Social Network Mechanisms of Behavior Change in Alcohol Use Disorder Recovery: A Longitudinal Observational Cohort Study

Social Network Mechanisms of Behavior Change in Alcohol Use Disorder Recovery: A Longitudinal Observational Cohort Study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Social Network Mechanisms of Behavior Change in Alcohol Use Disorder Recovery: A Longitudinal Observational Cohort Study Amanda Doggett, Kyla L. Belisario, Molly Garber, Samuel F. Acuff, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6658717/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Social factors play a pivotal role in both development of and recovery from alcohol use disorder (AUD), and social network analysis (SNA) provides a rigorous framework to understand these influences. The current study applied SNA to understand recovery from AUD, with a secondary aim of examining sex differences in social network influences. A cohort of adults with AUD making a significant recovery attempt ( N = 501) were followed over six waves during a one year period (83% retention) and completed assessments of egocentric SNA of their 20 closest alters and drinking behaviors. Hierarchical models run within a Bayesian imputation framework examined social network characteristics in relation to three recovery outcomes: abstinence, reduction in World Health Organization (WHO) drinking levels, and reductions in drinks/week. Follow-up analyses were stratified by sex. Three social network characteristics predicted abstinence: interaction frequency with alters, number of alters in mutual help organizations (MHOs), and number of family member alters. The latter two factors were also predictive of WHO drinking level and drinks/week. Alter heavy drinking days was negatively associated with reductions in WHO drinking level. Sex differences revealed greater network heavy drinking only impeded WHO level reductions for females, whereas having more MHO program members in one’s network only facilitated recovery for males. These findings reveal the importance of social networks, particularly the family, in AUD recovery. Results also highlight sex differences in how social networks influence recovery, with greater vulnerability to heavy drinking influences in females and greater benefit from MHO engagement for males. Epidemiology Psychology alcohol use disorder addiction recovery abstinence social network social influence Figures Figure 1 Figure 2 Introduction Alcohol is one of the most widely used substances and a major contributor to the global burden of disease, impacting health through both acute (e.g., injury, motor vehicle accidents, violence), and chronic mechanisms (e.g., the 200 + chronic diseases for which alcohol is a component cause) (Rehm et al., 2010 ). Alcohol use disorder (AUD) - characterized by uncontrolled heavy alcohol use which negatively impacts an individual’s health and wellbeing - is one of the most prevalent mental health disorders, with an estimated global prevalence of 5.1% (Carvalho et al., 2019 ; Rehm & Shield, 2019 ). Social factors play a central role in alcohol-related behaviours and exist across multiple levels of influence. At the broadest level, alcohol consumption is deeply embedded in social culture, particularly in high-income countries where rates of AUD are also the highest (Rehm & Shield, 2019 ). At the most granular individual level, social motivations for alcohol use are conceptualized as either enhancement (e.g., to improve mood) or coping (e.g., attenuate negative mood, social conformity) (Cooper et al., 1995 ; Kuntsche et al., 2005 ). In-between these most macro and micro levels are how social networks (i.e., the people we interact with such as friends, family, and coworkers) influence alcohol use and addiction. Literature supports the concept that human health is interconnected with the health of the social networks in which they are embedded, in particular for behavioural aspects like alcohol use (Rosenquist et al., 2010 ; Strickland & Acuff, 2023 ). However, much of the existing literature which examines alcohol use behaviours focuses on individual characteristics, and our understanding of social network influence is limited, particularly for AUD and AUD recovery. Social network analysis (SNA) provides an opportunity to directly study social network influences on alcohol behaviours by mapping the characteristics and connections between individuals in a social circle. There are two main types of SNA: sociocentric and egocentric. Sociocentric network analysis maps the connections between everyone in a single sample (e.g., all faculty in a department or all students in a single dorm), while egocentric network analysis takes a sample of unconnected individuals and maps their individual social circles (Perry et al., 2018 ). While the use of sociocentric analysis is limited to research contexts with a unique interconnected sample, egocentric analysis can be leveraged in most typical research contexts; in this type of research the term “ego” describes each individual in the sample, and each ego is asked to report characteristics and connections for the closest “alters” in their social network. Despite the potential utility of SNA, limited research has applied this approach to understand drinking and alcohol use disorder. Much of the existing literature focuses on adolescent (Ali & Dwyer, 2010 ; Mundt, n.d.) or college populations (Balestrieri et al., 2018 ; Meisel et al., 2015 ; A. M. Russell et al., 2020 , 2021 ), finding that drinking, social exclusion, and other substance use of alters can influence the ego’s own drinking behaviours (Balestrieri et al., 2018 ; Meisel et al., 2015 ; A. M. T. Russell et al., 2023 ). However, research focused on social network features of AUD populations is more sparse. Some previous literature has identified that AUD populations tend to have smaller and less diverse social networks (Mowbray et al., 2014 ), and recovery-specific research has identified that more low-risk pro-abstinence alters predicted abstinence of the ego (Eddie & Kelly, 2017 ; Longabaugh et al., 2010 ). Additional limitations to the social network and AUD recovery literature include studies exclusively focusing on abstinence as an outcome and the use of relatively small 5–10 alter social networks assessments (Eddie & Kelly, 2017 ; Longabaugh et al., 2010 ). The primary aim of this study was to add to limited existing research on social networks and recovery from AUD by investigating which features of social network structure and composition may facilitate or impede recovery. Specifically, among adults with AUD making a significant recovery attempt, this study examined egocentric social network features using a 20-alter social network evaluation as longitudinal predictors of three drinking outcomes: complete abstinence, drinking level based on the World Health Organization (WHO) Alcohol Risk Levels (World Health Organization, 2000 ), and quantitative number of drinks/week. Multiple outcomes were used to systematically evaluate influences across drinking outcomes. Abstinence has historically been used extensively but is necessarily a coarse outcome that cannot reflect some significant reductions in drinking; reductions in WHO levels have been validated as non-abstinent positive outcomes(Witkiewitz et al., 2017 ) and capture ordinal change in drinking levels; and alcohol consumption over time is a continuous drinking variable that provides the highest resolution. Thus, these different outcomes provide increasing levels of resolution, moving from dichotomous to ordinal to continuous. Finally, given evidence of substantive sex differences in social factors and AUD (Holzhauer, 2020 ), a secondary aim of this study was to examine differences in social network mechanisms in relation to the alcohol outcomes by sex. Methods Data and Participants The study sample consisted of N = 501 adults with DSM-5 AUD who reported they were initiating a significant recovery attempt, defined as either i) enrollment in a formal treatment program (22.1%) or ii) a self-initiated informal significant recovery attempt (78.8%). This study was initiated March 2019 and enrollment was on an ongoing basis, and self-declaration of a recovery attempt was added as an inclusion criteria after the onset of the COVID-19 pandemic in March 2020 due to the impacts to in-person treatment. Other inclusion criteria included alcohol being the primary substance for cases where individuals used multiple substances, and being 21–65 years of age. This was a multi-site study which collected data out of McMaster University in Hamilton, Ontario (N = 249) and Massachusetts General Hospital/Harvard University in Boston, Massachusetts (N = 252). Data collection consisted of 6 total waves; wave 1 (baseline) was pre-recovery attempt, while waves 2–6 were 1-,3-,6-,9-, and 12-month follow-ups. Remuneration was a $ 40 gift card for each wave of participation. This protocol was approved by the Hamilton Integrated Research Ethics Board (#3825) and the Partners Human Research Committee at Massachusetts General Hospital (#2017P002345). We report how we determined our sample size, all data exclusions (if any), all manipulations, and all measures in the study Measurement and Variables Alcohol consumption: Timeline Follow-Back (TLFB) The TLFB is a retrospective instrument that was developed to evaluate alcohol use at the day-level (M. B. Sobell et al., 1986 ). The TLFB is considered a valid instrument in populations of individuals with alcohol-related problems (Searles et al., 2000 ; L. C. Sobell et al., 2003 ), including those with comorbid conditions (Carey et al., 2004 ). At baseline, TLFBs were 90-day pre-recovery-attempt; the first two follow-up waves were 45-days, and all subsequent follow-ups were 90-days in length. For each day of the TLFB, participants were asked to report their alcohol consumption, other substance use behaviours, and attendance at any 12-step programs such as alcoholics anonymous (AA). During the follow-up waves, participants who may have otherwise been lost to follow-up due to data collection burden were offered the opportunity to instead report their average alcohol consumption via the Daily Drinking Questionnaire (Collins et al., 2011 ) (10% of observations across time). For the purposes of this study, TLFB and DDQ data were summarized to produce an average drinks per week indicator which was harmonious across instruments and waves. The study leveraged several TLFB indices to measure drinking changes following the significant recovery attempt. The first indicator of interest was abstinence, defined as having abstained from alcohol during all of the days from the TLFB, or reporting no alcohol consumption via the DDQ (i.e., average drinks per week = 0). Abstinence was examined in this study as it is the most physiologically relevant recovery definition carrying the lowest mortality risk (Roerecke et al., 2013 ), and research has supported that abstinence can better predict sustained recovery and improved quality of life compared to non-abstinent definitions (Eddie et al., 2022 ; Subbaraman & Witbrodt, 2014 ). However, research also strongly supports the need for non-abstinent recovery definitions, as non-abstinent outcomes have been associated with relevant improvements in physical and mental health, social functioning, and overall quality of life (Henssler et al., 2021 ; Witkiewitz et al., 2021 ). As such, we also examined alcohol consumption using the World Health Organization (WHO) Alcohol Risk Levels, which classify an individual’s drinking into low-risk (includes abstinence), medium-risk, high-risk, and very high-risk (World Health Organization, 2000 ). WHO Risk levels have been validated as clinical indicators in individuals with AUD, as reduced risk levels are associated with improvements in consequences and functioning (Hasin et al., 2017 ; Witkiewitz et al., 2017 ). The final indicator of interest was average drinks per week, which was included to provide the highest resolution continuous indicator of drinking. Social Network Variables Egocentric social networks were collected using a battery which elicited information on an individual’s 20 closest affiliates (termed “alters”). Each participant (termed “ego”) was asked to provide detailed information on all 20 of their alters, including: gender, relationship, how close they are, how often they interact, alcohol consumption, other substance use, and participation in mutual help organizations (MHOs) (e.g., alcoholics anonymous and any other “anonymous” substance program, or SMART recovery programs). Additionally, egos were asked to report the relationship connections between all alters (termed “ties”). If egos reported identical ties between all their alters (i.e. ‘straight-lined’ responses), this was considered a quality control indicator and variables leveraging tie data were marked as missing. Instances of straight-lining were not common in this study (n = 3 observations over all waves). In effort to mitigate potential loss to follow-up, similar to how DDQ was offered as an alternative to the TLFB, a brief 5-person social network battery was completed by some participants during follow-up waves (10.4% of observations); greater detail on how these data were used is described in Supplemental Materials. In this study, social network variables were summarized to key indicators of interest based on those identified in previous social network and alcohol use literature (Longabaugh et al., 2010 ; Meisel et al., 2015 ; A. M. T. Russell et al., 2023 ). Several indicators were averaged across alters, including: mean closeness to alters (ranged 1 (not close) to 4 (very close)), mean interaction with alters (range 1 (not in the last year) to 5 (daily)), and mean alcohol consumption frequency, heavy drinking frequency (sex-adjusted, defined as 5 or more drinks for males and 4 or more drinks for females), cannabis use frequency, and other illicit substance use frequency of alters (all range 1 (never) to 5 (4 + times per week)). Other indicators were scaled as number of alters out of 20 for greater interpretability (i.e., number of alters who were in MHOs, were treatment providers, were friends, were family, and were women). Lastly, social network density, which is a measure of the closeness of all the alters to each other was calculated using the R package igraph (Csárdi et al., 2024 ), and was scaled from 0 (no density, no connections between alters, to 20 (greatest density, all alters know each other)) for interpretability in the models. Control Variables Variables included in all models as covariates were age, ethnicity (racialized, non-racialized), and subjective evaluation of household’s financial situation (“Not enough to pay bills no matter how hard you try”, “Enough to pay bills, but have had to cut back”, “Enough to pay bills without cutting back, but no “extras””, and “Enough money for extras”) (Najdzionek et al., 2023 ). The latter permitted equivalent socioeconomic assessment at both sites without currency or cost of living adjustments. Additionally, a variable that controlled for severity of AUD at baseline was included and varied depending on model outcome; count of AUD symptoms (range 0–11) from the Diagnostic Assessment Research Tool for DSM-5(American Psychiatric Association, 2022 ) was used in the abstinence models, while baseline WHO drinking level and average drinks per week were used in their respective models. A time varying indicator of average number of MHO meetings attended per week was also included, as participation in this type programming has been positively associated with AUD recovery (Kelly et al., 2020 ). Study wave was included as a continuous indicator. Data Analysis As there was attrition (average retention across waves = 83%) and instances of item non-response present, a Bayesian joint imputation and analysis framework (Erler et al., 2021a ) was used to handle missing data. Specifically, alter-alter ties of the social network data required responses to a substantial number of questions ( C (20,2) = 190) and as such the social network density predictor had the highest degree of data imputed (36%). Wave-by-wave missing data (Supplemental Fig. 1) as well as further details on the missing data approach used in this study, including a list of auxiliary variables, are provided in Supplemental Materials. The principal analyses used were longitudinal linear mixed models (LMMs) and generalized linear mixed models (GLMMs). Three models examined three different drinking outcomes: abstinence (binary GLMM, modelling likelihood of abstinence), WHO drinking risk level (ordinal GLMM, modelling likelihood of decreasing risk level), and drinks per week (LMM, modelling likelihood of greater average drinks per week). All analyses were implemented in R using the JointAI R package (Erler, 2023 ), alongside the ordinal package (Christensen, 2023 ) to model the cumulative logit model GLMM. Convergence of imputation models and precision of posterior estimates was assessed using Gelman-Rubin criterion and Monte Carlo error, in alignment with documented recommendations (Erler et al., 2021b ). Secondary sex-stratified analyses were performed and full results are presented in Supplemental materials. Notably, although both biological sex and gender identity can both impact alcohol use and recovery from AUD in distinct ways, gender identity and biological sex overlapped by more than 98% in this sample. As such, only sex-stratified analyses were performed, but in the present sample these can by in large be a proxy for gender effects as well. Results Baseline Characteristics and Changes over the Recovery Attempt Table 1 presents demographic characteristics of participants at baseline (pre-recovery attempt); in this sample, average drinks per week was 45.95, and average AUD symptoms was 8.16 (severe AUD). Most participants were engaging in very high-risk drinking (76%) according to WHO risk levels. Sex-stratified characteristics are also given in Table 1 ; males consumed an average of 56.71 drinks per week while females consumed an average of 37.98 drinks per week. AUD symptom count was 8.41 AUD for males and 7.98 for females. Table 1 Demographic Characteristics of Participants at Baseline Total N = 501 Females N = 288 Males N = 213 Mean (SD) or n (%) where indicated Demographics Age 41.42 (11.22) 40.76 (10.88) 42.31 (11.62) Ethnicity (racialized), n (%) 104 (20.8%) 56 (19.5%) 48 (22.6%) Subjective financial situation: Enough for extras, n (%) 154 (30.7%) 86 (30.7%) 68 (32.5%) Average MHO meetings attended per week 0.41 (1.52) 0.41 (1.62) 0.41 (1.36%) Alcohol Use Drinks per week 45.95 (43.46) 37.98 (31.24) 56.71 (54.14) AUD Symptom Count 8.16 (2.49) 7.98 (2.53) 8.41 (2.43) WHO Risk Level – Very High Risk, n (%) 380 (75.8%) 234 (81.3%) 146 (68.5%) WHO Risk Level – High Risk, n (%) 95 (19.0%) 45 (15.6%) 50 (23.5%) WHO Risk Level – Medium Risk, n (%) 23 (4.6%) 9 (3.1%) 14 (6.6%) Social Network Features Mean closeness to alters 2.63 (0.52) 2.62 (0.50) 2.63 (0.55) Mean interaction with alters 3.15 (0.48) 3.19 (0.47) 3.10 (0.50) Mean alcohol consumption of alters 2.74 (0.78) 2.82 (0.75) 2.64 (0.80) Mean HDD of alters 2.24 (0.71) 2.67 (0.67) 2.12 (0.76) Mean cannabis use of alters 1.93 (0.66) 1.95 (0.65) 1.90 (0.67) Mean illicit drug use of alters 1.28 (0.34) 1.29 (0.34) 1.27 (0.35) # of alters in MHOs 1.75 (2.82) 1.57 (2.56) 1.99 (3.13) # of alters who are treatment providers 0.83 (1.66) 0.78 (1.46) 0.90 (1.89) # of alters who are friends 8.38 (4.09) 8.59 (3.88) 8.11 (4.35) # of alters who are family 5.82 (3.39) 6.00 (3.38) 5.59 (3.40) # of alters who are women 10.57 (3.40) 12.29 (2.72) 8.34 (2.85) SN Density 7.89 (3.71) 7.94 (3.68) 7.81 (3.77) Totals may not match sample sizes in cases of missing baseline characteristics (N = 2 missing ethnicity, N = 12 missing subjective finance) or marginal cases reporting low-risk alcohol use at baseline (N = 3) SD; standard deviation AUD; alcohol use disorder HDD; heavy drinking days SN; social network MHO; mutual help organization A sizable proportion of participants were able to achieve abstinence during the study; by 1-year (T6) 22% of participants were abstaining from alcohol, but, at the other end of the spectrum, 25% reported engaging in very high risk drinking at 1-year. A more granular presentation of abstinence and WHO trends wave-by-wave is available in Fig. 2 of Supplemental Materials. Social Network Predictors of Drinking Outcomes Table 2 presents the results of models (after missing data imputation) for abstinence, WHO drinking level, and drinks per week. Some social network indicators variables did not show any association with the recovery process, including closeness to alters, mean alcohol, cannabis, and other illicit substance consumption of alters, number of friend alters, and social network density. Indicators that did reveal significant associations with alcohol outcome are detailed below. Table 2 Bayesian Imputed Mixed Models of Abstinence, WHO Drinking Risk Level, and Drinks per Week Abstinence WHO Drinking Level Drinks per week OR 95% CI OR 95% CI β 95% CI Baseline Characteristics Age 1.01 (0.96, 1.05) 1 (0.98, 1.03) 0.05 (-0.1, 0.2) Sex – Female 0.28 (0.09, 0.81)* 0.32 (0.17, 0.57)* -1 (-4.81, 2.81) Ethnicity (racialized) 0.35 (0.1, 1.17) 0.88 (0.46, 1.69) -0.48 (-4.44, 3.46) Subjective financial siutation (Enough for extras) 0.59 (0.14, 2.48) 0.58 (0.25, 1.33) -2.58 (-7.59, 2.42) Site – MGH 0.98 (0.38, 2.52) 1.04 (0.61, 1.76) -0.76 (-3.91, 2.4) Baseline Drinking (Pre-Recovery Attempt) AUD Symptom Count 1.42 (1.17, 1.77)* - - - - WHO Drink Level – High Risk - - 3.81 (2, 7.34)* - - WHO Drink Level – Medium Risk - - 5.99 (1.81, 20.24)* - - Drinks per week - - - - 0.1 (0.05, 0.14)* Time varying Non-SN Variables Time 1.06 (0.93, 1.21) 1.04 (0.97, 1.12) 0.56 (0.11, 1.01) Weekly 12-step meetings attended a 1.33 (1.15, 1.55)* 1.35 (1.21, 1.52)* -0.92 (-1.42, -0.41)* Time varying SN Variables Closeness to alters a 1.49 (0.61, 3.59) 1.58 (0.99, 2.55) -2.51 (-6.05, 1.02) Interaction with alters a 2.54 (1.16, 5.64)* 1.52 (0.99, 2.33) -2.99 (-6.27, 0.31) Alter alcohol consumption a 0.57 (0.25, 1.28) 1.2 (0.78, 1.85) 2.26 (-0.75, 5.28) Alter HDD a 0.97 (0.39, 2.44) 0.49 (0.3, 0.79)* 2.43 (-1.01, 5.91) Alter cannabis use a 0.76 (0.37, 1.54) 1.12 (0.76, 1.65) -0.17 (-2.97, 2.6) Alter illicit drug use a 1.25 (0.42, 3.65) 0.74 (0.42, 1.33) 1.67 (-2.72, 6.12) # of alters in MHOs 1.28 (1.11, 1.47)* 1.13 (1.03, 1.24)* -0.73 (-1.35, -0.12)* # of treatment provider alters 0.93 (0.74, 1.15) 0.93 (0.81, 1.07) 1.25 (0.24, 2.24)* # of friend alters 0.97 (0.86, 1.08) 1.02 (0.96, 1.08) -0.29 (-0.76, 0.18) # of family alters 1.18 (1.02, 1.37)* 1.09 (1.01, 1.18)* -0.89 (-1.47, -0.32)* # of women alters 1.11 (0.97, 1.27) 1.05 (0.98, 1.13) -0.26 (-0.78, 0.26) SN Density 0.98 (0.88, 1.09) 1 (0.94, 1.06) -0.07 (-0.58, 0.43) *p < 0.05 a Mean; AUD; alcohol use disorder; HDD; heavy drinking days; SN; social network; MHO; mutual help organization For abstinence, three social network characteristics were positively predictive: interaction frequency with alters (OR: 1.54 (95% CI: 1.16, 5.64)), number of alters in MHOs (1.28 (1.11, 1.47)), and number of family member alters (1.18 (1.02, 1.37)). Figures 1 and 2 present illustrative examples of the impact of family and heavy drinking in one’s network, respectively. Both figures visualize three example networks of individuals who were abstinent from alcohol at 1-year alongside a propensity score matched control who were still consuming alcohol at 1-year. These select networks and their differences are for illustrative purposes; all abstinent participant networks with complete data and propensity score matched controls are visualized for the impact of family and alter heavy drinking in Supplemental Materials. Like abstinence, for WHO drinking level, number of alters in MHOs (OR: 1.13 (95% CI: 1.03, 1.24)), and number of family member alters (family alters: 1.09 (1.01, 1.18)) were significantly protective. Additionally, heavy drinking days of alters in the network was found to impede lower drinking level (0.49 (0.30, 0.79)). Like abstinence and WHO drinking level, for drinks per week, MHO member alters (β: -0.73 (95% CI: -1.35, -0.12) and family alters (-0.89 (-1.47, -0.32)) were significantly protective. Conversely, having more treatment providers in one’s network was associated with greater likelihood of more drinks per week (1.25 (0.24, 2.24)). Sex Differences Sex-stratified results are presented in Table 3 . Stratification revealed that having more MHO members in one’s network was only protective for males (abstinence: 1.52 (1.16, 2.06), WHO level reduction: 1.19 (1.02, 1.39))), but not for females (abstinence: 1.18 (0.96, 1.47)), WHO level reduction: 1.05 (0.91, 1.21)). Stratification also revealed that alter heavy drinking only impeded WHO drinking reductions for females (0.52 (0.29, 0.95)) but was non-significant for males (0.42 (0.17, 1.05)). Additionally, having more women in one’s network was associated with greater likelihood of abstinence (1.26 (1.06, 1.52)), and WHO level reduction (1.11 (1.02, 1.22)) for females only. Table 3 Sex-stratified Bayesian Imputed Mixed Models of Abstinence, WHO Drinking Risk Level, and Drinks per Week Abstinence OR (95% CI) WHO Drinking Level OR (95% CI) Drinks per week β (95% CI) Females Males Females Males Females Males Baseline Characteristics Age 0.97 (0.91, 1.03) 1.08 (0.98, 1.21) 0.99 (0.96, 1.03) 1.03 (0.98, 1.07) 0.05 (-0.14, 0.24) -0.01 (-0.26, 0.25) Ethnicity (racialized) 0.23 (0.04, 1.14) 0.34 (0.02, 5.07) 0.79 (0.33, 1.85) 0.81 (0.24, 2.7) -2.44 (-7.43, 2.57) 1.04 (-5.6, 7.68) Subjective financial siutation (Enough for extras) 0.39 (0.06, 2.57) 1.57 (0.04, 51.59) 0.29 (0.1, 0.82)* 2.19 (0.43, 11.37) -0.86 (-6.96, 5.25) -2.92 (-11.92, 6.09) Site – MGH 1.38 (0.4, 4.8) 0.67 (0.07, 6.26) 1.07 (0.54, 2.11) 0.79 (0.29, 2.12) 0.73 (-3.15, 4.61) -3.54 (-9.14, 2.1) AUD Symptom Count 1.27 (0.99, 1.68) 2 (1.22, 3.66)* - - - - WHO Drink Level – High Risk - - 3.86 (1.61, 9.38)* 5.64 (1.8, 18.49)* - - WHO Drink Level – Medium Risk - - 3.34 (0.52, 21.61) 12.36 (1.81, 88.02)* - - Drinks per week - - - - 0.14 (0.08, 0.2)* 0.09 (0.03, 0.15)* Time varying non-SN Variables Time 1 (0.84, 1.18) 1.28 (0.99, 1.66) 1 (0.92, 1.1) 1.12 (0.99, 1.26) 0.49 (0, 0.98) 0.4 (-0.48, 1.28) Weekly 12-step meetings attended a 1.25 (1.03, 1.53)* 1.66 (1.23, 2.35)* 1.31 (1.13, 1.51)* 1.53 (1.25, 1.89)* -0.66 (-1.25, -0.08)* -1.43 (-2.35, -0.52)* Time-varying SN Variables Closeness to alters a 1.08 (0.34, 3.5) 1.83 (0.31, 11.46) 1.36 (0.74, 2.48) 2.2 (0.95, 5.19) -0.91 (-5.03, 3.2) -4.19 (-10.46, 2.15) Interaction with alters a 3.08 (1.11, 8.63)* 6.51 (1.15, 44.93)* 1.82 (1.06, 3.14)* 1.23 (0.56, 2.7) -1.8 (-5.43, 1.85) -4 (-10.06, 2.12)* Alter alcohol consumption a 0.6 (0.21, 1.68) 0.26 (0.03, 1.81) 1.04 (0.61, 1.78) 1.58 (0.7, 3.57) 2.74 (-0.6, 6.09) -0.02 (-5.78, 5.75) Alter HDD a 0.67 (0.21, 2.21) 4.18 (0.5, 39.36) 0.52 (0.29, 0.95)* 0.42 (0.17, 1.05) 2.29 (-1.46, 6.01) 1.64 (-5.16, 8.38) Alter cannabis use a 0.68 (0.25, 1.74) 1.14 (0.24, 5.76) 0.99 (0.59, 1.64) 1.38 (0.71, 2.71) -0.3 (-3.57, 2.91) 0.23 (-4.65, 5.05) Alter illicit drug use a 2.63 (0.6, 12) 0.18 (0.01, 1.79) 0.78 (0.33, 1.81) 0.61 (0.24, 1.5) 2.31 (-3.01, 7.7) 3.02 (-4.28, 10.38) # of alters in MHOs 1.18 (0.96, 1.47) 1.52 (1.16, 2.06)* 1.05 (0.91, 1.21) 1.19 (1.02, 1.39)* -0.57 (-1.42, 0.27) -0.89 (-1.88, 0.1) # of treatment provider alters 1.26 (0.88, 1.82) 0.65 (0.41, 0.97) 1.04 (0.83, 1.31) 0.86 (0.7, 1.05) 0.62 (-0.83, 2.03) 0.81 (-0.67, 2.29) # of friend alters 0.99 (0.85, 1.16) 0.9 (0.7, 1.13) 1.05 (0.97, 1.14) 0.98 (0.88, 1.08) -0.27 (-0.8, 0.26) -0.31 (-1.11, 0.5) # of family alters 1.24 (1.03, 1.5)* 1.43 (1.05, 2.01)* 1.12 (1.02, 1.22)* 1.13 (0.98, 1.29) -0.44 (-1.07, 0.18) -1.5 (-2.48, -0.52)* # of women alters 1.26 (1.06, 1.52)* 0.94 (0.69, 1.26) 1.11 (1.02, 1.22)* 0.95 (0.83, 1.07) -0.28 (-0.86, 0.31) -0.42 (-1.37, 0.54) SN Density 0.92 (0.79, 1.06) 0.93 (0.74, 1.16) 0.96 (0.89, 1.04) 1.01 (0.91, 1.14) -0.1 (-0.65, 0.45) 0.14 (-0.68, 1) *p < 0.05 a Mean; AUD; alcohol use disorder; HDD; heavy drinking days; SN; social network; MHO; mutual help organization Discussion The primary purpose of this study was to examine which social network features may facilitate or impede recovery for individuals with AUD and, secondarily, to examine differences in these factors by sex. Social network characteristics significant across all outcomes (abstinence, lower WHO drinking level, and lower drinks per week) included number of family member alters and MHO member alters. Meanwhile, social interaction, treatment providers alters, and alter heavy drinking demonstrated significance for select outcomes. Sex-specific analyses revealed that MHO alters offered a protective effect only for males, while having more women in one’s network was protective for females. Some characteristics showed no association with any outcome; for example, although previous research has suggested clustering of other substance use behaviours in social networks with alcohol use (Meisel et al., 2015 ), this study found that neither cannabis use nor illicit substance use of alters were significantly associated with any of the recovery outcomes. Alter closeness, number of friend alters, and social network density also did not demonstrate any association with recovery outcomes. Recovery Process Facilitators Family One of the most consistent findings across all models was that a greater number of family members in one’s network facilitated greater reductions in drinking. Although family are often inherently closer ties, our findings suggest that family presence confers additional benefits beyond closeness, as closeness of ties was accounted for in all models. A previous study which examined specific family features found that good adaptability (i.e., flexibility to adjust to changes) and cohesion (i.e., degree of emotional connection) within a family was protective against relapse for individuals with substance use disorders (Zhang & Zeng, 2023 ). Our findings further support the critical role of family in recovery, emphasizing the value of involving family members in treatment and support interventions. It is worth noting however that our findings and interpretation are on average and assume a positive support lens that may not apply to all individuals. For example, a family with poor adaptability and cohesion may still impede recovery for an individual regardless of how many family members exist in their network. Mutal Help Organizations The presence of network members affiliated with mutual help organizations (MHOs) (e.g., alcoholics anonymous) facilitated recovery across all drinking outcomes. This is consistent with existing research demonstrating that one way in which MHOs can be effective is through social network “transplantation”(Kaskutas et al., 2002 ; Kelly et al., 2011 ; Levitt et al., n.d.; The_persistent_influence_of_so , n.d.) whereby pro-drinking individuals are replaced with pro-recovery individuals. Importantly, these benefits persisted even when controlling for individual MHO attendance, which itself was associated with positive recovery outcomes, consistent with prior research (Kelly et al., 2020 ). Social Interaction This study also found that greater frequency of interaction with one’s network predicted greater likelihood of abstinence. This is consistent with a previous study which found that greater time spent with friends predicted percent days abstinent, although this protective effect was reversed when those friends were high-risk SUDs (Eddie & Kelly, 2017 ). Existing literature has also identified social isolation (i.e., little to no interaction with others) as risk factor for the development SUDs (Chou et al., 2011 ; Desai et al., 2024 ). Our findings extend this literature by highlighting the potential benefit of social interaction in the recovery process. Interestingly, neither closeness of ties nor social network density (i.e., how interconnected one’s social ties are) significantly impacted drinking outcomes, suggesting that interaction frequency may be more important than the number of close relationships or overall network cohesion. Alternatively, this may suggest that our findings could in-part reflect participation in substance-free reinforcing social situations, which are known to be inversely related to substance use (Acuff et al., 2019 ). Recovery Barriers Alter Heavy Drinking In this study, across the 1-year recovery process greater alter heavy drinking was found to be inversely associated with lower WHO drinking level; when disaggregated by sex, this effect was only significant for females. Previous research has consistently shown that drinking in one’s network influences individual drinking patterns (Balestrieri et al., 2018 ; A. M. Russell et al., 2020 ; A. M. T. Russell et al., 2023 ), but very limited research has examined this within AUD populations. Previous work in the sample from the present study found that social network drinking mediated the relationship between a person increasing their AA attendance and reducing their drinks per week 6-weeks later (Levitt et al., n.d.). External studies have also revealed that network drinking and time spent with high-risk friends impeded abstinence (Eddie & Kelly, 2017 ; Longabaugh et al., 2010 ). However, these studies do not present disaggregated findings by sex or gender, making it difficult to compare to the present studies sex-specific findings. Our results emphasize the need for future research examining the role of social network drinking in AUD recovery, particularly through a sex and gender lens. Treatment Providers in Network Having a greater number of treatment providers in one’s social network was associated with greater drinks per week. While this may appear counterintuitive, it may reflect a lack of broader social ties. Social network instruments ask individuals to list their closest social ties, and inclusion of treatment providers in one’s network of closest 20 connections implies a lack of ties with others (e.g., family and friends). Given that our study identified family and social interaction as recovery facilitators, it follows that individuals who displace these relationships with professional providers may be experiencing challenges in reducing their alcohol consumption. Moreover, it is also possible that this displacement is to ameliorate the influence of negative social network influences that may not promote recovery (e.g., pro-drinking individuals). It is also worth highlighting that more treatment providers in a social network could also proxy greater engagement in formal treatment, which may be more likely for those who are struggling in the recovery process. Sex differences Sex-disaggregated findings demonstrated that for females only, having more women in their social network was associated with greater likelihood of abstinence and lower WHO drinking level. Limited research has examined how gender composition of social networks can influence alcohol use, and those that do have mostly focused on non-SUD adolescent and young adult populations (Deutsch et al., 2014 ; Grard et al., 2018 ; Gustafsson et al., 2021 ). Recovery research has focused more on gender-based programming, demonstrating that women often feel more comfortable and supported in women-only recovery groups (Johnstone et al., 2023 ; McCrady et al., 2020 ). Our findings suggest this sense of social support and shared experience may extend beyond the recovery group context to social networks more broadly. Importantly, these findings are unlikely to be inherently tied to gender and instead likely reflect the different types of support which may be more frequently given by women to women. For example, emotional support (e.g., encouragement, communication) and tangible support (e.g., childcare during treatment) in one’s network have been found to be highly important for women in recovery from SUDs (Tracy et al., 2010 ). Sex-disaggregated analyses also indicated that the presence of MHO network members was only significantly related to recovery outcomes for males, but not females. This is consistent with a previous study which found that while both women and men benefit from AA, the social transplantation impacts are a greater benefit for men (Kelly & Hoeppner, 2013 ). This may stem from differential drinking contexts by gender (i.e., women with AUD drinking in more solitary contexts) (Kelly & Hoeppner, 2013 ), or because women in recovery may have higher baseline social support than men (Cano et al., 2017 ; Patton et al., 2024 ; Scoglio et al., 2023 ). It is important to clarify that the differences in our study only applied to social network composition; women still saw benefits of participating in MHOs themselves, and some existing research has suggested women may even benefit more from long-term AA participation than men (Moos et al., n.d.)). Implications Findings from this study yield several implications for clinical practice and future research. First, our results reinforce the importance of frequent social interaction and family support in recovery, supporting existing recommendations to incorporate family members into addiction treatment (Kourgiantakis et al., 2021 ; Ventura & Bagley, 2017 ). This research also demonstrates potential negative impacts of having heavy drinking individuals in the network, and the benefits that can be conferred when instead pro-recovery individuals (such as those in MHO organizations) are in one’s network. However, findings also replicate other findings revealing that there can be sex/gender differences in recovery, and women in particular may be more impacted by having a heavy drinking network and benefit less from the social transplantation mechanisms conferred by MHOs (even though women still benefit from MHO participation). From a clinical perspective, assessing the characteristics of an individual’s social circle—including family presence, degree of social interaction, and drinking behaviors of network members—may provide valuable insights into leverage points for AUD recovery. Future research should continue to examine social networks of those in recovery from AUD in other samples, with particular attention to gender and sex-specific effects. Strengths and Limitations This study has a number of strengths, including the use of a relatively large sample size and large social network assessment comparative to previous social network research, multiple study sites, and the use of multiple follow-up periods extending to 1-year post recovery attempt with high follow-up rates. This study also used several definitions of drinking in order to capture additional alcohol outcomes related to recovery that may exist upstream of complete abstinence. Lastly, the analytical approach used in this study was robust to missing data bias, which is particularly important to manage in substance use research (Claus et al., 2002 ; Hallgren et al., 2016 ; Hallgren & Witkiewitz, 2013 ). Nonetheless, the findings should also be interpreted with respect to several limitations. First, this study was a convenience sample with unknown generalizability; extension of findings to other populations should be done with caution, although this study nonetheless add to the growing body of literature examining the importance of social networks in AUD recovery. Next, because this study period overlapped with the COVID-19 pandemic, recovery trajectories and related influences of social networks may have been impacted (positively or negatively). In particular, the lack of in-person support during lockdowns may have negatively impacted some participants, but at the same time this may have caused some individuals to rely more on their social networks. Lastly, in this study we viewed social networks through a positive lens (e.g. assuming that interactions and relationships were positive and not deleterious); while this is likely true on average, it may not be true at the individual level. For example, while this study finds that on average those with more family members in their network were more likely to reduce their drinking (based on all definitions), at the individual-level, unsupportive family members could still negatively impact recovery. As such, clinicians or those working with AUD populations need to take caution when interpreting, applying, or leveraging findings of clinical epidemiological research like the present study, as the population average effects presented may not always translate to individual cases. (62–64) Conclusion This study examined the impact of social network characteristics on the recovery process for individuals with alcohol use disorder. More family members in one’s network and more frequent social interaction were consistently associated with better recovery-related outcomes. Some impacts varied by sex; having a heavy drinking network was only a barrier to recovery for females, while having more network members in mutual help organizations was associated with better alcohol outcomes only for males. Taken together, these findings add to the growing body of empirical work validating the central importance of social networks in successful AUD recovery and highlight specific and actionable network features that may inform future social network-based interventions. References Acuff SF, Dennhardt AA, Correia CJ, Murphy JG (2019) Measurement of substance-free reinforcement in addiction: A systematic review. Clinical Psychology Review, vol 70. Elsevier Inc, pp 79–90. https://doi.org/10.1016/j.cpr.2019.04.003 Ali MM, Dwyer DS (2010) Social network effects in alcohol consumption among adolescents. Addict Behav 35(4):337–342. https://doi.org/10.1016/j.addbeh.2009.12.002 American Psychiatric Association (2022) Substance-Related and Addictive Disorders. Diagnostic and Statistical Manual of Mental Disorders. American Psychiatric Association Publishing. https://doi.org/10.1176/appi.books.9780890425787.x16_Substance_Related_Disorders Balestrieri SG, Diguiseppi GT, Meisel MK, Clark MA, Ott MQ, Barnett NP (2018) U.S. college students’ social network characteristics and perceived social exclusion: A comparison between drinkers and nondrinkers based on past-month alcohol use. J Stud Alcohol Drug 79(6):862–867. https://doi.org/10.15288/JSAD.2018.79.862 Cano I, Best D, Edwards M, Lehman J (2017) Recovery capital pathways: Modelling the components of recovery wellbeing. Drug Alcohol Depend 181:11–19. https://doi.org/10.1016/j.drugalcdep.2017.09.002 Carey KB, Carey MP, Maisto SA, Henson JM (2004) Temporal stability of the timeline followback interview for alcohol and drug use with psychiatric outpatients. J Stud Alcohol 65(6):774–781. https://doi.org/10.15288/jsa.2004.65.774 Carvalho AF, Heilig M, Perez A, Probst C, Rehm J (2019) Alcohol use disorders. Lancet 394(10200):781–792. https://doi.org/10.1016/S0140-6736(19)31775-1 Chou K-L, Liang K, Sareen J (2011) The Association Between Social Isolation and DSM-IV Mood, Anxiety, and Substance Use Disorders: Wave 2 of the National Epidemiologic Survey on Alcohol and Related Conditions. J Clin Psychiatry 72:11. https://doi.org/10.4088/JCP.10m06019gry Christensen RHB (2023) Package ordinal. https://cran.r-project.org/web/packages/ordinal/ordinal.pdf Claus RE, Kindleberger LR, Dugan MC (2002) Predictors of attrition in a longitudinal study of substance abusers. J Psychoactive Drugs 34(1):69–74. https://doi.org/10.1080/02791072.2002.10399938 Collins RL, Parks GA, Marlatt GA (2011) Daily Drinking Questionnaire. In PsycTESTS Dataset . https://doi.org/10.1037/t02146-000 Cooper ML, Frone MR, Russell M, Mudar P (1995) Drinking to Regulate Positive and Negative Emotions: A Motivational Model of Alcohol Use. In Journal of Personality and Social Psychology (Vol. 69, Issue 5) Csárdi G, Nepusz T, Müller K, Horvát S, Traag V, Zanini F, Noom D (2024) igraph for R: R interface of the igraph library for graph theory and network analysis . https://doi.org/10.5281/ZENODO.14347716 Desai R, Karim S, Freeborn J, Trivedi C, Husain K, Jain S (2024) Contextualizing the Relationship Between Social Isolation and Substance Abuse. Prim Care Companion CNS Disorders 26(5). https://doi.org/10.4088/PCC.23M03679 Deutsch AR, Steinley D, Slutske WS (2014) The Role of Gender and Friends’ Gender on Peer Socialization of Adolescent Drinking: A Prospective Multilevel Social Network Analysis. J Youth Adolesc 43(9):1421–1435. https://doi.org/10.1007/s10964-013-0048-9 Eddie D, Bergman BG, Hoffman LA, Kelly JF (2022) Abstinence versus moderation recovery pathways following resolution of a substance use problem: Prevalence, predictors, and relationship to psychosocial well-being in a U.S. national sample. Alcoholism: Clin Experimental Res 46(2):312–325. https://doi.org/10.1111/acer.14765 Eddie D, Kelly JF (2017) How many or how much? Testing the relative influence of the number of social network risks versus the amount of time exposed to social network risks on post-treatment substance use. Drug Alcohol Depend 175:246–253. https://doi.org/10.1016/j.drugalcdep.2017.02.012 Erler NS (2023) Package Joint AI. https://cran.r-project.org/web/packages/JointAI/JointAI.pdf Erler NS, Rizopoulos D, Lesaffre EMEH (2021a) JointAI: Joint Analysis and Imputation of Incomplete Data in R. J Stat Softw 100(20):1–56. https://doi.org/10.18637/JSS.V100.I20 Erler NS, Rizopoulos D, Lesaffre EMEH (2021b) JointAI: Joint Analysis and Imputation of Incomplete Data in R. J Stat Softw 100(20):1–56. https://doi.org/10.18637/JSS.V100.I20 Grard A, Kunst A, Kuipers M, Richter M, Rimpela A, Federico B, Lorant V (2018) Same-Sex Friendship, School Gender Composition, and Substance Use: A Social Network Study of 50 European Schools. Subst Use Misuse 53(6):998–1007. https://doi.org/10.1080/10826084.2017.1392976 Gustafsson NK, Rydgren J, Rostila M, Miething A (2021) Social network characteristics and alcohol use by ethnic origin: An ego-based network study on peer similarity, social relationships, and coexisting drinking habits among young Swedes. PLoS ONE 16(4). https://doi.org/10.1371/journal.pone.0249120 Hallgren KA, Witkiewitz K (2013) Missing Data in Alcohol Clinical Trials: A Comparison of Methods. Alcoholism: Clin Experimental Res 37(12):2152–2160. https://doi.org/10.1111/acer.12205 Hallgren KA, Witkiewitz K, Kranzler HR, Falk DE, Litten RZ, O’Malley SS, Anton RF (2016) Missing Data in Alcohol Clinical Trials with Binary Outcomes. Alcoholism: Clin Experimental Res 40(7):1548–1557. https://doi.org/10.1111/acer.13106 Hasin DS, Wall M, Witkiewitz K, Kranzler HR, Falk D, Litten R, Mann K, O’Malley SS, Scodes J, Robinson RL, Anton R, Fertig J, Isenberg K, McCann D, Meulien D, Meyer R, O’Brien C, Ryan M, Silverman B, Zakine B (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(6):469–476. https://doi.org/10.1016/S2215-0366(17)30130-X Henssler J, Müller M, Carreira H, Bschor T, Heinz A, Baethge C (2021) Controlled drinking—non-abstinent versus abstinent treatment goals in alcohol use disorder: a systematic review, meta-analysis and meta-regression. Addiction, vol 116. Blackwell Publishing Ltd, pp 1973–1987. 8 https://doi.org/10.1111/add.15329 Holzhauer C (2020) Sex and Gender Effects in Recovery from Alcohol Use Disorder. Alcohol Research: Current Reviews , 40 (3), 2020. https://doi.org/10.35946/arcr.v40.3.03 Johnstone S, Dela Cruz GA, Kalb N, Tyagi SV, Potenza MN, George TP, Castle DJ (2023) A systematic review of gender-responsive and integrated substance use disorder treatment programs for women with co-occurring disorders. American Journal of Drug and Alcohol Abuse, vol 49. Taylor and Francis Ltd, pp 21–42. 1 https://doi.org/10.1080/00952990.2022.2130348 Kaskutas LA, Bond J, Humphreys K (2002) Social networks as mediators of the effect of Alcoholics Anonymous. Addiction 97(7):891–900. https://doi.org/10.1046/j.1360-0443.2002.00118.x Kelly JF, Hoeppner BB (2013) Does Alcoholics Anonymous work differently for men and women? A moderated multiple-mediation analysis in a large clinical sample. Drug Alcohol Depend 130(1–3):186–193. https://doi.org/10.1016/j.drugalcdep.2012.11.005 Kelly JF, Humphreys K, Ferri M (2020) Alcoholics Anonymous and other 12-step programs for alcohol use disorder. In Cochrane Database of Systematic Reviews (Vol. 2020, Issue 3). John Wiley and Sons Ltd. https://doi.org/10.1002/14651858.CD012880.pub2 Kelly JF, Stout RL, Magill M, Tonigan JS (2011) The role of Alcoholics Anonymous in mobilizing adaptive social network changes: A prospective lagged mediational analysis. Drug Alcohol Depend 114(2–3):119–126. https://doi.org/10.1016/j.drugalcdep.2010.09.009 Kourgiantakis T, Ashcroft R, Mohamud F, Fearing G, Sanders J (2021) Family-Focused Practices in Addictions: A Scoping Review. J Social Work Pract Addictions 21(1):18–53. https://doi.org/10.1080/1533256X.2020.1870287/ASSET/09BE2E64-23A2-47CF-B022-3B5093380EEF/ASSETS/IMAGES/WSWP_A_1870287_F0001_OC.JPG Kuntsche E, Knibbe R, Gmel G, Engels R (2005) Why do young people drink? A review of drinking motives. Clin Psychol Rev 25(7):841–861. https://doi.org/10.1016/j.cpr.2005.06.002 Levitt E, Singh D, Belisario K, Doggett A, Clifton A, Stout R, Kelly JF, MacKillop J (n.d.). Changes in Alcohol-related Social Network Composition Mediate the Effects of AA Meeting Attendance on Drinking following a Recovery Attempt in Adults with Alcohol Use Disorder Longabaugh R, Wirtz PW, Zywiak WH, O’malley SS (2010) Network Support as a Prognostic Indicator of Drinking Outcomes: The COMBINE Study. J Stud Alcohol Drug 71(6):837–846. https://doi.org/10.15288/jsad.2010.71.837 McCrady BS, Epstein EE, Fokas KF (2020) Treatment interventions for women with alcohol use disorder. Alcohol Research: Curr Reviews 40(2). https://doi.org/10.35946/arcr.v40.2.08 Meisel MK, Clifton AD, Mackillop J, Goodie AS (2015) A social network analysis approach to alcohol use and co-occurring addictive behavior in young adults. Addict Behav 51:72–79. https://doi.org/10.1016/j.addbeh.2015.07.009 Moos RH, Moos BS, Timko C (n.d.) (eds) Gender,Treatment and Self-Help in Remission from Alcohol Use Disorders. In Original Research Clinical Medicine & Research (Vol. 4). http://www.clinmedres.org Mowbray O, Quinn A, Cranford JA (2014) Social networks and alcohol use disorders: Findings from a nationally representative sample. Am J Drug Alcohol Abuse 40(3):181–186. https://doi.org/10.3109/00952990.2013.860984 Mundt M P. (n.d.). The Impact of Peer Social Networks on Adolescent Alcohol Use Initiation Najdzionek P, McIntyre-Wood C, Amlung M, MacKillop J (2023) Incorporating socioeconomic status into studies on delay discounting and health via subjective financial status: An initial validation in tobacco use. Exp Clin Psychopharmacol 31(2):475–481. https://doi.org/10.1037/pha0000628 Patton R, Chou J, Kestner T, Feeney E (2024) Exploring social connectedness, isolation, support, and recovery factors among women seeking substance use treatment. Women Health 64(3):202–215. https://doi.org/10.1080/03630242.2024.2308518 Perry BL, Pescosolido BA, Borgatti SP (2018) Egocentric Network Analysis. Cambridge University Press. https://doi.org/10.1017/9781316443255 Rehm J, Baliunas D, Borges GLG, Graham K, Lrving H, Kehoe T, Parry CD, Patra J, Popova S, Poznyak V, Roerecke M, Room R, Samokhvalov AV, Taylor B, DIMENSIONS OF ALCOHOL CONSUMPTION AND BURDEN OF DISEASE-AN OVERVIEW (2010) Addiction 105(5):817–843. https://doi.org/10.1111/j.1360-0443.2010.02899.x . THE RELATION BETWEEN DIFFERENT Rehm J, Shield KD (2019) Global burden of alcohol use disorders and alcohol liver disease. Biomedicines 7(4). https://doi.org/10.3390/biomedicines7040099 Roerecke M, Gual A, Rehm J (2013) Reduction of alcohol consumption and subsequent mortality in alcohol use disorders: Systematic review and meta-analyses. J Clin Psychiatry 74(12). https://doi.org/10.4088/JCP.13r08379 Rosenquist JN, Murabito J, Fowler JH, Christakis NA (2010) The spread of alcohol consumption behavior in a large social network. Ann Intern Med 152(7):426–433. https://doi.org/10.7326/0003-4819-152-7-201004060-00007 Russell AM, Barry AE, Patterson MS (2020) A comparison of global and egocentric network approaches for assessing peer alcohol use among college students in the United States. Drug Alcohol Rev 39(7):984–993. https://doi.org/10.1111/DAR.13140 Russell AM, Patterson MS, Barry AE (2021) College Students’ Perceptions of Peer Alcohol Use: A Social Network Analytic Approach. Subst Use Misuse 56(1):46–53. https://doi.org/10.1080/10826084.2020.1833929 Russell AMT, Monds L, Hing N, Kroll J, Russell AM, Thorne HB (2023) Social Associations and Alcohol Consumption in an Australian Community Sample: An Egocentric Social Network Analysis Psychology of Addictive Behaviors. Association 2024 38(2):211–221. https://doi.org/10.1037/adb0000954 Scoglio AAJ, McFarland G, Marquez CI, Matsumoto A, Lincoln AK (2023) Social Support and Associated Factors Among Men and Women in Pre-COVID Substance Use Treatment. Commun Ment Health J. https://doi.org/10.1007/s10597-023-01218-7 Searles JS, Helzer JE, Walter DE (2000) Comparison of Drinking Patterns Measured by Daily Reports and Timeline Follow Back. Psychol Addict Behav 14(3):277–286. https://doi.org/10.1037//OS93-164X.14.3.277 Sobell LC, Agrawal S, Sobell MB, Leo GI, Young LJ, Cunningham JA, Simco ER (2003) Comparison of a quick drinking screen with the timeline followback for individuals with alcohol problems. J Stud Alcohol 64(6):858–861. https://doi.org/10.15288/jsa.2003.64.858 Sobell MB, Sobell LC, Klajner F, Pavan D, Basian E (1986) The reliability of a timeline method for assessing normal drinker college students’ recent drinking history: Utility for alcohol research. Addict Behav 11(2):149–161. https://doi.org/10.1016/0306-4603(86)90040-7 Strickland JC, Acuff SF (2023) Role of social context in addiction etiology and recovery. Pharmacology Biochemistry and Behavior , 229 . https://doi.org/10.1016/j.pbb.2023.173603 Subbaraman MS, Witbrodt J (2014) Differences between abstinent and non-abstinent individuals in recovery from alcohol use disorders. Addict Behav 39(12):1730–1735. https://doi.org/10.1016/j.addbeh.2014.07.010 The_persistent_influence_of_so . (n.d.) Tracy EM, Munson MR, Peterson LT, Floersch JE (2010) Social support: A mixed blessing for women in substance abuse treatment. J Social Work Pract Addictions 10(3):257–282. https://doi.org/10.1080/1533256X.2010.500970 Ventura AS, Bagley SM (2017) To Improve Substance Use Disorder Prevention, Treatment and Recovery: Engage the Family. Journal of Addiction Medicine, vol 11. Lippincott Williams and Wilkins, pp 339–341. 5 https://doi.org/10.1097/ADM.0000000000000331 Witkiewitz K, Hallgren KA, Kranzler HR, Mann KF, Hasin DS, Falk DE, Litten RZ, O’Malley SS, Anton RF (2017) Clinical Validation of Reduced Alcohol Consumption After Treatment for Alcohol Dependence Using the World Health Organization Risk Drinking Levels. Alcoholism: Clin Experimental Res 41(1):179–186. https://doi.org/10.1111/acer.13272 Witkiewitz K, Wilson AD, Roos CR, Swan JE, Votaw VR, Stein ER, Pearson MR, Edwards KA, Tonigan JS, Hallgren KA, Montes KS, Maisto SA, Tucker JA (2021) Can Individuals With Alcohol Use Disorder Sustain Nonabstinent Recovery? Non-abstinent Outcomes 10 Years After Alcohol Use Disorder Treatment. J Addict Med 15(4):303–310. https://doi.org/10.1097/ADM.0000000000000760 World Health Organization (2000) International Guide for Monitoring Alcohol Consumption and Related Harm Zhang X, Zeng X (2023) Effects of family functioning on relapse among individuals with drug addiction in compulsory isolation: a chained mediation model. Curr Psychol 42(3):1701–1711. https://doi.org/10.1007/s12144-021-01561-6 Additional Declarations The authors declare potential competing interests as follows: JM is a principal in Beam Diagnostics, Inc. and has consulted to Clairvoyant Therapeutics, Inc. All other authors report no conflicts of interest. Supplementary Files SupplementalMaterials.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6658717","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":456702659,"identity":"d0f4995e-b46d-4570-8a30-c1217cae91a4","order_by":0,"name":"Amanda Doggett","email":"","orcid":"https://orcid.org/0000-0001-8538-432X","institution":"McMaster University","correspondingAuthor":false,"prefix":"","firstName":"Amanda","middleName":"","lastName":"Doggett","suffix":""},{"id":456701751,"identity":"4d285c7f-a21b-4288-9fa5-b40148ec3a21","order_by":1,"name":"Kyla L. Belisario","email":"","orcid":"https://orcid.org/0000-0001-5403-9531","institution":"McMaster University","correspondingAuthor":false,"prefix":"","firstName":"Kyla","middleName":"L.","lastName":"Belisario","suffix":""},{"id":456702658,"identity":"6e0be721-fda8-43d8-9737-f44c989c1f22","order_by":2,"name":"Molly Garber","email":"","orcid":"https://orcid.org/0000-0003-4595-6017","institution":"McMaster University","correspondingAuthor":false,"prefix":"","firstName":"Molly","middleName":"","lastName":"Garber","suffix":""},{"id":456702877,"identity":"62e4f005-c192-432b-9855-d2b5dd5cbe57","order_by":3,"name":"Samuel F. Acuff","email":"","orcid":"https://orcid.org/0000-0002-1934-2639","institution":"Harvard Medical School","correspondingAuthor":false,"prefix":"","firstName":"Samuel","middleName":"F.","lastName":"Acuff","suffix":""},{"id":456703680,"identity":"2f6e4209-24cd-4d74-9154-39243f551ff8","order_by":4,"name":"Liah Rahman","email":"","orcid":"https://orcid.org/0000-0001-7409-3068","institution":"McMaster University","correspondingAuthor":false,"prefix":"","firstName":"Liah","middleName":"","lastName":"Rahman","suffix":""},{"id":456706372,"identity":"9ec99436-793b-4441-816b-a9e45b3fbf54","order_by":5,"name":"Allan Clifton","email":"","orcid":"https://orcid.org/0000-0001-8249-8065","institution":"Vassar College","correspondingAuthor":false,"prefix":"","firstName":"Allan","middleName":"","lastName":"Clifton","suffix":""},{"id":456706373,"identity":"df365c32-d2aa-4125-acba-f9518a694e50","order_by":6,"name":"John F. Kelly","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABA0lEQVRIie3PMUvDQBTA8XccXJfg4fZC9Ts8CJRIRb9KipBbChkNKPamTIJrJeBncHQUhGQ511LoUpe4Ki5O0kuj0OUko+D9p8fxfnccgM/3BxvMs4azAgMJIIDp7hQBuJOwuRGWxAeh7k1urwUHkx/TY29SBpbkGESLerFmDycZ1c8vS8jHE+0id7LiQBiMVtNzYubs6N6oKAajfiHtKx1JkRWcQp0KOzy5SflNonJLZhTeNJZ8ucn+9vuW0FBV7eUksX1Fu8kQTUdwNRU4KWpLGh4nlYpcZA+z149PujqVpWrwvbggIVO2fLscH7rITgFB8jMn7rXdBut+ez6fz/fv2gBs6U0QKbGR3QAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0001-6915-0750","institution":"Harvard Medical School","correspondingAuthor":true,"prefix":"","firstName":"John","middleName":"F.","lastName":"Kelly","suffix":""},{"id":456706374,"identity":"d15febc5-6535-4121-ab40-c17f2085c8fb","order_by":7,"name":"James MacKillop","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA3klEQVRIiWNgGAWjYBACxgYQNrCQ4SdViwSPZAOYb0C0NgkegwPEamGedvjhxxkFEjzG1w4/3fDjzx858wbmhx/w2jA7zVhyA9BhZrfTzG72thkYyxxgM5bAryXBjPEBWEsO2w3eBoPEGQw8DAS0pH8DazGencN2888fg3qgFuYf+LXkmDGCHGYgncN2m4fNIEGCgYeNgC05xZIzgFokgH65LdtmbDiDmc3MAp8Ww9npGz/2/LGR45+d/Ozmmz9y8hLszY9v4NXSgCHEjE89EMgTkB8Fo2AUjIJRwMAAAPUEQwk3H6ftAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0000-0002-8695-1071","institution":"McMaster University","correspondingAuthor":true,"prefix":"","firstName":"James","middleName":"","lastName":"MacKillop","suffix":""}],"badges":[],"createdAt":"2025-05-13 21:14:37","currentVersionCode":1,"declarations":{"humanSubjects":true,"vertebrateSubjects":false,"conflictsOfInterestStatement":true,"humanSubjectEthicalGuidelines":true,"humanSubjectConsent":true,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-6658717/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6658717/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":82868167,"identity":"75669a77-8d95-4e78-84d9-62b47c353089","added_by":"auto","created_at":"2025-05-16 08:21:20","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":4463839,"visible":true,"origin":"","legend":"\u003cp\u003eSocial networks of 3 example participants who were abstinent at 1-year alongside respective propensity score matched individuals who were not abstinent at 1-year, demonstrating the potential impact that family members in the network can have on AUD recovery. \u0026nbsp;Each circle represents alters 1-20 and lines represent connections between alters. Node colour represents alter relationship (purple = family member, green = other relationship).\u003c/p\u003e","description":"","filename":"Fig1.png","url":"https://assets-eu.researchsquare.com/files/rs-6658717/v1/59ea495fb40261df8c1de3b9.png"},{"id":82868168,"identity":"a1cb60c7-1a27-44ad-8ea8-3ed042818762","added_by":"auto","created_at":"2025-05-16 08:21:20","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":3130931,"visible":true,"origin":"","legend":"\u003cp\u003eSocial networks of 3 example participants who were abstinent at 1-year alongside respective propensity score matched individuals who were not abstinent at 1-year, demonstrating the potential impact that network heavy drinking can have on AUD recovery. \u0026nbsp;Each circle represents alters 1-20 and lines represent connections between alters. Node colour represents alter heavy drinking days (HDD) frequency, whereby orange is never, and increasingly darker shades of blue indicates greater HDD (Monthly or Less, 2-4 times per Month, 2 times per week, 4+ times per week)\u003c/p\u003e","description":"","filename":"Fig2.png","url":"https://assets-eu.researchsquare.com/files/rs-6658717/v1/a9a067c5125563c9c4cee199.png"},{"id":82869521,"identity":"dbaea03a-1be7-44e2-ac4f-cb1e36c2e75a","added_by":"auto","created_at":"2025-05-16 08:37:26","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":7061961,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6658717/v1/14641551-7fe5-4a70-9dd7-04aa73683599.pdf"},{"id":82868172,"identity":"487df599-1771-46c7-9303-a93194d81f55","added_by":"auto","created_at":"2025-05-16 08:21:20","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":14151610,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementalMaterials.docx","url":"https://assets-eu.researchsquare.com/files/rs-6658717/v1/bea8969b5f0ab02040d689f4.docx"}],"financialInterests":"The authors declare potential competing interests as follows: JM is a principal in Beam Diagnostics, Inc. and has consulted to Clairvoyant Therapeutics, Inc. All other authors report no conflicts of interest.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eSocial Network Mechanisms of Behavior Change in Alcohol Use Disorder Recovery: A Longitudinal Observational Cohort Study\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAlcohol is one of the most widely used substances and a major contributor to the global burden of disease, impacting health through both acute (e.g., injury, motor vehicle accidents, violence), and chronic mechanisms (e.g., the 200\u0026thinsp;+\u0026thinsp;chronic diseases for which alcohol is a component cause) (Rehm et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Alcohol use disorder (AUD) - characterized by uncontrolled heavy alcohol use which negatively impacts an individual\u0026rsquo;s health and wellbeing - is one of the most prevalent mental health disorders, with an estimated global prevalence of 5.1% (Carvalho et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Rehm \u0026amp; Shield, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSocial factors play a central role in alcohol-related behaviours and exist across multiple levels of influence. At the broadest level, alcohol consumption is deeply embedded in social culture, particularly in high-income countries where rates of AUD are also the highest (Rehm \u0026amp; Shield, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). At the most granular individual level, social motivations for alcohol use are conceptualized as either enhancement (e.g., to improve mood) or coping (e.g., attenuate negative mood, social conformity) (Cooper et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e1995\u003c/span\u003e; Kuntsche et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). In-between these most macro and micro levels are how social networks (i.e., the people we interact with such as friends, family, and coworkers) influence alcohol use and addiction. Literature supports the concept that human health is interconnected with the health of the social networks in which they are embedded, in particular for behavioural aspects like alcohol use (Rosenquist et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Strickland \u0026amp; Acuff, \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). However, much of the existing literature which examines alcohol use behaviours focuses on individual characteristics, and our understanding of social network influence is limited, particularly for AUD and AUD recovery.\u003c/p\u003e \u003cp\u003eSocial network analysis (SNA) provides an opportunity to directly study social network influences on alcohol behaviours by mapping the characteristics and connections between individuals in a social circle. There are two main types of SNA: sociocentric and egocentric. Sociocentric network analysis maps the connections between everyone in a single sample (e.g., all faculty in a department or all students in a single dorm), while egocentric network analysis takes a sample of unconnected individuals and maps their individual social circles (Perry et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). While the use of sociocentric analysis is limited to research contexts with a unique interconnected sample, egocentric analysis can be leveraged in most typical research contexts; in this type of research the term \u0026ldquo;ego\u0026rdquo; describes each individual in the sample, and each ego is asked to report characteristics and connections for the closest \u0026ldquo;alters\u0026rdquo; in their social network.\u003c/p\u003e \u003cp\u003eDespite the potential utility of SNA, limited research has applied this approach to understand drinking and alcohol use disorder. Much of the existing literature focuses on adolescent (Ali \u0026amp; Dwyer, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Mundt, n.d.) or college populations (Balestrieri et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Meisel et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; A. M. Russell et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2020\u003c/span\u003e, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), finding that drinking, social exclusion, and other substance use of alters can influence the ego\u0026rsquo;s own drinking behaviours (Balestrieri et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Meisel et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; A. M. T. Russell et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). However, research focused on social network features of AUD populations is more sparse. Some previous literature has identified that AUD populations tend to have smaller and less diverse social networks (Mowbray et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), and recovery-specific research has identified that more low-risk pro-abstinence alters predicted abstinence of the ego (Eddie \u0026amp; Kelly, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Longabaugh et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Additional limitations to the social network and AUD recovery literature include studies exclusively focusing on abstinence as an outcome and the use of relatively small 5\u0026ndash;10 alter social networks assessments (Eddie \u0026amp; Kelly, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Longabaugh et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2010\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe primary aim of this study was to add to limited existing research on social networks and recovery from AUD by investigating which features of social network structure and composition may facilitate or impede recovery. Specifically, among adults with AUD making a significant recovery attempt, this study examined egocentric social network features using a 20-alter social network evaluation as longitudinal predictors of three drinking outcomes: complete abstinence, drinking level based on the World Health Organization (WHO) Alcohol Risk Levels (World Health Organization, \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2000\u003c/span\u003e), and quantitative number of drinks/week. Multiple outcomes were used to systematically evaluate influences across drinking outcomes. Abstinence has historically been used extensively but is necessarily a coarse outcome that cannot reflect some significant reductions in drinking; reductions in WHO levels have been validated as non-abstinent positive outcomes(Witkiewitz et al., \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) and capture ordinal change in drinking levels; and alcohol consumption over time is a continuous drinking variable that provides the highest resolution. Thus, these different outcomes provide increasing levels of resolution, moving from dichotomous to ordinal to continuous. Finally, given evidence of substantive sex differences in social factors and AUD (Holzhauer, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), a secondary aim of this study was to examine differences in social network mechanisms in relation to the alcohol outcomes by sex.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eData and Participants\u003c/h2\u003e \u003cp\u003e The study sample consisted of N\u0026thinsp;=\u0026thinsp;501 adults with DSM-5 AUD who reported they were initiating a significant recovery attempt, defined as either i) enrollment in a formal treatment program (22.1%) or ii) a self-initiated informal significant recovery attempt (78.8%). This study was initiated March 2019 and enrollment was on an ongoing basis, and self-declaration of a recovery attempt was added as an inclusion criteria after the onset of the COVID-19 pandemic in March 2020 due to the impacts to in-person treatment. Other inclusion criteria included alcohol being the primary substance for cases where individuals used multiple substances, and being 21\u0026ndash;65 years of age. This was a multi-site study which collected data out of McMaster University in Hamilton, Ontario (N\u0026thinsp;=\u0026thinsp;249) and Massachusetts General Hospital/Harvard University in Boston, Massachusetts (N\u0026thinsp;=\u0026thinsp;252). Data collection consisted of 6 total waves; wave 1 (baseline) was pre-recovery attempt, while waves 2\u0026ndash;6 were 1-,3-,6-,9-, and 12-month follow-ups. Remuneration was a \u003cspan\u003e$\u003c/span\u003e40 gift card for each wave of participation. This protocol was approved by the Hamilton Integrated Research Ethics Board (#3825) and the Partners Human Research Committee at Massachusetts General Hospital (#2017P002345). We report how we determined our sample size, all data exclusions (if any), all manipulations, and all measures in the study\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eMeasurement and Variables\u003c/h3\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eAlcohol consumption: Timeline Follow-Back (TLFB)\u003c/h2\u003e \u003cp\u003eThe TLFB is a retrospective instrument that was developed to evaluate alcohol use at the day-level (M. B. Sobell et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e1986\u003c/span\u003e). The TLFB is considered a valid instrument in populations of individuals with alcohol-related problems (Searles et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; L. C. Sobell et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2003\u003c/span\u003e), including those with comorbid conditions (Carey et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). At baseline, TLFBs were 90-day pre-recovery-attempt; the first two follow-up waves were 45-days, and all subsequent follow-ups were 90-days in length. For each day of the TLFB, participants were asked to report their alcohol consumption, other substance use behaviours, and attendance at any 12-step programs such as alcoholics anonymous (AA). During the follow-up waves, participants who may have otherwise been lost to follow-up due to data collection burden were offered the opportunity to instead report their average alcohol consumption via the Daily Drinking Questionnaire (Collins et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) (10% of observations across time). For the purposes of this study, TLFB and DDQ data were summarized to produce an average drinks per week indicator which was harmonious across instruments and waves.\u003c/p\u003e \u003cp\u003eThe study leveraged several TLFB indices to measure drinking changes following the significant recovery attempt. The first indicator of interest was abstinence, defined as having abstained from alcohol during all of the days from the TLFB, or reporting no alcohol consumption via the DDQ (i.e., average drinks per week\u0026thinsp;=\u0026thinsp;0). Abstinence was examined in this study as it is the most physiologically relevant recovery definition carrying the lowest mortality risk (Roerecke et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), and research has supported that abstinence can better predict sustained recovery and improved quality of life compared to non-abstinent definitions (Eddie et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Subbaraman \u0026amp; Witbrodt, \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). However, research also strongly supports the need for non-abstinent recovery definitions, as non-abstinent outcomes have been associated with relevant improvements in physical and mental health, social functioning, and overall quality of life (Henssler et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Witkiewitz et al., \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). As such, we also examined alcohol consumption using the World Health Organization (WHO) Alcohol Risk Levels, which classify an individual\u0026rsquo;s drinking into low-risk (includes abstinence), medium-risk, high-risk, and very high-risk (World Health Organization, \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2000\u003c/span\u003e). WHO Risk levels have been validated as clinical indicators in individuals with AUD, as reduced risk levels are associated with improvements in consequences and functioning (Hasin et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Witkiewitz et al., \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). The final indicator of interest was average drinks per week, which was included to provide the highest resolution continuous indicator of drinking.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eSocial Network Variables\u003c/h3\u003e\n\u003cp\u003eEgocentric social networks were collected using a battery which elicited information on an individual\u0026rsquo;s 20 closest affiliates (termed \u0026ldquo;alters\u0026rdquo;). Each participant (termed \u0026ldquo;ego\u0026rdquo;) was asked to provide detailed information on all 20 of their alters, including: gender, relationship, how close they are, how often they interact, alcohol consumption, other substance use, and participation in mutual help organizations (MHOs) (e.g., alcoholics anonymous and any other \u0026ldquo;anonymous\u0026rdquo; substance program, or SMART recovery programs). Additionally, egos were asked to report the relationship connections between all alters (termed \u0026ldquo;ties\u0026rdquo;). If egos reported identical ties between all their alters (i.e. \u0026lsquo;straight-lined\u0026rsquo; responses), this was considered a quality control indicator and variables leveraging tie data were marked as missing. Instances of straight-lining were not common in this study (n\u0026thinsp;=\u0026thinsp;3 observations over all waves). In effort to mitigate potential loss to follow-up, similar to how DDQ was offered as an alternative to the TLFB, a brief 5-person social network battery was completed by some participants during follow-up waves (10.4% of observations); greater detail on how these data were used is described in Supplemental Materials.\u003c/p\u003e \u003cp\u003eIn this study, social network variables were summarized to key indicators of interest based on those identified in previous social network and alcohol use literature (Longabaugh et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Meisel et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; A. M. T. Russell et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Several indicators were averaged across alters, including: mean closeness to alters (ranged 1 (not close) to 4 (very close)), mean interaction with alters (range 1 (not in the last year) to 5 (daily)), and mean alcohol consumption frequency, heavy drinking frequency (sex-adjusted, defined as 5 or more drinks for males and 4 or more drinks for females), cannabis use frequency, and other illicit substance use frequency of alters (all range 1 (never) to 5 (4\u0026thinsp;+\u0026thinsp;times per week)). Other indicators were scaled as number of alters out of 20 for greater interpretability (i.e., number of alters who were in MHOs, were treatment providers, were friends, were family, and were women). Lastly, social network density, which is a measure of the closeness of all the alters to each other was calculated using the R package igraph (Cs\u0026aacute;rdi et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), and was scaled from 0 (no density, no connections between alters, to 20 (greatest density, all alters know each other)) for interpretability in the models.\u003c/p\u003e\n\u003ch3\u003eControl Variables\u003c/h3\u003e\n\u003cp\u003eVariables included in all models as covariates were age, ethnicity (racialized, non-racialized), and subjective evaluation of household\u0026rsquo;s financial situation (\u0026ldquo;Not enough to pay bills no matter how hard you try\u0026rdquo;, \u0026ldquo;Enough to pay bills, but have had to cut back\u0026rdquo;, \u0026ldquo;Enough to pay bills without cutting back, but no \u0026ldquo;extras\u0026rdquo;\u0026rdquo;, and \u0026ldquo;Enough money for extras\u0026rdquo;) (Najdzionek et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The latter permitted equivalent socioeconomic assessment at both sites without currency or cost of living adjustments. Additionally, a variable that controlled for severity of AUD at baseline was included and varied depending on model outcome; count of AUD symptoms (range 0\u0026ndash;11) from the Diagnostic Assessment Research Tool for DSM-5(American Psychiatric Association, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) was used in the abstinence models, while baseline WHO drinking level and average drinks per week were used in their respective models. A time varying indicator of average number of MHO meetings attended per week was also included, as participation in this type programming has been positively associated with AUD recovery (Kelly et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Study wave was included as a continuous indicator.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eData Analysis\u003c/h2\u003e \u003cp\u003eAs there was attrition (average retention across waves\u0026thinsp;=\u0026thinsp;83%) and instances of item non-response present, a Bayesian joint imputation and analysis framework (Erler et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2021a\u003c/span\u003e) was used to handle missing data. Specifically, alter-alter ties of the social network data required responses to a substantial number of questions (\u003cem\u003eC\u003c/em\u003e (20,2)\u0026thinsp;=\u0026thinsp;190) and as such the social network density predictor had the highest degree of data imputed (36%). Wave-by-wave missing data (Supplemental Fig.\u0026nbsp;1) as well as further details on the missing data approach used in this study, including a list of auxiliary variables, are provided in Supplemental Materials.\u003c/p\u003e \u003cp\u003eThe principal analyses used were longitudinal linear mixed models (LMMs) and generalized linear mixed models (GLMMs). Three models examined three different drinking outcomes: abstinence (binary GLMM, modelling likelihood of abstinence), WHO drinking risk level (ordinal GLMM, modelling likelihood of decreasing risk level), and drinks per week (LMM, modelling likelihood of greater average drinks per week). All analyses were implemented in R using the JointAI R package (Erler, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), alongside the ordinal package (Christensen, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) to model the cumulative logit model GLMM. Convergence of imputation models and precision of posterior estimates was assessed using Gelman-Rubin criterion and Monte Carlo error, in alignment with documented recommendations (Erler et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2021b\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSecondary sex-stratified analyses were performed and full results are presented in Supplemental materials. Notably, although both biological sex and gender identity can both impact alcohol use and recovery from AUD in distinct ways, gender identity and biological sex overlapped by more than 98% in this sample. As such, only sex-stratified analyses were performed, but in the present sample these can by in large be a proxy for gender effects as well.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eBaseline Characteristics and Changes over the Recovery Attempt\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e presents demographic characteristics of participants at baseline (pre-recovery attempt); in this sample, average drinks per week was 45.95, and average AUD symptoms was 8.16 (severe AUD). Most participants were engaging in very high-risk drinking (76%) according to WHO risk levels. Sex-stratified characteristics are also given in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e; males consumed an average of 56.71 drinks per week while females consumed an average of 37.98 drinks per week. AUD symptom count was 8.41 AUD for males and 7.98 for females.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDemographic Characteristics of Participants at Baseline\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;501\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFemales\u003c/p\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;288\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMales\u003c/p\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;213\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eMean (SD) or n (%) where indicated\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDemographics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e41.42 (11.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e40.76 (10.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e42.31 (11.62)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEthnicity (racialized), n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e104 (20.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e56 (19.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e48 (22.6%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSubjective financial situation: Enough for extras, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e154 (30.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e86 (30.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e68 (32.5%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAverage MHO meetings attended per week\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.41 (1.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.41 (1.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.41 (1.36%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eAlcohol Use\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDrinks per week\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e45.95 (43.46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e37.98 (31.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e56.71 (54.14)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAUD Symptom Count\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.16 (2.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.98 (2.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.41 (2.43)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWHO Risk Level \u0026ndash; Very High Risk, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e380 (75.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e234 (81.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e146 (68.5%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWHO Risk Level \u0026ndash; High Risk, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e95 (19.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e45 (15.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e50 (23.5%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWHO Risk Level \u0026ndash; Medium Risk, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23 (4.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9 (3.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14 (6.6%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eSocial Network Features\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean closeness to alters\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.63 (0.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.62 (0.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.63 (0.55)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean interaction with alters\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.15 (0.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.19 (0.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.10 (0.50)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean alcohol consumption of alters\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.74 (0.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.82 (0.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.64 (0.80)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean HDD of alters\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.24 (0.71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.67 (0.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.12 (0.76)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean cannabis use of alters\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.93 (0.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.95 (0.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.90 (0.67)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean illicit drug use of alters\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.28 (0.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.29 (0.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.27 (0.35)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e# of alters in MHOs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.75 (2.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.57 (2.56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.99 (3.13)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e# of alters who are treatment providers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.83 (1.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.78 (1.46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.90 (1.89)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e# of alters who are friends\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.38 (4.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.59 (3.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.11 (4.35)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e# of alters who are family\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.82 (3.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.00 (3.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.59 (3.40)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e# of alters who are women\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10.57 (3.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12.29 (2.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.34 (2.85)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSN Density\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.89 (3.71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.94 (3.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.81 (3.77)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eTotals may not match sample sizes in cases of missing baseline characteristics (N\u0026thinsp;=\u0026thinsp;2 missing ethnicity, N\u0026thinsp;=\u0026thinsp;12 missing subjective finance) or marginal cases reporting low-risk alcohol use at baseline (N\u0026thinsp;=\u0026thinsp;3)\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eSD; standard deviation\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eAUD; alcohol use disorder\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eHDD; heavy drinking days\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eSN; social network\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eMHO; mutual help organization\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eA sizable proportion of participants were able to achieve abstinence during the study; by 1-year (T6) 22% of participants were abstaining from alcohol, but, at the other end of the spectrum, 25% reported engaging in very high risk drinking at 1-year. A more granular presentation of abstinence and WHO trends wave-by-wave is available in Fig.\u0026nbsp;2 of Supplemental Materials.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eSocial Network Predictors of Drinking Outcomes\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e presents the results of models (after missing data imputation) for abstinence, WHO drinking level, and drinks per week. Some social network indicators variables did not show any association with the recovery process, including closeness to alters, mean alcohol, cannabis, and other illicit substance consumption of alters, number of friend alters, and social network density. Indicators that did reveal significant associations with alcohol outcome are detailed below.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBayesian Imputed Mixed Models of Abstinence, WHO Drinking Risk Level, and Drinks per Week\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eAbstinence\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eWHO Drinking Level\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eDrinks per week\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eβ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"8\" nameend=\"c8\" namest=\"c1\"\u003e \u003cp\u003eBaseline Characteristics\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.96, 1.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.98, 1.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(-0.1, 0.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex \u0026ndash; Female\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.09, 0.81)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.17, 0.57)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(-4.81, 2.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEthnicity (racialized)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.1, 1.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.46, 1.69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(-4.44, 3.46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSubjective financial siutation (Enough for extras)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.14, 2.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.25, 1.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-2.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(-7.59, 2.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSite \u0026ndash; MGH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.38, 2.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.61, 1.76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(-3.91, 2.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"8\" nameend=\"c8\" namest=\"c1\"\u003e \u003cp\u003eBaseline Drinking (Pre-Recovery Attempt)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAUD Symptom Count\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(1.17, 1.77)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWHO Drink Level \u0026ndash; High Risk\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(2, 7.34)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWHO Drink Level \u0026ndash; Medium Risk\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(1.81, 20.24)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDrinks per week\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0.05, 0.14)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"8\" nameend=\"c8\" namest=\"c1\"\u003e \u003cp\u003eTime varying Non-SN Variables\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTime\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.93, 1.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.97, 1.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0.11, 1.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWeekly 12-step meetings attended\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(1.15, 1.55)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(1.21, 1.52)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(-1.42, -0.41)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"8\" nameend=\"c8\" namest=\"c1\"\u003e \u003cp\u003eTime varying SN Variables\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCloseness to alters\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.61, 3.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.99, 2.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-2.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(-6.05, 1.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInteraction with alters\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(1.16, 5.64)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.99, 2.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-2.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(-6.27, 0.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlter alcohol consumption\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.25, 1.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.78, 1.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(-0.75, 5.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlter HDD\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.39, 2.44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.3, 0.79)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(-1.01, 5.91)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlter cannabis use\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.37, 1.54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.76, 1.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(-2.97, 2.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlter illicit drug use\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.42, 3.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.42, 1.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(-2.72, 6.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e# of alters in MHOs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(1.11, 1.47)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(1.03, 1.24)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(-1.35, -0.12)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e# of treatment provider alters\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.74, 1.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.81, 1.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0.24, 2.24)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e# of friend alters\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.86, 1.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.96, 1.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(-0.76, 0.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e# of family alters\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(1.02, 1.37)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(1.01, 1.18)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(-1.47, -0.32)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e# of women alters\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.97, 1.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.98, 1.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(-0.78, 0.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSN Density\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.88, 1.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.94, 1.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(-0.58, 0.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003e*p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 \u003csup\u003ea\u003c/sup\u003e Mean; AUD; alcohol use disorder; HDD; heavy drinking days; SN; social network; MHO; mutual help organization\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eFor abstinence, three social network characteristics were positively predictive: interaction frequency with alters (OR: 1.54 (95% CI: 1.16, 5.64)), number of alters in MHOs (1.28 (1.11, 1.47)), and number of family member alters (1.18 (1.02, 1.37)). Figures\u0026nbsp;1 and 2 present illustrative examples of the impact of family and heavy drinking in one\u0026rsquo;s network, respectively. Both figures visualize three example networks of individuals who were abstinent from alcohol at 1-year alongside a propensity score matched control who were still consuming alcohol at 1-year. These select networks and their differences are for illustrative purposes; all abstinent participant networks with complete data and propensity score matched controls are visualized for the impact of family and alter heavy drinking in Supplemental Materials.\u003c/p\u003e \u003cp\u003eLike abstinence, for WHO drinking level, number of alters in MHOs (OR: 1.13 (95% CI: 1.03, 1.24)), and number of family member alters (family alters: 1.09 (1.01, 1.18)) were significantly protective. Additionally, heavy drinking days of alters in the network was found to impede lower drinking level (0.49 (0.30, 0.79)).\u003c/p\u003e \u003cp\u003eLike abstinence and WHO drinking level, for drinks per week, MHO member alters (β: -0.73 (95% CI: -1.35, -0.12) and family alters (-0.89 (-1.47, -0.32)) were significantly protective. Conversely, having more treatment providers in one\u0026rsquo;s network was associated with greater likelihood of more drinks per week (1.25 (0.24, 2.24)).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eSex Differences\u003c/h2\u003e \u003cp\u003eSex-stratified results are presented in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. Stratification revealed that having more MHO members in one\u0026rsquo;s network was only protective for males (abstinence: 1.52 (1.16, 2.06), WHO level reduction: 1.19 (1.02, 1.39))), but not for females (abstinence: 1.18 (0.96, 1.47)), WHO level reduction: 1.05 (0.91, 1.21)). Stratification also revealed that alter heavy drinking only impeded WHO drinking reductions for females (0.52 (0.29, 0.95)) but was non-significant for males (0.42 (0.17, 1.05)). Additionally, having more women in one\u0026rsquo;s network was associated with greater likelihood of abstinence (1.26 (1.06, 1.52)), and WHO level reduction (1.11 (1.02, 1.22)) for females only.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSex-stratified Bayesian Imputed Mixed Models of Abstinence, WHO Drinking Risk Level, and Drinks per Week\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eAbstinence\u003c/p\u003e \u003cp\u003eOR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eWHO Drinking Level\u003c/p\u003e \u003cp\u003eOR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eDrinks per week\u003c/p\u003e \u003cp\u003eβ (95% CI)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemales\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMales\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFemales\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMales\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eFemales\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMales\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003eBaseline Characteristics\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.97 (0.91, 1.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.08 (0.98, 1.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.99 (0.96, 1.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.03 (0.98, 1.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.05 (-0.14, 0.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.01 (-0.26, 0.25)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEthnicity (racialized)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.23 (0.04, 1.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.34 (0.02, 5.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.79 (0.33, 1.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.81 (0.24, 2.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-2.44 (-7.43, 2.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.04 (-5.6, 7.68)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSubjective financial siutation (Enough for extras)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.39 (0.06, 2.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.57 (0.04, 51.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.29 (0.1, 0.82)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.19 (0.43, 11.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.86 (-6.96, 5.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-2.92 (-11.92, 6.09)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSite \u0026ndash; MGH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.38 (0.4, 4.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.67 (0.07, 6.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.07 (0.54, 2.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.79 (0.29, 2.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.73 (-3.15, 4.61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-3.54 (-9.14, 2.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAUD Symptom Count\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.27 (0.99, 1.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 (1.22, 3.66)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWHO Drink Level \u0026ndash; High Risk\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.86 (1.61, 9.38)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.64 (1.8, 18.49)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWHO Drink Level \u0026ndash; Medium Risk\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.34 (0.52, 21.61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12.36 (1.81, 88.02)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDrinks per week\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.14 (0.08, 0.2)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.09 (0.03, 0.15)*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003eTime varying non-SN Variables\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTime\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (0.84, 1.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.28 (0.99, 1.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 (0.92, 1.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.12 (0.99, 1.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.49 (0, 0.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.4 (-0.48, 1.28)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWeekly 12-step meetings attended\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.25 (1.03, 1.53)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.66 (1.23, 2.35)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.31 (1.13, 1.51)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.53 (1.25, 1.89)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.66 (-1.25, -0.08)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-1.43 (-2.35, -0.52)*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003eTime-varying SN Variables\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCloseness to alters\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.08 (0.34, 3.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.83 (0.31, 11.46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.36 (0.74, 2.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.2 (0.95, 5.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.91 (-5.03, 3.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-4.19 (-10.46, 2.15)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInteraction with alters\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.08 (1.11, 8.63)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.51 (1.15, 44.93)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.82 (1.06, 3.14)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.23 (0.56, 2.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-1.8 (-5.43, 1.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-4 (-10.06, 2.12)*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlter alcohol consumption\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.6 (0.21, 1.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.26 (0.03, 1.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.04 (0.61, 1.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.58 (0.7, 3.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.74 (-0.6, 6.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.02 (-5.78, 5.75)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlter HDD\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.67 (0.21, 2.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.18 (0.5, 39.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.52 (0.29, 0.95)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.42 (0.17, 1.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.29 (-1.46, 6.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.64 (-5.16, 8.38)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlter cannabis use\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.68 (0.25, 1.74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.14 (0.24, 5.76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.99 (0.59, 1.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.38 (0.71, 2.71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.3 (-3.57, 2.91)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.23 (-4.65, 5.05)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlter illicit drug use\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.63 (0.6, 12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.18 (0.01, 1.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.78 (0.33, 1.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.61 (0.24, 1.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.31 (-3.01, 7.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.02 (-4.28, 10.38)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e# of alters in MHOs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.18 (0.96, 1.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.52 (1.16, 2.06)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.05 (0.91, 1.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.19 (1.02, 1.39)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.57 (-1.42, 0.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.89 (-1.88, 0.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e# of treatment provider alters\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.26 (0.88, 1.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.65 (0.41, 0.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.04 (0.83, 1.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.86 (0.7, 1.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.62 (-0.83, 2.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.81 (-0.67, 2.29)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e# of friend alters\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.99 (0.85, 1.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.9 (0.7, 1.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.05 (0.97, 1.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.98 (0.88, 1.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.27 (-0.8, 0.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.31 (-1.11, 0.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e# of family alters\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.24 (1.03, 1.5)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.43 (1.05, 2.01)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.12 (1.02, 1.22)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.13 (0.98, 1.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.44 (-1.07, 0.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-1.5 (-2.48, -0.52)*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e# of women alters\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.26 (1.06, 1.52)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.94 (0.69, 1.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.11 (1.02, 1.22)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.95 (0.83, 1.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.28 (-0.86, 0.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.42 (-1.37, 0.54)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSN Density\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.92 (0.79, 1.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.93 (0.74, 1.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.96 (0.89, 1.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.01 (0.91, 1.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.1 (-0.65, 0.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.14 (-0.68, 1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003e*p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 \u003csup\u003ea\u003c/sup\u003e Mean; AUD; alcohol use disorder; HDD; heavy drinking days; SN; social network; MHO; mutual help organization\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe primary purpose of this study was to examine which social network features may facilitate or impede recovery for individuals with AUD and, secondarily, to examine differences in these factors by sex. Social network characteristics significant across all outcomes (abstinence, lower WHO drinking level, and lower drinks per week) included number of family member alters and MHO member alters. Meanwhile, social interaction, treatment providers alters, and alter heavy drinking demonstrated significance for select outcomes. Sex-specific analyses revealed that MHO alters offered a protective effect only for males, while having more women in one\u0026rsquo;s network was protective for females. Some characteristics showed no association with any outcome; for example, although previous research has suggested clustering of other substance use behaviours in social networks with alcohol use (Meisel et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), this study found that neither cannabis use nor illicit substance use of alters were significantly associated with any of the recovery outcomes. Alter closeness, number of friend alters, and social network density also did not demonstrate any association with recovery outcomes.\u003c/p\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eRecovery Process Facilitators\u003c/h2\u003e \u003cdiv id=\"Sec15\" class=\"Section3\"\u003e \u003ch2\u003eFamily\u003c/h2\u003e \u003cp\u003eOne of the most consistent findings across all models was that a greater number of family members in one\u0026rsquo;s network facilitated greater reductions in drinking. Although family are often inherently closer ties, our findings suggest that family presence confers additional benefits beyond closeness, as closeness of ties was accounted for in all models. A previous study which examined specific family features found that good adaptability (i.e., flexibility to adjust to changes) and cohesion (i.e., degree of emotional connection) within a family was protective against relapse for individuals with substance use disorders (Zhang \u0026amp; Zeng, \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Our findings further support the critical role of family in recovery, emphasizing the value of involving family members in treatment and support interventions. It is worth noting however that our findings and interpretation are on average and assume a positive support lens that may not apply to all individuals. For example, a family with poor adaptability and cohesion may still impede recovery for an individual regardless of how many family members exist in their network.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eMutal Help Organizations\u003c/h2\u003e \u003cp\u003eThe presence of network members affiliated with mutual help organizations (MHOs) (e.g., alcoholics anonymous) facilitated recovery across all drinking outcomes. This is consistent with existing research demonstrating that one way in which MHOs can be effective is through social network \u0026ldquo;transplantation\u0026rdquo;(Kaskutas et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Kelly et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Levitt et al., n.d.; \u003cem\u003eThe_persistent_influence_of_so\u003c/em\u003e, n.d.) whereby pro-drinking individuals are replaced with pro-recovery individuals. Importantly, these benefits persisted even when controlling for individual MHO attendance, which itself was associated with positive recovery outcomes, consistent with prior research (Kelly et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eSocial Interaction\u003c/h2\u003e \u003cp\u003eThis study also found that greater frequency of interaction with one\u0026rsquo;s network predicted greater likelihood of abstinence. This is consistent with a previous study which found that greater time spent with friends predicted percent days abstinent, although this protective effect was reversed when those friends were high-risk SUDs (Eddie \u0026amp; Kelly, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Existing literature has also identified social isolation (i.e., little to no interaction with others) as risk factor for the development SUDs (Chou et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Desai et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Our findings extend this literature by highlighting the potential benefit of social interaction in the recovery process. Interestingly, neither closeness of ties nor social network density (i.e., how interconnected one\u0026rsquo;s social ties are) significantly impacted drinking outcomes, suggesting that interaction frequency may be more important than the number of close relationships or overall network cohesion. Alternatively, this may suggest that our findings could in-part reflect participation in substance-free reinforcing social situations, which are known to be inversely related to substance use (Acuff et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eRecovery Barriers\u003c/h2\u003e \u003cdiv id=\"Sec19\" class=\"Section3\"\u003e \u003ch2\u003eAlter Heavy Drinking\u003c/h2\u003e \u003cp\u003eIn this study, across the 1-year recovery process greater alter heavy drinking was found to be inversely associated with lower WHO drinking level; when disaggregated by sex, this effect was only significant for females. Previous research has consistently shown that drinking in one\u0026rsquo;s network influences individual drinking patterns (Balestrieri et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; A. M. Russell et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; A. M. T. Russell et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), but very limited research has examined this within AUD populations. Previous work in the sample from the present study found that social network drinking mediated the relationship between a person increasing their AA attendance and reducing their drinks per week 6-weeks later (Levitt et al., n.d.). External studies have also revealed that network drinking and time spent with high-risk friends impeded abstinence (Eddie \u0026amp; Kelly, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Longabaugh et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). However, these studies do not present disaggregated findings by sex or gender, making it difficult to compare to the present studies sex-specific findings. Our results emphasize the need for future research examining the role of social network drinking in AUD recovery, particularly through a sex and gender lens.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eTreatment Providers in Network\u003c/h2\u003e \u003cp\u003eHaving a greater number of treatment providers in one\u0026rsquo;s social network was associated with greater drinks per week. While this may appear counterintuitive, it may reflect a lack of broader social ties. Social network instruments ask individuals to list their closest social ties, and inclusion of treatment providers in one\u0026rsquo;s network of closest 20 connections implies a lack of ties with others (e.g., family and friends). Given that our study identified family and social interaction as recovery facilitators, it follows that individuals who displace these relationships with professional providers may be experiencing challenges in reducing their alcohol consumption. Moreover, it is also possible that this displacement is to ameliorate the influence of negative social network influences that may not promote recovery (e.g., pro-drinking individuals). It is also worth highlighting that more treatment providers in a social network could also proxy greater engagement in formal treatment, which may be more likely for those who are struggling in the recovery process.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003eSex differences\u003c/h2\u003e \u003cp\u003eSex-disaggregated findings demonstrated that for females only, having more women in their social network was associated with greater likelihood of abstinence and lower WHO drinking level. Limited research has examined how gender composition of social networks can influence alcohol use, and those that do have mostly focused on non-SUD adolescent and young adult populations (Deutsch et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Grard et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Gustafsson et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Recovery research has focused more on gender-based programming, demonstrating that women often feel more comfortable and supported in women-only recovery groups (Johnstone et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; McCrady et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Our findings suggest this sense of social support and shared experience may extend beyond the recovery group context to social networks more broadly. Importantly, these findings are unlikely to be inherently tied to gender and instead likely reflect the different types of support which may be more frequently given by women to women. For example, emotional support (e.g., encouragement, communication) and tangible support (e.g., childcare during treatment) in one\u0026rsquo;s network have been found to be highly important for women in recovery from SUDs (Tracy et al., \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2010\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSex-disaggregated analyses also indicated that the presence of MHO network members was only significantly related to recovery outcomes for males, but not females. This is consistent with a previous study which found that while both women and men benefit from AA, the social transplantation impacts are a greater benefit for men (Kelly \u0026amp; Hoeppner, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). This may stem from differential drinking contexts by gender (i.e., women with AUD drinking in more solitary contexts) (Kelly \u0026amp; Hoeppner, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), or because women in recovery may have higher baseline social support than men (Cano et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Patton et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Scoglio et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). It is important to clarify that the differences in our study only applied to social network composition; women still saw benefits of participating in MHOs themselves, and some existing research has suggested women may even benefit more from long-term AA participation than men (Moos et al., n.d.)).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003eImplications\u003c/h2\u003e \u003cp\u003eFindings from this study yield several implications for clinical practice and future research. First, our results reinforce the importance of frequent social interaction and family support in recovery, supporting existing recommendations to incorporate family members into addiction treatment (Kourgiantakis et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Ventura \u0026amp; Bagley, \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). This research also demonstrates potential negative impacts of having heavy drinking individuals in the network, and the benefits that can be conferred when instead pro-recovery individuals (such as those in MHO organizations) are in one\u0026rsquo;s network. However, findings also replicate other findings revealing that there can be sex/gender differences in recovery, and women in particular may be more impacted by having a heavy drinking network and benefit less from the social transplantation mechanisms conferred by MHOs (even though women still benefit from MHO participation). From a clinical perspective, assessing the characteristics of an individual\u0026rsquo;s social circle\u0026mdash;including family presence, degree of social interaction, and drinking behaviors of network members\u0026mdash;may provide valuable insights into leverage points for AUD recovery. Future research should continue to examine social networks of those in recovery from AUD in other samples, with particular attention to gender and sex-specific effects.\u003c/p\u003e \u003cdiv id=\"Sec23\" class=\"Section3\"\u003e \u003ch2\u003eStrengths and Limitations\u003c/h2\u003e \u003cp\u003eThis study has a number of strengths, including the use of a relatively large sample size and large social network assessment comparative to previous social network research, multiple study sites, and the use of multiple follow-up periods extending to 1-year post recovery attempt with high follow-up rates. This study also used several definitions of drinking in order to capture additional alcohol outcomes related to recovery that may exist upstream of complete abstinence. Lastly, the analytical approach used in this study was robust to missing data bias, which is particularly important to manage in substance use research (Claus et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Hallgren et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Hallgren \u0026amp; Witkiewitz, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Nonetheless, the findings should also be interpreted with respect to several limitations. First, this study was a convenience sample with unknown generalizability; extension of findings to other populations should be done with caution, although this study nonetheless add to the growing body of literature examining the importance of social networks in AUD recovery. Next, because this study period overlapped with the COVID-19 pandemic, recovery trajectories and related influences of social networks may have been impacted (positively or negatively). In particular, the lack of in-person support during lockdowns may have negatively impacted some participants, but at the same time this may have caused some individuals to rely more on their social networks. Lastly, in this study we viewed social networks through a positive lens (e.g. assuming that interactions and relationships were positive and not deleterious); while this is likely true on average, it may not be true at the individual level. For example, while this study finds that on average those with more family members in their network were more likely to reduce their drinking (based on all definitions), at the individual-level, unsupportive family members could still negatively impact recovery. As such, clinicians or those working with AUD populations need to take caution when interpreting, applying, or leveraging findings of clinical epidemiological research like the present study, as the population average effects presented may not always translate to individual cases. (62\u0026ndash;64)\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study examined the impact of social network characteristics on the recovery process for individuals with alcohol use disorder. More family members in one\u0026rsquo;s network and more frequent social interaction were consistently associated with better recovery-related outcomes. Some impacts varied by sex; having a heavy drinking network was only a barrier to recovery for females, while having more network members in mutual help organizations was associated with better alcohol outcomes only for males. Taken together, these findings add to the growing body of empirical work validating the central importance of social networks in successful AUD recovery and highlight specific and actionable network features that may inform future social network-based interventions.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAcuff SF, Dennhardt AA, Correia CJ, Murphy JG (2019) Measurement of substance-free reinforcement in addiction: A systematic review. Clinical Psychology Review, vol 70. Elsevier Inc, pp 79\u0026ndash;90. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.cpr.2019.04.003\u003c/span\u003e\u003cspan address=\"10.1016/j.cpr.2019.04.003\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAli MM, Dwyer DS (2010) Social network effects in alcohol consumption among adolescents. Addict Behav 35(4):337\u0026ndash;342. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.addbeh.2009.12.002\u003c/span\u003e\u003cspan address=\"10.1016/j.addbeh.2009.12.002\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAmerican Psychiatric Association (2022) Substance-Related and Addictive Disorders. Diagnostic and Statistical Manual of Mental Disorders. American Psychiatric Association Publishing. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1176/appi.books.9780890425787.x16_Substance_Related_Disorders\u003c/span\u003e\u003cspan address=\"10.1176/appi.books.9780890425787.x16_Substance_Related_Disorders\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBalestrieri SG, Diguiseppi GT, Meisel MK, Clark MA, Ott MQ, Barnett NP (2018) U.S. college students\u0026rsquo; social network characteristics and perceived social exclusion: A comparison between drinkers and nondrinkers based on past-month alcohol use. J Stud Alcohol Drug 79(6):862\u0026ndash;867. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.15288/JSAD.2018.79.862\u003c/span\u003e\u003cspan address=\"10.15288/JSAD.2018.79.862\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCano I, Best D, Edwards M, Lehman J (2017) Recovery capital pathways: Modelling the components of recovery wellbeing. Drug Alcohol Depend 181:11\u0026ndash;19. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.drugalcdep.2017.09.002\u003c/span\u003e\u003cspan address=\"10.1016/j.drugalcdep.2017.09.002\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCarey KB, Carey MP, Maisto SA, Henson JM (2004) Temporal stability of the timeline followback interview for alcohol and drug use with psychiatric outpatients. J Stud Alcohol 65(6):774\u0026ndash;781. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.15288/jsa.2004.65.774\u003c/span\u003e\u003cspan address=\"10.15288/jsa.2004.65.774\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCarvalho AF, Heilig M, Perez A, Probst C, Rehm J (2019) Alcohol use disorders. Lancet 394(10200):781\u0026ndash;792. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/S0140-6736(19)31775-1\u003c/span\u003e\u003cspan address=\"10.1016/S0140-6736(19)31775-1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChou K-L, Liang K, Sareen J (2011) The Association Between Social Isolation and DSM-IV Mood, Anxiety, and Substance Use Disorders: Wave 2 of the National Epidemiologic Survey on Alcohol and Related Conditions. J Clin Psychiatry 72:11. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.4088/JCP.10m06019gry\u003c/span\u003e\u003cspan address=\"10.4088/JCP.10m06019gry\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChristensen RHB (2023) \u003cem\u003ePackage ordinal.\u003c/em\u003e \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://cran.r-project.org/web/packages/ordinal/ordinal.pdf\u003c/span\u003e\u003cspan address=\"https://cran.r-project.org/web/packages/ordinal/ordinal.pdf\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eClaus RE, Kindleberger LR, Dugan MC (2002) Predictors of attrition in a longitudinal study of substance abusers. J Psychoactive Drugs 34(1):69\u0026ndash;74. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/02791072.2002.10399938\u003c/span\u003e\u003cspan address=\"10.1080/02791072.2002.10399938\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCollins RL, Parks GA, Marlatt GA (2011) Daily Drinking Questionnaire. In \u003cem\u003ePsycTESTS Dataset\u003c/em\u003e. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1037/t02146-000\u003c/span\u003e\u003cspan address=\"10.1037/t02146-000\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCooper ML, Frone MR, Russell M, Mudar P (1995) Drinking to Regulate Positive and Negative Emotions: A Motivational Model of Alcohol Use. In \u003cem\u003eJournal of Personality and Social Psychology\u003c/em\u003e (Vol. 69, Issue 5)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCs\u0026aacute;rdi G, Nepusz T, M\u0026uuml;ller K, Horv\u0026aacute;t S, Traag V, Zanini F, Noom D (2024) \u003cem\u003eigraph for R: R interface of the igraph library for graph theory and network analysis\u003c/em\u003e. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.5281/ZENODO.14347716\u003c/span\u003e\u003cspan address=\"10.5281/ZENODO.14347716\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDesai R, Karim S, Freeborn J, Trivedi C, Husain K, Jain S (2024) Contextualizing the Relationship Between Social Isolation and Substance Abuse. Prim Care Companion CNS Disorders 26(5). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.4088/PCC.23M03679\u003c/span\u003e\u003cspan address=\"10.4088/PCC.23M03679\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDeutsch AR, Steinley D, Slutske WS (2014) The Role of Gender and Friends\u0026rsquo; Gender on Peer Socialization of Adolescent Drinking: A Prospective Multilevel Social Network Analysis. J Youth Adolesc 43(9):1421\u0026ndash;1435. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s10964-013-0048-9\u003c/span\u003e\u003cspan address=\"10.1007/s10964-013-0048-9\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEddie D, Bergman BG, Hoffman LA, Kelly JF (2022) Abstinence versus moderation recovery pathways following resolution of a substance use problem: Prevalence, predictors, and relationship to psychosocial well-being in a U.S. national sample. Alcoholism: Clin Experimental Res 46(2):312\u0026ndash;325. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/acer.14765\u003c/span\u003e\u003cspan address=\"10.1111/acer.14765\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEddie D, Kelly JF (2017) How many or how much? Testing the relative influence of the number of social network risks versus the amount of time exposed to social network risks on post-treatment substance use. Drug Alcohol Depend 175:246\u0026ndash;253. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.drugalcdep.2017.02.012\u003c/span\u003e\u003cspan address=\"10.1016/j.drugalcdep.2017.02.012\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eErler NS (2023) \u003cem\u003ePackage Joint AI.\u003c/em\u003e \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://cran.r-project.org/web/packages/JointAI/JointAI.pdf\u003c/span\u003e\u003cspan address=\"https://cran.r-project.org/web/packages/JointAI/JointAI.pdf\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eErler NS, Rizopoulos D, Lesaffre EMEH (2021a) JointAI: Joint Analysis and Imputation of Incomplete Data in R. J Stat Softw 100(20):1\u0026ndash;56. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.18637/JSS.V100.I20\u003c/span\u003e\u003cspan address=\"10.18637/JSS.V100.I20\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eErler NS, Rizopoulos D, Lesaffre EMEH (2021b) JointAI: Joint Analysis and Imputation of Incomplete Data in R. J Stat Softw 100(20):1\u0026ndash;56. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.18637/JSS.V100.I20\u003c/span\u003e\u003cspan address=\"10.18637/JSS.V100.I20\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGrard A, Kunst A, Kuipers M, Richter M, Rimpela A, Federico B, Lorant V (2018) Same-Sex Friendship, School Gender Composition, and Substance Use: A Social Network Study of 50 European Schools. Subst Use Misuse 53(6):998\u0026ndash;1007. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/10826084.2017.1392976\u003c/span\u003e\u003cspan address=\"10.1080/10826084.2017.1392976\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGustafsson NK, Rydgren J, Rostila M, Miething A (2021) Social network characteristics and alcohol use by ethnic origin: An ego-based network study on peer similarity, social relationships, and coexisting drinking habits among young Swedes. PLoS ONE 16(4). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1371/journal.pone.0249120\u003c/span\u003e\u003cspan address=\"10.1371/journal.pone.0249120\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHallgren KA, Witkiewitz K (2013) Missing Data in Alcohol Clinical Trials: A Comparison of Methods. Alcoholism: Clin Experimental Res 37(12):2152\u0026ndash;2160. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/acer.12205\u003c/span\u003e\u003cspan address=\"10.1111/acer.12205\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHallgren KA, Witkiewitz K, Kranzler HR, Falk DE, Litten RZ, O\u0026rsquo;Malley SS, Anton RF (2016) Missing Data in Alcohol Clinical Trials with Binary Outcomes. Alcoholism: Clin Experimental Res 40(7):1548\u0026ndash;1557. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/acer.13106\u003c/span\u003e\u003cspan address=\"10.1111/acer.13106\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHasin DS, Wall M, Witkiewitz K, Kranzler HR, Falk D, Litten R, Mann K, O\u0026rsquo;Malley SS, Scodes J, Robinson RL, Anton R, Fertig J, Isenberg K, McCann D, Meulien D, Meyer R, O\u0026rsquo;Brien C, Ryan M, Silverman B, Zakine B (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(6):469\u0026ndash;476. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/S2215-0366(17)30130-X\u003c/span\u003e\u003cspan address=\"10.1016/S2215-0366(17)30130-X\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHenssler J, M\u0026uuml;ller M, Carreira H, Bschor T, Heinz A, Baethge C (2021) Controlled drinking\u0026mdash;non-abstinent versus abstinent treatment goals in alcohol use disorder: a systematic review, meta-analysis and meta-regression. Addiction, vol 116. Blackwell Publishing Ltd, pp 1973\u0026ndash;1987. 8 \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/add.15329\u003c/span\u003e\u003cspan address=\"10.1111/add.15329\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHolzhauer C (2020) Sex and Gender Effects in Recovery from Alcohol Use Disorder. \u003cem\u003eAlcohol Research: Current Reviews\u003c/em\u003e, \u003cem\u003e40\u003c/em\u003e(3), 2020. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.35946/arcr.v40.3.03\u003c/span\u003e\u003cspan address=\"10.35946/arcr.v40.3.03\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJohnstone S, Dela Cruz GA, Kalb N, Tyagi SV, Potenza MN, George TP, Castle DJ (2023) A systematic review of gender-responsive and integrated substance use disorder treatment programs for women with co-occurring disorders. American Journal of Drug and Alcohol Abuse, vol 49. Taylor and Francis Ltd, pp 21\u0026ndash;42. 1 \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/00952990.2022.2130348\u003c/span\u003e\u003cspan address=\"10.1080/00952990.2022.2130348\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKaskutas LA, Bond J, Humphreys K (2002) Social networks as mediators of the effect of Alcoholics Anonymous. Addiction 97(7):891\u0026ndash;900. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1046/j.1360-0443.2002.00118.x\u003c/span\u003e\u003cspan address=\"10.1046/j.1360-0443.2002.00118.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKelly JF, Hoeppner BB (2013) Does Alcoholics Anonymous work differently for men and women? A moderated multiple-mediation analysis in a large clinical sample. Drug Alcohol Depend 130(1\u0026ndash;3):186\u0026ndash;193. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.drugalcdep.2012.11.005\u003c/span\u003e\u003cspan address=\"10.1016/j.drugalcdep.2012.11.005\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKelly JF, Humphreys K, Ferri M (2020) Alcoholics Anonymous and other 12-step programs for alcohol use disorder. In \u003cem\u003eCochrane Database of Systematic Reviews\u003c/em\u003e (Vol. 2020, Issue 3). John Wiley and Sons Ltd. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/14651858.CD012880.pub2\u003c/span\u003e\u003cspan address=\"10.1002/14651858.CD012880.pub2\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKelly JF, Stout RL, Magill M, Tonigan JS (2011) The role of Alcoholics Anonymous in mobilizing adaptive social network changes: A prospective lagged mediational analysis. Drug Alcohol Depend 114(2\u0026ndash;3):119\u0026ndash;126. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.drugalcdep.2010.09.009\u003c/span\u003e\u003cspan address=\"10.1016/j.drugalcdep.2010.09.009\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKourgiantakis T, Ashcroft R, Mohamud F, Fearing G, Sanders J (2021) Family-Focused Practices in Addictions: A Scoping Review. J Social Work Pract Addictions 21(1):18\u0026ndash;53. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/1533256X.2020.1870287/ASSET/09BE2E64-23A2-47CF-B022-3B5093380EEF/ASSETS/IMAGES/WSWP_A_1870287_F0001_OC.JPG\u003c/span\u003e\u003cspan address=\"10.1080/1533256X.2020.1870287/ASSET/09BE2E64-23A2-47CF-B022-3B5093380EEF/ASSETS/IMAGES/WSWP_A_1870287_F0001_OC.JPG\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKuntsche E, Knibbe R, Gmel G, Engels R (2005) Why do young people drink? A review of drinking motives. Clin Psychol Rev 25(7):841\u0026ndash;861. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.cpr.2005.06.002\u003c/span\u003e\u003cspan address=\"10.1016/j.cpr.2005.06.002\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLevitt E, Singh D, Belisario K, Doggett A, Clifton A, Stout R, Kelly JF, MacKillop J (n.d.). \u003cem\u003eChanges in Alcohol-related Social Network Composition Mediate the Effects of AA Meeting Attendance on Drinking following a Recovery Attempt in Adults with Alcohol Use Disorder\u003c/em\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLongabaugh R, Wirtz PW, Zywiak WH, O\u0026rsquo;malley SS (2010) Network Support as a Prognostic Indicator of Drinking Outcomes: The COMBINE Study. J Stud Alcohol Drug 71(6):837\u0026ndash;846. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.15288/jsad.2010.71.837\u003c/span\u003e\u003cspan address=\"10.15288/jsad.2010.71.837\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMcCrady BS, Epstein EE, Fokas KF (2020) Treatment interventions for women with alcohol use disorder. Alcohol Research: Curr Reviews 40(2). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.35946/arcr.v40.2.08\u003c/span\u003e\u003cspan address=\"10.35946/arcr.v40.2.08\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMeisel MK, Clifton AD, Mackillop J, Goodie AS (2015) A social network analysis approach to alcohol use and co-occurring addictive behavior in young adults. Addict Behav 51:72\u0026ndash;79. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.addbeh.2015.07.009\u003c/span\u003e\u003cspan address=\"10.1016/j.addbeh.2015.07.009\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMoos RH, Moos BS, Timko C (n.d.) (eds) Gender,Treatment and Self-Help in Remission from Alcohol Use Disorders. In \u003cem\u003eOriginal Research Clinical Medicine \u0026amp; Research\u003c/em\u003e (Vol. 4). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.clinmedres.org\u003c/span\u003e\u003cspan address=\"http://www.clinmedres.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMowbray O, Quinn A, Cranford JA (2014) Social networks and alcohol use disorders: Findings from a nationally representative sample. Am J Drug Alcohol Abuse 40(3):181\u0026ndash;186. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3109/00952990.2013.860984\u003c/span\u003e\u003cspan address=\"10.3109/00952990.2013.860984\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMundt M P. (n.d.). \u003cem\u003eThe Impact of Peer Social Networks on Adolescent Alcohol Use Initiation\u003c/em\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNajdzionek P, McIntyre-Wood C, Amlung M, MacKillop J (2023) Incorporating socioeconomic status into studies on delay discounting and health via subjective financial status: An initial validation in tobacco use. Exp Clin Psychopharmacol 31(2):475\u0026ndash;481. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1037/pha0000628\u003c/span\u003e\u003cspan address=\"10.1037/pha0000628\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePatton R, Chou J, Kestner T, Feeney E (2024) Exploring social connectedness, isolation, support, and recovery factors among women seeking substance use treatment. Women Health 64(3):202\u0026ndash;215. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/03630242.2024.2308518\u003c/span\u003e\u003cspan address=\"10.1080/03630242.2024.2308518\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePerry BL, Pescosolido BA, Borgatti SP (2018) Egocentric Network Analysis. Cambridge University Press. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1017/9781316443255\u003c/span\u003e\u003cspan address=\"10.1017/9781316443255\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRehm J, Baliunas D, Borges GLG, Graham K, Lrving H, Kehoe T, Parry CD, Patra J, Popova S, Poznyak V, Roerecke M, Room R, Samokhvalov AV, Taylor B, DIMENSIONS OF ALCOHOL CONSUMPTION AND BURDEN OF DISEASE-AN OVERVIEW (2010) Addiction 105(5):817\u0026ndash;843. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/j.1360-0443.2010.02899.x\u003c/span\u003e\u003cspan address=\"10.1111/j.1360-0443.2010.02899.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. THE RELATION BETWEEN DIFFERENT\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRehm J, Shield KD (2019) Global burden of alcohol use disorders and alcohol liver disease. Biomedicines 7(4). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/biomedicines7040099\u003c/span\u003e\u003cspan address=\"10.3390/biomedicines7040099\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRoerecke M, Gual A, Rehm J (2013) Reduction of alcohol consumption and subsequent mortality in alcohol use disorders: Systematic review and meta-analyses. J Clin Psychiatry 74(12). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.4088/JCP.13r08379\u003c/span\u003e\u003cspan address=\"10.4088/JCP.13r08379\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRosenquist JN, Murabito J, Fowler JH, Christakis NA (2010) The spread of alcohol consumption behavior in a large social network. Ann Intern Med 152(7):426\u0026ndash;433. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.7326/0003-4819-152-7-201004060-00007\u003c/span\u003e\u003cspan address=\"10.7326/0003-4819-152-7-201004060-00007\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRussell AM, Barry AE, Patterson MS (2020) A comparison of global and egocentric network approaches for assessing peer alcohol use among college students in the United States. Drug Alcohol Rev 39(7):984\u0026ndash;993. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/DAR.13140\u003c/span\u003e\u003cspan address=\"10.1111/DAR.13140\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRussell AM, Patterson MS, Barry AE (2021) College Students\u0026rsquo; Perceptions of Peer Alcohol Use: A Social Network Analytic Approach. Subst Use Misuse 56(1):46\u0026ndash;53. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/10826084.2020.1833929\u003c/span\u003e\u003cspan address=\"10.1080/10826084.2020.1833929\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRussell AMT, Monds L, Hing N, Kroll J, Russell AM, Thorne HB (2023) Social Associations and Alcohol Consumption in an Australian Community Sample: An Egocentric Social Network Analysis Psychology of Addictive Behaviors. Association 2024 38(2):211\u0026ndash;221. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1037/adb0000954\u003c/span\u003e\u003cspan address=\"10.1037/adb0000954\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eScoglio AAJ, McFarland G, Marquez CI, Matsumoto A, Lincoln AK (2023) Social Support and Associated Factors Among Men and Women in Pre-COVID Substance Use Treatment. Commun Ment Health J. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s10597-023-01218-7\u003c/span\u003e\u003cspan address=\"10.1007/s10597-023-01218-7\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSearles JS, Helzer JE, Walter DE (2000) Comparison of Drinking Patterns Measured by Daily Reports and Timeline Follow Back. Psychol Addict Behav 14(3):277\u0026ndash;286. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1037//OS93-164X.14.3.277\u003c/span\u003e\u003cspan address=\"10.1037//OS93-164X.14.3.277\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSobell LC, Agrawal S, Sobell MB, Leo GI, Young LJ, Cunningham JA, Simco ER (2003) Comparison of a quick drinking screen with the timeline followback for individuals with alcohol problems. J Stud Alcohol 64(6):858\u0026ndash;861. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.15288/jsa.2003.64.858\u003c/span\u003e\u003cspan address=\"10.15288/jsa.2003.64.858\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSobell MB, Sobell LC, Klajner F, Pavan D, Basian E (1986) The reliability of a timeline method for assessing normal drinker college students\u0026rsquo; recent drinking history: Utility for alcohol research. Addict Behav 11(2):149\u0026ndash;161. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/0306-4603(86)90040-7\u003c/span\u003e\u003cspan address=\"10.1016/0306-4603(86)90040-7\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStrickland JC, Acuff SF (2023) Role of social context in addiction etiology and recovery. \u003cem\u003ePharmacology Biochemistry and Behavior\u003c/em\u003e, \u003cem\u003e229\u003c/em\u003e. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.pbb.2023.173603\u003c/span\u003e\u003cspan address=\"10.1016/j.pbb.2023.173603\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSubbaraman MS, Witbrodt J (2014) Differences between abstinent and non-abstinent individuals in recovery from alcohol use disorders. Addict Behav 39(12):1730\u0026ndash;1735. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.addbeh.2014.07.010\u003c/span\u003e\u003cspan address=\"10.1016/j.addbeh.2014.07.010\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e\u003cem\u003eThe_persistent_influence_of_so\u003c/em\u003e. (n.d.)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTracy EM, Munson MR, Peterson LT, Floersch JE (2010) Social support: A mixed blessing for women in substance abuse treatment. J Social Work Pract Addictions 10(3):257\u0026ndash;282. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/1533256X.2010.500970\u003c/span\u003e\u003cspan address=\"10.1080/1533256X.2010.500970\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVentura AS, Bagley SM (2017) To Improve Substance Use Disorder Prevention, Treatment and Recovery: Engage the Family. Journal of Addiction Medicine, vol 11. Lippincott Williams and Wilkins, pp 339\u0026ndash;341. 5 \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1097/ADM.0000000000000331\u003c/span\u003e\u003cspan address=\"10.1097/ADM.0000000000000331\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWitkiewitz K, Hallgren KA, Kranzler HR, Mann KF, Hasin DS, Falk DE, Litten RZ, O\u0026rsquo;Malley SS, Anton RF (2017) Clinical Validation of Reduced Alcohol Consumption After Treatment for Alcohol Dependence Using the World Health Organization Risk Drinking Levels. Alcoholism: Clin Experimental Res 41(1):179\u0026ndash;186. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/acer.13272\u003c/span\u003e\u003cspan address=\"10.1111/acer.13272\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWitkiewitz K, Wilson AD, Roos CR, Swan JE, Votaw VR, Stein ER, Pearson MR, Edwards KA, Tonigan JS, Hallgren KA, Montes KS, Maisto SA, Tucker JA (2021) Can Individuals With Alcohol Use Disorder Sustain Nonabstinent Recovery? Non-abstinent Outcomes 10 Years After Alcohol Use Disorder Treatment. J Addict Med 15(4):303\u0026ndash;310. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1097/ADM.0000000000000760\u003c/span\u003e\u003cspan address=\"10.1097/ADM.0000000000000760\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWorld Health Organization (2000) \u003cem\u003eInternational Guide for Monitoring Alcohol Consumption and Related Harm\u003c/em\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang X, Zeng X (2023) Effects of family functioning on relapse among individuals with drug addiction in compulsory isolation: a chained mediation model. Curr Psychol 42(3):1701\u0026ndash;1711. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s12144-021-01561-6\u003c/span\u003e\u003cspan address=\"10.1007/s12144-021-01561-6\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[{"identity":"3370d7ca-27d4-4efe-870a-f11eb73f3e58","identifier":"10.13039/100000002","name":"National Institutes of Health","awardNumber":"R01 AA025849","order_by":0},{"identity":"3892139b-1fb5-40d5-9b12-527e6b3fb793","identifier":"10.13039/100000002","name":"National Institutes of Health","awardNumber":"K24 AA022136","order_by":1},{"identity":"f048b846-d435-4e0f-a2f6-a022f5d56dba","identifier":"10.13039/100016810","name":"Peter Boris Centre for Addictions Research","awardNumber":"CRC-2020-00170","order_by":2},{"identity":"f3b50795-a167-444a-a633-17f9968b1171","identifier":"10.13039/501100000024","name":"Canadian Institutes of Health Research","awardNumber":"MFE 201016","order_by":3}],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"McMaster University","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"alcohol use disorder, addiction, recovery, abstinence, social network, social influence","lastPublishedDoi":"10.21203/rs.3.rs-6658717/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6658717/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eSocial factors play a pivotal role in both development of and recovery from alcohol use disorder (AUD), and social network analysis (SNA) provides a rigorous framework to understand these influences. The current study applied SNA to understand recovery from AUD, with a secondary aim of examining sex differences in social network influences. A cohort of adults with AUD making a significant recovery attempt (\u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;501) were followed over six waves during a one year period (83% retention) and completed assessments of egocentric SNA of their 20 closest alters and drinking behaviors. Hierarchical models run within a Bayesian imputation framework examined social network characteristics in relation to three recovery outcomes: abstinence, reduction in World Health Organization (WHO) drinking levels, and reductions in drinks/week. Follow-up analyses were stratified by sex. Three social network characteristics predicted abstinence: interaction frequency with alters, number of alters in mutual help organizations (MHOs), and number of family member alters. The latter two factors were also predictive of WHO drinking level and drinks/week. Alter heavy drinking days was negatively associated with reductions in WHO drinking level. Sex differences revealed greater network heavy drinking only impeded WHO level reductions for females, whereas having more MHO program members in one\u0026rsquo;s network only facilitated recovery for males. These findings reveal the importance of social networks, particularly the family, in AUD recovery. Results also highlight sex differences in how social networks influence recovery, with greater vulnerability to heavy drinking influences in females and greater benefit from MHO engagement for males.\u003c/p\u003e","manuscriptTitle":"Social Network Mechanisms of Behavior Change in Alcohol Use Disorder Recovery: A Longitudinal Observational Cohort Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-16 08:21:15","doi":"10.21203/rs.3.rs-6658717/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"961beb92-bc4a-427a-bc4c-967cd97721cc","owner":[],"postedDate":"May 16th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":48584203,"name":"Epidemiology"},{"id":48584204,"name":"Psychology"}],"tags":[],"updatedAt":"2025-05-16T08:21:15+00:00","versionOfRecord":[],"versionCreatedAt":"2025-05-16 08:21:15","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6658717","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6658717","identity":"rs-6658717","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}