BACKGROUND: The number of individuals living with dementia is increasing, negatively affecting families, communities, and health-care systems around the world. A successful response to these challenges requires an accurate understanding of the dementia disease burden. We aimed to present the first detailed analysis of the global prevalence, mortality, and overall burden of dementia as captured by the Global Burden of Diseases, Injuries, and Risk Factors (GBD) Study 2016, and highlight the most important messages for clinicians and neurologists. METHODS: GBD 2016 obtained data on dementia from vital registration systems, published scientific literature and surveys, and data from health-service encounters on deaths, excess mortality, prevalence, and incidence from 195 countries and territories from 1990 to 2016, through systematic review and additional data-seeking efforts. To correct for differences in cause of death coding across time and locations, we modelled mortality due to dementia using prevalence data and estimates of excess mortality derived from countries that were most likely to code deaths to dementia relative to prevalence. Data were analysed by standardised methods to estimate deaths, prevalence, years of life lost (YLLs), years of life lived with disability (YLDs), and disability-adjusted life-years (DALYs; computed as the sum of YLLs and YLDs), and the fractions of these metrics that were attributable to four risk factors that met GBD criteria for assessment (high body-mass index [BMI], high fasting plasma glucose, smoking, and a diet high in sugar-sweetened beverages). FINDINGS: In 2016, the global number of individuals who lived with dementia was 43·8 million (95% uncertainty interval [UI] 37·8-51·0), increased from 20.2 million (17·4-23·5) in 1990. This increase of 117% (95% UI 114-121) contrasted with a minor increase in age-standardised prevalence of 1·7% (1·0-2·4), from 701 cases (95% UI 602-815) per 100 000 population in 1990 to 712 cases (614-828) per 100 000 population in 2016. More women than men had dementia in 2016 (27·0 million, 95% UI 23·3-31·4, vs 16.8 million, 14.4-19.6), and dementia was the fifth leading cause of death globally, accounting for 2·4 million (95% UI 2·1-2·8) deaths. Overall, 28·8 million (95% UI 24·5-34·0) DALYs were attributed to dementia; 6·4 million (95% UI 3·4-10·5) of these could be attributed to the modifiable GBD risk factors of high BMI, high fasting plasma glucose, smoking, and a high intake of sugar-sweetened beverages. INTERPRETATION: The global number of people living with dementia more than doubled from 1990 to 2016, mainly due to increases in population ageing and growth. Although differences in coding for causes of death and the heterogeneity in case-ascertainment methods constitute major challenges to the estimation of the burden of dementia, future analyses should improve on the methods for the correction of these biases. Until breakthroughs are made in prevention or curative treatment, dementia will constitute an increasing challenge to health-care systems worldwide. FUNDING: Bill & Melinda Gates Foundation. ; AA received financial support from the Department of Science and Technology, Government of India, (New Delhi, India) through the INSPIRE Faculty program. MSBS received Australian Government Research and Training Program funding for post-graduates to study at the Australian National University (Canberra, ACT, Australia). FC acknowledges support from the European Union (FEDER funds POCI/01/0145/FEDER/007728 and POCI/01/0145/FEDER/007265) and National Funds (FCT/MEC, Fundação para a Ciência e a Tecnologia and Ministério da Educação e Ciência) under the Partnership Agreements PT2020 UID/MULTI/04378/2013 and PT2020 UID/QUI/50006/2013. EC is supported by an Australian Research Council Future fellowship (FT3 140100085). AK was supported by the Miguel Servet contract financed by the CP13/00150 and PI15/00862 projects, integrated into the National R + D + I and funded by the ISCIII (General Branch Evaluation and Promotion of Health Research) and the European Regional Development fund (ISCIII-FEDER). MOO is supported by grant U54HG007479 from the National Institutes of Health. TCR is a member of the Alzheimer Scotland Dementia Research Centre (University of Edinburgh, Edinburgh, UK) and is supported by Alzheimer Scotland. RT-S was partly supported by grant number PROMETEOII/2015/021 from Generalitat Valenciana and the national grant PI17/00719 from ISCIII-FEDER. TW acknowledges academic support from University of Rajarata (Mihintale, Sri Lanka). ; Sí
Background: Neurological disorders are increasingly recognised as major causes of death and disability worldwide. The aim of this analysis from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2016 is to provide the most comprehensive and up-to-date estimates of the global, regional, and national burden from neurological disorders. Methods: We estimated prevalence, incidence, deaths, and disability-adjusted life-years (DALYs; the sum of years of life lost [YLLs] and years lived with disability [YLDs]) by age and sex for 15 neurological disorder categories (tetanus, meningitis, encephalitis, stroke, brain and other CNS cancers, traumatic brain injury, spinal cord injury, Alzheimer's disease and other dementias, Parkinson's disease, multiple sclerosis, motor neuron diseases, idiopathic epilepsy, migraine, tension-type headache, and a residual category for other less common neurological disorders) in 195 countries from 1990 to 2016. DisMod-MR 2.1, a Bayesian meta-regression tool, was the main method of estimation of prevalence and incidence, and the Cause of Death Ensemble model (CODEm) was used for mortality estimation. We quantified the contribution of 84 risks and combinations of risk to the disease estimates for the 15 neurological disorder categories using the GBD comparative risk assessment approach. Findings: Globally, in 2016, neurological disorders were the leading cause of DALYs (276 million [95% UI 247–308]) and second leading cause of deaths (9·0 million [8·8–9·4]). The absolute number of deaths and DALYs from all neurological disorders combined increased (deaths by 39% [34–44] and DALYs by 15% [9–21]) whereas their age-standardised rates decreased (deaths by 28% [26–30] and DALYs by 27% [24–31]) between 1990 and 2016. The only neurological disorders that had a decrease in rates and absolute numbers of deaths and DALYs were tetanus, meningitis, and encephalitis. The four largest contributors of neurological DALYs were stroke (42·2% [38·6–46·1]), migraine (16·3% [11·7–20·8]), Alzheimer's and other dementias (10·4% [9·0–12·1]), and meningitis (7·9% [6·6–10·4]). For the combined neurological disorders, age-standardised DALY rates were significantly higher in males than in females (male-to-female ratio 1·12 [1·05–1·20]), but migraine, multiple sclerosis, and tension-type headache were more common and caused more burden in females, with male-to-female ratios of less than 0·7. The 84 risks quantified in GBD explain less than 10% of neurological disorder DALY burdens, except stroke, for which 88·8% (86·5–90·9) of DALYs are attributable to risk factors, and to a lesser extent Alzheimer's disease and other dementias (22·3% [11·8–35·1] of DALYs are risk attributable) and idiopathic epilepsy (14·1% [10·8–17·5] of DALYs are risk attributable). Interpretation: Globally, the burden of neurological disorders, as measured by the absolute number of DALYs, continues to increase. As populations are growing and ageing, and the prevalence of major disabling neurological disorders steeply increases with age, governments will face increasing demand for treatment, rehabilitation, and support services for neurological disorders. The scarcity of established modifiable risks for most of the neurological burden demonstrates that new knowledge is required to develop effective prevention and treatment strategies. Funding: Bill & Melinda Gates Foundation.
Background Neurological disorders are increasingly recognised as major causes of death and disability worldwide. The aim of this analysis from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2016 is to provide the most comprehensive and up-to-date estimates of the global, regional, and national burden from neurological disorders. Methods We estimated prevalence, incidence, deaths, and disability-adjusted life-years (DALYs; the sum of years of life lost [YLLs] and years lived with disability [YLDs]) by age and sex for 15 neurological disorder categories (tetanus, meningitis, encephalitis, stroke, brain and other CNS cancers, traumatic brain injury, spinal cord injury, Alzheimer's disease and other dementias, Parkinson's disease, multiple sclerosis, motor neuron diseases, idiopathic epilepsy, migraine, tension-type headache, and a residual category for other less common neurological disorders) in 195 countries from 1990 to 2016. DisMod-MR 2.1, a Bayesian meta-regression tool, was the main method of estimation of prevalence and incidence, and the Cause of Death Ensemble model (CODEm) was used for mortality estimation. We quantified the contribution of 84 risks and combinations of risk to the disease estimates for the 15 neurological disorder categories using the GBD comparative risk assessment approach. Findings Globally, in 2016, neurological disorders were the leading cause of DALYs (276 million [95% UI 247-308]) and second leading cause of deaths (9.0 million [8.8-9.4]). The absolute number of deaths and DALYs from all neurological disorders combined increased (deaths by 39% [34-44] and DALYs by 15% [9-21]) whereas their age-standardised rates decreased (deaths by 28% [26-30] and DALYs by 27% [24-31]) between 1990 and 2016. The only neurological disorders that had a decrease in rates and absolute numbers of deaths and DALYs were tetanus, meningitis, and encephalitis. The four largest contributors of neurological DALYs were stroke (42.2% [38.6-46.1]), migraine (16.3% [11.7-20.8]), Alzheimer's and other dementias (10.4% [9.0-124]), and meningitis (7.9% [6.6-10.4]). For the combined neurological disorders, age-standardised DALY rates were significantly higher in males than in females (male-to-female ratio 1.12 [1.05-1.20]), but migraine, multiple sclerosis, and tension-type headache were more common and caused more burden in females, with male-to-female ratios of less than 0.7. The 84 risks quantified in GBD explain less than 10% of neurological disorder DALY burdens, except stroke, for which 88.8% (86.5-90.9) of DALYs are attributable to risk factors, and to a lesser extent Alzheimer's disease and other dementias (22.3% [11.8-35.1] of DALYs are risk attributable) and idiopathic epilepsy (14.1% [10.8-17.5] of DALYs are risk attributable). Interpretation Globally, the burden of neurological disorders, as measured by the absolute number of DALYs, continues to increase. As populations are growing and ageing, and the prevalence of major disabling neurological disorders steeply increases with age, governments will face increasing demand for treatment, rehabilitation, and support services for neurological disorders. The scarcity of established modifiable risks for most of the neurological burden demonstrates that new knowledge is required to develop effective prevention and treatment strategies. Copyright (C) The Author(s). Published by Elsevier Ltd.
Publisher´s version (útgefin grein). ; Background Neurological disorders are increasingly recognised as major causes of death and disability worldwide. The aim of this analysis from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2016 is to provide the most comprehensive and up-to-date estimates of the global, regional, and national burden from neurological disorders.Methods We estimated prevalence, incidence, deaths, and disability-adjusted life-years (DALYs; the sum of years of life lost [YLLs] and years lived with disability [YLDs]) by age and sex for 15 neurological disorder categories (tetanus, meningitis, encephalitis, stroke, brain and other CNS cancers, traumatic brain injury, spinal cord injury, Alzheimer's disease and other dementias, Parkinson's disease, multiple sclerosis, motor neuron diseases, idiopathic epilepsy, migraine, tension-type headache, and a residual category for other less common neurological disorders) in 195 countries from 1990 to 2016. DisMod-MR 2.1, a Bayesian meta-regression tool, was the main method of estimation of prevalence and incidence, and the Cause of Death Ensemble model (CODEm) was used for mortality estimation. We quantified the contribution of 84 risks and combinations of risk to the disease estimates for the 15 neurological disorder categories using the GBD comparative risk assessment approach.Findings Globally, in 2016, neurological disorders were the leading cause of DALYs (276 million [95% UI 247–308]) and second leading cause of deaths (9·0 million [8·8–9·4]). The absolute number of deaths and DALYs from all neurological disorders combined increased (deaths by 39% [34–44] and DALYs by 15% [9–21]) whereas their age-standardised rates decreased (deaths by 28% [26–30] and DALYs by 27% [24–31]) between 1990 and 2016. The only neurological disorders that had a decrease in rates and absolute numbers of deaths and DALYs were tetanus, meningitis, and encephalitis. The four largest contributors of neurological DALYs were stroke (42·2% [38·6–46·1]), migraine (16·3% [11·7–20·8]), Alzheimer's and other dementias (10·4% [9·0–12·1]), and meningitis (7·9% [6·6–10·4]). For the combined neurological disorders, age-standardised DALY rates were significantly higher in males than in females (male-to-female ratio 1·12 [1·05–1·20]), but migraine, multiple sclerosis, and tension-type headache were more common and caused more burden in females, with male-to-female ratios of less than 0·7. The 84 risks quantified in GBD explain less than 10% of neurological disorder DALY burdens, except stroke, for which 88·8% (86·5–90·9) of DALYs are attributable to risk factors, and to a lesser extent Alzheimer's disease and other dementias (22·3% [11·8–35·1] of DALYs are risk attributable) and idiopathic epilepsy (14·1% [10·8–17·5] of DALYs are risk attributable).Interpretation Globally, the burden of neurological disorders, as measured by the absolute number of DALYs, continues to increase. As populations are growing and ageing, and the prevalence of major disabling neurological disorders steeply increases with age, governments will face increasing demand for treatment, rehabilitation, and support services for neurological disorders. The scarcity of established modifiable risks for most of the neurological burden demonstrates that new knowledge is required to develop effective prevention and treatment strategies. ; ROA is funded by the National Institutes of Health (U01HG010273). SMA acknowledges the International Centre for Casemix and Clinical Coding, Faculty of Medicine, National University of Malaysia and Department of Health Policy and Management, Faculty of Public Health, Kuwait University for the approval and support to participate in this research project. AAw acknowledges funding support from Department of Science and Technology, Government of India, New Delhi, through INSPIRE Faculty scheme. TBA acknowledges partial funding from the Institute of Medical Research and Medicinal Plant Studies. ABa is supported by the Public Health Agency of Canada. TWB was supported by the Alexander von Humboldt Foundation through the Alexander von Humboldt Professor Award, funded by the Federal Ministry of Education and Research. MSBS acknowledges support from the Australian Government Research and Training Program scholarship for a PhD degree at the Australian National University, Australia. JJC is supported by the Swedish Heart and Lung Foundation. FCar is supported by the European Union (FEDER funds POCI/01/0145/FEDER/007728 and POCI/01/0145/FEDER/007265) and National Funds (FCT/MEC, Fundacao para a Ciencia e a Tecnologia and Ministerio da Educacao e Ciencia) under the Partnership Agreements PT2020 UID/MULTI/04378/2013 and PT2020UID/QUI/50006/2013. EC is supported by an Australian Research Council Future Fellowship (FT3 140100085). KD is supported by a Wellcome Trust [Grant Number 201900] as part of his International Intermediate Fellowship. EF is supported by the European Union (FEDER funds POCI/01/0145/FEDER/007728 and POCI/01/0145/FEDER/007265) and National Funds (FCT/MEC, Fundacao para a Ciencia e a Tecnologia and Ministerio da Educacao e Ciencia) under the Partnership Agreements PT2020 UID/MULTI/04378/2013 and PT2020UID/QUI/50006/2013. SMSI is funded by the Institute for Physical Activity and Nutrition (IPAN), Deakin University and received funding from High Blood Pressure Research Council of Australia. YKa is a DBT/Wellcome Trust India Alliance Fellow in Public Health. YJK is supported by the Office of Research and Innovation at Xiamen University Malaysia. BL acknowledges funding from the National Institute for Health Research (NIHR) Oxford Biomedical Research Centre. WDL is supported in part by U10NS086484 NINDS. SLo is funded by the German Federal Ministry of Education and Research (nutriCARD, grant agreement number 01EA1411A). RML is supported by a National Health and Medical Research Council (NHMRC) of Australia Senior Research Fellowship. AMa and the Imperial College London are grateful for support from the NW London NIHR Collaboration for Leadership in Applied Health Research and Care. JJM is supported by the Danish National Research Foundation (Niels Bohr Professorship), and the John Cade Fellowship (APP1056929) from NHMRC. TMei acknowledges additional institutional support from the Competence Cluster for Nutrition and Cardiovascular Health (nutriCARD), Jena-Halle-Leipzig. IMV is supported by the Sistema Nacional de Investigacion (Panama). MOO is supported by SIREN U54 U54HG007479 and SIBS Genomics R01NS107900 grants. AMS was supported by a fellowship from the Egyptian Fulbright Mission Program. MMSM acknowledges the support from the Ministry of Education, Science and Technological Development, Republic of Serbia (contract no 175087). AShe is supported by Health Data Research UK. MBS' work on traumatic brain injury is supported by grants NIH U01 NS086090 (PI G Manley) from the National Institutes of Health (NIH) and DoD W81XWH-14-2-0176 (PI G Manley) from the United States Department of Defense. RTS is supported in part by grant number PROMETEOII/2015/021 from Generalitat Valenciana and the national grant PI17/00719 from ISCIIIFEDER. AGT was supported by a Fellowship from the NHMRC (Australia; 1042600. KBT acknowledges funding supports from the Maurice Wilkins Centre for Biodiscovery, Cancer Society of New Zealand, Health Research Council, Gut Cancer Foundation, and the University of Auckland. CY acknowledges support from the National Natural Science Foundation of China (grant number 81773552) and the Chinese NSFC International Cooperation and Exchange Program (grant number 71661167007). ; "Peer Reviewed"
BACKGROUND: Neurological disorders are increasingly recognised as major causes of death and disability worldwide. The aim of this analysis from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2016 is to provide the most comprehensive and up-to-date estimates of the global, regional, and national burden from neurological disorders. METHODS: We estimated prevalence, incidence, deaths, and disability-adjusted life-years (DALYs; the sum of years of life lost [YLLs] and years lived with disability [YLDs]) by age and sex for 15 neurological disorder categories (tetanus, meningitis, encephalitis, stroke, brain and other CNS cancers, traumatic brain injury, spinal cord injury, Alzheimer's disease and other dementias, Parkinson's disease, multiple sclerosis, motor neuron diseases, idiopathic epilepsy, migraine, tension-type headache, and a residual category for other less common neurological disorders) in 195 countries from 1990 to 2016. DisMod-MR 2.1, a Bayesian meta-regression tool, was the main method of estimation of prevalence and incidence, and the Cause of Death Ensemble model (CODEm) was used for mortality estimation. We quantified the contribution of 84 risks and combinations of risk to the disease estimates for the 15 neurological disorder categories using the GBD comparative risk assessment approach. FINDINGS: Globally, in 2016, neurological disorders were the leading cause of DALYs (276 million [95% UI 247-308]) and second leading cause of deaths (9·0 million [8·8-9·4]). The absolute number of deaths and DALYs from all neurological disorders combined increased (deaths by 39% [34-44] and DALYs by 15% [9-21]) whereas their age-standardised rates decreased (deaths by 28% [26-30] and DALYs by 27% [24-31]) between 1990 and 2016. The only neurological disorders that had a decrease in rates and absolute numbers of deaths and DALYs were tetanus, meningitis, and encephalitis. The four largest contributors of neurological DALYs were stroke (42·2% [38·6-46·1]), migraine (16·3% [11·7-20·8]), Alzheimer's and other dementias (10·4% [9·0-12·1]), and meningitis (7·9% [6·6-10·4]). For the combined neurological disorders, age-standardised DALY rates were significantly higher in males than in females (male-to-female ratio 1·12 [1·05-1·20]), but migraine, multiple sclerosis, and tension-type headache were more common and caused more burden in females, with male-to-female ratios of less than 0·7. The 84 risks quantified in GBD explain less than 10% of neurological disorder DALY burdens, except stroke, for which 88·8% (86·5-90·9) of DALYs are attributable to risk factors, and to a lesser extent Alzheimer's disease and other dementias (22·3% [11·8-35·1] of DALYs are risk attributable) and idiopathic epilepsy (14·1% [10·8-17·5] of DALYs are risk attributable). INTERPRETATION: Globally, the burden of neurological disorders, as measured by the absolute number of DALYs, continues to increase. As populations are growing and ageing, and the prevalence of major disabling neurological disorders steeply increases with age, governments will face increasing demand for treatment, rehabilitation, and support services for neurological disorders. The scarcity of established modifiable risks for most of the neurological burden demonstrates that new knowledge is required to develop effective prevention and treatment strategies. FUNDING: Bill & Melinda Gates Foundation. ; Bill & Melinda Gates Foundation. ; Sí
Background: Neurological disorders are increasingly recognised as major causes of death and disability worldwide. The aim of this analysis from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2016 is to provide the most comprehensive and up-to-date estimates of the global, regional, and national burden from neurological disorders. Methods: We estimated prevalence, incidence, deaths, and disability-adjusted life-years (DALYs; the sum of years of life lost [YLLs] and years lived with disability [YLDs]) by age and sex for 15 neurological disorder categories (tetanus, meningitis, encephalitis, stroke, brain and other CNS cancers, traumatic brain injury, spinal cord injury, Alzheimer's disease and other dementias, Parkinson's disease, multiple sclerosis, motor neuron diseases, idiopathic epilepsy, migraine, tension-type headache, and a residual category for other less common neurological disorders) in 195 countries from 1990 to 2016. DisMod-MR 2.1, a Bayesian meta-regression tool, was the main method of estimation of prevalence and incidence, and the Cause of Death Ensemble model (CODEm) was used for mortality estimation. We quantified the contribution of 84 risks and combinations of risk to the disease estimates for the 15 neurological disorder categories using the GBD comparative risk assessment approach. Findings: Globally, in 2016, neurological disorders were the leading cause of DALYs (276 million [95% UI 247–308]) and second leading cause of deaths (9·0 million [8·8–9·4]). The absolute number of deaths and DALYs from all neurological disorders combined increased (deaths by 39% [34–44] and DALYs by 15% [9–21]) whereas their age-standardised rates decreased (deaths by 28% [26–30] and DALYs by 27% [24–31]) between 1990 and 2016. The only neurological disorders that had a decrease in rates and absolute numbers of deaths and DALYs were tetanus, meningitis, and encephalitis. The four largest contributors of neurological DALYs were stroke (42·2% [38·6–46·1]), migraine (16·3% [11·7–20·8]), Alzheimer's and other dementias (10·4% [9·0–12·1]), and meningitis (7·9% [6·6–10·4]). For the combined neurological disorders, age-standardised DALY rates were significantly higher in males than in females (male-to-female ratio 1·12 [1·05–1·20]), but migraine, multiple sclerosis, and tension-type headache were more common and caused more burden in females, with male-to-female ratios of less than 0·7. The 84 risks quantified in GBD explain less than 10% of neurological disorder DALY burdens, except stroke, for which 88·8% (86·5–90·9) of DALYs are attributable to risk factors, and to a lesser extent Alzheimer's disease and other dementias (22·3% [11·8–35·1] of DALYs are risk attributable) and idiopathic epilepsy (14·1% [10·8–17·5] of DALYs are risk attributable). Interpretation: Globally, the burden of neurological disorders, as measured by the absolute number of DALYs, continues to increase. As populations are growing and ageing, and the prevalence of major disabling neurological disorders steeply increases with age, governments will face increasing demand for treatment, rehabilitation, and support services for neurological disorders. The scarcity of established modifiable risks for most of the neurological burden demonstrates that new knowledge is required to develop effective prevention and treatment strategies. Funding: Bill & Melinda Gates Foundation.
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"