The Society for Social Work and Research

2014 Annual Conference

January 15-19, 2014 I Grand Hyatt San Antonio I San Antonio, TX

98P
Using Latent Class Growth Analysis to Identify Quality of Life Trajectories Among Women With Substance Use Disorders

Schedule:
Friday, January 17, 2014
HBG Convention Center, Bridge Hall Street Level (San Antonio, TX)
* noted as presenting author
Hyunyong Park, MSSW, Doctoral Research Assistant, Case Western Reserve University, Cleveland, OH
Elizabeth M. Tracy, PhD, Professor, Case Western Reserve University, Cleveland, OH
Meeyoung O. Min, PhD, Research Assistant Professor, Case Western Reserve University, Cleveland, OH
Min Kyoung Jun, MSSA, Doctoral Research Assistant, Case Western Reserve University, Cleveland, OH
Background and Purpose:

Quality of life (QOL) has become an important component of recovery for women with substance use disorders (SUD). However, little is known about different trajectories of QOL among women who receive treatment for addictions. Using latent class growth analysis (LCGA), this study identified heterogeneous QOL trajectories of women with SUD and explored socio-demographic, clinical and personal network characteristics in reference to the identified QOL trajectories.

Methods:

This NIDA funded study interviewed 377 women at three county-funded substance abuse treatment programs at 1 weeks post intake and at follow-ups (1, 6, and 12 months later) (81% retention). The World Health Organization Quality of Life Measure abbreviated version assessed women’s perceived quality of life over time. This study utilized socio-demographic characteristics, clinical characteristics (e.g., dual disorders, Trauma Symptom Checklist-40), Abstinence Self-Efficacy, friends and recovery support for abstinence, personal network characteristics (network composition, social support, and network structure).

LCGA was performed to identify distinctive participant trajectories that follow similar patterns of change over 12 months on perceived QOL. The model fit was evaluated by the Bayesian Information Criteria (BIC) and the sample sizes of the smallest class. Bivariate analyses were performed to compare characteristics of change patterns in QOL.

Results: 

Majority of participants were African American (60%), received government assistance (72.5%), and had co-occurring mental and substance use disorders (73.4%). The mean years of substance use disorder was 6.4 (SD=7.8). The mean number of substance using people among 25 network members was 8.4 (SD=4.9). The mean of QOL at intake was 61.2 (SD=15.6).

Using LCGA, this study identified three distinctive growth trajectories in QOL: (1) Consistently high levels of QOL (n=108, 28.7%), (2) Consistently moderate levels of QOL (n=214, 56.8%), and (3) Decreasing levels of QOL (n=55, 14.6%).

Compared to the consistently high levels of QOL group, the decreasing QOL group was more likely to have dual disorders, higher levels of trauma symptomatology, lower levels of self-efficacy, lower levels of friend and recovery support for abstinence, fewer people who provided concrete, emotional, informational and sobriety support, more substance using people, and fewer reciprocal and close relationships at p ≤ .05. Compared to the consistently moderate levels of QOL group, the decreasing QOL group was more likely to have higher levels of trauma symptomatology, lower levels of friends and recovery support for abstinence, fewer people who provided emotional and sobriety support, and fewer close relationships at p ≤ .05. No significant differences were found in socio-demographics and personal network structure between the decreasing QOL group and either the consistently high levels of QOL group and consistently moderate levels of QOL group.

Conclusion and Implications:

Findings highlight three growth patterns in quality of life over time among women with SUD and its association with clinical and personal network characteristics. Implementation of effective treatment programs are needed that target individuals (14.6%) whose levels of quality of life gradually decrease. Future research will explore how different QOL growth patterns are associated with relapse and treatment outcomes over time.