Wang, LinweiMoqueet, NasheedSimkin, AnnaKnight, JesseMa, HuitingLachowsky, 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, StefanMishra, Sharmistha2022-03-082022-03-0820212021Wang, 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. DOI: https://doi.org/10.1097/QAD.0000000000002826https://doi.org/10.1097/QAD.0000000000002826http://hdl.handle.net/1828/13787Some 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). We would like to thank Kristy Yiu for supporting submission and project coordination, and Steven Tingley for helpful discussions surrounding model structure.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 postintervention (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.enHIVMSMpre-exposure prophylaxisserosortingsexual mixing patternsArticleSchool of Public Health and Social Policy