Global Health Metrics Measuring universal health coverage based on an index of effective coverage of health services in 204 countries and territories, 1990–2019: a systematic analysis for the Global Burden of Disease Study 2019 GBD 2019 Universal Health Coverage Collaborators* Summary Lancet 2020; 396: 1250–84 Background Achieving universal health coverage (UHC) involves all people receiving the health services they need, of Published Online high quality, without experiencing financial hardship. Making progress towards UHC is a policy priority for both August 27, 2020 countries and global institutions, as highlighted by the agenda of the UN Sustainable Development Goals (SDGs) and https://doi.org/10.1016/ WHO’s Thirteenth General Programme of Work (GPW13). Measuring effective coverage at the health-system level is S0140-6736(20)30750-9 important for understanding whether health services are aligned with countries’ health profiles and are of sufficient See Comment page 1130 quality to produce health gains for populations of all ages. *Collaborators are listed at the end of the paper Methods Based on the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2019, we assessed UHC Correspondence to: Prof Rafael Lozano, Institute for effective coverage for 204 countries and territories from 1990 to 2019. Drawing from a measurement framework Health Metrics and Evaluation, developed through WHO’s GPW13 consultation, we mapped 23 effective coverage indicators to a matrix representing University of Washington, health service types (eg, promotion, prevention, and treatment) and five population-age groups spanning from Seattle, WA 98105, USA rlozano@uw.edu reproductive and newborn to older adults (≥65 years). Effective coverage indicators were based on intervention coverage or outcome-based measures such as mortality-to-incidence ratios to approximate access to quality care; outcome-based measures were transformed to values on a scale of 0–100 based on the 2·5th and 97·5th percentile of location-year values. We constructed the UHC effective coverage index by weighting each effective coverage indicator relative to its associated potential health gains, as measured by disability-adjusted life-years for each location-year and population-age group. For three tests of validity (content, known-groups, and convergent), UHC effective coverage index performance was generally better than that of other UHC service coverage indices from WHO (ie, the current metric for SDG indicator 3.8.1 on UHC service coverage), the World Bank, and GBD 2017. We quantified frontiers of UHC effective coverage performance on the basis of pooled health spending per capita, representing UHC effective coverage index levels achieved in 2019 relative to country-level government health spending, prepaid private expenditures, and development assistance for health. To assess current trajectories towards the GPW13 UHC billion target—1 billion more people benefiting from UHC by 2023—we estimated additional population equivalents with UHC effective coverage from 2018 to 2023. Findings Globally, performance on the UHC effective coverage index improved from 45·8 (95% uncertainty interval 44·2–47·5) in 1990 to 60·3 (58·7–61·9) in 2019, yet country-level UHC effective coverage in 2019 still spanned from 95 or higher in Japan and Iceland to lower than 25 in Somalia and the Central African Republic. Since 2010, sub-Saharan Africa showed accelerated gains on the UHC effective coverage index (at an average increase of 2·6% [1·9–3·3] per year up to 2019); by contrast, most other GBD super-regions had slowed rates of progress in 2010–2019 relative to 1990–2010. Many countries showed lagging performance on effective coverage indicators for non-communicable diseases relative to those for communicable diseases and maternal and child health, despite non- communicable diseases accounting for a greater proportion of potential health gains in 2019, suggesting that many health systems are not keeping pace with the rising non-communicable disease burden and associated population health needs. In 2019, the UHC effective coverage index was associated with pooled health spending per capita (r=0·79), although countries across the development spectrum had much lower UHC effective coverage than is potentially achievable relative to their health spending. Under maximum efficiency of translating health spending into UHC effective coverage performance, countries would need to reach $1398 pooled health spending per capita (US$ adjusted for purchasing power parity) in order to achieve 80 on the UHC effective coverage index. From 2018 to 2023, an estimated 388·9 million (358·6–421·3) more population equivalents would have UHC effective coverage, falling well short of the GPW13 target of 1 billion more people benefiting from UHC during this time. Current projections point to an estimated 3·1 billion (3·0–3·2) population equivalents still lacking UHC effective coverage in 2023, with nearly a third (968·1 million [903·5–1040·3]) residing in south Asia. Interpretation The present study demonstrates the utility of measuring effective coverage and its role in supporting improved health outcomes for all people—the ultimate goal of UHC and its achievement. Global ambitions to 1250 www.thelancet.com Vol 396 October 17, 2020 Global Health Metrics accelerate progress on UHC service coverage are increasingly unlikely unless concerted action on non-communicable diseases occurs and countries can better translate health spending into improved performance. Focusing on effective coverage and accounting for the world’s evolving health needs lays the groundwork for better understanding how close—or how far—all populations are in benefiting from UHC. Funding Bill & Melinda Gates Foundation. Copyright © 2020 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license. Research in context Evidence before this study involved either direct measures of intervention coverage Various approaches have been proposed for monitoring (eg, antiretroviral therapy coverage) or outcome-based universal health coverage (UHC) service coverage, including indicators, such as mortality-to-incidence ratios, to approximate those from WHO (ie, the UHC service coverage index, access to quality care. We weighted each effective coverage the official UN measure for the Sustainable Development Goal indicator on the basis of potential health gains deliverable by indicator 3.8.1) and the World Bank. Currently available service health systems, as approximated by the disability-adjusted coverage metrics are heavily focused on infectious diseases as life-years associated with each effective coverage indicator, well as reproductive, neonatal, maternal, and child health, and aggregated them to produce the UHC effective coverage despite the recognition that advances towards UHC also index. Three types of validity were assessed (content, known require service provision for non-communicable diseases and groups, and convergent) for the UHC effective coverage index delivering interventions to a broader range of population-age and other multi-country service coverage measures (eg, the UHC groups. Inconsistent trend estimation across indicators, service coverage index for 2017, as estimated by WHO, and the if time series are generated, impedes measurements of GBD 2017 UHC service coverage index for 2017). We also progress—a priority emphasised in the member-state-led quantified relationships between pooled health spending per Political Declaration for the UN High-Level Meeting on capita (ie, government expenditures, prepaid private Universal Health Coverage in 2019. Although the 2014 WHO spending, and development assistance for health) and UHC and World Bank framework for UHC service coverage is effective coverage performance to examine how well explicitly focused on health-system effective coverage, countries are currently translating resources into improved efforts to date have focused on crude coverage or health- UHC effective coverage. Last, we estimated the number of system resource inputs, or a combination of both. Effective population equivalents covered by effective health services coverage at the health-system level, or the fraction of from 2018 to 2023—a key component of WHO’s GPW13— potential health gains delivered by a health system, has yet to by assuming a direct translation of the UHC effective coverage be incorporated into UHC monitoring efforts, even though index to a fractional metric and multiplying country-level WHO and member states have signalled increasing interest in population estimates. understanding the impact of UHC beyond service coverage Implications of all the available evidence alone. This study offers another step forward in measuring UHC Added value of this study effective coverage across settings, developing a measurement Drawing from the WHO Thirteenth General Programme of framework and methods for country and global stakeholders Work (GPW13) Expert Reference Group and Task Force on to better track progress in effective health service provision at Metrics recommendations on UHC monitoring and conceptual the population level. Our results highlight the importance of work on effective coverage of health systems, the present including non-communicable disease indicators alongside study offers a new measurement framework for UHC effective interventions for reproductive, neonatal, maternal, and child coverage, representing health needs and corresponding health and for infectious diseases, as well as capturing service types across the life course while accounting for potential health gains delivered by health systems at the potential health gains delivered to populations. population level. In combination, we expect these analytical The framework mapped 23 effective coverage indicators advances to better identify where countries have improved against five health service domains—promotion, prevention, effective health service delivery, and what health needs along treatment, rehabilitation, and palliation—and five population- the life course increasingly threaten further progress. Focusing age groups (ie, reproductive and newborn, children <5 years, on UHC effective coverage, both in terms of its measurement children and adolescents aged 5–19 years, adults aged and its capacity for instilling greater accountability for 20–64 years, and adults aged ≥65 years). Based on estimates improving health outcomes across the development spectrum, from the Global Burden of Diseases, Injuries, and Risk Factors lays a data-driven path towards achieving UHC for all Study (GBD) 2019, these 23 effective coverage indicators populations. www.thelancet.com Vol 396 October 17, 2020 1251 Global Health Metrics Introduction involving more complex care (eg, cancer or stroke). Universal health coverage (UHC) is viewed as a crucial Tracer or proxy indicators of effective coverage exist for avenue through which improved health for all can be certain interventions or cause groups (eg, cancers), and attained,1,2 by ensuring all people can receive quality recent health-system research by Kruk and colleagues health services they need, without experiencing financial used mortality-to-incidence rates to garner insights into hardship. Global agendas and actors have amplified calls health-care quality in low-income to middle-income for UHC in recent years, driven at least in part by the countries.18 Nevertheless, to date no multi-country UHC explicit inclusion of UHC achievement in target 3.8 of measure ment effort to our knowledge has sought to the UN Sustainable Development Goals (SDGs)3–5 and estimate effective coverage across health service domains heightened emphasis within recent UN resolutions1 and and population-age groups within a cohesive analytical WHO programmatic objectives (eg, the target of 1 billion platform. more people benefiting from UHC from 2018 to 2023 as Following the 2014 WHO/World Bank UHC monitoring part of WHO’s Thirteenth General Programme of Work framework and SDG adoption in 2015, several multi- [GPW13]).6,7 Regional and country-driven efforts to elevate country health service coverage indices have been UHC on policy agendas have occurred as well, both developed to inform UHC measurement.19–26 Although building upon long establ ished UHC programmes (eg, in each effort has shown recognition of prevailing data Japan,8 much of western Europe,9,10 and many countries limitations and challenges with operationalising UHC in Latin America11,12) and galvanising newer commitments service coverage across myriad settings,21,24 they each have to UHC implementation (eg, in India, Kenya, and South limitations in how well they capture country-level trends Africa).13 To better understand how actions and investments and health service needs across the life course.17,27–31 First, are delivering on the ultimate goal of UHC—improving current indices primarily rely solely on household survey health outcomes—it is essential to quantify and track point estimates from multi-country survey series, which trends in effective health service provision, as well as the can lead to various measurement limitations (ie, being extent to which advances in service coverage correspond primarily focused on low-income to middle-income with the potential health gains populations should countries; restricted sets of interventions captured; and experience. lags in data availability for understanding trends). In 2014, WHO and the World Bank published a UHC Second, most indices include either risk factor indicators measurement framework in which service coverage (eg, prevalence of non-smoking and non-raised blood was defined as a spectrum of services—promotion, pressure in the UHC service coverage index,19–21 the prevention, treatment, rehabilitation, and palliation— SDG indicator 3.8.14) or health-system inputs or process across the life cycle.14,15 This framework emphasised indicators (eg, health workers per capita and hospital the importance of providing services for individuals’ beds per capita in the UHC service coverage index; health needs throughout their lifespans and quantifying inpatient admission rates for Wagstaff and colleagues’ effective coverage of interventions delivered by health service coverage index23,24), or both. The use of such proxy systems. Conceptually, effective coverage unites inter- indicators, as well as those influenced by factors outside vention need, use, and quality into a single metric, the health system (eg, tobacco prevalence), for service representing the proportion of health gain that could coverage measurement could misattribute successes in be potentially received from an intervention relative to health service provision or misrepresent UHC service what is actually experienced.16,17 At the health-system coverage. With non-communicable diseases accounting level, effective coverage aims to capture the fraction of for at least 60% of early death and disability worldwide,32 total potential health gains actually delivered relative to the omission of non-communicable disease indicators what a health system could have theoretically delivered.16 beyond risk factor prevalence proxies or cancer screening To quantify such population-level health gains, Shengelia is at odds with the reality of countries’ populations and and colleagues outlined an approach to measure an health systems. Third, approaches used to construct aggregate of health-system effective coverage.16 Effective overall indices of UHC service coverage typically involve coverage is a powerful measure: this metric not only somewhat arbitrary weighting schemes (eg, a series of demands accountability of intervention availability and geometric means4,19–21 or weighted geometric means23,24), use, but also requires that the services received are of and thus might not capture the alignment of services sufficient quality to provide the health gains they are provided given a country’s health and demographic supposed to. Yet in practice, effective coverage has to date profile. Last, none of these approaches explicitly accounts been rarely measured, particularly across countries and for the potential health gains delivered through the over time. Minimal uptake of effective coverage as a health system, a limitation that inhibits our collective metric for UHC monitoring is at least partly due to data understanding of whether or how gains in UHC are challenges, as most health data systems are not able to improving health outcomes for all. capture all three intervention components together (ie, Recent developments from WHO indicate a revived need, use or receipt, and quality) and few data sources interest in using effective coverage for UHC monitoring; can adequately represent these dimensions for conditions these include the WHO GPW13 Expert Reference Group 1252 www.thelancet.com Vol 396 October 17, 2020 Global Health Metrics (ERG) Task Force on Metrics recommendations on Measurement of UHC effective coverage effective service coverage measurement33 and the WHA72 Framework and indicators resolution recommending country pilots on monitoring Development of the UHC effective coverage measure- UHC effective coverage.7 The GPW13 ERG also supported ment framework and selection of effective coverage initial efforts to map health services against population- indicators was based on consultation, methods testing, age groups within a measurement framework and to and refinement via the WHO ERG on the GPW13 identify indicator options across the life course in order to from 2017 to 2019;7,33,38,39 the background and details of estimate UHC effective coverage across countries.33 The this process are provided in the appendix 1 (pp 12–28). present analysis contributes to this endeavour through The resulting framework (figure 1) and currently the Global Burden of Diseases, Injuries, and Risk Factors included effective coverage indicators (table 1) sought to Study (GBD) 2019, mapping 23 effective coverage indi- represent the range of different health services that cators across health service types and population-age populations need across their lifespans while recog- groups for 204 countries and territories from 1990 to 2019. nising present data gaps and appeals for measurement Based on the construct of health-system effective coverage, parsimony (appendix 1 pp 18–28). we aggregated individual effective coverage indicators As applied in this analysis, the UHC effective coverage to produce an overall index using health gain weights, measurement framework involves 30 unique cells which were derived from country-specific disease burden from a matrix of five health service types—promotion, estimates relative to theoretical levels of burden avertable prevention, treatment, rehabilitation, and palliation— given intervention levels and associated effectiveness. We against five population-age groups (reproductive and compared the performance of this UHC effective coverage newborn, children younger than 5 years, children and measure against that of previous multi-country UHC adolescents aged 5–19 years, adults aged 20–64 years, service coverage indices21,24,26 on a series of validity tests. We and older adults aged ≥65 years). Treatment is sub- then assessed the relationships between pooled health divided into two separate groups: first, communicable spending per capita and index performance, aiming to diseases and maternal, newborn, and child health; and capture how close—or how far—countries were in second, non-communicable diseases. Effective coverage reaching UHC effective coverage frontiers relative to their indica tors were then mapped to these cells to represent current spending. Finally, we considered applications of needed health services across the life course. this index for current global and national UHC priorities, 23 effective coverage indicators were included in the such as translating index performance to the number of present analysis (table 1). As recognised in previous people covered by effective coverage for the GPW13 UHC studies,19–26 data for directly measuring effective inter- billion target. vention coverage are rarely available across health services, locations, and over time. Subsequently, we used viable Methods proxy measures and analytical techniques to approximate Overview effective coverage for conditions considered amenable to Our primary analysis involved three main steps: first, to health care.40–43 Criteria set forth by the WHO ERG guided use intervention coverage or compute proxy measures of selection of effective coverage indicat ors and preferred effective coverage for 23 indicators; second, to calculate measurement approaches (appendix 1 pp 12–28).33 Such the fraction of potential health gains associated with criteria stipulated that effective coverage indicators should each effective coverage indicator based on each location’s be currently measurable (ie, data and methods that disease burden profile; and third, to construct the overall support indicator measurement today); reflect differences UHC effective coverage index by weighting each effective in effective health services and not factors outside the coverage indicator relative to its health gains fraction. immediate scope of health systems and UHC (eg, tobacco We then did secondary analyses, assessing UHC effec- taxation and physical infrastructure such as roads and tive coverage performance relative to health spending water systems); and use indicators already encompassed and current trajectories towards the GPW13 UHC billion within the SDGs and GPW13, or draw from data systems target. Each step is summarised below and further required for monitoring of SDGs and GPW13. Several described in appendix 1 (pp 12–61). other indicator candidates were considered from 2017 See Online for appendix 1 This analysis uses estimates from the broader to 2019 (appendix 1 pp 12–28), but inadequate data avail- GBD 2019,34–36 covering 204 countries and territories from ability, access, or quality, or a combination of these factors, 1990 to 2019. Details of disease-specific, injury-specific, impeded their inclusion in the current analysis. and coverage-specific data inputs and processing, statis- Four effective coverage indicators were measures tical synthesis approaches, and final models are available of intervention coverage and 19 were mortality-based in the accompanying GBD 2019 capstone publications.34–36 measures to proxy access to quality of care (table 1; This study complies with the Guidelines for Accurate appendix 1 pp 30–32). For the mortality-based measures, and Transparent Health Estimates Reporting (GATHER) we primarily used mortality-to-incidence ratios (MIRs) statement,37 with further information provided in the and mortality-to-prevalence ratios (MPRs) for chronic or appendix 1 (pp 69–72). longer-term conditions (eg, diabetes or asthma). Without www.thelancet.com Vol 396 October 17, 2020 1253 Global Health Metrics better data on effective coverage, such mortality-based risk standardisation aims to better isolate variations in measures are viewed as suitable proxies,33,44–46 providing mortality associated with health-care access and quality good signals on what access to quality care should, at from differences in underlying risk exposures mainly minimum, avert or protect against even if the onset of related to factors outside the health system. disease cannot be wholly prevented. The main exception Effective coverage indicators for intervention coverage was ischaemic heart disease, for which GBD input data were kept on their natural scale (0–100%), whereas the coverage and quality on non-fatal outcomes were less 19 other effective coverage indicators were transformed robust than data on causes of death and related risks; to values on a 0–100 scale (appendix pp 31–33). Across subsequently, we used risk-standardised death rates locations and from 1990 to 2019, 0 was set by values at instead of MIRs or MPRs to proxy effective coverage. As the 97·5th percentile or higher (ie, “worst” levels of a statistical approach used in previous GBD analyses41,43 MIRs) and 100 by the 2·5th percentile or lower (ie, “best” and further described in the appendix 1 (pp 31–32), levels of MIRs). Health service type Population age group Promotion Prevention Treatment Rehabilitation Palliation Communicable diseases and MNCH NCDs Reproductive and Met need for Antenatal, Antenatal, peripartum, and newborn family planning peripartum, and postnatal care for newborn with modern postnatal care for babies contraception newborn babies Antenatal, peripartum, and Antenatal, postnatal care for mothers peripartum, and postnatal care for mothers Children younger DTP3 coverage LRI treatment Acute lymphoid leukaemia than 5 years MCV1 coverage Diarrhoea treatment treatment Children and ART coverage Acute lymphoid leukaemia adolescents treatment (5–19 years) Asthma treatment Epilepsy treatment Appendicitis treatment Paralytic ileus and intestinal obstruction treatment Adults ART coverage Diabetes treatment (20–64 years) TB treatment IHD treatment Stroke treatment CKD treatment COPD treatment Cervical cancer treatment Breast cancer treatment Uterine cancer treatment Colon and rectum cancer treatment Epilepsy treatment Appendicitis treatment Paralytic ileus and intestinal obstruction treatment Older adults ART coverage Diabetes treatment (≥65 years) TB treatment IHD treatment Stroke treatment CKD treatment COPD treatment Cervical cancer treatment Breast cancer treatment Uterine cancer treatment Colon and rectum cancer treatment Epilepsy treatment Appendicitis treatment Paralytic ileus and intestinal obstruction treatment Figure 1: UHC effective coverage measurement framework Additional information about the framework development process and selection of effective coverage indicators can be found in appendix 1 (pp 12–28). ART=antiretroviral therapy. DTP3=diphtheria-tetanus-pertussis vaccine, 3 doses. IHD=ischaemic heart disease. CKD=chronic kidney disease. COPD=chronic obstructive pulmonary disease. LRI=lower respiratory infection. MCV1=measles-containing-vaccine, 1 dose. MNCH=maternal, neonatal, and child health. NCDs=non-communicable diseases. TB=tuberculosis. UHC=universal health coverage. 1254 www.thelancet.com Vol 396 October 17, 2020 Global Health Metrics Effective coverage Metric Effective coverage indicator measurement Health gain weight inputs Effectiveness indicator category Numerator Denominator Reproductive and newborn Promotion Met need for family Coverage Females aged 15–49 years with Females aged 15–49 years with 50% of DALYs due to maternal 5 planning with modern demand for family planning met demand for family planning disorders for females aged contraception with modern contraception 10–54 years Prevention; Antenatal, peripartum, Early All-cause deaths during the Population of early neonates Early neonatal deaths multiplied 3 treatment, and postnatal care for neonatal first 7 days of life by life expectancy at birth (on the communicable newborn babies mortality rate basis of theoretical minimum risk diseases and MNCH life table) Prevention; Antenatal, peripartum, Maternal Deaths due to maternal disorders for Livebirths among females aged 50% of DALYs due to maternal 1 treatment, and postnatal care for mortality females aged 10–54 years 10–54 years disorders for females aged communicable mothers ratio 10–54 years diseases and MNCH Children younger than 5 years Prevention DTP3 vaccine coverage Coverage Receipt of three doses of DTP Children aged 12–23 months DALYs due to diphtheria, tetanus, 1 vaccine among children aged and pertussis for children younger 12–23 months than 5 years Prevention MCV1 coverage Coverage Receipt of MCV1 among children Children aged 12–23 months DALYs due to measles for children 1 aged 12–23 months younger than 5 years Treatment, LRI treatment MIR Mortality from LRIs for children Incidence of LRIs for children DALYs due to LRIs for children 1 communicable younger than 5 years younger than 5 years younger than 5 years diseases and MNCH Treatment, Diarrhoea treatment MIR Mortality from diarrhoeal diseases Incidence of diarrhoeal diseases DALYs due to diarrhoeal diseases 1 communicable for children younger than 5 years for children younger than 5 years for children younger than 5 years diseases and MNCH Treatment, NCDs Acute lymphoid MIR Mortality from acute lymphoid Incidence of acute lymphoid DALYs due to acute lymphoid 1 leukaemia treatment leukaemia for children aged leukaemia for children aged leukaemia for children aged 1–4 years 1–4 years 1–4 years Children and adolescents (5–19 years) Treatment, ART coverage Coverage Populations aged 5–19 years living Populations aged 5–19 years DALYs due to HIV for populations 1 communicable with HIV/AIDS and on ART living with HIV/AIDS aged 5–19 years diseases and MNCH Treatment, NCDs Acute lymphoid MIR Mortality from acute lymphoid Incidence of acute lymphoid DALYs due to acute lymphoid 1 leukaemia treatment leukaemia for populations aged leukaemia for populations aged leukaemia for populations 5–19 years 5–19 years 5–19 years Treatment, NCDs Asthma treatment MPR Mortality from asthma for Prevalence of asthma for DALYs due to asthma for 1 populations aged 5–19 years populations aged 5–19 years populations aged 5–19 years Treatment, NCDs Epilepsy treatment MPR Mortality from epilepsy for Prevalence of epilepsy for DALYs due to epilepsy for 3 populations aged 5–19 years populations aged 5–19 populations aged 5–19 years Treatment, NCDs Appendicitis treatment MIR Mortality from appendicitis for Incidence of appendicitis for DALYs due to appendicitis for 1 populations aged 5–19 years populations aged 5–19 years populations aged 5–19 years Treatment, NCDs Paralytic ileus and MIR Mortality from paralytic ileus and Incidence of paralytic ileus and DALYs due to paralytic ileus and 1 intestinal obstruction intestinal obstruction for intestinal obstruction for intestinal obstruction for treatment populations aged 5–19 years populations aged 5–19 years populations aged 5–19 years Adults (20–64 years) Treatment, ART coverage Coverage Population aged 20–64 years living Population aged 20–64 years DALYs due to HIV for populations 1 communicable with HIV/AIDS and on ART living with HIV/AIDS aged 20–64 years diseases and MNCH Treatment, Tuberculosis treatment MIR Mortality from tuberculosis for Incidence of tuberculosis for DALYs due to tuberculosis for 1 communicable populations aged 20–64 years populations aged 20–64 years populations aged 20–64 years diseases and MNCH Treatment, NCDs Diabetes treatment MPR Mortality from diabetes for Prevalence of diabetes for DALYs due to diabetes for 3 populations aged 20–64 years populations aged 20–64 years populations aged 20–64 years Treatment, NCDs IHD treatment RSDR Risk-standardised deaths from IHD Population aged 20–64 years DALYs due to IHD for 2 for populations aged 20–64 years populations aged 20–64 years Treatment, NCDs Stroke treatment MIR Mortality from stroke for Incidence of stroke for DALYs due to stroke for 2 populations aged 20–64 years populations aged 20–64 years populations aged 20–64 years Treatment, NCDs CKD treatment MPR Mortality from CKD for Incidence of CKD for DALYs due to CKD for 1 populations aged 20–64 years populations aged 20–64 years populations aged 20–64 years (Table 1 continues on next page) www.thelancet.com Vol 396 October 17, 2020 1255 Global Health Metrics Effective coverage Metric Effective coverage indicator measurement Health gain weight inputs Effectiveness indicator category Numerator Denominator (Continued from previous page) Treatment, NCDs COPD treatment MPR Mortality from COPD for Prevalence of COPD for DALYs due to COPD for 3 populations aged 20–64 years populations aged 20–64 years populations aged 20–64 years Treatment, NCDs Cervical cancer MIR Mortality from cervical cancer for Incidence of cervical cancer for DALYs due to cervical cancer for 1 treatment females aged 20–64 years females aged 20–64 years females aged 20–64 years Treatment, NCDs Breast cancer treatment MIR Mortality from breast cancer for Incidence of breast cancer for DALYs due to breast cancer for 1 females aged 20–64 years females aged 20–64 years females aged 20–64 years Treatment, NCDs Uterine cancer MIR Mortality from uterine cancer for Incidence of uterine cancer for DALYs due to uterine cancer for 1 treatment females aged 20–64 years females aged 20–64 years females aged 20–64 years Treatment, NCDs Colon/rectum cancer MIR Mortality from colon/rectum Incidence of colon/rectum for DALYs due to colon/rectum 1 treatment cancer for populations aged populations aged 20–64 years cancer for populations aged 20–64 years 20–64 years Treatment, NCDs Epilepsy treatment MPR Mortality from epilepsy for Prevalence of epilepsy for DALYs due to epilepsy for 3 populations aged 20–64 years populations aged 20–64 years populations aged 20–64 years Treatment, NCDs Appendicitis treatment MIR Mortality from appendicitis for Incidence of appendicitis for DALYs due to appendicitis for 1 populations aged 20–64 years populations aged 20–64 years populations aged 20–64 years Treatment, NCDs Paralytic ileus and MIR Mortality from paralytic ileus and Incidence of paralytic ileus and DALYs due to paralytic ileus and 1 intestinal obstruction intestinal obstruction for intestinal obstruction for intestinal obstruction for treatment populations aged 20–64 years populations aged 20–64 years populations aged 20–64 years Older adults (≥65 years) Treatment, ART coverage Coverage Population aged ≥65 years living Population aged ≥65 years DALYs due to HIV for populations 2 communicable with HIV/AIDS and on ART living with HIV/AIDS aged ≥65 years diseases and MNCH Treatment, Tuberculosis treatment MIR Mortality from tuberculosis for Incidence of tuberculosis for DALYs due to tuberculosis for 2 communicable populations aged ≥65 years populations aged ≥65 years populations aged ≥65 years diseases and MNCH Treatment, NCDs Diabetes treatment MPR Mortality from diabetes for Prevalence of diabetes for DALYs due to diabetes for 4 populations aged ≥65 years populations aged ≥65 years populations aged ≥65 years Treatment, NCDs IHD treatment RSDR Risk-standardised deaths from IHD Population aged ≥65 years DALYs due to IHD for 3 for populations aged ≥65 years populations aged ≥65 years Treatment, NCDs Stroke treatment MIR Mortality from stroke for Incidence of stroke for DALYs due to stroke for 3 populations aged ≥65 years populations aged ≥65 years populations aged ≥65 years Treatment, NCDs CKD treatment MPR Mortality from CKD for Incidence of CKD for DALYs due to CKD for 2 populations aged ≥65 years populations aged ≥65 years populations aged ≥65 years Treatment, NCDs COPD treatment MPR Mortality from COPD for Prevalence of COPD for DALYs due to COPD for 4 populations aged ≥65 years populations aged ≥65 years populations aged ≥65 years Treatment, NCDs Cervical cancer MIR Mortality from cervical cancer for Incidence of cervical cancer for DALYs due to cervical cancer for 2 treatment females aged ≥65 years females aged ≥65 years females aged ≥65 years Treatment, NCDs Breast cancer MIR Mortality from breast cancer for Incidence of breast cancer for DALYs due to breast cancer for 2 treatment females aged ≥65 years females aged ≥65 years females aged ≥65 years Treatment, NCDs Uterine cancer MIR Mortality from uterine cancer for Incidence of uterine cancer for DALYs due to uterine cancer for 2 treatment females aged ≥65 years females aged ≥65 years females aged ≥65 years Treatment, NCDs Colon/rectum cancer MIR Mortality from colon/rectum Incidence of colon/rectum DALYs due to colon/rectum 2 treatment cancer for populations aged cancer for populations aged cancer for populations aged ≥65 years ≥65 years ≥65 years Treatment, NCDs Epilepsy treatment MPR Mortality from epilepsy for Prevalence of epilepsy for DALYs due to epilepsy for 4 populations aged ≥65 years populations aged ≥65 years populations aged ≥65 years Treatment, NCDs Appendicitis treatment MIR Mortality from appendicitis for Incidence of appendicitis for DALYs due to appendicitis for 2 populations aged ≥65 years populations aged ≥65 years populations aged ≥65 years Treatment, NCDs Paralytic ileus and MIR Mortality from paralytic ileus and Incidence of paralytic ileus and DALYs due to paralytic ileus and 2 intestinal obstruction intestinal obstruction for intestinal obstruction for intestinal obstruction for treatment populations aged ≥65 years populations aged ≥65 years populations aged ≥65 years Additional information about the framework development process and selection of effective coverage indicators can be found in appendix 1 (pp 12–28). UHC=universal health coverage. DALYs=disability- adjusted life-years. MNCH=maternal, neonatal, and child health. DTP3=diphtheria-tetanus-pertussis vaccine, 3 doses. MCV1=measles-containing-vaccine, 1 dose. LRI=lower respiratory infection. MIR=mortality-to-incidence ratio. NCDs=non-communicable diseases. ART=antiretroviral therapy. MPR=mortality-to-prevalence ratio. IHD=ischaemic heart disease. RSDR=risk-standardised death rate. CKD=chronic kidney disease. COPD=chronic obstructive pulmonary disease. Table 1: Details of the 23 effective coverage indicators included in the UHC effective coverage index, by health service type 1256 www.thelancet.com Vol 396 October 17, 2020 Global Health Metrics Construction of UHC effective coverage index GBD super-region As outlined by previous work,14–17 population-level measures 100 Central Europe, eastern Europe, and central Asia of effective coverage should represent the fraction of total High income JPN health gains a health system could potentially provide, Latin America and Caribbean NOR North Africa and Middle East CHE given currently available interventions, that a health SGPSouth Asia SVN system actually delivers. This construct is thus grounded Southeast Asia, east Asia, and Oceania KOR AUT in the principle of comparability—all health systems ought Sub-Saharan Africa DNK PRT EST to maximise potential health gains for their populations— QAT80 GRC but also requires accounting for local health needs and PER SVK epidemiological profiles. For instance, if a country PRITHA HUN currently experiences a high burden of diabetes and a CHN BLR comparatively lower burden of HIV, at least equal or even MDV RUS BRA LKA MNE higher priority in expanding services for diabetes should BIHNAM CPV SRB occur relative to HIV in order to further support health 60 RWA BWA ZAF MKD gains. MWI MAR KAZ ZWE PHL STP UKRBLZ To construct the UHC effective coverage index, we KEN UGA weighted each effective coverage indicator relative to VIRBDI SEN GHA VCT GRD their health gain weights, a metric approximating the ETH GMB MNG AZE population health gains potentially deliverable by FJIMOZ SLE LAO PLW TKM health systems for each location-year. More detail is 40 LSO SLB GUY UZB provided in the appendix 1 (pp 32–35), but in brief, KIR GNB TUV VUT PNG calculations were based on three inputs for each FSM effective coverage indicator and corresponding pop- TCD GIN ulation-age group: estimates on the 0–100 scale, tar- geted disease burden, and effectiveness categories of SOM associated interventions or services (table 1). For effec- 20 CAF tiveness, incremental values were assumed by category (ie, 90% effectiveness for category 1, 70% for category 2, 20 40 60 80 100 50% for category 3, and so on), as informed by studies UHC effective coverage index, unweighted average published in the Cochrane Database of Systematic Reviews, the Tufts Cost-Effectiveness Analysis Registry Figure 2: Comparing the UHC effective coverage index in 2019 with health gains weighting to the and Global Health Cost-Effectiveness Analysis Registry, unweighted index (unweighted average of effective coverage indicators) in 2019Locations are colour-coded by GBD super-region, and are abbreviated according to their ISO3 codes. ISO3 codes and Disease Control Priorities, third edition (DCP3); and corresponding location names are listed in appendix 1 (pp 64–68). UHC=universal health coverage. sensitivity analyses on shifting each effective coverage GBD=Global Burden of Diseases, Injuries, and Risk Factors Study. indicator by one category (ie, moving each category 2 indicator up to category 1 and then down to category 3) population-age groups) from the UHC effective For the Cochrane Database of showed high correlations with current assignments coverage framework that were represented by indi- Systematic Reviews see (appendix 1 p 35). cators for each index. For known-groups validity, we https://www.cochranelibrary. com/cdsr/reviews As shown in figure 2, UHC effective coverage index assessed how well each index could discriminate For the Tufts Cost-Effectiveness estimates based on health gain weighting and an between 16 country-pairs for which previous studies Analysis Registry see unweighted average across effective coverage indicators show “country A” as having better performance or https://cevr.tuftsmedicalcenter. were positively associated (r=0·95); however, effects progress on UHC service coverage than a similar org/databases/cea-registry differed across countries. “country B”.11,23,47–55 These pairs were selected a priori, For the Global Health Cost- and for each index we calculated the fraction of pairs Effectiveness Analysis Registry see https://ghcearegistry.org/ Validation correctly ordered on the basis of mean estimates ghcearegistry/ Since no gold-standard measures of UHC service and accounting for uncertainty where available. For For more on Disease Control coverage currently exist, we used three types of validity convergent validity, we quantified how much variation Priorities, third edition see testing to compare UHC effective coverage index perfor- in healthy life expectancy could be explained by each https://dcp-3.org/ mance to previously published multi-country indices of index after removing the average relationship between UHC service coverage: the WHO UHC service coverage each index and overall socio demographic development index for 2017;21 UHC service coverage index from (as measured by Socio-demographic Index [SDI]). In GBD 2017;26 and service coverage index values from general, the UHC effective coverage index based on the World Bank.24 Further details of these analyses are health gain weights showed stronger performance provided in the appendix 1 (pp 38–52), with results across these three validity mea sures than previous summarised in table 2. UHC service coverage measures and the unweighted For content validity, we computed the percentage UHC effective coverage index (table 2; appendix 1 of 30 cells (ie, combinations of health services and pp 38–52). www.thelancet.com Vol 396 October 17, 2020 1257 UHC effective coverage index, health gains weighted Global Health Metrics Source Content validity Known-groups validity Convergent validity (variation of HALE (proportion of (proportion of 16 country explained, accounting for SDI) cells covered) pairs) Based on mean With Beta Standard R2 values uncertainty coefficient error UHC effective coverage index, health GBD 2019 40% 94% 63% 5·00 1·72 0·073 gains weighted (reported 2019) UHC effective coverage index, GBD 2019 40% 94% 56% 4·19 1·49 0·068 unweighted average (reported 2019) UHC service coverage index for SDGs GBD 2017 33% 94% 69% 4·30 1·76 0·053 (reported 2017) UHC service coverage index for SDG WHO 2019 20% 75% ·· 4·21 1·88 0·044 indicator 3.8.1 (reported 2017) Service coverage index (for most recent World Bank 17% 56% ·· 1·24 1·18 0·010 year reported) 2020 Content validity was evaluated on the basis of the percentage of 30 matrix cells of health service types against population-age groups covered by each index. Known-groups validity was evaluated on the basis of the percentage of 16 country pairs correctly ranked based on country A’s UHC or health-system performance being recognised as better than country B’s performance; details are found in appendix 1 (pp 45–47). Convergent validity was evaluated on the basis of how much index performance could explain variation in HALE after controlling for levels of sociodemographic development (as measured by SDI). UHC=Universal health coverage. HALE=healthy life expectancy. SDI=Socio-demographic Index. GBD=Global Burden of Diseases, Injuries, and Risk Factors Study. SDGs=UN Sustainable Development Goals. Table 2: Results for content, known-groups, and construct validity across multi-country health service indices for UHC service coverage measurement Relationship between health spending and UHC analysis, we used a similar approach currently recom- effective coverage mended by WHO:58 we applied index estimates as To better understand potential drivers of UHC effective fractional metrics and multiplied these values by coverage, we used stochastic frontier metaregression populations to approxim ate population equivalents to quantify UHC effective coverage frontiers—esti- with UHC effective coverage. mated maximum levels of UHC effective coverage index To assess UHC effective coverage trajectories and achieved given any amount of health spending per their contributions towards meeting the UHC 1 billion capita—and compared country-level UHC effective target, we first projected country-level UHC effective coverage performance relative to these frontiers. The coverage index estimates through to 2023. These magnitude of these gaps between the frontier and UHC projections were based on stochastic frontier meta- effective coverage index values provides insights into regression modelled relationships between UHC potential inefficiencies, as well as measurement error, in effective coverage index and total health spending per translating health spending into improved UHC effective capita; a related method has been used previously by coverage at the population level. Further analytical details GBD26,59 and is described further in the appendix 1 are in the appendix 1 (pp 53–59). (pp 60–61). Taking UHC effective coverage index as a Since UHC aims to minimise financial hardship fraction, we multiplied these values by country-level associated with receiving essential health services, we GBD-based population forecasts through to 2023.60 focused on assessing the relationship between pooled Last, we aggregated these estimates globally and by health spending per capita (ie, government spending, GBD super-region, and calculated additional population prepaid private health spending, and development equivalents with UHC effective coverage from 2018 (the assistance for health)56 and UHC effective coverage GPW13 baseline) to 2023. performance. Alternative analyses, wherein out-of-pocket spending was included (ie, total health expenditure) and Uncertainty analysis then development assistance for health was excluded GBD aims to propagate sources of uncertainty through (ie, pooled domestic health expenditures), were also done its estimation process,34–36 resulting in 1000 draws See Online for appendix 2 but are not reported here (appendix 2 pp 6–7). from the posterior distribution for each measure by location, age, sex, and year. We incorporated uncertainty Counting population equivalents with UHC effective quantified for each effective coverage indicator and coverage associated disease burden based on GBD 2019 esti- Spurred by the GPW13 UHC billion target,6 which mates, and did scaling, index construction, and UHC calls for 1 billion more people benefiting from UHC effective coverage index projections at the draw-level to by 2023, various approaches have been considered reflect uncertainty. We report 95% uncertainty intervals for translating performance metrics into the number (95% UIs) based on the ordinal 25th and 975th draws of people covered by health services.20,21,57,58 For this for each measure. 1258 www.thelancet.com Vol 396 October 17, 2020 Global Health Metrics UHC effective coverage index deciles 22·3 to <41·0 59·2 to <62·5 41·0 to <46·3 62·5 to <69·2 46·3 to <50·4 69·2 to <76·5 50·4 to <54·0 76·5 to <87·7 54·0 to <59·2 87·7 to 96·3 Eastern Caribbean and central America Persian Gulf Balkan Peninsula Southeast Asia West Africa Mediterranean Northern Europe Figure 3: Map of the UHC effective coverage index, by decile, in 2019 Deciles are based on the distribution of UHC effective coverage index values in 2019. UHC=universal health coverage. Role of the funding source the eighth (China and Thailand) and second deciles The funder of the study had no role in study design, data (Laos), with India and Indonesia occupying the third collection, data analysis, data interpretation, or writing of decile. Within Latin America, various countries scored in the report. The corresponding author had full access to the eighth or seventh deciles (eg, Chile, Colombia, Peru, all the data in the study and had final responsibility for and Brazil) but others saw UHC effective coverage index the decision to submit for publication. values within the fourth to fifth deciles (eg, Bolivia, Guatemala, and Nicaragua). Results Performance on the overall UHC effective coverage National UHC effective coverage patterns in 2019 index often corresponded with levels achieved across In 2019, UHC effective coverage performance showed individual effective coverage indicators (figure 4); for some strong geographical patterns (figure 3), but sizeable instance, countries with effective coverage index values heterogeneities also emerged. Various European coun- of 85 or higher generally had the vast majority of effec- tries, including Iceland, as well as Australia, Canada, tive coverage indicators exceeding 80. Although high- Japan, Singapore, and South Korea, comprised the highest performing locations usually had lower values for at least decile, followed by a more geographically diverse group in some subsets of indicators (eg, met need for family the ninth decile (eg, Costa Rica, Israel, New Zealand, planning or antiretroviral therapy coverage), such indi- Portugal, and the USA). Sub-Saharan Africa had among cators often represented areas of lower potential health the widest range of UHC effective coverage performances gains—especially relative to effective coverage indicators in 2019, with two countries ranking in the sixth decile proxying health services or interventions for conditions (Rwanda and South Africa) and 11 countries in the first with higher potential health gains in these countries decile; the countries in the first decile were mainly in (eg, cardiovascular diseases, cancers, and diabetes). western or central sub-Saharan Africa, but also spanned Countries and territories with fairly low overall UHC the continent (eg, Angola, Lesotho, Madagascar, and effective coverage index performance in 2019 (ie, <40) Somalia). Outside of sub-Saharan Africa, ten countries, scored similarly low across most effective coverage including Afghanistan, Haiti, Pakistan, and Papua New indicators, although vaccine coverage and proxies for Guinea, were also in the lowest decile in 2019. In east, lower respiratory infection and diarrhoea treatment were southeast, and south Asia, countries largely fell between among the main exceptions. www.thelancet.com Vol 396 October 17, 2020 1259 Global Health Metrics Index value 100 75 50 25 0 Afghanistan 39 43 18 11 62 65 92 62 45 60 5 18 15 18 7 21 27 41 9 39 20 23 67 50 Albania 70 10 56 81 92 98 100 90 41 92 58 84 81 88 65 60 31 94 70 81 68 57 98 99 Algeria 65 75 36 38 84 92 99 95 80 94 26 70 63 67 52 46 60 72 41 65 47 62 96 97 American Samoa 53 75 66 36 91 82 99 93 57 87 10 50 55 58 42 42 56 40 13 21 44 53 88 86 Andorra 92 88 100 100 98 99 100 100 39 95 99 98 93 100 97 99 94 96 68 89 79 74 98 97 Angola 39 32 18 19 54 42 80 65 22 46 6 19 18 21 11 50 38 28 24 50 28 56 79 63 Antigua and Barbuda 60 83 64 50 94 92 99 93 46 100 26 76 58 68 65 67 49 34 26 77 74 53 92 83 Argentina 61 73 58 44 92 86 99 95 55 67 26 66 68 76 53 79 51 53 28 67 78 71 95 70 Armenia 62 43 60 63 94 93 99 78 83 84 35 72 62 80 59 34 48 42 67 63 100 89 95 95 Australia 89 88 85 92 91 96 100 99 85 99 87 98 100 86 100 100 88 78 70 92 69 67 100 98 Austria 86 81 89 97 94 88 100 100 85 95 99 93 79 99 95 84 99 72 61 95 91 78 100 99 Azerbaijan 48 32 27 63 87 82 97 37 83 77 15 61 52 73 45 13 18 44 48 69 72 47 96 94 The Bahamas 61 86 60 36 89 90 99 91 76 63 14 72 63 67 57 56 53 43 28 71 65 65 89 74 Bahrain 71 69 88 52 99 100 100 100 70 96 49 79 66 85 69 65 81 35 46 81 48 67 98 94 Bangladesh 54 79 22 15 94 92 96 69 60 79 7 41 36 43 24 48 11 47 52 51 15 56 94 85 Barbados 61 67 47 45 91 90 100 95 86 89 28 77 64 71 66 68 56 31 35 77 62 60 93 74 Belarus 70 73 85 73 97 100 100 99 76 80 69 85 75 94 80 13 42 90 98 73 98 55 98 97 Belgium 87 86 90 92 96 99 100 100 85 96 99 94 71 98 91 96 78 91 72 86 77 70 99 92 Belize 54 66 48 39 89 82 97 85 54 60 11 65 52 59 47 66 53 24 18 57 50 52 87 70 Benin 45 28 9 12 73 81 77 32 56 53 7 20 18 20 12 51 33 30 17 52 27 45 75 51 Bermuda 78 87 81 60 99 98 100 99 54 100 82 95 83 87 92 76 86 57 56 86 83 91 97 98 Bhutan 51 80 21 20 99 100 95 70 65 75 8 45 40 48 28 43 33 47 27 29 23 54 96 87 Bolivia 52 52 33 22 95 92 98 80 64 71 8 49 42 43 49 62 46 26 4 52 60 56 88 55 Bosnia and Herzegovina 64 26 93 96 68 78 100 100 68 79 50 71 69 83 60 56 50 49 62 76 100 66 96 85 Botswana 58 78 20 32 85 84 86 71 94 66 10 47 45 49 29 60 37 10 14 45 23 50 87 68 Brazil 65 88 40 42 99 86 98 89 63 85 25 73 59 72 57 66 65 44 37 61 76 73 68 77 Brunei 66 69 65 51 98 98 96 94 62 87 31 77 76 69 66 54 74 41 30 65 68 54 97 86 Bulgaria 63 54 75 73 91 90 99 94 78 90 47 84 75 90 68 32 27 59 51 75 95 63 92 81 Burkina Faso 42 47 14 16 95 94 68 6 56 48 6 20 20 19 11 39 22 25 19 57 31 36 64 31 Burundi 50 38 19 12 91 95 69 72 75 49 6 14 12 14 7 38 26 18 23 28 18 36 48 40 Cape Verde 62 69 42 41 100 100 97 86 83 80 12 56 44 54 34 38 50 44 30 59 58 52 96 90 Cambodia 57 59 26 27 91 92 97 56 79 77 8 40 42 53 27 50 24 34 39 47 28 65 87 67 Cameroon 42 45 18 9 71 78 60 45 45 44 6 25 24 27 16 51 34 15 12 59 28 45 82 65 Canada 90 87 75 83 93 86 100 100 86 96 98 99 99 98 100 87 100 70 70 87 77 68 99 100 Central African Republic 22 20 5 3 49 48 21 33 37 13 5 1 1 1 0 20 7 19 9 23 10 31 42 19 Chad 31 16 10 4 58 39 21 25 40 28 6 11 8 10 6 51 31 28 15 39 16 35 65 23 Chile 74 78 68 61 93 93 100 99 75 64 60 80 75 85 69 98 62 73 44 82 86 70 97 88 (Figure 4 continues on next page) 1260 www.thelancet.com Vol 396 October 17, 2020 UHC effective coverage index Met need for family planning with modern contraception Antenatal, peripartum, and postnatal care for newborn babies Antenatal, postpartum, and postnatal care for mothers MCV1 coverage DTP3 coverage Diarrhoea treatment LRI treatment ART coverage TB treatment Acute lymphoid leukaemia treatment Breast cancer treatment Cervical cancer treatment Uterine cancer treatment Colon and rectum cancer treatment IHD treatment Stroke treatment Diabetes treatment CKD treatment COPD treatment Asthma treatment Epilepsy treatment Appendicitis treatment Paralytic ileus and intestinal obstruction treatment Global Health Metrics Index value 100 75 50 25 0 China 70 96 70 75 98 99 99 90 32 97 72 91 60 88 86 66 43 80 61 41 86 65 98 98 Colombia 74 87 56 42 94 91 99 93 79 87 30 84 67 82 71 73 80 76 55 67 83 77 91 89 Comoros 48 33 11 24 83 84 85 60 66 61 6 18 20 22 11 46 46 19 25 42 33 50 68 59 Congo 44 40 24 14 72 65 83 83 30 48 6 22 21 24 13 36 37 24 18 47 36 62 80 69 Cook Islands 62 78 100 95 96 83 100 98 21 90 35 73 73 85 69 56 78 23 37 75 74 79 95 94 Costa Rica 79 86 61 55 100 97 99 98 85 82 42 89 73 86 77 80 88 84 48 75 80 82 92 95 Côte d’Ivoire 43 40 7 13 72 75 86 46 54 42 7 19 18 19 12 51 43 25 16 48 31 44 78 60 Croatia 79 39 83 91 89 91 100 100 81 90 91 87 98 100 82 64 55 80 75 85 99 69 99 95 Cuba 73 89 89 46 99 99 100 97 76 96 49 88 74 83 79 52 63 85 51 73 70 74 90 90 Cyprus 80 85 92 100 89 99 100 100 33 94 98 98 64 98 99 77 69 66 52 77 100 85 100 89 Czech Republic 82 69 100 100 94 94 100 100 84 93 90 88 79 93 80 66 76 86 87 87 73 77 98 95 Democratic Republic of the Congo 45 23 19 7 82 73 81 75 78 40 6 15 13 15 8 42 25 31 22 33 20 46 79 62 Denmark 84 81 82 100 92 97 100 100 70 95 100 90 91 97 90 99 75 67 71 81 85 66 98 93 Djibouti 45 52 20 8 83 57 88 60 19 56 6 25 20 27 15 59 45 16 24 57 39 53 77 67 Dominica 52 83 29 32 86 93 98 86 47 71 10 62 51 57 44 64 51 38 14 59 47 40 85 62 Dominican Republic 52 84 27 32 83 75 97 92 71 62 10 64 54 59 46 28 42 28 23 56 35 55 80 94 Ecuador 64 72 51 38 75 81 99 92 63 75 16 69 61 68 68 80 70 37 13 65 75 65 94 80 Egypt 55 85 73 51 97 96 89 88 75 86 12 57 37 67 42 12 67 34 28 59 36 79 95 85 El Salvador 62 82 63 50 97 87 98 96 58 81 17 75 50 74 58 58 73 40 10 69 75 75 91 90 Equatorial Guinea 50 33 24 24 39 45 96 82 30 66 6 36 31 37 22 69 52 27 23 58 43 78 90 83 Eritrea 42 30 23 5 100 95 73 69 45 49 6 13 10 13 8 41 15 13 17 37 28 41 55 40 Estonia 82 73 100 96 91 100 100 99 86 79 82 90 93 100 91 61 72 78 74 84 87 53 100 99 eSwatini 53 81 28 34 93 88 79 61 93 40 6 26 23 27 16 56 32 1 3 43 20 40 79 56 Ethiopia 47 62 16 16 60 57 74 71 51 63 12 21 17 22 13 60 31 14 28 31 35 41 12 4 Federated States of Micronesia 34 61 55 29 74 60 98 88 12 58 8 36 44 52 30 16 24 10 2 2 22 37 75 72 Fiji 45 72 44 35 80 86 95 84 70 75 6 40 45 54 26 19 51 5 21 9 6 49 76 73 Finland 91 89 100 96 93 88 100 100 80 93 98 100 94 96 97 72 83 100 91 96 94 68 100 95 France 91 92 90 85 90 97 100 100 87 87 99 95 87 99 92 100 84 71 83 97 86 67 99 91 Gabon 53 49 29 20 69 78 92 86 72 69 7 34 29 35 19 45 46 21 14 55 40 70 88 83 The Gambia 48 30 20 4 89 93 91 72 33 55 6 22 20 24 13 43 38 24 16 48 32 37 80 52 Georgia 56 52 62 51 93 84 99 95 83 88 20 66 57 74 50 31 18 55 60 78 37 71 96 82 Germany 86 83 88 90 97 97 100 100 83 97 100 93 94 98 91 80 87 90 61 97 81 81 99 93 Ghana 49 43 17 21 94 93 89 70 37 50 6 32 30 35 20 45 38 25 17 47 27 68 67 61 Greece 80 61 90 90 97 100 100 99 86 82 97 92 70 98 89 67 54 98 59 89 99 85 99 94 Greenland 69 75 60 70 91 95 100 98 75 87 21 67 81 67 56 71 72 67 57 50 65 45 94 75 Grenada 50 79 45 43 92 88 99 87 59 94 13 68 55 59 53 51 45 31 16 65 66 54 92 61 Guam 64 83 54 50 95 90 100 90 24 92 19 63 67 78 57 28 81 55 34 35 63 85 96 97 (Figure 4 continues on next page) www.thelancet.com Vol 396 October 17, 2020 1261 UHC effective coverage index Met need for family planning with modern contraception Antenatal, peripartum, and postnatal care for newborn babies Antenatal, postpartum, and postnatal care for mothers MCV1 coverage DTP3 coverage Diarrhoea treatment LRI treatment ART coverage TB treatment Acute lymphoid leukaemia treatment Breast cancer treatment Cervical cancer treatment Uterine cancer treatment Colon and rectum cancer treatment IHD treatment Stroke treatment Diabetes treatment CKD treatment COPD treatment Asthma treatment Epilepsy treatment Appendicitis treatment Paralytic ileus and intestinal obstruction treatment Global Health Metrics Index value 100 75 50 25 0 Guatemala 52 68 54 28 82 78 92 79 67 59 9 58 36 49 36 64 59 31 16 51 48 44 78 67 Guinea 32 21 12 2 49 45 74 24 47 36 6 12 14 13 8 42 29 21 12 45 22 34 73 55 Guinea-Bissau 36 48 9 15 80 74 65 68 47 13 6 16 17 14 10 26 20 16 5 40 21 32 61 22 Guyana 41 57 28 22 95 91 94 83 62 54 7 53 43 47 35 21 24 23 5 58 43 36 64 52 Haiti 36 45 15 3 72 71 75 53 55 60 4 28 22 17 17 15 16 30 13 37 19 24 44 20 Honduras 54 78 46 30 93 89 93 95 85 59 8 57 34 58 39 54 21 63 13 33 46 51 70 84 Hungary 72 57 92 74 97 98 100 99 79 96 77 81 69 90 73 51 67 75 74 69 83 79 95 95 Iceland 95 87 95 100 94 91 100 100 86 98 100 99 98 100 99 93 96 100 88 99 89 78 100 99 India 47 75 17 22 94 90 80 59 72 76 15 36 35 43 23 34 26 54 34 29 32 39 47 53 Indonesia 49 78 32 27 82 77 89 85 48 70 4 43 41 55 33 38 33 9 39 42 23 96 43 32 Iran 70 78 55 68 99 98 99 96 40 89 43 77 64 83 62 52 68 62 56 69 52 74 96 98 Iraq 58 55 44 50 79 78 99 94 61 86 18 64 53 74 48 41 47 44 31 72 54 67 99 96 Ireland 90 80 87 97 91 95 100 100 84 93 100 97 93 100 97 87 82 98 80 87 75 73 100 97 Israel 81 83 95 90 98 96 100 100 84 94 93 88 84 95 83 99 87 57 45 90 79 72 99 93 Italy 89 70 95 93 93 100 100 100 82 99 99 97 97 100 99 96 74 81 76 92 97 83 98 98 Jamaica 57 85 36 37 100 96 99 96 77 79 14 74 59 63 59 81 44 27 27 67 50 60 91 85 Japan 96 60 100 92 97 98 95 99 78 92 98 100 99 97 100 99 98 100 83 96 92 89 100 98 Jordan 70 57 47 54 81 89 100 93 50 92 38 75 63 82 61 75 83 53 38 79 62 76 99 93 Kazakhstan 59 79 65 68 95 95 99 81 74 86 29 70 69 78 55 38 21 67 57 43 53 52 96 90 Kenya 52 74 23 11 77 84 77 67 63 58 7 22 14 21 11 63 42 19 26 42 32 56 23 8 Kiribati 36 41 33 13 80 70 92 85 42 28 6 19 27 29 15 23 19 3 5 0 1 21 41 28 Kuwait 82 85 66 89 95 98 100 97 83 99 73 87 74 91 81 73 85 92 66 80 69 77 100 99 Kyrgyzstan 53 66 37 49 90 92 96 75 56 85 14 60 54 73 39 30 19 63 50 56 83 31 94 91 Laos 44 67 22 21 70 62 90 52 70 50 7 29 34 42 19 35 26 33 19 41 3 68 88 64 Latvia 70 71 86 72 98 100 100 99 68 85 61 80 40 95 70 39 45 69 87 79 92 54 100 98 Lebanon 75 66 67 67 81 76 100 98 67 93 69 85 79 91 68 37 100 78 53 87 64 72 98 99 Lesotho 39 77 12 9 98 99 73 49 62 22 6 14 11 18 10 51 16 1 2 28 0 30 71 30 Liberia 48 47 18 1 92 89 68 72 42 59 5 21 19 22 13 55 38 34 16 63 35 43 75 47 Libya 66 67 62 55 87 87 99 97 48 89 21 65 57 74 47 47 72 71 40 78 55 59 95 95 Lithuania 70 72 93 84 90 100 100 99 66 80 55 78 68 74 69 34 57 77 97 76 92 57 99 97 Luxembourg 91 88 99 85 99 100 100 100 83 99 99 95 90 99 93 100 82 100 70 99 78 68 100 94 Madagascar 40 51 18 12 73 86 59 62 12 59 5 15 15 15 8 39 29 22 29 37 18 44 64 53 Malawi 56 72 16 16 93 92 86 63 82 46 6 18 20 19 10 53 35 23 40 43 32 39 65 47 Malaysia 67 77 73 46 90 90 100 98 76 92 23 66 63 78 61 43 59 64 42 58 48 84 95 82 Maldives 67 37 41 36 91 87 99 97 28 88 40 74 71 85 71 63 64 54 38 73 52 60 100 100 Mali 41 39 3 6 73 73 75 22 41 50 6 18 16 20 11 56 31 25 16 46 20 31 73 42 Malta 83 77 69 74 93 96 100 99 76 99 97 92 77 97 92 77 80 81 66 99 85 82 99 96 (Figure 4 continues on next page) 1262 www.thelancet.com Vol 396 October 17, 2020 UHC effective coverage index Met need for family planning with modern contraception Antenatal, peripartum, and postnatal care for newborn babies Antenatal, postpartum, and postnatal care for mothers MCV1 coverage DTP3 coverage Diarrhoea treatment LRI treatment ART coverage TB treatment Acute lymphoid leukaemia treatment Breast cancer treatment Cervical cancer treatment Uterine cancer treatment Colon and rectum cancer treatment IHD treatment Stroke treatment Diabetes treatment CKD treatment COPD treatment Asthma treatment Epilepsy treatment Appendicitis treatment Paralytic ileus and intestinal obstruction treatment Global Health Metrics Index value 100 75 50 25 0 Marshall Islands 44 86 46 27 88 64 98 82 37 73 7 29 37 43 18 11 30 35 8 4 16 35 68 67 Mauritania 53 29 18 3 80 69 82 73 79 69 6 31 27 33 20 74 55 19 24 63 52 62 85 68 Mauritius 56 75 52 46 85 85 99 96 80 85 24 69 60 72 63 53 62 21 11 70 34 57 99 98 Mexico 61 77 53 46 87 74 98 91 76 76 20 78 52 77 62 64 81 33 16 60 71 73 74 80 Moldova 62 60 48 66 87 92 99 83 69 88 29 71 62 84 62 24 35 84 85 71 100 48 97 93 Monaco 91 89 100 100 88 100 100 100 38 80 100 97 98 100 97 83 82 100 83 100 89 91 99 93 Mongolia 48 67 44 47 92 96 97 71 45 75 9 50 41 67 34 30 0 55 39 47 64 47 76 54 Montenegro 66 45 85 85 60 87 100 99 85 88 72 80 80 89 71 57 18 72 57 88 100 86 99 90 Morocco 58 73 42 34 91 96 95 91 77 79 12 54 40 57 32 16 53 58 34 57 40 69 94 92 Mozambique 44 47 14 19 97 95 84 50 50 24 5 16 15 15 8 51 27 14 19 48 30 42 56 17 Myanmar 47 78 20 25 84 73 93 48 71 76 8 36 42 52 25 48 3 23 33 41 4 58 94 85 Namibia 62 82 27 28 100 91 90 80 94 64 7 39 33 41 25 59 34 15 25 39 19 52 87 61 Nauru 42 53 44 28 100 99 98 75 18 73 9 51 58 66 43 9 13 8 5 25 22 40 78 82 Nepal 47 62 24 12 95 86 94 56 66 71 7 32 32 36 22 49 11 59 25 9 21 29 92 78 Netherlands 90 83 84 93 93 95 100 100 80 94 100 97 93 99 97 99 82 81 77 87 90 75 98 94 New Zealand 83 83 80 76 90 95 100 100 79 100 92 99 85 89 98 83 76 72 62 83 57 64 99 99 Nicaragua 57 87 58 49 100 100 97 90 33 82 19 77 52 69 59 56 62 42 6 53 68 67 90 89 Niger 35 43 16 9 64 60 42 26 58 46 7 14 12 14 6 41 28 18 22 42 22 33 69 28 Nigeria 38 39 6 15 61 54 41 1 50 65 14 28 24 29 18 49 43 19 30 58 46 50 58 44 Niue 49 77 50 41 98 82 99 82 21 88 22 66 58 80 54 21 47 27 21 53 38 60 89 91 North Korea 53 86 52 45 99 100 98 89 30 86 12 53 55 71 48 41 24 57 45 30 56 39 95 88 North Macedonia 61 19 61 85 85 87 100 98 83 86 53 79 60 88 62 49 20 52 60 66 95 72 99 82 Northern Mariana Islands 60 78 71 37 97 93 99 93 51 95 33 71 71 83 66 38 54 40 15 19 64 73 97 91 Norway 94 86 99 100 97 97 100 100 73 95 99 96 94 98 96 97 100 97 82 93 95 75 100 99 Oman 71 46 63 62 88 89 100 97 49 96 50 79 68 83 69 41 62 43 67 54 89 95 99 99 Pakistan 39 52 2 15 75 80 79 50 41 71 10 27 29 35 18 34 22 33 10 25 17 47 36 51 Palau 45 67 59 33 89 81 99 80 20 87 22 66 68 73 60 19 49 24 8 34 51 69 89 95 Palestine 61 66 59 64 99 99 100 98 1 84 20 65 52 74 49 47 51 40 38 65 66 48 96 94 Panama 71 76 58 39 94 82 96 92 71 73 31 83 67 81 67 82 74 56 41 73 61 74 94 91 Papua New Guinea 38 40 26 16 54 40 86 36 51 62 6 23 30 29 18 30 29 19 42 7 8 40 73 80 Paraguay 63 80 58 34 97 92 98 94 55 76 13 69 56 69 50 68 63 29 17 65 79 66 85 84 Peru 76 65 50 38 87 84 98 91 70 88 20 72 69 74 90 93 79 52 42 93 78 83 95 81 Philippines 55 57 41 39 84 81 95 79 13 86 3 43 48 60 33 37 42 22 16 52 21 83 61 66 Poland 73 47 89 95 92 95 100 99 87 93 75 73 32 91 51 67 57 77 80 84 99 64 97 89 Portugal 84 80 97 86 100 98 100 100 86 80 96 94 95 98 88 98 60 81 48 84 100 61 97 95 Puerto Rico 76 88 65 63 91 88 100 98 67 71 68 94 84 86 88 80 97 48 37 76 76 77 98 95 Qatar 80 71 69 59 99 93 100 99 67 97 72 85 69 91 78 75 97 49 58 97 60 95 100 97 (Figure 4 continues on next page) www.thelancet.com Vol 396 October 17, 2020 1263 UHC effective coverage index Met need for family planning with modern contraception Antenatal, peripartum, and postnatal care for newborn babies Antenatal, postpartum, and postnatal care for mothers MCV1 coverage DTP3 coverage Diarrhoea treatment LRI treatment ART coverage TB treatment Acute lymphoid leukaemia treatment Breast cancer treatment Cervical cancer treatment Uterine cancer treatment Colon and rectum cancer treatment IHD treatment Stroke treatment Diabetes treatment CKD treatment COPD treatment Asthma treatment Epilepsy treatment Appendicitis treatment Paralytic ileus and intestinal obstruction treatment Global Health Metrics Index value 100 75 50 25 0 Romania 70 51 73 70 87 85 100 89 86 84 58 77 69 87 70 47 38 79 70 71 100 71 97 88 Russia 69 62 78 73 100 100 100 95 60 90 59 84 78 98 66 28 32 66 92 69 78 87 96 91 Rwanda 59 57 24 15 96 97 86 73 96 56 6 24 23 25 15 61 30 16 27 38 39 44 72 66 Saint Kitts and Nevis 53 83 44 28 95 94 98 91 27 86 31 79 57 74 68 55 35 38 13 73 65 57 90 74 Saint Lucia 59 68 40 36 88 87 99 94 68 67 18 71 58 68 55 79 49 41 25 59 48 47 89 81 Saint Vincent and the Grenadines 49 82 45 37 98 93 98 92 36 70 11 64 52 53 45 46 46 28 21 64 57 46 89 76 Samoa 50 42 58 55 66 59 99 90 37 76 24 46 54 61 35 28 41 28 18 6 31 52 85 84 San Marino 93 88 86 99 87 89 100 100 37 92 100 95 95 99 94 99 81 98 89 100 95 95 98 94 São Tomé and Príncipe 55 58 39 27 94 94 98 77 82 70 7 36 34 35 20 39 52 56 6 48 30 54 89 79 Saudi Arabia 64 50 84 44 99 97 100 100 23 82 47 80 69 86 66 48 62 70 26 67 55 67 98 95 Senegal 50 53 17 6 86 93 84 70 59 55 6 22 18 22 13 51 42 35 15 50 27 45 79 50 Serbia 63 38 86 78 90 86 100 99 82 85 63 76 75 89 68 53 36 61 54 81 73 71 97 79 Seychelles 62 80 53 45 90 89 99 92 63 85 17 62 62 76 55 51 66 62 26 62 45 87 92 80 Sierra Leone 42 49 13 1 87 86 84 20 33 62 6 19 18 20 11 39 40 15 20 49 25 39 76 49 Singapore 92 77 100 98 95 93 99 99 75 100 94 99 93 89 99 77 100 100 76 95 79 94 100 100 Slovakia 78 71 85 89 94 95 100 98 88 91 78 85 91 90 86 50 66 78 73 84 85 68 98 88 Slovenia 90 65 100 84 90 92 100 100 85 95 96 86 98 95 85 97 76 89 94 94 99 85 100 97 Solomon Islands 39 80 40 13 98 86 94 76 37 54 6 35 44 49 27 1 3 6 22 0 20 24 74 78 Somalia 24 3 14 7 57 31 65 29 12 14 5 2 1 1 1 42 19 14 14 23 16 22 47 36 South Africa 60 81 20 28 79 63 71 80 81 64 17 42 45 44 26 79 59 21 22 53 48 59 72 71 South Korea 89 78 100 78 97 97 99 100 43 96 96 99 99 92 99 100 88 66 70 95 99 76 99 96 South Sudan 42 6 9 20 56 61 71 32 31 43 6 9 5 7 4 63 52 22 26 45 32 55 60 53 Spain 90 80 98 95 95 93 100 100 87 93 99 96 99 99 98 99 82 93 54 82 82 84 99 95 Sri Lanka 66 66 68 52 92 98 100 97 74 90 35 72 69 83 68 46 64 41 47 70 36 68 99 99 Sudan 52 31 21 18 84 77 90 87 13 77 9 46 40 47 29 32 49 68 33 48 32 41 89 87 Suriname 50 66 28 30 91 99 95 84 76 63 8 59 49 48 43 50 40 45 14 61 50 47 86 39 Sweden 90 83 99 99 98 98 100 100 82 98 100 98 85 99 92 87 93 83 83 100 87 72 100 100 Switzerland 93 87 78 90 95 98 100 100 86 98 100 97 77 99 99 99 93 93 80 99 90 82 99 97 Syria 58 56 52 63 76 68 99 95 49 92 20 69 58 78 53 15 53 69 43 66 17 75 96 94 Taiwan (province of China) 79 96 84 75 100 98 100 99 46 99 88 93 89 97 91 90 82 44 55 55 89 73 99 97 Tajikistan 48 53 30 60 100 93 87 45 74 65 8 46 36 61 25 13 4 34 65 39 95 21 89 79 Tanzania 55 44 16 12 87 91 93 65 75 53 6 23 21 25 14 59 39 15 24 51 41 47 71 59 Thailand 72 89 74 49 92 89 99 95 71 92 34 73 76 84 68 79 65 47 43 68 44 74 96 94 Timor-Leste 46 48 26 23 68 63 94 68 48 55 7 31 34 39 22 37 33 41 30 37 15 72 89 62 Togo 43 40 14 16 84 86 51 62 62 51 6 24 23 24 14 40 38 18 17 52 34 43 77 47 Tokelau 53 69 78 40 92 84 99 94 20 79 11 52 58 69 39 32 43 23 21 51 44 56 88 85 Tonga 52 52 57 35 68 68 100 91 37 76 6 44 39 60 37 52 61 19 23 21 46 59 83 73 (Figure 4 continues on next page) 1264 www.thelancet.com Vol 396 October 17, 2020 UHC effective coverage index Met need for family planning with modern contraception Antenatal, peripartum, and postnatal care for newborn babies Antenatal, postpartum, and postnatal care for mothers MCV1 coverage DTP3 coverage Diarrhoea treatment LRI treatment ART coverage TB treatment Acute lymphoid leukaemia treatment Breast cancer treatment Cervical cancer treatment Uterine cancer treatment Colon and rectum cancer treatment IHD treatment Stroke treatment Diabetes treatment CKD treatment COPD treatment Asthma treatment Epilepsy treatment Appendicitis treatment Paralytic ileus and intestinal obstruction treatment Global Health Metrics Index value 100 75 50 25 0 Trinidad and Tobago 56 57 46 48 90 77 99 94 83 74 13 69 48 59 57 47 60 24 24 67 42 56 92 79 Tunisia 68 73 62 53 94 96 100 97 52 95 46 78 69 84 62 39 60 79 50 77 57 67 98 98 Turkey 69 63 46 56 97 98 99 96 44 96 47 79 66 84 66 61 71 57 49 67 72 65 99 93 Turkmenistan 44 76 35 46 94 100 95 46 51 58 14 62 55 73 44 17 22 40 35 67 57 48 94 91 Tuvalu 40 47 56 33 67 63 99 86 19 69 8 40 48 57 28 17 28 18 13 31 30 39 79 78 Uganda 53 52 15 25 80 79 90 78 77 45 6 23 23 24 14 57 38 24 25 45 34 53 71 63 Ukraine 57 67 64 64 98 72 100 97 50 82 45 62 62 76 59 2 31 74 87 69 85 51 96 93 United Arab Emirates 63 78 83 67 92 96 100 100 51 89 17 60 43 74 49 56 76 49 19 50 54 68 95 96 UK 88 89 83 88 93 96 100 99 85 97 99 94 86 94 94 88 72 100 85 81 64 66 98 95 USA 82 81 70 59 93 93 100 99 86 88 61 99 82 100 98 73 97 72 49 72 68 88 97 99 Uruguay 69 72 64 61 97 90 99 97 66 89 37 67 70 73 57 87 58 53 45 69 61 62 96 78 Uzbekistan 42 83 43 54 91 94 99 33 78 66 15 61 53 75 44 5 9 27 38 59 68 33 95 92 Vanuatu 34 53 40 30 45 51 96 78 32 46 7 24 31 37 19 5 30 28 15 1 13 35 64 58 Venezuela 61 76 48 32 56 56 97 92 82 80 22 79 62 77 64 47 57 43 31 62 60 66 89 85 Vietnam 60 76 53 66 91 85 100 88 58 80 20 63 56 69 57 56 26 34 41 61 44 74 96 94 Virgin Islands 54 87 66 61 67 61 100 99 63 82 20 74 51 72 63 23 56 42 30 72 69 77 89 80 Yemen 49 43 19 21 72 66 85 85 56 73 6 38 30 41 21 28 39 57 35 45 31 40 84 78 Zambia 53 64 21 25 94 93 79 62 81 51 5 26 26 28 15 61 15 16 17 46 25 47 71 48 Zimbabwe 54 85 19 11 83 82 92 49 94 22 6 24 24 25 15 39 34 24 13 59 16 22 76 21 Figure 4: Performance on the UHC effective coverage index and 23 effective coverage indicators, by location, in 2019 Locations are reported in alphabetical order. The UHC effective coverage index and individual effective coverage indicators are reported on a scale of 0–100. Four indicators (met need for family planning, MCV1 coverage, DTP3 coverage, and ART coverage) are based on intervention coverage, whereas the remaining effective coverage indicators use measures such as mortality-to-incidence ratios to approximate access to quality care; inputs and measurement approaches for each indicator and index are further described in appendix 1 (pp 30–32). ART=antiretroviral therapy. CKD=chronic kidney disease. COPD=chronic obstructive pulmonary disease. DTP3=diphtheria, tetanus, pertussis vaccine, 3 doses. IHD=ischaemic heart disease. LRI=lower respiratory infection. MCV1=measles-containing vaccine, 1 dose. TB=tuberculosis. UHC=universal health coverage. Many countries with middle-range performance on maternal, and child health, several of these countries had UHC effective coverage (ie, about 45–70) in 2019 had a higher UHC effective coverage index performance under mixture of fairly high values on most indicators for a health gains weighting approach than under the communicable diseases and reproductive, neonatal, assumption that each effective coverage indicator could maternal, and child health but comparatively lower deliver equal health gains to populations across different scores on many non-communicable diseases, likely settings (figure 2). By contrast, in many other countries— mirroring their variable epidemiological profiles and especially those in Latin America, central and eastern thus populations’ health needs. For some countries, Europe, and Oceania—non-communicable diseases especially those in sub-Saharan Africa (eg, Namibia, accounted for a greater proportion of potential health Rwanda, and Kenya), communicable diseases (eg, HIV) gains by 2019; consequently, these countries’ relatively and reproductive, neonatal, maternal, and child health poor performances on several effective coverage indi- still ranked among indicators with highest potential cators proxying non-communicable disease services health gains in 2019, even though non-communicable underpinned lower overall UHC effective coverage index diseases such as cardiovascular diseases and diabetes are values. High levels of vaccine coverage and performance on the rise.35 With their fairly high levels of coverage or on effective coverage indicators such as maternal care services proxied by effective coverage indicators for still contributed to UHC effective coverage performance communicable diseases and for reproductive, neonatal, for such countries; however, these health areas generally www.thelancet.com Vol 396 October 17, 2020 1265 UHC effective coverage index Met need for family planning with modern contraception Antenatal, peripartum, and postnatal care for newborn babies Antenatal, postpartum, and postnatal care for mothers MCV1 coverage DTP3 coverage Diarrhoea treatment LRI treatment ART coverage TB treatment Acute lymphoid leukaemia treatment Breast cancer treatment Cervical cancer treatment Uterine cancer treatment Colon and rectum cancer treatment IHD treatment Stroke treatment Diabetes treatment CKD treatment COPD treatment Asthma treatment Epilepsy treatment Appendicitis treatment Paralytic ileus and intestinal obstruction treatment Global Health Metrics A UHC effective coverage index B Population equivalents with UHC effective coverage Years Global 1990–2010 2010–2019 Central Europe, eastern Europe, and central Asia High income Latin America and Caribbean North Africa and Middle East South Asia Southeast Asia, east Asia, and Oceania Sub-Saharan Africa 0 1 2 3 4 5 6 0 1 2 3 4 5 6 Annualised rate of change (%) Annualised rate of change (%) Figure 5: Annualised rate of change in the UHC effective coverage index (A) and population equivalents with UHC effective coverage (B), globally and by GBD super-region, 1990–2010 and 2010–2019 Values reflect the average annualised rate of change on the UHC effective coverage index and population equivalents with UHC effective coverage between each time period. Population equivalents are based on taking the UHC effective coverage index as a fraction and multiplying these values by the total population for a given location-year to approximate populations covered with UHC effective coverage. UHC=universal health coverage. GBD=Global Burden of Diseases, Injuries, and Risk Factors Study. represented a smaller fraction of population-level health from 2010 to 2019, surpassing its annualised rate of gains than many non-communicable diseases in these change from 1990 to 2010 (1·3% [1·0–1·7] average settings. Health gain weights, by country and territory, increase per year). Central Europe, eastern Europe, and for each effective coverage indicator are available in the central Asia also had significantly faster progress from For more on the online data appendix 2 (pp 11–13) and via online data tools. 2010 to 2019 (1·4% [0·8–1·8] average annual increase) tools see http://ghdx.healthdata. than from 1990 to 2010 (0·5% [0·4–0·6] annual increase). org/gbd-2019 Pace of progress on UHC effective coverage Since 1990, UHC effective coverage performance Relationship between health expenditure and UHC improved, albeit at variable rates of progress over time effective coverage and across GBD super-regions (figure 5). The global Country-level performance on UHC effective coverage average increased from 45·8 (95% UI 44·2–47·5) in 1990 widely varied across different levels of pooled health to 60·3 (58·7–61·9) in 2019, while the absolute range in spending per capita (figure 6), highlighting how increased performance essentially remained the same (ie, 73·0-point health spending is necessary but insufficient on its own difference in 1990 vs 74·0-point difference in 2019). to improve UHC effective coverage. Overall, the UHC By 2019, the UHC effective coverage index spanned effective coverage index was associated with pooled from 95 or higher in Japan (96·3 [95·0–97·4]) and health spending per capita (r=0·79), but this relationship Iceland (95·3 [93·6–96·8]) to lower than 25 in the was varied at different levels of spending. Up to about Central African Republic (22·3 [16·3–29·3]) and Somalia $2500 (US$, adjusted for purchasing power parity) in (23·9 [17·1–31·1]; appendix 2 pp 14–20). Globally, the pace pooled health spending per capita, increasingly higher of progress on UHC effect ive coverage was somewhat expenditures generally paralleled higher performance slower, albeit not significantly, from 2010 to 2019 (0·9% on UHC effective coverage index; beyond that, higher [0·6–1·2] annualised increase) than from 1990 to 2010 expenditures did not correspond as consistently with (1·0% [0·8–1·1] annualised increase). Similarly, at the further improvements in UHC effective coverage global level, annualised rates of change for population performance. equivalents with effective coverage were slightly lower The UHC effective coverage frontier charts the highest from 2010 to 2019 (2·0% [1·7–2·3]) than from 1990 to 2010 UHC effective coverage performances, as achieved by (2·3% [2·2–2·4]), although this difference was not countries in 2019, across different levels of pooled health significant. However, some of these patterns diverged by spending per capita (figure 6); in other words, this frontier GBD super-region (figure 5), as well as at the country represents the relative efficiency—or ineffic iency—with level (appendix 2, pp 14–20). For instance, in sub-Saharan which countries could translate their health spending Africa, UHC effective coverage index performance into improved UHC effective coverage. Countries improved at an average of 2·6% (1·9–3·3) per year including South Korea, Cyprus, Costa Rica, Peru, and 1266 www.thelancet.com Vol 396 October 17, 2020 Global Health Metrics Rwanda were among those setting this performance frontier at their corresponding levels of pooled health expenditure per capita. Conversely, countries across the sociodemographic spectrum (ie, Central African Republic, Lesotho, Turkmenistan, Saudi Arabia, and A UHC effective coverage index relative to pooled health spending per capita the USA) showed large gaps between their estimated 100 JPN UHC effective coverage index perform ances in 2019 and SGP SMR CHE ESP ISL FRA IRL SWE LUX AND NOR what could have been achievable on the UHC effective KOR AUSFIN GBR CAN NLD coverage frontier given these countries’ levels of pooled ITAPRT MLT NZL BEL DNKEST CZE AUT DEU USA health spending. To reach a UHC effective coverage index 80 CYP GRCCRI ISR KWT BMU of at least 80, under maximum efficiency, countries would PER QATLBN CHL SVK TWN CUB need to reach US$1398 in pooled health spending per THA PRIJOR BLR BHR URY GRL capita (per year). Equivalent analyses and figures for LKA MYS MDV BRN total health expendit ure per capita (ie, pooled health BRABHS GUM ARE SAU spending plus out-of-pocket spending) and pooled 60 ZAF ARGJAM BWA domestic health expenditure per capita (ie, pooled health MUS TTOVIR spending minus development assistance for health) are SUR provided in appendix 2 (pp 6–7). MNGFJI PLW TKM NRU GBD super-region GUY 40 TUV Central Europe, eastern Europe, and central Asia Counting population equivalents with effective LSOKIR High income coverage for the UHC billion target FSM Latin America and CaribbeanGIN TCD North Africa and Middle EastBased on current projections, an estimated 5·0 billion South Asia (95% UI 4·8–5·1) population equivalents would have UHC SOM Southeast Asia, east Asia, and Oceania CAF effective coverage in 2023 (table 3). This would translate to Sub-Saharan Africa20 388·9 million (358·6–421·3) more population equivalents 0 500 1000 2000 4000 8000 100 with UHC effective coverage over the five-year GPW13 Pooled health expenditure (US$ PPP), 2017 evaluation period (2019–23, with 2018 as the baseline), or the equivalent of adding an average of 77·8 million B UHC effective coverage index relative to log-transformed pooled health spending per capita (71·7–84·3) population equivalents per year during this 100 JPN NOR time. From 2018 to 2023, sub-Saharan Africa was estimated SGP CHEISL to contribute the most additional population equivalents KOR ANDGBR NLD with UHC effective coverage (ie, 94·5 million [83·6–104·8]). ITA AUT DEUPRT CZEMLT USADNK By 2023, an estimated 3·1 billion (3·0–3·2) population CYP GRC NZL80 CRI KWT QAT equivalents would not have UHC effective coverage, with PER LBN PRCI HLSVK TWN BMU nearly a third residing in south Asia (ie, an estimated THA POLJORCHN BLRLVAPAN BHR CUB 968·1 million [903·5–1040·3]). ALB TURLKA TUN URYMYS MDV GUM BRN GRLPRY PSE ARM MDAVEN MEX BRA SAU ARE Discussion VNM60 RWA LCAKHM IRQ SLVUKRNIC ZAF ARGMWI MUS BWA Summary of the main findings BGD TZA HND EGY SWZ TTOUGA BLZBOL KNA VIR The present study offers a new approach to monitoring YEM BDI SEN GHA KEN AZE BTN GRD SURNPL IDN progress on UHC service coverage: measuring country- COD ETHCOM GMB FJI MNG NIU ERI TGO IND level effective coverage and thus better representing how MLI BFA CIV TLS PLW 40 AFG NGA SLE TKM PAK SSD UZB GUY NRU well health systems are delivering health gains relative to SLB AGO TUVNER LSOGNB PNG their populations’ health needs. Amid global advances TCD HTI KIRVUT FSM on the UHC effective coverage index since 1990, our GIN findings show a gap of more than 70 points between SOM CAF locations with the highest and lowest levels of UHC 20 effective coverage remained in 2019. Particularly among 0 15 100 500 1000 2000 4000 8000 low-middle to middle-SDI countries, performance of Log−transformed pooled health expenditure (US$ PPP), 2017 effective coverage indicators for non-communicable diseases was far lower than levels reached for several Figure 6: UHC effective coverage index frontier relative to pooled health spending per capita (A) and log-transformed pooled health spending per capita (B) communicable diseases and maternal and child health Pooled health spending per capita includes government health expenditures, prepaid private expenditures, and indicators—a pattern suggesting that many countries’ development assistance for health. All health spending estimates are for 2017 measured in 2019 PPP-adjusted health systems and financing priorities are not moving US$ adjusted for inflation. The black line represents the frontier values estimated for UHC effective coverage in as quickly as their epidemiological and demographic 2019 relative to spending per capita in 2017. Locations are colour-coded by GBD super-region, with a subset abbreviated according to their ISO3 codes. ISO3 codes and corresponding location names are listed in appendix 1 transitions. Higher pooled health spending per capita (pp 64–68). UHC=universal health coverage. GBD=Global Burden of Diseases, Injuries, and Risk Factors Study. generally corresponded with higher UHC effective PPP=purchasing-power parity. www.thelancet.com Vol 396 October 17, 2020 1267 UHC effective coverage index, 2019 UHC effective coverage index, 2019 Global Health Metrics UHC effective coverage index (95% UIs) Population equivalents with UHC effective coverage (95% UI)* 2018 2023 Added from 2018–23 Covered in 2023 Not covered in 2023 Global 59·8 (58·3 to 61·3) 61·7 (60·1 to 63·3) 388·9 (358·6 to 421·3) 5·0 billion (4·8 to 5·1) 3·1 billion (3·0 to 3·2) Central Europe, eastern 63·2 (61·0 to 65·5) 65·2 (62·7 to 67·6) 9·1 (7·5 to 10·9) 273·0 (262·5 to 282·8) 145·5 (135·7 to 156·1) Europe, and central Asia High income 85·8 (84·3 to 87·1) 87·1 (85·5 to 88·5) 31·6 (28·8 to 34·3) 958·3 (940·7 to 972·8) 141·5 (127·0 to 159·1) Latin America and Caribbean 63·2 (61·1 to 65·1) 65·6 (63·3 to 67·8) 33·6 (30·8 to 36·5) 398·5 (384·7 to 412·0) 209·0 (195·6 to 222·8) North Africa and Middle East 60·0 (57·9 to 61·9) 61·9 (59·6 to 64·0) 43·0 (39·8 to 45·9) 402·3 (387·6 to 416·1) 247·8 (233·9 to 262·5) South Asia 46·0 (42·6 to 49·2) 48·4 (44·6 to 51·9) 88·9 (73·5 to 102·8) 909·4 (837·2 to 974·0) 968·1 (903·5 to 1040·3) Southeast Asia, east Asia, and 64·2 (60·7 to 67·6) 66·9 (63·0 to 70·5) 88·2 (74·4 to 102·8) 1·5 billion (1·4 to 1·5) 726·3 (647·9 to 811·6) Oceania Sub-Saharan Africa 43·9 (41·4 to 46·5) 46·2 (43·3 to 49·1) 94·5 (83·6 to 104·8) 555·6 (521·1 to 590·1) 647·1 (612·7 to 681·7) Population equivalents based on taking the UHC effective coverage index as a fraction and multiplying these values by total population for a given location-year to approximate populations covered with UHC effective coverage. UHC=universal health coverage. GBD=Global Burden of Diseases, Injuries, and Risk Factors Study. 95% UI=95% uncertainty interval. *Reported in millions unless otherwise indicated. Table 3: Projected UHC effective coverage performance in 2023 and additional population equivalents with UHC effective coverage from 2018 to 2023, globally and by GBD super-region coverage. Nonetheless, country-level performance varied 80, and then $2538 per capita to reach 90 and $3424 per widely and many countries fell well below levels achieved capita to reach 95. At present, the only countries by other countries with similar amounts of health achieving 90 or higher on the UHC effective coverage expenditures, emphasising the importance of increasing index and such levels of pooled health spending per both health-system efficiencies and funding for UHC. capita are within the high-income GBD super-region. To achieve at least 80 on the UHC effective coverage Substantially increasing total health spending could be index, countries would need to reach $1398 pooled one avenue for elevating UHC effective coverage spending per capita—and do so under maximum performance; however, many countries still have high efficiency. An estimated 388·9 million more population out-of-pocket spending relative to their total spending,56,61 equivalents would have UHC effective coverage between which is strongly related to household catastrophic 2018 and 2023, falling well short of the GPW13 target of health expenditures and directly counter to improving 1 billion more people benefiting from UHC during this financial risk protection within UHC. Focusing on time. Genuinely advancing toward UHC requires domestic heath spending while also elevating efficiency prioritising—and thus monitoring—effective coverage could be another viable route; our results show that many and health systems’ capacities for improving outcomes countries would theoretically achieve much higher UHC for all people throughout the world. effective coverage if they could better translate current amounts of pooled spending per capita into improved Past progress, current challenges, and accelerating performance. How to best address such inefficiencies future gains on UHC effective coverage will markedly vary across contexts, and will require By 2019, UHC effective coverage improved substan tially accounting for country-level differences in health-sys- for many countries, and for some countries the pace of tem orientations and structures, political stability and progress has accelerated since 2010. This was particularly governance systems, and distribution of health resources evident in sub-Saharan Africa; this GBD super-region among populations. Further examination of approaches nearly doubled its average annual improvem ents from used by countries near or at the UHC effective coverage 2010 to 2019 compared to 1990–2010. Such gains could be frontier relative to their pooled health spending (eg, related to heightened funding—and thus prioritisation— Rwanda, Peru, South Korea, and Costa Rica) might help for HIV, vaccines and childhood infectious diseases, and identify tractable policy pathways to improved efficiency. maternal health during the Millennium Development Poor performance on various non-communicable Goal (MDG) era.61,62 As further illustrated by the UHC diseases has severely hindered progress on UHC effective coverage frontier, up to about $2500 per capita, effective coverage in many countries—a trend that is rising levels of UHC effective coverage index generally likely to only worsen until quality health services for paralleled pooled health spending; this trend highlights non-communicable diseases are better prioritised by the important role of increasing funding for UHC to countries and development partners alike. Especially jumpstart progress, particularly for countries that still among low-middle SDI to middle-SDI countries, earlier have very low UHC effective coverage in 2019. Yet even advances on UHC effective coverage were mainly at the frontier, reaching better UHC effective coverage propelled by improving health services focused on performance requires much higher pooled health communicable diseases, child health, and maternal care. spending per year: an estimated $1398 per capita to reach As cardiovascular disease, diabetes, cancers, and other 1268 www.thelancet.com Vol 396 October 17, 2020 Global Health Metrics non-communicable diseases became leading causes of Current challenges and future directions for measuring early death and disability, they also emerged as population UHC effective coverage health needs with the highest potential health gains— Our measurement framework is grounded in the that is, where health systems could increasingly deliver construct of effective coverage at the health-system level,16 the most improved outcomes via effective coverage of aiming to represent a country’s ability to improve health interventions and services. Re-orienting countries’ health outcomes in accordance with the health needs and disease systems towards providing effective health services for burden of its population. From this perspective, effec- non-communicable disease is not trivial, especially if tive coverage should capture the fraction of potential their prior focus (and funding) had a more limited population-level health gains actually delivered by the scope for the types of services provided, equipment health system, relative to what the health system could used, and health workforce training required. However, have provided at maximum performance of current continued inaction also has likely costs: if health interventions or services. As such, we used health gain systems remain too focused on health problems of the weights to construct the overall UHC effective coverage past, and fail to effectively respond to where the largest index and to more heavily weight effective coverage potential health gains exist today, it can be increasingly indicators for which a given country’s health could difficult to translate current levels of health spending produce greater health gains through available interven- into improved UHC effective coverage. For instance, tions. By contrast, the unweighted average of effective only a few high-SDI countries (eg, Japan, Switzerland, coverage indicators implies equal potential health gains and South Korea) averaged non-communicable disease irrespective of a country’s epidemiological profile or performance equal to or higher than effective coverage effectiveness of the associated interventions or ser vices, or indicators focused on communicable diseases and a combination of both. Equally weighting interventions maternal and child health by 2019.63 Unless deliberate and their potential for improving health is directly counter efforts are taken now to recalibrate health-system and to the reality of UHC programmes, which are subject to funding priorities, the ability to alter current trajectories each country’s unique health-system structures, political for UHC effective coverage could diminish. demands, and health priorities. To capture what can—or To catalyse faster gains in the SDG era, WHO’s should—be achievable through health systems’ provision GPW13 set forth its bold billion targets,6 with the UHC of effective services, we believe the health gains weighting target calling for 1 billion more people benefiting from approach can better track country-led UHC investments UHC by 2023, relative to 2018. Current projections have and policy implementation. Going forward, assessments the world falling well short of this ambition, with an of UHC effective coverage should strive to apply this estimated 388·9 million (358·6–421·3) more population method beyond the national level, aiming to capture equivalents having UHC effective coverage by 2023. inequalities in potential health gains not only by location Even these estimates are likely to be optimistic, as they but also within population-age groups, by sex, and across do not account for trends in financial risk protection— other important sociodemographic dimensions (eg, race/ the other key dimension of UHC—nor do they explicitly ethnicity and migrant status). account for populations’ needs for multiple health Routinely measuring UHC effective coverage requires services. Nonetheless, this initial assessment offers the existence and maintenance of several functional data important considerations for the remaining years of systems. Many, if not most, indicators or data systems, or GPW13 and then through to 2030. With more than both, that are needed to measure effective coverage 3 billion popu lation equivalents estimated to lack UHC indicators are already encompassed within the health- effective coverage in 2023, targeting populous regions related SDGs, which UN member states have committed or countries that currently have low UHC effective to monitoring. These include functional vital registration coverage and investing in service expansion could be systems that accurately record causes of death; periodic one option to accelerating future progress. For instance, household surveys that include biomarker data and south Asia, in combination with southeast Asia, east information on intervention coverage; and disease Asia, and Oceania, was estimated to have nearly incidence registries based on administrative systems and 1·7 billion population equivalents without UHC notifications for specific causes (eg, cancers and kidney effective coverage in 2023. However, on the basis of disease).64 Deliberate investments by national govern- current levels of health spending, many countries in ments, as well as international agencies where appropriate, these regions already fell below their potential UHC are important for strengthening these data systems and effective coverage perform ance in 2019. For most identifying how they can be used together to monitor countries, heightened health spending alone is unlikely trends in effective coverage. to deliver on ambitious UHC targets; rather, a com- The UHC effective coverage index and corresponding bination of improving alignment of health systems UHC effective coverage measurement framework repre- with population health needs and bolstering efficiencies sent important steps towards capturing a range of needed is likely to chart faster and perhaps more sustained health services across the life course; nonetheless, as gains. underscored by its multi-year development process www.thelancet.com Vol 396 October 17, 2020 1269 Global Health Metrics (appendix 1 pp 6–21), considerable gaps persist between could be assessed across settings and inform efforts to the breadth of the original candidate effective coverage incorporate effective coverage into UHC monitoring. indicators and those used in the present analysis. Minimal Continuing to advance effective coverage measurement data on rehabilitative services and palliation across of UHC in the future, especially if the main alternative is countries and over time hindered their direct inclusion or adhering to past measures with known drawbacks and the use of suitable proxy effective coverage indicators. narrow operationalisations of health services for all Recent steps by WHO (ie, publishing its first world report populations, is strongly supported by the broader GBD on vision65 and upcoming report on hearing,66 and its study and its collaborators. GPW13 indicator on oral morphine availability6) suggest that data collection for these areas could be increasingly Limitations prioritised. A similar paucity of routinely collected data Our study is subject to limitations beyond those already on mental health services and substance use disorder described. First, this analysis draws from GBD 2019 interventions precluded their use in the current UHC estimates of outcomes, intervention coverage, and SDG effective coverage index. In the future, triangulation of indicators,34–36 and thus broader GBD 2019 limitations data sources including administrative records, health also apply to the present study (eg, availability and quality facility records, and community-based surveys might of vital registration data, model coherence between inform such meas urements.67 Effective coverage indi- cause-specific mortality and non-fatal measures, and cators on emergency services and trauma care were also new modelling approaches for risk factors and related considered but ultimately excluded because of data outcomes). In the case of ischaemic heart disease, for limitations and ongoing methodological challenges example, new data on the interplay of household air (ie, appropriately isolating improvements in effective pollution, blood pressure, and ischaemic heart disease health services from advances in transportation safety). mortality resulted in implausible risk-standardised death For non-communicable diseases, we relied on out- rates for many low-SDI to low-middle SDI countries come-based effective coverage indicators, preferring to when we only accounted for joint exposures to metabolic approxi mate access to quality non-communicable disease risks considered amenable to health care.41,43 We thus care through measures such as MIRs rather than included household and outdoor air pollution in risk assuming that risk exposure, screening rates, or health- standardisation and plan to further examine these risk system inputs, or a combination of these factors, can mediation pathways in the future. appropriately capture effective service provision for non- Second, health gain weights were based on classifying communicable diseases. Many national data systems intervention sets into five effectiveness categories, as already collect data on cause-specific mortality and informed by published literature provided by Cochrane, disease incidence or prevalence, and when analysed the Tufts Cost-Effectiveness Analysis Registry, and together they should reflect variations in access to and DCP3.For some effective coverage indicators, especially quality of health services and serve as good proxy mea- treat ment of more chronic conditions, distilling a wide sures amid imperfect data realities for non-communicable range of reported effectiveness on available interven- disease services. Conversely, using indicators such as tions into a summary assessment was quite difficult. non-tobacco use and non-elevated blood pressure4,19,21 or Sensitivity analyses based on shifting each indicator’s inpatient admission rates pushes the world further away categorisation up and down one group showed similar from understanding improved outcomes delivered by overall UHC effective coverage index values (appendix 1 health systems and effective service provision. If, or p 35). Formally simulating the range of effectiveness when, the quantity and quality of data for measuring across interventions and incorporating this uncertainty health services for non-communicable diseases improve, into health gains weighting is an important future we would prefer to use more direct measures of avenue for measurement of the UHC effective coverage effec tive coverage over outcome-based proxy indicators. index. For instance, our ideal effective coverage indicator for Third, due to limited data quantity or quality (or both), diabetes treatment would be the proportion of people we could not include several original candidates for with diabetes on treatment and meeting specified effective coverage indicators (appendix 1 pp 12–28), treatment targets such as glycated haemoglobin lower including seven expressly recommended by the GPW13 than 8%. Household surveys such as the WHO STEPwise ERG: HPV vaccination, hepatitis C treatment, effective approach to surveillance (STEPS) are increasingly col- management of hypertension and diabetes, cataract lecting these data, and time series estimates by location surgery, refractive error correction, and dental care.33 and population-age group could be easily derived if As data availability improves alongside methods for sufficient access to such microdata is possible. estimating these indicators across countries, we plan to In sum, the indicators included in the present study test the inclusion of these indicators, and thus some are not meant to be prescriptive; rather, our primary country-level UHC effective coverage index values and objective was to establish a robust, comparable measure- rankings might change. Since data are generally more ment framework from which UHC effective coverage easily available for better-funded interventions and health 1270 www.thelancet.com Vol 396 October 17, 2020 Global Health Metrics areas, it is possible that our current estimates of UHC health needs across the lifespan, we strengthen the effective coverage are overly optimistic. evidence base for bringing UHC closer to reality for all. Fourth, we excluded several effective coverage indi- Contributors cators for which high potential health gains could only be Please see appendix 1 for more detailed information about individual achieved in select locations because of local exposures authors’ contributions to the research, divided into the following (ie, malaria and neglected tropical diseases) or current categories: managing the estimation process; writing the first draft of the manuscript; providing data or critical feedback on data sources; introduction status (eg, pneumococcal conjugate vaccine developing methods or computational machinery; applying analytical [PCV]). Subsequently, our results might under-estimate methods to produce estimates; providing critical feedback on methods or UHC effective coverage in some locations (eg, countries results; drafting the work or revising it critically for important intellectual with high coverage of effective malaria interventions) or content; extracting, cleaning, or cataloguing data; designing or coding figures and tables; and managing the overall research enterprise. over-estimate performance in others (eg, countries that have a high pneumonia burden but have yet to introduce GBD 2019 Universal Health Coverage CollaboratorsRafael Lozano, Nancy Fullman, John Everett Mumford, Megan Knight, PCV). Additional methodological testing is needed to Celine M Barthelemy, Cristiana Abbafati, Hedayat Abbastabar, better incorporate these locally relevant intervention Foad Abd-Allah, Mohammad Abdollahi, Aidin Abedi, needs within a global measurement framework. Hassan Abolhassani, Akine Eshete Abosetugn, Lucas Guimarães Abreu, Michael R M Abrigo, Abdulaziz Khalid Abu Haimed, Fifth, we did not explicitly account for the effects Abdelrahman I Abushouk, Maryam Adabi, Oladimeji M Adebayo, of potential community-level interventions and their Victor Adekanmbi, Jaimie D Adelson, Olatunji O Adetokunboh, contribution to potential health gains (eg, herd immunity Davoud Adham, Shailesh M Advani, Ashkan Afshin, Gina Agarwal, garnered from very high coverage of MCV1 or DTP3). Pradyumna Agasthi, Seyed Mohammad Kazem Aghamir, Anurag Agrawal, Tauseef Ahmad, Rufus Olusola Akinyemi, Future work should consider whether or how such effects Fares Alahdab, Ziyad Al-Aly, Khurshid Alam, Samuel B Albertson, can be incorporated into this measurement framework, Yihun Mulugeta Alemu, Robert Kaba Alhassan, Muhammad Ali, particularly given the toll of recent measles outbreaks Saqib Ali, Vahid Alipour, Syed Mohamed Aljunid, François Alla, worldwide.68 Majid Abdulrahman Hamad Almadi, Ali Almasi, Amir Almasi-Hashiani, Nihad A Almasri, Hesham M Al-Mekhlafi, Abdulaziz M Almulhim, Sixth, results of our known-groups validity testing might Jordi Alonso, Rajaa M Al-Raddadi, Khalid A Altirkawi, have varied if more or different country-pairs were selected Nelson Alvis-Guzman, Nelson J Alvis-Zakzuk, Saeed Amini, (appendix 1 pp 45–47). Showing performance based on Mostafa Amini-Rarani, Fatemeh Amiri, Arianna Maever L Amit, country means and uncertainty underscores the need to Dickson A Amugsi, Robert Ancuceanu, Deanna Anderlini, Catalina Liliana Andrei, Sofia Androudi, Fereshteh Ansari, further strengthen data collection and overarching mea- Alireza Ansari-Moghaddam, Carl Abelardo T Antonio, surement for UHC effective coverage at the country level. Catherine M Antony, Ernoiz Antriyandarti, Davood Anvari, Furthermore, it stresses the importance of estimating and Razique Anwer, Jalal Arabloo, Morteza Arab-Zozani, reporting uncertainty in monitoring UHC, a limitation of Aleksandr Y Aravkin, Olatunde Aremu, Johan Ärnlöv, Malke Asaad, Mehran Asadi-Aliabadi, Ali A Asadi-Pooya, Charlie Ashbaugh, current WHO and World Bank service coverage indices. Seyyed Shamsadin Athari, Maha Moh’d Wahbi Atout, Marcel Ausloos, Seventh, approximating populations with UHC effec- Leticia Avila-Burgos, Beatriz Paulina Ayala Quintanilla, Getinet Ayano, tive coverage by assuming the UHC effective coverage Martin Amogre Ayanore, Getie Lake Aynalem, Yared Asmare Aynalem, index as a fractional metric and multiplying by popula- Muluken Altaye Ayza, Samad Azari, Peter S Azzopardi, Darshan B B, Ebrahim Babaee, Ashish D Badiye, Mohammad Amin Bahrami, tion does not account for multimorbidities, nor does it Atif Amin Baig, Mohammad Hossein Bakhshaei, Ahad Bakhtiari, represent the distribution of needed services received Shankar M Bakkannavar, Arun Balachandran, Shelly Balassyano, within a given population. Measuring UHC effective Maciej Banach, Srikanta K Banerjee, Palash Chandra Banik, coverage at increasing granularity (ie, subnational loca- Agegnehu Bante Bante, Simachew Animen Bante, Suzanne Lyn Barker-Collo, Till Winfried Bärnighausen, Lope H Barrero, tions and by disaggregated age groups or sex, or both) Quique Bassat, Sanjay Basu, Bernhard T Baune, Mohsen Bayati, could help improve our understanding of the distribution Bayisa Abdissa Baye, Neeraj Bedi, Ettore Beghi, Masoud Behzadifar, of health services within a given population. Tariku Tesfaye Tesfaye Bekuma, Michelle L Bell, Isabela M Bensenor, Adam E Berman, Eduardo Bernabe, Robert S Bernstein, Akshaya Srikanth Bhagavathula, Dinesh Bhandari, Pankaj Bhardwaj, Conclusion Anusha Ganapati Bhat, Krittika Bhattacharyya, Suraj Bhattarai, This study provides a new measurement framework and Zulfiqar A Bhutta, Ali Bijani, Boris Bikbov, Ver Bilano, Antonio Biondi, metric on UHC effective coverage, supporting country Binyam Minuye Birihane, Moses John Bockarie, Somayeh Bohlouli, and global stakeholders in their efforts to track improved Hunduma Amensisa Bojia, Srinivasa Rao Rao Bolla, Archith Boloor, Oliver J Brady, Dejana Braithwaite, Paul Svitil Briant, Andrew M Briggs, performance over time. By striving to capture potential Nikolay Ivanovich Briko, Sharath Burugina Nagaraja, Reinhard Busse, health gains delivered by health systems, we hope to Zahid A Butt, Florentino Luciano Caetano dos Santos, better diagnose and address challenges that otherwise Lucero Cahuana-Hurtado, Luis Alberto Cámera, Rosario Cárdenas, Giulia Carreras, Juan J Carrero, Felix Carvalho, impede the ultimate objective of UHC: improving health Joao Mauricio Castaldelli-Maia, Carlos A Castañeda-Orjuela, for all people and leaving no one behind. If current Giulio Castelpietra, Franz Castro, Ferrán Catalá-López, Kate Causey, trends hold, the world will fall short of delivering on its Christopher R Cederroth, Kelly M Cercy, Ester Cerin, UHC ambitions for the GPW13 and SDGs. Although this Joht Singh Chandan, Angela Y Chang, Jaykaran Charan, Vijay Kumar Chattu, Sarika Chaturvedi, Ken Lee Chin, outcome is not yet inevitable, the window for meaningful Daniel Youngwhan Cho, Jee-Young Jasmine Choi, Hanne Christensen, action and health-system changes is rapidly narrowing. Dinh-Toi Chu, Michael T Chung, Liliana G Ciobanu, Massimo Cirillo, By focusing on UHC effective coverage and populations’ Haley Comfort, Kelly Compton, Paolo Angelo Cortesi, Vera Marisa Costa, www.thelancet.com Vol 396 October 17, 2020 1271 Global Health Metrics Ewerton Cousin, Saad M A Dahlawi, Giovanni Damiani, Lalit Dandona, Dian Kusuma, Hmwe Hmwe Kyu, Carlo La Vecchia, Ben Lacey, Rakhi Dandona, Jiregna Darega Gela, Aso Mohammad Darwesh, Dharmesh Kumar Lal, Ratilal Lalloo, Iván Landires, Ahmad Daryani, Aditya Prasad Dash, Gail Davey, Van Charles Lansingh, Anders O Larsson, Savita Lasrado, Claudio Alberto Dávila-Cervantes, Kairat Davletov, Jan-Walter De Neve, Kathryn Mei-Ming Lau, Paolo Lauriola, Jeffrey V Lazarus, Edgar Denova-Gutiérrez, Kebede Deribe, Nikolaos Dervenis, Jorge R Ledesma, Paul H Lee, Shaun Wen Huey Lee, Andrew T Leever, Rupak Desai, Samath Dhamminda Dharmaratne, Kate E LeGrand, James Leigh, Matilde Leonardi, Shanshan Li, Govinda Prasad Dhungana, Mostafa Dianatinasab, Diana Dias da Silva, Lee-Ling Lim, Stephen S Lim, Xuefeng Liu, Giancarlo Logroscino, Daniel Diaz, Ilse N Dippenaar, Hoa Thi Do, Fariba Dorostkar, Alan D Lopez, Platon D Lopukhov, Paulo A Lotufo, Alton Lu, Jianing Ma, Leila Doshmangir, Bruce B Duncan, Andre Rodrigues Duraes, Mohammed Madadin, Phetole Walter Mahasha, Morteza Mahmoudi, Arielle Wilder Eagan, David Edvardsson, Iman El Sayed, Azeem Majeed, Jeadran N Malagón-Rojas, Shokofeh Maleki, Maha El Tantawi, Islam Y Elgendy, Iqbal RF Elyazar, Khalil Eskandari, Deborah Carvalho Malta, Borhan Mansouri, Sharareh Eskandarieh, Saman Esmaeilnejad, Alireza Esteghamati, Mohammad Ali Mansournia, Santi Martini, Oluchi Ezekannagha, Tamer Farag, Mohammad Farahmand, Francisco Rogerlândio Martins-Melo, Ira Martopullo, Emerito Jose A Faraon, Carla Sofia e Sá Farinha, Andrea Farioli, Benjamin Ballard Massenburg, Claudia I Mastrogiacomo, Pawan Sirwan Faris, Andre Faro, Mehdi Fazlzadeh, Valery L Feigin, Manu Raj Mathur, Colm McAlinden, Martin McKee, Eduarda Fernandes, Pietro Ferrara, Garumma Tolu Feyissa, Irina Filip, Carlo Eduardo Medina-Solís, Birhanu Geta Meharie, Florian Fischer, James L Fisher, Luisa Sorio Flor, Nataliya A Foigt, Man Mohan Mehndiratta, Entezar Mehrabi Nasab, Fereshteh Mehri, Morenike Oluwatoyin Folayan, Artem Alekseevich Fomenkov, Ravi Mehrotra, Teferi Mekonnen, Addisu Melese, Peter T N Memiah, Masoud Foroutan, Joel Msafiri Francis, Weijia Fu, Takeshi Fukumoto, Walter Mendoza, Ritesh G Menezes, George A Mensah, Atte Meretoja, João M Furtado, Mohamed M Gad, Abhay Motiramji Gaidhane, Tuomo J Meretoja, Tomislav Mestrovic, Bartosz Miazgowski, Emmanuela Gakidou, Natalie C Galles, Silvano Gallus, Irmina Maria Michalek, Erkin M Mirrakhimov, Maryam Mirzaei, William M Gardner, Biniyam Sahiledengle Geberemariyam, Mehdi Mirzaei-Alavijeh, Philip B Mitchell, Babak Moazen, Abiyu Mekonnen Gebrehiwot, Gebreamlak Gebremedhn Gebremeskel, Masoud Moghadaszadeh, Efat Mohamadi, Dara K Mohammad, Leake G Gebremeskel, Hailay Abrha Gesesew, Keyghobad Ghadiri, Yousef Mohammad, Naser Mohammad Gholi Mezerji, Mansour Ghafourifard, Ahmad Ghashghaee, Nermin Ghith, Abdollah Mohammadian-Hafshejani, Jemal Abdu Mohammed, Asadollah Gholamian, Syed Amir Gilani, Paramjit Singh Gill, Shafiu Mohammed, Ali H Mokdad, Lorenzo Monasta, Tiffany K Gill, Themba G Ginindza, Mojgan Gitimoghaddam, Stefania Mondello, Masoud Moradi, Maziar Moradi-Lakeh, Giorgia Giussani, Mustefa Glagn, Elena V Gnedovskaya, Rahmatollah Moradzadeh, Paula Moraga, Joana Morgado-da-Costa, Myron Anthony Godinho, Salime Goharinezhad, Sameer Vali Gopalani, Shane Douglas Morrison, Abbas Mosapour, Jonathan F Mosser, Amir Hossein Goudarzian, Bárbara Niegia Garcia Goulart, Amin Mousavi Khaneghah, Moses K Muriithi, Ghulam Mustafa, Mohammed Ibrahim Mohialdeen Gubari, Rafael Alves Guimarães, Ashraf F Nabhan, Mehdi Naderi, Ahamarshan Jayaraman Nagarajan, Rashid Abdi Guled, Teklemariam Gultie, Yuming Guo, Rahul Gupta, Mohsen Naghavi, Behshad Naghshtabrizi, Rajeev Gupta, Nima Hafezi-Nejad, Abdul Hafiz, Mukhammad David Naimzada, Vinay Nangia, Jobert Richie Nansseu, Teklehaimanot Gereziher Haile, Randah R Hamadeh, Sajid Hameed, Vinod C Nayak, Javad Nazari, Rawlance Ndejjo, Ionut Negoi, Samer Hamidi, Chieh Han, Hannah Han, Ruxandra Irina Negoi, Subas Neupane, Kiirithio N Ngari, Demelash Woldeyohannes Handiso, Asif Hanif, Graeme J Hankey, Georges Nguefack-Tsague, Josephine W Ngunjiri, Cuong Tat Nguyen, Josep Maria Haro, Ahmed I Hasaballah, Md. 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Development, Jakarta, Indonesia; University of Basel, Basel, Switzerland Declaration of interests (F Tediosi PhD); Division of Biostatistics and Epidemiology Ali Almasi reports a patent null pending. Robert Ancuceanu reports (B Thakur PhD), Texas Tech University, El Paso, TX, USA; Timiryazev receiving consultancy and speakers’ fees from various pharmaceutical Institute of Plant Physiology (M V Titova PhD), Russian Academy of companies. Ettore Beghi reports grants from the Italian Ministry of Sciences, Moscow, Russia; Department of Medicine Health, grants from SOBI, and personal fees from Arvelle Therapeutics, (Prof M Tonelli MD), University of Calgary, Calgary, AB, Canada; outside the submitted work. Hanne Christensen reports personal fees Institute of Public Health (R Topor-Madry PhD), Jagiellonian University from Bristol-Myers Squibb, Bayer, Boehringer-Ingelheim, outside the Medical College, Kraków, Poland; Agency for Health Technology submitted work. Vivekanand Jha reports grants from GlaxoSmithKline, Assessment and Tariff System, Warsaw, Poland (R Topor-Madry PhD); grants from Baxter Healthcare, personal fees from NephroPlus, grants Modestum LTD, London, UK (M R Tovani-Palone PhD); Department of from Biocon, grants from Zydus Cadilla, outside the submitted work. Health Economics (B X Tran PhD), Hanoi Medical University, Hanoi, Jacek Jerzy Jozwiak reports personal fees from Amgen, Alab, Teva, Vietnam; Lee Kong Chian School of Medicine (L Tudor Car PhD), Synexus, and Boehringer Ingelheim, outside the submitted work. Nanyang Technological University, Singapore, Singapore; Department of Srinivasa Vittal Katikireddi reports support from the Medical Research Microbiology (I Ullah PhD), Iqra National University, Peshawar, Council and from the Scottish Government Chief Scientist Office, during Pakistan; TB Culture Laboratory (I Ullah PhD), Mufti Mehmood the conduct of the study. Walter Mendoza is Program Analyst in Memorial Teaching Hospital, Dera Ismail Khan, Pakistan; Department Population and Development at the United Nations Population Fund- of Community Medicine (C D Umeokonkwo MPH), Alex Ekwueme UNFPA Country Office in Peru, an institution which does not necessarily Federal University Teaching Hospital Abakaliki, Abakaliki, Nigeria; endorse this study. Jonathan F Mosser reports grants from the Bill and Amity Institute of Biotechnology (E Upadhyay PhD), Amity University Melinda Gates Foundation, during the conduct of the study. Rajasthan, Jaipur, India; Velez Sarsfield Hospital, Buenos Aires, Shuhei Nomura reports grants from the Ministry of Education, Culture, Argentina (Prof P R Valdez MEd); Department of Nephrology Sports, Science, and Technology of Japan. Thomas Pilgrim reports grants (Prof S Varughese FRCP), Christian Medical College and Hospital and personal fees from Biotronik and Boston Scientific, grants from (CMC), Vellore, India; UKK Institute, Tampere, Finland Edwards Lifesciences, and personal fees from HighLife SAS for his work (Prof T J Vasankari MD); Raffles Neuroscience Centre as a member of clinical event committee for a study sponsored by (Prof N Venketasubramanian MBBS), Raffles Hospital, Singapore, HighLife Sas, outside the submitted work. Maarten J Postma reports Singapore; Yong Loo Lin School of Medicine grants and personal fees from MSD, GlaxoSmithKline, Pfizer, (Prof N Venketasubramanian MBBS), National University of Singapore, Boehringer Ingelheim, Novavax, Bristol-Myers Squibb, AstraZeneca, Singapore, Singapore; Occupational Health Unit (Prof F S Violante MD), Sanofi, IQVIA, and Seqirus; personal fees from Quintiles, Novartis, Sant’Orsola Malpighi Hospital, Bologna, Italy; Foundation University and Pharmerit; 2% of stocks from Ingress Health, 100% of stocks from Medical College (Prof Y Waheed PhD), Foundation University PAG, being an advisor to Asc Academics; and grants from Bayer, Islamabad, Islamabad, Pakistan; Cultures, Societies and Global Studies, BioMerieux, WHO, the EU, FIND, Antilope, DIKTI, LPDP, and Budi, & Integrated Initiative for Global Health (R G Wamai PhD), outside the submitted work. Elisabetta Pupillo reports grants from AIFA, Northeastern University, Boston, MA, USA; Cardiology Department outside the submitted work. Miloje Savic is an employee of (Prof R G Weintraub MB), Royal Children’s Hospital, Melbourne, VIC, GlaxoSmithKline Biologicals, Wavre, Belgium, and holds GlaxoSmithKline Australia; Leonard Davis Institute of Health Economics (J Weiss MA), restricted shares. Aletta Elisabeth Schutte reports personal fees from Population Studies Center (J Weiss MA), University of Pennsylvania, Omron Healthcare, Servier, Novartis, Takeda, and Abbott, outside the Philadelphia, PA, USA; Demographic Change and Aging Research Area submitted work. Mark G Shrime reports grants from Mercy Ships and (A Werdecker PhD), Competence Center of Mortality-Follow-Up of the Damon Runyon Cancer Research Foundation, outside the submitted German National Cohort (R Westerman DSc), Federal Institute for work. Jasvinder A Singh reports personal fees from Crealta/Horizon, Population Research, Wiesbaden, Germany; NIHR Biomedical Research Medisys, Fidia, UBM LLC, Trio health, Medscape, WebMD, Clinical Care Centre (Prof C D A Wolfe MD), Guy’s and St.Thomas’ Hospital and options, Clearview healthcare partners, Putnam associates, Spherix, Kings College London, London, UK; Department of Orthopaedics Practice Point communications, the National Institutes of Health and the (Prof A Wu MD), Wenzhou Medical University, Wenzhou, China; American College of Rheumatology; personal fees from Simply Department of Behavior and Operation Management (Y Xie MD), Speaking, holding stock in Amarin pharmaceuticals and Viking Beijing Advanced Innovation Center for Big Data-based Precision pharmaceuticals, non-financial support from the FDA Arthritis Advisory Medicine, Beijing, China; Clinical Cancer Research Center Committee, non-financial support from Steering committee of (S Yahyazadeh Jabbari MD), Milad General Hospital, Tehran, Iran; OMERACT, an international organization that develops measures for Research and Development Center for Health Services clinical trials and receives arm’s length funding from 12 pharmaceutical (Prof K Yamagishi MD), University of Tsukuba, Tsukuba, Japan; School companies, non-financial support from the Veterans Affairs www.thelancet.com Vol 396 October 17, 2020 1281 Global Health Metrics Rheumatology Field Advisory Committee, and non-financial support support from the NIHR Oxford Biomedical Research Centre and the from the Editor and the Director of the UAB Cochrane Musculoskeletal BHF Centre of Research Excellence, Oxford. Iván Landires is a member Group Satellite Center on Network Meta-analysis, outside the submitted of the Sistema Nacional de Investigación (SNI), which is supported by work. Jeffrey D Stanaway reports grants from the Bill and Melinda Gates the Secretaría Nacional de Ciencia Tecnología e Innovación (SENACYT), Foundation, during the conduct of the study. Fotis Topouzis reports Panamá. Jeffrey V Lazarus acknowledges support by a Spanish Ministry grants from Pfizer, Thea, Rheon, Pharmaten, Bayer, and Bausch & Lomb; of Science, Innovation and Universities Miguel Servet grant (Instituto de and grants and personal fees from Novartis and Omikron, outside the Salud Carlos III/ESF, European Union [CP18/00074]). Peter T N Memiah submitted work. Riaz Uddin worked as a visiting fellow at Deakin acknowledges CODESRIA; HISTP. Subas Neupane acknowledges partial University Institute for Physical Activity and Nutrition (IPAN), which support from the Competitive State Research Financing of the Expert paid for his travel (including flights and transport), accommodation, Responsibility area of Tampere University Hospital. Shuhei Nomura and meals from Deakin University, outside the submitted work. acknowledges support from the Ministry of Education, Culture, Sports, Data sharing Science, and Technology of Japan (18K10082). Alberto Ortiz acknowledges support by ISCIII PI19/00815, DTS18/00032, To download the data used in these analyses, please visit the Global Health ISCIII-RETIC REDinREN RD016/0009 Fondos FEDER, FRIAT, Data Exchange at http://ghdx.healthdata.org/gbd-2019. Comunidad de Madrid B2017/BMD-3686 CIFRA2-CM. These funding Acknowledgments sources had no role in the writing of the manuscript or the decision to Lucas Guimarães Abreu acknowledges support from Coordenação de submit it for publication. George C Patton acknowledges support from a Aperfeiçoamento de Pessoal de Nível Superior - Brasil (Capes) - Finance National Health & Medical Research Council Fellowship. Code 001, Conselho Nacional de Desenvolvimento Científico e Marina Pinheiro acknowledges support from FCT for funding through Tecnológico (CNPq) and Fundação de Amparo à Pesquisa do Estado de program DL 57/2016 - Norma transitória. Alberto Raggi, David Sattin, Minas Gerais (FAPEMIG). Olatunji O Adetokunboh acknowledges South and Silvia Schiavolin acknowledge support by a grant from the Italian African Department of Science & Innovation, and National Research Ministry of Health (Ricerca Corrente, Fondazione Istituto Neurologico C Foundation. Anurag Agrawal acknowledges support from the Wellcome Besta, Linea 4 - Outcome Research: dagli Indicatori alle Raccomandazioni Trust DBT India Alliance Senior Fellowship IA/CPHS/14/1/501489. Cliniche). Daniel Cury Ribeiro acknowledges support from the Rufus Olusola Akinyemi acknowledges Grant U01HG010273 from the Sir Charles Hercus Health Research Fellowship - Health Research National Institutes of Health (NIH) as part of the H3Africa Consortium. Council of New Zealand (18/111). Perminder S Sachdev acknowledges Rufus Olusola Akinyemi is further supported by the FLAIR fellowship funding from the NHMRC Australia. Abdallah M Samy acknowledges funded by the UK Royal Society and the African Academy of Sciences. support from a fellowship from the Egyptian Fulbright Mission Program. Syed Mohamed Aljunid acknowledges the Department of Health Policy Milena M Santric-Milicevic acknowledges support from the Ministry of and Management, Faculty of Public Health, Kuwait University and Education, Science and Technological Development of the Republic of International Centre for Casemix and Clinical Coding, Faculty of Serbia (Contract No. 175087). Rodrigo Sarmiento-Suárez acknowledges Medicine, National University of Malaysia for the approval and support to institutional support from University of Applied and Environmental participate in this research project. Marcel Ausloos, Claudiu Herteliu, Sciences in Bogota, Colombia, and Carlos III Institute of Health in and Adrian Pana acknowledge partial support by a grant of the Romanian Madrid, Spain. Maria Inês Schmidt acknowledges grants from the National Authority for Scientific Research and Innovation, CNDS- Foundation for the Support of Research of the State of Rio Grande do Sul UEFISCDI, project number PN-III-P4-ID-PCCF-2016-0084. Till Winfried (IATS and PrInt) and the Brazilian Ministry of Health. Bärnighausen acknowledges support from the Alexander von Humboldt Sheikh Mohammed Shariful Islam acknowledges a fellowship from the Foundation through the Alexander von Humboldt Professor award, National Heart Foundation of Australia and Deakin University. funded by the German Federal Ministry of Education and Research. Aziz Sheikh acknowledges support from Health Data Research UK. Juan J Carrero was supported by the Swedish Research Council Kenji Shibuya acknowledges Japan Ministry of Education, Culture, (2019-01059). Felix Carvalho acknowledges UID/MULTI/04378/2019 and Sports, Science and Technology. Joan B Soriano acknowledges support by UID/QUI/50006/2019 support with funding from FCT/MCTES through Centro de Investigación en Red de Enfermedades Respiratorias national funds. Vera Marisa Costa acknowledges support from grant (CIBERES), Instituto de Salud Carlos III (ISCIII), Madrid, Spain. (SFRH/BHD/110001/2015), received by Portuguese national funds Rafael Tabarés-Seisdedos acknowledges partial support from grant through Fundação para a Ciência e a Tecnologia (FCT), IP, under the PI17/00719 from ISCIII-FEDER. Santosh Kumar Tadakamadla Norma Transitória DL57/2016/CP1334/CT0006. Jan-Walter De Neve acknowledges support from the National Health and Medical Research acknowledges support from the Alexander von Humboldt Foundation. Council Early Career Fellowship, Australia. Marcello Tonelli Kebede Deribe acknowledges support by Wellcome Trust grant number acknowledges the David Freeze Chair in Health Services Research at the 201900/Z/16/Z as part of his International Intermediate Fellowship. University of Calgary, AB, Canada. Claudiu Herteliu acknowledges partial support by a grant co-funded by European Fund for Regional Development through Operational Program Editorial note: the Lancet Group takes a neutral position with respect to for Competitiveness, Project ID P_40_382. Praveen Hoogar territorial claims in published maps and institutional affiliations. acknowledges the Centre for Bio Cultural Studies (CBiCS), Manipal References Academy of Higher Education(MAHE), Manipal and Centre for Holistic 1 UNGA. Resolution 74/2: political declaration of the high-level Development and Research (CHDR), Kalghatgi. Bing-Fang Hwang meeting on universal health coverage. Oct 18, 2019. https://undocs. acknowledges support from China Medical University (CMU108-MF-95), org/en/A/RES/74/2 (accessed Nov 15, 2019). Taichung, Taiwan. Mihajlo Jakovljevic acknowledges the Serbian part of 2 Schmidt H, Gostin LO, Emanuel EJ. Public health, universal health this GBD contribution was co-funded through the Grant OI175014 of coverage, and Sustainable Development Goals: can they coexist? the Ministry of Education Science and Technological Development of the Lancet 2015; 386: 928–30. Republic of Serbia. Aruna M Kamath acknowledges funding from the 3 UN. 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