Male partner age, viral load, and HIV infection in adolescent girls and young women: evidence from eight sub-Saharan African countries.

OBJECTIVE: We aimed to elucidate the role of partnerships with older men in the HIV epidemic among adolescent girls and young women (AGYW) aged 15-24 years in sub-Saharan Africa. DESIGN: Analysis of Population-based HIV Impact Assessments in Eswatini, Lesotho, Malawi, Namibia, Tanzania, Uganda, Zambia, and Zimbabwe. METHODS: We examined associations between reported partner age and recent HIV infection among AGYW, incorporating male population-level HIV characteristics by age-band. Recent HIV infection was defined using the LAg avidity assay algorithm. Viremia was defined as a viral load of more than 1000 copies/ml, regardless of serostatus. Logistic regression compared recent infection in AGYW with older male partners to those reporting younger partners. Dyadic analysis examined cohabitating male partner age, HIV status, and viremia to assess associations with AGYW infection. RESULTS: Among 17 813 AGYW, increasing partner age was associated with higher odds of recent infection, peaking for partners aged 35-44 (adjusted odds ratio = 8.94, 95% confidence interval: 2.63-30.37) compared with partners aged 15-24. Population-level viremia was highest in this male age-band. Dyadic analyses of 5432 partnerships confirmed the association between partner age-band and prevalent HIV infection (male spousal age 35-44-adjusted odds ratio = 3.82, 95% confidence interval: 2.17-6.75). Most new infections were in AGYW with partners aged 25-34, as most AGYW had partners in this age-band. CONCLUSION: These results provide evidence that men aged 25-34 drive most AGYW infections, but partners over 9 years older than AGYW in the 35-44 age-band confer greater risk. Population-level infectiousness and male age group should be incorporated into identifying high-risk typologies in AGYW.

Redefining the HIV epidemic in Nigeria: from national to state level.

BACKGROUND: Governments are increasingly recognizing the need to focus limited HIV resources on specific geographic areas and specific populations to have a greater impact. Nigeria, with the second largest HIV epidemic in the world, is an important example of where more localized programming has the potential to improve the efficiency of the HIV response. METHODS: Using Spectrum software we modelled the Nigerian HIV epidemic using two methods: First, we created national HIV estimates using trends in urban and rural areas. Second, we created national HIV estimates using trends from each of the 37 states in Nigeria and aggregated these results. In both instances we used HIV surveillance data from antenatal clinics and household surveys and aggregated the trends to determine the national epidemic. RESULTS: The state models showed divergent trends in the 37 states. Comparing the national results calculated from the two methods resulted in different conclusions. In the aggregated state files, adult HIV incidence in Nigeria was stable between 2005 and 2013 (change of -6%), whereas the urban and rural file suggested incidence was decreasing over the same time (change of -50%). This difference was also reflected in the HIV prevalence trends, although the two methods showed similar trends in AIDS-related mortality. The two models had similar adult HIV prevalence in 2013: 3.0% (2.0-4.5%) in the aggregated state files versus 3.2% (3.0-3.5%) in the urban/rural file. CONCLUSION: The state-level estimates provide insight into the variations of the HIV epidemic in each state and provide useful information for programme managers. However, the reliability of the results is highly dependent on the amount and quality of data available from each sub-national area.