Measuring Progress Toward Universal Health Coverage
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This document discusses measuring progress toward universal health coverage using the WHO's "UHC cube" framework of coverage of people, services, and costs. It summarizes that access, financial protection, and equitable access are still limited in many low and middle income countries based on various studies. The document then explores using a wealth index or Progress out of Poverty Index to routinely monitor equity in health program enrollment and compare clients to national distributions as part of measuring progress toward more equitable universal health coverage.
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Measuring Progress Toward Universal Health Coverage
- 1. Measuring to Manage Progress toward Universal Health Coverage Ben Bellows On behalf of the Social Franchise Metrics Working Group NHIS 10th Anniversary International Conference on UHC Accra
- 2. UHC is multidimensional & aspirational Access: Expand coverage to wider be? How universal can vouchers really population Despite growing evidence for vouchers’ impressive in terms Scope:impactand quality of of equity, Improve care, they financial protection remain for & specific tool to enable qualitynow aquantity of underserved groups to access priority services. However the WHO’s offered health services ‘cube’ frames progress towards UHC in terms of the share of people, services and costs covered, with Financial protection: a focus on growing these three dimensions Improve size of Given this as far as possible . understanding of subsidies UHC, UHC important can or how really be? vouchers’ contribution to reduce informal charges vouchers The first point to remember is that xi do not have to be targeted. For example, all families were eligible for the wildly successful family planning voucher programmes in Korea and Taiwan in the 60s-90s. Even among targeted voucher programmes, some Figure 1: WHO's Universal Health Coverage 'Cube' Pitfall 1: Social Health Insurance can emphasise curative care at the expense of public health and preventative care
- 3. 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), 122533.
- 4. 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
- 5. Selected constructs and metrics for UHC measurement Quality 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
- 6. 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, trustwort hy, quick application process • Time-delimited membership • Recognition of solidarity • Recourse for appeal
- 7. Pilot study: Find a good routine, monitoring equity indicator Progress out of Poverty Index (PPI) • Wealth Index (WI) Multidimensional Poverty Index (MPI) 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
- 8. PPI tools
- 10. Results & indicator attributes Wealth Index PPI Relative measure Uses DHS data to compare client sample to national wealth quintiles Low-cost because DHS data is publicly available Absolute measure Asset list gives likelihood that a client is under $1.25/day poverty threshold Expensive: unique asset weights developed for each country 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 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 Clients Benin Pakistan Philippines Vietnam $1.25/da y Franchise 19% 17% 17% 8% National 47% 21% 18% 17% Franchise 61% 72% 51% 51% National 75% 60% 42% 43% 0 3 (Middle) Threshold Only 6% of Benin franchise clients are from the bottom 40% of the population $2.50/da y 19% of Benin franchise clients living under the $1.25/day threshold vs. 47% of the national population
- 11. Selection criteria Criteria PPI Wealth Index Easy to Collect and Interpret Easy to collect Easy to collect Easy to calculate Difficult to calculate Easy to interpret poverty threshold Quintiles widely used/understood $20,000-$25,000 per country Inexpensive Requires some upkeep costs Based on publicly-available DHS Percent of clients under poverty line easily Wealth quintiles accurate and validated Low Cost Comparable to National Context comparable to national poverty rate Difficult/impossible subgroup analysis e.g.: comparison to national distribution Easy subgroup analysis just urban, or just FP clients 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
- 12. 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
- 13. 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.
- 14. Respondents who had ever used the HealthyBaby voucher in Uganda (20102011) 35% 30% 25% 20% 15% 10% 5% 0% Poorest quintile Poorer quintile Middle quintile Richer quintile Richest quintile
- 15. Does NHIS enrollment vary by wealth quintile? 50% 40% Women (DHS 2008) All (SHINE, 2009) 30% 20% 10% 0% Poorest Less poor Middle Less rich Richest
- 16. Conclusions: Active equity targeting is key component of UHC • • • • Tools exist that can cost-effectively identifying the poor who, in the absence of 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?
- 17. 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
Editor's Notes
- In spite of greater economic convergence globally, as low-income countries grow into middle-income country economies,intra-country inequalities – economic, social, and health status – risk beingexacerbated.To expand healthcare, guidance is needed to identify how to understand what we mean by expansion. Is is increasing access to the current healthcare package to new users, is it adding more or better healthcare for current beneficiaries, or is healthcare expansion to be understood as lower prices and greater protection from out-of-pocket spending on health services?
- There is certainly a case to be made that current levels of access to important maternal and neonatal services is not equitable along a socio-economic gradient. In a Lancet article published 2012, Barros and colleagues conducted a 54 country equity analysis of 12 maternal, newborn, and child health interventions using nationally representative demographic and health survey data. The MNCH services circled in red are, among the 12 MNCH services in the study, the most inequitable. Interestingly they have become the most frequently subsidized services by equity-targeted programs in the past 15 years as well.
- Worldwide, about 150 million people a year face catastrophic health-care costs because of direct payments such as user fees, while 100 million are driven below the poverty line.
- It’s said that the refocusing on “universal health coverage” is repackaging old ideas. These selected constructs and metrics are examples of commonly used approaches to measure quality of care, financial protection, and equitable access that have been used under UHC with greater purpose. These metrics are commonly collected in national household surveys, multiple indicator cluster surveys, and the Demographic and Health Surveys. In UHC, why measure equity, quality or financial protection? The answers will vary depending who you ask. Report progress toward UHC targetsManage claims costs Improve supply of servicesTarget disadvantaged populations
- Various implementers each want to know something a little different:Program Managers: assess progress; benchmark against other programs; identify areas for improvement (geographic, managers, disease areas)>>Metrics should be: Easy to collect (fast, inexpensive), Easy to interpret by managers and field staff Agency Headquarters: compare between country programs; direct new investment; advocate for funding; assess business model effectiveness>> Metrics should be: Standardized, Comparable across countries, Easy to explainOther Stakeholders: assess program effectiveness compared to stakeholder goals and competing business models>> Metrics should be: Comparable to global standard, Rigorous 4. Note that we didn’t come up with best “method,” came up with compromise. We had to balance the perspectives of program managers, agency headquarters, and other stakeholders (donors). Coming up with a compromise increases the likelihood that the metric will get used.
- MPI - didn’t meet our 4 criteria as it wasn’t easy to collect or interpretRemember that our selection criteria were:>>Easy to collect and interpret>>Low cost>>Comparable to national context>>Comparable across countries 3. The piloting process:4. Three organizations: World Health Partners, MSI, PSI5. Included wealth index and/or PPI questions into previously scheduled client exit interviews6. Countries:India – Bihar, urban and ruralMadagascar - 3 provinces, urban and ruralBenin – Surrounding Cotonou, urban and peri-urbanDRC – in Kinshasa and a provincial capital, urbanMali – in Bamako, urban7. Data analysis conducted by UCSF (May), MSI (Kenzo), and PSI (Nirali)8. Pilot objectives:Assess each metric individually for ease of implementationCompare metrics with regard to data obtained and ease of analysisPropose an aligned metricGather information to write a manual for implementation and analysis, that all franchises can use
- 1. Background (WI):WI is a relative measure of poverty that generates national wealth quintiles based on a series of mostly “yes/no” asset questions found in DHS surveys. WI is a widely accepted and validated measure of poverty.Highly relevant and accurate intra-country comparisons due to use of a relative measure. The same 20 questions are used and compared against asset/household indicators from DHS surveys to place clients in a wealth quintile distribution. Technical experts from SFMWG determined the most common 20 indicators from DHS surveys and used those in the pilots.Social franchise clients are asked the same questions and can then be compared to national distribution.Clients are easily stratified into incredibly useful subgroups (e.g. family planning users, those living with HIV/AIDS, etc.)Questions can be folded into any exit or client interview or administered independently.With WI, social franchisors can collect data, but they have little-to-no role in analysisWI analysis requires greater technical capacity and access to statistical software (e.g. Stata, SPSS), which requires centralized analysis or outside consultants to conduct analysis. For our pilots, technical experts from SFMWG conducted analysis. They created a series of .do files for streamlining the creation of each country’s WI analysis. Relatively inexpensive to expand coverage globally and to keep it updated2. Benin Finding3. Background (PPI):PPI is an absolute measure of poverty that generates the probability that an individual’s household is living under the World Bank’s $1.25/day poverty line. (an elegant and simple measure)10 country-specific questions yield an individual’s likelihood that s/he is below the $1.25 poverty line.A program’s clientele can then be evaluated and compared to national poverty distributions Questions can be folded into any exit or client interview or administered independentlyGrameen Foundation and Schreiner, generate the country-specific ten-question surveys and poverty probability tables for PPI. Grameen takes ownership over the development of the measure, but, once completed, they post all methodology and make the tools freely available online.Using the “look-up tables” provided by Grameen Foundation, anyone with a pencil and paper can calculate an individual’s poverty likelihood. The community-based provider, the regional manager, and the M&E officer at global headquarters can all derive usefulness from PPI analysis.The simplicity of PPI makes it a powerful reporting tool – it is easy to understand the statement, “85% of our clients are below the global poverty line.”PPI delivers a high degree of global context – because it uses the World Bank’s global $1.25 poverty line – while using country-specific questions. Once a PPI is developed for a country, anyone can freely access the tool.4. Benin FindingBoth metrics give similar resultsWe called out Benin b/c it’s where both metrics were pilotedThese programs are blindedIf anyone questions the findings, note that some programs are trying to maximize quality or cost recovery, which can account for not reaching the poorOne goal alone is not enough to evaluate a program – remember, we have 5
- Remember our 4 criteria:Easy to collect and interpret – data analysis is more difficult for WI; centralized data analysis or a system of quality control is requiredComparable across Countries – both metrics met this criterion; although it is more challenging to explain WI since it’s a relative measure of povertyOur biggest challenges were with cost and comparability within a national context.Cost:The development of each country PPI and every subsequent update will cost, according to Grameen Foundation, between $20,000-25,000 (USD). Approximately $10,000 -15,000 is for data collection, program management, and field testing costs, and approximately $10,000 is for Mark Schreiner’s group for statistical analysis. If social franchisors collect data themselves, only this portion of the cost would be saved. Statistical analysis will still cost.NOTE: Once a PPI is developed for a country, all the tools, the tables, and the construction documentation is made available to the public at no cost. If a national program were to take on PPI as their preferred equity measure, its constituent 10 indicators would need to be updated every 4-5 years at the same or higher cost. How could this be fixed?NOTE: Grameen has mostly used a reactive funding model for their existing PPIs – they’ve often waited until funders approach them about creating a country’s PPI. However, Grameen has reported that they plan on shifting to a more proactive approach to finding funders for future PPIs. If (a big “if”) outside funders are found to create PPIs for all countries, there would be great cost savings for our member organizations.One additional, potential area for cost sharing is the creation of “coalitions” to pool the resources of all PPI users in a specific country. For example, in Indonesia, Grameen reports 10 separate organizations using PPI – thus, if all if those organizations formed a funding coalition, the average contribution per organization would be $2,000.Many countries where social franchises and our member organizations (MSI/PSI/DKT combined represent the greatest geographic distribution) have a limited number of users – decreasing the likelihood of developing a robust coalition to fund each nation.Example: India, Pakistan, Peru, Ecuador all have over 10 separate organizations using PPI in those countries. Yet Kenya, Mali, Nigeria, Sri Lanka, and Costa Rica have an average of under 1.5 organizations using PPI in those countries. PPI scorecards currently exist for 45 countries, with a couple more in development (e.g. Zambia). Of the 35 countries with active social franchises, PPI is currently unavailable in 11: Burundi, Cameroon, Democratic Republic of Congo, Laos, Madagascar, Mozambique, Somaliland, Sudan, Togo, Zambia, Zimbabwe.Of the countries with an MSI presence, PPI is currently unavailable in 6: Zambia, Zimbabwe, Madagascar, Mongolia, Papua New Guinea, South SudanOf the countries with a PSI presence, PPI is currently unavailable in 22: Angola, Belize, Cameroon, Central African Republic, Costa Rica, Cote D’Ivoire, Democratic Republic of Congo, Guinea, Laos, Lesotho, Liberia, Madagascar, Mozambique, Namibia, Panama, Paraguay, Somaliland, Swaziland, Thailand, Togo, Zambia, ZimbabweOf the countries with a DKT presence, PPI is currently unavailable in 6: DRC, Malaysia, Mozambique, Sudan, Thailand, and Turkey5. Comparable to a National ContextPPI data can only be stratified by populations that exist in the source household income survey data. If an indicator is present (e.g. urban vs. rural), then PPI allows for the ability to compare the urban/rural proportion of your clients living under the $1.25 poverty line to the national urban/rural proportion living under $1.25.However, because the source data for PPI are not standardized (many countries income/expenditure surveys vary from others), not every country will have the same subgroup breakdowns available for analysis. Grameen also reported that because income/expenditure surveys are the source material for PPI, certain demographic subgroups of interest to health program, such as family planning users, are not available in the source data for PPI, meaning stratification would not be possible along for those groups.
- Randomly select social franchises – likely via PPSConduct exit surveys – N is approximately 400 (has a measurement error of approx 10%)But sample wouldn’t be powered to equity, it would likely be powered to something else, like FP usersA sample of 400 also doesn’t account for clustering. If you want to stratify, you’d need to increase the sample sizeNeed sample questions hereData analysisPCA – develop wealth quintiles using only the 20 questions from the DHS, capture the mean and standard deviation for each of the 20 variablesStandardize to the client exit survey sampleDivide the client exit survey sample into the same quintiles (using the DHS cutoffs)Lessons learned:Certain wording in some of the questions was difficult to translate into locally-understandable terms (e.g. “hectares”).The survey questions for both PPI and WI were not hard to ask, but given the sensitive nature of income- and asset-related questions, more training may be required for providers.Local NHIS offices may lack the technical expertise or resources available to conduct the level of analysis necessary to derive usefulness from the Wealth Index tool. For Wealth Index, it is currently designed to have the same 20 questions for every country – all of which are assets and household characteristics. However, we recently learned that Macro has already completed principle component analysis and generated factor weights for nearly every DHS country. Using their work would save time and money, but it would mean adding 10-15 questions for most countries. All questions would still be asset and household characteristic questions from DHS surveys. We are waiting for a few more pieces of information before deciding what to do.The alternative to using Macro’s prepared factor weights is to stay the course with our selected 20 questions and hire a technical consultant to generate the factor weight sheets for all of our countries. It’s true, 30-35 is a lot compared to 10 questions. The “Yes/No” nature of many of the asset indicators is the saving grace – the pilots from equity found that the 20-question survey did not add significantly more time than the 10- question guide. Looking over the sample factor weight sheets that we have from Macro, it looks like most of the additional questions are “Yes/No” asset ownership questions. Wealth Index offers the capacity to stratify an analysis by subgroups. For example, say you wanted to know how wealthy your program’s family planning clients were, relative to other family planning users in the country. Because those indicators are available in the DHS survey, it would be relatively easy to construct nationally representative wealth quintiles for family planning users and then analyze how your program’s family planning clients fit into that distribution. Further subgroup stratifications are also possible, provided the indicators are present in DHS surveys. If you want to know poverty status of rural family planning users in a specific region of a country, Wealth Index has the capacity to do this analysis. DHS surveys often contain standardized indicators across different countries.
- For today’s talk I want to focus on equitable access or “equity” in health insurance coverage as having insurance is a major contributor to seeking or accessing health services. In Ghana, according to a recent DHS survey, NHIS enrollment varies significantly by wealth quintile.