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Mathematical modelling of the influence of serosorting on the population-level HIV transmission impact of pre-exposure prophylaxis

OBJECTIVES: 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. DESIGN: We developed a compartmental HIV transmission mode...

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Detalles Bibliográficos
Autores principales: Wang, Linwei, Moqueet, Nasheed, Simkin, Anna, Knight, Jesse, Ma, Huiting, Lachowsky, Nathan J., Armstrong, Heather L., Tan, Darrell H.S., Burchell, Ann N., Hart, Trevor A., Moore, David M., Adam, Barry D., Macfadden, Derek R., Baral, Stefan, Mishra, Sharmistha
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Lippincott Williams & Wilkins 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8183492/
https://www.ncbi.nlm.nih.gov/pubmed/33534205
http://dx.doi.org/10.1097/QAD.0000000000002826
Descripción
Sumario:OBJECTIVES: 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. DESIGN: We developed a compartmental HIV transmission model parameterized with bio-behavioural and HIV surveillance data among MSM in Canada. METHODS: We separately fit the model with serosorting and without serosorting [counterfactual; sero-proportionate mixing (random partner-selection proportional to availability by HIV status)], and reproduced stable HIV epidemics 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 pre-specified coverage by year-one and compared absolute difference in relative HIV-incidence reduction 10 years post-intervention (PrEP-impact) between models with serosorting vs. sero-proportionate mixing; and counterfactual scenarios when PrEP users immediately stopped vs. continued serosorting. We examined sensitivity of results to PrEP-effectiveness (44–99%; reflecting varying dosing or adherence levels) and coverage (10–50%). RESULTS: Models with serosorting predicted a larger PrEP-impact than models with sero-proportionate mixing under all PrEP-effectiveness and coverage assumptions [median (interquartile 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%). CONCLUSION: 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. Our findings suggest the need to monitor sexual mixing patterns to inform PrEP implementation and evaluation.