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This paper investigates the impact of Social Discount Rate (SDR) choice on intergenerational equity issues caused by Public-Private Partnerships (PPPs) projects. Indeed, more PPPs mean more debt being accumulated for future generations leading to a fiscal deficit crisis. The paper draws on how the SDR level taken today distributes societies on the Social Welfare Function (SWF). This is done by answering two sub-questions: (i) What is the risk of PPPs' debts being off-balance sheet? (ii) How do public policies, based on the envisaged SDR, position society within different ethical perspectives? The answers are obtained from a discussion of the different SDRs (applied in the UK for examples) according to the merits of the pertinent ethical theories, namely libertarian, egalitarian, utilitarian and Rawlsian. We find that public policymakers can manipulate the SDR to make PPPs looking like a better option than the traditional financing form. However, this antagonises the Value for Money principle. We also point out that public policy is not harmonised with ethical theories. We find that at present (in the UK), the SDR is somewhere between weighted utilitarian and Rawlsian societies in the trade-off curve. Alas, our study finds no evidence that the (UK) government is using a sophisticated system to keep pace with the accumulated off-balance sheet debts. Thus, the exact prediction of the final state is hardly made because of the uncertainty factor. We conclude that our study hopefully provides a good analytical framework for policymakers in order to draw on the merits of ethical theories before initiating public policies like PPPs.

The concept of "primacy" as introduced by Jefferson in 1939 in urban geography leads to the notion of "dominant city" also known as the primate city. Practically, the notion was extended by Sheppard in view of discussing some "hierarchy". The type of dominance is not universal nor any hierarchy reversal. Both can be time and sample dependent. Thus, as an example taking into consideration the existence of both pieces of the puzzle, we consider and discuss the Bulgarian urban system. It is also interesting to compare data on two groups of cities in different time intervals: (i) the whole Bulgaria city system which contains about 250 cities, - studied in the time interval between 2004 and 2011, and (ii) a system of 33 cities, - studied over the time interval 1887 till 2010. These latter cities are selected because the population was already over 10 000 inhabitants in 1946. It is shown that new additional indices are interestingly introduced in order to compensate defects in the Sheppard index. Numerical illustrations are illuminated through a "length ratio" measure, which allows to distinguish the (often) observed departures from the hyperbolic ranking seen by Jefferson. ; : This work has been performed in the framework of COST Action IS1104 "The EU in the new economic complex geography: models, tools and policy evaluation". We acknowledge some support through the project 'Evolution spatiale et temporelle d'infrastructures régionales et économiques en Bulgarie et en Fédération Wallonie-Bruxelles' within the intergovernemental agreement for cooperation between the Republic of Bulgaria and the Communauté Française de Belgique. ; Peer-reviewed ; Publisher Version

The file associated with this record is under embargo until 24 months after publication, in accordance with the publisher's self-archiving policy. The full text may be available through the publisher links provided above. ; This work presents a text mining context and its use for a deep analysis of the messages delivered by politicians. Specifically, we deal with an expert systems-based exploration of the rhetoric dynamics of a large collection of US Presidents' speeches, ranging from Washington to Trump. In particular, speeches are viewed as complex expert systems whose structures can be effectively analyzed through rank-size laws. The methodological contribution of the paper is twofold. First, we develop a text mining-based procedure for the construction of the dataset by using a web scraping routine on the Miller Center website – the repository site collecting the speeches. Second, we explore the implicit structure of the discourse data by implementing a rank-size procedure over the individual speeches, being the words of each speech ranked in terms of their frequencies. The scientific significance of the proposed combination of text-mining and rank-size approaches can be found in its flexibility and generality, which let it be reproducible to a wide set of expert systems and text mining contexts. The usefulness of the proposed method and of the speeches analysis is demonstrated by the findings themselves. Indeed, in terms of impact, it is worth noting that interesting conclusions of social, political and linguistic nature on how 45 United States Presidents, from April 30, 1789 till February 28, 2017 delivered political messages can be carried out. Indeed, the proposed analysis shows some remarkable regularities, not only inside a given speech, but also among different speeches. Moreover, under a purely methodological perspective, the presented contribution suggests possible ways of generating a linguistic decision-making algorithm. ; Peer-reviewed ; Post-print

The file associated with this record is under embargo until 12 months after publication, in accordance with the publisher's self-archiving policy. The full text may be available through the publisher links provided above. ; This paper considers an often forgotten relationship, the time delay between a cause and its effect in economies and finance. We treat the case of Foreign Direct Investment (FDI) and economic growth, - measured through a country Gross Domestic Product (GDP). The pertinent data refers to 43 countries, over 1970-2015, - for a total of 4278 observations. When countries are grouped according to the Inequality-Adjusted Human Development Index (IHDI), it is found that a time lag dependence effect exists in FDI-GDP correlations. This is established through a time-dependent Pearson 's product-moment correlation coefficient matrix. Moreover, such a Pearson correlation coefficient is observed to evolve from positive to negative values depending on the IHDI, from low to high. It is "politically and policy "relevant" that the correlation is statistically significant providing the time lag is less than 3 years. A "rank-size" law is demonstrated. It is recommended to reconsider such a time lag effect when discussing previous analyses whence conclusions on international business, and thereafter on forecasting. ; Peer-reviewed ; Post-print

BACKGROUND: Comprehensive and comparable estimates of health spending in each country are a key input for health policy and planning, and are necessary to support the achievement of national and international health goals. Previous studies have tracked past and projected future health spending until 2040 and shown that, with economic development, countries tend to spend more on health per capita, with a decreasing share of spending from development assistance and out-of-pocket sources. We aimed to characterise the past, present, and predicted future of global health spending, with an emphasis on equity in spending across countries. METHODS: We estimated domestic health spending for 195 countries and territories from 1995 to 2016, split into three categories-government, out-of-pocket, and prepaid private health spending-and estimated development assistance for health (DAH) from 1990 to 2018. We estimated future scenarios of health spending using an ensemble of linear mixed-effects models with time series specifications to project domestic health spending from 2017 through 2050 and DAH from 2019 through 2050. Data were extracted from a broad set of sources tracking health spending and revenue, and were standardised and converted to inflation-adjusted 2018 US dollars. Incomplete or low-quality data were modelled and uncertainty was estimated, leading to a complete data series of total, government, prepaid private, and out-of-pocket health spending, and DAH. Estimates are reported in 2018 US dollars, 2018 purchasing-power parity-adjusted dollars, and as a percentage of gross domestic product. We used demographic decomposition methods to assess a set of factors associated with changes in government health spending between 1995 and 2016 and to examine evidence to support the theory of the health financing transition. We projected two alternative future scenarios based on higher government health spending to assess the potential ability of governments to generate more resources for health. FINDINGS: Between 1995 and 2016, health spending grew at a rate of 4·00% (95% uncertainty interval 3·89-4·12) annually, although it grew slower in per capita terms (2·72% [2·61-2·84]) and increased by less than $1 per capita over this period in 22 of 195 countries. The highest annual growth rates in per capita health spending were observed in upper-middle-income countries (5·55% [5·18-5·95]), mainly due to growth in government health spending, and in lower-middle-income countries (3·71% [3·10-4·34]), mainly from DAH. Health spending globally reached $8·0 trillion (7·8-8·1) in 2016 (comprising 8·6% [8·4-8·7] of the global economy and $10·3 trillion [10·1-10·6] in purchasing-power parity-adjusted dollars), with a per capita spending of US$5252 (5184-5319) in high-income countries, $491 (461-524) in upper-middle-income countries, $81 (74-89) in lower-middle-income countries, and $40 (38-43) in low-income countries. In 2016, 0·4% (0·3-0·4) of health spending globally was in low-income countries, despite these countries comprising 10·0% of the global population. In 2018, the largest proportion of DAH targeted HIV/AIDS ($9·5 billion, 24·3% of total DAH), although spending on other infectious diseases (excluding tuberculosis and malaria) grew fastest from 2010 to 2018 (6·27% per year). The leading sources of DAH were the USA and private philanthropy (excluding corporate donations and the Bill & Melinda Gates Foundation). For the first time, we included estimates of China's contribution to DAH ($644·7 million in 2018). Globally, health spending is projected to increase to $15·0 trillion (14·0-16·0) by 2050 (reaching 9·4% [7·6-11·3] of the global economy and $21·3 trillion [19·8-23·1] in purchasing-power parity-adjusted dollars), but at a lower growth rate of 1·84% (1·68-2·02) annually, and with continuing disparities in spending between countries. In 2050, we estimate that 0·6% (0·6-0·7) of health spending will occur in currently low-income countries, despite these countries comprising an estimated 15·7% of the global population by 2050. The ratio between per capita health spending in high-income and low-income countries was 130·2 (122·9-136·9) in 2016 and is projected to remain at similar levels in 2050 (125·9 [113·7-138·1]). The decomposition analysis identified governments' increased prioritisation of the health sector and economic development as the strongest factors associated with increases in government health spending globally. Future government health spending scenarios suggest that, with greater prioritisation of the health sector and increased government spending, health spending per capita could more than double, with greater impacts in countries that currently have the lowest levels of government health spending. INTERPRETATION: Financing for global health has increased steadily over the past two decades and is projected to continue increasing in the future, although at a slower pace of growth and with persistent disparities in per-capita health spending between countries. Out-of-pocket spending is projected to remain substantial outside of high-income countries. Many low-income countries are expected to remain dependent on development assistance, although with greater government spending, larger investments in health are feasible. In the absence of sustained new investments in health, increasing efficiency in health spending is essential to meet global health targets. FUNDING: Bill & Melinda Gates Foundation. ; Bill & Melinda Gates Foundation ; Sí

High-resolution estimates of HIV burden across space and time provide an important tool for tracking and monitoring the progress of prevention and control efforts and assist with improving the precision and efficiency of targeting efforts. We aimed to assess HIV incidence and HIV mortality for all second-level administrative units across sub-Saharan Africa. ; his work was primarily supported by the Bill & Melinda Gates Foundation (grant OPP1132415). Additionally, O Adetokunboh acknowledges the support of the Department of Science and Innovation, and National Research Foundation of South Africa. M Ausloos, A Pana, and C Herteliu are partially supported by a grant of the Romanian National Authority for Scientific Research and Innovation, Executive Agency for Higher Education, Research, Development and Innovation Funding (Romania; project number PN-III-P4-ID-PCCF-2016-0084). T W Bärnighausen was supported by the Alexander von Humboldt Foundation through the Alexander von Humboldt Professor award, funded by the German Federal Ministry of Education and Research. M J Bockarie is supported by the European and Developing Countries Clinical Trials Partnership. F Carvalho and E Fernandes acknowledge support from Portuguese national funds (Fundação para a Ciência e Tecnologia and Ministério da Ciência, Tecnologia e Ensino Superior; UIDB/50006/2020, UIDB/04378/2020, and UIDP/04378/2020. K Deribe is supported by the Wellcome Trust (grant 201900/Z/16/Z) as part of his International Intermediate Fellowship. B-F Hwang was partially supported by China Medical University (CMU107-Z-04), Taichung, Taiwan. M Jakovljevic acknowledges support of the Serbia Ministry of Education Science and Technological Development (grant OI 175 014). M N Khan acknowledges the support of Jatiya Kabi Kazi Nazrul Islam University, Bangladesh. Y J Kim was supported by the Research Management Centre, Xiamen University Malaysia, Malaysia, (XMUMRF/2020-C6/ITCM/0004). K Krishnan is supported by University Grants Commission Centre of Advanced Study, (CAS II), awarded to the Department of Anthropology, Panjab University, Chandigarh, India. M Kumar would like to acknowledge National Institutes of Health and Fogarty International Cente (K43TW010716). I Landires is a member of the Sistema Nacional de Investigación, which is supported by the Secretaría Nacional de Ciencia, Tecnología e Innovación, Panama. W Mendoza is a program analyst in population and development at the UN Population Fund Country Office in Peru, which does not necessarily endorse this study. M Phetole received institutional support from the Grants, Innovation and Product Development Unit, South African Medical Research Council. O Odukoya acknowledges support from the Fogarty International Center of the US National Institutes of Health (K43TW010704). The content is solely the responsibility of the authors and does not necessarily represent the official views of the US National Institutes of Health. O Oladimeji is grateful for the support from Walter Sisulu University, Eastern Cape, South Africa, the University of Botswana, Botswana, and the University of Technology of Durban, Durban, South Africa. J R Padubidri acknowledges support from Kasturba Medical College, Mangalore, Manipal Academy of Higher Education, India. G C Patton is supported by an Australian Government National Health and Medical Research Council research fellowship. P Rathi acknowledges Kasturba Medical College, Mangalore, Manipal Academy of Higher Education, Manipal India. A I Ribeiro was supported by National Funds through Fundação para a Ciência e Tecnologia, under the programme of Stimulus of Scientific Employment–Individual Support (CEECIND/02386/2018). A M Samy acknowledges the support of the Egyptian Fulbright Mission Program. F Sha was supported by the Shenzhen Social Science Fund (SZ2020C015) and the Shenzhen Science and Technology Program (KQTD20190929172835662). A Sheikh is supported by Health Data Research UK. N Taveira acknowledges partial funding by Fundação para a Ciência e Tecnologia, Portugal, and Aga Khan Development Network—Portugal Collaborative Research Network in Portuguese-speaking countries in Africa (332821690), and by the European and Developing Countries Clinical Trials Partnership (RIA2016MC-1615). C S Wiysonge is supported by the South African Medical Research Council. Y Zhang was supported by the Science and Technology Research Project of Hubei Provincial Department of Education (Q20201104) and Open Fund Project of Hubei Province Key Laboratory of Occupational Hazard Identification and Control (OHIC2020Y01).Editorial note: the Lancet Group takes a neutral position with respect to territorial claims in published maps and institutional affiliations

High-resolution estimates of HIV burden across space and time provide an important tool for tracking and monitoring the progress of prevention and control efforts and assist with improving the precision and efficiency of targeting efforts. We aimed to assess HIV incidence and HIV mortality for all second-level administrative units across sub-Saharan Africa. ; his work was primarily supported by the Bill & Melinda Gates Foundation (grant OPP1132415). Additionally, O Adetokunboh acknowledges the support of the Department of Science and Innovation, and National Research Foundation of South Africa. M Ausloos, A Pana, and C Herteliu are partially supported by a grant of the Romanian National Authority for Scientific Research and Innovation, Executive Agency for Higher Education, Research, Development and Innovation Funding (Romania; project number PN-III-P4-ID-PCCF-2016-0084). T W Bärnighausen was supported by the Alexander von Humboldt Foundation through the Alexander von Humboldt Professor award, funded by the German Federal Ministry of Education and Research. M J Bockarie is supported by the European and Developing Countries Clinical Trials Partnership. F Carvalho and E Fernandes acknowledge support from Portuguese national funds (Fundação para a Ciência e Tecnologia and Ministério da Ciência, Tecnologia e Ensino Superior; UIDB/50006/2020, UIDB/04378/2020, and UIDP/04378/2020. K Deribe is supported by the Wellcome Trust (grant 201900/Z/16/Z) as part of his International Intermediate Fellowship. B-F Hwang was partially supported by China Medical University (CMU107-Z-04), Taichung, Taiwan. M Jakovljevic acknowledges support of the Serbia Ministry of Education Science and Technological Development (grant OI 175 014). M N Khan acknowledges the support of Jatiya Kabi Kazi Nazrul Islam University, Bangladesh. Y J Kim was supported by the Research Management Centre, Xiamen University Malaysia, Malaysia, (XMUMRF/2020-C6/ITCM/0004). K Krishnan is supported by University Grants Commission Centre of Advanced Study, (CAS II), awarded to the Department of Anthropology, Panjab University, Chandigarh, India. M Kumar would like to acknowledge National Institutes of Health and Fogarty International Cente (K43TW010716). I Landires is a member of the Sistema Nacional de Investigación, which is supported by the Secretaría Nacional de Ciencia, Tecnología e Innovación, Panama. W Mendoza is a program analyst in population and development at the UN Population Fund Country Office in Peru, which does not necessarily endorse this study. M Phetole received institutional support from the Grants, Innovation and Product Development Unit, South African Medical Research Council. O Odukoya acknowledges support from the Fogarty International Center of the US National Institutes of Health (K43TW010704). The content is solely the responsibility of the authors and does not necessarily represent the official views of the US National Institutes of Health. O Oladimeji is grateful for the support from Walter Sisulu University, Eastern Cape, South Africa, the University of Botswana, Botswana, and the University of Technology of Durban, Durban, South Africa. J R Padubidri acknowledges support from Kasturba Medical College, Mangalore, Manipal Academy of Higher Education, India. G C Patton is supported by an Australian Government National Health and Medical Research Council research fellowship. P Rathi acknowledges Kasturba Medical College, Mangalore, Manipal Academy of Higher Education, Manipal India. A I Ribeiro was supported by National Funds through Fundação para a Ciência e Tecnologia, under the programme of Stimulus of Scientific Employment–Individual Support (CEECIND/02386/2018). A M Samy acknowledges the support of the Egyptian Fulbright Mission Program. F Sha was supported by the Shenzhen Social Science Fund (SZ2020C015) and the Shenzhen Science and Technology Program (KQTD20190929172835662). A Sheikh is supported by Health Data Research UK. N Taveira acknowledges partial funding by Fundação para a Ciência e Tecnologia, Portugal, and Aga Khan Development Network—Portugal Collaborative Research Network in Portuguese-speaking countries in Africa (332821690), and by the European and Developing Countries Clinical Trials Partnership (RIA2016MC-1615). C S Wiysonge is supported by the South African Medical Research Council. Y Zhang was supported by the Science and Technology Research Project of Hubei Provincial Department of Education (Q20201104) and Open Fund Project of Hubei Province Key Laboratory of Occupational Hazard Identification and Control (OHIC2020Y01).Editorial note: the Lancet Group takes a neutral position with respect to territorial claims in published maps and institutional affiliations

Background The rapid spread of COVID-19 renewed the focus on how health systems across the globe are financed, especially during public health emergencies. Development assistance is an important source of health financing in many low-income countries, yet little is known about how much of this funding was disbursed for COVID-19. We aimed to put development assistance for health for COVID-19 in the context of broader trends in global health financing, and to estimate total health spending from 1995 to 2050 and development assistance for COVID-19 in 2020.

Importance: Cancer and other noncommunicable diseases (NCDs) are now widely recognized as a threat to global development. The latest United Nations high-level meeting on NCDs reaffirmed this observation and also highlighted the slow progress in meeting the 2011 Political Declaration on the Prevention and Control of Noncommunicable Diseases and the third Sustainable Development Goal. Lack of situational analyses, priority setting, and budgeting have been identified as major obstacles in achieving these goals. All of these have in common that they require information on the local cancer epidemiology. The Global Burden of Disease (GBD) study is uniquely poised to provide these crucial data. Objective: To describe cancer burden for 29 cancer groups in 195 countries from 1990 through 2017 to provide data needed for cancer control planning. Evidence Review: We used the GBD study estimation methods to describe cancer incidence, mortality, years lived with disability, years of life lost, and disability-adjusted life-years (DALYs). Results are presented at the national level as well as by Socio-demographic Index (SDI), a composite indicator of income, educational attainment, and total fertility rate. We also analyzed the influence of the epidemiological vs the demographic transition on cancer incidence. Findings: In 2017, there were 24.5 million incident cancer cases worldwide (16.8 million without nonmelanoma skin cancer [NMSC]) and 9.6 million cancer deaths. The majority of cancer DALYs came from years of life lost (97%), and only 3% came from years lived with disability. The odds of developing cancer were the lowest in the low SDI quintile (1 in 7) and the highest in the high SDI quintile (1 in 2) for both sexes. In 2017, the most common incident cancers in men were NMSC (4.3 million incident cases); tracheal, bronchus, and lung (TBL) cancer (1.5 million incident cases); and prostate cancer (1.3 million incident cases). The most common causes of cancer deaths and DALYs for men were TBL cancer (1.3 million deaths and 28.4 million DALYs), liver cancer (572000 deaths and 15.2 million DALYs), and stomach cancer (542000 deaths and 12.2 million DALYs). For women in 2017, the most common incident cancers were NMSC (3.3 million incident cases), breast cancer (1.9 million incident cases), and colorectal cancer (819000 incident cases). The leading causes of cancer deaths and DALYs for women were breast cancer (601000 deaths and 17.4 million DALYs), TBL cancer (596000 deaths and 12.6 million DALYs), and colorectal cancer (414000 deaths and 8.3 million DALYs). Conclusions and Relevance: The national epidemiological profiles of cancer burden in the GBD study show large heterogeneities, which are a reflection of different exposures to risk factors, economic settings, lifestyles, and access to care and screening. The GBD study can be used by policy makers and other stakeholders to develop and improve national and local cancer control in order to achieve the global targets and improve equity in cancer care.

Importance Cancer and other noncommunicable diseases (NCDs) are now widely recognized as a threat to global development. The latest United Nations high-level meeting on NCDs reaffirmed this observation and also highlighted the slow progress in meeting the 2011 Political Declaration on the Prevention and Control of Noncommunicable Diseases and the third Sustainable Development Goal. Lack of situational analyses, priority setting, and budgeting have been identified as major obstacles in achieving these goals. All of these have in common that they require information on the local cancer epidemiology. The Global Burden of Disease (GBD) study is uniquely poised to provide these crucial data. Objective To describe cancer burden for 29 cancer groups in 195 countries from 1990 through 2017 to provide data needed for cancer control planning. Evidence Review We used the GBD study estimation methods to describe cancer incidence, mortality, years lived with disability, years of life lost, and disability-adjusted life-years (DALYs). Results are presented at the national level as well as by Socio-demographic Index (SDI), a composite indicator of income, educational attainment, and total fertility rate. We also analyzed the influence of the epidemiological vs the demographic transition on cancer incidence. Findings In 2017, there were 24.5 million incident cancer cases worldwide (16.8 million without nonmelanoma skin cancer [NMSC]) and 9.6 million cancer deaths. The majority of cancer DALYs came from years of life lost (97%), and only 3% came from years lived with disability. The odds of developing cancer were the lowest in the low SDI quintile (1 in 7) and the highest in the high SDI quintile (1 in 2) for both sexes. In 2017, the most common incident cancers in men were NMSC (4.3 million incident cases); tracheal, bronchus, and lung (TBL) cancer (1.5 million incident cases); and prostate cancer (1.3 million incident cases). The most common causes of cancer deaths and DALYs for men were TBL cancer (1.3 million deaths and 28.4 million DALYs), liver cancer (572 000 deaths and 15.2 million DALYs), and stomach cancer (542 000 deaths and 12.2 million DALYs). For women in 2017, the most common incident cancers were NMSC (3.3 million incident cases), breast cancer (1.9 million incident cases), and colorectal cancer (819 000 incident cases). The leading causes of cancer deaths and DALYs for women were breast cancer (601 000 deaths and 17.4 million DALYs), TBL cancer (596 000 deaths and 12.6 million DALYs), and colorectal cancer (414 000 deaths and 8.3 million DALYs). Conclusions and Relevance The national epidemiological profiles of cancer burden in the GBD study show large heterogeneities, which are a reflection of different exposures to risk factors, economic settings, lifestyles, and access to care and screening. The GBD study can be used by policy makers and other stakeholders to develop and improve national and local cancer control in order to achieve the global targets and improve equity in cancer

BACKGROUND:Achieving universal health coverage (UHC) involves all people receiving the health services they need, of high quality, without experiencing financial hardship. Making progress towards UHC is a policy priority for both countries and global institutions, as highlighted by the agenda of the UN Sustainable Development Goals (SDGs) and WHO's Thirteenth General Programme of Work (GPW13). Measuring effective coverage at the health-system level is important for understanding whether health services are aligned with countries' health profiles and are of sufficient quality to produce health gains for populations of all ages. METHODS:Based on the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2019, we assessed UHC effective coverage for 204 countries and territories from 1990 to 2019. Drawing from a measurement framework developed through WHO's GPW13 consultation, we mapped 23 effective coverage indicators to a matrix representing health service types (eg, promotion, prevention, and treatment) and five population-age groups spanning from 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 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.

Publisher's version (útgefin grein) ; Background Achieving universal health coverage (UHC) involves all people receiving the health services they need, of high quality, without experiencing financial hardship. Making progress towards UHC is a policy priority for both countries and global institutions, as highlighted by the agenda of the UN Sustainable Development Goals (SDGs) and WHO's Thirteenth General Programme of Work (GPW13). Measuring effective coverage at the health-system level is important for understanding whether health services are aligned with countries' health profiles and are of sufficient quality to produce health gains for populations of all ages. Methods Based on the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2019, we assessed UHC effective coverage for 204 countries and territories from 1990 to 2019. Drawing from a measurement framework developed through WHO's GPW13 consultation, we mapped 23 effective coverage indicators to a matrix representing health service types (eg, promotion, prevention, and treatment) and five population-age groups spanning from 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 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. ; Lucas Guimaraes Abreu acknowledges support from Coordenacao de Aperfeicoamento de Pessoal de Nivel Superior -Brasil (Capes) -Finance Code 001, Conselho Nacional de Desenvolvimento Cientifico e Tecnologico (CNPq) and Fundacao de Amparo a Pesquisa do Estado de Minas Gerais (FAPEMIG). Olatunji O Adetokunboh acknowledges South African Department of Science & Innovation, and National Research Foundation. Anurag Agrawal acknowledges support from the Wellcome Trust DBT India Alliance Senior Fellowship IA/CPHS/14/1/501489. Rufus Olusola Akinyemi acknowledges Grant U01HG010273 from the National Institutes of Health (NIH) as part of the H3Africa Consortium. Rufus Olusola Akinyemi is further supported by the FLAIR fellowship funded by the UK Royal Society and the African Academy of Sciences. Syed Mohamed Aljunid acknowledges the Department of Health Policy and Management, Faculty of Public Health, Kuwait University and International Centre for Casemix and Clinical Coding, Faculty of Medicine, National University of Malaysia for the approval and support to participate in this research project. Marcel Ausloos, Claudiu Herteliu, and Adrian Pana acknowledge partial support by a grant of the Romanian National Authority for Scientific Research and Innovation, CNDSUEFISCDI, project number PN-III-P4-ID-PCCF-2016-0084. Till Winfried Barnighausen acknowledges support from the Alexander von Humboldt Foundation through the Alexander von Humboldt Professor award, funded by the German Federal Ministry of Education and Research. Juan J Carrero was supported by the Swedish Research Council (2019-01059). Felix Carvalho acknowledges UID/MULTI/04378/2019 and UID/QUI/50006/2019 support with funding from FCT/MCTES through national funds. Vera Marisa Costa acknowledges support from grant (SFRH/BHD/110001/2015), received by Portuguese national funds through Fundacao para a Ciencia e a Tecnologia (FCT), IP, under the Norma TransitA3ria DL57/2016/CP1334/CT0006. Jan-Walter De Neve acknowledges support from the Alexander von Humboldt Foundation. Kebede Deribe acknowledges support by Wellcome Trust grant number 201900/Z/16/Z as part of his International Intermediate Fellowship. Claudiu Herteliu acknowledges partial support by a grant co-funded by European Fund for Regional Development through Operational Program for Competitiveness, Project ID P_40_382. Praveen Hoogar acknowledges the Centre for Bio Cultural Studies (CBiCS), Manipal Academy of Higher Education(MAHE), Manipal and Centre for Holistic Development and Research (CHDR), Kalghatgi. Bing-Fang Hwang acknowledges support from China Medical University (CMU108-MF-95), Taichung, Taiwan. Mihajlo Jakovljevic acknowledges the Serbian part of this GBD contribution was co-funded through the Grant OI175014 of the Ministry of Education Science and Technological Development of the Republic of Serbia. Aruna M Kamath acknowledges funding from the National Institutes of Health T32 grant (T32GM086270). Srinivasa Vittal Katikireddi acknowledges funding from the Medical Research Council (MC_UU_12017/13 & MC_UU_12017/15), Scottish Government Chief Scientist Office (SPHSU13 & SPHSU15) and an NRS Senior Clinical Fellowship (SCAF/15/02). Yun Jin Kim acknowledges support from the Research Management Centre, Xiamen University Malaysia (XMUMRF/2018-C2/ITCM/0001). Kewal Krishan acknowledges support from the DST PURSE grant and UGC Center of Advanced Study (CAS II) awarded to the Department of Anthropology, Panjab University, Chandigarh, India. Manasi Kumar acknowledges support from K43 TW010716 Fogarty International Center/NIMH. Ben Lacey acknowledges support from the NIHR Oxford Biomedical Research Centre and the BHF Centre of Research Excellence, Oxford. Ivan Landires is a member of the Sistema Nacional de InvestigaciA3n (SNI), which is supported by the Secretaria Nacional de Ciencia Tecnologia e Innovacion (SENACYT), Panama. Jeffrey V Lazarus acknowledges support by a Spanish Ministry of Science, Innovation and Universities Miguel Servet grant (Instituto de Salud Carlos III/ESF, European Union [CP18/00074]). Peter T N Memiah acknowledges CODESRIA; HISTP. Subas Neupane acknowledges partial support from the Competitive State Research Financing of the Expert Responsibility area of Tampere University Hospital. Shuhei Nomura acknowledges support from the Ministry of Education, Culture, Sports, Science, and Technology of Japan (18K10082). Alberto Ortiz acknowledges support by ISCIII PI19/00815, DTS18/00032, ISCIII-RETIC REDinREN RD016/0009 Fondos FEDER, FRIAT, Comunidad de Madrid B2017/BMD-3686 CIFRA2-CM. These funding sources had no role in the writing of the manuscript or the decision to submit it for publication. George C Patton acknowledges support from a National Health & Medical Research Council Fellowship. Marina Pinheiro acknowledges support from FCT for funding through program DL 57/2016 -Norma transitA3ria. Alberto Raggi, David Sattin, and Silvia Schiavolin acknowledge support by a grant from the Italian Ministry of Health (Ricerca Corrente, Fondazione Istituto Neurologico C Besta, Linea 4 -Outcome Research: dagli Indicatori alle Raccomandazioni Cliniche). Daniel Cury Ribeiro acknowledges support from the Sir Charles Hercus Health Research Fellowship -Health Research Council of New Zealand (18/111). Perminder S Sachdev acknowledges funding from the NHMRC Australia. Abdallah M Samy acknowledges support from a fellowship from the Egyptian Fulbright Mission Program. Milena M Santric-Milicevic acknowledges support from the Ministry of Education, Science and Technological Development of the Republic of Serbia (Contract No. 175087). Rodrigo Sarmiento-Suarez acknowledges institutional support from University of Applied and Environmental Sciences in Bogota, Colombia, and Carlos III Institute of Health in Madrid, Spain. Maria Ines Schmidt acknowledges grants from the Foundation for the Support of Research of the State of Rio Grande do Sul (IATS and PrInt) and the Brazilian Ministry of Health. Sheikh Mohammed Shariful Islam acknowledges a fellowship from the National Heart Foundation of Australia and Deakin University. Aziz Sheikh acknowledges support from Health Data Research UK. Kenji Shibuya acknowledges Japan Ministry of Education, Culture, Sports, Science and Technology. Joan B Soriano acknowledges support by Centro de Investigacion en Red de Enfermedades Respiratorias (CIBERES), Instituto de Salud Carlos III (ISCIII), Madrid, Spain. Rafael Tabares-Seisdedos acknowledges partial support from grant PI17/00719 from ISCIII-FEDER. Santosh Kumar Tadakamadla acknowledges support from the National Health and Medical Research Council Early Career Fellowship, Australia. Marcello Tonelli acknowledges the David Freeze Chair in Health Services Research at the University of Calgary, AB, Canada. ; "Peer Reviewed"