptoms of mood and anxiety disorders that do not meet full diagnostic criteria, and as a result, often remain untreated despite experiencing substantial distress and functional impairment. Such comorbid psychological distress is associated with greater health and social burden in individuals with StUDs and other substance use disorders,^6-9 including greater legal, family, and medical problems.^10-13 These social and health factors, in turn, can impact the outcomes and effectiveness of StUD treatment.

Although prior studies estimate that over 40% of individuals with StUDs experience significant psychological distress,^4,14,15 research examining the impact of distress on StUD treatment outcomes is limited. While one study found that comorbid psychiatric disorders may not directly impede recovery, the study found that the presence and severity of depressive symptoms can negatively affect treatment completion.¹⁶ Other studies have found psychological distress to be associated with higher craving and lower self-efficacy to refrain from drug use, poorer treatment retention, higher methadone dosing, and more substance-related complications, including hepatitis C and HIV risk behaviors.^11,17,18

Furthermore, sex differences in the association of psychological distress with StUD outcomes and treatment response are rarely examined, while these differences have been consistently observed in stimulant use behaviors, StUDs, and psychological distress. Men are more likely to have a higher prevalence and report higher frequency of stimulant use,¹⁹ whereas women tend to progress more rapidly to dependence following initiation.^20-22 Women with StUDs are also more likely to present with co-occurring symptoms of depression, anxiety, and distress,^4,23,24 while men often demonstrate antisocial and risk-taking behaviors.^25,26 Importantly, comorbid psychological distress appears to interact with substance use differently among men and women, with women more likely than men to use substances as a way of coping with psychological distress.^23,27

Collectively, these findings underscore the need for consideration of sex-specific pathways in the relationship between psychological distress and StUD outcomes and treatment response to inform the development of more integrated, sex-responsive treatment strategies. However, prior studies have been limited by small sample sizes, heterogeneous measures of psychological distress, and limited assessments by sex, making it difficult to draw robust conclusions. Building on our research team’s previous work demonstrating that comorbid psychological distress decreases StUD treatment retention and that sex differences shape methamphetamine use disorder treatment outcomes,^14,18 the current study used harmonized individual participant-level data from 4 randomized controlled trials (RCTs) of behavioral interventions for StUDs to examine the effect of comorbid psychological distress on treatment outcomes. The study capitalized on a large pooled and harmonized sample of participants from 4 RCTs using very similar methodology and measures to assess the associations of psychological distress, measured consistently across trials, with outcomes and behavioral treatment response for StUDs. We further assessed whether these associations differed between men and women.

Methods

Study Design and Sample

Data were drawn from 4 completed multisite RCTs of behavioral interventions for StUDs. The data are publicly available through the National Institute on Drug Abuse (NIDA) Data Share Website (https://datashare.nida.nih.gov). These 4 RCTs were selected because the trials shared a core set of measures and employed similar study designs, eligibility criteria, and quality assurance procedures. The data harmonization methods and original studies have been previously described.²⁸

The combined dataset comprised 1866 participants who were at least 18 years old and had a Diagnostic and Statistical Manual of Mental Disorders, 4th edition (DSM-IV) diagnosis of a StUD. Participants were excluded from the trials if they were pregnant, had severe psychiatric disorders that required ongoing treatment, were at significant risk for suicide, or were deemed unable to safely participate in RCTs based on the investigators’ judgment.

Each of the StUD RCTs (Table 1) examined the effects of one behavioral intervention, including motivational interviewing,^29,30 12-step,³¹ and smoking cessation,³² compared to treatment as usual (TAU). Across all 4 RCTs, TAU included standard weekly group counseling sessions provided at community treatment programs along with family counseling as needed. The duration of the trials ranged from 8 to 12 weeks. The current study involves the secondary analysis of publicly available deidentified data of trial participants; therefore, analysis was deemed exempt from review by the Institutional Review Board of the Johns Hopkins Bloomberg School of Public Health.

Table 1.

Any Stimulant Use Disorder RCT Characteristics and Participant Demographic Characteristics.

NIDA RCT #	Treatment type	Treatment arms	Year	Duration in weeks	N	Age, years mean (SD)	Men (%)	Non-Hispanic White (%)
CTN0006	Behavioral	MI vs TAU	2001-2003	12	454	35.9 (8.6)	45.2	35.7
CTN0007	Behavioral	MI vs TAU	2001-2003	12	403	41.9 (8.5)	55.0	26.1
CTN0031	Behavioral	12-Step vs TAU	2008-2010	8	471	38.3 (9.7)	41.2	46.2
CTN0046	Behavioral	Smoking cessation vs TAU	2010-2012	10	538	36.9 (10.0)	52.0	51.7

Abbreviations: MI, motivational interviewing; TAU, treatment as usual; RCT, randomized controlled trial.

Measures

Psychological distress was measured using the psychiatric domain of the Addiction Severity Index (ASI-Psych), a standardized semi-structured clinical interview. The ASI-Psych provides a comprehensive assessment of psychological distress by incorporating a wider range of symptoms that frequently co-occur and shape functioning and recovery among individuals with substance use disorders,³³ including depression, anxiety, psychosis, cognitive difficulties, problems with impulse control and aggression, and suicidal ideation and attempts. Importantly, ASI administration guidelines instruct interviewers to distinguish symptoms that reflect underlying psychological problems from those attributable to acute intoxication or withdrawal, helping ensure that participant responses reflect broader psychological distress rather than transient substance-related effects.³⁴

The ASI-psych composite score was derived from 11 items, which include 7 yes/no items evaluating the presence of psychological symptoms, excluding symptoms directly attributable to substance use, that were experienced for a significant duration within the past 30 days. These symptoms included serious depression, anxiety or tension, hallucinations, trouble understanding or concentrating, trouble controlling violent behaviors, and thoughts or attempts of suicide. The eighth item assessed the use of prescribed medications for emotional or psychological issues. The remaining 3 items measured the number of days participants reported experiencing emotional or psychological problems (scored continuously from 0 to 30), the degree to which these problems were considered troublesome, and the perceived importance of treatment for these problems. The latter 2 items were scored on a 5-point ordinal scale ranging from 0 (“not at all”) to 4 (“extremely”). Composite scores were calculated according to guidelines from the Composite Scores Manual,³⁵ yielding values from 0 to 1, with higher scores indicating more severe psychological distress. A dichotomous variable was also created to distinguish between individuals with and without significant psychological distress in the past 30 days. A threshold of 0.246 was validated in a large multi-site sample of individuals with substance use disorders, the majority of whom had StUD.³³ The cutoff demonstrated moderate accuracy for detecting psychiatric disorders based on diagnoses from the Structured Clinical Interview for DSM or Mini International Neuropsychiatry Interview (Area Under the Curve = 0.74) among individuals with StUDs.

The primary treatment outcome was any reduction in the frequency of stimulant use from baseline to the end of trial, measured using the ASI drug domain. Consistent with prior studies,^36-38 the frequency of use was categorized into 3 levels: no use (0 days), low-frequency of use (1-4 days), and high-frequency of use (≥5 days). Because of sample size limitations with the three-category variable, we derived a binary outcome variable to indicate any reduction in stimulant use, that is, any versus no reduction. Participants who transitioned from high-frequency to low-frequency use or to abstinence were classified as having reduced their use, whereas participants whose use remained the same or increased were categorized as having no reduction in use.

The secondary treatment outcome included results from urine drug screens for any stimulant and other substances (eg, opioids, benzodiazepines, cannabis, and barbiturates) at the end of the trial. Binary outcome variables were created for each substance based on results from the 2 most recent assessments prior to trial completion, that is, positive versus negative. If either assessment yielded a positive result, it was considered treatment failure. Conversely, 2 consecutive negative tests were considered treatment success. If one test was negative and the other was missing, the outcome was coded as missing.

Covariates included participants’ sociodemographic and clinical characteristics, including age, sex (man/woman), marital status (legally married/cohabiting, divorced/widowed/separated, and single/never married), homelessness, years of education completed, history of chronic medical illness, and race-ethnicity (non-Hispanic White, non-Hispanic Black, Hispanic, and other). The “other” category included participants who identified as Multi-Racial, Native Hawaiian or Other Pacific Islander, American Indian/Alaska Native, and Asian.

Statistical Analysis

Of the total study sample (n = 1866), 3 participants had no information available for any of the study measurements, and 5 had missing ASI-psych items, precluding the computation of the ASI-psych composite score. Therefore, the analytic sample was limited to 1858 participants with complete data.

First, we compared sociodemographic and clinical characteristics for men and women with and without psychological distress using chi-squared tests for binary and categorical variables and t-tests for continuous variables. We also compared the psychological symptoms from the ASI-psych between men and women.

Next, we conducted a series of regression analyses using the methods of one-stage individual participant data (IPD) meta-analysis,³⁹ to assess the association between psychological distress and each of the 3 outcomes: (1) reduction in the frequency of stimulant use, (2) urine toxicology ascertained stimulant use at the end of the trial, and (3) urine toxicology ascertained use of other drugs at the end of the trial. IPD meta-analysis methods were used to enhance methodological rigor and account for between-trial variability.^40,41

Using both binary (ASI-psych score ≥0.246) and continuous (ASI-psych composite score 0-1) exposure variables of psychological distress, 3 hierarchical logistic regression models were conducted for each outcome. Model A was the crude model, Model B adjusted for sociodemographic covariates, including age, sex, race-ethnicity, education, treatment arm, and duration of the trials. We also incorporated clustering by trial in the model to account for heterogeneity across trials. Model C further addressed missing outcome data using inverse probability weighting (IPW). All the analyses were conducted for the total sample and also separately for men and women. We report odds ratios (OR), adjusted odds ratios (aOR), and their corresponding 95% confidence intervals (95% CI).

Finally, we examined if the overall treatment effect (behavioral intervention vs TAU) on StUD treatment outcomes was moderated by psychological distress using two-way interaction terms (psychological distress × StUD treatment) in both the total sample and stratified by sex. These models were adjusted for sociodemographic covariates and accounted for missing outcome data. All statistical analyses were performed in Stata/MP version 18.0.⁴²

Results

Description of Sample

Of the total sample (n = 1858), 48.2% experienced serious psychological distress. Compared to men, women had higher prevalence of psychological distress (57.1% vs 38.7%, P < .001) and had a higher average ASI-Psych composite score (0.25 vs 0.19, P < .001). Baseline sociodemographic and clinical characteristics associated with psychological distress varied among men and women (Table 2). Among individuals with psychological distress, women were more likely to be younger than 35 years (42.5% vs 33.9%), whereas men were more likely than women to be over 50 years of age (14.9% vs 7.7%); a similar pattern was observed among participants without psychological distress. For education, among those with psychological distress, women had slightly higher mean years of education compared to men (12.1 vs 11.7), whereas among those without psychological distress, men had higher mean years of education (12.0 vs 11.7). Differences in race/ethnicity were small and did not follow a specific pattern across both groups.

Table 2.

Baseline Sociodemographic and Clinical Characteristics, Stratified by Gender.

Baseline characteristics	Total	Psychological distress			No psychological distress
	Total	Men	Women	P-value	Men	Women	P-value
	n = 1858n (%)	n = 348n (%)	n = 548n (%)	P-value	n = 550n (%)	n = 412n (%)	P-value
Age, years				.002			.001
<35	712 (38.3)	118 (33.9)	233 (42.5)		199 (36.2)	162 (39.3)
35-50	943 (50.7)	176 (50.6)	270 (49.3)		274 (49.8)	223 (54.1)
>50	198 (10.7)	52 (14.9)	42 (7.7)		77 (14.00)	27 (6.6)
Unknown	5 (0.3)	2 (0.6)	3 (0.5)		0 (0.00)	0 (0.00)
Race/ethnicity				.023			.025
Non-Hispanic White	759 (40.8)	151 (43.4)	239 (43.6)		194 (35.3)	175 (42.5)
Non-Hispanic Black	726 (39.1)	122 (35.1)	201 (36.7)		241 (43.8)	162 (39.3)
Hispanic	221 (11.9)	52 (14.9)	49 (8.9)		79 (14.4)	41 (9.9)
Non-Hispanic other	149 (8.0)	23 (6.6)	58 (10.6)		36 (6.5)	32 (7.8)
Unknown	3 (0.2)	0 (0.0)	1 (0.2)		0 (0.00)	2 (0.5)
Marital status				.807			.335
Married/cohabitation	303 (16.3)	51 (14.7)	89 (16.2)		97 (17.6)	66 (16.0)
Divorced/widowed/separated	1,153 (62.1)	224 (64.4)	344 (62.9)		329 (59.8)	256 (62.1)
Never married	400 (21.5)	73 (20.9)	115 (20.9)		124 (22.6)	88 (21.4)
Unknown	2 (0.1)	0 (0.0)	0 (0.00)		0 (0.00)	2 (0.5)
Homelessness	122 (6.6)	34 (9.8)	47 (8.6)	.544	29 (5.3)	12 (2.9)	.197
History of chronic disease	760 (40.9)	168 (48.3)	273 (49.8)	.653	185 (33.6)	134 (32.5)	.717
Education, mean (SD)	11.9 (1.9)	11.7 (1.9)	12.1 (2.0)	.044	12.0 (1.7)	11.7 (1.9)	.048

Bold values indicates statistical significance (P < 0.05).

Symptoms of Psychological Distress Among Men and Women

Overall, the prevalence of psychological distress symptoms was higher among women compared to men (Table 3). Women had significantly higher prevalence of serious depression (40.7% vs 31.2%), serious anxiety and tension (50.6% vs 39.0%), trouble understanding or concentrating (44.8% vs 31.7%), trouble controlling violent behavior (15.8% vs 11.4%), and serious thoughts of suicide (7.3% vs 3.9%). Women were also significantly more likely than men to have a greater mean number of days of experiencing psychological or emotional problems (11.1 vs 6.9 days) and to have been prescribed medication for psychological impairment (33.5% vs 15.5%). Furthermore, women were significantly more likely than men to report being “considerably” or “extremely” troubled or bothered by psychological or emotional problems (40.7% vs 26.5%) and to perceive treatment for psychological problems as more important (48.5% vs 29.2%).

Table 3.

Distribution of Psychological Distress (ASI-Psych) Symptoms in the Past 30 Days, Stratified by Gender.

Psychological distress symptoms	Total, n = 1858	Men, n = 898	Women, n = 960	P-value
Psychological distress symptoms	n (%)	n (%)	n (%)	P-value
Serious depression	671 (36.1)	280 (31.2)	391 (40.7)	<.001
Serious anxiety or tension	836 (45.0)	350 (39.0)	486 (50.6)	<.001
Hallucinations	110 (5.9)	45 (5.0)	65 (6.8)	.108
Trouble understanding or concentrating	715 (38.5)	285 (31.7)	430 (44.8)	<.001
Trouble controlling violent behavior	254 (13.7)	102 (11.4)	152 (15.8)	.005
Serious thoughts of suicide	105 (5.6)	35 (3.9)	70 (7.3)	.002
Attempted suicide	27 (1.45)	10 (1.1)	17 (1.8)	.237
Been prescribed medication for psychological/emotional problems	461 (24.8)	139 (15.5)	322 (33.5)	<.001
Rated considerably or extremely troubled or bothered by psychological or emotional problems	629 (33.8)	238 (26.5)	391 (40.7)	<.001
Rated considerably or extremely for importance of treatment for psychological problems	728 (39.2)	262 (29.2)	466 (48.5)	<.001
Days experienced psychological or emotional problems, mean (SD)	9.1 (11.57)	6.9 (10.45)	11.1 (12.19)	<.001

Abbreviation: ASI-Psych, psychiatric domain of the Addiction Severity Index. Bold values indicates statistical significance (P < 0.05).

Association of Psychological Distress With StUD Outcomes

We did not find any significant associations between psychological distress and StUD outcomes in the crude models (Model A). However, after adjusting for covariates in Model B, in the total sample, using the dichotomous ASI-psych exposure variable (Table 4A), individuals with psychological distress had lower odds of achieving reduced frequency of stimulant use (aOR = 0.73; 95% CI = 0.55, 0.98; P = .035). When IPW was applied to address missingness (Model C), the association was slightly attenuated and the statistical significance was reduced to a trend level (aOR = 0.77; 95% CI = 0.57, 1.03, P = .083).

Table 4.

The Association of Psychological Distress and Outcomes of Stimulant Use Disorders, Stratified Analysis by Gender.

Treatment outcomes	Model A^a		Model B^b		Model C^c
Treatment outcomes	OR (95% CI)	P-value	aOR (95% CI)	P-value	aOR (95% CI)	P-value
(A) Dichotomous exposure: ASI-Psych ≥0.246
Outcome 1: Any reduced frequency of use from baseline to the end of trial
Total sample	0.84 (0.46, 1.54)	.578	0.73 (0.55, 0.98)	.035	0.77 (0.57, 1.03)	.083
Men	0.76 (0.46, 1.28)	.312	0.64 (0.43, 0.96)	.033	0.70 (0.49, 0.99)	.046
Women	0.89 (0.44, 1.78)	.737	0.83 (0.63, 1.09)	.185	0.84 (0.60, 1.19)	.333
Outcome 2: Positive urine test for stimulant at end of trial
Total sample	1.18 (0.71, 1.97)	.515	1.27 (0.82, 1.98)	.281	1.29 (0.81, 2.08)	.285
Men	1.16 (0.93, 1.44)	.185	1.22 (1.01, 1.48)	.048	1.23 (1.03, 1.47)	.024
Women	1.18 (0.53, 2.65)	.678	1.29 (0.66, 2.54)	.457	1.33 (0.62, 2.84)	.455
Outcome 3: Positive urine test for other drugs at end of trial
Total sample	1.15 (0.80, 1.64)	.450	1.22 (0.92, 1.62)	.164	1.22 (0.92, 1.61)	.163
Men	1.02 (0.88, 1.20)	.758	1.03 (0.83, 1.29)	.756	1.03 (0.85, 1.25)	.764
Women	1.40 (0.85, 2.30)	.186	1.44 (0.95, 2.20)	.087	1.44 (0.96, 2.14)	.075
(B) Continuous exposure: ASI-Psych composite score (0-1)
Outcome 1: Any reduced frequency of use from baseline to the end of trial
Total sample	0.76 (0.18, 3.19)	.713	0.57 (0.23, 1.40)	.219	0.60 (0.26, 1.39)	.232
Men	0.57 (0.15, 2.18)	.411	0.38 (0.15, 0.95)	.040	0.43 (0.20, 0.92)	.029
Women	0.90 (0.22, 3.67)	.887	0.78 (0.34, 1.78)	.559	0.79 (0.32, 1.93)	.600
Outcome 2: Positive urine test for stimulant at end of trial
Total sample	1.50 (0.48, 4.74)	.485	1.76 (0.62, 5.02)	.291	1.84 (0.61, 5.57)	.280
Men	1.63 (0.97, 2.75)	.063	2.01 (1.38, 2.91)	<.001	2.00 (1.41, 2.85)	<.001
Women	1.36 (0.24, 7.59)	.722	1.56 (0.32, 7.52)	.580	1.73 (0.30, 9.78)	.536
Outcome 3: Positive urine test for other drugs at end of trial
Total sample	1.41 (0.61, 3.27)	.426	1.68 (0.75, 3.76)	.211	1.66 (0.74, 3.72)	.221
Men	1.40 (1.02, 1.90)	.034	1.54 (0.87, 2.74)	.141	1.54 (0.93, 2.57)	.098
Women	1.69 (0.51, 5.62)	.391	1.77 (0.57, 5.54)	.325	1.74 (0.55, 5.51)	.345

Abbreviations: OR, odds ratio; aOR, adjusted odds ratio; CI, confidence interval; ASI-Psych, psychiatric domain of the Addiction Severity Index; RCT, randomized controlled trial. Bold values indicates statistical significance (P < 0.05).

Model A was not adjusted for any covariates; ^bModel B was adjusted demographic characteristics (sex, age, race, and education), treatment arm and duration of treatment, and clustering within RCTs; ^cModel C was adjusted demographic characteristics (sex, age, race, and education), treatment arm and duration of treatment, clustering within RCTs, and inverse probability weighting (IPW) to address missingness in outcome measures.

Sex-stratified analyses revealed notable differences in these associations in Models B and C. Using the dichotomous ASI-psych exposure variable, men with psychological distress had a higher likelihood of experiencing unfavorable outcomes, such as a lower odds of reduced frequency of stimulant use (aOR = 0.64; 95% CI = 0.43, 0.96; P = .033), and higher odds of a positive urine test for stimulants (aOR = 1.22; 95% CI = 1.01, 1.48; P = .048). Similarly, the continuous ASI-psych exposure variable (Table 4B) was associated with lower odds of reduced frequency of stimulant use (aOR = 0.38; 95% CI = 0.15, 0.95; P = .040) and higher odds of a positive urine test for stimulants (aOR = 2.01; 95% CI = 1.38, 2.91; P < .001) among men. These results remained consistent in the IPW models, demonstrating lower odds of reduced use (dichotomous exposure: aOR = 0.70; 95% CI = 0.49, 0.99; P = .046; continuous exposure: aOR = 0.43; 95% CI = 0.20, 0.92; P = .029), and higher odds of a positive urine test for stimulants (dichotomous exposure: aOR = 1.23; 95% CI = 1.03, 1.47; P = .024; continuous exposure: aOR = 2.00; 95% CI = 1.41, 2.85; P < .001) associated with ASI-psych in men.

In contrast, women with psychological distress did not significantly differ from those without psychological distress across outcomes of reduced frequency of stimulant use and positive stimulant urine test using both dichotomous and continuous ASI-psych. Additionally, men and women with distress did not significantly differ from their counterparts without distress on the outcome of positive urine test for other drugs at the end of the trial.

Impact of Psychological Distress on Treatment Effect

In the total sample, no statistically significant interactions were found between psychological distress and treatment (behavioral intervention vs placebo) for any of the StUD treatment outcomes (Table 5).

Table 5.

Treatment Effect on Outcomes of Stimulant Use Disorders Among Those With and Without Psychological Distress, Stratified by Gender.

Exposure	Any reduced frequency of use from baseline to the end of trial			Positive urine test for stimulant at end of trial			Positive urine test for other drugs at end of trial
	Study arms		Two-way interaction test^a	Study arms		Two-way interaction test^a	Study arms		Two-way interaction test^a
	Active (%)	Placebo (%)	Two-way interaction test^a	Active (%)	Placebo (%)	Two-way interaction test^a	Active (%)	Placebo (%)	Two-way interaction test^a
Total
Psychological distress	60.8	58.5	χ² = 0.16	32.1	31.9	χ² = 0.15	19.4	22.2	χ² = 0.01
No psychological distress	65.2	64.6	P = .688	25.7	27.1	P = .703	17.0	19.7	P = .915
Men
Psychological distress	56.5	58.4	χ² = 0.76	31.1	29.3	χ² = 0.91	20.7	21.2	χ² = 0.11
No psychological distress	67.2	61.8	P = .383	24.8	28.2	P = .340	19.1	22.1	P = .735
Women
Psychological distress	64.0	59.0	χ² = 31.10	32.1	33.0	χ² = 0.34	18.4	22.8	χ² = 0.01
No psychological distress	62.0	67.8	P < .001	27.8	26.3	P = .562	13.8	16.9	P = .909

Bold values indicates statistical significance (P < 0.05).

Indicates a two-way interaction (stimulant use disorder treatment × psychological distress).

Sex-stratified analyses showed that psychological distress moderated the treatment effect on achieving reduced frequency of stimulant use (χ² = 31.10, P < .001) in women. Among women with psychological distress, those who received the behavioral intervention were more likely to have reduced stimulant use compared to those who received TAU (64.0% vs 59.0%, respectively). Conversely, among women without psychological distress, those who received the behavioral intervention were less likely to have reduced stimulant use compared to TAU (62.0% vs 67.8%, respectively). However, no significant interaction was found for reduced frequency of stimulant use from baseline to the end of trial among men. Similarly, psychological distress did not moderate treatment effect on achieving a positive urine test for stimulants and other drugs, neither for men nor for women.

Discussion

In this study of 4 multisite RCTs of behavioral interventions for StUDs, we found that comorbid psychological distress was common among individuals with StUDs, affecting nearly half of the sample, with significantly higher prevalence and severity among women compared to men. Women reported higher rates of depressive and anxiety symptoms, greater use of prescribed medication for psychological problems, and a greater perceived need for treatment. These findings are consistent with previous research demonstrating high rates of psychiatric comorbidity among individuals with substance use disorders,^43-46 particularly among women.^20,47,48

The impact of psychological distress on StUD outcomes differed by sex. Among men, psychological distress was consistently associated with unfavorable outcomes, such as lower odds of reduced frequency of stimulant use and higher odds of a stimulant-positive urine test at the end of treatment. One plausible explanation for this finding is that distress among men may exacerbate risk-taking behaviors or impair impulse control, contributing to poorer treatment response.⁴⁹ Additionally, cultural and social norms around masculinity often discourage men from expressing emotional vulnerability and seeking psychological support.^50-52 Consequently, psychological distress in men may remain untreated, increasing the likelihood of maladaptive coping strategies such as continued stimulant use.

In contrast, although psychological distress was more prevalent and severe among women, its negative impact on treatment outcomes appeared to be less pronounced. One possible explanation is that women experiencing distress may perceive a greater need for treatment,^53,54 leading to earlier engagement with treatment systems, thereby mitigating adverse outcomes. This corresponds with our finding that women with psychological distress who received behavioral interventions demonstrated higher rates of reduced stimulant use compared to those receiving TAU, suggesting that distressed women may have derived greater benefit from treatment.

Our findings highlight the need to better understand the mechanisms linking psychological distress with outcomes of StUDs and the impact of StUD treatments. Although the precise mechanism remains unclear, emotional stress has been associated with increased drug craving and drug-seeking behavior among individuals with StUDs,^55-57 while depressive symptoms may reinforce the effects of stimulants and increase vulnerability to relapse.⁵⁸ Moreover, the physiological and psychological discomfort associated with withdrawal during treatment may further exacerbate distress,⁵⁹ creating a negative feedback loop in which substance use becomes a primary coping strategy to alleviate anxiety and dysphoria.⁶⁰ These interrelated processes may disproportionately affect men, who are less likely to seek support for psychological distress and may rely more heavily on stimulants as a coping mechanism, thereby contributing to poorer outcomes. In contrast, in women, psychological distress may have a buffering effect by increasing perceived need for care and treatment engagement. Future research needs to corroborate the study’s findings and further examine the possible mechanisms for sex differences.

This study has several limitations. First, the RCTs excluded participants with severe psychiatric disorders, potentially underestimating the impact of comorbid psychological distress on treatment outcomes. Second, diagnoses of StUDs in these trials were based on DSM-IV criteria. Although the DSM-5 introduced changes by combining abuse and dependence into a single substance use disorder with severity specifiers and adding craving as a criterion, prior research indicates that DSM-IV substance use dependence more closely aligns with DSM-5 moderate and severe substance use disorder.⁶¹ Third, the use of a binary outcome variable indicating any reduction in the frequency of stimulant use (ie, any vs no reduction) may treat small and large changes equivalently. However, the small sample size (<5%) in the “high- to low-frequency use” or “reduced use” category precluded our ability to model more granular changes. Fourth, the trials were relatively short, limiting our ability to assess long-term associations between distress and treatment outcomes. Fifth, the 4 RCTs included in our study had different durations and varied in the nature, quality, and intensity of substance use disorder treatments. We attempted to account for these differences by adjusting all analyses for duration and type of treatment. Lastly, our study was limited to adults who were willing to participate in an RCT, which does not necessarily represent all individuals with StUDs. Furthermore, the RCTs were all based in the United States. Therefore, generalizability of findings to individuals receiving StUD treatment in usual care settings and settings outside of the United States may be limited.

Nonetheless, our findings have important clinical implications for StUD treatment. Routine clinical screening for psychological distress can help identify individuals who may not voluntarily disclose stressful experiences or psychiatric symptoms, or who may attribute such symptoms solely to substance use. Systematic screening may also detect subclinical symptoms that would otherwise go unrecognized but that still interfere with treatment engagement and recovery. Brief, validated screening tools that are feasible for use in busy clinical settings, such as the General Health Questionnaire-12 or the Kessler-6,^5,62,63 have strong psychometric properties and can facilitate identification of psychological distress, enabling timely referral to mental health providers and the integration of supportive services within StUD care.

The findings also highlight the need for sex-responsive approaches to StUD treatment. Women had higher levels of psychological distress, and distressed women receiving behavioral interventions demonstrated greater reductions in stimulant use compared to TAU, suggesting that expanding and tailoring psychosocial support for women may enhance treatment response. Prior research on sex-responsive and women-centered substance use treatment models has demonstrated improvements in retention, reduced substance use, and enhanced psychosocial outcomes compared to mixed-gender approaches, particularly among women with complex trauma histories.^64-67 Therefore, StUD treatment programs for women should extend beyond addressing psychological distress to address broader psychosocial vulnerabilities, including relational risks, trauma histories, and caregiving responsibilities, that shape their substance use trajectories.²⁷ For men, who may be less likely to verbalize psychological symptoms and seek mental health services, routine psychological distress screening within clinical settings may improve detection and facilitate timely intervention. Future research should not only investigate sex-specific differences in the long-term effects of psychological distress on StUD treatment outcomes but also explore the mechanisms that may underlie these differences. Additionally, evaluation of sex-responsive treatment models is critical to determine their effectiveness and improve treatment retention, reduce relapse, and promote recovery.

Footnotes

Authors’ Note

Part of the study findings were presented at the 2025 Annual Meeting of the Association for Medical Education and Research in Substance Abuse (AMERSA).

ORCID iD

Himani Byregowda

Ethical Considerations

Institutional Review Board approval was not required.

Author Contributions

HB: Formal analysis (lead); investigation (lead); visualization (lead); writing—review and editing (lead); and writing—review and editing (lead).

MA-E: Conceptualization (supporting); formal analysis (supporting); methodology (supporting); and writing—review and editing (supporting).

RS: Conceptualization (supporting); formal analysis (supporting); methodology (supporting); and writing—review and editing (supporting).

RM: Conceptualization (lead); supervision (supporting); validation (supporting); project administration (supporting); writing—review and editing (supporting).

RMC: Conceptualization (lead); supervision (lead); validation (lead); project administration (lead); writing—review and editing (supporting).

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by the National Institutes of Health (R01DA058008) to MPIs: Dr. Ramin Mojtabai and Dr. Rosa M. Crum. Dr. Mojtabai’s work was supported in part by the Louisiana Board of Regents Endowed Chairs for Eminent Scholars program. Contents are solely the responsibility of the authors and do not necessarily represent the official views of the NIH.

Declaration of Conflicting Interests

The authors declared the following potential conflicts of interest with respect to the research, authorship, and/or publication of this article: Dr. Mojtabai has received royalties and consulting fees from UpToDate, Medscape, and MindMed, as well as providing consultation regarding social media litigation on behalf of the Plaintiffs.

References

Substance Abuse and Mental Health Services Administration. Key Substance Use and Mental Health Indicators in the United States: Results From the 2024 National Survey on Drug Use and Health. Center for Behavioral Health Statistics and Quality, Substance Abuse and Mental Health Services Administration; 2025. https://www.samhsa.gov/data/sites/default/files/reports/rpt56287/2024-nsduh-annual-national-report.pdf

Magidson

Liu

Lejuez

Blanco

Comparison of the course of substance use disorders among individuals with and without generalized anxiety disorder in a nationally representative sample. J Psychiatr Res. 2012;46(5):659-666. doi:10.1016/j.jpsychires.2012.02.011

Pettinati

O’Brien

Dundon

WD.

Current status of co-occurring mood and substance use disorders: a new therapeutic target. Am J Psychiatry. 2013;170(1):23-30. doi:10.1176/appi.ajp.2012.12010112

Warden

Sanchez

Greer

, et al. Demographic and clinical characteristics of current comorbid psychiatric disorders in a randomized clinical trial for adults with stimulant use disorders. Psychiatry Res. 2016;246:136-141. doi:10.1016/j.psychres.2016.09.007

Drapeau

Marchand

Beaulieu-Prévost

. Epidemiology of psychological distress. In: L’Abate

, ed. Mental Illnesses-Understanding, Prediction and Control. London IntechOpen; 2011:134-155. https://www.intechopen.com/chapters/25512

Crum

Green

Stuart

, et al. Transitions through stages of alcohol involvement: The potential role of mood disorders. Drug Alcohol Depend. 2018;189:116-124. doi:10.1016/j.drugalcdep.2018.02.027

Burns

Teesson

O’Neill

The impact of comorbid anxiety and depression on alcohol treatment outcomes. Addiction. 2005;100(6):787-796. doi:10.1111/j.1360-0443.2005.001069.x

Burns

Teesson

Alcohol use disorders comorbid with anxiety, depression and drug use disorders. Findings from the Australian National Survey of Mental Health and Well Being. Drug Alcohol Depend. 2002;68(3):299-307. doi:10.1016/s0376-8716(02)00220-x

Dodge

Sindelar

Sinha

The role of depression symptoms in predicting drug abstinence in outpatient substance abuse treatment. J Subst Abuse Treat. 2005;28(2):189-196. doi:10.1016/j.jsat.2004.12.005

10.

Leventhal

Mooney

DeLaune

Schmitz

JM.

Using addiction severity profiles to differentiate cocaine-dependent patients with and without comorbid major depression. Am J Addict. 2006;15(5):362-369. doi:10.1080/10550490600860148

11.

Schmitz

Stotts

Averill

, et al. Cocaine dependence with and without comorbid depression: a comparison of patient characteristics. Drug Alcohol Depend. 2000;60(2):189-198. doi:10.1016/s0376-8716(99)00157-x

12.

Rubin

Aharonovich

Bisaga

Levin

Raby

Nunes

EV.

Early abstinence in cocaine dependence: influence of comorbid major depression. Am J Addict. 2007;16(4):283-290. doi:10.1080/10550490701389880

13.

Kim

Burlaka

Gender differences in suicidal behaviors: mediation role of psychological distress between alcohol abuse/dependence and suicidal behaviors. Arch Suicide Res. 2018;22(3):405-419. doi:10.1080/13811118.2017.1355284

14.

Amin-Esmaeili

Byregowda

Susukida

Mojtabai

Crum

RM.

Gender differences in the impact of psychological distress on methamphetamine use disorder outcomes and treatment effect. Addiction. Published online January 18, 2026. doi:10.1111/add.70315

15.

Tan

Abd Rashid

Guan

NC.

Anxiety and depression among amphetamine-type stimulant users: the association with religiosity and religious coping. Malays J Med Sci. 2020;27(4):51-63. doi:10.21315/mjms2020.27.4.5

16.

Daigre

Perea-Ortueta

Berenguer

, et al. Psychiatric factors affecting recovery after a long term treatment program for substance use disorder. Psychiatry Res. 2019;276:283-289. doi:10.1016/j.psychres.2019.05.026

17.

Gelkopf

Weizman

Melamed

Adelson

Bleich

Does psychiatric comorbidity affect drug abuse treatment outcome? A prospective assessment of drug abuse, treatment tenure and infectious diseases in an Israeli methadone maintenance clinic. Isr J Psychiatry Relat Sci. 2006;43(2):126-136.

18.

Byregowda

Amin-Esmaeili

Susukida

Mojtabai

Crum

. Association between psychiatric comorbidity and retention in substance use disorder treatment: results from multi-site randomized controlled trials of pharmacological and behavioral interventions. J Dual Diagn. Published online January 1, 2026:1-13. doi:10.1080/15504263.2025.2606018

19.

McHugh

Korte

Bichon

Weiss

RD.

Gender differences in the prevalence of stimulant misuse in the United States: 2015-2019. Am J Addict. 2024;33(3):283-289. doi:10.1111/ajad.13501

20.

Fonseca

Robles-Martínez

Tirado-Muñoz

, et al. A gender perspective of addictive disorders. Curr Addict Rep. 2021;8(1):89-99. doi:10.1007/s40429-021-00357-9

21.

Becker

McClellan

Reed

BG.

Sex differences, gender and addiction. J Neurosci Res. 2017;95(1-2):136-147. doi:10.1002/jnr.23963

22.

Zakiniaeiz

Potenza

MN.

Gender-related differences in addiction: a review of human studies. Curr Opin Behav Sci. 2018;23:171-175. doi:10.1016/j.cobeha.2018.08.004

23.

Greenfield

Back

Lawson

Brady

KT.

Substance abuse in women. Psychiatr Clin North Am. 2010;33(2):339-355. doi:10.1016/j.psc.2010.01.004

24.

Grant

Stinson

Dawson

, et al. Prevalence and co-occurrence of substance use disorders and independent mood and anxiety disorders: results from the National Epidemiologic Survey on Alcohol and Related Conditions. Arch Gen Psychiatry. 2004;61(8):807-816. doi:10.1001/archpsyc.61.8.807

25.

Martin

Neighbors

Griffith

DM.

The experience of symptoms of depression in men vs women: analysis of the National Comorbidity Survey Replication: analysis of the national comorbidity survey replication. JAMA Psychiatry. 2013;70(10):1100-1106. doi:10.1001/jamapsychiatry.2013.1985

26.

Cole

Davidson

MM.

Exploring men’s perceptions about male depression. Psychol Men Masc. 2019;20(4):459-466. doi:10.1037/men0000176

27.

Harris

MTH

Laks

Stahl

Bagley

Saia

Wechsberg

. Gender dynamics in substance use and treatment: a women’s focused approach. Med Clin North Am. 2022;106(1):219-234. doi:10.1016/j.mcna.2021.08.007

28.

Susukida

Amin-Esmaeili

Mayo-Wilson

Mojtabai

Data management in substance use disorder treatment research: implications from data harmonization of National Institute on Drug Abuse-funded randomized controlled trials. Clin Trials. 2021;18(2):215-225. doi:10.1177/1740774520972687

29.

Petry

Peirce

Stitzer

, et al. Effect of prize-based incentives on outcomes in stimulant abusers in outpatient psychosocial treatment programs: a national drug abuse treatment clinical trials network study: a national drug abuse treatment clinical trials network study. Arch Gen Psychiatry. 2005;62(10):1148-1156. doi:10.1001/archpsyc.62.10.1148

30.

Peirce

Petry

Stitzer

, et al. Effects of lower-cost incentives on stimulant abstinence in methadone maintenance treatment: a National Drug Abuse Treatment Clinical Trials Network study: a national drug abuse treatment clinical trials network study. Arch Gen Psychiatry. 2006;63(2):201-208. doi:10.1001/archpsyc.63.2.201

31.

Donovan

Daley

Brigham

, et al. Stimulant abuser groups to engage in 12-step: a multisite trial in the National Institute on Drug Abuse Clinical Trials Network. J Subst Abuse Treat. 2013;44(1):103-114. doi:10.1016/j.jsat.2012.04.004

32.

Winhusen

Brigham

Kropp

, et al. A randomized trial of concurrent smoking-cessation and substance use disorder treatment in stimulant-dependent smokers. J Clin Psychiatry. 2014;75(4):336-343. doi:10.4088/JCP.13m08449

33.

Susukida

Mojtabai

Amin-Esmaeili

Validation of Addiction Severity Index (ASI) for assessment of psychiatric comorbidity in multi-site randomized controlled trials. J Dual Diagn. 2020;16(3):312-321. doi:10.1080/15504263.2020.1741755

34.

McLellan

Cacciola

Alterman

Rikoon

Carise

The Addiction Severity Index at 25: origins, contributions and transitions. Am J Addict. 2006;15(2):113-124. doi:10.1080/10550490500528316

35.

McGahan

Griffith

Parente

McLellann

AT.

Composite Scores Manual. Treatment Research Institute; 1986.

36.

Amin-Esmaeili

Farokhnia

Susukida

, et al. Reduced drug use as an alternative valid outcome in individuals with stimulant use disorders: findings from 13 multisite randomized clinical trials. Addiction. 2024;119(5):833-843. doi:10.1111/add.16409

37.

Amin-Esmaeili

Susukida

Johnson

, et al. Patterns of reduced use and abstinence in multi-site randomized controlled trials of pharmacotherapies for cocaine and methamphetamine use disorders. Drug Alcohol Depend. 2021;226:108904. doi:10.1016/j.drugalcdep.2021.108904

38.

Roos

Nich

Mun

, et al. Clinical validation of reduction in cocaine frequency level as an endpoint in clinical trials for cocaine use disorder. Drug Alcohol Depend. 2019;205(107648):107648. doi:10.1016/j.drugalcdep.2019.107648

39.

Burke

Ensor

Riley

RD.

Meta-analysis using individual participant data: one-stage and two-stage approaches, and why they may differ. Stat Med. 2017;36(5):855-875. doi:10.1002/sim.7141

40.

Abo-Zaid

Guo

Deeks

, et al. Individual participant data meta-analyses should not ignore clustering. J Clin Epidemiol. 2013;66(8):865-873.e4. doi:10.1016/j.jclinepi.2012.12.017

41.

Debray

TPA

Moons

KGM

van Valkenhoef

, et al. Get real in individual participant data (IPD) meta-analysis: a review of the methodology: methods for IPD Meta-Analysis. Res Synth Methods. 2015;6(4):293-309. doi:10.1002/jrsm.1160

42.

StataCorp LLC. Stata Statistical Software. 2025. Accessed August 18, 2025. https://www.stata.com/company/

43.

Lee

Jain

Duthely

Ikeda

Santos

GM.

Stimulant use associated with psychosocial factors, HIV risk, and concurrent hazardous alcohol use among US adults: exploratory cross-sectional questionnaire study. JMIR Form Res. 2023;7(1):e45717. doi:10.2196/45717

44.

Duncan

Ward

Kippen

Dietze

Sutton

A narrative systematic review of associations and temporality between use of methamphetamine, ecstasy/MDMA, or cocaine with anxiety or depressive symptoms. Addict Behav. 2024;153(107988):107988. doi:10.1016/j.addbeh.2024.107988

45.

McKetin

Leung

Stockings

, et al. Mental health outcomes associated with of the use of amphetamines: a systematic review and meta-analysis. EClinicalMedicine. 2019;16:81-97. doi:10.1016/j.eclinm.2019.09.014

46.

Wang

Amphetamines abuse and depression: focus on TRPC channels. Exp Neurol. 2023;364(114391):114391. doi:10.1016/j.expneurol.2023.114391

47.

Zilberman

Tavares

Blume

el-Guebaly

Substance use disorders: sex differences and psychiatric comorbidities. Can J Psychiatry. 2003;48(1):5-13. doi:10.1177/070674370304800103

48.

Franke

Neumann

Proebstl

, et al. Psychiatric comorbidity and psychopathology of methamphetamine users—are there gender differences? Int J Ment Health Addict. 2023;21(4):2632-2649. doi:10.1007/s11469-021-00743-4

49.

Seidler

Dawes

Rice

Oliffe

Dhillon

HM.

The role of masculinity in men’s help-seeking for depression: a systematic review. Clin Psychol Rev. 2016;49:106-118. doi:10.1016/j.cpr.2016.09.002

50.

Seidler

Rice

Kealy

Oliffe

Ogrodniczuk

JS.

What gets in the way? Men’s perspectives of barriers to mental health services. Int J Soc Psychiatry. 2020;66(2):105-110. doi:10.1177/0020764019886336

51.

Susukida

Mojtabai

Mendelson

Sex differences in help seeking for mood and anxiety disorders in the National Comorbidity Survey-Replication: sex differences in help seeking for mood and anxiety disorders. Depress Anxiety. 2015;32(11):853-860. doi:10.1002/da.22366

52.

Levant

Powell

. The gender role strain paradigm. In: Levant

Wong

, eds. The Psychology of Men and Masculinities. American Psychological Association; 2017:15-43. doi:10.1037/0000023-002

53.

Wendt

Shafer

Gender and attitudes about mental health help seeking: results from national data. Health Soc Work. 2016;41(1):e20-e28. doi:10.1093/hsw/hlv089

54.

Lee

Hwang

Ball

Lee

Albright

DL.

Mental health literacy affects mental health attitude: is there a gender difference?

Am J Health Behav. 2020;44(3):282-291. doi:10.5993/AJHB.44.3.1

55.

Brady

Sinha

Co-occurring mental and substance use disorders: the neurobiological effects of chronic stress. Am J Psychiatry. 2005;162(8):1483-1493. doi:10.1176/appi.ajp.162.8.1483

56.

Parisi

Landicho

Hudak

Leknes

Froeliger

Garland

EL.

Emotional distress and pain catastrophizing predict cue-elicited opioid craving among chronic pain patients on long-term opioid therapy. Drug Alcohol Depend. 2022;233(109361):109361. doi:10.1016/j.drugalcdep.2022.109361

57.

Sinha

Fuse

Aubin

O’Malley

SS.

Psychological stress, drug-related cues and cocaine craving. Psychopharmacology (Berl). 2000;152(2):140-148. doi:10.1007/s002130000499

58.

Rounsaville

BJ.

Treatment of cocaine dependence and depression. Biol Psychiatry. 2004;56(10):803-809. doi:10.1016/j.biopsych.2004.05.009

59.

Regina

Gokarakonda

Attia

FN.

Withdrawal syndromes. In: StatPearls. StatPearls Publishing; 2025. Accessed August 20, 2025. https://www.ncbi.nlm.nih.gov/books/NBK459239/

60.

Sinha

Chronic stress, drug use, and vulnerability to addiction. Ann N Y Acad Sci. 2008;1141(1):105-130. doi:10.1196/annals.1441.030

61.

Compton

Dawson

Goldstein

Grant

BF.

Crosswalk between DSM-IV dependence and DSM-5 substance use disorders for opioids, cannabis, cocaine and alcohol. Drug Alcohol Depend. 2013;132(1-2):387-390. doi:10.1016/j.drugalcdep.2013.02.036

62.

Kessler

Andrews

Colpe

, et al. Short screening scales to monitor population prevalences and trends in non-specific psychological distress. Psychol Med. 2002;32(6):959-976. doi:10.1017/s0033291702006074

63.

Martin

AJ.

Assessing the multidimensionality of the 12-item General Health Questionnaire. Psychol Rep. 1999;84(3 Pt 1):927-935. doi:10.2466/pr0.1999.84.3.927

64.

Johnstone

Dela Cruz

Kalb

, et al. A systematic review of gender-responsive and integrated substance use disorder treatment programs for women with co-occurring disorders. Am J Drug Alcohol Abuse. 2023;49(1):21-42. doi:10.1080/00952990.2022.2130348

65.

Niv

Hser

YI.

Women-only and mixed-gender drug abuse treatment programs: service needs, utilization and outcomes. Drug Alcohol Depend. 2007;87(2-3):194-201. doi:10.1016/j.drugalcdep.2006.08.017

66.

Valeri

Sugarman

Reilly

McHugh

Fitzmaurice

Greenfield

SF.

Group therapy for women with substance use disorders: in-session affiliation predicts women’s substance use treatment outcomes. J Subst Abuse Treat. 2018;94:60-68. doi:10.1016/j.jsat.2018.08.008

67.

Greenfield

Brooks

Gordon

, et al. Substance abuse treatment entry, retention, and outcome in women: a review of the literature. Drug Alcohol Depend. 2007;86(1):1-21. doi:10.1016/j.drugalcdep.2006.05.012