Patterns of Personal Networks and Their Relationship to Quality of Life Among Women with Substance Use Disorders

Background/Rationale

Substance abuse treatment often emphasizes the role of personal networks as coping resource to improve treatment outcomes.  Literature has also recognized quality of life (QOL) as one of important treatment outcomes because higher levels of QOL are associated with decreased odds of relapse post treatment.  However, mixed findings about the effect of personal networks have been reported, which might be related to the complexity of personal network characteristics (e.g., non-linear interaction between personal network variables).  The present study employed Latent Class Analysis (LCA) to assess the non-linear interactions among personal network variables.  The present study also examined the unique effect of personal network patterns on quality of life after controlling for social-demographic and clinical characteristics at baseline.

Methods/Methodology

The present study interviewed 272 women at three county-funded substance abuse treatment programs at 1 week and at 6 and 12 months post treatment intake.  Personal network characteristics were assessed using EgoNet program.  Women listed 25 network members and their characteristics (e.g., type, quality, and structure of relationships).  The 26-item WHO Quality of Life-BREF (WHOQOL-BREF) assessed quality of life at 12 months post intake (α=.91). 

LCA was conducted to explore the underlying patterns of personal networks.  The model fit was evaluated by the Bayesian Information Criteria, the size of smallest group, solution stability, and the interpretability for practical purposes.  Hierarchical linear regression was performed to examine the unique effect of underlying patterns of personal networks on quality of life at 12 months after controlling for socio-demographics and clinical characteristics.

Results

The majority of respondents were African American (63.2%) with a mean age of 36.5 (SD = 10.4).  43% of women had less than high school education. Approximately 73% women had previous treatment history and 73% women were diagnosed with co-occurring psychiatric and substance use disorders.

LCA identified three underlying subgroups of women who had similar personal network characteristics: (a) family-centered networks (36.7%), (b) treatment-centered networks (26.2%), and (c) substance-using negative networks (37.1%).  Results of hierarchical linear regression shows that including patterns of personal networks increased 3% in variance of quality of life in the model (p<.01).  Compared to women with family-centered networks, those with substance-using negative networks were more likely to report lower levels of quality of life (b=-6.44, SE=2.33, p=.006).  Compared to women with treatment-centered networks, those with substance using negative networks were associated with lower levels of quality of life at 12 months post treatment intake (b=-5.89, SE=2.63, p=.026).  In terms of covariates, higher levels of trauma symptoms and lower levels of treatment motivation were associated with lower levels quality of life at 12 months.

Conclusions

Findings highlight the distinctive patterns of personal network characteristics among women with SUD.  Findings also indicate that personal network patterns were associated with quality of life even after controlling for socio-demographic and clinical characteristics. Women with substance-using negative networks reported significantly lower levels of quality of life than those with other network patterns.  Identifying personal network patterns may be useful to provide tailored personal network-based interventions to improve quality of life.