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Appendices Table of Contents
Table of Contents ...... 1 Appendix 0.A Key messages and key findings ...... 3 Appendix 1.A. Extreme poverty persists ...... 5 Appendix 1.B. Prevalence of 5 of 8 deprivations in education and living standards (poorest billion poverty), and other selected indicators ...... 7 Appendix 1.C. Years of Life Lived with Disability and Years of Life Lost among the Poorest Billion ...... 16 Appendix 1.D. Causes of death by socioeconomic status at INDEPTH Network HDSS sites .... 42 Appendix 1.E. Primary data on extreme poverty and morbidity due to NCDIs ...... 43 Appendix 1.F. Expert perspectives on extreme poverty, disease occurrence, and case-fatality ... 44 Appendix 1.G. NCDI DALY rate comparison between the poorest billion and high-income countries ...... 46 Appendix 1.H. Behavioral, Metabolic, and Environmental Risk Factor Exposure among the Poorest Billion ...... 48 Appendix 1.I. Risk-attributable disease burden ...... 52 Appendix 1.J. Infectious Risks for NCDIs among the Poorest Billion ...... 56 Appendix 1.K. Disease-specific Health Adjusted Age at Death (HAAD) ...... 60 Appendix 2.A. Equity Scoring ...... 62 Appendix 2.B. Health-sector NCDI Interventions in the DCP3 Essential Universal Health Coverage Package (EUHC), plus additional interventions, organized by cause group ...... 64 Appendix 2.C. Health-sector NCDI Interventions in the DCP3 Essential Universal Health Coverage Package (EUHC), plus additional interventions, organized by health system platform and integrated care team (ICT) ...... 76 Appendix 2.D. Intersectoral NCDI Interventions from DCP3 Essential UHC Package ...... 89 Appendix 2.E. Cost-effectiveness and Equity of health sector interventions in the DCP3 Essential Universal Health Coverage Package (EUHC), plus additional interventions, organized by cause groups and by level of the health system ...... 93 Appendix 2.F. Prototypical Staffing of Integrated Care Teams for NCDIs ...... 95 Appendix 2.G. Costing interventions grouped by integrated care teams (ICTs) ...... 96 Appendix 2.H. Mapping “sentinel” NCDI conditions onto Integrated Care Teams ...... 97 Appendix 2.I. Impact estimation for implementation of DCP3 Essential Universal Health Coverage Package (EUHC) and injury prevention interventions ...... 98 Appendix 3.A. Health financing and expenditures on NCDIs in low- and lower-middle-income countries ...... 100 Appendix 3.B. Modelling catastrophic expenditure due to NCDIs among the Poorest Billion . 102 Appendix 3.C. Development Assistance for Health targeted to NCDs in the Poorest Countries103
Appendices Page 1 Appendix 3.D. Projected health financing capacity in low- and lower-middle-income countries, 2017-2030 ...... 106 Appendix 4.A. NCDs and the Poorest Billion on two separate tracks: 1948-2015 ...... 109 Appendix 4.B. Review of Global Governance Documents ...... 110 Appendix 4.C. Review of National NCD Strategic Plans ...... 117 Appendix 4.D. Review of Poverty Reduction Strategic Papers ...... 125 Appendix 4.E. Composition, current status, and examples of country-level impacts of NCDI Poverty Commissions, Groups, and Consortia established since 2016 ...... 128 Appendix 5.A.: Voices of NCDI Poverty ...... 130 References ...... 138
Appendices Page 2 Appendix 0.A Key messages and key findings
Here, we briefly describe methods used to generate the key messages and key findings and refer to additional sections of the Appendix for more detail. The original research outputs of the Commission are enumerated in Table 1.
Working Group Original Research Outputs Poverty and Burden of NCDIs 1. Who are the world’s poor? A new profile of global multidimensional poverty 2. Burden of Disease among the World’s Poorest Billion People 3. Risk-attributable NCDI Burden among the Poorest Billion 4. Comparison of mortality by socioeconomic status across seven health and demographic surveillance systems 5. Lifetime loss of health by disease categories 6. Global burden of NCDs from infectious causes Integrated Intervention Impact 7. Cost and impact of interventions for NCDIs in low- and lower-middle income countries 8. Mortality impacts of interventions for unintentional injuries 9. Mapping priority interventions to integrated care teams 10. Availability of Equipment and Medications for NCDIs at Public First-Referral Level Hospitals Financing 11. Public expenditure on NCDs in India: a budget-based analysis 12. Disaggregating catastrophic expenditure by disease area 13. Domestic spending on NCDIs in low- and middle-income countries 14. Analysis of external NCD financing targeted to the poorest countries 15. Projected health financing capacity versus NCDI intervention costs in the poorest countries History, Advocacy and Governance 16. The origins of the 4 x 4 framework for NCDs at WHO 17. Textual Analysis of NCD Strategic Plans 18. Textual analysis of NCD framing at global institutions National NCDI Poverty Commissions 19. The Afghanistan NCDI Poverty Commission Report 20. The Ethiopia NCDI Commission Report 21. The Kenya NCDI Poverty Commission Report 22. The Liberia NCDI Poverty Commission Report 23. The Malawi NCDI Poverty Commission Report 24. The Nepal NCDI Poverty Commission Report 25. The Mozambique NDI Poverty Commission Report 26. The Haiti NCDI Poverty Commission Report Table 1: Commission Original Research Outputs Key findings pertaining to the disease burden among the poorest billion were supported by analysis of estimates from the Global Burden of Disease Study and poverty data from a number of household surveys across countries as described in Section 1 of the Commission report. The data on poverty are further described in Appendix 1.A and Appendix 1.B. We defined “avoidable” burden in the poorest billion as disability-adjusted life years (DALYs) in excess of how many DALYs there would be if the poorest billion experienced age- and cause-specific DALY rates estimated for high-income countries. The estimates of disease burden among the poorest billion are further described in Appendix 1.C.
Key messages pertaining to interventions for reducing the burden of NCDIs among the poorest billion drew on previous work from the Disease Control Priorities 3rd Edition (DCP3) as described in Section 2 of the Commission report. We examined the impact of scaling up the interventions included in the DCP3 set of essential interventions for Universal Health Coverage (EUHC) among the poorest billion living in low- and lower-middle-income countries (LLMICs) by applying effect sizes to Global Burden of Disease estimates and weighting for population sizes in the poorest billion. We also included the implementation of a set of evidence-based interventions to prevent drowning and road traffic injury deaths. Further explanation of these methods can be found in Appendix 2.I.
Key findings pertaining to financing of health services that address NCDIs drew on evidence from National Health Accounts from the WHO Global Health Estimates, estimates of donor assistance for health from the Institute for Health Metrics and Evaluation, and a novel analysis attributing catastrophic health expenditure by cause. Further detail on these estimates can be found in Section 3 of the Commission report and Appendix 3.A-D.
Key findings pertaining to policy and governance were supported by analysis of archival documents from the World Health Organization, as well as global and national policy documents as discussed in Section 4. Description of the methodology underlying the document analysis can be found in Appendix 4.A-C.
Appendices Page 4 Appendix 1.A. Extreme poverty persists
A first step for the Commission was to review what was already known about those living in poverty. In order to support analyses of disease burden specifically among the poorest billion at global, national, sub-national, and household levels we adapted a non-monetary poverty index derived from aggregated household microdata (see Appendix 1.B below). We were also interested in looking at historical trends and future projections regarding the prevalence and geographical distribution of poverty. We found that approaches based on an international monetary poverty line were helpful to understand changes in poverty over time (which is not possible to the same degree using non-monetary indices). This multifaceted approach to poverty assessment is consistent with the recommendations of the 2017 Commission on Global Poverty.1
Historical poverty calculations and forecasts from the World Bank reveal a remarkable consistency to estimates of around one billion people living in extreme monetary poverty over the past two centuries of rapid population and economic growth (see Figure 1).2-5 Although the number of people living in extreme poverty has remained relatively constant, the geography of this kind of suffering has changed dramatically. Once a global phenomenon affecting all but the lucky few, extreme poverty was virtually eradicated in Europe and North America by the mid- 20th century, and became increasingly rare in Latin America and East Asia as the 20th century drew to close (particularly with falling poverty in China). Extreme poverty is now heavily concentrated in sub-Saharan Africa and South Asia. Projections from the World Bank suggest that by 2030, 87% of extreme poverty will be limited to sub-Saharan Africa alone.6 At the same time, many of the countries in these regions have graduated from low-income to lower-middle- income status, despite large within-country inequalities. As a consequence, a larger fraction of the extreme poor are now living in lower-middle-income countries.7 The World Bank’s most recent estimate has been that if historical trends in economic growth persist, around 6% of the world’s population (500 million people) will still be living in extreme poverty in 2030.6
Appendices Page 5
Figure 1: Historical and projected estimates of population living in extreme poverty – 1820-2030
Appendices Page 6 Appendix 1.B. Prevalence of 5 of 8 deprivations in education and living standards (poorest billion poverty), and other selected indicators
For our definition and analysis of the poorest billion, we utilized the aggregated dataset assembled by the Oxford Poverty and Human Development Initiative (OPHI). This dataset had previously been used to construct the global Multidimensional Poverty Index (MPI), which has been included in the United Nations’ Human Development Report since 2010.8,9 The primary sources for the OPHI data repository cover 106 low- and middle-income countries and include data from Demographic and Health Surveys (DHS), the Multiple Indicators Cluster Surveys (MICS), as well as other high-quality national household surveys with similar content (see Table 2).10 We only included surveys conducted in or after 2005 in our analysis, and 98 percent of the population (5.7 billion) in these surveyed countries had surveys conducted in or after 2010. We were not able to find high-quality recent surveys from two low-income countries (Eritrea and North Korea), eight lower-middle income countries (Angola, Cabo Verde, Kiribati, Kosovo, the Federated States of Micronesia, Papua New Guinea, the Solomon Islands, and Sri Lanka), and 24 upper-middle income countries (American Samoa, Botswana, Bulgaria, Costa Rica, Cuba, Dominica, Equatorial Guinea, Fiji, Grenada, Iran, Lebanon, Malaysia, the Marshall Islands, Mauritius, Nauru, Paraguay, Romania, the Russian Federation, Samoa, St. Vincent and the Grenadines, Tonga, Turkey, Tuvalu, and Venezuela) with a total population of 527 million people (country classifications from World Bank for year 2017). For these countries, we used the average prevalence of poorest billion population by age and sex from the corresponding country income groups. For consistency with burden of disease estimates, we used 2017 population estimates from the Global Burden of Disease Study 2017. 11 Kosovo, Nauru, and Tuvalu are not included in the additional population estimate of 527 million because they were not included in the GBD Study.
Country Survey Survey Year Sub-Saharan Africa Benin DHS 2012 Burkina Faso DHS 2010 Burundi DHS 2010 Cameroon DHS 2011 Central African Republic MICS 2010 Chad DHS 2015 Comoros DHS-MICS 2012 Congo DHS 2012 Congo, Democratic Republic of the DHS 2014 Côte d'Ivoire DHS 2012 Ethiopia DHS 2011 Gabon DHS 2012 Gambia DHS 2013 Ghana DHS 2014
Appendices Page 7 Country Survey Survey Year Guinea DHS-MICS 2012 Guinea-Bissau MICS 2014 Kenya DHS 2014 Lesotho DHS 2014 Liberia DHS 2013 Madagascar DHS 2009 Malawi DHS 2016 Mali DHS 2013 Mauritania MICS 2011 Mozambique DHS 2011 Namibia DHS 2013 Niger DHS 2012 Nigeria DHS 2013 Rwanda DHS 2015 Sao Tome and Principe MICS 2014 Senegal DHS 2015 Sierra Leone DHS 2013 Somalia MICS 2006 South Africa NIDS 2015 South Sudan MICS 2010 Sudan MICS 2014 Swaziland MICS 2014 Tanzania DHS 2016 Togo DHS 2014 Uganda DHS 2011 Zambia DHS 2014 Zimbabwe DHS 2015
South Asia Afghanistan DHS 2016 Bangladesh DHS 2014 Bhutan MICS 2010 India IHDS 2012 Maldives DHS 2009 Nepal MICS 2014 Pakistan DHS 2013
Middle East and North Africa Algeria MICS 2013 Djibouti MICS 2006
Appendices Page 8 Country Survey Survey Year Egypt DHS 2014 Iraq MICS 2011 Jordan DHS 2012 Libya PAPFAM 2007 Morocco PAPFAM 2011 Palestine MICS 2014 Syrian Arab Republic PAPFAM 2009 Tunisia MICS 2012 Yemen DHS 2013 Latin America Belize MICS 2011 Bolivia DHS 2008 Brazil PNAD 2014 Colombia DHS 2010 Dominican Republic MICS 2014 Ecuador ECV 2014 El Salvador MICS 2014 Guatemala DHS 2015 Guyana MICS 2014 Haiti DHS 2012 Honduras DHS 2012 Jamaica JSLC 2012 Mexico MICS 2015 Nicaragua DHS 2012 Peru DHS-Cont 2012 Saint Lucia MICS 2012 Suriname MICS 2010 East and Central Europe Albania DHS 2009 Armenia DHS 2010 Azerbaijan DHS 2006 Belarus MICS 2005 Bosnia and Herzegovina MICS 2012 Georgia MICS 2005 Kazakhstan MICS 2015 Kyrgyzstan MICS 2014 Macedonia MICS 2011 Moldova MICS 2012 Montenegro MICS 2013 Serbia MICS 2014
Appendices Page 9 Country Survey Survey Year Tajikistan DHS 2012 Turkmenistan MICS 2016 Ukraine MICS 2012 Uzbekistan MICS 2006 East Asia and Pacific Cambodia DHS 2014 China CFPS 2014 Indonesia DHS 2012 Lao MICS/DHS 2012 Mongolia MICS 2013 Myanmar DHS 2016 Philippines DHS 2013 Thailand MICS 2012 Timor-Leste DHS 2010 Vanuatu MICS 2007 Vietnam MICS 2014 DHS = Demographic and Health Surveillance Survey; MICS = Multiple Indicator Cluster Survey; PAPFAM = Pan Arab Project for Family Health (Syrian Arab Republic, Libya, Morocco); NIDS = National Income Dynamics Study (South Africa); ENNyS = Encuesta Nacional de Nutrición y Salud (Argentia); CFPS = China Family Panel Studies; ECV = Encuesta de Condiciones de Vida (Ecuador); IHDS = India Human Development Survey; JSLC = Jamaica Survey of Living Conditions; PNAD = Pesquisa Nacional por Amostra de Domicilios (Brazil) Table 2: Countries with household surveys available since 2005 used to calculate poverty rates In contrast to the MPI (which includes health and nutritional indicators), we limited the Commission’s poverty index (PI) to include only indicators of deprivation in education and living standards in order to avoid confounding (see Table 3). To estimate the level of deprivation found among the world’s poorest billion, we calculated the age and sex-specific prevalence of each indicator for each country for which we had data available. We assumed no change in indicator prevalence since the time of the most recent household survey and multiplied this rate by 2017 population estimates.
Indicators Definition MPI* PI** Health (2) Child mortality + - years. Nutrition 2 + - the median weight (moderate and severe underweight). Education (2) Years of Schooling + + years of schooling. School Attendance Any school-aged child (e.g. 5-15) is not attending school up to the + + age at which he/she would complete class 8. Living Standards (6) Cooking Fuel The household cooks with dung, wood or charcoal. + + Improved Sanitation The household does not have a flush toilet or latrine, or does not + + have or must share one of the following with other households: a ventilated improved pit or composting toilet.
Appendices Page 10 Safe Drinking Water The household does not have piped water, a public tap, a + + borehole or pump, a protected well or spring or rainwater within a 30 minutes roundtrip walk. Electricity The household has no electricity. + + Flooring The household has a dirt, sand, dung or ‘other’ (unspecified) type + + of floor. Assets The household does not own a car or truck and does not own + + more than one of the following: a radio, TV, telephone, bicycle, motorbike or refrigerator. * MPI = Multidimensional Poverty Index; ** Commission PI = Poverty Index Table 3: Poverty Index We examined the number of people in 2017 living in each World Bank region with at least 5 of 8 deprivations in education and/or living standards compared with those living with at least 4 of 8 deprivations. We also compared the number of people living beyond these poverty index thresholds with those living below the international monetary poverty line of $1.9 per day (2011 Purchasing Power Parity) used by the World Bank to define extreme poverty (see Table 4). We estimated that the total number of people living with at least 5 of 8 poverty index deprivations was 873 million in 2017 (34 million in countries without recent household survey data), which was similar to the number living in extreme monetary poverty in that same year (768 million). We chose the threshold of 5 of 8 poverty index deprivations as our definition of “poorest billion” poverty.
Population Meeting Poverty Definitions
(millions) in 2017 Poverty Index Monetary Poverty 5 of 8 4 of 8 World Bank Region $1.90 PPP* per day indicators indicators East Asia & Pacific 41 88 74 Europe, Central Asia, Middle East, &North 17 29 18 Africa Latin America & Caribbean 15 28 30 South Asia 288 549 249 Sub-Saharan Africa 513 647 390 Other High-Income Countries ND ND 7 Global 873 1,340 768 * PPP = Purchasing Power Parity. ND = No Data. Sources: Poverty Index = author’s calculations; Monetary Poverty: World Bank: http://iresearch.worldbank.org/PovcalNet/home.aspx Table 4: Where are the Poorest Billion by Region? We found that Poorest Billion live largely in low and lower-middle income countries (LLMICs), with 59% living in sub-Saharan Africa, and 33% living in South Asia (see Table 5). In these countries and regions, the Poorest Billion are largely found in rural areas and are nearly universally deprived in access to non-biomass cooking fuels. On average, the Poorest Billion in South Asia and in lower-middle income countries suffer fewer deprivation, are somewhat older on average, and are less likely to have household members employed in agriculture as compared with the Poorest Billion in sub-Saharan Africa and low-income countries.
Appendices Page 11 Country Region Income Group Lower- Low South sub-Saharan Total Middle Income Asia Africa Income Pattern of Deprivation^ Low Household Education 48% 48% 49% 51% 47% Low Child School Attendance 40% 46% 37% 33% 45% Biomass Cooking Fuel 98% 100% 99% 100% 100% Unimproved Sanitation 92% 92% 93% 95% 92% Unsafe Drinking Water 58% 75% 44% 26% 76% No Electricity 86% 97% 77% 69% 97% Poor Flooring 88% 88% 89% 94% 88% Few Assets 72% 70% 74% 82% 67% Age Distribution# Median 5-year Age Group 15-19 15-19 15-19 20-24 15-19 Under age 50 (% of PB*) 87% 90% 85% 83% 91% Under age 40 (% of PB*) 79% 83% 77% 74% 83% Under age 30 (% of PB*) 67% 72% 64% 61% 73% Under age 5 (% of PB*) 16% 18% 15% 13% 18% Percent Rural^ 92% 91% 93% 95% 91% Percent Employed in 65% 77% 53% 50% 74% Agriculture (n=35 countries) *PB = Poorest Billion, ^ Based on countries with available surveys, # Including LMICs without surveys and imputed prevalence of poorest billion as described in text Table 5 of 8 poverty index deprivations) in 2017 The world’s poorest people are not concentrated in particular countries or large sub-national geographies (see Table 6). Although there are around 47 countries with more than 2 million “poorest billion” people each, the poor are widely dispersed in many of these countries. In fact, most of the world’s poorest (62%) live in countries in which they are the minority. All of the countries with more than 50 percent extreme poverty prevalence are in sub-Saharan Africa (with the sole exception of Timor-Leste), and these countries only account for 38 percent of the world’s poorest. Only about 52 percent of the poorest live in countries where they are the majority even in a single large sub-national region. Similarly, only 58 percent of the poorest live in countries where they are the majority in rural areas.
Appendices Page 12 # of Countries % of Poorest Billion Countries with large numbers of the poorest people 47 97% Countries with a high national prevalence of poorest billion poverty 5 15% 18 38% 41 65% 63 96% Countries with poverty concentrated in sub-national regions - 18 52% - 58 98% Countries with high poverty prevalence in rural areas poorest billion 31 58% 52 96% Table 6: National and sub-national concentration of the poorest billion One consequence of the geographical diffuseness of poorest billion poverty is that there are very few countries (Niger, Somalia, Central African Republic, Ethiopia, South Sudan, and Chad), in which national summary data reflects the situation of the world’s poorest. It would be difficult to characterize the poorest based on national data sets in a country such as Brazil, for example, where 500,000 people live in extreme poverty, but no sub-national region has more than 5 percent poverty prevalence.
In order to facilitate our analysis, we identified 55 LLMICs that had at least one sub-national region with at least 25% poorest billion poverty at a sub-national level (see Table 7). These 55 “poorest billion countries” have a combined population of 820 million people living in poorest billion poverty (94% of the poorest billion).
Appendices Page 13 No. Nurses & Income GNI/ deprivations deprivations Physicians Country midwives Group capita12 in 2017 in 2017 per 1,000 per 1,000 (millions) Sub-Saharan Africa South Sudan LIC ND 88.7 8.8 ND ND Chad LIC $640 82.6 12.6 0.5 3.6 Ethiopia* LIC $740 81.5 83.8 1 8.4 Niger LIC $360 77.7 16.6 0.5 3.1 Somalia LIC ND 76.3 12.9 0.2 0.6 Central African Republic LIC $420 74.9 3.5 0.6 2 Burundi LIC $280 72.8 7.9 0.5 6.8 Congo, Democratic LIC $460 68.9 55.8 0.9 4.7 Republic of the Madagascar* LIC $400 65.2 17 1.8 1.1 Mozambique* LIC $430 65.1 19.6 0.7 4.4 Burkina Faso LIC $590 64.7 13.7 0.6 5.7 Sierra Leone* LIC $520 62.2 4.9 ND 10 Uganda* LIC $620 58.4 22.8 0.9 6.3 Mali LIC $770 58.1 11.8 1.4 3.8 Tanzania* LIC $970 56.2 30.4 0.4 4.1 Guinea LIC $830 55.3 6.5 0.8 3.8 Guinea-Bissau LIC $680 52.4 1 2 14 Rwanda* LIC $730 49.2 6.2 1.3 8.3 Malawi* LIC $340 48.5 8.3 0.2 2.5 Liberia* LIC $620 48.3 2.3 0.4 1 Zambia* LMIC $1,300 46.7 8.1 0.9 8.9 Sudan LMIC $2,390 46 18.5 4.1 8.3 Benin LIC $800 44.4 5.1 1.6 6.1 Mauritania LMIC $1,120 43 1.7 1.8 10.3 Kenya* LMIC $1,440 38.9 18.8 2 15.4 Cameroon LMIC $1,340 36.4 10.1 ND ND Nigeria LMIC $2,100 33.9 69.8 3.8 14.5 Congo LMIC $1,480 31.5 1.5 ND ND Togo LIC $590 31.2 2.3 0.5 3 Senegal LIC $1,280 30.8 4.5 0.7 3.1 Côte d'Ivoire LMIC $1,480 29.4 7.3 2.3 8.5 Zimbabwe* LIC $1,370 28.6 4.2 0.8 11.5 Namibia UM $4,800 25.3 0.6 ND ND Lesotho LMIC $1,300 25.1 0.5 ND ND Gambia LIC $650 23.6 0.5 1.1 16.3 Comoros LIC $1,280 22.9 0.2 1.7 9.2 South Asia Bangladesh LMIC $1,520 25.2 39.6 5.3 3.1 Afghanistan* LIC $550 25.1 8.3 2.8 3.2 Pakistan LMIC $1,500 16.4 35.1 9.8 5 India* LMIC $1,830 14.3 197.5 7.8 21.1 Nepal* LIC $850 12.6 3.8 6.5 26.9 Bhutan LMIC $2,890 11 0.1 3.7 15.1 Latin America and the Caribbean Haiti* LIC $760 39 4.6 ND ND Bolivia LMIC $3,090 14.5 1.7 16.1 7.4 Nicaragua LMIC $2,090 11.4 0.7 9.1 13.8 Guatemala UM $4,060 9.9 1.7 ND ND Middle East and North Africa Yemen LIC $1,060 20.7 6.3 3.1 7.3 Djibouti LMIC $2,040 15.8 0.2 2.2 5.3 East Asia Pacific Timor-Leste LMIC $1,810 51.2 0.7 7.2 16.7 Vanuatu LMIC $2,810 27.8 0.1 1.7 13.9 Lao LMIC $2,240 16.7 1.2 5 9.8 Cambodia LMIC $1,230 13.6 2.2 1.7 9.5
Appendices Page 14 No. Nurses & Income GNI/ deprivations deprivations Physicians Country midwives Group capita12 in 2017 in 2017 per 1,000 per 1,000 (millions) Myanmar LMIC $1,200 12.9 6.8 8.6 9.8 Mongolia LMIC $3,230 12.6 0.4 28.9 39.8 Indonesia LMIC $3,530 3.6 9.2 3.8 20.6 *Countries that have organized National NCD Poverty Commissions/Groups/Consortia; LIC = Low-income country, LMI = Lower-middle-income country, ND = no data; Robles Aguilar and Sumner 2020. 13 Original analysis using data from household surveys up to 2016. Source for health worker rates from Global Health Observatory 2016. 14 Per capita GNI from year 2017. Population calculated from GBD 2017. Doctor and nurse/midwife estimates taken from most recent year from 2012 to 2017. Table 7: Characteristics of low- and lower-middle income countries with at least one sub-national regional
Appendices Page 15 Appendix 1.C. Years of Life Lived with Disability and Years of Life Lost among the Poorest Billion
For our first analysis, we have built on the work done previously by Gwatkin and colleagues. 15,16 We utilized publicly available national estimates for 195 countries regarding age- and sex-specific mortality, prevalence, incidence, and disability weights for 360 causes of death and disability from the Global Burden of Disease (GBD) 2017 Study.17,18 These estimates use a Bayesian meta- regression framework described elsewhere in detail, and represent the most comprehensive review and synthesis of epidemiological data that is currently available. 19 Although these estimates have been previously analyzed extensively according to World Bank income group, and more recently by an index that integrates national education, fertility, and income data (the sociodemographic index or SDI), here we have made estimates specifically for the approximately 873 million people in the world who experienced five or more of the eight deprivations on our poverty index (the poorest billion).
We have tried to address the problem that current models of disease burden only generate estimates for national (and increasingly sub- national) disease rates but that most of the poorest are a minority in countries where they live. 20 If there are major differences in disease rates among the poorest compared with the less-poor or non-poor within countries (as suggested by theories of epidemiologic polarization), then aggregating from national estimates may be misleading.21 We pursued an ecological analysis to address this issue, predicting within-country difference based on between-country trends. We regressed YLD rates and death rates (Measure ratel) from each cause of morbidity and mortality on the proportion of the population in the poorest billion across countries (PPBl), by age group and sex, and with r) to better isolate country-to-country variation not driven by large regional differences.
log( ) = + + +
From these regressions, we predicted rates in hypothetical populations with 100% prevalence of extreme poverty and 0% prevalence of extreme poverty. Then, in each country, we scaled these rates to national rates using the proportion of the population in the poorest billion, resulting in predicted rates for the population in the poorest billion and the population not in the poorest billion that were consistent with national-level estimates from the GBD. First, we solved for the scalar (k) in the equation below for each location (l), age group (a), sex (s), and cause (c). Then, we multiplied the rates by k to make the adjustment.