title: 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.
  
  
  OBJECTIVE
  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.
authors.affiliation: a Department of Health , Behavior & Society, Johns Hopkins University , Baltimore , Maryland , USA.
authors.forename: Jingyan
authors.initials: J
authors.lastname: Yang
authors.affiliation: a Department of Health , Behavior & Society, Johns Hopkins University , Baltimore , Maryland , USA.
authors.forename: Carl
authors.initials: C
authors.lastname: Latkin
authors.affiliation: a Department of Health , Behavior & Society, Johns Hopkins University , Baltimore , Maryland , USA.
authors.forename: Melissa
authors.initials: M
authors.lastname: Davey-Rothwell
authors.affiliation: b Department of Epidemiology , Columbia University , New York , New York , USA.
authors.forename: Mansi
authors.initials: M
authors.lastname: Agarwal
mesh_terms: D000328:Adult; D015142:Baltimore / Q000453:epidemiology; D003863:Depression / Q000453:epidemiology*; D004522:Educational Status; D005260:Female; D006703:Ill-Housed Persons / Q000706:statistics & numerical data*; D006801:Humans; D016014:Linear Models; D008137:Longitudinal Studies; D008297:Male; D008875:Middle Aged; D055361:Multilevel Analysis; D015999:Multivariate Analysis; D015995:Prevalence; D012307:Risk Factors; D012907:Smoking / Q000453:epidemiology*; D012944:Social Support; D019966:Substance-Related Disorders / Q000453:epidemiology*; D014505:Urban Population / Q000706:statistics & numerical data*
keywords: depression; drug users; longitudinal; network size; social influence