Roxanne Beauclair1, Niel Hens2, Wim Delva3. 1. International Centre for Reproductive Health, Ghent University, Gent, Belgium; The South African Department of Science and Technology-National Research Foundation (DST-NRF) Centre of Excellence in Epidemiological Modelling and Analysis (SACEMA), Stellenbosch University, Stellenbosch, South Africa. Electronic address: Roxanne.Beauclair@ugent.be. 2. Center for Statistics, I-BioStat, Hasselt University, Martelarenlaan 42, BE3500 Hasselt, Belgium; Centre for Health Economics Research and Modelling Infectious Diseases and Centre for the Evaluation of Vaccination, Vaccine & Infectious Disease Institute, University of Antwerp, Antwerp, Belgium. 3. International Centre for Reproductive Health, Ghent University, Gent, Belgium; The South African Department of Science and Technology-National Research Foundation (DST-NRF) Centre of Excellence in Epidemiological Modelling and Analysis (SACEMA), Stellenbosch University, Stellenbosch, South Africa; Center for Statistics, I-BioStat, Hasselt University, Martelarenlaan 42, BE3500 Hasselt, Belgium; Rega Institute for Medical Research, KU Leuven, Herestraat 49, 3000 Leuven, Belgium; Department of Global Health Faculty of Medicine and Health Sciences, Stellenbosch University, Stellenbosch, South Africa.
Abstract
BACKGROUND: Age-disparate relationships are thought to put young women at increased risk of HIV, though current evidence is inconclusive. Studying population-level age-mixing patterns as well as individual-level measures of age difference variation may provide insight into the persistence and magnitude of the epidemic in South Africa. METHODS: We used data from a survey in Cape Town (n = 506) to describe age-mixing dynamics in the four population strata of HIV negative and HIV positive male and female participants. Mixed-effects models were used to calculate the average increase in partner age for each year increase in age of participant, the average partner age for 15 year olds, and the between-subject and the within-subject standard deviation of partner ages. We conducted 2000 bootstrap replications of the models. Using negative binomial models, we also explored whether HIV status was associated with participants having a larger range in partner ages. RESULTS: HIV positive women had large variability in partner ages at the population level, and at the individual level had nearly three times the expected range of partner ages compared to HIV negative women. This pattern may increase the potential for HIV transmission across birth cohorts and may partially explain the persistence of the epidemic in South Africa. Young men, who have been previously absent from the age-disparity discourse, also choose older partners who may be putting them at increased risk of HIV infection due to the high HIV prevalence among older age categories of women.
BACKGROUND: Age-disparate relationships are thought to put young women at increased risk of HIV, though current evidence is inconclusive. Studying population-level age-mixing patterns as well as individual-level measures of age difference variation may provide insight into the persistence and magnitude of the epidemic in South Africa. METHODS: We used data from a survey in Cape Town (n = 506) to describe age-mixing dynamics in the four population strata of HIV negative and HIV positive male and female participants. Mixed-effects models were used to calculate the average increase in partner age for each year increase in age of participant, the average partner age for 15 year olds, and the between-subject and the within-subject standard deviation of partner ages. We conducted 2000 bootstrap replications of the models. Using negative binomial models, we also explored whether HIV status was associated with participants having a larger range in partner ages. RESULTS: HIV positive women had large variability in partner ages at the population level, and at the individual level had nearly three times the expected range of partner ages compared to HIV negative women. This pattern may increase the potential for HIV transmission across birth cohorts and may partially explain the persistence of the epidemic in South Africa. Young men, who have been previously absent from the age-disparity discourse, also choose older partners who may be putting them at increased risk of HIV infection due to the high HIV prevalence among older age categories of women.