Bansi-Matharu Loveleen, Moolla Haroon, Citron Daniel T, Stover John, Pickles Michael, Martin-Hughes Rowan, Boily Marie-Claude, Nyirenda Rose, Mudimu Edinah, Ten Brink Debra, Johnson Leigh F, Mugurungi Owen, Cambiano Valentina, Dimitrov Dobromir, Smith Jenny, Glaubius Robert, Taramusi Issac, Mpofu Amon, Phillips Andrew, Bershteyn Anna
UCL Centre for Clinical Research, Epidemiology, Modelling and Evaluation, Institute for Global Health, University College London, London, UK.
Centre for Infectious Disease Epidemiology and Research, University of Cape Town, Cape Town, South Africa.
Lancet Glob Health. 2025 Jun;13(6):e1006-e1019. doi: 10.1016/S2214-109X(25)00121-4.
Although HIV incidence has considerably decreased in eastern, central, and southern Africa, new HIV infections continue to be a major public health challenge in the region. We aimed to investigate where in the HIV treatment cascade new transmissions are occurring in Malawi, Zimbabwe, and South Africa (the three countries involved in the Modelling to Inform HIV Programmes in Sub-Saharan Africa project).
In this model comparison study, we used six well described and independently calibrated HIV transmission dynamics models that have been used to inform HIV policy in Africa (Optima HIV, EMOD, Goals, Thembisa, PopART-IBM, and HIV Synthesis) to estimate and predict the proportion of annual new HIV transmissions attributable to people living with HIV who are undiagnosed, have been diagnosed but have not yet started antiretroviral therapy (ART), are receiving ART, and have interrupted ART in Malawi, Zimbabwe, and South Africa from 2010 to 2040 stratified by the age and sex of the individual acquiring HIV.
Despite the different model structures and underlying assumptions, the six models were well aligned in relation to key HIV epidemic characteristics (including population estimates and HIV prevalence) in each of the three settings. There was, however, considerable variation in the predicted number of new infections, particularly in Malawi and Zimbabwe where this number ranged from fewer than 10 000 new infections to over 30 000 new infections in 2024. Most model results suggested that the mean age of HIV acquisition has been increasing since 2000, with men acquiring HIV at an older age than women in all three settings. All models attributed fewer than 5% of transmissions to individuals who had been diagnosed but had not yet started ART. In Malawi, the proportion of transmissions attributable to undiagnosed people with HIV in 2024 ranged from 33·3% to 75·3% across the models, and transmissions attributable to individuals who had experienced interrupted treatment ranged from 8·4% to 20·1%. In Zimbabwe, the proportion of transmissions attributable to undiagnosed individuals in 2024 ranged from 29·8% to 64·6% across the models and the proportion of transmissions attributable to individuals who had interrupted treatment ranged from 4·7% to 21·5%. In South Africa, 21·8-46·4% of transmissions in 2024 were attributable to undiagnosed individuals and 27·6-58·9% of transmissions were attributable to individuals who had interrupted treatment.
Across the three study settings, a substantial proportion of new HIV transmissions were attributable to undiagnosed individuals and people who have received interrupted ART, reinforcing the importance of continuing HIV testing and ART re-engagement and retention interventions.
The Bill & Melinda Gates Foundation.
尽管在非洲东部、中部和南部，艾滋病毒发病率已大幅下降，但新的艾滋病毒感染仍是该地区一项重大的公共卫生挑战。我们旨在调查在马拉维、津巴布韦和南非（参与撒哈拉以南非洲艾滋病毒规划建模项目的三个国家）的艾滋病毒治疗流程中，新传播发生在哪些环节。
在这项模型比较研究中，我们使用了六个描述详尽且独立校准的艾滋病毒传播动力学模型（Optima HIV、EMOD、Goals、Thembisa、PopART - IBM和HIV Synthesis），这些模型曾用于为非洲的艾滋病毒政策提供参考，以估计和预测2010年至2040年期间，在马拉维、津巴布韦和南非，按感染艾滋病毒个体的年龄和性别分层，因未被诊断出感染艾滋病毒、已被诊断但尚未开始抗逆转录病毒疗法（ART）、正在接受ART以及中断ART的艾滋病毒感染者导致的年度新艾滋病毒传播比例。
尽管六个模型的结构和潜在假设不同，但在这三种环境中的每一种环境下，它们在关键的艾滋病毒流行特征（包括人口估计数和艾滋病毒流行率）方面都具有良好的一致性。然而，预测的新感染数量存在相当大的差异，特别是在马拉维和津巴布韦，2024年新感染数量从不到10000例到超过30000例不等。大多数模型结果表明，自2000年以来，感染艾滋病毒的平均年龄一直在增加，在所有三种环境中，男性感染艾滋病毒的年龄都比女性大。所有模型都将不到5%的传播归因于已被诊断但尚未开始接受ART的个体。在马拉维，2024年各模型中因未被诊断出感染艾滋病毒的人导致的传播比例在33.3%至75.3%之间，因经历治疗中断的个体导致的传播比例在8.4%至20.1%之间。在津巴布韦，2024年各模型中因未被诊断出感染艾滋病毒的个体导致的传播比例在29.8%至64.6%之间，因治疗中断的个体导致的传播比例在4.7%至21.5%之间。在南非，2024年21.8 - 46.4%的传播归因于未被诊断出感染艾滋病毒的个体，27.6 - 58.9%的传播归因于经历治疗中断的个体。
在这三个研究环境中，相当大比例的新艾滋病毒传播归因于未被诊断出感染艾滋病毒的个体和接受过中断ART治疗的人，这强化了持续进行艾滋病毒检测以及重新参与和维持ART干预措施的重要性。
比尔及梅琳达·盖茨基金会。
