WPS8577 Policy Research Working Paper 8577 The 2018 Health Equity and Financial Protection Indicators Database Overview and Insights Adam Wagstaff Patrick Eozenou Sven Neelsen Marc Smitz Development Research Group & Health Nutrition and Population Global Practice October 2018 Policy Research Working Paper 8577 Abstract The 2018 database on Health Equity and Financial Protec- equity side and the financial protection side. The paper tion indicators provides data on equity in the delivery of also provides illustrative uses of the database, including health service interventions and health outcomes, and on the extent of and trends in inequity in maternal and child financial protection in health. This paper provides a brief health intervention coverage, the extent of inequities in history of the database, gives an overview of the contents women’s cancer screening and inpatient care utilization, of the 2018 version of the database, and then gets into and trends and inequalities in the incidence of catastrophic the details of the construction of its two sides—the health health expenditures. This paper is a product of the Development Research Group and the Health Nutrition and Population Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank. org/research. The lead author may be contacted at awagstaff@worldbank.org. The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent. Produced by the Research Support Team The 2018 Health Equity and Financial Protection Indicators Database: Overview and Insights Adam Wagstaffa*, Patrick Eozenoub, Sven Neelsenb, and Marc Smitzb a Development Research Group, The World Bank, 1818 H Street, NW, Washington DC 20433, USA b Health, Nutrition and Population Global Practice, The World Bank, 1818 H Street, NW, Washington DC 20433, USA Keywords: Health indicators; health equity; health and inequality; out-of-pocket health expenditures; financial protection; health and poverty; millennium development goals; sustainable development goals; universal health coverage; non-communicable diseases JEL codes: I1, I3, J13 2    Acknowledgments We are indebted to the task team leaders of the 2000, 2007 and 2012 databases, Davidson Gwatkin and Caryn Bredenkamp, without whose efforts the 2018 database would not have been possible. We are grateful to Leander Buisman who assisted in the processing of the microdata, to Caryn Bredenkamp, Tania Dmytraczenko, Olivier Dupriez, Rose Mungai, Minh Cong Nguyen, Marco Ranzani, Aparnaa Somanathan, Ajay Tandon and Joao Pedro Wagner De Azevedo who provided access to microdata, and to Qinghua Zhao for help with PovcalNet. We are grateful to Rantimi Adetunji, Nastassha Arreza, Amanda Kerr, Lingrui Liu, Jie Ren and Margarida Rodrigues for their tireless research assistance. We acknowledge the contributions of our collaborators on several projects whose ideas helped shape the 2018 database, including Daniel Cotlear, Tania Dmytraczenko and Owen Smith at the World Bank, Gabriela Flores at WHO Geneva, and Gisele Almeida at PAHO/WHO. Finally, we are grateful to Michele Gragnolati, Christoph Kurowski and Magnus Lindelow for their support, and to Tony Fujs, Karthik Ramanathan and Tariq Khokhar for engineering the online products. The findings, interpretations and conclusions expressed in this paper are entirely those of the authors, and do not necessarily represent the views of the World Bank, its Executive Directors, or the governments of the countries they represent. Accessing the database A portal version of the 2018 database with visualization functionality can be accessed at http://datatopics.worldbank.org/health-equity-and-financial-protection/. The data set itself can be accessed and downloaded, indicator by indicator, or in its entirety, from https://datacatalog.worldbank.org/node/142861 from which model Stata ‘do’ files can be downloaded to replicate the datapoints in the HEFPI data set. Citing the database The reference citation for the data is: Wagstaff, Adam, Eozenou, Patrick, Neelsen, Sven and Smitz, Marc. 2018. The Health Equity and Financial Protection Indicators Database 2018. World Bank: Washington, DC. * Corresponding author: Adam Wagstaff. Development Research Group, The World Bank, 1818 H Street, NW, Washington DC 20433, USA. Tel: +1 202 473 0566, awagstaff@worldbank.org   3    List of Abbreviations ANC Antenatal care ARI Acute respiratory infection BCG Bacillus Calmette–Guérin BMI Body Mass Index CATA Catastrophic (health) expenditures CPI Consumer price index CWIQ Core Welfare Indicators Questionnaire DDH World Bank Development Data Hub DHS Demographic and Health Survey E123 Enquêtes 1-2-3 EAPPOV East Asia & Pacific harmonized household survey collection ECAPOV Europe & Central Asia harmonized household survey collection ECHP European Community Household Panel EHIS European Health Interview Survey EUROSTAT-HBS Eurostat Household Budget Survey FP Financial protection HBS Household Budget Survey HEFPI Health Equity and Financial Protection Indicators HEIDE Household Expenditure and Income Data for Transitional Economies HIES Household Income & Expenditure Survey ICP International Comparison Program IFG Impaired fasting glycaemia IFLS Indonesia Family Life Survey IMPOV Impoverishing (health) expenditures IMR Infant mortality rate IP Inpatient ISSP International Social Survey Program ITN Insecticide treated bed net LAM Lactational amenorrhea method LCUs Local currency units LIS Luxembourg Income Study LMIC Low and middle-income country LSMS Living Standards Measurement Study LWS Luxembourg Wealth Study MCH Maternal and child health MCSS Multi-Country Survey Study on Health and Responsiveness MDGs Millennium Development Goals MICS Multiple Indicator Cluster Survey MMR Measles-Mumps-Rubella MNAPOV Middle East & North Africa harmonized household survey collection NCD Noncommunicable disease OECD Organization for Economic Co-operation and Development ORS Oral Rehydration Salts 4    PAP Cervical cancer screening PCA Principal components analysis PL Poverty Line PPP Purchasing power parity RHS Reproductive Health Survey SAGE Study on global AGEing and adult health SARLF South Asia Labor Flagship harmonized survey collection SARMD South Asia harmonized household survey collection SBA Skilled birth attendance SDGs Sustainable Development Goals SEDLAC Socio-Economic Database for Latin America and the Caribbean SHES Standardized household expenditure surveys SHIP Sub-Saharan Africa harmonized household survey collection STEPS Stepwise Approach to Surveillance U5MR Under-5 mortality rate UHC Universal Health Coverage UK-GHS United Kingdom General Household Survey UN United Nations UNICEF United Nations Children's Fund UNICO Universal Health Coverage Study Series US$ United States Dollars US-NHIS United States National Health Interview Survey WB World Bank WDI World Development Indicators WEO World Economic Outlook WHO World Health Organization WHS World Health Survey 5    Introduction Among the many shifts of emphasis that have been evident in global health over the last 25 years or so, two stand out. One is the concern over equity: there has been a growing realization that the poor continue to lag behind the better off in receipt of key health interventions and health outcomes, and that international goals couched in terms of population averages could perfectly possibly be met without faster progress among the poor. The other is a concern over out-of-pocket health spending: getting people the health interventions they need is one part of the overall goal of any health system; the other is to ensure that families do not end up impoverished or otherwise suffer financial hardship by paying large sums of money out-of-pocket to ensure family members get the services they need. Neither of these concerns was reflected in the Millennium Development Goals (MDGs), which focused on population averages and made no mention of out-of-pocket expenses. By contrast, both are reflected in the Sustainable Development Goals (SDGs): the SDG equity commitment to ‘leave no one behind’ calls for data that are disaggregated by inter alia living standards; and the SDG commitment to universal health coverage (UHC) explicitly includes a commitment to ‘financial risk protection’. This paper provides an overview of an international database on Health Equity and Financial Protection Indicators (HEFPI). The data set provides data on the delivery of health service interventions, health outcomes, and ‘financial protection’ in health, at both the population level and for subpopulations defined by household living standards. The data are computed from well-known household surveys that have been conducted by, or in partnership with, national governments, such as the Demographic and Health Survey (DHS) and the Living Standards Measurement Study (LSMS). None of our data comes from official reports by national governments, in part because such data do not lend themselves to disaggregation by household living standards, and in part because of concerns about accuracy, especially where governments do not face incentives to report accurate numbers (Murray et al. 2003; Lim et al. 2008; Sandefur and Glassman 2015; World Bank n.d.). Where we have been able to access the raw microdata from household surveys, we have done so. 6    Sometimes this was because there was no estimate reported in the survey report or online tool. But often it was because indicator definitions can vary from one survey ‘family’ to another, and sometimes even within a survey family, either over time or between the survey report and the online tool. Although we have re-analyzed the raw microdata, the estimates we report are simply harmonized direct (re)calculations of the quantities reported in the survey reports and online tools. In line with the growing concerns about the use of modeling in global health data sets (AbouZahr et al. 2017; Boerma et al. 2018), we do not predict missing data – we do not produce forecasts for country-years where there is no survey.1 The downside is that our data set is, as a result, full of gaps. The upside is that, insofar as the surveys we use are reliable, differences over time or across countries ought to reflect reality rather than modeling assumptions; conversely, when real changes occur on the ground, they ought to get reflected in our numbers, rather than being smoothed away by the modeling process. Our data set is freely downloadable, and a data visualization tool is also available.2 To ensure our data are reproducible, and in line with GATHER (Guidelines for Accurate and Transparent Health Estimates Reporting) (Stevens et al. 2016), we document thoroughly our methods and highlight the differences between our definitions and others’, and provide the essential computer code that ought to make it possible for anyone trying to reproduce our results to do so.3 The GATHER table showing pages where the various parts of the database construction process are recorded, is included as Table A2 in the Annex. The HEFPI database can be used to analyze a variety of topics. With the database one can see not just how the population fares but also how different ‘wealth’ or income groups fare on the indicators used in global goals, such as the MDGs and the SDGs: the database allows ‘snapshot’ comparisons as well as comparisons of trends (cf. Wagstaff 2002; Victora et al. 2003; Wagstaff et al. 2014). One can zoom in on a specific topic, such as child malnutrition, and see whether inequalities                                                                1 Nor do we replace estimates directly calculated from the survey microdata by modeled estimates. 2 See frontmatter. 3 See frontmatter. 7    have narrowed over time (cf. Bredenkamp et al. 2014). One can document changes and differences in ‘financial protection’ in health; one can see, for example, whether the incidence of ‘catastrophic’ and ‘impoverishing’ health expenditures varies across countries (cf. Wagstaff and Eozenou 2014) or has changed over time in a specific country, before or after a reform, or relative to trends in neighboring countries. One can analyze (equality in) service coverage and financial protection simultaneously under the UHC umbrella (cf. Wagstaff et al. 2015; Wagstaff et al. 2016). More generally, the database is likely to be useful for analyzing any health system regarding how well it does in terms of delivering health services and improving health outcomes, but not compromising families’ ability to pay for goods and services other than health care. This paper provides a brief history of the HEFPI database, gives an overview of the contents of the 2018 version of the database, and then gets into the detail of the construction of its two sides – the health equity side, and the financial protection side. It also provides illustrative uses of the data set. A brief history of the HEFPI database The 2018 HEFPI database is, in effect, the fourth in a series of similar World Bank databases, all of which draw exclusively on data from household surveys – see Figure 1. The first two (Gwatkin et al. 2000; Gwatkin et al. 2007) showed gaps within and between countries on various indicators of service coverage and health outcomes in the MDG areas of maternal and child health (MCH), and communicable disease. The 2000 data set covered just 42 countries and drew data from just 42 surveys in the Demographic and Health Survey (DHS) family. More DHS surveys were added in 2007. The 2012 database (Bredenkamp et al. 2012b, c, d, e, a) also included data from UNICEF’s Multiple Indicator Cluster Survey (MICS) and the World Health Organization’s (WHO’s) World Health Survey (WHS). Data on service coverage and health outcomes in all three databases were presented for the population and for ‘wealth’ quintiles, the latter being formed by applying principal components analysis (PCA) to a variety of indicators capturing the ownership of assets (e.g. car and 8    television) and the characteristics of the household’s home (e.g. type of floor and roof) as proposed by Filmer and Pritchett (1999, 2001). The 2012 database also expanded the range of the health data: it included indicators of adult health, including noncommunicable disease (NCD) indicators, covering: health status, e.g. arthritis; risky behavior, e.g. smoking; preventive care, e.g. cervical cancer screening; and receipt of curative care, e.g. inpatient admissions. The 2012 database also expanded the range of countries, going from 95 countries in the developing world to 109 countries at all levels of development. Finally, the 2012 database expanded the scope of the exercise from just health equity to health equity and financial protection: the new indicators included covered both catastrophic health expenditures and impoverishing health expenditures (cf. Wagstaff and van Doorslaer 2003). Figure 1: Evolution of the World Bank’s Data on Health Equity and Financial Protection 2000 2007 2012 2018 42 countries – all  56 countries – all  109 countries – 193 countries – developing  developing  incl. some high‐ goal is global  countries countries income coverage 42 surveys – all  95 surveys – all  285 surveys – 1,654 surveys – DHS DHS DHS, MICS &  DHS, MICS, WHS,  WHS LSMS, HBS, etc.  34 indicators – all  115 indicators – 73 indicators – 51 indicators – services or  all services or  incl. 4 FP  incl. more NCD  outcomes outcomes indicators  and FP indicators Focus on equity  Focus on equity  Not just MDG  Levels of and  in MDG  in MDG  indicators – some  equity in MDG  indicators indicators NCD and FP  and SDG  indicators indicators, incl.  FP The 2018 edition of the HEFPI database continues this broadening-out. In addition to the ‘traditional’ MCH and communicable disease indicators from the DHS and MICS, it includes more data on adult NCD indicators, drawing on data from the WHS, the DHS, the Stepwise Approach to Surveillance (STEPS) surveys, and many other regional and national surveys. In addition, the database expands dramatically the number of financial protection datapoints – from 54 to 563. This expansion builds on World Bank research on monitoring progress towards UHC – initially in Latin 9    America (with PAHO) (Wagstaff et al. 2015), then in the Universal Health Coverage Study Series (UNICO) countries (Wagstaff et al. 2016), and more recently globally (with WHO) (Wagstaff et al. 2018a; Wagstaff et al. 2018b). The 2018 HEFPI data set includes the datapoints contributed by the World Bank to the joint 2017 WHO-World Bank data set (80% of the total), but also many others generated (by the World Bank) since. With the new datapoints, the financial protection part of the 2018 HEFPI data set is larger than the 2017 WHO-World Bank data set and covers more countries. On both the health equity and financial protection sides of the HEFPI database, the country coverage has expanded as well – from 109 countries in 2012 to 193. The number of household surveys used has increased even more dramatically – from 285 in 2012 to over 1,600. The datapoints in the 2018 HEFPI data set, like those in the previous three data sets, have, wherever possible, been computed from the original microdata. For some surveys, this was not possible, and we have had to make do either with published reports (as in the case, for example, of the STEPS surveys) or with studies by researchers who have used the same methods as us (see e.g. Van Doorslaer and Masseria 2004; Van Doorslaer et al. 2006a). One goal behind re-analyzing the original microdata was to ensure maximum consistency across surveys, countries and years. Sometimes, this means that our data are not identical to those on the websites and in the reports of the organizations that produced the microdata. For example, we use the same (2006) standards for childhood stunting and underweight in all surveys. The DHS reports, by contrast, use whatever standard was in force at the time the survey was done. In addition to ensuring consistency, there was a second reason to re-analyze the microdata: to ensure we have data for different ‘wealth’ or income groups, and a summary measure of inequality, namely the concentration index and its standard error (Kakwani et al. 1997). Overview of the 2018 HEFPI database Figure 2 gives an overview of the 2018 HEFPI database in terms of indicators. The darker shaded boxes contain indicators used already in the MDGs. The lighter shaded boxes contain 10    indicators that did not feature in the MDGs, but do feature in, or are consistent with, the SDGs. To make way for the newer indicators, and for the extensive financial protection data, some of the MDG-era indicators included in previous versions of the HEFPI database have been retired. The retained MDG-era indicators feature prominently in the official and supplemental MDG monitoring indicators (Wagstaff and Claeson 2004), as well as in indicator lists for current global goal- monitoring exercises, such as the ‘Countdown to 2030 for Maternal, Newborn, and Child Survival’ (cf. Victora et al. 2015) and the SDGs (e.g. SDG target 2 on ending hunger and improving nutrition and SDG 3.8 on achieving UHC).4 The SDG-era indicators also feature in current global goal- monitoring exercises, including broad exercises like the SDGs, as well as in more specific exercises like the UN General Assembly’s 2011 Political Declaration on NCDs.5                                                                4The SDG indicators are listed at https://unstats.un.org/sdgs/indicators/indicators-list/. 5The indicators being used to monitor progress on the NCDs are listed at http://www.who.int/nmh/global_monitoring_framework/en/. 11    Figure 2: Structure of the 2018 HEFPI database Health Equity and Financial Protection Health equity Financial Protection Catastrophic expenditures Impoverishing Service coverage Health outcomes (CATA) expenditures (IMPOV) IMPOV $1.90-a-day, and Prevention Treatment MDG era SDG era CATA 10% other $ PLs IMPOV 60% median per MDG era SDG era MDG era SDG era Infant mortality (IMR) Adult BMI CATA25% capita consumption Cervical cancer screening Skilled birth attendance Under-five mortality Antenatal visits (4+) Inpatient admissions Adult overweight (PAP) (SBA) (U5MR) Treatment of child with Treatment for Stunting among Child immunization Breast cancer screening acute respiratory infection Adult obesity hypertension under-5s (ARI) Sleeping under insecticide- Treatment of child with Treatment for Underweight among Hypertension testing Adult height treated bednet diarrhea diabetes under-5s Prevalence of raised blood Contraception prevalence Cholesterol testing HIV prevalence pressure Raised blood glucose and Family planning demands Diabetes testing impaired fasting glycaemia satisfied (IFG) Condom use during risky intercourse Figure 3 shows the surveys used in the 2018 HEFPI database, where the size of each block is proportional to the fraction of total datapoints contributed by the survey in question. In contrast to the 2000 database, which was based entirely on DHS surveys, the 2018 database uses a variety of different surveys, albeit still, for the most part, highly standardized surveys from a few multi- country programs. For the MDG-era health service coverage and health outcome indicators, the DHS accounts for the majority of datapoints, but the MICS and the WHS are also important sources, contributing over 30% of the MDG-era service coverage datapoints. For the SDG-era health service coverage and health outcome indicators, the DHS is also an important source, but other sources are also important. These include the STEPS and the WHS, as well as the European Community Household Panel (ECHP), the International Social Survey Program (ISSP), the Eurobarometer, and 12    the European Health Interview Survey (EHIS). The data for the two financial protection indicators (catastrophic and impoverishing payments) come from household income and expenditure surveys (HIES), household budget surveys (HBS), or multipurpose household surveys like the World Bank’s Living Standards Measurement Study (LSMS). Very few come from a highly standardized multi- country program – the LSMS is an exception. Figure 3: Surveys used in the 2018 HEFPI database Figure 4 shows the geographic coverage of the 2018 HEFPI database. Darker shaded countries have data on more indicators, or more years of data, or both. Indonesia and Peru have a large number of datapoints. In both countries, the datapoints come not only from multi-country initiatives like the DHS, which in Peru’s case includes a continuous DHS (ENDES), but also from country-specific surveys like the SUSENAS and the Indonesia Family Life Survey (IFLS) in the case of Indonesia, and an annual HIES (ENAHO) in the case of Peru. The shade of the country on the map also reflects variation across countries in microdata access for World Bank staff: countries like Ireland, Peru, South Africa, the United Kingdom and the United States have strong open access policies guaranteeing access to bona fide researchers from around the world. Many European countries and some other OECD countries, as well as many developing countries such as China and several countries in the Middle East, have tighter rules that make it hard if not impossible for researchers not affiliated with a national institution to access microdata. Some European countries 13    restrict access even to microdata that have been harmonized and completely anonymized by the European Union’s statistical agency EUROSTAT. Some countries provide access but charge a fee and/or require the researcher conduct the analysis onsite. Figure 4: Geographic coverage of the 2018 HEFPI database Health equity data In this section, we report details of the health equity part of the 2018 HEFPI data set, listing the indicators included, the reasons for including them, their sources and definitions, how they were computed, how we derived data for different subpopulations, our quality checks, and lastly some illustrations of the use of the data. Indicators included The indicators in the health equity part of the 2018 HEFPI database are listed in Tables 1, 2 and 3 and shown in Figure 2. They are commonly used in international monitoring exercises and global health publications. We were guided in our choice of indicators by the MDGs (cf. Wagstaff and 14    Claeson 2004), the ‘Countdown to 2030 for Maternal, Newborn, and Child Survival’ (cf. Victora et al. 2015), the SDGs,6 the STEPS7 indicators and other NCD indicators used to track progress relating to the UN General Assembly’s 2011 Political Declaration on NCDs,8 and the ongoing discussions relating to the measurement of service coverage in the context of UHC9 (cf. Boerma et al. 2014a; Boerma et al. 2014b; Hogan et al. 2018). We include 18 indicators of health service utilization, of which 12 are preventative and 6 curative. The other 28 indicators are health and anthropometric outcomes for both adults and children. Data search and data sources We set out to assemble as large a data set as possible of household surveys. To this end, we undertook inventories of the microdata catalogs of the International Household Survey Network10 and the World Bank,11 the Institute of Health Metrics’ Global Health Data Exchange,12 and several household survey collections. We also searched for household surveys online. This search identified 1,153 surveys from 193 countries – see Figure 5. The surveys include country-specific surveys as well as multi-country household survey collections, notably the DHS, the ECHP, the Eurobarometer, the European Health Interview Survey (EHIS), the LSMS, the Multi-Country Survey Study on Health and Responsiveness (MCSS), the MICS, the Reproductive Health Survey (RHS), the STEPS, the World Bank’s Europe and Central Asia Household Health Survey, and the WHS. Table 4 summarizes the key details of these survey ‘families’. For 863 of the surveys identified, we were able                                                                6 The SDG indicators are listed at https://unstats.un.org/sdgs/indicators/indicators-list/. 7 STEPS survey reports and fact sheets are available at http://www.who.int/ncds/surveillance/steps/en/. 8 The indicators being used to monitor progress on the NCDs are listed at http://www.who.int/nmh/global_monitoring_framework/en/. 9 See also the discussions of UHC service coverage indicators at http://www.who.int/healthinfo/universal_health_coverage/UHC_Meeting_Nov2015_Report.pdf. 10 http://www.ihsn.org/. 11 http://microdata.worldbank.org/index.php/home. 12 http://ghdx.healthdata.org/.     15    to obtain the microdata and compute by ourselves the numbers included in the database. For the remaining 290 surveys (e.g. the STEPS), the microdata were inaccessible. In this case, we extracted information from survey reports, and in a few cases from research papers authored by researchers who had used the same methods we use.13 All datapoints in the data set are labeled with their data source in the referenceid variable – see annex for naming conventions. Figure 5: Data sources for the health equity part of the 2018 HEFPI database 1,153 surveys  identified 227 DHS 118 STEPS 114 MICS 101 ECHP 70 WHS 43 Eurobarometer 39 EHIS 35 MCSS 352 other national or  32 ISSP multicountry surveys 863 accessible and  290 inaccessible, summary data taken from publication, e.g. STEPS microdata analyzed                                                                13 E.g. van Doorslaer and Masseria (2004) and van Doorslaer et al. (2006a). 16    Table 1: Health equity: Service coverage (prevention) Indicator Definition Main Data Source Pregnancies with 4 or more Percentage of most recent births in last two years with at least 4 antenatal care visits (women age DHS, MICS, WHS antenatal care visits (% of total) 18-49 at the time of the survey) Immunization, full (% of children Percentage of children age 15-23 months who received Bacillus Calmette–Guérin (BCG), DHS, MICS ages 15-23 months) measles/Measles-Mumps-Rubella (MMR), 3 doses of polio (excluding polio given at birth) and 3 doses of diphtheria-pertussis-tetanus (DPT)/Pentavalent vaccinations, either verified by vaccination card or by recall of respondent Immunization, measles (% of Percentage of children age 15-23 months who received measles or MMR vaccination, either DHS, MICS, WHS children ages 15-23 months) verified by vaccination card or by recall of respondent Use of insecticide-treated bed nets Percentage of children under 5 who slept under an insecticide treated bed net (ITN) the night DHS, MICS (% of under-5 population) before the survey. A bed net is considered treated if it a) is a long-lasting treated net, b) a pre- treated net that was purchased or soaked in insecticides less than 12 months ago, or c) a non-pre- MDG treated net which was soaked in insecticides less than 12 months ago. MICS 2 data points (MICS surveys pre-2002) consider bed nets treated if they were ever treated.) Contraceptive prevalence, modern Percentage of women age 15-49 who are married or live in union and currently use a modern DHS, MICS methods (% of women ages 15-49) method of contraception. Modern methods are defined as female sterilization, male sterilization, the contraceptive pill, intrauterine contraceptive device (IUD), injectables, implants, female condom, male condom, diaphragm, contraceptive foam and contraceptive jelly, lactational amenorrhea method (LAM), emergency contraception, country-specific modern methods and other modern contraceptive methods respondent mentioned. Unmet need for contraception (% of Percentage of women age 15-49 who are married or live in union who do not want to become DHS women ages 15-49) pregnant but are not using contraception (revised definition by Bradley et al. (2012)).14 Condom use in last intercourse (% Percentage of women age 18-49 who had more than one sexual partner in the last 12 months and DHS, MICS,15 WHS of female at risk population) used a condom during last intercourse Pap smear in last 5 years Percentage of women who received a pap smear in the last 5 years (preferably age 30-49 but age DHS, EHIS, groups may vary) Eurobarometer, STEPS, US-NHIS, WHS SDG Mammography in last 2 years Percentage of women who received a mammogram in the last 2 years (preferably age 50-69 but DHS, EHIS, age groups may vary) Eurobarometer, SAGE, STEPS, US- NHIS, WHS                                                                14 A flowchart showing how our unmet need variable is constructed is available at https://dhsprogram.com/topics/upload/Figure-2-Revised-unmet-need-definition-flowchart- Bradley-et-al-AS25.pdf. Further methodological details and code are available at https://dhsprogram.com/topics/Unmet-Need.cfm. 15 Our condom use in last intercourse data do not include points from the MICS 3 wave because unlike subsequent waves, MICS 3 only collected sexual intercourse data for women aged 15-24. 17    Indicator Definition Main Data Source Blood pressure measured in last 12 Percentage of population over 18 having their blood pressure measured by health professional in Eurobarometer months (% of population age 18 and the last year older) Cholesterol measured in last five Percentage of adult population at risk (overweight or obese and older than 20, male and older EHIS years (% of population at risk of than 34) having their cholesterol levels measured in the last 5 years high cholesterol) Blood sugar measured in last 5 Percentage of population aged 40-69 at increased risk of diabetes (overweight, obese) having their EHIS years (% of population at risk of blood sugar measured in the last 5 years diabetes) 18    Table 2: Health equity: Service coverage (treatment) Indicator Definition Main Data Source Births attended by skilled health Percentage of most recent births in last 2 years attended by any skilled health personnel (women DHS, MICS, WHS staff (% of total) age 18-49 at the time of the survey). Definition of skilled varies by country and survey but always includes doctor, nurse, midwife and auxiliary midwife). Acute respiratory infections treated Percentage of children under 5 with cough and rapid breathing in the two weeks preceding the DHS, MICS (% of children under 5 with cough survey (DHS, WHS) who had a consultation with a formal health care provider (excluding MDG and rapid breathing) pharmacies and visits to ‘other’ health care providers). MICS data points use sample of children under 5 with cough and rapid breathing in the 2 weeks preceding the survey which originated from the chest. The definition of formal health care providers varies by country and data source. Diarrhea treatment (% of children Percentage of children under 5 with diarrhea in the 2 weeks before the survey who were given DHS, MICS under 5 with diarrhea who received oral rehydration salts (ORS) TREATMENT ORS) Inpatient care use in last 12 months Percentage of population age 18 and older using inpatient care in the last 12 months DHS, ECHP, (% of population age 18 and older) ENAHO, Eurobarometer, EHIS, ISSP, LSMS, MCSS, US-NHIS, SLC, SUSENAS, UK- SDG GHS, WHS Treated for high blood pressure (% Percentage of adult population being treated for high blood pressure (age-range may vary) Eurobarometer, of adult population) EHIS Treated for raised blood glucose or Percentage of adult population being treated for raised blood glucose or diabetes (age-range may DHS, diabetes (% of adult population) vary) Eurobarometer, EHIS 19    Table 3: Health equity: Outcomes Indicator Definition Main Data Source Mortality rate, infant (deaths per Deaths of children before their 1st birthday per 1,000 live births. Sample: children born up to 5 years DHS 1,000 live births) before the survey for full population mortality estimates, and up to 10 years before the survey for wealth quintile specific mortality estimates Mortality rate, under-5 (deaths Deaths of children before their 5th birthday per 1,000 live births. Sample: children born up to 5 years DHS per 1,000 live births) before the survey for full population mortality estimates, and up to 10 years before the survey for wealth quintile specific mortality estimates MDG Prevalence of stunting, height for Percentage of children under 5 with a Height-for-Age z-score <-2 standard deviations from the DHS, MICS age (% of children under 5) reference median (z-score calculated using WHO 2006 Child Growth Standards) Prevalence of underweight, Percentage of children under 5 with a Weight-for-Age z-score <-2 standard deviations from the DHS, MICS weight for age (% of children reference median (z-score calculated using WHO 2006 Child Growth Standards) under 5) Prevalence of HIV, total (% of Percentage of population age 15-49 who had blood tests that are positive for HIV1 or HIV2 DHS population ages 15-49) Height in meters, adults (age 18 Mean height in meters of population aged 18 and older ECHP, ISSP, WHS and older) Height in meters, men (age 18 Mean height in meters of males aged 18 and older ECHP, ISSP, WHS and older) Height in meters, women (age 18 Mean height in meters of females aged 18 and older ECHP, ISSP, WHS and older) Height in meters, women (age 15- Mean height in meters of females aged 15-49 DHS 49) BMI, adults (age 18 and older) Mean BMI of population aged 18 or older ECHP, EHIS, ISSP, STEPS, WHS BMI, men (age 18 and older) Mean BMI of male population aged 18 or older ECHP, EHIS, ISSP, STEPS, WHS BMI, women (age 18 and older) Mean BMI of female population aged 18 or older ECHP, EHIS, ISSP, STEPS, WHS SDG BMI, women (age 15-49) Mean BMI of female population aged 15-49 (excludes currently pregnant women and women having DHS given birth in the three months preceding the survey) Prevalence of overweight, BMI (% Percentage of population aged 18 or older with BMI above 25 ECHP, EHIS, ISSP, of population 18 and older) STEPS, WHS Prevalence of overweight among Percentage of male population aged 18 or older with BMI above 25 ECHP, EHIS, ISSP, men, BMI (% of males 18 and STEPS, WHS older) Prevalence of overweight among Percentage of female population aged 18 or older with BMI above 25 ECHP, EHIS, ISSP, women, BMI (% of females age 18 STEPS, WHS and older) Prevalence of overweight among Percentage of female population aged 15-49 with BMI above 25 (excludes currently pregnant women DHS women, BMI (% of females age and women having given birth in the three months preceding the survey) 15-49) Prevalence of obesity, BMI (% of Percentage of population aged 18 or older with BMI above 30 ECHP, EHIS, ISSP, population 18 and older) STEPS, WHS 20    Indicator Definition Main Data Source Prevalence of obesity among men, Percentage of males aged 18 and older with BMI above 30 ECHP, EHIS, ISSP, BMI (% of males ages 18 and STEPS, WHS older) Prevalence of obesity among Percentage of females aged 18 and older with BMI above 30 ECHP, EHIS, ISSP, women, BMI (% of females ages STEPS, WHS 18 and older) Prevalence of obesity among Percentage of females aged 15-49 with BMI above 30 (excludes currently pregnant women and women DHS women, BMI (% of females age having given birth in the three months preceding the survey) 15-49) Mean diastolic blood pressure, Mean diastolic blood pressure (mmHg) in adult population (age-range may vary) DHS, STEPS adult population (mmHg) Mean systolic blood pressure, Mean systolic blood pressure (mmHg) in adult population (age-range may vary) DHS, STEPS adult population (mmHg) High blood pressure or being Percentage of adult population with high blood pressure or on treatment for high blood pressure (age- DHS, STEPS treated for high blood pressure range may vary) (% of adult population) Mean fasting blood glucose, adult Mean fasting blood glucose (mmol/L) in adult population (age-range may vary) STEPS population (mmol/L) Impaired fasting glycaemia (% of Percentage of adult population with impaired fasting glycaemia (age-range may vary) STEPS adult population) Mean cholesterol, adult Mean cholesterol (mmol/L) in adult population (age-range may vary) STEPS population (mmol/L) High cholesterol or on treatment Percentage of adult population with high cholesterol or on treatment for high cholesterol (age-range STEPS for high cholesterol (% of adult may vary) population) 21    Table 4: Survey ‘families’ used in health equity side of HEFPI database Survey Title in full Core topics No. of countries History Data collection Sample size Further method information on data and access DHS Demographic Population, health, and 91 low and middle-income Ongoing, dating Face-to-face Typically https://dhsprogra and Health nutrition, with a focus on countries (LMIC), 74 in back to 1984. interviews, 4,000- m.com/ Survey reproductive, maternal and HEFPI (data sets with First HEFPI physical 15,000 child health restricted access and those data from 1990 measurements, households collected before 1990 (Phase 2) biochemical excluded, several collected measurements from 2016 to be added) ECHP European Multipurpose panel survey with 15 European high-income 1994-2001 Face-to-face Typically http://ec.europa.e Community adult health module countries, 14 in HEFPI interviews 4,000- u/eurostat/web/m Household Panel (Germany micro-data not 12,000 icrodata/europea available) adults n-community- household-panel EHIS European Health Adult self-perceived health, 31 middle and high-income Ongoing, dating Face-to-face Typically http://ec.europa.e Interview Survey chronic conditions, disease countries (European Union, back to 2006 interviews 1,000- u/eurostat/web/m specific morbidity, physical and Iceland, Norway, and 10,000 icrodata/europea sensory functional limitations, Turkey), 20 in HEFPI (data adults n-health- hospitalization, consultations, sets with restricted access interview-survey unmet needs, use of medicines, excluded) preventive actions, height and weight, health behaviors Eurobaro Eurobarometer Multipurpose survey with 28 European high and Ongoing, since Face-to-face Typically http://ec.europa.e meter changing focus, adult health middle-income countries, all 1974, health interviews 1,000 u/commfrontoffic module in 2003 and 2006 in HEFPI modules in 2003 adults e/publicopinion/in and 2006 dex.cfm/General/i ndex ISSP International Multipurpose survey with 32 middle and high-income Ongoing since Face-to-face Typically https://www.gesis Social Survey changing focus, adult health countries in 2011 wave, all in 1985, health interviews, 1,000-2,500 .org/issp/modules/ Program module in 2011 HEFPI module in 2011 telephone adults issp-modules-by- interviews, topic/health-and- postal and web health-care/ surveys 22    Survey Title in full Core topics No. of countries History Data collection Sample size Further method information on data and access MCSS Multi-Country Adult health state descriptions, 60 countries of all income 2000-2001 Face-to-face Typically http://apps.who.i Survey Study on health conditions, screening, levels and worldwide, 35 in interviews, 600-6,000 nt/healthinfo/syst Health and health state valuations, health HEFPI (postal surveys telephone adults ems/surveydata/i Responsiveness system responsiveness, adult excluded, several countries to interviews, ndex.php/catalog/ mortality be added) postal survey mcss/about MICS Multiple Population, health, and 108 LMIC with completed Ongoing since Face-to-face On average http://mics.unicef. Indicator Cluster nutrition, with a focus on surveys, 89 with available 1995, first interviews, 11,000 org/ Survey reproductive, maternal and data, 73 in HEFPI (MICS 5 to HEFPI data physical households child health be added, a number of earlier from 1999 measurements in MICS 5 wave surveys to be added) wave STEPS Stepwise Adult non-communicable 111 LMIC, 94 in HEFPI Ongoing, dating Face-to-face Typically http://www.who.i Approach to disease-related health status (subnational surveys back to 2001 interviews, 1,000- nt/ncds/surveilla Surveillance and health behaviors excluded, several to be added) physical 10,000 nce/steps/en/ measurements, adults biochemical measurements WHS World Health Health expenditure, health 70 countries of all income 2002-2004 Face-to-face Typically http://www.who.i Survey insurance coverage, adult levels and worldwide, all in interviews between nt/healthinfo/sur health state descriptions, HEFPI 1,000 and vey/en/ health state valuation, risk 8,000 factors, chronic conditions, adults mortality, health care utilization, health systems responsiveness and social capital. All surveys (we use) are nationally representative household surveys 23    Indicator definitions Tables 1, 2 and 3 also show the definitions of the indicators. In choosing exact definitions of indicators, we have been guided by the same initiatives that guided us in our choice of indicators (see above), but also by the constraints imposed by the data and a desire to have a common definition irrespective of the data source. Our definitions sometimes differ from those used in reports and online tools derived from the same surveys we have used (e.g. DHS reports,16 DHS STATcompiler,17 MICS reports,18 UNICEF website,19 the WHS reports,20 and the World Bank’s ‘Health, Nutrition and Population Statistics by Wealth Quintile’ databank,21 which contains the data from the DHS and MICS reports), and from the WHO’s Health Equity Monitor22 which also contains summary statistics at the population level and for wealth quintiles based on analysis of microdata from the DHS and MICS surveys. We summarize the differences in definitions between our definitions and others’ in Annex Table A1. Important examples to highlight include:  Generally, when computing percentages of the population covered by certain services (e.g. fully immunized), we exclude cases with missing information from the denominator. The DHS and MICS reports, by contrast, typically do not, and instead treat missing values the same as if the respondent had answered No when asked about having accessed the respective service. As a result, DHS and MICS reports typically have a lower service coverage rates than us.                                                                16 https://dhsprogram.com/publications/publication-search.cfm?type=5. 17 https://www.statcompiler.com/en/. 18 http://mics.unicef.org/surveys. 19 See https://data.unicef.org/. Population rates are also available for some indicators and some MICS surveys via the MICS COMPILER tool at http://www.micscompiler.org/. 20 http://apps.who.int/healthinfo/systems/surveydata/index.php/catalog/whs/about. 21 http://databank.worldbank.org/data/source/health-nutrition-and-population-statistics-by-wealth-quintile. These data can be more conveniently imported into Stata using the Stata module WBOPENDATA (Azevedo 2016). 22 Data available at http://www.who.int/gho/health_equity/en/0 and indicator definitions at http://www.equidade.org/resources/indicators.pdf. 24     We always compute skilled birth attendance and antenatal care utilization rates for births in the past two years. The comparison databases, by contrast, use births in the reference period of the original survey question – e.g. births over the last 5 years for DHS, over the last year in the second MICS wave, and over the last 2 years from the third MICS wave onwards.  We apply a consistent definition of full immunization in terms of both required vaccines and the age by which a child has to have received them to be considered fully immunized, whereas the vaccines and age-groups vary within and across the DHS and MICS comparison databases. For example, some MICS surveys measure full immunization at age 18-29 months and others at age 12-23 months, whereas we consistently use the 15-23 months age-group.23 Also, full immunization rates in the MICS comparison databases vary in whether they consider pentavalent vaccination as an alternative to standard DPT vaccination, while we consistently consider it an alternative.  As mentioned above, we use a consistent definition of ARI (cough and rapid breathing) across all DHS surveys to define the sample for which we compute our ARI treatment indicator.24  We use a consistent definition of modern contraception methods across all surveys in the database which includes the lactational amenorrhea method (LAM). LAM is considered a modern method in STATcompiler and DHS reports but not in MICS reports and the MICS data in WHO’s Health Equity Monitor.                                                                23 We depart from the 12-23 months age-group that is used in most DHS and MICS reports to account for the fact that immunization schedule age-ranges for the first dose of measles vaccination vary internationally from 9 to 15 months. Countries with higher measles prevalence are recommended to immunize children at an earlier age, despite the vaccines being more effective when administered later (World Health Organization 2017). 24 Our definition of appropriate care-seeking for children with ARIs excludes other public or private providers since it is unclear if a medical consultation took place. 25     We use the same WHO 2006 standards for childhood stunting and underweight in all surveys, whereas the DHS and MICS reports use whatever standard was in force at the time the survey was done. The change of standards apparently makes a difference (Ergo et al. 2009). Indicator computation With the exception of the infant and under-5 mortality rates, which we calculate using the same life-table synthetic-cohort probability method employed in DHS reports and programmed in the Stata module SYNCMRATES (Masset 2016), and the childhood anthropometric z-score indicators from the MICS, which are computed throughout using 2006 WHO growth standards and programmed in WHO’s package IGROWUP,25 all indicators are based on simple population-weighted means of variables constructed from the questions in the survey.26 Where an indicator is available in more than one survey for a given year, we average over all data points.27 Sometimes the way the data were collected in the surveys prevented us from achieving 100% consistency of definition across surveys. For a number of indicators such as pap smear and mammogram rates, and blood pressure and cholesterol levels, the sampled age-ranges differ (see notes in Tables 1-3). For other indicators, we could not fully eliminate conceptual inconsistencies. For example, the MICS only asks about care-seeking for children with coughing and rapid breathing if the caretaker reports that the respiratory problems originate from the chest. For MICS data, our                                                                25 Program and documentation can be downloaded from http://www.who.int/childgrowth/software/en/. For the DHS, we use the 2006 WHO growth standard z-scores provided in the datasets to compute rates of childhood stunting and underweight. 26 Generally, construction of both the health equity and financial protection sides of the data set follows the methods summarized in O’Donnell et al. (2008). 27 We apply a different aggregation rule for our cancer screening variables – pap smear and mammography – when we have multiple data points for a given year that differ in terms of the age-range of the women for whom the surveys obtained screening rates. The rule we apply here is as follows: Our preferred age-ranges are 30-49 for pap smears and 50-69 for mammography. If for a given year, data are only available for another than the preferred age-range, we use the data point which minimizes the absolute difference between the number of years of the data point’s age-range which are outside the preferred age-range (inclusion errors) and the number of years of the preferred age-range which are not included in the data point’s age-range (exclusion errors). If, for instance, pap smear data for a given year are only available for women aged 25-44 and 35-59, we would choose the 25-44 data point (both age-ranges have an exclusion error of five years, but the inclusion error for the 35-59 age-range is ten years compared to five years for the 25-44 age-range). 26    acute respiratory infection (ARI) treatment variable is therefore only defined for the subgroup of children who experience cough and breathing difficulties coming from the chest. By contrast, many DHS do not include a question on where the respiratory problems originate, and the medical care seeking question is asked for all children with cough or breathing problems. The absence of a question on whether the breathing problems come from the chest prevents us from computing an ARI treatment variable for the DHS that is identical to that of the MICS. We are, however, able to obtain an ARI treatment variable that is consistent across all DHSs by defining ARIs (and thus the sample for which the treatment variable is computed) as having a cough which coincides with difficulties breathing.28 Other indicators for which we do not achieve full consistency are 4+ antenatal care visits, children under 5 sleeping under insecticide-treated bed nets and measles immunization.29 In some cases where the original survey questions differ, we try to harmonize indicators using an ex post adjustment of the population (and quintile) rates. Specifically, we flag and subsequently adjust cancer screening and inpatient utilization rates whenever the reported recall period differs from our preferred recall period: 5 years for pap smears and 2 years for mammograms according to WHO recommended screening intervals for our preferred age-groups 30-49 (pap smears) and 50-69 (mammograms) (World Health Organization 2013a, 2014), and 12 months as the most                                                                28 ARI treatment rates constructed from WHS data are substantially higher than those obtained from DHS and MICS. We suspect these differences to be due to divergences in the survey methodology: Among other things, WHS data are only available for the first health care seeking response if the youngest child under 5 in the household suffered an ARI, whereas DHS and MICS ARI treatment data come from all health care seeking responses of all children under 5 with an ARI in a household. The HEFPI database therefore does not include ARI treatment data from the WHS. 29 For antenatal care, the MICS 2 antenatal care visit data only refer to visits to a specific provider, whereas all later MICS waves and all DHSs do not impose this limitation. For all DHSs and all MICSs from 2002 onwards, a bed net is considered treated if it a) is a long-lasting treated net, b) a pre-treated net that was purchased or soaked in insecticides less than 12 months ago, or c) a non-pre-treated net which was soaked in insecticides less than 12 months ago. By contrast, data limitations in the MICS 2 wave (collected before 2002) restrict our definition of treated nets to those ever treated. For antenatal care, the MICS 2 antenatal care visit data only refer to visits to a specific list of providers, whereas all later MICS waves and all DHSs do not impose this limitation. WHS measles immunization data are only available for the youngest child in the household, whereas our DHS and MICS measles immunization data come from all children under 5 in a household.     27    frequently used (and, we would argue, most sensible) recall period for inpatient care. Concretely, when a survey reported pap-smear (mammogram) utilization data for a recall period other than 5 (2) years, we transform the reported utilization rates to a 5 (2) year recall using the formula for the / probability of an event over multiple trials, 1 1 , where x is the percentage of women obtaining pap smears (mammograms) over the survey’s reported recall period z (in years), and y is our preferred recall period of 5 (2) years.30 To obtain the adjustment factors for inpatient care data with less than 12 months recall, we exploit the availability of both 4-week and 12-month recall inpatient utilization data in the MCSS. Using the observed 4-week and 12-month inpatient utilization rates across 54 MCSS countries, and assuming zero utilization at time zero, we fit a nonlinear model of the relationship between time and utilization.31 We then use the model to estimate hypothetical utilization rates for 2-week, 3-month, and 6-month recall periods. The adjustment factors are obtained by dividing the observed 12-month utilization rate by the respective estimated rates (and the observed 4-week rate).32 Finally, the observed rate for the respective shorter recall period is multiplied with its adjustment factor to estimate the 12-month utilization rate. Data processing process Figure 6 summarizes the data-processing steps. Whenever possible, we first generate from the raw household survey microdata. In these microdata sets, we generate a standardized or harmonized microdata set that contains our standardized indicators. The rates for each survey are then compared to the rates reported elsewhere in a quality-control exercise (see below for further details). Rejected datapoints are dropped. The remaining datapoints are consolidated into a ‘meso data set’, which has one row per survey-indicator combination, e.g. Armenia/2010/DHS/cervical                                                                30 For surveys where the recall period is unspecified (‘Have you ever had a pap smear/mammogram?’), we assume , where and are the upper and lower bounds, respectively, of the age-group for which the survey question is asked (e.g. 49 and 30 for pap smear, and 69 and 50 for mammograms). 31 The fitted model takes the form y = -4E-05x2 + 0.0044x. 32 The adjustment factors to a 12-month recall utilization rate are 14.82 for 2-week, 7.58 for 4-week, 2.54 for 3-month and 1.47 for 6-month recall. 28    cancer screening. If the raw microdata are not available, we make use of summary statistics in existing reports and papers. Figure 6: HEFPI health equity data-processing steps Identify HH surveys Access HH surveys Compute harmonized variables Compute population and quintile mean outcomes, CIs and their SEs Collapse micro‐data to country‐year‐indicator level Conduct data quality checks Drop rejected datapoints Get population and quintile means from published work where microdata inaccessible Merge data from publications into meso data HEFPI HE meso data set Comparisons across subpopulations The HEFPI database presents not only sample averages but also subpopulation averages and measures of inequality. Households are ranked by either household per capita consumption or income or the Filmer-Pritchett (1999, 2001) wealth index. Subsequent to Gwatkin et al. (2000), the organization responsible for the DHS (then Macro International) decided to include the wealth index in each public-release DHS data set; UNICEF, which is responsible for the MICS, subsequently decided to do the same with the MICS. A handful of earlier MICS surveys33 and the WHS do not include a wealth index. We therefore created wealth indices for these surveys using principal component analysis (PCA). For the MICS surveys, we included the same asset variables as the                                                                33 Namely the Comoros 2000, Lesotho 2000, Eswatini (formerly Swaziland) 2000, Iraq 2006, and Djibouti 2006. 29    standard MICS wealth index, and for the WHS we used all the questions on assets available in the WHS survey. Averages are presented for each quintile of households; because the number of births and child deaths are not equal across household wealth quintiles, there are typically more children in the lower wealth quintiles. The poorest quintile thus contains 20 percent of households but typically more than 20 percent of children. In addition to presenting the quintile means, the 2018 HEFPI database, like previous HEFPI databases, also includes the concentration index and its standard error (Kakwani et al. 1997). This captures the degree of inequality (by wealth) in each indicator. A negative value indicates, on average, higher values among the poor; a positive value indicates, on average, higher values among the better off. The minimum is -1, and the maximum is +1. The concentration index and its standard error were computed using individual-level data via the Stata module CONINDEX (O'Donnell et al. 2015, 2016), with per capita household consumption or income or the wealth index as the ranking variable; it is therefore not affected by the fact that the quintiles are quintiles of households. When quintiles were very small, the quintile mean is not included in the data set. A quintile-specific datapoint is excluded if the sample size in any quintile was less than the following thresholds: 250 per quintile for infant and child mortality estimates and 25 per quintile for all other indicators; this follows the practice of Gwatkin et al. (2007). Data-quality checks On the health equity side of the HEFPI data set, our quality checks involve checking population and quintile specific rates from DHS and MICS against rates published by WHO’s Health Equity Monitor, MICS reports, DHS STATcompiler and World Bank’s ‘Health, Nutrition and Population Statistics by Wealth Quintile’ databank whenever our and the publishers’ indicator definitions align (e.g. for the infant mortality and diarrhea treatment indicators on STATcompiler, 30    see Annex Table A1). We noted and investigated discrepancies, and corrected any coding mistakes. No datapoints were excluded – we are sure any discrepancies are due to definition differences. However, even when indicator definitions are identical, for many country-year-survey- indicator combinations, we do not have a published number to compare ours with: STATcompiler does not tabulate all indicators by wealth; older MICS surveys do not tabulate any indicators by wealth; and some surveys are not (yet) included on the DHS STATcompiler and MICS reports. For surveys other than the DHS and MICS, there are no equivalents of DHS STATcompiler and MICS reports. In these cases, and DHS and MICS surveys without published data population and quintile rates, we ran some basic checks, the most important of which involved making sure that population rates were within a reasonable interval, e.g. proportion indicators should be in the interval [0, 1], under-five mortality should not be above 400 per 1,000, systolic blood pressure should be in the interval (90, 250), cholesterol (in mmol per L) should be in the interval (3, 8), women’s weight (in kg) should be in the interval (40, 120), and men’s height (in meters) should be in the interval (1.3, 2.8). None of our datapoints failed these basic checks; therefore, no datapoints were excluded. Illustrations using the health equity data Figure7 shows inequalities across wealth quintiles in immunization for selected African countries in the 1990s. The gaps are much larger in some countries than others. But the figure also illustrates how even the better off in some countries do poorly. For example, children in the richest 20% of the population in Ghana were being immunized at the same rate (65%) as the poorest 20% of children in Kenya. 31    Figure 7: Immunization inequalities in selected African countries in the 1990s 4 Chad; 1996 24 Cote d'Ivoire; 1994 38 80 Ghana; 1993 16 65 Kenya; 1993 65 87 Mali; 1995 17 57 0 10 20 30 40 50 60 70 80 90 100 Poorest quintile Richest quintile Figure 8 shows trends in MCH inequalities between the 1990s and 2010s for a panel of 25 countries that have complete data for the 1990s, the 2000s and the 2010s. For some indicators (e.g. immunization and the treatment of ARI), the rate for the richest 20% has barely changed, reflecting in part the fact the rate was already high, while the rate for the poorest 20% has increased, thereby closing the gap between the poor and better off. For other indicators (e.g. ANC and the treatment of diarrhea), we see increases among both the poorest 20% and the richest 20%, even if the proportionate increase is typically still larger for the poorest 20%. 32    Figure 8: Trends in MCH inequalities from the 1990s to the 2010s Richest quintile 1990s Poorest quintile 2010s Richest quintile 2010s Poorest quintile 1990s ANC 31 45 69 77 Immunization 37 51 66 67 SBA 24 40 77 89 Treat Acute Resp. Infection 40 46 61 65 Treat Diarrhea 28 38 44 55 Met Need for Family Planning 67 7375 80 0 10 20 30 40 50 60 70 80 90 100 25 countries with data for 1990s, 2000s and 2010s Figure 9 shows averages across country income groups in 8 service coverage indicators used in two recent studies of progress towards UHC (Wagstaff et al. 2015; Wagstaff et al. 2016). Unsurprisingly, high-income countries have higher service coverage rates than middle-income countries which in turn have higher rates than low-income countries. The gaps are especially marked for the two cancer screening variables. Also shown in Figure 9 are the values for Thailand and Zimbabwe, which have higher values on most indicators than their peers. 33    Figure 9: Levels of coverage of select service coverage indicators by level of development High income Upper middle inc Lower middle inc Low income Thailand Zimbabwe Mammogram 100 80 IP admission PAP smear 60 40 20 Treat Diarrhea 0 ANC Treat ARI Full immunization SBA Inpatient admission in last year as % of equivalent WHO benchmark (~9%) Finally, Figure 10 compares levels of and inequalities in inpatient (IP) admission rates and pap smears between low- and high-income countries. In the latter, inpatient admission rates are higher among the poorest 20% of the population, reflecting the greater need for inpatient care (van Doorslaer et al. 1992; van Doorslaer et al. 2000). Moreover, in high-income countries, there is little evidence of underutilization – even the top income group is admitted at a rate that is in line with the WHO benchmark of 0.1 inpatient admissions per capita (World Health Organization 2013b), which translates into just over 0.09 persons per capita having at least one admission per year. In low- income countries, by contrast, the income gradient is reversed, and even the richest 20% of the population, on average, underutilize inpatient care according to the WHO benchmark. In the case of pap smears, we see a positive gradient in both low- and high-income countries. However, in the high- income countries, even the poorest 20% are getting screened at a rate of over 70%. In low-income countries, by contrast, even the richest 20% are getting screened at barely 10%. 34    Figure 10: Levels and inequalities in inpatient admission rates and pap smears compared Inpatient admission last 12 months Pap smear last 5 yrs, women age 30‐49 12% 90% 80% 10% 70% 8% 60% 50% 6% 40% 4% 30% 20% 2% 10% 0% 0% High income countries Low income countries High income countries Low income countries Q1 (poorest) Q2 Q3 Q4 Q5 (richest) Q1 (poorest) Q2 Q3 Q4 Q5 (richest) Financial protection data In this section, we report details of the financial protection part of the 2018 HEFPI data set, listing the indicators included, the reasons for including them, their sources and definitions, how they were computed, how we derived data for different subpopulations, our quality checks, and lastly some illustrations of the use of the data. Indicators included The indicators in the financial protection part of the 2018 HEFPI database (cf. Figure 2) are: (i) the incidence of ‘catastrophic’ health expenditures (health expenditures exceeding a certain percentage, x, of a household’s total consumption or income), and (ii) the incidence of ‘impoverishing’ health expenditures (expenditures without which the household would have been above the poverty line, but because of the expenditures is below the poverty line). Indicator (i) (catastrophic expenditures) is one of the two UHC SDGs (3.8.2). The catastrophic expenditure indicator tells us whether health expenditures cause household consumption or income to fall by more than x percent, while the second tells us whether health expenditures were sufficiently large to push the household 35    into poverty. The two indicators are therefore complements, and the impoverishment indicator sheds light on the poverty SDG (target 1.1). Both indicators are widely used in the literatures on financial protection in health (Wagstaff and van Doorslaer 2003; van Doorslaer et al. 2006b; van Doorslaer et al. 2007; Wagstaff et al. 2018a; Wagstaff et al. 2018b). Some studies decide whether health expenditures are catastrophic by comparing them to total consumption, while others compare them to total income. Similarly, some studies decide whether health expenditures are impoverishing by comparing income net of health expenditures and income gross of health expenditures, while others compare consumption net and gross of health expenditures. There is no right or wrong approach. However, it is important to keep in mind that total consumption includes health expenditures, and will therefore increase when a health shock occurs if the household finances part of the health expenditure through borrowing or dissaving rather than entirely through cutting back consumption on other budget items. A household with a large health expenditure may therefore appear to be richer (in terms of consumption) than one that incurs only small health expenditures. As a result, we may end up with the rather perverse result that large health expenditures (possibly catastrophic ones, too) are more common among ‘rich’ households, and less common among ‘poor’ households (WHO and World Bank 2017). By contrast, income is not directly affected by health expenditures. (It may be affected indirectly in the sense that the same health shock that causes the health expenditures may also reduce labor income.) Thus, when computing catastrophic and impoverishing health expenditures, and especially when looking at inequality in catastrophic health expenditures, it is probably preferable to use income rather than consumption (WHO and World Bank 2017). Data search and data sources As with the health equity side of the HEFPI database, our goal is to assemble as large a data set as possible of surveys suitable for the analysis of financial protection. Again. we undertook inventories of the microdata catalogs of the International Household Survey Network and the World 36    Bank, and of several household survey collections. We also searched for household surveys online. Through this process, we have so far identified, tried to access and (where possible) vetted 1,752 household survey data sets from 178 countries – see Table 5. We are in the process of identifying, trying to access and vetting other data sets, some of which will be added to the HEFPI data set in due course. Of the 1,752 surveys, 299 are currently inaccessible and 465 lack key variables. The remaining 988 data sets were accessed, many through the World Bank Development Data Hub (DDH).34 They were then vetted and those that had the necessary information were analyzed; after a series of quality checks (see below for details), 570 data sets were kept. Most of the surveys are HIES or HBS surveys, or multipurpose household surveys like the LSMS. Very few come from a highly standardized multi-country survey program like the LSMS. However, many of the datapoints come from versions of the microdata that have been harmonized ex post, such as the Luxembourg Income Study (LIS) and various World Bank ex post harmonized data set ‘collections’ (e.g. ECAPOV). Such ex-post harmonization exercises consist of applying a common set of standards and guidelines to the construction of specific variables such as total income, or a consumption aggregate across different data sets. Table 6 summarizes the key details of these survey ‘collections’. Sometimes we have produced and compared results from both the original ‘master’ data set and the ex-post harmonized ‘adaptation’. Indeed, sometimes we have produced and compared estimates from different adaptations. In addition, both masters and adaptations can be updated, so we have recorded the version of each master and adaptation used. All datapoints in the data set are labeled with their data source in the referenceid variable – see annex for naming conventions.                                                                34 https://datacatalog.worldbank.org/. 37    Table 5: Data sources for the financial protection part of the 2018 HEFPI database Inaccessible Key variable(s) missing Analyzed – dropped Analyzed – kept Total CWIQ 1 6 7 E123 2 2 EAPPOV 1 3 4 ECAPOV 20 7 14 221 262 EUROSTAT 24 23 47 HBS 109 136 1 3 249 HEIDE 6 2 8 HIES 164 277 77 158 676 LIS 98 54 152 LSMS 2 20 18 11 51 MCSS 1 11 12 MNAPOV 3 2 11 16 SARLF 3 3 SARMD 5 5 SEDLAC 7 7 SHES 1 1 96 58 156 SHIP 20 26 46 WHS 50 50 Total 299 465 417 572 1,753 38    Table 6: Survey ‘collections’ used in financial protection side of HEFPI database Collection Title in full Type of collection Institution Geographic Type of survey(s) Further information on data and access focus CWIQ Core Welfare Multi-country World Bank Developing Multipurpose survey Some CWIQ surveys publicly accessible via Indicators survey initiative originally, but world the World Bank Microdata Catalog.35 More Questionnaire – somewhat have been used by CWIQ surveys can be accessed by World Bank standardized other institutions staff via the World Bank Microdata Library.36 too See also the Institute for Health Metric’s Global Health Data Exchange (GHDx) entries for CWIQ surveys.37 E123 Enquêtes 1-2-3 Multi-country DIAL Research Developing Multipurpose survey Details available (in French only) on the survey initiative Unit, France world Enquêtes 1-2-3 page of the DIAL website.38 – somewhat standardized EAPPOV World Bank East Ex post World Bank World Bank’s HBS, HIES, Accessible only to World Bank staff via World Asia & Pacific harmonized East Asia & multipurpose surveys Bank Microdata Library. harmonized Pacific region household survey collection ECAPOV World Bank Europe Ex post World Bank World Bank’s HBS, HIES, Accessible only to World Bank staff via World and Central Asia harmonized Europe & multipurpose surveys Bank Microdata Library. harmonized Central Asia household survey region collection EUROSTAT- Eurostat HBS Public Ex post European European HBS Details available on the Eurostat microdata HBS Use Files harmonized Commission – Union website.39 Eurostat HBS Household Budget National survey – National Global HBS Survey-specific Survey raw data governments HEIDE World Bank Ex post World Bank World Bank’s HBS, HIES, Details of the HEIDE data set can be found in Household harmonized Europe & multipurpose surveys the World Bank Microdata Catalog, and can Expenditure and Central Asia be found by searching for HEIDE in the study Income Data for region description.                                                                35 The World Bank Microdata Catalog can be accessed at http://microdata.worldbank.org/index.php/home and is open access. 36 The World Bank Microdata Library can be accessed at http://microdatalib.worldbank.org/index.php/home and is accessible only to World Bank staff. 37 Search for CWIQ on the ‘series and systems’ page of the GHDx site at http://ghdx.healthdata.org/series_and_systems. 38 The relevant page is http://www.dial.ird.fr/enquetes-statistiques/enquetes-1-2-3. 39 The Eurostat microdata access website is http://ec.europa.eu/eurostat/web/microdata.     39    Collection Title in full Type of collection Institution Geographic Type of survey(s) Further information on data and access focus Transitional Economies HIES Household Income & Individual National Global HIES Survey-specific Expenditure Survey country survey – governments raw data LIS Luxembourg Income Ex post Luxembourg Global, mostly HBS, HIES, Details of the LIS and how to access the data Study harmonized Income Study OECD multipurpose surveys are at the LIS website.40 LSMS World Bank Living Multi-country World Bank Global, Multipurpose The LSMS project is described at the LSMS Standards survey initiative developing surveys website.41 Most microdata sets are publicly Measurement Study – somewhat countries accessible via the World Bank Microdata standardized Catalog.42 MCSS WHO Multi-Country Multi-country World Health Global Health survey, Details of the survey and how to access the Survey Study on survey initiative Organization including out-of- microdata can be found at the MCSS Health and – highly pocket expenses and webpage.43 Responsiveness standardized some data on household consumption MNAPOV World Bank Middle Ex post World Bank World Bank’s Accessible only to World Bank staff via World East & North Africa harmonized Middle East Bank Microdata Library. harmonized & North household survey Africa region collection SARLF World Bank South Variety of World Bank World Bank’s Multiple types, many Accessible only to World Bank staff via World Asia Labor Flagship surveys in South South Asia not relevant to the Bank Microdata Library. harmonized survey Asia countries, region current database collection often raw data SARMD World Bank South Ex post World Bank World Bank’s HBS, HIES, Accessible only to World Bank staff via World Asia harmonized harmonized South Asia multipurpose surveys Bank Microdata Library. household survey region collection SEDLAC Socio-Economic Ex post CEDLAS and the Latin America HBS, HIES, Details of the SEDLAC project are at the Database for Latin harmonized World Bank & Caribbean multipurpose surveys SEDLAC website.44 Website gives no details America and the of how to access to microdata. World Bank Caribbean staff can access microdata via the World Bank Microdata Library.                                                                40 The LIS website is at http://www.lisdatacenter.org/our-data/lis-database/. 41 The LSMS website is at http://www.lisdatacenter.org/our-data/lis-database/. 42 The relevant webpage is http://microdata.worldbank.org/index.php/catalog/lsms. 43 The relevant page is http://apps.who.int/healthinfo/systems/surveydata/index.php/catalog/mcss/about. 44 The SEDLAC website is at http://www.cedlas.econo.unlp.edu.ar/wp/en/estadisticas/sedlac/. 40    Collection Title in full Type of collection Institution Geographic Type of survey(s) Further information on data and access focus SHES World Bank Ex post World Bank Global HBS, HIES, Surveys produced by World Bank’s Data standardized harmonized multipurpose surveys Group as part of the International household Comparison program. Accessible only to expenditure surveys World Bank staff SHIP World Bank Sub- Ex post World Bank World Bank’s HBS, HIES, Some publicly accessible via the World Bank Saharan Africa harmonized Sub-Saharan multipurpose surveys Microdata Catalog. Rest accessible only to harmonized Africa region World Bank staff via World Bank Microdata household survey Library. collection WHS WHO World Health Multi-country World Health Global Health survey, Details of the WHS survey can be found at the Survey survey initiative Organization including out-of- WHS webpage, and access is via the WHO – highly pocket expenses and Central Data Catalog.45  standardized some data on household consumption                                                                45 The WHS webpage is http://www.who.int/healthinfo/survey/en/. The WHO Central Data Catalog is at http://apps.who.int/healthinfo/systems/surveydata/index.php/catalog. 41    Indicator definitions The first indicator is the incidence of ‘catastrophic’ health expenditures, defined as health expenditures exceeding a certain percentage, x, of a household’s total consumption or income. The 2018 HEFPI database includes two thresholds for catastrophic expenditures (i.e. x): 10% and 25%. The SDG 3.8.2 indicator is the 10% threshold. The second indicator is the incidence of ‘impoverishing’ health expenditures, defined as expenditures without which the household would have been above the poverty line, but because of the expenditures is below the poverty line. In the HEFPI 2018 database, we use two absolute international poverty lines ($1.90-a-day and $3.20-a-day in 2011 purchasing power parity (PPP) dollars) and one relative poverty line (50% of median consumption or income). Indicator computation Measuring out-of-pocket health expenditures Out-of-pocket spending includes not only payments made by the user at the point of use but also cost-sharing and informal payments, both in kind and in cash, but it excludes payments by a third-party payer. Many household expenditure surveys include questions on health spending, but, being general surveys, most have some shortcomings in terms of identifying out-of-pocket health spending. First, it is sometimes not clear whether the spending reported is gross or net of any reimbursement by third parties (e.g., private insurance company or government agency), in which case out-of-pocket spending could be overestimated. Whenever possible, health insurance premiums should be excluded from out-of-pocket payments, and reimbursement from any type of health insurance scheme should be included to produce a net estimate of out-of-pocket payments. Second, recall periods are sometimes inappropriate, particularly in general expenditure surveys, in which the last 3 months 42    and the last 12 months are used frequently, periods that are probably too long for items such as outpatient care and medicines. Multipurpose surveys are better in that spending data are gathered via a health module that varies recall period by type of service. Third, variations in comprehensiveness probably exist across surveys. A review of 100 survey questionnaires cited in Wagstaff et al. (2018a) found that, in 80% of surveys, questions were asked about spending on pharmaceutical products, hospital services, medical services, and paramedical services. Measuring income Because, as mentioned above, total consumption increases after a health shock and pushes sick households up the consumption distribution while income does not, catastrophic (and impoverishing) payments computed with reference to income are easier to interpret, especially when looking at inequalities in catastrophic payments (Wagstaff et al. 2018a). It is customary to distinguish four main components in the measurement of income: (i) wage income from labor services; (ii) rental income from the supply of land, capital, or other assets; (iii) self-employment income; and (iv) current transfers from government or nongovernment agencies or other households. It is sometimes claimed (cf. e.g. Deaton and Zaidi 2002) that developing-country surveys (including the LSMS surveys) do well on (i) but less well on the other components of income. Recent initiatives such as the FAO RIGA project (Quiñones et al. 2009) are changing this. Sometimes income is not measured at all in developing-country surveys, so using it is not an option. The 2018 HEFPI database uses income rather than consumption for all high-income countries, and for certain upper middle-income countries. For most developing countries, we use consumption. Many of the high- income country datapoints come from ex-post data harmonization efforts like the LIS – see below. Measuring consumption Surveys have differed a great deal in the level of detail of their consumption modules. The LSMS surveys, which have been designed and implemented with the explicit objective of measuring living standards, have included somewhere in the region of 20 to 40 food items and a similar number 43    of nonfood items. Because of this heterogeneity, it is not possible to provide general guidelines on how to construct consumption aggregates or to fully account for the methodological challenges and pitfalls in this process.46 Here, we restrict ourselves to a general overview of the steps of the process. Most surveys collect data on four main classes of consumption: (i) food items, (ii) nonfood, nondurable items, (iii) consumer durables, and (iv) housing. Consumption is measured with a particular reference period in mind. Although the reference period varies, many surveys aim to accurately measure the total consumption of the household in the past year. In this way, temporary drops in consumption are ignored, and it is still possible to capture changes in living standards of a single individual or household over time. The reference period should be distinguished from the recall period, which refers to the time period for which respondents are asked to report consumption in the survey. Recall periods tend to differ for different types of goods, such that reporting on goods that tend to be purchased infrequently is based on a longer time period. A balance has to be struck between capturing a sufficiently long period so that the consumption during the period is representative of the reference period (year) as a whole and making it sufficiently short such that households can remember expenditures and consumption with reasonable accuracy. Surveys have taken different approaches to striking that balance. In general, there are three steps in the construction of a consumption-based living standards measure: (i) construct an aggregate of different components of consumption (e.g. food consumption, non-food consumption, consumer durables, housing, etc.), (ii) make adjustments for cost of living differences, and (iii) make adjustments for household size and composition. Deaton and Zaidi (2002) provide overarching principles and detailed guidelines for the construction of consumption aggregates. Moreover, data harmonization efforts are often conducted to ensure that consumption aggregates are comparable across countries and over time, which is crucial for poverty estimation                                                                46 See Deaton and Zaidi (2002) for methodological guidance on the measurement of consumption aggregates. 44    and comparability. In the 2018 HEFPI database, we rely on several data harmonization efforts (see Table 6):  LIS database. The LIS acquires data sets with income, wealth, employment, and demographic data from many high- and middle-income countries, harmonizes them to enable cross-national comparisons, and makes them publicly available in two databases, the LIS database and the Luxembourg Wealth Study database. For the 2018 HEFPI data set, we use all available datapoints from the LIS database.  World Bank regional harmonized databases. Regional teams at the World Bank are also working to produce adaptations of raw country level survey data sets using regionally harmonized definitions and aggregation methods (e.g. the ECAPOV data sets for Europe and Central Asia, the EAPPOV data set for East Asia and Pacific, the SHIP for Sub-Saharan Africa, the MNAPOV for Middle East and North Africa, the SEDLAC for Latin America and the Caribbean, and the SARMD for South Asia). These harmonized data sets are used for the global monitoring of poverty by the World Bank. We also use these harmonized data sets whenever possible. Supplemental indicators used in computing absolute impoverishment When we measure impoverishment using the absolute $1.90-a-day and $3.20-a-day poverty lines in 2011 PPP dollars, we have to take into account that the survey values are in local currency units (LCUs), and that they refer to the year of the survey which is not necessarily 2011, the year to which the PPP conversion factors that we use refer. The PPP conversion factor tells us the number of LCUs that would have been needed in 2011 to buy the same amounts of goods and services in the country as one US dollar would have bought in the USA in 2011. Multiplying the conversion factor by 1.9 or 3.2 gives us the amount of LCUs that would have been needed in 2011 to buy the same amount of goods and services in the country as US$1.90 or US$3.20 would have bought in the USA. This is the poverty line (per day) in the country in question in 2011. We then need to take into 45    account inflation in the country between the survey date and 2011, for which we need the country’s consumer price index (CPI). Thus, for a survey conducted in year t, we compute the poverty line in LCUs using the formula: ∗ ∗ , where is the poverty line expressed in $US (either $1.90 or $3.20 in the 2018 HEFPI data set), is the country’s implied PPP for year t, is the country’s PPP for 2011, is the country’s CPI in year t, and is the country’s CPI in 2011. We thus convert prices in the survey year in the country in question to 2011 prices in that country, and then apply the 2011 PPP conversion rate to convert 2011 LCUs of that country into international dollars. In the 2018 HEFPI database, we use the 2014 version of the 2011 PPP factors produced by the International Comparison Program (ICP). These PPP factors cover 199 countries and are expressed in terms of 2011 prices. We extract the PPP conversion factor series PA.NUS.PRVT.PP from the World Development Indicator (WDI) database. This conversion factor is for private consumption, i.e., household final consumption expenditure. Whenever possible, we rely on the CPI series used by PovcalNet.47 When the country’s CPI series are not available, we rely on the International Monetary Fund’s World Economic Outlook (WEO) data or on World Bank’s WDI CPI series. Computing the incidence of catastrophic and impoverishing expenditures The incidence rates of catastrophic and impoverishing health expenditures are computed using (the code underlying) the Stata module FPRO (Eozenou and Wagstaff 2018). Where health expenditure data are individual-level, the data are aggregated to the household level. A household is defined as incurring catastrophic expenses if its out-of-pocket health expenditures strictly exceed the                                                                47 PovcalNet can be accessed at http://iresearch.worldbank.org/PovcalNet/povOnDemand.aspx. 46    threshold. A household is defined as impoverished if it is not poor based on consumption or income gross of out-of-pocket health expenditures, but is poor based on consumption or income net of out-of- pocket health expenditures. To take into account differences in household size and weights, we use an ‘aweight’ equal to the product of household size and household weight when computing the population-level incidence of (i.e. the percentage of households incurring) catastrophic and impoverishing expenditures. Data processing process The data-processing process begins by generating in the microdata standardized or harmonized variables that correspond to our indicators – see Figure 11. (Surveys that do not allow us to estimate out-of-pocket expenditures and income or (non-health) consumption are, of course, excluded.) Population rates of catastrophic expenditures (CATA) (and, where income is used, rates for specific quintiles) are then computed from each standardized microdata set. The computation of rates of impoverishment (IMPOV) require supplementary data at the national level on PPPs and the CPI; these data are merged into the microdata and the impoverishment rate is then computed. The data are then consolidated into a ‘meso data set’, which has one row per country-year-survey- adaptation-indicator combination, e.g. Estonia/2010/HBS/ECAPOV adaptation/catastrophic expenditures/10% threshold. For quality checking we compare the country-year datapoints to data from other sources (see below for details) and the necessary data are merged into the meso data and then the quality checks are performed. Rejected datapoints are dropped, and the resultant data set is the financial protection HEFPI meso data set. 47    Figure 11: HEFPI financial protection data-processing steps Identify HH surveys Access HH surveys Compute harmonized variables Compute CATA Get PPP and CPI data Merge PPP and CPI data into microdata Compute IMPOV Collapse micro‐data to country‐year‐indicator level Get PovcalNet, WBI and GHED data Merge PovcalNet, WBI and WHO‐NHA data into meso data Conduct data quality checks Drop rejected datapoints HEFPI FP meso data set Comparisons across subpopulations For the reasons indicated above, and in line with findings reported in the World Bank-WHO 2017 Global Monitoring Report on UHC (WHO and World Bank 2017), we avoid reporting inequalities in catastrophic health expenditures across consumption quintiles, and report inequalities only across income quintiles. Inevitably, therefore, data on inequalities in catastrophic expenditures are available only for high-income and some upper middle-income countries. Inequalities in the incidence of catastrophic health expenditures are computed using the same code underlying the Stata module FPRO (Eozenou and Wagstaff 2018). Data-quality checks As explained above, on the health equity side of the HEFPI data set, there are published data we can check our results against for some country-year-survey-indicator combinations. This is not the case with the financial protection side of the data set. For sure, there are multi-country studies of catastrophic and impoverishing health expenditures, but these studies vary in the methods they use, and none uses exactly the same methods and data sets we do. The two papers by 48    van Doorslaer et al. (2006b; 2007) come closest to our work, but the poverty line in van Doorslaer et al. (2006b) is the old international poverty line, and in any case the number of surveys analyzed is 11 compared to our 600+ surveys.48 Therefore, instead of comparing our financial protection results to published numbers, we perform external and internal consistency checks.49 For the former, we perform three checks to determine the following: 1. Whether the welfare aggregate is aligned with the welfare aggregate reported in PovcalNet. Data sets are flagged if the absolute value of the relative difference between the measure of welfare derived from the survey is more than 10% apart from the value reported in PovcalNet (in log terms). 2. Whether the poverty headcount is aligned with the poverty headcount reported in PovcalNet. Data sets are flagged if the poverty headcount derived from the survey is more than 10 percentage points apart from the value reported in PovcalNet. 3. Whether the budget share of health expenditures derived from the survey is aligned with the aggregate budget share of health out-of-pocket expenditures. Data sets are flagged if the health budget share derived from the survey data is more than 5 percentage points apart from the aggregate budget share for health payments. The aggregate budget share for health is constructed by taking the ratio between aggregate out-of-pocket expenditures expressed in local currency units (in nominal terms for the year of the survey), and aggregate consumption, also expressed in nominal local currency units. The information for aggregate out-of-pocket expenditures is extracted from the WHO Global Health Expenditure Database                                                                48 The data in Wagstaff et al. (2018a; 2018b) are not a potential comparator source either, as 80% of the datapoints there come from a preliminary version of the 2018 HEFPI database, the rest coming from WHO. 49 These checks were refined during the collaborative studies with WHO staff (Wagstaff et al. 2018a; Wagstaff et al. 2018b). 49    (GHED), and aggregate consumption is obtained from the World Bank World Development Indicators data set. These external checks led to the dropping of some survey ‘families’ (notably WHO’s WHS and MCSS survey families) and some survey adaptations for some countries (e.g. LIS for the USA), because some or all of the above flags were raised consistently. Our internal consistency checks involve examining the full set of datapoints available for each country. When different datapoints were available for a given country-year combination (for example when a datapoint is derived from the raw data, and when there is also another datapoint derived from an adaptation of the raw data set), longitudinal consistency across the different datapoints was favored. For example, in the case of Mexico, the LIS-based estimates were used for all years, since they were the best in terms of the external flags for the vast majority of years, even though for some years they were not the best. Illustrations using the financial protection data Figure 12 shows trends in catastrophic expenditures (using the 10% threshold) in selected countries, all of which have enacted reforms aimed at expanding and deepening health insurance coverage (Wagstaff et al. 2016). In both Indonesia and Georgia, the incidence of catastrophic expenditures has been rising, but the rise has been far more pronounced in Georgia, and the base was higher as well. Despite the recent rise in Indonesia, the rate at the end of the series is still lower than that in the other three countries. In both Mexico and the US, the incidence of catastrophic expenditures has come down, in Mexico’s case the drop apparently happened somewhat after the ‘Seguro Popular’ reform, while in the US the drop apparently began somewhat before the Affordable Care Act or ‘Obamacare’ reform; the drop in Mexico has been more pronounced. 50    Figure 12: Trends in catastrophic expenditures (10% threshold) in selected countries 35% 30% Georgia 25% 20% 15% 10% Mexico 5% USA Indonesia 0% 1980 1990 2000 2010 2020 Figure 13 shows median incidence rates of catastrophic health expenditures by income quintile for 35 upper middle-income and high-income countries. On average, the lower income quintiles are over twice as likely to experience catastrophic health expenditures than the top income quintile. The inequalities across consumption quintiles (not shown here) look quite different reflecting the point made earlier about households borrowing and dissaving to finance health expenses, making them look relatively well off in consumption terms. 51    Figure 13: Inequalities in catastrophic health expenditures (10% threshold) by income quintile. Median across 35 upper middle-income and high-income countries 9% 8% 7% 6% 5% 4% 3% 2% 1% 0% Q1 (lowest income) Q2 Q3 Q4 Q5 (highest income) Figure 14 shows the incidence of catastrophic health expenditures across the world for the latest year of data, using the 10% threshold. Latin American countries tends to have quite high rates, as do several Asian countries. Many African countries have quite low rates, reflecting in some cases the fact people simply do not receive the health services they need. 52    Figure 14: Incidence of catastrophic health expenditures (10% threshold), latest year Conclusions The 2018 HEFPI database builds on three previous World Bank databases on the same theme, and continues the process begun in the last two databases of broadening the scope of the exercise. Like the previous three databases, the 2018 database highlights wherever possible the gaps across wealth (or consumption or income) quintiles in service coverage and health outcomes. The 2018 database continues the trend started by the 2003 database of expanding beyond MDG service coverage indicators to include NCD indicators, and expanding the geographic coverage beyond low- and middle-income countries. 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World Health Organization (2017). ‘Measles vaccines: WHO position paper, April 2017 - Recommendations.’ Vaccine. 57    Annex Table A1: Comparison of selected indicator definitions between HEFPI and other databases and survey reports Comparison with HEFPI definition Health Equity Indicator HEFPI definition DHS reports and Monitor (DHS, MICS reports WHS reports STATcompiler MICS, RHS) Service coverage (prevention) 4+ antenatal Percentage of most recent births in last two years with Reference periods for Reference period for Reference periods for Reference period for care visits at least 4 antenatal care visits (women age 18-49 at the most recent births most recent birth is births may be last 3 or most recent births is time of the survey) vary: last 3 or 5 years last 2 years; women 5 years; uses all births last 5 years for DHS and last 2 age 15-49 over reference period; years for MICS data women age 15-49 points; women age 15- 49 Immunization, Percentage of children age 15-23 months who received Age groups vary across Age groups (12-23/18- Age groups (12-23/18- Not available (survey full Bacillus Calmette–Guérin (BCG), measles/Measles- surveys (12-23/18- 29/15-26 months) and 29/15-26 months) and does not collect BCG Mumps-Rubella (MMR), 3 doses of polio (excluding 29/15-26 months) vaccines defining full vaccines defining full and polio vaccination polio given at birth) and 3 doses of diphtheria- vaccination vary vaccination vary data) pertussis-tetanus (DPT)/Pentavalent vaccinations, across surveys across surveys either verified by vaccination card or by recall of respondent Immunization, Percentage of children age 15-23 months who received Age groups vary across Age groups vary across Age groups vary across Sample of children age measles measles or MMR vaccination, either verified by surveys (12-23/18- surveys (12-23/18- surveys (12-23/18- 12-23 months from vaccination card or by recall of respondent 29/15-26 months) 29/15-26 months) 29/15-26 months) households where they are the youngest child under 5 Use of Percentage of children under 5 who slept under an Identical to HEFPI Identical to HEFPI Identical to HEFPI Not available insecticide- insecticide treated bed net (ITN) the night before the treated bed survey. A bed net is considered treated if it a) is a long- nets, children lasting treated net, b) a pre-treated net that was under 5 purchased or soaked in insecticides less than 12 months ago, or c) a non-pre-treated net which was soaked in insecticides less than 12 months ago. MICS 2 data points (MICS surveys pre-2002) consider bed nets treated if they were ever treated.) Contraceptive Percentage of women age 15-49 who are married or live Lactational Lactational Identical to HEFPI Not available prevalence, in union and currently use a modern method of amenorrhea method amenorrhea method modern contraception. Modern methods are defined as female (LAM) not considered (LAM) not considered methods sterilization, male sterilization, the contraceptive pill, modern contraception modern contraception. intrauterine contraceptive device (IUD), injectables, for MICS data points implants, female condom, male condom, diaphragm, 58    Comparison with HEFPI definition Health Equity Indicator HEFPI definition DHS reports and Monitor (DHS, MICS reports WHS reports STATcompiler MICS, RHS) contraceptive foam and contraceptive jelly, lactational amenorrhea method (LAM), emergency contraception, country-specific modern methods and other modern contraceptive methods respondent mentioned. Unmet need Percentage of women age 15-49 who are married or live Not reported Identical to HEFPI Identical to HEFPI Not available for in union who do not want to become pregnant but are contraception not using contraception (revised definition by Bradley et al.(2012)). Condom use in Percentage of women age 18-49 who had more than one Not reported Age group is 15-49 Age group is 15-49 Identical to HEFPI last sexual partner in the last 12 months and used a intercourse, at- condom during last intercourse risk females Pap smear Percentage of women who received a pap smear in the Not available Not available Not available Age group is 18-69; last 5 years (preferably age 30-49 but age groups may reference period is 3 vary) years Mammography Percentage of women who received a mammogram in Not available Not available Not available Age-group 40-60; the last 2 years (preferably age 50-69 but age groups mammography may vary) indicator comprises not only mammogram but also breast examination; reference period is 3 years Service coverage (treatment) Births Percentage of most recent births in last 2 years Reference periods for Most recent births in Reference periods for Reference period for attended by attended by any skilled health personnel (women age most recent births last 2 years; women births may be last 3 or most recent births is skilled health 18-49 at the time of the survey). Definition of skilled vary: last 3 or 5 years age 15-49 5 years; uses all births last 5 years; skilled staff varies by country and survey but always includes for DHS and last 2 over reference period; providers limited to doctor, nurse, midwife and auxiliary midwife). years for MICS data women age 15-49 doctor, nurses and points; women age 15- midwives (auxiliary 49 nurses/midwives excluded) Acute Percentage of children under 5 with cough and rapid Acute respiratory Acute respiratory Definition of acute Not available respiratory breathing in the two weeks preceding the survey (DHS, infection defined as infections defined as respiratory infections infections WHS) who had a consultation with a formal health cough with rapid cough with rapid varies: for some treated care provider (excluding pharmacies and visits to breathing which breathing which surveys defined as ‘other’ health care providers). MICS data points use originates from the originates from the cough with rapid sample of children under 5 with cough and rapid chest (DHS surveys chest; visits to ‘other’ breathing which breathing in the 2 weeks preceding the survey which without data on where public and private originates from the originated from the chest. The definition of formal breathing problems providers considered chest, for others as health care providers varies by country and data originate omitted); appropriate care- cough with rapid source. visits to ‘other’ private seeking breathing only; visits and public providers to ‘other’ private and public providers 59    Comparison with HEFPI definition Health Equity Indicator HEFPI definition DHS reports and Monitor (DHS, MICS reports WHS reports STATcompiler MICS, RHS) considered appropriate considered appropriate care-seeking care-seeking Diarrhea Percentage of children under 5 with diarrhea in the 2 Identical to HEFPI Identical to HEFPI Identical to HEFPI Not available treatment weeks before the survey who were given oral rehydration salts (ORS) Inpatient care Percentage of population age 18 and older using Not available Not available Not available Reference period is 3 use inpatient care in the last 12 months years Health outcomes Mortality rate, Deaths of children before their 1st birthday per 1,000 Identical to HEFPI Not available Identical to HEFPI Not available infant live births. Sample: children born up to 5 years before the survey for full population mortality estimates, and up to 10 years before the survey for wealth quintile specific mortality estimates Mortality rate, Deaths of children before their 5th birthday per 1,000 Identical to HEFPI Not available Identical to HEFPI Not available under-5 live births. Sample: children born up to 5 years before the survey for full population mortality estimates, and up to 10 years before the survey for wealth quintile specific mortality estimates Prevalence of Percentage of children under 5 with a Height-for-Age z- Identical to HEFPI Use current growth Reports use current Not available stunting score <-2 standard deviations from the reference standards which growth standards median (z-score calculated using WHO 2006 Child change over time which change over Growth Standards) time. STATcompiler uses WHO 2006 Child Growth Standards like HEFPI Prevalence of Percentage of children under 5 with a Weight-for-Age Identical to HEFPI Identical to HEFPI Identical to HEFPI Not available underweight z-score <-2 standard deviations from the reference median (z-score calculated using WHO 2006 Child Growth Standards) Prevalence of Percentage of population age 15-49 who had blood tests Not available Not available Identical to HEFPI Not available HIV that are positive for HIV1 or HIV2 60    Table A2: GATHER checklist Item Checklist item Reported on page # # Objectives and funding 1 Define the indicator(s), populations 21; 37 (including age, sex, and geographic entities), and time period(s) for which estimates were made. 2 List the funding sources for the work. World Bank Data Inputs For all data inputs from multiple sources that are synthesized as part of the study: 3 Describe how the data were identified 13; 33 and how the data were accessed. 4 Specify the inclusion and exclusion 28; 44 criteria. Identify all ad-hoc exclusions. 5 Provide information on all included 19; 36 data sources and their main characteristics. For each data source used, report reference information or contact name/institution, population represented, data collection method, year(s) of data collection, sex and age range, diagnostic criteria or measurement method, and sample size, as relevant. 6 Identify and describe any categories of input data that have potentially important biases (e.g., based on characteristics listed in item 5). For data inputs that contribute to the analysis but were not synthesized as part of the study: 7 Describe and give sources for any Published data from e.g. STEPS p14; FP other data inputs. supplemental data p41 ff For all data inputs: 8 Provide all data inputs in a file Raw data signposted in tables 4 and 5; format from which data can be example Stata code for producing meso data efficiently extracted (e.g., a and meso data downloadable spreadsheet rather than a PDF), including all relevant meta-data listed in item 5. For any data inputs that cannot be shared because of ethical or legal reasons, such as third- party ownership, provide a contact name or the name of the institution that retains the right to the data. Data analysis 9 Provide a conceptual overview of the 26; 43 data analysis method. A diagram may be helpful. 10 Provide a detailed description of all 23; 38 steps of the analysis, including mathematical formulae. This description should cover, as relevant, data cleaning, data pre-processing, data adjustments and weighting of       61    data sources, and mathematical or statistical model(s). 11 Describe how candidate models were N/A evaluated and how the final model(s) were selected. 12 Provide the results of an evaluation of N/A model performance, if done, as well as the results of any relevant sensitivity analysis. 13 Describe methods for calculating Confidence intervals reported for uncertainty of the estimates. State concentration indices, p27 which sources of uncertainty were, and were not, accounted for in the uncertainty analysis. 14 State how analytic or statistical Example Stata code for producing meso data source code used to generate and meso data downloadable estimates can be accessed. Results and Discussion 15 Provide published estimates in a file https://datacatalog.worldbank.org/node/142861 format from which data can be efficiently extracted. 16 Report a quantitative measure of the Reported in database uncertainty of the estimates (e.g. uncertainty intervals). 17 Interpret results in light of existing Annex table A1 compares HEFPI definitions evidence. If updating a previous set of with definitions used in other reports and estimates, describe the reasons for websites changes in estimates. 18 Discuss limitations of the estimates. Include a discussion of any modelling assumptions or data limitations that affect interpretation of the estimates.       62    Details of survey naming convention used in data set All data points in the 2018 HEFPI database are labelled as follows: CCC_YYYY_SSSS_ vNN _M_vNN_A_HHHH where - CCC = WDI country code (3 letters) - YYYY = survey year; we use the year when data collection started - SSSS = survey acronym (e.g., LSMS, HBS, etc.) - vNN_M = the ‘M’ comes from Master file. The Master file is the full data set, typically as provided by the country. vNN is the version name; NN is a sequential number; it starts with 01 and when a newer version is available it is named v02, then v03 etc. - vNN_A_HHHH = ‘A’ for Adaptations, and ‘HHHH’ for the name of the adaptation. These are often subsets of the data, such as harmonized data sets. The vNN follows the same rule as the numbering for original data, but this one refers for the version of the adaptation. For instance, the datapoint based on the ECAPOV harmonized version of the 2009 Tajikistan Living Standards Survey would be labeled ‘TJK_2009_TLSS_v01_M_v01_A_ECAPOV’.