2A5 Connecting the Dots: A Glimpse into the Sexual Networks of Syphilis Cases in the San Francisco Bay Area

Wednesday, September 21, 2016: 4:20 PM
Salon C
Rilene A. Chew Ng, DrPH, Sexually Transmitted Diseases Control Branch, California Department of Public Health, Richmond, CA, Robert P. Kohn, MPH, Applied Research, Community Health, Epidemiology, and Surveillance (ARCHES) Branch, Population Health Division, San Francisco Department of Public Health, San Francisco, CA and Heidi Bauer, MD, MS, MPH, STD Control Branch, California Department of Public Health, Richmond, CA

Background: The number of early syphilis cases in the San Francisco Bay Area Region (SFBAR) increased from 828 cases in 2008 to 1837 in 2014. This region is comprised of 8 counties collectively referred to as California Project Area (CPA) counties, as well as SF County. Limited data sharing functionality and siloed surveillance systems pose a barrier to counties "seeing" beyond their borders and understanding how they both contribute to and are impacted by disease in surrounding areas.

Methods: We used routinely collected syphilis surveillance and partner notification data from 2008-2014 to identify sexual networks in the SFBAR and assess the proportion of partnerships occurring between persons from different counties (i.e., interjurisdictional). A probabilistic matching algorithm was used to merge 2008-2011 case-based data with 2012-2014 patient-based data, resulting in a single patient-based registry. County and regional network diagrams were generated. Nodes (persons) were characterized by county of residence and were connected based on named sexual partnerships. To assess SF's impact on regional network connectivity, we generated a CPA-only network excluding SF, in addition to an overall SFBAR network.

Results: In the CPA, 729 networks and 1796 partnerships were identified, with the majority (77%) consisting of 2-3 persons and the largest network consisting of 50 persons. At least one interjurisdictional partnership was observed in 37% (n=267) of CPA networks and 33% (n=600) of CPA partnerships. Incorporating SF data increased the size of the largest network to 3879 persons and 4550 partnerships, of which 41% (n=1869) were interjurisdictional.

Conclusions: While limited connectivity was observed in the CPA network alone, incorporating SF revealed a large regional network with a high proportion of interjurisdictional partnerships. Further analysis of routinely collected network data and increased support for data sharing between counties may reveal opportunities for leveraging limited DIS resources for more effective partner follow-up and interruption of disease transmission.