An epidemiological evaluation of the use of microbiological tools for identifying gonorrhoea infection networks.

We aimed to assess the utility of various techniques for identifying gonorrhoea infection networks. All residents of a non-metropolitan North Carolina county visiting a sexually transmitted disease (STD) clinic during a 17-month period were screened for gonorrhoea. Infection networks were estimated by serovar type combined with antibiotic resistance, arbitrarily primed polymerase chain reaction (AP-PCR), or temporal clustering. The residential addresses of infected patients were geocoded and mapped. Among 2 serovar types, the presence of distinguishing characteristics of a network, based on questionnaire data, was assessed with prevalence ratios and 95% confidence intervals (CIs) relative to those not in the network. Twenty-five serovar types were identified among 759 gonorrhoea infections. In one serovar, the networks further delineated by temporal clusters correlated with particular AP-PCR types. In most instances, however, different typing techniques painted different network pictures. No refined serovar network stood out as having a particular set of characteristics that could be used to shape intervention. Teasing out an individual infection network with unique characteristics will require the development and use of other microbiological tools.