Bidirectional Influence: A Longitudinal Analysis of Size of Drug Network and Depression Among Inner-City Residents in Baltimore, Maryland. (15th October 2015)

• Record Type: Journal Article
• Authors: Yang, Jingyan; Latkin, Carl; Davey-Rothwell, Melissa; Agarwal, Mansi
• Is Part Of: Substance use & misuse. Volume 50:Number 12(2015)
• Page Start: 1544
• Page End: 1551
• Publication Date: 2015-10-15
• Subjects: depression -- network size -- longitudinal -- drug users -- social influence; Narcotic habit -- Periodicals; Alcoholism -- Periodicals; Substance abuse -- Periodicals; Behavior, Addictive -- Periodicals; Sustance-Related Disorders -- Periodicals; 362.2905
• DOI: 10.3109/10826084.2015.1023452
• Languages: English
• ISSNs: 1082-6084

Abstract

Abstract : Background: The prevalence of depression among drug users is high. It has been recognized that drug use behaviors can be influenced and spread through social networks. Objectives: We investigated the directional relationship between social network factors and depressive symptoms among a sample of inner-city residents in Baltimore, MD. Methods: We performed a longitudinal study of four-wave data collected from a network-based HIV/STI prevention intervention for women and network members, consisting of both men and women. Our primary outcome and exposure were depression using CESD scale and social network characteristics, respectively. Linear-mixed model with clustering adjustment was used to account for both repeated measurement and network design. Results: Of the 746 participants, those who had high levels of depression tended to be female, less educated, homeless, smokers, and did not have a main partner. In the univariate longitudinal model, larger size of drug network was significantly associated with depression (OR = 1.38, p < .001). This relationship held after controlling for age, gender, homeless in the past 6 months, college education, having a main partner, cigarette smoking, perceived health, and social support network (aOR = 1.19, p = .001). In the univariate mixed model using depression to predict size of drug network, the data suggested that depression was associated with larger size of drug network (coef. = 1.23, p < .001) and the same relation held in multivariate model (adjusted coef. = 1.08, p = .001). Conclusions: The results suggest that larger size of drug network is a risk factor for depression, and vice versa. Further intervention strategies to reduce depression should address social networks factors.