1. David Wilson Cost-effectiveness of HIV financing

2. Global HIV resourcing

3. Resulted in tremendous health and economic savings E.g. Avahan achieved scale and coverage –HIV prevalence declined significantly –19.6% to 16.4% among FSWs (aOR=0.81, p=0.04) Source: Ramesh BM. IBBA two rounds analysis with FSWs in Karnataka, 5 districts. STI 2010; 86 (Suppl 1): i17; http://www.aidstar-one.com/sites/default/files/technical_consultations/mixed_epidemics/day_2/Avahan_program_Gina_Dallabetta.pdf

4. But not enough money to do everything 2.3 (1.9-2.7) million newly infected in 2012 35.3 (29.1-35.3) million PLHIV and growing Source: UNAIDS 2013 global report

5. Much money has been wasted Administrative and ‘other management’ costs Programs have not operated most efficiently Programs have not achieved scale and coverage Available money has not been allocated to programs which have the largest impact –Proven effective and feasible programs of the greatest cost-effectiveness –Many implemented programs have not been cost-effective (Craig et al JIAS 2014)

6. Epidemiology of HIV in Asia-Pacific 86% of all 5 million PLHIV in Asia-Pacific are in 5 countries (India, China, Thailand, Indonesia, Vietnam) –97% in 10 countries 70% of new infections in the KAPs Source: Kirby Institute estimates based on UNAIDS HIV and AIDS data hub for Asia Pacific

7. Inefficient allocations HIV prevention funding in Asia poorly targeted Source: UNAIDS The Gap Report (2014): UNAIDS HIV and AIDS data hub for Asia Pacific based on AIDSinfo Online Database; Craig et al JIAS (2014) 17:18822

8. 27/77 provinces in Thailand account for 70% of new HIV infections 43% of Philippines epidemic in Manila MSM 73% in just 3 cities Need to focus limited resources by geography and population group

9. Deciding HIV budget allocations / GF concept sheets / operational plans Know your epidemic, know your program costs, know your program impact, know your desired outcome Allocate based on all this knowledge to have the best possible (i.e. optimal) impact Investing for the biggest impact: optimization / allocative efficiency

10. Allocations should be based on objectives Minimize incidence Minimize deaths Minimize DALYs Minimize money to achieve multiple targets in a national strategy Different objectives Different allocations Determine the allocation of resources or spending required that best meets the objective

11. Mathematical optimization Formal mathematical approach, with epidemiological model, taken to find the precise “best” / “optimal” solution to meet the objective according to the known epidemiology, costs and outcomes of programs Allocation minimizing outcome Current allocation programme 1 programme 2 UNSW- World Bank allocative efficiency tool

12. E.g. An African country (specific country not disclosed) Packages include condoms, HTC, SBCC $5.6 million per year Expected new infections, 2013-2020 Infections (‘000s) Same money, but avert 15% incidence

13. Minimize incidence: different budget amounts An example from an Asian country

14. Large amounts of money on indirect or other management costs Large indirect costs: ~50% $5.6 million per year Program efficiency can free up this money for direct program efforts for greater impact –E.g. Efficiency study in Ukraine (UNSW, WB, UNAIDS) NSP costs can reduce by 18% OST costs can reduce by at least half (stand alone); 43% for integrated sites ART costs can reduce by 28% (1 st line) and 41% (2 nd line)

15. Great need to invest smarter: focussed and efficient investments “I simply wish that in a matter which so closely concerns the wellbeing of the human race, no decision shall be made without all the knowledge which a little analysis and calculation can provide”. - Daniel Bernoulli, 1760