http://informahealthcare.com/ada ISSN: 0095-2990 (print), 1097-9891 (electronic) Am J Drug Alcohol Abuse, 2014; 40(3): 179–180 ! 2014 Informa Healthcare USA, Inc. DOI: 10.3109/00952990.2013.875553

COMMENTARY

The role of social networks in recovery from alcohol and drug abuse Amy A. Mericle, PhD Research Scientist, Treatment Research Institute, Philadelphia, PA, and Affiliate Scientist, Alcohol Research Group, Public Health Institute, Emeryville, CA, USA

Address correspondence to Amy A. Mericle, PhD, Treatment Research Institute, 150 S. Independence Mall West, Philadelphia, PA 19130, USA. Tel: +1 215 399 0980. E-mail: [email protected]

dependence have the smallest networks (those with alcohol abuse fell in between and were significantly different from both). Regarding network diversity, however, the social networks of individuals with alcohol abuse looked similar to those individuals with no history of AUD, but the social networks of individuals with alcohol dependence were significantly less diverse than the other two groups. At the multivariate level, examining the effects of network size and network diversity while simultaneously controlling for social and clinical factors, social network size was no longer associated with AUD status. And although this model showed that social network diversity was a significant predictor of AUD status, the observed relative risk ratio was relatively weak. These findings lead the authors to conclude that individuals with AUD have social networks similar to those without AUD in the general population and that interventions focused on reducing alcohol use could benefit from harnessing these networks. However, there are a number of important limitations of this study. This study only uses an overall measure of network size and a gross measure of network diversity; it does not include measures of alcohol and drug use among network members and several other network factors that have been found to influence AUD and recovery. Additionally, although the study includes measures of lifetime and past year AUD, it does not include measures of current alcohol use, which would be an important indicator of sobriety and where individuals may be in their recovery from AUD. Finally, the cross-sectional nature of their analyses prohibits assessment of causal relationships and how social network characteristics may vary over the life span and affect alcohol use. Thus, while this article represents an important step in understanding basic network characteristics of individuals with and without AUD in the general population, it does not point to the characteristics of networks that would need to be harnessed to reduce alcohol use. Stone and colleagues (11), while not drawing from a nationally representative sample, further this line of inquiry by examining how social network characteristics of individuals in the recovery process change over time. Specifically, this study uses multilevel modeling to examine characteristics of individuals (egos) and network members (alters) that affect

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Social capital, in general, and social networks, in particular, have long been noted to be important predictors of health and well-being. The preponderance of evidence suggests that individuals who are actively involved in their communities and socially engaged with others have been found to live longer, to be healthier, and to experience less psychological distress (1–3). Social factors, including social network characteristics, are also known to be associated with alcohol and drug abuse (4,5). More recently, it has been posited that these factors may influence recovery from substance abuse by facilitating entry into treatment and by promoting ongoing sobriety (6–9). Understanding the role that social networks play in the genesis of substance abuse could lead to the development of prevention and intervention strategies geared toward reducing the tremendous burden of addiction; understanding the role that social networks play in promoting sobriety could lead to the development of strategies to support long-term recovery from addiction over the life span. Two articles in this issue further this important line of inquiry (see Mowbray et al. and Stone et al.). The article by Mowbray and colleagues (10) explores social ties and social network characteristics of respondents participating in the National Epidemiologic Survey on Alcohol and Related Conditions (NESARC) study. Specifically, this article examines whether network size and network diversity differ among individuals with and without alcohol use disorders (AUD) and whether these characteristics are predictive of AUD status after controlling for other social and clinical factors known to be related to AUD. Importantly, these authors use a nationally representative sample to explore how these social networking characteristics vary among individuals with no lifetime AUD, individuals with past-year diagnosis of alcohol abuse, and individuals with past-year alcohol dependence. At the bivariate level, these authors found that network size varied by AUD status. Individuals with no history of AUD had the largest networks and individuals with alcohol

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network member retention among ex-offenders interviewed at discharge from inpatient substance abuse treatment and at 6-months post-discharge. Although many ego (e.g. network size) and alter characteristics (e.g. relationship to the ego, use of substances, criminal history, and frequency of contact with the ego) have been examined in relation to abstinence and recidivism in prior studies, the focus of this study is on how social networks change in these domains during the first six months of recovery. The authors of this study find that alters who were related to the individual in recovery, did not use drugs, were embedded in smaller networks, and had more frequent contact with the individual in recovery were more likely to be retained in the recovery individual’s network. Alter drinking status and criminal history were not found to be related to network retention. The authors of this study note several limitations to this study, primarily high rates of attrition and the exclusion of outcome variables in the model. However, their work identifies factors related to network stability and turnover in the early recovery process and highlights the need for future studies that specifically examine the degree to which network changes are related to relapse, recidivism, and long-term recovery. These types of studies are critical to developing specific social network interventions and strategies that cull and retain certain network members to promote favorable outcomes. While these articles add to an important and growing literature on the role of social networks among individuals with substance use disorders, their most important contribution is to highlight the need for more research in this area, particularly studies that provide information that could be used to develop social network interventions to reduce substance abuse and promote recovery. The need for this research is especially timely as new recovery support services, such as peer-based recovery support delivered in community recovery centers, recovery residences, and collegiate recovery communities (12) provide an opportunity to study the characteristics and dynamics (13) of these intentional recovery support networks. Studies of these recovery support services will be critical to understanding the therapeutic value of these services, to identifying characteristics of them that lead to long-term recovery, and ultimately to developing specific social network interventions to reduce substance abuse and promote recovery.

Am J Drug Alcohol Abuse, 2014; 40(3): 179–180

Acknowledgements This work was supported by funds from the Pennsylvania Department of Health (SAP #4100057685). The author would also like to thank James R. McKay for comments on early drafts of this commentary.

Declaration of interest The author reports no conflicts of interest. The author alone is responsible for the content and writing of the paper.

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