measuring to manage progress toward universal health coverage
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Measuring to Manage Progress toward Universal Health Coverage. Ben Bellows On behalf of the Social Franchise Metrics Working Group NHIS 10 th Anniversary International Conference on UHC Accra. UHC is multidimensional & aspirational. Access : Expand coverage to wider population - PowerPoint PPT PresentationTRANSCRIPT
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Measuring to Manage Progress toward Universal Health CoverageBen BellowsOn behalf of the Social Franchise Metrics Working GroupNHIS 10th Anniversary International Conference on UHCAccra
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UHC is multidimensional & aspirationalAccess: Expand coverage to wider populationScope: Improve quality & quantity of health services offeredFinancial protection: Improve size of subsidies or reduce informal charges
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Access is far from universal in 54 LMIC
• Of 12 MNH interventions in a review of public data across 54 countries, family planning was the third most inequitable
*Barros, A. J. D., Ronsmans, C., et al. (2012). “Equity in maternal, newborn, and child health interventions in Countdown to 2015: a retrospective review of survey data from 54 countries”. Lancet, 379(9822), 1225-33.
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Limited financial protection is common in 51 LMIC*• 13–32% of household expenditures over
4 weeks went to healthcare• 25% poor households incurred potentially
catastrophic healthcare expenses • >40% of households used savings,
borrowed money, or sold assets to pay for care
• 41-56% of households spent 100% of health care expenditures on medicines
*Wagner, Graves, Reiss, LeCates, Zhang, Ross-Degnan. 2011. “Access to care and medicines, burden of health care expenditures, and risk protection: Results from the World Health Survey” Health Policy. 100(2-3):151-158
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Selected constructs and metrics for UHC measurementQuality of care:• Donabedian framework (structure, process, outcomes)• Investment in facility infrastructure
Financial protection:• Out-of-pocket spending on health paid for by the patient at the
point of service • Proportion of household consumption that is spent on healthcare
Equitable access:• Geographic proximity• Above or below a poverty line • Member of a wealth quintile
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Preferred characteristics in a UHC equity measure
• Program Managers• Quick, inexpensive to
collect• Easy to interpret by
managers and field staff
• Agency Headquarters • Standardized & comparable
nationally• Easy to explain to policy
makers
• Other Stakeholders• Comparable internationally
• Clients• Transparent,
trustworthy, quick application process
• Time-delimited membership
• Recognition of solidarity
• Recourse for appeal
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Pilot study: Find a good routine, monitoring equity indicator
• MPI dismissed: not feasible to collect• PPI and Wealth Index piloted in 5 countries in
2012 as part of franchise client exit interviews
• Results compared against selection criteria
Progress out of Poverty Index
(PPI)
Wealth Index (WI) Multi-
dimensional Poverty Index
(MPI)
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PPI tools
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DHS questions
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Quintile India Madag Benin DRC Mali
n=797 n=853 n=535 n=242 n=293
1 (Poorest) 27.9 2.1 3.4 0 0
2 (Poorer) 22.5 9.3 2.4 0 0
3 (Middle) 21.7 25.4 4.3 0 0.3
4 (Richer) 15.3 38.6 13.1 9.1 13.9
5 (Richest) 12.7 24.6 76.8 90.9 85.7
Results & indicator attributes
Wealth IndexRelative measureUses DHS data to compare client sample
to national wealth quintilesLow-cost because DHS data is publicly
available
PPIAbsolute measureAsset list gives likelihood that a client is
under $1.25/day poverty threshold Expensive: unique asset weights developed
for each country
Only 6% of Benin franchise clients are from the bottom 40% of the population
Threshold Clients Benin Pakistan Philippines Vietnam
$1.25/day
Franchise 19% 17% 17% 8%
National 47% 21% 18% 17%
$2.50/day
Franchise 61% 72% 51% 51%
National 75% 60% 42% 43%
19% of Benin franchise clients living under the $1.25/day threshold vs. 47% of the national population
BOTH METRICS GIVE SIMILAR RESULTS
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Selection criteria
Criteria PPI Wealth IndexEasy to Collect and Interpret
Easy to collect Easy to calculate Easy to interpret poverty threshold
Easy to collect Difficult to calculate Quintiles widely used/understood
Low Cost $20,000-$25,000 per country Requires some upkeep costs
Inexpensive Based on publicly-available DHS
Comparable to National Context
Percent of clients under poverty line easily comparable to national poverty rate
Difficult/impossible subgroup analysis e.g.: just urban, or just FP clients
Wealth quintiles accurate and validated comparison to national distribution
Easy subgroup analysis
Comparable Across Countries
Percentage of clients under $1.25/day standard across countries
Can discuss percentage of clients that fall within bottom 40%, but measure is relative to a country
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Using Wealth Index routinely
• Randomly select NHIS facilities or enrollment centers• Conduct exit surveys among clients• 20 questions about household characteristics• Adds approximately 10 minutes to each interview
• Centralized data analysis in M&E unit – takes about 8 hours
• Build capacity through a tool kit and standard syntax files• Conduct surveys on quarterly or semi-annual basis
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Uganda & Kenya: Equity targeting for program enrollment
• Uganda & Kenya voucher programs• Every client identified in the community
using a short targeting tool• Voucher expires after a year and can
only be used for one service package.
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Respondents who had ever used the HealthyBaby voucher in Uganda (2010-2011)
Poorest quintile
Poorer quintile
Middle quintile
Richer quintile
Richest quintile
0%5%
10%15%20%25%30%35%
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Does NHIS enrollment vary by wealth quintile?
Poorest Less poor Middle Less rich Richest0%
10%
20%
30%
40%
50%Women (DHS 2008)All (SHINE, 2009)
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Conclusions: Active equity targeting is key component of UHC
• Tools exist that can cost-effectively identify the poor for enrollment who, in the absence of the active identification, would not have become NHI members
• Monitor samples of clients for reporting against performance targets
• Use for beneficiary identification and enrollment
• Consider: Are other exemptions as effective to achieve the same objective?
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Thank you
Social Franchising Metrics Working Group• Bill & Melinda Gates Foundation• DKT• International Planned Parenthood Federation• Johns Hopkins• Marie Stopes International• Population Services International • Rockefeller Foundation • Population Council • University of California San Francisco • USAID• World Health Partners