Sexual partnership age pairings and risk of HIV acquisition in rural South Africa

Abstract

Objective:To quantify the contribution of specific sexual partner age groups to the risk of HIV acquisition in men and women in a hyperendemic region of South Africa.Design:We conducted a population-based cohort study among women (15–49 years of age) and men (15–55 years of age) between 2004 and 2015 in KwaZulu-Natal, South Africa.Methods:Generalized additive models were used to estimate smoothed HIV incidence rates across partnership age pairings in men and women. Cox proportional hazards regression was used to estimate the relative risk of HIV acquisition by partner age group.Results:A total of 882 HIV seroconversions were observed in 15 935 person-years for women, incidence rate = 5.5 per 100 person-years [95% confidence interval (CI), 5.2–5.9] and 270 HIV seroconversions were observed in 9372 person-years for men, incidence rate = 2.9 per 100 person-years (95% CI, 2.6–3.2). HIV incidence was highest among 15–24-year-old women reporting partnerships with 30–34-year-old men, incidence rate = 9.7 per 100 person-years (95% CI, 7.2–13.1). Risk of HIV acquisition in women was associated with male partners aged 25–29 years (adjusted hazard ratio; aHR = 1.44, 95% CI, 1.02–2.04) and 30–34 years (aHR = 1.50, 95% CI, 1.08–2.09) relative to male partners aged 35 and above. Risk of HIV acquisition in men was associated with 25–29-year-old (aHR = 1.72, 95% CI, 1.02–2.90) and 30–34-year-old women (aHR = 2.12, 95% CI, 1.03–4.39) compared to partnerships with women aged 15–19 years.Conclusion:Age of sexual partner is a major risk factor for HIV acquisition in both men and women, independent of one's own age. Partner age pairings play a critical role in driving the cycle of HIV transmission.

HIV Risks in Sexual Networks of Heterosexual Men in South Africa

“…HIV incidence was found to increase with increasing age and recent infection was highest at younger ages and long-term infections were common in older ages. These findings are consistent with many South African data which aims to understand the risk factors for HIV infection in men (Jewkes et al, 2006) and assessing sexual partnership and age pairing as a risk of HIV acquisition (Akullian et al, 2017), however, these are based on epidemiological data without the use of formal network designs. In sexual networks, the younger ages identified to be associated with HIV infection suggested that while low HIV prevalence is observed in young males, their risk to new HIV infections is similar to their female counterparts (Zeh et al, 2016).…”

“…Further, husbands or males are found to have the highest number of reported sexual partners and may result in higher transmission probabilities and unlinked cases are the internal source of infection and have occasional sexual relationships as an external source of infection. Current epidemiological data indicate sexual partnerships are an important risk factor for HIV infection in South Africa (Akullian et al, 2017;Beauclair, Hens, & Delva, 2015;Kalichman et al, 2013), however at this point, it is not clear whether sequential or concurrent relationships pose similar or different risks to sexual partners. In this review, we report on sexual partnerships as an important risk factor which warrants further investigation.…”

HIV self‐testing: breaking the barriers to uptake of testing among men and adolescents in sub‐Saharan Africa, experiences from STAR demonstration projects in Malawi, Zambia and Zimbabwe

“…Consistent with previous reports , all distribution models had high male participation in each country. Strategies that provide men with greater coverage of HIV testing and care are urgently needed both to address the disproportionately high testing gap and mortality from HIV in men, and also to reduce risk of onward transmission of HIV . Peak HIV prevalence for men in southern Africa is now in the 40‐ to 49‐year‐old age‐group , with older men among least likely to have accessed standard HIV testing services .…”

Opportunities and Challenges in HIV Treatment as Prevention Research: Results from the ANRS 12249 Cluster-Randomized Trial and Associated Population Cohort

“…These large sample sizes taken from a complete population followed longitudinally for well over a decade permit powerful statistical inference. This in turn can facilitate a deep and nuanced understanding of underlying causal risk factors and processes and a quantification of dynamic incidence patterns among different population sub-strata allowing for identification of particularly vulnerable sub-groups [39][40][41][42].…”