Identifying priority populations for HIV interventions using acquisition and transmission indicators: a combined analysis of 15 mathematical models from ten African countries
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Notice bibliographique
Résumé
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 (I 1 ) measured the annual proportion of all new infections acquired by a specific population, the direct transmission indicator (I 2 ) measured the annual proportion of all new infections directly transmitted by a specific population, and the 1-year transmission population-attributable fractions (tPAFs; I 3 ) and 10-year tPAFs (I 4 ) 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: I 1 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 (I 2 ) in only two settings. In six of ten settings, I 4 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 (I 1 ) more infections in 2020 (median of 1·7 times higher across models) than they directly transmitted (I 2 ), 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.
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Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle