Mathematical modelling of the influence of serosorting on the population-level HIV transmission impact of pre-exposure prophylaxis
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 model parameterized with bio-behavioural and HIV surveillance data among men who have sex with men 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-1 and compared absolute difference in relative HIV-incidence reduction ten-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 (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%).
Conclusions: 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.
1113-1125
Wang, Linwei
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Moqueet, Nasheed
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Simkin, Anna
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Knight, Jesse
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Ma, Huiting
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Lachowsky, Nathan J.
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Armstrong, Heather
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Tan, Darrell
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Burchell, Ann
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Hart, Trevor
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Moore, David M.
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Adam, Barry
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MacFadden, Derek
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Baral, Stefan
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Mishra, Sharmistha
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1 June 2021
Wang, Linwei
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Moqueet, Nasheed
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Simkin, Anna
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Knight, Jesse
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Ma, Huiting
94efc877-96dd-455f-976d-25ddea1ca073
Lachowsky, Nathan J.
87634bac-759c-4e7b-9f16-22fb37e87cf6
Armstrong, Heather
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Tan, Darrell
27633611-3301-4bc3-9e65-8d202e019cbd
Burchell, Ann
e321a3c9-bad3-4417-858d-b6fc64044806
Hart, Trevor
49f351fd-ecb1-4fae-a272-93d1050e0aca
Moore, David M.
b3bb7f8f-4409-412e-959b-bcda959a8d2d
Adam, Barry
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MacFadden, Derek
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Baral, Stefan
a40b2229-4d12-42dc-b0e8-8ead14ecd8eb
Mishra, Sharmistha
8417142a-9fed-431d-a5b9-2beb05ec704d
Wang, Linwei, Moqueet, Nasheed, Simkin, Anna, Knight, Jesse, Ma, Huiting, Lachowsky, Nathan J., Armstrong, Heather, Tan, Darrell, Burchell, Ann, Hart, Trevor, Moore, David M., Adam, Barry, MacFadden, Derek, Baral, Stefan and Mishra, Sharmistha
(2021)
Mathematical modelling of the influence of serosorting on the population-level HIV transmission impact of pre-exposure prophylaxis.
AIDS, 35 (7), .
(doi:10.1097/QAD.0000000000002826).
Abstract
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 men who have sex with men 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-1 and compared absolute difference in relative HIV-incidence reduction ten-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 (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%).
Conclusions: 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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Wang (2021) PrEP serosort Modeling_manuscript_AIDS_final
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Accepted/In Press date: 11 January 2021
e-pub ahead of print date: 29 January 2021
Published date: 1 June 2021
Additional Information:
Copyright © 2021 The Author(s). Published by Wolters Kluwer Health, Inc.
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Local EPrints ID: 446563
URI: http://eprints.soton.ac.uk/id/eprint/446563
ISSN: 0269-9370
PURE UUID: 7d0bdddd-d7a6-4e04-aa05-adcbfee516f2
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Date deposited: 15 Feb 2021 17:31
Last modified: 17 Mar 2024 06:15
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Contributors
Author:
Linwei Wang
Author:
Nasheed Moqueet
Author:
Anna Simkin
Author:
Jesse Knight
Author:
Huiting Ma
Author:
Nathan J. Lachowsky
Author:
Darrell Tan
Author:
Ann Burchell
Author:
Trevor Hart
Author:
David M. Moore
Author:
Barry Adam
Author:
Derek MacFadden
Author:
Stefan Baral
Author:
Sharmistha Mishra
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