Identifying priority populations for HIV interventions using acquisition and transmission indicators: a combined analysis of 15 mathematical models from ten African countries
File(s)
Published version
Author(s)
Type
Journal Article
Abstract
Background
Characterising disparities in HIV infection across populations by gender, age, and HIV risk is key information to guide intervention priorities. We aimed to assess how indicators measuring HIV acquisitions, transmissions, or potential long-term infections influence estimates of the contribution of different populations to new infections, including key populations (including female sex workers, their clients, men who have sex with men).
Methods
In this mathematical model comparison analysis, we evaluated four indicators using nine models representing 15 different settings across Africa. The acquisition indicator (I1) measured the annual proportion of all new infections acquired by a specific population, the direct transmission indicator (I2) measured the annual proportion of all new infections directly transmitted by a specific population, and the 1-year transmission population-attributable fractions (tPAFs; I3) and 10-year tPAFs (I4) measured the proportion of new infections averted if transmission involving a specific population was blocked over a specific time period. We compared estimates of the four indicators across seven populations and 15 settings and assessed if the contribution of specific populations ranked differently across indicators for ten settings.
Findings
Different indicators identified distinct priority populations as the largest contributors: I1 identified women aged 25 years and older outside key populations as contributing the most to acquired infections in eight of ten settings in 2020, but to direct transmissions (I2) in only two settings. In six of ten settings, I4 identified non-key population men aged 25 years and older and clients of female sex workers as the largest contributors to HIV transmission. Notably, non-key population women aged 15–24 years acquired (I1) more infections in 2020 (median of 1·7 times higher across models) than they directly transmitted (I2), whereas more infections were transmitted than acquired in non-key population men aged 25 years and older (median 1·4 times more) and clients of female sex workers (1·6 times more) in all but one model. Estimates of the 10-year tPAFs accounting for transmission in the long-term were substantially larger than the direct transmission indicator for all populations, especially for female sex workers (2·0 times higher).
Interpretation
Indicators that reflect HIV acquisitions and transmissions in the short and long term can be used to capture the complexity of HIV epidemics across different populations and timeframes. The added nuance would improve the effectiveness of the HIV prevention response across all populations at risk.
Funding
US National Institutes of Health and UK Medical Research Council.
Translation
For the French translation of the abstract see Supplementary Materials section.
Characterising disparities in HIV infection across populations by gender, age, and HIV risk is key information to guide intervention priorities. We aimed to assess how indicators measuring HIV acquisitions, transmissions, or potential long-term infections influence estimates of the contribution of different populations to new infections, including key populations (including female sex workers, their clients, men who have sex with men).
Methods
In this mathematical model comparison analysis, we evaluated four indicators using nine models representing 15 different settings across Africa. The acquisition indicator (I1) measured the annual proportion of all new infections acquired by a specific population, the direct transmission indicator (I2) measured the annual proportion of all new infections directly transmitted by a specific population, and the 1-year transmission population-attributable fractions (tPAFs; I3) and 10-year tPAFs (I4) measured the proportion of new infections averted if transmission involving a specific population was blocked over a specific time period. We compared estimates of the four indicators across seven populations and 15 settings and assessed if the contribution of specific populations ranked differently across indicators for ten settings.
Findings
Different indicators identified distinct priority populations as the largest contributors: I1 identified women aged 25 years and older outside key populations as contributing the most to acquired infections in eight of ten settings in 2020, but to direct transmissions (I2) in only two settings. In six of ten settings, I4 identified non-key population men aged 25 years and older and clients of female sex workers as the largest contributors to HIV transmission. Notably, non-key population women aged 15–24 years acquired (I1) more infections in 2020 (median of 1·7 times higher across models) than they directly transmitted (I2), whereas more infections were transmitted than acquired in non-key population men aged 25 years and older (median 1·4 times more) and clients of female sex workers (1·6 times more) in all but one model. Estimates of the 10-year tPAFs accounting for transmission in the long-term were substantially larger than the direct transmission indicator for all populations, especially for female sex workers (2·0 times higher).
Interpretation
Indicators that reflect HIV acquisitions and transmissions in the short and long term can be used to capture the complexity of HIV epidemics across different populations and timeframes. The added nuance would improve the effectiveness of the HIV prevention response across all populations at risk.
Funding
US National Institutes of Health and UK Medical Research Council.
Translation
For the French translation of the abstract see Supplementary Materials section.
Date Issued
2026-01-01
Date Acceptance
2025-07-17
Citation
The Lancet HIV, 2026, 13 (1), pp.e30-e39
ISSN
2405-4704
Publisher
Elsevier
Start Page
e30
End Page
e39
Journal / Book Title
The Lancet HIV
Volume
13
Issue
1
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
2025-11-20