Conventional behavioral risk paradigms focus on individual factors for the understanding of substance abuse and associated infectious disease transmission patterns, largely ignoring social context. Accumulating evidence suggests that the structure of social networks influences the magnitude and direction of drug-associated disease transmission. A clearer view of how the """"""""architecture"""""""" of social networks influences health risks may lead to the development of improved risk-reduction and disease prevention efforts. This proposal seeks assistance for the secondary analysis of a 5-year (1988-1992) prospective study of the social networks of persons at presumed high risk for HIV infection (drug abusers) in Colorado Springs, CO. This is perhaps the largest infectious disease social networks dataset currently available to analysis and disease modelers. Preliminary findings for this study implicate network structure as a barrier to HIV transmission and point to opportunities for targeted disease intervention. Proposed are detailed analyses of a large network of drug abusers comprising more than 8,000 uniquely identified and connected (via drug use, sexual activity, or living arrangements) persons named by nearly 600 respondents in the process of nearly 1,000 personal interviews. Analyses envisaged are: to examine population mixing patterns in a network context; to study in fine detail the stability of network structure over time and how network dynamics may facilitate or constrain HIV transmission; to elucidate the patterns or risk behavior in network context over time, particularly the epidemiology of drug abuse and drug and sex partner selection; to determine the minimum number of informants needed to accurately describe network structure; to study the stability of network structure as characterized by data collected from different population samples; and to study the validity and reliability of informant reports. This is a proposed 2-year collaborative effort linking social scientist, ethnographers, and computer programming experts to elucidate network features as they relate to drug abuse and its associated disease transmission opportunities. Based on these secondary analyses, we expect to recommend how to obtain and use social network data to design network-informed strategies to promote efficacious harm reduction and disease intervention techniques in the social networks of drug abusers.
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