alexa Can Social Networks Inform Treatment Use for Persons with Co-Occurring Substance Use and Mental Health Problems? | OMICS International
ISSN: 2155-6105
Journal of Addiction Research & Therapy

Like us on:

Make the best use of Scientific Research and information from our 700+ peer reviewed, Open Access Journals that operates with the help of 50,000+ Editorial Board Members and esteemed reviewers and 1000+ Scientific associations in Medical, Clinical, Pharmaceutical, Engineering, Technology and Management Fields.
Meet Inspiring Speakers and Experts at our 3000+ Global Conferenceseries Events with over 600+ Conferences, 1200+ Symposiums and 1200+ Workshops on Medical, Pharma, Engineering, Science, Technology and Business

Can Social Networks Inform Treatment Use for Persons with Co-Occurring Substance Use and Mental Health Problems?

Orion Mowbray*

University of Michigan School of Social Work, 1080 S. University, Ann Arbor, MI 48109, USA

*Corresponding Author:
Orion Mowbray
1080 S. University
Ann Arbor, MI 48109, USA
Tel: 734-260-4730
E-mail: [email protected]

Received October 19, 2012; Accepted October 23, 2012; Published October 30, 2012

Citation: Mowbray O (2012) Can Social Networks Inform Treatment Use for Persons with Co-Occurring Substance Use and Mental Health Problems? J Addict Res Ther 3:e115. doi:10.4172/2155-6105.1000e115

Copyright: © 2012 Mowbray O. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Visit for more related articles at Journal of Addiction Research & Therapy

Introduction

Social networks matter to addiction researchers and treatment providers. They are an integral part in understanding how and why people do the things they do. For persons with co-occurring substance use problems and mental illness, the need to understand their social networks is very timely, as persons with Co-occurring substance use Disorders and mental illness (CODs) represent one of the hardest groups to reach among those in need of treatment services [1] and cost more than others to treat [2]. Examining the social networks of persons with CODs may assist in understanding why some persons seek treatment services, while a great many others do not. This paper outlines how social networks can offer insight into the dynamic processes associated with entering and persisting through treatment among persons with CODs. The importance of co-occurring disorders Persons with Cooccurring substance use Disorders and mental illness (CODs) represent a very sizable portion of individuals in need of mental health treatment. The National Epidemiologic Survey on Alcohol-Related Conditions (NESARC) shows that 18.9% of individuals with a 12-month alcohol use disorder also report a co-occurring mood disorder, with 17.1% reporting a co-occurring anxiety disorder [3]. From a lifetime perspective, the National Co-morbidity survey shows us that as many as 52% of individuals with a lifetime alcohol use disorder also experienced a mental health disorder [4]. Additionally, of those with a lifetime drug use disorder, 59% experienced a lifetime mental health disorder [5]. Furthermore, only small portions of persons with CODs who need treatment actually receive it. National estimates of 3 persons with CODs suggest that upwards of 75% do not receive any treatment, or believe they do not need it [6]. However, emerging research that examines the role of others, or those within one’s social network, reveals that a large portion of the decision to initiate mental health treatment may stem from the influence of close personal relationships [7]. Often times either perception from family members that treatment is effective [8], or the report of the positive feelings associated with interaction among family members [9] are well linked to a person’s desire to initiate treatment for CODs. This suggests that family members, as well as others in a social network, play a vital role in seeking treatment.

The Network Episode Model

The Network Episode Model of treatment use suggests that people decide to enter treatment and form attitudes about substance use and mental health treatment the same way they acquire other information through social networks [10]. For many, there is a large base to consult with during an illness, including both primary reference groups (e.g. close friends and family), as well as secondary groups (co-workers, neighbors, medical professionals) [11]. Network episode models suggest that the decision to enter treatment is not a calculated decision. Rather, seeking treatment in influenced by only a subset of potential factors regarding illness management, which is in part, a function of the social networks an individual belongs to. Finally, network episode models suggest that treatment use is a dynamic process, where individuals often combine a series of decisions over time. Many individuals “muddle through” the decision to enter treatment due to the consequences associated with previous choices on how to manage addiction and mental health [12].

To date, there is sufficient research from the fields of mental health that demonstrate the importance of understanding treatment use from a network episode model [13], but there is little research that examines how network episode models influence treatment use for persons with CODs. A recent application of network episode modeling examining service utilization by the homeless with high rates of CODs shows that the number of persons in a social network, as well as increased length of time an individual has been managing their addiction or mental illness increases the likelihood of treatment initiation [14]. In a related study, it was shown that persons from social networks of health professionals play a significant role in whether or not treatment is initiated. Among those who felt coerced by medical professionals in their social network, fewer service contacts were formed, and evaluations of the quality of medical professionals diminished [15]. While these studies among persons with CODs make a meaningful contribution to the potential validity of the NEM in explaining treatment initiation, there are still many critical research gaps needed to understand how social networks influence persons with CODs to seek treatment.

Social Network Overlap

Some social networks, such as family and close friends can have a positive impact on treatment initiation, provided that the dominant view of treatment services within the network is favorable [16]. However, the number of substance users within one’s social network is inversely related poor treatment outcomes among substance users [17]. To address this discrepancy, research is needed to understand the degree to which supportive networks overlap, or differentiate from social networks that discourage treatment use, such as social networks with a high number of substance users or is composed of those with distrust for treatment systems of addiction and mental health.

Types of Social Networks

Research on network episode models focus almost exclusively on the close, personal networks that are composed of family and close friends. However, to understand how social networks influence the pursuit of treatment use among those with CODs, networks from all levels must be considered. Future research concerning social networks must examine how ties to networks of health professionals influence the pursuit of treatment services. This matter is of particular importance to the social networks of persons with CODs, as persons with CODs show higher network ties to mental health treatment professionals and fewer ties to close family and friends than those with SMI or an SUD alone [18].

Interaction within Social Networks

When examining the interaction found within social networks, there is an overwhelming emphasis on the topic of social support in the literature on persons with substance use disorders and mental illness [19]. While there is sufficient evidence to show that a strong social support network is related to treatment use among persons with substance use disorders and mental illness, it is unclear how this concept may impact persons with CODs, especially when social support is considered as an array of support types, and not just one monolithic measure.

Social Network Methodology

In many studies, treatment use is still considered a dichotomous outcome [20]. However, network episode models suggest that treatment use is not a single event, but is a process where individuals combine treatment services over time. Future research examining network episode models among persons with CODs may contribute to the larger field of services research by redefining how treatment use is operationalized to better reflect the common experiences of treatment use among those with addiction and mental illness. Finally, applying network episode models among persons with CODs may benefit greatly from the use of more longitudinal studies. If network models are conceived of as a dynamic process, then longitudinal research is a must. Longitudinal research can give a better understanding of how social networks change over time, how persons move in and out of treatment services, and how previous treatment experiences influence the current search for care.

Conclusion

Network episode models are useful tools for understanding how social networks influence treatment use. The belief that those around us influence who we are and what we do is not a new concept. In fact, it is one of the oldest in behavioral science research. These terms have been a part of behavior science research for nearly a century and are not expected to diminish in their importance any time soon. While there are many critical research gaps needed to be filled in network episode modeling, the theoretical basis for network episode models and early empirical work on network episode models among those with CODs provide valuable contributions to the study of treatment use for addiction and mental illness.

References

Select your language of interest to view the total content in your interested language
Post your comment

Share This Article

Article Usage

  • Total views: 12516
  • [From(publication date):
    December-2012 - Jun 06, 2020]
  • Breakdown by view type
  • HTML page views : 8697
  • PDF downloads : 3819
Top