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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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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
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author 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
author_facet 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
author_sort Wang, Linwei
collection PubMed
description 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.
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spelling pubmed-81834922021-06-09 Mathematical modelling of the influence of serosorting on the population-level HIV transmission impact of pre-exposure prophylaxis 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 AIDS Epidemiology and Social 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. Lippincott Williams & Wilkins 2021-06-01 2021-03-01 /pmc/articles/PMC8183492/ /pubmed/33534205 http://dx.doi.org/10.1097/QAD.0000000000002826 Text en Copyright © 2021 The Author(s). Published by Wolters Kluwer Health, Inc. https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution-Non Commercial-No Derivatives License 4.0 (CCBY-NC-ND), where it is permissible to download and share the work provided it is properly cited. The work cannot be changed in any way or used commercially without permission from the journal. http://creativecommons.org/licenses/by-nc-nd/4.0 (https://creativecommons.org/licenses/by-nc-nd/4.0/)
spellingShingle Epidemiology and Social
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
Mathematical modelling of the influence of serosorting on the population-level HIV transmission impact of pre-exposure prophylaxis
title Mathematical modelling of the influence of serosorting on the population-level HIV transmission impact of pre-exposure prophylaxis
title_full Mathematical modelling of the influence of serosorting on the population-level HIV transmission impact of pre-exposure prophylaxis
title_fullStr Mathematical modelling of the influence of serosorting on the population-level HIV transmission impact of pre-exposure prophylaxis
title_full_unstemmed Mathematical modelling of the influence of serosorting on the population-level HIV transmission impact of pre-exposure prophylaxis
title_short Mathematical modelling of the influence of serosorting on the population-level HIV transmission impact of pre-exposure prophylaxis
title_sort mathematical modelling of the influence of serosorting on the population-level hiv transmission impact of pre-exposure prophylaxis
topic Epidemiology and Social
url 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
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