Cost-effectiveness of HIV financing
David Wilson
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
Need to focus limited resources by
geography and population group
• 27/77 provinces in Thailand account
for 70% of new HIV infections
• 43% of Philippines epidemic
in Manila MSM
• 73% in just 3 cities
Investing for the biggest impact:
optimization / allocative efficiency
• 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
Allocations should be based on objectives
Different
objectives
•
•
•
•
Minimize incidence
Minimize deaths
Minimize DALYs
Minimize money to
achieve multiple
targets in a national
strategy
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
Current allocation
Allocation
minimizing
outcome
UNSW- World Bank allocative efficiency tool
E.g. An African country (specific country not disclosed)
$5.6 million per year
Expected new
infections,
2013-2020
Infections (‘000s)
Packages
include
condoms,
HTC,
SBCC
Same money,
but avert 15%
incidence
Minimize incidence: different budget amounts
An example from an Asian country
Large amounts of money on indirect or other
management costs
$5.6 million per year
Large
indirect
costs: ~50%
• 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