Concurrent partnerships in Cape Town, South Africa: race and sex differences in prevalence and duration of overlap

Prevalence and Risk Factors for HIV Infection Among Heterosexual Men Recruited from Socializing Venues in Rural KwaZulu-Natal, South Africa

Young heterosexual men have low uptake of HIV prevention and treatment services and represent an important key population that may require novel strategies. We recruited 1271 heterosexual men, 12 years and older from socializing venues such as "shebeens", transport hubs, "spaza" shops, and community centers in rural KwaZulu-Natal, South Africa. Participants completed a questionnaire and were tested for HIV serostatus. Generalized estimating equations (GEE) with exchangeable covariance structure estimated factors independently associated with prevalent HIV infection. Median age was 25 years [Interquartile range (IQR) 21-29]. HIV prevalence was 15.5% [95% confidence interval (CI) 11.0-21.9] and increased significantly by age. Factors associated with higher odds of HIV infection were being 25 years and older [adjusted odds ratio (aOR) 4.82, 95% CI 3.47-6.69; p < 0.001), not completing high school (aOR 1.60, 95% CI 1.39-1.85; p < 0.001), not using condoms at first sex (aOR 1.43, 95% CI 1.20-1.70; p < 0.001), consuming alcohol (aOR 1.63, 95% CI 1.15-2.31; p = 0.006) or substances (aOR 1.37, 95% CI 1.31-1.44; p < 0.001), and absence of medical circumcision (aOR 2.05, 95% CI 1.71-2.44; p < 0.001). Risk was lower among those testing for HIV in last 12 months (aOR 0.54, 95% CI 0.36-0.80; p = 0.002). Greater effort is needed to implement innovative programs within settings that are easily accessible and where heterosexual men are likely to be.

Assessing the uncertainty around age-mixing patterns in HIV transmission inferred from phylogenetic trees

Understanding age-mixing patterns in Human Immunodeficiency Virus (HIV) transmission networks can enhance the design and implementation of HIV prevention strategies in sub-Saharan Africa. Due to ethical consideration, it is less likely possible to conduct a benchmark study to assess which sampling strategy, and sub-optimal sampling coverage which can yield best estimates for these patterns. We conducted a simulation study, using phylogenetic trees to infer estimates of age-mixing patterns in HIV transmission, through the computation of proportions of pairings between men and women, who were phylogenetically linked across different age groups (15-24 years, 25-39 years, and 40-49 years); and the means, and standard deviations of their age difference. We investigated also the uncertainty around these estimates as a function of the sampling coverage in four sampling strategies: when missing sequence data were missing completely at random (MCAR), and missing at random (MAR) with at most 30%-50%-70% of women in different age groups being in the sample. The results suggested that age-mixing patterns in HIV transmission can be unveiled from proportions of phylogenetic pairings between men and women across age groups; and the mean, and standard deviation of their age difference. A 55% sampling coverage was sufficient to provide the best values of estimates of age-mixing patterns in HIV transmission with MCAR scenario. But we should be cautious in interpreting proportions of men phylogenetically linked to women because they may be overestimated or underestimated, even at higher sampling coverage. The findings showed that, MCAR was the best sampling strategy. This means, it is advisable not to use sequence data collected in settings where we can find a systematic imbalance of age and gender to investigate age-mixing in HIV transmission. If not possible, ensure to take into consideration the imbalance in interpreting the results.

The role of age-mixing patterns in HIV transmission dynamics: Novel hypotheses from a field study in Cape Town, South Africa

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.

Concurrency and other sexual partnership patterns reported in a survey of young people in rural Northern Tanzania

African adolescents and young adults remain at substantial risk of infection with HIV and other sexually transmitted infections (STIs), and AIDS is the leading cause of death among African adolescents (10–19 years). Sexual partnership patterns influence transmission risk of sexually transmitted infections. We describe patterns reported by youth (15-30y) in a community-based survey in Tanzania. Among participants reporting multiple partners, we investigated factors associated with reported concurrency. Female (N = 6513) and male (N = 7301) participants had median ages of 21 and 22 years, respectively. Most participants (92%) reported having previously been sexually active, of whom 15% of males and <1% of females reported ≥4 partners in the past year. The point prevalence of concurrency was 2.3% (95%CI 1.9–2.9) for females and 10.6% (95%CI 9.3–12.1) for males. High levels of multiple and concurrent partnerships were reported by those previously married. Females were more likely than males to report having spousal/regular partners and longer partnership lengths. Compared to males, the partnerships reported by females were less likely to be new partnerships, and more likely to be defined by the respondent as still ‘ongoing’. Females reporting younger sexual debut were more likely to report concurrent sexual partners. Far fewer young women reported multiple and concurrent partnerships, but we cannot definitively conclude that concurrency was uncommon for women, because stigma towards women’s multiple sexual partnerships might contribute to substantial under-reporting, as was found in extensive qualitative research in the study population. This study provides one of the most comprehensive quantitative descriptions of partnership patterns of young people in an African setting. Interventions addressing sexual risk among youth should involve male partners, empower women to protect themselves within different types of partnerships, and encourage a greater openness about young people’s sexual relationships.

Connecting the dots: network data and models in HIV epidemiology

Effective HIV prevention requires knowledge of the structure and dynamics of the social networks across which infections are transmitted. These networks most commonly comprise chains of sexual relationships, but in some populations, sharing of contaminated needles is also an important, or even the main mechanism that connects people in the network. Whereas network data have long been collected during survey interviews, new data sources have become increasingly common in recent years, because of advances in molecular biology and the use of partner notification services in HIV prevention and treatment programmes. We review current and emerging methods for collecting HIV-related network data, as well as modelling frameworks commonly used to infer network parameters and map potential HIV transmission pathways within the network. We discuss the relative strengths and weaknesses of existing methods and models, and we propose a research agenda for advancing network analysis in HIV epidemiology. We make the case for a combination approach that integrates multiple data sources into a coherent statistical framework.