Nadia Nguyen1,2, Kimberly A Powers1, William C Miller3, Annie Green Howard4,5, Carolyn T Halpern5,6, James P Hughes7,8, Jing Wang8, Rhian Twine9, F Xavier Gomez-Olive9,10, Catherine MacPhail9,11,12, Kathleen Kahn9,10,13, Audrey E Pettifor1,5,9. 1. Department of Epidemiology, Gillings School of Global Public Health, The University of North Carolina at Chapel Hill, Chapel Hill, NC. 2. HIV Center for Clinical and Behavioral Studies, New York State Psychiatric Institute, Columbia University, New York, NY. 3. Division of Epidemiology, The Ohio State University, Columbus, OH. 4. Department of Biostatistics, Gillings School of Global Public Health, The University of North Carolina at Chapel Hill, Chapel Hill, NC. 5. Carolina Population Center, The University of North Carolina at Chapel Hill, Chapel Hill, NC. 6. Department of Maternal and Child Health, Gillings School of Global Public Health, The University of North Carolina at Chapel Hill, Chapel Hill, NC. 7. Department of Biostatistics, University of Washington, Seattle, WA. 8. Fred Hutchinson Cancer Research Center, Seattle, WA. 9. Medical Research Council/Wits Rural Public Health and Health Transitions Research Unit (Agincourt), School of Public Health, Faculty of the Health Sciences, University of the Witwatersrand, Johannesburg, South Africa. 10. INDEPTH Network, Accra, Ghana. 11. Wits Reproductive Health and HIV Research Institute, University of the Witwatersrand, Johannesburg, South Africa. 12. School of Health and Society, University of Wollongong, Wollongong, New South Wales, Australia. 13. Epidemiology and Global Health Unit, Department of Public Health and Clinical Medicine, Umeå University, Umeå, Sweden.
Abstract
BACKGROUND: Sexual partners are the primary source of incident HIV infection among adolescent girls and young women (AGYW) in sub-Saharan Africa. Identifying partner types at greatest risk of HIV transmission could guide the design of tailored HIV prevention interventions. METHODS: We conducted a secondary analysis of data from AGYW (aged 13-23 years) enrolled in a randomized controlled trial of cash transfers for HIV prevention in South Africa. Annually, AGYW reported behavioral and demographic characteristics of their 3 most recent sexual partners, categorized each partner using prespecified labels, and received HIV testing. We used latent class analysis (LCA) to identify partner types from reported characteristics, and generalized estimating equations to estimate the relationship between both LCA-identified and prespecified partner types and incident HIV infection. RESULTS:Across 2140 AGYW visits, 1034 AGYW made 2968 partner reports and 63 AGYW acquired HIV infection. We identified 5 LCA partner types, which we named monogamous HIV-negative peer partner; one-time protected in-school peer partner; out-of-school older partner; anonymous out-of-school peer partner; and cohabiting with children in-school peer partner. Compared to AGYW with only monogamous HIV-negative peer partners, AGYW with out-of-school older partners had 2.56 times the annual risk of HIV infection (95% confidence interval: 1.23 to 5.33), whereas AGYW with anonymous out-of-school peer partners had 1.72 times the risk (95% confidence interval: 0.82 to 3.59). Prespecified partner types were not associated with incident HIV. CONCLUSION: By identifying meaningful combinations of partner characteristics and predicting the corresponding risk of HIV acquisition among AGYW, LCA-identified partner types may provide new insights for the design of tailored HIV prevention interventions.
RCT Entities:
BACKGROUND: Sexual partners are the primary source of incident HIV infection among adolescent girls and young women (AGYW) in sub-Saharan Africa. Identifying partner types at greatest risk of HIV transmission could guide the design of tailored HIV prevention interventions. METHODS: We conducted a secondary analysis of data from AGYW (aged 13-23 years) enrolled in a randomized controlled trial of cash transfers for HIV prevention in South Africa. Annually, AGYW reported behavioral and demographic characteristics of their 3 most recent sexual partners, categorized each partner using prespecified labels, and received HIV testing. We used latent class analysis (LCA) to identify partner types from reported characteristics, and generalized estimating equations to estimate the relationship between both LCA-identified and prespecified partner types and incident HIV infection. RESULTS: Across 2140 AGYW visits, 1034 AGYW made 2968 partner reports and 63 AGYW acquired HIV infection. We identified 5 LCA partner types, which we named monogamous HIV-negative peer partner; one-time protected in-school peer partner; out-of-school older partner; anonymous out-of-school peer partner; and cohabiting with children in-school peer partner. Compared to AGYW with only monogamous HIV-negative peer partners, AGYW with out-of-school older partners had 2.56 times the annual risk of HIV infection (95% confidence interval: 1.23 to 5.33), whereas AGYW with anonymous out-of-school peer partners had 1.72 times the risk (95% confidence interval: 0.82 to 3.59). Prespecified partner types were not associated with incident HIV. CONCLUSION: By identifying meaningful combinations of partner characteristics and predicting the corresponding risk of HIV acquisition among AGYW, LCA-identified partner types may provide new insights for the design of tailored HIV prevention interventions.
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