Skip to main content
NCBI home page
Social Work Research logoLink to Social Work Research
. 2024 Jul 16;48(3):201–211. doi: 10.1093/swr/svae012

Predictors of National Institute on Alcohol Abuse and Alcoholism (NIAAA) Alcohol Recovery among Individuals in Alcohol Treatment: Implications for Social Work

Charles LaBarre 1,✉, Clara M Bradizza 2, Braden K Linn 3, Junru Zhao 4, Kyler S Knapp 5, Gregory E Wilding 6, Paul R Stasiewicz 7
PMCID: PMC11371134  PMID: 39234355

Abstract

The National Institute on Alcohol Abuse and Alcoholism’s (NIAAA) definition of alcohol use disorder (AUD) recovery stipulates two criteria: remission from DSM-5 AUD and cessation of heavy drinking. Importantly, these criteria allow for consideration of nonabstinent alcohol treatment outcomes. However, researchers have yet to assess potential predictors of the NIAAA recovery outcome. The current study examined associations between mental health and coping predictors of NIAAA recovery status in an AUD treatment sample. At baseline (BL) and end-of-treatment (EOT) research interviews in a clinical trial, participants (N = 118) completed questionnaires assessing alcohol dependence, mental health, and confidence levels in reducing heavy drinking, as well as alcohol use and DSM-5 AUD symptom endorsement. Logistic regression models tested the associations between chosen predictors and the odds of achieving NIAAA recovery. Twenty-four percent of individuals (n = 28) met both criteria for NIAAA recovery at EOT. Higher levels of BL state anxiety and anxiety sensitivity predicted lower odds of achieving NIAAA recovery, while greater confidence to reduce heavy drinking predicted increased odds of NIAAA recovery. Social workers are encouraged to continue assessing and addressing mental health in AUD treatment to help individuals with alcohol problems achieve their AUD recovery goals.

Keywords: alcohol misuse, alcohol treatment, National Institute on Alcohol Abuse and Alcoholism recovery, nonabstinence

Most individuals who experience problems related to alcohol use disorder (AUD) do not utilize available treatments. Data from the 2012–2013 National Epidemiologic Survey on Alcohol and Related Conditions-III indicate that approximately 23% of adults with a previously diagnosed AUD reported ever receiving any form of alcohol treatment, including inpatient, outpatient, or medication-assisted interventions (Fan et al., 2019). More recently, data from the 2021 National Survey of Drug Use and Health indicate that 1.3 million U.S. adults 18 years or older, or 4.6% of adults with a past-year AUD diagnosis, received alcohol treatment within the previous year (Center for Behavioral Health Statistics and Quality, 2023). Low AUD treatment utilization in recent years may, in part, be attributed to comparative increases in alcohol use in response to COVID-19 (Grossman et al., 2020). Overall, statistical trends convey that alcohol use problems are increasing in prevalence among the general population while AUD treatment utilization remains relatively low.

Among the subset of individuals with alcohol problems who enter AUD treatment, there is considerable heterogeneity in treatment goals. Many individuals enter AUD treatment with a desire to moderate their drinking (Hasin et al., 2017; Henssler et al., 2020). Researchers have found that individuals in AUD treatment often succeed in reducing their drinking to less harmful levels (Witkiewitz et al., 2017; Witkiewitz et al., 2019). Relatedly, there has been a movement toward using myriad clinical assessment tools to evaluate drinking reductions. The U.S. Food and Drug Administration (2015) recognizes low-risk drinking (i.e., below heavy drinking guidelines per the National Institute on Alcohol Abuse and Alcoholism [NIAAA]) as an acceptable outcome for AUD clinical trials. The World Health Organization (WHO) developed a scale that conceptualizes AUD outcomes in terms of risk levels determined by reduced drinking in grams of ethanol, with a recommendation that individuals reduce their alcohol use by one or two WHO risk levels (Kirouac et al., 2017). Evidence confirms that the WHO risk scale is suitable for assessing well-being, functioning, and overall physical health improvements in AUD treatment settings (Knox et al., 2018; Richards et al., 2024; Witkiewitz et al., 2017). In light of this research, there has been a movement toward developing a new recovery definition that allows for the assessment of nonabstinent AUD outcomes (Witkiewitz et al., 2020).

Despite emerging research, there persists an emphasis on abstinence within AUD treatment settings (Henssler et al., 2020). Existing evidence suggests that this emphasis can act as a barrier to treatment among those who prefer to reduce or moderate their drinking (Probst et al., 2015). Researchers have advocated for greater acceptance of nonabstinence goals to better engage such individuals in alcohol treatment (Connor et al., 2016). More recently, researchers have argued that acknowledging nonabstinence treatment goals is integral to honoring social work values of self-determination and client-centered practice (Xin et al., 2023). The continued use and development of evaluative tools that assess nonabstinent alcohol outcomes represent an essential step toward engaging a broader population of clients in AUD treatment.

A New AUD Recovery Definition

The NIAAA recently finalized a new operational definition of AUD recovery (Hagman et al., 2022). The NIAAA recovery definition specifies that individuals must meet two criteria: remission of DSM-5 AUD (aside from the craving item) and reduction of drinking below heavy drinking thresholds (i.e., no more than three drinks per day or seven per week for women; four drinks per day or 14 per week for men). The definition also designates improvements in wellness and functioning as integral to recovery (Hagman et al., 2022). Broadly, NIAAA-defined AUD recovery represents a shift away from a singular emphasis on alcohol abstinence and reflects the growing body of literature indicating the importance of biopsychosocial wellness in recovery (Kaskutas et al., 2014; Witkiewitz et al., 2019). Preliminary research of NIAAA recovery suggests that achieving both NIAAA recovery criteria (i.e., DSM-5 remission and no heavy drinking) at the end of treatment (EOT) predicts drinking reductions up to three months posttreatment and, therefore, could be a suitable AUD outcome among those wanting to reduce their drinking (Linn et al., 2023).

Current Study

Researchers have thoroughly investigated predictors of abstinence and traditional drinking outcomes in clinical treatment (e.g., drinks per day; Adamson et al., 2009). However, researchers have yet to examine potential predictors of the NIAAA outcome. Informed by an increased emphasis on the importance of wellness and functioning in AUD recovery (Hagman et al., 2022), the current study aims to assess baseline (BL) mental health symptomology and confidence levels to reduce heavy drinking as predictors of achieving (a) both of the NIAAA recovery criteria at EOT and (b) either of the two NIAAA recovery criteria (i.e., percentage of heavy drinking days [PHDD] and DSM-5 symptom count) separately, within an AUD treatment sample.

Depression and anxiety disorders are each highly associated with AUD diagnosis (Brière et al., 2014; Lai et al., 2015). Furthermore, emerging evidence suggests that negative emotionality (e.g., depression, state anxiety, anxiety sensitivity, and psychological distress levels) is associated with NIAAA recovery up to three months post-AUD treatment (Linn et al., 2023). Self-efficacy and coping, characterized by confidence levels to reduce heavy drinking, is theoretically related to the low-risk drinking NIAAA criterion (Hagman et al., 2022) and is a consistent predictor of AUD treatment outcomes in empirical research (Kruger et al., 2021). This prior research informs our selection of predictors for the current study. We hypothesized that depression, anxiety, and psychological distress levels, as well as confidence in not drinking heavily, would predict NIAAA recovery status at posttreatment, with higher pretreatment mental health symptoms predicting a decreased likelihood of achieving NIAAA recovery status and higher confidence levels predicting an increased likelihood of achieving NIAAA recovery status. Second, when examining NIAAA criteria separately, we hypothesized that confidence levels to not drink heavily would be associated with reduced heavy drinking at EOT. Finally, we hypothesized that depression, anxiety, and psychological distress would be associated with a greater DSM-5 AUD symptom count at EOT. The decision to examine NIAAA criteria together and separately is informed by the exploratory nature of this study and the novelty of the NIAAA criteria. This investigation is intended to expand the literature on NIAAA recovery and inform social work research and practice through the identification of factors associated with achieving NIAAA recovery status at the end of AUD treatment.

Method

Study Design and Sample Characteristics

Data for the current study were drawn from a randomized controlled trial of individuals seeking outpatient alcohol treatment from February 2017 to October 2022 (N = 194; 50% female; Mage = 50.38, SDage = 10.76). The parent study was approved by the State University of New York at Buffalo institutional review board. Participants were recruited through paid radio and television advertisements targeted toward those seeking to reduce their drinking, including radio stations with a majority of racial and ethnic minority listeners in the city where data collection took place. Individuals interested in the project called in response to these advertisements and were screened for eligibility. Participants between the ages of 18 and 68 were eligible for the study if they (a) met the criteria for a moderate or severe AUD per DSM-5 criteria (American Psychiatric Association [APA], 2013), (b) reported frequently drinking heavily in response to negative affect situations, and (c) lived within commuting distance of the outpatient clinic. Participants were deemed ineligible for the study if they (a) had received AUD treatment during the past six months, (b) were mandated to seek AUD treatment, (c) met DSM-5 criteria for a psychotic or bipolar disorder, or (d) met DSM-5 criteria for a substance use disorder other than cannabis or nicotine use disorder (co-occuring cannabis or tobacco use did not disqualify from participation). In total, 530 individuals were phone screened, and 354 were eligible to participate in the BL assessment. Of those, 106 were either lost to contact or not interested in participating further in the study. Of the remaining 248 individuals who attended and completed the BL assessment in person or via video teleconferencing, 194 were eligible to participate and enrolled in the study. In the current study, participants completed follow-up interviews at BL and EOT to assess alcohol use, mental health, and DSM-5 symptom endorsement. No remuneration was offered for attendance at the BL assessment or treatment sessions. Participants were compensated $45 for completing the EOT assessment.

Before the COVID-19 pandemic, research and treatment sessions primarily occurred in person within a university-funded outpatient clinic. Beginning in April 2020, all sessions occurred through video conferencing technology to comply with social distancing and mandated safety precautions. After the shift to video conferencing, additional eligibility criteria included access to the internet, a smartphone or a laptop, and access to a private space for treatment sessions and research appointments. Of the 194 participants, 139 were recruited for the study before these adaptations, while 55 were recruited after these shifts and thus completed all treatment and research sessions via video conferencing. All questionnaire data were collected electronically by the research team using computerized software.

Measures: Study Inclusion Criteria and Covariates

Inventory of Drug-Taking Situations‒Alcohol Version (IDTS-A)

The IDTS-A is a 50-item measure that assesses how frequently individuals report drinking heavily in various situations during the past year (Sklar et al., 1997). For each item, the scale ranges from 1 = never to 4 = almost always. The eight subscales of the IDTS-A include testing personal control, urges and temptations to use, conflict with others, social pressure to use, pleasant emotions, pleasant times with others, unpleasant emotions, and physical discomfort (Sklar et al., 1997). To be eligible for the parent study and meet the criteria for negative affect drinking, participants had to have scored above the midpoint on either the unpleasant emotions subscale or the conflict with others subscale.

Mini International Neuropsychiatric Interview (MINI)

The MINI (Sheehan et al., 1998) was adapted for this study based on DSM-5 AUD diagnostic criteria and other mental health diagnoses (e.g., major depression and generalized anxiety disorder). Study enrollment began during the transition from DSM-IV to DSM-5 before the revised MINI was published. Therefore, the substance use disorder modules were modified (i.e., adding a craving item) to reflect revised DSM-5 diagnostic criteria. The MINI was used to determine if individuals met the criteria for moderate or severe AUD (i.e., endorsing at least four of the 11 DSM-5 items), an inclusion criterion for the parent study.

Demographic Characteristics

Demographic characteristics of the sample (e.g., gender, age, employment status), alcohol use, and treatment history were obtained via questionnaires administered at the BL assessment before treatment. All measures used in this study, described in the following sections, had good to excellent internal reliability (Cronbach’s αs ranged from = .70 to .97).

Alcohol Dependence Scale (ADS)

The ADS is a psychometrically robust measure that contains 29 items assessing symptoms associated with alcohol dependence (Skinner & Allen, 1982). Two-choice items are scored as either 0 or 1, three-choice items are scored as 0, 1, or 2, and four-choice items are scored as 0, 1, 2, or 3. Higher total scores are indicative of increased alcohol dependence severity. The ADS provides critical context regarding the extent of alcohol dependence severity among individuals with AUD and was therefore delivered at BL and EOT interviews. Furthermore, given that alcohol dependence severity is a consistent predictor of posttreatment alcohol use (Adamson et al., 2009), the ADS was used as a covariate in logistic and PHDD regression analyses.

Measures: Predictors

Anxiety Sensitivity Index (ASI)

The ASI is a valid and reliable measure of anxiety sensitivity that includes 18 items (Peterson & Reiss, 1993) that assess how individuals would feel in hypothetical situations eliciting feelings of anxiety (Peterson & Heilbronner, 1987). Possible responses range from 0 = very little to 4 = very much. Higher scores are indicative of higher levels of anxiety sensitivity.

State-Trait Anxiety Inventory (STAI)

The STAI is a 20-item measure of self-reported anxiety (Spielberger et al., 1983), and it contains two 10-item subscales (i.e., state and trait). Each item is scored on a scale ranging from 1 = almost never to 4 = almost always. Higher cumulative scores indicate higher levels of anxiety. For this study, the STAI state subscale was used in analyses.

Beck Depression Inventory-II (BDI-II)

The BDI-II is a 21-item measure of depression symptoms (Beck et al., 1996). For each of the 21 items, possible responses range from 0 to 3, with total BDI-II scores ranging from 0 to 63. Higher total scores indicate greater depression symptomology (Beck et al., 1996).

Brief Symptom Inventory (BSI)

The BSI contains 53 items and measures psychological distress and psychopathology (Derogatis, 1993). The nine primary subscales are calculated using a five-point scale ranging from 0 = not at all to 4 = extremely and include somatization, obsessive–compulsivity, interpersonal sensitivity, depression, anxiety, hostility, phobic anxiety, paranoid ideation, and psychoticism. For the current study, the BSI total score was used in the primary analyses as an index of psychological distress.

Drug-Taking Confidence Questionnaire (DTCQ)

The DTCQ is a 50-item measure that assesses confidence to not drink heavily in situations that present temptations to drink (Sklar et al., 1997). For each item, participants rate their confidence on a six-point scale ranging from 0% to 100% in increments of 20%, with higher percentages indicating increased confidence to not drink heavily in a given situation. This measure is a companion to the IDTS-A described earlier (i.e., identical item content). It was selected for this study due to its relevance to the NIAAA recovery definition, which stipulates the cessation of heavy drinking (Hagman et al., 2022). The eight subscales for this measure include unpleasant emotions (10 items), physical discomfort (five items), pleasant emotions (five items), testing personal control (five items), urges and temptations to use (five items), conflict with others (10 items), social pressure to use (five items), and pleasant times with others (five items; Sklar et al., 1997). Items within each subscale were totaled and averaged together for analysis.

Measures: Outcome Variables

Timeline Followback (TLFB)

The TLFB is a retrospective, calendar-based interview that assesses daily alcohol use (Sobell & Sobell, 1996). Participants completed the TLFB at BL for the previous six months and at EOT to assess drinking during treatment. PHDD is derived from participant reports of daily drinking before and during treatment. For this study, we used PHDD during the final two weeks of treatment for analysis.

NIAAA Recovery

The NIAAA recovery outcome was operationalized as a categorical variable based on NIAAA recovery criteria (Hagman et al., 2022). Participants were identified as achieving NIAAA recovery if they did not endorse any of the DSM-5 AUD criteria at EOT (aside from the craving item) and reported no heavy drinking during the final two weeks of treatment. DSM-5 AUD criteria include 11 items that assess alcohol tolerance, withdrawal, and a range of social consequences related to drinking (e.g., consequences related to work, family, and other relationships; APA, 2013). For primary statistical analyses, the NIAAA recovery outcome was coded as either meeting or not meeting both criteria.

Data Analysis

Analytic Plan

Statistical analyses were conducted using Stata (version 17; StataCorp, 2021). Descriptive statistics were computed to ascertain the characteristics of the study sample. Second, binary logistic regression models assessed relationships between the BL predictors, including depression, anxiety, psychological distress, and confidence in not drinking heavily, with the outcome of NIAAA recovery status at EOT. Third, we examined associations between the same set of BL predictors and posttreatment NIAAA recovery status using linear regressions; analyses were conducted separately for PHDD and DSM-5 symptom count. Based on evidence that individuals reduce their drinking in more significant amounts during the first weeks of AUD treatment, followed by stability (Maisto et al., 2021), PHDD during the final two weeks of treatment was used in statistical analyses to reflect a more accurate representation of alcohol consumption patterns and NIAAA recovery at EOT (Linn et al., 2023). Finally, considering the literature conveying a potential impact of pretreatment drinking on AUD outcomes (LaBarre, Stasiewicz, Linn, & Bradizza, 2022; Stasiewicz et al., 2013), sensitivity analyses were conducted to determine the effect of BL PHDD on logistic regression model results. Chosen predictors were standardized for analysis to minimize potential biases in estimates due to the differential scaling of the predictors (Cohen et al., 2003). The alpha value of .05 was chosen as the threshold for statistical significance within each model. In the logistic regression models, we controlled for age, gender, marital status, education, employment, religious status, and alcohol dependence due to these factors’ prominence in the alcohol literature as potential confounders of AUD treatment outcomes (Adamson et al., 2009).

Missing Data

Data were missing at EOT interviews within the parent study. First, 12 participants did not attend a treatment session after the BL assessment. Second, the DSM-5 measure was added to the EOT interview after enrollment was initiated, and therefore, six participants never completed the EOT DSM-5 assessment. Third, approximately one-third of participants chose to opt out of a full EOT interview that reassessed DSM-5 AUD and instead chose to complete a more brief EOT assessment that captured a subset of variables directly relevant to the parent study aims (i.e., alcohol consumption). Consequently, case-wise deletion procedures were used among these cases in the primary analyses because we could not assess the DSM-5 remission criterion of NIAAA recovery. Of the 176 individuals who attended treatment after the addition of the DSM-5 instrument to the EOT study protocol, 118 participants completed both the DSM-5 interview and the TLFB to assess heavy drinking at EOT.

Results

Table 1 presents demographic characteristics and BL alcohol variables. Of the 118 participants available for the primary analysis, 47.5% identified as female, 92.4% were White, and 70.3% were employed full-time or students. Approximately 24% (n = 28) of the analytic sample achieved NIAAA recovery status at EOT.

Table 1:

Demographic Characteristics and Baseline Alcohol Variables (N = 118)

Variable M (SD) n (%)
Age (years) 51.22 (10.7)
Gender
 Male 62 (52.5)
 Female 56 (47.5)
Race
  White 109 (92.4)
  Non-White 9 (7.6)
Education
  High school or less 23 (19.5)
  More than high school 95 (80.5)
Religious status
  Religious 101 (85.6)
  Nonreligious 17 (14.4)
Marital status
  Single or divorced/separated 57 (48.3)
  Married or partnered 61 (51.7)
Employment status
  Unemployed or retired 35 (29.7)
  Employed full-time/student 83 (70.3)
Alcohol variables
  ADS 14.8 (6.8)
  SIP 21.0 (8.4)
  PDA (baseline) 0.26 (0.24)
  PHDD (baseline) 0.59 (0.33)
Open in a new tab

Notes: ADS = Alcohol Dependence Scale; SIP = Short Inventory of Problems; PDA = percentage of days abstinent; PHDD = percentage of heavy drinking days. PDA and PHDD are computed from Timeline Followback.

The results of the logistic regression analyses are presented in Table 2. Notably, higher ASI (odds ratio [OR] = 0.43; 95% CI [0.24, 0.79], p = .005) and STAI (OR = 0.53; 95% CI [0.29, 0.98], p = .037) scores at BL were associated with significantly lower odds of meeting NIAAA recovery at EOT. A Pearson correlation coefficient was computed to assess the linear relationship between ASI and STAI. There was a low correlation between the ASI and STAI variables, r(116) = 0.35, p = .0001. Higher DTCQ scores at BL were associated with significantly higher odds of achieving NIAAA recovery at EOT (OR = 3.02; 95% CI [1.72, 5.46], p < .001). Finally, when controlling for BL PHDD in sensitivity analyses, ASI, STAI, and DTCQ remained statistically significant and retained similar effect sizes in logistic regression analyses (p < .05). No other significant changes were found in sensitivity analyses.

Table 2:

Associations between Baseline Demographic Characteristics, Mental Health, and NIAAA Recovery among an AUD Treatment Sample (N = 118)

Baseline Variable Standardized Coef. OR [95% CI]
Gender
  1 = female 0.27 0.56 [0.22, 1.45]
Age 0.02 1.00 [0.96, 1.05]
Employment status
  1 = employed 0.53 0.99 [0.35, 2.82]
Partnered status
  1 = Partnered or married 1.11 2.25 [0.85, 5.91]
Educational status
  1 = some college or more 0.35 0.65 [0.22, 1.87]
Religious status
  1 = religious-identifying 0.43 0.65 [0.18, 2.4]
Alcohol dependence score 0.28 1.13 [0.70, 1.82]
Anxiety sensitivity (ASI) 0.13** 0.43 [0.24, 0.79]
State anxiety (SAI) 0.16* 0.53 [0.29, 0.98]
Depression (BDI-II) 0.19 0.65 [0.36, 1.15]
Psychological distress (BSI) 0.20 0.62 [0.33, 1.17]
Drinking self-efficacy (DTCQ) 0.90*** 3.02 [1.72, 5.46]
Open in a new tab

Notes: NIAAA = National Institute on Alcohol Abuse and Alcoholism; AUD = alcohol use disorder; ASI = Anxiety Sensitivity Index; SAI = State Anxiety Inventory; BDI-II = Beck’s Depression Inventory (version II); BSI = Brief Symptom Inventory; DTCQ = Drug-Taking Confidence Questionnaire. The NIAAA defines heavy drinking (percentage of heavy drinking days) for women as four or more drinks per day and eight or more drinks per week, and for men as five or more drinks per day and 15 or more drinks per week.

*

p < .05.

**

p < .01.

***

p <.001.

Table 3 presents linear regression results examining the two NIAAA recovery criteria as separate outcome variables. Of note, greater confidence to not drink heavily and partnered or married status was associated with lower EOT PHDD and explained a significant proportion of variance in EOT PHDD: R2 = .21, F(8, N = 103) = 3.51, p = .001. Furthermore, ASI [R2 = .11, F(7, N = 105) = 1.79, p = .01], STAI [R2 = .08, F(7, N = 105) = 1.33, p = .05], and BDI [R2 = .08, F(7, N = 105) = 1.34, p = .048] were associated with higher DSM-5 symptom count at EOT, while higher DTCQ scores were associated with lower DSM-5 symptom count at EOT [R2 = .23, F(8, N = 102) = 3.82, p < .001]. No other significant associations were found in these secondary analyses.

Table 3:

Associations between Baseline Variables and Heavy Drinking; DSM-5 Outcomes at EOT

PHDD (N = 151)
 DSM-5 Symptom Count (N = 118)
Variable Regression Coefficient [95% CI] Regression Coefficient [95% CI]
Gender (1 = female) 0.019 [–0.078, 0.117] 0.140 [–1.03, 1.31]
Age (years) –0.002 [0.007, 0.003] 0.014 [–0.042, 0.071]
Employment status (1 = employed or student) 0.048 [–0.060, 0.157] 0.457 [–0.821, 1.74]
Religious status (1 = religious identifying) 0.013 [–0.121, 0.147] –0.911 [–2.58, 0.758]
Education status (1 = some college or more) 0.006 [–0.110, 0.122] 0.036 [–1.37, 1.44]
Marital status (1 = married or partnered) –0.140* [–0.238, –0.040] –1.10 [–2.27, 0.066]
Alcohol dependence (ADS) –0.016 [–0.065, 0.034] 0.334 [–0.245, 0.923]
Anxiety sensitivity (ASI) 0.022 [–0.029, 0.073] 0.734* [0.183, 1.29]
State anxiety (SAI) 0.025 [–0.031, 0.082] 0.648* [–0.0002, 1.30]
Depression (BDI-II) 0.023 [–0.035, 0.081] 0.654* [0.005, 1.30]
Psychological distress (BSI) 0.021 [–0.032, 0.075] 0.360 [–0.360, 1.08]
Drinking self-efficacy (DTCQ) –0.122* [–0.169, –0.075] –1.30* [–1.80, –0.795]
Open in a new tab

Notes: PHDD = percentage of heavy drinking days; ADS = Alcohol Dependence Scale; ASI = Anxiety Sensitivity Scale; SAI = State Anxiety Inventory; BDI-II = Beck’s Depression Inventory (version II); BSI = Brief Symptom Inventory; DTCQ = Drug-Taking Consequences Questionnaire (adapted for alcohol use). PHDD is computed from the Timeline Followback.

*

p < .05.

Discussion

This study aimed to assess (a) the relationship between mental health symptoms and coping characteristics assessed at BL (e.g., anxiety, depression symptoms) and NIAAA recovery status at EOT and (b) the associations between select BL variables and heavy drinking during treatment and DSM-5 symptom count, separately, at EOT. Study results indicate that several mental health variables assessed at BL were associated with NIAAA recovery status at EOT. Pretreatment levels of state anxiety and anxiety sensitivity, two distinct anxiety constructs, predicted a lower likelihood of achieving NIAAA recovery at EOT. These associations were significant even after accounting for BL heavy drinking. Furthermore, increases in both state anxiety and anxiety sensitivity were associated with increased DSM-5 AUD symptom count posttreatment. A wealth of literature confirms that those with anxiety disorders often experience barriers to reducing their drinking within treatment settings (e.g., Smith & Randall, 2012) and are less likely to initiate or complete treatment (Buckner & Schmidt, 2009). Thus, they are less likely to reap its benefits (i.e., resulting in reduced drinking or abstinence). Further research is needed to assess and elucidate the relationship between anxiety and NIAAA recovery status among individuals with less pronounced levels of negative affect or without co-occurring mental health conditions in AUD treatment settings.

Contrary to expectations, psychological distress levels assessed by BSI (Derogatis, 1993) did not predict a decreased likelihood of achieving NIAAA recovery status at EOT. Higher depression scores on the BDI-II (Beck et al., 1996) at BL were associated with a higher posttreatment DSM-5 symptom count but not the NIAAA recovery outcome. This finding suggests that individuals with higher-than-average depression levels entering treatment had more difficulty meeting the DSM-5 remission criterion of NIAAA recovery. Researchers have shown that negative emotionality, including depression at EOT, predicts EOT NIAAA recovery (Linn et al., 2023). However, it remains unclear to what extent depression levels may impact NIAAA recovery throughout treatment and whether fluctuations in each domain over treatment interact with one’s ability to meet recovery criteria. This could be investigated in future research.

Greater confidence levels to not drink heavily were significantly associated with a greater likelihood of achieving NIAAA recovery status, less heavy drinking, and lower endorsement of DSM-5 symptoms at posttreatment. Furthermore, the DTCQ logistic regression model accounted for a significantly greater variance in meeting NIAAA recovery status than the anxiety models (20% versus 10% to 13% in anxiety models). These findings align with a wealth of research showing positive relationships between self-efficacy and reduced drinking both inside and outside of AUD treatment settings (Greenfield et al., 2000; Maisto et al., 2000). Therefore, coping and self-efficacy, more broadly, may have clinical utility as a target of assessment and intervention. Several interventions targeting cannabis use disorders (e.g., contingency management) have been developed and have shown efficacy in targeting self-efficacy and motivation levels during treatment (Litt et al., 2013). These techniques could be worthy of investigation in alcohol use treatments moving forward and could be useful in helping individuals meet their recovery goals.

Social Work Practice and Policy Implications

Findings from this study hold important implications for social work research and practice. Results indicate that a quarter of the study sample met NIAAA recovery criteria at the end of 12 weeks of cognitive‒behavioral treatment. Furthermore, various mental health symptoms impacted participants meeting NIAAA recovery criteria. Emergent empirical evidence indicates that NIAAA recovery status at posttreatment predicts short-term drinking outcomes (Linn et al., 2023), and, therefore, achievement and maintenance of NIAAA recovery status may be a worthy assessment and intervention component before and after treatment. The NIAAA recovery outcome could be used to assess recovery posttreatment, in aftercare settings, or within recovery-focused interventions in social work practice contexts.

Study results also suggest that individuals with elevated depression or anxiety symptoms or lower confidence levels to reduce heavy drinking may require greater attention in clinical settings (see Barlow et al., 2017). Tailored interventions or more informal checkups during and after treatment could help such individuals meet and maintain NIAAA recovery status. Individuals evidencing lower confidence levels to reduce heavy drinking may benefit from interventions or practices, whether formally or informally, that strengthen their commitment to reducing drinking in various situations that present temptations to drink heavily. Strategies could include developing coping strategies related to specific situations that present temptations to drink or discussing areas of strength or those needing improvement during treatment to prepare for posttreatment recovery goals (Litt et al., 2013). Researchers have developed several interventions to assist those with co-occurring AUD and anxiety disorders, suggesting the potential utility of adapted interventions among select populations (Smith & Randall, 2012). Interventions that target emotion regulation processes, furthermore, may also have utility in reducing one’s anxiety or depression levels throughout treatment (Stasiewicz et al., 2013). Those who have higher anxiety levels may benefit from the use of these techniques to manage their anxiety alongside reducing their drinking. Social work practitioners are encouraged to use their breadth of skills to meet the needs of individuals with pronounced mental health symptomology or low confidence levels related to reducing their drinking.

An awareness of controlled drinking goals has been recommended for engaging individuals in treatment who seek to reduce, rather than eliminate, their drinking (Connor et al., 2016). Therefore, it is vital for social work practitioners to move toward using the NIAAA recovery criteria to accommodate populations rejecting abstinence as a desired outcome (Hasin et al., 2017; Xin et al., 2023). Furthermore, because research indicates that individuals with AUD often cycle in and out of heavy alcohol consumption in early recovery (Maisto et al., 2020), it could be that one’s NIAAA recovery status could also fluctuate. Social work researchers are poised to continue this line of research and use the NIAAA recovery definition to assess recovery longitudinally and guide client progress in clinical and community-based settings.

In reflection of shifting societal attitudes on addiction and recovery, many clinical social workers now accept reduced or nonhazardous drinking goals for their clients (Xin et al., 2023). Harm reduction strategies that support reduced drinking have been shown to reduce alcohol consumption and related harm (Marlatt & Witkiewitz, 2002). Treatment organizations have integrated related frameworks on a policy level to inform intervention approaches. In practice, however, treatments continue to emphasize abstinence; thus, a subset of treatment-seeking populations remain reluctant to access AUD treatment (Connor et al., 2016; Probst et al., 2015). Continued research and validation of the NIAAA recovery criteria could provide further support for policies supporting the heterogeneity of treatment goals. Such approaches may also improve treatment accessibility and retention among a broader population of treatment-seeking individuals with an AUD.

Limitations

While this study had many methodological strengths, there remain three main limitations. First, this sample comprises individuals who drink in response to negative affect. Thus, findings may not generalize to all AUD phenotypes. While anxiety was a predictor of NIAAA recovery and is typically elevated among negative affect drinkers (Blevins et al., 2016), other treatment samples may not evidence this same association. Furthermore, it may be that the relatively low percentage of individuals who met NIAAA criteria at EOT could be attributed to the unique nature of the sample with respect to AUD severity and negative affectivity. Second, despite our efforts to reach a diverse audience in recruitment, the accrued sample was relatively homogenous, particularly concerning race and ethnicity, age, and educational status. Therefore, findings from this study would not extend to racial minorities, younger adults, or those without a postsecondary education. Researchers have identified that several populations (e.g., Black Americans and young adults) are underrepresented in alcohol recovery research (Wagner & Baldwin, 2020). Therefore, researchers should endeavor to recruit minoritized or underresearched groups in ongoing scholarship. Finally, COVID-19 may have impacted a subsample within the parent study whose treatment sessions and follow-up assessments were completed during the initial stages of the pandemic. A previous analysis found that about half of the participants within the parent study who began treatment after the onset of COVID-19 in the United States identified a protective effect of the pandemic on their drinking, while others reported increased drinking in response to the pandemic (LaBarre, Stasiewicz, Linn, Zhao, et al., 2022). Therefore, there may be several COVID-19-related variables not assessed in the current study that mediated or moderated the relationship between mental health and alcohol outcomes.

Conclusion

The NIAAA recovery definition presents many opportunities for social work. Results of the present study conveyed that lower BL anxiety and anxiety sensitivity, as well as higher confidence levels related to reducing heavy drinking, predicted meeting the NIAAA recovery criteria at the end of AUD treatment. Based on these insights, researchers are encouraged to continue ascertaining factors related to NIAAA recovery status among individuals with alcohol problems. Further insights will help move the social work and AUD fields toward greater acceptance of nonabstinent recovery and afford insight into what individuals in AUD recovery need to meet and maintain their drinking goals.

Contributor Information

Charles LaBarre, LMSW, is a PhD candidate, School of Social Work, State University of New York at Buffalo, 1021 Main Street, Buffalo, NY 14203, USA.

Clara M Bradizza, PhD, is professor, School of Social Work, State University of New York at Buffalo, Buffalo, NY, USA.

Braden K Linn, PhD, LMSW, is assistant professor, Department of Family and Community Medicine, College of Medicine, Pennsylvania State University, Hershey, PA, USA.

Junru Zhao, PhD, is a statistician, School of Social Work, State University of New York at Buffalo, Buffalo, NY, USA.

Kyler S Knapp, PhD, is National Institute on Alcohol Abuse and Alcoholism (NIAAA) T32 postdoctoral fellow, Clinical and Research Institute on Addictions, School of Social Work, State University of New York at Buffalo, Buffalo, NY, USA.

Gregory E Wilding, PhD, is professor, Department of Biostatistics, School of Public Health and Health Professions, State University of New York at Buffalo, Buffalo, NY, USA.

Paul R Stasiewicz, PhD, is professor and Janet B. Wattles endowed chair, School of Social Work, State University of New York at Buffalo, Buffalo, NY, USA.

 

This work was supported by NIAAA of the National Institutes of Health [R01 AA024628, T32 AA007583].

References

  1. Adamson S. J., Sellman J. D., Frampton C. M. A. (2009). Patient predictors of alcohol treatment outcome: A systematic review. Journal of Substance Abuse Treatment, 36, 75–86. 10.1016/j.jsat.2008.05.007 [DOI] [PubMed] [Google Scholar]
  2. American Psychiatric Association. (2013). Diagnostic and statistical manual of mental disorders (5th ed.). Washington, DC. [Google Scholar]
  3. Barlow D. H., Farchione T. J., Bullis J. R., Gallagher M. W., Murray-Latin H., Sauer-Zavala S., Bentley K. H., Thompson-Hollands J., Conklin L. R., Boswell J. F., Ametaj A., Carl J. R., Boettcher H. T., Cassiello-Robbins C. (2017). The unified protocol for transdiagnostic treatment of emotional disorders compared with diagnosis-specific protocols for anxiety disorders: A randomized clinical trial. JAMA Psychiatry, 74, 875–884. 10.1001/jamapsychiatry.2017.2164 [DOI] [PMC free article] [PubMed] [Google Scholar]
  4. Beck A. T., Steer R. A., Brown G. K. (1996). Manual for the Beck Depression Inventory-II. Psychological Corporation. [Google Scholar]
  5. Blevins C. E., Abrantes A. M., Stephens R. S. (2016). Motivational pathways from antecedents of alcohol use to consequences: A structural model of using alcohol to cope with negative affect. American Journal of Drug and Alcohol Abuse, 42, 395–403. 10.3109/00952990.2016.1141915 [DOI] [PubMed] [Google Scholar]
  6. Brière F. N., Rohde P., Seeley J. R., Klein D., Lewinsohn P. M. (2014). Comorbidity between major depression and alcohol use disorder from adolescence to adulthood. Comprehensive Psychiatry, 55, 526–533. 10.1016/j.comppsych.2013.10.007 [DOI] [PMC free article] [PubMed] [Google Scholar]
  7. Buckner J. D., Schmidt N. B. (2009). A randomized pilot study of motivation enhancement therapy to increase utilization of cognitive–behavioral therapy for social anxiety. Behaviour Research and Therapy, 47, 710–715. 10.1016/j.brat.2009.04.009 [DOI] [PubMed] [Google Scholar]
  8. Center for Behavioral Health Statistics and Quality. (2023). Key substance use and mental health indicators in the United States. Substance Abuse and Mental Health Services Administration. https://www.samhsa.gov/data/ [Google Scholar]
  9. Cohen J., Cohen P., West S. G., Aiken L. S. (2003). Applied multiple regression/correlation analysis for the behavioral sciences (3rd ed.). Routledge. [Google Scholar]
  10. Connor J. P., Haber P. S., Hall W. D. (2016). Alcohol use disorders. The Lancet, 387, 988–998. 10.1016/S0140-6736(15)00122-1 [DOI] [PubMed] [Google Scholar]
  11. Derogatis L. (1993). The Brief Symptom Inventory (BSI): Administration, scoring and procedures manual (3rd ed.). National Computer Systems. [Google Scholar]
  12. Fan A. Z., Chou S. P., Zhang H., Jung J., Grant B. F. (2019). Prevalence and correlates of past-year recovery from DSM-5 alcohol use disorder: Results from National Epidemiologic Survey on Alcohol and Related Conditions-III. Alcoholism: Clinical and Experimental Research, 43, 2406–2420. 10.1111/acer.14192 [DOI] [PubMed] [Google Scholar]
  13. Greenfield S. F., Hufford M. R., Vagge L. M., Muenz L. R., Costello M. E., Weiss R. D. (2000). The relationship of self-efficacy expectancies to relapse among alcohol dependent men and women: A prospective study. Journal of Studies on Alcohol, 61, 345–351. 10.15288/jsa.2000.61.345 [DOI] [PubMed] [Google Scholar]
  14. Grossman E. R., Benjamin-Neelon S. E., Sonnenschein S. (2020). Alcohol consumption during the COVID-19 pandemic: A cross-sectional survey of U.S. adults. International Journal of Environmental Research and Public Health, 17, Article 24. 10.3390/ijerph17249189 [DOI] [PMC free article] [PubMed] [Google Scholar]
  15. Hagman B. T., Falk D., Litten R., Koob G. F. (2022). Defining recovery from alcohol use disorder: Development of an NIAAA research definition. American Journal of Psychiatry. Advance online publication. 10.1176/appi.ajp.21090963 [DOI] [PubMed] [Google Scholar]
  16. Hasin D. S., Wall M., Witkiewitz K., Kranzler H. R., Falk D., Litten R., Mann K., O’Malley Scodes, J., Robinson R. L., Anton R. (2017). Change in non-abstinent WHO drinking risk levels and alcohol dependence: A 3 year follow-up study in the US general population. Lancet Psychiatry, 4, 469–476. 10.1016/S2215-0366(17)30130-X [DOI] [PMC free article] [PubMed] [Google Scholar]
  17. Henssler J., Müller M., Carreira H., Bschor T., Heinz A., Baethge C. (2020). Controlled drinking—Non-abstinent versus abstinent treatment goals in alcohol use disorder: A systematic review, meta-analysis, and meta-regression. Addiction, 116, 1973–1987. 10.1111/add.15329 [DOI] [PubMed] [Google Scholar]
  18. Kaskutas L. A., Borkman T. J., Laudet A., Ritter L. A., Witbrodt J., Subbaraman M. S., Stunz A., Bond J. (2014). Elements that define recovery: The experiential perspective. Journal of Studies on Alcohol and Drugs, 75, 999–1010. 10.15288/jsad.2014.75.999 [DOI] [PMC free article] [PubMed] [Google Scholar]
  19. Kirouac M., Stein E. R., Pearson M. R., Witkiewitz K. (2017). Viability of the World Health Organization quality of life measure to assess changes in quality of life following treatment for alcohol use disorder. Quality of Life Research, 26, 2987–2997. 10.1007/s11136-017-1631-4 [DOI] [PMC free article] [PubMed] [Google Scholar]
  20. Knox J., Wall M., Witkiewitz K., Kranzler H. R., Falk D., Litten R., Mann K., O’Malley S. S., Scodes J., Anton R., Hasin D. S., For  the Alcohol Clinical Trials (ACTIVE) Workgroup. (2018). Reduction in nonabstinent WHO drinking risk levels and change in risk for liver disease and positive AUDIT-C scores: Prospective 3-year follow-up results in the U.S. general population. Alcohol: Clinical and Experimental Research, 42, 2256–2265. 10.1111/acer.13884 [DOI] [PMC free article] [PubMed] [Google Scholar]
  21. Kruger E. S., Serier K. N., Pfund R. A., McKay J. R., Witkiewitz K. (2021). Integrative data analysis of self-efficacy in 4 clinical trials for alcohol use disorder. Alcohol: Clinical and Experimental Research, 45, 2347–2356. 10.1111/acer.14713 [DOI] [PMC free article] [PubMed] [Google Scholar]
  22. LaBarre C., Stasiewicz P. R., Linn B. K., Bradizza C. M. (2022). Pretreatment change in substance use behaviors: Implications for the social work field [Viewpoint]. Health & Social Work, 47, 229‒231. 10.1093/hsw/hlac011 [DOI] [PMC free article] [PubMed] [Google Scholar]
  23. LaBarre C., Stasiewicz P. R., Linn B. K., Zhao J., Wilding G. E., Bradizza C. M. (2022). The influence of COVID-19 on alcohol consumption among individuals in outpatient treatment: Emerging clinical practices. Best Practices in Mental Health, 18, 18‒32. https://www.ingentaconnect.com/contentone/follmer/bpmh/2022/00000018/00000001/art00004 [Google Scholar]
  24. Lai H. M. X., Clearly M., Sitharthan T., Hunt G. E. (2015). Prevalence of comorbid substance use, anxiety and mood disorders in epidemiological surveys, 1990–2014: A systematic review and meta-analysis. Drug and Alcohol Dependence, 154, 1–13. 10.1016/j.drugalcdep.2015.05.031 [DOI] [PubMed] [Google Scholar]
  25. Linn B. K., Zhao J., Stasiewicz P. R., LaBarre C., Wilding G. E., Bradizza C. M. (2023). Relationship of negative emotionality, NIAAA recovery, and 3- and 6-month drinking outcomes among adults in treatment for alcohol use disorder. Drug and Alcohol Dependence, 242, Article 109695. 10.1016/j.drugalcdep.2022.109695 [DOI] [PubMed] [Google Scholar]
  26. Litt M. D., Kadden R. M., Petry N. M. (2013). Behavioral treatment for marijuana dependence: Randomized trial of contingency management and self-efficacy enhancement. Addictive Behaviors, 38, 1764–1775. 10.1016/j.addbeh.2012.08.011 [DOI] [PMC free article] [PubMed] [Google Scholar]
  27. Maisto S. A., Connors G. J., Zywiak W. H. (2000). Alcohol treatment changes in coping skills, self-efficacy, and levels of alcohol use and related problems 1 year following treatment initiation. Psychology of Addictive Behaviors, 14, 257–266. 10.1037/0893-164X.14.3.257 [DOI] [PubMed] [Google Scholar]
  28. Maisto S. A., Hallgren K. A., Roos C. R., Swan J. E., 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, 1119–1132. 10.1037/ccp0000615 [DOI] [PMC free article] [PubMed] [Google Scholar]
  29. Maisto S. A., Hallgren K. A., Swan J. E., Roos C., Witkiewitz K. (2021). Within-AUD outpatient treatment heavy drinking transitions and associations with long-term outcomes. Drug and Alcohol Dependence, 228, Article 108968. 10.1016/j.drugalcdep.2021.108968 [DOI] [PMC free article] [PubMed] [Google Scholar]
  30. Marlatt G. A., Witkiewitz K. (2002). Harm reduction approaches to alcohol use: Health promotion, prevention, and treatment. Addictive Behaviors, 27,867–886. 10.1016/S0306-4603(02)00294-0 [DOI] [PubMed] [Google Scholar]
  31. Peterson R. A., Heilbronner R. L. (1987). The Anxiety Sensitivity Index: Construct validity and factor analytic structure. Journal of Anxiety Disorders, 1, 117–121. 10.1016/0887-6185(87)90002-8 [DOI] [Google Scholar]
  32. Peterson R. A., Reiss S. (1993). Anxiety Sensitivity Index revised text manual. IDS Publishing. [Google Scholar]
  33. Probst C., Manthey J., Martinez A., Rehm J. (2015). Alcohol use disorder severity and reported reasons not to seek treatment: A cross-sectional study in European primary care practices. Substance Abuse Treatment, Prevention, & Policy, 10, Article 32. 10.1186/s13011-015-0028-z [DOI] [PMC free article] [PubMed] [Google Scholar]
  34. Richards D. K., Tuchman F. R., Hallgren K. A., Kranzler H. R., Aubin H. J., O’Malley S. S., Mann K., Aldridge A., Anton R. F., Witkiewitz K. (2024). Reductions in World Health Organization risk drinking level are associated with reductions in alcohol use disorder diagnosis and criteria: Evidence from an alcohol pharmacotherapy trial. Journal of Addiction Medicine. Advance online publication.  10.1097/ADM.0000000000001303 [DOI] [PMC free article] [PubMed] [Google Scholar]
  35. Sheehan D. V., Lecrubier Y., Sheehan K. H., Amorim P., Janavs J., Weiller E., Hergueta T., Dunbar G. C. (1998). The Mini-International Neuropsychiatric Interview (MINI): The development and validation of a structured diagnostic psychiatric interview for DSM-IV and ICD-10. Journal of Clinical Psychiatry, 59, 22–33. [PubMed] [Google Scholar]
  36. Skinner H. A., Allen B. A. (1982). Alcohol dependence syndrome: Measurement and validation. Journal of Abnormal Psychology, 91, 199–209. 10.1037/0021-843X.91.3.199 [DOI] [PubMed] [Google Scholar]
  37. Sklar S. M., Annis H. M., Turner N. E. (1997). Development and validation of the Drug-Taking Confidence Questionnaire: A measure of coping self-efficacy. Addictive Behaviors, 22, 655‒670. 10.1016/S0306-4603(97)00006-3 [DOI] [PubMed] [Google Scholar]
  38. Smith J. P., Randall C. L. (2012). Anxiety and alcohol use disorders: Comorbidity and treatment considerations. Alcohol Research: Current Reviews, 34, 414–431. [DOI] [PMC free article] [PubMed] [Google Scholar]
  39. Sobell L. C., Sobell M. B. (1996). Timeline Followback: User’s guide. Addiction Research Foundation. [Google Scholar]
  40. Spielberger C. D., Gorsuch R. L., Lushene R., Vagg P. R., Jacobs G. A. (1983). Manual for the State-Trait Anxiety Inventory. Consulting Psychologists Press. [Google Scholar]
  41. Stasiewicz P. R., Bradizza C. M., Schlauch R. C., Coffey S. F., Gulliver S. B., Gudleski G. D., Bole C. W. (2013). Affect regulation training (ART) for alcohol use disorders: Development of a novel intervention for negative affect drinkers. Journal of Substance Abuse Treatment, 45, 433‒443. 10.1016/j.jsat.2013.05.012 [DOI] [PMC free article] [PubMed] [Google Scholar]
  42. StataCorp. (2021). Stata statistical software: Release 17 [Software]. Author.
  43. U.S. Food & Drug Administration. (2015, February 9). Alcoholism: Developing drugs for treatment. Center for Drug Evaluation and Research (CDER; ). https://www.fda.gov/files/drugs/published/Alcoholism---Developing-Drugs-for-Treatment.pdf [Google Scholar]
  44. Wagner E. F., Baldwin J. A. (2020). Recovery in special emphasis populations. Alcohol Research: Current Reviews, 40, Article 5. 10.35946/arcr.v40.3.05 [DOI] [PMC free article] [PubMed] [Google Scholar]
  45. Witkiewitz K., Montes K. S., Schwebel F. J., Tucker J. A. (2020). What is recovery?  Alcohol Research: Current Reviews, 40, Article 1. 10.35946/arcr.v40.3.01 [DOI] [PMC free article] [PubMed] [Google Scholar]
  46. Witkiewitz K., Pearson M. R., Hallgren K. A., Maisto S. A., Roos C. R., Kirouac M., Wilson A. D., Montes K. S., Heather N. (2017). Who achieves low risk drinking during alcohol treatment? An analysis of patients in three alcohol clinical trials. Addiction, 112, 2112–2121. 10.1111/add.13870 [DOI] [PMC free article] [PubMed] [Google Scholar]
  47. Witkiewitz K., Wilson A. D., Pearson M. R., Montes K. S., Kirouac M., Roos C. R., Hallgren K. A., Maisto S. A. (2019). Profiles of recovery from alcohol use disorder at three years following treatment: Can the definition of recovery be extended to include high-functioning heavy drinkers?  Addiction, 114, 69–80. 10.1111/add.14403 [DOI] [PMC free article] [PubMed] [Google Scholar]
  48. Xin Y., España M., Davis A. K. (2023). The acceptability of non-abstinent treatment goals among clinical social workers in the United States. Journal of Social Work Practice, 37, 339–354. 10.1080/02650533.2022.2034768 [DOI] [Google Scholar]

Articles from Social Work Research are provided here courtesy of Oxford University Press

RESOURCES