The End of AIDS: The Road to Implementation David Wilson Global HIV/AIDS Program Director The World Bank UCLA AIDS Institute Grand Rounds Friday, March 14, 2014

Why worry? Proven interventions? Achieve with full implementation? Actual implementation? Investing smarter and implementing better? Maximizing impact and investment return

AIDS fastest growing cause of disease burden globally in last 20 years IHME/World Bank, 2013

AIDS and malaria greatest causes of disease burden in Sub-Saharan Africa IHME/World Bank, 2013

AIDS by far the largest cause of disease burden in many African countries, including Uganda

Estimated national HIV incidence encouraging Measured sub-national HIV incidence worrying The end of AIDS?

Estimated national incidence declines  Estimated national HIV incidence fell by 20%  39 countries (23 African) - declines > 25% UNAIDS, 2012

Measured sub-national HIV incidence (trial cohorts)

Smarter investments, greater impact Understand transmission dynamics Use proven interventions For the right people In the right places

The core transmission dynamics distinction: Concentrated, generalized and mixed epidemics Epidemics CONCENTRATED if effective SW (sex worker), MSM (men-having-sex-with-men) and IDU (injection drug user) programs would prevent wider epidemic Epidemics GENERALIZED if epidemics would persist despite effective SW, MSM and IDU programs Epidemics MIXED if transmission sustained BOTH by SW, MSM and IDU and wider population

Concentrated, generalized and mixed epidemics Concentrated Generalized Mixed/uncertain

Transmission dynamics: Indonesia’s contrasting epidemics in one country Jakarta’s concentrated epidemic Papua’s generalized epidemic Wilson, 2012

Transmission dynamics: an improved Modes of Transmission model in Cross River State, Nigeria Old model < 20% concentrated transmission New model ~ 50% concentrated transmission

Understanding transmission dynamics avoids misunderstanding – India example District HIV prevalence in antenatal women Percentage of new infections in low risk general population Bagalkot (high prevalence) 2.1%38.4% Shimoga (Medium prevalence) 1.0%57.9% Varanasi (low prevalence) 0.25%77.2% Mishra, 2012

Concentrated epidemics: Why worry? Key populationOverall risk of HIV infection, relative to general population Sex workers13-fold higher Men-having-sex-with- men 13.5 fold higher Injection drug user20-fold higher UNAIDS, 2012

Concentrated epidemics – SW Why worry? Proven interventions? Achieve with full implementation? Actual implementation? Investing smarter and implementing better?

Concentrated epidemics – SW Why worry – SW HIV rates 13.5-fold higher UNAIDS, %

Concentrated epidemics – SW Proven interventions Concentrated SW epidemics - know what to do in real world at scale and have checked several SW epidemics Effective SW programs have six integrated components: – Behavior change communication – Condom promotion – Tailored sexual health services – HIV testing and treatment – Solidarity and group empowerment – Supportive local and national legal environment

Concentrated epidemics – SW Proven interventions in Burkina Faso

Concentrated epidemics – SW Implementation and coverage limited 75% very low or don’t report 91% of funding international Services for MSW and TGSW almost non-existent 75% very low or no report UNAIDS, 2012

Concentrated epidemics – SW Low investment in high impact SW interventions – Benin

In India, targeted interventions alone may nearly eliminate HIV – at lower cost Blanchard, 2012 MysoreBelgaumBellaryGuntur Size of general population480,000460,000490,000620,000 Size of FSW population % gen pop HIV positive0.94%0.63%1.36%1.9% Cost of testing general population every 5 years (US$) 960,000920,000980,0001,240,000 Estimated annual test-and- treat cost 4,600,0003,200,0006,300,00010,600,000 Annual core group intervention cost 470,000400,000570,0001,200,000

The complexity of sex work in Africa poses a particular implementation challenge

Much better epidemic analysis and characterization – sources of transmission and geographic variation Increase investment and scale of SOP defined, socially franchised, quality assured, formal SW interventions Strengthen real-time data use for continuous program refinement Increase focus on high transmission geographic areas Develop and evaluate effective interventions for hotspots and informal sex workers Concentrated epidemics - SW The FIVE Things We Must Do Better

Concentrated epidemics – MSM Why worry? Proven interventions? Achieve with full implementation? Actual implementation? Investing smarter and implementing better?

Concentrated epidemics – MSM Why worry – MSM HIV rates 13.5-fold higher 20% UNAIDS, 2012

Concentrated epidemics – MSM Proven interventions? Despite developed world successes, few developing country MSM programs have demonstrably reduced HIV incidence PREP reduced HIV among MSM by 44% (90% among fully adherent) but we don’t even reach MSM in most developing countries with information and condoms In developing countries, scarcely know how to reach hidden MSM, reduce stigma, deliver at scale and change policy Still need to navigate between southern unwillingness to address male-male sexuality and northern temptation to frame response within western constructs

Concentrated epidemics – MSM Implementation and coverage limited 70% very low or don’t report 90% of funding international LMIC reliant on external funding 70% very low or no report UNAIDS, 2012

Concentrated epidemics – MSM Few reached by HIV prevention services

Concentrated epidemics – IDU Why worry? Proven interventions? Achieve with full implementation? Actual implementation? Investing smarter and implementing better?

Concentrated epidemics – IDU Why worry – IDU HIV rates 20-fold higher 60% UNAIDS, 2012

Concentrated epidemics – IDU Proven interventions? Three proven interventions (plus other 6 WHO interventions) NSP OST ART

Concentrated epidemics – IDU Proven interventions? NSP HIV prevalence in 99 city study (MacDonald et al, 2003) 19% per year in cities with NSP 8% in cities without NSP OST RCTs (Mattick et al, 2003) Observational studies (Mattick, 1998) Cochrane review (Gowing, 2008) Amsterdam cohort – – 60% incidence reduction Meta-analysis (Mcarthur BMJ2012) – 60% incidence reduction

Concentrated epidemics – IDU Implementation and coverage limited 86% low coverage or no report UNAIDS, 2012

Concentrated epidemics – IDU Low access to basic services 90% 92% 85% Million IHRA, 2012

Generalized epidemics Why worry? Proven interventions? Achieve with full implementation? Actual implementation? Investing smarter and implementing better?

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Generalized epidemics: Why worry? Why they are so different? FHI, 2002

Generalized epidemics: Why worry? Why they are so different? Sources of infection – KwaZulu-Natal (South Africa) example, 2012

Generalized epidemics: Why worry? Why they are so different? National household HIV prevalence, Swaziland, 2012

Generalized epidemics Do we have proven approaches? TrialCompleted/StoppedEffective Microbicides101 Behavior change80 STI treatment71 HIV vaccines41 PEP10 Male circumcision-male acquisition33 HIV treatment as prevention11 PREP32 Financial incentives33 Total4111 Weiss, Abu_Raddad, Padian

Generalized epidemics - VMMC Do we have proven approaches? VMMC clinical trial efficacy at least 60% VMMC longer term effectiveness greater 67% years, Kenya 73% years, Uganda 76% - 3 years, South Africa

Generalized epidemics - VMMC What can we achieve with full implementation? 80% implementation will avert 3.4 million or 22% of new HIV infections in 14 priority countries Cost-effective - net savings per VMMC $1,100 at age 20 VMMC amortization 7 years at age 20 (11 years at 30 and 25 years at 45) Haaker, 2013 Savings per circumcision

Generalized epidemics - VMMC Actual implementation is seriously off-target PEPFAR, 2013 Total

Generalized epidemics - TAsP Do we have proven approaches? TAsP clinical trial efficacy 96%+ TAsP real world effectiveness lower? – Infection 34% lower in area with 30%-40% ART coverage (the effect saturation point) than area with <10% coverage in KZN (Tanser et al, 2013) – Infection 26% lower in discordant couples in China - for transfusion or sexually infected but not IDU infected indexes (Jia, 2012) – No difference in discordant couples in Uganda (Birungi et al. 2013) – HIV infections continue to rise in highly treated MSM communities in developed countries (Wilson et al, 2012) – With ~85% on ART at CD350, Swaziland has measured HIV incidence of 2.4% on top of 26% adult prevalence (SHIMS, 2013)

Generalized epidemics - TAsP Do we have proven approaches? HPTN 071 (PopART) TasPBotswana/ HSPH SEARCH SitesLusaka, CTownSouth AfricaMochudi/BotsKenya, Uganda DesignCluster RCT 55,000 3 arm Cluster RCT 1,250 2 arm Paired cluster RCT, 5,000, 2 arm Paired cluster RCT, 32 10,000, 2 arm InterventionImmediate ART if HIV+ HCT, VMMC, condom, risk reduction counselling Immediate ART if HIV+ HCT home-based ART for CD4 10,000 HCT, VMMC, PMTCT-B Immediate ART if HIV+ Combination HIV prevention package Outcome2 y HIV incidence in cohort Cumulat 2 yr HIV incidence in cohort Cumulat HIV incidence yrs, cross-sectional

CD4 ≤ 200 CD4 ≤ TB/HIV HBV/HIV CD4 ≤ TB/HIV HBV/HIV + CD4 ≤ 500 “Test and treat” All HIV ART regardless of CD4 count for:  HIV-SD couples  Pregnant women ART regardless of CD4 count for:  HIV-SD couples  Pregnant women + TB/HIV HBV/HIV SD couples Pregnant Children < 5 Apollo et al, million 17 million 21 million 26 million 32 million 11 million 17 million 21 million 26 million 32 million Recommended since 2010 Recommended until million on ART - 26 million eligible at CD500 and 32 million eligible for “test and treat” Generalized epidemics - TAsP What does full implementation look like?

Generalized epidemics - ART What is actual implementation like? US treatment cascade - 28% virally suppressed

Implementability of TasP: Retention WHO/UNAIDS 2012 <60% <50%

Implementability of TasP: Viral load Kranzer et al, % 30%

Generalized epidemics - ART What is actual implementation like? Acquired HIV drug resistance in low resource settings Stadeli et al, 2013

Generalized epidemics - ART What is actual implementation like? SDRM = Surveillance Drug Resistance Mutations HIV drug resistance in ARV-naive populations

Barninghausen et al examined cost-effectiveness of ART, MC and TasP in South Africa from All cost-effective at WHO rule of 3x/per capita GDP Significant cost savings through optimal intervention mix without compromising prevention or mortality High ART+MC coverage similar HIV incidence reduction as TasP High ART+MC coverage $5 billion less expensive than TasP Increased MC ($1,100 per infection averted) outperforms ART ($6,800) and TasP ($8,400) Most cost-effective prevention and mortality scenario is MC first then ART - 50% ART and 60% MC coverage optimal MC more cost-effective than TasP because cost is one-ninth, accrued once versus lifetime As only half needing ART at CD4<350 receive it, increasing treatment in this group should precede treatment expansion to earlier disease stages Generalized epidemics - ART Cost-effectiveness in South Africa

Generalized epidemics – Financial incentives Do we have proven approaches? Three World Bank RCTs show financial incentives reduce STI/HIV transmission – In Tanzania, people offered up to $60 each annually to stay STI- free had 25 percent lower STI prevalence (De Walque et al 2012) – In Malawi, girls and parents offered up to $15 monthly to stay in school had 60% lower HIV prevalence - whether they stayed in school or not (Ozler et al, 2012) – In Lesotho, adolescents offered a lottery ticket to win up to $50 or $100 every four months if they stayed STI and HIV-free had a 25% lower HIV incidence - 33% lower among girls and 31% in the $100 arm (De Walque et al 2012)

TanzaniaMalawiLesotho SitesIfakara, TanzaniaZomba district, Malawi5 rural/periurban districts DesignRCT, 2399 adults yrs, 3 arms Cluster RCT, 1,289 never-married females yrs in 176 EAs, 3 arms RCT, 3,426 adults yrs, 3 arms Intervention and incentive All: STI testing and treatment every 4 mths for 1 yr Low value and higher value CCT for those STI free CCT (for school attendance) and UCT payment (no school attendance required) CCTs and UCTs to student and parent, randomised amounts ($1-10) All: STI testing, counselling and treatment every 4 mths for 2 years High and low lottery tickets for those free of curable STIs EndpointCombined prevalence of 4 STIs (HIV, HSV-2 & syphilis secondary endpoints) Prevalence of HIV and HSV-2 at 18 mths HIV incidence Generalized epidemics – Financial incentives Do we have proven approaches?

Watts, % reduction in HIV risk

Generalized epidemics – Financial incentives Do we have proven approaches? Evidence robust enough? Are financial incentives: – scalable? – affordable? – durable? What to do next with this evidence?

How much can geographic targeting and a “hotspot” focus help - Indonesia Half of Indonesia’s epidemic historically in Papua with 2% of population SDRM = Surveillance Drug Resistance Mutations Papua Java KPA, 2013

Philippines 43% of Philippines epidemic Manila MSM 73% in just 3 cities Manila MSM 43% NDOH, 2013 CEBU IDU 14% CALAMBA MSM 16%

India NACO, %+ of India’s epidemic historically in 4 – 35 high burden southern states

Thailand UNAIDS, 2013

Pakistan Blanchard, % of Pakistan’s IDUs in 4 cities

54% of new infections in 9 highest burden counties (Nairobi, Homa Bay, Kisumu, Siaya, Migori, Mombasa, Turkana, Busia and Kisi) Less than 2% of new infections in 8 lowest burden counties (Wajir, Mandera, West Pokot, Tana River, Marsabit, Lamu, Wajir) Kenya UNAIDS, 2013

South Africa

KwaZulu-Natal - spatial clustering of sero- conversions Tanser et al, 2011.

Even greater concentration of sero- conversions per square mile Tanser et al, 2012

Spatial clustering in Sub-Saharan Africa DHS+ analysis OECD, 2013 Abu-Raddad, 2013 Abu-Raddad et al,, 2012

Spatial clustering in Sub-Saharan Africa DHS+ analysis (Abu-Raddad et al, 2013) OECD, 2013 Great spatial variation with “valleys” (high prevalence), “dams” (which obstruct transmission) and “islands” (of very high or low prevalence) Overall, 14% live in high prevalence clusters and 16% live in low prevalence clusters However, average masks great sub-national variations: – Far more clustering in low prevalence concentrated epidemics (highest in Senegal, also high prevalence “island” clusters in Burkina Faso, DRC, Sierra Leone and Ethiopia – Far less clustering in high prevalence, generalized, diffused epidemics (none in Swaziland, little in Zambia and Lesotho) – “Dams” (most obviously MC) with low prevalence even in high prevalence countries (Kenya, Tanzania, Malawi)

OECD, 2013 Major opportunity for better geographic targeting in large, diverse countries with heterogeneous epidemics (Indonesia, India, Nigeria) – but we knew that Significant opportunity for hotspot targeting, especially in lower prevalence, concentrated epidemics Also opportunity for “de-targeting” low transmission areas Need rigorous test – not uncritical advocacy - of feasibility and utility of geographic and “hotspot” targeting 80:20 rule or a 55:45 rule? Feasibility and utility will determine whether geographic targeting incremental or fundamental impact How much can geographic targeting and a hotspot focus help? Summary

Schwartlander et al (2011). The Lancet 2011; 377: (DOI: /S (11) ; KFF and UNAIDS, 2011 Declining international AIDS financing and significant resource gap

Declines in overall development assistance OECD, % decline in % decline in 2012

South Africa’s growing AIDS and health budget South Africa’s AIDS budget grew 500% in a decade to $1.9 billion, the second largest globally By 2015, AIDS projected to reach 38% of total health budget

South Africa’s budget balance,

Post-MDG High Level Panel Report Word Cloud Wagstaff, 2013

Conclusion At best flat financing and competing priorities With existing implementation and rate of expansion, we are likely to prevent less than half of new infections in next decade With full implementation of interventions, we could prevent over half of new infections Bridging the gap between full and actual implementation means moving from age of advocacy to era of implementation - intelligent, inquiring, critical, tailored, targeted, painstaking implementation Ending AIDS requires more and better tools