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Background: The number of early syphilis cases in San Francisco has increased steadily from 354 cases in 2007 to 1017 cases in 2013. Network dynamic theory indicates the importance of core groups in sustaining STD epidemics. We used an innovative algorithm to identify sexual networks from routinely collected case data to better understand the epidemiology of recent syphilis cases and explore possible new approaches to disease control.
Methods: All case data are maintained in a single patient-based registry along with all other San Francisco STD screening and surveillance data. All sexual partnerships identified during syphilis and HIV partner services activities in 2013 were analyzed using an iterative algorithm that matched all cases and contacts against all others and enumerated network membership.
Results: A total of 286 networks were identified. Of these, 229 (80 percent) consisted of only 2 or 3 individuals. Eleven (11) networks of 10 or more clients were identified, including a network of 435 individuals. Clients in this "mega-network" were more likely to be HIV-positive (P <0.001 from Chi-square) and to have had more cases of syphilis in the past (p<0.0001 from ANOVA) than clients in other networks or cases in no network at all. Cases in the mega-network had been filed in 69 different "lots" of syphilis cases during the year.
Conclusions: There were many more connections between clients than we had found by reviewing cases one at a time. Further analysis of network data may reveal ways to reach core groups and prevent infection and further disease transmission. Using the network algorithm on new cases and newly named partners may help us prioritize work and to identify connections between cases that are not readily apparent.