Sexual network position and risk of sexually transmitted infections

Objectives:A population-based sexual network study was used to identify sexual network structures associated with sexually transmitted infection (STI) risk, and to evaluate the degree to which the use of network-level data furthers the understanding of STI risk.Methods:Participants (n = 655) were fr...

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Veröffentlicht in:Sexually transmitted infections 2009-12, Vol.85 (7), p.493-498
Hauptverfasser: Fichtenberg, C M, Muth, S Q, Brown, B, Padian, N S, Glass, T A, Ellen, J M
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Sprache:eng
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Zusammenfassung:Objectives:A population-based sexual network study was used to identify sexual network structures associated with sexually transmitted infection (STI) risk, and to evaluate the degree to which the use of network-level data furthers the understanding of STI risk.Methods:Participants (n = 655) were from the baseline and 12-month follow-up waves of a 2001–2 population-based longitudinal study of sexual networks among urban African–American adolescents. Sexual network position was characterised as the interaction between degree (number of partners) and two-reach centrality (number of partners’ partners), resulting in the following five positions: confirmed dyad, unconfirmed dyad, periphery of non-dyadic component, centre of star-like component and interior of non-star component. STI risk was measured as laboratory-confirmed infection with gonorrhoea and/or chlamydia.Results:Results of logistic regression models with generalised estimating equations showed that being in the centre of a sexual network component increased the odds of infection at least sixfold compared with being in a confirmed dyad. Individuals on the periphery of non-dyadic components were nearly five times more likely to be infected than individuals in confirmed dyads, despite having only one partner. Measuring network position using only individual-based information led to twofold underestimates of the associations between STI risk and network position.Conclusions:These results demonstrate the importance of measuring sexual network structure using network data to fully capture the probability of exposure to an infected partner.
ISSN:1368-4973
1472-3263
DOI:10.1136/sti.2009.036681