Bidirectional Influence: A Longitudinal Analysis of Size of Drug Network and Depression Among Inner-City Residents in Baltimore, Maryland

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.

Keywords: :

• depression
• network size
• longitudinal
• drug users
• social influence

THE AUTHORS

Jingyan Yang, MHS, is currently a DrPH student in the Mailman School of Public Health at Columbia University. Prior to this, she was Senior Biostatistician in the Bloomberg School of Public Health at Johns Hopkins University. Ms. Yang is a statistician whose substantive research has focused on understanding the dynamics of social network characteristics among marginalized population via complex modeling. In particular, She is very interested in assessing the effect of drug network characteristics on drug use behaviors, depression, drug stigma and alcohol abuse.

Carl Latkin, Health Behavior and Society, Johns Hopkins Bloomberg School of Public Health. His work focuses on HIV and STI prevention among people who use drugs, the psychosocial well-being of people with HIV/AIDS, substance use and HIV risk behaviors, and measurement of social-contextual factors including social and personal network analysis and neighborhood and other geographic factors.

Melissa Davey-Rothwell, PhD, CHES, Associate Scientist, Health Behavior and Society, Johns Hopkins Bloomberg School of Public Health. Her research focuses on individual and social-network factors associated with norms, neighborhoods, and behaviors such as substance use and sexual practices. In addition, she develops, evaluates, and disseminates behavioral interventions for at-risk populations.

Mansi Agarwal, MPH, is a PhD student in epidemiology at the Columbia University Mailman School of Public Health. Her research interests include infectious disease surveillance in low-resource settings, HIV and aging, and quality of life outcomes on long-term treatments of HIV and cancer.

GLOSSARY

• Social affiliation: Social affiliation is the desire or tendency to be with others of one's own kind. The assumption is that individual actors in groups or events are an indicator of an underlying social tie.

• Social contagion: Social contagion is the collective behavior made possible in social events and conditions. The assumption is that moods and thoughts become contagious within certain types of crowds. Once infected with these moods, behavior becomes illogical and people tend to do things they normally would not do.

• Social identity: Social identity is the portion of an individual's self-concept derived from perceived membership in a relevant social group.

• Social influence: Social influence is a broad term that relates to many different phenomena, which occurs when one's emotions, opinions, or behaviors are affected by others.

• Social learning: Social learning is a cognitive process that happens in a social context and can occur purely through instruction, imitation, or observation.

• Social network theory (SNT): Social network theory takes social relationships as nodes and ties. Nodes are the individual actors within the networks, and ties are the relationships between the individual actors. Various kinds of ties can be connected between nodes. In simple scenario, a social network is a map of all of the relevant ties between the nodes being studied.