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Record W4408562139 · doi:10.1101/2025.03.17.25324139

Identifying priority populations for HIV interventions using acquisition and transmission indicators: a combined analysis of 15 mathematical models from 10 African countries

2025· preprint· en· W4408562139 on OpenAlex
Romain Silhol, Ross D. Booton, Kate M. Mitchell, James Stannah, Oliver Stevens, Dobromir Dimitrov, Anna Bershteyn, Leigh F. Johnson, Sherrie L. Kelly, Hae‐Young Kim, Mathieu Maheu‐Giroux, Rowan Martin‐Hughes, Sharmistha Mishra, Jack Stone, Robyn M. Stuart, John Stover, Peter Vickerman, David Wilson, Stefan Baral, Deborah Donnell, Jeffrey W. Eaton, Marie‐Claude Boily

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenuemedRxiv · 2025
Typepreprint
Languageen
FieldEconomics, Econometrics and Finance
TopicHIV/AIDS Impact and Responses
Canadian institutionsUniversity of TorontoMcGill University
FundersEuropean and Developing Countries Clinical Trials PartnershipMedical Research CouncilNational Institutes of HealthCanada Research ChairsWellcome Trust
KeywordsPsychological interventionHuman immunodeficiency virus (HIV)Transmission (telecommunications)EconometricsComputer scienceGeographyVirologyPsychologyEconomicsBiologyTelecommunications

Abstract

fetched live from OpenAlex

Abstract Background Characterising disparities in HIV infection across populations by gender, age, and HIV risk is key information to guide intervention priorities. We compared 9 models representing 15 different settings across Africa 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 (KPs, including female sex workers (FSW), their clients, men who have sex with men). Methods We evaluated four indicators: I 1 ) acquisition indicator measuring the annual fraction of all new infections acquired by a specific population, I 2 ) direct transmission indicator measuring the annual fraction of all new infections directly transmitted by a specific population, I 3 ) 1-year and I 4 ) 10-year transmission population-attributable fractions ( tPAFs ). tPAFs measure the fraction 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 7 populations and 15 settings and assessed if the contribution of specific populations is ranked differently across indicators for 10 settings. Findings Indicators identified distinct priority populations as the largest contributors: The acquisition indicator (I 1 ) identified women aged 25+ years outside KPs as contributing the most to acquired infections in 8/10 settings in 2020, but to direct transmissions (I 2 ) in only two settings. In 6/10 settings, the 10-year tPAFs (I 4 ) identified non-KP men aged 25+ years and clients of FSW as the largest contributors to HIV transmission. Notably, non-KP women aged 15-24 years acquired (I 1 ) more infections in 2020 (median of 1·7-fold across models) than they directly transmitted (I 2 ), while non-KP men aged 25+ years and clients of FSWs transmitted more infections than they acquired in all but one model (median: 1·4 and 1·6-fold, respectively). 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 FSW (median: 2·0-fold). Interpretation Indicators that reflect HIV acquisitions and transmissions over the short and long term can be utilised 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 NIH, MRC. Research in context Evidence before this study Measures of the distribution of HIV acquisition across population groups are commonly used for assessing the contribution of populations to new HIV infections and prevention priorities. However, alternative indicators documented in the literature reflect transmissions or potential long-term effects. It is unclear how the choice of indicator affects the identification of populations that require additional prevention and treatment efforts to accelerate progress towards ending AIDS. We searched PubMed on March 08, 2025, with the terms (HIV) AND (Africa*) AND (acqui*) AND (transm*) AND (model*), with no language or publication date restriction, and identified no meta-assessment or mathematical model comparison studying differences in estimates of the fraction of all infections acquired and transmitted by a population when using different epidemiological indicators. Added value of this study Using estimates from 9 models representing 15 different epidemic settings across Africa, we studied indicators of HIV epidemic contribution for 7 populations, including female sex workers, their clients, men who have sex with men, and non-key populations stratified by gender and age. We measured four commonly reported indicators of HIV contribution. One focused on acquired infections and the other three focused on transmissions. We found that estimates from these different indicators can differ greatly for the same model and population, to the extent that they identify different populations for prioritising interventions to accelerate HIV incidence declines. The acquisition-focused indicator (i.e. fraction of all infections acquired by a given population), the most used and communicated by UNAIDS, substantially underestimates the large contribution of men, and particularly male clients of female sex workers, to ongoing HIV transmission. Implications of all the available evidence The choice of indicators measuring a population’s contribution to the HIV epidemic should be carefully considered and precisely defined. Modelling teams working in partnership with government, implementers, funders, and community members should systematically report both acquisition- and (long-term) transmission-focused indicators, instead of only measuring acquisitions in the short term as currently done. Multiple indicators will more comprehensively capture the potential impact of prevention efforts addressing acquisition and transmission risks of different vulnerable populations.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.515
Threshold uncertainty score0.901

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.116
GPT teacher head0.343
Teacher spread0.227 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it