Identifying gaps in the HIV treatment cascade in Africa: a model comparison study
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Author(s)
Type
Journal Article
Abstract
Background
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).
Methods
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.
Findings
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.
Interpretation
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.
Funding
The Bill & Melinda Gates Foundation.
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).
Methods
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.
Findings
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.
Interpretation
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.
Funding
The Bill & Melinda Gates Foundation.
Date Issued
2025-06-01
Date Acceptance
2025-03-14
Citation
The Lancet Global Health, 2025, 13 (6), pp.e1006-e1019
ISSN
2214-109X
Publisher
Elsevier BV
Start Page
e1006
End Page
e1019
Journal / Book Title
The Lancet Global Health
Volume
13
Issue
6
Copyright Statement
© 2025 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license.
License URL
Publication Status
Published
Date Publish Online
2025-05-21