An Epidemiological Evaluation of the use of Microbiological Tools for Identifying Gonorrhoea Infection Networks

• Published in: International journal of STD & AIDS Vol. 10; no. 5; pp. 316 - 323
• Main Authors: Pierce, Richard L, Thomas, James C, Sparling, P Frederick, Fisher, William, Davis, Rachel H, Alcorn, Timothy, Cohen, Myron
• Format: Journal Article
• Language: English
• Published: London, England SAGE Publications 01.05.1999; Royal Society of Medicine Press; Sage Publications Ltd
• Subjects: Adult; Age Factors; AIDS/HIV; Bacterial diseases; Bacterial diseases of the genital system; Bacteriological Techniques; Biological and medical sciences; Condoms; Contact Tracing; Gonorrhea - epidemiology; Gonorrhea - transmission; Health Knowledge, Attitudes, Practice; Human bacterial diseases; Humans; Infectious diseases; Marital Status; Medical sciences; Microbial Sensitivity Tests; Neisseria gonorrhoeae; North Carolina - epidemiology; Polymerase Chain Reaction; Risk Factors; Rural Health; Serotyping; Sexual Behavior; North Carolina; United States; North America; America; evaluation studies; Gonorrhoea; network; Human; Drug susceptibility test; Identification; Method; Epidemiology; Infection; Antibiotic; Microbiological investigation; Gonococcal infection; Sexually transmitted disease; Geographic distribution; Bacteriosis; Rural area; Serotype; Antibacterial agent
• ISSN: 0956-4624; 1758-1052
• DOI: 10.1258/0956462991914186

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.