1. HIV Modelling & Economics Estimating the potential impact and efficiency of PrEP for FSWs and MSM in Bangalore, southern India K.M. Mitchell 1, H.J. Prudden 1, B.M. Ramesh 2.3, R. Washington 2,4, S. Isac 2, S. Rajaram 5, F. Terris-Prestholt 1, C.H. Watts 1, P. Vickerman 1,6 1 London School of Hygiene and Tropical Medicine, London, UK, 2 Karnataka Health Promotion Trust, Bangalore, India,, 3 University of Manitoba, Winnipeg, Canada, 4 St John's Research Institute, Bangalore, India, 5 CHARME-India Project, Bangalore, India, 6 University of Bristol, Bristol, UK Improving health worldwide www.lshtm.ac.uk

2. HIV Modelling & Economics Bangalore: background In Bangalore, HIV infection is concentrated amongst – female sex workers (FSWs) HIV prevalence 8.0% (2009) – FSWs’ commercial clients 2.4% (2007) – men who have sex with men (MSM) 16.5% (2009) Low general population HIV prevalence (ANC 0.4%, 2009) Following previous interventions, reported condom use by MSM and FSW is high (75-90% always use condoms with high-risk partners) Not all acts or partnerships are covered by condoms

3. HIV Modelling & Economics Pre-exposure prophylaxis (PrEP) Given to HIV negative individuals at high risk of HIV infection Daily oral tablet containing antiretroviral drugs MSM: iPrex trial showed 44% reduction in HIV incidence (Grant et al 2010 NEJM) Heterosexuals: Trials show 0-75% reduction in HIV incidence (Baeten et al 2012 NEJM, Thigpen et al 2012 NEJM, Van Damme et al 2012 NEJM, Amico et al 2014 CID) Trial efficacy strongly related to drug adherence (Amico et al 2014 CID) FSW: trials & demonstration projects planned or in progress

4. HIV Modelling & Economics What could be the additional impact of providing PrEP for FSW and/or MSM in this setting?

5. HIV Modelling & Economics Population characteristics FSWHigh-risk MSM Population size~12000~18000 Condom use high-risk partners 90%75% HIV prevalence8%16.5% High-risk partners/year~38070-130 % with marital/cohabiting partner 69%2-20%

6. HIV Modelling & Economics Methods: mathematical modelling Mathematical models widely used to estimate the impact of different HIV interventions and inform policy Deterministic model which describes HIV transmission between MSM, FSW, clients and low-risk members of the general population Model inputs: – Behavioural data from Bangalore on sexual partners, frequency of sex acts, condom use, rates of HIV testing, linkage to ART, rates of dropout from ART – Biological data from the literature on the probability of infection per sex act, condom and ART efficacy, HIV progression rates and death rates on and off ART

7. HIV Modelling & Economics Methods: model fitting Model was fitted to data from Bangalore: – HIV prevalence for MSM, FSW and clients – Overall ART coverage data Model inputs were randomly sampled many times Only combinations which gave outputs agreeing with the data were retained for further analysis

8. HIV Modelling & Economics Methods: modelled interventions Model used to estimate the impact of PrEP – number of infections averted over 10 years PrEP intervention starting in January 2014 20% annual dropout from PrEP intervention PrEP intervention characteristics: – Targeted to FSW, high-risk (HR-) MSM or both – Effectiveness (efficacy*adherence) 30% or 60% – Coverage after 5 years: 20%, 40% or 60% of target group

9. HIV Modelling & Economics Results: targeting FSWs, impact among FSWs 40% coverage of FSWs and 60% PrEP effectiveness → ~23% of FSW infections averted Higher impact at higher coverage and/or effectiveness Similar results for MSM

10. HIV Modelling & Economics Results: impact in whole population 40% coverage of FSWs and 60% PrEP effectiveness → 3.4% of infections averted overall Targeting MSM at same coverage has lower population-level impact Targeting both groups – impact almost additive Impact increases over time

11. HIV Modelling & Economics Results: HIV infections averted by subgroup More infections averted in general population when FSW targeted

12. HIV Modelling & Economics How efficient is providing PrEP for FSW and/or MSM in this setting? Efficiency = person-years of PrEP per life-year gained

13. HIV Modelling & Economics Results: PrEP efficiency in whole population 40% coverage and 60% PrEP effectiveness: PrEP most efficient when targeting FSWs only or FSW+MSM Efficiency improves over time

14. HIV Modelling & Economics Conclusions What could be the additional impact of providing PrEP for FSW and/or MSM in this setting? – Substantial reduction in incidence amongst target group – Smaller reduction in incidence in the whole population – Larger impact from targeting FSW vs MSM How efficient is providing PrEP for FSW and/or MSM in this setting? – More efficient to target FSW than MSM – More efficient in the longer term – Could higher efficiency be achieved if PrEP is targeted to those unable to use condoms consistently?

15. HIV Modelling & Economics This work was supported by the Bill and Melinda Gates Foundation* *The *views expressed herein are those of the authors and do not necessarily reflect the official policy or position of the Bill & Melinda Gates Foundation. Acknowledgements London School of Hygiene & Tropical Medicine – Fern Terris-Prestholt – Holly Prudden – Anna Foss – Charlotte Watts University of Bristol – Peter Vickerman Karnataka Health Promotion Trust – BM Ramesh – Reynold Washington – Shajy Isac CHARME India Project – S Rajaram

16. HIV Modelling & Economics