David Wilson Global HIV/AIDS Program DirectorDecision and Delivery Science Global Lead

Investing inHIV Prevention

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mathematical optimization equation

For resource vector R such that ΣR=c(t) and bounded by constraints rmin(t)≤Ri≤rmax(t) with outcome 0=f(R), find R that minimizes 0

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Optimization algorithm example

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Concentrated epidemics | Proven approaches for SW, MSM and IDU

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Optimizing HIV prevention investments to prevent 19,000 new infections in SudanWith the same $6.4 million in 2013, Sudan could avert anadditional 19,000 infections (36% of cumulative HIV infections) from 2014–20 by reallocating funds from general population as follows:ART 12%–22%SW clients 4%–10%FSW 4%–15% MSM 2%–6%

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generalized

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Generalized epidemics | Why they are so different, Swaziland

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Generalized epidemics | Why they are so different

FHI, 2002

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Sources of infection example, KwaZulu-Natal (South Africa), 2012

Generalized epidemics | Why they are so different

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Generalized epidemics | Proven approaches

Trial Completed/Stopped EffectiveMicrobicides 10 1Behavior change 9 1STI treatment 7 1HIV vaccines 4 1PEP 1 0Male circumcision 3 3ART-based prevention 9 7Cash transfers 5 3Total 45 16

Weiss, Abu_Raddad,Padian

Impact of optimized allocations on generalized epidemics in SwazilandSwaziland could reduce new infections by 30% by 2018 by making a single change to allocations:Increase VMMC from <1% to 8% of HIV spendingplus Sustain and expand ART, PMTCT, BCC, condomswithin existing budgets

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Geographical prioritization in Indonesia

Greatest gains by targeting by program and location in Malawi

Source: Draft of populated Optima model, not for citation

Develop detailed district level allocation results in Malawi

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Implementation Efficiency in Kazakhstan

Source: Optima draft report and data spreadsheets based on NASA/ GARPR

Kazakhstan could halve HIV incidence and mortality within existing budget with reduced ART and management costs

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Strengthening the treatment cascade among MSM in Bangkok, Thailand

Strengthening the circumcision cascade in Swaziland

Limitation: Varying sources with differing denominators

South Africa VL/CD4 data for program improvement

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Results on viral suppression

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Exploring viral suppression data

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Results on immune recovery

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Implications

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The third 90 is possible―132 ART sites have viral suppression of 90% or higher―learn from successFailing clinics need extra VLS supportOlder males and those with <200 CD4 count at initiation need to be prioritizedSecondary analysis of large sets of routine data and novel approaches in record linkage (“big data”) can contribute to program improvement

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Optima | Wider health applications

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Conclusions | Priorities for better prevention Increase allocative efficiency (still lowest-hanging fruit)―effective

10–30% budget increase to be harvested Interventions, populations localities, mixes Increase ART, VMMC, KPs, reduce general population

behavioral interventions Reduce management and procurement costs (ART squeeze) Improve procurement of supplies and services Strengthen contracting, performance management and incentives Innovate, integrate, decentralize and simplify services―maximize

simplicity of early ART (more tablets, less tests) and role of community health workers and adherence clubs

Emphasize demand demand, demand, especially in lower volume sites