Influence of serosorting and intervention-mediated changes in serosorting on the population-level HIV transmission impact of pre-exposure prophylaxis among men who have sex with men: a mathematical modelling study
Notice bibliographique
Résumé
ABSTRACT Background HIV pre-exposure prophylaxis (PrEP) may change serosorting patterns. We examined the influence of serosorting on the population-level HIV transmission impact of PrEP, and how impact could change if PrEP users stopped serosorting. Methods We developed a compartmental HIV transmission model parameterized with bio-behavioural and HIV surveillance data among men who have sex with men in Canada. We separately fit the model with serosorting and without serosorting (random partner-selection proportional to availability by HIV-status (sero-proportionate)), and reproduced stable HIV epidemics (2013-2018) with HIV-prevalence 10.3%-24.8%, undiagnosed fraction 4.9%-15.8%, and treatment coverage 82.5%-88.4%. We simulated PrEP-intervention reaching stable coverage by year-1 and compared absolute difference in relative HIV-incidence reduction 10-year post-intervention (PrEP-impact) between: models with serosorting vs. sero-proportionate mixing; and scenarios in which PrEP users stopped vs. continued serosorting. We examined sensitivity of results to PrEP-effectiveness (44%-99%) and coverage (10%-50%). Findings Models with serosorting predicted a larger PrEP-impact compared with models with sero-proportionate mixing under all PrEP-effectiveness and coverage assumptions (median (inter-quartile-range): 8.1%(5.5%-11.6%)). PrEP users” stopping serosorting reduced PrEP-impact compared with when PrEP users continued serosorting: reductions in PrEP-impact were minimal (2.1%(1.4%-3.4%)) under high PrEP-effectiveness (86%-99%); however, could be considerable (10.9%(8.2%-14.1%)) under low PrEP effectiveness (44%) and high coverage (30%-50%). Interpretation Models assuming sero-proportionate mixing may underestimate population-level HIV-incidence reductions due to PrEP. PrEP-mediated changes in serosorting could lead to programmatically-important reductions in PrEP-impact under low PrEP-effectiveness (e.g. poor adherence/retention). Our findings suggest the need to monitor sexual mixing patterns to inform PrEP implementation and evaluation. Funding Canadian Institutes of Health Research RESEARCH IN CONTEXT Evidence before this study We searched PubMed for full-text journal articles published between Jan 1, 2010, and Dec 31, 2017, using the MeSH terms “pre-exposure prophylaxis (PrEP)” and “homosexuality, male” and using key words (“pre-exposure prophylaxis” or “preexposure prophylaxis” or “PrEP”) and (“men who have sex with men” or “MSM”) in titles and abstracts. Search results (520 records) were reviewed to identify publications which examined the population-level HIV transmission impact or population-level cost-effectiveness of PrEP in high-income settings. We identified a total of 18 modelling studies of PrEP impact among men who have sex with men (MSM) and four studies were based on the same model with minor variations (thus only the most recent one was included). Among the 15 unique models of PrEP impact, three included serosorting. A total of nine models have assessed the individual-level behaviour change among those on PrEP and its influence on the transmission impact of PrEP. Specifically, the models examined increases in number of partners and reductions in condom use. Most models predicted that realistic increases in partner number or decreases in condom use would not fully offset, but could weaken, PrEP”s impact on reducing HIV transmission. We did not identify any study that examined the influence of serosorting patterns on the estimated transmission impact of PrEP at the population-level, or what could happen to HIV incidence if the use of PrEP changes serosorting patterns. Added value of this study We used a mathematical model of HIV transmission to estimate the influence of serosorting and PrEP-mediated changes in serosorting on the transmission impact of PrEP at the population-level among MSM. We found the impact of PrEP was higher under epidemics with serosorting, compared with comparable epidemics simulated assuming sero-proportionate mixing. Under epidemics with serosorting, when PrEP users stopped serosorting (while other men continue to serosort among themselves) we found a reduced PrEP impact compared with scenarios when PrEP users continued to serosort. The magnitude of reduction in PrEP impact was minimal if PrEP-effectiveness was high; however, could be programmatically-meaningful in the context of low PrEP-effectiveness (e.g., poor adherence or retention) and high PrEP coverage. To our knowledge, our study is the first to directly examine the influence of serosorting and PrEP-mediated changes in serosorting on the transmission impact of PrEP and its underlying mechanism. Implications of all the available evidence Our findings suggest that models which do not consider baseline patterns of serosorting among MSM could potentially underestimate PrEP impact. In addition to monitoring individual-level behavioural change such as condom use, our findings highlight the need to monitor population-level sexual mixing patterns and their changes over time among MSM in the design and evaluation of PrEP implementation.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,003 | 0,013 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,003 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,002 | 0,001 |
| Science ouverte | 0,002 | 0,001 |
| Intégrité de la recherche | 0,002 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».