Health and budget impact of combined HIV prevention S. Vermeersch, hict L. Annemans, UGent
Outline Context and objectives Modeling approach Model validation Results Conclusions Disclosures The study was performed independently by Sebastian Vermeersch and supervised by Lieven Annemans hict and Lieven Annemans received financial compensation to perform this study as part of a consultancy agreement with Gilead. 2
Luxembourg United Kingdom Malta Belgium San Marino Portugal Ireland Spain France Greece West Switzerland Italy Israel Norway Netherlands Denmark Germany Andorra Sweden Iceland Finland Austria Monaco New HIV diagnoses rates per 100 000 population, by country, in 2014 (West European WHO region) 4th highest! 0 2 4 6 8 10 12 14 European Centre for Disease Prevention and Control, WHO Regional Office for Europe. HIV/AIDS surveillance in Europe 2014.
Context & objectives Can we make a difference? Prevention!? Impact of prevention on Epidemiology Budget 4 4
Model scope & terminology Treatment cost only Outreach Treatment as Prevention (TasP) Prevention Pre-exposure prophylaxis (PreP) General prevention Not included 5
Modeling approach 6
Model mechanics Year # NET LOSS = # Patients in medical follow-up # New HIV diagnoses Entering follow-up # Death + # inflow (re-entering follow-up) - #outflow (lost to follow-up) Year+1? # Patients in medical follow-up
Model mechanics # NET LOSS = # Patients in medical follow-up # New HIV diagnoses Entering follow-up # Death + # inflow (re-entering follow-up) - #outflow (lost to follow-up)?
Model mechanics # NET LOSS = # Patients in medical follow-up # New HIV diagnoses Entering follow-up # Death + # inflow (re-entering follow-up) - #outflow (lost to follow-up) # unknown # Known, untreated # Treated, VL>200 copies/ml
Modeling prevention: Outreach & TasP # NET LOSS = # Patients in medical follow-up # New HIV diagnoses Entering follow-up # Death + # inflow (re-entering follow-up) - #outflow (lost to follow-up) # unknown # Known, untreated # Treated, VL>200 copies/ml Outreach TasP
Modeling prevention: PreP # NET LOSS = # Patients in medical follow-up # New HIV diagnoses Entering follow-up # Death + # inflow (re-entering follow-up) - #outflow (lost to follow-up) PreP Population @ risk # treated PreP # infections avoided
Costs included in the model HIV treatment: 12 330/year 1 PreP: 6 328/year (before 2017) 4 929/year (generics & cluster opening 2017) 2 596/year (post 2017) 1 Vandijck et al., 2015 12
Additional principles (Sub)population differences MSM, hetero, PWID Differing infectiousness Undiagnosed, diagnosed but untreated, treated and VL>=200 HIV infections in migrants occurring prior to migration Effect of aging HIV population 13
Model validation 14
16.000 Model validation - Actual vs estimated 14.000 12.000 10.000 8.000 6.000 4.000 2.000 0 # patients in medical follow-up # new HIV diagnoses 2008 2009 2010 2011 2012 2013 2014 2015 Adults in medical follow-up (Actual) New diagnoses (actual) Adults in medical follow-up (estimated) New diagnoses (estimated)
Results 16
Epidemiology 17
1.300 1.200 1.100 1.000 900 800 700 600 500 New HIV diagnoses in the absence of additional prevention effort 3% reduction in % undiagnosed (13% -> 10%) 3% increase in % patients on ART (89% -> 92%) +21% 400 2012 2014 2016 2018 2020 2022 2024 2026 2028 2030 2032
1.300 1.200 1.100 1.000 900 800 700 600 500 New HIV diagnoses - TasP 3% reduction in % undiagnosed (13% -> 10%) 7% (+4%) increase in % patients on ART (89% -> 96%) 400 2012 2014 2016 2018 2020 2022 2024 2026 2028 2030 2032 Old world New world -12% +6%
1.300 1.200 1.100 1.000 900 800 700 New HIV diagnoses - Outreach 600 500 8% (+5%) reduction in % undiagnosed (13% -> 5%) 3% increase in % patients on ART (89% -> 92%) 400 2012 2014 2016 2018 2020 2022 2024 2026 2028 2030 2032 Old world New world -28% -41%
1.300 1.200 1.100 1.000 900 800 700 600 500 New HIV diagnoses - PreP 3% reduction in % undiagnosed (13% -> 10%) 3% increase in % patients on ART (89% -> 92%) 2 600 patients treated with PreP (90% MSM) 400 2012 2014 2016 2018 2020 2022 2024 2026 2028 2030 2032 Old world New world -14% +5%
New HIV diagnoses Combined prevention 1.300 1.200 1.100 1.000 900 800-52% -60% 700 600 500 400 2012 2014 2016 2018 2020 2022 2024 2026 2028 2030 2032 Old world New world
Budget 23
MILIONS 260 Total budget in the absence of additional prevention 240 220 200 +64% 180 160 140 120 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030
MILLIONS 8 Budget impact - TasP 6 4 3,1 4,3 5,4 6,4 5,7 5,0 4,3 3,5 2,7 2 1,7 1,8 0,9 0-0,0-2 - 4-0,9-1,9-3,0 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030
MILLIONS 10 5 0-5 - 10-15 - 20-25 - 30-35 Budget impact - Outreach 2,4 3,8 4,7 5,1 4,9 1,9 - -1,2-4,3-7,6-10,9-14,3-17,9-21,5-25,2-29,1-33,0 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030
MILLIONS 6 Budget impact - PreP 4 2 0 - - - 2,6 2,5 3,4 4,1 4,6 3,4 2,2 1,0-0,3-2 -1,6-4 - 6-8 -2,9-4,3-5,6-7,1 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030
MILLIONS 20 10 0-10 - 20-30 - 40-50 Budget impact Combined prevention 11,5 12,8 14,3 11,1 4,1 6,9 7,6 2,4 - -2,9-8,3-13,8-19,4-25,2-31,0-37,0-43,0 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030
MILLIONS 260 Total budget Combined prevention 240 220-17% 200 180 160 140 120 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030 Old world New world
Conclusions 30
Conclusions Can we make a difference?? Impact of prevention on Epidemiology Budget 31 31
Conclusions Can we make a difference? Yes, we can! Not investing in prevention = not an option! new diagnoses total budget Added value of combined prevention: new diagnoses total budget 32 32
New HIV diagnoses and rates per 100 000 population, by country, in 2014 (West European WHO region) Luxembourg United Kingdom Malta Belgium San Marino Portugal Ireland Spain France Greece West Switzerland Italy Israel Norway Netherlands Denmark Belgium (2) Germany Andorra Sweden Iceland Finland Austria Monaco 0 2 4 6 8 10 12 33 14
Acknowledgements Acknowledgements Prof.Dr. Goffard Prof.Dr. Laga Prof.Dr. De Wit Prof.Dr. Callens Dr. Van Beckhoven Contact Sebastian Vermeersch sebastian.vermeersch@hict.be Lieven Annemans lieven.annemans@ugent.be 34
Data sources Main data source: WIV-ISP. Epidemiologie van aids en hiv infectie in België. Toestand op 31 december 2014. Additional references: Smit et al. Future challenges for clinical care of an ageing population infected with HIV: a modelling study. Lancet Infect Dis 2015;15:810-818 Skarbinsky et al. Human immunodeficiency virus transmission at each stage of the care continuum in the United States. JAMA Intern Med 2015; 175(4):588-596 Zheng et al. HIV acquisition post-migration. Evidence from four European Countries. Poster at the 21st International AIDS Conference (AIDS 2016). Grant et al. Preexposure Chemoprophylaxis for HIV Prevention in Men Who Have Sex with Men. N Engl J Med 2010; 363:2587-2599 Thigpen et al. Antiretroviral Preexposure Prophylaxis for Heterosexual HIV Transmission in Botswana. N Engl J Med 2012; 367:423-434 Choopanya et al. Antiretroviral prophylaxis for HIV infection in injecting drug users in Bangkok, Thailand (the Bangkok Tenofovir Study): a randomised, double-blind, placebo-controlled phase 3 trial. The Lancet 2013; 381:2083-2090 35