Identification of Networks of Sexually Transmitted Infection: A Molecular, Geographic, and Social Network Analysis
BackgroundDespite widespread efforts to control it, Chlamydia trachomatis remains the most frequently diagnosed bacterial sexually transmitted infection (STI). Analysis of sexual networks has been proposed as a novel tool for control of and research into STI. In the present study, we combine molecul...
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Veröffentlicht in: | The Journal of infectious diseases 2005-03, Vol.191 (6), p.899-906 |
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Sprache: | eng |
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Zusammenfassung: | BackgroundDespite widespread efforts to control it, Chlamydia trachomatis remains the most frequently diagnosed bacterial sexually transmitted infection (STI). Analysis of sexual networks has been proposed as a novel tool for control of and research into STI. In the present study, we combine molecular genotype data, analysis of geographic clusters, and sociodemographic descriptors to facilitate analysis of large sexual networks MethodsIndividual chlamydia genotypes found in Manitoba, Canada, were analyzed to identify geographic clusters, and the identified clusters were further characterized by statistical analysis of sociodemographic variables ResultsA total of 10 geographic clusters of chlamydia-genotype infection were identified. Clusters in Winnipeg showed no or little geographic overlap and could be further differentiated on the basis of the sociodemographic characteristics of the individuals within a cluster. Several clusters in northern Manitoba overlapped geographically but, nonetheless, could be differentiated on the basis of the sociodemographic characteristics of the infected individuals ConclusionsOn the basis of results of the combined analyses, each geographic cluster appeared to represent a relatively distinct transmission network within the larger sexual network. The geographic analysis of the molecular data provided a basis for establishment of potential epidemiological connections between small groups of unlinked individuals. Analytic approaches of the type described here would help to decipher the patterns that exist within large social network data sets and would be applicable to many types of infectious agents |
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ISSN: | 0022-1899 1537-6613 |
DOI: | 10.1086/427661 |