Syphilis in Chicago: Epidemiology, Sexual Networking and Modeling for Prevention

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Project Title: Syphilis in Chicago: Epidemiology, Sexual Networking and Modeling for Prevention ABSTRACT The incidence of syphilis has been increasing in the US for at least 10 years, especially among men who have sex with men (MSM). Syphilis seroprevalence is higher among HIV-infected MSM, an indicator of condomless sexual contact and associated with incident HIV infection. Chicago is one of the major epicenters of the syphilis epidemic and includes the largest contiguous African American population in the United States. We propose to identify and characterize those at greatest risk for incident new and recurrent syphilis infections and better define the patterns of transmission, reinfection and co-infection within the social and sexual networks of a diverse group of MSM (16-39 years of age) who are disproportionately impacted. The study, STI Chicago Epidemiology Network Study (SCENES), will enroll 150 high-risk MSM using a recently developed two-step (2S) network recruitment approach in three of Chicago's highest prevalence neighborhoods and monitor them quarterly for 2 years for incident syphilis and other STIs including HIV. We will collect detailed social and sexual network, and risk behavior data and create a syphilis transmission model that is built upon our existing MSM agent-based HIV transmission model platform to incorporate detailed sexual network dynamics, syphilis treatment and reinfection. We will also work closely with our existing Chicago Department of Public Health programs and outreach clinics in order to coordinate treatment of STIs, determine previous treatment and infection histories, engage clients in care and provision of condoms and PrEP as is our current practice. This proposal will contribute valuable new knowledge about syphilis incidence in the highest prevalence neighborhoods in Chicago, and provide a clear understanding of syphilis mono- and co-infection networks. Sexual network analysis using dynamic exponential random graph models that are embedded within our existing agent-based model platform will provide an opportunity to include future genetic network data and serve as a framework for testing emerging prevention interventions prior to conducting clinical trials or implementing prevention strategies.