HIV Modelling & Economics
Group discussion on selected HIV/AIDS
costing and cost-effectiveness articles
Michelle Remme
([email protected])
HIV Modelling and Economics, Social and Mathematical Epidemiology Group
London School of Hygiene and Tropical Medicine
Improving health worldwide
www.lshtm.ac.uk
HIV Modelling & Economics
Why are these Vfm / efficiency
measures useful?
• UNAIDS Investment Framework calls for more efficient HIV
investments – this will require evidence
• Inputs into decisions on:
– resource allocation (allocative efficiency – what interventions are most
efficient where?)
– programme optimisation (technical efficiency – how can these
interventions be implemented more efficiently)
Prioritising most efficient interventions
Source: http://www.givingwhatwecan.org/where-to-give/charity-evaluation/health/hiv-aids
HIV Modelling & Economics
Some useful resources
• Recent systematic reviews:
–
–
–
–
–
–
–
Siapka et al (under review): unit costs and efficiency of 6 basic HIV/AIDS programmes
Guinness et al (under review): cost-effectiveness of harm reduction programmes
Beck et al (2013): cost and cost-effectiveness of HIV community services
Sweeney et al (2012): economies of scope/efficiency gains from integration
Galarraga et al (2011): unit costs of ART and PMTCT
Santa-Ana-Tellez (2011): costs of interventions for OVC
Galarraga et al (2010): cost-effectiveness of HIV prevention
 comparable and quality data is limited
• Databases/ compilation of cost/CEA data:
–
–
–
–
LSHTM/UNAIDS inventory
Futures unit cost database
Disease Control Priorities (2nd edition)
WHO CHOICE
Growing evidence base in LMICs
1990-1994
1995-1999
2000-2004
2005-2009
35
Number of studies
30
25
20
15
10
5
0
Eastern Europe North Africa and Africa - West
and Central Asia Middle East
and Central
Latin America Asia and Pacific Africa - East and
Southern
Source: LSHTM/UNAIDS
How to interpret the growing evidence base for priority-setting and programming?
HIV Modelling & Economics
Next hour
Learning objectives
• Know what to consider when interpreting a cost or cost-effectiveness analysis
• Understand how scale, scope, quality and costing methods can influence study
results and conclusions
Overview
16:30 – 16:35
Introduction, format and assignment
16:35 – 16:45
Individually – read article (Abstract, Methods, Results)
16:45 – 17:05
Group work – discuss main findings and potential limitations
17:05 – 17:25
Feedback from groups
17:25 – 17:30
Closing remarks
HIV Modelling & Economics
Selected papers
•
•
•
•
•
•
•
Guinness et al – HIV prevention programmes for FSWs in India
Obure et al – Cost of HCT in Kenya and Swaziland
Marseille et al – Determinants of costs &cost-effectiveness of ART in Zambia
Rosen et al – Cost and outcomes of different ART models in South Africa
Menzies et al – Cost of PEPFAR ART programmes in 5 countries
Meyer-Rath & Over – Cost functions in modelling with TasP example
Shrestha et al – Comparison of costing methods for HCT in the USA*
HIV Modelling & Economics
Discussion points
1. What are the key findings?
Pick one figure/table that summarises these best
2. What do these findings mean / why are they relevant for HIV policy
and programming?
3. As an HIV policymaker or programme manager, (how) would you
use these results?
4. What are some key methodological limitations?
Drummond checklist
5. How do these limitations influence the interpretation of the study
results?
HIV Modelling & Economics
(Guinness et al, 2005, WHO Bulletin)
HIV Modelling & Economics
Key issues
• U-shaped relationship between unit cost and scale
– Suggesting that efficiency gains can be realised by increasing scale until a cost-minimising
point, which was found to be relatively low (1,000 – 1,700 FSWs reached)
– Despite high share of variable costs in cost profile
– Resource requirement estimates that assume constant unit cost are over- or
underestimating the real cost of delivering services
– Scale can be measured in different ways and may not always be modifiable by the
programme, i.e. defined by duration of programme, reachability/size of target population
•
Limitations
– Small sample size and variation in service package provided – is the output/service
provided comparable? Is it of equal quality (effectiveness) across 17 NGOs?
– Retrospective costing based on routine monitoring systems could have errors
HIV Modelling & Economics
(Obure et al, 2012, Sex Transm Infect)
HIV Modelling & Economics
Key issues
•
Variation in average costs per client counselled and tested in both Kenya and
Swaziland suggest potential for efficiency gains
•
Kenya: PITC was delivered at a lower average cost and reached HIV-positive clients
at lower cost than VCT (see Bautista-Arredondo et al, 2008, AIDS for targeting efficiency)
– VCT in provincial and district hospitals more efficient at identifying HIV-positive clients
•
Swaziland: public facilities providing both PITC and VCT are more efficient at
identifying HIV-positive clients than private (possibly more ‘voluntary’)
•
Variation in average costs driven by human resource costs, and in particular staff
workload (little variation in salaries) – this can be explained by different models,
i.e. Individual or group counselling, use of staff for multiple services
•
Limitations:
– HIV-positive tests may not have been first tests  may be overestimating cost-effectiveness
– Caution: VCT and PITC are not perfect substitutes
HIV Modelling & Economics
(Marseille et al, 2012, PLoS One)
HIV Modelling & Economics
Key issues
• Determinants of costs
–
–
–
–
–
Large variation in average cost per ART patient-year, suggesting potential inefficiencies
On-site costs represented 2/3 of total average costs
Sites with higher adherence had higher unit costs (quality costs more)
Average costs decrease with scale, through effect on off-site costs
ART appears more efficient at hospitals  could this be partly scale or scope effect?
Equity considerations?
– On-site cost savings through rural/private sites can be offset by higher centralised support
costs to these sites
• Determinants of cost-effectiveness
– Counterfactual in CEA is important: no intervention or existing intervention (CTX)
– Complexity of patient case load increases cost, but not an inefficiency
– Adherence may explain cost-effectiveness more than cost per se
HIV Modelling & Economics
(Rosen et al, 2008, Trop Med Int Health)
HIV Modelling & Economics
Key issues
•
4 different models of ART delivery had different average costs per patient
initiated (US$ 756 – 1,126)
– likely due to scale, factor prices (access to public sector prices), scope (integration within
primary care clinic or hospital), intensity of care provided (number of monitoring visits or
lab tests)
•
For the quality-adjusted output, i.e. patient in care and responding, average
costs were higher due to higher resource use (more drugs, lab tests, clinic visits)
[US$ 903 – 1,210]
•
Average cost to produce a patient in care and responding (incorporating costs of
patients no longer in care or not responding) is even higher (US$ 1,128 – 1,723)
•
If the quality of the treatment programme increases, total costs will increase,
but the average cost of producing quality-adjusted outputs will fall  or costeffectiveness/value for money will increase
•
Cost variation increases when patient outcomes are factored in
– see interpretation of efficiency in Site 4 and Site 2, before and after adjusting for quality
HIV Modelling & Economics
Fig. 2. Change in median per-patient financial costs in successive 6-month periods, from start of
HIV treatment scale-up in eachsite through 2006–2007 (2009 US$).
(Menzies et al., 2011, AIDS)
HIV Modelling & Economics
Key issues
•
Annual per patient costs varied widely, esp. when ARV costs excluded:
– Price differentials and different stages of programme development
– Service package varied per site - supportive care could include OI prophylaxis & treatment,
nutritional support, adherence and community-based interventions
•
Newly initiated ART patients (first 6 months) cost 15-20% more than established
ART patients (50% more, excl. ARVs) –more frequent monitoring
•
Average costs higher for paediatric patients
•
Per patients financial costs dropped rapidly over 1st year (46.8% between 1st
and 2nd 6-month periods) and additional 29.5% the following year
•
Costs decline as sites mature
– Cost reductions most important for investment costs (scale effect)
– Recurrent costs also drop, presumably due to learning by doing
HIV Modelling & Economics
(Meyer-Rath & Over, 2012, PLoS Medicine)
HIV Modelling & Economics
Key issues
• Determinants of costs include: patient health status, treatment regimen,
factor prices, programme/facility scale, facility experience, facility type,
quality of care, technical efficiency
• Most modelling studies (e.g. for ART scale-up) only consider patient health
status and treatment regimen
• Flexible cost functions incorporating various cost determinants would be
more appropriate than current cost accounting identities
• Replication of Granich et al (2012) model of TasP with a cost function that
incorporates scale/level of delivery finds inefficiencies of small scale could
add up to 42% to total future programme cost
HIV Modelling & Economics
(Shrestha et al, 2012, J Public Health Management Practice)
HIV Modelling & Economics
Key issues
• Different costing methods lead to different estimates that can be up to 78%
lower than microcosting (direct measurement) or 61% higher
– Programme budget costing (where programme costs = total funding)  highest estimate
– Gross costing (Medicaid payment for HIV testing used as unit cost)  lowest estimate
• Policy implications: depending which methods is used, conclusions on a
programme’s cost-effectiveness and eligibility for resource allocation could
differ
• Different methods can respond to different questions, but when comparing
the relative value for money of interventions, it is important to understand
if the costing methods underpinning the economic evaluation are
comparable
• Microcosting generally considered to be preferred, but underestimates
waste as it assumes that unused resources are used by other programmes/
for other services
HIV Modelling & Economics
Concluding remarks
•
Determinants of costs and cost-effectiveness (from economic perspective)
–
–
–
–
–
–
–
–
–
–
–
•
Complexity of patient case mix
Factor input prices
Scale
Type/level of facility
Geographic setting (urban/rural)
Programme/site maturity
Targeting
Quality / adherence
Above service level costs
Discount rates
Costing methods
Gaps:
–
–
–
–
Economies of scope/integration
Above service level/health system costs
Programme management characteristics
Dealing with multiple (non-HIV) outcomes in cost-effectiveness analysis
HIV Modelling & Economics
Thank you
http://blogs.lshtm.ac.uk/samemodellingandeconomics/
https://strive.lshtm.ac.uk