David Wilson
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Transcript of David Wilson
David Wilson
Cost-effectiveness of HIV financing
Global HIV resourcing
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
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
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)
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
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
• 27/77 provinces in Thailand account for 70% of new HIV infections
• 43% of Philippines epidemicin Manila MSM
• 73% in just 3 cities
Need to focus limited resources by geography and population group
• 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
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
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
E.g. An African country (specific country not disclosed)
Packages include condoms,HTC,SBCC
$5.6 million per year
Expected new infections, 2013-2020
Infe
ction
s (‘0
00s)
Same money, but avert 15% incidence
Minimize incidence: different budget amountsAn example from an Asian country
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% (1st line) and 41% (2nd line)
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