Beyond the trials: Translating research results into public health impact (an update on modelling) Catherine Hankins MD MSc FRCPC Chief Scientific Adviser to UNAIDS Office of the Deputy Executive Director The Promise and Perils of antiretroviral-based prevention: Making it a reality on the ground Satellite Session at AIDS2010 Sunday, 18 July 2010

Beyond the trials: Translating research results into public health impact (an update on modelling) What modelling can contribute to decision-making Modelling approaches Modelling and stakeholder deliberations Initial findings Questions from modellers

What modelling can contribute to decision-making Explicit assumptions – testable predictions Framework for data analysis Projections From outcome to impact (effect for cost- effectiveness analysis) Assessing: –Perverse outcomes –Combining interventions –Impact of new technologies Setting coverage targets Advocacy

What works for HIV prevention: Results from randomised controlled trials (RCTs) with HIV incidence end points Review: 37 HIV prevention RCTs on 39 interventions:  PrEP : 1Microfinance: 1 STI treatment: 9  Behavioural: 7Diaphragm: 1 Vaccines: 4  Microbicides: 12Male circumcision: 4 Efficacy Study Effect size (CI) STD treatment (Mwanza) 42% (21; 58) Circumcision (Orange Farm, Rakai, Kisumu) 57% (42; 68) HIV Vaccine (Thai RV144) 31% (1; 51) 0% % Padian NS, et al. Weighing the gold in the gold standard: challenges in HIV prevention research. AIDS 2010, 24:621–635 Courtesy CAPRISA

Key Equations r a,s,t = r u x AvgRisk a,s,t x BC a,s,t x MCeffect a,s,t x PrevEffect t r u = r’/[(1-MC%) + MC% x (1-Red)] r’ = initial force of infection, from curve fit AvgRisk = e -αI/N reduction in average risk among susceptible population as highest risk populations become infected BC = e -εD behavior change (D = Cumulative AIDS deaths) MCeffect = (1-MC% t ) + ∑ p (P p xMC%xRed p )/∑ p P PrevEffect = future scale-up of prevention coverage

Discounted Costs and Savings Discounted net costs per infection averted ∑ t {Costs t /(1+d) t } / ∑ t {IA t /(1+d) t } Net savings per infection averted ∑ t {(ART$ t -MC$)/(1+d) t } / ∑ t {IA t /(1+d) t }

Male circumcision modelling approaches and settings P.I.WilliamsNagelkerkeGrayHallettWhiteAlsallaq Model type Closed- solution analysis & determin- istic Stochastic Determin -istic Stochastic network simulation Closed- solution analysis & determin -istic SettingAfrica & South Africa Botswana and Nyanza, Kenya Rural Uganda Southern /Eastern Africa Kisumu, Kenya Kisumu, Kenya & Africa IAS Cape Town MOPDC106. Hankins et al. Informing Decision-making on Male Circumcision for HIV Prevention in High HIV Prevalence Settings: Insights from Modelling

Expert Review Group Consensus In high HIV prevalence/low male circumcision settings, models predict that: One HIV infection averted for 5-15 male circumcisions performed Costs to avert one HIV infection: $ (10-year time horizon) If confined to newly or already circumcised men and their partners, any behavioural risk enhancement/compensation has only a small population-level effect on the anticipated impact of MC service scale-up on HIV incidence MC scale-up acts synergistically with other HIV prevention strategies Women will benefit indirectly, although the effect will be smaller than the direct effect for men and will take longer to develop IAS Cape Town 2009 MOPDC106. Hankins et al. Informing Decision-making on Male Circumcision for HIV Prevention in High HIV Prevalence Settings: Insights from Modelling

Population-level Impacts by Coverage Hankins et al. Male circumcision for HIV prevention in high HIV prevalence settings: What can mathematical modelling contribute to informed decision making? PLoS Medicine 2009;6, September 8

Male Circumcision: Moving from Research to Implementation Modelling informed user-friendly pragmatic Decision-Makers’ Programme Planning Tool (DMPPT) DMPPT allows decision makers to indirectly access main modelling findings to estimate:  HIV incidence, AIDS deaths, overall costs, net cost per infection averted as a function of: procedures performed, service delivery mode, age at circumcision, and rate and shape of scale-up (linear, S- shaped)

Importance of epidemiological context in deciding who should be prioritised for PrEP  Different epidemiological contexts by region and epidemic type? Diversity of settings  In which key populations will PrEP be most effective and worth implementing?  Discordant couples  Adolescents in high prevalence settings  Sex workers  Men who have sex with men  People who inject drugs

PrEP Modelling Overview Existing modelling work is limited but rapidly expanding: –General population, heterosexual transmission in sub- Saharan Africa Abbas et. al., 2007 –General population, sex workers & their clients in Africa and India Vissers et. al., 2008 –Men who have sex with men in New York City Desai et. al., 2008 –Men who have sex with men USA Patiel et. al –Impact of resistance on PrEP effectiveness in Zimbabwe Van de Vijver et. al., 2009

Copyright © 2010 AIDS. Published by Lippincott Williams & Wilkins. 13 Abbas et. al.Desai et. al. Increases in sexual risk behaviour may erode or reverse preventive benefit of PrEP Note different scales for increased risk

Communicating about partial protection

PrEP Modelling Overview: Preliminary Findings Considerable potential preventive benefit if PrEP: shows high effectiveness is prioritised for those at highest risk of HIV exposure does not lead to risk compensation (increased partner numbers, decreased condom use, choice of more risky partners) adherence is high Circulating drug resistance would have a limited impact on PrEP effectiveness Risk of resistance is highest when PrEP is used by someone who has HIV infection already

PrEP Modelling UNAIDS/WHO PrEP Modelling Meeting, Geneva March 2010 (consensus meeting planned for 2011) Georgetown/Imperial/WHO/UNAIDS supported by BMGF to convene 5 regional consultations for which Imperial is preparing region-specific modelling scenarios: –West Africa –East Africa –Southern Africa –Latin America –Asia

PrEP in Combination Prevention Status quo Intervention to scale (incr. condom use and prompt treatment initiation) + Targeted effective PreP + The missing piece? Numbers based on extrapolation to Urban Benin; *PreP intervention is to 60% of sex workers & clients; 70% efficacy and 80% adherence, for 10 years. ** The missing piece required to reduce incidence by 90% in 2031 and eventually stop the epidemic is a 60% efficacy vaccine delivered to half the population.

Other contexts: Preliminary Results Hyper-endemic setting  Prioritisation has less pay-off  For same coverage & efficacy, modestly higher proportion of infections averted IDU and MSM  Per exposure efficacy could interact with route of exposure  Effectiveness modulated by rate of exposure  Capacity to prioritise specific populations will also vary

PrEP Serodiscordant couples PrEP Serodiscordant couples (UNAIDS/WHO Modelling Group and Imperial College)  Treatment of partner is an option (most risk is from known source)  Conception and first months of partner’s treatment (until viral load undetectable) provide additional windows for PrEP use  Potential additional benefit of PrEP depends on pattern of antiretroviral treatment initiation  How infectious are individuals in stable partnerships that don’t need treatment?  Contribution of stable couples to overall epidemic and extent of forward benefits for reduced transmission in wider population is unclear

Questions from modellers What efficacy range will be acceptable: what would be worth implementing and where? What range of HIV testing frequency? –Frequency will determine length of time a person with acute infection will be on suboptimal therapy Implications of development of resistance on the cost of eventual treatment – use of a non- TDF first line regimen Implications for transmissibility during primary infection

Questions from modellers Pending bridging studies in pregnant women, would delivery be for men only? Should indirect effects for women be modelled? What level of adherence likely in which populations? What discontinuation rate and why (toxicity vs. other reasons) Potential for risk compensation (behavioural risk enhancement)? –Defined how? (increased frequency, number of partners, risk of partners, unprotected sex acts)

Questions from modellers Heterogeneity in susceptibility to infection by host genetics, mode of transmission, founder virus/es Assumptions on the extent of scale-up of other interventions when PrEP introduced and potential synergies or displacement of resources/attention: MC, ART, condom promotion Policy re discordant couples: treat the HIV-positive person and provide PrEP one year to the HIV- negative person? Time to achieve target coverage? (S-shaped, linear) Service delivery: cadre of health worker, site, SMS texting….

Acknowledgements John Stover Geoff Garnett Tim Hallett Ide Cremin Brian Williams Lori Bollinger Salim Abdool Karim Quarraisha Abdool Karim Toby Kasper

Guiding principles: Respect Mutual understanding Scientific and ethical integrity Transparency Accountability Community autonomy

Thank you for your attention