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. Author manuscript; available in PMC: 2025 Aug 1.
Published in final edited form as: J Subst Use Addict Treat. 2024 May 11;163:209394. doi: 10.1016/j.josat.2024.209394

Examining Cocaine Use Reductions and Long-Term Outcomes in Two Clinical Trials of Continuing Care for Cocaine Dependence

Victoria R Votaw 1,2,3,4, Felicia R Tuchman 1, Corey R Roos 5, Hanna M Hebden 1,2, James R McKay 6,7, Brian D Kiluk 5, Katie Witkiewitz 1,2
PMCID: PMC11180556  NIHMSID: NIHMS1996118  PMID: 38735481

Abstract

Background:

There is a need to identify clinically meaningful non-abstinent endpoints for cocaine use disorder (CUD) clinical trials. In this study, we sought to replicate and extend prior work validating reductions in cocaine use frequency levels as an endpoint by examining associations between reductions in cocaine use frequency and long-term functioning outcomes.

Methods:

We conducted a secondary analysis of two randomized clinical trials (N=445; 77.5% male; mean age=42.18 years; 86.5% Black, 10.8% non-Hispanic white) that evaluated telephone-based continuing care for a 12- and 24-month period. Cocaine use frequency levels, measured with the Timeline Followback, were (1) abstinence (no past-month cocaine use), (2) low-frequency use (1–4 days of use/month), and (3) high-frequency use (5+ days of use/month).

Results:

Among those who completed the 12-month follow-up (n=392), most reduced from high-frequency use at baseline to abstinence at the 12-month follow-up (n=243; 62.0%). An additional 21.2% (n=83) reported either high-to-low-frequency use (n=35; 8.9%) or low use-to-abstinence (n=48; 12.2%); 16.8% of participants (n=66) did not change or increased their cocaine frequency level. Compared to those who had no change/increases in frequency levels, at least a one-level reduction from baseline to 12-month follow-up (i.e., high-to-low-frequency use, high-to-abstinence, low-to-abstinence) was concurrently associated with lower levels of negative consequences at the 12-month follow-up and prospectively with lower levels of cocaine use and consequences at 24-month follow-up, with effect sizes in the medium-to-large range. Those who reduced to abstinence generally had fewer drug use consequences at the 12-month follow-up than those who reduced to a low-frequency level; however, these groups did not significantly differ on any outcomes at the 24-month follow-up.

Conclusions:

Categorical reductions in cocaine use frequency levels, including those short of abstinence, are associated with less cocaine use and lower problem severity up to two years following treatment entry. Low-frequency cocaine use following the initial treatment phase does not appear to forebode worsening functioning, such as escalations in cocaine use.

Keywords: cocaine use disorder, endpoints, harm reduction, non-abstinence endpoints

1. Introduction

The treatment of cocaine use disorder (CUD) is hampered by a lack of medications to treat CUD (Brandt et al., 2021) and mixed efficacy of psychosocial interventions (Kampman, 2019). Many prior studies have used abstinence as the primary outcome, which many people do not achieve. Thus, prior trials likely failed to capture meaningful reductions in cocaine use, which also aligns with some individuals’ goals to reduce use and/or consequences of use (Brandt et al., 2021; Paquette et al., 2022). Continuous reductions in cocaine use (e.g., percentage reduction in cocaine use from baseline to the last month of treatment) are associated with functional outcomes and cocaine use at follow-up assessments, though evidence is equivocal regarding whether continuous reductions in cocaine use are sensitive to active treatment effects (Carroll et al., 2014, 2018).

The Food and Drug Administration (FDA) has signaled openness to non-abstinence endpoints for stimulant use disorders in medication development trials but emphasized the need for practical measures of reductions in use (FDA, 2023). Specifically, the FDA has suggested that endpoints emphasize within-person change, focus on days of use per period of time, and identify the proportions of “responders” per treatment condition (Food and Drug Administration, 2023). These recommendations are consistent with those put forward by the Analgesic, Anesthetic, and Addiction Clinical Trial Translations, Innovations, Opportunities, and Networks (ACTTION) workgroup that highlighted the need for endpoints focusing on days of use rather than quantity of use per day/episode (Kiluk et al., 2016) Taken together, there is a need to validate binary, yet meaningful, endpoints for patterns of stimulant use.

In the alcohol research field, a growing body of literature has supported categorical reductions in World Health Organization (WHO) risk drinking levels, evaluated by the average number of drinks per day over a given period, as an endpoint for alcohol clinical trials (Votaw & Witkiewitz, 2022). Studies have shown that reductions in WHO drinking risk levels are more achievable than abstinence, sensitive to treatment effects, stable across long-term follow-ups, and associated with improved mental health, quality of life, and physical health (Aldridge et al., 2016; Falk et al., 2019; Hartwell et al., 2021; Witkiewitz et al., 2017, 2019, 2020, 2021). Accordingly, research on WHO drinking risk levels provides a roadmap for identifying non-abstinent endpoints in CUD trials.

Recent research has focused on validating cocaine “risk” levels, defined by cocaine use frequency, paralleling research on WHO risk drinking levels. In an initial examination of cocaine use frequency categories, Kiluk and colleagues (2017) showed that individuals who were abstinent or reported 1–4 days of cocaine use in the last month of treatment were more likely to report an absence of problems in psychosocial functioning domains at follow-ups compared to those with higher frequency cocaine use. Further supporting the utility of 1–4 days of cocaine use as a low-frequency level, Roos and colleagues (2019) used a data-driven latent variable modeling approach that revealed three common patterns of cocaine use during treatment: abstinence, low-frequency use (approximately 1 day/week) and persistent frequent use (approximately 4 days/week). Individuals who demonstrated a low-frequency use pattern reported similar psychosocial functioning at follow-ups compared to those who were abstinent (Roos, Nich, Mun, Mendonca, et al., 2019).

Building from this work, Roos and colleagues (2019) defined three cocaine use frequency levels: (1) abstinence (no past-month cocaine use), (2) low-frequency use (1–4 days of use/month), and (3) high-frequency use (5+ days of use/month). To examine reductions in cocaine use frequency levels from pre-treatment to the end of treatment (a 12-week period), three categorical outcomes were operationalized: (1) no change in cocaine use frequency or an increase in cocaine use frequency (low-frequency use to high-frequency use), (2) a one-level reduction in cocaine use frequency (high-frequency use to low-frequency use or low-frequency use to abstinence), or (3) a two-level reduction in cocaine use frequency (high-frequency use to abstinence). In a pooled dataset of seven randomized clinical trials (RCTs) for CUD, achieving at least a one-level reduction in cocaine frequency level from pre-treatment to the end of treatment as compared to no change or increase in cocaine frequency was associated with less cocaine use and better psychosocial functioning at 6- and 12-month follow-ups. Although attention was paid to the binary outcome of achieving at least a one-level reduction to identify “responders,” additional analyses indicated that results were similar across those who reduced from high-to-low frequency use (a one-level reduction) and those who reduced from high-to-abstinence (a two-level reduction). More recently, an individual-level meta-analysis of 13 pharmacotherapy trials for stimulant use disorder (cocaine or methamphetamine) used these categorical reductions in cocaine use frequency levels and showed that reductions from high-to-low frequency use were more achievable than abstinence (Aminesmaeili et al., 2023). In addition, reductions to either low-frequency use or abstinence (i.e., at least a one-level reduction) were associated with decreased craving, depression, and substance use severity, and increased psychosocial functioning (Aminesmaeili et al., 2023). These findings indicate the utility of categorical reductions in cocaine use frequency levels, and particularly the outcome of achieving at least a one-level reduction, as an endpoint in clinical trials of CUD treatment.

Current Study

This study extended these findings by leveraging data from two trials of telephone-based continuing care following CUD treatment (McKay et al., 2005; McKay, Van Horn, Lynch, et al., 2013). We examined the association between reductions in cocaine use frequency levels and functioning outcomes over longer follow-up periods (i.e., 24-months), which is an indicator of a meaningful endpoint (FDA, 2023; Kiluk et al., 2016). Specifically, we evaluated associations between the categorical reductions in cocaine use frequency levels from baseline to the 12-month follow-up and (1) concurrent consequences and psychosocial functioning outcomes at the 12-month follow-up and (2) prospective cocaine use, consequences, and psychosocial functioning outcomes at the 24-month follow-up.

We hypothesized that achieving at least a one-level reduction in cocaine use frequency would be associated with better outcomes compared to not changing or increasing cocaine use frequency levels. Further, we hypothesized that among those with high-frequency cocaine use at baseline, those who reduced their use to low-frequency would evidence similar substance use and psychosocial functioning outcomes to those who achieved abstinence.

2. Methods

2.1. Participants and Data Sources

We conducted secondary data analysis of two RCTs (N=445) that evaluated telephone-based continuing care following CUD treatment. Detailed information on study procedures, inclusion/exclusion criteria, and participant eligibility (e.g., via a CONSORT diagram) have been previously reported (McKay et al., 2005; McKay, Van Horn, Lynch, et al., 2013). Aims, hypotheses, and statistical analyses were pre-registered: https://osf.io/254ft. Demographic data for participants across both studies can be found in Table 1.

Table 1.

Demographic Information for Participants (N=445)

Total Study 1 Study 2
N (%) or Mean (SD) N (%) or Mean (SD) N (%) or Mean (SD)
Sample size 445 (100%) 207 (46.5%) 238 (53.5%)
Male Sex 345 (77.5%) 165 (79.7%) 180 (75.6%)
Age 42.18 (7.69) 40.64 (7.67) 43.55 (7.47)
Years of education 11.86 (1.79) 12.23 (1.77) 11.53 (1.74)
Racial/ethnic identity
 Black 385 (86.5%) 175 (84.5%) 210 (88.2%)
 White 48 (10.8%) 29 (14.0%) 19 (8.0%)
 Puerto Rican 6 (1.3%) 2 (1.0%) 4 (1.7%)
 Other Hispanic 2 (0.4%) 0 (0.0%) 2 (0.8%)
 Native 1 (0.2%) 1 (0.5%) 0 (0.0%)
 Hispanic Mexican 1 (0.2%) 0 (0.0%) 1 (0.4%)
 Hispanic Cuban 1 (0.2%) 0 (0.0%) 1 (0.4%)
 Missing 1 (0.2%) 0 (0.0%) 1 (0.4%)
Marital status
 Never married 201 (45.2%) 80 (38.6%) 121 (50.8%)
 Divorced 84 (18.9%) 43 (20.8%) 41 (17.4%)
 Separated 82 (18.4%) 46 (22.2%) 36 (15.1%)
 Married 54 (12.1%) 33 (15.9%) 21 (8.8%)
 Widowed 10 (2.2%) 5 (2.4%) 5 (2.1%)
 Unmarried couple 12 (2.7%) NA 12 (5.0%)
 Missing 2 (0.4%) 0 (0.0%) 2 (0.8%)
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Note: Participants in Study 2, but not Study 1, were able to endorse their marital status as “a member of an unmarried couple.”

The first study was an RCT of 359 adults with cocaine and/or alcohol dependence who received 12 weeks of standard continuing care, relapse prevention, or telephone monitoring (McKay et al., 2005) following one month of intensive outpatient treatment (IOP). For the present study, we used data collected at baseline (collected at the end of IOP) and 12- and 24-month follow-ups. Given the focus of the present analysis on cocaine use frequency levels, we only included individuals who met the criteria for lifetime cocaine dependence and reported at least one day of cocaine use during the period we used to assess baseline cocaine use (see section 2.2.1), rendering a sample size of 207 from this trial.

The second RCT enrolled 321 participants in intensive outpatient treatment for cocaine dependence and randomized participants after 3–4 weeks of IOP to standard continuing care, telephone monitoring and counseling, or telephone monitoring and counseling plus incentives; the two telephone monitoring groups received up to 24 months of continuing care, with decreased frequency of monitoring and counseling with time (McKay, Van Horn, Lynch, et al., 2013). Consistent with the first trial, we used data collected at baseline (collected in weeks 3–4 of IOP) and 12- and 24-month follow-ups. Although all participants in this second RCT met the criteria for lifetime DSM-IV cocaine dependence, we excluded participants with no days of cocaine use during the baseline period, rendering a sample size of 238 from this trial.

2.2. Measures

All measures described below were administered in both RCTs.

2.2.1. Cocaine Use and Cocaine Use Frequency Levels

The Timeline Follow-Back (TLFB), a calendar-based method, was used to assessed daily cocaine use at baseline and 12- and 24-month follow-ups (Miller, 1996; Sobell & Sobell, 1995). Participants completed the TLFB for a six-month period at baseline, a three-month period at the 12-month follow-up, and a six-month period at the 24-month follow-up. For baseline cocaine use frequency, we used a variable representing the 30-day period six months before randomization because participants were in abstinence-based inpatient and IOP in the month(s) immediately before randomization, thus limiting cocaine use in these months. To illustrate this point, Supplementary Table 1 presents cocaine use frequency levels and treatment characteristics (also collected on the TLFB) for each month during the six months prior to baseline among all participants with cocaine dependence (N=602). Rates of abstinence from cocaine use in the month before baseline (68.1%) and two months before baseline (36.9%) were high, as were rates of being in any controlled environment (1 month before baseline: 30.7%; 2 months before baseline: 42.9%), receiving inpatient drug treatment (1 month before baseline: 26.6%; 2 months before baseline: 36.0%), and receiving outpatient drug treatment (1 month before baseline: 98.0%; 2 months before baseline: 55.0%). The period representing the 30-day period six months before baseline evidenced the lowest rates of abstinence (26.1%), the highest rates of high-frequency cocaine use (61.1%), and the lowest rates of receiving any drug treatment (inpatient: 6.5%; outpatient: 26.1%). Accordingly, using this period as our “baseline” cocaine use frequency increased our sample size for analyses focusing on those with high-frequency cocaine use at baseline, and decreased the likelihood that participants were receiving treatment during this period. For the 12- and 24-month follow-up assessments, we measured cocaine use frequency in the 30 days before the assessment. Thus, these periods were 18 and 30 months following this baseline. However, we will use the terms 12- and 24-month follow-ups throughout the manuscript, given these refer to the measurement periods after the baseline assessment.

Cocaine use frequency levels were defined as (1) abstinence (no past-month cocaine use), (2) low-frequency use (1–4 days of use), and (3) high-frequency use (5+ days of use). To better characterize these cocaine frequency levels, average cocaine use days by cocaine frequency level for each assessment are presented in Supplementary Table 2. Categorical changes in cocaine use frequency levels from baseline to the 12-month follow-up were defined as: (1) no change in cocaine use frequency or an increase in cocaine use frequency (low-to-high frequency use), (2) a one-level reduction in cocaine use frequency (high-to-low frequency use or low- to-abstinence), or (3) a two-level reduction in cocaine use frequency (high-to-abstinence).

2.2.2. Functional Outcomes of Reductions in Cocaine Use Frequency Levels

The studies administered the Addiction Severity Index (ASI) at all time points of interest (McLellan et al., 1992). The ASI is a well-established, semi-structured interview measuring problem severity across seven subscales: medical, employment, alcohol use, drug use, legal, family/social, and psychological. For these seven subscales, composite scores are calculated, which range from 0–1, with higher scores indicating greater problems.

The Inventory of Drug Use Consequences (InDUC) is a 50-item self-report questionnaire that assesses consequences of substance use. Although this measure includes consequences in five domains (i.e., physical, social, intrapersonal, impulse control, and interpersonal consequences), prior work indicates that these subscales are highly interdependent and that items load strongly onto a single latent factor (Blanchard et al., 2003). At the baseline assessment, participants completed the lifetime version (response options include 0=no and 1=yes; range=0–50; ω coefficient=0.878), and at the 12- and 24-month post-treatment assessments, participants completed the recent version assessing consequences in the past three months (response options include 0=never to 3=daily or almost daily; range=0–150; ωs≥0.986) (Tonigan & Miller, 2002).

2.3. Statistical Analysis

The study analyzed descriptive statistics in IBM SPSS Statistics (Version 28) to characterize cocaine use frequency levels at baseline and the 12-month follow-up and categorical reductions in cocaine use frequency levels over this period.

Separate multivariable linear regression analyses examined associations between reductions in cocaine use frequency levels (focal independent variable) and various outcomes at the 12- and 24-month follow-up assessments, including: ASI drug, alcohol, psychological, medical, legal, employment, and family domains; InDUC drug use consequences; and TLFB cocaine use days (only at the 24-month follow-up). All models controlled for age, sex, race, years of education, and trial, and baseline values of outcomes of interests. These regression analyses were conducted both in the entire sample, wherein we examined achieving at least a one-level reduction in cocaine use frequency as the focal independent variable, and among those with high-frequency cocaine use at baseline, wherein we examined all three reduction levels (i.e., no change or increase, one-level reduction, two-level reduction) as the focal independent variables. To determine effect sizes, we computed Hedge’s g for mean differences in outcomes by reduction level at the 12- and 24-month follow-ups (conducted in SPSS Version 28).

Sensitivity analyses confirmed cocaine abstinence at the 12-month follow-up with urine toxicology results. For these, we re-estimated the regression models by using two different methods for categorizing discrepancies between self-reported cocaine abstinence and cocaine-positive urine toxicology results: 1) coding the cocaine reduction level as “missing” for those with missing urine toxicology and coding the cocaine reduction level as “no change or increase” for those who reported cocaine abstinence but provided a cocaine-positive urine toxicology result, and 2) coding the cocaine reduction level as “missing” both for those with missing urine toxicology and for those who reported cocaine abstinence but provided a cocaine-positive urine toxicology result.

We used a false discovery rate procedure (Benjamini & Hochberg, 1995) to adjust the p-values for multiple testing within each set of analyses (i.e., multinomial logistic regression analyses, linear regression analyses in the entire sample, linear regression analyses in the high-frequency use sample). In addition, code and output files for all regression analyses are available at https://osf.io/d7uqr/.

3. Results

3.1. Cocaine Frequency Levels

Descriptive statistics for cocaine frequency levels at baseline (i.e., the 30-day period six months before randomization) and at the 12-month follow-up are presented in Table 2. Most participants reported high-frequency cocaine use at baseline (n=368; 82.7%) and cocaine abstinence at the 12-month follow-up (n=291; 72.4%). Among those who completed the 12-month follow-up (n=392), the most common frequency level reduction from baseline to the 12-month follow-up was from high-frequency use at baseline to abstinence (n=243; 62.0%), which reflects a two-level reduction. An additional 21.2% (n=83) reported a one-level reduction, including either high-frequency use to low-frequency use (n=35; 8.9%) or low-frequency use to abstinence (n=48; 12.2%). Lastly, 16.8% of participants (n=66) did not change or increased their cocaine frequency level.

Table 2.

Cocaine Frequency Levels at Baseline and the 12-Month Follow-Up and Level Reductions Over Time

Cocaine Frequency Level Baseline n (%) 12-month follow-up n (%)
Abstinence (0 cocaine use days in the past month) 0 (0%) 291 (74.2%)
Low frequency (1–4 cocaine use days in past month) 77 (17.3%) 48 (12.2%)
High frequency (5+ cocaine use days in the past month 368 (82.7%) 53 (13.5%)
Change in Cocaine Frequency Level from Baseline to 12-month Follow-Up n (%)
Increase 1 Level 9 (2.3%)
No change 57 (14.5%)
Decrease 1 Level 83 (21.2%)
Decrease 2 Levels 243 (62.0%)
All Categories of Level Reduction from Baseline to 12-Month Follow-Up n (%)
High Freq Baseline -> High Freq 12-Month 44 (11.2%)
High Freq Baseline -> Low Freq 12-Month 35 (8.9%)
High Freq Baseline -> Abstinence 12-Month 243 (62.0%)
Low Freq Baseline -> High Freq 12-Month 9 (2.3%)
Low Freq Baseline -> Low Freq 12-Month 13 (3.3%)
Low Freq Baseline -> Abstinence 12-Month 48 (12.2%)
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Note. The sample size at baseline was 445. The sample size for the 12-month follow-up was 392 (i.e., those with TLFB data at the 12-month follow-up). The percentages reported for the 12-month follow-up, including reductions in cocaine use frequency levels, are only considering those with complete data.

3.2. At Least a One-Level Reduction in Frequency Level as a Predictor of Outcomes

Table 3 presents the associations between achieving at least a one-level reduction in cocaine use frequency levels from baseline to the 12-month follow-up, compared to no change or increase in frequency levels, with outcomes at the 12-and 24-month follow-up. At the 12-month follow-up, those who achieved at least a one-level reduction had lower problems in the ASI alcohol, drug, and legal domains and fewer drug use consequences than those who reported no change or an increase in cocaine use frequency levels. These effects largely remained at the 24-month follow-up, except that the association between achieving a one-level reduction and lower problems in the ASI legal domain was not statistically significant. Descriptive statistics for 12- and 24-month follow-up outcomes by each cocaine frequency level reduction category in the entire sample are reported in Supplementary Table 3.

Table 3.

At Least One-Level Reduction in Cocaine Frequency Level as a Predictor of 12- and 24-Month Outcomes in the Full Sample (n=445)

12-Month Outcomes
No Change or Increase (n=58) At Least One-Level Reduction (n=294)
Variable Mean (SD) B (SE) Hedge’s g
ASI Alcohol 0.23 (0.22) 0.10 (0.16) −0.26 (0.05)* −0.76
ASI Drug 0.19 (0.11) 0.07 (0.08) −0.46 (0.04)* −1.31
ASI Employment 0.70 (0.27) 0.74 (0.28) −0.06 (0.04) 0.13
ASI Legal 0.13 (0.21) 0.04 (0.12) −0.22 (0.05)* −0.63
ASI Medical 0.41 (0.39) 0.35 (0.36) −0.04 (0.05) −0.15
ASI Psychological 0.28 (0.23) 0.20 (0.22) −0.11 (0.05) −0.35
ASI Social 0.24 (0.22) 0.15 (0.19) −0.12 (0.05) −0.47
Drug Use Consequences 57.53 (31.19) 20.59 (31.70) −0.38 (0.05)* −1.16
24-Month Outcomes
No Change or Increase (n=61) At Least One-Level Reduction (n=289)
ASI Alcohol 0.19 (0.20) 0.11 (0.19) −0.14 (0.05)* −0.42
ASI Drug 0.13 (0.11) 0.07 (0.10) −0.19 (0.05)* −0.54
ASI Employment 0.71 (0.24) 0.72 (0.28) −0.06 (0.04) 0.04
ASI Legal 0.10 (0.21) 0.04 (0.11) −0.12 (0.05) −0.44
ASI Medical 0.32 (0.33) 0.36 (0.39) 0.04 (0.05) 0.11
ASI Psychological 0.27 (0.25) 0.19 (0.21) −0.12 (0.05) −0.38
ASI Social 0.19 (0.20) 0.15 (0.21) −0.04 (0.05) −0.18
Drug Use Consequences 41.30 (36.81) 22.07 (34.71) −0.23 (0.05)* −0.55
Cocaine Use Days 5.34 (8.60) 1.46 (4.85) −0.24 (0.05)* −0.68
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Note.

*

p≤0.014 for 12-month outcomes, p≤0.009 for 24-month outcomes

SD=standard deviation, B=standardized regression coefficient, SE=standard error; descriptive statistics and Hedge’s g represent observed values from those with available data, with reported sample sizes representing the number of participants with any available data on the follow-up outcomes; the linear regression analyses used multiple imputation for missing data (n=445); no change refers to individuals who were in the high-frequency group at baseline and remained in the high-frequency group at the 12-month follow-up OR those who were in the low-frequency group at baseline and increase to high-frequency at the 12-month follow-up, at least a one-level reduction refers to those who reduced one (i.e., high-to-low, low-to-abstinence) or two frequency levels (i.e., high-to-abstinence) from baseline to the 12-month follow-up.

1.1. Reductions in Cocaine Use Levels and Outcomes among those in the High-Frequency Level at Baseline

The associations between reductions in cocaine use frequency levels and psychosocial outcomes at the 12-month and 24-month follow-ups among those in the high-frequency level at baseline are in Table 4. Compared to those who reported no change in their cocaine use frequency level from the baseline to the 12-month follow-up (i.e., high-to-high), those who reduced two levels to abstinence had lower problem severity in the ASI alcohol use, drug use, and legal domains and fewer drug use consequences at the 12-month follow-up. Compared to those who reported no change in their cocaine use frequency level, those who reduced one level to low-frequency use had lower problem severity in the ASI drug use domain. Compared to those who reduced one level to low-frequency use, those who reduced two levels to abstinence had lower problem severity in the ASI alcohol and drug use domains and fewer drug use consequences.

Table 4.

Comparison of 12-Month and 24-Month Follow-Up Outcomes by Cocaine Frequency Level Reductions Among those with High-Frequency Use at Baseline (n=368)

12-Month Outcomes
No Change (n=38) One-Level Reduction (n=35) Two-Level Reduction (n=215) Two-Level Reduction vs. No Change or Increase One-Level Reduction vs. No Change or Increase One-Level Reduction vs. Two-Level Reduction
Variable Mean (SD) B (SE) Hedge’s g B (SE) Hedge’s g B (SE) Hedge’s g
ASI Alcohol 0.23 (0.24) 0.18 (0.20) 0.09 (0.14) −0.35 (0.07)* −0.91 −0.12 (0.07) −0.24 0.14 (0.05)* 0.62
ASI Drug 0.19 (0.12) 0.15 (0.07) 0.06 (0.08) −0.61 (0.06)* −1.57 −0.19 (0.06)* −0.40 0.25 (0.05)* 1.22
ASI Employment 0.71 (0.26) 0.70 (0.29) 0.73 (0.28) −0.08 (0.06) 0.10 −0.04 (0.06) −0.01 0.02 (0.04) −0.10
ASI Legal 0.12 (0.18) 0.08 (0.15) 0.04 (0.12) −0.25 (0.07)* −0.63 −0.10 (0.07) −0.26 0.08 (0.05) 0.31
ASI Medical 0.32 (0.40) 0.34 (0.37) 0.34 (0.37) 0.00 (0.07) 0.07 0.01 (0.07) 0.07 0.01 (0.05) 0.00
ASI Psychological 0.26 (0.24) 0.25 (0.23) 0.19 (0.21) −0.16 (0.07) −0.36 −0.07 (0.06) −0.05 0.05 (0.05) 0.31
ASI Social 0.20 (0.21) 0.22 (0.22) 0.14 (0.18) −0.10 (0.07) −0.36 0.00 (0.07) 0.09 0.08 (0.05) 0.46
Drug Use Consequences 57.36 (31.06) 42.63 (23.65) 16.64 (30.63) −0.49 (0.07)* −1.33 −0.15 (0.07) −0.53 0.22 (0.05)* 0.87
24-Month Outcomes
No Change (n=40) One-Level Reduction (n=33) Two-Level Reduction (n=216) Two-Level Reduction vs. No Change or Increase One-Level Reduction vs. No Change or Increase One-Level Reduction vs. Two-Level Reduction
ASI Alcohol 0.19 (0.21) 0.15 (0.18) 0.10 (0.19) −0.17 (0.07) −0.43 −0.06 (0.07) −0.17 0.06 (0.06) 0.26
ASI Drug 0.15 (0.12) 0.11 (0.09) 0.07 (0.10) −0.30 (0.07)* −0.77 −0.11 (0.07) −0.37 0.11 (0.06) 0.39
ASI Employment 0.71 (0.22) 0.76 (0.26) 0.71 (0.28) −0.09 (0.06) 0.01 0.02 (0.06) 0.22 0.09 (0.05) 0.18
ASI Legal 0.14 (0.24) 0.08 (0.13) 0.04 (0.11) −0.27 (0.07)* −0.76 −0.11 (0.07) −0.31 0.09 (0.05) 0.39
ASI Medical 0.32 (0.31) 0.34 (0.38) 0.34 (0.38) 0.01 (0.07) 0.05 0.02 (0.07) 0.05 0.02 (0.06) −0.01
ASI Psychological 0.26 (0.25) 0.22 (0.24) 0.17 (0.20) −0.17 (0.07) −0.42 −0.08 (0.07) −0.14 0.05 (0.06) 0.26
ASI Social 0.19 (0.19) 0.25 (0.24) 0.13 (0.19) −0.09 (0.07) −0.31 0.06 (0.07) −0.29 0.13 (0.06) 0.61
Drug Use Consequences 43.70 (36.34) 35.25 (33.21) 19.12 (33.52) −0.35 (0.07)* −0.72 −0.11 (0.07) −0.24 0.15 (0.06) 0.48
Cocaine Use Days 7.33 (9.99) 2.52 (3.98) 1.45 (5.31) −0.39 (0.07)* −0.94 −0.22 (0.07)* −0.61 0.07 (0.06) 0.21
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Note.

*

p≤0.008 for 12-month outcomes, p≤0.004 for 24-month outcomes

SD=standard deviation, B=standardized regression coefficient, SE=standard error; descriptive statistics and Hedge’s g represent observed values from those with available data, with reported sample sizes representing the number of participants with any available data on the follow-up outcomes; the linear regression analyses used multiple imputation for missing data (n=368); no change refers to individuals who were in the high-frequency group at baseline and remained in the high-frequency group at the 12-month follow-up, a one-level reduction refers to those who reduced from high-frequency use at baseline to low-frequency use at the 12-month follow-up, a two-level reduction refers to those who reduced from the high-frequency use at baseline to abstinence at the 12-month follow-up.

In prospective analyses, compared to those who reported no change in their cocaine use frequency level from the baseline to the 12-month follow-up (i.e., high-to-high), those who reduced two levels to abstinence had lower problem severity in the ASI drug use and legal domains and fewer drug use consequences and cocaine use days at the 24-month follow-up. Compared to those who continued to use at a high-frequency level, those who reduced one level from high-frequency use at baseline to low-frequency use at the 12-month follow-up had fewer cocaine use days at the 24-month follow-up. There were no statistically significant differences at the 24-month follow-up between those who reduced from high-frequency use to low-frequency use (i.e., a one-level reduction) and those who reduced from high-frequency use to abstinence (i.e., a two-level reduction).

1.2. Sensitivity Analyses

Fifty-nine participants had missing urine toxicology results at the 12-month follow-up but had TLFB data for this assessment; these participants were coded as missing in both sensitivity analysis coding schemes. An additional 44 participants reported abstinence at the 12-month follow-up on the TLFB but had a cocaine-positive urine toxicology result at the same assessment; these participants were coded as “no change” in sensitivity analysis coding #1 (thus increasing the sample size of this group from 66 in primary analyses to 101 in the analyses with sensitivity analysis coding #1) and missing in sensitivity analysis coding #2. Results for sensitivity analysis based on urine toxicology coding #1 (i.e., coding the cocaine reduction level as missing for those with missing urine toxicology and as “no change” for those who reported abstinence, but a positive urine toxicology result) are presented in Supplementary Tables 4 and 5. Results for sensitivity analysis coding #2 (i.e., coding the cocaine reduction level as missing for those with missing urine toxicology and for those who reported abstinence but provided a positive urine toxicology result) are in Supplementary Tables 6 and 7. Results across both coding systems were largely consistent with the results of the primary analyses, with some minor exceptions due to those who did not change cocaine use frequency levels (i.e., high-to-high) demonstrating lower problem severity across outcomes examined using sensitivity analysis coding #1 than in primary analyses. Thus, it likely produces statistical bias to categorize those with a cocaine-positive urine toxicology result as a high-frequency use level when the participant is self-reporting abstinence.

1.3. Post-Hoc (Not Pre-Registered) Analyses Examining Stability of Cocaine Frequency Levels From the 12-Month to 24-Month Follow-Up among those in the High-Frequency Level at Baseline

Given that there were no statistically significant differences in 24-month psychosocial outcomes between those who reduced from high-to-low frequency use (i.e., a one-level reduction) and those who reduced from high-to-abstinence (i.e., a two-level reduction), we examined the stability of cocaine frequency levels from the 12- to 24-month follow-up among those who reported high-frequency cocaine use at baseline to determine if these two groups reported similar levels of cocaine use at 24-months. Among those who reported abstinence at 12 months, most (n=182; 84.3%) were still abstinent at 24 months, though some transitioned to low-frequency (n=15; 6.9%) and high-frequency (n=19; 8.8%) use. Half of the individuals who reported low-frequency use at 12 months reported abstinence at 24 months (n=18; 54.5%), followed by low-frequency use (n=9; 27.3%) and high-frequency use (n=6; 18.2%). Those who reported high-frequency cocaine use at 12 months were split across the three cocaine frequency levels at 24 months (abstinence: n=14; 35.0%; low-frequency use: n=9; 22.5%; high-frequency use: n=17; 42.5%). These rates did not appear to differ substantially by study (results not reported), despite those in Study 2 receiving continuing care until the 24-month follow-up.

2. Discussion

This study sought to replicate and extend prior work examining the validity of categorical reductions in cocaine use frequency levels (Aminesmaeili et al., 2023; Roos, Nich, Mun, Babuscio, et al., 2019) by examining associations between these reductions and outcomes measured over longer-term follow-up periods. Individuals who achieved at least a one-level reduction in cocaine use frequency levels from baseline to the 12-month follow-up had more favorable outcomes concerning cocaine use and consequences concurrently at the 12-month follow-up and prospectively at the 24-month follow-up. Effect sizes for statistically significant results were in the medium-to-large range. These results suggest that categorical reductions in cocaine use frequency levels are meaningfully associated with cocaine use and related consequences up to two years following completion of IOP.

Analyses conducted among participants in the high-frequency group at baseline showed that participants who reduced from high-to-low frequency use had greater problem severity (i.e., on ASI drug and alcohol composites and drug use consequences) than those who reduced to abstinence at the 12-month follow-up. However, the high-to-low frequency group had lower ASI drug scores at the 12-month follow-up and less cocaine use at the 24-month follow-up than those who continued to use at a high-frequency level. Our findings are in contrast with the prior study by Roos and colleagues (2019) which showed those in the high-to-low frequency group had similar outcomes compared to those in the high-to-abstinence group at the end of treatment (Roos, Nich, Mun, Babuscio, et al., 2019). In addition, a recent individual-level meta-analysis showed that the high-to-low frequency group demonstrated better outcomes than the group who did not reduce their use on a range of outcomes (e.g., ASI drug, alcohol, legal subscales; depression severity; substance use severity; global improvement; craving), though the group who achieved abstinence was functioning better than both of the groups with continued stimulant use. Despite few statistically significant differences between the high-to-low and high-to-high frequency groups, descriptive statistics and Hedge’s g reported in Table 4 indicate that the high-to-low frequency group had lower ASI alcohol and legal scores, drug use consequences, and cocaine use days at the 12- and 24-month follow-ups, with effect sizes in the small-to-medium range (though not statistically significant, except cocaine use days at the 24-month follow-up). Only 9% (n=35) of the present sample reported high-to-low frequency use and most participants were abstinent at the 12-month follow-up, whereas prior work showed that participants were more likely to achieve low-frequency use than they were to achieve abstinence (Aminesmaeili et al., 2023; Roos, Nich, Mun, Babuscio, et al., 2019). Taken together with our decision to use a stringent p-value for indicating statistical significance, the small sample size of the high-to-low-frequency group in the present analysis may have limited our ability to identify differences in functional outcomes between this group and the high-to-high frequency group. Additional research is needed to further evaluate the clinical benefits of reducing cocaine use from high-to-low frequency, including the impacts of this reduction on physical health (Knox et al., 2020; Witkiewitz et al., 2018), healthcare costs (Aldridge et al., 2016), and cardiovascular, neuroimaging, and molecular biomarkers of cocaine use (pending further validation of several biomarkers) (Bough et al., 2014; McCann et al., 2015).

Notably, the high-to-abstinence group did not differ from the high-to-low-frequency group on ASI employment, legal, medical, psychological, and social composites at the 12-month follow-up and there were no differences between these two groups at the 24-month follow-up. These findings are consistent with previous work indicating weak associations between drug use and psychosocial functioning (Kiluk et al., 2019). Given that clients most commonly report improved quality of life as a treatment goal (Peers Speak Out, 2021), there is a need for endpoints that consider psychosocial functioning (with or without substance use) (Kiluk et al., 2017; Votaw & Witkiewitz, 2022) and interventions that directly seek to increase natural, non-drug reinforcers, such as employment, hobbies, and social support (McKay, 2017).

Additionally, our study demonstrated that most individuals in the high-to-low-frequency group were abstinent at the 24-month follow-up. This finding illustrates the nature of recovery as a dynamic process as opposed to a static endpoint (Maisto et al., 2016), with many individuals transitioning between low- and high-risk patterns of use following treatment (Maisto et al., 2020), including transitions between cocaine use and abstinence (McKay, Van Horn, Rennert, et al., 2013). Categorical reductions in cocaine use offer a snapshot rather than a full depiction of a dynamic recovery process. Nevertheless, given the need to balance endpoints that are practical, meaningful, and representative of recovery processes, the present analysis indicates that considering a one-level reduction in cocaine use frequency levels a “successful” endpoint is likely more robust to this limitation than continuous abstinence.

Importantly, most participants in this study received 4 weeks or more of intensive treatment and aftercare interventions up to 24 months in duration, while most RCTs and episodes of care comprise short-term outpatient treatment. Providers in traditional, abstinence-oriented intensive treatment settings may be less likely to support low-frequency use as a recovery goal than those in outpatient treatment settings (Rosenberg et al., 2020), which might partially explain the high rates of abstinence that were observed in the present sample. In addition, the time frame for assessing reductions in cocaine use frequency levels in previous work covered 12 weeks (Roos, Nich, Mun, Babuscio, et al., 2019), which reflects a common duration of treatment in RCTs, whereas this study covered 18 months. One implication of this methodological difference is that reductions in cocaine use frequency levels is a valid and meaningful endpoint across both short- and long-term periods, which is important given treatment episodes in naturalistic settings may be longer than 12 weeks.

To determine who is most likely to benefit from low-frequency cocaine use and achieve success with this treatment goal, there is a need to examine predictions of reductions in cocaine use frequency levels and moderators of the associations between reductions in cocaine use frequency and functional outcomes. Evidence from the alcohol research field indicates that lower drug use severity and negative mood symptoms might be associated with a greater likelihood of achieving a “successful” non-abstinent outcome (Votaw & Witkiewitz, 2022). Additionally, those in treatment settings with greater support for non-abstinence goals and who have less internalized stigma might also be more likely to achieve low-frequency cocaine use and experience benefits associated with this outcome. Social determinants of health and systemic discrimination might also impact the ability to achieve and benefit from low-frequency cocaine use among historically marginalized populations. For example, many Black communities experience targeted and disproportionate legal consequences for any substance use, including more arrests and harsher sentences (Alexander, 2011; Substance Abuse and Mental Health Services Administration, 2020), which might impede the ability to experience functional benefits from low-frequency cocaine use. Likewise, people of color may also receive less support in pursuing individual recovery goals (Substance Abuse and Mental Health Services Administration, 2020), particularly given evidence of racial disparities in other harm reduction interventions (e.g., receipt of naloxone) (Kinnard et al., 2021). Investigating such health disparities will be an important line of future research to determine if interventions are needed to increase provider knowledge of the benefits associated with harm reduction interventions and provide guidance on helping patients from historically marginalized backgrounds select individualized recovery goals.

2.1. Limitations

Results should be interpreted in the context of limitations. First, we used the 30-day period in the six months before randomization to establish the baseline cocaine use frequency level. This decision stemmed from the fact that a substantial number of participants underwent intensive treatment in the month(s) immediately before randomization and were abstinent over this period. However, given that this period was distal to the baseline assessment, cocaine use days in the 30-day period six months before randomization might be impacted by recall bias. Second, we pooled data across two RCTs with differing durations of continuing care to increase our sample size, which could have affected the results. The two studies included in the present review were also conducted in the early-to-mid 2000s. Since this time, drug supply and trends in drug use have substantially shifted, with increased prevalence of overdose deaths related to fentanyl and cocaine (Nolan et al., 2019). There is a need to replicate this work in contemporary samples and examine the impacts of non-abstinence endpoints on outcomes such as overdose risk and co-occurring substance use and use disorders (Knox et al., 2020).

We examined ASI outcomes to replicate prior work on cocaine use frequency levels and ASI outcomes (Roos, Nich, Mun, Babuscio, et al., 2019) and because this measure was included in both studies. However, research on the psychometric properties of the ASI has yielded equivocal findings regarding its reliability and validity (Mäkelä, 2004), which may have partly contributed to some null findings. Future research is needed to examine the associations between reduction in cocaine use frequency levels and other outcomes such as physical health, healthcare costs, and risk behaviors for infectious disease, as well as other previously mentioned outcomes (e.g., overdose, other substance use). Similarly, we chose to use the three cocaine frequency levels (and reductions in these levels) that have been examined in prior work (Aminesmaeili et al., 2023; Roos, Nich, Mun, Babuscio, et al., 2019), but an additional cocaine frequency level capturing “moderate” cocaine use might also have clinical utility. In prior work, “moderate-frequency” cocaine use (5–14 days of use in the past month) was associated with better psychosocial functioning than “high-frequency” cocaine use (15+ days of use), though to a lesser extent than low-frequency use and abstinence (Kiluk et al., 2017). Of note, the mean days of cocaine use among those in the high-frequency use group at the 12-month follow-up was 13.45 (SD=8.08) (see Supplementary Table 2), which would be considered “moderate” in this prior work. Future research considering a moderate-frequency level in the validation of categorical reductions in cocaine use frequency would provide insight into the utility of this frequency level.

2.2. Conclusions

Identification of non-abstinent endpoints for drug use disorders is essential to be more responsive to some clients’ goals (Paquette et al., 2022; Votaw & Witkiewitz, 2022) and will likely aid in identifying effective psychosocial and pharmacological interventions (Brandt et al., 2021; Volkow et al., 2018). Results of the present analysis add to a prior study (Roos, Nich, Mun, Babuscio, et al., 2019) indicating at least a one-level reduction in cocaine use frequency levels demonstrated associations with functional outcomes at the 12- and 24-month follow-ups and reductions in cocaine use were largely sustained at the longer-term follow-up. Future directions to further validate this endpoint include: 1) examining moderators of the associations between reductions in cocaine use frequency levels and psychosocial functioning outcomes, 2) evaluating other functional outcomes (e.g., physical health, healthcare costs, overdose risk, other substance use), and 3) determining how this endpoint compares to abstinence in detecting active treatment effects.

Supplementary Material

1

Highlights.

  • Reduced cocaine frequency predicts long-term drug-related and psychosocial outcomes

  • Reductions in cocaine use frequency were sustained over long-term follow-ups

  • Low-frequency cocaine use outcomes do not forebode worsening outcomes

  • Low-frequency cocaine use might be a precursor to later abstinence for some people

Footnotes

Effort for this analysis was supported, in part, by NIAAA grants F31AA029266 (Votaw, PI), R01DA020623 (McKay, PI), R01DA10262 (McKay, PI), R01AA10341 (McKay, PI), R01AA025539 (Witkiewitz, PI), R01AA022328 (Witkiewitz, PI), T32AA018108 (Witkiewitz, PI), and K23AT011342 (Roos, PI). All authors declare no conflicts of interest

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References

  1. Aldridge AP, Zarkin GA, Dowd WN, & Bray JW (2016). The Relationship Between End-of-Treatment Alcohol Use and Subsequent Healthcare Costs: Do Heavy Drinking Days Predict Higher Healthcare Costs? Alcoholism: Clinical and Experimental Research, 40(5), 1122–1128. 10.1111/acer.13054 [DOI] [PMC free article] [PubMed] [Google Scholar]
  2. Alexander M (2011). The New Jim Crow: Mass Incarcertation in the Age of Colorblindness. New Press. [Google Scholar]
  3. Aminesmaeili M, Farokhnia M, Susukida R, Leggio L, Johnson RM, Crum RM, & Mojtabai R (2023). Reduced drug use as an alternative valid outcome in individuals with stimulant use disorders: Findings from 13 multisite randomized clinical trials. Addiction, n/a(n/a). 10.1111/add.16409 [DOI] [PMC free article] [PubMed] [Google Scholar]
  4. Benjamini Y, & Hochberg Y (1995). Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing. Journal of the Royal Statistical Society: Series B (Methodological), 57(1), 289–300. 10.1111/j.2517-6161.1995.tb02031.x [DOI] [Google Scholar]
  5. Blanchard KA, Morgenstern J, Morgan TJ, Lobouvie EW, & Bux DA (2003). Assessing consequences of substance use: Psychometric properties of the inventory of drug use consequences. Psychology of Addictive Behaviors: Journal of the Society of Psychologists in Addictive Behaviors, 17(4), 328–331. 10.1037/0893-164X.17.4.328 [DOI] [PubMed] [Google Scholar]
  6. Bough KJ, Amur S, Lao G, Hemby SE, Tannu NS, Kampman KM, Schmitz JM, Martinez D, Merchant KM, Green C, Sharma J, Dougherty AH, & Moeller FG (2014). Biomarkers for the development of new medications for cocaine dependence. Neuropsychopharmacology: Official Publication of the American College of Neuropsychopharmacology, 39(1), 202–219. 10.1038/npp.2013.210 [DOI] [PMC free article] [PubMed] [Google Scholar]
  7. Brandt L, Chao T, Comer SD, & Levin FR (2021). Pharmacotherapeutic strategies for treating cocaine use disorder—What do we have to offer? Addiction, 116(4), 694–710. 10.1111/add.15242 [DOI] [PMC free article] [PubMed] [Google Scholar]
  8. Carroll KM, Kiluk BD, Nich C, DeVito EE, Decker S, LaPaglia D, Duffey D, Babuscio TA, & Ball SA (2014). Toward empirical identification of a clinically meaningful indicator of treatment outcome: Features of candidate indicators and evaluation of sensitivity to treatment effects and relationship to one year follow up cocaine use outcomes. Drug and Alcohol Dependence, 137, 3–19. 10.1016/j.drugalcdep.2014.01.012 [DOI] [PMC free article] [PubMed] [Google Scholar]
  9. Carroll KM, Nich C, DeVito EE, Shi JM, & Sofuoglu M (2018). Galantamine and Computerized Cognitive Behavioral Therapy for Cocaine Dependence: A Randomized Clinical Trial. The Journal of Clinical Psychiatry, 79(1), 17m11669. 10.4088/JCP.17m11669 [DOI] [PMC free article] [PubMed] [Google Scholar]
  10. Falk DE, O’Malley SS, Witkiewitz K, Anton RF, Litten RZ, Slater M, Kranzler HR, Mann KF, Hasin DS, Johnson B, Meulien D, Ryan M, Fertig J, & for the Alcohol Clinical Trials Initiative (ACTIVE) Workgroup. (2019). Evaluation of Drinking Risk Levels as Outcomes in Alcohol Pharmacotherapy Trials: A Secondary Analysis of 3 Randomized Clinical Trials. JAMA Psychiatry, 76(4), 374. 10.1001/jamapsychiatry.2018.3079 [DOI] [PMC free article] [PubMed] [Google Scholar]
  11. Food and Drug Administration. (2023). Stimulant Use Disorders: Developing Drugs for Treatment (Draft Guidance). U.S. Department of Health and Human Services, Food and Drug Administration, Center for Drug Evaluation and Research (CDER). https://www.fda.gov/media/172703/download [Google Scholar]
  12. Hartwell EE, Feinn R, Witkiewitz K, Pond T, & Kranzler HR (2021). World Health Organization risk drinking levels as a treatment outcome measure in topiramate trials. Alcoholism: Clinical and Experimental Research, 45(8), 1664–1671. 10.1111/acer.14652 [DOI] [PMC free article] [PubMed] [Google Scholar]
  13. Kampman KM (2019). The treatment of cocaine use disorder. Science Advances, 5(10), eaax1532. 10.1126/sciadv.aax1532 [DOI] [PMC free article] [PubMed] [Google Scholar]
  14. Kiluk BD, Babuscio TA, Nich C, & Carroll KM (2017). Initial validation of a proxy indicator of functioning as a potential tool for establishing a clinically meaningful cocaine use outcome. Drug and Alcohol Dependence, 179, 400–407. 10.1016/j.drugalcdep.2017.07.020 [DOI] [PMC free article] [PubMed] [Google Scholar]
  15. Kiluk BD, Carroll KM, Duhig A, Falk DE, Kampman K, Lai S, Litten RZ, McCann DJ, Montoya ID, Preston KL, Skolnick P, Weisner C, Woody G, Chandler R, Detke MJ, Dunn K, Dworkin RH, Fertig J, Gewandter J, … Strain EC (2016). Measures of outcome for stimulant trials: ACTTION recommendations and research agenda. Drug and Alcohol Dependence, 158, 1–7. 10.1016/j.drugalcdep.2015.11.004 [DOI] [PMC free article] [PubMed] [Google Scholar]
  16. Kiluk BD, Fitzmaurice GM, Strain EC, & Weiss RD (2019). What defines a clinically meaningful outcome in the treatment of substance use disorders: Reductions in direct consequences of drug use or improvement in overall functioning? Addiction, 114(1), 9–15. 10.1111/add.14289 [DOI] [PMC free article] [PubMed] [Google Scholar]
  17. Kinnard EN, Bluthenthal RN, Kral AH, Wenger LD, & Lambdin BH (2021). The naloxone delivery cascade: Identifying disparities in access to naloxone among people who inject drugs in Los Angeles and San Francisco, CA. Drug and Alcohol Dependence, 225, 108759. 10.1016/j.drugalcdep.2021.108759 [DOI] [PubMed] [Google Scholar]
  18. Knox J, Scodes J, Witkiewitz K, Kranzler HR, Mann K, O’Malley SS, Wall M, Anton R, Hasin DS, & For the Alcohol Clinical Trials (ACTIVE) Workgroup. (2020). Reduction in World Health Organization Risk Drinking Levels and Cardiovascular Disease. Alcoholism: Clinical and Experimental Research, 44(8), 1625–1635. 10.1111/acer.14386 [DOI] [PMC free article] [PubMed] [Google Scholar]
  19. Maisto SA, Hallgren KA, Roos CR, Swan JE, & Witkiewitz K (2020). Patterns of transitions between relapse to and remission from heavy drinking over the first year after outpatient alcohol treatment and their relation to long-term outcomes. Journal of Consulting and Clinical Psychology, 88(12), 1119–1132. 10.1037/ccp0000615 [DOI] [PMC free article] [PubMed] [Google Scholar]
  20. Maisto SA, Witkiewitz K, Moskal D, & Wilson AD (2016). Is the construct of relapse heuristic, and does it advance alcohol use disorder clinical practice? Journal of Studies on Alcohol and Drugs. 10.15288/jsad.2016.77.849 [DOI] [PMC free article] [PubMed] [Google Scholar]
  21. Mäkelä K (2004). Studies of the reliability and validity of the Addiction Seventy Index. Addiction, 99(4), 398–410. 10.1111/j.1360-0443.2003.00665.x [DOI] [PubMed] [Google Scholar]
  22. McCann DJ, Ramey T, & Skolnick P (2015). Outcome Measures in Medication Trials for Substance Use Disorders. Current Treatment Options in Psychiatry, 2(2), 113–121. 10.1007/s40501-015-0038-5 [DOI] [Google Scholar]
  23. McKay JR (2017). Making the hard work of recovery more attractive for those with substance use disorders. Addiction, 112(5), 751–757. 10.1111/add.13502 [DOI] [PMC free article] [PubMed] [Google Scholar]
  24. McKay JR, Lynch KG, Shepard DS, & Pettinati HM (2005). The Effectiveness of Telephone-Based Continuing Care for Alcohol and Cocaine Dependence: 24 Month Outcomes. Archives of General Psychiatry, 62(2), 199–2007. 10.1037/0022-006X.75.5.775 [DOI] [PubMed] [Google Scholar]
  25. McKay JR, Van Horn DHA, Lynch KG, Ivey M, Cary MS, Drapkin ML, Coviello DM, & Plebani JG (2013). An adaptive approach for identifying cocaine dependent patients who benefit from extended continuing care. Journal of Consulting and Clinical Psychology, 81(6), 1063–1073. 10.1037/a0034265 [DOI] [PMC free article] [PubMed] [Google Scholar]
  26. McKay JR, Van Horn D, Rennert L, Drapkin M, Ivey M, & Koppenhaver J (2013). Factors in sustained recovery from cocaine dependence. Journal of Substance Abuse Treatment, 45(2), 163–172. 10.1016/j.jsat.2013.02.007 [DOI] [PMC free article] [PubMed] [Google Scholar]
  27. McLellan AT, Kushner H, Metzger D, Peters R, Smith I, Grissom G, Pettinati H, & Argeriou M (1992). The fifth edition of the addiction severity index. Journal of Substance Abuse Treatment, 9(3), 199–213. 10.1016/0740-5472(92)90062-S [DOI] [PubMed] [Google Scholar]
  28. Miller WR (1996). Form 90: A Structured Assessment Interview for Drinking and Related Behaviors: Test Manual: (563242012–001) [dataset]. American Psychological Association. 10.1037/e563242012-001 [DOI] [Google Scholar]
  29. Nolan ML, Shamasunder S, Colon-Berezin C, Kunins HV, & Paone D (2019). Increased Presence of Fentanyl in Cocaine-Involved Fatal Overdoses: Implications for Prevention. Journal of Urban Health, 96(1), 49–54. 10.1007/s11524-018-00343-z [DOI] [PMC free article] [PubMed] [Google Scholar]
  30. Paquette CE, Daughters SB, & Witkiewitz K (2022). Expanding the continuum of substance use disorder treatment: Nonabstinence approaches. Clinical Psychology Review, 91, 102110. 10.1016/j.cpr.2021.102110 [DOI] [PMC free article] [PubMed] [Google Scholar]
  31. Peers Speak Out: Priority Outcomes for Substance Use Treatment and Services. (2021). Community Catalyst. https://communitycatalyst.org/resource/peers-speak-out/ [Google Scholar]
  32. Roos CR, Nich C, Mun CJ, Babuscio TA, Mendonca J, Miguel AQC, DeVito EE, Yip SW, Witkiewitz K, Carroll KM, & Kiluk BD (2019). Clinical validation of reduction in cocaine frequency level as an endpoint in clinical trials for cocaine use disorder. Drug and Alcohol Dependence, 205, 107648. 10.1016/j.drugalcdep.2019.107648 [DOI] [PMC free article] [PubMed] [Google Scholar]
  33. Roos CR, Nich C, Mun CJ, Mendonca J, Babuscio TA, Witkiewitz K, Carroll KM, & Kiluk BD (2019). Patterns of Cocaine Use During Treatment: Associations With Baseline Characteristics and Follow-Up Functioning. Journal of Studies on Alcohol and Drugs, 80(4), 431–440. 10.15288/jsad.2019.80.431 [DOI] [PMC free article] [PubMed] [Google Scholar]
  34. Rosenberg H, Grant J, & Davis AK (2020). Acceptance of Non-Abstinence as an Outcome Goal for Individuals Diagnosed With Substance Use Disorders: A Narrative Review of Published Research. Journal of Studies on Alcohol and Drugs, 81(4), 405–415. 10.15288/jsad.2020.81.405 [DOI] [PubMed] [Google Scholar]
  35. Sobell MB, & Sobell LC (1995). Controlled drinking after 25 years: How important was the great debate? Addiction, 90(9), 1149–1153. 10.1080/09652149541392 [DOI] [PubMed] [Google Scholar]
  36. Substance Abuse and Mental Health Services Administration. (2020). The opioid crisis and the Black/African American Population: An Urgent Issue (PEP20–05-02–001). Substance Abuse and Mental Health Services Administration (SAMHSA), U.S. Department of Health and Human Services (HHS). [Google Scholar]
  37. Tonigan JS, & Miller WR (2002). The Inventory of Drug Use Consequences (InDUC): Test-retest stability and sensitivity to detect change. Psychology of Addictive Behaviors, 16(2), 165–168. 10.1037/0893-164X.16.2.165 [DOI] [PubMed] [Google Scholar]
  38. Volkow ND, Woodcock J, Compton WM, Throckmorton DC, Skolnick P, Hertz S, & Wargo EM (2018). Medication development in opioid addiction: Meaningful clinical end points. Science Translational Medicine, 10(434), eaan2595. 10.1126/scitranslmed.aan2595 [DOI] [PubMed] [Google Scholar]
  39. Votaw VR, & Witkiewitz K (2022). The clinical benefits of non-abstinent outcomes in alcohol use disorder treatment: Evidence from clinical trials and treatment implications. In Alcoholism and Alcohol Related Disorders. Springer Nature. [Google Scholar]
  40. Witkiewitz K, Falk DE, Litten RZ, Hasin DS, Kranzler HR, Mann KF, O’Malley SS, & Anton RF (2019). Maintenance of World Health Organization Risk Drinking Level Reductions and Posttreatment Functioning Following a Large Alcohol Use Disorder Clinical Trial. Alcoholism: Clinical and Experimental Research, 43(5), 979–987. 10.1111/acer.14018 [DOI] [PMC free article] [PubMed] [Google Scholar]
  41. Witkiewitz K, Hallgren KA, Kranzler HR, Mann KF, Hasin DS, Falk DE, Litten RZ, O’Malley SS, & Anton RF (2017). Clinical Validation of Reduced Alcohol Consumption After Treatment for Alcohol Dependence Using the World Health Organization Risk Drinking Levels. Alcoholism: Clinical and Experimental Research, 41(1), 179–186. 10.1111/acer.13272 [DOI] [PMC free article] [PubMed] [Google Scholar]
  42. Witkiewitz K, Heather N, Falk DE, Litten RZ, Hasin DS, Kranzler HR, Mann KF, O’Malley SS, & Anton RF (2020). World Health Organization risk drinking level reductions are associated with improved functioning and are sustained among patients with mild, moderate and severe alcohol dependence in clinical trials in the United States and United Kingdom. Addiction, 115(9), 1668–1680. 10.1111/add.15011 [DOI] [PMC free article] [PubMed] [Google Scholar]
  43. Witkiewitz K, Kranzler HR, Hallgren KA, Hasin DS, Aldridge AP, Zarkin GA, Mann KF, O’Malley SS, & Anton RF (2021). Stability of Drinking Reductions and Long-term Functioning Among Patients with Alcohol Use Disorder. Journal of General Internal Medicine, 36(2), 404–412. 10.1007/s11606-020-06331-x [DOI] [PMC free article] [PubMed] [Google Scholar]
  44. Witkiewitz K, Kranzler HR, Hallgren KA, O’Malley SS, Falk DE, Litten RZ, Hasin DS, Mann KF, & Anton RF (2018). Drinking Risk Level Reductions Associated with Improvements in Physical Health and Quality of Life Among Individuals with Alcohol Use Disorder. Alcoholism: Clinical and Experimental Research, 42(12), 2453–2465. 10.1111/acer.13897 [DOI] [PMC free article] [PubMed] [Google Scholar]

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