{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,5]],"date-time":"2025-03-05T05:13:45Z","timestamp":1741151625209,"version":"3.38.0"},"reference-count":34,"publisher":"SAGE Publications","issue":"5","license":[{"start":{"date-parts":[[1999,5,1]],"date-time":"1999-05-01T00:00:00Z","timestamp":925516800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Int J STD AIDS"],"published-print":{"date-parts":[[1999,5]]},"abstract":"<jats:p> 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. <\/jats:p>","DOI":"10.1258\/0956462991914186","type":"journal-article","created":{"date-parts":[[2002,10,16]],"date-time":"2002-10-16T10:10:33Z","timestamp":1034763033000},"page":"316-323","source":"Crossref","is-referenced-by-count":2,"title":["An Epidemiological Evaluation of the use of Microbiological Tools for Identifying Gonorrhoea Infection Networks"],"prefix":"10.1177","volume":"10","author":[{"given":"Richard L","family":"Pierce","sequence":"first","affiliation":[{"name":"Department of Epidemiology, School of Public Health, University of North Carolina, Chapel Hill, North Carolina, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"James C","family":"Thomas","sequence":"additional","affiliation":[{"name":"Department of Epidemiology, School of Public Health, University of North Carolina, Chapel Hill, North Carolina, 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