BibTex format
@article{Bansi-Matharu:2025:10.1016/s2214-109x(25)00121-4,
author = {Bansi-Matharu, L and Moolla, H and Citron, DT and Stover, J and Pickles, M and Martin-Hughes, R and Boily, M-C and Nyirenda, R and Mudimu, E and ten, Brink D and Johnson, LF and Mugurungi, O and Cambiano, V and Dimitrov, D and Smith, J and Glaubius, R and Taramusi, I and Mpofu, A and Phillips, A and Bershteyn, A},
doi = {10.1016/s2214-109x(25)00121-4},
journal = {The Lancet Global Health},
pages = {e1006--e1019},
title = {Identifying gaps in the HIV treatment cascade in Africa: a model comparison study},
url = {http://dx.doi.org/10.1016/s2214-109x(25)00121-4},
volume = {13},
year = {2025}
}
RIS format (EndNote, RefMan)
TY - JOUR
AB - BackgroundAlthough 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).MethodsIn 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.FindingsDespite 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 10000 new infections to over 30000 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 experien
AU - Bansi-Matharu,L
AU - Moolla,H
AU - Citron,DT
AU - Stover,J
AU - Pickles,M
AU - Martin-Hughes,R
AU - Boily,M-C
AU - Nyirenda,R
AU - Mudimu,E
AU - ten,Brink D
AU - Johnson,LF
AU - Mugurungi,O
AU - Cambiano,V
AU - Dimitrov,D
AU - Smith,J
AU - Glaubius,R
AU - Taramusi,I
AU - Mpofu,A
AU - Phillips,A
AU - Bershteyn,A
DO - 10.1016/s2214-109x(25)00121-4
EP - 1019
PY - 2025///
SN - 2214-109X
SP - 1006
TI - Identifying gaps in the HIV treatment cascade in Africa: a model comparison study
T2 - The Lancet Global Health
UR - http://dx.doi.org/10.1016/s2214-109x(25)00121-4
UR - https://doi.org/10.1016/s2214-109x(25)00121-4
VL - 13
ER -