Sexual Partner Types and Incident HIV Infection Among Rural South African Adolescent Girls and Young Women Enrolled in HPTN 068: A Latent Class Analysis

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 LCAidentified 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-ofschool 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.

Vulnerability profiles and prevalence of HIV and other sexually transmitted infections among adolescent girls and young women in Ethiopia: A latent class analysis

“…Latent class analysis (LCA) is a statistical method used to evaluate multiple risk factors simultaneously to uncover classes, or groups of individuals, with distinct patterns of characteristics [27,28]. Utilizing a person-centered framework [27,29], LCA methods have been used to examine sexual partnership types [30,31], sexual risk behaviors [32][33][34][35], and latent constructs of stigma [36]. Here, we use LCA methods to characterize patterns of structural and social vulnerability, and their association with STI and HIV prevalence, among AGYW in Ethiopia.…”

Examining the relationship between physical and sexual violence and psychosocial health in young people living with HIV in rural South Africa

“…Participants' experiences with physical and sexual violence were captured using items developed by the World Health Organization [37]. Though this instrument was designed to assess experiences of violence in women, it has since been utilized among adolescents and men in sub-Saharan Africa [37][38][39][40][41][42]. Using this tool, participants were asked to indicate if a partner, family member or friend had ever slapped or thrown things at them, pushed or shoved them, hit them, kicked, dragged or beat them, choked or burnt them, or used or threatened to use a gun, knife or other weapon against them.…”

Profiles of HIV Care Disruptions Among Adult Patients Lost to Follow-up in Zambia: A Latent Class Analysis

“…This allows it to extend prior studies that identified associations only with individual barriers to care (or categories of barriers) and outcomes such as retention and mortality. 11 , 24 , 25 For this reason, LCA is also often considered a patient-centered 26 —as opposed to variable-centered—analytic method, and has been used in several other studies to assess the interplay between multidimensional constructs such as HIV acquisition risk, 27 – 31 health care seeking behaviors, 32 , 33 and engagement patterns 34 , 35 at the person-level. Ultimately, the use of LCA should be extended as it can synthesize highly dimensional data to provide a more comprehensive and patient-centered understanding of the drivers of patient behaviors.…”