The findings and conclusions in these presentations have not been formally disseminated by the Centers for Disease Control and Prevention and should not be construed to represent any agency determination or policy.

Tuesday, May 9, 2006
111

Molecular Epidemiology, Venue Networks, and Risk Factors for Outbreak Strains of Fluoroquinolone Resistant Neisseria gonorrhoeae in California

Sheldon R. Morris1, Heidi M. Bauer1, Joan Knapp2, David Trees2, Paul Hannah3, Doug Moore3, Susan Wang4, and Gail Bolan1. (1) STD Control Branch, California Department of Health Services, Richmond, CA, USA, (2) Division of STD Prevention, Centers for Disease Control and Prevention, Atlanta, GA, USA, (3) Orange County Public Health Laboratory, Santa Ana, CA, (4) Division of Viral Hepatitis, Centers for Disease Control and Prevention, Atlanta, GA, USA


Background:
Identifying outbreaks in the appropriate at-risk populations could lead to more effective interventions. Molecular typing and network analysis are methods of interest to achieve this in STD control.

Objective:
To use strain typing to describe an outbreak of fluoroquinolone resistant Neisseria gonorrhoeae (QRNG) in California within the context of contact tracing, venue network mapping, and risk factor analysis.

Method:
82 QRNG cases between January 2000 and June 2002 were identified by sentinel surveillance. Epidemiologic information was collected from medical charts, field records, and, after January 2001, interviews. QRNG strain types were defined by auxotype, serovar, Lip, gyrA and parC genotypes (ASLGP), antibiograms, and pulsed-field gel electrophoresis (PFGE). A network map of cases by strain type was created for the outbreak connecting contacts and venues where cases met partners in the past three months. Risk factors associated with outbreak strains were determined using multivariate regression analysis.

Result:
Twenty ASLGP strain types were identified. Two outbreak strain types accounted for 70% of cases. Seven dyads and one triad were identified from contact tracing. With addition of epidemiological data on venues (including clubs, bathhouses and internet sites) there was a core group of 17. Strain typing supported the connection of the members of the core group (17) and an additional 36 cases for a total of 54. An outbreak strain was associated with men who have sex with men with an adjusted odds ratio 25.0 (95% CI 2.0-312); whereas travel history was a negative predictor with an adjusted odds ratio 0.1 (95% CI 0.0-0.7).

Conclusion:
Strain typing can help distinguish outbreak cases from background cases and may allow more targeted intervention strategies. Mapping venue networks may identify sites with priority for intervention.

Implications:
Use of strain typing and network mapping may improve efficiency of disease outbreak control, however they need to available in a timely fashion.