Bidirectional Influence: A Longitudinal Analysis of Size of Drug Network and Depression Among Inner-City Residents in Baltimore, Maryland.
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| Title: | Bidirectional Influence: A Longitudinal Analysis of Size of Drug Network and Depression Among Inner-City Residents in Baltimore, Maryland. |
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| Authors: | Yang, Jingyan (AUTHOR), Latkin, Carl (AUTHOR), Davey-Rothwell, Melissa (AUTHOR), Agarwal, Mansi (AUTHOR) |
| Source: | Substance Use & Misuse. Oct2015, Vol. 50 Issue 12, p1544-1551. 8p. 3 Charts, 1 Graph. |
| Subjects: | Mental depression risk factors, Substance abuse & psychology, Automatic data collection systems, Chi-squared test, Confidence intervals, Mental depression, Interviewing, Longitudinal method, Poisson distribution, Probability theory, Research funding, Social networks, Statistics, T-test (Statistics), Logistic regression analysis, Secondary analysis, Socioeconomic factors, Repeated measures design, Psychology of drug abusers, Odds ratio |
| Geographic Terms: | 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. [ABSTRACT FROM AUTHOR] |
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| Database: | Psychology and Behavioral Sciences Collection |
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| 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. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 10826084 |
| DOI: | 10.3109/10826084.2015.1023452 |
