Predictors of concerned others’ Al-Anon attendance and involvement following adults’ entry into treatment for an alcohol use disorder

Abstract

Background:

Alcohol use disorders (AUDs) negatively impact the health of persons consuming alcohol and their concerned others (COs, their family and friends). It is important to identify factors that affect COs’ likelihood of participating in supportive services, such as Al-Anon, that can improve their outcomes. We used the Stress-Strain-Coping-Support model to identify potential predictors of Al-Anon attendance and involvement among COs of adults entering treatment for an AUD.

Methods:

Participants were 279 dyads of patients entering residential treatment for AUD and their CO. Outcomes were COs’ Al-Anon attendance and participation. The study examined patient and CO characteristics at baseline as predictors of COs’ Al-Anon attendance and involvement at baseline and 3-, 6-, and 12-month follow-ups, after controlling for COs’ demographics.

Results:

COs who were of older age, White, and religious were more likely to attend and be involved in Al-Anon, and COs who were currently living with the patient were more likely to be involved in Al-Anon. Patients with more Al-Anon involvement and criminal justice engagement had COs who were more likely to attend and/or be involved in Al-Anon. Further, COs who used more nurturing communication were more likely to attend Al-Anon, and COs who were abstinent from alcohol, and with poorer mental health, were more likely to attend and be involved in Al-Anon.

Conclusions:

Understanding factors affecting COs’ use of Al-Anon may inform treatment program and provider efforts to improve the use of these effective and widely available services among COs.

Short Overview

This study found that characteristics of concerned others (COs) of adults entering treatment for an alcohol use disorder such as older age, being White, religious, living with the patient, use of nurturing communication, alcohol use, and poor mental health were associated with COs’ Al-Anon participation. Patient characteristics of AA involvement and the criminal justice system were also associated with COs’ Al-Anon participation. These findings may inform treatment program and provider efforts to improve the use of Al-Anon among COs.

Introduction

Alcohol use disorders (AUDs) negatively impact the health of both the persons drinking and their concerned others (COs, their family and friends) (Carvalho et al., 2019; Birkeland et al., 2018). In the US, over half of adults (54%) reported that a family member had ever been addicted to alcohol (Sparks et al., 2023) and 53 million adults experienced at least one harm from another person’s drinking, including family or financial harm, harassment, or physical aggression (Nayak et al., 2019). A multinational survey of adults who knew a heavy drinker found that between 10% and 48% reported experiencing at least one harm due to another’s drinking, including emotional hurt or neglect, physical or verbal assault, or feeling threatened (Stanesby et al., 2018). COs also experience high rates of worry and stress about the person drinking. Among adult family members of a person with a substance use disorder, 36% reported symptoms of anxiety, stress, and/or depression (Olafsdottir et al., 2018). Partly because of such harms, COs have a lower quality of life and poorer mental health than other groups (Birkeland et al., 2018; Timko et al., 2022a). It is therefore critical to identify factors that affect COs’ likelihood of participating in supportive services, such as Al-Anon, that can improve their outcomes. This line of research informs efforts to identify COs who may be less likely to access support services from which they could benefit, as well as strategies to link them to help.

Benefits and Correlates of Al-Anon Participation

Al-Anon (short for Al-Anon Family Groups), a 12-step mutual-help group (MHG) for COs, is widely available with over 24,000 Al-Anon groups available in over 133 countries (Al-Anon, n.d.-a). In the US, there are about 11,000 in-person groups and another 900 groups are available virtually (Al-Anon World Service Office, 2022). Al-Anon, which is free and has no requirements for membership, involves COs’ sharing their experiences in applying the Al-Anon principles (e.g., belief in a higher power, admitting powerlessness over alcohol) to problems due to a person in their lives who drinks (Al-Anon, 2024-b). Because Al-Anon is the most available form of help shown to improve the health of COs (Timko et al., 2014), it is important to facilitate its use by identifying factors associated with more Al-Anon participation.

Participation in Al-Anon can have many benefits to COs. Among newcomers to Al-Anon, those with sustained attendance, compared to those who discontinued, reported better quality of life, well-being, ability to handle problems due to the person drinking and cope with life’s problems, and increased self-esteem and less depression at 6-month follow-up (Timko et al., 2015). Newcomers with sustained Al-Anon attendance, compared to those who discontinued, also reported less verbal and physical abuse and a better relationship with the person drinking (Timko et al., 2016). The potential benefits of Al-Anon may be due to members’ feeling connected to other members, receiving assistance working toward important goals, and exposure to sponsors and peers (Timko et al., 2015).

Prior studies have identified correlates of COs’ initial participation in Al-Anon (Timko et al., 2013) and participation in the following 6 months (Timko et al., 2014). Correlates of COs’ Al-Anon participation can be categorized using the Stress-Strain-Coping-Support (SSCS) model, which includes five domains of stressors (patients’ drinking, and COs’ stress, strain, coping, and support; Figure 1) that affect COs’ experiences related to another’s drinking (Orford et al., 2013). In the SSCS model, COs’ central stressor is the severity of the person’s drinking problem. COs’ stress factors include perceived stigma, CO-patient communication, and the quality of their relationship. Strain is the adverse impact of the person’s drinking on COs’ health including problems with their own substance use, mental health, and/or well-being. Coping includes positive and negative strategies COs use to manage the person’s drinking and related stressful experiences. Support for COs includes the presence of trusted others such as friends, family, and providers on whom the CO can rely for support and discussing personal problems.

Figure 1.

The stress-strain-coping-support model

A survey to identify reasons for initial attendance to Al-Anon found that problems with COs’ overall quality of life, including being stressed and angry, and problems in their relationship with the person drinking (stress and strain factors in the SSCS model) were the main contributors (Timko et al., 2013). COs’ dealing with problems due to the patient’s drinking, and feeling stressed, anxious, and unable to relax (strain factor) were also common motivators for COs’ Al-Anon attendance. Among newcomers to Al-Anon, being unemployed, having adult (vs. younger) children, and being less satisfied with their financial situation (strain factor) were positively associated with COs’ likelihood of sustaining Al-Anon attendance for 6 months (Timko et al., 2014). In addition, COs who continued to attend Al-Anon were more likely to report having a diagnosed medical condition, physical abuse, and lower quality of life and well-being (strain factor) than those who discontinued (Timko et al., 2014). Compared to COs who discontinued Al-Anon, COs who continued were also less likely to use avoidance coping (e.g., trying not to think about a problem or crisis; coping factor), and more likely to report problems in their relationship with the person drinking (stress factor), despite having more contact with that person, and more concern about the person’s alcohol use (Timko et al., 2014). In contrast, COs who discontinued Al-Anon were less likely to be troubled by the person’s drinking (central stressor), and to have a medical problem, report physical abuse, and be satisfied with their quality of life and well-being (strain factor).

Missing from the literature is a more comprehensive examination of variables, guided by the SSCS conceptual model, that may serve as potential predictors of COs’ Al-Anon attendance and involvement. This line of research could inform the identification of characteristics or subgroups of COs in need of help and efforts to link them to supportive services. In addition to Al-Anon attendance, it is important to also examine Al-Anon involvement as an outcome given findings showing that COs may experience greater benefit from Al-Anon when they have more intensive, sustained engagement in Al-Anon practices (Timko et al., 2016). No studies have utilized a conceptual framework of potential stressors exerted on COs by the person drinking to guide the selection of potential predictors of their Al-Anon attendance and involvement over time. Thus, in the present study, we used the SSCS conceptual model to guide the selection of potential predictors of COs’ Al-Anon attendance and involvement over 12 months.

Present study

The present study used data from CO-patient dyads who participated in a randomized controlled trial (RCT) testing the effectiveness of an intervention, Al-Anon Intensive Referral (AIR), relative to usual care (UC), to facilitate COs’ participation in Al-Anon (Timko et al., 2022b). Findings from the parent RCT showed no effect of study condition on the primary outcome of COs’ participation in Al-Anon at 12 months post-randomization (Timko et al., 2022b). The present study examined associations of patient and CO characteristics at baseline with COs’ Al-Anon participation (attendance and involvement) at baseline and 3-, 6-, and 12-month follow-ups, after controlling for COs’ demographics.

Examining patient and CO characteristics as potential predictors of COs’ Al-Anon attendance and involvement may have important benefits. For example, identifying patient characteristics (e.g., substance use severity) associated with these outcomes may inform treatment programs’ efforts to help the CO-patient dyad such as determining the intensity of intervention (brief vs. more intensive) most appropriate for treating the patient’s substance use. Identifying characteristics of COs associated with their use of Al-Anon, after the patient’s severity has been considered, is also important as it may inform efforts to improve COs’ outcomes in their own right, including helping them access or continue their engagement in supportive services when needed.

Materials and methods

Sample and procedures

COs

All procedures were Institutional Review Board compliant. To recruit COs for the parent RCT, patients were asked to name potential COs (>18 years old, supportive of the patient’s treatment entry) and provide their COs’ contact information. We attempted to contact a CO, when identified, for each patient entering AUD residential treatment. Of 413 COs approached, 68% provided consent and were enrolled, completed the study baseline assessment, and received $25. COs were paid $25 for each subsequent follow-up. Reasons for COs not enrolling (n=134) were that they did not respond to repeated contact attempts (47%), declined to participate when contacted (27%), did not attend scheduled enrollment sessions (14%), and their contact information from the patient was incorrect (12%) (Timko et al., 2022b). After completing the baseline assessment, COs were randomly assigned to study condition--UC or AIR. COs randomized to UC received the treatment program’s offer of educational sessions designed for COs including information about Al-Anon (e.g., how it can help, how to find a meeting, what to expect when attending), while those assigned to AIR received sessions with an Al-Anon Coach to facilitate participation in Al-Anon (Timko et al., 2022b). Department of Veterans Affairs and community treatment programs at three US geographically dispersed sites (West, Midwest, and South) were selected to increase the generalizability of findings. All treatment sites provided similar UC in terms of addiction treatment services to patients and their COs.

Patients

Patients entering AUD residential treatment were recruited to participate. Patients who had a participating CO (n=279) were a subset of 402 patients enrolled in the parent trial, of whom 123 did not have a CO. To enroll patient participants, those who agreed were first screened for study eligibility (>18 years old, spoke English, no conservator); then, eligible patients were asked to provide informed consent. Of 453 patients approached, 94% agreed to be screened and 96% of those screened were eligible. Ninety-eight percent of eligible patients were enrolled, completed the study’s baseline assessment, and received $25. Participants were paid $25 for each subsequent follow-up.

The study attempted to follow all enrolled COs and patients 3, 6, and 12 months after baseline by telephone. Among COs, follow-up rates were 80%, 71%, and 75% at 3, 6, and 12 months, respectively. Among patients with a participating CO, follow-up rates were 84%, 75%, and 76% at 3, 6, and 12 months, respectively.

Measures

Outcomes

Outcomes were assessed at baseline and each follow-up.

CO Al-Anon attendance and involvement.

At baseline and at 12-month follow-up, COs reported Al-Anon attendance as the number of Al-Anon meetings attended in the past 6 months. At the 3- and 6-month follow-ups, COs reported the number of Al-Anon meetings they had attended in the past 3 months. Because most participants (79% at baseline to 83% at 12-month interview) had not attended any Al-Anon meetings, we dichotomized the number of meetings attended on each occasion as 0 or >0.

To measure involvement, at baseline and each follow-up, COs answered yes or no to 12 items describing aspects of participating in Al-Anon’s program (e.g., Have you had an Al-Anon group sponsor; Have you read Al-Anon group literature). The number of “yes” responses was summed to indicate Al-Anon involvement. At baseline and at 12-month follow-up, items referred to the past 6 months; at 3- and 6-month follow-ups, items referred to the past 3 months. Al-Anon involvement was categorized into 0, 1-2, or ≥3 practices to examine associations between potential predictors and level of engagement in Al-Anon practices (Timko et al., 2016).

Predictors

Predictors were assessed at baseline and included CO and patient characteristics.

CO characteristics

Demographics.

COs reported on their demographics including: age (years); gender (male or female); marital status (married/living with partner vs. separated, divorced, widowed, never married); race (American Indian/Alaskan Native, Asian, Black or African America, Native Hawaiian/Other Pacific Islander, White or multiracial; categorized as White or not); education (0-12 years vs. ≥13 years); income (<$30,000 vs. ≥$30,000); employment status (working for pay vs. not); health insurance status (yes vs. no); and religious practices (religious vs. spiritual vs. atheist, agnostic, or unsure).

Relationship to patient and cohabitation.

The CO’s relationship to the patient was coded as marital (current or former spouse/partner), family (parent, sibling, offspring) or other (friend or other relative). COs also reported whether they were currently living with the patient (yes or no).

Stress

Stigma.

COs’ perceived stigma related to the patient’s alcohol use was assessed with the Stigma Scale (King et al., 2007). Items were rated on a 5-point scale (1=strongly disagree, 5=strongly agree). Disclosure stigma was the mean of 5 items (α=0.84 at baseline), e.g., I feel the need to hide my drinker’s alcohol use from my friends. Positive aspect stigma was the mean of 4 items (α=0.68 at baseline), e.g., My relationship with someone who has an alcohol use disorder has made me more accepting of other people. Discrimination stigma was the mean of 5 items (α=0.76 at baseline), e.g., People have insulted me because of my drinker’s alcohol use. The phrase “my drinker” referred to the patient.

Communication.

COs rated their own communication toward their patient (LePoire, 2006). Items were rated on a 5-point scale (1=strongly disagree, 5=strongly agree). Inconsistent communication was the mean of 6 items, e.g., Sometimes my communication about my drinker’s alcohol use contradicts my behavior (α=0.63 at baseline). Controlling communication was the mean of 3 items, e.g., I express concern regarding alcohol use through ultimatums (α=0.61 at baseline). Nurturing communication was the mean of 3 items, e.g., I often make excuses for my drinker’s alcohol use, I occasionally cover for my drinker’s alcohol use from friends, family, and/or employers (α=0.70 at baseline).

CO-patient relationship and conflict tactics.

The CO rated their relationship with the patient on the Health and Daily Living (HDL) form (Moos et al., 1992) in terms of how often 11 events occurred (0=Never, 4=Very Often). The HDL has been used extensively in prior research and is psychometrically sound (Bi et al., 2015; Holahan et al., 2018). Relationship stressors was the sum of 5 items, e.g., The drinker expected more from you than he or she gave (α=0.89 at baseline). Relationship resources was the sum of 6 items, e.g., The drinker cooperated with things asked of him or her (α=0.89 at baseline).

CO-patient conflict was assessed using an adapted form of the Conflict Tactics Scale (CTS; Straus and Douglas, 2004), which measures tactics used when conflict occurs in dyads. The present study used the violence and negotiation subscales. Use of violence was coded as “yes” if one or more of five items was endorsed, e.g., I pushed, shoved, or slapped my drinker. Use of negotiation was the sum (frequency of occurrence) of 2 items, e.g., I explained my side or suggested a compromise for a disagreement with my drinker.

Strain

Well-being.

The HDL (Moos et al., 1992) was used to measure COs’ mental health and quality of life. Mental health was calculated using the frequency of occurrence (0=very often, 4=never) of 10 items (How often have you experienced feeling, e.g., hopeful, depressed; α=0.82 at baseline). Negative items were reverse scored, and all items were averaged such that higher scores indicated better mental health. Quality of life was defined as the mean of 5 items representing the frequency (0=never, 4=very often) of being satisfied, e.g., with your overall quality of life and well-being (α=0.76 at baseline), with higher scores representing better quality of life. COs also reported the number of years they had been troubled by the patient’s drinking.

Alcohol use and MHG attendance.

COs reported their alcohol use on a single item (taken from the Brief Addiction Monitor [BAM]; Cacciola et al., 2013) assessing the number of days they consumed any alcohol in the past 30 days. Responses were dichotomized as any alcohol use (at least one day) vs. no alcohol use (zero days) in the past 30 days. COs also reported (yes or no) on whether they had ever attended MHGs, such as Alcoholics Anonymous (AA), for their own substance use; this did not include Al-Anon.

Coping

COs’ coping was assessed using the HDL (Moos et al., 1992). COs rated items with respect to their patient’s drinking on a scale from 0 (definitely no) to 3 (definitely yes). Approach coping was the mean of 24 items (α=0.87 at baseline), e.g., Did you think of different ways to deal with the problem? Avoidance coping was the mean of 24 items (α=0.83 at baseline), e.g., Did you try not to think about the problem?

Support

Social support was the mean of two items from the HDL assessing the CO’s number of (a) close friends with whom they felt at ease and could talk to about personal problems, and (b) people they could expect real help from in times of trouble.

Patient characteristics

Substance use.

Patients’ substance use was assessed by self-report using the BAM (Cacciola et al., 2013). The BAM consists of 17 items assessing alcohol and drug use in the past 30 days. It has been used extensively to measure outcomes of persons in treatment for a substance use disorder (e.g., Stockin et al., 2019, Blonigen et al. 2015). From the BAM items, three subscales are derived that measure substance use, risk factors, and protective factors. The substance use subscale is the sum of three items measuring drug and alcohol use over the past 30 days, e.g., How many days did you drink any alcohol, with responses from 0=0 to 4=16-30 days. Scores for the substance use subscale range from 0-12 with higher scores indicating more substance use. Risk factors for substance use is the sum of six items, e.g., In the past 30 days, how much were you bothered by cravings or urges to drink alcohol or use drugs (0=not at all, 4=extremely). Scores range from 0-24, with higher scores indicating more risk. Protective factors for substance use are the sum of six items, e.g., Do you have enough income to pay for necessities such as housing, transportation, food and clothing for yourself and your dependents? (no=0, yes=4). Scores range from 0-24, with higher scores indicating more protection.

AA.

Patients reported whether they attended an AA meeting in the past 6 months (yes or no). In addition, patients answered yes or no to each of 12 items describing AA Involvement (e.g., Have you had a home group). The number of “yes” responses was summed to measure their level of engagement (Cucciare et al., 2024).

Number of alcohol treatment episodes.

Patients reported the number of times (episodes, not number of days or sessions) they had been treated for alcohol use over their lifetime, including (a) inpatient and residential, and (b) outpatient treatment.

Patient-CO relationship and conflict tactics.

The patient rated their relationship with the CO on the HDL (Moos et al., 1992) in terms of how often 11 events occurred (0=Never, 4=Very Often) over the past 6 months. Relationship stressors was the sum of 5 items, e.g., The CO expected more from you than he or she gave (α=0.85 at baseline). Relationship resources was the sum of 6 items, e.g., The CO cooperated with things asked of him or her (α=0.83 at baseline).

Patient-CO conflict was assessed using the adapted form of the Conflict Tactics Scale (CTS; Straus and Douglas, 2004). Use of violence was coded as “yes” if one or more of five items was endorsed, e.g., I pushed, shoved, or slapped my CO. Use of negotiation was the sum (frequency of occurrence) of 2 items, e.g., I explained my side or suggested a compromise for a disagreement with my CO.

Criminal justice involvement.

Patients’ involvement with the justice system was assessed using five self-report items: (1) was the current AUD treatment episode prompted by the criminal justice system (no/yes), (2) were they currently on parole or probation (no/yes), (3) were they presently awaiting charges, trial, or sentencing (no/yes), and (4) the number of times they had been arrested or charged and (5) incarcerated in their lifetime.

Analysis Plan

Thirty-five percent of participants had missing values. To address missing data, variables with missing values were imputed using multiple imputation methods with the assumption that all missing data were missing at random (Timko et al., 2022b). Forty imputed data sets were generated, and all analyses were performed using each individual imputed data set. The final parameter estimates were calculated by averaging the parameter estimates from each of the forty models. SAS 9.4 was used for analyses. A type I error rate of .05 was used for all analyses.

Descriptive statistics for the outcomes of COs’ Al-Anon attendance and involvement were calculated at baseline and 3-, 6-, and 12-month follow-ups. Descriptive statistics for CO and patient predictors, measured at baseline, were also calculated. Bivariate analyses (after adjusting for time) were performed between each predictor and outcome using generalized linear mixed models. Predictors with p values less than 0.20 identified in the bivariate analyses from either model (attendance, involvement; supplementary Tables 1a and 1b) were included in the multivariable models. Outcomes were binary for COs’ Al-Anon attendance and ordinal for Al-Anon involvement. Data were in a repeated measures structure by time; thus, generalized linear mixed models were used for the two outcomes with logit link and cumulative logit link, respectively. The linearity assumption between the continuous predictors and the outcomes in the forms of the corresponding link functions was checked and appropriate forms of the variables were used when the linearity assumption was violated. Interactions between each predictor and time were tested and dropped if not significant. Odds ratios and their 95% confidence intervals were calculated for each predictor.

Results

Descriptive statistics

For COs’ Al-Anon attendance, 21.0% of COs reported attending Al-Anon at baseline (Table 1), 24.0% at 3 months, 17.7% at 6 months, and 16.1% at 12 months (Figure 2). For Al-Anon involvement, 26.1% reported any involvement at baseline (Table 1), 38.6% at 3 months, 35.0% at 6 months, and 30.4% at 12 months (Figure 2).

Table 1.

Baseline descriptive statistics (n=279 concerned other [CO]-patient dyads)

Variables	n (%) or M (SD)
Outcomes (CO)
Attended Al-Anon in past 6 months	59 (21.00)
Al-Anon involvement past 6 months
0	206 (73.93)
1-2	35 (12.66)
3+	37 (13.41)
Predictors
CO demographics
Age	51.91 (14.74)
Gender: Women	215 (77.06)
Marital status
Married/living with partner	142 (50.72)
Separated/divorced/widowed/never married	137 (49.28)
Race: White	184 (65.86)
Education
0-12 years	89 (31.90)
≥13 years	190 (68.10)
Income: ≥$30,000	146 (52.30)
Employed	162 (57.97)
Has health insurance	234 (83.81)
Religious practices
Atheist/Agnostic/Unsure	20 (7.11)
Spiritual	93 (33.21)
Religious	167 (59.69)
CO relationship to patient
Marital (current or former spouse or partner)	92 (32.97)
Family (parent, sibling or offspring)	137 (49.10)
Other (other relative, friend)	50 (17.92)
Currently living with patient	102 (36.52)
Patient Characteristics
Substance use: Brief Addiction Monitor (BAM)
BAM Use	6.79 (4.09)
BAM Risk factors	15.65 (5.64)
BAM Protective factors	13.07 (4.22)
Alcoholics Anonymous (AA) past 6 months
Attended AA meeting	258 (92.44)
AA involvement	5.67 (3.30)
Number of alcohol treatment episodes over their lifetime
Inpatient or residential
0	29 (10.51)
1	75 (26.89)
2+	175 (62.60)
Outpatient
0	123 (44.10)
1	74 (26.56)
2+	82 (29.34)
Patient-CO relationship
Stressors	7.99 (5.53)
Resources	17.26 (5.26)
Patient-CO conflict tactics
Any violence in past 6 months	156 (55.88)
Negotiation	40.20 (68.16)
Criminal justice involvement
Current treatment prompted by the criminal justice system	70 (25.22)
Currently on parole or probation	86 (30.71)
≥1 lifetime incarceration	185 (66.39)
Currently awaiting charges, trial, or sentencing	55 (19.80)
≥1 lifetime arrests or charges	227 (81.25)
CO characteristics
Stress
Stigma
Disclosure	2.59 (1.02)
Positive Aspect	3.79 (0.70)
Discrimination	2.46 (0.91)
Communication
Inconsistent	2.43 (0.74)
Controlling	3.47 (0.91)
Nurturing	2.22 (1.02)
CO-patient relationship
Stressors	11.30 (5.91)
Resources	13.11 (5.75)
CO-patient conflict tactics
Any Violence in the past 6 months	170 (60.82)
Negotiation	57.06 (119.4)
Strain
Well-being
Mental health	2.31 (0.76)
Quality of life	2.92 (0.70)
Years troubled by patient’s drinking	10.06 (10.63)
Alcohol use and mutual-help groups
Consumed alcohol in the past 30 days	140 (50.31)
Attended mutual-help group for own substance use in lifetime	148 (53.07)
Coping
Approach	1.73 (0.60)
Avoidance	1.21 (0.53)
Support	5.88 (6.54)

Notes. M=mean, SD=standard deviation. Because data were imputed with multiple imputed data sets, sample sizes (n’s) were calculated by multiplying 279 by the corresponding percentages. Percentages were the average values from multiple imputed data sets. BAM=Brief Addiction Monitor

Figure 2.

Proportion of COs reporting Al-Anon attendance and involvement at each follow-up.

Descriptive statistics for COs’ demographic characteristics are presented in Table 1. COs had a mean age of 51.9 years (SD=14.7). Most were women, had at least some college, an annual income of at least $30,000, and health insurance, and were employed and married or living with an intimate partner. Most COs were White (64.9%). COs were also African American (20.0%), American Indian or Alaskan Native (2.9%), Native Hawaiian or Other Pacific Islander (2.5%), Asian (1.4%) or identified as more than one race (2.9%) (data not shown). Most COs reported being religious. The CO’s relationship to the patient was predominantly non-marital and most COs were not cohabitating with the patient.

Most patients were men (76.2%), White (66.3%), unmarried (81.5%), and unemployed (75.3%) (data not shown). Patients’ mean age was 43.0 years old (SD=13.2), mean education was 13.1 years (SD=2.1), and mean annual income was $25,187 (SD=$25,949) (data not shown).

Multivariable models for COs’ Al-Anon attendance

Table 2 presents results for the generalized linear mixed model predicting COs’ Al-Anon attendance. COs’ age was positively associated with COs’ Al-Anon attendance. For a one year increase in age, the odds of Al-Anon attendance increased 4%. COs who were White were more likely to attend Al-Anon than COs in the other racial/ethnic group. In addition, COs who self-identified as religious were more likely to attend Al-Anon meetings than COs who self-identified as spiritual. When patients had more AA involvement, COs were more likely to attend Al-Anon meetings; for a one unit increase in patients’ AA involvement, the odds of COs’ Al-Anon attendance increased 16%. COs’ use of more nurturing communication was positively associated with Al-Anon attendance; for a one unit increase in nurturing communication, the odds of Al-Anon attendance increased 73%. COs who abstained from alcohol in the past 30 days were more likely to attend Al-Anon than COs reporting any days of drinking.

Table 2.

Generalized linear mixed model for associations between patient and CO characteristics and COs’ Al-Anon attendance.

	OR (95% CI)
CO demographics
Age	1.04 (1.01, 1.08)
Race: White (ref: non-White)	5.25 (1.88, 14.64)
Religious practice (ref: spiritual)
Atheist/Agnostic/Unsure	1.09 (0.11,10.58)
Religious	3.73 (1.36,10.22)
Currently living with patient: Yes (ref: no)	1.60 (0.63, 4.07)
Patient Characteristics
Alcoholics Anonymous (AA)
AA involvement	1.16 (1.01, 1.34)
Number of alcohol outpatient treatment episodes over their lifetime (ref: 2+)
0	1.27 (0.42, 3.81)
1	2.80 (0.87, 8.97)
Criminal justice involvement
Currently awaiting charges, trial, or sentencing: Yes (ref: no)	2.14 (0.69, 6.63)
Current treatment prompted by the criminal justice system (ref: no)	0.89 (0.31, 2.61)
CO characteristics
Stress
Stigma
Positive aspect	1.63 (0.79, 3.37)
Communication
Nurturing	1.73 (1.08, 2.76)
CO-patient relationship
Stressors	1.07 (0.98, 1.16)
Strain
Well-being
MH heath
at time=baseline & MH=1.8 (25th quartile)	0.55 (0.24, 1.27)
at time=baseline & MH=2.4 (median)	0.41 (0.10, 1.68)
at time=baseline & MH=4 (max)	0.19 (0.00, 7.72)
at time=3-month & MH=1.8 (25th quartile)	0.42 (0.16, 1.11)
at time=3-month & MH=2.4 (median)	0.16 (0.03, 0.88)
at time=3-month & MH=4 (max)	0.01 (0.00, 1.08)
at time=6-month & MH=1.8	0.52 (0.13, 2.08)
at time=6-month & MH=2.4 (median)	0.02 (0.00, 0.39)
at time=6-month & MH=4 (max)	0.00 (0.00, 0.02)
at time=12-month & MH=1.8 (25th quartile)	0.46 (0.14, 1.49)
at time=12-month & MH=2.4 (median)	0.13 (0.01, 1.17)
at time=12-month & MH=4 (max)	0.00 (0.00, 1.51)
Alcohol use and mutual-help groups
Consumed alcohol in the past 30 days: No (ref: Yes)	3.58 (1.41, 9.09)
Attended mutual-help group for own substance use in lifetime: Yes (ref: no)	1.37 (0.55, 3.40)
Coping
Approach	1.80 (0.74, 4.36)
Time (ref: baseline)
3-month at MH score =1.8 (25th quartile)	1.73 (0.77, 3.90)
at MH score =2.4 (median)	1.69 (0.78, 3.65)
at MH score =2.9 (75th quartile)	1.22 (0.49, 3.03)
6-month at MH score =1.8 (25th quartile)	0.86 (0.33, 2.24)
at MH score =2.4 (median)	1.49 (0.62, 3.61)
at MH score =2.9 (75th quartile)	0.62 (0.18, 2.10)
12-month at MH score =1.8 (25th quartile)	0.58 (0.24, 1.42)
at MH score =2.4 (median)	0.63 (0.27, 1.47)
at MH score =2.9 (75th quartile)	0.43 (0.14, 1.28)

Notes. ORs that do not include 1 (in bold) are statistically significant at p<.05; ref=reference category. CI=confidence interval.

The model showed a quadratic relationship between COs’ mental health and their Al-Anon attendance in l the log of odds and significant interactions between COs’ mental health and its quadratic term with time respectively; therefore, the effect of COs’ mental health differed by time and mental health scores (Figure 3). Figure 3 showed that the mental heath effect was not significant at baseline and the 12-month follow-up as the odds ratio bars crossed with the OR=1 line. The mental heath effect was significant at 3- and 6-month follow-up, but only for subgroups of the COs as the figure showed some of the bars did not cross the OR=1 line. The mental health effect was significant only for COs with their scores between 2 and 3.5 at the 3-month follow-up and for COs with their mental health scores > 2 at the 6-month follow-up. Since the ORs for these significant ones were all < 1 (i.e. the bars were on the left side of line OR=1), this indicated that higher mental health scores were negatively associated with attendance for COs in these two follow-ups and for COs having their mental health scores in these ranges, meaning higher (better) mental health was associated lower probability of Al-Anon attendance.

Figure 3.

Odds ratios for the Al Anon attendance model for mental health by time

Multivariable models for COs’ Al-Anon involvement

Table 3 presents results for the generalized linear mixed model for COs’ Al-Anon involvement. COs had more Al-Anon involvement at the 3- and 6-month follow-ups than at baseline. COs’ age was positively associated with Al-Anon involvement. A one-year increase in COs’ age was associated with a 3% increase in their odds of reporting more Al-Anon involvement. COs who were White reported more Al-Anon involvement than COs who were not White. In addition, COs who self-identified as religious reported more Al-Anon involvement than COs who self-identified as spiritual. COs who were living with the patient reported more Al-Anon involvement than COs who were not. When patients reported more AA involvement, COs reported more Al-Anon involvement; specifically, for a one unit increase in patients’ AA involvement, the odds of COs having more Al-Anon involvement increased 12%.

Table 3.

Generalized linear mixed model for associations between patient and CO characteristics and COs’ Al-Anon involvement.

Variable	OR (95% CI)
CO demographics
Age	1.03 (1.01, 1.06)
Race: White (ref: non-White)	2.93 (1.46, 5.88)
Religious practice (ref: Spiritual)
Atheist/Agnostic/Unsure	1.14 (0.25, 5.25)
Religious	2.27 (1.12, 4.60)
Currently living with patient: Yes (ref: no)	2.26 (1.15, 4.45)
Patient Characteristics
Alcoholics Anonymous (AA)
AA involvement	1.12 (1.01, 1.24)
Number of alcohol outpatient treatment episodes over their lifetime (ref: 2+)
0	0.94 (0.43, 2.04)
1	1.66 (0.73, 3.77)
Criminal justice involvement
Currently awaiting charges, trial, or sentencing: Yes (ref: no)	2.28 (1.004, 5.17)
Current treatment prompted by the criminal justice system (ref: no)	1.52 (0.70, 3.32)
CO characteristics
Stress
Stigma
Positive aspect	1.57 (0.92, 2.67)
Communication
Nurturing	1.21 (0.87, 1.69)
CO-patient relationship
Stressors	1.06 (0.997, 1.13)
Strain
Well-being
Mental health	0.59 (0.36, 0.97)
Alcohol use and mutual-help groups
Consumed alcohol in the past 30 days: No (ref: Yes)	2.84 (1.46, 5.51)
Attended mutual-help group for own substance use in lifetime: Yes (ref: no)	1.13 (0.58, 2.20)
Coping
Approach	1.69 (0.89, 3.19)
Time: (ref=baseline)
3-month	2.50 (1.48, 4.22)
6-month	1.80 (1.04, 3.12)
12-month	1.47 (0.85, 2.53)

Notes. ORs that do not include 1 (in bold) are statistically significant at p<.05; ref=reference category. CI=confidence interval.

COs of patients currently engaged with the criminal justice system (awaiting charges, trial, or sentencing) reported more involvement in Al-Anon than COs of patients who were not engaged. COs with better mental health scores reported less Al-Anon involvement. Specifically, for a one unit increase in mental health scores (indicating better mental health), the odds of COs’ having more Al-Anon involvement decreased 41%. COs with no alcohol use in the past 30 days had more Al-Anon involvement than COs with any alcohol use.

Discussion

This study examined patient and CO characteristics in relation to COs’ Al-Anon attendance and involvement over 12 months following patients’ entry into residential treatment for an AUD. Results show that COs’ Al-Anon involvement, but not their attendance, increased at 3- and 6-month follow-ups relative to baseline. COs’ characteristics of being older, White, religious, and living with the patient were associated with more Al-Anon attendance and/or involvement. In terms of the SSCS model, greater patient severity, indicated by more AA involvement and criminal justice engagement (awaiting charges, trial, or sentencing), was associated with COs’ greater Al-Anon attendance and/or involvement. The CO stress factor of nurturing communication (e.g., making excuses for the patient’s drinking) was associated with more Al-Anon attendance, while the strain-related factor of no alcohol use was associated with both more Al-Anon attendance and involvement. Furthermore, the CO strain factor of COs’ poorer mental health was associated with their greater Al-Anon attendance and involvement.

Our findings that COs reporting older age and identifying as White were more likely to attend and be involved in Al-Anon are consistent with prior research. Al-Anon members tend to be slightly older than people new to Al-Anon (Timko et al., 2013). The 2024 Al-Anon membership survey found an average age of 63.4 years, with members attending their first meeting at 44.4 years old (Al-Anon, 2024). Most Al-Anon newcomers (94%; Timko et al., 2013) and members (86%, Al-Anon, 2024) are White. Members of another mutual-help group for COs, Learn to Cope, also found that most COs (98%) were White (Kelly et al., 2017). Together, our findings suggest a need for providers and treatment programs to conduct outreach to the population of younger and racially diverse COs.

The need for outreach to younger and racially diverse COs is supported by evidence that COs can experience many benefits from participating in Al-Anon such as improvements in their understanding of the features and course of AUDs, their own depression and self-acceptance, and their relationship with the person using substances (Cutter and Cutter, 1987). For example, COs attending Learn to Cope reported improvements in their ability to cope with the patient’s substance use and communicate with the patient, reductions in self-blame for the patient’s substance use, and increased proficiency to help the patient with their substance use (Kelly et al., 2017). Outreach efforts could be informed by misconceptions about mutual-help which include that it is designed for older people (Labbe et al., 2015) or problem-solving barriers to attending due to lack of time, transportation, or finances (due to not being able to take time off work). Younger people and racial minorities may face barriers to attending MHGs including being underrepresented in meetings resulting in a perception of not being able to relate to others or experiencing potentially challenging encounters (Labbe et al., 2015; Zemore et al., 2024). Racial minorities may also experience high mistrust of other members, discrimination, and lack of support for help seeking (Zemore et al., 2024). Outreach efforts might focus on educating younger people about Al-Anon (its purpose, how to find a meeting, availability of virtual meetings) to inform them of possible benefits to their health and well-being and to address potential facilitators of meeting attendance such as saving time by attending online meetings. Efforts could also focus on helping racially diverse COs be aware of Al-Anon or Learn to Cope meetings (in person or online) that are tailored to racial minorities. Learn to Cope is a peer-led network that provides support and addiction-focused education to participants. In Learn to Cope, participants share personal experiences, exchange information, and receive education through lectures by addiction professionals. For example, due to the COVID-19 pandemic, Al-Anon now offers more meetings online and specifically for persons of color (called People of Color meetings).

We also found that COs who reported being religious were more likely to attend Al-Anon than those reporting to be spiritual. Other research shows that the degree of match between one’s individual beliefs and those of a substance use recovery support group can affect how much the individual participates in that group, and their outcomes (Atkins and Hawdon, 2007). A national survey of people in recovery for a substance use disorder found that respondents whose personal beliefs matched those of their primary support group (e.g., AA, SMART Recovery, Women for Sobriety) participated more in those groups and had better outcomes (Atkins and Hawdon, 2007). Reasons people may choose not to participate in MHGs, such as those based on the 12 steps, include dislike of their perceived religious aspects and/or the perception that group content is old-fashioned and non-scientific (Tonigan, 2007; Kelly et al., 2024). Findings from the present study suggest that it may be helpful for treatment programs and providers to educate COs on the types of help services available and important aspects of those services (e.g., science-based, being open to people self-identified as religious or not) that may affect their appeal. For example, SMART Recovery is a secular, science-based alternative to Al-Anon and may be appealing to COs who are not interested in the perceived religious components of Al-Anon. Educating COs on available help options and their components has potential to increase their appeal and support COs in finding the option that is right for them.

Guided by the SSCS model, this study examined indicators of five groups of potential stressors: the severity of patients’ drinking, and stress, strain, coping, and support factors affecting COs’ functioning, in relation to COs Al-Anon attendance and involvement. Regarding patients’ drinking severity, we found that COs of patients with more AA involvement reported more Al-Anon attendance and involvement. Further, COs of patients awaiting charges, trial, or sentencing were also more likely to be involved in Al-Anon. Reasons for this may be that patient and CO participation in AA and Al-Anon, respectively, may indicate that the COs foster a shared understanding of the patient’s AUD as both are grounded in the 12-step philosophy. Alternatively, patients’ involvement in AA and/or the justice system may provide COs with an opportunity or window to obtain help for themselves.

Specifically, in the SSCS model, COs are viewed as people exposed to highly stressful circumstances and conditions of adversity through repeated contact with the patient (Orford et al., 2013). COs of family members with alcohol problems report feeling chronic insecurity, concern and fear about their own personal safety, lack of trust in others, and feeling “shut off” from their emotions and other people (Johannessen et al., 2022). These experiences may be made worse for COs living with the patient, which is consistent with our finding that living with the person drinking is associated with more CO Al-Anon involvement. Thus, some COs may worry less, experience a sense of relief that the patient is safe and/or have renewed hope that the patient’s drinking will improve upon the patient participating in AA or being involved in the criminal justice system where treatment can sometimes be accessed. Indeed, about 34% of substance use disorder treatment referrals are made through the criminal justice system (Smith and Strashny, 2016). Offenders may be linked to treatment in several ways, including as an alternative to incarceration, or mandated as a condition of probation or parole supervision (Belenko et al., 2013). COs may see the patient’s participation in AA or their involvement in the criminal justice system as an opportunity to seek help for themselves. Treatment programs and providers might help COs connect to MHGs, including Al-Anon, before the patient chooses to obtain help or enter treatment. One quarter to one third of newcomers to Al-Anon report obtaining help from a mental or medical care provider in the past 6 months (Timko et al., 2013). These care visits represent an opportunity for providers to identify COs who may need help and educate them on the types, availability, and components of MHGs, and their primary purpose, which for Al-Anon is to support the CO whether the person drinking seeks help or treatment. Also, research might determine whether interventions such as intensive referral (education, goal setting, linking to current member; Timko et al., 2006, Timko et al., 2011), conducted simultaneously for both patients and COs, helps each person of the dyad connect to relevant supportive services.

We found that COs using more nurturing communication (an SSCS model stress factor) were more likely to attend Al-Anon meetings. It is possible that the CO’s use of “nurturing” communication (e.g., caretaking when the drinker has been drinking, which may serve to reinforce drinking) is an indicator of losing hope that the person drinking will ever change and/or as a way to avoid conflict with the person drinking (Rotunda et al., 2004)—both of which are key reasons people start participating in Al-Anon (Timko et al., 2013). Therefore, the use of nurturing communication may be an indicator that the CO is finding it difficult to maintain hope and cope with problems due to the person drinking, which in turn, is contributing to their motivation to seek help for themselves.

Our results showed that COs’ reporting abstinence from alcohol and/or poorer mental health were more likely to attend and report more Al-Anon involvement. These findings are consistent with research showing that having high motivation to maintain sobriety, wanting to connect with a support system and community, and to find hope are primary reasons people join 12-step groups (Kelly et al., 2024). Indeed, bonding with other people, having role models, and opportunities to be engaged in rewarding activities are some reasons why COs may benefit from Al-Anon (Timko et al., 2016). COs having mental health difficulties may seek out Al-Anon to find a “healthy” community in which to connect and to find hope, and to learn ways to more effectively cope with problems related to the person’s drinking. Seeking help may be more difficult for COs who drink to cope with difficult emotions and/or the stressful circumstances related to the person’s alcohol use. Among newcomers to Al-Anon, 60% had a drink containing alcohol in the last 30 days and 14% reported 5 or more drinks on a single occasion (Timko et al., 2013), suggesting low rates of heavy drinking among those initiating attendance to Al-Anon. This suggests that treatment programs and providers might encourage COs who are using alcohol to seek help for themselves which may include supportive services (e.g., MHGs) where they can learn healthier ways to respond to challenges, learn from others facing similar problems, and obtain support.

Limitations and conclusions

This study has several strengths and limitations. Strengths of this study are that we collected data from dyads and had high follow-up rates. Data were collected that tapped CO and patient characteristics contained in the SSCS model, allowing us to explore their unique contributions to COs’ Al-Anon attendance and involvement. However, some characteristics selected to represent CO and patient severity may not fully capture individuals’ motivations for engaging in a particular behavior. For example, for the domain of COs’ strain, the variable of any CO alcohol use in the past 30 days, may include COs who are social drinkers and/or those whose alcohol use is not impacted by the patient’s drinking. Also, AA participation in the past 6 months, an indicator of patient severity, may include people who are seeking help before their drinking escalates to a problematic level. Future studies might address this limitation by including interview questions that more fully capture motivations for specific behavior such as drinking or AA use.

An additional limitation of this study is that we cannot claim causality between predictors and COs’ Al-Anon participation in our models. The study’s findings may also be limited to COs of people who entered residential AUD treatment and may not generalize to COs of people who are unable to access treatment, choose not to obtain it, or obtain AUD treatment in other settings such as intensive outpatient or primary care. Additionally, we did not ask COs about their potential use of other forms of mutual help sought by family and friends of persons with a substance use disorder such as Learn to Cope or SMART Recovery. This limitation may have resulted in underestimating help seeking among COs enrolled in this study.

The findings from this study shed light on the potential associations between COs’ demographics (age, race, religious practices) and SSCS model factors including indicators of patients’ substance use severity (AA participation and criminal justice involvement), and COs’ stress (nurturing communication) and strain (mental health and alcohol use) factors that may affect COs participation in Al-Anon. A next step in this line of work might include examining whether predictors of Al-Anon participation identified in this study moderate associations between efforts to link COs to Al-Anon (as described in our parent trial) and COs’ Al-Anon participation. Understanding factors affecting COs’ use of Al-Anon may inform the efforts of treatment programs and other health care providers who encounter COs (such as mental health and primary care providers) to improve the use of these effective and widely available services among this population at risk for poor outcomes. Further, such efforts are critically important as some COs may choose to pursue autonomy and gain independence from the person drinking, regardless of whether the patient chooses to seek help or treatment (Orford et al., 2013). Findings from the present study may help advise treatment programs’ and providers’ efforts, such as offering education about the availability, types, components, and purpose of available supportive services, to help facilitate COs’ use of these services, including Al-Anon, irrespective of patients’ decisions to seek help or treatment.