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

Linwei Wang; Nasheed Moqueet; Anna Simkin; Jesse Knight; Huiting Ma; Nathan J. Lachowsky; Heather L. Armstrong; Darrell H.S. Tan; Ann N. Burchell; Trevor A. Hart; David M. Moore; Barry D. Adam; Derek R. MacFadden; Stefan Baral; Sharmistha Mishra

doi:10.1097/QAD.0000000000002826

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

Linwei Wang, Nasheed Moqueet, Anna Simkin, Jesse Knight, Huiting Ma, Nathan J. Lachowsky, Heather L. Armstrong, Darrell H.S. Tan, Ann N. Burchell, Trevor A. Hart, David M. Moore, Barry D. Adam, Derek R. MacFadden, Stefan Baral, Sharmistha Mishra

Bloomberg School of Public Health

Research output: Contribution to journal › Article › peer-review

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 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.

Original language	English (US)
Pages (from-to)	1113-1125
Number of pages	13
Journal	AIDS
Volume	35
Issue number	7
DOIs	https://doi.org/10.1097/QAD.0000000000002826
State	Published - Jun 1 2021

Keywords

HIV
MSM
pre-exposure prophylaxis
serosorting
sexual mixing patterns

ASJC Scopus subject areas

Immunology and Allergy
Immunology
Infectious Diseases

Access to Document

10.1097/QAD.0000000000002826

Cite this

Wang, L., Moqueet, N., Simkin, A., Knight, J., Ma, H., Lachowsky, N. J., Armstrong, H. L., Tan, D. H. S., Burchell, A. N., Hart, T. A., Moore, D. M., Adam, B. D., MacFadden, D. R., Baral, S., & Mishra, S. (2021). Mathematical modelling of the influence of serosorting on the population-level HIV transmission impact of pre-exposure prophylaxis. AIDS, 35(7), 1113-1125. https://doi.org/10.1097/QAD.0000000000002826

Wang, L, Moqueet, N, Simkin, A, Knight, J, Ma, H, Lachowsky, NJ, Armstrong, HL, Tan, DHS, Burchell, AN, Hart, TA, Moore, DM, Adam, BD, MacFadden, DR, Baral, S & Mishra, S 2021, 'Mathematical modelling of the influence of serosorting on the population-level HIV transmission impact of pre-exposure prophylaxis', AIDS, vol. 35, no. 7, pp. 1113-1125. https://doi.org/10.1097/QAD.0000000000002826

@article{74e4f92047a5453c969b8fcac978dbc3,

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

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 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.",

keywords = "HIV, MSM, pre-exposure prophylaxis, serosorting, sexual mixing patterns",

author = "Linwei Wang and Nasheed Moqueet and Anna Simkin and Jesse Knight and Huiting Ma and Lachowsky, {Nathan J.} and Armstrong, {Heather L.} and Tan, {Darrell H.S.} and Burchell, {Ann N.} and Hart, {Trevor A.} and Moore, {David M.} and Adam, {Barry D.} and MacFadden, {Derek R.} and Stefan Baral and Sharmistha Mishra",

note = "Funding Information: Some of the model parameters in the current modelling article drew on estimates published in Wang et al. 2019 ( https://doi.org/10.1093/aje/kwz231 ).We acknowledge the Engage study and its funders (Canadian Institutes of Health Research (CIHR) Team Grant [TE2-138299]; CIHR Canadian HIV Trials Network [CTN 300]; Canadian Foundation for AIDS Research [Engage]; Canadian Blood Services [MSM2017LP-OD]; Ontario HIV Treatment Network (OHTN) [1051]; Ryerson University [no related grant number]; and Public Health Agency of Canada [4500370314]), which supported the independently published results in Wang et al. 2019 ( https://doi.org/10.1093/aje/kwz231 ). Funding Information: This study was funded by the Canadian Institutes of Health Research (CIHR) foundation grant [grant number FN-13455]. Funding Information: S.M. and D.H.S.T. are supported by a CIHR and the Ontario HIV Treatment Network (OHTN) New Investigator Award. T.A.H. is supported by an OHTN Applied HIV Research Chair Award. D.M.M. and N.J.L. are supported by Scholar Awards from the Michael Smith Foundation for Health Research (#5209, #16863). N.M. was supported by the CIHR-funded Canadian HIV Trials Network Postdoctoral Fellowship. We would like to thank Kristy Yiu for supporting submission and project coordination, and Steven Tingley for helpful discussions surrounding model structure. Publisher Copyright: {\textcopyright} 2021 Lippincott Williams and Wilkins. All rights reserved.",

year = "2021",

month = jun,

day = "1",

doi = "10.1097/QAD.0000000000002826",

language = "English (US)",

volume = "35",

pages = "1113--1125",

journal = "AIDS",

issn = "0269-9370",

publisher = "Lippincott Williams and Wilkins",

number = "7",

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TY - JOUR

T1 - Mathematical modelling of the influence of serosorting on the population-level HIV transmission impact of pre-exposure prophylaxis

AU - Wang, Linwei

AU - Moqueet, Nasheed

AU - Simkin, Anna

AU - Knight, Jesse

AU - Ma, Huiting

AU - Lachowsky, Nathan J.

AU - Armstrong, Heather L.

AU - Tan, Darrell H.S.

AU - Burchell, Ann N.

AU - Hart, Trevor A.

AU - Moore, David M.

AU - Adam, Barry D.

AU - MacFadden, Derek R.

AU - Baral, Stefan

AU - Mishra, Sharmistha

N1 - Funding Information: Some of the model parameters in the current modelling article drew on estimates published in Wang et al. 2019 ( https://doi.org/10.1093/aje/kwz231 ).We acknowledge the Engage study and its funders (Canadian Institutes of Health Research (CIHR) Team Grant [TE2-138299]; CIHR Canadian HIV Trials Network [CTN 300]; Canadian Foundation for AIDS Research [Engage]; Canadian Blood Services [MSM2017LP-OD]; Ontario HIV Treatment Network (OHTN) [1051]; Ryerson University [no related grant number]; and Public Health Agency of Canada [4500370314]), which supported the independently published results in Wang et al. 2019 ( https://doi.org/10.1093/aje/kwz231 ). Funding Information: This study was funded by the Canadian Institutes of Health Research (CIHR) foundation grant [grant number FN-13455]. Funding Information: S.M. and D.H.S.T. are supported by a CIHR and the Ontario HIV Treatment Network (OHTN) New Investigator Award. T.A.H. is supported by an OHTN Applied HIV Research Chair Award. D.M.M. and N.J.L. are supported by Scholar Awards from the Michael Smith Foundation for Health Research (#5209, #16863). N.M. was supported by the CIHR-funded Canadian HIV Trials Network Postdoctoral Fellowship. We would like to thank Kristy Yiu for supporting submission and project coordination, and Steven Tingley for helpful discussions surrounding model structure. Publisher Copyright: © 2021 Lippincott Williams and Wilkins. All rights reserved.

PY - 2021/6/1

Y1 - 2021/6/1

N2 - 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.

AB - 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.

KW - HIV

KW - MSM

KW - pre-exposure prophylaxis

KW - serosorting

KW - sexual mixing patterns

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

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